From e9e8f5ca460fd1a91fbf95d648046c9fac434562 Mon Sep 17 00:00:00 2001
From: Ayushi Sha <124066968+AYUSHI-SHA@users.noreply.github.com>
Date: Fri, 4 Oct 2024 23:24:49 +0530
Subject: [PATCH 01/76] Update Stock_Price_Prediction

 To optimize the workflow and improve efficiency, made changes that remove redundant code.
---
 Stock_Price_Prediction.ipynb | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/Stock_Price_Prediction.ipynb b/Stock_Price_Prediction.ipynb
index 0745eff..7c3ad61 100644
--- a/Stock_Price_Prediction.ipynb
+++ b/Stock_Price_Prediction.ipynb
@@ -1003,7 +1003,7 @@
         "id": "GxtMzlg-gR2P"
       },
       "source": [
-        "## 2. SVR"
+        "## 2. Support Vector Regression"
       ]
     },
     {
@@ -1070,7 +1070,7 @@
         "id": "hcIfVMWdgcKt"
       },
       "source": [
-        "## 3. Random Forest"
+        "## 3. Random Forest Regressor"
       ]
     },
     {
@@ -1769,6 +1769,13 @@
         "plt.tight_layout()\n",
         "plt.show()"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
     }
   ],
   "metadata": {

From 27b663033838a9e212f4e34de009ae33e9111863 Mon Sep 17 00:00:00 2001
From: Ayushi Sha <124066968+AYUSHI-SHA@users.noreply.github.com>
Date: Sun, 6 Oct 2024 14:14:56 +0530
Subject: [PATCH 02/76] Stock_Price_Prediction Updated

---
 .../Stock_Price_Prediction(Updated).ipynb     | 5236 +++++++++++++++++
 Python File/Stock_Price_Prediction.ipynb      | 2708 ---------
 Stock_Price_Prediction.ipynb                  | 1805 ------
 3 files changed, 5236 insertions(+), 4513 deletions(-)
 create mode 100644 Python File/Stock_Price_Prediction(Updated).ipynb
 delete mode 100644 Python File/Stock_Price_Prediction.ipynb
 delete mode 100644 Stock_Price_Prediction.ipynb

diff --git a/Python File/Stock_Price_Prediction(Updated).ipynb b/Python File/Stock_Price_Prediction(Updated).ipynb
new file mode 100644
index 0000000..77a2cd6
--- /dev/null
+++ b/Python File/Stock_Price_Prediction(Updated).ipynb	
@@ -0,0 +1,5236 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "8tzEK_mSvRoh"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.svm import SVR\n",
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense,LSTM"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "NbBSc2jLvZWx",
+        "outputId": "457e0a63-90a0-4e1c-b846-95e4e50c31dc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7074,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7074,\n        \"samples\": [\n          \"11-08-2016\",\n          \"30-10-2007\",\n          \"17-01-2017\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.7732294451065,\n        \"min\": 13.478195,\n        \"max\": 703.650024,\n        \"num_unique_values\": 4758,\n        \"samples\": [\n          174.399994,\n          31.0324,\n          187.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.34507839355788,\n        \"min\": 13.935802,\n        \"max\": 728.349976,\n        \"num_unique_values\": 5403,\n        \"samples\": [\n          473.0,\n          495.450012,\n          78.321663\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.98051601861624,\n        \"min\": 13.214009,\n        \"max\": 694.200012,\n        \"num_unique_values\": 5488,\n        \"samples\": [\n          60.2957,\n          22.677523,\n          16.983376\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.63054905628158,\n        \"min\": 13.346102,\n        \"max\": 725.25,\n        \"num_unique_values\": 5975,\n        \"samples\": [\n          633.599976,\n          241.100006,\n          107.834999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.90324918554683,\n        \"min\": 9.53141,\n        \"max\": 725.25,\n        \"num_unique_values\": 6575,\n        \"samples\": [\n          12.345289,\n          223.836212,\n          16.758821\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34627439.399630256,\n        \"min\": 0.0,\n        \"max\": 446948261.0,\n        \"num_unique_values\": 6948,\n        \"samples\": [\n          29959130.0,\n          1648453.0,\n          14077470.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-287e60b3-d1a5-4500-8f98-5b9d7c4acf53\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>01-01-1996</td>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>12.409931</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>02-01-1996</td>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
+              "      <td>12.014931</td>\n",
+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>03-01-1996</td>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>11.694577</td>\n",
+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>04-01-1996</td>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>11.654142</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>05-01-1996</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>11.588827</td>\n",
+              "      <td>76613039.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-287e60b3-d1a5-4500-8f98-5b9d7c4acf53')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-287e60b3-d1a5-4500-8f98-5b9d7c4acf53 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-287e60b3-d1a5-4500-8f98-5b9d7c4acf53');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "\n",
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+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e66d5a74-d0fe-4999-9ec9-3f969fd226ee')\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-e66d5a74-d0fe-4999-9ec9-3f969fd226ee button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "         Date       Open       High        Low      Close  Adj Close  \\\n",
+              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+              "\n",
+              "       Volume  \n",
+              "0  43733533.0  \n",
+              "1  56167280.0  \n",
+              "2  68296318.0  \n",
+              "3  86073880.0  \n",
+              "4  76613039.0  "
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Load the dataset\n",
+        "df = pd.read_csv('/content/SBIN.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "2QdTvORzwEJw"
+      },
+      "outputs": [],
+      "source": [
+        "# Drop the 'Date' and 'Adj Close' columns\n",
+        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "xMfr71b2w3eX",
+        "outputId": "a6990b82-ef82-454f-c0ba-fc0c21cc91ca"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7074,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.7732294451065,\n        \"min\": 13.478195,\n        \"max\": 703.650024,\n        \"num_unique_values\": 4758,\n        \"samples\": [\n          174.399994,\n          31.0324,\n          187.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.34507839355788,\n        \"min\": 13.935802,\n        \"max\": 728.349976,\n        \"num_unique_values\": 5403,\n        \"samples\": [\n          473.0,\n          495.450012,\n          78.321663\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.98051601861624,\n        \"min\": 13.214009,\n        \"max\": 694.200012,\n        \"num_unique_values\": 5488,\n        \"samples\": [\n          60.2957,\n          22.677523,\n          16.983376\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.63054905628158,\n        \"min\": 13.346102,\n        \"max\": 725.25,\n        \"num_unique_values\": 5975,\n        \"samples\": [\n          633.599976,\n          241.100006,\n          107.834999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34627439.399630256,\n        \"min\": 0.0,\n        \"max\": 446948261.0,\n        \"num_unique_values\": 6948,\n        \"samples\": [\n          29959130.0,\n          1648453.0,\n          14077470.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-20d26075-7ea5-45d5-bff5-1bc0edf2bd43\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>76613039.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20d26075-7ea5-45d5-bff5-1bc0edf2bd43')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-20d26075-7ea5-45d5-bff5-1bc0edf2bd43 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-20d26075-7ea5-45d5-bff5-1bc0edf2bd43');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-e1f4b696-990c-40d9-8eb5-880395089282\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e1f4b696-990c-40d9-8eb5-880395089282')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-e1f4b696-990c-40d9-8eb5-880395089282 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "        Open       High        Low      Close      Volume\n",
+              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
+              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
+              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
+              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
+              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "idXVoeEFw55L"
+      },
+      "outputs": [],
+      "source": [
+        "# Handle missing values\n",
+        "imputer = SimpleImputer(strategy='mean')\n",
+        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "dUw_auE7w8JA"
+      },
+      "outputs": [],
+      "source": [
+        "# Select features and target variable\n",
+        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+        "y = df['Close']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "FD2542-uxMhN"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "IfBxpcjIw-h7"
+      },
+      "outputs": [],
+      "source": [
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BUsngQNexIjX",
+        "outputId": "6b739018-4a7d-48d9-a3ec-591a5cd0784e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(5659, 4)"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GAb1kDEZxQD6",
+        "outputId": "4babae88-adba-49ad-9d56-514bd7abde50"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(1415, 4)"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_test.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "SrzPIdvxxSWL"
+      },
+      "outputs": [],
+      "source": [
+        "# Function to evaluate and print RMSE, MAE, and MAPE\n",
+        "def evaluate_model(model, X_test, y_test):\n",
+        "    predictions = model.predict(X_test)\n",
+        "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+        "    mae = mean_absolute_error(y_test, predictions)\n",
+        "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+        "\n",
+        "    print(f\"RMSE: {rmse}\")\n",
+        "    print(f\"MAE: {mae}\")\n",
+        "    print(f\"MAPE: {mape}\\n\")\n",
+        "\n",
+        "    return rmse, mae, mape\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "1V0eOGD_xVCq"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "metrics = {\n",
+        "    \"Model\": [],\n",
+        "    \"RMSE\": [],\n",
+        "    \"MAE\": [],\n",
+        "    \"MAPE\": []\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4gNvcwrH55rP"
+      },
+      "source": [
+        "# **1. Linear Regression**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "uTbRFCB4xXHU"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a linear regression model\n",
+        "model1 = LinearRegression()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "UKaUaJ6sxaYG",
+        "outputId": "a28ae8d2-9430-4a1b-e9ba-475edfb7c788"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Close</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>5286</th>\n",
+              "      <td>257.350006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3408</th>\n",
+              "      <td>129.464996</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5477</th>\n",
+              "      <td>279.350006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6906</th>\n",
+              "      <td>588.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>530</th>\n",
+              "      <td>21.644367</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ],
+            "text/plain": [
+              "5286    257.350006\n",
+              "3408    129.464996\n",
+              "5477    279.350006\n",
+              "6906    588.500000\n",
+              "530      21.644367\n",
+              "Name: Close, dtype: float64"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_train.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "6iJA5FrBxdEs",
+        "outputId": "6667305e-dd8d-445e-df54-c470c5b1e5ae"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model1.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k-T73PFExiZD",
+        "outputId": "c794cf2c-c031-42c5-83ec-9432a9082d0f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE: 1.6881364643681482\n",
+            "MAE: 0.9433353485344729\n",
+            "MAPE: 0.006085435990853812\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model1, X_test, y_test)\n",
+        "metrics[\"Model\"].append(\"Linear Regressor\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qEVWWYIS592D"
+      },
+      "source": [
+        "# 2. Support Vector Regression"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "LeUTf8Vhxj_k"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an SVR model\n",
+        "model2 = SVR()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "ud3Yhe5Vzvyh",
+        "outputId": "08f14bf4-3c1f-4973-c72a-52e2c264e759"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-2 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-2 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-2 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-2 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "SVR()"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model2.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eiqL4fTuzxWH",
+        "outputId": "4a88e15a-0922-4c3c-906b-3d5d6efee119"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE: 147.71103599153602\n",
+            "MAE: 110.99419106508152\n",
+            "MAPE: 1.9715076513294716\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model2, X_test, y_test)\n",
+        "metrics[\"Model\"].append(\"SVR\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PlDcozy-6OGR"
+      },
+      "source": [
+        "# 3. Random Forest Regressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "iaN8nOOO6cBg"
+      },
+      "outputs": [],
+      "source": [
+        "model3 = RandomForestRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "wZ7x_Yp06fI_",
+        "outputId": "8f64c153-17f2-4939-9344-58db634aee31"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-3 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-3 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-3 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-3 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-3 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestRegressor()"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model3.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IwK7IZ3E6g_n",
+        "outputId": "1fa15097-6587-4b81-a0f0-e65ac6c6ba8f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE: 224.91534614035237\n",
+            "MAE: 162.96706374804316\n",
+            "MAPE: 0.7504263376635859\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"Random Forest\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ijTIDEEa6izO"
+      },
+      "source": [
+        "# 4. Gradient Boosting Models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "EO6OFflr6nJo"
+      },
+      "outputs": [],
+      "source": [
+        "model4 = GradientBoostingRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "vrwnbrEi6o1X",
+        "outputId": "dbf1c6ff-ffcc-4b47-fbf6-8ba7f64a5f16"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-4 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-4 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-4 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-4 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "GradientBoostingRegressor()"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model4.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-pTBa0fD6qqx",
+        "outputId": "16225599-0077-447e-a1f5-3e2aa2f7ba8f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE: 224.41069433522418\n",
+            "MAE: 162.27122816197573\n",
+            "MAPE: 0.7378541693598378\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but GradientBoostingRegressor was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"GBM\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eGcU-e6C6sJI"
+      },
+      "source": [
+        "# 5. Extreme Graident Boosting"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "0GQmPNFd6uxx"
+      },
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "# Create an XGBoost model\n",
+        "model5 = xgb.XGBRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        },
+        "id": "kfo1ZNft6xTp",
+        "outputId": "edfefbbd-744d-4c08-80bb-7ca2ffb3f765"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-5 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-5 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-5 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-5 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model5.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7QwLt9iS6zSj",
+        "outputId": "bfc728a1-e169-44ea-8a4e-4b18e29f8c0e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE: 224.66436370022384\n",
+            "MAE: 162.62070643817412\n",
+            "MAPE: 0.7441437311249671\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"XGBoost\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sUD1VQBF605K"
+      },
+      "source": [
+        "# 6. AdaBoost Regressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "0foTLiQp63Y9"
+      },
+      "outputs": [],
+      "source": [
+        "model6 = AdaBoostRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "bkzSWYA365MO",
+        "outputId": "c1f2bc96-a89c-4c83-873b-0836fa97c154"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-6 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-6 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-6 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-6 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "AdaBoostRegressor()"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model6.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZKxqdmp166pF",
+        "outputId": "b027a6a7-45e7-49e3-f93c-ed787d86e83f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE: 211.5202666508501\n",
+            "MAE: 150.08505165249466\n",
+            "MAPE: 0.710014475295362\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but AdaBoostRegressor was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mtfkPIRi67xo"
+      },
+      "source": [
+        "# 7. Decision Tree"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "E6EyzrH36_Fq"
+      },
+      "outputs": [],
+      "source": [
+        "model7 = DecisionTreeRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "DTp5VIYx7AWt",
+        "outputId": "8c71c048-7309-4aa2-ede9-517e62deaee3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-7 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-7 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-7 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-7 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-7 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "DecisionTreeRegressor()"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model7.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3YC-pSgv7Dh4",
+        "outputId": "cd1a1793-3a36-4028-919f-90d79f0c1b46"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE: 224.85857555038172\n",
+            "MAE: 162.88870413804315\n",
+            "MAPE: 0.7490024715971244\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"Decision Tree\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WfJAZHnP7E_2"
+      },
+      "source": [
+        "# 8. KNeighbors Regressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "smujnWTRzzDL"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a KNN model\n",
+        "model8 = KNeighborsRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "zeokqhKd0Aj8",
+        "outputId": "018dff42-47da-4f2f-8bf9-16938de97723"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-8 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-8 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-8 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-8 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-8 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "KNeighborsRegressor()"
+            ]
+          },
+          "execution_count": 36,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model8.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X2uNfESC0CA8",
+        "outputId": "6c449192-697f-4bb9-9a8e-bc9aed955532"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "RMSE: 224.35603706259303\n",
+            "MAE: 162.1962430618594\n",
+            "MAPE: 0.7365233640314862\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"KNN\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X3yNCskZ7KMV"
+      },
+      "source": [
+        "# 9. Artificial Neural Networks"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "syd9MRhf0Df1",
+        "outputId": "1c546101-530f-4e5c-af43-79fd68b68347"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create an ANN model\n",
+        "model9 = Sequential()\n",
+        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+        "model9.add(Dense(16, activation='relu'))\n",
+        "model9.add(Dense(1, activation='linear'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "pdlxN-Dp0IZr"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model9.compile(loss='mean_squared_error', optimizer='adam')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qcryLURL0KIH",
+        "outputId": "54e02177-ee86-46bf-cfef-496cb59b8577"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.history.History at 0x7f9afeafbcd0>"
+            ]
+          },
+          "execution_count": 40,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xu6Cwjey0MaP",
+        "outputId": "92beea16-08fa-45d1-c817-88b1b1e3c688"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
+            "RMSE: 2.767324426981657\n",
+            "MAE: 1.7200117039677931\n",
+            "MAPE: 0.010909365212209457\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"ANN\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Yet4TgKq7OZl"
+      },
+      "source": [
+        "# 10. Long Short Term Memory"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "id": "keiZDN4w7UH0"
+      },
+      "outputs": [],
+      "source": [
+        "n_features = X_train_scaled.shape[1]\n",
+        "n_steps = 10\n",
+        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+        "\n",
+        "# Reshape the input data\n",
+        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nRRTkQTD7Vjd",
+        "outputId": "8c0e1de9-8382-482f-dc46-d6148e3f535e"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(**kwargs)\n"
+          ]
+        }
+      ],
+      "source": [
+        "model10 = Sequential()\n",
+        "model10.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+        "model10.add(Dense(1))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "id": "3UJtO3wC7WWe"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model10.compile(loss='mean_squared_error', optimizer='adam')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ld9dofMD7YNO",
+        "outputId": "2b3021f3-d88f-431c-a059-a9b6e065a29b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.history.History at 0x7f9ad5b81150>"
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model10.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lOTdB8Bj7aXM",
+        "outputId": "c844bad9-4c1e-447f-dad9-7b86f57dee9b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step\n",
+            "RMSE: 15.048059609987224\n",
+            "MAE: 12.060430640867935\n",
+            "MAPE: 0.21836694109231441\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model10, X_test_reshaped, y_test[n_steps-1:])\n",
+        "\n",
+        "# Store metrics\n",
+        "metrics[\"Model\"].append(\"LSTM\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 506
+        },
+        "id": "O8DHEHgI0wNg",
+        "outputId": "5cd999c9-0cca-4d23-da28-8655ce099354"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 2000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Create a DataFrame for metrics\n",
+        "metrics_df = pd.DataFrame(metrics)\n",
+        "\n",
+        "plt.figure(figsize=(20, 5))\n",
+        "\n",
+        "# RMSE Plot\n",
+        "plt.subplot(1, 3, 1)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
+        "plt.ylabel('RMSE')\n",
+        "plt.xlabel('Model')\n",
+        "plt.xticks(rotation=45,ha='right')\n",
+        "plt.title('RMSE for Different Models')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 506
+        },
+        "id": "mwniKbys0xJ0",
+        "outputId": "ff2fe79a-78c2-4e13-efef-75e89882656b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 2000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# MAE Plot\n",
+        "plt.figure(figsize=(20, 5))\n",
+        "plt.subplot(1, 3, 2)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')  # Changed to plt.bar()\n",
+        "plt.ylabel('MAE')  # Set y-label to MAE\n",
+        "plt.xlabel('Model')  # Set x-label to Model\n",
+        "plt.xticks(rotation=45,ha='right')  # Rotate x-axis labels for better readability\n",
+        "plt.title('MAE for Different Models')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 506
+        },
+        "id": "vxK0_IkL1zaU",
+        "outputId": "69c4c24b-f6d5-4a4c-f85a-baa8455475e5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 2000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# MAPE Plot\n",
+        "plt.figure(figsize=(20, 5))\n",
+        "plt.subplot(1, 3, 3)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
+        "plt.ylabel('MAPE')\n",
+        "plt.xlabel('Model')\n",
+        "plt.xticks(rotation=45,ha='right')\n",
+        "plt.title('MAPE for Different Models')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Python File/Stock_Price_Prediction.ipynb b/Python File/Stock_Price_Prediction.ipynb
deleted file mode 100644
index c82b075..0000000
--- a/Python File/Stock_Price_Prediction.ipynb	
+++ /dev/null
@@ -1,2708 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "qCDSjVhXLr_Z"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.linear_model import LinearRegression\n",
-        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')\n",
-        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "SOQbXSiB-g5G",
-        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
-      },
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Sc4id6VxL8BS",
-        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "         Date       Open       High        Low      Close  Adj Close  \\\n",
-              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
-              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
-              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
-              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
-              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
-              "\n",
-              "       Volume  \n",
-              "0  43733533.0  \n",
-              "1  56167280.0  \n",
-              "2  68296318.0  \n",
-              "3  86073880.0  \n",
-              "4  76613039.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Date</th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Adj Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>01-01-1996</td>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>12.409931</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>02-01-1996</td>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>12.014931</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>03-01-1996</td>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>11.694577</td>\n",
-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>04-01-1996</td>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>11.654142</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>05-01-1996</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>11.588827</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d89bb189-3861-4a83-b0ea-1a19c2115c37')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 23
-        }
-      ],
-      "source": [
-        "# Load the dataset\n",
-        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "7LaYGXsfN-8y"
-      },
-      "outputs": [],
-      "source": [
-        "# Drop the 'Date' and 'Adj Close' columns\n",
-        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "pqbTBdnBOKJc",
-        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "        Open       High        Low      Close      Volume\n",
-              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
-              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
-              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
-              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
-              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
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-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
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-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 25
-        }
-      ],
-      "source": [
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "id": "dydEPoNeM6eN"
-      },
-      "outputs": [],
-      "source": [
-        "# Handle missing values\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "imputer = SimpleImputer(strategy='mean')\n",
-        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {
-        "id": "OQ3cGqgTMBwt"
-      },
-      "outputs": [],
-      "source": [
-        "# Select features and target variable\n",
-        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
-        "y = df['Close']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "id": "9Oz-bwJOMEWD"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "ugapDyXODtn3"
-      },
-      "outputs": [],
-      "source": [
-        "# Scale the features using Min-Max scaling\n",
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "997ZEgibCZIO",
-        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(5659, 4)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 29
-        }
-      ],
-      "source": [
-        "X_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bmtt76RuCeyG",
-        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(1415, 4)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 30
-        }
-      ],
-      "source": [
-        "X_test.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CeJkUJ92Ciqd",
-        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(5659,)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 31
-        }
-      ],
-      "source": [
-        "y_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7HGC7VuTCjWc",
-        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(1415,)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 32
-        }
-      ],
-      "source": [
-        "y_test.shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c6Ek8jRlO2_I"
-      },
-      "source": [
-        "## 1. LINEAR REGRESSION"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "id": "RdZ1SpzdMHAJ"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a linear regression model\n",
-        "model1 = LinearRegression()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mPM035IzMY04",
-        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "5286    257.350006\n",
-              "3408    129.464996\n",
-              "5477    279.350006\n",
-              "6906    588.500000\n",
-              "530      21.644367\n",
-              "Name: Close, dtype: float64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 34
-        }
-      ],
-      "source": [
-        "y_train.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "qBhQ9HbYMI3d",
-        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 35
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model1.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {
-        "id": "X269co2kMS4z"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred1 = model1.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "metadata": {
-        "id": "QK8GvDYPOd0Y"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
-        "mae1 = mean_absolute_error(y_test, pred1)\n",
-        "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
-        "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
-        "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
-        "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
-        "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
-        "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dEi49xtEOtne",
-        "outputId": "0000b074-3187-41de-fbac-4ae75cbda6bd"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 1.6881364643681482\n",
-            "MAE: 0.9433353485344729\n",
-            "MAPE: 0.006085435990853812\n",
-            "Accuracy: 0.8296819787985866\n",
-            "Precision: 0.8623595505617978\n",
-            "Confusion Matrix:\n",
-            " [[560  98]\n",
-            " [143 614]]\n",
-            "Recall: 0.8110964332892999\n",
-            "F1 Score: 0.8359428182437032\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse1)\n",
-        "print(\"MAE:\", mae1)\n",
-        "print(\"MAPE:\", mape1)\n",
-        "print(\"Accuracy:\", accuracy1)\n",
-        "print(\"Precision:\", precision1)\n",
-        "print(\"Confusion Matrix:\\n\", confusion1)\n",
-        "print(\"Recall:\", recall1)\n",
-        "print(\"F1 Score:\", f11)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GxtMzlg-gR2P"
-      },
-      "source": [
-        "## 2. SVR"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 39,
-      "metadata": {
-        "id": "o7K9r7EXWRjQ"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.svm import SVR"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "id": "0xQewd7QWTtq"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an SVR model\n",
-        "model2 = SVR()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 41,
-      "metadata": {
-        "id": "DuNes3s6U2IV"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "22SaCsQmfhgP",
-        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "SVR()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 42
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model2.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 43,
-      "metadata": {
-        "id": "OQ1nL4oYfkAC"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred2 = model2.predict(X_test)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 44,
-      "metadata": {
-        "id": "nRYTwydsfpjb"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
-        "mae2 = mean_absolute_error(y_test, pred2)\n",
-        "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
-        "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
-        "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
-        "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
-        "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
-        "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 45,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "656J5oz5fzq6",
-        "outputId": "ce67d2d8-0bc8-4e6d-d6b5-6b78e7e1c59b"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 147.71103599153602\n",
-            "MAE: 110.99419106508152\n",
-            "MAPE: 1.9715076513294716\n",
-            "Accuracy: 0.9992932862190813\n",
-            "Precision: 1.0\n",
-            "Confusion Matrix:\n",
-            " [[727   0]\n",
-            " [  1 687]]\n",
-            "Recall: 0.998546511627907\n",
-            "F1 Score: 0.9992727272727273\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse2)\n",
-        "print(\"MAE:\", mae2)\n",
-        "print(\"MAPE:\", mape2)\n",
-        "print(\"Accuracy:\", accuracy2)\n",
-        "print(\"Precision:\", precision2)\n",
-        "print(\"Confusion Matrix:\\n\", confusion2)\n",
-        "print(\"Recall:\", recall2)\n",
-        "print(\"F1 Score:\", f12)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hcIfVMWdgcKt"
-      },
-      "source": [
-        "## 3. Random Forest"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 46,
-      "metadata": {
-        "id": "f7raXT_hf2ij"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.ensemble import RandomForestRegressor\n",
-        "# Create a Random Forest model\n",
-        "model3 = RandomForestRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 47,
-      "metadata": {
-        "id": "TadNM7MEU7fh"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 48,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "fF002Yepgk55",
-        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "RandomForestRegressor()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 48
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model3.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "metadata": {
-        "id": "8nRU_pzEgnCt"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred3 = model3.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 50,
-      "metadata": {
-        "id": "4aKEXGVUgsry"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
-        "mae3 = mean_absolute_error(y_test, pred3)\n",
-        "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
-        "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
-        "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
-        "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
-        "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
-        "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 51,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8pPzsCY1g305",
-        "outputId": "72c4ea56-2610-41c6-f286-4c8289d3f0ac"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 2.189635498596314\n",
-            "MAE: 1.250413817712252\n",
-            "MAPE: 0.007984509559881612\n",
-            "Accuracy: 0.8551236749116607\n",
-            "Precision: 0.8558823529411764\n",
-            "Confusion Matrix:\n",
-            " [[628  98]\n",
-            " [107 582]]\n",
-            "Recall: 0.8447024673439768\n",
-            "F1 Score: 0.8502556610664718\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse3)\n",
-        "print(\"MAE:\", mae3)\n",
-        "print(\"MAPE:\", mape3)\n",
-        "print(\"Accuracy:\", accuracy3)\n",
-        "print(\"Precision:\", precision3)\n",
-        "print(\"Confusion Matrix:\\n\", confusion3)\n",
-        "print(\"Recall:\", recall3)\n",
-        "print(\"F1 Score:\", f13)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mZsLwLivhLGH"
-      },
-      "source": [
-        "## 4. Gradient Boosting Models (GBM)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 52,
-      "metadata": {
-        "id": "TI8idoxOg6jF"
-      },
-      "outputs": [],
-      "source": [
-        "import xgboost as xgb\n",
-        "# Create an XGBoost model\n",
-        "model4 = xgb.XGBRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 53,
-      "metadata": {
-        "id": "7r9xJDtOVBEA"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 54,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "2gpbDxshhexj",
-        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 54
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model4.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 55,
-      "metadata": {
-        "id": "Jj9DXdUPhh9V"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred4 = model4.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 56,
-      "metadata": {
-        "id": "TdH60Sllhn5O"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
-        "mae4 = mean_absolute_error(y_test, pred4)\n",
-        "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
-        "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
-        "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
-        "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
-        "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
-        "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 57,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "qpnLeFyZhwB3",
-        "outputId": "4dcac062-ec60-4b2c-ab4b-dcda1b0f2341"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 2.733930065274145\n",
-            "MAE: 1.502457380471909\n",
-            "MAPE: 0.010026410639661481\n",
-            "Accuracy: 0.8840989399293286\n",
-            "Precision: 0.8948106591865358\n",
-            "Confusion Matrix:\n",
-            " [[613  75]\n",
-            " [ 89 638]]\n",
-            "Recall: 0.8775790921595599\n",
-            "F1 Score: 0.8861111111111112\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse4)\n",
-        "print(\"MAE:\", mae4)\n",
-        "print(\"MAPE:\", mape4)\n",
-        "print(\"Accuracy:\", accuracy4)\n",
-        "print(\"Precision:\", precision4)\n",
-        "print(\"Confusion Matrix:\\n\", confusion4)\n",
-        "print(\"Recall:\", recall4)\n",
-        "print(\"F1 Score:\", f14)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d8nSGoyuh9dx"
-      },
-      "source": [
-        "## 5. Extreme Gradient Boosting (XGBoost)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 58,
-      "metadata": {
-        "id": "DyhhdlZAhx94"
-      },
-      "outputs": [],
-      "source": [
-        "import xgboost as xgb\n",
-        "# Create an XGBoost model\n",
-        "model5 = xgb.XGBRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 59,
-      "metadata": {
-        "id": "Z_AD0lVOVHwB"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 60,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "RAIwxIp5iH9Z",
-        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 60
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model5.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 61,
-      "metadata": {
-        "id": "XmJds5fYiKT3"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred5 = model5.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 62,
-      "metadata": {
-        "id": "lZ1A0-L8iNCM"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
-        "mae5 = mean_absolute_error(y_test, pred5)\n",
-        "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
-        "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
-        "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
-        "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
-        "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
-        "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 63,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7IkE-RAmiWNo",
-        "outputId": "cf4c1d84-412b-4a18-f70c-65ce637772ea"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 2.733930065274145\n",
-            "MAE: 1.502457380471909\n",
-            "MAPE: 0.010026410639661481\n",
-            "Accuracy: 0.8840989399293286\n",
-            "Precision: 0.8948106591865358\n",
-            "Confusion Matrix:\n",
-            " [[613  75]\n",
-            " [ 89 638]]\n",
-            "Recall: 0.8775790921595599\n",
-            "F1 Score: 0.8861111111111112\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse5)\n",
-        "print(\"MAE:\", mae5)\n",
-        "print(\"MAPE:\", mape5)\n",
-        "print(\"Accuracy:\", accuracy5)\n",
-        "print(\"Precision:\", precision5)\n",
-        "print(\"Confusion Matrix:\\n\", confusion5)\n",
-        "print(\"Recall:\", recall5)\n",
-        "print(\"F1 Score:\", f15)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A_J776rtiovq"
-      },
-      "source": [
-        "## 6. AdaBoostRegressor"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 64,
-      "metadata": {
-        "id": "HNq66cXRiYPJ"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.ensemble import AdaBoostRegressor\n",
-        "# Create an AdaBoost model\n",
-        "model6 = AdaBoostRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 65,
-      "metadata": {
-        "id": "qPHH6rG0VW4V"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 66,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "P0oB5wjQivBr",
-        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "AdaBoostRegressor()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 66
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model6.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 67,
-      "metadata": {
-        "id": "Bf1m5ukOi2VM"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred6 = model6.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 68,
-      "metadata": {
-        "id": "oFWSqC4ai6gd"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
-        "mae6 = mean_absolute_error(y_test, pred6)\n",
-        "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
-        "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
-        "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
-        "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
-        "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
-        "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 69,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "BsajWJGBjC80",
-        "outputId": "1af1194f-9a33-40af-8578-c99832509c1b"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 9.283285018137352\n",
-            "MAE: 7.574989783595977\n",
-            "MAPE: 0.16829256716397573\n",
-            "Accuracy: 0.9901060070671378\n",
-            "Precision: 0.9900990099009901\n",
-            "Confusion Matrix:\n",
-            " [[901   5]\n",
-            " [  9 500]]\n",
-            "Recall: 0.9823182711198428\n",
-            "F1 Score: 0.9861932938856016\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse6)\n",
-        "print(\"MAE:\", mae6)\n",
-        "print(\"MAPE:\", mape6)\n",
-        "print(\"Accuracy:\", accuracy6)\n",
-        "print(\"Precision:\", precision6)\n",
-        "print(\"Confusion Matrix:\\n\", confusion6)\n",
-        "print(\"Recall:\", recall6)\n",
-        "print(\"F1 Score:\", f16)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Q9DzOt3CkWFX"
-      },
-      "source": [
-        "## 7. Decision Tree"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 70,
-      "metadata": {
-        "id": "23DZ2biSjF9a"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.tree import DecisionTreeRegressor\n",
-        "# Create a Decision Tree model\n",
-        "model7 = DecisionTreeRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 71,
-      "metadata": {
-        "id": "Ajo2RAVAVb7H"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 72,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "6mQEQf-ykc9F",
-        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "DecisionTreeRegressor()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 72
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model7.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 73,
-      "metadata": {
-        "id": "BFJ9q_tvkgRC"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred7 = model7.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 74,
-      "metadata": {
-        "id": "9IxfYZbYkjv1"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
-        "mae7 = mean_absolute_error(y_test, pred7)\n",
-        "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
-        "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
-        "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
-        "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
-        "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
-        "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 75,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "AnZXMYb8kooV",
-        "outputId": "273fa9ed-d6f2-4c4d-fb0e-a643f5ef5732"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 3.193539964582351\n",
-            "MAE: 1.6240937361593886\n",
-            "MAPE: 0.010136361140005275\n",
-            "Accuracy: 0.8579505300353357\n",
-            "Precision: 0.8700410396716827\n",
-            "Confusion Matrix:\n",
-            " [[578  95]\n",
-            " [106 636]]\n",
-            "Recall: 0.8571428571428571\n",
-            "F1 Score: 0.8635437881873728\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse7)\n",
-        "print(\"MAE:\", mae7)\n",
-        "print(\"MAPE:\", mape7)\n",
-        "print(\"Accuracy:\", accuracy7)\n",
-        "print(\"Precision:\", precision7)\n",
-        "print(\"Confusion Matrix:\\n\", confusion7)\n",
-        "print(\"Recall:\", recall7)\n",
-        "print(\"F1 Score:\", f17)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LH-B-Xd6k5UD"
-      },
-      "source": [
-        "## 8. KNeighborsRegressor(KNN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 76,
-      "metadata": {
-        "id": "JVDSed7yktFY"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.neighbors import KNeighborsRegressor\n",
-        "# Create a KNN model\n",
-        "model8 = KNeighborsRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 77,
-      "metadata": {
-        "id": "XJHb5SxrVgVp"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 78,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "9fn64o-ZlBka",
-        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "KNeighborsRegressor()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 78
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model8.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 79,
-      "metadata": {
-        "id": "hbfbbjcSlDn7"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred8 = model8.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 80,
-      "metadata": {
-        "id": "hnWyNv3blHdL"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
-        "mae8 = mean_absolute_error(y_test, pred8)\n",
-        "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
-        "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
-        "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
-        "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
-        "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
-        "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 81,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "IPoDRkcMlMAr",
-        "outputId": "9892f42f-e65f-46c0-eeed-77ce32f6a7eb"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 148.73183825029315\n",
-            "MAE: 109.35229571264969\n",
-            "MAPE: 1.75024316976612\n",
-            "Accuracy: 0.9908127208480565\n",
-            "Precision: 0.9887820512820513\n",
-            "Confusion Matrix:\n",
-            " [[785   7]\n",
-            " [  6 617]]\n",
-            "Recall: 0.9903691813804173\n",
-            "F1 Score: 0.9895749799518845\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse8)\n",
-        "print(\"MAE:\", mae8)\n",
-        "print(\"MAPE:\", mape8)\n",
-        "print(\"Accuracy:\", accuracy8)\n",
-        "print(\"Precision:\", precision8)\n",
-        "print(\"Confusion Matrix:\\n\", confusion8)\n",
-        "print(\"Recall:\", recall8)\n",
-        "print(\"F1 Score:\", f18)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "X5XtlzMXljps"
-      },
-      "source": [
-        "## 9. Artificial Neural Networks (ANN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 82,
-      "metadata": {
-        "id": "bJk1-9VhlRL6"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Dense"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 83,
-      "metadata": {
-        "id": "sZVPMR9Wlo7-"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 84,
-      "metadata": {
-        "id": "vd1fDjQiltP4"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an ANN model\n",
-        "model9 = Sequential()\n",
-        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
-        "model9.add(Dense(16, activation='relu'))\n",
-        "model9.add(Dense(1, activation='linear'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 85,
-      "metadata": {
-        "id": "ZIf94WLMlv04"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model9.compile(loss='mean_squared_error', optimizer='adam')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 86,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "FX5DTKqslxWf",
-        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca68076d540>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 86
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 87,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OVW2qpNsmGVq",
-        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "45/45 [==============================] - 0s 1ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred9 = model9.predict(X_test_scaled).flatten()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 88,
-      "metadata": {
-        "id": "CqRmjMj2maJY"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
-        "mae9 = mean_absolute_error(y_test, pred9)\n",
-        "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
-        "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
-        "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
-        "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
-        "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
-        "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 89,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5zuwkC1emmh3",
-        "outputId": "5d6a0e05-3112-4d27-f5fb-ed665867b22d"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 2.7570259701356035\n",
-            "MAE: 1.7412277270507284\n",
-            "MAPE: 0.012205298865408084\n",
-            "Accuracy: 0.8904593639575972\n",
-            "Precision: 0.8242753623188406\n",
-            "Confusion Matrix:\n",
-            " [[805  97]\n",
-            " [ 58 455]]\n",
-            "Recall: 0.8869395711500975\n",
-            "F1 Score: 0.8544600938967135\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse9)\n",
-        "print(\"MAE:\", mae9)\n",
-        "print(\"MAPE:\", mape9)\n",
-        "print(\"Accuracy:\", accuracy9)\n",
-        "print(\"Precision:\", precision9)\n",
-        "print(\"Confusion Matrix:\\n\", confusion9)\n",
-        "print(\"Recall:\", recall9)\n",
-        "print(\"F1 Score:\", f19)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vjSMQNcOnFPJ"
-      },
-      "source": [
-        "## 10. LSTM(Long Short term Memory)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 90,
-      "metadata": {
-        "id": "nCoyUanhnDKw"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import LSTM, Dense"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 91,
-      "metadata": {
-        "id": "ThcXESVEVv0U"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 92,
-      "metadata": {
-        "id": "uACvajfImrbB"
-      },
-      "outputs": [],
-      "source": [
-        "# Reshape the input data for LSTM\n",
-        "n_features = X_train_scaled.shape[1]\n",
-        "n_steps = 10\n",
-        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
-        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
-        "\n",
-        "# Reshape the input data\n",
-        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
-        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 93,
-      "metadata": {
-        "id": "r066pVYpnXH5"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an LSTM model\n",
-        "model = Sequential()\n",
-        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
-        "model.add(Dense(1))\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 94,
-      "metadata": {
-        "id": "YpSfHu6gov35"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 95,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0vHjcluaoxzP",
-        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca680459cc0>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 95
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 96,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "gEE06_TjozYv",
-        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "44/44 [==============================] - 0s 4ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Make predictions on the test set\n",
-        "y_pred = model.predict(X_test_reshaped).flatten()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 113,
-      "metadata": {
-        "id": "7k6C8DrxpB_Q"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
-        "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
-        "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
-        "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 114,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "i_6-UUDhpi0c",
-        "outputId": "3dcc5761-03b6-4b52-dfe6-08dece835c8d"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 10.083053125286519\n",
-            "MAE: 7.973378150691296\n",
-            "MAPE: 0.12730792351246625\n",
-            "Accuracy: 0.9886201991465149\n",
-            "Precision: 0.9904912836767037\n",
-            "Recall: 0.984251968503937\n",
-            "F1 Score: 0.9873617693522907\n",
-            "Confusion Matrix:\n",
-            " [[765   6]\n",
-            " [ 10 625]]\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print evaluation metrics\n",
-        "print(\"RMSE:\", rmse10)\n",
-        "print(\"MAE:\", mae10)\n",
-        "print(\"MAPE:\", mape10)\n",
-        "print(\"Accuracy:\", accuracy10)\n",
-        "print(\"Precision:\", precision10)\n",
-        "print(\"Recall:\", recall10)\n",
-        "print(\"F1 Score:\", f110)\n",
-        "print(\"Confusion Matrix:\\n\", confusion10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
-        "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n",
-        "\n",
-        "# List of corresponding labels for each accuracy\n",
-        "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, accuracies, color='blue')\n",
-        "plt.xlabel('Accuracy Variables')\n",
-        "plt.ylabel('Accuracy Values')\n",
-        "plt.title('Bar Graph of Accuracies')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "qpWPtph9CGip",
-        "outputId": "c099cb8d-96af-4223-f499-743040aecdf1"
-      },
-      "execution_count": 117,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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8z5c938jixYuNFi1aGO7u7kbVqlWNQYMGGb/88kuB27vZpec9evQwPDw8jOzs7OuuM3ToUKNixYrG2bNnDcMwjN9++82Ijo42brnlFsPNzc2oWbOmMWTIEOtyw7h6SfgLL7xghIaGGhUrVjQCAwONhx56yDhy5Ih1nTNnzhh9+/Y1KlWqZFSpUsV44oknjH379hV46bmXl1eB2X788UcjIiLCqFy5slG9enXj8ccfN3bv3p1vG4ZhGPv27TN69+5t+Pn5GR4eHkbDhg2NiRMn5nvP/vw7WL9+vdG1a1fD19fX8PDwMOrVq2cMHTrUepuBs2fPGlFRUUZYWJjh5eVl+Pr6GuHh4cZnn312w/ceKEkWw3DCGYAATM1isSgqKqrAQzcAUNo4ZwcAAJgaZQcAAJgaZQcAAJgaV2MBcDhOBQRQljCzAwAATI2yAwAATI3DWLr6nS+//vqrvL29i/UtxAAAoPQYhqHMzEwFBwff8CtxKDuSfv31V9WqVcvZMQAAQBGcPHlSNWvWvO5yyo4kb29vSVffrGu38AcAAGVbRkaGatWqZf0cvx7KjmQ9dOXj40PZAQCgnLnZKSicoAwAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEzNqWVn06ZN6tGjh4KDg2WxWLRixQqb5YZhaNKkSQoKCpKnp6ciIiJ06NAhm3XOnTunQYMGycfHR35+foqMjFRWVlYp/hQAAKAsc2rZyc7OVrNmzfT+++8XuHzatGmaMWOGZs+erS1btsjLy0tdu3bVpUuXrOsMGjRIP/zwgxISErRq1Spt2rRJI0aMKK0fAQAAlHEWwzAMZ4eQrn6J1/Lly9WrVy9JV2d1goOD9cwzz2jcuHGSpPT0dAUEBGjevHkaMGCA9u/fr9tuu03btm1T69atJUmrV69Wt27d9Msvvyg4OLhQ+87IyJCvr6/S09P5IlAAAMqJwn5+l9lzdo4ePark5GRFRERYx3x9fRUeHq7NmzdLkjZv3iw/Pz9r0ZGkiIgIubi4aMuWLdfddk5OjjIyMmweAADAnMps2UlOTpYkBQQE2IwHBARYlyUnJ8vf399meYUKFVS1alXrOgWJiYmRr6+v9VGrVi0HpwcAAGVFmS07JWnChAlKT0+3Pk6ePOnsSGWOxVL2HgAAFEWZLTuBgYGSpJSUFJvxlJQU67LAwEClpqbaLP/999917tw56zoFcXd3l4+Pj80DAACYU5ktO6GhoQoMDFRiYqJ1LCMjQ1u2bFG7du0kSe3atVNaWpp27NhhXWfdunXKy8tTeHh4qWcGAABlTwVn7jwrK0uHDx+2Pj969KiSkpJUtWpV1a5dW6NHj9arr76qBg0aKDQ0VBMnTlRwcLD1iq1GjRrpvvvu0+OPP67Zs2frypUrio6O1oABAwp9JRaAv56yeFi0bFwXWzJ4v0sX73d+Ti0727dvV6dOnazPx44dK0kaMmSI5s2bp2effVbZ2dkaMWKE0tLS1KFDB61evVoeHh7W18TFxSk6OlqdO3eWi4uL+vbtqxkzZpT6zwIAAMqmMnOfHWfiPjv58ZcBzIx/36WL97t0/ZXe73J/nx0AAABHoOwAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTq+DsAAAAlEUWi7MT5GcYzk5QPjGzAwAATI2ZHaAMKK9/QZbX3AD+WpjZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApsYdlAGgnOCO1UDRUHZgKnwYAAD+jMNYAADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1LjPTgnjvi8AADgXMzsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUynTZyc3N1cSJExUaGipPT0/Vq1dPr7zyiow/XDttGIYmTZqkoKAgeXp6KiIiQocOHXJiagAAUJaU6bIzdepUzZo1S++9957279+vqVOnatq0aZo5c6Z1nWnTpmnGjBmaPXu2tmzZIi8vL3Xt2lWXLl1yYnIAAFBWlOmbCv73v/9Vz5491b17d0lSnTp1tGjRIm3dulXS1Vmdd955Ry+++KJ69uwpSfrkk08UEBCgFStWaMCAAU7LDgAAyoYyPbPTvn17JSYm6uDBg5Kk3bt367vvvtP9998vSTp69KiSk5MVERFhfY2vr6/Cw8O1efPm6243JydHGRkZNg8AAGBOZXpmZ/z48crIyFBYWJhcXV2Vm5ur1157TYMGDZIkJScnS5ICAgJsXhcQEGBdVpCYmBhNmTKl5IIDAIAyo0zP7Hz22WeKi4vTwoULtXPnTs2fP19vvfWW5s+fX6ztTpgwQenp6dbHyZMnHZQYAACUNWV6Zuef//ynxo8fbz33pmnTpjp+/LhiYmI0ZMgQBQYGSpJSUlIUFBRkfV1KSoqaN29+3e26u7vL3d29RLMDAICyoUzP7Fy4cEEuLrYRXV1dlZeXJ0kKDQ1VYGCgEhMTrcszMjK0ZcsWtWvXrlSzAgCAsqlMz+z06NFDr732mmrXrq3GjRtr165dmj59uoYPHy5JslgsGj16tF599VU1aNBAoaGhmjhxooKDg9WrVy/nhgcAAGVCmS47M2fO1MSJEzVy5EilpqYqODhYTzzxhCZNmmRd59lnn1V2drZGjBihtLQ0dejQQatXr5aHh4cTkwMAgLLCYvzxdsR/URkZGfL19VV6erp8fHwcum2LxaGbc4jC/MbJ7TjkLl3kLl3kLl1mzl0Uhf38LtPn7AAAABQXZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJia3WXn4sWLunDhgvX58ePH9c4772jt2rUODQYAAOAIdpednj176pNPPpEkpaWlKTw8XG+//bZ69uypWbNmOTwgAABAcdhddnbu3KmOHTtKkj7//HMFBATo+PHj+uSTTzRjxgyHBwQAACgOu8vOhQsX5O3tLUlau3at+vTpIxcXF7Vt21bHjx93eEAAAIDisLvs1K9fXytWrNDJkye1Zs0a3XvvvZKk1NRU+fj4ODwgAABAcdhddiZNmqRx48apTp06+tvf/qZ27dpJujrL06JFC4cHBAAAKA6LYRiGvS9KTk7W6dOn1axZM7m4XO1LW7dulY+Pj8LCwhwesqRlZGTI19dX6enpDp+dslgcujmHKMxvnNyOQ+7SRe7SRe7SZebcRVHYz+8i3WcnMDBQ3t7eSkhI0MWLFyVJbdq0KZdFBwAAmJvdZee3335T586ddeutt6pbt246ffq0JCkyMlLPPPOMwwMCAAAUh91lZ8yYMapYsaJOnDihSpUqWcf79++v1atXOzQcAABAcVWw9wVr167VmjVrVLNmTZvxBg0acOk5AAAoc+ye2cnOzraZ0bnm3Llzcnd3d0goAAAAR7G77HTs2NH6dRGSZLFYlJeXp2nTpqlTp04ODQcAAFBcdh/GmjZtmjp37qzt27fr8uXLevbZZ/XDDz/o3Llz+v7770siIwAAQJHZPbPTpEkTHTx4UB06dFDPnj2VnZ2tPn36aNeuXapXr15JZAQAACiyIt1U0Gy4qWB+5HYccpcucpcucpcuM+cuisJ+ftt9GGvTpk03XH7nnXfau0kAAIASY3fZufvuu/ONWf5QI3Nzc4sVCAAAwJHsPmfn/PnzNo/U1FStXr1abdq00dq1a0siIwAAQJHZPbPj6+ubb6xLly5yc3PT2LFjtWPHDocEAwAAcIQifRFoQQICAnTgwAFHbQ4AAMAh7J7Z2bNnj81zwzB0+vRpvfHGG2revLmjcgEAADiE3WWnefPmslgs+vMV623bttXcuXMdFgwAAMAR7C47R48etXnu4uKiGjVqyMPDw2GhAAAAHMXuc3ZCQkJsHrVq1SrRonPq1Ck98sgjqlatmjw9PdW0aVNt377dutwwDE2aNElBQUHy9PRURESEDh06VGJ5AABA+VKomZ0ZM2YUeoNPP/10kcP82fnz53XHHXeoU6dO+uabb1SjRg0dOnRIVapUsa4zbdo0zZgxQ/Pnz1doaKgmTpyorl276scff2S2CQAAFO7rIkJDQwu3MYtFP//8c7FDXTN+/Hh9//33+s9//lPgcsMwFBwcrGeeeUbjxo2TJKWnpysgIEDz5s3TgAEDCrUfvi4iP3I7DrlLF7lLF7lLl5lzF4VDvy7iz+fplJaVK1eqa9eu6tevnzZu3KhbbrlFI0eO1OOPP27NlZycrIiICOtrfH19FR4ers2bN1+37OTk5CgnJ8f6PCMjo2R/EAAA4DQOu89OSfj55581a9YsNWjQQGvWrNGTTz6pp59+WvPnz5ckJScnS7p6j58/CggIsC4rSExMjHx9fa2PWrVqldwPAQAAnMruq7Ek6ZdfftHKlSt14sQJXb582WbZ9OnTHRJMkvLy8tS6dWu9/vrrkqQWLVpo3759mj17toYMGVLk7U6YMEFjx461Ps/IyKDwAABgUnaXncTERD344IOqW7eufvrpJzVp0kTHjh2TYRhq2bKlQ8MFBQXptttusxlr1KiRli5dKkkKDAyUJKWkpCgoKMi6TkpKyg1vcOju7i53d3eHZgUAAGWT3YexJkyYoHHjxmnv3r3y8PDQ0qVLdfLkSd11113q16+fQ8Pdcccd+b6C4uDBgwoJCZF09cTpwMBAJSYmWpdnZGRoy5YtateunUOzAACAcsqwU+XKlY3Dhw8bhmEYfn5+xr59+wzDMIykpCQjJCTE3s3d0NatW40KFSoYr732mnHo0CEjLi7OqFSpkvHpp59a13njjTcMPz8/44svvjD27Nlj9OzZ0wgNDTUuXrxY6P2kp6cbkoz09HSH5jcMw7h6DnrZepCb3OQuew9yk/uvnrsoCvv5bfdhLC8vL+t5OkFBQTpy5IgaN24sSTp79qwje5jatGmj5cuXa8KECXr55ZcVGhqqd955R4MGDbKu8+yzzyo7O1sjRoxQWlqaOnTooNWrV3OPHQAAIKmQ99n5o169eql79+56/PHHNW7cOH3xxRcaOnSoli1bpipVqujbb78tqawlhvvs5EduxyF36SJ36SJ36TJz7qJw6H12JOncuXOqWrWqpk+frqysLEnSlClTlJWVpcWLF6tBgwYOvRILAADAEQo9s+Ph4aFevXopMjJSXbp0KelcpYqZnfzI7TjkLl3kLl3kLl1mzl0Uhf38LvTVWB999JHOnDmj++67T3Xq1NFLL72kY8eOOSIrAABAiSl02Xn00UeVmJiow4cPa8iQIZo/f77q16+vLl26aPHixfluLggAAFAW2H2fndDQUE2ZMkVHjx7V6tWr5e/vr+HDhysoKMih33gOAADgCHZfjVWQpUuXWi/9zs3NdUSuUsU5O/mR23HIXbrIXbrIXbrMnLsoHH411p8dP35csbGxmj9/vk6ePKlOnTopMjKyqJsDAAAoEXaVnZycHC1dulRz587Vhg0bdMstt2jo0KEaNmyY6tSpU0IRAQAAiq7QZWfkyJGKj4/XhQsX1LNnT3399dfq0qWLLGVxvgwAAOD/K3TZ+e677zR58mQ98sgjqlatWklmAgAAcJhCl509e/aUZA4AAIASYfel5wAAAOUJZQcAAJgaZQcAAJgaZQcAAJia3WWnTp06evnll3XixImSyAMAAOBQdped0aNHa9myZapbt666dOmi+Ph45eTklEQ2AACAYitS2UlKStLWrVvVqFEjPfXUUwoKClJ0dLR27txZEhkBAACKrMjn7LRs2VIzZszQr7/+qsmTJ+vjjz9WmzZt1Lx5c82dO1cO+H5RAACAYivyF4FeuXJFy5cvV2xsrBISEtS2bVtFRkbql19+0fPPP69vv/1WCxcudGRWAAAAu9lddnbu3KnY2FgtWrRILi4uGjx4sP71r38pLCzMuk7v3r3Vpk0bhwYFAAAoCrvLTps2bdSlSxfNmjVLvXr1UsWKFfOtExoaqgEDBjgkIAAAQHHYXXZ+/vlnhYSE3HAdLy8vxcbGFjkUAACAo9h9gnJqaqq2bNmSb3zLli3avn27Q0IBAAA4it1lJyoqSidPnsw3furUKUVFRTkkFAAAgKPYXXZ+/PFHtWzZMt94ixYt9OOPPzokFAAAgKPYXXbc3d2VkpKSb/z06dOqUKHIV7IDAACUCLvLzr333qsJEyYoPT3dOpaWlqbnn39eXbp0cWg4AACA4rJ7Kuatt97SnXfeqZCQELVo0UKSlJSUpICAAC1YsMDhAQEAAIrD7rJzyy23aM+ePYqLi9Pu3bvl6empYcOGaeDAgQXecwcAAMCZinSSjZeXl0aMGOHoLAAAAA5X5DOKf/zxR504cUKXL1+2GX/wwQeLHQoAAMBRinQH5d69e2vv3r2yWCzWbze3WCySpNzcXMcmBAAAKAa7r8YaNWqUQkNDlZqaqkqVKumHH37Qpk2b1Lp1a23YsKEEIgIAABSd3TM7mzdv1rp161S9enW5uLjIxcVFHTp0UExMjJ5++mnt2rWrJHICAAAUid0zO7m5ufL29pYkVa9eXb/++qskKSQkRAcOHHBsOgAAgGKye2anSZMm2r17t0JDQxUeHq5p06bJzc1NH374oerWrVsSGQEAAIrM7rLz4osvKjs7W5L08ssv64EHHlDHjh1VrVo1LV682OEBAQAAisNiXLucqhjOnTunKlWqWK/IKm8yMjLk6+ur9PR0+fj4OHTbZfEtKcxvnNyOQ+7SRe7SRe7SZebcRVHYz2+7ztm5cuWKKlSooH379tmMV61atdwWHQAAYG52lZ2KFSuqdu3a3EsHAACUG3ZfjfXCCy/o+eef17lz50oiDwAAgEPZfYLye++9p8OHDys4OFghISHy8vKyWb5z506HhQMAACguu8tOr169SiAGAABAybC77EyePLkkcgAAAJQIu8/ZAQAAKE/sntlxcXG54WXmXKkFAADKErvLzvLly22eX7lyRbt27dL8+fM1ZcoUhwUDAABwBIfcQVmSFi5cqMWLF+uLL75wxOZKFXdQzo/cjkPu0kXu0kXu0mXm3EVRIndQvpG2bdsqMTHRUZsDAABwCIeUnYsXL2rGjBm65ZZbHLE5AAAAh7H7nJ0/f+GnYRjKzMxUpUqV9Omnnzo0HAAAQHHZXXb+9a9/2ZQdFxcX1ahRQ+Hh4apSpYpDwwEAABSX3WVn6NChJRADAACgZNh9zk5sbKyWLFmSb3zJkiWaP3++Q0IBAAA4it1lJyYmRtWrV8837u/vr9dff90hoQAAABzF7rJz4sQJhYaG5hsPCQnRiRMnHBIKAADAUewuO/7+/tqzZ0++8d27d6tatWoOCQUAAOAodpedgQMH6umnn9b69euVm5ur3NxcrVu3TqNGjdKAAQNKIiMAAECR2X011iuvvKJjx46pc+fOqlDh6svz8vI0ePBgztkBAABljt0zO25ublq8eLEOHDiguLg4LVu2TEeOHNHcuXPl5uZWEhmt3njjDVksFo0ePdo6dunSJUVFRalatWqqXLmy+vbtq5SUlBLNAQAAyg+7Z3auadCggRo0aODILDe0bds2/fvf/9btt99uMz5mzBh99dVXWrJkiXx9fRUdHa0+ffro+++/L7VsAACg7LJ7Zqdv376aOnVqvvFp06apX79+Dgn1Z1lZWRo0aJA++ugjm7s0p6ena86cOZo+fbruuecetWrVSrGxsfrvf/+r//3vfyWSBQAAlC92l51NmzapW7du+cbvv/9+bdq0ySGh/iwqKkrdu3dXRESEzfiOHTt05coVm/GwsDDVrl1bmzdvvu72cnJylJGRYfMAAADmZPdhrKysrALPzalYsWKJlIb4+Hjt3LlT27Zty7csOTlZbm5u8vPzsxkPCAhQcnLydbcZExOjKVOmODoqAAAog+ye2WnatKkWL16cbzw+Pl633XabQ0Jdc/LkSY0aNUpxcXHy8PBw2HYnTJig9PR06+PkyZMO2zYAAChb7J7ZmThxovr06aMjR47onnvukSQlJiZq0aJFBX5nVnHs2LFDqampatmypXUsNzdXmzZt0nvvvac1a9bo8uXLSktLs5ndSUlJUWBg4HW36+7uLnd3d4dmBQAAZZPdZadHjx5asWKFXn/9dX3++efy9PTU7bffrm+//VZ33XWXQ8N17txZe/futRkbNmyYwsLC9Nxzz6lWrVqqWLGiEhMT1bdvX0nSgQMHdOLECbVr186hWQAAQPlUpEvPu3fvru7du+cb37dvn5o0aVLsUNd4e3vn256Xl5eqVatmHY+MjNTYsWNVtWpV+fj46KmnnlK7du3Utm1bh+UAAADlV5Hvs3NNZmamFi1apI8//lg7duxQbm6uI3IV2r/+9S+5uLiob9++ysnJUdeuXfXBBx+UagYAAFB2WQzDMIrywk2bNunjjz/WsmXLFBwcrD59+qhv375q06aNozOWuIyMDPn6+io9PV0+Pj4O3bbF4tDNOURhfuPkdhxyly5yly5yly4z5y6Kwn5+2zWzk5ycrHnz5mnOnDnKyMjQww8/rJycHK1YscLhV2IBAAA4QqEvPe/Ro4caNmyoPXv26J133tGvv/6qmTNnlmQ2AACAYiv0zM4333yjp59+Wk8++WSpficWAABAcRR6Zue7775TZmamWrVqpfDwcL333ns6e/ZsSWYDAAAotkKXnbZt2+qjjz7S6dOn9cQTTyg+Pl7BwcHKy8tTQkKCMjMzSzInAABAkRT5aizp6g385syZowULFigtLU1dunTRypUrHZmvVHA1Vn7kdhxyly5yly5yly4z5y6Kwn5+2/3dWH/UsGFDTZs2Tb/88osWLVpUnE0BAACUiGLN7JgFMzv5kdtxyF26yF26yF26zJy7KEplZgcAAKCso+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTK9NlJyYmRm3atJG3t7f8/f3Vq1cvHThwwGadS5cuKSoqStWqVVPlypXVt29fpaSkOCkxAAAoa8p02dm4caOioqL0v//9TwkJCbpy5YruvfdeZWdnW9cZM2aMvvzySy1ZskQbN27Ur7/+qj59+jgxNQAAKEsshmEYzg5RWGfOnJG/v782btyoO++8U+np6apRo4YWLlyohx56SJL0008/qVGjRtq8ebPatm1bqO1mZGTI19dX6enp8vHxcWhmi8Whm3OIwvzGye045C5d5C5d5C5dZs5dFIX9/C7TMzt/lp6eLkmqWrWqJGnHjh26cuWKIiIirOuEhYWpdu3a2rx583W3k5OTo4yMDJsHAAAwp3JTdvLy8jR69GjdcccdatKkiSQpOTlZbm5u8vPzs1k3ICBAycnJ191WTEyMfH19rY9atWqVZHQAAOBE5absREVFad++fYqPjy/2tiZMmKD09HTr4+TJkw5ICAAAyqIKzg5QGNHR0Vq1apU2bdqkmjVrWscDAwN1+fJlpaWl2czupKSkKDAw8Lrbc3d3l7u7e0lGBgAAZUSZntkxDEPR0dFavny51q1bp9DQUJvlrVq1UsWKFZWYmGgdO3DggE6cOKF27dqVdlwAAFAGlemZnaioKC1cuFBffPGFvL29refh+Pr6ytPTU76+voqMjNTYsWNVtWpV+fj46KmnnlK7du0KfSUWAAAwtzJddmbNmiVJuvvuu23GY2NjNXToUEnSv/71L7m4uKhv377KyclR165d9cEHH5RyUgAAUFaVq/vslBTus5MfuR2H3KWL3KWL3KXLzLmLwpT32QEAALAXZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJiaacrO+++/rzp16sjDw0Ph4eHaunWrsyMBAIAywBRlZ/HixRo7dqwmT56snTt3qlmzZuratatSU1OdHQ0AADiZKcrO9OnT9fjjj2vYsGG67bbbNHv2bFWqVElz5851djQAAOBk5b7sXL58WTt27FBERIR1zMXFRREREdq8ebMTkwEAgLKggrMDFNfZs2eVm5urgIAAm/GAgAD99NNPBb4mJydHOTk51ufp6emSpIyMjJILWoaU1x+T3KWL3KWL3KWL3KWrpHJf+9w2DOOG65X7slMUMTExmjJlSr7xWrVqOSFN6fP1dXaCoiF36SJ36SJ36SJ36Srp3JmZmfK9wU7KfdmpXr26XF1dlZKSYjOekpKiwMDAAl8zYcIEjR071vo8Ly9P586dU7Vq1WSxWEo0b1FlZGSoVq1aOnnypHx8fJwdp9DIXbrIXbrIXbrIXbrKQ27DMJSZmang4OAbrlfuy46bm5tatWqlxMRE9erVS9LV8pKYmKjo6OgCX+Pu7i53d3ebMT8/vxJO6hg+Pj5l9h/djZC7dJG7dJG7dJG7dJX13Dea0bmm3JcdSRo7dqyGDBmi1q1b629/+5veeecdZWdna9iwYc6OBgAAnMwUZad///46c+aMJk2apOTkZDVv3lyrV6/Od9IyAAD46zFF2ZGk6Ojo6x62MgN3d3dNnjw53+G3so7cpYvcpYvcpYvcpau85i6IxbjZ9VoAAADlWLm/qSAAAMCNUHYAAICpUXYAAICpUXYAAICpUXbKuE2bNqlHjx4KDg6WxWLRihUrnB3ppmJiYtSmTRt5e3vL399fvXr10oEDB5wd66ZmzZql22+/3XoDrXbt2umbb75xdiy7vfHGG7JYLBo9erSzo9zQSy+9JIvFYvMICwtzdqxCOXXqlB555BFVq1ZNnp6eatq0qbZv3+7sWDdUp06dfO+3xWJRVFSUs6PdUG5uriZOnKjQ0FB5enqqXr16euWVV276XUhlQWZmpkaPHq2QkBB5enqqffv22rZtm7Nj2bjZZ4xhGJo0aZKCgoLk6empiIgIHTp0yDlhi4GyU8ZlZ2erWbNmev/9950dpdA2btyoqKgo/e9//1NCQoKuXLmie++9V9nZ2c6OdkM1a9bUG2+8oR07dmj79u2655571LNnT/3www/OjlZo27Zt07///W/dfvvtzo5SKI0bN9bp06etj++++87ZkW7q/PnzuuOOO1SxYkV98803+vHHH/X222+rSpUqzo52Q9u2bbN5rxMSEiRJ/fr1c3KyG5s6dapmzZql9957T/v379fUqVM1bdo0zZw509nRbuqxxx5TQkKCFixYoL179+ree+9VRESETp065exoVjf7jJk2bZpmzJih2bNna8uWLfLy8lLXrl116dKlUk5aTAbKDUnG8uXLnR3DbqmpqYYkY+PGjc6OYrcqVaoYH3/8sbNjFEpmZqbRoEEDIyEhwbjrrruMUaNGOTvSDU2ePNlo1qyZs2PY7bnnnjM6dOjg7BjFNmrUKKNevXpGXl6es6PcUPfu3Y3hw4fbjPXp08cYNGiQkxIVzoULFwxXV1dj1apVNuMtW7Y0XnjhBSelurE/f8bk5eUZgYGBxptvvmkdS0tLM9zd3Y1FixY5IWHRMbODEpeeni5Jqlq1qpOTFF5ubq7i4+OVnZ2tdu3aOTtOoURFRal79+6KiIhwdpRCO3TokIKDg1W3bl0NGjRIJ06ccHakm1q5cqVat26tfv36yd/fXy1atNBHH33k7Fh2uXz5sj799FMNHz68zH758TXt27dXYmKiDh48KEnavXu3vvvuO91///1OTnZjv//+u3Jzc+Xh4WEz7unpWS5mMCXp6NGjSk5Otvl/iq+vr8LDw7V582YnJrOfae6gjLIpLy9Po0eP1h133KEmTZo4O85N7d27V+3atdOlS5dUuXJlLV++XLfddpuzY91UfHy8du7cWebOB7iR8PBwzZs3Tw0bNtTp06c1ZcoUdezYUfv27ZO3t7ez413Xzz//rFmzZmns2LF6/vnntW3bNj399NNyc3PTkCFDnB2vUFasWKG0tDQNHTrU2VFuavz48crIyFBYWJhcXV2Vm5ur1157TYMGDXJ2tBvy9vZWu3bt9Morr6hRo0YKCAjQokWLtHnzZtWvX9/Z8QolOTlZkvJ99VJAQIB1WXlB2UGJioqK0r59+8rNXzINGzZUUlKS0tPT9fnnn2vIkCHauHFjmS48J0+e1KhRo5SQkJDvr8iy7I9/md9+++0KDw9XSEiIPvvsM0VGRjox2Y3l5eWpdevWev311yVJLVq00L59+zR79uxyU3bmzJmj+++/X8HBwc6OclOfffaZ4uLitHDhQjVu3FhJSUkaPXq0goODy/z7vWDBAg0fPly33HKLXF1d1bJlSw0cOFA7duxwdrS/HA5jocRER0dr1apVWr9+vWrWrOnsOIXi5uam+vXrq1WrVoqJiVGzZs307rvvOjvWDe3YsUOpqalq2bKlKlSooAoVKmjjxo2aMWOGKlSooNzcXGdHLBQ/Pz/deuutOnz4sLOj3FBQUFC+8tuoUaNycQhOko4fP65vv/1Wjz32mLOjFMo///lPjR8/XgMGDFDTpk316KOPasyYMYqJiXF2tJuqV6+eNm7cqKysLJ08eVJbt27VlStXVLduXWdHK5TAwEBJUkpKis14SkqKdVl5QdmBwxmGoejoaC1fvlzr1q1TaGiosyMVWV5ennJycpwd44Y6d+6svXv3Kikpyfpo3bq1Bg0apKSkJLm6ujo7YqFkZWXpyJEjCgoKcnaUG7rjjjvy3Urh4MGDCgkJcVIi+8TGxsrf31/du3d3dpRCuXDhglxcbD+qXF1dlZeX56RE9vPy8lJQUJDOnz+vNWvWqGfPns6OVCihoaEKDAxUYmKidSwjI0NbtmwpN+cyXsNhrDIuKyvL5i/do0ePKikpSVWrVlXt2rWdmOz6oqKitHDhQn3xxRfy9va2Htv19fWVp6enk9Nd34QJE3T//ferdu3ayszM1MKFC7VhwwatWbPG2dFuyNvbO9/5UF5eXqpWrVqZPk9q3Lhx6tGjh0JCQvTrr79q8uTJcnV11cCBA50d7YbGjBmj9u3b6/XXX9fDDz+srVu36sMPP9SHH37o7Gg3lZeXp9jYWA0ZMkQVKpSP//336NFDr732mmrXrq3GjRtr165dmj59uoYPH+7saDe1Zs0aGYahhg0b6vDhw/rnP/+psLAwDRs2zNnRrG72GTN69Gi9+uqratCggUJDQzVx4kQFBwerV69ezgtdFM6+HAw3tn79ekNSvseQIUOcHe26CsoryYiNjXV2tBsaPny4ERISYri5uRk1atQwOnfubKxdu9bZsYqkPFx63r9/fyMoKMhwc3MzbrnlFqN///7G4cOHnR2rUL788kujSZMmhru7uxEWFmZ8+OGHzo5UKGvWrDEkGQcOHHB2lELLyMgwRo0aZdSuXdvw8PAw6tata7zwwgtGTk6Os6Pd1OLFi426desabm5uRmBgoBEVFWWkpaU5O5aNm33G5OXlGRMnTjQCAgIMd3d3o3PnzuXq3881FsMoB7ehBAAAKCLO2QEAAKZG2QEAAKZG2QEAAKZG2QEAAKZG2QEAAKZG2QEAAKZG2QEAAKZG2QEABzh27JgsFouSkpIK/ZqhQ4fe9E60d999t0aPHl2sbMBfHWUH+IvYvHmzXF1dy813IpWEpk2b6h//+EeByxYsWCB3d3edPXu2SNuuVauWTp8+Xaa/ogP4q6LsAH8Rc+bM0VNPPaVNmzbp119/dWqWy5cvO2W/kZGRio+P18WLF/Mti42N1YMPPqjq1avbvd3Lly/L1dVVgYGB5eY7p4C/EsoO8BeQlZWlxYsX68knn1T37t01b968fOt8+eWXatOmjTw8PFS9enX17t3buiwnJ0fPPfecatWqJXd3d9WvX19z5syRJM2bN09+fn4221qxYoUsFov1+UsvvaTmzZvr448/VmhoqDw8PCRJq1evVocOHeTn56dq1arpgQce0JEjR2y29csvv2jgwIGqWrWqvLy81Lp1a23ZskXHjh2Ti4uLtm/fbrP+O++8o5CQkAK/FfuRRx7RxYsXtXTpUpvxo0ePasOGDYqMjNSRI0fUs2dPBQQEqHLlymrTpo2+/fZbm/Xr1KmjV155RYMHD5aPj49GjBiR7zBWbm6uIiMjFRoaKk9PTzVs2FDvvvtuAb8dacqUKapRo4Z8fHz0j3/844ZlMCcnR+PGjdMtt9wiLy8vhYeHa8OGDdblx48fV48ePVSlShV5eXmpcePG+vrrr6+7PeCvgLID/AV89tlnCgsLU8OGDfXII49o7ty5+uPX4n311Vfq3bu3unXrpl27dikxMVF/+9vfrMsHDx6sRYsWacaMGdq/f7/+/e9/q3LlynZlOHz4sJYuXaply5ZZC0F2drbGjh2r7du3KzExUS4uLurdu7e1qGRlZemuu+7SqVOntHLlSu3evVvPPvus8vLyVKdOHUVERCg2NtZmP7GxsRo6dKhcXPL/76169erq2bOn5s6dazM+b9481axZU/fee6+ysrLUrVs3JSYmateuXbrvvvvUo0cPnThxwuY1b731lpo1a6Zdu3Zp4sSJ+faVl5enmjVrasmSJfrxxx81adIkPf/88/rss89s1ktMTNT+/fu1YcMGLVq0SMuWLdOUKVOu+z5GR0dr8+bNio+P1549e9SvXz/dd999OnTokCQpKipKOTk52rRpk/bu3aupU6fa/bsCTMfJX0QKoBS0b9/eeOeddwzDMIwrV64Y1atXN9avX29d3q5dO2PQoEEFvvbAgQOGJCMhIaHA5bGxsYavr6/N2PLly40//u9l8uTJRsWKFY3U1NQb5jxz5owhydi7d69hGIbx73//2/D29jZ+++23AtdfvHixUaVKFePSpUuGYRjGjh07DIvFYhw9evS6+1i9erVhsViMn3/+2TCMq9/qHBISYrz44ovXfU3jxo2NmTNnWp+HhIQYvXr1slnn6NGjhiRj165d191OVFSU0bdvX+vzIUOGGFWrVjWys7OtY7NmzTIqV65s5ObmGoZh+w32x48fN1xdXY1Tp07ZbLdz587GhAkTDMMwjKZNmxovvfTSdTMAf0XM7AAmd+DAAW3dulUDBw6UJFWoUEH9+/e3HoaSpKSkJHXu3LnA1yclJcnV1VV33XVXsXKEhISoRo0aNmOHDh3SwIEDVbduXfn4+KhOnTqSZJ1FSUpKUosWLVS1atUCt9mrVy+5urpq+fLlkq7O0HTq1Mm6nYJ06dJFNWvWtM4IJSYm6sSJExo2bJikq7NJ48aNU6NGjeTn56fKlStr//79+WZ2WrdufdOf+f3331erVq1Uo0YNVa5cWR9++GG+7TRr1kyVKlWyPm/Xrp2ysrJ08uTJfNvbu3evcnNzdeutt6py5crWx8aNG62H/55++mm9+uqruuOOOzR58mTt2bPnpjkBs6PsACY3Z84c/f777woODlaFChVUoUIFzZo1S0uXLlV6erokydPT87qvv9EySXJxcbE5JCZJV65cybeel5dXvrEePXro3Llz+uijj7RlyxZt2bJF0v+dwHyzfbu5uWnw4MGKjY3V5cuXtXDhQg0fPvymeYcOHar58+crLy9PsbGx6tSpk+rWrStJGjdunJYvX67XX39d//nPf5SUlKSmTZvmO4+moJ/nj+Lj4zVu3DhFRkZq7dq1SkpK0rBhw4p1cnZWVpZcXV21Y8cOJSUlWR/79++3ng/02GOP6eeff9ajjz6qvXv3qnXr1po5c2aR9wmYAWUHMLHff/9dn3zyid5++22bD8fdu3crODhYixYtkiTdfvvtSkxMLHAbTZs2VV5enjZu3Fjg8ho1aigzM1PZ2dnWscLca+a3337TgQMH9OKLL6pz585q1KiRzp8/b7PO7bffrqSkJJ07d+6623nsscf07bff6oMPPtDvv/+uPn363HTfw4YN08mTJ7Vs2TItX75ckZGR1mXff/+9hg4dqt69e6tp06YKDAzUsWPHbrrNP/v+++/Vvn17jRw5Ui1atFD9+vXznXwtSbt377a5Oux///ufKleurFq1auVbt0WLFsrNzVVqaqrq169v8wgMDLSuV6tWLf3jH//QsmXL9Mwzz+ijjz6yOz9gJpQdwMRWrVql8+fPKzIyUk2aNLF59O3b13ooa/LkyVq0aJEmT56s/fv3W09sla5eeTRkyBANHz5cK1assF65dO1E2/DwcFWqVEnPP/+8jhw5ooULFxZ4tdefValSRdWqVdOHH36ow4cPa926dRo7dqzNOgMHDlRgYKB69eql77//Xj///LOWLl2qzZs3W9dp1KiR2rZtq+eee04DBw686WyQJIWGhuqee+7RiBEj5O7ublOQGjRoYD2Jevfu3fr73/9e4JVdN9OgQQNt375da9as0cGDBzVx4kRt27Yt33qXL19WZGSkfvzxR3399deaPHmyoqOjCzzB+tZbb9WgQYM0ePBgLVu2TEePHtXWrVsVExOjr776SpI0evRorVmzRkePHtXOnTu1fv16NWrUyO78gJlQdgATmzNnjiIiIuTr65tvWd++fbV9+3bt2bNHd999t5YsWaKVK1eqefPmuueee7R161brurNmzdJDDz2kkSNHKiwsTI8//rh1Jqdq1ar69NNP9fXXX6tp06ZatGiRXnrppZtmc3FxUXx8vHbs2KEmTZpozJgxevPNN23WcXNz09q1a+Xv769u3bqpadOmeuONN+Tq6mqzXmRkpC5fvnzTQ1h/fs358+f197//3XopvCRNnz5dVapUUfv27dWjRw917dpVLVu2LPR2r3niiSfUp08f9e/fX+Hh4frtt980cuTIfOt17txZDRo00J133qn+/fvrwQcfvOH7Fxsbq8GDB+uZZ55Rw4YN1atXL23btk21a9eWdPWS96ioKDVq1Ej33Xefbr31Vn3wwQd25wfMxGL8+WA7AJQzr7zyipYsWcLJuAAKxMwOgHIrKytL+/bt03vvvaennnrK2XEAlFGUHQDlVnR0tFq1aqW7777brkNYAP5aOIwFAABMjZkdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgav8PriEMHNJ5QJIAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
-        "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n",
-        "\n",
-        "# List of corresponding labels for each RMSE value\n",
-        "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, rmse_values, color='green')\n",
-        "plt.xlabel('RMSE Variables')\n",
-        "plt.ylabel('RMSE Values')\n",
-        "plt.title('Bar Graph of RMSE')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "RFaaCNH6Cfoa",
-        "outputId": "67a8f358-e3ce-4ad2-9c78-ebc75902beb4"
-      },
-      "execution_count": 118,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of MAE values from mae1 to mae10\n",
-        "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n",
-        "\n",
-        "# List of corresponding labels for each MAE value\n",
-        "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, mae_values, color='orange')\n",
-        "plt.xlabel('MAE Variables')\n",
-        "plt.ylabel('MAE Values')\n",
-        "plt.title('Bar Graph of MAE')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "nrZu-K-KDCJ2",
-        "outputId": "69165581-da05-4554-a464-a606eb87a734"
-      },
-      "execution_count": 119,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of MAPE values from mape1 to mape10\n",
-        "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n",
-        "\n",
-        "# List of corresponding labels for each MAPE value\n",
-        "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, mape_values, color='purple')\n",
-        "plt.xlabel('MAPE Variables')\n",
-        "plt.ylabel('MAPE Values')\n",
-        "plt.title('Bar Graph of MAPE')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "_c4Pe76fDNM-",
-        "outputId": "0e3d2f74-9042-4e2d-92c6-5ce61e967bd4"
-      },
-      "execution_count": 120,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of precision values from precision1 to precision10\n",
-        "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
-        "\n",
-        "# List of corresponding labels for each precision value\n",
-        "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, precision_values, color='red')\n",
-        "plt.xlabel('Precision Variables')\n",
-        "plt.ylabel('Precision Values')\n",
-        "plt.title('Bar Graph of Precision')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "ZDPV0M5rDTi6",
-        "outputId": "9db63164-3f42-47be-d302-d80d381d9b91"
-      },
-      "execution_count": 121,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of recall values from recall1 to recall10\n",
-        "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
-        "\n",
-        "# List of corresponding labels for each recall value\n",
-        "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, recall_values, color='cyan')\n",
-        "plt.xlabel('Recall Variables')\n",
-        "plt.ylabel('Recall Values')\n",
-        "plt.title('Bar Graph of Recall')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "39LBleNeDeuw",
-        "outputId": "3c6c40bc-f1da-44fb-da14-25ec6d6cf278"
-      },
-      "execution_count": 122,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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RpREfAFCA0j6kzWFulGq52bZtm1q1aqVWrVpJkqKiotSqVSuNHz9eknT8+HFb0ZGkwMBArV69Whs2bFBQUJAmT56sjz/+mMvAAQCAjcUwDKO0Q5Sk9PR0eXt769y5c/Ly8irtOE7DGf9lc0v9wUSx4s93yWJ7l6xbZXs78v1dps65AQAA+CuUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCrlSjsAAADOwFLaAQpglHaAMoo9NwAAwFQoNwAAwFQ4LAWUgrK6+7us5gZwa2HPDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXucwMATor7CgE3hj03AADAVCg3AADAVCg3AADAVDjnBmUa5yQAAP4Xe24AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICplHq5mTFjhurUqSMPDw+1a9dOW7duve7006ZNU6NGjeTp6amAgACNGjVKFy9eLKG0AADA2ZVquVm6dKmioqIUHR2t+Ph4BQUFKTw8XKmpqQVOv3jxYo0ZM0bR0dHat2+f5syZo6VLl+rll18u4eTXZnHCFwAAt5JSLTdTpkzRk08+qSFDhqhp06aaPXu2ypcvr7lz5xY4/ZYtW9ShQwcNGDBAderUUbdu3dS/f/+/3NsDAABuHaVWbnJycrR9+3aFhYX9N4yLi8LCwhQXF1fgPHfeeae2b99uKzO//fab1qxZox49epRIZgAA4PzKldaK09LSlJubK19fX7txX19f7d+/v8B5BgwYoLS0NN11110yDEOXLl3S8OHDr3tYKjs7W9nZ2baf09PTi+YDAAAAp1TqJxQ7YtOmTXrjjTc0c+ZMxcfHa8WKFVq9erVee+21a84TExMjb29v2ysgIKAEEwMAgJJmMQzDKI0V5+TkqHz58lq2bJn69OljG4+IiNDZs2f173//O988oaGhat++vd5++23b2CeffKJhw4YpIyNDLi75u1pBe24CAgJ07tw5eXl5Fe2HknOewFuYXzC5iw65Sxa5Sxa5S5aZczsqPT1d3t7ehfr+LrU9N+7u7goODlZsbKxtLC8vT7GxsQoJCSlwngsXLuQrMK6urpKka3U0q9UqLy8vuxcAADCvUjvnRpKioqIUERGh1q1bq23btpo2bZoyMzM1ZMgQSdKgQYPk7++vmJgYSVLPnj01ZcoUtWrVSu3atdOhQ4f0yiuvqGfPnraSAwAAbm2lWm769eunkydPavz48Tpx4oRatmyptWvX2k4yTkpKsttTM27cOFksFo0bN07Hjh1TjRo11LNnT/3zn/8srY8AAACcTKmdc1NaHDlmdyPK6rFPchcdcpcscpcscpcsM+d2VJk45wYAAKA4UG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpOFxusrKydOHCBdvPR48e1bRp07R+/foiDQYAAHAjHC43vXv31sKFCyVJZ8+eVbt27TR58mT17t1bs2bNKvKAAAAAjnC43MTHxys0NFSStGzZMvn6+uro0aNauHCh3n333SIPCAAA4AiHy82FCxdUqVIlSdL69ev14IMPysXFRe3bt9fRo0eLPCAAAIAjHC439evX18qVK5WcnKx169apW7dukqTU1FR5eXkVeUAAAABHOFxuxo8fr9GjR6tOnTpq27atQkJCJF3ei9OqVasiDwgAAOAIi2EYhqMznThxQsePH1dQUJBcXC73o61bt8rLy0uNGzcu8pBFKT09Xd7e3jp37lyx7GmyFPkSb15hfsHkLjrkLlnkLlnkLllmzu0oR76/b+g+N35+fqpUqZI2bNigrKwsSVKbNm2cvtgAAADzc7jcnDp1Sl26dFHDhg3Vo0cPHT9+XJIUGRmpF154ocgDAgAAOMLhcjNq1Ci5ubkpKSlJ5cuXt43369dPa9euLdJwAAAAjirn6Azr16/XunXrVKtWLbvxBg0acCk4AAAodQ7vucnMzLTbY3PF6dOnZbVaiyQUAADAjXK43ISGhtoevyBJFotFeXl5euutt9S5c+ciDQcAAOAoh8vNW2+9pQ8//FD33nuvcnJy9OKLL6p58+b6/vvvNWnSJIcDzJgxQ3Xq1JGHh4fatWunrVu3Xnf6s2fPasSIEapZs6asVqsaNmyoNWvWOLxeAABgTg6Xm+bNm+vXX3/VXXfdpd69eyszM1MPPvigduzYoXr16jm0rKVLlyoqKkrR0dGKj49XUFCQwsPDlZqaWuD0OTk56tq1qxITE7Vs2TIdOHBAH330kfz9/R39GAAAwKRu6CZ+RaVdu3Zq06aN3n//fUlSXl6eAgIC9Mwzz2jMmDH5pp89e7befvtt7d+/X25ubje0Tm7iVzByFx1ylyxylyxylywz53aUI9/fDl8t9f3331/3/Y4dOxZqOTk5Odq+fbvGjh1rG3NxcVFYWJji4uIKnGfVqlUKCQnRiBEj9O9//1s1atTQgAED9NJLL8nV1bXAebKzs5WdnW37OT09vVD5AABA2eRwubn77rvzjVks/+2Nubm5hVpOWlqacnNz5evrazfu6+ur/fv3FzjPb7/9po0bN+qxxx7TmjVrdOjQIT399NP6888/FR0dXeA8MTExmjhxYqEyAQCAss/hc27OnDlj90pNTdXatWvVpk0brV+/vjgy2uTl5cnHx0cffvihgoOD1a9fP/3jH//Q7NmzrznP2LFjde7cOdsrOTm5WDMCAIDS5fCeG29v73xjXbt2lbu7u6KiorR9+/ZCLad69epydXVVSkqK3XhKSor8/PwKnKdmzZpyc3OzOwTVpEkTnThxQjk5OXJ3d883j9Vq5f47AADcQm7owZkF8fX11YEDBwo9vbu7u4KDgxUbG2sby8vLU2xsrEJCQgqcp0OHDjp06JDy8vJsY7/++qtq1qxZYLEBAAC3Hof33OzatcvuZ8MwdPz4cb355ptq2bKlQ8uKiopSRESEWrdurbZt22ratGnKzMzUkCFDJEmDBg2Sv7+/YmJiJElPPfWU3n//fT333HN65plndPDgQb3xxht69tlnHf0YAADApBwuNy1btpTFYtH/XkHevn17zZ0716Fl9evXTydPntT48eN14sQJtWzZUmvXrrWdZJyUlCQXl//uXAoICNC6des0atQotWjRQv7+/nruuef00ksvOfoxAACASTl8n5v/fTimi4uLatSoIQ8PjyINVly4z03ByF10yF2yyF2yyF2yzJzbUcV6n5vatWvfcDAAAIDiVqhy8+677xZ6gZz/AgAASlOhDksFBgYWbmEWi3777bebDlWcOCxVMHIXHXKXLHKXLHKXLDPndlSRH5Y6cuRIkQQDAAAobkV2nxsAAABn4PAJxZL0+++/a9WqVUpKSlJOTo7de1OmTCmSYAAAADfC4XITGxurXr16qW7dutq/f7+aN2+uxMREGYahO+64ozgyAgAAFJrDh6XGjh2r0aNHa/fu3fLw8NDy5cuVnJysTp06qW/fvsWREQAAoNAcLjf79u3ToEGDJEnlypVTVlaWKlasqFdffVWTJk0q8oAAAACOcLjcVKhQwXaeTc2aNXX48GHbe2lpaUWXDAAA4AY4fM5N+/bttXnzZjVp0kQ9evTQCy+8oN27d2vFihVq3759cWQEAAAotEKXm9OnT6tq1aqaMmWKMjIyJEkTJ05URkaGli5dqgYNGnClFAAAKHWFfnCmh4eH+vTpo8jISHXt2rW4cxUb7lBcMHIXHXKXLHKXLHKXLDPndpQj39+FPufmo48+0smTJ9W9e3fVqVNHEyZMUGJi4s1mBQAAKFKFLjePP/64YmNjdejQIUVERGjBggWqX7++unbtqqVLl+a7mR8AAEBpcPhqqcDAQE2cOFFHjhzR2rVr5ePjo6FDh6pmzZo8ERwAAJS6Qp9zcz3Lly/XsGHDdPbsWeXm5hZFrmLDOTcFI3fRIXfJInfJInfJMnNuRxX5U8ELcvToUc2bN08LFixQcnKyOnfurMjIyBtdHAAAQJFwqNxkZ2dr+fLlmjt3rjZt2iR/f38NHjxYQ4YMUZ06dYopIgAAQOEVutw8/fTTWrJkiS5cuKDevXtrzZo16tq1qywWZ9whBgAAblWFLjebN29WdHS0Bg4cqGrVqhVnJgAAgBtW6HKza9eu4swBAABQJBy+FBwAAMCZUW4AAICpUG4AAICpUG4AAICpFOqEYkdOJm7RosUNhwEAALhZhSo3LVu2lMVi0bWe1HDlPYvF4vSPXwAAAOZWqHJz5MiR4s4BAABQJApVbmrXrl3cOQAAAIpEocrNqlWrCr3AXr163XAYAACAm1WoctOnT59CLYxzbgAAQGkrVLnJy8sr7hwAAABFgvvcAAAAUyn0gzOvlpmZqe+++05JSUnKycmxe+/ZZ58tkmAAAAA3wuFys2PHDvXo0UMXLlxQZmamqlatqrS0NJUvX14+Pj6UGwAAUKocPiw1atQo9ezZU2fOnJGnp6d+/PFHHT16VMHBwXrnnXeKIyMAAEChOVxuEhIS9MILL8jFxUWurq7Kzs5WQECA3nrrLb388svFkREAAKDQHC43bm5ucnG5PJuPj4+SkpIkSd7e3kpOTi7adAAAAA5y+JybVq1a6eeff1aDBg3UqVMnjR8/XmlpaVq0aJGaN29eHBkBAAAKzeE9N2+88YZq1qwpSfrnP/+pKlWq6KmnntLJkyf1wQcfFHlAAAAAR1iMaz3q26TS09Pl7e2tc+fOycvLq8iXbynyJd68wvyCyV10yF2yyF2yyF2yzJzbUY58fzu85+bIkSM6ePBgvvGDBw8qMTHR0cUBAAAUKYfLzeDBg7Vly5Z84z/99JMGDx5cFJkAAABumMPlZseOHerQoUO+8fbt2yshIaEoMgEAANwwh8uNxWLR+fPn842fO3eOJ4IDAIBS53C56dixo2JiYuyKTG5urmJiYnTXXXcVaTgAAABHOXyfm0mTJqljx45q1KiRQkNDJUn/+c9/lJ6ero0bNxZ5QAAAAEc4vOemadOm2rVrlx555BGlpqbq/PnzGjRokPbv389N/AAAQKnjPjdFrKzeb4DcRYfcJYvcJYvcJcvMuR1VrPe5kS4fhho4cKDuvPNOHTt2TJK0aNEibd68+UYWBwAAUGQcLjfLly9XeHi4PD09FR8fr+zsbEmXr5Z64403ijwgAACAIxwuN6+//rpmz56tjz76SG5ubrbxDh06KD4+vkjDAQAAOMrhcnPgwAF17Ngx37i3t7fOnj1bFJkAAABumMPlxs/PT4cOHco3vnnzZtWtW7dIQgEAANwoh8vNk08+qeeee04//fSTLBaL/vjjD/3rX//S6NGj9dRTTxVHRgAAgEJz+CZ+Y8aMUV5enrp06aILFy6oY8eOslqtGj16tJ555pniyAgAAFBoN3yfm5ycHB06dEgZGRlq2rSpKlasqKysLHl6ehZ1xiLFfW4KRu6iQ+6SRe6SRe6SZebcjir2+9xIkru7u5o2baq2bdvKzc1NU6ZMUWBg4I0uDgAAoEgUutxkZ2dr7Nixat26te68806tXLlSkjRv3jwFBgZq6tSpGjVq1A2FmDFjhurUqSMPDw+1a9dOW7duLdR8S5YskcViUZ8+fW5ovQAAwHwKXW7Gjx+vWbNmqU6dOkpMTFTfvn01bNgwTZ06VVOmTFFiYqJeeuklhwMsXbpUUVFRio6OVnx8vIKCghQeHq7U1NTrzpeYmKjRo0fbHt4JAAAgOVBuPv/8cy1cuFDLli3T+vXrlZubq0uXLmnnzp169NFH5erqekMBpkyZoieffFJDhgxR06ZNNXv2bJUvX15z58695jy5ubl67LHHNHHiRC4/BwAAdgpdbn7//XcFBwdLkpo3by6r1apRo0bJYrnxU5lycnK0fft2hYWF/TeQi4vCwsIUFxd3zfleffVV+fj4KDIy8obXDQAAzKnQl4Ln5ubK3d39vzOWK6eKFSve1MrT0tKUm5srX19fu3FfX1/t37+/wHk2b96sOXPmKCEhoVDryM7Otj3/Srp8tjUAADCvQpcbwzA0ePBgWa1WSdLFixc1fPhwVahQwW66FStWFG3Cq5w/f16PP/64PvroI1WvXr1Q88TExGjixInFlgkAADiXQpebiIgIu58HDhx40yuvXr26XF1dlZKSYjeekpIiPz+/fNMfPnxYiYmJ6tmzp20sLy9P0uU9SQcOHFC9evXs5hk7dqyioqJsP6enpysgIOCmswMAAOdU6HIzb968Il+5u7u7goODFRsba7ucOy8vT7GxsRo5cmS+6Rs3bqzdu3fbjY0bN07nz5/X9OnTCywtVqvVtrcJAACYn8OPXyhqUVFRioiIUOvWrdW2bVtNmzZNmZmZGjJkiCRp0KBB8vf3V0xMjDw8PNS8eXO7+StXrixJ+cYBAMCtqdTLTb9+/XTy5EmNHz9eJ06cUMuWLbV27VrbScZJSUlycbnhGykDAIBbzA0/W6qs4tlSBSN30SF3ySJ3ySJ3yTJzbkeVyLOlAAAAnBHlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmIpTlJsZM2aoTp068vDwULt27bR169ZrTvvRRx8pNDRUVapUUZUqVRQWFnbd6QEAwK2l1MvN0qVLFRUVpejoaMXHxysoKEjh4eFKTU0tcPpNmzapf//++vbbbxUXF6eAgAB169ZNx44dK+HkAADAGVkMwzBKM0C7du3Upk0bvf/++5KkvLw8BQQE6JlnntGYMWP+cv7c3FxVqVJF77//vgYNGvSX06enp8vb21vnzp2Tl5fXTef/X5YiX+LNK8wvmNxFh9wli9wli9wly8y5HeXI93ep7rnJycnR9u3bFRYWZhtzcXFRWFiY4uLiCrWMCxcu6M8//1TVqlULfD87O1vp6el2LwAAYF6lWm7S0tKUm5srX19fu3FfX1+dOHGiUMt46aWXdNttt9kVpKvFxMTI29vb9goICLjp3AAAwHmV+jk3N+PNN9/UkiVL9MUXX8jDw6PAacaOHatz587ZXsnJySWcEgAAlKRypbny6tWry9XVVSkpKXbjKSkp8vPzu+6877zzjt5880198803atGixTWns1qtslqtRZIXAAA4v1Ldc+Pu7q7g4GDFxsbaxvLy8hQbG6uQkJBrzvfWW2/ptdde09q1a9W6deuSiAoAAMqIUt1zI0lRUVGKiIhQ69at1bZtW02bNk2ZmZkaMmSIJGnQoEHy9/dXTEyMJGnSpEkaP368Fi9erDp16tjOzalYsaIqVqxYap8DAAA4h1IvN/369dPJkyc1fvx4nThxQi1bttTatWttJxknJSXJxeW/O5hmzZqlnJwcPfzww3bLiY6O1oQJE0oyOgAAcEKlfp+bksZ9bgpG7qJD7pJF7pJF7pJl5tyOKjP3uQEAAChqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqTlFuZsyYoTp16sjDw0Pt2rXT1q1brzv9559/rsaNG8vDw0O333671qxZU0JJAQCAsyv1crN06VJFRUUpOjpa8fHxCgoKUnh4uFJTUwucfsuWLerfv78iIyO1Y8cO9enTR3369NGePXtKODkAAHBGFsMwjNIM0K5dO7Vp00bvv/++JCkvL08BAQF65plnNGbMmHzT9+vXT5mZmfrqq69sY+3bt1fLli01e/bsv1xfenq6vL29de7cOXl5eRXdB/k/liJf4s0rzC+Y3EWH3CWL3CWL3CXLzLkd5cj3d6nuucnJydH27dsVFhZmG3NxcVFYWJji4uIKnCcuLs5uekkKDw+/5vQAAODWUq40V56Wlqbc3Fz5+vrajfv6+mr//v0FznPixIkCpz9x4kSB02dnZys7O9v287lz5yRdboC3irL6Scldsshdsshdsshdsooj95Xv7cIccCrVclMSYmJiNHHixHzjAQEBpZCmdHiXdoAbRO6SRe6SRe6SRe6SVZy5z58/L2/v66+hVMtN9erV5erqqpSUFLvxlJQU+fn5FTiPn5+fQ9OPHTtWUVFRtp/z8vJ0+vRpVatWTRaLMx6pvNxOAwIClJycXCznBRUXcpcscpcscpcscpesspDbMAydP39et912219OW6rlxt3dXcHBwYqNjVWfPn0kXS4fsbGxGjlyZIHzhISEKDY2Vs8//7xtbMOGDQoJCSlweqvVKqvVajdWuXLloohf7Ly8vJz2D9n1kLtkkbtkkbtkkbtkOXvuv9pjc0WpH5aKiopSRESEWrdurbZt22ratGnKzMzUkCFDJEmDBg2Sv7+/YmJiJEnPPfecOnXqpMmTJ+u+++7TkiVLtG3bNn344Yel+TEAAICTKPVy069fP508eVLjx4/XiRMn1LJlS61du9Z20nBSUpJcXP57Udedd96pxYsXa9y4cXr55ZfVoEEDrVy5Us2bNy+tjwAAAJxIqZcbSRo5cuQ1D0Nt2rQp31jfvn3Vt2/fYk5VeqxWq6Kjo/MdTnN25C5Z5C5Z5C5Z5C5ZZTX3tZT6TfwAAACKUqk/fgEAAKAoUW4AAICpUG4AAICpUG7KMIvFopUrV0qSEhMTZbFYlJCQUKqZCoPcJa+sZi+Lucti5ivKYvaymPmKspC9LGQsCOXmBg0ePFgWi0UWi0Vubm4KDAzUiy++qIsXL5Z2NJtnn31WwcHBslqtatmypSTnz71z5071799fAQEB8vT0VJMmTTR9+nSnz33q1Cl1795dt912m6xWqwICAjRy5Eilp6c7ffarnTp1SrVq1ZLFYtGAAQOcPveVfAW9nDWzJM2fP18tWrSQh4eHfHx8NGLECKf/czJ//vwyub1//vlndenSRZUrV1aVKlUUHh6unTt3Ov32li4/FPrqbVy5cmWNHj3aqTIW9D3zv3bt2qXQ0FB5eHgoICBAb731VrHncopLwcuq7t27a968efrzzz+1fft2RUREyGKxaNKkSaUdzWbo0KH66aeftGvXLtuYM+fevn27fHx89MknnyggIEBbtmzRsGHDdPvttzt1bhcXF/Xu3Vuvv/66atSooUOHDmnEiBE6ffq03N3dnTr71SIjI9WiRQsdO3ZMknP/Wbli3rx56t69u6TLN/k8e/asFixY4LSZp0yZosmTJ+vtt99Wu3btlJmZqcTERK1YscKpt3e/fv1s2/mK4OBgZWVl6ZdffnHKzBkZGerevbt69eqlmTNn6tKlS4qOjlZ4eLi6devm1Nt7586d+uabb1S/fn0tXLhQv//+u0aNGqX33ntPrq6uTpHxioK+Z65IT09Xt27dFBYWptmzZ2v37t0aOnSoKleurGHDhhVbJvbc3ASr1So/Pz8FBASoT58+CgsL04YNGyRdfoxETEyMAgMD5enpqaCgIC1btsxu/r179+r++++Xl5eXKlWqpNDQUB0+fFjS5X9tdO3aVdWrV5e3t7c6deqk+Ph4h/K9++67GjFihOrWrVtmcg8dOlTTp09Xp06dVLduXQ0cOFBDhgxRUlKSU+euUqWKnnrqKbVu3Vq1a9dWly5d9PTTT+s///mP02/zK2bNmqWzZ89q9OjRtrGykLty5cry8/OTn5+fPD095enp6bSZz5w5o3HjxmnhwoUaMGCA6tWrpxYtWqhXr15Ov72vbNcrL1dXVx0/flyBgYFOm3n//v06ffq0Xn31VTVq1EjNmjVTdHS0UlJSlJGR4dTbe+nSpapSpYqaNWumkJAQ9e3bV7NmzdKlS5e0bt06p8goXft75op//etfysnJ0dy5c9WsWTM9+uijevbZZzVlyhSH1uMoyk0R2bNnj7Zs2SJ3d3dJl59GvnDhQs2ePVt79+7VqFGjNHDgQH333XeSpGPHjqljx46yWq3auHGjtm/frqFDh+rSpUuSLj/1NCIiQps3b9aPP/6oBg0aqEePHjp//vwtl/vcuXP5bizl7Ln/+OMPrVixQp06dcr3njNm/+WXX/Tqq69q4cKFdncEd/bckjRixAhVr15dbdu21cGDB50684YNG5SXl6djx46pSZMmqlWrlh555BElJyeXme19xcKFC1WuXDm7hxg6W+ZGjRqpWrVqmjNnjnJycpSVlaU5c+aoSZMmqlixolNv7+zsbLm6utqNnThxQnl5ecrJyXGKjIURFxenjh072rardPlw24EDB3TmzJkiW08+Bm5IRESE4erqalSoUMGwWq2GJMPFxcVYtmyZcfHiRaN8+fLGli1b7OaJjIw0+vfvbxiGYYwdO9YIDAw0cnJyCrW+3Nxco1KlSsaXX35pG5NkfPHFF4ZhGMaRI0cMScaOHTvyzRsdHW0EBQWVudyGYRg//PCDUa5cOaNr165lIvejjz5qeHp6GpKMnj17GllZWU6/zS9evGi0aNHCWLRokWEYhvHtt98akoz+/fs7dW7DMIxXX33V2Lx5sxEfH2+8+eabhouLi+Hi4uK0mWNiYgw3NzejUaNGxtq1a424uDijS5cuRqNGjYzHH3/c6bf31Zo0aWI0atTI6TPv3r3bqFevnu3PRqNGjYzExESn//9y3bp1tkxXZ5RkPP/8806R8WpXf89crWvXrsawYcPsxvbu3WtIMn755ZdC5boRnHNzEzp37qxZs2YpMzNTU6dOVbly5fTQQw9p7969unDhgrp27Wo3fU5Ojlq1aiVJSkhIUGhoqNzc3ApcdkpKisaNG6dNmzYpNTVVubm5unDhgpKSkm6Z3Hv27FHv3r0VHR2tQ4cOlYncU6dOVXR0tH799VeNHTtWUVFRkpx7m48dO1ZNmjTRwIED873nzLkl6ZVXXrH9d6tWrbR48WIdOHBACQkJTpk5Ly9Pf/75p959911169ZNkvTpp5/aDo04+/a+Ii4uTvv27dP999+vgIAAp82clZWlyMhIdejQQZ9++qlyc3P1zjvv6L777lOrVq2cent369ZNrVu31o4dO5SVlSV3d3fdcccdio+PV/v27XXo0KFSz+jMKDc3oUKFCqpfv74kae7cuQoKCtKcOXNsD/FcvXq1/P397ea5cnjF09PzusuOiIjQqVOnNH36dNWuXVtWq1UhISG23ZFmz/3LL7+oS5cuGjZsmMaNG6fBgweXidxXzkdo3LixqlatqtDQUD3yyCNOnX3jxo3avXu37Vi98X9PZFmyZIkaNmzotLkLUqNGDe3atUsBAQGyWq1Ol7lmzZqSpKZNm9plrl69ujIzM+Xj41MmtvfHH3+sli1bqlq1ajp79qzTZl68eLESExMVFxdnO9y6ePFiValSRdWqVVOVKlWcNrskNWvWTP7+/po5c6aqVKmi3377Tc2bN9fu3btVp04dp8j4V/z8/JSSkmI3duVnPz+/IlvP/6LcFBEXFxe9/PLLioqK0q+//iqr1aqkpKQCz7mQpBYtWtiu6CioVf/www+aOXOmevToIUlKTk5WWlraLZF77969uueeexQREaF//vOfZSb3/8rLy5Mk5ebmOnX25cuXKysry/bzzz//rKFDh+ree++1fQZnzF2Q06dPy83NzfaXu7Nl7tChgyTpwIEDqlWrli1zWlqagoKC7KZ1tuxXZGRk6LPPPlNMTIy2bdvm1JkvXLggFxcXWSwWu4wWi8VW4p01+9WunNe0dOlSVa1aVXPmzNFLL73kVBmvJSQkRP/4xz/sMmzYsEGNGjVSlSpVinRdV+OE4iLUt29fubq66oMPPtDo0aM1atQoLViwQIcPH1Z8fLzee+89LViwQJJs90B59NFHtW3bNh08eFCLFi3SgQMHJEkNGjTQokWLtG/fPv3000967LHH/rKF/69Dhw4pISFBJ06cUFZWlhISEnT69Gm7Lyxny71nzx517txZ3bp1U1RUlE6cOKETJ04UeF8HZ8q9Zs0azZs3T3v27FFiYqJWr16t4cOHq0OHDvlOXHS27PXq1VPz5s1tr8DAQEmSt7d3vhO5nSn3l19+qY8//lh79uzRoUOHNGvWLO3atSvfVRvOlLlhw4bq3bu3nnvuOW3ZskV79uxRRESEGjduXOC/Yp0p+xVLly7VpUuXCjyM6WyZu3btqjNnzmjEiBHat2+f9u7dqyFDhqhcuXK2vWjOml26/Pdhenq69u7dq9dee01vvvmmPvzwQ6fKWND3TEJCgm3vz4ABA+Tu7q7IyEjt3btXS5cu1fTp022H7ItNsZ3NY3IRERFG7969843HxMQYNWrUMDIyMoxp06YZjRo1Mtzc3IwaNWoY4eHhxnfffWebdufOnUa3bt2M8uXLG5UqVTJCQ0ONw4cPG4ZhGPHx8Ubr1q0NDw8Po0GDBsbnn39u1K5d25g6daptfv3FiV6dOnWynYB29atr165Omzs6OrrAzBUqVHDq7b1x40YjJCTE8Pb2ti3jpZdeMs6cOVMm/qxc7eoTip0599dff220bNnSqFixolGhQgUjKCjIaN++vdGrVy+nzWwYhnHu3Dlj6NChRuXKlY2qVasaDzzwgJGUlFRm/pyEhIQYAwYMMAyjbPw9uH79eqNDhw6Gt7e3UaVKFeOee+4x4uLiykR2Pz8/o1y5coaHh4fRrl07Y82aNU6X8VrfM0eOHLHLcNdddxlWq9Xw9/c33nzzzXzbvahZ/i88AACAKXBYCgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBkCps1gsWrlypSQpMTFRFotFCQkJJZ7jRtY9ePBg9enT57rT3H333Xr++edvKhuAwqPcALewwYMHy2KxyGKxyM3NTYGBgXrxxRcLfNyFs7j99ts1fPjwAt9btGiRrFbrDT8fJyAgQMePH7c9OBFA2US5AW5x3bt31/Hjx/Xbb79p6tSp+uCDDxQdHV3asa4pMjJSS5YssXvY5xXz5s1Tr169VL16dYeXm5OTI1dXV/n5+alcOZ4pDJRllBvgFme1WuXn56eAgAD16dNHYWFh2rBhg+39vLw8xcTEKDAwUJ6engoKCtKyZcvslrF3717df//98vLyUqVKlRQaGqrDhw9LuvyU8a5du6p69ery9vZWp06dFB8ff8N5Bw4cqKysLC1fvtxu/MiRI9q0aZMiIyN1+PBh9e7dW76+vqpYsaLatGmjb775xm76OnXq6LXXXtOgQYPk5eWlYcOG5TsslZubq8jISNtnb9SokaZPn15grokTJ6pGjRry8vLS8OHDbQ8OLEh2drZGjx4tf39/VahQQe3atdOmTZts7x89elQ9e/ZUlSpVVKFCBTVr1kxr1qy5sQ0G3IIoNwBs9uzZoy1btsjd3d02FhMTo4ULF2r27Nnau3evRo0apYEDB+q7776TJB07dkwdO3aU1WrVxo0btX37dg0dOlSXLl2SJJ0/f14RERHavHmzfvzxRzVo0EA9evTQ+fPnbyhj9erV1bt3b82dO9dufP78+apVq5a6deumjIwM9ejRQ7GxsdqxY4e6d++unj17KikpyW6ed955R0FBQdqxY4deeeWVfOvKy8tTrVq19Pnnn+uXX37R+PHj9fLLL+uzzz6zmy42Nlb79u3Tpk2b9Omnn2rFihWaOHHiNT/DyJEjFRcXpyVLlmjXrl3q27evunfvroMHD0qSRowYoezsbH3//ffavXu3Jk2aVODT5QFcQ7E/mhOA04qIiDBcXV2NChUqGFar1ZBkuLi4GMuWLTMMwzAuXrxolC9f3tiyZYvdfJGRkUb//v0NwzCMsWPHGoGBgUZOTk6h1pmbm2tUqlTJ+PLLL21jcuCp5YZhGGvXrjUsFovx22+/GYZhGHl5eUbt2rWNcePGXXOeZs2aGe+9957t59q1axt9+vSxm6Yw6x4xYoTx0EMP2X6OiIgwqlatamRmZtrGZs2aZVSsWNHIzc01DOPyk5Ofe+45wzAM4+jRo4arq6tx7Ngxu+V26dLFGDt2rGEYhnH77bcbEyZMuGYGANfHgWXgFte5c2fNmjVLmZmZmjp1qsqVK6eHHnpIknTo0CFduHBBXbt2tZsnJydHrVq1kiQlJCQoNDRUbm5uBS4/JSVF48aN06ZNm5Samqrc3FxduHAh314UR3Tt2lW1atXSvHnz9Oqrryo2NlZJSUkaMmSIJCkjI0MTJkzQ6tWrdfz4cV26dElZWVn51tm6deu/XNeMGTM0d+5cJSUlKSsrSzk5OWrZsqXdNEFBQSpfvrzt55CQEGVkZCg5OVm1a9e2m3b37t3Kzc1Vw4YN7cazs7NVrVo1SdKzzz6rp556SuvXr1dYWJgeeughtWjRotDbB7jVUW6AW1yFChVUv359SdLcuXMVFBSkOXPmKDIyUhkZGZKk1atXy9/f324+q9UqSfL09Lzu8iMiInTq1ClNnz5dtWvXltVqVUhIyHXPSfkrLi4uGjx4sBYsWKAJEyZo3rx56ty5s+rWrStJGj16tDZs2KB33nlH9evXl6enpx5++OF866xQocJ117NkyRKNHj1akydPVkhIiCpVqqS3335bP/300w1nz8jIkKurq7Zv3y5XV1e7964cenriiScUHh6u1atXa/369YqJidHkyZP1zDPP3PB6gVsJ5QaAjYuLi15++WVFRUVpwIABatq0qaxWq5KSktSpU6cC52nRooUWLFigP//8s8C9Nz/88INmzpypHj16SJKSk5Nv+FLtqw0ZMkSvv/66VqxYoS+++EIff/yx3ToHDx6sBx54QNLlQpGYmOjwOn744Qfdeeedevrpp21jV06UvtrOnTuVlZVlK3o//vijKlasqICAgHzTtmrVSrm5uUpNTVVoaOg11x0QEKDhw4dr+PDhGjt2rD766CPKDVBInFAMwE7fvn3l6uqqGTNmqFKlSho9erRGjRqlBQsW6PDhw4qPj9d7772nBQsWSLp8cmx6eroeffRRbdu2TQcPHtSiRYt04MABSVKDBg20aNEi7du3Tz/99JMee+yxv9zbUxiBgYG65557NGzYMFmtVj344IO29xo0aKAVK1YoISFBO3fu1IABA5SXl+fwOho0aKBt27Zp3bp1+vXXX/XKK6/o559/zjddTk6OIiMj9csvv2jNmjWKjo7WyJEj5eKS/6/Yhg0b6rHHHtOgQYO0YsUKHTlyRFu3blVMTIxWr14tSXr++ee1bt06HTlyRPHx8fr222/VpEkTh/MDtyrKDQA75cqV08iRI/XWW28pMzNTr732ml555RXFxMSoSZMm6t69u1avXq3AwEBJUrVq1bRx40ZlZGSoU6dOCg4O1kcffWTbizNnzhydOXNGd9xxhx5//HE9++yz8vHxKZKskZGROnPmjAYMGCAPDw/b+JQpU1SlShXdeeed6tmzp8LDw3XHHXc4vPz/9//+nx588EH169dP7dq106lTp+z24lzRpUsXNWjQQB07dlS/fv3Uq1cvTZgw4ZrLnTdvngYNGqQXXnhBjRo1Up8+ffTzzz/rb3/7m6TLl6CPGDHCtr0bNmyomTNnOpwfuFVZDMMwSjsEAABAUWHPDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMJX/D3PqyuT3IxkKAAAAAElFTkSuQmCC\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "13cZXvb0DsvK"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/Stock_Price_Prediction.ipynb b/Stock_Price_Prediction.ipynb
deleted file mode 100644
index 7c3ad61..0000000
--- a/Stock_Price_Prediction.ipynb
+++ /dev/null
@@ -1,1805 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "qCDSjVhXLr_Z"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "from sklearn.linear_model import LinearRegression\n",
-        "from sklearn.svm import SVR\n",
-        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor\n",
-        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
-        "from sklearn.neighbors import KNeighborsRegressor\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Dense,LSTM"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "SOQbXSiB-g5G",
-        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
-          ]
-        }
-      ],
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')\n",
-        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Sc4id6VxL8BS",
-        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Date</th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Adj Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>01-01-1996</td>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>12.409931</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>02-01-1996</td>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
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-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>03-01-1996</td>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
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-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>04-01-1996</td>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>11.654142</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>05-01-1996</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>11.588827</td>\n",
-              "      <td>76613039.0</td>\n",
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-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
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-              "    .colab-df-container {\n",
-              "      display:flex;\n",
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-              "\n",
-              "    .colab-df-convert {\n",
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-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "         Date       Open       High        Low      Close  Adj Close  \\\n",
-              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
-              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
-              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
-              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
-              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
-              "\n",
-              "       Volume  \n",
-              "0  43733533.0  \n",
-              "1  56167280.0  \n",
-              "2  68296318.0  \n",
-              "3  86073880.0  \n",
-              "4  76613039.0  "
-            ]
-          },
-          "execution_count": 23,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Load the dataset\n",
-        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "7LaYGXsfN-8y"
-      },
-      "outputs": [],
-      "source": [
-        "# Drop the 'Date' and 'Adj Close' columns\n",
-        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "pqbTBdnBOKJc",
-        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7696e222-c2c8-45d3-99ad-84f50b7bfd98')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "        Open       High        Low      Close      Volume\n",
-              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
-              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
-              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
-              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
-              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
-            ]
-          },
-          "execution_count": 25,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "id": "dydEPoNeM6eN"
-      },
-      "outputs": [],
-      "source": [
-        "# Handle missing values\n",
-        "imputer = SimpleImputer(strategy='mean')\n",
-        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {
-        "id": "OQ3cGqgTMBwt"
-      },
-      "outputs": [],
-      "source": [
-        "# Select features and target variable\n",
-        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
-        "y = df['Close']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "id": "9Oz-bwJOMEWD"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "ugapDyXODtn3"
-      },
-      "outputs": [],
-      "source": [
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "997ZEgibCZIO",
-        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(5659, 4)"
-            ]
-          },
-          "execution_count": 29,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "X_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bmtt76RuCeyG",
-        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(1415, 4)"
-            ]
-          },
-          "execution_count": 30,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "X_test.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CeJkUJ92Ciqd",
-        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(5659,)"
-            ]
-          },
-          "execution_count": 31,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "y_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7HGC7VuTCjWc",
-        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(1415,)"
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "y_test.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Function to evaluate and print RMSE, MAE, and MAPE\n",
-        "def evaluate_model(model, X_test, y_test):\n",
-        "    predictions = model.predict(X_test)\n",
-        "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
-        "    mae = mean_absolute_error(y_test, predictions)\n",
-        "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
-        "\n",
-        "    print(f\"RMSE: {rmse}\")\n",
-        "    print(f\"MAE: {mae}\")\n",
-        "    print(f\"MAPE: {mape}\\n\")\n",
-        "    \n",
-        "    return rmse, mae, mape\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "metrics = {\n",
-        "    \"Model\": [],\n",
-        "    \"RMSE\": [],\n",
-        "    \"MAE\": [],\n",
-        "    \"MAPE\": []\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c6Ek8jRlO2_I"
-      },
-      "source": [
-        "## 1. LINEAR REGRESSION"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "id": "RdZ1SpzdMHAJ"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a linear regression model\n",
-        "model1 = LinearRegression()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mPM035IzMY04",
-        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "5286    257.350006\n",
-              "3408    129.464996\n",
-              "5477    279.350006\n",
-              "6906    588.500000\n",
-              "530      21.644367\n",
-              "Name: Close, dtype: float64"
-            ]
-          },
-          "execution_count": 34,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "y_train.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "qBhQ9HbYMI3d",
-        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "LinearRegression()"
-            ]
-          },
-          "execution_count": 35,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model1.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {
-        "id": "X269co2kMS4z"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Linear Regressor\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GxtMzlg-gR2P"
-      },
-      "source": [
-        "## 2. Support Vector Regression"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "id": "0xQewd7QWTtq"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an SVR model\n",
-        "model2 = SVR()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "22SaCsQmfhgP",
-        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "SVR()"
-            ]
-          },
-          "execution_count": 42,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model2.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 43,
-      "metadata": {
-        "id": "OQ1nL4oYfkAC"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"SVR\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hcIfVMWdgcKt"
-      },
-      "source": [
-        "## 3. Random Forest Regressor"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 46,
-      "metadata": {
-        "id": "f7raXT_hf2ij"
-      },
-      "outputs": [],
-      "source": [
-        "model3 = RandomForestRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 48,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "fF002Yepgk55",
-        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "RandomForestRegressor()"
-            ]
-          },
-          "execution_count": 48,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model3.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "metadata": {
-        "id": "8nRU_pzEgnCt"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Random Forest\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mZsLwLivhLGH"
-      },
-      "source": [
-        "## 4. Gradient Boosting Models (GBM)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 52,
-      "metadata": {
-        "id": "TI8idoxOg6jF"
-      },
-      "outputs": [],
-      "source": [
-        "model4 = GradientBoostingRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 54,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "2gpbDxshhexj",
-        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ]
-          },
-          "execution_count": 54,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model4.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 55,
-      "metadata": {
-        "id": "Jj9DXdUPhh9V"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"GBM\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d8nSGoyuh9dx"
-      },
-      "source": [
-        "## 5. Extreme Gradient Boosting (XGBoost)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 58,
-      "metadata": {
-        "id": "DyhhdlZAhx94"
-      },
-      "outputs": [],
-      "source": [
-        "import xgboost as xgb\n",
-        "# Create an XGBoost model\n",
-        "model5 = xgb.XGBRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 60,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "RAIwxIp5iH9Z",
-        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ]
-          },
-          "execution_count": 60,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model5.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"XGBoost\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A_J776rtiovq"
-      },
-      "source": [
-        "## 6. AdaBoostRegressor"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 64,
-      "metadata": {
-        "id": "HNq66cXRiYPJ"
-      },
-      "outputs": [],
-      "source": [
-        "model6 = AdaBoostRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 66,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "P0oB5wjQivBr",
-        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "AdaBoostRegressor()"
-            ]
-          },
-          "execution_count": 66,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model6.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 67,
-      "metadata": {
-        "id": "Bf1m5ukOi2VM"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Q9DzOt3CkWFX"
-      },
-      "source": [
-        "## 7. Decision Tree"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 70,
-      "metadata": {
-        "id": "23DZ2biSjF9a"
-      },
-      "outputs": [],
-      "source": [
-        "model7 = DecisionTreeRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 72,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "6mQEQf-ykc9F",
-        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "DecisionTreeRegressor()"
-            ]
-          },
-          "execution_count": 72,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model7.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 73,
-      "metadata": {
-        "id": "BFJ9q_tvkgRC"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Decision Tree\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LH-B-Xd6k5UD"
-      },
-      "source": [
-        "## 8. KNeighborsRegressor(KNN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 76,
-      "metadata": {
-        "id": "JVDSed7yktFY"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a KNN model\n",
-        "model8 = KNeighborsRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 78,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "9fn64o-ZlBka",
-        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "KNeighborsRegressor()"
-            ]
-          },
-          "execution_count": 78,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model8.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 79,
-      "metadata": {
-        "id": "hbfbbjcSlDn7"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"KNN\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "X5XtlzMXljps"
-      },
-      "source": [
-        "## 9. Artificial Neural Networks (ANN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 84,
-      "metadata": {
-        "id": "vd1fDjQiltP4"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an ANN model\n",
-        "model9 = Sequential()\n",
-        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
-        "model9.add(Dense(16, activation='relu'))\n",
-        "model9.add(Dense(1, activation='linear'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 85,
-      "metadata": {
-        "id": "ZIf94WLMlv04"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model9.compile(loss='mean_squared_error', optimizer='adam')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 86,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "FX5DTKqslxWf",
-        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca68076d540>"
-            ]
-          },
-          "execution_count": 86,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 87,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OVW2qpNsmGVq",
-        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "45/45 [==============================] - 0s 1ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"ANN\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vjSMQNcOnFPJ"
-      },
-      "source": [
-        "## 10. LSTM(Long Short term Memory)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 92,
-      "metadata": {
-        "id": "uACvajfImrbB"
-      },
-      "outputs": [],
-      "source": [
-        "# Reshape the input data for LSTM\n",
-        "n_features = X_train_scaled.shape[1]\n",
-        "n_steps = 10\n",
-        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
-        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
-        "\n",
-        "# Reshape the input data\n",
-        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
-        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 93,
-      "metadata": {
-        "id": "r066pVYpnXH5"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an LSTM model\n",
-        "model = Sequential()\n",
-        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
-        "model.add(Dense(1))\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 94,
-      "metadata": {
-        "id": "YpSfHu6gov35"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 95,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0vHjcluaoxzP",
-        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca680459cc0>"
-            ]
-          },
-          "execution_count": 95,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 96,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "gEE06_TjozYv",
-        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "44/44 [==============================] - 0s 4ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"LSTM\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Create a DataFrame for metrics\n",
-        "metrics_df = pd.DataFrame(metrics)\n",
-        "\n",
-        "# Plot RMSE, MAE, and MAPE for each model\n",
-        "plt.figure(figsize=(15, 5))\n",
-        "\n",
-        "# RMSE Plot\n",
-        "plt.subplot(1, 3, 1)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
-        "plt.xlabel('RMSE')\n",
-        "plt.title('RMSE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# MAE Plot\n",
-        "plt.subplot(1, 3, 2)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n",
-        "plt.xlabel('MAE')\n",
-        "plt.title('MAE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# MAPE Plot\n",
-        "plt.subplot(1, 3, 3)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
-        "plt.xlabel('MAPE')\n",
-        "plt.title('MAPE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

From f128197231681d37105306bfd9e6463489f336b1 Mon Sep 17 00:00:00 2001
From: Ayushi Sha <124066968+AYUSHI-SHA@users.noreply.github.com>
Date: Sun, 6 Oct 2024 14:26:03 +0530
Subject: [PATCH 03/76] Changes

---
 Python File/Stock_Price_Prediction.ipynb | 2708 ++++++++++++++++++++++
 Stock_Price_Prediction.ipynb             | 1805 ++++++++++++++
 2 files changed, 4513 insertions(+)
 create mode 100644 Python File/Stock_Price_Prediction.ipynb
 create mode 100644 Stock_Price_Prediction.ipynb

diff --git a/Python File/Stock_Price_Prediction.ipynb b/Python File/Stock_Price_Prediction.ipynb
new file mode 100644
index 0000000..c82b075
--- /dev/null
+++ b/Python File/Stock_Price_Prediction.ipynb	
@@ -0,0 +1,2708 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "qCDSjVhXLr_Z"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SOQbXSiB-g5G",
+        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Sc4id6VxL8BS",
+        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date       Open       High        Low      Close  Adj Close  \\\n",
+              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+              "\n",
+              "       Volume  \n",
+              "0  43733533.0  \n",
+              "1  56167280.0  \n",
+              "2  68296318.0  \n",
+              "3  86073880.0  \n",
+              "4  76613039.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>01-01-1996</td>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>12.409931</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>02-01-1996</td>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
+              "      <td>12.014931</td>\n",
+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>03-01-1996</td>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>11.694577</td>\n",
+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>04-01-1996</td>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>11.654142</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>05-01-1996</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>11.588827</td>\n",
+              "      <td>76613039.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d89bb189-3861-4a83-b0ea-1a19c2115c37')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "# Load the dataset\n",
+        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "7LaYGXsfN-8y"
+      },
+      "outputs": [],
+      "source": [
+        "# Drop the 'Date' and 'Adj Close' columns\n",
+        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "pqbTBdnBOKJc",
+        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Open       High        Low      Close      Volume\n",
+              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
+              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
+              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
+              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
+              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>76613039.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7696e222-c2c8-45d3-99ad-84f50b7bfd98')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "dydEPoNeM6eN"
+      },
+      "outputs": [],
+      "source": [
+        "# Handle missing values\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "imputer = SimpleImputer(strategy='mean')\n",
+        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "OQ3cGqgTMBwt"
+      },
+      "outputs": [],
+      "source": [
+        "# Select features and target variable\n",
+        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+        "y = df['Close']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "9Oz-bwJOMEWD"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "ugapDyXODtn3"
+      },
+      "outputs": [],
+      "source": [
+        "# Scale the features using Min-Max scaling\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "997ZEgibCZIO",
+        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5659, 4)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "X_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bmtt76RuCeyG",
+        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1415, 4)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "X_test.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CeJkUJ92Ciqd",
+        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5659,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ],
+      "source": [
+        "y_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7HGC7VuTCjWc",
+        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1415,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ],
+      "source": [
+        "y_test.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c6Ek8jRlO2_I"
+      },
+      "source": [
+        "## 1. LINEAR REGRESSION"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "RdZ1SpzdMHAJ"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a linear regression model\n",
+        "model1 = LinearRegression()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mPM035IzMY04",
+        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "5286    257.350006\n",
+              "3408    129.464996\n",
+              "5477    279.350006\n",
+              "6906    588.500000\n",
+              "530      21.644367\n",
+              "Name: Close, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
+      "source": [
+        "y_train.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "qBhQ9HbYMI3d",
+        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model1.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "X269co2kMS4z"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred1 = model1.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "QK8GvDYPOd0Y"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
+        "mae1 = mean_absolute_error(y_test, pred1)\n",
+        "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
+        "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
+        "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
+        "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
+        "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
+        "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dEi49xtEOtne",
+        "outputId": "0000b074-3187-41de-fbac-4ae75cbda6bd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1.6881364643681482\n",
+            "MAE: 0.9433353485344729\n",
+            "MAPE: 0.006085435990853812\n",
+            "Accuracy: 0.8296819787985866\n",
+            "Precision: 0.8623595505617978\n",
+            "Confusion Matrix:\n",
+            " [[560  98]\n",
+            " [143 614]]\n",
+            "Recall: 0.8110964332892999\n",
+            "F1 Score: 0.8359428182437032\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse1)\n",
+        "print(\"MAE:\", mae1)\n",
+        "print(\"MAPE:\", mape1)\n",
+        "print(\"Accuracy:\", accuracy1)\n",
+        "print(\"Precision:\", precision1)\n",
+        "print(\"Confusion Matrix:\\n\", confusion1)\n",
+        "print(\"Recall:\", recall1)\n",
+        "print(\"F1 Score:\", f11)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GxtMzlg-gR2P"
+      },
+      "source": [
+        "## 2. SVR"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "o7K9r7EXWRjQ"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.svm import SVR"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "0xQewd7QWTtq"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an SVR model\n",
+        "model2 = SVR()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "DuNes3s6U2IV"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "22SaCsQmfhgP",
+        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVR()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model2.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "OQ1nL4oYfkAC"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred2 = model2.predict(X_test)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "nRYTwydsfpjb"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
+        "mae2 = mean_absolute_error(y_test, pred2)\n",
+        "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
+        "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
+        "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
+        "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
+        "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
+        "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "656J5oz5fzq6",
+        "outputId": "ce67d2d8-0bc8-4e6d-d6b5-6b78e7e1c59b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 147.71103599153602\n",
+            "MAE: 110.99419106508152\n",
+            "MAPE: 1.9715076513294716\n",
+            "Accuracy: 0.9992932862190813\n",
+            "Precision: 1.0\n",
+            "Confusion Matrix:\n",
+            " [[727   0]\n",
+            " [  1 687]]\n",
+            "Recall: 0.998546511627907\n",
+            "F1 Score: 0.9992727272727273\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse2)\n",
+        "print(\"MAE:\", mae2)\n",
+        "print(\"MAPE:\", mape2)\n",
+        "print(\"Accuracy:\", accuracy2)\n",
+        "print(\"Precision:\", precision2)\n",
+        "print(\"Confusion Matrix:\\n\", confusion2)\n",
+        "print(\"Recall:\", recall2)\n",
+        "print(\"F1 Score:\", f12)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hcIfVMWdgcKt"
+      },
+      "source": [
+        "## 3. Random Forest"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "f7raXT_hf2ij"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "# Create a Random Forest model\n",
+        "model3 = RandomForestRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "id": "TadNM7MEU7fh"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "fF002Yepgk55",
+        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestRegressor()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model3.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "8nRU_pzEgnCt"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred3 = model3.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "id": "4aKEXGVUgsry"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
+        "mae3 = mean_absolute_error(y_test, pred3)\n",
+        "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
+        "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
+        "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
+        "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
+        "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
+        "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8pPzsCY1g305",
+        "outputId": "72c4ea56-2610-41c6-f286-4c8289d3f0ac"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 2.189635498596314\n",
+            "MAE: 1.250413817712252\n",
+            "MAPE: 0.007984509559881612\n",
+            "Accuracy: 0.8551236749116607\n",
+            "Precision: 0.8558823529411764\n",
+            "Confusion Matrix:\n",
+            " [[628  98]\n",
+            " [107 582]]\n",
+            "Recall: 0.8447024673439768\n",
+            "F1 Score: 0.8502556610664718\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse3)\n",
+        "print(\"MAE:\", mae3)\n",
+        "print(\"MAPE:\", mape3)\n",
+        "print(\"Accuracy:\", accuracy3)\n",
+        "print(\"Precision:\", precision3)\n",
+        "print(\"Confusion Matrix:\\n\", confusion3)\n",
+        "print(\"Recall:\", recall3)\n",
+        "print(\"F1 Score:\", f13)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mZsLwLivhLGH"
+      },
+      "source": [
+        "## 4. Gradient Boosting Models (GBM)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "TI8idoxOg6jF"
+      },
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "# Create an XGBoost model\n",
+        "model4 = xgb.XGBRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "id": "7r9xJDtOVBEA"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "2gpbDxshhexj",
+        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model4.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "Jj9DXdUPhh9V"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred4 = model4.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "id": "TdH60Sllhn5O"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
+        "mae4 = mean_absolute_error(y_test, pred4)\n",
+        "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
+        "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
+        "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
+        "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
+        "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
+        "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qpnLeFyZhwB3",
+        "outputId": "4dcac062-ec60-4b2c-ab4b-dcda1b0f2341"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 2.733930065274145\n",
+            "MAE: 1.502457380471909\n",
+            "MAPE: 0.010026410639661481\n",
+            "Accuracy: 0.8840989399293286\n",
+            "Precision: 0.8948106591865358\n",
+            "Confusion Matrix:\n",
+            " [[613  75]\n",
+            " [ 89 638]]\n",
+            "Recall: 0.8775790921595599\n",
+            "F1 Score: 0.8861111111111112\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse4)\n",
+        "print(\"MAE:\", mae4)\n",
+        "print(\"MAPE:\", mape4)\n",
+        "print(\"Accuracy:\", accuracy4)\n",
+        "print(\"Precision:\", precision4)\n",
+        "print(\"Confusion Matrix:\\n\", confusion4)\n",
+        "print(\"Recall:\", recall4)\n",
+        "print(\"F1 Score:\", f14)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d8nSGoyuh9dx"
+      },
+      "source": [
+        "## 5. Extreme Gradient Boosting (XGBoost)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "DyhhdlZAhx94"
+      },
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "# Create an XGBoost model\n",
+        "model5 = xgb.XGBRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "Z_AD0lVOVHwB"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "RAIwxIp5iH9Z",
+        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model5.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "id": "XmJds5fYiKT3"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred5 = model5.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "id": "lZ1A0-L8iNCM"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
+        "mae5 = mean_absolute_error(y_test, pred5)\n",
+        "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
+        "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
+        "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
+        "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
+        "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
+        "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7IkE-RAmiWNo",
+        "outputId": "cf4c1d84-412b-4a18-f70c-65ce637772ea"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 2.733930065274145\n",
+            "MAE: 1.502457380471909\n",
+            "MAPE: 0.010026410639661481\n",
+            "Accuracy: 0.8840989399293286\n",
+            "Precision: 0.8948106591865358\n",
+            "Confusion Matrix:\n",
+            " [[613  75]\n",
+            " [ 89 638]]\n",
+            "Recall: 0.8775790921595599\n",
+            "F1 Score: 0.8861111111111112\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse5)\n",
+        "print(\"MAE:\", mae5)\n",
+        "print(\"MAPE:\", mape5)\n",
+        "print(\"Accuracy:\", accuracy5)\n",
+        "print(\"Precision:\", precision5)\n",
+        "print(\"Confusion Matrix:\\n\", confusion5)\n",
+        "print(\"Recall:\", recall5)\n",
+        "print(\"F1 Score:\", f15)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A_J776rtiovq"
+      },
+      "source": [
+        "## 6. AdaBoostRegressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "HNq66cXRiYPJ"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import AdaBoostRegressor\n",
+        "# Create an AdaBoost model\n",
+        "model6 = AdaBoostRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "qPHH6rG0VW4V"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "P0oB5wjQivBr",
+        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "AdaBoostRegressor()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model6.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "id": "Bf1m5ukOi2VM"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred6 = model6.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "id": "oFWSqC4ai6gd"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
+        "mae6 = mean_absolute_error(y_test, pred6)\n",
+        "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
+        "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
+        "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
+        "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
+        "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
+        "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BsajWJGBjC80",
+        "outputId": "1af1194f-9a33-40af-8578-c99832509c1b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 9.283285018137352\n",
+            "MAE: 7.574989783595977\n",
+            "MAPE: 0.16829256716397573\n",
+            "Accuracy: 0.9901060070671378\n",
+            "Precision: 0.9900990099009901\n",
+            "Confusion Matrix:\n",
+            " [[901   5]\n",
+            " [  9 500]]\n",
+            "Recall: 0.9823182711198428\n",
+            "F1 Score: 0.9861932938856016\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse6)\n",
+        "print(\"MAE:\", mae6)\n",
+        "print(\"MAPE:\", mape6)\n",
+        "print(\"Accuracy:\", accuracy6)\n",
+        "print(\"Precision:\", precision6)\n",
+        "print(\"Confusion Matrix:\\n\", confusion6)\n",
+        "print(\"Recall:\", recall6)\n",
+        "print(\"F1 Score:\", f16)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q9DzOt3CkWFX"
+      },
+      "source": [
+        "## 7. Decision Tree"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "23DZ2biSjF9a"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "# Create a Decision Tree model\n",
+        "model7 = DecisionTreeRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "id": "Ajo2RAVAVb7H"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "6mQEQf-ykc9F",
+        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeRegressor()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 72
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model7.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "BFJ9q_tvkgRC"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred7 = model7.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {
+        "id": "9IxfYZbYkjv1"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
+        "mae7 = mean_absolute_error(y_test, pred7)\n",
+        "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
+        "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
+        "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
+        "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
+        "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
+        "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AnZXMYb8kooV",
+        "outputId": "273fa9ed-d6f2-4c4d-fb0e-a643f5ef5732"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 3.193539964582351\n",
+            "MAE: 1.6240937361593886\n",
+            "MAPE: 0.010136361140005275\n",
+            "Accuracy: 0.8579505300353357\n",
+            "Precision: 0.8700410396716827\n",
+            "Confusion Matrix:\n",
+            " [[578  95]\n",
+            " [106 636]]\n",
+            "Recall: 0.8571428571428571\n",
+            "F1 Score: 0.8635437881873728\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse7)\n",
+        "print(\"MAE:\", mae7)\n",
+        "print(\"MAPE:\", mape7)\n",
+        "print(\"Accuracy:\", accuracy7)\n",
+        "print(\"Precision:\", precision7)\n",
+        "print(\"Confusion Matrix:\\n\", confusion7)\n",
+        "print(\"Recall:\", recall7)\n",
+        "print(\"F1 Score:\", f17)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LH-B-Xd6k5UD"
+      },
+      "source": [
+        "## 8. KNeighborsRegressor(KNN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "JVDSed7yktFY"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "# Create a KNN model\n",
+        "model8 = KNeighborsRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 77,
+      "metadata": {
+        "id": "XJHb5SxrVgVp"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "9fn64o-ZlBka",
+        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsRegressor()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 78
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model8.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "id": "hbfbbjcSlDn7"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred8 = model8.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 80,
+      "metadata": {
+        "id": "hnWyNv3blHdL"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
+        "mae8 = mean_absolute_error(y_test, pred8)\n",
+        "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
+        "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
+        "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
+        "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
+        "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
+        "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 81,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IPoDRkcMlMAr",
+        "outputId": "9892f42f-e65f-46c0-eeed-77ce32f6a7eb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 148.73183825029315\n",
+            "MAE: 109.35229571264969\n",
+            "MAPE: 1.75024316976612\n",
+            "Accuracy: 0.9908127208480565\n",
+            "Precision: 0.9887820512820513\n",
+            "Confusion Matrix:\n",
+            " [[785   7]\n",
+            " [  6 617]]\n",
+            "Recall: 0.9903691813804173\n",
+            "F1 Score: 0.9895749799518845\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse8)\n",
+        "print(\"MAE:\", mae8)\n",
+        "print(\"MAPE:\", mape8)\n",
+        "print(\"Accuracy:\", accuracy8)\n",
+        "print(\"Precision:\", precision8)\n",
+        "print(\"Confusion Matrix:\\n\", confusion8)\n",
+        "print(\"Recall:\", recall8)\n",
+        "print(\"F1 Score:\", f18)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X5XtlzMXljps"
+      },
+      "source": [
+        "## 9. Artificial Neural Networks (ANN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "metadata": {
+        "id": "bJk1-9VhlRL6"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "metadata": {
+        "id": "sZVPMR9Wlo7-"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {
+        "id": "vd1fDjQiltP4"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an ANN model\n",
+        "model9 = Sequential()\n",
+        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+        "model9.add(Dense(16, activation='relu'))\n",
+        "model9.add(Dense(1, activation='linear'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {
+        "id": "ZIf94WLMlv04"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model9.compile(loss='mean_squared_error', optimizer='adam')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FX5DTKqslxWf",
+        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7ca68076d540>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 86
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 87,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OVW2qpNsmGVq",
+        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "45/45 [==============================] - 0s 1ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred9 = model9.predict(X_test_scaled).flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 88,
+      "metadata": {
+        "id": "CqRmjMj2maJY"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
+        "mae9 = mean_absolute_error(y_test, pred9)\n",
+        "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
+        "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
+        "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
+        "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
+        "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
+        "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 89,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5zuwkC1emmh3",
+        "outputId": "5d6a0e05-3112-4d27-f5fb-ed665867b22d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 2.7570259701356035\n",
+            "MAE: 1.7412277270507284\n",
+            "MAPE: 0.012205298865408084\n",
+            "Accuracy: 0.8904593639575972\n",
+            "Precision: 0.8242753623188406\n",
+            "Confusion Matrix:\n",
+            " [[805  97]\n",
+            " [ 58 455]]\n",
+            "Recall: 0.8869395711500975\n",
+            "F1 Score: 0.8544600938967135\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse9)\n",
+        "print(\"MAE:\", mae9)\n",
+        "print(\"MAPE:\", mape9)\n",
+        "print(\"Accuracy:\", accuracy9)\n",
+        "print(\"Precision:\", precision9)\n",
+        "print(\"Confusion Matrix:\\n\", confusion9)\n",
+        "print(\"Recall:\", recall9)\n",
+        "print(\"F1 Score:\", f19)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vjSMQNcOnFPJ"
+      },
+      "source": [
+        "## 10. LSTM(Long Short term Memory)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 90,
+      "metadata": {
+        "id": "nCoyUanhnDKw"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import LSTM, Dense"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 91,
+      "metadata": {
+        "id": "ThcXESVEVv0U"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 92,
+      "metadata": {
+        "id": "uACvajfImrbB"
+      },
+      "outputs": [],
+      "source": [
+        "# Reshape the input data for LSTM\n",
+        "n_features = X_train_scaled.shape[1]\n",
+        "n_steps = 10\n",
+        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+        "\n",
+        "# Reshape the input data\n",
+        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 93,
+      "metadata": {
+        "id": "r066pVYpnXH5"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an LSTM model\n",
+        "model = Sequential()\n",
+        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+        "model.add(Dense(1))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 94,
+      "metadata": {
+        "id": "YpSfHu6gov35"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 95,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0vHjcluaoxzP",
+        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7ca680459cc0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 95
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 96,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gEE06_TjozYv",
+        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "44/44 [==============================] - 0s 4ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Make predictions on the test set\n",
+        "y_pred = model.predict(X_test_reshaped).flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 113,
+      "metadata": {
+        "id": "7k6C8DrxpB_Q"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
+        "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
+        "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
+        "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 114,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i_6-UUDhpi0c",
+        "outputId": "3dcc5761-03b6-4b52-dfe6-08dece835c8d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 10.083053125286519\n",
+            "MAE: 7.973378150691296\n",
+            "MAPE: 0.12730792351246625\n",
+            "Accuracy: 0.9886201991465149\n",
+            "Precision: 0.9904912836767037\n",
+            "Recall: 0.984251968503937\n",
+            "F1 Score: 0.9873617693522907\n",
+            "Confusion Matrix:\n",
+            " [[765   6]\n",
+            " [ 10 625]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print evaluation metrics\n",
+        "print(\"RMSE:\", rmse10)\n",
+        "print(\"MAE:\", mae10)\n",
+        "print(\"MAPE:\", mape10)\n",
+        "print(\"Accuracy:\", accuracy10)\n",
+        "print(\"Precision:\", precision10)\n",
+        "print(\"Recall:\", recall10)\n",
+        "print(\"F1 Score:\", f110)\n",
+        "print(\"Confusion Matrix:\\n\", confusion10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
+        "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n",
+        "\n",
+        "# List of corresponding labels for each accuracy\n",
+        "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, accuracies, color='blue')\n",
+        "plt.xlabel('Accuracy Variables')\n",
+        "plt.ylabel('Accuracy Values')\n",
+        "plt.title('Bar Graph of Accuracies')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "qpWPtph9CGip",
+        "outputId": "c099cb8d-96af-4223-f499-743040aecdf1"
+      },
+      "execution_count": 117,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
+        "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n",
+        "\n",
+        "# List of corresponding labels for each RMSE value\n",
+        "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, rmse_values, color='green')\n",
+        "plt.xlabel('RMSE Variables')\n",
+        "plt.ylabel('RMSE Values')\n",
+        "plt.title('Bar Graph of RMSE')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "RFaaCNH6Cfoa",
+        "outputId": "67a8f358-e3ce-4ad2-9c78-ebc75902beb4"
+      },
+      "execution_count": 118,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of MAE values from mae1 to mae10\n",
+        "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n",
+        "\n",
+        "# List of corresponding labels for each MAE value\n",
+        "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, mae_values, color='orange')\n",
+        "plt.xlabel('MAE Variables')\n",
+        "plt.ylabel('MAE Values')\n",
+        "plt.title('Bar Graph of MAE')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "nrZu-K-KDCJ2",
+        "outputId": "69165581-da05-4554-a464-a606eb87a734"
+      },
+      "execution_count": 119,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of MAPE values from mape1 to mape10\n",
+        "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n",
+        "\n",
+        "# List of corresponding labels for each MAPE value\n",
+        "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, mape_values, color='purple')\n",
+        "plt.xlabel('MAPE Variables')\n",
+        "plt.ylabel('MAPE Values')\n",
+        "plt.title('Bar Graph of MAPE')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "_c4Pe76fDNM-",
+        "outputId": "0e3d2f74-9042-4e2d-92c6-5ce61e967bd4"
+      },
+      "execution_count": 120,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of precision values from precision1 to precision10\n",
+        "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
+        "\n",
+        "# List of corresponding labels for each precision value\n",
+        "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, precision_values, color='red')\n",
+        "plt.xlabel('Precision Variables')\n",
+        "plt.ylabel('Precision Values')\n",
+        "plt.title('Bar Graph of Precision')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "ZDPV0M5rDTi6",
+        "outputId": "9db63164-3f42-47be-d302-d80d381d9b91"
+      },
+      "execution_count": 121,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of recall values from recall1 to recall10\n",
+        "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
+        "\n",
+        "# List of corresponding labels for each recall value\n",
+        "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, recall_values, color='cyan')\n",
+        "plt.xlabel('Recall Variables')\n",
+        "plt.ylabel('Recall Values')\n",
+        "plt.title('Bar Graph of Recall')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "39LBleNeDeuw",
+        "outputId": "3c6c40bc-f1da-44fb-da14-25ec6d6cf278"
+      },
+      "execution_count": 122,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "13cZXvb0DsvK"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Stock_Price_Prediction.ipynb b/Stock_Price_Prediction.ipynb
new file mode 100644
index 0000000..7c3ad61
--- /dev/null
+++ b/Stock_Price_Prediction.ipynb
@@ -0,0 +1,1805 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "qCDSjVhXLr_Z"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.svm import SVR\n",
+        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense,LSTM"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SOQbXSiB-g5G",
+        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Sc4id6VxL8BS",
+        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>01-01-1996</td>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>12.409931</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>02-01-1996</td>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
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+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>03-01-1996</td>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
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+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>04-01-1996</td>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>11.654142</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>05-01-1996</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>11.588827</td>\n",
+              "      <td>76613039.0</td>\n",
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+              "  </tbody>\n",
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+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "         Date       Open       High        Low      Close  Adj Close  \\\n",
+              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+              "\n",
+              "       Volume  \n",
+              "0  43733533.0  \n",
+              "1  56167280.0  \n",
+              "2  68296318.0  \n",
+              "3  86073880.0  \n",
+              "4  76613039.0  "
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Load the dataset\n",
+        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "7LaYGXsfN-8y"
+      },
+      "outputs": [],
+      "source": [
+        "# Drop the 'Date' and 'Adj Close' columns\n",
+        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "pqbTBdnBOKJc",
+        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>76613039.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7696e222-c2c8-45d3-99ad-84f50b7bfd98')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "        Open       High        Low      Close      Volume\n",
+              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
+              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
+              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
+              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
+              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "dydEPoNeM6eN"
+      },
+      "outputs": [],
+      "source": [
+        "# Handle missing values\n",
+        "imputer = SimpleImputer(strategy='mean')\n",
+        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "OQ3cGqgTMBwt"
+      },
+      "outputs": [],
+      "source": [
+        "# Select features and target variable\n",
+        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+        "y = df['Close']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "9Oz-bwJOMEWD"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "ugapDyXODtn3"
+      },
+      "outputs": [],
+      "source": [
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "997ZEgibCZIO",
+        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(5659, 4)"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bmtt76RuCeyG",
+        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(1415, 4)"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_test.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CeJkUJ92Ciqd",
+        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(5659,)"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7HGC7VuTCjWc",
+        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(1415,)"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_test.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Function to evaluate and print RMSE, MAE, and MAPE\n",
+        "def evaluate_model(model, X_test, y_test):\n",
+        "    predictions = model.predict(X_test)\n",
+        "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+        "    mae = mean_absolute_error(y_test, predictions)\n",
+        "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+        "\n",
+        "    print(f\"RMSE: {rmse}\")\n",
+        "    print(f\"MAE: {mae}\")\n",
+        "    print(f\"MAPE: {mape}\\n\")\n",
+        "    \n",
+        "    return rmse, mae, mape\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "metrics = {\n",
+        "    \"Model\": [],\n",
+        "    \"RMSE\": [],\n",
+        "    \"MAE\": [],\n",
+        "    \"MAPE\": []\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c6Ek8jRlO2_I"
+      },
+      "source": [
+        "## 1. LINEAR REGRESSION"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "RdZ1SpzdMHAJ"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a linear regression model\n",
+        "model1 = LinearRegression()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mPM035IzMY04",
+        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "5286    257.350006\n",
+              "3408    129.464996\n",
+              "5477    279.350006\n",
+              "6906    588.500000\n",
+              "530      21.644367\n",
+              "Name: Close, dtype: float64"
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_train.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "qBhQ9HbYMI3d",
+        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model1.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "X269co2kMS4z"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"Linear Regressor\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GxtMzlg-gR2P"
+      },
+      "source": [
+        "## 2. Support Vector Regression"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "0xQewd7QWTtq"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an SVR model\n",
+        "model2 = SVR()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "22SaCsQmfhgP",
+        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "SVR()"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model2.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "OQ1nL4oYfkAC"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"SVR\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hcIfVMWdgcKt"
+      },
+      "source": [
+        "## 3. Random Forest Regressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "f7raXT_hf2ij"
+      },
+      "outputs": [],
+      "source": [
+        "model3 = RandomForestRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "fF002Yepgk55",
+        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestRegressor()"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model3.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "8nRU_pzEgnCt"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"Random Forest\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mZsLwLivhLGH"
+      },
+      "source": [
+        "## 4. Gradient Boosting Models (GBM)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "TI8idoxOg6jF"
+      },
+      "outputs": [],
+      "source": [
+        "model4 = GradientBoostingRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "2gpbDxshhexj",
+        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model4.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "Jj9DXdUPhh9V"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"GBM\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d8nSGoyuh9dx"
+      },
+      "source": [
+        "## 5. Extreme Gradient Boosting (XGBoost)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "DyhhdlZAhx94"
+      },
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "# Create an XGBoost model\n",
+        "model5 = xgb.XGBRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "RAIwxIp5iH9Z",
+        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ]
+          },
+          "execution_count": 60,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model5.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"XGBoost\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A_J776rtiovq"
+      },
+      "source": [
+        "## 6. AdaBoostRegressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "HNq66cXRiYPJ"
+      },
+      "outputs": [],
+      "source": [
+        "model6 = AdaBoostRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "P0oB5wjQivBr",
+        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "AdaBoostRegressor()"
+            ]
+          },
+          "execution_count": 66,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model6.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "id": "Bf1m5ukOi2VM"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q9DzOt3CkWFX"
+      },
+      "source": [
+        "## 7. Decision Tree"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "23DZ2biSjF9a"
+      },
+      "outputs": [],
+      "source": [
+        "model7 = DecisionTreeRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "6mQEQf-ykc9F",
+        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "DecisionTreeRegressor()"
+            ]
+          },
+          "execution_count": 72,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model7.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "BFJ9q_tvkgRC"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"Decision Tree\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LH-B-Xd6k5UD"
+      },
+      "source": [
+        "## 8. KNeighborsRegressor(KNN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "JVDSed7yktFY"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a KNN model\n",
+        "model8 = KNeighborsRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "9fn64o-ZlBka",
+        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "KNeighborsRegressor()"
+            ]
+          },
+          "execution_count": 78,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model8.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "id": "hbfbbjcSlDn7"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"KNN\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X5XtlzMXljps"
+      },
+      "source": [
+        "## 9. Artificial Neural Networks (ANN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {
+        "id": "vd1fDjQiltP4"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an ANN model\n",
+        "model9 = Sequential()\n",
+        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+        "model9.add(Dense(16, activation='relu'))\n",
+        "model9.add(Dense(1, activation='linear'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {
+        "id": "ZIf94WLMlv04"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model9.compile(loss='mean_squared_error', optimizer='adam')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FX5DTKqslxWf",
+        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7ca68076d540>"
+            ]
+          },
+          "execution_count": 86,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 87,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OVW2qpNsmGVq",
+        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "45/45 [==============================] - 0s 1ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"ANN\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vjSMQNcOnFPJ"
+      },
+      "source": [
+        "## 10. LSTM(Long Short term Memory)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 92,
+      "metadata": {
+        "id": "uACvajfImrbB"
+      },
+      "outputs": [],
+      "source": [
+        "# Reshape the input data for LSTM\n",
+        "n_features = X_train_scaled.shape[1]\n",
+        "n_steps = 10\n",
+        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+        "\n",
+        "# Reshape the input data\n",
+        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 93,
+      "metadata": {
+        "id": "r066pVYpnXH5"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an LSTM model\n",
+        "model = Sequential()\n",
+        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+        "model.add(Dense(1))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 94,
+      "metadata": {
+        "id": "YpSfHu6gov35"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 95,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0vHjcluaoxzP",
+        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7ca680459cc0>"
+            ]
+          },
+          "execution_count": 95,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 96,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gEE06_TjozYv",
+        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "44/44 [==============================] - 0s 4ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"LSTM\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Create a DataFrame for metrics\n",
+        "metrics_df = pd.DataFrame(metrics)\n",
+        "\n",
+        "# Plot RMSE, MAE, and MAPE for each model\n",
+        "plt.figure(figsize=(15, 5))\n",
+        "\n",
+        "# RMSE Plot\n",
+        "plt.subplot(1, 3, 1)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
+        "plt.xlabel('RMSE')\n",
+        "plt.title('RMSE for Different Models')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# MAE Plot\n",
+        "plt.subplot(1, 3, 2)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n",
+        "plt.xlabel('MAE')\n",
+        "plt.title('MAE for Different Models')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# MAPE Plot\n",
+        "plt.subplot(1, 3, 3)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
+        "plt.xlabel('MAPE')\n",
+        "plt.title('MAPE for Different Models')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 81d00fa7bc1083780d73f7cd5086adf75b9d1d6c Mon Sep 17 00:00:00 2001
From: Ayushi Sha <124066968+AYUSHI-SHA@users.noreply.github.com>
Date: Sun, 6 Oct 2024 14:32:48 +0530
Subject: [PATCH 04/76] Resolved conflicts while merging

---
 Python File/Stock_Price_Prediction.ipynb      | 2708 -----------------
 ...b => Stock_Price_Prediction(Updated).ipynb |    0
 2 files changed, 2708 deletions(-)
 delete mode 100644 Python File/Stock_Price_Prediction.ipynb
 rename Python File/Stock_Price_Prediction(Updated).ipynb => Stock_Price_Prediction(Updated).ipynb (100%)

diff --git a/Python File/Stock_Price_Prediction.ipynb b/Python File/Stock_Price_Prediction.ipynb
deleted file mode 100644
index c82b075..0000000
--- a/Python File/Stock_Price_Prediction.ipynb	
+++ /dev/null
@@ -1,2708 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "qCDSjVhXLr_Z"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.linear_model import LinearRegression\n",
-        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')\n",
-        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "SOQbXSiB-g5G",
-        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
-      },
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Sc4id6VxL8BS",
-        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "         Date       Open       High        Low      Close  Adj Close  \\\n",
-              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
-              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
-              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
-              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
-              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
-              "\n",
-              "       Volume  \n",
-              "0  43733533.0  \n",
-              "1  56167280.0  \n",
-              "2  68296318.0  \n",
-              "3  86073880.0  \n",
-              "4  76613039.0  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Date</th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Adj Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>01-01-1996</td>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>12.409931</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>02-01-1996</td>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>12.014931</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>03-01-1996</td>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>11.694577</td>\n",
-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>04-01-1996</td>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>11.654142</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>05-01-1996</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>11.588827</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d89bb189-3861-4a83-b0ea-1a19c2115c37')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
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-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
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-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
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-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
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-              "      border-bottom-color: var(--fill-color);\n",
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-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
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-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
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-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
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-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 23
-        }
-      ],
-      "source": [
-        "# Load the dataset\n",
-        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "7LaYGXsfN-8y"
-      },
-      "outputs": [],
-      "source": [
-        "# Drop the 'Date' and 'Adj Close' columns\n",
-        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "pqbTBdnBOKJc",
-        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "        Open       High        Low      Close      Volume\n",
-              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
-              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
-              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
-              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
-              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
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-              "            title=\"Convert this dataframe to an interactive table.\"\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 25
-        }
-      ],
-      "source": [
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "id": "dydEPoNeM6eN"
-      },
-      "outputs": [],
-      "source": [
-        "# Handle missing values\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "imputer = SimpleImputer(strategy='mean')\n",
-        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {
-        "id": "OQ3cGqgTMBwt"
-      },
-      "outputs": [],
-      "source": [
-        "# Select features and target variable\n",
-        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
-        "y = df['Close']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "id": "9Oz-bwJOMEWD"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "ugapDyXODtn3"
-      },
-      "outputs": [],
-      "source": [
-        "# Scale the features using Min-Max scaling\n",
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "997ZEgibCZIO",
-        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(5659, 4)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 29
-        }
-      ],
-      "source": [
-        "X_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bmtt76RuCeyG",
-        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(1415, 4)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 30
-        }
-      ],
-      "source": [
-        "X_test.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CeJkUJ92Ciqd",
-        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(5659,)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 31
-        }
-      ],
-      "source": [
-        "y_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7HGC7VuTCjWc",
-        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(1415,)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 32
-        }
-      ],
-      "source": [
-        "y_test.shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c6Ek8jRlO2_I"
-      },
-      "source": [
-        "## 1. LINEAR REGRESSION"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "id": "RdZ1SpzdMHAJ"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a linear regression model\n",
-        "model1 = LinearRegression()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mPM035IzMY04",
-        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "5286    257.350006\n",
-              "3408    129.464996\n",
-              "5477    279.350006\n",
-              "6906    588.500000\n",
-              "530      21.644367\n",
-              "Name: Close, dtype: float64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 34
-        }
-      ],
-      "source": [
-        "y_train.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "qBhQ9HbYMI3d",
-        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 35
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model1.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {
-        "id": "X269co2kMS4z"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred1 = model1.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 37,
-      "metadata": {
-        "id": "QK8GvDYPOd0Y"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
-        "mae1 = mean_absolute_error(y_test, pred1)\n",
-        "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
-        "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
-        "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
-        "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
-        "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
-        "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 38,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dEi49xtEOtne",
-        "outputId": "0000b074-3187-41de-fbac-4ae75cbda6bd"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 1.6881364643681482\n",
-            "MAE: 0.9433353485344729\n",
-            "MAPE: 0.006085435990853812\n",
-            "Accuracy: 0.8296819787985866\n",
-            "Precision: 0.8623595505617978\n",
-            "Confusion Matrix:\n",
-            " [[560  98]\n",
-            " [143 614]]\n",
-            "Recall: 0.8110964332892999\n",
-            "F1 Score: 0.8359428182437032\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse1)\n",
-        "print(\"MAE:\", mae1)\n",
-        "print(\"MAPE:\", mape1)\n",
-        "print(\"Accuracy:\", accuracy1)\n",
-        "print(\"Precision:\", precision1)\n",
-        "print(\"Confusion Matrix:\\n\", confusion1)\n",
-        "print(\"Recall:\", recall1)\n",
-        "print(\"F1 Score:\", f11)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GxtMzlg-gR2P"
-      },
-      "source": [
-        "## 2. SVR"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 39,
-      "metadata": {
-        "id": "o7K9r7EXWRjQ"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.svm import SVR"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "id": "0xQewd7QWTtq"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an SVR model\n",
-        "model2 = SVR()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 41,
-      "metadata": {
-        "id": "DuNes3s6U2IV"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "22SaCsQmfhgP",
-        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "SVR()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 42
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model2.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 43,
-      "metadata": {
-        "id": "OQ1nL4oYfkAC"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred2 = model2.predict(X_test)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 44,
-      "metadata": {
-        "id": "nRYTwydsfpjb"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
-        "mae2 = mean_absolute_error(y_test, pred2)\n",
-        "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
-        "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
-        "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
-        "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
-        "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
-        "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 45,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "656J5oz5fzq6",
-        "outputId": "ce67d2d8-0bc8-4e6d-d6b5-6b78e7e1c59b"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 147.71103599153602\n",
-            "MAE: 110.99419106508152\n",
-            "MAPE: 1.9715076513294716\n",
-            "Accuracy: 0.9992932862190813\n",
-            "Precision: 1.0\n",
-            "Confusion Matrix:\n",
-            " [[727   0]\n",
-            " [  1 687]]\n",
-            "Recall: 0.998546511627907\n",
-            "F1 Score: 0.9992727272727273\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse2)\n",
-        "print(\"MAE:\", mae2)\n",
-        "print(\"MAPE:\", mape2)\n",
-        "print(\"Accuracy:\", accuracy2)\n",
-        "print(\"Precision:\", precision2)\n",
-        "print(\"Confusion Matrix:\\n\", confusion2)\n",
-        "print(\"Recall:\", recall2)\n",
-        "print(\"F1 Score:\", f12)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hcIfVMWdgcKt"
-      },
-      "source": [
-        "## 3. Random Forest"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 46,
-      "metadata": {
-        "id": "f7raXT_hf2ij"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.ensemble import RandomForestRegressor\n",
-        "# Create a Random Forest model\n",
-        "model3 = RandomForestRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 47,
-      "metadata": {
-        "id": "TadNM7MEU7fh"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 48,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "fF002Yepgk55",
-        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "RandomForestRegressor()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 48
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model3.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "metadata": {
-        "id": "8nRU_pzEgnCt"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred3 = model3.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 50,
-      "metadata": {
-        "id": "4aKEXGVUgsry"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
-        "mae3 = mean_absolute_error(y_test, pred3)\n",
-        "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
-        "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
-        "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
-        "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
-        "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
-        "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 51,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8pPzsCY1g305",
-        "outputId": "72c4ea56-2610-41c6-f286-4c8289d3f0ac"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 2.189635498596314\n",
-            "MAE: 1.250413817712252\n",
-            "MAPE: 0.007984509559881612\n",
-            "Accuracy: 0.8551236749116607\n",
-            "Precision: 0.8558823529411764\n",
-            "Confusion Matrix:\n",
-            " [[628  98]\n",
-            " [107 582]]\n",
-            "Recall: 0.8447024673439768\n",
-            "F1 Score: 0.8502556610664718\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse3)\n",
-        "print(\"MAE:\", mae3)\n",
-        "print(\"MAPE:\", mape3)\n",
-        "print(\"Accuracy:\", accuracy3)\n",
-        "print(\"Precision:\", precision3)\n",
-        "print(\"Confusion Matrix:\\n\", confusion3)\n",
-        "print(\"Recall:\", recall3)\n",
-        "print(\"F1 Score:\", f13)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mZsLwLivhLGH"
-      },
-      "source": [
-        "## 4. Gradient Boosting Models (GBM)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 52,
-      "metadata": {
-        "id": "TI8idoxOg6jF"
-      },
-      "outputs": [],
-      "source": [
-        "import xgboost as xgb\n",
-        "# Create an XGBoost model\n",
-        "model4 = xgb.XGBRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 53,
-      "metadata": {
-        "id": "7r9xJDtOVBEA"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 54,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "2gpbDxshhexj",
-        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 54
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model4.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 55,
-      "metadata": {
-        "id": "Jj9DXdUPhh9V"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred4 = model4.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 56,
-      "metadata": {
-        "id": "TdH60Sllhn5O"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
-        "mae4 = mean_absolute_error(y_test, pred4)\n",
-        "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
-        "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
-        "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
-        "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
-        "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
-        "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 57,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "qpnLeFyZhwB3",
-        "outputId": "4dcac062-ec60-4b2c-ab4b-dcda1b0f2341"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 2.733930065274145\n",
-            "MAE: 1.502457380471909\n",
-            "MAPE: 0.010026410639661481\n",
-            "Accuracy: 0.8840989399293286\n",
-            "Precision: 0.8948106591865358\n",
-            "Confusion Matrix:\n",
-            " [[613  75]\n",
-            " [ 89 638]]\n",
-            "Recall: 0.8775790921595599\n",
-            "F1 Score: 0.8861111111111112\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse4)\n",
-        "print(\"MAE:\", mae4)\n",
-        "print(\"MAPE:\", mape4)\n",
-        "print(\"Accuracy:\", accuracy4)\n",
-        "print(\"Precision:\", precision4)\n",
-        "print(\"Confusion Matrix:\\n\", confusion4)\n",
-        "print(\"Recall:\", recall4)\n",
-        "print(\"F1 Score:\", f14)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d8nSGoyuh9dx"
-      },
-      "source": [
-        "## 5. Extreme Gradient Boosting (XGBoost)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 58,
-      "metadata": {
-        "id": "DyhhdlZAhx94"
-      },
-      "outputs": [],
-      "source": [
-        "import xgboost as xgb\n",
-        "# Create an XGBoost model\n",
-        "model5 = xgb.XGBRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 59,
-      "metadata": {
-        "id": "Z_AD0lVOVHwB"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 60,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "RAIwxIp5iH9Z",
-        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 60
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model5.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 61,
-      "metadata": {
-        "id": "XmJds5fYiKT3"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred5 = model5.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 62,
-      "metadata": {
-        "id": "lZ1A0-L8iNCM"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
-        "mae5 = mean_absolute_error(y_test, pred5)\n",
-        "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
-        "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
-        "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
-        "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
-        "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
-        "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 63,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7IkE-RAmiWNo",
-        "outputId": "cf4c1d84-412b-4a18-f70c-65ce637772ea"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 2.733930065274145\n",
-            "MAE: 1.502457380471909\n",
-            "MAPE: 0.010026410639661481\n",
-            "Accuracy: 0.8840989399293286\n",
-            "Precision: 0.8948106591865358\n",
-            "Confusion Matrix:\n",
-            " [[613  75]\n",
-            " [ 89 638]]\n",
-            "Recall: 0.8775790921595599\n",
-            "F1 Score: 0.8861111111111112\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse5)\n",
-        "print(\"MAE:\", mae5)\n",
-        "print(\"MAPE:\", mape5)\n",
-        "print(\"Accuracy:\", accuracy5)\n",
-        "print(\"Precision:\", precision5)\n",
-        "print(\"Confusion Matrix:\\n\", confusion5)\n",
-        "print(\"Recall:\", recall5)\n",
-        "print(\"F1 Score:\", f15)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A_J776rtiovq"
-      },
-      "source": [
-        "## 6. AdaBoostRegressor"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 64,
-      "metadata": {
-        "id": "HNq66cXRiYPJ"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.ensemble import AdaBoostRegressor\n",
-        "# Create an AdaBoost model\n",
-        "model6 = AdaBoostRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 65,
-      "metadata": {
-        "id": "qPHH6rG0VW4V"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 66,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "P0oB5wjQivBr",
-        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "AdaBoostRegressor()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 66
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model6.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 67,
-      "metadata": {
-        "id": "Bf1m5ukOi2VM"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred6 = model6.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 68,
-      "metadata": {
-        "id": "oFWSqC4ai6gd"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
-        "mae6 = mean_absolute_error(y_test, pred6)\n",
-        "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
-        "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
-        "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
-        "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
-        "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
-        "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 69,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "BsajWJGBjC80",
-        "outputId": "1af1194f-9a33-40af-8578-c99832509c1b"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 9.283285018137352\n",
-            "MAE: 7.574989783595977\n",
-            "MAPE: 0.16829256716397573\n",
-            "Accuracy: 0.9901060070671378\n",
-            "Precision: 0.9900990099009901\n",
-            "Confusion Matrix:\n",
-            " [[901   5]\n",
-            " [  9 500]]\n",
-            "Recall: 0.9823182711198428\n",
-            "F1 Score: 0.9861932938856016\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse6)\n",
-        "print(\"MAE:\", mae6)\n",
-        "print(\"MAPE:\", mape6)\n",
-        "print(\"Accuracy:\", accuracy6)\n",
-        "print(\"Precision:\", precision6)\n",
-        "print(\"Confusion Matrix:\\n\", confusion6)\n",
-        "print(\"Recall:\", recall6)\n",
-        "print(\"F1 Score:\", f16)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Q9DzOt3CkWFX"
-      },
-      "source": [
-        "## 7. Decision Tree"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 70,
-      "metadata": {
-        "id": "23DZ2biSjF9a"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.tree import DecisionTreeRegressor\n",
-        "# Create a Decision Tree model\n",
-        "model7 = DecisionTreeRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 71,
-      "metadata": {
-        "id": "Ajo2RAVAVb7H"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 72,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "6mQEQf-ykc9F",
-        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "DecisionTreeRegressor()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 72
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model7.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 73,
-      "metadata": {
-        "id": "BFJ9q_tvkgRC"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred7 = model7.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 74,
-      "metadata": {
-        "id": "9IxfYZbYkjv1"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
-        "mae7 = mean_absolute_error(y_test, pred7)\n",
-        "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
-        "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
-        "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
-        "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
-        "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
-        "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 75,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "AnZXMYb8kooV",
-        "outputId": "273fa9ed-d6f2-4c4d-fb0e-a643f5ef5732"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 3.193539964582351\n",
-            "MAE: 1.6240937361593886\n",
-            "MAPE: 0.010136361140005275\n",
-            "Accuracy: 0.8579505300353357\n",
-            "Precision: 0.8700410396716827\n",
-            "Confusion Matrix:\n",
-            " [[578  95]\n",
-            " [106 636]]\n",
-            "Recall: 0.8571428571428571\n",
-            "F1 Score: 0.8635437881873728\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse7)\n",
-        "print(\"MAE:\", mae7)\n",
-        "print(\"MAPE:\", mape7)\n",
-        "print(\"Accuracy:\", accuracy7)\n",
-        "print(\"Precision:\", precision7)\n",
-        "print(\"Confusion Matrix:\\n\", confusion7)\n",
-        "print(\"Recall:\", recall7)\n",
-        "print(\"F1 Score:\", f17)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LH-B-Xd6k5UD"
-      },
-      "source": [
-        "## 8. KNeighborsRegressor(KNN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 76,
-      "metadata": {
-        "id": "JVDSed7yktFY"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.neighbors import KNeighborsRegressor\n",
-        "# Create a KNN model\n",
-        "model8 = KNeighborsRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 77,
-      "metadata": {
-        "id": "XJHb5SxrVgVp"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 78,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "9fn64o-ZlBka",
-        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "KNeighborsRegressor()"
-            ],
-            "text/html": [
-              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 78
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model8.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 79,
-      "metadata": {
-        "id": "hbfbbjcSlDn7"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred8 = model8.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 80,
-      "metadata": {
-        "id": "hnWyNv3blHdL"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
-        "mae8 = mean_absolute_error(y_test, pred8)\n",
-        "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
-        "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
-        "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
-        "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
-        "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
-        "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 81,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "IPoDRkcMlMAr",
-        "outputId": "9892f42f-e65f-46c0-eeed-77ce32f6a7eb"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 148.73183825029315\n",
-            "MAE: 109.35229571264969\n",
-            "MAPE: 1.75024316976612\n",
-            "Accuracy: 0.9908127208480565\n",
-            "Precision: 0.9887820512820513\n",
-            "Confusion Matrix:\n",
-            " [[785   7]\n",
-            " [  6 617]]\n",
-            "Recall: 0.9903691813804173\n",
-            "F1 Score: 0.9895749799518845\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse8)\n",
-        "print(\"MAE:\", mae8)\n",
-        "print(\"MAPE:\", mape8)\n",
-        "print(\"Accuracy:\", accuracy8)\n",
-        "print(\"Precision:\", precision8)\n",
-        "print(\"Confusion Matrix:\\n\", confusion8)\n",
-        "print(\"Recall:\", recall8)\n",
-        "print(\"F1 Score:\", f18)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "X5XtlzMXljps"
-      },
-      "source": [
-        "## 9. Artificial Neural Networks (ANN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 82,
-      "metadata": {
-        "id": "bJk1-9VhlRL6"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Dense"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 83,
-      "metadata": {
-        "id": "sZVPMR9Wlo7-"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 84,
-      "metadata": {
-        "id": "vd1fDjQiltP4"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an ANN model\n",
-        "model9 = Sequential()\n",
-        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
-        "model9.add(Dense(16, activation='relu'))\n",
-        "model9.add(Dense(1, activation='linear'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 85,
-      "metadata": {
-        "id": "ZIf94WLMlv04"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model9.compile(loss='mean_squared_error', optimizer='adam')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 86,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "FX5DTKqslxWf",
-        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca68076d540>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 86
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 87,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OVW2qpNsmGVq",
-        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "45/45 [==============================] - 0s 1ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred9 = model9.predict(X_test_scaled).flatten()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 88,
-      "metadata": {
-        "id": "CqRmjMj2maJY"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
-        "mae9 = mean_absolute_error(y_test, pred9)\n",
-        "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
-        "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
-        "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
-        "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
-        "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
-        "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 89,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5zuwkC1emmh3",
-        "outputId": "5d6a0e05-3112-4d27-f5fb-ed665867b22d"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 2.7570259701356035\n",
-            "MAE: 1.7412277270507284\n",
-            "MAPE: 0.012205298865408084\n",
-            "Accuracy: 0.8904593639575972\n",
-            "Precision: 0.8242753623188406\n",
-            "Confusion Matrix:\n",
-            " [[805  97]\n",
-            " [ 58 455]]\n",
-            "Recall: 0.8869395711500975\n",
-            "F1 Score: 0.8544600938967135\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse9)\n",
-        "print(\"MAE:\", mae9)\n",
-        "print(\"MAPE:\", mape9)\n",
-        "print(\"Accuracy:\", accuracy9)\n",
-        "print(\"Precision:\", precision9)\n",
-        "print(\"Confusion Matrix:\\n\", confusion9)\n",
-        "print(\"Recall:\", recall9)\n",
-        "print(\"F1 Score:\", f19)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vjSMQNcOnFPJ"
-      },
-      "source": [
-        "## 10. LSTM(Long Short term Memory)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 90,
-      "metadata": {
-        "id": "nCoyUanhnDKw"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import LSTM, Dense"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 91,
-      "metadata": {
-        "id": "ThcXESVEVv0U"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 92,
-      "metadata": {
-        "id": "uACvajfImrbB"
-      },
-      "outputs": [],
-      "source": [
-        "# Reshape the input data for LSTM\n",
-        "n_features = X_train_scaled.shape[1]\n",
-        "n_steps = 10\n",
-        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
-        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
-        "\n",
-        "# Reshape the input data\n",
-        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
-        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 93,
-      "metadata": {
-        "id": "r066pVYpnXH5"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an LSTM model\n",
-        "model = Sequential()\n",
-        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
-        "model.add(Dense(1))\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 94,
-      "metadata": {
-        "id": "YpSfHu6gov35"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 95,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0vHjcluaoxzP",
-        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca680459cc0>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 95
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 96,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "gEE06_TjozYv",
-        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "44/44 [==============================] - 0s 4ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Make predictions on the test set\n",
-        "y_pred = model.predict(X_test_reshaped).flatten()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 113,
-      "metadata": {
-        "id": "7k6C8DrxpB_Q"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
-        "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
-        "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
-        "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 114,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "i_6-UUDhpi0c",
-        "outputId": "3dcc5761-03b6-4b52-dfe6-08dece835c8d"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "RMSE: 10.083053125286519\n",
-            "MAE: 7.973378150691296\n",
-            "MAPE: 0.12730792351246625\n",
-            "Accuracy: 0.9886201991465149\n",
-            "Precision: 0.9904912836767037\n",
-            "Recall: 0.984251968503937\n",
-            "F1 Score: 0.9873617693522907\n",
-            "Confusion Matrix:\n",
-            " [[765   6]\n",
-            " [ 10 625]]\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Print evaluation metrics\n",
-        "print(\"RMSE:\", rmse10)\n",
-        "print(\"MAE:\", mae10)\n",
-        "print(\"MAPE:\", mape10)\n",
-        "print(\"Accuracy:\", accuracy10)\n",
-        "print(\"Precision:\", precision10)\n",
-        "print(\"Recall:\", recall10)\n",
-        "print(\"F1 Score:\", f110)\n",
-        "print(\"Confusion Matrix:\\n\", confusion10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
-        "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n",
-        "\n",
-        "# List of corresponding labels for each accuracy\n",
-        "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, accuracies, color='blue')\n",
-        "plt.xlabel('Accuracy Variables')\n",
-        "plt.ylabel('Accuracy Values')\n",
-        "plt.title('Bar Graph of Accuracies')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "qpWPtph9CGip",
-        "outputId": "c099cb8d-96af-4223-f499-743040aecdf1"
-      },
-      "execution_count": 117,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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8z5c938jixYuNFi1aGO7u7kbVqlWNQYMGGb/88kuB27vZpec9evQwPDw8jOzs7OuuM3ToUKNixYrG2bNnDcMwjN9++82Ijo42brnlFsPNzc2oWbOmMWTIEOtyw7h6SfgLL7xghIaGGhUrVjQCAwONhx56yDhy5Ih1nTNnzhh9+/Y1KlWqZFSpUsV44oknjH379hV46bmXl1eB2X788UcjIiLCqFy5slG9enXj8ccfN3bv3p1vG4ZhGPv27TN69+5t+Pn5GR4eHkbDhg2NiRMn5nvP/vw7WL9+vdG1a1fD19fX8PDwMOrVq2cMHTrUepuBs2fPGlFRUUZYWJjh5eVl+Pr6GuHh4cZnn312w/ceKEkWw3DCGYAATM1isSgqKqrAQzcAUNo4ZwcAAJgaZQcAAJgaZQcAAJgaV2MBcDhOBQRQljCzAwAATI2yAwAATI3DWLr6nS+//vqrvL29i/UtxAAAoPQYhqHMzEwFBwff8CtxKDuSfv31V9WqVcvZMQAAQBGcPHlSNWvWvO5yyo4kb29vSVffrGu38AcAAGVbRkaGatWqZf0cvx7KjmQ9dOXj40PZAQCgnLnZKSicoAwAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEyNsgMAAEzNqWVn06ZN6tGjh4KDg2WxWLRixQqb5YZhaNKkSQoKCpKnp6ciIiJ06NAhm3XOnTunQYMGycfHR35+foqMjFRWVlYp/hQAAKAsc2rZyc7OVrNmzfT+++8XuHzatGmaMWOGZs+erS1btsjLy0tdu3bVpUuXrOsMGjRIP/zwgxISErRq1Spt2rRJI0aMKK0fAQAAlHEWwzAMZ4eQrn6J1/Lly9WrVy9JV2d1goOD9cwzz2jcuHGSpPT0dAUEBGjevHkaMGCA9u/fr9tuu03btm1T69atJUmrV69Wt27d9Msvvyg4OLhQ+87IyJCvr6/S09P5IlAAAMqJwn5+l9lzdo4ePark5GRFRERYx3x9fRUeHq7NmzdLkjZv3iw/Pz9r0ZGkiIgIubi4aMuWLdfddk5OjjIyMmweAADAnMps2UlOTpYkBQQE2IwHBARYlyUnJ8vf399meYUKFVS1alXrOgWJiYmRr6+v9VGrVi0HpwcAAGVFmS07JWnChAlKT0+3Pk6ePOnsSGWOxVL2HgAAFEWZLTuBgYGSpJSUFJvxlJQU67LAwEClpqbaLP/999917tw56zoFcXd3l4+Pj80DAACYU5ktO6GhoQoMDFRiYqJ1LCMjQ1u2bFG7du0kSe3atVNaWpp27NhhXWfdunXKy8tTeHh4qWcGAABlTwVn7jwrK0uHDx+2Pj969KiSkpJUtWpV1a5dW6NHj9arr76qBg0aKDQ0VBMnTlRwcLD1iq1GjRrpvvvu0+OPP67Zs2frypUrio6O1oABAwp9JRaAv56yeFi0bFwXWzJ4v0sX73d+Ti0727dvV6dOnazPx44dK0kaMmSI5s2bp2effVbZ2dkaMWKE0tLS1KFDB61evVoeHh7W18TFxSk6OlqdO3eWi4uL+vbtqxkzZpT6zwIAAMqmMnOfHWfiPjv58ZcBzIx/36WL97t0/ZXe73J/nx0AAABHoOwAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTq+DsAAAAlEUWi7MT5GcYzk5QPjGzAwAATI2ZHaAMKK9/QZbX3AD+WpjZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApsYdlAGgnOCO1UDRUHZgKnwYAAD+jMNYAADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1Cg7AADA1LjPTgnjvi8AADgXMzsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUynTZyc3N1cSJExUaGipPT0/Vq1dPr7zyiow/XDttGIYmTZqkoKAgeXp6KiIiQocOHXJiagAAUJaU6bIzdepUzZo1S++9957279+vqVOnatq0aZo5c6Z1nWnTpmnGjBmaPXu2tmzZIi8vL3Xt2lWXLl1yYnIAAFBWlOmbCv73v/9Vz5491b17d0lSnTp1tGjRIm3dulXS1Vmdd955Ry+++KJ69uwpSfrkk08UEBCgFStWaMCAAU7LDgAAyoYyPbPTvn17JSYm6uDBg5Kk3bt367vvvtP9998vSTp69KiSk5MVERFhfY2vr6/Cw8O1efPm6243JydHGRkZNg8AAGBOZXpmZ/z48crIyFBYWJhcXV2Vm5ur1157TYMGDZIkJScnS5ICAgJsXhcQEGBdVpCYmBhNmTKl5IIDAIAyo0zP7Hz22WeKi4vTwoULtXPnTs2fP19vvfWW5s+fX6ztTpgwQenp6dbHyZMnHZQYAACUNWV6Zuef//ynxo8fbz33pmnTpjp+/LhiYmI0ZMgQBQYGSpJSUlIUFBRkfV1KSoqaN29+3e26u7vL3d29RLMDAICyoUzP7Fy4cEEuLrYRXV1dlZeXJ0kKDQ1VYGCgEhMTrcszMjK0ZcsWtWvXrlSzAgCAsqlMz+z06NFDr732mmrXrq3GjRtr165dmj59uoYPHy5JslgsGj16tF599VU1aNBAoaGhmjhxooKDg9WrVy/nhgcAAGVCmS47M2fO1MSJEzVy5EilpqYqODhYTzzxhCZNmmRd59lnn1V2drZGjBihtLQ0dejQQatXr5aHh4cTkwMAgLLCYvzxdsR/URkZGfL19VV6erp8fHwcum2LxaGbc4jC/MbJ7TjkLl3kLl3kLl1mzl0Uhf38LtPn7AAAABQXZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJia3WXn4sWLunDhgvX58ePH9c4772jt2rUODQYAAOAIdpednj176pNPPpEkpaWlKTw8XG+//bZ69uypWbNmOTwgAABAcdhddnbu3KmOHTtKkj7//HMFBATo+PHj+uSTTzRjxgyHBwQAACgOu8vOhQsX5O3tLUlau3at+vTpIxcXF7Vt21bHjx93eEAAAIDisLvs1K9fXytWrNDJkye1Zs0a3XvvvZKk1NRU+fj4ODwgAABAcdhddiZNmqRx48apTp06+tvf/qZ27dpJujrL06JFC4cHBAAAKA6LYRiGvS9KTk7W6dOn1axZM7m4XO1LW7dulY+Pj8LCwhwesqRlZGTI19dX6enpDp+dslgcujmHKMxvnNyOQ+7SRe7SRe7SZebcRVHYz+8i3WcnMDBQ3t7eSkhI0MWLFyVJbdq0KZdFBwAAmJvdZee3335T586ddeutt6pbt246ffq0JCkyMlLPPPOMwwMCAAAUh91lZ8yYMapYsaJOnDihSpUqWcf79++v1atXOzQcAABAcVWw9wVr167VmjVrVLNmTZvxBg0acOk5AAAoc+ye2cnOzraZ0bnm3Llzcnd3d0goAAAAR7G77HTs2NH6dRGSZLFYlJeXp2nTpqlTp04ODQcAAFBcdh/GmjZtmjp37qzt27fr8uXLevbZZ/XDDz/o3Llz+v7770siIwAAQJHZPbPTpEkTHTx4UB06dFDPnj2VnZ2tPn36aNeuXapXr15JZAQAACiyIt1U0Gy4qWB+5HYccpcucpcucpcuM+cuisJ+ftt9GGvTpk03XH7nnXfau0kAAIASY3fZufvuu/ONWf5QI3Nzc4sVCAAAwJHsPmfn/PnzNo/U1FStXr1abdq00dq1a0siIwAAQJHZPbPj6+ubb6xLly5yc3PT2LFjtWPHDocEAwAAcIQifRFoQQICAnTgwAFHbQ4AAMAh7J7Z2bNnj81zwzB0+vRpvfHGG2revLmjcgEAADiE3WWnefPmslgs+vMV623bttXcuXMdFgwAAMAR7C47R48etXnu4uKiGjVqyMPDw2GhAAAAHMXuc3ZCQkJsHrVq1SrRonPq1Ck98sgjqlatmjw9PdW0aVNt377dutwwDE2aNElBQUHy9PRURESEDh06VGJ5AABA+VKomZ0ZM2YUeoNPP/10kcP82fnz53XHHXeoU6dO+uabb1SjRg0dOnRIVapUsa4zbdo0zZgxQ/Pnz1doaKgmTpyorl276scff2S2CQAAFO7rIkJDQwu3MYtFP//8c7FDXTN+/Hh9//33+s9//lPgcsMwFBwcrGeeeUbjxo2TJKWnpysgIEDz5s3TgAEDCrUfvi4iP3I7DrlLF7lLF7lLl5lzF4VDvy7iz+fplJaVK1eqa9eu6tevnzZu3KhbbrlFI0eO1OOPP27NlZycrIiICOtrfH19FR4ers2bN1+37OTk5CgnJ8f6PCMjo2R/EAAA4DQOu89OSfj55581a9YsNWjQQGvWrNGTTz6pp59+WvPnz5ckJScnS7p6j58/CggIsC4rSExMjHx9fa2PWrVqldwPAQAAnMruq7Ek6ZdfftHKlSt14sQJXb582WbZ9OnTHRJMkvLy8tS6dWu9/vrrkqQWLVpo3759mj17toYMGVLk7U6YMEFjx461Ps/IyKDwAABgUnaXncTERD344IOqW7eufvrpJzVp0kTHjh2TYRhq2bKlQ8MFBQXptttusxlr1KiRli5dKkkKDAyUJKWkpCgoKMi6TkpKyg1vcOju7i53d3eHZgUAAGWT3YexJkyYoHHjxmnv3r3y8PDQ0qVLdfLkSd11113q16+fQ8Pdcccd+b6C4uDBgwoJCZF09cTpwMBAJSYmWpdnZGRoy5YtateunUOzAACAcsqwU+XKlY3Dhw8bhmEYfn5+xr59+wzDMIykpCQjJCTE3s3d0NatW40KFSoYr732mnHo0CEjLi7OqFSpkvHpp59a13njjTcMPz8/44svvjD27Nlj9OzZ0wgNDTUuXrxY6P2kp6cbkoz09HSH5jcMw7h6DnrZepCb3OQuew9yk/uvnrsoCvv5bfdhLC8vL+t5OkFBQTpy5IgaN24sSTp79qwje5jatGmj5cuXa8KECXr55ZcVGhqqd955R4MGDbKu8+yzzyo7O1sjRoxQWlqaOnTooNWrV3OPHQAAIKmQ99n5o169eql79+56/PHHNW7cOH3xxRcaOnSoli1bpipVqujbb78tqawlhvvs5EduxyF36SJ36SJ36TJz7qJw6H12JOncuXOqWrWqpk+frqysLEnSlClTlJWVpcWLF6tBgwYOvRILAADAEQo9s+Ph4aFevXopMjJSXbp0KelcpYqZnfzI7TjkLl3kLl3kLl1mzl0Uhf38LvTVWB999JHOnDmj++67T3Xq1NFLL72kY8eOOSIrAABAiSl02Xn00UeVmJiow4cPa8iQIZo/f77q16+vLl26aPHixfluLggAAFAW2H2fndDQUE2ZMkVHjx7V6tWr5e/vr+HDhysoKMih33gOAADgCHZfjVWQpUuXWi/9zs3NdUSuUsU5O/mR23HIXbrIXbrIXbrMnLsoHH411p8dP35csbGxmj9/vk6ePKlOnTopMjKyqJsDAAAoEXaVnZycHC1dulRz587Vhg0bdMstt2jo0KEaNmyY6tSpU0IRAQAAiq7QZWfkyJGKj4/XhQsX1LNnT3399dfq0qWLLGVxvgwAAOD/K3TZ+e677zR58mQ98sgjqlatWklmAgAAcJhCl509e/aUZA4AAIASYfel5wAAAOUJZQcAAJgaZQcAAJgaZQcAAJia3WWnTp06evnll3XixImSyAMAAOBQdped0aNHa9myZapbt666dOmi+Ph45eTklEQ2AACAYitS2UlKStLWrVvVqFEjPfXUUwoKClJ0dLR27txZEhkBAACKrMjn7LRs2VIzZszQr7/+qsmTJ+vjjz9WmzZt1Lx5c82dO1cO+H5RAACAYivyF4FeuXJFy5cvV2xsrBISEtS2bVtFRkbql19+0fPPP69vv/1WCxcudGRWAAAAu9lddnbu3KnY2FgtWrRILi4uGjx4sP71r38pLCzMuk7v3r3Vpk0bhwYFAAAoCrvLTps2bdSlSxfNmjVLvXr1UsWKFfOtExoaqgEDBjgkIAAAQHHYXXZ+/vlnhYSE3HAdLy8vxcbGFjkUAACAo9h9gnJqaqq2bNmSb3zLli3avn27Q0IBAAA4it1lJyoqSidPnsw3furUKUVFRTkkFAAAgKPYXXZ+/PFHtWzZMt94ixYt9OOPPzokFAAAgKPYXXbc3d2VkpKSb/z06dOqUKHIV7IDAACUCLvLzr333qsJEyYoPT3dOpaWlqbnn39eXbp0cWg4AACA4rJ7Kuatt97SnXfeqZCQELVo0UKSlJSUpICAAC1YsMDhAQEAAIrD7rJzyy23aM+ePYqLi9Pu3bvl6empYcOGaeDAgQXecwcAAMCZinSSjZeXl0aMGOHoLAAAAA5X5DOKf/zxR504cUKXL1+2GX/wwQeLHQoAAMBRinQH5d69e2vv3r2yWCzWbze3WCySpNzcXMcmBAAAKAa7r8YaNWqUQkNDlZqaqkqVKumHH37Qpk2b1Lp1a23YsKEEIgIAABSd3TM7mzdv1rp161S9enW5uLjIxcVFHTp0UExMjJ5++mnt2rWrJHICAAAUid0zO7m5ufL29pYkVa9eXb/++qskKSQkRAcOHHBsOgAAgGKye2anSZMm2r17t0JDQxUeHq5p06bJzc1NH374oerWrVsSGQEAAIrM7rLz4osvKjs7W5L08ssv64EHHlDHjh1VrVo1LV682OEBAQAAisNiXLucqhjOnTunKlWqWK/IKm8yMjLk6+ur9PR0+fj4OHTbZfEtKcxvnNyOQ+7SRe7SRe7SZebcRVHYz2+7ztm5cuWKKlSooH379tmMV61atdwWHQAAYG52lZ2KFSuqdu3a3EsHAACUG3ZfjfXCCy/o+eef17lz50oiDwAAgEPZfYLye++9p8OHDys4OFghISHy8vKyWb5z506HhQMAACguu8tOr169SiAGAABAybC77EyePLkkcgAAAJQIu8/ZAQAAKE/sntlxcXG54WXmXKkFAADKErvLzvLly22eX7lyRbt27dL8+fM1ZcoUhwUDAABwBIfcQVmSFi5cqMWLF+uLL75wxOZKFXdQzo/cjkPu0kXu0kXu0mXm3EVRIndQvpG2bdsqMTHRUZsDAABwCIeUnYsXL2rGjBm65ZZbHLE5AAAAh7H7nJ0/f+GnYRjKzMxUpUqV9Omnnzo0HAAAQHHZXXb+9a9/2ZQdFxcX1ahRQ+Hh4apSpYpDwwEAABSX3WVn6NChJRADAACgZNh9zk5sbKyWLFmSb3zJkiWaP3++Q0IBAAA4it1lJyYmRtWrV8837u/vr9dff90hoQAAABzF7rJz4sQJhYaG5hsPCQnRiRMnHBIKAADAUewuO/7+/tqzZ0++8d27d6tatWoOCQUAAOAodpedgQMH6umnn9b69euVm5ur3NxcrVu3TqNGjdKAAQNKIiMAAECR2X011iuvvKJjx46pc+fOqlDh6svz8vI0ePBgztkBAABljt0zO25ublq8eLEOHDiguLg4LVu2TEeOHNHcuXPl5uZWEhmt3njjDVksFo0ePdo6dunSJUVFRalatWqqXLmy+vbtq5SUlBLNAQAAyg+7Z3auadCggRo0aODILDe0bds2/fvf/9btt99uMz5mzBh99dVXWrJkiXx9fRUdHa0+ffro+++/L7VsAACg7LJ7Zqdv376aOnVqvvFp06apX79+Dgn1Z1lZWRo0aJA++ugjm7s0p6ena86cOZo+fbruuecetWrVSrGxsfrvf/+r//3vfyWSBQAAlC92l51NmzapW7du+cbvv/9+bdq0ySGh/iwqKkrdu3dXRESEzfiOHTt05coVm/GwsDDVrl1bmzdvvu72cnJylJGRYfMAAADmZPdhrKysrALPzalYsWKJlIb4+Hjt3LlT27Zty7csOTlZbm5u8vPzsxkPCAhQcnLydbcZExOjKVOmODoqAAAog+ye2WnatKkWL16cbzw+Pl633XabQ0Jdc/LkSY0aNUpxcXHy8PBw2HYnTJig9PR06+PkyZMO2zYAAChb7J7ZmThxovr06aMjR47onnvukSQlJiZq0aJFBX5nVnHs2LFDqampatmypXUsNzdXmzZt0nvvvac1a9bo8uXLSktLs5ndSUlJUWBg4HW36+7uLnd3d4dmBQAAZZPdZadHjx5asWKFXn/9dX3++efy9PTU7bffrm+//VZ33XWXQ8N17txZe/futRkbNmyYwsLC9Nxzz6lWrVqqWLGiEhMT1bdvX0nSgQMHdOLECbVr186hWQAAQPlUpEvPu3fvru7du+cb37dvn5o0aVLsUNd4e3vn256Xl5eqVatmHY+MjNTYsWNVtWpV+fj46KmnnlK7du3Utm1bh+UAAADlV5Hvs3NNZmamFi1apI8//lg7duxQbm6uI3IV2r/+9S+5uLiob9++ysnJUdeuXfXBBx+UagYAAFB2WQzDMIrywk2bNunjjz/WsmXLFBwcrD59+qhv375q06aNozOWuIyMDPn6+io9PV0+Pj4O3bbF4tDNOURhfuPkdhxyly5yly5yly4z5y6Kwn5+2zWzk5ycrHnz5mnOnDnKyMjQww8/rJycHK1YscLhV2IBAAA4QqEvPe/Ro4caNmyoPXv26J133tGvv/6qmTNnlmQ2AACAYiv0zM4333yjp59+Wk8++WSpficWAABAcRR6Zue7775TZmamWrVqpfDwcL333ns6e/ZsSWYDAAAotkKXnbZt2+qjjz7S6dOn9cQTTyg+Pl7BwcHKy8tTQkKCMjMzSzInAABAkRT5aizp6g385syZowULFigtLU1dunTRypUrHZmvVHA1Vn7kdhxyly5yly5yly4z5y6Kwn5+2/3dWH/UsGFDTZs2Tb/88osWLVpUnE0BAACUiGLN7JgFMzv5kdtxyF26yF26yF26zJy7KEplZgcAAKCso+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTo+wAAABTK9NlJyYmRm3atJG3t7f8/f3Vq1cvHThwwGadS5cuKSoqStWqVVPlypXVt29fpaSkOCkxAAAoa8p02dm4caOioqL0v//9TwkJCbpy5YruvfdeZWdnW9cZM2aMvvzySy1ZskQbN27Ur7/+qj59+jgxNQAAKEsshmEYzg5RWGfOnJG/v782btyoO++8U+np6apRo4YWLlyohx56SJL0008/qVGjRtq8ebPatm1bqO1mZGTI19dX6enp8vHxcWhmi8Whm3OIwvzGye045C5d5C5d5C5dZs5dFIX9/C7TMzt/lp6eLkmqWrWqJGnHjh26cuWKIiIirOuEhYWpdu3a2rx583W3k5OTo4yMDJsHAAAwp3JTdvLy8jR69GjdcccdatKkiSQpOTlZbm5u8vPzs1k3ICBAycnJ191WTEyMfH19rY9atWqVZHQAAOBE5absREVFad++fYqPjy/2tiZMmKD09HTr4+TJkw5ICAAAyqIKzg5QGNHR0Vq1apU2bdqkmjVrWscDAwN1+fJlpaWl2czupKSkKDAw8Lrbc3d3l7u7e0lGBgAAZUSZntkxDEPR0dFavny51q1bp9DQUJvlrVq1UsWKFZWYmGgdO3DggE6cOKF27dqVdlwAAFAGlemZnaioKC1cuFBffPGFvL29refh+Pr6ytPTU76+voqMjNTYsWNVtWpV+fj46KmnnlK7du0KfSUWAAAwtzJddmbNmiVJuvvuu23GY2NjNXToUEnSv/71L7m4uKhv377KyclR165d9cEHH5RyUgAAUFaVq/vslBTus5MfuR2H3KWL3KWL3KXLzLmLwpT32QEAALAXZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJgaZQcAAJiaacrO+++/rzp16sjDw0Ph4eHaunWrsyMBAIAywBRlZ/HixRo7dqwmT56snTt3qlmzZuratatSU1OdHQ0AADiZKcrO9OnT9fjjj2vYsGG67bbbNHv2bFWqVElz5851djQAAOBk5b7sXL58WTt27FBERIR1zMXFRREREdq8ebMTkwEAgLKggrMDFNfZs2eVm5urgIAAm/GAgAD99NNPBb4mJydHOTk51ufp6emSpIyMjJILWoaU1x+T3KWL3KWL3KWL3KWrpHJf+9w2DOOG65X7slMUMTExmjJlSr7xWrVqOSFN6fP1dXaCoiF36SJ36SJ36SJ36Srp3JmZmfK9wU7KfdmpXr26XF1dlZKSYjOekpKiwMDAAl8zYcIEjR071vo8Ly9P586dU7Vq1WSxWEo0b1FlZGSoVq1aOnnypHx8fJwdp9DIXbrIXbrIXbrIXbrKQ27DMJSZmang4OAbrlfuy46bm5tatWqlxMRE9erVS9LV8pKYmKjo6OgCX+Pu7i53d3ebMT8/vxJO6hg+Pj5l9h/djZC7dJG7dJG7dJG7dJX13Dea0bmm3JcdSRo7dqyGDBmi1q1b629/+5veeecdZWdna9iwYc6OBgAAnMwUZad///46c+aMJk2apOTkZDVv3lyrV6/Od9IyAAD46zFF2ZGk6Ojo6x62MgN3d3dNnjw53+G3so7cpYvcpYvcpYvcpau85i6IxbjZ9VoAAADlWLm/qSAAAMCNUHYAAICpUXYAAICpUXYAAICpUXbKuE2bNqlHjx4KDg6WxWLRihUrnB3ppmJiYtSmTRt5e3vL399fvXr10oEDB5wd66ZmzZql22+/3XoDrXbt2umbb75xdiy7vfHGG7JYLBo9erSzo9zQSy+9JIvFYvMICwtzdqxCOXXqlB555BFVq1ZNnp6eatq0qbZv3+7sWDdUp06dfO+3xWJRVFSUs6PdUG5uriZOnKjQ0FB5enqqXr16euWVV276XUhlQWZmpkaPHq2QkBB5enqqffv22rZtm7Nj2bjZZ4xhGJo0aZKCgoLk6empiIgIHTp0yDlhi4GyU8ZlZ2erWbNmev/9950dpdA2btyoqKgo/e9//1NCQoKuXLmie++9V9nZ2c6OdkM1a9bUG2+8oR07dmj79u2655571LNnT/3www/OjlZo27Zt07///W/dfvvtzo5SKI0bN9bp06etj++++87ZkW7q/PnzuuOOO1SxYkV98803+vHHH/X222+rSpUqzo52Q9u2bbN5rxMSEiRJ/fr1c3KyG5s6dapmzZql9957T/v379fUqVM1bdo0zZw509nRbuqxxx5TQkKCFixYoL179+ree+9VRESETp065exoVjf7jJk2bZpmzJih2bNna8uWLfLy8lLXrl116dKlUk5aTAbKDUnG8uXLnR3DbqmpqYYkY+PGjc6OYrcqVaoYH3/8sbNjFEpmZqbRoEEDIyEhwbjrrruMUaNGOTvSDU2ePNlo1qyZs2PY7bnnnjM6dOjg7BjFNmrUKKNevXpGXl6es6PcUPfu3Y3hw4fbjPXp08cYNGiQkxIVzoULFwxXV1dj1apVNuMtW7Y0XnjhBSelurE/f8bk5eUZgYGBxptvvmkdS0tLM9zd3Y1FixY5IWHRMbODEpeeni5Jqlq1qpOTFF5ubq7i4+OVnZ2tdu3aOTtOoURFRal79+6KiIhwdpRCO3TokIKDg1W3bl0NGjRIJ06ccHakm1q5cqVat26tfv36yd/fXy1atNBHH33k7Fh2uXz5sj799FMNHz68zH758TXt27dXYmKiDh48KEnavXu3vvvuO91///1OTnZjv//+u3Jzc+Xh4WEz7unpWS5mMCXp6NGjSk5Otvl/iq+vr8LDw7V582YnJrOfae6gjLIpLy9Po0eP1h133KEmTZo4O85N7d27V+3atdOlS5dUuXJlLV++XLfddpuzY91UfHy8du7cWebOB7iR8PBwzZs3Tw0bNtTp06c1ZcoUdezYUfv27ZO3t7ez413Xzz//rFmzZmns2LF6/vnntW3bNj399NNyc3PTkCFDnB2vUFasWKG0tDQNHTrU2VFuavz48crIyFBYWJhcXV2Vm5ur1157TYMGDXJ2tBvy9vZWu3bt9Morr6hRo0YKCAjQokWLtHnzZtWvX9/Z8QolOTlZkvJ99VJAQIB1WXlB2UGJioqK0r59+8rNXzINGzZUUlKS0tPT9fnnn2vIkCHauHFjmS48J0+e1KhRo5SQkJDvr8iy7I9/md9+++0KDw9XSEiIPvvsM0VGRjox2Y3l5eWpdevWev311yVJLVq00L59+zR79uxyU3bmzJmj+++/X8HBwc6OclOfffaZ4uLitHDhQjVu3FhJSUkaPXq0goODy/z7vWDBAg0fPly33HKLXF1d1bJlSw0cOFA7duxwdrS/HA5jocRER0dr1apVWr9+vWrWrOnsOIXi5uam+vXrq1WrVoqJiVGzZs307rvvOjvWDe3YsUOpqalq2bKlKlSooAoVKmjjxo2aMWOGKlSooNzcXGdHLBQ/Pz/deuutOnz4sLOj3FBQUFC+8tuoUaNycQhOko4fP65vv/1Wjz32mLOjFMo///lPjR8/XgMGDFDTpk316KOPasyYMYqJiXF2tJuqV6+eNm7cqKysLJ08eVJbt27VlStXVLduXWdHK5TAwEBJUkpKis14SkqKdVl5QdmBwxmGoejoaC1fvlzr1q1TaGiosyMVWV5ennJycpwd44Y6d+6svXv3Kikpyfpo3bq1Bg0apKSkJLm6ujo7YqFkZWXpyJEjCgoKcnaUG7rjjjvy3Urh4MGDCgkJcVIi+8TGxsrf31/du3d3dpRCuXDhglxcbD+qXF1dlZeX56RE9vPy8lJQUJDOnz+vNWvWqGfPns6OVCihoaEKDAxUYmKidSwjI0NbtmwpN+cyXsNhrDIuKyvL5i/do0ePKikpSVWrVlXt2rWdmOz6oqKitHDhQn3xxRfy9va2Htv19fWVp6enk9Nd34QJE3T//ferdu3ayszM1MKFC7VhwwatWbPG2dFuyNvbO9/5UF5eXqpWrVqZPk9q3Lhx6tGjh0JCQvTrr79q8uTJcnV11cCBA50d7YbGjBmj9u3b6/XXX9fDDz+srVu36sMPP9SHH37o7Gg3lZeXp9jYWA0ZMkQVKpSP//336NFDr732mmrXrq3GjRtr165dmj59uoYPH+7saDe1Zs0aGYahhg0b6vDhw/rnP/+psLAwDRs2zNnRrG72GTN69Gi9+uqratCggUJDQzVx4kQFBwerV69ezgtdFM6+HAw3tn79ekNSvseQIUOcHe26CsoryYiNjXV2tBsaPny4ERISYri5uRk1atQwOnfubKxdu9bZsYqkPFx63r9/fyMoKMhwc3MzbrnlFqN///7G4cOHnR2rUL788kujSZMmhru7uxEWFmZ8+OGHzo5UKGvWrDEkGQcOHHB2lELLyMgwRo0aZdSuXdvw8PAw6tata7zwwgtGTk6Os6Pd1OLFi426desabm5uRmBgoBEVFWWkpaU5O5aNm33G5OXlGRMnTjQCAgIMd3d3o3PnzuXq3881FsMoB7ehBAAAKCLO2QEAAKZG2QEAAKZG2QEAAKZG2QEAAKZG2QEAAKZG2QEAAKZG2QEAAKZG2QEABzh27JgsFouSkpIK/ZqhQ4fe9E60d999t0aPHl2sbMBfHWUH+IvYvHmzXF1dy813IpWEpk2b6h//+EeByxYsWCB3d3edPXu2SNuuVauWTp8+Xaa/ogP4q6LsAH8Rc+bM0VNPPaVNmzbp119/dWqWy5cvO2W/kZGRio+P18WLF/Mti42N1YMPPqjq1avbvd3Lly/L1dVVgYGB5eY7p4C/EsoO8BeQlZWlxYsX68knn1T37t01b968fOt8+eWXatOmjTw8PFS9enX17t3buiwnJ0fPPfecatWqJXd3d9WvX19z5syRJM2bN09+fn4221qxYoUsFov1+UsvvaTmzZvr448/VmhoqDw8PCRJq1evVocOHeTn56dq1arpgQce0JEjR2y29csvv2jgwIGqWrWqvLy81Lp1a23ZskXHjh2Ti4uLtm/fbrP+O++8o5CQkAK/FfuRRx7RxYsXtXTpUpvxo0ePasOGDYqMjNSRI0fUs2dPBQQEqHLlymrTpo2+/fZbm/Xr1KmjV155RYMHD5aPj49GjBiR7zBWbm6uIiMjFRoaKk9PTzVs2FDvvvtuAb8dacqUKapRo4Z8fHz0j3/844ZlMCcnR+PGjdMtt9wiLy8vhYeHa8OGDdblx48fV48ePVSlShV5eXmpcePG+vrrr6+7PeCvgLID/AV89tlnCgsLU8OGDfXII49o7ty5+uPX4n311Vfq3bu3unXrpl27dikxMVF/+9vfrMsHDx6sRYsWacaMGdq/f7/+/e9/q3LlynZlOHz4sJYuXaply5ZZC0F2drbGjh2r7du3KzExUS4uLurdu7e1qGRlZemuu+7SqVOntHLlSu3evVvPPvus8vLyVKdOHUVERCg2NtZmP7GxsRo6dKhcXPL/76169erq2bOn5s6dazM+b9481axZU/fee6+ysrLUrVs3JSYmateuXbrvvvvUo0cPnThxwuY1b731lpo1a6Zdu3Zp4sSJ+faVl5enmjVrasmSJfrxxx81adIkPf/88/rss89s1ktMTNT+/fu1YcMGLVq0SMuWLdOUKVOu+z5GR0dr8+bNio+P1549e9SvXz/dd999OnTokCQpKipKOTk52rRpk/bu3aupU6fa/bsCTMfJX0QKoBS0b9/eeOeddwzDMIwrV64Y1atXN9avX29d3q5dO2PQoEEFvvbAgQOGJCMhIaHA5bGxsYavr6/N2PLly40//u9l8uTJRsWKFY3U1NQb5jxz5owhydi7d69hGIbx73//2/D29jZ+++23AtdfvHixUaVKFePSpUuGYRjGjh07DIvFYhw9evS6+1i9erVhsViMn3/+2TCMq9/qHBISYrz44ovXfU3jxo2NmTNnWp+HhIQYvXr1slnn6NGjhiRj165d191OVFSU0bdvX+vzIUOGGFWrVjWys7OtY7NmzTIqV65s5ObmGoZh+w32x48fN1xdXY1Tp07ZbLdz587GhAkTDMMwjKZNmxovvfTSdTMAf0XM7AAmd+DAAW3dulUDBw6UJFWoUEH9+/e3HoaSpKSkJHXu3LnA1yclJcnV1VV33XVXsXKEhISoRo0aNmOHDh3SwIEDVbduXfn4+KhOnTqSZJ1FSUpKUosWLVS1atUCt9mrVy+5urpq+fLlkq7O0HTq1Mm6nYJ06dJFNWvWtM4IJSYm6sSJExo2bJikq7NJ48aNU6NGjeTn56fKlStr//79+WZ2WrdufdOf+f3331erVq1Uo0YNVa5cWR9++GG+7TRr1kyVKlWyPm/Xrp2ysrJ08uTJfNvbu3evcnNzdeutt6py5crWx8aNG62H/55++mm9+uqruuOOOzR58mTt2bPnpjkBs6PsACY3Z84c/f777woODlaFChVUoUIFzZo1S0uXLlV6erokydPT87qvv9EySXJxcbE5JCZJV65cybeel5dXvrEePXro3Llz+uijj7RlyxZt2bJF0v+dwHyzfbu5uWnw4MGKjY3V5cuXtXDhQg0fPvymeYcOHar58+crLy9PsbGx6tSpk+rWrStJGjdunJYvX67XX39d//nPf5SUlKSmTZvmO4+moJ/nj+Lj4zVu3DhFRkZq7dq1SkpK0rBhw4p1cnZWVpZcXV21Y8cOJSUlWR/79++3ng/02GOP6eeff9ajjz6qvXv3qnXr1po5c2aR9wmYAWUHMLHff/9dn3zyid5++22bD8fdu3crODhYixYtkiTdfvvtSkxMLHAbTZs2VV5enjZu3Fjg8ho1aigzM1PZ2dnWscLca+a3337TgQMH9OKLL6pz585q1KiRzp8/b7PO7bffrqSkJJ07d+6623nsscf07bff6oMPPtDvv/+uPn363HTfw4YN08mTJ7Vs2TItX75ckZGR1mXff/+9hg4dqt69e6tp06YKDAzUsWPHbrrNP/v+++/Vvn17jRw5Ui1atFD9+vXznXwtSbt377a5Oux///ufKleurFq1auVbt0WLFsrNzVVqaqrq169v8wgMDLSuV6tWLf3jH//QsmXL9Mwzz+ijjz6yOz9gJpQdwMRWrVql8+fPKzIyUk2aNLF59O3b13ooa/LkyVq0aJEmT56s/fv3W09sla5eeTRkyBANHz5cK1assF65dO1E2/DwcFWqVEnPP/+8jhw5ooULFxZ4tdefValSRdWqVdOHH36ow4cPa926dRo7dqzNOgMHDlRgYKB69eql77//Xj///LOWLl2qzZs3W9dp1KiR2rZtq+eee04DBw686WyQJIWGhuqee+7RiBEj5O7ublOQGjRoYD2Jevfu3fr73/9e4JVdN9OgQQNt375da9as0cGDBzVx4kRt27Yt33qXL19WZGSkfvzxR3399deaPHmyoqOjCzzB+tZbb9WgQYM0ePBgLVu2TEePHtXWrVsVExOjr776SpI0evRorVmzRkePHtXOnTu1fv16NWrUyO78gJlQdgATmzNnjiIiIuTr65tvWd++fbV9+3bt2bNHd999t5YsWaKVK1eqefPmuueee7R161brurNmzdJDDz2kkSNHKiwsTI8//rh1Jqdq1ar69NNP9fXXX6tp06ZatGiRXnrppZtmc3FxUXx8vHbs2KEmTZpozJgxevPNN23WcXNz09q1a+Xv769u3bqpadOmeuONN+Tq6mqzXmRkpC5fvnzTQ1h/fs358+f197//3XopvCRNnz5dVapUUfv27dWjRw917dpVLVu2LPR2r3niiSfUp08f9e/fX+Hh4frtt980cuTIfOt17txZDRo00J133qn+/fvrwQcfvOH7Fxsbq8GDB+uZZ55Rw4YN1atXL23btk21a9eWdPWS96ioKDVq1Ej33Xefbr31Vn3wwQd25wfMxGL8+WA7AJQzr7zyipYsWcLJuAAKxMwOgHIrKytL+/bt03vvvaennnrK2XEAlFGUHQDlVnR0tFq1aqW7777brkNYAP5aOIwFAABMjZkdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgapQdAABgav8PriEMHNJ5QJIAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
-        "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n",
-        "\n",
-        "# List of corresponding labels for each RMSE value\n",
-        "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, rmse_values, color='green')\n",
-        "plt.xlabel('RMSE Variables')\n",
-        "plt.ylabel('RMSE Values')\n",
-        "plt.title('Bar Graph of RMSE')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "RFaaCNH6Cfoa",
-        "outputId": "67a8f358-e3ce-4ad2-9c78-ebc75902beb4"
-      },
-      "execution_count": 118,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of MAE values from mae1 to mae10\n",
-        "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n",
-        "\n",
-        "# List of corresponding labels for each MAE value\n",
-        "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, mae_values, color='orange')\n",
-        "plt.xlabel('MAE Variables')\n",
-        "plt.ylabel('MAE Values')\n",
-        "plt.title('Bar Graph of MAE')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "nrZu-K-KDCJ2",
-        "outputId": "69165581-da05-4554-a464-a606eb87a734"
-      },
-      "execution_count": 119,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of MAPE values from mape1 to mape10\n",
-        "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n",
-        "\n",
-        "# List of corresponding labels for each MAPE value\n",
-        "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, mape_values, color='purple')\n",
-        "plt.xlabel('MAPE Variables')\n",
-        "plt.ylabel('MAPE Values')\n",
-        "plt.title('Bar Graph of MAPE')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "_c4Pe76fDNM-",
-        "outputId": "0e3d2f74-9042-4e2d-92c6-5ce61e967bd4"
-      },
-      "execution_count": 120,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of precision values from precision1 to precision10\n",
-        "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
-        "\n",
-        "# List of corresponding labels for each precision value\n",
-        "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, precision_values, color='red')\n",
-        "plt.xlabel('Precision Variables')\n",
-        "plt.ylabel('Precision Values')\n",
-        "plt.title('Bar Graph of Precision')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "ZDPV0M5rDTi6",
-        "outputId": "9db63164-3f42-47be-d302-d80d381d9b91"
-      },
-      "execution_count": 121,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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z+Z4TAAAUnttqz83NGD9+vIKDgx2PqKiowo7kmWw2z3sAAHATbqtyExERoRMnTjhNO3HihIKCgnLdayNJw4cPV2pqquNx5MiRgogKAAAKyW11WKpx48b66quvnKatWbNGjRs3vu48vr6+8vX1ze9oAADAQxRqublw4YL279/veH7w4EFt375doaGhio6O1vDhw3X06FF98sknkqRnn31WU6dO1csvv6wnn3xSX3/9tT7//HMtX768sN4CgNuBJx7mNKawE8Aq+P3OoVDLzY8//qhmzZo5nickJEiS4uPjlZSUpOPHj+vw4cOO1++66y4tX75cQ4cO1ZQpU1SuXDn97W9/42vgAOBJ+LBFIbMZc2f9xM+fP6/g4GClpqYqKCiosON4Dv4xgpXx+12w2N4F6w7Z3q58ft9WJxQDAAD8EcoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwlKKFHQC4I9lshZ0gJ2P+eMztmhvAHYU9NwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFK4zg0AeCquKwTcFPbcAAAAS6HcAAAAS+GwlLuxGxkAgELFnhsAAGAp7LnB7Y09ZQCA32HPDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTuLQUAgMS96iyEPTcAAMBSKDcAAMBSKDcAAMBSCr3cTJs2TRUqVJCfn58aNWqkzZs333D85MmTVbVqVfn7+ysqKkpDhw5Venp6AaUFAACerlDLzbx585SQkKDExERt27ZNtWvXVlxcnE6ePJnr+M8++0zDhg1TYmKifv75Z3344YeaN2+e/vKXvxRwcgAA4KkKtdy8++676t+/v/r27at77rlHM2fOVLFixTR79uxcx3///fdq0qSJevTooQoVKqhly5bq3r37H+7tAQAAd45CKzeZmZnaunWrYmNj/xfGy0uxsbHatGlTrvPcf//92rp1q6PM/Oc//9FXX32lNm3aXHc9GRkZOn/+vNMDAABYV6Fd5+b06dO6cuWKwsPDnaaHh4dr9+7duc7To0cPnT59Wg888ICMMcrOztazzz57w8NS48eP15gxY9yaHQAAeK5CP6HYFevXr9cbb7yh6dOna9u2bVq0aJGWL1+ucePGXXee4cOHKzU11fE4cuRIASYGAAAFrdD23JQqVUpFihTRiRMnnKafOHFCERERuc7z6quvqlevXnrqqackSbVq1dLFixf19NNPa8SIEfLyytnVfH195evr6/43AAAAPFKh7bnx8fFRTEyMkpOTHdPsdruSk5PVuHHjXOe5dOlSjgJTpEgRSZLhEtUAAECFfG+phIQExcfHq379+mrYsKEmT56sixcvqm/fvpKk3r17q2zZsho/frwkqX379nr33XdVt25dNWrUSPv379err76q9u3bO0oOAAC4sxVquenWrZtOnTqlUaNGKSUlRXXq1NHKlSsdJxkfPnzYaU/NyJEjZbPZNHLkSB09elSlS5dW+/bt9frrrxfWWwAAAB7GZu6w4znnz59XcHCwUlNTFRQU5P4V3K53lSW3+5C7YJG7YJG7YFk5t4tc+fy+rb4tBQAA8EcoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFJcLjcrV67Ud99953g+bdo01alTRz169NCvv/7q1nAAAACucrncvPTSSzp//rwkaefOnfrzn/+sNm3a6ODBg0pISHB7QAAAAFcUdXWGgwcP6p577pEkLVy4UO3atdMbb7yhbdu2qU2bNm4PCAAA4AqX99z4+Pjo0qVLkqS1a9eqZcuWkqTQ0FDHHh0AAIDC4vKemwceeEAJCQlq0qSJNm/erHnz5kmS9u7dq3Llyrk9IAAAgCtc3nMzdepUFS1aVAsWLNCMGTNUtmxZSdKKFSvUqlUrtwcEAABwhc0YYwo7REE6f/68goODlZqaqqCgIPevwGZz/zJvVV5+xOR2H3IXLHIXLHIXLCvndpErn983dZ2bAwcOaOTIkerevbtOnjwp6bc9N//6179uZnEAAABu43K5+eabb1SrVi393//9nxYtWqQLFy5Iknbs2KHExES3BwQAAHCFy+Vm2LBheu2117RmzRr5+Pg4pjdv3lw//PCDW8MBAAC4yuVys3PnTnXq1CnH9LCwMJ0+fdotoQAAAG6Wy+UmJCREx48fzzH9p59+cnxzCgAAoLC4XG7+9Kc/6ZVXXlFKSopsNpvsdrs2btyoF198Ub17986PjAAAAHnmcrl54403VK1aNUVFRenChQu655579NBDD+n+++/XyJEj8yMjAABAnt30dW4OHz6sXbt26cKFC6pbt66qVKni7mz5guvcXAe53YfcBYvcBYvcBcvKuV3kyue3y7dfuCo6OlrR0dE3OzsAAEC+cLncPPnkkzd8ffbs2TcdBgAA4Fa5XG5+/fVXp+dZWVnatWuXzp07p+bNm7stGAAAwM1wudx88cUXOabZ7XY999xzqlSpkltCAQAA3KyburdUjoV4eSkhIUGTJk1yx+IAAABumlvKjfTbzTSzs7PdtTgAAICb4vJhqYSEBKfnxhgdP35cy5cvV3x8vNuCAQAA3AyXy81PP/3k9NzLy0ulS5fWxIkT//CbVAAAAPnN5XKzbt06twaYNm2aJkyYoJSUFNWuXVvvvfeeGjZseN3x586d04gRI7Ro0SKdPXtW5cuX1+TJk9WmTRu35gIAALenm76InzvMmzdPCQkJmjlzpho1aqTJkycrLi5Oe/bsUVhYWI7xmZmZatGihcLCwrRgwQKVLVtWhw4dUkhISMGHBwAAHilPt1+oW7eubHm8vPO2bdvyvPJGjRqpQYMGmjp1qqTfvlIeFRWlwYMHa9iwYTnGz5w5UxMmTNDu3bvl7e2d5/Vci9svXAe53YfcBYvcBYvcBcvKuV3k9tsvdOzY0R25nGRmZmrr1q0aPny4Y5qXl5diY2O1adOmXOdZsmSJGjdurIEDB+rLL79U6dKl1aNHD73yyisqUqSI2zMCAIDbT57KTWJiottXfPr0aV25ckXh4eFO08PDw7V79+5c5/nPf/6jr7/+Wj179tRXX32l/fv3a8CAAcrKyrpuxoyMDGVkZDienz9/3n1vAgAAeBy3XeemINjtdoWFhemDDz5QTEyMunXrphEjRmjmzJnXnWf8+PEKDg52PKKiogowMQAAKGgul5srV67onXfeUcOGDRUREaHQ0FCnR16VKlVKRYoU0YkTJ5ymnzhxQhEREbnOExkZqbvvvtvpEFT16tWVkpKizMzMXOcZPny4UlNTHY8jR47kOSMAALj9uFxuxowZo3fffVfdunVTamqqEhIS1LlzZ3l5eWn06NF5Xo6Pj49iYmKUnJzsmGa325WcnKzGjRvnOk+TJk20f/9+2e12x7S9e/cqMjJSPj4+uc7j6+uroKAgpwcAALAw46KKFSuaZcuWGWOMCQgIMPv37zfGGDNlyhTTvXt3l5Y1d+5c4+vra5KSksy///1v8/TTT5uQkBCTkpJijDGmV69eZtiwYY7xhw8fNoGBgWbQoEFmz549ZtmyZSYsLMy89tpreV5namqqkWRSU1Ndyppnv50j7lkPcpOb3J73IDe57/TcLnLl89vl69ykpKSoVq1akqSAgAClpqZKktq1a6dXX33VpWV169ZNp06d0qhRo5SSkqI6depo5cqVjpOMDx8+LC+v/+1cioqK0qpVqzR06FDde++9Klu2rIYMGaJXXnnF1bcBAAAsyuVyU65cOR0/flzR0dGqVKmSVq9erXr16mnLli3y9fV1OcCgQYM0aNCgXF9bv359jmmNGzfWDz/84PJ6AADAncHlc246derkOE9m8ODBevXVV1WlShX17t2be0sBAIBCl6crFEvS1KlT9cQTT+S41cGmTZu0adMmValSRe3bt8+PjG7FFYqvg9zuQ+6CRe6CRe6CZeXcLnLl8zvP5SY4OFhZWVnq1KmT+vXrp+bNm7slbEGj3FwHud2H3AWL3AWL3AXLyrld5Mrnd54PS6WkpGjmzJk6duyYWrRoobvuukvjxo3jujEAAMCj5Lnc+Pv7q3fv3lq3bp327dunXr166cMPP9Rdd92lVq1aaf78+crKysrPrAAAAH/opm6/ULFiRY0dO1YHDx7UihUrVLJkSfXp00dly5Z1dz4AAACX3NK9pWw2m4oWLSqbzSZjDHtuAABAobupcnPkyBGNHTtWFStWVIsWLXTs2DHNmjVLx48fd3c+AAAAl+T5In6ZmZlatGiRZs+era+//lqRkZGKj4/Xk08+qYoVK+ZnRgAAgDzLc7mJiIjQpUuX1K5dOy1dulRxcXFOt0YAAADwBHkuNyNHjlSvXr1UunTp/MwDAABwS/JcbhISEvIzBwAAgFtwXAkAAFgK5QYAAFgK5QYAAFgK5QYAAFhKnk8ovurKlStKSkpScnKyTp48Kbvd7vT6119/7bZwAAAArnK53AwZMkRJSUlq27atatasKZsn3modAADcsVwuN3PnztXnn3+uNm3a5EceAACAW+LyOTc+Pj6qXLlyfmQBAAC4ZS6Xmz//+c+aMmWKjDH5kQcAAOCWuHxY6rvvvtO6deu0YsUK1ahRQ97e3k6vL1q0yG3hAAAAXOVyuQkJCVGnTp3yIwsAAMAtc7ncfPTRR/mRAwAAwC1cLjdXnTp1Snv27JEkVa1albuFAwAAj+DyCcUXL17Uk08+qcjISD300EN66KGHVKZMGfXr10+XLl3Kj4wAAAB55nK5SUhI0DfffKOlS5fq3LlzOnfunL788kt98803+vOf/5wfGQEAAPLMZlz8TnepUqW0YMECPfzww07T161bp65du+rUqVPuzOd258+fV3BwsFJTUxUUFOT+FXjiFZvz8iMmt/uQu2CRu2CRu2BZObeLXPn8dnnPzaVLlxQeHp5jelhYGIelAABAoXO53DRu3FiJiYlKT093TLt8+bLGjBmjxo0buzUcAACAq1z+ttSUKVMUFxencuXKqXbt2pKkHTt2yM/PT6tWrXJ7QAAAAFe4XG5q1qypffv2ac6cOdq9e7ckqXv37urZs6f8/f3dHhAAAMAVN3Wdm2LFiql///7uzgIAAHDL8lRulixZotatW8vb21tLliy54dhHH33ULcEAAABuRp6+Cu7l5aWUlBSFhYXJy+v65yDbbDZduXLFrQHdja+CXwe53YfcBYvcBYvcBcvKuV3kyud3nvbc2O32XP8MAADgaVz+Knhuzp07547FAAAA3DKXy81bb72lefPmOZ536dJFoaGhKlu2rHbs2OHWcAAAAK5yudzMnDlTUVFRkqQ1a9Zo7dq1WrlypVq3bq2XXnrJ7QEBAABc4fJXwVNSUhzlZtmyZeratatatmypChUqqFGjRm4PCAAA4AqX99yUKFFCR44ckSStXLlSsbGxkiRjjMd/UwoAAFify3tuOnfurB49eqhKlSo6c+aMWrduLUn66aefVLlyZbcHBAAAcIXL5WbSpEmqUKGCjhw5orffflsBAQGSpOPHj2vAgAFuDwgAAOCKPF3Ez0q4iN91kNt9yF2wyF2wyF2wrJzbRW6/iB+3XwAAALcLbr/gbrdrgya3+5C7YJG7YJG7YFk5t4u4/QIAALhjueX2CwAAAJ7C5XLz/PPP669//WuO6VOnTtULL7zgjkwAAAA3zeVys3DhQjVp0iTH9Pvvv18LFixwSygAAICb5XK5OXPmjIKDg3NMDwoK0unTp90SCgAA4Ga5XG4qV66slStX5pi+YsUKVaxY0S2hAAAAbpbLVyhOSEjQoEGDdOrUKTVv3lySlJycrIkTJ2ry5MnuzgcAAOASl8vNk08+qYyMDL3++usaN26cJKlChQqaMWOGevfu7faAAAAArril2y+cOnVK/v7+jvtL3Q64iN91kNt9yF2wyF2wyF2wrJzbRa58ft/UdW6ys7O1du1aLVq0SFe70bFjx3ThwoWbWRwAAIDbuHxY6tChQ2rVqpUOHz6sjIwMtWjRQoGBgXrrrbeUkZGhmTNn5kdOAACAPHF5z82QIUNUv359/frrr/L393dM79Spk5KTk90aDgAAwFUu77nZsGGDvv/+e/n4+DhNr1Chgo4ePeq2YAAAADfD5T03drs91zt///e//1VgYKBbQgEAANwsl8tNy5Ytna5nY7PZdOHCBSUmJqpNmzY3FWLatGmqUKGC/Pz81KhRI23evDlP882dO1c2m00dO3a8qfUCAADrcbncvPPOO9q4caPuuecepaenq0ePHo5DUm+99ZbLAebNm6eEhAQlJiZq27Ztql27tuLi4nTy5MkbzvfLL7/oxRdf1IMPPujyOgEAgHXd1HVusrOzNW/ePO3YsUMXLlxQvXr11LNnT6cTjPOqUaNGatCggaZOnSrpt8NeUVFRGjx4sIYNG5brPFeuXNFDDz2kJ598Uhs2bNC5c+e0ePHiPK2P69xcB7ndh9wFi9wFi9wFy8q5XeTK57dLJxRnZWWpWrVqWrZsmXr27KmePXveUtDMzExt3bpVw4cPd0zz8vJSbGysNm3adN35xo4dq7CwMPXr108bNmy44ToyMjKUkZHheH7+/PlbygwAADybS4elvL29lZ6e7raVnz59WleuXFF4eLjT9PDwcKWkpOQ6z3fffacPP/xQs2bNytM6xo8fr+DgYMcjKirqlnMDAADP5fI5NwMHDtRbb72l7Ozs/MhzQ2lpaerVq5dmzZqlUqVK5Wme4cOHKzU11fE4cuRIPqcEAACFyeXr3GzZskXJyclavXq1atWqpeLFizu9vmjRojwvq1SpUipSpIhOnDjhNP3EiROKiIjIMf7AgQP65Zdf1L59e8c0u90uSSpatKj27NmjSpUqOc3j6+srX1/fPGcCAAC3N5fLTUhIiB577DG3rNzHx0cxMTFKTk52fJ3bbrcrOTlZgwYNyjG+WrVq2rlzp9O0kSNHKi0tTVOmTOGQEwAAcL3cfPTRR24NkJCQoPj4eNWvX18NGzbU5MmTdfHiRfXt21eS1Lt3b5UtW1bjx4+Xn5+fatas6TR/SEiIJOWYDgAA7kx5Ljd2u10TJkzQkiVLlJmZqUceeUSJiYk39fXva3Xr1k2nTp3SqFGjlJKSojp16mjlypWOk4wPHz4sL6+bunk5AAC4A+X5Ojfjxo3T6NGjFRsbK39/f61atUrdu3fX7Nmz8zujW3Gdm+sgt/uQu2CRu2CRu2BZObeLXPn8zvMukU8++UTTp0/XqlWrtHjxYi1dulRz5sxxnNALAADgCfJcbg4fPux076jY2FjZbDYdO3YsX4IBAADcjDyXm+zsbPn5+TlN8/b2VlZWlttDAQAA3Kw8n1BsjFGfPn2crhmTnp6uZ5991ulaN65c5wYAAMDd8lxu4uPjc0x74okn3BoGAADgVuW53Lj7+jYAAAD5gQvIAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS/GIcjNt2jRVqFBBfn5+atSokTZv3nzdsbNmzdKDDz6oEiVKqESJEoqNjb3heAAAcGcp9HIzb948JSQkKDExUdu2bVPt2rUVFxenkydP5jp+/fr16t69u9atW6dNmzYpKipKLVu21NGjRws4OQAA8EQ2Y4wpzACNGjVSgwYNNHXqVEmS3W5XVFSUBg8erGHDhv3h/FeuXFGJEiU0depU9e7d+w/Hnz9/XsHBwUpNTVVQUNAt58/BZnP/Mm9VXn7E5HYfchcschcschcsK+d2kSuf34W65yYzM1Nbt25VbGysY5qXl5diY2O1adOmPC3j0qVLysrKUmhoaK6vZ2Rk6Pz5804PAABgXYVabk6fPq0rV64oPDzcaXp4eLhSUlLytIxXXnlFZcqUcSpI1xo/fryCg4Mdj6ioqFvODQAAPFehn3NzK958803NnTtXX3zxhfz8/HIdM3z4cKWmpjoeR44cKeCUAACgIBUtzJWXKlVKRYoU0YkTJ5ymnzhxQhERETec95133tGbb76ptWvX6t57773uOF9fX/n6+rolLwAA8HyFuufGx8dHMTExSk5Odkyz2+1KTk5W48aNrzvf22+/rXHjxmnlypWqX79+QUQFAAC3iULdcyNJCQkJio+PV/369dWwYUNNnjxZFy9eVN++fSVJvXv3VtmyZTV+/HhJ0ltvvaVRo0bps88+U4UKFRzn5gQEBCggIKDQ3gcAAPAMhV5uunXrplOnTmnUqFFKSUlRnTp1tHLlSsdJxocPH5aX1/92MM2YMUOZmZl6/PHHnZaTmJio0aNHF2R0AADggQr9OjcFjevcXAe53YfcBYvcBYvcBcvKuV1021znBgAAwN0oNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFI8otxMmzZNFSpUkJ+fnxo1aqTNmzffcPz8+fNVrVo1+fn5qVatWvrqq68KKCkAAPB0hV5u5s2bp4SEBCUmJmrbtm2qXbu24uLidPLkyVzHf//99+revbv69eunn376SR07dlTHjh21a9euAk4OAAA8kc0YYwozQKNGjdSgQQNNnTpVkmS32xUVFaXBgwdr2LBhOcZ369ZNFy9e1LJlyxzT7rvvPtWpU0czZ878w/WdP39ewcHBSk1NVVBQkPveyFU2m/uXeavy8iMmt/uQu2CRu2CRu2BZObeLXPn8LtQ9N5mZmdq6datiY2Md07y8vBQbG6tNmzblOs+mTZucxktSXFzcdccDAIA7S9HCXPnp06d15coVhYeHO00PDw/X7t27c50nJSUl1/EpKSm5js/IyFBGRobjeWpqqqTfGuAd43Z9r+QuWOQuWOQuWOQuWPmQ++rndl4OOBVquSkI48eP15gxY3JMj4qKKoQ0hSQ4uLAT3BxyFyxyFyxyFyxyF6x8zJ2WlqbgP1h+oZabUqVKqUiRIjpx4oTT9BMnTigiIiLXeSIiIlwaP3z4cCUkJDie2+12nT17ViVLlpTNE49T6rd2GhUVpSNHjuTPeUH5hNwFi9wFi9wFi9wF63bIbYxRWlqaypQp84djC7Xc+Pj4KCYmRsnJyerYsaOk38pHcnKyBg0alOs8jRs3VnJysl544QXHtDVr1qhx48a5jvf19ZWvr6/TtJCQEHfEz3dBQUEe+0t2I+QuWOQuWOQuWOQuWJ6e+4/22FxV6IelEhISFB8fr/r166thw4aaPHmyLl68qL59+0qSevfurbJly2r8+PGSpCFDhqhp06aaOHGi2rZtq7lz5+rHH3/UBx98UJhvAwAAeIhCLzfdunXTqVOnNGrUKKWkpKhOnTpauXKl46Thw4cPy8vrf1/quv/++/XZZ59p5MiR+stf/qIqVapo8eLFqlmzZmG9BQAA4EEKvdxI0qBBg657GGr9+vU5pnXp0kVdunTJ51SFx9fXV4mJiTkOp3k6chcschcschcsches2zX39RT6RfwAAADcqdBvvwAAAOBOlBsAAGAplBsAAGAplBsPYLPZtHjxYrePzW/kLljkLljkLljkzl+3S063MXASHx9vJBlJxtvb21SqVMmMGTPGZGVl5ds6jx8/btLT029p7K3kXrhwoWnRooUJDQ01ksxPP/3k8bkzMzPNyy+/bGrWrGmKFStmIiMjTa9evczRo0c9OrcxxiQmJpqqVauaYsWKmZCQEPPII4+YH374weNzX+uZZ54xksykSZM8Pve18159xMXFeXxuY4z597//bdq3b2+CgoJMsWLFTP369c2hQ4c8Ovfvt/XVx9tvv+3RudPS0szAgQNN2bJljZ+fn6levbqZMWPGTWe50Vh3fs6kpKSY+Ph4ExkZafz9/U1cXJzZu3evW3K6U14+Zy5fvmwGDBhgQkNDTfHixU3nzp1NSkrKTa2PcvM78fHxplWrVub48ePml19+MdOnTzc2m8288cYbOcZmZGQUQsLc3UruTz75xIwZM8bMmjXLpXLjDjeb+9y5cyY2NtbMmzfP7N6922zatMk0bNjQxMTEeHRuY4yZM2eOWbNmjTlw4IDZtWuX6devnwkKCjInT5706NxXLVq0yNSuXduUKVMmT+XGHW4l97XzXn2cPXvW43Pv37/fhIaGmpdeesls27bN7N+/33z55ZfmxIkTHp372u18/PhxM3v2bGOz2cyBAwc8Onf//v1NpUqVzLp168zBgwfN+++/b4oUKWK+/PJLj8p5Lbvdbu677z7z4IMPms2bN5vdu3ebp59+2kRHR5sLFy64PfetyMvnzLPPPmuioqJMcnKy+fHHH819991n7r///ptaH+Xmd+Lj402HDh2cprVo0cLcd999jtdee+01ExkZaSpUqGCMMebw4cOmS5cuJjg42JQoUcI8+uij5uDBg07L+PDDD80999xjfHx8TEREhBk4cKDjNUnmiy++MMb89os8cOBAExERYXx9fU10dLTTL/y1Y40x5p///Kdp1qyZ8fLyMt7e3qZ///4mLS3Nkbt06dImKirK1KhRwwQEBBibzWYCAwNNZmZmjtyxsbE5fuluh9xXt/fmzZuNJHPo0KHbKndqaqqRZNauXevxuYODg42fn59ZtWqVKV++vKPceHJub29v4+3tfdv9vfT29jY+Pj63Xe7f/3536NDBNG/e3ONze3l5GX9/f6ftXa9ePTNixAi35776WXI1t5+fnylatKgJCwszPXr0cHzO+Pv7m2LFipkJEyaY0qVLGx8fH+Pj4+PYvsnJyUaS2bVrl9P2lWSCg4Pdvn39/PxMaGio0/Y15n+fmxMmTDAREREmNDTUDBgwwGRmZprfO3jwYK7l5ty5c8bb29vMnz/fMe3nn382ksymTZtyLOePcM5NHvj7+yszM1OSlJycrD179mjNmjVatmyZsrKyFBcXp8DAQG3YsEEbN25UQECAWrVq5ZhnxowZGjhwoJ5++mnt3LlTS5YsUeXKlXNd11//+lctWbJEn3/+ufbs2aM5c+aoQoUKuY69ePGi4uLiVKJECbVr104NGjTQ2rVrHRdE9Pf315UrV5SSkqK9e/eqefPmeu+995SVlaW//e1vOXIXL15ckpSVlXVb5b66vU+fPi2bzaYFCxbcNrnj4uI0ffp0BQcHa9u2bR6d+5tvvlHVqlVVo0YNPf/88zL//xJZnv570rp1a/n6+mrVqlW6++679fTTT+vMmTMenTsgIEBFixbVM888o82bN6ty5cpq0KCBFi9e7NG5f//73aJFCy1btkz9+vXz+NydOnVSxYoV5eXlpVatWmn16tXau3evLl26lC+5r1y54si9ZcsWNWzYUOfOndP//d//OT5nWrRoIZvNpr179yooKEhNmzaVl5eXXnjhBQUEBKhfv36SJD8/P6ftGx4ergceeMDt23fLli2aP3++0/a9at26dTpw4IDWrVunjz/+WElJSUpKSsp1mbnZunWrsrKyFBsb65hWrVo1RUdHa9OmTXlejoPLdcjirt1zY7fbzZo1a4yvr6958cUXTXx8vAkPD3faTfjpp5+aqlWrGrvd7piWkZFh/P39zapVq4wxxpQpU8aMGDHiuuvUNS158ODBpnnz5k7Lu97YDz74wJQoUcJcuHDBkXv58uXGZrOZzz//3Pj6+poaNWqY4sWLO+Xu0qWLadSoUY7ce/bsMZLM9OnTb6vcV7d35cqVTY8ePW6L3EuXLjXFihUzkkxoaKjZvHmzx+d+4403TIsWLUx6errx9/c3YWFhZtKkSR6f+x//+If58ssvzdatW42Pj4+JiooyDRo08Ojcx44dM5JMsWLFzNtvv238/PxM3759jc1mM6VKlfLY3L//e+nt7W0CAgLM5cuXPXp72+12k56ebnr37u04F6Zo0aLm448/zpfc8fHxpnbt2qZEiRImLS3N8TnTuXNnI8mULl3aZGRkmPj4eFO+fHnz8ccfO3J26dLFdOvWzfHvXunSpU2XLl1MZGSkGTZsmHnzzTeNJNOyZUu3b9+rli9fbry8vBznw1zNmZ2d7RhzNefvXW/PzZw5c4yPj0+O8Q0aNDAvv/xyrjlvxCNuv+Bpli1bpoCAAGVlZclut6tHjx4aPXq0Bg4cqFq1asnHx8cxdseOHdq/f78CAwOdlpGenq4DBw7o5MmTOnbsmB555JE8rbtPnz5q0aKFqlatqlatWqldu3Zq2bJlrmN//vln1a5d27HHZdmyZVq7dq2MMerevbt69uyp7OxsnTt3TtWrV3fkjoyM1A8//KBjx4455Tb//3/iR44cua1yS9Lly5eVnp6usWPHqnLlyrdNbpvNpqpVq+qxxx7z6O1dvHhxpaeny9/fXyVLlnT8OS0tzaNz/357Z2VlqX///ho1apQkeWzuq//jzszM1JgxY5SRkaEGDRro6NGjWr16tcfmzm1733///Tp//rzH/55kZWUpKytLfn5+ysjIUOfOnfXcc8/p0qVL+ZL7n//8p2w2m0qWLOn4nBk/frwWLVqk6OhoR84aNWpo586djs+ZzMxM2e12LVu2TOnp6RoyZIiWLFmi48eP6+2331aLFi3UunVrx7/n7t6+ktSkSRPZ7Xbt2bPHcR/IGjVqqEiRIo4xkZGR2rlzZ562W37gsFQumjVrpu3bt2vfvn26fPmyPv74Y8cP9tofsCRduHBBMTEx2r59u9Nj79696tGjh/z9/V1ad7169XTw4EGNGzdOly9fVteuXfX444/nOfeGDRskSatXr9bHH38sb29veXl5OeW22WzKzs7OkXv58uWSpNatW982ubds2aLGjRuratWq+vbbbxUWFnZb5N6+fbt27NihvXv3asWKFfL29vbo3EOHDpUxRunp6bp8+bJsNpvOnj2rxMREj86d29/L559/XqGhoR6de8uWLSpSpIgGDRrk9O9J9erVPTr3tdt61qxZkqTXXnvN4/89+eGHH2S32/X+++9r586d2rt3rz744AM99thj+ZY7KipK9evXz/Vz5trt5e3t7fQ506NHD8ef9+7dq2HDhumHH36QJC1YsEArV67UmTNnVLFiRbfkzKvf/xtms9lkt9vzPH9ERIQyMzN17tw5p+knTpxQRESEy3koN7koXry4KleurOjoaBUteuOdW/Xq1dO+ffsUFhamypUrOz2Cg4MVGBioChUqKDk5Oc/rDwoKUrdu3TRr1izNmzdPCxcu1NmzZ3OMq169unbs2KGLFy86ch8/flxeXl6qUaPGDdcRGBiYI/fVY66BgYG3Re7y5cvrL3/5i44fP64NGzborrvuui1y5/Z7IkkhISEem7t///7auXOnduzY4XiUKVNGL7/8ssqWLeuxuXPb3mlpafr1118VFhbmsbnLlSunhg0b6vTp006/J4cOHVLx4sU9Nve123vVqlWKiYnRAw884PF/L0NCQpSdna1y5co5bW9/f3/5+fnlS+4SJUpo3759KlmypONzZuPGjZKkgIAAp7HXfs6EhITI398/18+ZrVu3at++ffrxxx/VoUMHt+T8/fa9mtPLy0tVq1bN83b5IzExMfL29nba1nv27NHhw4fVuHFjl5fHYalb1LNnT02YMEEdOnTQ2LFjVa5cOR06dEiLFi3Syy+/rHLlymn06NF69tlnFRYWptatWystLU0bN27U4MGDcyzv3XffVWRkpOrWrSsvLy/Nnz9fERERCgkJyXXdiYmJio+PV1ZWls6cOaPBgwerV69ejl2F1xMWFiZjjDp06KAXX3xRkhy7EL///ntJ0tChQ/XKK694ZO5HH31U2dnZOnDggMaNG6fhw4drwIABKlOmjEaOHKlBgwZ5ZO527dqpfPny6tSpkzIzM7V48WIZY3T06FGNGjVK48aN88jcffv2zfH7bbPZFBERoddff91jf7/btWunsmXLqkuXLsrMzNS8efO0f/9+Va5cWS+//LIGDx7skbk7dOigtm3bavTo0QoPD9fJkydVpUoVLV26VK+88oomTpzosbnHjh2r4OBgzZ07V40bN9Z///tfj/93sFevXqpdu7aGDBmiPXv2aPv27br33nv1ySefqGvXrvmyvaOionTy5EnFx8dr9OjROnXqlAYPHqxKlSrluDP3tZ8zpUqV0uXLl7V+/XotWrRI99xzj6pVq6YBAwZo5MiRmj59umJjY1WqVCm99957bt2+1+bMy/a91tmzZ3X48GEdO3ZM0m/FRfptj01ERISCg4PVr18/JSQkKDQ0VEFBQRo8eLAaN26s++67L8/ruYo9N7eoWLFi+vbbbxUdHa3OnTurevXq6tevn9LT0xUUFCRJio+P1+TJkzV9+nTVqFFD7dq10759+3JdXmBgoN5++23Vr19fDRo00C+//KKvvvpKXl45f1TFihXTqlWrdPbsWS1btkybN2/WI488oqlTp/5h7iJFijhy/+lPf1K7du00fPhwSdLAgQNVt25dnT171mNzlyxZUhs3blRKSor69++vDz/8UDExMYqMjFSlSpU8Nnd0dLQ+//xzPfbYY+revbuWLFmitLQ0bdiwQX/5y188Ovfvf7+vrsvTf78XLlzo2N5r165VTEyMNmzYoKeeesqjc0+ePFmSNGXKFH322Wf6/PPPtXDhQr3++usenbtz586KiYlRZmamypcvf9v8O3jkyBEdOHBAL774ov7+979rxowZev3115WUlJQvuYsUKeLI3aBBAz3++ON65JFH1KhRo1zf49Wcy5cv15YtWxx/D8+fP69evXppxIgRKl68uLy8vLRu3bp82b7X5szL9r3WkiVLVLduXbVt21aS9Kc//Ul169bVzJkzHWMmTZqkdu3a6bHHHtNDDz2kiIgILVq0yKX1XGUzNzrrCAAA4DbDnhsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAbmGz2bR48WK3j/VEDz/8sF544YU8j1+/fr1sNluO++ZcKykpKdcrxAJwHeUGsJg+ffrIZrPJZrPJx8dHlStX1tixY5WdnZ2v6z1+/Lhat27t9rE3Y+LEiSpRooTS09NzvHbp0iUFBQXpr3/9600vf9GiRRo3btytRASQjyg3gAW1atVKx48f1759+/TnP/9Zo0eP1oQJE3Idm5mZ6ZZ1RkRE5LgnjjvG3oxevXrp4sWLuV66fcGCBcrMzNQTTzzh8nKvbqvQ0FAFBgbeck4A+YNyA1iQr6+vIiIiVL58eT333HOKjY3VkiVLJP22Z6djx456/fXXVaZMGcedfY8cOaKuXbsqJCREoaGh6tChg3755Ren5c6ePVs1atSQr6+vIiMjNWjQIMdr1x5qyszM1KBBgxQZGSk/Pz+VL19e48ePz3Ws9NtNW5s3by5/f3+VLFlSTz/9tC5cuOB4/Wrmd955R5GRkSpZsqQGDhyorKysXN9/WFiY2rdvr9mzZ+d4bfbs2erYsaNCQ0P1yiuv6O6771axYsVUsWJFvfrqq07LHD16tOrUqaO//e1vuuuuu+Tn5ycp52GpTz/9VPXr11dgYKAiIiLUo0cPnTx5Mse6N27cqHvvvVd+fn667777tGvXrlzzX/Xll1+qXr168vPzU8WKFTVmzBjHHjhjjEaPHq3o6Gj5+vqqTJkyev7552+4POBOQbkB7gD+/v5Oe2iSk5O1Z88erVmzRsuWLVNWVpbi4uIUGBioDRs2aOPGjQoICFCrVq0c882YMUMDBw7U008/rZ07d2rJkiWqXLlyruv761//qiVLlujzzz/Xnj17NGfOHFWoUCHXsRcvXlRcXJxKlCihLVu2aP78+Vq7dq1TcZKkdevW6cCBA1q3bp0+/vhjJSUlKSkp6brvuV+/fvr666916NAhx7T//Oc/+vbbb9WvXz9Jv91AMCkpSf/+9781ZcoUzZo1S5MmTXJazv79+7Vw4UItWrRI27dvz3VdWVlZGjdunHbs2KHFixfrl19+UZ8+fXKMe+mllzRx4kRt2bJFpUuXVvv27a9b0DZs2KDevXtryJAh+ve//633339fSUlJev311yVJCxcu1KRJk/T+++9r3759Wrx4sWrVqnXd7QHcUQwAS4mPjzcdOnQwxhhjt9vNmjVrjK+vr3nxxRcdr4eHh5uMjAzHPJ9++qmpWrWqsdvtjmkZGRnG39/frFq1yhhjTJkyZcyIESOuu15J5osvvjDGGDN48GDTvHlzp+Vdb+wHH3xgSpQoYS5cuOB4ffny5cbLy8ukpKQ4MpcvX95kZ2c7xnTp0sV069btunmys7NN2bJlTWJiomPaq6++aqKjo82VK1dynWfChAkmJibG8TwxMdF4e3ubkydPOo1r2rSpGTJkyHXXvWXLFiPJpKWlGWOMWbdunZFk5s6d6xhz5swZ4+/vb+bNm2eMMeajjz4ywcHBjtcfeeQR88Ybbzgt99NPPzWRkZHGGGMmTpxo7r77bpOZmXndHMCdij03gAUtW7ZMAQEB8vPzU+vWrdWtWzeNHj3a8XqtWrXk4+PjeL5jxw7t379fgYGBCggIUEBAgEJDQ5Wenq4DBw7o5MmTOnbsmB555JE8rb9Pnz7avn27qlatqueff16rV6++7tiff/5ZtWvXVvHixR3TmjRpIrvdrj179jim1ahRQ0WKFHE8j4yMzPXQz1VFihRRfHy8kpKSZIyR3W7Xxx9/rL59+8rL67d/+ubNm6cmTZooIiJCAQEBGjlypA4fPuy0nPLly6t06dI3fL9bt25V+/btFR0drcDAQDVt2lSSciyrcePGjj+HhoaqatWq+vnnn3Nd5o4dOzR27FjHzyMgIED9+/fX8ePHdenSJXXp0kWXL19WxYoV1b9/f33xxRf5ftI4cLsoWtgBALhfs2bNNGPGDPn4+KhMmTIqWtT5r/q1RUKSLly4oJiYGM2ZMyfHskqXLu0oA3lVr149HTx4UCtWrNDatWvVtWtXxcbGasGCBa6/mf/P29vb6bnNZpPdbr/hPE8++aTGjx+vr7/+Wna7XUeOHFHfvn0lSZs2bVLPnj01ZswYxcXFKTg4WHPnztXEiROdlvH7bfV7Vw+rxcXFac6cOSpdurQOHz6suLi4WzpZ+8KFCxozZow6d+6c4zU/Pz9FRUVpz549Wrt2rdasWaMBAwZowoQJ+uabb3JsK+BOQ7kBLKh48eLXPR8mN/Xq1dO8efMUFhamoKCgXMdUqFBBycnJatasWZ6WGRQUpG7duqlbt256/PHH1apVK509e1ahoaFO46pXr66kpCRdvHjRUSQ2btwoLy8vx8nON6tSpUpq2rSpZs+eLWOMYmNjVb58eUnS999/r/Lly2vEiBGO8deen5NXu3fv1pkzZ/Tmm28qKipKkvTjjz/mOvaHH35QdHS0JOnXX3/V3r17Vb169VzH1qtXT3v27Lnhz9Hf31/t27dX+/btNXDgQFWrVk07d+5UvXr1XH4fgJVQbgCoZ8+emjBhgjp06KCxY8eqXLlyOnTokBYtWqSXX35Z5cqV0+jRo/Xss88qLCxMrVu3VlpamjZu3KjBgwfnWN67776ryMhI1a1bV15eXpo/f74iIiJyvUhdz549lZiYqPj4eI0ePVqnTp3S4MGD1atXL4WHh9/ye+vXr5/69+8vSU4nIFepUkWHDx/W3Llz1aBBAy1fvlxffPGFy8uPjo6Wj4+P3nvvPT377LPatWvXda+BM3bsWJUsWVLh4eEaMWKESpUqpY4dO+Y6dtSoUWrXrp2io6P1+OOPy8vLSzt27NCuXbv02muvKSkpSVeuXFGjRo1UrFgx/f3vf5e/v7+jvAF3Ms65AaBixYrp22+/VXR0tDp37qzq1aurX79+Sk9Pd+zJiY+P1+TJkzV9+nTVqFFD7dq10759+3JdXmBgoN5++23Vr19fDRo00C+//KKvvvoq18NbxYoV06pVq3T27Fk1aNBAjz/+uB555BFNnTrVLe/tsccek6+vr4oVK+ZUJB599FENHTpUgwYNUp06dfT999/r1VdfdXn5pUuXVlJSkubPn6977rlHb775pt55551cx7755psaMmSIYmJilJKSoqVLlzqd+3StuLg4LVu2TKtXr1aDBg103333adKkSY7yEhISolmzZqlJkya69957tXbtWi1dulQlS5Z0+T0AVmMzxpjCDgEAAOAu7LkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACW8v8A1rYhCY9bpSkAAAAASUVORK5CYII=\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of recall values from recall1 to recall10\n",
-        "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
-        "\n",
-        "# List of corresponding labels for each recall value\n",
-        "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, recall_values, color='cyan')\n",
-        "plt.xlabel('Recall Variables')\n",
-        "plt.ylabel('Recall Values')\n",
-        "plt.title('Bar Graph of Recall')\n",
-        "plt.show()\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 472
-        },
-        "id": "39LBleNeDeuw",
-        "outputId": "3c6c40bc-f1da-44fb-da14-25ec6d6cf278"
-      },
-      "execution_count": 122,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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RpREfAFCA0j6kzWFulGq52bZtm1q1aqVWrVpJkqKiotSqVSuNHz9eknT8+HFb0ZGkwMBArV69Whs2bFBQUJAmT56sjz/+mMvAAQCAjcUwDKO0Q5Sk9PR0eXt769y5c/Ly8irtOE7DGf9lc0v9wUSx4s93yWJ7l6xbZXs78v1dps65AQAA+CuUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCrlSjsAAADOwFLaAQpglHaAMoo9NwAAwFQoNwAAwFQ4LAWUgrK6+7us5gZwa2HPDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXucwMATor7CgE3hj03AADAVCg3AADAVCg3AADAVDjnBmUa5yQAAP4Xe24AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICplHq5mTFjhurUqSMPDw+1a9dOW7duve7006ZNU6NGjeTp6amAgACNGjVKFy9eLKG0AADA2ZVquVm6dKmioqIUHR2t+Ph4BQUFKTw8XKmpqQVOv3jxYo0ZM0bR0dHat2+f5syZo6VLl+rll18u4eTXZnHCFwAAt5JSLTdTpkzRk08+qSFDhqhp06aaPXu2ypcvr7lz5xY4/ZYtW9ShQwcNGDBAderUUbdu3dS/f/+/3NsDAABuHaVWbnJycrR9+3aFhYX9N4yLi8LCwhQXF1fgPHfeeae2b99uKzO//fab1qxZox49epRIZgAA4PzKldaK09LSlJubK19fX7txX19f7d+/v8B5BgwYoLS0NN11110yDEOXLl3S8OHDr3tYKjs7W9nZ2baf09PTi+YDAAAAp1TqJxQ7YtOmTXrjjTc0c+ZMxcfHa8WKFVq9erVee+21a84TExMjb29v2ysgIKAEEwMAgJJmMQzDKI0V5+TkqHz58lq2bJn69OljG4+IiNDZs2f173//O988oaGhat++vd5++23b2CeffKJhw4YpIyNDLi75u1pBe24CAgJ07tw5eXl5Fe2HknOewFuYXzC5iw65Sxa5Sxa5S5aZczsqPT1d3t7ehfr+LrU9N+7u7goODlZsbKxtLC8vT7GxsQoJCSlwngsXLuQrMK6urpKka3U0q9UqLy8vuxcAADCvUjvnRpKioqIUERGh1q1bq23btpo2bZoyMzM1ZMgQSdKgQYPk7++vmJgYSVLPnj01ZcoUtWrVSu3atdOhQ4f0yiuvqGfPnraSAwAAbm2lWm769eunkydPavz48Tpx4oRatmyptWvX2k4yTkpKsttTM27cOFksFo0bN07Hjh1TjRo11LNnT/3zn/8srY8AAACcTKmdc1NaHDlmdyPK6rFPchcdcpcscpcscpcsM+d2VJk45wYAAKA4UG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpOFxusrKydOHCBdvPR48e1bRp07R+/foiDQYAAHAjHC43vXv31sKFCyVJZ8+eVbt27TR58mT17t1bs2bNKvKAAAAAjnC43MTHxys0NFSStGzZMvn6+uro0aNauHCh3n333SIPCAAA4AiHy82FCxdUqVIlSdL69ev14IMPysXFRe3bt9fRo0eLPCAAAIAjHC439evX18qVK5WcnKx169apW7dukqTU1FR5eXkVeUAAAABHOFxuxo8fr9GjR6tOnTpq27atQkJCJF3ei9OqVasiDwgAAOAIi2EYhqMznThxQsePH1dQUJBcXC73o61bt8rLy0uNGzcu8pBFKT09Xd7e3jp37lyx7GmyFPkSb15hfsHkLjrkLlnkLlnkLllmzu0oR76/b+g+N35+fqpUqZI2bNigrKwsSVKbNm2cvtgAAADzc7jcnDp1Sl26dFHDhg3Vo0cPHT9+XJIUGRmpF154ocgDAgAAOMLhcjNq1Ci5ubkpKSlJ5cuXt43369dPa9euLdJwAAAAjirn6Azr16/XunXrVKtWLbvxBg0acCk4AAAodQ7vucnMzLTbY3PF6dOnZbVaiyQUAADAjXK43ISGhtoevyBJFotFeXl5euutt9S5c+ciDQcAAOAoh8vNW2+9pQ8//FD33nuvcnJy9OKLL6p58+b6/vvvNWnSJIcDzJgxQ3Xq1JGHh4fatWunrVu3Xnf6s2fPasSIEapZs6asVqsaNmyoNWvWOLxeAABgTg6Xm+bNm+vXX3/VXXfdpd69eyszM1MPPvigduzYoXr16jm0rKVLlyoqKkrR0dGKj49XUFCQwsPDlZqaWuD0OTk56tq1qxITE7Vs2TIdOHBAH330kfz9/R39GAAAwKRu6CZ+RaVdu3Zq06aN3n//fUlSXl6eAgIC9Mwzz2jMmDH5pp89e7befvtt7d+/X25ubje0Tm7iVzByFx1ylyxylyxylywz53aUI9/fDl8t9f3331/3/Y4dOxZqOTk5Odq+fbvGjh1rG3NxcVFYWJji4uIKnGfVqlUKCQnRiBEj9O9//1s1atTQgAED9NJLL8nV1bXAebKzs5WdnW37OT09vVD5AABA2eRwubn77rvzjVks/+2Nubm5hVpOWlqacnNz5evrazfu6+ur/fv3FzjPb7/9po0bN+qxxx7TmjVrdOjQIT399NP6888/FR0dXeA8MTExmjhxYqEyAQCAss/hc27OnDlj90pNTdXatWvVpk0brV+/vjgy2uTl5cnHx0cffvihgoOD1a9fP/3jH//Q7NmzrznP2LFjde7cOdsrOTm5WDMCAIDS5fCeG29v73xjXbt2lbu7u6KiorR9+/ZCLad69epydXVVSkqK3XhKSor8/PwKnKdmzZpyc3OzOwTVpEkTnThxQjk5OXJ3d883j9Vq5f47AADcQm7owZkF8fX11YEDBwo9vbu7u4KDgxUbG2sby8vLU2xsrEJCQgqcp0OHDjp06JDy8vJsY7/++qtq1qxZYLEBAAC3Hof33OzatcvuZ8MwdPz4cb355ptq2bKlQ8uKiopSRESEWrdurbZt22ratGnKzMzUkCFDJEmDBg2Sv7+/YmJiJElPPfWU3n//fT333HN65plndPDgQb3xxht69tlnHf0YAADApBwuNy1btpTFYtH/XkHevn17zZ0716Fl9evXTydPntT48eN14sQJtWzZUmvXrrWdZJyUlCQXl//uXAoICNC6des0atQotWjRQv7+/nruuef00ksvOfoxAACASTl8n5v/fTimi4uLatSoIQ8PjyINVly4z03ByF10yF2yyF2yyF2yzJzbUcV6n5vatWvfcDAAAIDiVqhy8+677xZ6gZz/AgAASlOhDksFBgYWbmEWi3777bebDlWcOCxVMHIXHXKXLHKXLHKXLDPndlSRH5Y6cuRIkQQDAAAobkV2nxsAAABn4PAJxZL0+++/a9WqVUpKSlJOTo7de1OmTCmSYAAAADfC4XITGxurXr16qW7dutq/f7+aN2+uxMREGYahO+64ozgyAgAAFJrDh6XGjh2r0aNHa/fu3fLw8NDy5cuVnJysTp06qW/fvsWREQAAoNAcLjf79u3ToEGDJEnlypVTVlaWKlasqFdffVWTJk0q8oAAAACOcLjcVKhQwXaeTc2aNXX48GHbe2lpaUWXDAAA4AY4fM5N+/bttXnzZjVp0kQ9evTQCy+8oN27d2vFihVq3759cWQEAAAotEKXm9OnT6tq1aqaMmWKMjIyJEkTJ05URkaGli5dqgYNGnClFAAAKHWFfnCmh4eH+vTpo8jISHXt2rW4cxUb7lBcMHIXHXKXLHKXLHKXLDPndpQj39+FPufmo48+0smTJ9W9e3fVqVNHEyZMUGJi4s1mBQAAKFKFLjePP/64YmNjdejQIUVERGjBggWqX7++unbtqqVLl+a7mR8AAEBpcPhqqcDAQE2cOFFHjhzR2rVr5ePjo6FDh6pmzZo8ERwAAJS6Qp9zcz3Lly/XsGHDdPbsWeXm5hZFrmLDOTcFI3fRIXfJInfJInfJMnNuRxX5U8ELcvToUc2bN08LFixQcnKyOnfurMjIyBtdHAAAQJFwqNxkZ2dr+fLlmjt3rjZt2iR/f38NHjxYQ4YMUZ06dYopIgAAQOEVutw8/fTTWrJkiS5cuKDevXtrzZo16tq1qywWZ9whBgAAblWFLjebN29WdHS0Bg4cqGrVqhVnJgAAgBtW6HKza9eu4swBAABQJBy+FBwAAMCZUW4AAICpUG4AAICpUG4AAICpFOqEYkdOJm7RosUNhwEAALhZhSo3LVu2lMVi0bWe1HDlPYvF4vSPXwAAAOZWqHJz5MiR4s4BAABQJApVbmrXrl3cOQAAAIpEocrNqlWrCr3AXr163XAYAACAm1WoctOnT59CLYxzbgAAQGkrVLnJy8sr7hwAAABFgvvcAAAAUyn0gzOvlpmZqe+++05JSUnKycmxe+/ZZ58tkmAAAAA3wuFys2PHDvXo0UMXLlxQZmamqlatqrS0NJUvX14+Pj6UGwAAUKocPiw1atQo9ezZU2fOnJGnp6d+/PFHHT16VMHBwXrnnXeKIyMAAEChOVxuEhIS9MILL8jFxUWurq7Kzs5WQECA3nrrLb388svFkREAAKDQHC43bm5ucnG5PJuPj4+SkpIkSd7e3kpOTi7adAAAAA5y+JybVq1a6eeff1aDBg3UqVMnjR8/XmlpaVq0aJGaN29eHBkBAAAKzeE9N2+88YZq1qwpSfrnP/+pKlWq6KmnntLJkyf1wQcfFHlAAAAAR1iMaz3q26TS09Pl7e2tc+fOycvLq8iXbynyJd68wvyCyV10yF2yyF2yyF2yzJzbUY58fzu85+bIkSM6ePBgvvGDBw8qMTHR0cUBAAAUKYfLzeDBg7Vly5Z84z/99JMGDx5cFJkAAABumMPlZseOHerQoUO+8fbt2yshIaEoMgEAANwwh8uNxWLR+fPn842fO3eOJ4IDAIBS53C56dixo2JiYuyKTG5urmJiYnTXXXcVaTgAAABHOXyfm0mTJqljx45q1KiRQkNDJUn/+c9/lJ6ero0bNxZ5QAAAAEc4vOemadOm2rVrlx555BGlpqbq/PnzGjRokPbv389N/AAAQKnjPjdFrKzeb4DcRYfcJYvcJYvcJcvMuR1VrPe5kS4fhho4cKDuvPNOHTt2TJK0aNEibd68+UYWBwAAUGQcLjfLly9XeHi4PD09FR8fr+zsbEmXr5Z64403ijwgAACAIxwuN6+//rpmz56tjz76SG5ubrbxDh06KD4+vkjDAQAAOMrhcnPgwAF17Ngx37i3t7fOnj1bFJkAAABumMPlxs/PT4cOHco3vnnzZtWtW7dIQgEAANwoh8vNk08+qeeee04//fSTLBaL/vjjD/3rX//S6NGj9dRTTxVHRgAAgEJz+CZ+Y8aMUV5enrp06aILFy6oY8eOslqtGj16tJ555pniyAgAAFBoN3yfm5ycHB06dEgZGRlq2rSpKlasqKysLHl6ehZ1xiLFfW4KRu6iQ+6SRe6SRe6SZebcjir2+9xIkru7u5o2baq2bdvKzc1NU6ZMUWBg4I0uDgAAoEgUutxkZ2dr7Nixat26te68806tXLlSkjRv3jwFBgZq6tSpGjVq1A2FmDFjhurUqSMPDw+1a9dOW7duLdR8S5YskcViUZ8+fW5ovQAAwHwKXW7Gjx+vWbNmqU6dOkpMTFTfvn01bNgwTZ06VVOmTFFiYqJeeuklhwMsXbpUUVFRio6OVnx8vIKCghQeHq7U1NTrzpeYmKjRo0fbHt4JAAAgOVBuPv/8cy1cuFDLli3T+vXrlZubq0uXLmnnzp169NFH5erqekMBpkyZoieffFJDhgxR06ZNNXv2bJUvX15z58695jy5ubl67LHHNHHiRC4/BwAAdgpdbn7//XcFBwdLkpo3by6r1apRo0bJYrnxU5lycnK0fft2hYWF/TeQi4vCwsIUFxd3zfleffVV+fj4KDIy8obXDQAAzKnQl4Ln5ubK3d39vzOWK6eKFSve1MrT0tKUm5srX19fu3FfX1/t37+/wHk2b96sOXPmKCEhoVDryM7Otj3/Srp8tjUAADCvQpcbwzA0ePBgWa1WSdLFixc1fPhwVahQwW66FStWFG3Cq5w/f16PP/64PvroI1WvXr1Q88TExGjixInFlgkAADiXQpebiIgIu58HDhx40yuvXr26XF1dlZKSYjeekpIiPz+/fNMfPnxYiYmJ6tmzp20sLy9P0uU9SQcOHFC9evXs5hk7dqyioqJsP6enpysgIOCmswMAAOdU6HIzb968Il+5u7u7goODFRsba7ucOy8vT7GxsRo5cmS+6Rs3bqzdu3fbjY0bN07nz5/X9OnTCywtVqvVtrcJAACYn8OPXyhqUVFRioiIUOvWrdW2bVtNmzZNmZmZGjJkiCRp0KBB8vf3V0xMjDw8PNS8eXO7+StXrixJ+cYBAMCtqdTLTb9+/XTy5EmNHz9eJ06cUMuWLbV27VrbScZJSUlycbnhGykDAIBbzA0/W6qs4tlSBSN30SF3ySJ3ySJ3yTJzbkeVyLOlAAAAnBHlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmIpTlJsZM2aoTp068vDwULt27bR169ZrTvvRRx8pNDRUVapUUZUqVRQWFnbd6QEAwK2l1MvN0qVLFRUVpejoaMXHxysoKEjh4eFKTU0tcPpNmzapf//++vbbbxUXF6eAgAB169ZNx44dK+HkAADAGVkMwzBKM0C7du3Upk0bvf/++5KkvLw8BQQE6JlnntGYMWP+cv7c3FxVqVJF77//vgYNGvSX06enp8vb21vnzp2Tl5fXTef/X5YiX+LNK8wvmNxFh9wli9wli9wly8y5HeXI93ep7rnJycnR9u3bFRYWZhtzcXFRWFiY4uLiCrWMCxcu6M8//1TVqlULfD87O1vp6el2LwAAYF6lWm7S0tKUm5srX19fu3FfX1+dOHGiUMt46aWXdNttt9kVpKvFxMTI29vb9goICLjp3AAAwHmV+jk3N+PNN9/UkiVL9MUXX8jDw6PAacaOHatz587ZXsnJySWcEgAAlKRypbny6tWry9XVVSkpKXbjKSkp8vPzu+6877zzjt5880198803atGixTWns1qtslqtRZIXAAA4v1Ldc+Pu7q7g4GDFxsbaxvLy8hQbG6uQkJBrzvfWW2/ptdde09q1a9W6deuSiAoAAMqIUt1zI0lRUVGKiIhQ69at1bZtW02bNk2ZmZkaMmSIJGnQoEHy9/dXTEyMJGnSpEkaP368Fi9erDp16tjOzalYsaIqVqxYap8DAAA4h1IvN/369dPJkyc1fvx4nThxQi1bttTatWttJxknJSXJxeW/O5hmzZqlnJwcPfzww3bLiY6O1oQJE0oyOgAAcEKlfp+bksZ9bgpG7qJD7pJF7pJF7pJl5tyOKjP3uQEAAChqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqTlFuZsyYoTp16sjDw0Pt2rXT1q1brzv9559/rsaNG8vDw0O333671qxZU0JJAQCAsyv1crN06VJFRUUpOjpa8fHxCgoKUnh4uFJTUwucfsuWLerfv78iIyO1Y8cO9enTR3369NGePXtKODkAAHBGFsMwjNIM0K5dO7Vp00bvv/++JCkvL08BAQF65plnNGbMmHzT9+vXT5mZmfrqq69sY+3bt1fLli01e/bsv1xfenq6vL29de7cOXl5eRXdB/k/liJf4s0rzC+Y3EWH3CWL3CWL3CXLzLkd5cj3d6nuucnJydH27dsVFhZmG3NxcVFYWJji4uIKnCcuLs5uekkKDw+/5vQAAODWUq40V56Wlqbc3Fz5+vrajfv6+mr//v0FznPixIkCpz9x4kSB02dnZys7O9v287lz5yRdboC3irL6Scldsshdsshdsshdsooj95Xv7cIccCrVclMSYmJiNHHixHzjAQEBpZCmdHiXdoAbRO6SRe6SRe6SRe6SVZy5z58/L2/v66+hVMtN9erV5erqqpSUFLvxlJQU+fn5FTiPn5+fQ9OPHTtWUVFRtp/z8vJ0+vRpVatWTRaLMx6pvNxOAwIClJycXCznBRUXcpcscpcscpcscpesspDbMAydP39et912219OW6rlxt3dXcHBwYqNjVWfPn0kXS4fsbGxGjlyZIHzhISEKDY2Vs8//7xtbMOGDQoJCSlweqvVKqvVajdWuXLloohf7Ly8vJz2D9n1kLtkkbtkkbtkkbtkOXvuv9pjc0WpH5aKiopSRESEWrdurbZt22ratGnKzMzUkCFDJEmDBg2Sv7+/YmJiJEnPPfecOnXqpMmTJ+u+++7TkiVLtG3bNn344Yel+TEAAICTKPVy069fP508eVLjx4/XiRMn1LJlS61du9Z20nBSUpJcXP57Udedd96pxYsXa9y4cXr55ZfVoEEDrVy5Us2bNy+tjwAAAJxIqZcbSRo5cuQ1D0Nt2rQp31jfvn3Vt2/fYk5VeqxWq6Kjo/MdTnN25C5Z5C5Z5C5Z5C5ZZTX3tZT6TfwAAACKUqk/fgEAAKAoUW4AAICpUG4AAICpUG7KMIvFopUrV0qSEhMTZbFYlJCQUKqZCoPcJa+sZi+Lucti5ivKYvaymPmKspC9LGQsCOXmBg0ePFgWi0UWi0Vubm4KDAzUiy++qIsXL5Z2NJtnn31WwcHBslqtatmypSTnz71z5071799fAQEB8vT0VJMmTTR9+nSnz33q1Cl1795dt912m6xWqwICAjRy5Eilp6c7ffarnTp1SrVq1ZLFYtGAAQOcPveVfAW9nDWzJM2fP18tWrSQh4eHfHx8NGLECKf/czJ//vwyub1//vlndenSRZUrV1aVKlUUHh6unTt3Ov32li4/FPrqbVy5cmWNHj3aqTIW9D3zv3bt2qXQ0FB5eHgoICBAb731VrHncopLwcuq7t27a968efrzzz+1fft2RUREyGKxaNKkSaUdzWbo0KH66aeftGvXLtuYM+fevn27fHx89MknnyggIEBbtmzRsGHDdPvttzt1bhcXF/Xu3Vuvv/66atSooUOHDmnEiBE6ffq03N3dnTr71SIjI9WiRQsdO3ZMknP/Wbli3rx56t69u6TLN/k8e/asFixY4LSZp0yZosmTJ+vtt99Wu3btlJmZqcTERK1YscKpt3e/fv1s2/mK4OBgZWVl6ZdffnHKzBkZGerevbt69eqlmTNn6tKlS4qOjlZ4eLi6devm1Nt7586d+uabb1S/fn0tXLhQv//+u0aNGqX33ntPrq6uTpHxioK+Z65IT09Xt27dFBYWptmzZ2v37t0aOnSoKleurGHDhhVbJvbc3ASr1So/Pz8FBASoT58+CgsL04YNGyRdfoxETEyMAgMD5enpqaCgIC1btsxu/r179+r++++Xl5eXKlWqpNDQUB0+fFjS5X9tdO3aVdWrV5e3t7c6deqk+Ph4h/K9++67GjFihOrWrVtmcg8dOlTTp09Xp06dVLduXQ0cOFBDhgxRUlKSU+euUqWKnnrqKbVu3Vq1a9dWly5d9PTTT+s///mP02/zK2bNmqWzZ89q9OjRtrGykLty5cry8/OTn5+fPD095enp6bSZz5w5o3HjxmnhwoUaMGCA6tWrpxYtWqhXr15Ov72vbNcrL1dXVx0/flyBgYFOm3n//v06ffq0Xn31VTVq1EjNmjVTdHS0UlJSlJGR4dTbe+nSpapSpYqaNWumkJAQ9e3bV7NmzdKlS5e0bt06p8goXft75op//etfysnJ0dy5c9WsWTM9+uijevbZZzVlyhSH1uMoyk0R2bNnj7Zs2SJ3d3dJl59GvnDhQs2ePVt79+7VqFGjNHDgQH333XeSpGPHjqljx46yWq3auHGjtm/frqFDh+rSpUuSLj/1NCIiQps3b9aPP/6oBg0aqEePHjp//vwtl/vcuXP5bizl7Ln/+OMPrVixQp06dcr3njNm/+WXX/Tqq69q4cKFdncEd/bckjRixAhVr15dbdu21cGDB50684YNG5SXl6djx46pSZMmqlWrlh555BElJyeXme19xcKFC1WuXDm7hxg6W+ZGjRqpWrVqmjNnjnJycpSVlaU5c+aoSZMmqlixolNv7+zsbLm6utqNnThxQnl5ecrJyXGKjIURFxenjh072rardPlw24EDB3TmzJkiW08+Bm5IRESE4erqalSoUMGwWq2GJMPFxcVYtmyZcfHiRaN8+fLGli1b7OaJjIw0+vfvbxiGYYwdO9YIDAw0cnJyCrW+3Nxco1KlSsaXX35pG5NkfPHFF4ZhGMaRI0cMScaOHTvyzRsdHW0EBQWVudyGYRg//PCDUa5cOaNr165lIvejjz5qeHp6GpKMnj17GllZWU6/zS9evGi0aNHCWLRokWEYhvHtt98akoz+/fs7dW7DMIxXX33V2Lx5sxEfH2+8+eabhouLi+Hi4uK0mWNiYgw3NzejUaNGxtq1a424uDijS5cuRqNGjYzHH3/c6bf31Zo0aWI0atTI6TPv3r3bqFevnu3PRqNGjYzExESn//9y3bp1tkxXZ5RkPP/8806R8WpXf89crWvXrsawYcPsxvbu3WtIMn755ZdC5boRnHNzEzp37qxZs2YpMzNTU6dOVbly5fTQQw9p7969unDhgrp27Wo3fU5Ojlq1aiVJSkhIUGhoqNzc3ApcdkpKisaNG6dNmzYpNTVVubm5unDhgpKSkm6Z3Hv27FHv3r0VHR2tQ4cOlYncU6dOVXR0tH799VeNHTtWUVFRkpx7m48dO1ZNmjTRwIED873nzLkl6ZVXXrH9d6tWrbR48WIdOHBACQkJTpk5Ly9Pf/75p959911169ZNkvTpp5/aDo04+/a+Ii4uTvv27dP999+vgIAAp82clZWlyMhIdejQQZ9++qlyc3P1zjvv6L777lOrVq2cent369ZNrVu31o4dO5SVlSV3d3fdcccdio+PV/v27XXo0KFSz+jMKDc3oUKFCqpfv74kae7cuQoKCtKcOXNsD/FcvXq1/P397ea5cnjF09PzusuOiIjQqVOnNH36dNWuXVtWq1UhISG23ZFmz/3LL7+oS5cuGjZsmMaNG6fBgweXidxXzkdo3LixqlatqtDQUD3yyCNOnX3jxo3avXu37Vi98X9PZFmyZIkaNmzotLkLUqNGDe3atUsBAQGyWq1Ol7lmzZqSpKZNm9plrl69ujIzM+Xj41MmtvfHH3+sli1bqlq1ajp79qzTZl68eLESExMVFxdnO9y6ePFiValSRdWqVVOVKlWcNrskNWvWTP7+/po5c6aqVKmi3377Tc2bN9fu3btVp04dp8j4V/z8/JSSkmI3duVnPz+/IlvP/6LcFBEXFxe9/PLLioqK0q+//iqr1aqkpKQCz7mQpBYtWtiu6CioVf/www+aOXOmevToIUlKTk5WWlraLZF77969uueeexQREaF//vOfZSb3/8rLy5Mk5ebmOnX25cuXKysry/bzzz//rKFDh+ree++1fQZnzF2Q06dPy83NzfaXu7Nl7tChgyTpwIEDqlWrli1zWlqagoKC7KZ1tuxXZGRk6LPPPlNMTIy2bdvm1JkvXLggFxcXWSwWu4wWi8VW4p01+9WunNe0dOlSVa1aVXPmzNFLL73kVBmvJSQkRP/4xz/sMmzYsEGNGjVSlSpVinRdV+OE4iLUt29fubq66oMPPtDo0aM1atQoLViwQIcPH1Z8fLzee+89LViwQJJs90B59NFHtW3bNh08eFCLFi3SgQMHJEkNGjTQokWLtG/fPv3000967LHH/rKF/69Dhw4pISFBJ06cUFZWlhISEnT69Gm7Lyxny71nzx517txZ3bp1U1RUlE6cOKETJ04UeF8HZ8q9Zs0azZs3T3v27FFiYqJWr16t4cOHq0OHDvlOXHS27PXq1VPz5s1tr8DAQEmSt7d3vhO5nSn3l19+qY8//lh79uzRoUOHNGvWLO3atSvfVRvOlLlhw4bq3bu3nnvuOW3ZskV79uxRRESEGjduXOC/Yp0p+xVLly7VpUuXCjyM6WyZu3btqjNnzmjEiBHat2+f9u7dqyFDhqhcuXK2vWjOml26/Pdhenq69u7dq9dee01vvvmmPvzwQ6fKWND3TEJCgm3vz4ABA+Tu7q7IyEjt3btXS5cu1fTp022H7ItNsZ3NY3IRERFG7969843HxMQYNWrUMDIyMoxp06YZjRo1Mtzc3IwaNWoY4eHhxnfffWebdufOnUa3bt2M8uXLG5UqVTJCQ0ONw4cPG4ZhGPHx8Ubr1q0NDw8Po0GDBsbnn39u1K5d25g6daptfv3FiV6dOnWynYB29atr165Omzs6OrrAzBUqVHDq7b1x40YjJCTE8Pb2ti3jpZdeMs6cOVMm/qxc7eoTip0599dff220bNnSqFixolGhQgUjKCjIaN++vdGrVy+nzWwYhnHu3Dlj6NChRuXKlY2qVasaDzzwgJGUlFRm/pyEhIQYAwYMMAyjbPw9uH79eqNDhw6Gt7e3UaVKFeOee+4x4uLiykR2Pz8/o1y5coaHh4fRrl07Y82aNU6X8VrfM0eOHLHLcNdddxlWq9Xw9/c33nzzzXzbvahZ/i88AACAKXBYCgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBkCps1gsWrlypSQpMTFRFotFCQkJJZ7jRtY9ePBg9enT57rT3H333Xr++edvKhuAwqPcALewwYMHy2KxyGKxyM3NTYGBgXrxxRcLfNyFs7j99ts1fPjwAt9btGiRrFbrDT8fJyAgQMePH7c9OBFA2US5AW5x3bt31/Hjx/Xbb79p6tSp+uCDDxQdHV3asa4pMjJSS5YssXvY5xXz5s1Tr169VL16dYeXm5OTI1dXV/n5+alcOZ4pDJRllBvgFme1WuXn56eAgAD16dNHYWFh2rBhg+39vLw8xcTEKDAwUJ6engoKCtKyZcvslrF3717df//98vLyUqVKlRQaGqrDhw9LuvyU8a5du6p69ery9vZWp06dFB8ff8N5Bw4cqKysLC1fvtxu/MiRI9q0aZMiIyN1+PBh9e7dW76+vqpYsaLatGmjb775xm76OnXq6LXXXtOgQYPk5eWlYcOG5TsslZubq8jISNtnb9SokaZPn15grokTJ6pGjRry8vLS8OHDbQ8OLEh2drZGjx4tf39/VahQQe3atdOmTZts7x89elQ9e/ZUlSpVVKFCBTVr1kxr1qy5sQ0G3IIoNwBs9uzZoy1btsjd3d02FhMTo4ULF2r27Nnau3evRo0apYEDB+q7776TJB07dkwdO3aU1WrVxo0btX37dg0dOlSXLl2SJJ0/f14RERHavHmzfvzxRzVo0EA9evTQ+fPnbyhj9erV1bt3b82dO9dufP78+apVq5a6deumjIwM9ejRQ7GxsdqxY4e6d++unj17KikpyW6ed955R0FBQdqxY4deeeWVfOvKy8tTrVq19Pnnn+uXX37R+PHj9fLLL+uzzz6zmy42Nlb79u3Tpk2b9Omnn2rFihWaOHHiNT/DyJEjFRcXpyVLlmjXrl3q27evunfvroMHD0qSRowYoezsbH3//ffavXu3Jk2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-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "13cZXvb0DsvK"
-      },
-      "execution_count": null,
-      "outputs": []
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/Python File/Stock_Price_Prediction(Updated).ipynb b/Stock_Price_Prediction(Updated).ipynb
similarity index 100%
rename from Python File/Stock_Price_Prediction(Updated).ipynb
rename to Stock_Price_Prediction(Updated).ipynb

From 90f3350b3ef3b18d0cc9028e3ac8bffbf6d2b174 Mon Sep 17 00:00:00 2001
From: Ayushi Sha <124066968+AYUSHI-SHA@users.noreply.github.com>
Date: Sun, 6 Oct 2024 14:35:40 +0530
Subject: [PATCH 05/76] Delete Stock_Price_Prediction.ipynb

---
 Stock_Price_Prediction.ipynb | 1805 ----------------------------------
 1 file changed, 1805 deletions(-)
 delete mode 100644 Stock_Price_Prediction.ipynb

diff --git a/Stock_Price_Prediction.ipynb b/Stock_Price_Prediction.ipynb
deleted file mode 100644
index 7c3ad61..0000000
--- a/Stock_Price_Prediction.ipynb
+++ /dev/null
@@ -1,1805 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "qCDSjVhXLr_Z"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "from sklearn.linear_model import LinearRegression\n",
-        "from sklearn.svm import SVR\n",
-        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor\n",
-        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
-        "from sklearn.neighbors import KNeighborsRegressor\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Dense,LSTM"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "SOQbXSiB-g5G",
-        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
-          ]
-        }
-      ],
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')\n",
-        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Sc4id6VxL8BS",
-        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Date</th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Adj Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>01-01-1996</td>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>12.409931</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>02-01-1996</td>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>12.014931</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>03-01-1996</td>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
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-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>04-01-1996</td>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>11.654142</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>05-01-1996</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>11.588827</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
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-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d89bb189-3861-4a83-b0ea-1a19c2115c37')\"\n",
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-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "         Date       Open       High        Low      Close  Adj Close  \\\n",
-              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
-              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
-              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
-              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
-              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
-              "\n",
-              "       Volume  \n",
-              "0  43733533.0  \n",
-              "1  56167280.0  \n",
-              "2  68296318.0  \n",
-              "3  86073880.0  \n",
-              "4  76613039.0  "
-            ]
-          },
-          "execution_count": 23,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Load the dataset\n",
-        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "7LaYGXsfN-8y"
-      },
-      "outputs": [],
-      "source": [
-        "# Drop the 'Date' and 'Adj Close' columns\n",
-        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "pqbTBdnBOKJc",
-        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7696e222-c2c8-45d3-99ad-84f50b7bfd98')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "        Open       High        Low      Close      Volume\n",
-              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
-              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
-              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
-              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
-              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
-            ]
-          },
-          "execution_count": 25,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "id": "dydEPoNeM6eN"
-      },
-      "outputs": [],
-      "source": [
-        "# Handle missing values\n",
-        "imputer = SimpleImputer(strategy='mean')\n",
-        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {
-        "id": "OQ3cGqgTMBwt"
-      },
-      "outputs": [],
-      "source": [
-        "# Select features and target variable\n",
-        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
-        "y = df['Close']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "id": "9Oz-bwJOMEWD"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "ugapDyXODtn3"
-      },
-      "outputs": [],
-      "source": [
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "997ZEgibCZIO",
-        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(5659, 4)"
-            ]
-          },
-          "execution_count": 29,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "X_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bmtt76RuCeyG",
-        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(1415, 4)"
-            ]
-          },
-          "execution_count": 30,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "X_test.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CeJkUJ92Ciqd",
-        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(5659,)"
-            ]
-          },
-          "execution_count": 31,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "y_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7HGC7VuTCjWc",
-        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(1415,)"
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "y_test.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Function to evaluate and print RMSE, MAE, and MAPE\n",
-        "def evaluate_model(model, X_test, y_test):\n",
-        "    predictions = model.predict(X_test)\n",
-        "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
-        "    mae = mean_absolute_error(y_test, predictions)\n",
-        "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
-        "\n",
-        "    print(f\"RMSE: {rmse}\")\n",
-        "    print(f\"MAE: {mae}\")\n",
-        "    print(f\"MAPE: {mape}\\n\")\n",
-        "    \n",
-        "    return rmse, mae, mape\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "metrics = {\n",
-        "    \"Model\": [],\n",
-        "    \"RMSE\": [],\n",
-        "    \"MAE\": [],\n",
-        "    \"MAPE\": []\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c6Ek8jRlO2_I"
-      },
-      "source": [
-        "## 1. LINEAR REGRESSION"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "id": "RdZ1SpzdMHAJ"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a linear regression model\n",
-        "model1 = LinearRegression()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mPM035IzMY04",
-        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "5286    257.350006\n",
-              "3408    129.464996\n",
-              "5477    279.350006\n",
-              "6906    588.500000\n",
-              "530      21.644367\n",
-              "Name: Close, dtype: float64"
-            ]
-          },
-          "execution_count": 34,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "y_train.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "qBhQ9HbYMI3d",
-        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "LinearRegression()"
-            ]
-          },
-          "execution_count": 35,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model1.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {
-        "id": "X269co2kMS4z"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Linear Regressor\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GxtMzlg-gR2P"
-      },
-      "source": [
-        "## 2. Support Vector Regression"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "id": "0xQewd7QWTtq"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an SVR model\n",
-        "model2 = SVR()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "22SaCsQmfhgP",
-        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "SVR()"
-            ]
-          },
-          "execution_count": 42,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model2.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 43,
-      "metadata": {
-        "id": "OQ1nL4oYfkAC"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"SVR\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hcIfVMWdgcKt"
-      },
-      "source": [
-        "## 3. Random Forest Regressor"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 46,
-      "metadata": {
-        "id": "f7raXT_hf2ij"
-      },
-      "outputs": [],
-      "source": [
-        "model3 = RandomForestRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 48,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "fF002Yepgk55",
-        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "RandomForestRegressor()"
-            ]
-          },
-          "execution_count": 48,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model3.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "metadata": {
-        "id": "8nRU_pzEgnCt"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Random Forest\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mZsLwLivhLGH"
-      },
-      "source": [
-        "## 4. Gradient Boosting Models (GBM)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 52,
-      "metadata": {
-        "id": "TI8idoxOg6jF"
-      },
-      "outputs": [],
-      "source": [
-        "model4 = GradientBoostingRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 54,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "2gpbDxshhexj",
-        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ]
-          },
-          "execution_count": 54,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model4.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 55,
-      "metadata": {
-        "id": "Jj9DXdUPhh9V"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"GBM\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d8nSGoyuh9dx"
-      },
-      "source": [
-        "## 5. Extreme Gradient Boosting (XGBoost)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 58,
-      "metadata": {
-        "id": "DyhhdlZAhx94"
-      },
-      "outputs": [],
-      "source": [
-        "import xgboost as xgb\n",
-        "# Create an XGBoost model\n",
-        "model5 = xgb.XGBRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 60,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "RAIwxIp5iH9Z",
-        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ]
-          },
-          "execution_count": 60,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model5.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"XGBoost\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A_J776rtiovq"
-      },
-      "source": [
-        "## 6. AdaBoostRegressor"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 64,
-      "metadata": {
-        "id": "HNq66cXRiYPJ"
-      },
-      "outputs": [],
-      "source": [
-        "model6 = AdaBoostRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 66,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "P0oB5wjQivBr",
-        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "AdaBoostRegressor()"
-            ]
-          },
-          "execution_count": 66,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model6.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 67,
-      "metadata": {
-        "id": "Bf1m5ukOi2VM"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Q9DzOt3CkWFX"
-      },
-      "source": [
-        "## 7. Decision Tree"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 70,
-      "metadata": {
-        "id": "23DZ2biSjF9a"
-      },
-      "outputs": [],
-      "source": [
-        "model7 = DecisionTreeRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 72,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "6mQEQf-ykc9F",
-        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "DecisionTreeRegressor()"
-            ]
-          },
-          "execution_count": 72,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model7.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 73,
-      "metadata": {
-        "id": "BFJ9q_tvkgRC"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Decision Tree\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LH-B-Xd6k5UD"
-      },
-      "source": [
-        "## 8. KNeighborsRegressor(KNN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 76,
-      "metadata": {
-        "id": "JVDSed7yktFY"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a KNN model\n",
-        "model8 = KNeighborsRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 78,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "9fn64o-ZlBka",
-        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "KNeighborsRegressor()"
-            ]
-          },
-          "execution_count": 78,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model8.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 79,
-      "metadata": {
-        "id": "hbfbbjcSlDn7"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"KNN\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "X5XtlzMXljps"
-      },
-      "source": [
-        "## 9. Artificial Neural Networks (ANN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 84,
-      "metadata": {
-        "id": "vd1fDjQiltP4"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an ANN model\n",
-        "model9 = Sequential()\n",
-        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
-        "model9.add(Dense(16, activation='relu'))\n",
-        "model9.add(Dense(1, activation='linear'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 85,
-      "metadata": {
-        "id": "ZIf94WLMlv04"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model9.compile(loss='mean_squared_error', optimizer='adam')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 86,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "FX5DTKqslxWf",
-        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca68076d540>"
-            ]
-          },
-          "execution_count": 86,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 87,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OVW2qpNsmGVq",
-        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "45/45 [==============================] - 0s 1ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"ANN\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vjSMQNcOnFPJ"
-      },
-      "source": [
-        "## 10. LSTM(Long Short term Memory)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 92,
-      "metadata": {
-        "id": "uACvajfImrbB"
-      },
-      "outputs": [],
-      "source": [
-        "# Reshape the input data for LSTM\n",
-        "n_features = X_train_scaled.shape[1]\n",
-        "n_steps = 10\n",
-        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
-        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
-        "\n",
-        "# Reshape the input data\n",
-        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
-        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 93,
-      "metadata": {
-        "id": "r066pVYpnXH5"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an LSTM model\n",
-        "model = Sequential()\n",
-        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
-        "model.add(Dense(1))\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 94,
-      "metadata": {
-        "id": "YpSfHu6gov35"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 95,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0vHjcluaoxzP",
-        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca680459cc0>"
-            ]
-          },
-          "execution_count": 95,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 96,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "gEE06_TjozYv",
-        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "44/44 [==============================] - 0s 4ms/step\n"
-          ]
-        }
-      ],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"LSTM\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Create a DataFrame for metrics\n",
-        "metrics_df = pd.DataFrame(metrics)\n",
-        "\n",
-        "# Plot RMSE, MAE, and MAPE for each model\n",
-        "plt.figure(figsize=(15, 5))\n",
-        "\n",
-        "# RMSE Plot\n",
-        "plt.subplot(1, 3, 1)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
-        "plt.xlabel('RMSE')\n",
-        "plt.title('RMSE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# MAE Plot\n",
-        "plt.subplot(1, 3, 2)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n",
-        "plt.xlabel('MAE')\n",
-        "plt.title('MAE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# MAPE Plot\n",
-        "plt.subplot(1, 3, 3)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
-        "plt.xlabel('MAPE')\n",
-        "plt.title('MAPE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

From 4b421087d26d78f51843af2722cf856b9c1f38dc Mon Sep 17 00:00:00 2001
From: Ayushi Sha <124066968+AYUSHI-SHA@users.noreply.github.com>
Date: Sun, 6 Oct 2024 14:45:27 +0530
Subject: [PATCH 06/76] conflicts resolving

---
 Python File/Stock_Price_Prediction.ipynb | 2708 ++++++++++++++++++++++
 Stock_Price_Prediction.ipynb             | 1805 ++++++++++++++
 2 files changed, 4513 insertions(+)
 create mode 100644 Python File/Stock_Price_Prediction.ipynb
 create mode 100644 Stock_Price_Prediction.ipynb

diff --git a/Python File/Stock_Price_Prediction.ipynb b/Python File/Stock_Price_Prediction.ipynb
new file mode 100644
index 0000000..c82b075
--- /dev/null
+++ b/Python File/Stock_Price_Prediction.ipynb	
@@ -0,0 +1,2708 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "qCDSjVhXLr_Z"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SOQbXSiB-g5G",
+        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Sc4id6VxL8BS",
+        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date       Open       High        Low      Close  Adj Close  \\\n",
+              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+              "\n",
+              "       Volume  \n",
+              "0  43733533.0  \n",
+              "1  56167280.0  \n",
+              "2  68296318.0  \n",
+              "3  86073880.0  \n",
+              "4  76613039.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>01-01-1996</td>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>12.409931</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>02-01-1996</td>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
+              "      <td>12.014931</td>\n",
+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>03-01-1996</td>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>11.694577</td>\n",
+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>04-01-1996</td>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>11.654142</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>05-01-1996</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>11.588827</td>\n",
+              "      <td>76613039.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d89bb189-3861-4a83-b0ea-1a19c2115c37')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "# Load the dataset\n",
+        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "7LaYGXsfN-8y"
+      },
+      "outputs": [],
+      "source": [
+        "# Drop the 'Date' and 'Adj Close' columns\n",
+        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "pqbTBdnBOKJc",
+        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        Open       High        Low      Close      Volume\n",
+              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
+              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
+              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
+              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
+              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>76613039.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7696e222-c2c8-45d3-99ad-84f50b7bfd98')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "dydEPoNeM6eN"
+      },
+      "outputs": [],
+      "source": [
+        "# Handle missing values\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "imputer = SimpleImputer(strategy='mean')\n",
+        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "OQ3cGqgTMBwt"
+      },
+      "outputs": [],
+      "source": [
+        "# Select features and target variable\n",
+        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+        "y = df['Close']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "9Oz-bwJOMEWD"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "ugapDyXODtn3"
+      },
+      "outputs": [],
+      "source": [
+        "# Scale the features using Min-Max scaling\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "997ZEgibCZIO",
+        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5659, 4)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "X_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bmtt76RuCeyG",
+        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1415, 4)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "X_test.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CeJkUJ92Ciqd",
+        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5659,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ],
+      "source": [
+        "y_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7HGC7VuTCjWc",
+        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1415,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ],
+      "source": [
+        "y_test.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c6Ek8jRlO2_I"
+      },
+      "source": [
+        "## 1. LINEAR REGRESSION"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "RdZ1SpzdMHAJ"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a linear regression model\n",
+        "model1 = LinearRegression()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mPM035IzMY04",
+        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "5286    257.350006\n",
+              "3408    129.464996\n",
+              "5477    279.350006\n",
+              "6906    588.500000\n",
+              "530      21.644367\n",
+              "Name: Close, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
+      "source": [
+        "y_train.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "qBhQ9HbYMI3d",
+        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model1.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "X269co2kMS4z"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred1 = model1.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "QK8GvDYPOd0Y"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
+        "mae1 = mean_absolute_error(y_test, pred1)\n",
+        "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
+        "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
+        "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
+        "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
+        "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
+        "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dEi49xtEOtne",
+        "outputId": "0000b074-3187-41de-fbac-4ae75cbda6bd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1.6881364643681482\n",
+            "MAE: 0.9433353485344729\n",
+            "MAPE: 0.006085435990853812\n",
+            "Accuracy: 0.8296819787985866\n",
+            "Precision: 0.8623595505617978\n",
+            "Confusion Matrix:\n",
+            " [[560  98]\n",
+            " [143 614]]\n",
+            "Recall: 0.8110964332892999\n",
+            "F1 Score: 0.8359428182437032\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse1)\n",
+        "print(\"MAE:\", mae1)\n",
+        "print(\"MAPE:\", mape1)\n",
+        "print(\"Accuracy:\", accuracy1)\n",
+        "print(\"Precision:\", precision1)\n",
+        "print(\"Confusion Matrix:\\n\", confusion1)\n",
+        "print(\"Recall:\", recall1)\n",
+        "print(\"F1 Score:\", f11)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GxtMzlg-gR2P"
+      },
+      "source": [
+        "## 2. SVR"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "o7K9r7EXWRjQ"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.svm import SVR"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "0xQewd7QWTtq"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an SVR model\n",
+        "model2 = SVR()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "DuNes3s6U2IV"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "22SaCsQmfhgP",
+        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVR()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model2.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "OQ1nL4oYfkAC"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred2 = model2.predict(X_test)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "nRYTwydsfpjb"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
+        "mae2 = mean_absolute_error(y_test, pred2)\n",
+        "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
+        "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
+        "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
+        "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
+        "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
+        "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "656J5oz5fzq6",
+        "outputId": "ce67d2d8-0bc8-4e6d-d6b5-6b78e7e1c59b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 147.71103599153602\n",
+            "MAE: 110.99419106508152\n",
+            "MAPE: 1.9715076513294716\n",
+            "Accuracy: 0.9992932862190813\n",
+            "Precision: 1.0\n",
+            "Confusion Matrix:\n",
+            " [[727   0]\n",
+            " [  1 687]]\n",
+            "Recall: 0.998546511627907\n",
+            "F1 Score: 0.9992727272727273\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse2)\n",
+        "print(\"MAE:\", mae2)\n",
+        "print(\"MAPE:\", mape2)\n",
+        "print(\"Accuracy:\", accuracy2)\n",
+        "print(\"Precision:\", precision2)\n",
+        "print(\"Confusion Matrix:\\n\", confusion2)\n",
+        "print(\"Recall:\", recall2)\n",
+        "print(\"F1 Score:\", f12)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hcIfVMWdgcKt"
+      },
+      "source": [
+        "## 3. Random Forest"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "f7raXT_hf2ij"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "# Create a Random Forest model\n",
+        "model3 = RandomForestRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "id": "TadNM7MEU7fh"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "fF002Yepgk55",
+        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestRegressor()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model3.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "8nRU_pzEgnCt"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred3 = model3.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "id": "4aKEXGVUgsry"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
+        "mae3 = mean_absolute_error(y_test, pred3)\n",
+        "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
+        "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
+        "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
+        "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
+        "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
+        "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8pPzsCY1g305",
+        "outputId": "72c4ea56-2610-41c6-f286-4c8289d3f0ac"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 2.189635498596314\n",
+            "MAE: 1.250413817712252\n",
+            "MAPE: 0.007984509559881612\n",
+            "Accuracy: 0.8551236749116607\n",
+            "Precision: 0.8558823529411764\n",
+            "Confusion Matrix:\n",
+            " [[628  98]\n",
+            " [107 582]]\n",
+            "Recall: 0.8447024673439768\n",
+            "F1 Score: 0.8502556610664718\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse3)\n",
+        "print(\"MAE:\", mae3)\n",
+        "print(\"MAPE:\", mape3)\n",
+        "print(\"Accuracy:\", accuracy3)\n",
+        "print(\"Precision:\", precision3)\n",
+        "print(\"Confusion Matrix:\\n\", confusion3)\n",
+        "print(\"Recall:\", recall3)\n",
+        "print(\"F1 Score:\", f13)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mZsLwLivhLGH"
+      },
+      "source": [
+        "## 4. Gradient Boosting Models (GBM)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "TI8idoxOg6jF"
+      },
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "# Create an XGBoost model\n",
+        "model4 = xgb.XGBRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {
+        "id": "7r9xJDtOVBEA"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "2gpbDxshhexj",
+        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 54
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model4.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "Jj9DXdUPhh9V"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred4 = model4.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {
+        "id": "TdH60Sllhn5O"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
+        "mae4 = mean_absolute_error(y_test, pred4)\n",
+        "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
+        "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
+        "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
+        "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
+        "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
+        "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qpnLeFyZhwB3",
+        "outputId": "4dcac062-ec60-4b2c-ab4b-dcda1b0f2341"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 2.733930065274145\n",
+            "MAE: 1.502457380471909\n",
+            "MAPE: 0.010026410639661481\n",
+            "Accuracy: 0.8840989399293286\n",
+            "Precision: 0.8948106591865358\n",
+            "Confusion Matrix:\n",
+            " [[613  75]\n",
+            " [ 89 638]]\n",
+            "Recall: 0.8775790921595599\n",
+            "F1 Score: 0.8861111111111112\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse4)\n",
+        "print(\"MAE:\", mae4)\n",
+        "print(\"MAPE:\", mape4)\n",
+        "print(\"Accuracy:\", accuracy4)\n",
+        "print(\"Precision:\", precision4)\n",
+        "print(\"Confusion Matrix:\\n\", confusion4)\n",
+        "print(\"Recall:\", recall4)\n",
+        "print(\"F1 Score:\", f14)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d8nSGoyuh9dx"
+      },
+      "source": [
+        "## 5. Extreme Gradient Boosting (XGBoost)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "DyhhdlZAhx94"
+      },
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "# Create an XGBoost model\n",
+        "model5 = xgb.XGBRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "Z_AD0lVOVHwB"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "RAIwxIp5iH9Z",
+        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model5.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "id": "XmJds5fYiKT3"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred5 = model5.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "id": "lZ1A0-L8iNCM"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
+        "mae5 = mean_absolute_error(y_test, pred5)\n",
+        "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
+        "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
+        "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
+        "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
+        "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
+        "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7IkE-RAmiWNo",
+        "outputId": "cf4c1d84-412b-4a18-f70c-65ce637772ea"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 2.733930065274145\n",
+            "MAE: 1.502457380471909\n",
+            "MAPE: 0.010026410639661481\n",
+            "Accuracy: 0.8840989399293286\n",
+            "Precision: 0.8948106591865358\n",
+            "Confusion Matrix:\n",
+            " [[613  75]\n",
+            " [ 89 638]]\n",
+            "Recall: 0.8775790921595599\n",
+            "F1 Score: 0.8861111111111112\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse5)\n",
+        "print(\"MAE:\", mae5)\n",
+        "print(\"MAPE:\", mape5)\n",
+        "print(\"Accuracy:\", accuracy5)\n",
+        "print(\"Precision:\", precision5)\n",
+        "print(\"Confusion Matrix:\\n\", confusion5)\n",
+        "print(\"Recall:\", recall5)\n",
+        "print(\"F1 Score:\", f15)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A_J776rtiovq"
+      },
+      "source": [
+        "## 6. AdaBoostRegressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "HNq66cXRiYPJ"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import AdaBoostRegressor\n",
+        "# Create an AdaBoost model\n",
+        "model6 = AdaBoostRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "qPHH6rG0VW4V"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "P0oB5wjQivBr",
+        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "AdaBoostRegressor()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model6.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "id": "Bf1m5ukOi2VM"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred6 = model6.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "id": "oFWSqC4ai6gd"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
+        "mae6 = mean_absolute_error(y_test, pred6)\n",
+        "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
+        "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
+        "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
+        "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
+        "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
+        "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BsajWJGBjC80",
+        "outputId": "1af1194f-9a33-40af-8578-c99832509c1b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 9.283285018137352\n",
+            "MAE: 7.574989783595977\n",
+            "MAPE: 0.16829256716397573\n",
+            "Accuracy: 0.9901060070671378\n",
+            "Precision: 0.9900990099009901\n",
+            "Confusion Matrix:\n",
+            " [[901   5]\n",
+            " [  9 500]]\n",
+            "Recall: 0.9823182711198428\n",
+            "F1 Score: 0.9861932938856016\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse6)\n",
+        "print(\"MAE:\", mae6)\n",
+        "print(\"MAPE:\", mape6)\n",
+        "print(\"Accuracy:\", accuracy6)\n",
+        "print(\"Precision:\", precision6)\n",
+        "print(\"Confusion Matrix:\\n\", confusion6)\n",
+        "print(\"Recall:\", recall6)\n",
+        "print(\"F1 Score:\", f16)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q9DzOt3CkWFX"
+      },
+      "source": [
+        "## 7. Decision Tree"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "23DZ2biSjF9a"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "# Create a Decision Tree model\n",
+        "model7 = DecisionTreeRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "id": "Ajo2RAVAVb7H"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "6mQEQf-ykc9F",
+        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeRegressor()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 72
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model7.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "BFJ9q_tvkgRC"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred7 = model7.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {
+        "id": "9IxfYZbYkjv1"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
+        "mae7 = mean_absolute_error(y_test, pred7)\n",
+        "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
+        "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
+        "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
+        "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
+        "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
+        "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AnZXMYb8kooV",
+        "outputId": "273fa9ed-d6f2-4c4d-fb0e-a643f5ef5732"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 3.193539964582351\n",
+            "MAE: 1.6240937361593886\n",
+            "MAPE: 0.010136361140005275\n",
+            "Accuracy: 0.8579505300353357\n",
+            "Precision: 0.8700410396716827\n",
+            "Confusion Matrix:\n",
+            " [[578  95]\n",
+            " [106 636]]\n",
+            "Recall: 0.8571428571428571\n",
+            "F1 Score: 0.8635437881873728\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse7)\n",
+        "print(\"MAE:\", mae7)\n",
+        "print(\"MAPE:\", mape7)\n",
+        "print(\"Accuracy:\", accuracy7)\n",
+        "print(\"Precision:\", precision7)\n",
+        "print(\"Confusion Matrix:\\n\", confusion7)\n",
+        "print(\"Recall:\", recall7)\n",
+        "print(\"F1 Score:\", f17)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LH-B-Xd6k5UD"
+      },
+      "source": [
+        "## 8. KNeighborsRegressor(KNN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "JVDSed7yktFY"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "# Create a KNN model\n",
+        "model8 = KNeighborsRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 77,
+      "metadata": {
+        "id": "XJHb5SxrVgVp"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "9fn64o-ZlBka",
+        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsRegressor()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 78
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model8.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "id": "hbfbbjcSlDn7"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred8 = model8.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 80,
+      "metadata": {
+        "id": "hnWyNv3blHdL"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
+        "mae8 = mean_absolute_error(y_test, pred8)\n",
+        "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
+        "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
+        "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
+        "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
+        "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
+        "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 81,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IPoDRkcMlMAr",
+        "outputId": "9892f42f-e65f-46c0-eeed-77ce32f6a7eb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 148.73183825029315\n",
+            "MAE: 109.35229571264969\n",
+            "MAPE: 1.75024316976612\n",
+            "Accuracy: 0.9908127208480565\n",
+            "Precision: 0.9887820512820513\n",
+            "Confusion Matrix:\n",
+            " [[785   7]\n",
+            " [  6 617]]\n",
+            "Recall: 0.9903691813804173\n",
+            "F1 Score: 0.9895749799518845\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse8)\n",
+        "print(\"MAE:\", mae8)\n",
+        "print(\"MAPE:\", mape8)\n",
+        "print(\"Accuracy:\", accuracy8)\n",
+        "print(\"Precision:\", precision8)\n",
+        "print(\"Confusion Matrix:\\n\", confusion8)\n",
+        "print(\"Recall:\", recall8)\n",
+        "print(\"F1 Score:\", f18)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X5XtlzMXljps"
+      },
+      "source": [
+        "## 9. Artificial Neural Networks (ANN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "metadata": {
+        "id": "bJk1-9VhlRL6"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "metadata": {
+        "id": "sZVPMR9Wlo7-"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {
+        "id": "vd1fDjQiltP4"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an ANN model\n",
+        "model9 = Sequential()\n",
+        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+        "model9.add(Dense(16, activation='relu'))\n",
+        "model9.add(Dense(1, activation='linear'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {
+        "id": "ZIf94WLMlv04"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model9.compile(loss='mean_squared_error', optimizer='adam')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FX5DTKqslxWf",
+        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7ca68076d540>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 86
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 87,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OVW2qpNsmGVq",
+        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "45/45 [==============================] - 0s 1ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred9 = model9.predict(X_test_scaled).flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 88,
+      "metadata": {
+        "id": "CqRmjMj2maJY"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
+        "mae9 = mean_absolute_error(y_test, pred9)\n",
+        "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
+        "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
+        "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
+        "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
+        "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
+        "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 89,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5zuwkC1emmh3",
+        "outputId": "5d6a0e05-3112-4d27-f5fb-ed665867b22d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 2.7570259701356035\n",
+            "MAE: 1.7412277270507284\n",
+            "MAPE: 0.012205298865408084\n",
+            "Accuracy: 0.8904593639575972\n",
+            "Precision: 0.8242753623188406\n",
+            "Confusion Matrix:\n",
+            " [[805  97]\n",
+            " [ 58 455]]\n",
+            "Recall: 0.8869395711500975\n",
+            "F1 Score: 0.8544600938967135\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse9)\n",
+        "print(\"MAE:\", mae9)\n",
+        "print(\"MAPE:\", mape9)\n",
+        "print(\"Accuracy:\", accuracy9)\n",
+        "print(\"Precision:\", precision9)\n",
+        "print(\"Confusion Matrix:\\n\", confusion9)\n",
+        "print(\"Recall:\", recall9)\n",
+        "print(\"F1 Score:\", f19)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vjSMQNcOnFPJ"
+      },
+      "source": [
+        "## 10. LSTM(Long Short term Memory)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 90,
+      "metadata": {
+        "id": "nCoyUanhnDKw"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import LSTM, Dense"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 91,
+      "metadata": {
+        "id": "ThcXESVEVv0U"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 92,
+      "metadata": {
+        "id": "uACvajfImrbB"
+      },
+      "outputs": [],
+      "source": [
+        "# Reshape the input data for LSTM\n",
+        "n_features = X_train_scaled.shape[1]\n",
+        "n_steps = 10\n",
+        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+        "\n",
+        "# Reshape the input data\n",
+        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 93,
+      "metadata": {
+        "id": "r066pVYpnXH5"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an LSTM model\n",
+        "model = Sequential()\n",
+        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+        "model.add(Dense(1))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 94,
+      "metadata": {
+        "id": "YpSfHu6gov35"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 95,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0vHjcluaoxzP",
+        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7ca680459cc0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 95
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 96,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gEE06_TjozYv",
+        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "44/44 [==============================] - 0s 4ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Make predictions on the test set\n",
+        "y_pred = model.predict(X_test_reshaped).flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 113,
+      "metadata": {
+        "id": "7k6C8DrxpB_Q"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
+        "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
+        "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
+        "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 114,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i_6-UUDhpi0c",
+        "outputId": "3dcc5761-03b6-4b52-dfe6-08dece835c8d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 10.083053125286519\n",
+            "MAE: 7.973378150691296\n",
+            "MAPE: 0.12730792351246625\n",
+            "Accuracy: 0.9886201991465149\n",
+            "Precision: 0.9904912836767037\n",
+            "Recall: 0.984251968503937\n",
+            "F1 Score: 0.9873617693522907\n",
+            "Confusion Matrix:\n",
+            " [[765   6]\n",
+            " [ 10 625]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print evaluation metrics\n",
+        "print(\"RMSE:\", rmse10)\n",
+        "print(\"MAE:\", mae10)\n",
+        "print(\"MAPE:\", mape10)\n",
+        "print(\"Accuracy:\", accuracy10)\n",
+        "print(\"Precision:\", precision10)\n",
+        "print(\"Recall:\", recall10)\n",
+        "print(\"F1 Score:\", f110)\n",
+        "print(\"Confusion Matrix:\\n\", confusion10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
+        "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n",
+        "\n",
+        "# List of corresponding labels for each accuracy\n",
+        "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, accuracies, color='blue')\n",
+        "plt.xlabel('Accuracy Variables')\n",
+        "plt.ylabel('Accuracy Values')\n",
+        "plt.title('Bar Graph of Accuracies')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "qpWPtph9CGip",
+        "outputId": "c099cb8d-96af-4223-f499-743040aecdf1"
+      },
+      "execution_count": 117,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
+        "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n",
+        "\n",
+        "# List of corresponding labels for each RMSE value\n",
+        "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, rmse_values, color='green')\n",
+        "plt.xlabel('RMSE Variables')\n",
+        "plt.ylabel('RMSE Values')\n",
+        "plt.title('Bar Graph of RMSE')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "RFaaCNH6Cfoa",
+        "outputId": "67a8f358-e3ce-4ad2-9c78-ebc75902beb4"
+      },
+      "execution_count": 118,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of MAE values from mae1 to mae10\n",
+        "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n",
+        "\n",
+        "# List of corresponding labels for each MAE value\n",
+        "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, mae_values, color='orange')\n",
+        "plt.xlabel('MAE Variables')\n",
+        "plt.ylabel('MAE Values')\n",
+        "plt.title('Bar Graph of MAE')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "nrZu-K-KDCJ2",
+        "outputId": "69165581-da05-4554-a464-a606eb87a734"
+      },
+      "execution_count": 119,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of MAPE values from mape1 to mape10\n",
+        "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n",
+        "\n",
+        "# List of corresponding labels for each MAPE value\n",
+        "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, mape_values, color='purple')\n",
+        "plt.xlabel('MAPE Variables')\n",
+        "plt.ylabel('MAPE Values')\n",
+        "plt.title('Bar Graph of MAPE')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "_c4Pe76fDNM-",
+        "outputId": "0e3d2f74-9042-4e2d-92c6-5ce61e967bd4"
+      },
+      "execution_count": 120,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of precision values from precision1 to precision10\n",
+        "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
+        "\n",
+        "# List of corresponding labels for each precision value\n",
+        "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, precision_values, color='red')\n",
+        "plt.xlabel('Precision Variables')\n",
+        "plt.ylabel('Precision Values')\n",
+        "plt.title('Bar Graph of Precision')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "ZDPV0M5rDTi6",
+        "outputId": "9db63164-3f42-47be-d302-d80d381d9b91"
+      },
+      "execution_count": 121,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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z+Z4TAAAUnttqz83NGD9+vIKDgx2PqKiowo7kmWw2z3sAAHATbqtyExERoRMnTjhNO3HihIKCgnLdayNJw4cPV2pqquNx5MiRgogKAAAKyW11WKpx48b66quvnKatWbNGjRs3vu48vr6+8vX1ze9oAADAQxRqublw4YL279/veH7w4EFt375doaGhio6O1vDhw3X06FF98sknkqRnn31WU6dO1csvv6wnn3xSX3/9tT7//HMtX768sN4CgNuBJx7mNKawE8Aq+P3OoVDLzY8//qhmzZo5nickJEiS4uPjlZSUpOPHj+vw4cOO1++66y4tX75cQ4cO1ZQpU1SuXDn97W9/42vgAOBJ+LBFIbMZc2f9xM+fP6/g4GClpqYqKCiosON4Dv4xgpXx+12w2N4F6w7Z3q58ft9WJxQDAAD8EcoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwlKKFHQC4I9lshZ0gJ2P+eMztmhvAHYU9NwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFK4zg0AeCquKwTcFPbcAAAAS6HcAAAAS+GwlLuxGxkAgELFnhsAAGAp7LnB7Y09ZQCA32HPDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTKDQAAsBTuLQUAgMS96iyEPTcAAMBSKDcAAMBSKDcAAMBSCr3cTJs2TRUqVJCfn58aNWqkzZs333D85MmTVbVqVfn7+ysqKkpDhw5Venp6AaUFAACerlDLzbx585SQkKDExERt27ZNtWvXVlxcnE6ePJnr+M8++0zDhg1TYmKifv75Z3344YeaN2+e/vKXvxRwcgAA4KkKtdy8++676t+/v/r27at77rlHM2fOVLFixTR79uxcx3///fdq0qSJevTooQoVKqhly5bq3r37H+7tAQAAd45CKzeZmZnaunWrYmNj/xfGy0uxsbHatGlTrvPcf//92rp1q6PM/Oc//9FXX32lNm3aXHc9GRkZOn/+vNMDAABYV6Fd5+b06dO6cuWKwsPDnaaHh4dr9+7duc7To0cPnT59Wg888ICMMcrOztazzz57w8NS48eP15gxY9yaHQAAeK5CP6HYFevXr9cbb7yh6dOna9u2bVq0aJGWL1+ucePGXXee4cOHKzU11fE4cuRIASYGAAAFrdD23JQqVUpFihTRiRMnnKafOHFCERERuc7z6quvqlevXnrqqackSbVq1dLFixf19NNPa8SIEfLyytnVfH195evr6/43AAAAPFKh7bnx8fFRTEyMkpOTHdPsdruSk5PVuHHjXOe5dOlSjgJTpEgRSZLhEtUAAECFfG+phIQExcfHq379+mrYsKEmT56sixcvqm/fvpKk3r17q2zZsho/frwkqX379nr33XdVt25dNWrUSPv379err76q9u3bO0oOAAC4sxVquenWrZtOnTqlUaNGKSUlRXXq1NHKlSsdJxkfPnzYaU/NyJEjZbPZNHLkSB09elSlS5dW+/bt9frrrxfWWwAAAB7GZu6w4znnz59XcHCwUlNTFRQU5P4V3K53lSW3+5C7YJG7YJG7YFk5t4tc+fy+rb4tBQAA8EcoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFJcLjcrV67Ud99953g+bdo01alTRz169NCvv/7q1nAAAACucrncvPTSSzp//rwkaefOnfrzn/+sNm3a6ODBg0pISHB7QAAAAFcUdXWGgwcP6p577pEkLVy4UO3atdMbb7yhbdu2qU2bNm4PCAAA4AqX99z4+Pjo0qVLkqS1a9eqZcuWkqTQ0FDHHh0AAIDC4vKemwceeEAJCQlq0qSJNm/erHnz5kmS9u7dq3Llyrk9IAAAgCtc3nMzdepUFS1aVAsWLNCMGTNUtmxZSdKKFSvUqlUrtwcEAABwhc0YYwo7REE6f/68goODlZqaqqCgIPevwGZz/zJvVV5+xOR2H3IXLHIXLHIXLCvndpErn983dZ2bAwcOaOTIkerevbtOnjwp6bc9N//6179uZnEAAABu43K5+eabb1SrVi393//9nxYtWqQLFy5Iknbs2KHExES3BwQAAHCFy+Vm2LBheu2117RmzRr5+Pg4pjdv3lw//PCDW8MBAAC4yuVys3PnTnXq1CnH9LCwMJ0+fdotoQAAAG6Wy+UmJCREx48fzzH9p59+cnxzCgAAoLC4XG7+9Kc/6ZVXXlFKSopsNpvsdrs2btyoF198Ub17986PjAAAAHnmcrl54403VK1aNUVFRenChQu655579NBDD+n+++/XyJEj8yMjAABAnt30dW4OHz6sXbt26cKFC6pbt66qVKni7mz5guvcXAe53YfcBYvcBYvcBcvKuV3kyue3y7dfuCo6OlrR0dE3OzsAAEC+cLncPPnkkzd8ffbs2TcdBgAA4Fa5XG5+/fVXp+dZWVnatWuXzp07p+bNm7stGAAAwM1wudx88cUXOabZ7XY999xzqlSpkltCAQAA3KyburdUjoV4eSkhIUGTJk1yx+IAAABumlvKjfTbzTSzs7PdtTgAAICb4vJhqYSEBKfnxhgdP35cy5cvV3x8vNuCAQAA3AyXy81PP/3k9NzLy0ulS5fWxIkT//CbVAAAAPnN5XKzbt06twaYNm2aJkyYoJSUFNWuXVvvvfeeGjZseN3x586d04gRI7Ro0SKdPXtW5cuX1+TJk9WmTRu35gIAALenm76InzvMmzdPCQkJmjlzpho1aqTJkycrLi5Oe/bsUVhYWI7xmZmZatGihcLCwrRgwQKVLVtWhw4dUkhISMGHBwAAHilPt1+oW7eubHm8vPO2bdvyvPJGjRqpQYMGmjp1qqTfvlIeFRWlwYMHa9iwYTnGz5w5UxMmTNDu3bvl7e2d5/Vci9svXAe53YfcBYvcBYvcBcvKuV3k9tsvdOzY0R25nGRmZmrr1q0aPny4Y5qXl5diY2O1adOmXOdZsmSJGjdurIEDB+rLL79U6dKl1aNHD73yyisqUqSI2zMCAIDbT57KTWJiottXfPr0aV25ckXh4eFO08PDw7V79+5c5/nPf/6jr7/+Wj179tRXX32l/fv3a8CAAcrKyrpuxoyMDGVkZDienz9/3n1vAgAAeBy3XeemINjtdoWFhemDDz5QTEyMunXrphEjRmjmzJnXnWf8+PEKDg52PKKiogowMQAAKGgul5srV67onXfeUcOGDRUREaHQ0FCnR16VKlVKRYoU0YkTJ5ymnzhxQhEREbnOExkZqbvvvtvpEFT16tWVkpKizMzMXOcZPny4UlNTHY8jR47kOSMAALj9uFxuxowZo3fffVfdunVTamqqEhIS1LlzZ3l5eWn06NF5Xo6Pj49iYmKUnJzsmGa325WcnKzGjRvnOk+TJk20f/9+2e12x7S9e/cqMjJSPj4+uc7j6+uroKAgpwcAALAw46KKFSuaZcuWGWOMCQgIMPv37zfGGDNlyhTTvXt3l5Y1d+5c4+vra5KSksy///1v8/TTT5uQkBCTkpJijDGmV69eZtiwYY7xhw8fNoGBgWbQoEFmz549ZtmyZSYsLMy89tpreV5namqqkWRSU1Ndyppnv50j7lkPcpOb3J73IDe57/TcLnLl89vl69ykpKSoVq1akqSAgAClpqZKktq1a6dXX33VpWV169ZNp06d0qhRo5SSkqI6depo5cqVjpOMDx8+LC+v/+1cioqK0qpVqzR06FDde++9Klu2rIYMGaJXXnnF1bcBAAAsyuVyU65cOR0/flzR0dGqVKmSVq9erXr16mnLli3y9fV1OcCgQYM0aNCgXF9bv359jmmNGzfWDz/84PJ6AADAncHlc246derkOE9m8ODBevXVV1WlShX17t2be0sBAIBCl6crFEvS1KlT9cQTT+S41cGmTZu0adMmValSRe3bt8+PjG7FFYqvg9zuQ+6CRe6CRe6CZeXcLnLl8zvP5SY4OFhZWVnq1KmT+vXrp+bNm7slbEGj3FwHud2H3AWL3AWL3AXLyrld5Mrnd54PS6WkpGjmzJk6duyYWrRoobvuukvjxo3jujEAAMCj5Lnc+Pv7q3fv3lq3bp327dunXr166cMPP9Rdd92lVq1aaf78+crKysrPrAAAAH/opm6/ULFiRY0dO1YHDx7UihUrVLJkSfXp00dly5Z1dz4AAACX3NK9pWw2m4oWLSqbzSZjDHtuAABAobupcnPkyBGNHTtWFStWVIsWLXTs2DHNmjVLx48fd3c+AAAAl+T5In6ZmZlatGiRZs+era+//lqRkZGKj4/Xk08+qYoVK+ZnRgAAgDzLc7mJiIjQpUuX1K5dOy1dulRxcXFOt0YAAADwBHkuNyNHjlSvXr1UunTp/MwDAABwS/JcbhISEvIzBwAAgFtwXAkAAFgK5QYAAFgK5QYAAFgK5QYAAFhKnk8ovurKlStKSkpScnKyTp48Kbvd7vT6119/7bZwAAAArnK53AwZMkRJSUlq27atatasKZsn3modAADcsVwuN3PnztXnn3+uNm3a5EceAACAW+LyOTc+Pj6qXLlyfmQBAAC4ZS6Xmz//+c+aMmWKjDH5kQcAAOCWuHxY6rvvvtO6deu0YsUK1ahRQ97e3k6vL1q0yG3hAAAAXOVyuQkJCVGnTp3yIwsAAMAtc7ncfPTRR/mRAwAAwC1cLjdXnTp1Snv27JEkVa1albuFAwAAj+DyCcUXL17Uk08+qcjISD300EN66KGHVKZMGfXr10+XLl3Kj4wAAAB55nK5SUhI0DfffKOlS5fq3LlzOnfunL788kt98803+vOf/5wfGQEAAPLMZlz8TnepUqW0YMECPfzww07T161bp65du+rUqVPuzOd258+fV3BwsFJTUxUUFOT+FXjiFZvz8iMmt/uQu2CRu2CRu2BZObeLXPn8dnnPzaVLlxQeHp5jelhYGIelAABAoXO53DRu3FiJiYlKT093TLt8+bLGjBmjxo0buzUcAACAq1z+ttSUKVMUFxencuXKqXbt2pKkHTt2yM/PT6tWrXJ7QAAAAFe4XG5q1qypffv2ac6cOdq9e7ckqXv37urZs6f8/f3dHhAAAMAVN3Wdm2LFiql///7uzgIAAHDL8lRulixZotatW8vb21tLliy54dhHH33ULcEAAABuRp6+Cu7l5aWUlBSFhYXJy+v65yDbbDZduXLFrQHdja+CXwe53YfcBYvcBYvcBcvKuV3kyud3nvbc2O32XP8MAADgaVz+Knhuzp07547FAAAA3DKXy81bb72lefPmOZ536dJFoaGhKlu2rHbs2OHWcAAAAK5yudzMnDlTUVFRkqQ1a9Zo7dq1WrlypVq3bq2XXnrJ7QEBAABc4fJXwVNSUhzlZtmyZeratatatmypChUqqFGjRm4PCAAA4AqX99yUKFFCR44ckSStXLlSsbGxkiRjjMd/UwoAAFify3tuOnfurB49eqhKlSo6c+aMWrduLUn66aefVLlyZbcHBAAAcIXL5WbSpEmqUKGCjhw5orffflsBAQGSpOPHj2vAgAFuDwgAAOCKPF3Ez0q4iN91kNt9yF2wyF2wyF2wrJzbRW6/iB+3XwAAALcLbr/gbrdrgya3+5C7YJG7YJG7YFk5t4u4/QIAALhjueX2CwAAAJ7C5XLz/PPP669//WuO6VOnTtULL7zgjkwAAAA3zeVys3DhQjVp0iTH9Pvvv18LFixwSygAAICb5XK5OXPmjIKDg3NMDwoK0unTp90SCgAA4Ga5XG4qV66slStX5pi+YsUKVaxY0S2hAAAAbpbLVyhOSEjQoEGDdOrUKTVv3lySlJycrIkTJ2ry5MnuzgcAAOASl8vNk08+qYyMDL3++usaN26cJKlChQqaMWOGevfu7faAAAAArril2y+cOnVK/v7+jvtL3Q64iN91kNt9yF2wyF2wyF2wrJzbRa58ft/UdW6ys7O1du1aLVq0SFe70bFjx3ThwoWbWRwAAIDbuHxY6tChQ2rVqpUOHz6sjIwMtWjRQoGBgXrrrbeUkZGhmTNn5kdOAACAPHF5z82QIUNUv359/frrr/L393dM79Spk5KTk90aDgAAwFUu77nZsGGDvv/+e/n4+DhNr1Chgo4ePeq2YAAAADfD5T03drs91zt///e//1VgYKBbQgEAANwsl8tNy5Ytna5nY7PZdOHCBSUmJqpNmzY3FWLatGmqUKGC/Pz81KhRI23evDlP882dO1c2m00dO3a8qfUCAADrcbncvPPOO9q4caPuuecepaenq0ePHo5DUm+99ZbLAebNm6eEhAQlJiZq27Ztql27tuLi4nTy5MkbzvfLL7/oxRdf1IMPPujyOgEAgHXd1HVusrOzNW/ePO3YsUMXLlxQvXr11LNnT6cTjPOqUaNGatCggaZOnSrpt8NeUVFRGjx4sIYNG5brPFeuXNFDDz2kJ598Uhs2bNC5c+e0ePHiPK2P69xcB7ndh9wFi9wFi9wFy8q5XeTK57dLJxRnZWWpWrVqWrZsmXr27KmePXveUtDMzExt3bpVw4cPd0zz8vJSbGysNm3adN35xo4dq7CwMPXr108bNmy44ToyMjKUkZHheH7+/PlbygwAADybS4elvL29lZ6e7raVnz59WleuXFF4eLjT9PDwcKWkpOQ6z3fffacPP/xQs2bNytM6xo8fr+DgYMcjKirqlnMDAADP5fI5NwMHDtRbb72l7Ozs/MhzQ2lpaerVq5dmzZqlUqVK5Wme4cOHKzU11fE4cuRIPqcEAACFyeXr3GzZskXJyclavXq1atWqpeLFizu9vmjRojwvq1SpUipSpIhOnDjhNP3EiROKiIjIMf7AgQP65Zdf1L59e8c0u90uSSpatKj27NmjSpUqOc3j6+srX1/fPGcCAAC3N5fLTUhIiB577DG3rNzHx0cxMTFKTk52fJ3bbrcrOTlZgwYNyjG+WrVq2rlzp9O0kSNHKi0tTVOmTOGQEwAAcL3cfPTRR24NkJCQoPj4eNWvX18NGzbU5MmTdfHiRfXt21eS1Lt3b5UtW1bjx4+Xn5+fatas6TR/SEiIJOWYDgAA7kx5Ljd2u10TJkzQkiVLlJmZqUceeUSJiYk39fXva3Xr1k2nTp3SqFGjlJKSojp16mjlypWOk4wPHz4sL6+bunk5AAC4A+X5Ojfjxo3T6NGjFRsbK39/f61atUrdu3fX7Nmz8zujW3Gdm+sgt/uQu2CRu2CRu2BZObeLXPn8zvMukU8++UTTp0/XqlWrtHjxYi1dulRz5sxxnNALAADgCfJcbg4fPux076jY2FjZbDYdO3YsX4IBAADcjDyXm+zsbPn5+TlN8/b2VlZWlttDAQAA3Kw8n1BsjFGfPn2crhmTnp6uZ5991ulaN65c5wYAAMDd8lxu4uPjc0x74okn3BoGAADgVuW53Lj7+jYAAAD5gQvIAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS/GIcjNt2jRVqFBBfn5+atSokTZv3nzdsbNmzdKDDz6oEiVKqESJEoqNjb3heAAAcGcp9HIzb948JSQkKDExUdu2bVPt2rUVFxenkydP5jp+/fr16t69u9atW6dNmzYpKipKLVu21NGjRws4OQAA8EQ2Y4wpzACNGjVSgwYNNHXqVEmS3W5XVFSUBg8erGHDhv3h/FeuXFGJEiU0depU9e7d+w/Hnz9/XsHBwUpNTVVQUNAt58/BZnP/Mm9VXn7E5HYfchcschcschcsK+d2kSuf34W65yYzM1Nbt25VbGysY5qXl5diY2O1adOmPC3j0qVLysrKUmhoaK6vZ2Rk6Pz5804PAABgXYVabk6fPq0rV64oPDzcaXp4eLhSUlLytIxXXnlFZcqUcSpI1xo/fryCg4Mdj6ioqFvODQAAPFehn3NzK958803NnTtXX3zxhfz8/HIdM3z4cKWmpjoeR44cKeCUAACgIBUtzJWXKlVKRYoU0YkTJ5ymnzhxQhERETec95133tGbb76ptWvX6t57773uOF9fX/n6+rolLwAA8HyFuufGx8dHMTExSk5Odkyz2+1KTk5W48aNrzvf22+/rXHjxmnlypWqX79+QUQFAAC3iULdcyNJCQkJio+PV/369dWwYUNNnjxZFy9eVN++fSVJvXv3VtmyZTV+/HhJ0ltvvaVRo0bps88+U4UKFRzn5gQEBCggIKDQ3gcAAPAMhV5uunXrplOnTmnUqFFKSUlRnTp1tHLlSsdJxocPH5aX1/92MM2YMUOZmZl6/PHHnZaTmJio0aNHF2R0AADggQr9OjcFjevcXAe53YfcBYvcBYvcBcvKuV1021znBgAAwN0oNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFIoNwAAwFI8otxMmzZNFSpUkJ+fnxo1aqTNmzffcPz8+fNVrVo1+fn5qVatWvrqq68KKCkAAPB0hV5u5s2bp4SEBCUmJmrbtm2qXbu24uLidPLkyVzHf//99+revbv69eunn376SR07dlTHjh21a9euAk4OAAA8kc0YYwozQKNGjdSgQQNNnTpVkmS32xUVFaXBgwdr2LBhOcZ369ZNFy9e1LJlyxzT7rvvPtWpU0czZ878w/WdP39ewcHBSk1NVVBQkPveyFU2m/uXeavy8iMmt/uQu2CRu2CRu2BZObeLXPn8LtQ9N5mZmdq6datiY2Md07y8vBQbG6tNmzblOs+mTZucxktSXFzcdccDAIA7S9HCXPnp06d15coVhYeHO00PDw/X7t27c50nJSUl1/EpKSm5js/IyFBGRobjeWpqqqTfGuAd43Z9r+QuWOQuWOQuWOQuWPmQ++rndl4OOBVquSkI48eP15gxY3JMj4qKKoQ0hSQ4uLAT3BxyFyxyFyxyFyxyF6x8zJ2WlqbgP1h+oZabUqVKqUiRIjpx4oTT9BMnTigiIiLXeSIiIlwaP3z4cCUkJDie2+12nT17ViVLlpTNE49T6rd2GhUVpSNHjuTPeUH5hNwFi9wFi9wFi9wF63bIbYxRWlqaypQp84djC7Xc+Pj4KCYmRsnJyerYsaOk38pHcnKyBg0alOs8jRs3VnJysl544QXHtDVr1qhx48a5jvf19ZWvr6/TtJCQEHfEz3dBQUEe+0t2I+QuWOQuWOQuWOQuWJ6e+4/22FxV6IelEhISFB8fr/r166thw4aaPHmyLl68qL59+0qSevfurbJly2r8+PGSpCFDhqhp06aaOHGi2rZtq7lz5+rHH3/UBx98UJhvAwAAeIhCLzfdunXTqVOnNGrUKKWkpKhOnTpauXKl46Thw4cPy8vrf1/quv/++/XZZ59p5MiR+stf/qIqVapo8eLFqlmzZmG9BQAA4EEKvdxI0qBBg657GGr9+vU5pnXp0kVdunTJ51SFx9fXV4mJiTkOp3k6chcschcschcsches2zX39RT6RfwAAADcqdBvvwAAAOBOlBsAAGAplBsAAGAplBsPYLPZtHjxYrePzW/kLljkLljkLljkzl+3S063MXASHx9vJBlJxtvb21SqVMmMGTPGZGVl5ds6jx8/btLT029p7K3kXrhwoWnRooUJDQ01ksxPP/3k8bkzMzPNyy+/bGrWrGmKFStmIiMjTa9evczRo0c9OrcxxiQmJpqqVauaYsWKmZCQEPPII4+YH374weNzX+uZZ54xksykSZM8Pve18159xMXFeXxuY4z597//bdq3b2+CgoJMsWLFTP369c2hQ4c8Ovfvt/XVx9tvv+3RudPS0szAgQNN2bJljZ+fn6levbqZMWPGTWe50Vh3fs6kpKSY+Ph4ExkZafz9/U1cXJzZu3evW3K6U14+Zy5fvmwGDBhgQkNDTfHixU3nzp1NSkrKTa2PcvM78fHxplWrVub48ePml19+MdOnTzc2m8288cYbOcZmZGQUQsLc3UruTz75xIwZM8bMmjXLpXLjDjeb+9y5cyY2NtbMmzfP7N6922zatMk0bNjQxMTEeHRuY4yZM2eOWbNmjTlw4IDZtWuX6devnwkKCjInT5706NxXLVq0yNSuXduUKVMmT+XGHW4l97XzXn2cPXvW43Pv37/fhIaGmpdeesls27bN7N+/33z55ZfmxIkTHp372u18/PhxM3v2bGOz2cyBAwc8Onf//v1NpUqVzLp168zBgwfN+++/b4oUKWK+/PJLj8p5Lbvdbu677z7z4IMPms2bN5vdu3ebp59+2kRHR5sLFy64PfetyMvnzLPPPmuioqJMcnKy+fHHH819991n7r///ptaH+Xmd+Lj402HDh2cprVo0cLcd999jtdee+01ExkZaSpUqGCMMebw4cOmS5cuJjg42JQoUcI8+uij5uDBg07L+PDDD80999xjfHx8TEREhBk4cKDjNUnmiy++MMb89os8cOBAExERYXx9fU10dLTTL/y1Y40x5p///Kdp1qyZ8fLyMt7e3qZ///4mLS3Nkbt06dImKirK1KhRwwQEBBibzWYCAwNNZmZmjtyxsbE5fuluh9xXt/fmzZuNJHPo0KHbKndqaqqRZNauXevxuYODg42fn59ZtWqVKV++vKPceHJub29v4+3tfdv9vfT29jY+Pj63Xe7f/3536NDBNG/e3ONze3l5GX9/f6ftXa9ePTNixAi35776WXI1t5+fnylatKgJCwszPXr0cHzO+Pv7m2LFipkJEyaY0qVLGx8fH+Pj4+PYvsnJyUaS2bVrl9P2lWSCg4Pdvn39/PxMaGio0/Y15n+fmxMmTDAREREmNDTUDBgwwGRmZprfO3jwYK7l5ty5c8bb29vMnz/fMe3nn382ksymTZtyLOePcM5NHvj7+yszM1OSlJycrD179mjNmjVatmyZsrKyFBcXp8DAQG3YsEEbN25UQECAWrVq5ZhnxowZGjhwoJ5++mnt3LlTS5YsUeXKlXNd11//+lctWbJEn3/+ufbs2aM5c+aoQoUKuY69ePGi4uLiVKJECbVr104NGjTQ2rVrHRdE9Pf315UrV5SSkqK9e/eqefPmeu+995SVlaW//e1vOXIXL15ckpSVlXVb5b66vU+fPi2bzaYFCxbcNrnj4uI0ffp0BQcHa9u2bR6d+5tvvlHVqlVVo0YNPf/88zL//xJZnv570rp1a/n6+mrVqlW6++679fTTT+vMmTMenTsgIEBFixbVM888o82bN6ty5cpq0KCBFi9e7NG5f//73aJFCy1btkz9+vXz+NydOnVSxYoV5eXlpVatWmn16tXau3evLl26lC+5r1y54si9ZcsWNWzYUOfOndP//d//OT5nWrRoIZvNpr179yooKEhNmzaVl5eXXnjhBQUEBKhfv36SJD8/P6ftGx4ergceeMDt23fLli2aP3++0/a9at26dTpw4IDWrVunjz/+WElJSUpKSsp1mbnZunWrsrKyFBsb65hWrVo1RUdHa9OmTXlejoPLdcjirt1zY7fbzZo1a4yvr6958cUXTXx8vAkPD3faTfjpp5+aqlWrGrvd7piWkZFh/P39zapVq4wxxpQpU8aMGDHiuuvUNS158ODBpnnz5k7Lu97YDz74wJQoUcJcuHDBkXv58uXGZrOZzz//3Pj6+poaNWqY4sWLO+Xu0qWLadSoUY7ce/bsMZLM9OnTb6vcV7d35cqVTY8ePW6L3EuXLjXFihUzkkxoaKjZvHmzx+d+4403TIsWLUx6errx9/c3YWFhZtKkSR6f+x//+If58ssvzdatW42Pj4+JiooyDRo08Ojcx44dM5JMsWLFzNtvv238/PxM3759jc1mM6VKlfLY3L//e+nt7W0CAgLM5cuXPXp72+12k56ebnr37u04F6Zo0aLm448/zpfc8fHxpnbt2qZEiRImLS3N8TnTuXNnI8mULl3aZGRkmPj4eFO+fHnz8ccfO3J26dLFdOvWzfHvXunSpU2XLl1MZGSkGTZsmHnzzTeNJNOyZUu3b9+rli9fbry8vBznw1zNmZ2d7RhzNefvXW/PzZw5c4yPj0+O8Q0aNDAvv/xyrjlvxCNuv+Bpli1bpoCAAGVlZclut6tHjx4aPXq0Bg4cqFq1asnHx8cxdseOHdq/f78CAwOdlpGenq4DBw7o5MmTOnbsmB555JE8rbtPnz5q0aKFqlatqlatWqldu3Zq2bJlrmN//vln1a5d27HHZdmyZVq7dq2MMerevbt69uyp7OxsnTt3TtWrV3fkjoyM1A8//KBjx4455Tb//3/iR44cua1yS9Lly5eVnp6usWPHqnLlyrdNbpvNpqpVq+qxxx7z6O1dvHhxpaeny9/fXyVLlnT8OS0tzaNz/357Z2VlqX///ho1apQkeWzuq//jzszM1JgxY5SRkaEGDRro6NGjWr16tcfmzm1733///Tp//rzH/55kZWUpKytLfn5+ysjIUOfOnfXcc8/p0qVL+ZL7n//8p2w2m0qWLOn4nBk/frwWLVqk6OhoR84aNWpo586djs+ZzMxM2e12LVu2TOnp6RoyZIiWLFmi48eP6+2331aLFi3UunVrx7/n7t6+ktSkSRPZ7Xbt2bPHcR/IGjVqqEiRIo4xkZGR2rlzZ562W37gsFQumjVrpu3bt2vfvn26fPmyPv74Y8cP9tofsCRduHBBMTEx2r59u9Nj79696tGjh/z9/V1ad7169XTw4EGNGzdOly9fVteuXfX444/nOfeGDRskSatXr9bHH38sb29veXl5OeW22WzKzs7OkXv58uWSpNatW982ubds2aLGjRuratWq+vbbbxUWFnZb5N6+fbt27NihvXv3asWKFfL29vbo3EOHDpUxRunp6bp8+bJsNpvOnj2rxMREj86d29/L559/XqGhoR6de8uWLSpSpIgGDRrk9O9J9erVPTr3tdt61qxZkqTXXnvN4/89+eGHH2S32/X+++9r586d2rt3rz744AM99thj+ZY7KipK9evXz/Vz5trt5e3t7fQ506NHD8ef9+7dq2HDhumHH36QJC1YsEArV67UmTNnVLFiRbfkzKvf/xtms9lkt9vzPH9ERIQyMzN17tw5p+knTpxQRESEy3koN7koXry4KleurOjoaBUteuOdW/Xq1dO+ffsUFhamypUrOz2Cg4MVGBioChUqKDk5Oc/rDwoKUrdu3TRr1izNmzdPCxcu1NmzZ3OMq169unbs2KGLFy86ch8/flxeXl6qUaPGDdcRGBiYI/fVY66BgYG3Re7y5cvrL3/5i44fP64NGzborrvuui1y5/Z7IkkhISEem7t///7auXOnduzY4XiUKVNGL7/8ssqWLeuxuXPb3mlpafr1118VFhbmsbnLlSunhg0b6vTp006/J4cOHVLx4sU9Nve123vVqlWKiYnRAw884PF/L0NCQpSdna1y5co5bW9/f3/5+fnlS+4SJUpo3759KlmypONzZuPGjZKkgIAAp7HXfs6EhITI398/18+ZrVu3at++ffrxxx/VoUMHt+T8/fa9mtPLy0tVq1bN83b5IzExMfL29nba1nv27NHhw4fVuHFjl5fHYalb1LNnT02YMEEdOnTQ2LFjVa5cOR06dEiLFi3Syy+/rHLlymn06NF69tlnFRYWptatWystLU0bN27U4MGDcyzv3XffVWRkpOrWrSsvLy/Nnz9fERERCgkJyXXdiYmJio+PV1ZWls6cOaPBgwerV69ejl2F1xMWFiZjjDp06KAXX3xRkhy7EL///ntJ0tChQ/XKK694ZO5HH31U2dnZOnDggMaNG6fhw4drwIABKlOmjEaOHKlBgwZ5ZO527dqpfPny6tSpkzIzM7V48WIZY3T06FGNGjVK48aN88jcffv2zfH7bbPZFBERoddff91jf7/btWunsmXLqkuXLsrMzNS8efO0f/9+Va5cWS+//LIGDx7skbk7dOigtm3bavTo0QoPD9fJkydVpUoVLV26VK+88oomTpzosbnHjh2r4OBgzZ07V40bN9Z///tfj/93sFevXqpdu7aGDBmiPXv2aPv27br33nv1ySefqGvXrvmyvaOionTy5EnFx8dr9OjROnXqlAYPHqxKlSrluDP3tZ8zpUqV0uXLl7V+/XotWrRI99xzj6pVq6YBAwZo5MiRmj59umJjY1WqVCm99957bt2+1+bMy/a91tmzZ3X48GEdO3ZM0m/FRfptj01ERISCg4PVr18/JSQkKDQ0VEFBQRo8eLAaN26s++67L8/ruYo9N7eoWLFi+vbbbxUdHa3OnTurevXq6tevn9LT0xUUFCRJio+P1+TJkzV9+nTVqFFD7dq10759+3JdXmBgoN5++23Vr19fDRo00C+//KKvvvpKXl45f1TFihXTqlWrdPbsWS1btkybN2/WI488oqlTp/5h7iJFijhy/+lPf1K7du00fPhwSdLAgQNVt25dnT171mNzlyxZUhs3blRKSor69++vDz/8UDExMYqMjFSlSpU8Nnd0dLQ+//xzPfbYY+revbuWLFmitLQ0bdiwQX/5y188Ovfvf7+vrsvTf78XLlzo2N5r165VTEyMNmzYoKeeesqjc0+ePFmSNGXKFH322Wf6/PPPtXDhQr3++usenbtz586KiYlRZmamypcvf9v8O3jkyBEdOHBAL774ov7+979rxowZev3115WUlJQvuYsUKeLI3aBBAz3++ON65JFH1KhRo1zf49Wcy5cv15YtWxx/D8+fP69evXppxIgRKl68uLy8vLRu3bp82b7X5szL9r3WkiVLVLduXbVt21aS9Kc//Ul169bVzJkzHWMmTZqkdu3a6bHHHtNDDz2kiIgILVq0yKX1XGUzNzrrCAAA4DbDnhsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAbmGz2bR48WK3j/VEDz/8sF544YU8j1+/fr1sNluO++ZcKykpKdcrxAJwHeUGsJg+ffrIZrPJZrPJx8dHlStX1tixY5WdnZ2v6z1+/Lhat27t9rE3Y+LEiSpRooTS09NzvHbp0iUFBQXpr3/9600vf9GiRRo3btytRASQjyg3gAW1atVKx48f1759+/TnP/9Zo0eP1oQJE3Idm5mZ6ZZ1RkRE5LgnjjvG3oxevXrp4sWLuV66fcGCBcrMzNQTTzzh8nKvbqvQ0FAFBgbeck4A+YNyA1iQr6+vIiIiVL58eT333HOKjY3VkiVLJP22Z6djx456/fXXVaZMGcedfY8cOaKuXbsqJCREoaGh6tChg3755Ren5c6ePVs1atSQr6+vIiMjNWjQIMdr1x5qyszM1KBBgxQZGSk/Pz+VL19e48ePz3Ws9NtNW5s3by5/f3+VLFlSTz/9tC5cuOB4/Wrmd955R5GRkSpZsqQGDhyorKysXN9/WFiY2rdvr9mzZ+d4bfbs2erYsaNCQ0P1yiuv6O6771axYsVUsWJFvfrqq07LHD16tOrUqaO//e1vuuuuu+Tn5ycp52GpTz/9VPXr11dgYKAiIiLUo0cPnTx5Mse6N27cqHvvvVd+fn667777tGvXrlzzX/Xll1+qXr168vPzU8WKFTVmzBjHHjhjjEaPHq3o6Gj5+vqqTJkyev7552+4POBOQbkB7gD+/v5Oe2iSk5O1Z88erVmzRsuWLVNWVpbi4uIUGBioDRs2aOPGjQoICFCrVq0c882YMUMDBw7U008/rZ07d2rJkiWqXLlyruv761//qiVLlujzzz/Xnj17NGfOHFWoUCHXsRcvXlRcXJxKlCihLVu2aP78+Vq7dq1TcZKkdevW6cCBA1q3bp0+/vhjJSUlKSkp6brvuV+/fvr666916NAhx7T//Oc/+vbbb9WvXz9Jv91AMCkpSf/+9781ZcoUzZo1S5MmTXJazv79+7Vw4UItWrRI27dvz3VdWVlZGjdunHbs2KHFixfrl19+UZ8+fXKMe+mllzRx4kRt2bJFpUuXVvv27a9b0DZs2KDevXtryJAh+ve//633339fSUlJev311yVJCxcu1KRJk/T+++9r3759Wrx4sWrVqnXd7QHcUQwAS4mPjzcdOnQwxhhjt9vNmjVrjK+vr3nxxRcdr4eHh5uMjAzHPJ9++qmpWrWqsdvtjmkZGRnG39/frFq1yhhjTJkyZcyIESOuu15J5osvvjDGGDN48GDTvHlzp+Vdb+wHH3xgSpQoYS5cuOB4ffny5cbLy8ukpKQ4MpcvX95kZ2c7xnTp0sV069btunmys7NN2bJlTWJiomPaq6++aqKjo82VK1dynWfChAkmJibG8TwxMdF4e3ubkydPOo1r2rSpGTJkyHXXvWXLFiPJpKWlGWOMWbdunZFk5s6d6xhz5swZ4+/vb+bNm2eMMeajjz4ywcHBjtcfeeQR88Ybbzgt99NPPzWRkZHGGGMmTpxo7r77bpOZmXndHMCdij03gAUtW7ZMAQEB8vPzU+vWrdWtWzeNHj3a8XqtWrXk4+PjeL5jxw7t379fgYGBCggIUEBAgEJDQ5Wenq4DBw7o5MmTOnbsmB555JE8rb9Pnz7avn27qlatqueff16rV6++7tiff/5ZtWvXVvHixR3TmjRpIrvdrj179jim1ahRQ0WKFHE8j4yMzPXQz1VFihRRfHy8kpKSZIyR3W7Xxx9/rL59+8rL67d/+ubNm6cmTZooIiJCAQEBGjlypA4fPuy0nPLly6t06dI3fL9bt25V+/btFR0drcDAQDVt2lSSciyrcePGjj+HhoaqatWq+vnnn3Nd5o4dOzR27FjHzyMgIED9+/fX8ePHdenSJXXp0kWXL19WxYoV1b9/f33xxRf5ftI4cLsoWtgBALhfs2bNNGPGDPn4+KhMmTIqWtT5r/q1RUKSLly4oJiYGM2ZMyfHskqXLu0oA3lVr149HTx4UCtWrNDatWvVtWtXxcbGasGCBa6/mf/P29vb6bnNZpPdbr/hPE8++aTGjx+vr7/+Wna7XUeOHFHfvn0lSZs2bVLPnj01ZswYxcXFKTg4WHPnztXEiROdlvH7bfV7Vw+rxcXFac6cOSpdurQOHz6suLi4WzpZ+8KFCxozZow6d+6c4zU/Pz9FRUVpz549Wrt2rdasWaMBAwZowoQJ+uabb3JsK+BOQ7kBLKh48eLXPR8mN/Xq1dO8efMUFhamoKCgXMdUqFBBycnJatasWZ6WGRQUpG7duqlbt256/PHH1apVK509e1ahoaFO46pXr66kpCRdvHjRUSQ2btwoLy8vx8nON6tSpUpq2rSpZs+eLWOMYmNjVb58eUnS999/r/Lly2vEiBGO8deen5NXu3fv1pkzZ/Tmm28qKipKkvTjjz/mOvaHH35QdHS0JOnXX3/V3r17Vb169VzH1qtXT3v27Lnhz9Hf31/t27dX+/btNXDgQFWrVk07d+5UvXr1XH4fgJVQbgCoZ8+emjBhgjp06KCxY8eqXLlyOnTokBYtWqSXX35Z5cqV0+jRo/Xss88qLCxMrVu3VlpamjZu3KjBgwfnWN67776ryMhI1a1bV15eXpo/f74iIiJyvUhdz549lZiYqPj4eI0ePVqnTp3S4MGD1atXL4WHh9/ye+vXr5/69+8vSU4nIFepUkWHDx/W3Llz1aBBAy1fvlxffPGFy8uPjo6Wj4+P3nvvPT377LPatWvXda+BM3bsWJUsWVLh4eEaMWKESpUqpY4dO+Y6dtSoUWrXrp2io6P1+OOPy8vLSzt27NCuXbv02muvKSkpSVeuXFGjRo1UrFgx/f3vf5e/v7+jvAF3Ms65AaBixYrp22+/VXR0tDp37qzq1aurX79+Sk9Pd+zJiY+P1+TJkzV9+nTVqFFD7dq10759+3JdXmBgoN5++23Vr19fDRo00C+//KKvvvoq18NbxYoV06pVq3T27Fk1aNBAjz/+uB555BFNnTrVLe/tsccek6+vr4oVK+ZUJB599FENHTpUgwYNUp06dfT999/r1VdfdXn5pUuXVlJSkubPn6977rlHb775pt55551cx7755psaMmSIYmJilJKSoqVLlzqd+3StuLg4LVu2TKtXr1aDBg103333adKkSY7yEhISolmzZqlJkya69957tXbtWi1dulQlS5Z0+T0AVmMzxpjCDgEAAOAu7LkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACW8v8A1rYhCY9bpSkAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of recall values from recall1 to recall10\n",
+        "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
+        "\n",
+        "# List of corresponding labels for each recall value\n",
+        "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, recall_values, color='cyan')\n",
+        "plt.xlabel('Recall Variables')\n",
+        "plt.ylabel('Recall Values')\n",
+        "plt.title('Bar Graph of Recall')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "39LBleNeDeuw",
+        "outputId": "3c6c40bc-f1da-44fb-da14-25ec6d6cf278"
+      },
+      "execution_count": 122,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "13cZXvb0DsvK"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Stock_Price_Prediction.ipynb b/Stock_Price_Prediction.ipynb
new file mode 100644
index 0000000..7c3ad61
--- /dev/null
+++ b/Stock_Price_Prediction.ipynb
@@ -0,0 +1,1805 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "qCDSjVhXLr_Z"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.svm import SVR\n",
+        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense,LSTM"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SOQbXSiB-g5G",
+        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Sc4id6VxL8BS",
+        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Adj Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>01-01-1996</td>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>12.409931</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>02-01-1996</td>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
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+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>03-01-1996</td>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
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+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>04-01-1996</td>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>11.654142</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>05-01-1996</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>11.588827</td>\n",
+              "      <td>76613039.0</td>\n",
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+              "  </tbody>\n",
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+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "         Date       Open       High        Low      Close  Adj Close  \\\n",
+              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+              "\n",
+              "       Volume  \n",
+              "0  43733533.0  \n",
+              "1  56167280.0  \n",
+              "2  68296318.0  \n",
+              "3  86073880.0  \n",
+              "4  76613039.0  "
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Load the dataset\n",
+        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "7LaYGXsfN-8y"
+      },
+      "outputs": [],
+      "source": [
+        "# Drop the 'Date' and 'Adj Close' columns\n",
+        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "pqbTBdnBOKJc",
+        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Volume</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>18.691147</td>\n",
+              "      <td>18.978922</td>\n",
+              "      <td>18.540184</td>\n",
+              "      <td>18.823240</td>\n",
+              "      <td>43733533.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18.894005</td>\n",
+              "      <td>18.964767</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>18.224106</td>\n",
+              "      <td>56167280.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>18.327892</td>\n",
+              "      <td>18.568489</td>\n",
+              "      <td>17.643839</td>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>68296318.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>17.502312</td>\n",
+              "      <td>17.832542</td>\n",
+              "      <td>17.223972</td>\n",
+              "      <td>17.676863</td>\n",
+              "      <td>86073880.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>17.738192</td>\n",
+              "      <td>17.785366</td>\n",
+              "      <td>17.459852</td>\n",
+              "      <td>17.577793</td>\n",
+              "      <td>76613039.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7696e222-c2c8-45d3-99ad-84f50b7bfd98')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "        Open       High        Low      Close      Volume\n",
+              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
+              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
+              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
+              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
+              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "dydEPoNeM6eN"
+      },
+      "outputs": [],
+      "source": [
+        "# Handle missing values\n",
+        "imputer = SimpleImputer(strategy='mean')\n",
+        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "OQ3cGqgTMBwt"
+      },
+      "outputs": [],
+      "source": [
+        "# Select features and target variable\n",
+        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+        "y = df['Close']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "9Oz-bwJOMEWD"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "ugapDyXODtn3"
+      },
+      "outputs": [],
+      "source": [
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "997ZEgibCZIO",
+        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(5659, 4)"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bmtt76RuCeyG",
+        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(1415, 4)"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "X_test.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CeJkUJ92Ciqd",
+        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(5659,)"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_train.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7HGC7VuTCjWc",
+        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(1415,)"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_test.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Function to evaluate and print RMSE, MAE, and MAPE\n",
+        "def evaluate_model(model, X_test, y_test):\n",
+        "    predictions = model.predict(X_test)\n",
+        "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+        "    mae = mean_absolute_error(y_test, predictions)\n",
+        "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+        "\n",
+        "    print(f\"RMSE: {rmse}\")\n",
+        "    print(f\"MAE: {mae}\")\n",
+        "    print(f\"MAPE: {mape}\\n\")\n",
+        "    \n",
+        "    return rmse, mae, mape\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "metrics = {\n",
+        "    \"Model\": [],\n",
+        "    \"RMSE\": [],\n",
+        "    \"MAE\": [],\n",
+        "    \"MAPE\": []\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c6Ek8jRlO2_I"
+      },
+      "source": [
+        "## 1. LINEAR REGRESSION"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "RdZ1SpzdMHAJ"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a linear regression model\n",
+        "model1 = LinearRegression()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mPM035IzMY04",
+        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "5286    257.350006\n",
+              "3408    129.464996\n",
+              "5477    279.350006\n",
+              "6906    588.500000\n",
+              "530      21.644367\n",
+              "Name: Close, dtype: float64"
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "y_train.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "qBhQ9HbYMI3d",
+        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model1.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "X269co2kMS4z"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"Linear Regressor\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GxtMzlg-gR2P"
+      },
+      "source": [
+        "## 2. Support Vector Regression"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "0xQewd7QWTtq"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an SVR model\n",
+        "model2 = SVR()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "22SaCsQmfhgP",
+        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "SVR()"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model2.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "OQ1nL4oYfkAC"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"SVR\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hcIfVMWdgcKt"
+      },
+      "source": [
+        "## 3. Random Forest Regressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "f7raXT_hf2ij"
+      },
+      "outputs": [],
+      "source": [
+        "model3 = RandomForestRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "fF002Yepgk55",
+        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestRegressor()"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model3.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "8nRU_pzEgnCt"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"Random Forest\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mZsLwLivhLGH"
+      },
+      "source": [
+        "## 4. Gradient Boosting Models (GBM)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "TI8idoxOg6jF"
+      },
+      "outputs": [],
+      "source": [
+        "model4 = GradientBoostingRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "2gpbDxshhexj",
+        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model4.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "Jj9DXdUPhh9V"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"GBM\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d8nSGoyuh9dx"
+      },
+      "source": [
+        "## 5. Extreme Gradient Boosting (XGBoost)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "DyhhdlZAhx94"
+      },
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "# Create an XGBoost model\n",
+        "model5 = xgb.XGBRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "RAIwxIp5iH9Z",
+        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)"
+            ]
+          },
+          "execution_count": 60,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model5.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"XGBoost\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A_J776rtiovq"
+      },
+      "source": [
+        "## 6. AdaBoostRegressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "id": "HNq66cXRiYPJ"
+      },
+      "outputs": [],
+      "source": [
+        "model6 = AdaBoostRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "P0oB5wjQivBr",
+        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "AdaBoostRegressor()"
+            ]
+          },
+          "execution_count": 66,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model6.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "id": "Bf1m5ukOi2VM"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q9DzOt3CkWFX"
+      },
+      "source": [
+        "## 7. Decision Tree"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "23DZ2biSjF9a"
+      },
+      "outputs": [],
+      "source": [
+        "model7 = DecisionTreeRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "6mQEQf-ykc9F",
+        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "DecisionTreeRegressor()"
+            ]
+          },
+          "execution_count": 72,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model7.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "BFJ9q_tvkgRC"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"Decision Tree\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LH-B-Xd6k5UD"
+      },
+      "source": [
+        "## 8. KNeighborsRegressor(KNN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "JVDSed7yktFY"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a KNN model\n",
+        "model8 = KNeighborsRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "9fn64o-ZlBka",
+        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "KNeighborsRegressor()"
+            ]
+          },
+          "execution_count": 78,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model8.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 79,
+      "metadata": {
+        "id": "hbfbbjcSlDn7"
+      },
+      "outputs": [],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"KNN\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X5XtlzMXljps"
+      },
+      "source": [
+        "## 9. Artificial Neural Networks (ANN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {
+        "id": "vd1fDjQiltP4"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an ANN model\n",
+        "model9 = Sequential()\n",
+        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+        "model9.add(Dense(16, activation='relu'))\n",
+        "model9.add(Dense(1, activation='linear'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {
+        "id": "ZIf94WLMlv04"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model9.compile(loss='mean_squared_error', optimizer='adam')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FX5DTKqslxWf",
+        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7ca68076d540>"
+            ]
+          },
+          "execution_count": 86,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 87,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OVW2qpNsmGVq",
+        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "45/45 [==============================] - 0s 1ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"ANN\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vjSMQNcOnFPJ"
+      },
+      "source": [
+        "## 10. LSTM(Long Short term Memory)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 92,
+      "metadata": {
+        "id": "uACvajfImrbB"
+      },
+      "outputs": [],
+      "source": [
+        "# Reshape the input data for LSTM\n",
+        "n_features = X_train_scaled.shape[1]\n",
+        "n_steps = 10\n",
+        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+        "\n",
+        "# Reshape the input data\n",
+        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 93,
+      "metadata": {
+        "id": "r066pVYpnXH5"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an LSTM model\n",
+        "model = Sequential()\n",
+        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+        "model.add(Dense(1))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 94,
+      "metadata": {
+        "id": "YpSfHu6gov35"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 95,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0vHjcluaoxzP",
+        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<keras.src.callbacks.History at 0x7ca680459cc0>"
+            ]
+          },
+          "execution_count": 95,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Train the model\n",
+        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 96,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gEE06_TjozYv",
+        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "44/44 [==============================] - 0s 4ms/step\n"
+          ]
+        }
+      ],
+      "source": [
+        "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
+        "metrics[\"Model\"].append(\"LSTM\")\n",
+        "metrics[\"RMSE\"].append(rmse)\n",
+        "metrics[\"MAE\"].append(mae)\n",
+        "metrics[\"MAPE\"].append(mape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Create a DataFrame for metrics\n",
+        "metrics_df = pd.DataFrame(metrics)\n",
+        "\n",
+        "# Plot RMSE, MAE, and MAPE for each model\n",
+        "plt.figure(figsize=(15, 5))\n",
+        "\n",
+        "# RMSE Plot\n",
+        "plt.subplot(1, 3, 1)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
+        "plt.xlabel('RMSE')\n",
+        "plt.title('RMSE for Different Models')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# MAE Plot\n",
+        "plt.subplot(1, 3, 2)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n",
+        "plt.xlabel('MAE')\n",
+        "plt.title('MAE for Different Models')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# MAPE Plot\n",
+        "plt.subplot(1, 3, 3)\n",
+        "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
+        "plt.xlabel('MAPE')\n",
+        "plt.title('MAPE for Different Models')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 5a6e093e313f9b13816b25a007c84d5e84c78bdc Mon Sep 17 00:00:00 2001
From: spandana2004 <110383668+spandana2004@users.noreply.github.com>
Date: Sun, 6 Oct 2024 17:58:31 +0530
Subject: [PATCH 07/76] reduced_redundancy_stock_price_prediction

This file has reduced redundant code for print statements for training and also for evaluation of the Stock Price prediction code.
---
 ...ed_redundancy_stock_price_prediction.ipynb | 289 ++++++++++++++++++
 1 file changed, 289 insertions(+)
 create mode 100644 reduced_redundancy_stock_price_prediction.ipynb

diff --git a/reduced_redundancy_stock_price_prediction.ipynb b/reduced_redundancy_stock_price_prediction.ipynb
new file mode 100644
index 0000000..37e1d9d
--- /dev/null
+++ b/reduced_redundancy_stock_price_prediction.ipynb
@@ -0,0 +1,289 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d6a21042",
+   "metadata": {},
+   "source": [
+    "DEPENDENCIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "15cc2438",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "import xgboost as xgb\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1a47080a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the dataset\n",
+    "df = pd.read_csv('SBIN.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8b945263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data preprocessing\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "af430abe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select features and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "763efa5e",
+   "metadata": {},
+   "source": [
+    "TRAINING AND TESTING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2dddc0b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1c523190",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "73b110e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to train and evaluate model\n",
+    "def train_and_evaluate_model(model, X_train, y_train, X_test, y_test, params=None):\n",
+    "    if params:\n",
+    "        model = GridSearchCV(model, params, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n",
+    "    model.fit(X_train, y_train)\n",
+    "    predictions = model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate evaluation metrics\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+    "    mae = mean_absolute_error(y_test, predictions)\n",
+    "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+    "    accuracy = accuracy_score(y_test > predictions, y_test > predictions.round())\n",
+    "    precision = precision_score(y_test > predictions, y_test > predictions.round())\n",
+    "    confusion = confusion_matrix(y_test > predictions, y_test > predictions.round())\n",
+    "    recall = recall_score(y_test > predictions, y_test > predictions.round())\n",
+    "    f1 = f1_score(y_test > predictions, y_test > predictions.round())\n",
+    "\n",
+    "    # Print the evaluation metrics\n",
+    "    print(f\"RMSE: {rmse}, MAE: {mae}, MAPE: {mape}, Accuracy: {accuracy}, Precision: {precision}, Confusion Matrix:\\n{confusion}, Recall: {recall}, F1 Score: {f1}\")\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "68f21263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define models and their hyperparameters\n",
+    "models_params = {\n",
+    "    'Linear Regression': (LinearRegression(), None),\n",
+    "    'SVR': (SVR(), {'C': [0.1, 1], 'epsilon': [0.01, 0.1, 0.5], 'kernel': ['sigmoid']}),\n",
+    "    'Random Forest': (RandomForestRegressor(), None),\n",
+    "    'Gradient Boosting': (GradientBoostingRegressor(), None),\n",
+    "    'XGBoost': (xgb.XGBRegressor(), None),\n",
+    "    'AdaBoost': (AdaBoostRegressor(), None),\n",
+    "    'Decision Tree': (DecisionTreeRegressor(), None),\n",
+    "    'KNeighbors': (KNeighborsRegressor(), {'n_neighbors': [3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]})\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "af1992c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Linear Regression Model:\n",
+      "RMSE: 1.6881364651923592, MAE: 0.9433353486266863, MAPE: 0.006085435968276418, Accuracy: 0.8296819787985866, Precision: 0.8623595505617978, Confusion Matrix:\n",
+      "[[560  98]\n",
+      " [143 614]], Recall: 0.8110964332892999, F1 Score: 0.8359428182437032\n",
+      "\n",
+      "SVR Model:\n",
+      "RMSE: 134.5606234817444, MAE: 76.05976804010935, MAPE: 0.39705639844596075, Accuracy: 0.9978798586572438, Precision: 0.9970544918998527, Confusion Matrix:\n",
+      "[[735   2]\n",
+      " [  1 677]], Recall: 0.9985250737463127, F1 Score: 0.997789240972734\n",
+      "\n",
+      "Random Forest Model:\n",
+      "RMSE: 2.191031983578823, MAE: 1.254384854932792, MAPE: 0.007957611370512454, Accuracy: 0.8551236749116607, Precision: 0.8556851311953353, Confusion Matrix:\n",
+      "[[623  99]\n",
+      " [106 587]], Recall: 0.847041847041847, F1 Score: 0.851341551849166\n",
+      "\n",
+      "Gradient Boosting Model:\n",
+      "RMSE: 2.697993647984777, MAE: 1.6922021460047614, MAPE: 0.011881429758725463, Accuracy: 0.9017667844522969, Precision: 0.8982300884955752, Confusion Matrix:\n",
+      "[[667  69]\n",
+      " [ 70 609]], Recall: 0.8969072164948454, F1 Score: 0.8975681650700075\n",
+      "\n",
+      "XGBoost Model:\n",
+      "RMSE: 2.733930065274145, MAE: 1.502457380471909, MAPE: 0.010026410639661481, Accuracy: 0.8840989399293286, Precision: 0.8948106591865358, Confusion Matrix:\n",
+      "[[613  75]\n",
+      " [ 89 638]], Recall: 0.8775790921595599, F1 Score: 0.8861111111111112\n",
+      "\n",
+      "AdaBoost Model:\n",
+      "RMSE: 9.915909571695305, MAE: 8.180488940261938, MAPE: 0.1986601163272193, Accuracy: 0.992226148409894, Precision: 0.986, Confusion Matrix:\n",
+      "[[911   7]\n",
+      " [  4 493]], Recall: 0.9919517102615694, F1 Score: 0.9889669007021062\n",
+      "\n",
+      "Decision Tree Model:\n",
+      "RMSE: 3.0678929179050054, MAE: 1.6473595220251132, MAPE: 0.010260672105066157, Accuracy: 0.8572438162544169, Precision: 0.8650137741046832, Confusion Matrix:\n",
+      "[[585  98]\n",
+      " [104 628]], Recall: 0.8579234972677595, F1 Score: 0.8614540466392319\n",
+      "\n",
+      "KNeighbors Model:\n",
+      "RMSE: 3.261285106039988, MAE: 1.8071640369846882, MAPE: 0.014187783844224503, Accuracy: 0.8968197879858657, Precision: 0.9097744360902256, Confusion Matrix:\n",
+      "[[664  60]\n",
+      " [ 86 605]], Recall: 0.8755426917510853, F1 Score: 0.8923303834808259\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train and evaluate all models\n",
+    "for name, (model, params) in models_params.items():\n",
+    "    print(f\"\\n{name} Model:\")\n",
+    "    train_and_evaluate_model(model, X_train_scaled, y_train, X_test_scaled, y_test, params)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "fb66a3c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# KNN Hyperparameter Tuning\n",
+    "def knn_hyperparameter_tuning(X_train, y_train):\n",
+    "    k_values = range(1, 31)  # Example range for k\n",
+    "    mse_values = []\n",
+    "    \n",
+    "    for k in k_values:\n",
+    "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
+    "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
+    "        mse_values.append(mse)\n",
+    "    \n",
+    "    return k_values, mse_values\n",
+    "\n",
+    "# Perform KNN Hyperparameter Tuning\n",
+    "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
+    "\n",
+    "# Plotting the results\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
+    "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
+    "plt.xlabel('Number of Neighbors (k)')\n",
+    "plt.ylabel('Mean Squared Error (MSE)')\n",
+    "plt.xticks(k_values)  # Show all k values on x-axis\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b410ecfc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 535e5c69172733a1ef338dc040154a9353ee2db5 Mon Sep 17 00:00:00 2001
From: Ayushi Sha <124066968+AYUSHI-SHA@users.noreply.github.com>
Date: Sun, 6 Oct 2024 18:55:28 +0530
Subject: [PATCH 08/76] Updated codes

---
 Stock_Price_Prediction(Updated).ipynb |   6 +-
 Stock_prediction_Data_Analysis.ipynb  | 135 +++++++++++++++++++++-----
 2 files changed, 115 insertions(+), 26 deletions(-)

diff --git a/Stock_Price_Prediction(Updated).ipynb b/Stock_Price_Prediction(Updated).ipynb
index 77a2cd6..c108568 100644
--- a/Stock_Price_Prediction(Updated).ipynb
+++ b/Stock_Price_Prediction(Updated).ipynb
@@ -697,10 +697,8 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "id": "idXVoeEFw55L"
-      },
+      "execution_count": null,
+      "metadata": {},
       "outputs": [],
       "source": [
         "# Handle missing values\n",
diff --git a/Stock_prediction_Data_Analysis.ipynb b/Stock_prediction_Data_Analysis.ipynb
index cf6979c..2da9745 100644
--- a/Stock_prediction_Data_Analysis.ipynb
+++ b/Stock_prediction_Data_Analysis.ipynb
@@ -568,6 +568,46 @@
     "import seaborn as sns"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96a4fe8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to create scatter plots\n",
+    "def create_scatter_plot(x_data, y_data, size_data=None, title=None, x_label=None, y_label=None, figsize=(20,10)):\n",
+    "    plt.figure(figsize=figsize)\n",
+    "    sns.scatterplot(x=x_data, y=y_data, size=size_data)\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    if x_label:\n",
+    "        plt.xlabel(x_label)\n",
+    "    if y_label:\n",
+    "        plt.ylabel(y_label)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25e91892",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to create line plots\n",
+    "def create_line_plot(x_data, y_data, title=None, x_label=None, y_label=None, figsize=(20,10)):\n",
+    "    plt.figure(figsize=figsize)\n",
+    "    sns.lineplot(x=x_data, y=y_data)\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    if x_label:\n",
+    "        plt.xlabel(x_label)\n",
+    "    if y_label:\n",
+    "        plt.ylabel(y_label)\n",
+    "    plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -591,6 +631,15 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "69d2c9c2",
+   "metadata": {},
+   "source": [
+    "Early 1990s spike: There is a high trading volume around the mid-1990s, peaking at over 4 × 10^8 (400 million trades).\n",
+    "Post-1996 to 2020: The volume significantly decreases, showing lower levels of activity with some fluctuations and minor peaks, particularly after 2016.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 29,
@@ -609,10 +658,23 @@
     }
    ],
    "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "sns.lineplot(x = df['Date'], y = df['High'] - df['Low'])\n",
-    "plt.ylabel(\"price variation\")\n",
-    "plt.show()"
+    "create_line_plot(df['Date'], df['High'] - df['Low'], \n",
+    "                 title='Price Variation Over Time', x_label='Date', y_label='Price Variation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b64048ab",
+   "metadata": {},
+   "source": [
+    "#### Stable period until 2004: \n",
+    "The price variation was relatively low and stable until around 2003-2004, generally staying under 10 units.\n",
+    "#### 2004-2008 increase: \n",
+    "Starting from 2004, price variations gradually began to increase, peaking sharply just before the 2008 financial crisis. Some spikes went beyond 40 units.\n",
+    "#### 2008-2020 fluctuations:\n",
+    "After the 2008 peak, price variation showed continuous fluctuations with noticeable peaks, though they were more frequent post-2016.\n",
+    "#### 2020 onward:\n",
+    "The recent period (2020-2024) shows significant and frequent fluctuations, with peaks reaching 60+ units, possibly influenced by events like the COVID-19 pandemic and other global factors."
    ]
   },
   {
@@ -633,9 +695,18 @@
     }
    ],
    "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "sns.lineplot(x = df['Adj Close'], y = df['Volume'])\n",
-    "plt.show()"
+    "create_line_plot(df['Adj Close'], df['Volume'], \n",
+    "                 title='Adjusted Close vs Volume', \n",
+    "                 x_label='Adjusted Close', \n",
+    "                 y_label='Volume')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22e7a95e",
+   "metadata": {},
+   "source": [
+    "The overall trend seems to be somewhat volatile, with periods of upward and downward movement."
    ]
   },
   {
@@ -656,9 +727,16 @@
     }
    ],
    "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "sns.scatterplot(x = df['Open'], y = df['Adj Close'], size = df['Volume'])\n",
-    "plt.show()"
+    "create_scatter_plot(df['Open'], df['Adj Close'], size_data=df['Volume'], \n",
+    "                    title='Open vs Adjusted Close', x_label='Open', y_label='Adj Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc30e23a",
+   "metadata": {},
+   "source": [
+    "The scatter points generally show an upward trend, indicating a positive correlation between the opening and closing prices. This means that, in general, when the stock opens higher, it tends to close higher as well."
    ]
   },
   {
@@ -679,9 +757,16 @@
     }
    ],
    "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "sns.scatterplot(x = df['Close'], y = df['Adj Close'], size = df['Volume'])\n",
-    "plt.show()"
+    "create_scatter_plot(df['Close'], df['Adj Close'], size_data=df['Volume'], \n",
+    "                    title='Close vs Adjusted Close', x_label='Close', y_label='Adj Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01218d24",
+   "metadata": {},
+   "source": [
+    "The scatter points show a very strong upward trend, indicating a strong positive correlation between the closing price and the adjusted closing price. This means that, in general, when the stock closes higher, the adjusted closing price is also higher."
    ]
   },
   {
@@ -702,10 +787,8 @@
     }
    ],
    "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "sns.scatterplot(x = df['Open'], y = df['Close'], size= df['Volume'])\n",
-    "plt.legend()\n",
-    "plt.show()"
+    "create_scatter_plot(df['Open'], df['Close'], size_data=df['Volume'], \n",
+    "                    title='Open vs Close', x_label='Open', y_label='Close')"
    ]
   },
   {
@@ -726,11 +809,10 @@
     }
    ],
    "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "sns.lineplot(x = df['Open']-df['Close'], y = df['High'] - df['Low'])\n",
-    "plt.xlabel(\"Stock Variation\")\n",
-    "plt.ylabel(\"price variation\")\n",
-    "plt.show()"
+    "create_line_plot(df['Open'] - df['Close'], df['High'] - df['Low'], \n",
+    "                 title='Stock Variation vs Price Variation', \n",
+    "                 x_label='Stock Variation (Open - Close)', \n",
+    "                 y_label='Price Variation (High - Low)')"
    ]
   },
   {
@@ -899,6 +981,15 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3084d625",
+   "metadata": {},
+   "source": [
+    "The high correlation between 'Open,' 'High,' 'Low,' 'Close,' and 'Adj Close' shows these features are highly interdependent and tend to move together in the same direction.\n",
+    "The negative correlation of 'Volume' with price-related features suggests that increased trading volume does not necessarily coincide with an increase in stock prices."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 59d4ebf25e1cb0c62d44f651c3c0bb5791a8027d Mon Sep 17 00:00:00 2001
From: Pankaj Kumar Goyal <140732455+Pankaj4152@users.noreply.github.com>
Date: Sun, 6 Oct 2024 19:06:57 +0530
Subject: [PATCH 09/76] Added MACD , MACD Graph and SMA graph

---
 .../Stock_Price_Prediction_REMOTE_20502.ipynb | 248 +++++++++++++++++-
 1 file changed, 245 insertions(+), 3 deletions(-)

diff --git a/Python File/Stock_Price_Prediction_REMOTE_20502.ipynb b/Python File/Stock_Price_Prediction_REMOTE_20502.ipynb
index 5312833..c08a8c8 100644
--- a/Python File/Stock_Price_Prediction_REMOTE_20502.ipynb	
+++ b/Python File/Stock_Price_Prediction_REMOTE_20502.ipynb	
@@ -2,7 +2,7 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 10,
       "metadata": {
         "id": "qCDSjVhXLr_Z"
       },
@@ -10,6 +10,7 @@
       "source": [
         "import pandas as pd\n",
         "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
         "from sklearn.model_selection import train_test_split\n",
         "from sklearn.linear_model import LinearRegression\n",
         "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score"
@@ -17,7 +18,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -298,6 +299,40 @@
         "df.head()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### SMA Graph"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
+        "\n",
+        "ax1.plot(df[\"SMA_10\"], label = \"SMA_10\", color='Red', linewidth=1, alpha=0.8)\n",
+        "ax1.plot(df[\"SMA_50\"], label = \"SMA_50\", color='Green', linewidth=1, alpha=0.8)\n",
+        "ax1.plot(df[\"Close\"], label = \"Close\", color='Blue', linewidth=2)\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -360,7 +395,6 @@
       ],
       "source": [
         "# Create subplots\n",
-        "import matplotlib.pyplot as plt\n",
         "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})\n",
         "\n",
         "# price graph\n",
@@ -502,6 +536,214 @@
         "df.head()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### MACD"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Calculate the short-term and long-term EMAs\n",
+        "df['EMA_12'] = df['Close'].ewm(span=12, adjust=False).mean()\n",
+        "df['EMA_26'] = df['Close'].ewm(span=26, adjust=False).mean()\n",
+        "\n",
+        "# Calculate MACD and Signal line\n",
+        "df['MACD'] = df['EMA_12'] - df['EMA_26']  # MACD line\n",
+        "df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()  # Signal line"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Open</th>\n",
+              "      <th>High</th>\n",
+              "      <th>Low</th>\n",
+              "      <th>Close</th>\n",
+              "      <th>Volume</th>\n",
+              "      <th>SMA_10</th>\n",
+              "      <th>SMA_50</th>\n",
+              "      <th>EMA_12</th>\n",
+              "      <th>EMA_26</th>\n",
+              "      <th>MACD</th>\n",
+              "      <th>Signal_Line</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>22.649218</td>\n",
+              "      <td>22.734135</td>\n",
+              "      <td>22.276527</td>\n",
+              "      <td>22.635065</td>\n",
+              "      <td>39637704.0</td>\n",
+              "      <td>23.102579</td>\n",
+              "      <td>20.537146</td>\n",
+              "      <td>22.635065</td>\n",
+              "      <td>22.635065</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>22.172739</td>\n",
+              "      <td>22.361443</td>\n",
+              "      <td>21.837790</td>\n",
+              "      <td>22.116129</td>\n",
+              "      <td>39109363.0</td>\n",
+              "      <td>22.929443</td>\n",
+              "      <td>20.603004</td>\n",
+              "      <td>22.555229</td>\n",
+              "      <td>22.596625</td>\n",
+              "      <td>-0.041397</td>\n",
+              "      <td>-0.008279</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>51</th>\n",
+              "      <td>22.092541</td>\n",
+              "      <td>22.186892</td>\n",
+              "      <td>21.889683</td>\n",
+              "      <td>22.083105</td>\n",
+              "      <td>27429706.0</td>\n",
+              "      <td>22.757250</td>\n",
+              "      <td>20.680184</td>\n",
+              "      <td>22.482594</td>\n",
+              "      <td>22.558587</td>\n",
+              "      <td>-0.075992</td>\n",
+              "      <td>-0.021822</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>52</th>\n",
+              "      <td>22.073669</td>\n",
+              "      <td>22.686958</td>\n",
+              "      <td>21.960447</td>\n",
+              "      <td>22.611477</td>\n",
+              "      <td>27421227.0</td>\n",
+              "      <td>22.635065</td>\n",
+              "      <td>20.777649</td>\n",
+              "      <td>22.502422</td>\n",
+              "      <td>22.562505</td>\n",
+              "      <td>-0.060082</td>\n",
+              "      <td>-0.029474</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>53</th>\n",
+              "      <td>22.691675</td>\n",
+              "      <td>22.889814</td>\n",
+              "      <td>22.465231</td>\n",
+              "      <td>22.587889</td>\n",
+              "      <td>36343129.0</td>\n",
+              "      <td>22.626573</td>\n",
+              "      <td>20.875870</td>\n",
+              "      <td>22.515571</td>\n",
+              "      <td>22.564385</td>\n",
+              "      <td>-0.048814</td>\n",
+              "      <td>-0.033342</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Open       High        Low      Close      Volume     SMA_10  \\\n",
+              "49  22.649218  22.734135  22.276527  22.635065  39637704.0  23.102579   \n",
+              "50  22.172739  22.361443  21.837790  22.116129  39109363.0  22.929443   \n",
+              "51  22.092541  22.186892  21.889683  22.083105  27429706.0  22.757250   \n",
+              "52  22.073669  22.686958  21.960447  22.611477  27421227.0  22.635065   \n",
+              "53  22.691675  22.889814  22.465231  22.587889  36343129.0  22.626573   \n",
+              "\n",
+              "       SMA_50     EMA_12     EMA_26      MACD  Signal_Line  \n",
+              "49  20.537146  22.635065  22.635065  0.000000     0.000000  \n",
+              "50  20.603004  22.555229  22.596625 -0.041397    -0.008279  \n",
+              "51  20.680184  22.482594  22.558587 -0.075992    -0.021822  \n",
+              "52  20.777649  22.502422  22.562505 -0.060082    -0.029474  \n",
+              "53  20.875870  22.515571  22.564385 -0.048814    -0.033342  "
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### MACD Graph"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot the MACD\n",
+        "fig, ax = plt.subplots(2, 1, figsize=(10, 8))\n",
+        "\n",
+        "\n",
+        "# Plot stock price on first subplot (bold)\n",
+        "ax[0].plot( df['Close'], label='Close Price', color='blue', linewidth=3)\n",
+        "ax[0].set_title('Stock Price')\n",
+        "ax[0].set_ylabel('Price')\n",
+        "\n",
+        "# Plot MACD and Signal line on second subplot\n",
+        "ax[1].plot( df['MACD'], label='MACD', color='green', linewidth=2)\n",
+        "ax[1].plot( df['Signal_Line'], label='Signal Line', color='red', linestyle='--', alpha=0.7)\n",
+        "ax[1].set_title('MACD')\n",
+        "ax[1].set_ylabel('MACD Value')\n",
+        "\n",
+        "# Show legends\n",
+        "ax[0].legend()\n",
+        "ax[1].legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

From a89781f4dbc5288b8fa9e9b1e6c1fafae4cc0886 Mon Sep 17 00:00:00 2001
From: Simran Shaikh <simmoshaikh20.2003@gmail.com>
Date: Sun, 6 Oct 2024 14:39:33 -0500
Subject: [PATCH 10/76] Added More Charts

---
 .ipynb_checkpoints/More_Charts.ipynb      | 358 ++++++++++++++++++++++
 .ipynb_checkpoints/candlestick_chart.html |  14 +
 2 files changed, 372 insertions(+)
 create mode 100644 .ipynb_checkpoints/More_Charts.ipynb
 create mode 100644 .ipynb_checkpoints/candlestick_chart.html

diff --git a/.ipynb_checkpoints/More_Charts.ipynb b/.ipynb_checkpoints/More_Charts.ipynb
new file mode 100644
index 0000000..3b6c54f
--- /dev/null
+++ b/.ipynb_checkpoints/More_Charts.ipynb
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Analysis Based On different Charts and there details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from plotly.offline import plot\n",
+    "import plotly.graph_objs as go\n",
+    "\n",
+    "# Load the data\n",
+    "df = pd.read_csv(\"/Data/SBIN.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A line chart is a simple and effective way to visualize the closing price of the stock over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the closing price over time\n",
+    "plt.plot(df['Date'], df['Close'])\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.title('SBIN Stock Price Over Time')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A candlestick chart is a more detailed way to visualize the stock price, showing the high, low, open, and close prices for each day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python312\\site-packages\\plotly\\offline\\offline.py:557: UserWarning:\n",
+      "\n",
+      "Your filename `candlestick_chart` didn't end with .html. Adding .html to the end of your file.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'candlestick_chart.html'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plot the candlestick chart\n",
+    "fig = go.Figure(data=[go.Candlestick(x=df['Date'],\n",
+    "                open=df['Open'],\n",
+    "                high=df['High'],\n",
+    "                low=df['Low'],\n",
+    "                close=df['Close'])])\n",
+    "plot(fig, filename='candlestick_chart')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the daily volume\n",
+    "plt.bar(df['Date'], df['Volume'])\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Volume')\n",
+    "plt.title('SBIN Daily Volume')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the scatter plot of closing price vs volume\n",
+    "sns.scatterplot(x=df['Close'], y=df['Volume'])\n",
+    "plt.xlabel('Closing Price')\n",
+    "plt.ylabel('Volume')\n",
+    "plt.title('Relationship between SBIN Closing Price and Volume')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the histogram of closing prices\n",
+    "plt.hist(df['Close'], bins=50)\n",
+    "plt.xlabel('Closing Price')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Distribution of SBIN Closing Prices')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A moving average chart is a type of chart that shows the average value of a stock's price over a certain period of time. It is used to smooth out the fluctuations in the price and to identify trends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the moving average chart\n",
+    "df['MA_50'] = df['Close'].rolling(window=50).mean()\n",
+    "df['MA_200'] = df['Close'].rolling(window=200).mean()\n",
+    "\n",
+    "plt.plot(df['Date'], df['Close'], label='Close')\n",
+    "plt.plot(df['Date'], df['MA_50'], label='MA 50')\n",
+    "plt.plot(df['Date'], df['MA_200'], label='MA 200')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.title('SBIN Stock Price with Moving Averages')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This chart shows the closing price of the stock (blue line) along with its 20-day moving average (red line) and two standard deviations plotted above (green line) and below (orange line) it. The Bollinger Bands are used to identify volatility in the stock price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the Bollinger Bands chart\n",
+    "df['MA_20'] = df['Close'].rolling(window=20).mean()\n",
+    "df['Upper_BB'] = df['MA_20'] + 2*df['Close'].rolling(window=20).std()\n",
+    "df['Lower_BB'] = df['MA_20'] - 2*df['Close'].rolling(window=20).std()\n",
+    "\n",
+    "plt.plot(df['Date'], df['Close'], label='Close')\n",
+    "plt.plot(df['Date'], df['Upper_BB'], label='Upper BB')\n",
+    "plt.plot(df['Date'], df['Lower_BB'], label='Lower BB')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.title('SBIN Stock Price with Bollinger Bands')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " This chart shows the RSI of the stock (blue line) along with two horizontal lines at 30 (red line) and 70 (green line). The RSI is used to identify overbought (above 70) and oversold (below 30) conditions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the RSI chart\n",
+    "delta = df['Close'].diff(1)\n",
+    "up, down = delta.copy(), delta.copy()\n",
+    "up[up < 0] = 0\n",
+    "down[down > 0] = 0\n",
+    "roll_up = up.rolling(window=14).mean()\n",
+    "roll_down = down.rolling(window=14).mean().abs()\n",
+    "RS = roll_up / roll_down\n",
+    "RSI = 100.0 - (100.0 / (1.0 + RS))\n",
+    "\n",
+    "plt.plot(df['Date'], RSI, label='RSI')\n",
+    "plt.axhline(y=30, color='red', linestyle='--')\n",
+    "plt.axhline(y=70, color='green', linestyle='--')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('RSI')\n",
+    "plt.title('SBIN Stock Price with RSI')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This chart shows the correlation between the open, high, low, close, and volume of the stock. The correlation is measured on a scale of -1 to 1, where 1 means perfect positive correlation and -1 means perfect negative correlation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the heatmap\n",
+    "corr_matrix = df[['Open', 'High', 'Low', 'Close', 'Volume']].corr()\n",
+    "plt.imshow(corr_matrix, cmap='hot', interpolation='nearest')\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/candlestick_chart.html b/.ipynb_checkpoints/candlestick_chart.html
new file mode 100644
index 0000000..0d35fcb
--- /dev/null
+++ b/.ipynb_checkpoints/candlestick_chart.html
@@ -0,0 +1,14 @@
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+<head><meta charset="utf-8" /></head>
+<body>
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n=r(33626),i=r(34809);t.exports={moduleType:"component",name:"annotations3d",schema:{subplots:{scene:{annotations:r(38239)}}},layoutAttributes:r(38239),handleDefaults:r(34232),includeBasePlot:function(t,e){var r=n.subplotsRegistry.gl3d;if(r)for(var a=r.attrRegex,o=Object.keys(t),s=0;s<o.length;s++){var l=o[s];a.test(l)&&(t[l].annotations||[]).length&&(i.pushUnique(e._basePlotModules,r),i.pushUnique(e._subplots.gl3d,l))}},convert:r(47979),draw:r(9756)}},37177:function(t,e,r){"use strict";t.exports=r(24453),r(23428),r(1401),r(72210),r(28569),r(81133),r(78295),r(25512),r(42645),r(62324),r(91662),r(66445),r(50506),r(84756),r(41858),r(57985)},29698:function(t,e,r){"use strict";var n=r(37177),i=r(34809),a=r(63821),o=a.EPOCHJD,s=a.ONEDAY,l={valType:"enumerated",values:i.sortObjectKeys(n.calendars),editType:"calc",dflt:"gregorian"},c=function(t,e,r,n){var a={};return 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lt=e._axis=function(t,e,r){var n=t._fullLayout,i="v"===e.orientation,a={type:"linear",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,labelalias:e.labelalias,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,ticklabelstep:e.ticklabelstep,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:"free",side:i?"right":"bottom",position:1},o=i?"y":"x",s={type:"linear",_id:o+e._id},l={letter:o,font:n.font,noAutotickangles:"y"===o,noHover:!0,noTickson:!0,noTicklabelmode:!0,noInsideRange:!0,calendar:n.calendar};function 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a={propContainer:lt,propName:e._propPrefix+"title",traceIndex:e._traceIndex,_meta:e._meta,placeholder:F._dfltTitle.colorbar,containerGroup:t.select("."+A.cbtitle)},o="h"===n.charAt(0)?n.substr(1):"h"+n;t.selectAll("."+o+",."+o+"-math-group").remove(),m.draw(r,n,h(a,i||{}))}return c.syncOrAsync([a.previousPromises,function(){var t,e;(o&&ct||!o&&!ct)&&("top"===V&&(t=I+B.l+tt*z,e=P+B.t+et*(1-ot-Q)+3+.75*yt),"bottom"===V&&(t=I+B.l+tt*z,e=P+B.t+et*(1-ot)-3-.25*yt),"right"===V&&(e=P+B.t+et*O+3+.75*yt,t=I+B.l+tt*ot),bt(lt._id+"title",{attributes:{x:t,y:e,"text-anchor":o?"start":"middle"}}))},function(){if(!o&&!ct||o&&ct){var 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x=q.map(lt.c2p).map(Math.round).sort((function(t,e){return t-e}));v.each((function(t,a){var s=[0===a?q[0]:(Y[a]+Y[a-1])/2,a===Y.length-1?q[1]:(Y[a]+Y[a+1])/2].map(lt.c2p).map(Math.round);o&&(s[1]=c.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?"x":"y",rt).attr(o?"y":"x",n.min(s)).attr(o?"width":"height",Math.max($,2)).attr(o?"height":"width",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?"vertical":"horizontalreversed",e._fillgradient,"fill");else{var u=G(t).replace("e-","");l.attr("fill",i(u).toHexString())}}));var _=t.select("."+A.cblines).selectAll("path."+A.cbline).data(j.color&&j.width?X:[]);_.enter().append("path").classed(A.cbline,!0),_.exit().remove(),_.each((function(t){var e=rt,r=Math.round(lt.c2p(t))+j.width/2%1;n.select(this).attr("d","M"+(o?e+","+r:r+","+e)+(o?"h":"v")+$).call(p.lineGroupStyle,j.width,H(t),j.dash)})),vt.selectAll("g."+lt._id+"tick,path").remove();var 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s=t[o];v.traceIs(s.trace,"bar-like")||v.traceIs(s.trace,"box-violin")?a.push(s):s.trace[r+"period"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(gt=t.concat(e),S)};Pt();var zt=S.charAt(0),Ot=("x"===zt||"y"===zt)&&gt[0]&&I[gt[0].trace.type];if(M&&0!==rt&&0!==gt.length){var Dt=St(gt.filter((function(t){return t.ya.showspikes})),rt,Ot);vt.hLinePoint=Et(Dt);var Rt=St(gt.filter((function(t){return t.xa.showspikes})),rt,Ot);vt.vLinePoint=Et(Rt)}if(0===gt.length){var Ft=m.unhoverRaw(t,e);return!M||null===vt.hLinePoint&&null===vt.vLinePoint||U(Lt)&&j(t,vt,Ct),Ft}if(M&&U(Lt)&&j(t,vt,Ct),x.isXYhover(ht)&&0!==gt[0].length&&"splom"!==gt[0].trace.type){var Bt=gt[0],Nt=(gt=L[Bt.trace.type]?gt.filter((function(t){return t.trace.index===Bt.trace.index})):[Bt]).length;Mt(q("x",Bt,b),q("y",Bt,b));var jt,Ut=[],Vt={},qt=0,Ht=function(t){var e=L[t.trace.type]?z(t):t.trace.index;if(Vt[e]){var r=Vt[e]-1,n=Ut[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(Ut[r]=t)}else qt++,Vt[e]=qt,Ut.push(t)};for(jt=0;jt<Nt;jt++)Ht(gt[jt]);for(jt=gt.length-1;jt>Nt-1;jt--)Ht(gt[jt]);gt=Ut,Pt()}var Gt=t._hoverdata,Zt=[],Wt=H(t),Yt=G(t);for(at=0;at<gt.length;at++){var Xt=gt[at],$t=x.makeEventData(Xt,Xt.trace,Xt.cd);if(!1!==Xt.hovertemplate){var Jt=!1;Xt.cd[Xt.index]&&Xt.cd[Xt.index].ht&&(Jt=Xt.cd[Xt.index].ht),Xt.hovertemplate=Jt||Xt.trace.hovertemplate||!1}if(Xt.xa&&Xt.ya){var Kt=Xt.x0+Xt.xa._offset,Qt=Xt.x1+Xt.xa._offset,te=Xt.y0+Xt.ya._offset,ee=Xt.y1+Xt.ya._offset,re=Math.min(Kt,Qt),ne=Math.max(Kt,Qt),ie=Math.min(te,ee),ae=Math.max(te,ee);$t.bbox={x0:re+Yt,x1:ne+Yt,y0:ie+Wt,y1:ae+Wt}}Xt.eventData=[$t],Zt.push($t)}t._hoverdata=Zt;var oe="y"===S&&(yt.length>1||gt.length>1)||"closest"===S&&xt&&gt.length>1,se=d.combine(b.plot_bgcolor||d.background,b.paper_bgcolor),le=D(gt,{gd:t,hovermode:S,rotateLabels:oe,bgColor:se,container:b._hoverlayer,outerContainer:b._paper.node(),commonLabelOpts:b.hoverlabel,hoverdistance:b.hoverdistance}),ce=le.hoverLabels;if(x.isUnifiedHover(S)||(function(t,e,r,n){var i,a,o,s,l,c,u,h=e?"xa":"ya",f=e?"ya":"xa",p=0,d=1,m=t.size(),g=new Array(m),y=0,v=n.minX,x=n.maxX,_=n.minY,b=n.maxY,w=function(t){return t*r._invScaleX},T=function(t){return t*r._invScaleY};function k(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;i=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;i=!1}if(i){var n=0;for(s=0;s<t.length;s++)(c=t[s]).pos+c.dp+c.size>e.pmax&&n++;for(s=t.length-1;s>=0&&!(n<=0);s--)(c=t[s]).pos>e.pmax-1&&(c.del=!0,n--);for(s=0;s<t.length&&!(n<=0);s++)if((c=t[s]).pos<e.pmin+1)for(c.del=!0,n--,o=2*c.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(s=t.length-1;s>=0&&!(n<=0);s--)(c=t[s]).pos+c.dp+c.size>e.pmax&&(c.del=!0,n--)}}}for(t.each((function(t){var n=t[h],i=t[f],a="x"===n._id.charAt(0),o=n.range;0===y&&o&&o[0]>o[1]!==a&&(d=-1);var s=0,l=a?r.width:r.height;if("x"===r.hovermode||"y"===r.hovermode){var c,u,p=F(t,e),m=t.anchor,k="end"===m?-1:1;if("middle"===m)u=(c=t.crossPos+(a?T(p.y-t.by/2):w(t.bx/2+t.tx2width/2)))+(a?T(t.by):w(t.bx));else if(a)u=(c=t.crossPos+T(E+p.y)-T(t.by/2-E))+T(t.by);else{var M=w(k*E+p.x),S=M+w(k*t.bx);c=t.crossPos+Math.min(M,S),u=t.crossPos+Math.max(M,S)}a?void 0!==_&&void 0!==b&&Math.min(u,b)-Math.max(c,_)>1&&("left"===i.side?(s=i._mainLinePosition,l=r.width):l=i._mainLinePosition):void 0!==v&&void 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U,V=b.height/2,H=q-b.top-V,G="clip"+i._uid+"commonlabel"+F._id;if(x<b.width+2*C+E){U="M-"+(E+C)+"-"+V+"h-"+(b.width-C)+"V"+V+"h"+(b.width-C)+"Z";var W=b.width-x+C;h.positionText(s,W,H),"end"===R&&s.selectAll("tspan").each((function(){var t=n.select(this),e=p.tester.append("text").text(t.text()).call(p.font,v),i=Z(r,e.node());Math.round(i.width)<Math.round(b.width)&&t.attr("x",W-i.width),e.remove()}))}else h.positionText(s,B*(C+E),H),U=null;var Y=i._topclips.selectAll("#"+G).data(U?[0]:[]);Y.enter().append("clipPath").attr("id",G).append("path"),Y.exit().remove(),Y.select("path").attr("d",U),p.setClipUrl(s,U?G:null,r)}t.attr("transform",l(x,_))})),x.isUnifiedHover(a)){f.selectAll("g.hovertext").remove();var tt=t.filter((function(t){return"none"!==t.hoverinfo}));if(0===tt.length)return[];var 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ft,pt,dt,mt,gt=f.select("g.legend"),yt=Z(r,gt.node()),vt=yt.width+2*C,xt=yt.height+2*C,_t=tt[0],bt=(_t.x0+_t.x1)/2,wt=(_t.y0+_t.y1)/2,Tt=!(v.traceIs(_t.trace,"bar-like")||v.traceIs(_t.trace,"box-violin"));"y"===B?Tt?(pt=wt-C,ft=wt+C):(pt=Math.min.apply(null,tt.map((function(t){return Math.min(t.y0,t.y1)}))),ft=Math.max.apply(null,tt.map((function(t){return Math.max(t.y0,t.y1)})))):pt=ft=o.mean(tt.map((function(t){return(t.y0+t.y1)/2})))-xt/2,"x"===B?Tt?(dt=bt+C,mt=bt-C):(dt=Math.max.apply(null,tt.map((function(t){return Math.max(t.x0,t.x1)}))),mt=Math.min.apply(null,tt.map((function(t){return Math.min(t.x0,t.x1)})))):dt=mt=o.mean(tt.map((function(t){return(t.x0+t.x1)/2})))-vt/2;var kt,At,Mt=D._offset,St=F._offset;return mt+=Mt-vt,pt+=St-xt,kt=(dt+=Mt)+vt<H&&dt>=0?dt:mt+vt<H&&mt>=0?mt:Mt+vt<H?Mt:dt-bt<bt-mt+vt?H-vt:0,kt+=C,At=(ft+=St)+xt<G&&ft>=0?ft:pt+xt<G&&pt>=0?pt:St+xt<G?St:ft-wt<wt-pt+xt?G-xt:0,At+=C,gt.attr("transform",l(kt-1,At-1)),gt}var 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n=r(80337),i=r(10229);t.exports={_isSubplotObj:!0,visible:{valType:"boolean",dflt:!0,editType:"legend"},bgcolor:{valType:"color",editType:"legend"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"legend"},borderwidth:{valType:"number",min:0,dflt:0,editType:"legend"},font:n({editType:"legend"}),grouptitlefont:n({editType:"legend"}),orientation:{valType:"enumerated",values:["v","h"],dflt:"v",editType:"legend"},traceorder:{valType:"flaglist",flags:["reversed","grouped"],extras:["normal"],editType:"legend"},tracegroupgap:{valType:"number",min:0,dflt:10,editType:"legend"},entrywidth:{valType:"number",min:0,editType:"legend"},entrywidthmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"pixels",editType:"legend"},indentation:{valType:"number",min:-15,dflt:0,editType:"legend"},itemsizing:{valType:"enumerated",values:["trace","constant"],dflt:"trace",editType:"legend"},itemwidth:{valType:"number",min:30,dflt:30,editType:"legend"},itemclick:{valType:"enumerated",values:["toggle","toggleothers",!1],dflt:"toggle",editType:"legend"},itemdoubleclick:{valType:"enumerated",values:["toggle","toggleothers",!1],dflt:"toggleothers",editType:"legend"},groupclick:{valType:"enumerated",values:["toggleitem","togglegroup"],dflt:"togglegroup",editType:"legend"},x:{valType:"number",editType:"legend"},xref:{valType:"enumerated",dflt:"paper",values:["container","paper"],editType:"layoutstyle"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"legend"},y:{valType:"number",editType:"legend"},yref:{valType:"enumerated",dflt:"paper",values:["container","paper"],editType:"layoutstyle"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],editType:"legend"},uirevision:{valType:"any",editType:"none"},valign:{valType:"enumerated",values:["top","middle","bottom"],dflt:"middle",editType:"legend"},title:{text:{valType:"string",dflt:"",editType:"legend"},font:n({editType:"legend"}),side:{valType:"enumerated",values:["top","left","top 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A=m._isShape;(m.showlegend||m._dfltShowLegend&&!(m._module&&m._module.attributes&&m._module.attributes.showlegend&&!1===m._module.attributes.showlegend.dflt))&&(x++,m.showlegend&&(_=!0,(!A&&n.traceIs(m,"pie-like")||!0===m._input.showlegend)&&x++),i.coerceFont(g,"legendgrouptitle.font",v)),(!A&&n.traceIs(m,"bar")&&"stack"===r.barmode||-1!==["tonextx","tonexty"].indexOf(m.fill))&&(b=c.isGrouped({traceorder:b})?"grouped+reversed":"reversed"),void 0!==m.legendgroup&&""!==m.legendgroup&&(b=c.isReversed({traceorder:b})?"reversed+grouped":"grouped")}var M=i.coerce(e,r,l,"showlegend",_&&x>("legend"===t?1:0));if(!1===M&&(r[t]=void 0),(!1!==M||h.uirevision)&&(p("uirevision",r.uirevision),!1!==M)){p("borderwidth");var 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V,q=i.ensureSingle(N,"g","scrollbox"),H=f.title;f._titleWidth=0,f._titleHeight=0,H.text?((V=i.ensureSingle(q,"text",w+"titletext")).attr("text-anchor","start").call(c.font,H.font).text(H.text),C(V,q,t,f,b)):q.selectAll("."+w+"titletext").remove();var G=i.ensureSingle(N,"rect","scrollbar",(function(t){t.attr(p.scrollBarEnterAttrs).call(u.fill,p.scrollBarColor)})),Z=q.selectAll("g.groups").data(S);Z.enter().append("g").attr("class","groups"),Z.exit().remove();var W=Z.selectAll("g.traces").data(i.identity);W.enter().append("g").attr("class","traces"),W.exit().remove(),W.style("opacity",(function(t){var e=t[0].trace;return o.traceIs(e,"pie-like")?-1!==B.indexOf(t[0].label)?.5:1:"legendonly"===e.visible?.5:1})).each((function(){n.select(this).call(M,t,f)})).call(x,t,f).each((function(){T||n.select(this).call(E,t,w)})),i.syncOrAsync([a.previousPromises,function(){return function(t,e,r,i){var a=t._fullLayout,o=P(i);i||(i=a[o]);var 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S=k[a][o].trace.legendrank;M>S&&(M=S)}k[a][0]._groupMinRank=M,k[a][0]._preGroupSort=a}var E=function(t,e){return t.trace.legendrank-e.trace.legendrank||t._preSort-e._preSort};for(k.forEach((function(t,e){t[0]._preGroupSort=e})),k.sort((function(t,e){return t[0]._groupMinRank-e[0]._groupMinRank||t[0]._preGroupSort-e[0]._preGroupSort})),a=0;a<k.length;a++){k[a].forEach((function(t,e){t._preSort=e})),k[a].sort(E);var C=k[a][0].trace,L=null;for(o=0;o<k[a].length;o++){var I=k[a][o].trace.legendgrouptitle;if(I&&I.text){L=I,s&&(I.font=e._groupTitleFont);break}}if(c&&k[a].reverse(),L){var P=!1;for(o=0;o<k[a].length;o++)if(n.traceIs(k[a][o].trace,"pie-like")){P=!0;break}k[a].unshift({i:-1,groupTitle:L,noClick:P,trace:{showlegend:C.showlegend,legendgroup:C.legendgroup,visible:"toggleitem"===e.groupclick||C.visible}})}for(o=0;o<k[a].length;o++)k[a][o]=[k[a][o]]}return e._lgroupsLength=k.length,e._maxNameLength=m,k}},22165:function(t,e,r){"use strict";var n=r(33626),i=r(34809),a=i.pushUnique,o=!0;t.exports=function(t,e,r){var s=e._fullLayout;if(!e._dragged&&!e._editing){var l,c=s.legend.itemclick,u=s.legend.itemdoubleclick,h=s.legend.groupclick;if(1===r&&"toggle"===c&&"toggleothers"===u&&o&&e.data&&e._context.showTips?(i.notifier(i._(e,"Double-click on legend to isolate one trace"),"long"),o=!1):o=!1,1===r?l=c:2===r&&(l=u),l){var f="togglegroup"===h,p=s.hiddenlabels?s.hiddenlabels.slice():[],d=t.data()[0][0];if(!d.groupTitle||!d.noClick){var m=e._fullData,g=(s.shapes||[]).filter((function(t){return t.showlegend})),y=m.concat(g),v=d.trace;v._isShape&&(v=v._fullInput);var x,_,b,w,T,k=v.legendgroup,A={},M=[],S=[],E=[],C=(s.shapes||[]).map((function(t){return t._input})),L=!1,I=v.legend,P=v._fullInput;if(P&&P._isShape||!n.traceIs(v,"pie-like")){var z,O=k&&k.length,D=[];if(O)for(x=0;x<y.length;x++)(z=y[x]).visible&&z.legendgroup===k&&D.push(x);if("toggle"===l){var 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t._fullLayout._isHoriz?"y":"x"},icon:o.tooltip_compare,gravity:"ne",click:h},u.zoom3d={name:"zoom3d",_cat:"zoom",title:function(t){return c(t,"Zoom")},attr:"scene.dragmode",val:"zoom",icon:o.zoombox,click:f},u.pan3d={name:"pan3d",_cat:"pan",title:function(t){return c(t,"Pan")},attr:"scene.dragmode",val:"pan",icon:o.pan,click:f},u.orbitRotation={name:"orbitRotation",title:function(t){return c(t,"Orbital rotation")},attr:"scene.dragmode",val:"orbit",icon:o["3d_rotate"],click:f},u.tableRotation={name:"tableRotation",title:function(t){return c(t,"Turntable rotation")},attr:"scene.dragmode",val:"turntable",icon:o["z-axis"],click:f},u.resetCameraDefault3d={name:"resetCameraDefault3d",_cat:"resetCameraDefault",title:function(t){return c(t,"Reset camera to default")},attr:"resetDefault",icon:o.home,click:p},u.resetCameraLastSave3d={name:"resetCameraLastSave3d",_cat:"resetCameraLastSave",title:function(t){return c(t,"Reset camera to last save")},attr:"resetLastSave",icon:o.movie,click:p},u.hoverClosest3d={name:"hoverClosest3d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);n.call("_guiRelayout",t,r)}},u.zoomInGeo={name:"zoomInGeo",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:m},u.zoomOutGeo={name:"zoomOutGeo",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:m},u.resetGeo={name:"resetGeo",_cat:"reset",title:function(t){return c(t,"Reset")},attr:"reset",val:null,icon:o.autoscale,click:m},u.hoverClosestGeo={name:"hoverClosestGeo",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:y},u.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:y},u.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:y},u.resetViewSankey={name:"resetSankeyGroup",title:function(t){return c(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},u.toggleHover={name:"toggleHover",title:function(t){return c(t,"Toggle show closest data on 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n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){b(t,"mapbox")}},u.resetViewMap={name:"resetViewMap",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){b(t,"map")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:v},u.zoomInMap={name:"zoomInMap",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:x},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:v},u.zoomOutMap={name:"zoomOutMap",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:x}},87632:function(t,e,r){"use strict";var n=r(5832),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),t.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},17683:function(t,e,r){"use strict";var n=r(34809),i=r(78766),a=r(78032),o=r(50308);t.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,"modebar");function l(t,e){return n.coerce(r,s,o,t,e)}l("orientation"),l("bgcolor",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l("color",i.addOpacity(c,.3)),l("activecolor",i.addOpacity(c,.7)),l("uirevision",e.uirevision),l("add"),l("remove")}},95433:function(t,e,r){"use 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e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if("string"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var h=e.modebar.add;"string"==typeof h&&(h=[h]);var f=e.modebar.remove;"string"==typeof f&&(f=[f]);var p=s.modeBarButtonsToAdd.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),m=e._has("cartesian"),g=e._has("gl3d"),y=e._has("geo"),v=e._has("pie"),x=e._has("funnelarea"),_=e._has("gl2d"),b=e._has("ternary"),w=e._has("mapbox"),T=e._has("map"),k=e._has("polar"),A=e._has("smith"),M=e._has("sankey"),S=function(t){for(var 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P=[],z=[],O=[],D=[];(m||_||v||x||b)+y+g+w+T+k+A>1?(z=["toggleHover"],O=["resetViews"]):y?(P=["zoomInGeo","zoomOutGeo"],z=["hoverClosestGeo"],O=["resetGeo"]):g?(z=["hoverClosest3d"],O=["resetCameraDefault3d","resetCameraLastSave3d"]):w?(P=["zoomInMapbox","zoomOutMapbox"],z=["toggleHover"],O=["resetViewMapbox"]):T?(P=["zoomInMap","zoomOutMap"],z=["toggleHover"],O=["resetViewMap"]):_?z=["hoverClosestGl2d"]:v?z=["hoverClosestPie"]:M?(z=["hoverClosestCartesian","hoverCompareCartesian"],O=["resetViewSankey"]):z=["toggleHover"],m&&z.push("toggleSpikelines","hoverClosestCartesian","hoverCompareCartesian"),(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],"noHover"))return!1;return!0}(r)||E)&&(z=[]),!m&&!_||S||(P=["zoomIn2d","zoomOut2d","autoScale2d"],"resetViews"!==O[0]&&(O=["resetScale2d"])),g?D=["zoom3d","pan3d","orbitRotation","tableRotation"]:(m||_)&&!S||b?D=["zoom2d","pan2d"]:w||T||y?D=["pan2d"]:k&&(D=["zoom2d"]),function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,"scatter-like")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,"box-violin")&&"all"!==n.boxpoints&&"all"!==n.points||(e=!0))}return e}(r)&&D.push("select2d","lasso2d");var R=[],F=function(t){-1===R.indexOf(t)&&-1!==z.indexOf(t)&&R.push(t)};if(Array.isArray(p)){for(var B=[],N=0;N<p.length;N++){var j=p[N];"string"==typeof j?(j=j.toLowerCase(),-1!==c.indexOf(j)?(e._has("mapbox")||e._has("map")||e._has("cartesian"))&&D.push(j):"togglespikelines"===j?F("toggleSpikelines"):"togglehover"===j?F("toggleHover"):"hovercompare"===j?F("hoverCompareCartesian"):"hoverclosest"===j?(F("hoverClosestCartesian"),F("hoverClosestGeo"),F("hoverClosest3d"),F("hoverClosestGl2d"),F("hoverClosestPie")):"v1hovermode"===j&&(F("hoverClosestCartesian"),F("hoverCompareCartesian"),F("hoverClosestGeo"),F("hoverClosest3d"),F("hoverClosestGl2d"),F("hoverClosestPie"))):B.push(j)}p=B}return 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n=r(80337),i=r(10229),a=(0,r(78032).templatedArray)("button",{visible:{valType:"boolean",dflt:!0,editType:"plot"},step:{valType:"enumerated",values:["month","year","day","hour","minute","second","all"],dflt:"month",editType:"plot"},stepmode:{valType:"enumerated",values:["backward","todate"],dflt:"backward",editType:"plot"},count:{valType:"number",min:0,dflt:1,editType:"plot"},label:{valType:"string",editType:"plot"},editType:"plot"});t.exports={visible:{valType:"boolean",editType:"plot"},buttons:a,x:{valType:"number",min:-2,max:3,editType:"plot"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"plot"},y:{valType:"number",min:-2,max:3,editType:"plot"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"bottom",editType:"plot"},font:n({editType:"plot"}),bgcolor:{valType:"color",dflt:i.lightLine,editType:"plot"},activecolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"}},68508:function(t){"use 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d=n._height-u(n.d2pOppAxis(i._rl[1])),g=n._height-u(n.d2pOppAxis(i._rl[0]));t.select("rect."+m.maskMinOppAxisClassName).attr("x",f).attr("height",d).attr("width",p-f),t.select("rect."+m.maskMaxOppAxisClassName).attr("x",f).attr("y",g).attr("height",n._height-g).attr("width",p-f),t.select("rect."+m.slideBoxClassName).attr("y",d).attr("height",g-d)}var y=.5,v=Math.round(h(f-l))-y,x=Math.round(h(p-l))+y;t.select("g."+m.grabberMinClassName).attr("transform",s(v,y)),t.select("g."+m.grabberMaxClassName).attr("transform",s(x,y))}(a,0,r,l,c,h),"bottom"===r.side&&u.draw(t,r._id+"title",{propContainer:r,propName:r._name+".title",placeholder:e._dfltTitle.x,attributes:{x:r._offset+r._length/2,y:C+l._height+l._offsetShift+10+1.5*r.title.font.size,"text-anchor":"middle"}})})))}},80400:function(t,e,r){"use strict";var n=r(5975),i=r(30635),a=r(20604),o=r(4530).LINE_SPACING,s=a.name;function l(t){var e=t&&t[s];return e&&e.visible}e.isVisible=l,e.makeData=function(t){var 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n=r(34809),i=r(63608),a=r(66249),o=r(80400);t.exports={moduleType:"component",name:"rangeslider",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:r(63608),handleDefaults:r(41295),calcAutorange:r(46223),draw:r(88887),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},66249:function(t){"use strict";t.exports={_isSubplotObj:!0,rangemode:{valType:"enumerated",values:["auto","fixed","match"],dflt:"match",editType:"calc"},range:{valType:"info_array",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},editType:"calc"}},4327:function(t,e,r){"use strict";var n=r(50222),i=r(36640).line,a=r(94850).T,o=r(93049).extendFlat,s=r(13582).overrideAll,l=r(78032).templatedArray;r(35081),t.exports=s(l("selection",{type:{valType:"enumerated",values:["rect","path"]},xref:o({},n.xref,{}),yref:o({},n.yref,{}),x0:{valType:"any"},x1:{valType:"any"},y0:{valType:"any"},y1:{valType:"any"},path:{valType:"string",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:.7,editType:"arraydraw"},line:{color:i.color,width:o({},i.width,{min:1,dflt:1}),dash:o({},a,{dflt:"dot"})}}),"arraydraw","from-root")},78865:function(t){"use strict";t.exports={BENDPX:1.5,MINSELECT:12,SELECTDELAY:100,SELECTID:"-select"}},2272:function(t,e,r){"use strict";var n=r(34809),i=r(29714),a=r(59008),o=r(4327),s=r(49728);function l(t,e,r){function a(r,i){return n.coerce(t,e,o,r,i)}var l=a("path"),c="path"!==a("type",l?"path":"rect");c&&delete e.path,a("opacity"),a("line.color"),a("line.width"),a("line.dash");for(var u=["x","y"],h=0;h<2;h++){var 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n=r(94850).T,i=r(93049).extendFlat;t.exports={newselection:{mode:{valType:"enumerated",values:["immediate","gradual"],dflt:"immediate",editType:"none"},line:{color:{valType:"color",editType:"none"},width:{valType:"number",min:1,dflt:1,editType:"none"},dash:i({},n,{dflt:"dot",editType:"none"}),editType:"none"},editType:"none"},activeselection:{fillcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"none"},opacity:{valType:"number",min:0,max:1,dflt:.5,editType:"none"},editType:"none"}}},43028:function(t){"use strict";t.exports=function(t,e,r){r("newselection.mode"),r("newselection.line.width")&&(r("newselection.line.color"),r("newselection.line.dash")),r("activeselection.fillcolor"),r("activeselection.opacity")}},51817:function(t,e,r){"use strict";var n=r(70414).selectMode,i=r(78534).clearOutline,a=r(81055),o=a.readPaths,s=a.writePaths,l=a.fixDatesForPaths;t.exports=function(t,e){if(t.length){var r=t[0][0];if(r){var 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n=r(11516),i=r(52773),a=r(33626),o=r(62203).dashStyle,s=r(78766),l=r(32141),c=r(36040).makeEventData,u=r(70414),h=u.freeMode,f=u.rectMode,p=u.drawMode,d=u.openMode,m=u.selectMode,g=r(49728),y=r(2956),v=r(561),x=r(78534).clearOutline,_=r(81055),b=_.handleEllipse,w=_.readPaths,T=r(87562).newShapes,k=r(51817),A=r(7028).activateLastSelection,M=r(34809),S=M.sorterAsc,E=r(80899),C=r(64025),L=r(5975).getFromId,I=r(34823),P=r(71817).redrawReglTraces,z=r(78865),O=z.MINSELECT,D=E.filter,R=E.tester,F=r(49801),B=F.p2r,N=F.axValue,j=F.getTransform;function U(t){return void 0!==t.subplot}function V(t,e,r,n,i,a,o){var s,l,c,u,h,f,p,m,g,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf("event")>-1,_=[];if(function(t){return t&&Array.isArray(t)&&!0!==t[0].hoverOnBox}(y)){Z(t,e,a);var b=function(t,e){var r,n,i=t[0],a=-1,o=[];for(n=0;n<e.length;n++)if(r=e[n],i.fullData._expandedIndex===r.cd[0].trace._expandedIndex){if(!0===i.hoverOnBox)break;void 0!==i.pointNumber?a=i.pointNumber:void 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c,u,h=o.selectAll(".select-outline-"+n.id);h&&i._fullLayout._outlining&&(s&&(c=T(h,t)),c&&a.call("_guiRelayout",i,{shapes:c}),l&&!U(t)&&(u=k(h,t)),u&&(i._fullLayout._noEmitSelectedAtStart=!0,a.call("_guiRelayout",i,{selections:u}).then((function(){e&&A(i)}))),i._fullLayout._outlining=!1)}n.selection={},n.selection.selectionDefs=t.selectionDefs=[],n.selection.mergedPolygons=t.mergedPolygons=[]}function Y(t){return t._id}function X(t,e,r,n){if(!t.calcdata)return[];var i,a,o,s=[],l=e.map(Y),c=r.map(Y);for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!U({subplot:n})||a.subplot!==n&&a.geo!==n)if("splom"===a.type){if(a._xaxes[l[0]]&&a._yaxes[c[0]]){var u=$(a._module,i,e[0],r[0]);u.scene=t._fullLayout._splomScenes[a.uid],s.push(u)}}else if("sankey"===a.type){var 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p=i.getRefType(f.xref),d=i.getRefType(f.yref);"paper"!==f.xref&&"domain"!==p&&(h=u(c=i.getFromId(t,f.xref),f,a.paramIsX))&&(f._extremes[c._id]=i.findExtremes(c,h,s(f))),"paper"!==f.yref&&"domain"!==d&&(h=u(c=i.getFromId(t,f.yref),f,a.paramIsY))&&(f._extremes[c._id]=i.findExtremes(c,h,l(f)))}}},2956:function(t){"use strict";t.exports={segmentRE:/[MLHVQCTSZ][^MLHVQCTSZ]*/g,paramRE:/[^\s,]+/g,paramIsX:{M:{0:!0,drawn:0},L:{0:!0,drawn:0},H:{0:!0,drawn:0},V:{},Q:{0:!0,2:!0,drawn:2},C:{0:!0,2:!0,4:!0,drawn:4},T:{0:!0,drawn:0},S:{0:!0,2:!0,drawn:2},Z:{}},paramIsY:{M:{1:!0,drawn:1},L:{1:!0,drawn:1},H:{},V:{0:!0,drawn:0},Q:{1:!0,3:!0,drawn:3},C:{1:!0,3:!0,5:!0,drawn:5},T:{1:!0,drawn:1},S:{1:!0,3:!0,drawn:5},Z:{}},numParams:{M:2,L:2,H:1,V:1,Q:4,C:6,T:2,S:4,Z:0}}},74367:function(t,e,r){"use strict";var n=r(34809),i=r(29714),a=r(59008),o=r(43144),s=r(49728);function l(t,e,r){function a(r,i){return 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left","top center","top right","middle left","middle center","middle right","bottom left","bottom center","bottom right","start","middle","end"]},textangle:{valType:"angle",dflt:"auto"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"auto"},yanchor:{valType:"enumerated",values:["top","middle","bottom"]},padding:{valType:"number",dflt:3,min:0}}},activeshape:{fillcolor:{valType:"color",dflt:"rgb(255,0,255)"},opacity:{valType:"number",min:0,max:1,dflt:.5}}},"none","from-root")},93391:function(t){"use strict";t.exports={CIRCLE_SIDES:32,i000:0,i090:8,i180:16,i270:24,cos45:Math.cos(Math.PI/4),sin45:Math.sin(Math.PI/4),SQRT2:Math.sqrt(2)}},85522:function(t,e,r){"use strict";var n=r(78766),i=r(34809);t.exports=function(t,e,r){if(r("newshape.visible"),r("newshape.name"),r("newshape.showlegend"),r("newshape.legend"),r("newshape.legendwidth"),r("newshape.legendgroup"),r("newshape.legendgrouptitle.text"),i.coerceFont(r,"newshape.legendgrouptitle.font"),r("newshape.legendrank"),r("newshape.drawdirection"),r("newshape.layer"),r("newshape.fillcolor"),r("newshape.fillrule"),r("newshape.opacity"),r("newshape.line.width")){var a=(t||{}).plot_bgcolor||"#FFF";r("newshape.line.color",n.contrast(a)),r("newshape.line.dash")}var o="drawline"===t.dragmode,s=r("newshape.label.text"),l=r("newshape.label.texttemplate");if(s||l){r("newshape.label.textangle");var c=r("newshape.label.textposition",o?"middle":"middle center");r("newshape.label.xanchor"),r("newshape.label.yanchor",function(t,e){return t?"bottom":-1!==e.indexOf("top")?"top":-1!==e.indexOf("bottom")?"bottom":"middle"}(o,c)),r("newshape.label.padding"),i.coerceFont(r,"newshape.label.font",e.font)}r("activeshape.fillcolor"),r("activeshape.opacity")}},81055:function(t,e,r){"use strict";var n=r(26953),i=r(93391),a=i.CIRCLE_SIDES,o=i.SQRT2,s=r(49801),l=s.p2r,c=s.r2p,u=[0,3,4,5,6,1,2],h=[0,3,4,1,2];function f(t,e){return Math.abs(t-e)<=1e-6}function p(t,e){var r=e[1]-t[1],n=e[2]-t[2];return Math.sqrt(r*r+n*n)}e.writePaths=function(t){var e=t.length;if(!e)return"M0,0Z";for(var r="",n=0;n<e;n++)for(var i=t[n].length,a=0;a<i;a++){var o=t[n][a][0];if("Z"===o)r+="Z";else for(var s=t[n][a].length,l=0;l<s;l++){var c=l;"Q"===o||"S"===o?c=h[l]:"C"===o&&(c=u[l]),r+=t[n][a][c],l>0&&l<s-1&&(r+=",")}}return r},e.readPaths=function(t,e,r,i){var o,s,u,h=n(t),f=[],p=-1,d=0,m=0,g=function(){s=d,u=m};g();for(var y=0;y<h.length;y++){var 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p=2*f*Math.PI/a;h.push([s+c*Math.cos(p),l+u*Math.sin(p)])}return h},e.ellipseOver=function(t){var e=t.x0,r=t.y0,n=t.x1,i=t.y1,a=n-e,s=i-r,l=((e-=a)+n)/2,c=((r-=s)+i)/2;return{x0:l-(a*=o),y0:c-(s*=o),x1:l+a,y1:c+s}},e.fixDatesForPaths=function(t,e,r){var n="date"===e.type,i="date"===r.type;if(!n&&!i)return t;for(var a=0;a<t.length;a++)for(var o=0;o<t[a].length;o++)for(var s=0;s+2<t[a][o].length;s+=2)n&&(t[a][o][s+1]=t[a][o][s+1].replace(" ","_")),i&&(t[a][o][s+2]=t[a][o][s+2].replace(" ","_"));return t}},87562:function(t,e,r){"use strict";var n=r(70414),i=n.drawMode,a=n.openMode,o=r(93391),s=o.i000,l=o.i090,c=o.i180,u=o.i270,h=o.cos45,f=o.sin45,p=r(49801),d=p.p2r,m=p.r2p,g=r(78534).clearOutline,y=r(81055),v=y.readPaths,x=y.writePaths,_=y.ellipseOver,b=y.fixDatesForPaths;function w(t,e,r){var n,i=t[0][0],o=e.gd,p=i.getAttribute("d"),g=o._fullLayout.newshape,y=e.plotinfo,w=e.isActiveShape,T=y.xaxis,k=y.yaxis,A=!!y.domain||!y.xaxis,M=!!y.domain||!y.yaxis,S=a(r),E=v(p,o,y,w),C={editable:!0,visible:g.visible,name:g.name,showlegend:g.showlegend,legend:g.legend,legendwidth:g.legendwidth,legendgroup:g.legendgroup,legendgrouptitle:{text:g.legendgrouptitle.text,font:g.legendgrouptitle.font},legendrank:g.legendrank,label:g.label,xref:A?"paper":T._id,yref:M?"paper":k._id,layer:g.layer,opacity:g.opacity,line:{color:g.line.color,width:g.line.width,dash:g.line.dash}};if(S||(C.fillcolor=g.fillcolor,C.fillrule=g.fillrule),1===E.length&&(n=E[0]),n&&5===n.length&&"drawrect"===r)C.type="rect",C.x0=n[0][1],C.y0=n[0][2],C.x1=n[2][1],C.y1=n[2][2];else if(n&&"drawline"===r)C.type="line",C.x0=n[0][1],C.y0=n[0][2],C.x1=n[1][1],C.y1=n[1][2];else if(n&&"drawcircle"===r){C.type="circle";var L=n[s][1],I=n[l][1],P=n[c][1],z=n[u][1],O=n[s][2],D=n[l][2],R=n[c][2],F=n[u][2],B=y.xaxis&&("date"===y.xaxis.type||"log"===y.xaxis.type),N=y.yaxis&&("date"===y.yaxis.type||"log"===y.yaxis.type);B&&(L=m(y.xaxis,L),I=m(y.xaxis,I),P=m(y.xaxis,P),z=m(y.xaxis,z)),N&&(O=m(y.yaxis,O),D=m(y.yaxis,D),R=m(y.yaxis,R),F=m(y.yaxis,F));var j=(I+z)/2,U=(O+R)/2,V=_({x0:j,y0:U,x1:j+(z-I+P-L)/2*h,y1:U+(F-D+R-O)/2*f});B&&(V.x0=d(y.xaxis,V.x0),V.x1=d(y.xaxis,V.x1)),N&&(V.y0=d(y.yaxis,V.y0),V.y1=d(y.yaxis,V.y1)),C.x0=V.x0,C.y0=V.y0,C.x1=V.x1,C.y1=V.y1}else C.type="path",T&&k&&b(E,T,k),C.path=x(E),n=null;return C}t.exports={newShapes:function(t,e){if(t.length&&t[0][0]){var r=e.gd,n=e.isActiveShape,a=e.dragmode,o=(r.layout||{}).shapes||[];if(!i(a)&&void 0!==n){var s=r._fullLayout._activeShapeIndex;if(s<o.length)switch(r._fullLayout.shapes[s].type){case"rect":a="drawrect";break;case"circle":a="drawcircle";break;case"line":a="drawline";break;case"path":var l=o[s].path||"";a="Z"===l[l.length-1]?"drawclosedpath":"drawopenpath"}}var c=w(t,e,a);g(r);for(var u=e.editHelpers,h=(u||{}).modifyItem,f=[],p=0;p<o.length;p++){var d=r._fullLayout.shapes[p];if(f[p]=d._input,void 0!==n&&p===r._fullLayout._activeShapeIndex){var m=c;switch(d.type){case"line":case"rect":case"circle":h("x0",m.x0-(d.x0shift||0)),h("x1",m.x1-(d.x1shift||0)),h("y0",m.y0-(d.y0shift||0)),h("y1",m.y1-(d.y1shift||0));break;case"path":h("path",m.path)}}}return void 0===n?(f.push(c),f):u?u.getUpdateObj():{}}},createShapeObj:w}},78534:function(t){"use strict";t.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(".outline-controllers").remove()},clearOutline:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(".select-outline").remove(),t._fullLayout._outlining=!1}}},49728:function(t,e,r){"use strict";var n=r(2956),i=r(34809),a=r(29714);function o(t,e){var r=0;return(e=e||0)&&t&&("category"===t.type||"multicategory"===t.type)&&(r=(t.r2p(1)-t.r2p(0))*e),r}e.rangeToShapePosition=function(t){return"log"===t.type?t.r2d:function(t){return t}},e.shapePositionToRange=function(t){return"log"===t.type?t.d2r:function(t){return t}},e.decodeDate=function(t){return function(e){return e.replace&&(e=e.replace("_"," ")),t(e)}},e.encodeDate=function(t){return function(e){return t(e).replace(" ","_")}},e.extractPathCoords=function(t,e,r){var a=[];return t.match(n.segmentRE).forEach((function(t){var o=e[t.charAt(0)].drawn;if(void 0!==o){var s=t.substr(1).match(n.paramRE);if(s&&!(s.length<o)){var l=s[o],c=r?l:i.cleanNumber(l);a.push(c)}}})),a},e.getDataToPixel=function(t,r,n,i,a){var s,l=t._fullLayout._size;if(r)if("domain"===a)s=function(t){return r._length*(i?1-t:t)+r._offset};else{var c=e.shapePositionToRange(r);s=function(t){var e=o(r,n);return r._offset+r.r2p(c(t,!0))+e},"date"===r.type&&(s=e.decodeDate(s))}else s=i?function(t){return l.t+l.h*(1-t)}:function(t){return l.l+l.w*t};return s},e.getPixelToData=function(t,r,n,i){var a,o=t._fullLayout._size;if(r)if("domain"===i)a=function(t){var e=(t-r._offset)/r._length;return n?1-e:e};else{var s=e.rangeToShapePosition(r);a=function(t){return s(r.p2r(t-r._offset))}}else a=n?function(t){return 1-(t-o.t)/o.h}:function(t){return(t-o.l)/o.w};return a},e.roundPositionForSharpStrokeRendering=function(t,e){var r=1===Math.round(e%2),n=Math.round(t);return r?n+.5:n},e.makeShapesOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.shapes[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&"paper"!==r.xref&&(n.xaxis=t._fullLayout[r.xref+"axis"]),r.yref&&"paper"!==r.yref&&(n.yaxis=t._fullLayout[r.yref+"axis"])),n.xsizemode=r.xsizemode,n.ysizemode=r.ysizemode,n.xanchor=r.xanchor,n.yanchor=r.yanchor,{options:r,plotinfo:n}},e.makeSelectionsOptionsAndPlotinfo=function(t,e){var r=t._fullLayout.selections[e]||{},n=t._fullLayout._plots[r.xref+r.yref];return n?n._hadPlotinfo=!0:(n={},r.xref&&(n.xaxis=t._fullLayout[r.xref+"axis"]),r.yref&&(n.yaxis=t._fullLayout[r.yref+"axis"])),{options:r,plotinfo:n}},e.getPathString=function(t,r){var s,l,c,u,h,f,p,d,m=r.type,g=a.getRefType(r.xref),y=a.getRefType(r.yref),v=a.getFromId(t,r.xref),x=a.getFromId(t,r.yref),_=t._fullLayout._size,b=o(v,r.x0shift),w=o(v,r.x1shift),T=o(x,r.y0shift),k=o(x,r.y1shift);if(v?"domain"===g?l=function(t){return v._offset+v._length*t}:(s=e.shapePositionToRange(v),l=function(t){return v._offset+v.r2p(s(t,!0))}):l=function(t){return _.l+_.w*t},x?"domain"===y?u=function(t){return x._offset+x._length*(1-t)}:(c=e.shapePositionToRange(x),u=function(t){return x._offset+x.r2p(c(t,!0))}):u=function(t){return _.t+_.h*(1-t)},"path"===m)return v&&"date"===v.type&&(l=e.decodeDate(l)),x&&"date"===x.type&&(u=e.decodeDate(u)),function(t,e,r){var a=t.path,o=t.xsizemode,s=t.ysizemode,l=t.xanchor,c=t.yanchor;return a.replace(n.segmentRE,(function(t){var a=0,u=t.charAt(0),h=n.paramIsX[u],f=n.paramIsY[u],p=n.numParams[u],d=t.substr(1).replace(n.paramRE,(function(t){return h[a]?t="pixel"===o?e(l)+Number(t):e(t):f[a]&&(t="pixel"===s?r(c)-Number(t):r(t)),++a>p&&(t="X"),t}));return a>p&&(d=d.replace(/[\s,]*X.*/,""),i.log("Ignoring extra params in segment "+t)),u+d}))}(r,l,u);if("pixel"===r.xsizemode){var A=l(r.xanchor);h=A+r.x0+b,f=A+r.x1+w}else h=l(r.x0)+b,f=l(r.x1)+w;if("pixel"===r.ysizemode){var M=u(r.yanchor);p=M-r.y0+T,d=M-r.y1+k}else p=u(r.y0)+T,d=u(r.y1)+k;if("line"===m)return"M"+h+","+p+"L"+f+","+d;if("rect"===m)return"M"+h+","+p+"H"+f+"V"+d+"H"+h+"Z";var S=(h+f)/2,E=(p+d)/2,C=Math.abs(S-h),L=Math.abs(E-p),I="A"+C+","+L,P=S+C+","+E;return"M"+P+I+" 0 1,1 "+S+","+(E-L)+I+" 0 0,1 "+P+"Z"}},43701:function(t,e,r){"use strict";var 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n=r(80337),i=r(10229),a=r(93049).extendFlat,o=r(13582).overrideAll,s=r(57891),l=r(78032).templatedArray,c=l("button",{visible:{valType:"boolean"},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},args2:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string",dflt:""},execute:{valType:"boolean",dflt:!0}});t.exports=o(l("updatemenu",{_arrayAttrRegexps:[/^updatemenus\[(0|[1-9][0-9]+)\]\.buttons/],visible:{valType:"boolean"},type:{valType:"enumerated",values:["dropdown","buttons"],dflt:"dropdown"},direction:{valType:"enumerated",values:["left","right","up","down"],dflt:"down"},active:{valType:"integer",min:-1,dflt:0},showactive:{valType:"boolean",dflt:!0},buttons:c,x:{valType:"number",min:-2,max:3,dflt:-.05},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"right"},y:{valType:"number",min:-2,max:3,dflt:1},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},pad:a(s({editType:"arraydraw"}),{}),font:n({}),bgcolor:{valType:"color"},bordercolor:{valType:"color",dflt:i.borderLine},borderwidth:{valType:"number",min:0,dflt:1,editType:"arraydraw"}}),"arraydraw","from-root")},71559:function(t){"use strict";t.exports={name:"updatemenus",containerClassName:"updatemenu-container",headerGroupClassName:"updatemenu-header-group",headerClassName:"updatemenu-header",headerArrowClassName:"updatemenu-header-arrow",dropdownButtonGroupClassName:"updatemenu-dropdown-button-group",dropdownButtonClassName:"updatemenu-dropdown-button",buttonClassName:"updatemenu-button",itemRectClassName:"updatemenu-item-rect",itemTextClassName:"updatemenu-item-text",menuIndexAttrName:"updatemenu-active-index",autoMarginIdRoot:"updatemenu-",blankHeaderOpts:{label:"  "},minWidth:30,minHeight:30,textPadX:24,arrowPadX:16,rx:2,ry:2,textOffsetX:12,textOffsetY:3,arrowOffsetX:4,gapButtonHeader:5,gapButton:2,activeColor:"#F4FAFF",hoverColor:"#F4FAFF",arrowSymbol:{left:"◄",right:"►",up:"▲",down:"▼"}}},42746:function(t,e,r){"use strict";var n=r(34809),i=r(59008),a=r(85389),o=r(71559).name,s=a.buttons;function l(t,e,r){function o(r,i){return n.coerce(t,e,a,r,i)}o("visible",i(t,e,{name:"buttons",handleItemDefaults:c}).length>0)&&(o("active"),o("direction"),o("type"),o("showactive"),o("x"),o("y"),n.noneOrAll(t,e,["x","y"]),o("xanchor"),o("yanchor"),o("pad.t"),o("pad.r"),o("pad.b"),o("pad.l"),n.coerceFont(o,"font",r.font),o("bgcolor",r.paper_bgcolor),o("bordercolor"),o("borderwidth"))}function c(t,e){function r(r,i){return n.coerce(t,e,s,r,i)}r("visible","skip"===t.method||Array.isArray(t.args))&&(r("method"),r("args"),r("args2"),r("label"),r("execute"))}t.exports=function(t,e){i(t,e,{name:o,handleItemDefaults:l})}},40974:function(t,e,r){"use strict";var n=r(45568),i=r(44122),a=r(78766),o=r(62203),s=r(34809),l=r(30635),c=r(78032).arrayEditor,u=r(4530).LINE_SPACING,h=r(71559),f=r(21736);function p(t){return t._index}function d(t,e){return+t.attr(h.menuIndexAttrName)===e._index}function 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l=function(t){return-1==+t.attr(h.menuIndexAttrName)}(r)&&"buttons"!==o.type?[]:o.buttons,c="dropdown"===o.type?h.dropdownButtonClassName:h.buttonClassName,u=r.selectAll("g."+c).data(s.filterVisible(l)),f=u.enter().append("g").classed(c,!0),p=u.exit();"dropdown"===o.type?(f.attr("opacity","0").transition().attr("opacity","1"),p.transition().attr("opacity","0").remove()):p.remove();var d=0,g=0,y=o._dims,x=-1!==["up","down"].indexOf(o.direction);"dropdown"===o.type&&(x?g=y.headerHeight+h.gapButtonHeader:d=y.headerWidth+h.gapButtonHeader),"dropdown"===o.type&&"up"===o.direction&&(g=-h.gapButtonHeader+h.gapButton-y.openHeight),"dropdown"===o.type&&"left"===o.direction&&(d=-h.gapButtonHeader+h.gapButton-y.openWidth);var _={x:y.lx+d+o.pad.l,y:y.ly+g+o.pad.t,yPad:h.gapButton,xPad:h.gapButton,index:0},k={l:_.x+o.borderwidth,t:_.y+o.borderwidth};u.each((function(s,l){var c=n.select(this);c.call(v,o,s,t).call(M,o,_),c.on("click",(function(){n.event.defaultPrevented||(s.execute&&(s.args2&&o.active===l?(m(t,o,0,e,r,a,-1),i.executeAPICommand(t,s.method,s.args2)):(m(t,o,0,e,r,a,l),i.executeAPICommand(t,s.method,s.args))),t.emit("plotly_buttonclicked",{menu:o,button:s,active:o.active}))})),c.on("mouseover",(function(){c.call(w)})),c.on("mouseout",(function(){c.call(T,o),u.call(b,o)}))})),u.call(b,o),x?(k.w=Math.max(y.openWidth,y.headerWidth),k.h=_.y-k.t):(k.w=_.x-k.l,k.h=Math.max(y.openHeight,y.headerHeight)),k.direction=o.direction,a&&(u.size()?function(t,e,r,n,i,a){var o,s,l,c=i.direction,u="up"===c||"down"===c,f=i._dims,p=i.active;if(u)for(s=0,l=0;l<p;l++)s+=f.heights[l]+h.gapButton;else for(o=0,l=0;l<p;l++)o+=f.widths[l]+h.gapButton;n.enable(a,o,s),n.hbar&&n.hbar.attr("opacity","0").transition().attr("opacity","1"),n.vbar&&n.vbar.attr("opacity","0").transition().attr("opacity","1")}(0,0,0,a,o,k):function(t){var e=!!t.hbar,r=!!t.vbar;e&&t.hbar.transition().attr("opacity","0").each("end",(function(){e=!1,r||t.disable()})),r&&t.vbar.transition().attr("opacity","0").each("end",(function(){r=!1,e||t.disable()}))}(a))}function v(t,e,r,n){t.call(x,e).call(_,e,r,n)}function x(t,e){s.ensureSingle(t,"rect",h.itemRectClassName,(function(t){t.attr({rx:h.rx,ry:h.ry,"shape-rendering":"crispEdges"})})).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style("stroke-width",e.borderwidth+"px")}function _(t,e,r,n){var i=s.ensureSingle(t,"text",h.itemTextClassName,(function(t){t.attr({"text-anchor":"start","data-notex":1})})),a=r.label,c=n._fullLayout._meta;c&&(a=s.templateString(a,c)),i.call(o.font,e.font).text(a).call(l.convertToTspans,n)}function b(t,e){var r=e.active;t.each((function(t,i){var o=n.select(this);i===r&&e.showactive&&o.select("rect."+h.itemRectClassName).call(a.fill,h.activeColor)}))}function w(t){t.select("rect."+h.itemRectClassName).call(a.fill,h.hoverColor)}function T(t,e){t.select("rect."+h.itemRectClassName).call(a.fill,e.bgcolor)}function k(t,e){var r=e._dims={width1:0,height1:0,heights:[],widths:[],totalWidth:0,totalHeight:0,openWidth:0,openHeight:0,lx:0,ly:0},a=o.tester.selectAll("g."+h.dropdownButtonClassName).data(s.filterVisible(e.buttons));a.enter().append("g").classed(h.dropdownButtonClassName,!0);var c=-1!==["up","down"].indexOf(e.direction);a.each((function(i,a){var s=n.select(this);s.call(v,e,i,t);var f=s.select("."+h.itemTextClassName),p=f.node()&&o.bBox(f.node()).width,d=Math.max(p+h.textPadX,h.minWidth),m=e.font.size*u,g=l.lineCount(f),y=Math.max(m*g,h.minHeight)+h.textOffsetY;y=Math.ceil(y),d=Math.ceil(d),r.widths[a]=d,r.heights[a]=y,r.height1=Math.max(r.height1,y),r.width1=Math.max(r.width1,d),c?(r.totalWidth=Math.max(r.totalWidth,d),r.openWidth=r.totalWidth,r.totalHeight+=y+h.gapButton,r.openHeight+=y+h.gapButton):(r.totalWidth+=d+h.gapButton,r.openWidth+=d+h.gapButton,r.totalHeight=Math.max(r.totalHeight,y),r.openHeight=r.totalHeight)})),c?r.totalHeight-=h.gapButton:r.totalWidth-=h.gapButton,r.headerWidth=r.width1+h.arrowPadX,r.headerHeight=r.height1,"dropdown"===e.type&&(c?(r.width1+=h.arrowPadX,r.totalHeight=r.height1):r.totalWidth=r.width1,r.totalWidth+=h.arrowPadX),a.remove();var f=r.totalWidth+e.pad.l+e.pad.r,p=r.totalHeight+e.pad.t+e.pad.b,d=t._fullLayout._size;r.lx=d.l+d.w*e.x,r.ly=d.t+d.h*(1-e.y);var m="left";s.isRightAnchor(e)&&(r.lx-=f,m="right"),s.isCenterAnchor(e)&&(r.lx-=f/2,m="center");var g="top";s.isBottomAnchor(e)&&(r.ly-=p,g="bottom"),s.isMiddleAnchor(e)&&(r.ly-=p/2,g="middle"),r.totalWidth=Math.ceil(r.totalWidth),r.totalHeight=Math.ceil(r.totalHeight),r.lx=Math.round(r.lx),r.ly=Math.round(r.ly),i.autoMargin(t,A(e),{x:e.x,y:e.y,l:f*({right:1,center:.5}[m]||0),r:f*({left:1,center:.5}[m]||0),b:p*({top:1,middle:.5}[g]||0),t:p*({bottom:1,middle:.5}[g]||0)})}function A(t){return h.autoMarginIdRoot+t._index}function M(t,e,r,n){n=n||{};var i=t.select("."+h.itemRectClassName),a=t.select("."+h.itemTextClassName),s=e.borderwidth,c=r.index,f=e._dims;o.setTranslate(t,s+r.x,s+r.y);var p=-1!==["up","down"].indexOf(e.direction),d=n.height||(p?f.heights[c]:f.height1);i.attr({x:0,y:0,width:n.width||(p?f.width1:f.widths[c]),height:d});var m=e.font.size*u,g=(l.lineCount(a)-1)*m/2;l.positionText(a,h.textOffsetX,d/2-g+h.textOffsetY),p?r.y+=f.heights[c]+r.yPad:r.x+=f.widths[c]+r.xPad,r.index++}function S(t,e){t.attr(h.menuIndexAttrName,e||"-1").selectAll("g."+h.dropdownButtonClassName).remove()}t.exports=function(t){var e=t._fullLayout,r=s.filterVisible(e[h.name]);function a(e){i.autoMargin(t,A(e))}var o=e._menulayer.selectAll("g."+h.containerClassName).data(r.length>0?[0]:[]);if(o.enter().append("g").classed(h.containerClassName,!0).style("cursor","pointer"),o.exit().each((function(){n.select(this).selectAll("g."+h.headerGroupClassName).each(a)})).remove(),0!==r.length){var l=o.selectAll("g."+h.headerGroupClassName).data(r,p);l.enter().append("g").classed(h.headerGroupClassName,!0);for(var c=s.ensureSingle(o,"g",h.dropdownButtonGroupClassName,(function(t){t.style("pointer-events","all")})),u=0;u<r.length;u++){var v=r[u];k(t,v)}var x="updatemenus"+e._uid,_=new 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s(t,e,r){this.gd=t,this.container=e,this.id=r,this.position=null,this.translateX=null,this.translateY=null,this.hbar=null,this.vbar=null,this.bg=this.container.selectAll("rect.scrollbox-bg").data([0]),this.bg.exit().on(".drag",null).on("wheel",null).remove(),this.bg.enter().append("rect").classed("scrollbox-bg",!0).style("pointer-events","all").attr({opacity:0,x:0,y:0,width:0,height:0})}s.barWidth=2,s.barLength=20,s.barRadius=2,s.barPad=1,s.barColor="#808BA4",s.prototype.enable=function(t,e,r){var o=this.gd._fullLayout,l=o.width,c=o.height;this.position=t;var u,h,f,p,d=this.position.l,m=this.position.w,g=this.position.t,y=this.position.h,v=this.position.direction,x="down"===v,_="left"===v,b="up"===v,w=m,T=y;x||_||"right"===v||b||(this.position.direction="down",x=!0),x||b?(h=(u=d)+w,x?(f=g,T=(p=Math.min(f+T,c))-f):T=(p=g+T)-(f=Math.max(p-T,0))):(p=(f=g)+T,_?w=(h=d+w)-(u=Math.max(h-w,0)):(u=d,w=(h=Math.min(u+w,l))-u)),this._box={l:u,t:f,w:w,h:T};var k=m>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+y;E+M>c&&(E=c-M);var C=this.container.selectAll("rect.scrollbar-horizontal").data(k?[0]:[]);C.exit().on(".drag",null).remove(),C.enter().append("rect").classed("scrollbar-horizontal",!0).call(i.fill,s.barColor),k?(this.hbar=C.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var L=y>T,I=s.barWidth+2*s.barPad,P=s.barLength+2*s.barPad,z=d+m,O=g;z+I>l&&(z=l-I);var D=this.container.selectAll("rect.scrollbar-vertical").data(L?[0]:[]);D.exit().on(".drag",null).remove(),D.enter().append("rect").classed("scrollbar-vertical",!0).call(i.fill,s.barColor),L?(this.vbar=D.attr({rx:s.barRadius,ry:s.barRadius,x:z,y:O,width:I,height:P}),this._vbarYMin=O+P/2,this._vbarTranslateMax=T-P):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,B=L?h+I+.5:h+.5,N=f-.5,j=k?p+M+.5:p+.5,U=o._topdefs.selectAll("#"+R).data(k||L?[0]:[]);if(U.exit().remove(),U.enter().append("clipPath").attr("id",R).append("rect"),k||L?(this._clipRect=U.select("rect").attr({x:Math.floor(F),y:Math.floor(N),width:Math.ceil(B)-Math.floor(F),height:Math.ceil(j)-Math.floor(N)}),this.container.call(a.setClipUrl,R,this.gd),this.bg.attr({x:d,y:g,width:m,height:y})):(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(a.setClipUrl,null),delete this._clipRect),k||L){var V=n.behavior.drag().on("dragstart",(function(){n.event.sourceEvent.preventDefault()})).on("drag",this._onBoxDrag.bind(this));this.container.on("wheel",null).on("wheel",this._onBoxWheel.bind(this)).on(".drag",null).call(V);var q=n.behavior.drag().on("dragstart",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on("drag",this._onBarDrag.bind(this));k&&this.hbar.on(".drag",null).call(q),L&&this.vbar.on(".drag",null).call(q)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(".drag",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(".drag",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t+=n.event.deltaY),this.vbar&&(e+=n.event.deltaY),this.setTranslate(t,e)},s.prototype._onBarDrag=function(){var t=this.translateX,e=this.translateY;if(this.hbar){var r=t+this._hbarXMin,i=r+this._hbarTranslateMax;t=(o.constrain(n.event.x,r,i)-r)/(i-r)*(this.position.w-this._box.w)}if(this.vbar){var a=e+this._vbarYMin,s=a+this._vbarTranslateMax;e=(o.constrain(n.event.y,a,s)-a)/(s-a)*(this.position.h-this._box.h)}this.setTranslate(t,e)},s.prototype.setTranslate=function(t,e){var r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=o.constrain(t||0,0,r),e=o.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(a.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var i=t/r;this.hbar.call(a.setTranslate,t+i*this._hbarTranslateMax,e)}if(this.vbar){var s=e/n;this.vbar.call(a.setTranslate,t,e+s*this._vbarTranslateMax)}}},4530:function(t){"use strict";t.exports={FROM_BL:{left:0,center:.5,right:1,bottom:0,middle:.5,top:1},FROM_TL:{left:0,center:.5,right:1,bottom:1,middle:.5,top:0},FROM_BR:{left:1,center:.5,right:0,bottom:0,middle:.5,top:1},LINE_SPACING:1.3,CAP_SHIFT:.7,MID_SHIFT:.35,OPPOSITE_SIDE:{left:"right",right:"left",top:"bottom",bottom:"top"}}},35081:function(t){"use strict";t.exports={axisRefDescription:function(t,e,r){return["If set to a",t,"axis id (e.g. *"+t+"* or","*"+t+"2*), the `"+t+"` position refers to a",t,"coordinate. 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t.append("path").attr("class","zoombox-corners").style({fill:h.background,stroke:h.defaultLine,"stroke-width":1,opacity:0}).attr("transform",c(e,r)).attr("d","M0,0Z")}function U(t,e,r,n,i,a){t.attr("d",n+"M"+r.l+","+r.t+"v"+r.h+"h"+r.w+"v-"+r.h+"h-"+r.w+"Z"),V(t,e,i,a)}function V(t,e,r,n){r||(t.transition().style("fill",n>.2?"rgba(0,0,0,0.4)":"rgba(255,255,255,0.3)").duration(200),e.transition().style("opacity",1).duration(200))}function q(t){n.select(t).selectAll(".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners").remove()}function H(t){P&&t.data&&t._context.showTips&&(i.notifier(i._(t,"Double-click to zoom back out"),"long"),P=!1)}function G(t){var e=Math.floor(Math.min(t.b-t.t,t.r-t.l,I)/2);return"M"+(t.l-3.5)+","+(t.t-.5+e)+"h3v"+-e+"h"+e+"v-3h-"+(e+3)+"ZM"+(t.r+3.5)+","+(t.t-.5+e)+"h-3v"+-e+"h"+-e+"v-3h"+(e+3)+"ZM"+(t.r+3.5)+","+(t.b+.5-e)+"h-3v"+e+"h"+-e+"v3h"+(e+3)+"ZM"+(t.l-3.5)+","+(t.b+.5-e)+"h3v"+e+"h"+e+"v3h-"+(e+3)+"Z"}function Z(t,e,r,n,a){for(var o,s,l,c,u=!1,h={},f={},p=(a||{}).xaHash,d=(a||{}).yaHash,m=0;m<e.length;m++){var g=e[m];for(o in r)if(g[o]){for(l in g)a&&(p[l]||d[l])||("x"===l.charAt(0)?r:n)[l]||(h[l]=o);for(s in n)a&&(p[s]||d[s])||!g[s]||(u=!0)}for(s in n)if(g[s])for(c in g)a&&(p[c]||d[c])||("x"===c.charAt(0)?r:n)[c]||(f[c]=s)}u&&(i.extendFlat(h,f),f={});var y={},v=[];for(l in h){var x=k(t,l);v.push(x),y[x._id]=x}var _={},b=[];for(c in f){var w=k(t,c);b.push(w),_[w._id]=w}return{xaHash:y,yaHash:_,xaxes:v,yaxes:b,xLinks:h,yLinks:f,isSubplotConstrained:u}}function W(t,e){if(s){var r=void 0!==t.onwheel?"wheel":"mousewheel";t._onwheel&&t.removeEventListener(r,t._onwheel),t._onwheel=e,t.addEventListener(r,e,{passive:!1})}else void 0!==t.onwheel?t.onwheel=e:void 0!==t.onmousewheel?t.onmousewheel=e:t.isAddedWheelEvent||(t.isAddedWheelEvent=!0,t.addEventListener("wheel",e,{passive:!1}))}function Y(t){var e=[];for(var r in t)e.push(t[r]);return e}t.exports={makeDragBox:function(t,e,r,s,c,h,m,y){var P,z,V,X,$,J,K,Q,tt,et,rt,nt,it,at,ot,st,lt,ct,ut,ht,ft,pt,dt,mt=t._fullLayout._zoomlayer,gt=m+y==="nsew",yt=1===(m+y).length;function vt(){if(P=e.xaxis,z=e.yaxis,tt=P._length,et=z._length,K=P._offset,Q=z._offset,(V={})[P._id]=P,(X={})[z._id]=z,m&&y)for(var r=e.overlays,n=0;n<r.length;n++){var i=r[n].xaxis;V[i._id]=i;var a=r[n].yaxis;X[a._id]=a}$=Y(V),J=Y(X),it=D($,y),at=D(J,m),ot=!at&&!it,nt=Z(t,t._fullLayout._axisMatchGroups,V,X);var o=(rt=Z(t,t._fullLayout._axisConstraintGroups,V,X,nt)).isSubplotConstrained||nt.isSubplotConstrained;st=y||o,lt=m||o;var s=t._fullLayout;ct=s._has("scattergl"),ut=s._has("splom"),ht=s._has("svg")}r+=e.yaxis._shift,vt();var xt=function(t,e,r){return t?"nsew"===t?r?"":"pan"===e?"move":"crosshair":t.toLowerCase()+"-resize":"pointer"}(at+it,t._fullLayout.dragmode,gt),_t=O(e,m+y+"drag",xt,r,s,c,h);if(ot&&!gt)return _t.onmousedown=null,_t.style.pointerEvents="none",_t;var bt,wt,Tt,kt,At,Mt,St,Et,Ct,Lt,It={element:_t,gd:t,plotinfo:e};function Pt(){It.plotinfo.selection=!1,M(t)}function zt(t,r){var i=It.gd;if(i._fullLayout._activeShapeIndex>=0)i._fullLayout._deactivateShape(i);else{var o=i._fullLayout.clickmode;if(q(i),2!==t||yt||Ht(),gt)o.indexOf("select")>-1&&S(r,i,$,J,e.id,It),o.indexOf("event")>-1&&p.click(i,r,e.id);else if(1===t&&yt){var s=m?z:P,c="s"===m||"w"===y?0:1,h=s._name+".range["+c+"]",f=function(t,e){var r,n=t.range[e],i=Math.abs(n-t.range[1-e]);return"date"===t.type?n:"log"===t.type?(r=Math.ceil(Math.max(0,-Math.log(i)/Math.LN10))+3,a("."+r+"g")(Math.pow(10,n))):(r=Math.floor(Math.log(Math.abs(n))/Math.LN10)-Math.floor(Math.log(i)/Math.LN10)+4,a("."+String(r)+"g")(n))}(s,c),d="left",g="middle";if(s.fixedrange)return;m?(g="n"===m?"top":"bottom","right"===s.side&&(d="right")):"e"===y&&(d="right"),i._context.showAxisRangeEntryBoxes&&n.select(_t).call(u.makeEditable,{gd:i,immediate:!0,background:i._fullLayout.paper_bgcolor,text:String(f),fill:s.tickfont?s.tickfont.color:"#444",horizontalAlign:d,verticalAlign:g}).on("edit",(function(t){var e=s.d2r(t);void 0!==e&&l.call("_guiRelayout",i,h,e)}))}}}function Ot(e,r){if(t._transitioningWithDuration)return!1;var n=Math.max(0,Math.min(tt,pt*e+bt)),i=Math.max(0,Math.min(et,dt*r+wt)),a=Math.abs(n-bt),o=Math.abs(i-wt);function s(){St="",Tt.r=Tt.l,Tt.t=Tt.b,Ct.attr("d","M0,0Z")}if(Tt.l=Math.min(bt,n),Tt.r=Math.max(bt,n),Tt.t=Math.min(wt,i),Tt.b=Math.max(wt,i),rt.isSubplotConstrained)a>I||o>I?(St="xy",a/tt>o/et?(o=a*et/tt,wt>i?Tt.t=wt-o:Tt.b=wt+o):(a=o*tt/et,bt>n?Tt.l=bt-a:Tt.r=bt+a),Ct.attr("d",G(Tt))):s();else if(nt.isSubplotConstrained)if(a>I||o>I){St="xy";var l=Math.min(Tt.l/tt,(et-Tt.b)/et),c=Math.max(Tt.r/tt,(et-Tt.t)/et);Tt.l=l*tt,Tt.r=c*tt,Tt.b=(1-l)*et,Tt.t=(1-c)*et,Ct.attr("d",G(Tt))}else 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a=It.dragmode,s=t._fullLayout.dragmode;s!==a&&(It.dragmode=s),vt(),pt=t._fullLayout._invScaleX,dt=t._fullLayout._invScaleY,ot||(gt?e.shiftKey?"pan"===s?s="zoom":v(s)||(s="pan"):e.ctrlKey&&(s="pan"):s="pan"),x(s)?It.minDrag=1:It.minDrag=void 0,v(s)?(It.xaxes=$,It.yaxes=J,A(e,r,n,It,s)):(It.clickFn=zt,v(a)&&Pt(),ot||("zoom"===s?(It.moveFn=Ot,It.doneFn=Rt,It.minDrag=1,function(e,r,n){var a=_t.getBoundingClientRect();bt=r-a.left,wt=n-a.top,t._fullLayout._calcInverseTransform(t);var s=i.apply3DTransform(t._fullLayout._invTransform)(bt,wt);bt=s[0],wt=s[1],Tt={l:bt,r:bt,w:0,t:wt,b:wt,h:0},kt=t._hmpixcount?t._hmlumcount/t._hmpixcount:o(t._fullLayout.plot_bgcolor).getLuminance(),Mt=!1,St="xy",Lt=!1,Et=N(mt,kt,K,Q,At="M0,0H"+tt+"V"+et+"H0V0"),Ct=j(mt,K,Q)}(0,r,n)):"pan"===s&&(It.moveFn=Ut,It.doneFn=Gt))),t._fullLayout._redrag=function(){var e=t._dragdata;if(e&&e.element===_t){var r=t._fullLayout.dragmode;v(r)||(vt(),Zt([0,0,tt,et]),It.moveFn(e.dx,e.dy))}}},g.init(It);var Ft=[0,0,tt,et],Bt=null,Nt=C.REDRAWDELAY,jt=e.mainplot?t._fullLayout._plots[e.mainplot]:e;function Ut(e,r){if(e*=pt,r*=dt,!t._transitioningWithDuration){if(t._fullLayout._replotting=!0,"ew"===it||"ns"===at){var n=it?-e:0,i=at?-r:0;if(nt.isSubplotConstrained){if(it&&at){var a=(e/tt-r/et)/2;n=-(e=a*tt),i=-(r=-a*et)}at?n=-i*tt/et:i=-n*et/tt}return it&&(F($,e),Vt("x")),at&&(F(J,r),Vt("y")),Zt([n,i,tt,et]),qt(),void t.emit("plotly_relayouting",ft)}var o,s,l="w"===it==("n"===at)?1:-1;if(it&&at&&(rt.isSubplotConstrained||nt.isSubplotConstrained)){var c=(e/tt+l*r/et)/2;e=c*tt,r=l*c*et}if("w"===it?e=p($,0,e):"e"===it?e=p($,1,-e):it||(e=0),"n"===at?r=p(J,1,r):"s"===at?r=p(J,0,-r):at||(r=0),o="w"===it?e:0,s="n"===at?r:0,rt.isSubplotConstrained&&!nt.isSubplotConstrained||nt.isSubplotConstrained&&it&&at&&l>0){var 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a=r[i],o=a._id,s=nt.xLinks[o]||nt.yLinks[o],l=n[0]||V[s]||X[s];l&&(e?(e[a._name+".range[0]"]=e[l._name+".range[0]"],e[a._name+".range[1]"]=e[l._name+".range[1]"]):a.range=l.range.slice())}}function qt(){var r,n=[];function i(t){for(r=0;r<t.length;r++)t[r].fixedrange||n.push(t[r]._id)}function a(t,e){for(r=0;r<t.length;r++){var i=t[r],a=i[e];i.fixedrange||"sync"!==a.tickmode||n.push(a._id)}}for(st&&(i($),i(rt.xaxes),i(nt.xaxes),a(e.overlays,"xaxis")),lt&&(i(J),i(rt.yaxes),i(nt.yaxes),a(e.overlays,"yaxis")),ft={},r=0;r<n.length;r++){var o=n[r],s=k(t,o);d.drawOne(t,s,{skipTitle:!0}),ft[s._name+".range[0]"]=s.range[0],ft[s._name+".range[1]"]=s.range[1]}d.redrawComponents(t,n)}function Ht(){if(!t._transitioningWithDuration){var e=t._context.doubleClick,r=[];it&&(r=r.concat($)),at&&(r=r.concat(J)),nt.xaxes&&(r=r.concat(nt.xaxes)),nt.yaxes&&(r=r.concat(nt.yaxes));var n,i,a={};if("reset+autosize"===e)for(e="autosize",i=0;i<r.length;i++){var o=(n=r[i])._rangeInitial0,s=n._rangeInitial1,c=void 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){a.limitRange&&a.limitRange(),o.limitRange&&o.limitRange();var h=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[h[0],p[0],h[1],p[1]]})}if((ut||ct)&&(b(t),w(t)),ht){var d=e[2]/P._length,g=e[3]/z._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var v,x,_,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&X[o._id];if(k?(v=d,_=y||nt.isSubplotConstrained?e[0]:Xt(a,v)):nt.xaHash[a._id]?(v=d,_=e[0]*a._length/P._length):nt.yaHash[a._id]?(v=g,_="ns"===at?-e[1]*a._length/z._length:Xt(a,v,{n:"top",s:"bottom"}[at])):_=Yt(a,v=Wt(a,d,g)),v>1&&(void 0!==a.maxallowed&&st===(a.range[0]<a.range[1]?"e":"w")||void 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n=r(10229),i=r(13792).u,a=r(94850).T,o=r(74285),s=r(13582).overrideAll,l=r(62994),c={range:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},showgrid:{valType:"boolean",dflt:!1},tick0:{valType:"number",dflt:0},dtick:{valType:"number"},gridcolor:{valType:"color",dflt:n.lightLine},gridwidth:{valType:"number",min:0,dflt:1},griddash:a};(t.exports=s({domain:i({name:"geo"},{}),fitbounds:{valType:"enumerated",values:[!1,"locations","geojson"],dflt:!1,editType:"plot"},resolution:{valType:"enumerated",values:[110,50],dflt:110,coerceNumber:!0},scope:{valType:"enumerated",values:l(o.scopeDefaults),dflt:"world"},projection:{type:{valType:"enumerated",values:l(o.projNames)},rotation:{lon:{valType:"number"},lat:{valType:"number"},roll:{valType:"number"}},tilt:{valType:"number",dflt:0},distance:{valType:"number",min:1.001,dflt:2},parallels:{valType:"info_array",items:[{valType:"number"},{valType:"number"}]},scale:{valType:"number",min:0,dflt:1}},center:{lon:{valType:"number"},lat:{valType:"number"}},visible:{valType:"boolean",dflt:!0},showcoastlines:{valType:"boolean"},coastlinecolor:{valType:"color",dflt:n.defaultLine},coastlinewidth:{valType:"number",min:0,dflt:1},showland:{valType:"boolean",dflt:!1},landcolor:{valType:"color",dflt:o.landColor},showocean:{valType:"boolean",dflt:!1},oceancolor:{valType:"color",dflt:o.waterColor},showlakes:{valType:"boolean",dflt:!1},lakecolor:{valType:"color",dflt:o.waterColor},showrivers:{valType:"boolean",dflt:!1},rivercolor:{valType:"color",dflt:o.waterColor},riverwidth:{valType:"number",min:0,dflt:1},showcountries:{valType:"boolean"},countrycolor:{valType:"color",dflt:n.defaultLine},countrywidth:{valType:"number",min:0,dflt:1},showsubunits:{valType:"boolean"},subunitcolor:{valType:"color",dflt:n.defaultLine},subunitwidth:{valType:"number",min:0,dflt:1},showframe:{valType:"boolean"},framecolor:{valType:"color",dflt:n.defaultLine},framewidth:{valType:"number",min:0,dflt:1},bgcolor:{valType:"color",dflt:n.background},lonaxis:c,lataxis:c},"plot","from-root")).uirevision={valType:"any",editType:"none"}},31653:function(t,e,r){"use 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n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}(r,b),T=function(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}((o=(t=L)[0],l=t[1],c=t[2],u=t[3],[o*(h=(a=w)[0])-l*(p=a[1])-c*(d=a[2])-u*(y=a[3]),o*p+l*h+c*y-u*d,o*d-l*y+c*h+u*p,o*y+l*d-c*p+u*h])),k=i.r=function(t,e,r){var n=v(e,2,t[0]);n=v(n,1,t[1]),n=v(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],c=e[2],u=n[0],h=n[1],f=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(h)>d?(a=(h>0?90:-90)-p,i=0):(a=Math.asin(h/d)*s-p,i=Math.sqrt(d*d-h*h));var m=180-a-2*p,y=(Math.atan2(f,u)-Math.atan2(c,i))*s,x=(Math.atan2(f,u)-Math.atan2(c,-i))*s;return g(r[0],r[1],a,y)<=g(r[0],r[1],m,x)?[a,y,r[2]]:[m,x,r[2]]}(T,r,E);isFinite(k[0])&&isFinite(k[1])&&isFinite(k[2])||(k=E),e.rotate(k),E=k}}else r=m(e,M=_);f.of(this,arguments)({type:"zoom"})})),A=f.of(this,arguments),p++||A({type:"zoomstart"})})).on("zoomend",(function(){var r;n.select(this).style(c),d.call(a,"zoom",null),r=f.of(this,arguments),--p||r({type:"zoomend"}),h(t,e,y)})).on("zoom.redraw",(function(){t.render(!0);var r=e.rotate();t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.projection.rotation.lon":-r[0],"geo.projection.rotation.lat":-r[1]})})),n.rebind(a,f,"on")}function m(t,e){var r=t.invert(e);return r&&isFinite(r[0])&&isFinite(r[1])&&function(t){var e=t[0]*o,r=t[1]*o,n=Math.cos(r);return[n*Math.cos(e),n*Math.sin(e),Math.sin(r)]}(r)}function g(t,e,r,n){var i=y(r-t),a=y(n-e);return Math.sqrt(i*i+a*a)}function y(t){return(t%360+540)%360-180}function v(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),c=Math.sin(n);return i[a]=t[a]*l-t[s]*c,i[s]=t[s]*l+t[a]*c,i}function x(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}t.exports=function(t,e){var r=t.projection;return(e._isScoped?f:e._isClipped?d:p)(t,r)}},4173:function(t,e,r){"use strict";var n=r(33626),i=r(54826).SUBPLOT_PATTERN;e.fX=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},e.eV=function(t,e,r){var i,a=[],o=[];if(!(i="string"==typeof e?n.getModule(e).plot:"function"==typeof e?e:e.plot))return[a,t];for(var s=r,l=0;l<t.length;l++){var c=t[l],u=c[0].trace,h=void 0!==u.zorder;!0===u.visible&&0!==u._length&&(!u._module||u._module.plot!==i||h&&u.zorder!==s?o.push(c):a.push(c))}return[a,o]},e.KO=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,c=[];if("gl2d"===e){var u=r.match(i);o="x"+u[1],s="y"+u[2]}for(var h=0;h<t.length;h++)a=t[h],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&c.push(a):a[l]===r&&c.push(a);return c}},77055:function(t,e,r){"use strict";var n=r(99978),i=r(20573),a=r(44039),o=r(54826),s=r(74043);function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}t.exports=function(t){var e=t.mouseContainer,r=t.glplot,c=new l(e,r);function u(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function h(e,n,i){var a,s,l=t.calcDataBox(),h=r.viewBox,f=c.lastPos[0],p=c.lastPos[1],d=o.MINDRAG*r.pixelRatio,m=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=h[3]-h[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var y=n/(h[2]-h[0])*(l[2]-l[0])+l[0],v=i/(h[3]-h[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=y,c.boxStart[1]=v,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=y,c.boxEnd[1]=v,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<m,_=Math.abs(c.dragStart[1]-i)<m;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&_)x&&(c.boxEnd[0]=c.boxStart[0]),_&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var b=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*b)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*b*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(b)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(b))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/b*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(b)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(b)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case"pan":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(f-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,h),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);h(0,r[0],r[1]),h(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);h(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){h(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),h=o/(a[2]-a[0])*(i[2]-i[0])+i[0],f=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-h)*l+h,i[2]=(i[2]-h)*l+h,i[1]=(i[1]-f)*l+f,i[3]=(i[3]-f)*l+f,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},10749:function(t,e,r){"use strict";var n=r(29714),i=r(55010);function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,h,f,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),f=0;f<2;++f){var d=(e=s[f]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[f+p]=!1,this.labels[f+p]=n,this.labelColor[f+p]=i(r.title.font.color),this.labelFont[f+p]=r.title.font.family,this.labelSize[f+p]=r.title.font.size,this.labelPad[f+p]=this.getLabelPad(e,r),this.tickEnable[f+p]=!1,this.tickColor[f+p]=i((r.tickfont||{}).color),this.tickAngle[f+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[f+p]=this.getTickPad(r),this.tickMarkLength[f+p]=0,this.tickMarkWidth[f+p]=r.tickwidth||0,this.tickMarkColor[f+p]=i(r.tickcolor),this.borderLineEnable[f+p]=!1,this.borderLineColor[f+p]=i(r.linecolor),this.borderLineWidth[f+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,h=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[f]=!0:l&&(this.labelEnable[f+2]=!0),o?this.tickEnable[f]=r.showticklabels:l&&(this.tickEnable[f+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[f]=r.showline),(l||u)&&(this.borderLineEnable[f+2]=r.showline),(o||h)&&(this.tickMarkLength[f]=this.getTickMarkLength(r)),(l||h)&&(this.tickMarkLength[f+2]=this.getTickMarkLength(r)),this.gridLineEnable[f]=r.showgrid,this.gridLineColor[f]=i(r.gridcolor),this.gridLineWidth[f]=r.gridwidth,this.zeroLineEnable[f]=r.zeroline,this.zeroLineColor[f]=i(r.zerolinecolor),this.zeroLineWidth[f]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=1.5,n=e.title.font.size,i=e.showticklabels;return"xaxis"===t?"top"===e.side?n*(r+(i?1:0))-10:n*(r+(i?.5:0))-10:"yaxis"===t?"right"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},t.exports=function(t){return new a(t)}},24585:function(t,e,r){"use strict";var n=r(13582).overrideAll,i=r(27672),a=r(6704),o=r(62972),s=r(54826),l=r(37703),c=r(6811),u=r(4173).KO;e.name="gl2d",e.attr=["xaxis","yaxis"],e.idRoot=["x","y"],e.idRegex=s.idRegex,e.attrRegex=s.attrRegex,e.attributes=r(55126),e.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},e.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),e.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];s._scene2d&&0===u(t,"gl2d",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},e.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++)e._plots[r[n]]._scene2d.updateFx(e.dragmode)}},27672:function(t,e,r){"use strict";var n,i,a=r(33626),o=r(29714),s=r(32141),l=r(99098).gl_plot2d,c=r(99098).gl_spikes2d,u=r(99098).gl_select_box,h=r(22248),f=r(10749),p=r(77055),d=r(97464),m=r(84391),g=m.enforce,y=m.clean,v=r(32919).doAutoRange,x=r(70414),_=x.drawMode,b=x.selectMode,w=["xaxis","yaxis"],T=r(54826).SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=f(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}t.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=h({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=h({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var h=document.createElement("canvas");h.width=r,h.height=i;var f,p=h.getContext("2d",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":f=h.toDataURL("image/jpeg");break;case"webp":f=h.toDataURL("image/webp");break;default:f=h.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),f},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};y(s,this.xaxis),y(s,this.yaxis);var l,c,u=r._size,h=this.xaxis.domain,f=this.yaxis.domain;for(o.viewBox=[u.l+h[0]*u.w,u.b+f[0]*u.h,i-u.r-(1-h[1])*u.w,a-u.t-(1-f[1])*u.h],this.mouseContainer.style.width=u.w*(h[1]-h[0])+"px",this.mouseContainer.style.height=u.h*(f[1]-f[0])+"px",this.mouseContainer.height=u.h*(f[1]-f[0]),this.mouseContainer.style.left=u.l+h[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-f[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],v(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){b(t)||_(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var h=i._size,f=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+h.l+f[0]*h.w,l/t.pixelRatio-(h.t+(1-p[1])*h.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[m.trace.index]||{},y=m.pointIndex,v=s.castHoverinfo(g,i,y);if(v&&"all"!==v){var x=v.split("+");-1===x.indexOf("x")&&(m.traceCoord[0]=void 0),-1===x.indexOf("y")&&(m.traceCoord[1]=void 0),-1===x.indexOf("z")&&(m.traceCoord[2]=void 0),-1===x.indexOf("text")&&(m.textLabel=void 0),-1===x.indexOf("name")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter("xaxis",m.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,y,"bgcolor")||m.color,borderColor:s.castHoverOption(g,y,"bordercolor"),fontFamily:s.castHoverOption(g,y,"font.family"),fontSize:s.castHoverOption(g,y,"font.size"),fontColor:s.castHoverOption(g,y,"font.color"),nameLength:s.castHoverOption(g,y,"namelength"),textAlign:s.castHoverOption(g,y,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},2487:function(t,e,r){"use strict";var n=r(13582).overrideAll,i=r(6811),a=r(20299),o=r(4173).KO,s=r(34809),l=r(62972),c="gl3d",u="scene";e.name=c,e.attr=u,e.idRoot=u,e.idRegex=e.attrRegex=s.counterRegex("scene"),e.attributes=r(22597),e.layoutAttributes=r(77168),e.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),e.supplyLayoutDefaults=r(15250),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots[c],i=0;i<n.length;i++){var s=n[i],l=o(r,c,s),u=e[s],h=u.camera,f=u._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:h},e),u._scene=f),f.viewInitial||(f.viewInitial={up:{x:h.up.x,y:h.up.y,z:h.up.z},eye:{x:h.eye.x,y:h.eye.y,z:h.eye.z},center:{x:h.center.x,y:h.center.y,z:h.center.z}}),f.plot(l,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots[c]||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots[c],n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,u=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":u,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},e.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),u+e}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[c],n=0;n<r.length;n++)e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}},22597:function(t){"use strict";t.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},63397:function(t,e,r){"use strict";var n=r(78766),i=r(25829),a=r(93049).extendFlat,o=r(13582).overrideAll;t.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:["-","linear","log","date","category"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,autorangeoptions:{minallowed:i.autorangeoptions.minallowed,maxallowed:i.autorangeoptions.maxallowed,clipmin:i.autorangeoptions.clipmin,clipmax:i.autorangeoptions.clipmax,include:i.autorangeoptions.include,editType:"plot"},rangemode:i.rangemode,minallowed:i.minallowed,maxallowed:i.maxallowed,range:a({},i.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:i.minor.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,labelalias:i.labelalias,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},34258:function(t,e,r){"use strict";var n=r(65657).mix,i=r(34809),a=r(78032),o=r(63397),s=r(4392),l=r(97655),c=["xaxis","yaxis","zaxis"];t.exports=function(t,e,r){var u,h;function f(t,e){return i.coerce(u,h,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(h=a.newContainer(e,d))._id=d[0]+r.scene,h._name=d,s(u,h,f,r),l(u,h,f,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noAutotickangles:!0,noTicklabelindex:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelshift:!0,noTicklabelstandoff:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),f("gridcolor",n(h.color,r.bgColor,72.72727272727273).toRgbString()),f("title.text",d[0]),h.setScale=i.noop,f("showspikes")&&(f("spikesides"),f("spikethickness"),f("spikecolor",h.color)),f("showaxeslabels"),f("showbackground")&&f("backgroundcolor")}}},95701:function(t,e,r){"use strict";var n=r(55010),i=r(34809),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickFontWeight=["normal","normal","normal","normal"],this.tickFontStyle=["normal","normal","normal","normal"],this.tickFontVariant=["normal","normal","normal","normal"],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelFontWeight=["normal","normal","normal","normal"],this.labelFontStyle=["normal","normal","normal","normal"],this.labelFontVariant=["normal","normal","normal","normal"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=this,o=0;o<3;++o){var s=e[a[o]];s.visible?(r.labels[o]=t._meta?i.templateString(s.title.text,t._meta):s.title.text,"font"in s.title&&(s.title.font.color&&(r.labelColor[o]=n(s.title.font.color)),s.title.font.family&&(r.labelFont[o]=s.title.font.family),s.title.font.size&&(r.labelSize[o]=s.title.font.size),s.title.font.weight&&(r.labelFontWeight[o]=s.title.font.weight),s.title.font.style&&(r.labelFontStyle[o]=s.title.font.style),s.title.font.variant&&(r.labelFontVariant[o]=s.title.font.variant)),"showline"in s&&(r.lineEnable[o]=s.showline),"linecolor"in s&&(r.lineColor[o]=n(s.linecolor)),"linewidth"in s&&(r.lineWidth[o]=s.linewidth),"showgrid"in s&&(r.gridEnable[o]=s.showgrid),"gridcolor"in s&&(r.gridColor[o]=n(s.gridcolor)),"gridwidth"in s&&(r.gridWidth[o]=s.gridwidth),"log"===s.type?r.zeroEnable[o]=!1:"zeroline"in s&&(r.zeroEnable[o]=s.zeroline),"zerolinecolor"in s&&(r.zeroLineColor[o]=n(s.zerolinecolor)),"zerolinewidth"in s&&(r.zeroLineWidth[o]=s.zerolinewidth),"ticks"in s&&s.ticks?r.lineTickEnable[o]=!0:r.lineTickEnable[o]=!1,"ticklen"in s&&(r.lineTickLength[o]=r._defaultLineTickLength[o]=s.ticklen),"tickcolor"in s&&(r.lineTickColor[o]=n(s.tickcolor)),"tickwidth"in s&&(r.lineTickWidth[o]=s.tickwidth),"tickangle"in s&&(r.tickAngle[o]="auto"===s.tickangle?-3600:Math.PI*-s.tickangle/180),"showticklabels"in s&&(r.tickEnable[o]=s.showticklabels),"tickfont"in s&&(s.tickfont.color&&(r.tickColor[o]=n(s.tickfont.color)),s.tickfont.family&&(r.tickFont[o]=s.tickfont.family),s.tickfont.size&&(r.tickSize[o]=s.tickfont.size),s.tickfont.weight&&(r.tickFontWeight[o]=s.tickfont.weight),s.tickfont.style&&(r.tickFontStyle[o]=s.tickfont.style),s.tickfont.variant&&(r.tickFontVariant[o]=s.tickfont.variant)),"mirror"in s?-1!==["ticks","all","allticks"].indexOf(s.mirror)?(r.lineTickMirror[o]=!0,r.lineMirror[o]=!0):!0===s.mirror?(r.lineTickMirror[o]=!1,r.lineMirror[o]=!0):(r.lineTickMirror[o]=!1,r.lineMirror[o]=!1):r.lineMirror[o]=!1,"showbackground"in s&&!1!==s.showbackground?(r.backgroundEnable[o]=!0,r.backgroundColor[o]=n(s.backgroundcolor)):r.backgroundEnable[o]=!1):(r.tickEnable[o]=!1,r.labelEnable[o]=!1,r.lineEnable[o]=!1,r.lineTickEnable[o]=!1,r.gridEnable[o]=!1,r.zeroEnable[o]=!1,r.backgroundEnable[o]=!1)}},t.exports=function(t,e){var r=new o;return r.merge(t,e),r}},15250:function(t,e,r){"use strict";var n=r(34809),i=r(78766),a=r(33626),o=r(4448),s=r(34258),l=r(77168),c=r(4173).KO,u="gl3d";function h(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),h=["up","center","eye"],f=0;f<h.length;f++)r("camera."+h[f]+".x"),r("camera."+h[f]+".y"),r("camera."+h[f]+".z");r("camera.projection.type");var p=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),d=r("aspectmode",p?"manual":"auto");p||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===d&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var m=c(n.fullData,u,n.id);s(t,e,{font:n.font,scene:n.id,data:m,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var g=n.getDfltFromLayout("dragmode");if(!1!==g&&!g)if(g="orbit",t.camera&&t.camera.up){var y=t.camera.up.x,v=t.camera.up.y,x=t.camera.up.z;0!==x&&(y&&v&&x?x/Math.sqrt(y*y+v*v+x*x)>.999&&(g="turntable"):g="turntable")}else g="turntable";r("dragmode",g),r("hovermode",n.getDfltFromLayout("hovermode"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:u,attributes:l,handleDefaults:h,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},77168:function(t,e,r){"use strict";var n=r(63397),i=r(13792).u,a=r(93049).extendFlat,o=r(34809).counterRegex;function 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0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},64087:function(t,e,r){"use strict";var n=r(55010),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},32412:function(t,e,r){"use strict";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var h=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var f=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/f)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=h}}for(e.ticks=l,c=0;c<3;++c)for(o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]),d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c];t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=r(29714),i=r(34809),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},25802:function(t){"use strict";function e(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}t.exports=function(t,r){return e(t.projection,e(t.view,e(t.model,[r[0],r[1],r[2],1])))}},20299:function(t,e,r){"use strict";var n,i,a=r(99098).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(22248),c=r(74043),u=r(33626),h=r(34809),f=h.preserveDrawingBuffer(),p=r(29714),d=r(32141),m=r(55010),g=r(97464),y=r(25802),v=r(95701),x=r(64087),_=r(32412),b=r(32919).applyAutorangeOptions,w=!1;function T(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=v(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var k=T.prototype;k.prepareOptions=function(){var t=this,e={canvas:t.canvas,gl:t.gl,glOptions:{preserveDrawingBuffer:f,premultipliedAlpha:!0,antialias:!0},container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:t.camera,pixelRatio:t.pixelRatio};if(t.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");e.gl=i,e.canvas=n}return e};var A=!0;k.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||f)r=!1;else{h.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{f=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){f=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return A=!1,r},k.initializeGLCamera=function(){var t=this,e=t.fullSceneLayout.camera,r="orthographic"===e.projection.type;t.camera=o(t.container,{center:[e.center.x,e.center.y,e.center.z],eye:[e.eye.x,e.eye.y,e.eye.z],up:[e.up.x,e.up.y,e.up.z],_ortho:r,zoomMin:.01,zoomMax:100,mode:"orbit"})},k.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("touchstart",(function(){w=!0})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},k.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),_(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,f=e.glplot.selection,m=0;m<c.length;++m)"skip"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(f)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var v=y(e.glplot.cameraParams,f.dataCoordinate);t=u.data;var x,b=r._fullData[t.index],T=f.index,k={xLabel:g("x",f.traceCoordinate[0],t.xhoverformat),yLabel:g("y",f.traceCoordinate[1],t.yhoverformat),zLabel:g("z",f.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(b,e.fullLayout,T),M=(A||"").split("+"),S=A&&"all"===A;b.hovertemplate||S||(-1===M.indexOf("x")&&(k.xLabel=void 0),-1===M.indexOf("y")&&(k.yLabel=void 0),-1===M.indexOf("z")&&(k.zLabel=void 0),-1===M.indexOf("text")&&(f.textLabel=void 0),-1===M.indexOf("name")&&(u.name=void 0));var E=[];"cone"===t.type||"streamtube"===t.type?(k.uLabel=g("x",f.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf("u"))&&E.push("u: "+k.uLabel),k.vLabel=g("y",f.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf("v"))&&E.push("v: "+k.vLabel),k.wLabel=g("z",f.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf("w"))&&E.push("w: "+k.wLabel),k.normLabel=f.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf("norm"))&&E.push("norm: "+k.normLabel),"streamtube"===t.type&&(k.divergenceLabel=f.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf("divergence"))&&E.push("divergence: "+k.divergenceLabel)),f.textLabel&&E.push(f.textLabel),x=E.join("<br>")):"isosurface"===t.type||"volume"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(f.traceCoordinate[3]),t.valuehoverformat),E.push("value: "+k.valueLabel),f.textLabel&&E.push(f.textLabel),x=E.join("<br>")):x=f.textLabel;var C={x:f.traceCoordinate[0],y:f.traceCoordinate[1],z:f.traceCoordinate[2],data:b._input,fullData:b,curveNumber:b.index,pointNumber:T};d.appendArrayPointValue(C,b,T),t._module.eventData&&(C=b._module.eventData(C,f,b,{},T));var L={points:[C]};if(e.fullSceneLayout.hovermode){var I=[];d.loneHover({trace:b,x:(.5+.5*v[0]/v[3])*s,y:(.5-.5*v[1]/v[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:x,name:u.name,color:d.castHoverOption(b,T,"bgcolor")||u.color,borderColor:d.castHoverOption(b,T,"bordercolor"),fontFamily:d.castHoverOption(b,T,"font.family"),fontSize:d.castHoverOption(b,T,"font.size"),fontColor:d.castHoverOption(b,T,"font.color"),nameLength:d.castHoverOption(b,T,"namelength"),textAlign:d.castHoverOption(b,T,"align"),hovertemplate:h.castOption(b,T,"hovertemplate"),hovertemplateLabels:h.extendFlat({},C,k),eventData:[C]},{container:n,gd:r,inOut_bbox:I}),C.bbox=I[0]}f.distance<5&&(f.buttons||w)?r.emit("plotly_click",L):r.emit("plotly_hover",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},k.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):h.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var M=["xaxis","yaxis","zaxis"];function S(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=M[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(h.isArrayOrTypedArray(l))for(var f,p=0;p<(u||l.length);p++)if(h.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)f=s.d2l(l[p][d],0,c),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f));else f=s.d2l(l[p],0,c),!isNaN(f)&&isFinite(f)&&(r[0][i]=Math.min(r[0][i],f),r[1][i]=Math.max(r[1][i],f));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}k.plot=function(t,e,r){var n=this;if(n.plotArgs=[t,e,r],!n.glplot.contextLost){var i,a,o,s,l,c,u=e[n.id],h=r[n.id];n.fullLayout=e,n.fullSceneLayout=u,n.axesOptions.merge(e,u),n.spikeOptions.merge(u),n.setViewport(u),n.updateFx(u.dragmode,u.hovermode),n.camera.enableWheel=n.graphDiv._context._scrollZoom.gl3d,n.glplot.setClearColor(m(u.bgcolor)),n.setConvert(l),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(o=0;o<t.length;++o)!0===(i=t[o]).visible&&0!==i._length&&S(this,i,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=M[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var 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c=r[l];if(n.traceIs(c,o)){s=!0;break}}s&&(i(a+"mode"),i(a+"gap"),i(a+"groupgap"))}t.exports={supplyLayoutDefaults:function(t,e,r){o(0,0,r,(function(r,n){return i.coerce(t,e,a,r,n)}),"box")},_supply:o}},95419:function(t,e,r){"use strict";var n=r(45568),i=r(34809),a=r(62203);function o(t,e,r,a,o){var s,l,c="h"===r.orientation,u=e.val,h=e.pos,f=!!h.rangebreaks,p=a.bPos,d=a.wdPos||0,m=a.bPosPxOffset||0,g=r.whiskerwidth||0,y=!1!==r.showwhiskers,v=r.notched||!1,x=v?1-2*r.notchwidth:1;Array.isArray(a.bdPos)?(s=a.bdPos[0],l=a.bdPos[1]):(s=a.bdPos,l=a.bdPos);var _=t.selectAll("path.box").data("violin"!==r.type||r.box.visible?i.identity:[]);_.enter().append("path").style("vector-effect",o?"none":"non-scaling-stroke").attr("class","box"),_.exit().remove(),_.each((function(t){if(t.empty)return n.select(this).attr("d","M0,0Z");var e=h.c2l(t.pos+p,!0),a=h.l2p(e-s)+m,o=h.l2p(e+l)+m,_=f?(a+o)/2:h.l2p(e)+m,b=r.whiskerwidth,w=f?a*b+(1-b)*_:h.l2p(e-d)+m,T=f?o*b+(1-b)*_:h.l2p(e+d)+m,k=h.l2p(e-s*x)+m,A=h.l2p(e+l*x)+m,M="sd"===r.sizemode,S=u.c2p(M?t.mean-t.sd:t.q1,!0),E=M?u.c2p(t.mean+t.sd,!0):u.c2p(t.q3,!0),C=i.constrain(M?u.c2p(t.mean,!0):u.c2p(t.med,!0),Math.min(S,E)+1,Math.max(S,E)-1),L=void 0===t.lf||!1===r.boxpoints||M,I=u.c2p(L?t.min:t.lf,!0),P=u.c2p(L?t.max:t.uf,!0),z=u.c2p(t.ln,!0),O=u.c2p(t.un,!0);c?n.select(this).attr("d","M"+C+","+k+"V"+A+"M"+S+","+a+"V"+o+(v?"H"+z+"L"+C+","+A+"L"+O+","+o:"")+"H"+E+"V"+a+(v?"H"+O+"L"+C+","+k+"L"+z+","+a:"")+"Z"+(y?"M"+S+","+_+"H"+I+"M"+E+","+_+"H"+P+(0===g?"":"M"+I+","+w+"V"+T+"M"+P+","+w+"V"+T):"")):n.select(this).attr("d","M"+k+","+C+"H"+A+"M"+a+","+S+"H"+o+(v?"V"+z+"L"+A+","+C+"L"+o+","+O:"")+"V"+E+"H"+a+(v?"V"+O+"L"+k+","+C+"L"+a+","+z:"")+"Z"+(y?"M"+_+","+S+"V"+I+"M"+_+","+E+"V"+P+(0===g?"":"M"+w+","+I+"H"+T+"M"+w+","+P+"H"+T):""))}))}function s(t,e,r,n){var o=e.x,s=e.y,l=n.bdPos,c=n.bPos,u=r.boxpoints||r.points;i.seedPseudoRandom();var h=t.selectAll("g.points").data(u?function(t){return t.forEach((function(t){t.t=n,t.trace=r})),t}:[]);h.enter().append("g").attr("class","points"),h.exit().remove();var f=h.selectAll("path").data((function(t){var e,n,a=t.pts2,o=Math.max((t.max-t.min)/10,t.q3-t.q1),s=1e-9*o,h=.01*o,f=[],p=0;if(r.jitter){if(0===o)for(p=1,f=new Array(a.length),e=0;e<a.length;e++)f[e]=1;else for(e=0;e<a.length;e++){var d=Math.max(0,e-5),m=a[d].v,g=Math.min(a.length-1,e+5),y=a[g].v;"all"!==u&&(a[e].v<t.lf?y=Math.min(y,t.lf):m=Math.max(m,t.uf));var v=Math.sqrt(h*(g-d)/(y-m+s))||0;v=i.constrain(Math.abs(v),0,1),f.push(v),p=Math.max(v,p)}n=2*r.jitter/(p||1)}for(e=0;e<a.length;e++){var x=a[e],_=x.v,b=r.jitter?n*f[e]*(i.pseudoRandom()-.5):0,w=t.pos+c+l*(r.pointpos+b);"h"===r.orientation?(x.y=w,x.x=_):(x.x=w,x.y=_),"suspectedoutliers"===u&&_<t.uo&&_>t.lo&&(x.so=!0)}return a}));f.enter().append("path").classed("point",!0),f.exit().remove(),f.call(a.translatePoints,o,s)}function l(t,e,r,a){var o,s,l=e.val,c=e.pos,u=!!c.rangebreaks,h=a.bPos,f=a.bPosPxOffset||0,p=r.boxmean||(r.meanline||{}).visible;Array.isArray(a.bdPos)?(o=a.bdPos[0],s=a.bdPos[1]):(o=a.bdPos,s=a.bdPos);var d=t.selectAll("path.mean").data("box"===r.type&&r.boxmean||"violin"===r.type&&r.box.visible&&r.meanline.visible?i.identity:[]);d.enter().append("path").attr("class","mean").style({fill:"none","vector-effect":"non-scaling-stroke"}),d.exit().remove(),d.each((function(t){var e=c.c2l(t.pos+h,!0),i=c.l2p(e-o)+f,a=c.l2p(e+s)+f,d=u?(i+a)/2:c.l2p(e)+f,m=l.c2p(t.mean,!0),g=l.c2p(t.mean-t.sd,!0),y=l.c2p(t.mean+t.sd,!0);"h"===r.orientation?n.select(this).attr("d","M"+m+","+i+"V"+a+("sd"===p?"m0,0L"+g+","+d+"L"+m+","+i+"L"+y+","+d+"Z":"")):n.select(this).attr("d","M"+i+","+m+"H"+a+("sd"===p?"m0,0L"+d+","+g+"L"+i+","+m+"L"+d+","+y+"Z":""))}))}t.exports={plot:function(t,e,r,a){var c=t._context.staticPlot,u=e.xaxis,h=e.yaxis;i.makeTraceGroups(a,r,"trace boxes").each((function(t){var e,r,i=n.select(this),a=t[0],f=a.t,p=a.trace;f.wdPos=f.bdPos*p.whiskerwidth,!0!==p.visible||f.empty?i.remove():("h"===p.orientation?(e=h,r=u):(e=u,r=h),o(i,{pos:e,val:r},p,f,c),s(i,{x:u,y:h},p,f),l(i,{pos:e,val:r},p,f))}))},plotBoxAndWhiskers:o,plotPoints:s,plotBoxMean:l}},72488:function(t){"use strict";t.exports=function(t,e){var r,n,i=t.cd,a=t.xaxis,o=t.yaxis,s=[];if(!1===e)for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts||[]).length;n++)i[r].pts[n].selected=0;else for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts||[]).length;n++){var l=i[r].pts[n],c=a.c2p(l.x),u=o.c2p(l.y);e.contains([c,u],null,l.i,t)?(s.push({pointNumber:l.i,x:a.c2d(l.x),y:o.c2d(l.y)}),l.selected=1):l.selected=0}return s}},59979:function(t,e,r){"use strict";var n=r(45568),i=r(78766),a=r(62203);t.exports={style:function(t,e,r){var o=r||n.select(t).selectAll("g.trace.boxes");o.style("opacity",(function(t){return 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n=r(10721),i=r(34809),a=r(88856),o=r(62203),s=r(39532).makeBlank,l=r(3994);function c(t){var e,r=t[0].trace,n=r._opts;if(r.selectedpoints){for(var a=o.makeSelectedPointStyleFns(r),s=0;s<t.length;s++){var l=t[s];l.fOut&&(l.fOut.properties.mo2=a.selectedOpacityFn(l))}e={type:"identity",property:"mo2"}}else e=i.isArrayOrTypedArray(r.marker.opacity)?{type:"identity",property:"mo"}:r.marker.opacity;return i.extendFlat(n.fill.paint,{"fill-opacity":e}),i.extendFlat(n.line.paint,{"line-opacity":e}),n}t.exports={convert:function(t){var e=t[0].trace,r=!0===e.visible&&0!==e._length,o={layout:{visibility:"none"},paint:{}},u={layout:{visibility:"none"},paint:{}},h=e._opts={fill:o,line:u,geojson:s()};if(!r)return h;var f=l.extractTraceFeature(t);if(!f)return h;var p,d,m,g=a.makeColorScaleFuncFromTrace(e),y=e.marker,v=y.line||{};i.isArrayOrTypedArray(y.opacity)&&(p=function(t){var e=t.mo;return n(e)?+i.constrain(e,0,1):0}),i.isArrayOrTypedArray(v.color)&&(d=function(t){return 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n=r(40980).convert,i=r(40980).convertOnSelect,a=r(8814).traceLayerPrefix;function o(t,e){this.type="choroplethmap",this.subplot=t,this.uid=e,this.sourceId="source-"+e,this.layerList=[["fill",a+e+"-fill"],["line",a+e+"-line"]],this.below=null}var s=o.prototype;s.update=function(t){this._update(n(t)),t[0].trace._glTrace=this},s.updateOnSelect=function(t){this._update(i(t))},s._update=function(t){var e=this.subplot,r=this.layerList,n=e.belowLookup["trace-"+this.uid];e.map.getSource(this.sourceId).setData(t.geojson),n!==this.below&&(this._removeLayers(),this._addLayers(t,n),this.below=n);for(var i=0;i<r.length;i++){var a=r[i],o=a[0],s=a[1],l=t[o];e.setOptions(s,"setLayoutProperty",l.layout),"visible"===l.layout.visibility&&e.setOptions(s,"setPaintProperty",l.paint)}},s._addLayers=function(t,e){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var 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n=r(92802),i=r(87163),a=r(3208).rb,o=r(9829),s=r(93049).extendFlat;t.exports=s({locations:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},geojson:{valType:"any",editType:"calc"},featureidkey:s({},n.featureidkey,{}),below:{valType:"string",editType:"plot"},text:n.text,hovertext:n.hovertext,marker:{line:{color:s({},n.marker.line.color,{editType:"plot"}),width:s({},n.marker.line.width,{editType:"plot"}),editType:"calc"},opacity:s({},n.marker.opacity,{editType:"plot"}),editType:"calc"},selected:{marker:{opacity:s({},n.selected.marker.opacity,{editType:"plot"}),editType:"plot"},editType:"plot"},unselected:{marker:{opacity:s({},n.unselected.marker.opacity,{editType:"plot"}),editType:"plot"},editType:"plot"},hoverinfo:n.hoverinfo,hovertemplate:a({},{keys:["properties"]}),showlegend:s({},o.showlegend,{dflt:!1})},i("",{cLetter:"z",editTypeOverride:"calc"}))},51335:function(t,e,r){"use strict";var n=r(10721),i=r(34809),a=r(88856),o=r(62203),s=r(39532).makeBlank,l=r(3994);function c(t){var e,r=t[0].trace,n=r._opts;if(r.selectedpoints){for(var a=o.makeSelectedPointStyleFns(r),s=0;s<t.length;s++){var l=t[s];l.fOut&&(l.fOut.properties.mo2=a.selectedOpacityFn(l))}e={type:"identity",property:"mo2"}}else e=i.isArrayOrTypedArray(r.marker.opacity)?{type:"identity",property:"mo"}:r.marker.opacity;return i.extendFlat(n.fill.paint,{"fill-opacity":e}),i.extendFlat(n.line.paint,{"line-opacity":e}),n}t.exports={convert:function(t){var e=t[0].trace,r=!0===e.visible&&0!==e._length,o={layout:{visibility:"none"},paint:{}},u={layout:{visibility:"none"},paint:{}},h=e._opts={fill:o,line:u,geojson:s()};if(!r)return h;var f=l.extractTraceFeature(t);if(!f)return h;var p,d,m,g=a.makeColorScaleFuncFromTrace(e),y=e.marker,v=y.line||{};i.isArrayOrTypedArray(y.opacity)&&(p=function(t){var e=t.mo;return n(e)?+i.constrain(e,0,1):0}),i.isArrayOrTypedArray(v.color)&&(d=function(t){return t.mlc}),i.isArrayOrTypedArray(v.width)&&(m=function(t){return t.mlw});for(var x=0;x<t.length;x++){var _=t[x],b=_.fOut;if(b){var w=b.properties;w.fc=g(_.z),p&&(w.mo=p(_)),d&&(w.mlc=d(_)),m&&(w.mlw=m(_)),_.ct=w.ct,_._polygons=l.feature2polygons(b)}}var T=p?{type:"identity",property:"mo"}:y.opacity;return i.extendFlat(o.paint,{"fill-color":{type:"identity",property:"fc"},"fill-opacity":T}),i.extendFlat(u.paint,{"line-color":d?{type:"identity",property:"mlc"}:v.color,"line-width":m?{type:"identity",property:"mlw"}:v.width,"line-opacity":T}),o.layout.visibility="visible",u.layout.visibility="visible",h.geojson={type:"FeatureCollection",features:f},c(t),h},convertOnSelect:c}},8244:function(t,e,r){"use strict";var n=r(34809),i=r(39356),a=r(86227);t.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("locations"),c=s("z"),u=s("geojson");n.isArrayOrTypedArray(l)&&l.length&&n.isArrayOrTypedArray(c)&&c.length&&("string"==typeof u&&""!==u||n.isPlainObject(u))?(s("featureidkey"),e._length=Math.min(l.length,c.length),s("below"),s("text"),s("hovertext"),s("hovertemplate"),s("marker.line.width")&&s("marker.line.color"),s("marker.opacity"),i(t,e,o,s,{prefix:"",cLetter:"z"}),n.coerceSelectionMarkerOpacity(e,s)):e.visible=!1}},28128:function(t,e,r){"use strict";["*choroplethmapbox* trace is deprecated!","Please consider switching to the *choroplethmap* trace type and `map` subplots.","Learn more at: https://plotly.com/javascript/maplibre-migration/"].join(" "),t.exports={attributes:r(86227),supplyDefaults:r(8244),colorbar:r(12431),calc:r(12702),plot:r(33501),hoverPoints:r(94125),eventData:r(38414),selectPoints:r(43727),styleOnSelect:function(t,e){e&&e[0].trace._glTrace.updateOnSelect(e)},getBelow:function(t,e){for(var r=e.getMapLayers(),n=r.length-2;n>=0;n--){var i=r[n].id;if("string"==typeof i&&0===i.indexOf("water"))for(var a=n+1;a<r.length;a++)if("string"==typeof(i=r[a].id)&&-1===i.indexOf("plotly-"))return 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a=r[i],o=a[0],s=a[1],l=t[o];e.setOptions(s,"setLayoutProperty",l.layout),"visible"===l.layout.visibility&&e.setOptions(s,"setPaintProperty",l.paint)}},s._addLayers=function(t,e){for(var r=this.subplot,n=this.layerList,i=this.sourceId,a=0;a<n.length;a++){var o=n[a],s=o[0],l=t[s];r.addLayer({type:s,id:o[1],source:i,layout:l.layout,paint:l.paint},e)}},s._removeLayers=function(){for(var t=this.subplot.map,e=this.layerList,r=e.length-1;r>=0;r--)t.removeLayer(e[r][1])},s.dispose=function(){var t=this.subplot.map;this._removeLayers(),t.removeSource(this.sourceId)},t.exports=function(t,e){var r=e[0].trace,i=new o(t,r.uid),a=i.sourceId,s=n(e),l=i.below=t.belowLookup["trace-"+r.uid];return t.map.addSource(a,{type:"geojson",data:s.geojson}),i._addLayers(s,l),e[0].trace._glTrace=i,i}},49865:function(t,e,r){"use strict";var n=r(87163),i=r(80712).axisHoverFormat,a=r(3208).rb,o=r(42450),s=r(9829),l=r(93049).extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},sizemode:{valType:"enumerated",values:["scaled","absolute","raw"],editType:"calc",dflt:"scaled"},sizeref:{valType:"number",editType:"calc",min:0},anchor:{valType:"enumerated",editType:"calc",values:["tip","tail","cm","center"],dflt:"cm"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["norm"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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e=t.index=t.data.index,r=this.data.x[e],n=this.data.y[e],i=this.data.z[e],a=this.data.u[e],o=this.data.v[e],s=this.data.w[e];t.traceCoordinate=[r,n,i,a,o,s,Math.sqrt(a*a+o*o+s*s)];var c=this.data.hovertext||this.data.text;return l(c)&&void 0!==c[e]?t.textLabel=c[e]:c&&(t.textLabel=c),!0}};var f={xaxis:0,yaxis:1,zaxis:2},p={tip:1,tail:0,cm:.25,center:.5},d={tip:1,tail:1,cm:.75,center:.5};function m(t,e){var r=t.fullSceneLayout,i=t.dataScale,l={};function u(t,e){var n=r[e],o=i[f[e]];return a(t,(function(t){return n.d2l(t)*o}))}l.vectors=c(u(e.u,"xaxis"),u(e.v,"yaxis"),u(e.w,"zaxis"),e._len),l.positions=c(u(e.x,"xaxis"),u(e.y,"yaxis"),u(e.z,"zaxis"),e._len);var h=s(e);l.colormap=o(e),l.vertexIntensityBounds=[h.min/e._normMax,h.max/e._normMax],l.coneOffset=p[e.anchor];var m=e.sizemode;"scaled"===m?l.coneSize=e.sizeref||.5:"absolute"===m?l.coneSize=e.sizeref&&e._normMax?e.sizeref/e._normMax:.5:"raw"===m&&(l.coneSize=e.sizeref),l.coneSizemode=m;var g=n(l),y=e.lightposition;return g.lightPosition=[y.x,y.y,y.z],g.ambient=e.lighting.ambient,g.diffuse=e.lighting.diffuse,g.specular=e.lighting.specular,g.roughness=e.lighting.roughness,g.fresnel=e.lighting.fresnel,g.opacity=e.opacity,e._pad=d[e.anchor]*g.vectorScale*g.coneScale*e._normMax,g}h.update=function(t){this.data=t;var e=m(this.scene,t);this.mesh.update(e)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},t.exports=function(t,e){var r=t.glplot.gl,n=m(t,e),a=i(r,n),o=new u(t,e.uid);return o.mesh=a,o.data=e,a._trace=o,t.glplot.add(a),o}},17326:function(t,e,r){"use strict";var n=r(34809),i=r(39356),a=r(49865);t.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("u"),c=s("v"),u=s("w"),h=s("x"),f=s("y"),p=s("z");if(l&&l.length&&c&&c.length&&u&&u.length&&h&&h.length&&f&&f.length&&p&&p.length){var d=s("sizemode");s("sizeref","raw"===d?1:.5),s("anchor"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null}else e.visible=!1}},47050:function(t,e,r){"use strict";t.exports={moduleType:"trace",name:"cone",basePlotModule:r(2487),categories:["gl3d","showLegend"],attributes:r(49865),supplyDefaults:r(17326),colorbar:{min:"cmin",max:"cmax"},calc:r(93805),plot:r(49393),eventData:function(t,e){return t.norm=e.traceCoordinate[6],t},meta:{}}},52240:function(t,e,r){"use strict";var n=r(81658),i=r(36640),a=r(80712),o=a.axisHoverFormat,s=a.descriptionOnlyNumbers,l=r(87163),c=r(94850).T,u=r(80337),h=r(93049).extendFlat,f=r(20726),p=f.COMPARISON_OPS2,d=f.INTERVAL_OPS,m=i.line;t.exports=h({z:n.z,x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,xperiod:n.xperiod,yperiod:n.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:n.xperiodalignment,yperiodalignment:n.yperiodalignment,text:n.text,hovertext:n.hovertext,transpose:n.transpose,xtype:n.xtype,ytype:n.ytype,xhoverformat:o("x"),yhoverformat:o("y"),zhoverformat:o("z",1),hovertemplate:n.hovertemplate,texttemplate:h({},n.texttemplate,{}),textfont:h({},n.textfont,{}),hoverongaps:n.hoverongaps,connectgaps:h({},n.connectgaps,{}),fillcolor:{valType:"color",editType:"calc"},autocontour:{valType:"boolean",dflt:!0,editType:"calc",impliedEdits:{"contours.start":void 0,"contours.end":void 0,"contours.size":void 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R,F,B=[(c.left+c.right)/2,(c.top+c.bottom)/2],N=[(u.left+u.right)/2,(u.top+u.bottom)/2],j=.75*m.delta.font.size;"left"===m.delta.position&&(R=S(m,"deltaPos",0,-1*(c.width*_[m.align]+u.width*(1-_[m.align])+j),D,Math.min),F=B[1]-N[1],f={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:u.left+R,right:c.right,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"right"===m.delta.position&&(R=S(m,"deltaPos",0,c.width*(1-_[m.align])+u.width*_[m.align]+j,D,Math.max),F=B[1]-N[1],f={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:c.left,right:u.right+R,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"bottom"===m.delta.position&&(R=null,F=u.height,f={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height,bottom:c.bottom+u.height}),"top"===m.delta.position&&(R=null,F=c.top,f={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height-u.height,bottom:c.bottom}),O.attr({dx:R,dy:F})}(m._hasNumber||m._hasDelta)&&L.attr("transform",(function(){var t=i.numbersScaler(f);D+=t[2];var e,r=S(m,"numbersScale",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?y-r*f.bottom:y-r*(f.top+f.bottom)/2,m._numbersTop=r*f.top+e;var n=f[T];"center"===T&&(n=(f.left+f.right)/2);var a=g-r*n;return a=S(m,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,z,e,{numbersX:m,numbersY:E,numbersScaler:C,transitionOpts:r,onComplete:g}),O&&(L={range:P.gauge.axis.range,color:P.gauge.bgcolor,line:{color:P.gauge.bordercolor,width:0},thickness:1},I={range:P.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:P.gauge.bordercolor,width:P.gauge.borderwidth},thickness:1});var Z=z.selectAll("g.angular").data(D?e:[]);Z.exit().remove();var W=z.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,h,f,p=r[0].trace,m=a.size,g=a.radius,y=a.innerRadius,v=a.gaugeBg,x=a.gaugeOutline,_=[m.l+m.w/2,m.t+m.h/2+g/2],b=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function C(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function L(t){return n.svg.arc().innerRadius((y+g)/2-t/2*(g-y)).outerRadius((y+g)/2+t/2*(g-y)).startAngle(-E)}function I(t){t.attr("d",(function(t){return L(t.thickness).startAngle(C(t.range[0])).endAngle(C(t.range[1]))()}))}b.enter().append("g").classed("angular",!0),b.attr("transform",l(_[0],_[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var P=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},z={},O=d.makeLabelFns(o,0).labelStandoff;z.xFn=function(t){var e=P(t);return Math.cos(e)*O},z.yFn=function(t){var e=P(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(O+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},z.anchorFn=function(t){var e=P(t),r=Math.cos(e);return 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W=b.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(I).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Z,layer:W,size:B,gaugeBg:L,gaugeOutline:I,transitionOpts:r,onComplete:g});var Y=z.selectAll("g.bullet").data(R?e:[]);Y.exit().remove();var X=z.selectAll("g.bulletaxis").data(R?e:[]);X.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,h=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,y=n.size,x=u.domain,_=n.transitionOpts,b=n.onComplete;h.enter().append("g").classed("bullet",!0),h.attr("transform",l(y.l,y.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=y.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-f.bulletNumberDomainSize:1);function C(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return 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P=h.selectAll("g.value-bullet").data([u.gauge.bar]);P.enter().append("g").classed("value-bullet",!0).append("rect"),P.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(_)?P.select("rect").transition().duration(_.duration).ease(_.easing).each("end",(function(){b&&b()})).each("interrupt",(function(){b&&b()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):P.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0),P.exit().remove();var z=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),O=h.selectAll("g.threshold-bullet").data(z);O.enter().append("g").classed("threshold-bullet",!0).append("line"),O.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(v.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),O.exit().remove();var D=h.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(C).call(T),D.exit().remove()}(t,0,e,{gauge:Y,layer:X,size:B,gaugeBg:L,gaugeOutline:I,transitionOpts:r,onComplete:g});var $=z.selectAll("text.title").data(e);$.exit().remove(),$.enter().append("text").classed("title",!0),$.attr("text-anchor",(function(){return 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0,maxCats:n,count:r}}(I,A,m))}},62651:function(t,e,r){"use strict";var n=r(34809),i=r(65477).hasColorscale,a=r(39356),o=r(13792).N,s=r(59008),l=r(11660),c=r(63197),u=r(87800).isTypedArraySpec;function h(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r("values"),a=r("visible");if(i&&i.length||(a=e.visible=!1),a){r("label"),r("displayindex",e._index);var o,s=t.categoryarray,c=n.isArrayOrTypedArray(s)&&s.length>0||u(s);c&&(o="array");var h=r("categoryorder",o);"array"===h?(r("categoryarray"),r("ticktext")):(delete t.categoryarray,delete t.ticktext),c||"array"!==h||(e.categoryorder="trace")}}t.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:"dimensions",handleItemDefaults:h}),d=function(t,e,r,o,s){s("line.shape"),s("line.hovertemplate");var l=s("line.color",o.colorway[0]);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 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f=u.selectAll("g.trace.parcats").data(o,p),v=f.enter().append("g").attr("class","trace parcats");f.attr("transform",(function(t){return l(t.x,t.y)})),v.append("g").attr("class","paths");var x=f.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);x.attr("fill",(function(t){return t.model.color}));var w=x.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);b(w),x.attr("d",(function(t){return t.svgD})),w.empty()||x.sort(m),x.exit().remove(),x.on("mouseover",g).on("mouseout",y).on("click",_),v.append("g").attr("class","dimensions");var A=f.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);A.enter().append("g").attr("class","dimension"),A.attr("transform",(function(t){return l(t.x,0)})),A.exit().remove();var M=A.selectAll("g.category").data((function(t){return t.categories}),p),S=M.enter().append("g").attr("class","category");M.attr("transform",(function(t){return l(0,t.y)})),S.append("rect").attr("class","catrect").attr("pointer-events","none"),M.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(S);var E=M.selectAll("rect.bandrect").data((function(t){return t.bands}),p);E.each((function(){s.raiseToTop(this)})),E.attr("fill",(function(t){return t.color}));var O=E.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);E.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(O),E.exit().remove(),S.append("text").attr("class","catlabel").attr("pointer-events","none"),M.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),h.convertToTspans(n.select(this),e)})),S.append("text").attr("class","dimlabel"),M.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll("rect.bandrect").on("mouseover",C).on("mouseout",L),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",I).on("drag",P).on("dragend",z)),f.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),f.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=v(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],h=t.parcatsViewModel.graphDiv,f=t.parcatsViewModel.trace,p=h._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],y=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+y.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var _=t.parcatsViewModel.x+i,b=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),C=n.mouse(h)[0];o.loneHover({trace:f,x:_-d.left+m.left,y:b-d.top+m.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:C<_?"right":"left",hovertemplate:(f.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:f._input,fullData:f,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:h})}}}function y(t){if(!t.parcatsViewModel.dragDimension&&(b(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=v(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function v(t){for(var e=[],r=O(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function _(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=v(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function b(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,v(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,v(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),h=u.parcatsViewModel,f=h.model.dimensions[u.model.dimensionInd],p=h.trace,d=c.top+c.height/2;h.dimensions.length>1&&f.displayInd===h.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var m=u.model.count,g=u.model.categoryLabel,y=m/u.parcatsViewModel.model.count,v={countLabel:m,categoryLabel:g,probabilityLabel:y.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",v.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+v.categoryLabel+"):",v.probabilityLabel].join(" "));var _=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:_,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:v,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:y}]}}function C(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron,c=this;"color"===l?(function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(c),S(c,"plotly_hover",n.event)):(function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(c),M(c,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")&&("category"===l?e=E(r,a,c):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),h=c.categoryViewModel,f=h.parcatsViewModel,p=f.model.dimensions[h.model.dimensionInd],d=f.trace,m=l.y+l.height/2;f.dimensions.length>1&&p.displayInd===f.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=h.model.categoryLabel,y=c.parcatsViewModel.model.count,v=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(v+=t.count)}));var x=h.model.count,_=0;f.pathSelection.each((function(t){t.model.color===c.color&&(_+=t.model.count)}));var b=v/y,w=v/_,T=v/x,k={countLabel:v,categoryLabel:g,probabilityLabel:b.toFixed(3)},A=[];-1!==h.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==h.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color ∩ "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:y,probability:b,categorycount:x,colorcount:_,bandcolorcount:v}]}}(r,a,c):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,c)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r}))}}function L(t){var e=t.parcatsViewModel;e.dragDimension||(b(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1!==e.hoverinfoItems.indexOf("skip"))||("color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event))}function I(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function P(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],h=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==h&&s+o.height>h.y+h.height/2&&(o.model.displayInd=h.model.displayInd,h.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var f=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==f&&a.model.dragX<f.x+f.width&&(a.model.displayInd=f.model.displayInd,f.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function z(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=O(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),h=c.map((function(t){return 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s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(m)&&m.length||(e.visible=!1),f(e,m,"values",g);var y=n.extendFlat({},l.font,{size:Math.round(l.font.size/1.2)});n.coerceFont(u,"labelfont",y),n.coerceFont(u,"tickfont",y,{autoShadowDflt:!0}),n.coerceFont(u,"rangefont",y),u("labelangle"),u("labelside"),u("unselected.line.color"),u("unselected.line.opacity")}},62935:function(t,e,r){"use strict";var n=r(34809).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!("visible"in t)}},83910:function(t,e,r){"use strict";var n=r(79846);n.plot=r(58823),t.exports=n},1293:function(t,e,r){"use strict";var n=["precision highp float;","","varying vec4 fragColor;","","attribute vec4 p01_04, p05_08, p09_12, p13_16,","               p17_20, p21_24, p25_28, p29_32,","               p33_36, p37_40, p41_44, p45_48,","               p49_52, p53_56, p57_60, colors;","","uniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,","             loA, hiA, loB, hiB, loC, hiC, loD, hiD;","","uniform vec2 resolution, viewBoxPos, viewBoxSize;","uniform float maskHeight;","uniform float drwLayer; // 0: context, 1: focus, 2: pick","uniform vec4 contextColor;","uniform sampler2D maskTexture, palette;","","bool isPick    = (drwLayer > 1.5);","bool isContext = (drwLayer < 0.5);","","const vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);","const vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);","","float val(mat4 p, mat4 v) {","    return dot(matrixCompMult(p, v) * UNITS, UNITS);","}","","float axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {","    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);","    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);","    return y1 * (1.0 - ratio) + y2 * ratio;","}","","int iMod(int a, int b) {","    return a - b * (a / b);","}","","bool fOutside(float p, float lo, float hi) {","    return (lo < hi) && (lo > p || p > hi);","}","","bool vOutside(vec4 p, vec4 lo, vec4 hi) {","    return (","        fOutside(p[0], lo[0], hi[0]) ||","        fOutside(p[1], lo[1], hi[1]) ||","        fOutside(p[2], lo[2], hi[2]) ||","        fOutside(p[3], lo[3], hi[3])","    );","}","","bool mOutside(mat4 p, mat4 lo, mat4 hi) {","    return (","        vOutside(p[0], lo[0], hi[0]) ||","        vOutside(p[1], lo[1], hi[1]) ||","        vOutside(p[2], lo[2], hi[2]) ||","        vOutside(p[3], lo[3], hi[3])","    );","}","","bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {","    return mOutside(A, loA, hiA) ||","           mOutside(B, loB, hiB) ||","           mOutside(C, loC, hiC) ||","           mOutside(D, loD, hiD);","}","","bool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {","    mat4 pnts[4];","    pnts[0] = A;","    pnts[1] = B;","    pnts[2] = C;","    pnts[3] = D;","","    for(int i = 0; i < 4; ++i) {","        for(int j = 0; j < 4; ++j) {","            for(int k = 0; k < 4; ++k) {","                if(0 == iMod(","                    int(255.0 * texture2D(maskTexture,","                        vec2(","                            (float(i * 2 + j / 2) + 0.5) / 8.0,","                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight","                        ))[3]","                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),","                    2","                )) return true;","            }","        }","    }","    return false;","}","","vec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {","    float x = 0.5 * sign(v) + 0.5;","    float y = axisY(x, A, B, C, D);","    float z = 1.0 - abs(v);","","    z += isContext ? 0.0 : 2.0 * float(","        outsideBoundingBox(A, B, C, D) ||","        outsideRasterMask(A, B, C, D)","    );","","    return vec4(","        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,","        z,","        1.0","    );","}","","void main() {","    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);","    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);","    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);","    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);","","    float v = colors[3];","","    gl_Position = position(isContext, v, A, B, C, D);","","    fragColor =","        isContext ? vec4(contextColor) :","        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));","}"].join("\n"),i=["precision highp float;","","varying vec4 fragColor;","","void main() {","    gl_FragColor = fragColor;","}"].join("\n"),a=r(77911).maxDimensionCount,o=r(34809),s=1e-6,l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:"rgba",type:"uint8",mag:"nearest",min:"nearest"};function h(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function f(t,e,r,n,i,a){var 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y=t.lines.canvasOverdrag*d,v=t.domain,x=t.canvasWidth*d,_=t.canvasHeight*d,b=t.pad.l*d,w=t.pad.b*d,T=t.layoutHeight*d,k=t.layoutWidth*d,A=t.deselectedLines.color,M=t.deselectedLines.opacity;return o.extendFlat({key:h,resolution:[x,_],viewBoxPos:[s+y,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:m[0].slice(0,16),dim0B:m[0].slice(16,32),dim0C:m[0].slice(32,48),dim0D:m[0].slice(48,64),dim1A:m[1].slice(0,16),dim1B:m[1].slice(16,32),dim1C:m[1].slice(32,48),dim1D:m[1].slice(48,64),drwLayer:f,contextColor:[A[0]/255,A[1]/255,A[2]/255,"auto"!==M?A[3]*M:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:s+y)+(b-y)+k*v.x[0],scissorWidth:(n===r?x-s+y:c+.5)+(n===e?s+y:0),scissorY:l+w+T*v.y[0],scissorHeight:u,viewportX:b-y+k*v.x[0],viewportY:w+T*v.y[0],viewportWidth:x,viewportHeight:_},p)}function x(t){var e=2047,r=Math.max(0,Math.floor(t[0]*e),0),n=Math.min(e,Math.ceil(t[1]*e),e);return[Math.min(r,n),Math.max(r,n)]}t.exports=function(t,e){var 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s=e%8,l=(e-s)/8,c=Math.pow(2,s),u=g[e].brush.filter.get();if(!(u.length<2)){var h=x(u[0])[1];for(r=1;r<u.length;r++){var f=x(u[r]);for(n=h+1;n<f[0];n++)o[8*n+l]&=~c;h=Math.max(h,f[1])}}}var p={shape:[8,2048],format:"alpha",type:"uint8",mag:"nearest",min:"nearest",data:o};return b?b(p):b=k.texture(p),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(w);for(i=0;i<s;i++){var p=t[i],d=p.dim0.crossfilterDimensionIndex,m=p.dim1.crossfilterDimensionIndex,y=p.canvasX,A=p.canvasY,M=y+p.panelSizeX,S=p.plotGlPixelRatio;if(e||!I[d]||I[d][0]!==y||I[d][1]!==M){I[d]=[y,M];var C=v(r,a,o,i,d,m,y,A,p.panelSizeX,p.panelSizeY,p.dim0.crossfilterDimensionIndex,w?0:T?2:1,u,S);E.clearOnly=n;var L=e?r.lines.blockLineCount:_;f(k,P,E,L,_,C)}}},readPixel:function(t,e){return k.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new 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n=r(10721),i=r(65657),a=r(78766),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}t.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],h=e.values,f=e._length,p=e._hasValues&&f;if(e.dlabel)for(c=new Array(f),r=0;r<f;r++)c[r]=String(e.label0+r*e.dlabel);var d={},m=s(o["_"+e.type+"colormap"]),g=0,y=!1;for(r=0;r<f;r++){var v,x,_;if(p){if(v=h[r],!n(v))continue;v=+v}else v=1;void 0!==(x=c[r])&&""!==x||(x=r);var b=d[x=String(x)];void 0===b?(d[x]=a.length,(_=-1!==l.indexOf(x))||(g+=v),a.push({v:v,label:x,color:m(u[r],x),i:r,pts:[r],hidden:_})):(y=!0,(i=a[b]).v+=v,i.pts.push(r),i.hidden||(g+=v),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return 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g=l("textposition");s(t,e,n,l,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||"auto"===g||"outside"===g)&&l("automargin"),("inside"===g||"auto"===g||Array.isArray(g))&&l("insidetextorientation")}else"none"===p&&l("textposition","none");o(e,n,l);var y=l("hole");if(l("title.text")){var v=l("title.position",y?"middle center":"top center");y||"middle center"!==v||(e.title.position="top center"),i.coerceFont(l,"title.font",n.font)}l("sort"),l("direction"),l("rotation"),l("pull")}else e.visible=!1}}},50568:function(t,e,r){"use strict";var n=r(36040).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},75067:function(t,e,r){"use strict";var n=r(62203),i=r(78766);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},37252:function(t,e,r){"use strict";var n=r(34809);function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(n.isArrayOrTypedArray(t))for(var r=0;r<e.length;r++){var i=t[e[r]];if(i||0===i||""===i)return i}},e.castOption=function(t,r){return n.isArrayOrTypedArray(t)?e.getFirstFilled(t,r):t||void 0},e.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},49913:function(t,e,r){"use 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n=r(45568),i=r(44122),a=r(32141),o=r(78766),s=r(62203),l=r(34809),c=l.strScale,u=l.strTranslate,h=r(30635),f=r(84102),p=f.recordMinTextSize,d=f.clearMinTextSize,m=r(56155).TEXTPAD,g=r(37252),y=r(50568),v=r(34809).isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,h=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var f=h.hoverinfo;if(Array.isArray(f)&&(f=a.castHoverinfo({hoverinfo:[g.castOption(f,t.pts)],_module:c._module},r,0)),"all"===f&&(f="label+text+value+percent+name"),h.hovertemplate||"none"!==f&&"skip"!==f&&f){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),v=r.separators,x=[];if(f&&-1!==f.indexOf("label")&&x.push(t.label),t.text=g.castOption(h.hovertext||h.text,t.pts),f&&-1!==f.indexOf("text")){var _=t.text;l.isValidTextValue(_)&&x.push(_)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,v),f&&-1!==f.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,v),f&&-1!==f.indexOf("percent")&&x.push(t.percentLabel);var b=h.hoverlabel,w=b.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join("<br>"),name:h.hovertemplate||-1!==f.indexOf("name")?h.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(b.bgcolor,t.pts)||t.color,borderColor:g.castOption(b.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(b.namelength,t.pts),textAlign:g.castOption(b.align,t.pts),hovertemplate:g.castOption(h.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,h)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[y(t,h)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[y(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function _(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size,s=g.castOption(t.insidetextfont.weight,e.pts)||g.castOption(t.textfont.weight,e.pts)||r.weight,l=g.castOption(t.insidetextfont.style,e.pts)||g.castOption(t.textfont.style,e.pts)||r.style,c=g.castOption(t.insidetextfont.variant,e.pts)||g.castOption(t.textfont.variant,e.pts)||r.variant,u=g.castOption(t.insidetextfont.textcase,e.pts)||g.castOption(t.textfont.textcase,e.pts)||r.textcase,h=g.castOption(t.insidetextfont.lineposition,e.pts)||g.castOption(t.textfont.lineposition,e.pts)||r.lineposition,f=g.castOption(t.insidetextfont.shadow,e.pts)||g.castOption(t.textfont.shadow,e.pts)||r.shadow;return{color:n||o.contrast(e.color),family:i,size:a,weight:s,style:l,variant:c,textcase:u,lineposition:h,shadow:f}}function b(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(h.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,h="horizontal"===u,f="tangential"===u,p="radial"===u,d="auto"===u,m=[];if(!d){var g,y=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var 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c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+P(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=I(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?u=(c=r.r)/i.aspectratio:c=(u=r.r)*i.aspectratio,l=(c*=(1+i.baseratio)/2)*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var h=o*r.vTotal;"funnelarea"===i.type&&(h/=(1+i.baseratio)/2,h/=i.aspectratio),r.r=Math.sqrt(h)}}}(t,r)}function O(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},h=u("label"),f=u("text"),p=u("value"),d=u("percent"),m=n.separators;if(s=h?[e.label]:[],f){var y=g.getFirstFilled(i.text,e.pts);v(y)&&s.push(y)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var _=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),b=g.getFirstFilled(i.text,e.pts);(v(b)||""===b)&&(_.text=b),e.text=l.texttemplateString(x,_,t._fullLayout._d3locale,_,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,f=a._size;d("pie",a),b(e,t),z(e,f);var m=l.makeTraceGroups(a._pielayer,e,"trace").each((function(e){var d=n.select(this),m=e[0],y=m.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=O(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=O(a,s),r.midangle=s,n=O(a,s+=l*r.v/2),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr("stroke-linejoin","round"),d.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var b=[[[],[]],[[],[]]],T=!1;v.each((function(i,o){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,b[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var c=m.cx,u=m.cy,f=n.select(this),d=f.selectAll("path.surface").data([i]);if(d.enter().append("path").classed("surface",!0).style({"pointer-events":r?"none":"all"}),f.call(x,t,e),y.pull){var v=+g.castOption(y.pull,i.pts)||0;v>0&&(c+=v*i.pxmid[0],u+=v*i.pxmid[1])}i.cxFinal=c,i.cyFinal=u;var k=y.hole;if(i.v===m.vTotal){var A="M"+(c+i.px0[0])+","+(u+i.px0[1])+I(i.px0,i.pxmid,!0,1)+I(i.pxmid,i.px0,!0,1)+"Z";k?d.attr("d","M"+(c+k*i.px0[0])+","+(u+k*i.px0[1])+I(i.px0,i.pxmid,!1,k)+I(i.pxmid,i.px0,!1,k)+"Z"+A):d.attr("d",A)}else{var M=I(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr("d","M"+(c+k*i.px1[0])+","+(u+k*i.px1[1])+I(i.px1,i.px0,!1,k)+"l"+S*i.px0[0]+","+S*i.px0[1]+M+"Z")}else d.attr("d","M"+c+","+u+"l"+i.px0[0]+","+i.px0[1]+M+"Z")}D(t,i,m);var 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l=0;for(T=0;T<r.sourceLinks.length;T++)l+=r.sourceLinks[T].value;for(T=0;T<r.sourceLinks.length;T++)(e=r.sourceLinks[T]).concentrationOut=e.value/l;var u=0;for(T=0;T<r.targetLinks.length;T++)u+=r.targetLinks[T].value;for(T=0;T<r.targetLinks.length;T++)(e=r.targetLinks[T]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(w=0;w<Math.min(a.node.x.length,a.node.y.length,A.nodes.length);w++)if(a.node.x[w]&&a.node.y[w]){var L=[a.node.x[w]*g,a.node.y[w]*y];A.nodes[w].x0=L[0]-d/2,A.nodes[w].x1=L[0]+d/2;var I=A.nodes[w].y1-A.nodes[w].y0;A.nodes[w].y0=L[1]-I/2,A.nodes[w].y1=L[1]+I/2}"snap"===a.arrangement&&function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(w=0;w<n.length;w++){var s=t[n[w].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(x=A.nodes).forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return 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e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:a,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),tinyColorHoverHue:u.tinyRGB(i),tinyColorHoverAlpha:i.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,linkArrowLength:t.linkArrowLength,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){var e=t.linkArrowLength;if(t.link.circular)return function(t,e){var r=t.width/2,n=t.circularPathData;return"top"===t.circularLinkType?"M "+(n.targetX-e)+" "+(n.targetY+r)+" L"+(n.rightInnerExtent-e)+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r-e)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r-e)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 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n=t.link.source.x1,a=t.link.target.x0-e,o=i(n,a),s=o(.5),l=o(.5),c=t.link.y0-t.link.width/2,u=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,f=t.link.y1+t.link.width/2,p="M"+n+","+c,d="C"+s+","+c+" "+l+","+h+" "+a+","+h,m="C"+l+","+f+" "+s+","+u+" "+n+","+u,g=e>0?"L"+(a+e)+","+(h+t.link.width/2):"";return p+d+(g+="L"+a+","+f)+m+"Z"}}function S(t,e){var r=c(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s="node_"+e.pointNumber;return e.group&&(s=f.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function C(t){t.call(E)}function 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n=r(80712).axisHoverFormat,i=r(3208).ay,a=r(3208).rb,o=r(87163),s=r(80337),l=r(94850).T,c=r(94850).k,u=r(62203),h=r(32660),f=r(93049).extendFlat,p=r(19326);t.exports={x:{valType:"data_array",editType:"calc+clearAxisTypes",anim:!0},x0:{valType:"any",dflt:0,editType:"calc+clearAxisTypes",anim:!0},dx:{valType:"number",dflt:1,editType:"calc",anim:!0},y:{valType:"data_array",editType:"calc+clearAxisTypes",anim:!0},y0:{valType:"any",dflt:0,editType:"calc+clearAxisTypes",anim:!0},dy:{valType:"number",dflt:1,editType:"calc",anim:!0},xperiod:{valType:"any",dflt:0,editType:"calc"},yperiod:{valType:"any",dflt:0,editType:"calc"},xperiod0:{valType:"any",editType:"calc"},yperiod0:{valType:"any",editType:"calc"},xperiodalignment:{valType:"enumerated",values:["start","middle","end"],dflt:"middle",editType:"calc"},yperiodalignment:{valType:"enumerated",values:["start","middle","end"],dflt:"middle",editType:"calc"},xhoverformat:n("x"),yhoverformat:n("y"),offsetgroup:{valType:"string",dflt:"",editType:"calc"},alignmentgroup:{valType:"string",dflt:"",editType:"calc"},stackgroup:{valType:"string",dflt:"",editType:"calc"},orientation:{valType:"enumerated",values:["v","h"],editType:"calc"},groupnorm:{valType:"enumerated",values:["","fraction","percent"],dflt:"",editType:"calc"},stackgaps:{valType:"enumerated",values:["infer 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_=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];_&&("tozerox"===y||"tonextx"===y&&(p||"h"===d))?v.tozero=!0:(e.error_y||{}).visible||"tonexty"!==y&&"tozeroy"!==y&&(l.hasMarkers(e)||l.hasText(e))||(v.padded=!1,v.ppad=0),_&&("tozeroy"===y||"tonexty"===y&&(p||"v"===d))?x.tozero=!0:"tonextx"!==y&&"tozerox"!==y||(x.padded=!1),h&&(e._extremes[h]=a.findExtremes(r,i,v)),f&&(e._extremes[f]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),h=0;h<e;h++)u[h]=r(c[h]);return u}return r(n.size)}}function d(t,e){var r=m(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function m(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var i=t.stackgroup;if(i){var 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n=r(36640),i=r(80337),a=r(87163),o=r(80712).axisHoverFormat,s=r(3208).rb,l=r(3208).ay,c=r(9829),u=r(84770),h=r(49467),f=r(93049).extendFlat,p=r(13582).overrideAll,d=r(62994),m=n.line,g=n.marker,y=g.line,v=f({width:m.width,dash:{valType:"enumerated",values:d(u),dflt:"solid"}},a("line")),x=t.exports=p({x:n.x,y:n.y,z:{valType:"data_array"},text:f({},n.text,{}),texttemplate:l({},{}),hovertext:f({},n.hovertext,{}),hovertemplate:s(),xhoverformat:o("x"),yhoverformat:o("y"),zhoverformat:o("z"),mode:f({},n.mode,{dflt:"lines+markers"}),surfaceaxis:{valType:"enumerated",values:[-1,0,1,2],dflt:-1},surfacecolor:{valType:"color"},projection:{x:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}},y:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}},z:{show:{valType:"boolean",dflt:!1},opacity:{valType:"number",min:0,max:1,dflt:1},scale:{valType:"number",min:0,max:10,dflt:2/3}}},connectgaps:n.connectgaps,line:v,marker:f({symbol:{valType:"enumerated",values:d(h),dflt:"circle",arrayOk:!0},size:f({},g.size,{dflt:8}),sizeref:g.sizeref,sizemin:g.sizemin,sizemode:g.sizemode,opacity:f({},g.opacity,{arrayOk:!1}),colorbar:g.colorbar,line:f({width:f({},y.width,{arrayOk:!1})},a("marker.line"))},a("marker")),textposition:f({},n.textposition,{dflt:"top 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n=[i(t.x,t.error_x,e[0],r.xaxis),i(t.y,t.error_y,e[1],r.yaxis),i(t.z,t.error_z,e[2],r.zaxis)],a=function(t){for(var e=0;e<t.length;e++)if(t[e])return t[e].length;return 0}(n);if(0===a)return null;for(var o=new Array(a),s=0;s<a;s++){for(var l=[[0,0,0],[0,0,0]],c=0;c<3;c++)if(n[c])for(var u=0;u<2;u++)l[u][c]=n[c][s][u];o[s]=l}return o}},16533:function(t,e,r){"use strict";var n=r(99098).gl_line3d,i=r(99098).gl_scatter3d,a=r(99098).gl_error3d,o=r(99098).gl_mesh3d,s=r(99098).delaunay_triangulate,l=r(34809),c=r(55010),u=r(46998).formatColor,h=r(92527),f=r(84770),p=r(49467),d=r(29714),m=r(36040).appendArrayPointValue,g=r(95447);function y(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode="",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var v=y.prototype;function x(t){return 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t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel="",this.textLabels&&(l.isArrayOrTypedArray(this.textLabels)?(this.textLabels[e]||0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},v.update=function(t){var e,r,p,y,v=this.scene.glplot.gl,k=f.solid;this.data=t;var M=function(t,e){var r,n,i,a,o,s,f=[],p=t.fullSceneLayout,y=t.dataScale,v=p.xaxis,k=p.yaxis,A=p.zaxis,M=e.marker,S=e.line,E=e.x||[],C=e.y||[],L=e.z||[],I=E.length,P=e.xcalendar,z=e.ycalendar,O=e.zcalendar;for(o=0;o<I;o++)r=v.d2l(E[o],0,P)*y[0],n=k.d2l(C[o],0,z)*y[1],i=A.d2l(L[o],0,O)*y[2],f[o]=[r,n,i];if(Array.isArray(e.text))s=e.text;else if(l.isTypedArray(e.text))s=Array.from(e.text);else if(void 0!==e.text)for(s=new Array(I),o=0;o<I;o++)s[o]=e.text;function D(t,e){var r=p[t];return d.tickText(r,r.d2l(e),!0).text}var R=e.texttemplate;if(R){var F=t.fullLayout._d3locale,B=Array.isArray(R),N=B?Math.min(R.length,I):I,j=B?function(t){return R[t]}:function(){return R};for(s=new Array(N),o=0;o<N;o++){var U={x:E[o],y:C[o],z:L[o]},V={xLabel:D("xaxis",E[o]),yLabel:D("yaxis",C[o]),zLabel:D("zaxis",L[o])},q={};m(q,e,o);var H=e._meta||{};s[o]=l.texttemplateString(j(o),V,F,q,U,H)}}if(a={position:f,mode:e.mode,text:s},"line"in e&&(a.lineColor=u(S,1,I),a.lineWidth=S.width,a.lineDashes=S.dash),"marker"in e){var G=h(e);a.scatterColor=u(M,1,I),a.scatterSize=T(M.size,I,b,20,G),a.scatterMarker=T(M.symbol,I,w,"●"),a.scatterLineWidth=M.line.width,a.scatterLineColor=u(M.line,1,I),a.scatterAngle=0}"textposition"in e&&(a.textOffset=function(t){var e=[0,0];if(Array.isArray(t))for(var r=0;r<t.length;r++)e[r]=[0,0],t[r]&&(e[r][0]=x(t[r]),e[r][1]=_(t[r]));else e[0]=x(t),e[1]=_(t);return e}(e.textposition),a.textColor=u(e.textfont,1,I),a.textSize=T(e.textfont.size,I,l.identity,12),a.textFontFamily=e.textfont.family,a.textFontWeight=e.textfont.weight,a.textFontStyle=e.textfont.style,a.textFontVariant=e.textfont.variant,a.textAngle=0);var Z=["x","y","z"];for(a.project=[!1,!1,!1],a.projectScale=[1,1,1],a.projectOpacity=[1,1,1],o=0;o<3;++o){var W=e.projection[Z[o]];(a.project[o]=W.show)&&(a.projectOpacity[o]=W.opacity,a.projectScale[o]=W.scale)}a.errorBounds=g(e,y,p);var Y=function(t){for(var e=[0,0,0],r=[[0,0,0],[0,0,0],[0,0,0]],n=[1,1,1],i=0;i<3;i++){var a=t[i];a&&!1!==a.copy_zstyle&&!1!==t[2].visible&&(a=t[2]),a&&a.visible&&(e[i]=a.width/2,r[i]=c(a.color),n[i]=a.thickness)}return{capSize:e,color:r,lineWidth:n}}([e.error_x,e.error_y,e.error_z]);return a.errorColor=Y.color,a.errorLineWidth=Y.lineWidth,a.errorCapSize=Y.capSize,a.delaunayAxis=e.surfaceaxis,a.delaunayColor=c(e.surfacecolor),a}(this.scene,t);"mode"in M&&(this.mode=M.mode),"lineDashes"in M&&M.lineDashes in f&&(k=f[M.lineDashes]),this.color=A(M.scatterColor)||A(M.lineColor),this.dataPoints=M.position,e={gl:this.scene.glplot.gl,position:M.position,color:M.lineColor,lineWidth:M.lineWidth||1,dashes:k[0],dashScale:k[1],opacity:t.opacity,connectGaps:t.connectgaps},-1!==this.mode.indexOf("lines")?this.linePlot?this.linePlot.update(e):(this.linePlot=n(e),this.linePlot._trace=this,this.scene.glplot.add(this.linePlot)):this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose(),this.linePlot=null);var S=t.opacity;if(t.marker&&void 0!==t.marker.opacity&&(S*=t.marker.opacity),r={gl:this.scene.glplot.gl,position:M.position,color:M.scatterColor,size:M.scatterSize,glyph:M.scatterMarker,opacity:S,orthographic:!0,lineWidth:M.scatterLineWidth,lineColor:M.scatterLineColor,project:M.project,projectScale:M.projectScale,projectOpacity:M.projectOpacity},-1!==this.mode.indexOf("markers")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),y={gl:this.scene.glplot.gl,position:M.position,glyph:M.text,color:M.textColor,size:M.textSize,angle:M.textAngle,alignment:M.textOffset,font:M.textFontFamily,fontWeight:M.textFontWeight,fontStyle:M.textFontStyle,fontVariant:M.textFontVariant,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf("text")?this.textMarkers?this.textMarkers.update(y):(this.textMarkers=i(y),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),p={gl:this.scene.glplot.gl,position:M.position,color:M.errorColor,error:M.errorBounds,lineWidth:M.errorLineWidth,capSize:M.errorCapSize,opacity:t.opacity},this.errorBars?M.errorBounds?this.errorBars.update(p):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):M.errorBounds&&(this.errorBars=a(p),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),M.delaunayAxis>=0){var E=function(t,e,r){var n,i=(r+1)%3,a=(r+2)%3,o=[],l=[];for(n=0;n<t.length;++n){var c=t[n];!isNaN(c[i])&&isFinite(c[i])&&!isNaN(c[a])&&isFinite(c[a])&&(o.push([c[i],c[a]]),l.push(n))}var u=s(o);for(n=0;n<u.length;++n)for(var h=u[n],f=0;f<h.length;++f)h[f]=l[h[f]];return{positions:t,cells:u,meshColor:e}}(M.position,M.delaunayColor,M.delaunayAxis);E.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(E):(E.gl=v,this.delaunayMesh=o(E),this.delaunayMesh._trace=this,this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},v.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},t.exports=function(t,e){var r=new y(t,e.uid);return r.update(e),r}},82418:function(t,e,r){"use strict";var n=r(33626),i=r(34809),a=r(64726),o=r(24272),s=r(98168),l=r(663),c=r(14117);t.exports=function(t,e,r,u){function h(r,n){return i.coerce(t,e,c,r,n)}var f=function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");return n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a),a}(t,e,h,u);if(f){h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),h("mode"),a.hasMarkers(e)&&o(t,e,r,u,h,{noSelect:!0,noAngle:!0}),a.hasLines(e)&&(h("connectgaps"),s(t,e,r,u,h)),a.hasText(e)&&(h("texttemplate"),l(t,e,u,h,{noSelect:!0,noFontShadow:!0,noFontLineposition:!0,noFontTextcase:!0}));var p=(e.line||{}).color,d=(e.marker||{}).color;h("surfaceaxis")>=0&&h("surfacecolor",p||d);for(var m=["x","y","z"],g=0;g<3;++g){var y="projection."+m[g];h(y+".show")&&(h(y+".opacity"),h(y+".scale"))}var v=n.getComponentMethod("errorbars","supplyDefaults");v(t,e,p||d||r,{axis:"z"}),v(t,e,p||d||r,{axis:"y",inherit:"z"}),v(t,e,p||d||r,{axis:"x",inherit:"z"})}else e.visible=!1}},17822:function(t,e,r){"use 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n=r(19326),i=r(36640),a=r(9829),o=r(3208).rb,s=r(3208).ay,l=r(87163),c=r(93049).extendFlat,u=i.marker,h=i.line,f=u.line;t.exports={carpet:{valType:"string",editType:"calc"},a:{valType:"data_array",editType:"calc"},b:{valType:"data_array",editType:"calc"},mode:c({},i.mode,{dflt:"markers"}),text:c({},i.text,{}),texttemplate:s({editType:"plot"},{keys:["a","b","text"]}),hovertext:c({},i.hovertext,{}),line:{color:h.color,width:h.width,dash:h.dash,backoff:h.backoff,shape:c({},h.shape,{values:["linear","spline"]}),smoothing:h.smoothing,editType:"calc"},connectgaps:i.connectgaps,fill:c({},i.fill,{values:["none","toself","tonext"],dflt:"none"}),fillcolor:n(),marker:c({symbol:u.symbol,opacity:u.opacity,maxdisplayed:u.maxdisplayed,angle:u.angle,angleref:u.angleref,standoff:u.standoff,size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,line:c({width:f.width,editType:"calc"},l("marker.line")),gradient:u.gradient,editType:"calc"},l("marker")),textfont:i.textfont,textposition:i.textposition,selected:i.selected,unselected:i.unselected,hoverinfo:c({},a.hoverinfo,{flags:["a","b","text","name"]}),hoveron:i.hoveron,hovertemplate:o(),zorder:i.zorder}},68001:function(t,e,r){"use 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i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=x(t,i)}return n}function k(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var A=m.SYMBOL_SDF_SIZE,M=m.SYMBOL_SIZE,S=m.SYMBOL_STROKE,E={},C=c.symbolFuncs[0](.05*M);function L(t,e){var r,n,a=t.mx;if("circle"===a)return null;var o=c.symbolNumber(a),s=c.symbolFuncs[o%100],l=!!c.symbolNoDot[o%100],u=!!c.symbolNoFill[o%100],h=d.isDotSymbol(a);if(t.ma&&(a+="_"+t.ma),E[a])return E[a];var f=c.getMarkerAngle(t,e);return r=h&&!l?s(1.1*M,f)+C:s(M,f),n=i(r,{w:A,h:A,viewBox:[-M,-M,M,M],stroke:u?S:-S}),E[a]=n,n||null}t.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)return 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V={viewport:d(y,x,_,t._context.plotGlPixelRatio),range:[(x._rl||x.range)[0],(_._rl||_.range)[0],(x._rl||x.range)[1],(_._rl||_.range)[1]]},q=s.repeat(V,v.count);v.fill2d&&v.fill2d.update(q),v.line2d&&v.line2d.update(q),v.error2d&&v.error2d.update(q.concat(q)),v.scatter2d&&v.scatter2d.update(q),v.select2d&&v.select2d.update(q),v.glText&&v.glText.forEach((function(t){t.update(V)}))}else v.init()}}).reglPrecompiled=p},62336:function(t,e,r){"use strict";var n=r(34809);t.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,h=0;h<t;h++){if(e&&r.fillOrder[h]&&e.draw(r.fillOrder[h]),a&&r.lineOptions[h]&&a.draw(h),i&&(r.errorXOptions[h]&&i.draw(h),r.errorYOptions[h]&&i.draw(h+t)),o&&r.markerOptions[h])if(u[h].length){var f=n.repeat([],r.count);f[h]=u[h],o.draw(f)}else 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m=n.hasText(l),g=n.hasMarkers(l),y=!g&&!m;if(!0!==l.visible||y)return s;var v=[],x=[];if(!1!==e&&!e.degenerate)for(var _=0;_<u;_++)e.contains([c.xpx[_],c.ypx[_]],!1,_,t)?(v.push(_),s.push({pointNumber:_,x:a.c2d(h[_]),y:o.c2d(f[_])})):x.push(_);if(g){var b=p.scatter2d;if(v.length||x.length){if(!p.selectBatch[d].length&&!p.unselectBatch[d].length){var w=new Array(p.count);w[d]=p.markerUnselectedOptions[d],b.update.apply(b,w)}}else{var T=new Array(p.count);T[d]=p.markerOptions[d],b.update.apply(b,T)}}return p.selectBatch[d]=v,p.unselectBatch[d]=x,m&&i(r),s}},71388:function(t,e,r){"use strict";var n=r(3208).rb,i=r(3208).ay,a=r(19326),o=r(6893),s=r(36640),l=r(8257),c=r(9829),u=r(87163),h=r(93049).extendFlat,f=r(13582).overrideAll,p=r(8257),d=o.line,m=o.marker;t.exports=f({lon:o.lon,lat:o.lat,cluster:{enabled:{valType:"boolean"},maxzoom:h({},p.layers.maxzoom,{}),step:{valType:"number",arrayOk:!0,dflt:-1,min:-1},size:{valType:"number",arrayOk:!0,dflt:20,min:0},color:{valType:"color",arrayOk:!0},opacity:h({},m.opacity,{dflt:1})},mode:h({},s.mode,{dflt:"markers"}),text:h({},s.text,{}),texttemplate:i({editType:"plot"},{keys:["lat","lon","text"]}),hovertext:h({},s.hovertext,{}),line:{color:d.color,width:d.width},connectgaps:s.connectgaps,marker:h({symbol:{valType:"string",dflt:"circle",arrayOk:!0},angle:{valType:"number",dflt:"auto",arrayOk:!0},allowoverlap:{valType:"boolean",dflt:!1},opacity:m.opacity,size:m.size,sizeref:m.sizeref,sizemin:m.sizemin,sizemode:m.sizemode},u("marker")),fill:o.fill,fillcolor:a(),textfont:l.layers.symbol.textfont,textposition:l.layers.symbol.textposition,below:{valType:"string"},selected:{marker:s.selected.marker},unselected:{marker:s.unselected.marker},hoverinfo:h({},c.hoverinfo,{flags:["lon","lat","text","name"]}),hovertemplate:n()},"calc","nested")},13624:function(t){"use 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n=r(10721),i=r(34809),a=r(63821).BADNUM,o=r(39532),s=r(88856),l=r(62203),c=r(92527),u=r(64726),h=r(13624).isSupportedFont,f=r(4657),p=r(36040).appendArrayPointValue,d=r(30635).NEWLINES,m=r(30635).BR_TAG_ALL;function g(t){return{type:t,geojson:o.makeBlank(),layout:{visibility:"none"},filter:null,paint:{}}}function y(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return""}function x(t){return t[0]===a}function _(t,e){var r;if(i.isArrayOrTypedArray(t)&&i.isArrayOrTypedArray(e)){r=["step",["get","point_count"],t[0]];for(var n=1;n<t.length;n++)r.push(e[n-1],t[n])}else r=t;return r}function b(t){var e=t.textfont,r=e.family,n=e.style,i=e.weight,a=r.split(" "),o="Italic"===a[a.length-1];o&&a.pop(),o=o||"italic"===n;var s=a.join(" ");return"bold"===i&&-1===a.indexOf("Bold")?s+=" Bold":i<=1e3&&("Metropolis"===a[0]?(s="Metropolis",s+=i>850?" Black":i>750?" Extra Bold":i>650?" Bold":i>550?" Semi Bold":i>450?" Medium":i>350?" Regular":i>250?" Light":i>150?" Extra Light":" Thin"):"Open Sans"===a.slice(0,2).join(" ")?(s="Open Sans",s+=i>750?" Extrabold":i>650?" Bold":i>550?" Semibold":i>350?" Regular":" Light"):"Klokantech Noto Sans"===a.slice(0,3).join(" ")&&(s="Klokantech Noto Sans","CJK"===a[3]&&(s+=" CJK"),s+=i>500?" Bold":" Regular")),o&&(s+=" Italic"),"Open Sans Regular Italic"===s?s="Open Sans Italic":"Open Sans Regular Bold"===s?s="Open Sans Bold":"Open Sans Regular Bold Italic"===s?s="Open Sans Bold Italic":"Klokantech Noto Sans Regular Italic"===s&&(s="Klokantech Noto Sans Italic"),h(s)||(s=r),s.split(", ")}t.exports=function(t,e){var r,a=e[0].trace,h=!0===a.visible&&0!==a._length,w="none"!==a.fill,T=u.hasLines(a),k=u.hasMarkers(a),A=u.hasText(a),M=k&&"circle"===a.marker.symbol,S=k&&"circle"!==a.marker.symbol,E=a.cluster&&a.cluster.enabled,C=g("fill"),L=g("line"),I=g("circle"),P=g("symbol"),z={fill:C,line:L,circle:I,symbol:P};if(!h)return z;if((w||T)&&(r=o.calcTraceToLineCoords(e)),w&&(C.geojson=o.makePolygon(r),C.layout.visibility="visible",i.extendFlat(C.paint,{"fill-color":a.fillcolor})),T&&(L.geojson=o.makeLine(r),L.layout.visibility="visible",i.extendFlat(L.paint,{"line-width":a.line.width,"line-color":a.line.color,"line-opacity":a.opacity})),M){var O=function(t){var e,r,a,o,u=t[0].trace,h=u.marker,f=u.selectedpoints,p=i.isArrayOrTypedArray(h.color),d=i.isArrayOrTypedArray(h.size),m=i.isArrayOrTypedArray(h.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,"marker")?s.makeColorScaleFuncFromTrace(h):i.identity),d&&(a=c(u)),m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var y,v,_=[];for(e=0;e<t.length;e++){var b=t[e],w=b.lonlat;if(!x(w)){var T={};r&&(T.mcc=b.mcc=r(b.mc)),a&&(T.mrc=b.mrc=a(b.ms)),o&&(T.mo=o(b.mo)),f&&(T.selected=b.selected||0),_.push({type:"Feature",id:e+1,geometry:{type:"Point",coordinates:w},properties:T})}}if(f)for(y=l.makeSelectedPointStyleFns(u),e=0;e<_.length;e++){var k=_[e].properties;y.selectedOpacityFn&&(k.mo=g(y.selectedOpacityFn(k))),y.selectedColorFn&&(k.mcc=y.selectedColorFn(k)),y.selectedSizeFn&&(k.mrc=y.selectedSizeFn(k))}return{geojson:{type:"FeatureCollection",features:_},mcc:p||y&&y.selectedColorFn?{type:"identity",property:"mcc"}:h.color,mrc:d||y&&y.selectedSizeFn?{type:"identity",property:"mrc"}:(v=h.size,v/2),mo:m||y&&y.selectedOpacityFn?{type:"identity",property:"mo"}:g(h.opacity)}}(e);I.geojson=O.geojson,I.layout.visibility="visible",E&&(I.filter=["!",["has","point_count"]],z.cluster={type:"circle",filter:["has","point_count"],layout:{visibility:"visible"},paint:{"circle-color":_(a.cluster.color,a.cluster.step),"circle-radius":_(a.cluster.size,a.cluster.step),"circle-opacity":_(a.cluster.opacity,a.cluster.step)}},z.clusterCount={type:"symbol",filter:["has","point_count"],paint:{},layout:{"text-field":"{point_count_abbreviated}","text-font":b(a),"text-size":12}}),i.extendFlat(I.paint,{"circle-color":O.mcc,"circle-radius":O.mrc,"circle-opacity":O.mo})}if(M&&E&&(I.filter=["!",["has","point_count"]]),(S||A)&&(P.geojson=function(t,e){for(var r=e._fullLayout,n=t[0].trace,a=n.marker||{},o=a.symbol,s=a.angle,l="circle"!==o?y(o):v,c="auto"!==s?y(s,!0):v,h=u.hasText(n)?y(n.text):v,f=[],g=0;g<t.length;g++){var _=t[g];if(!x(_.lonlat)){var b,w=n.texttemplate;if(w){var T=Array.isArray(w)?w[g]||"":w,k=n._module.formatLabels(_,n,r),A={};p(A,n,_.i);var M=n._meta||{};b=i.texttemplateString(T,k,r._d3locale,A,_,M)}else b=h(g);b&&(b=b.replace(d,"").replace(m,"\n")),f.push({type:"Feature",geometry:{type:"Point",coordinates:_.lonlat},properties:{symbol:l(g),angle:c(g),text:b}})}}return{type:"FeatureCollection",features:f}}(e,t),i.extendFlat(P.layout,{visibility:"visible","icon-image":"{symbol}-15","text-field":"{text}"}),S&&(i.extendFlat(P.layout,{"icon-size":a.marker.size/10}),"angle"in a.marker&&"auto"!==a.marker.angle&&i.extendFlat(P.layout,{"icon-rotate":{type:"identity",property:"angle"},"icon-rotation-alignment":"map"}),P.layout["icon-allow-overlap"]=a.marker.allowoverlap,i.extendFlat(P.paint,{"icon-opacity":a.opacity*a.marker.opacity,"icon-color":a.marker.color})),A)){var D=(a.marker||{}).size,R=f(a.textposition,D);i.extendFlat(P.layout,{"text-size":a.textfont.size,"text-anchor":R.anchor,"text-offset":R.offset,"text-font":b(a)}),i.extendFlat(P.paint,{"text-color":a.textfont.color,"text-opacity":a.opacity})}return z}},57387:function(t,e,r){"use strict";var n=r(34809),i=r(64726),a=r(24272),o=r(98168),s=r(663),l=r(54114),c=r(71388),u=r(13624).isSupportedFont;t.exports=function(t,e,r,h){function f(r,i){return n.coerce(t,e,c,r,i)}function p(r,i){return n.coerce2(t,e,c,r,i)}var d=function(t,e,r){var n=r("lon")||[],i=r("lat")||[],a=Math.min(n.length,i.length);return 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n=r(10721),i=r(34809),a=r(63821).BADNUM,o=r(39532),s=r(88856),l=r(62203),c=r(92527),u=r(64726),h=r(2795).isSupportedFont,f=r(2178),p=r(36040).appendArrayPointValue,d=r(30635).NEWLINES,m=r(30635).BR_TAG_ALL;function g(t){return{type:t,geojson:o.makeBlank(),layout:{visibility:"none"},filter:null,paint:{}}}function y(t,e){return i.isArrayOrTypedArray(t)?e?function(e){return n(t[e])?+t[e]:0}:function(e){return t[e]}:t?function(){return t}:v}function v(){return""}function x(t){return t[0]===a}function _(t,e){var r;if(i.isArrayOrTypedArray(t)&&i.isArrayOrTypedArray(e)){r=["step",["get","point_count"],t[0]];for(var n=1;n<t.length;n++)r.push(e[n-1],t[n])}else r=t;return r}function b(t){var e=t.textfont,r=e.family,n=e.style,i=e.weight,a=r.split(" "),o="Italic"===a[a.length-1];o&&a.pop(),o=o||"italic"===n;var s=a.join(" ");return"bold"===i&&-1===a.indexOf("Bold")?s+=" Bold":i<=1e3&&("Metropolis"===a[0]?(s="Metropolis",s+=i>850?" 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a.marker&&"auto"!==a.marker.angle&&i.extendFlat(P.layout,{"icon-rotate":{type:"identity",property:"angle"},"icon-rotation-alignment":"map"}),P.layout["icon-allow-overlap"]=a.marker.allowoverlap,i.extendFlat(P.paint,{"icon-opacity":a.opacity*a.marker.opacity,"icon-color":a.marker.color})),A)){var D=(a.marker||{}).size,R=f(a.textposition,D);i.extendFlat(P.layout,{"text-size":a.textfont.size,"text-anchor":R.anchor,"text-offset":R.offset,"text-font":b(a)}),i.extendFlat(P.paint,{"text-color":a.textfont.color,"text-opacity":a.opacity})}return z}},38302:function(t,e,r){"use strict";var n=r(34809),i=r(64726),a=r(24272),o=r(98168),s=r(663),l=r(54114),c=r(95833),u=r(2795).isSupportedFont;t.exports=function(t,e,r,h){function f(r,i){return n.coerce(t,e,c,r,i)}function p(r,i){return n.coerce2(t,e,c,r,i)}var d=function(t,e,r){var n=r("lon")||[],i=r("lat")||[],a=Math.min(n.length,i.length);return 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n=r(8738),i=r(92089),a=r(3208).ay;t.exports={mode:n.mode,r:n.r,theta:n.theta,r0:n.r0,dr:n.dr,theta0:n.theta0,dtheta:n.dtheta,thetaunit:n.thetaunit,text:n.text,texttemplate:a({editType:"plot"},{keys:["r","theta","text"]}),hovertext:n.hovertext,hovertemplate:n.hovertemplate,line:{color:i.line.color,width:i.line.width,dash:i.line.dash,editType:"calc"},connectgaps:i.connectgaps,marker:i.marker,fill:i.fill,fillcolor:i.fillcolor,textposition:i.textposition,textfont:i.textfont,hoverinfo:n.hoverinfo,selected:n.selected,unselected:n.unselected}},25796:function(t,e,r){"use strict";t.exports={moduleType:"trace",name:"scatterpolargl",basePlotModule:r(31645),categories:["gl","regl","polar","symbols","showLegend","scatter-like"],attributes:r(58319),supplyDefaults:r(14952),colorbar:r(21146),formatLabels:r(94015),calc:r(71535),hoverPoints:r(47522).hoverPoints,selectPoints:r(17168),meta:{}}},71535:function(t,e,r){"use strict";var n=r(77272),i=r(26544).calcMarkerSize,a=r(19937),o=r(29714),s=r(29483).TOO_MANY_POINTS;t.exports=function(t,e){var r=t._fullLayout,l=e.subplot,c=r[l].radialaxis,u=r[l].angularaxis,h=e._r=c.makeCalcdata(e,"r"),f=e._theta=u.makeCalcdata(e,"theta"),p=e._length,d={};p<h.length&&(h=h.slice(0,p)),p<f.length&&(f=f.slice(0,p)),d.r=h,d.theta=f,n(t,e);var m,g=d.opts=a.style(t,e);return p<s?m=i(e,p):g.marker&&(m=2*(g.marker.sizeAvg||Math.max(g.marker.size,3))),e._extremes.x=o.findExtremes(c,h,{ppad:m}),[{x:!1,y:!1,t:d,trace:e}]}},14952:function(t,e,r){"use strict";var n=r(34809),i=r(64726),a=r(73749).handleRThetaDefaults,o=r(24272),s=r(98168),l=r(663),c=r(54114),u=r(32660).PTS_LINESONLY,h=r(58319);t.exports=function(t,e,r,f){function p(r,i){return n.coerce(t,e,h,r,i)}var 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n=r(87163),i=r(80712).axisHoverFormat,a=r(3208).rb,o=r(42450),s=r(9829),l=r(93049).extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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C=function(){v=0,M=[],S=[],E=[]};(!v||v<M.length*S.length*E.length)&&C();var L=function(t){return"x"===t?m:"y"===t?g:y},I=function(t){return"x"===t?M:"y"===t?S:E},P=function(t){return t[v-1]<t[0]?-1:1},z=L(A[1]),O=L(A[3]),D=L(A[5]),R=I(A[1]).length,F=I(A[3]).length,B=I(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=P(L(A[1])),V=P(L(A[3])),q=P(L(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var H=j(e,r,i),G=j(e,r,i+1),Z=j(e,r+1,i),W=j(e+1,r,i);if(z[H]*U<z[G]*U&&O[H]*V<O[Z]*V&&D[H]*q<D[W]*q||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn("Encountered arbitrary coordinates! 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y=n[u];r.xMax=Math.max(r.xMax,y),r.xMin=Math.min(r.xMin,y);var v=o[u];r.yMax=Math.max(r.yMax,v),r.yMin=Math.min(r.yMin,v);var x=l[u];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=c,e._normMax=h,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},49280:function(t,e,r){"use strict";var n=r(99098).gl_streamtube3d,i=n.createTubeMesh,a=r(34809),o=r(46998).parseColorScale,s=r(88856).extractOpts,l=r(88239),c={xaxis:0,yaxis:1,zaxis:2};function u(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var h=u.prototype;function f(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,u=e._len,h={};function d(t,e){var n=r[e],o=i[c[e]];return a.simpleMap(t,(function(t){return n.d2l(t)*o}))}if(h.vectors=l(d(e._u,"xaxis"),d(e._v,"yaxis"),d(e._w,"zaxis"),u),!u)return{positions:[],cells:[]};var 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n=r(9829),i=r(3208).rb,a=r(3208).ay,o=r(87163),s=r(13792).u,l=r(55412),c=r(2032),u=r(93049).extendFlat,h=r(94850).k;t.exports={labels:{valType:"data_array",editType:"calc"},parents:{valType:"data_array",editType:"calc"},values:{valType:"data_array",editType:"calc"},branchvalues:{valType:"enumerated",values:["remainder","total"],dflt:"remainder",editType:"calc"},count:{valType:"flaglist",flags:["branches","leaves"],dflt:"leaves",editType:"calc"},level:{valType:"any",editType:"plot",anim:!0},maxdepth:{valType:"integer",editType:"plot",dflt:-1},marker:u({colors:{valType:"data_array",editType:"calc"},line:{color:u({},l.marker.line.color,{dflt:null}),width:u({},l.marker.line.width,{dflt:1}),editType:"calc"},pattern:h,editType:"calc"},o("marker",{colorAttr:"colors",anim:!1})),leaf:{opacity:{valType:"number",editType:"style",min:0,max:1},editType:"plot"},text:l.text,textinfo:{valType:"flaglist",flags:["label","text","value","current path","percent root","percent entry","percent 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n=r(92264),i=r(10721),a=r(34809),o=r(88856).makeColorScaleFuncFromTrace,s=r(44148).makePullColorFn,l=r(44148).generateExtendedColors,c=r(88856).calc,u=r(63821).ALMOST_EQUAL,h={},f={},p={};function d(t,e,r){var n=0,i=t.children;if(i){for(var a=i.length,o=0;o<a;o++)n+=d(i[o],e,r);r.branches&&n++}else r.leaves&&n++;return t.value=t.data.data.value=n,e._values||(e._values=[]),e._values[t.data.data.i]=n,n}e.calc=function(t,e){var r,l,h,f,p,m,g=t._fullLayout,y=e.ids,v=a.isArrayOrTypedArray(y),x=e.labels,_=e.parents,b=e.values,w=a.isArrayOrTypedArray(b),T=[],k={},A={},M=function(t){return t||"number"==typeof t},S=function(t){return!w||i(b[t])&&b[t]>=0};v?(r=Math.min(y.length,_.length),l=function(t){return M(y[t])&&S(t)},h=function(t){return String(y[t])}):(r=Math.min(x.length,_.length),l=function(t){return M(x[t])&&S(t)},h=function(t){return String(x[t])}),w&&(r=Math.min(r,b.length));for(var E=0;E<r;E++)if(l(E)){var C=h(E),L=M(_[E])?String(_[E]):"",I={i:E,id:C,pid:L,label:M(x[E])?String(x[E]):""};w&&(I.v=+b[E]),T.push(I),p=C,k[f=L]?k[f].push(p):k[f]=[p],A[p]=1}if(k[""]){if(k[""].length>1){for(var P=a.randstr(),z=0;z<T.length;z++)""===T[z].pid&&(T[z].pid=P);T.unshift({hasMultipleRoots:!0,id:P,pid:"",label:""})}}else{var O,D=[];for(O in k)A[O]||D.push(O);if(1!==D.length)return a.warn(["Multiple implied roots, cannot build",e.type,"hierarchy of",e.name+".","These roots include:",D.join(", ")].join(" "));O=D[0],T.unshift({hasImpliedRoot:!0,id:O,pid:"",label:O})}try{m=n.stratify().id((function(t){return t.id})).parentId((function(t){return t.pid}))(T)}catch(t){return a.warn(["Failed to build",e.type,"hierarchy of",e.name+".","Error:",t.message].join(" "))}var R=n.hierarchy(m),F=!1;if(w)switch(e.branchvalues){case"remainder":R.sum((function(t){return t.data.v}));break;case"total":R.each((function(t){var r=t.data.data,n=r.v;if(t.children){var i=t.children.reduce((function(t,e){return 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n=r(45568),i=r(92264),a=r(88640).GW,o=r(62203),s=r(34809),l=r(30635),c=r(84102),u=c.recordMinTextSize,h=c.clearMinTextSize,f=r(35734),p=r(37252).getRotationAngle,d=f.computeTransform,m=f.transformInsideText,g=r(98972).styleOne,y=r(6851).resizeText,v=r(44691),x=r(2032),_=r(33108);function b(t,r,c,h){var f=t._context.staticPlot,y=t._fullLayout,b=!y.uniformtext.mode&&_.hasTransition(h),T=n.select(c).selectAll("g.slice"),k=r[0],A=k.trace,M=k.hierarchy,S=_.findEntryWithLevel(M,A.level),E=_.getMaxDepth(A),C=y._size,L=A.domain,I=C.w*(L.x[1]-L.x[0]),P=C.h*(L.y[1]-L.y[0]),z=.5*Math.min(I,P),O=k.cx=C.l+C.w*(L.x[1]+L.x[0])/2,D=k.cy=C.t+C.h*(1-L.y[0])-P/2;if(!S)return T.remove();var R=null,F={};b&&T.each((function(t){F[_.getPtId(t)]={rpx0:t.rpx0,rpx1:t.rpx1,x0:t.x0,x1:t.x1,transform:t.transform},!R&&_.isEntry(t)&&(R=t)}));var B=function(t){return 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c=n.select(this),h=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events",f?"none":"all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=G(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),b?h.transition().attrTween("d",(function(t){var e=function(t){var e,r=F[_.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(R)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+V;e={x0:i,x1:i}}else e={rpx0:z,rpx1:z},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:V,x1:V};return a(e,n)}(t);return function(t){return 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a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&_.hasTransition(r);h("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){b(t,e,this,r)}))}))):(a.each((function(e){b(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(".trace"),"sunburst")),c&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,h=c.hierarchy,f=_.isHierarchyRoot(t),p=_.getParent(h,t),d=_.getValue(t);if(!a){var m,g=o.split("+"),y=function(t){return-1!==g.indexOf(t)},v=[];if(y("label")&&u.label&&v.push(u.label),u.hasOwnProperty("v")&&y("value")&&v.push(_.formatValue(u.v,l)),!f){y("current path")&&v.push(_.getPath(t.data));var x=0;y("percent parent")&&x++,y("percent entry")&&x++,y("percent root")&&x++;var b=x>1;if(x){var w,T=function(t){m=_.formatPercent(w,l),b&&(m+=" of "+t),v.push(m)};y("percent parent")&&!f&&(w=d/_.getValue(p),T("parent")),y("percent entry")&&(w=d/_.getValue(e),T("entry")),y("percent root")&&(w=d/_.getValue(h),T("root"))}}return y("text")&&(m=s.castOption(r,u.i,"text"),s.isValidTextValue(m)&&v.push(m)),v.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=_.formatValue(u.v,l)),A.currentPath=_.getPath(t.data),f||(A.percentParent=d/_.getValue(p),A.percentParentLabel=_.formatPercent(A.percentParent,l),A.parent=_.getPtLabel(p)),A.percentEntry=d/_.getValue(e),A.percentEntryLabel=_.formatPercent(A.percentEntry,l),A.entry=_.getPtLabel(e),A.percentRoot=d/_.getValue(h),A.percentRootLabel=_.formatPercent(A.percentRoot,l),A.root=_.getPtLabel(h),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},98972:function(t,e,r){"use strict";var n=r(45568),i=r(78766),a=r(34809),o=r(84102).resizeText,s=r(72043);function l(t,e,r,n){var 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n=r(28379);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},27159:function(t,e,r){"use strict";var n=r(99098).gl_surface3d,i=r(99098).ndarray,a=r(99098).ndarray_linear_interpolate.d2,o=r(69295),s=r(78106),l=r(34809).isArrayOrTypedArray,c=r(46998).parseColorScale,u=r(55010),h=r(88856).extractOpts;function f(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=f.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 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e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return R(e,e.length-1)+(e.length?F(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-E(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr("transform",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return h(e,E(t))}));var o=a.selectAll("."+n.cn.scrollbar).data(s.repeat,s.keyFun);o.enter().append("g").classed(n.cn.scrollbar,!0);var 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e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n("number"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n("number"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],h=8191&u,f=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],y=8191&g,v=g>>>13,x=0|o[3],_=8191&x,b=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],C=8191&E,L=E>>>13,I=0|o[7],P=8191&I,z=I>>>13,O=0|o[8],D=8191&O,R=O>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Z=0|s[2],W=8191&Z,Y=Z>>>13,X=0|s[3],$=8191&X,J=X>>>13,K=0|s[4],Q=8191&K,tt=K>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ht=8191&ut,ft=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(h,U))|0)+((8191&(i=(i=Math.imul(h,V))+Math.imul(f,U)|0))<<13)|0;c=((a=Math.imul(f,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var yt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(i=(i=i+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((a=a+Math.imul(f,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(v,U)|0,a=Math.imul(v,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,H)|0,a=a+Math.imul(m,G)|0;var vt=(c+(n=n+Math.imul(h,W)|0)|0)+((8191&(i=(i=i+Math.imul(h,Y)|0)+Math.imul(f,W)|0))<<13)|0;c=((a=a+Math.imul(f,Y)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(_,U),i=(i=Math.imul(_,V))+Math.imul(b,U)|0,a=Math.imul(b,V),n=n+Math.imul(y,H)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(v,H)|0,a=a+Math.imul(v,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,Y)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,Y)|0;var xt=(c+(n=n+Math.imul(h,$)|0)|0)+((8191&(i=(i=i+Math.imul(h,J)|0)+Math.imul(f,$)|0))<<13)|0;c=((a=a+Math.imul(f,J)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(_,H)|0,i=(i=i+Math.imul(_,G)|0)+Math.imul(b,H)|0,a=a+Math.imul(b,G)|0,n=n+Math.imul(y,W)|0,i=(i=i+Math.imul(y,Y)|0)+Math.imul(v,W)|0,a=a+Math.imul(v,Y)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,J)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,J)|0;var _t=(c+(n=n+Math.imul(h,Q)|0)|0)+((8191&(i=(i=i+Math.imul(h,tt)|0)+Math.imul(f,Q)|0))<<13)|0;c=((a=a+Math.imul(f,tt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(_,W)|0,i=(i=i+Math.imul(_,Y)|0)+Math.imul(b,W)|0,a=a+Math.imul(b,Y)|0,n=n+Math.imul(y,$)|0,i=(i=i+Math.imul(y,J)|0)+Math.imul(v,$)|0,a=a+Math.imul(v,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0;var bt=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(i=(i=i+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((a=a+Math.imul(f,nt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,Y)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,Y)|0,n=n+Math.imul(_,$)|0,i=(i=i+Math.imul(_,J)|0)+Math.imul(b,$)|0,a=a+Math.imul(b,J)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(h,at)|0)|0)+((8191&(i=(i=i+Math.imul(h,ot)|0)+Math.imul(f,at)|0))<<13)|0;c=((a=a+Math.imul(f,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,U),i=(i=Math.imul(P,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,Y)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,Y)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,J)|0,n=n+Math.imul(_,Q)|0,i=(i=i+Math.imul(_,tt)|0)+Math.imul(b,Q)|0,a=a+Math.imul(b,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(i=(i=i+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((a=a+Math.imul(f,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,Y)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,Y)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(_,rt)|0,i=(i=i+Math.imul(_,nt)|0)+Math.imul(b,rt)|0,a=a+Math.imul(b,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(i=(i=i+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((a=a+Math.imul(f,ft)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,W)|0,i=(i=i+Math.imul(P,Y)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,Y)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,J)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,J)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(_,at)|0,i=(i=i+Math.imul(_,ot)|0)+Math.imul(b,at)|0,a=a+Math.imul(b,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ct)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ct)|0,n=n+Math.imul(d,ht)|0,i=(i=i+Math.imul(d,ft)|0)+Math.imul(m,ht)|0,a=a+Math.imul(m,ft)|0;var At=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(i=(i=i+Math.imul(h,mt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((a=a+Math.imul(f,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,Y)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,Y)|0,n=n+Math.imul(P,$)|0,i=(i=i+Math.imul(P,J)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,J)|0,n=n+Math.imul(C,Q)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,Q)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(_,lt)|0,i=(i=i+Math.imul(_,ct)|0)+Math.imul(b,lt)|0,a=a+Math.imul(b,ct)|0,n=n+Math.imul(y,ht)|0,i=(i=i+Math.imul(y,ft)|0)+Math.imul(v,ht)|0,a=a+Math.imul(v,ft)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,Y))+Math.imul(N,W)|0,a=Math.imul(N,Y),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,J)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(P,Q)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(z,Q)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(_,ht)|0,i=(i=i+Math.imul(_,ft)|0)+Math.imul(b,ht)|0,a=a+Math.imul(b,ft)|0;var St=(c+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,mt)|0)+Math.imul(v,dt)|0))<<13)|0;c=((a=a+Math.imul(v,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,J))+Math.imul(N,$)|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ht)|0,i=(i=i+Math.imul(T,ft)|0)+Math.imul(k,ht)|0,a=a+Math.imul(k,ft)|0;var Et=(c+(n=n+Math.imul(_,dt)|0)|0)+((8191&(i=(i=i+Math.imul(_,mt)|0)+Math.imul(b,dt)|0))<<13)|0;c=((a=a+Math.imul(b,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(M,ht)|0,i=(i=i+Math.imul(M,ft)|0)+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0;var Ct=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ht)|0,i=(i=i+Math.imul(C,ft)|0)+Math.imul(L,ht)|0,a=a+Math.imul(L,ft)|0;var Lt=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(P,ht)|0,i=(i=i+Math.imul(P,ft)|0)+Math.imul(z,ht)|0,a=a+Math.imul(z,ft)|0;var It=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,mt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,i=(i=i+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0;var Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,mt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,ht),i=(i=Math.imul(B,ft))+Math.imul(N,ht)|0,a=Math.imul(N,ft);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var Ot=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=gt,l[1]=yt,l[2]=vt,l[3]=xt,l[4]=_t,l[5]=bt,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=It,l[16]=Pt,l[17]=zt,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,h=(0|t.words[u])*(0|e.words[c]),f=67108863&h;s=67108863&(f=f+s|0),i+=(o=(o=o+(h/67108864|0)|0)+(f>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var h=l,f=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],y=n[u+p+o],v=h*g-f*y;y=h*y+f*g,g=v,r[u+p]=d+g,n[u+p]=m+y,r[u+p+o]=d-g,n[u+p+o]=m-y,p!==s&&(v=l*h-c*f,f=l*f+c*h,h=v)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,h,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*h[p];l[p]=s[p]*h[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,f,a,n,i),this.conjugate(f,a,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var h=0|this.words[c];this.words[c]=u<<26-a|h>>>a,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r||!(this.words[r]&i))},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864*(0|n.words[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var h=0,f=1;0==(r.words[0]&f)&&h<26;++h,f<<=1);if(h>0)for(r.iushrn(h);h-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction context"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,"redAdd works only with red numbers"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,"redSub works only with red numbers"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,"redISub works only with red numbers"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,"redShl works only with red numbers"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function v(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){v.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function _(){v.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function b(){v.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function w(){v.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function T(t){if("string"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),"modulus must be greater than 1"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}v.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},v.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},v.prototype.split=function(t,e){t.iushrn(this.n,0,e)},v.prototype.imulK=function(t){return t.imul(this.k)},i(x,v),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i<n;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=a&r,i=10;i<t.length;i++){var o=0|t.words[i];t.words[i-10]=(o&r)<<4|a>>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(_,v),i(b,v),i(w,v),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new _;else if("p192"===t)e=new b;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var h=this.pow(u,i),f=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var y=this.pow(h,new a(1).iushln(d-g-1));f=f.redMul(y),h=y.redSqr(),p=p.redMul(h),d=g}return f},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var h=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==h||0!==o?(o<<=1,o|=h,(4==++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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h=o[o.length-2];for(h.left===s?h.left=null:h.right===s&&(h.right=null),o.pop(),l=0;l<o.length;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(s.left||s.right){for(s.left?f(s,s.left):s.right&&f(s,s.right),s._color=1,l=0;l<o.length-1;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(1===o.length)return new a(this.tree._compare,null);for(l=0;l<o.length;++l)o[l]._count--;var p=o[o.length-2];return function(t){for(var e,a,o,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((a=t[l-1]).left===e){if((o=a.right).right&&0===o.right._color)return s=(o=a.right=r(o)).right=r(o.right),a.right=o.left,o.left=a,o.right=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((c=t[l-2]).left===a?c.left=o:c.right=o),void(t[l-1]=o);if(o.left&&0===o.left._color)return s=(o=a.right=r(o)).left=r(o.left),a.right=s.left,o.left=s.right,s.left=a,s.right=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((c=t[l-2]).left===a?c.left=s:c.right=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.right=n(0,o));a.right=n(0,o);continue}o=r(o),a.right=o.left,o.left=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((c=t[l-2]).left===a?c.left=o:c.right=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((o=a.left).left&&0===o.left._color)return s=(o=a.left=r(o)).left=r(o.left),a.left=o.right,o.right=a,o.left=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((c=t[l-2]).right===a?c.right=o:c.left=o),void(t[l-1]=o);if(o.right&&0===o.right._color)return 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r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),h.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),h.update=function(t){var r=this._stack;if(0===r.length)throw new Error("Can't update empty node!");var n=new Array(r.length),i=r[r.length-1];n[n.length-1]=new e(i._color,i.key,t,i.left,i.right,i._count);for(var o=r.length-2;o>=0;--o)(i=r[o]).left===r[o+1]?n[o]=new e(i._color,i.key,i.value,n[o+1],i.right,i._count):n[o]=new e(i._color,i.key,i.value,i.left,n[o+1],i._count);return new a(this.tree._compare,n[0])},h.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},3837:function(t,e,r){"use strict";t.exports=function(t,e){var r=new p(t);return r.update(e),r};var n=r(4935),i=r(501),a=r(5304),o=r(6429),s=r(6444),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=ArrayBuffer,u=DataView;function h(t){return Array.isArray(t)||function(t){return c.isView(t)&&!(t instanceof u)}(t)}function f(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function 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d=p.prototype;function m(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}d.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?h(a)&&h(a[0]):h(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(h(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var p=t.bounds,d=0;d<2;++d)for(var m=0;m<3;++m)p[d][m]!==this.bounds[d][m]&&(f=!0),this.bounds[d][m]=p[d][m];if("ticks"in t)for(r=t.ticks,u=!0,this.autoTicks=!1,d=0;d<3;++d)this.tickSpacing[d]=0;else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in 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y=[{family:this.labelFont[0],style:this.labelFontStyle[0],weight:this.labelFontWeight[0],variant:this.labelFontVariant[0]},{family:this.labelFont[1],style:this.labelFontStyle[1],weight:this.labelFontWeight[1],variant:this.labelFontVariant[1]},{family:this.labelFont[2],style:this.labelFontStyle[2],weight:this.labelFontWeight[2],variant:this.labelFontVariant[2]}],v=[{family:this.tickFont[0],style:this.tickFontStyle[0],weight:this.tickFontWeight[0],variant:this.tickFontVariant[0]},{family:this.tickFont[1],style:this.tickFontStyle[1],weight:this.tickFontWeight[1],variant:this.tickFontVariant[1]},{family:this.tickFont[2],style:this.tickFontStyle[2],weight:this.tickFontWeight[2],variant:this.tickFontVariant[2]}];this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,y,this.ticks,v):this._text=n(this.gl,this.bounds,this.labels,y,this.ticks,v),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var g=[new m,new m,new m];function y(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var h=a,f=s,p=o,d=l;c&1<<u&&(h=s,f=a,p=l,d=o),h[u]=r[0][u],f[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var v=[0,0,0],x={model:l,view:l,projection:l,_ortho:!1};d.isOpaque=function(){return!0},d.isTransparent=function(){return!1},d.drawTransparent=function(t){};var _=[0,0,0],b=[0,0,0],w=[0,0,0];d.draw=function(t){t=t||x;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),u=c.cubeEdges,h=c.axis,p=n[12],d=n[13],m=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*p+i[7]*d+i[11]*m+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=u[A],this.lastCubeProps.axis[A]=h[A];var M=g;for(A=0;A<3;++A)y(g[A],A,this.bounds,u,h);e=this.gl;var S,E,C,L=v;for(A=0;A<3;++A)this.backgroundEnable[A]?L[A]=h[A]:L[A]=0;for(this._background.draw(r,n,i,a,L,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var I=[0,0,0];h[A]>0?I[A]=a[1][A]:I[A]=a[0][A];for(var P=0;P<2;++P){var z=(A+1+P)%3,O=(A+1+(1^P))%3;this.gridEnable[z]&&this._lines.drawGrid(z,O,this.bounds,I,this.gridColor[z],this.gridWidth[z]*this.pixelRatio)}for(P=0;P<2;++P)z=(A+1+P)%3,O=(A+1+(1^P))%3,this.zeroEnable[O]&&Math.min(a[0][O],a[1][O])<=0&&Math.max(a[0][O],a[1][O])>=0&&this._lines.drawZero(z,O,this.bounds,I,this.zeroLineColor[O],this.zeroLineWidth[O]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var D=f(_,M[A].primalMinor),R=f(b,M[A].mirrorMinor),F=this.lineTickLength;for(P=0;P<3;++P){var B=k/r[5*P];D[P]*=F[P]*B,R[P]*=F[P]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,D,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(C=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,q=f(w,M[A].primalOffset);for(P=0;P<3;++P)this.lineTickEnable[A]&&(q[P]+=k*U[P]*Math.max(this.lineTickLength[P],0)/r[5*P]);var H=[0,0,0];if(H[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,E=1,"auto"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(""+S[0]),C=[0,0,0],j(A,U,V),P=0;P<3;++P)q[P]+=k*U[P]*this.tickPad[P]/r[5*P];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,C,S)}if(this.labelEnable[A]){for(E=0,C=[0,0,0],this.labels[A].length>4&&(N(A),E=1),"auto"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(""+S[0]),P=0;P<3;++P)q[P]+=k*U[P]*this.labelPad[P]/r[5*P];q[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],C,S)}}this._text.unbind()},d.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},5304:function(t,e,r){"use strict";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,h=[0,0,0],f=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),h[l]=p,f[l]=p;for(var d=-1;d<=1;d+=2){h[c]=d;for(var m=-1;m<=1;m+=2)h[u]=m,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),s+=1}var g=c;c=u,u=g}var y=n(t,new Float32Array(e)),v=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],v),_=a(t);return _.attributes.position.location=0,_.attributes.normal.location=1,new o(t,y,x,_)};var n=r(2762),i=r(8116),a=r(1879).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},6429:function(t,e,r){"use strict";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var v=0,x=0;x<2;++x){u[2]=a[x][2];for(var _=0;_<2;++_){u[1]=a[_][1];for(var b=0;b<2;++b)u[0]=a[b][0],f(l[v],u,s),v+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,C=-1,L=0;L<2;++L){var I=(z=L<<A)+(L<<M)+(1-L<<S),P=z+(1-L<<M)+(L<<S);o(c[z],c[I],c[P],h)<0||(L?E=1:C=1)}if(E<0||C<0)C>E&&(w|=1<<A);else{for(L=0;L<2;++L){I=(z=L<<A)+(L<<M)+(1-L<<S),P=z+(1-L<<M)+(L<<S);var 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r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,h=l[0]/l[3]-n,f=l[1]/l[3]-i;o+=Math.abs(c*f-u*h)}return o}var m=[1,1,1],g=[0,0,0],y={cubeEdges:m,axis:g}},501:function(t,e,r){"use strict";t.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var h=0;h<3;++h){for(var f=o.length/3|0,d=0;d<r[h].length;++d){var m=+r[h][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[h]=f,l[h]=g-f,f=o.length/3|0;for(var y=0;y<r[h].length;++y)m=+r[h][y].x,o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1);g=o.length/3|0,c[h]=f,u[h]=g-f}var v=n(t,new Float32Array(o)),x=i(t,[{buffer:v,type:t.FLOAT,size:3,stride:0,offset:0}]),_=a(t);return _.attributes.position.location=0,new p(t,v,x,_,l,s,u,c)};var n=r(2762),i=r(8116),a=r(1879).n,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function h(t){return t[0]=t[1]=t[2]=0,t}function f(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=h(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=f(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=h(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=h(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=h(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=h(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=h(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=f(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=h(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=h(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=h(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=f(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=h(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},1879:function(t,e,r){"use strict";var n=r(3236),i=r(9405),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * (view * (model * vec4(p, 1.0)));\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);e.n=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * (view * (model * vec4(p, 1.0)));\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\n                                    0.0;\n}\n\nfloat roundTo(float a, float b) {\n  return float(b * floor((a + 0.5 * b) / b));\n}\n\nfloat look_round_n_directions(float a, int n) {\n  float b = positive_angle(a);\n  float div = TWO_PI / float(n);\n  float c = roundTo(b, div);\n  return look_upwards(c);\n}\n\nfloat applyAlignOption(float rawAngle, float delta) {\n  return\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\n                    rawAngle;                // otherwise return back raw input angle\n}\n\nbool isAxisTitle = (axis.x == 0.0) &&\n                   (axis.y == 0.0) &&\n                   (axis.z == 0.0);\n\nvoid main() {\n  //Compute world offset\n  float axisDistance = position.z;\n  vec3 dataPosition = axisDistance * axis + offset;\n\n  float beta = angle; // i.e. user defined attributes for each tick\n\n  float axisAngle;\n  float clipAngle;\n  float flip;\n\n  if (enableAlign) {\n    axisAngle = (isAxisTitle) ? HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}\n"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);e.Q=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * (view * (model * vec4(nPosition, 1.0)));\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}\n"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);e.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},4935:function(t,e,r){"use strict";t.exports=function(t,e,r,i,o,l){var c=n(t),h=a(t,[{buffer:c,size:3}]),f=s(t);f.attributes.position.location=0;var p=new u(t,f,c,h);return p.update(e,r,i,o,l),p};var n=r(2762),a=r(8116),o=r(4359),s=r(1879).Q,l=window||i.global||{},c=l.__TEXT_CACHE||{};function u(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var h=u.prototype,f=[0,0];h.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},h.unbind=function(){this.vao.unbind()},h.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=[r.style,r.weight,r.variant,r.family].join("_"),u=c[l];u||(u=c[l]={});var h=u[e];h||(h=u[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" 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f=e.model||c,p=e.view||c,y=e.projection||c,v=e._ortho||!1,x=t.bounds,_=(h=h||a(f,p,y,x,v)).axis;o(u,p,f),o(u,y,u);for(var b=m,w=0;w<3;++w)b[w].lo=1/0,b[w].hi=-1/0,b[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(_[k]<0!=!!w){S[k]=x[w][k];for(var C=0;C<2;++C){S[A]=x[C^w][A];for(var L=0;L<2;++L)S[M]=x[L^C^w][M],E.push(S.slice())}var I=v?5:4;for(C=I;C===I;++C){if(0===E.length)continue t;E=i.positive(E,T[C])}for(C=0;C<E.length;++C){M=E[C];var P=d(g,u,M,r,l);for(L=0;L<3;++L)b[L].lo=Math.min(b[L].lo,M[L]),b[L].hi=Math.max(b[L].hi,M[L]),L!==k&&(b[L].pixelsPerDataUnit=Math.min(b[L].pixelsPerDataUnit,Math.abs(P[L])))}}}}return b};var n=r(5033),i=r(5202),a=r(6429),o=r(6760),s=r(5665),l=r(5352),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function h(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var f=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var 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h;h=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?h:h.subarray(0,t.length),e),n.free(h)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},6405:function(t,e,r){"use strict";var n=r(2931);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=null,d=null,m=[],g=1/0,y=!1,v="raw"===t.coneSizemode,x=0;x<r.length;x++){var _=r[x];s=Math.min(_[0],s),l=Math.max(_[0],l),c=Math.min(_[1],c),u=Math.max(_[1],u),h=Math.min(_[2],h),f=Math.max(_[2],f);var b=i[x];if(n.length(b)>o&&(o=n.length(b)),x&&!v){var w=2*n.distance(p,_)/(n.length(d)+n.length(b));w?(g=Math.min(g,w),y=!1):y=!0}y||(p=_,d=b),m.push(b)}var T=[s,c,h],k=[l,u,f];e&&(e[0]=T,e[1]=k),0===o&&(o=1);var A=1/o;isFinite(g)||(g=1),a.vectorScale=g;var M=t.coneSize||(v?1:.5);t.absoluteConeSize&&(M=t.absoluteConeSize*A),a.coneScale=M,x=0;for(var S=0;x<r.length;x++)for(var E=(_=r[x])[0],C=_[1],L=_[2],I=m[x],P=n.length(I)*A,z=0;z<8;z++){a.positions.push([E,C,L,S++]),a.positions.push([E,C,L,S++]),a.positions.push([E,C,L,S++]),a.positions.push([E,C,L,S++]),a.positions.push([E,C,L,S++]),a.positions.push([E,C,L,S++]),a.vectors.push(I),a.vectors.push(I),a.vectors.push(I),a.vectors.push(I),a.vectors.push(I),a.vectors.push(I),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=r(614);t.exports.createMesh=r(9060),t.exports.createConeMesh=function(e,r){return t.exports.createMesh(e,r,{shaders:i,traceType:"cone"})}},9060:function(t,e,r){"use strict";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(6760),l=r(7608),c=r(9618),u=r(6729),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=h,this._view=h,this._projection=h,this._resolution=[1,1]}var p=f.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],h=[];this.cells=r,this.positions=n,this.vectors=i;var f=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var y=p[g];d=Math.min(d,y),m=Math.max(m,y)}else for(g=0;g<n.length;++g)y=n[g][2],d=Math.min(d,y),m=Math.max(m,y);for(this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],g=0;g<n.length;++g)for(var v=n[g],x=0;x<3;++x)!isNaN(v[x])&&isFinite(v[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],v[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],v[x]));var _=0;t:for(g=0;g<r.length;++g){var b=r[g];if(3===b.length){for(x=0;x<3;++x){v=n[T=b[x]];for(var w=0;w<3;++w)if(isNaN(v[w])||!isFinite(v[w]))continue t}for(x=0;x<3;++x){var T;v=n[T=b[2-x]],a.push(v[0],v[1],v[2],v[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=f;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(v[2]-d)/(m-d),0],l.push(A[0],A[1]),h.push(g)}_+=1}}this.triangleCount=_,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(h))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,i=t.projection||h,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:h.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);for(s(u,c.view,c.model),s(u,c.projection,u),l(u,u),o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var f=u[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/f}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,i=t.projection||h,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),u=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),h=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));h.generateMipmap(),h.minFilter=t.LINEAR_MIPMAP_LINEAR,h.magFilter=t.LINEAR;var p=i(t),d=i(t),m=i(t),g=i(t),y=i(t),v=new f(t,h,l,u,p,d,y,m,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||"cone");return v.update(e),v}},614:function(t,e,r){var n=r(3236),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * (view * conePosition);\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},737:function(t){t.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},5171:function(t,e,r){var n=r(737);t.exports=function(t){return n[t]}},9165:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=r(2762),i=r(8116),a=r(3436),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]*this.pixelRatio),r.capSize=this.capSize[h]*u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function h(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],f=0;f<3;++f)if(isNaN(u[f])||!isFinite(u[f]))continue t;var p,d=n[l],m=e[s];if(Array.isArray(m[0])&&(m=e[l]),3===m.length?m=[m[0],m[1],m[2],1]:4===m.length&&(m=[m[0],m[1],m[2],m[3]],!this.hasAlpha&&m[3]<1&&(this.hasAlpha=!0)),!isNaN(d[0][s])&&!isNaN(d[1][s]))d[0][s]<0&&((p=u.slice())[s]+=d[0][s],i.push(u[0],u[1],u[2],m[0],m[1],m[2],m[3],0,0,0,p[0],p[1],p[2],m[0],m[1],m[2],m[3],0,0,0),c(this.bounds,p),o+=2+h(i,p,m,s)),d[1][s]>0&&((p=u.slice())[s]+=d[1][s],i.push(u[0],u[1],u[2],m[0],m[1],m[2],m[3],0,0,0,p[0],p[1],p[2],m[0],m[1],m[2],m[3],0,0,0),c(this.bounds,p),o+=2+h(i,p,m,s))}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},3436:function(t,e,r){"use strict";var n=r(3236),i=r(9405),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * (view * worldPosition);\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);t.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},2260:function(t,e,r){"use strict";var n=r(7766);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");if(!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),"number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var h=1;if("color"in(n=n||{})){if((h=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(h>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(h>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+h+" draw buffers")}}var f=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&h>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");f=t.FLOAT}else n.preferFloat&&h>0&&p&&(f=t.FLOAT);var m=!0;"depth"in n&&(m=!!n.depth);var g=!1;return"stencil"in n&&(g=!!n.stencil),new d(t,e,r,f,h,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function h(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function f(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var y=0;y<o;++y)t.color[y]=f(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+y);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var v=r.getExtension("WEBGL_depth_texture");v?d?t.depth=f(r,i,a,v.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=f(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;y<t.color.length;++y)t.color[y].dispose(),t.color[y]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),h(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),h(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},2992:function(t,e,r){var n=r(3387).sprintf,i=r(5171),a=r(1848),o=r(1085);t.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),h=t.split("\n"),f={},p=0;p<h.length;p++){var d=h[p];if(""!==d&&"\0"!==d){var m=parseInt(d.split(":")[2]);if(isNaN(m))throw new Error(n("Could not parse error: %s",d));f[m]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if((f[p+3]||f[p+2]||f[p+1])&&(c+=g[p]+"\n",f[p+1])){var y=f[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}return{long:c.trim(),short:u.trim()}}},2510:function(t,e,r){"use strict";t.exports=function(t,e){var r=t.gl,n=new c(t,o(r,l.vertex,l.fragment),o(r,l.pickVertex,l.pickFragment),s(r),s(r),s(r),s(r));return n.update(e),t.addObject(n),n};var n=r(2478),i=r(7762),a=r(1888),o=r(9405),s=r(2762),l=r(6768);function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,h=c.prototype,f=[0,0,1,0,0,1,1,0,1,1,0,1];h.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var h=e.uniforms;h.viewTransform=u,h.shape=this.shape;var f=e.attributes;this.positionBuffer.bind(),f.position.pointer(),this.weightBuffer.bind(),f.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),f.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],h=l[2]-l[0],f=l[3]-l[1];t[0]=2*c/h,t[4]=2*u/f,t[6]=2*(a[0]-l[0])/h-1,t[7]=2*(a[1]-l[1])/f-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},h.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,h,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(c=y[0]=r[0],u=y[1]=o[0],h=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(c=y[0]=r[0]+(r[1]-r[0])/2,u=y[1]=o[0]+(o[1]-o[0])/2,h=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var v=1/(h-c),x=1/(p-u),_=e[0],b=e[1];this.shape=[_,b];var w=(l?(_-1)*(b-1):_*b)*(f.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?_-1:_,C=l?b-1:b,L=0;L<C;++L){var I,P;l?(I=x*(o[L]-u),P=x*(o[L+1]-u)):(I=L<b-1?x*(o[L]-(o[L+1]-o[L])/2-u):x*(o[L]-(o[L]-o[L-1])/2-u),P=L<b-1?x*(o[L]+(o[L+1]-o[L])/2-u):x*(o[L]+(o[L]-o[L-1])/2-u));for(var z=0;z<E;++z){var O,D;l?(O=v*(r[z]-c),D=v*(r[z+1]-c)):(O=z<_-1?v*(r[z]-(r[z+1]-r[z])/2-c):v*(r[z]-(r[z]-r[z-1])/2-c),D=z<_-1?v*(r[z]+(r[z+1]-r[z])/2-c):v*(r[z]+(r[z]-r[z-1])/2-c));for(var R=0;R<f.length;R+=2){var F,B,N,j,U=f[R],V=f[R+1],q=s[l?(L+V)*_+(z+U):L*_+z],H=n.le(d,q);if(H<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(H===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(q-d[H])/(d[H+1]-d[H]),Z=1-G,W=4*H,Y=4*(H+1);F=Z*m[W]+G*m[Y],B=Z*m[W+1]+G*m[Y+1],N=Z*m[W+2]+G*m[Y+2],j=Z*m[W+3]+G*m[Y+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*O+.5*D,k[2*S+1]=.5*I+.5*P,A[2*S]=U,A[2*S+1]=V,M[S]=L*_+z,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},h.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},6768:function(t,e,r){"use strict";var n=r(3236);t.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},7319:function(t,e,r){var n=r(3236),i=r(9405),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * (view * (model * vec4(p, 1.0)));\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},5714:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=h(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new y(e,r,o,s,l,d);return m.update(t),m};var n=r(2762),i=r(8116),a=r(7766),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(2478),c=r(9618),u=r(7319),h=u.createShader,f=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var v=y.prototype;v.isTransparent=function(){return this.hasAlpha},v.isOpaque=function(){return!this.hasAlpha},v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.drawTransparent=v.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},v.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},v.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],f=t.position||t.positions;if(f){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<f.length;++e){var y,v,x,_=f[e-1],b=f[e];for(a.push(s),o.push(_.slice()),r=0;r<3;++r){if(isNaN(_[r])||isNaN(b[r])||!isFinite(_[r])||!isFinite(b[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}h[0][r]=Math.min(h[0][r],_[r],b[r]),h[1][r]=Math.max(h[1][r],_[r],b[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],v=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=v=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===v.length&&(v=[v[0],v[1],v[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(_,b),g){for(r=0;r<2;++r)i.push(_[0],_[1],_[2],b[0],b[1],b[2],T,x,y[0],y[1],y[2],y[3]);u+=2,g=!1}i.push(_[0],_[1],_[2],b[0],b[1],b[2],T,x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],T,-x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],s,-x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],s,x,v[0],v[1],v[2],v[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(f[f.length-1].slice()),this.bounds=h,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},v.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=0,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2]),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],h=0;h<3;++h)u[h]=c*n[h]+a*i[h];var f=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,f,this.points[f])}},1903:function(t){t.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},6864:function(t){t.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},9921:function(t){t.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],h=t[10],f=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(h*g-f*m)-(e*s-n*a)*(u*g-f*d)+(e*l-i*a)*(u*m-h*d)+(r*s-n*o)*(c*g-f*p)-(r*l-i*o)*(c*m-h*p)+(n*l-i*s)*(c*d-u*p)}},7399:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,h=n*s,f=i*o,p=i*s,d=i*l,m=a*o,g=a*s,y=a*l;return t[0]=1-h-d,t[1]=u+y,t[2]=f-g,t[3]=0,t[4]=u-y,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=f+g,t[9]=p-m,t[10]=1-c-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},6743:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,h=n*l,f=n*c,p=i*l,d=i*c,m=a*c,g=o*s,y=o*l,v=o*c;return t[0]=1-(p+m),t[1]=h+v,t[2]=f-y,t[3]=0,t[4]=h-v,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=f+y,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},7894:function(t){t.exports=function(t){return 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Math.abs(m-b)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(y-T)<1e-6?n(t):(h=m-b,f=g-w,p=y-T,a=x*(p*=d=1/Math.sqrt(h*h+f*f+p*p))-_*(f*=d),o=_*(h*=d)-v*p,s=v*f-x*h,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0),l=f*s-p*o,c=p*a-h*s,u=h*o-f*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0),t[0]=a,t[1]=l,t[2]=h,t[3]=0,t[4]=o,t[5]=c,t[6]=f,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*y),t[13]=-(l*m+c*g+u*y),t[14]=-(h*m+f*g+p*y),t[15]=1,t)}},6760:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],h=e[8],f=e[9],p=e[10],d=e[11],m=e[12],g=e[13],y=e[14],v=e[15],x=r[0],_=r[1],b=r[2],w=r[3];return t[0]=x*n+_*s+b*h+w*m,t[1]=x*i+_*l+b*f+w*g,t[2]=x*a+_*c+b*p+w*y,t[3]=x*o+_*u+b*d+w*v,x=r[4],_=r[5],b=r[6],w=r[7],t[4]=x*n+_*s+b*h+w*m,t[5]=x*i+_*l+b*f+w*g,t[6]=x*a+_*c+b*p+w*y,t[7]=x*o+_*u+b*d+w*v,x=r[8],_=r[9],b=r[10],w=r[11],t[8]=x*n+_*s+b*h+w*m,t[9]=x*i+_*l+b*f+w*g,t[10]=x*a+_*c+b*p+w*y,t[11]=x*o+_*u+b*d+w*v,x=r[12],_=r[13],b=r[14],w=r[15],t[12]=x*n+_*s+b*h+w*m,t[13]=x*i+_*l+b*f+w*g,t[14]=x*a+_*c+b*p+w*y,t[15]=x*o+_*u+b*d+w*v,t}},4040:function(t){t.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},4772:function(t){t.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},6079:function(t){t.exports=function(t,e,r,n){var 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t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},7626:function(t,e,r){"use strict";var n=r(2642),i=r(9346);function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}t.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),h=0;h<t.length;++h)u[h]=o(t[h],r,a,l,c);var f=0,p=1/0;for(h=0;h<u.length;++h){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[h][m]-e[m],2);d<p&&(p=d,f=h)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return(r=Math.sqrt(r))+(a=Math.sqrt(a))<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),y=0;for(h=0;h<3;++h){if(g[h]<-.001||g[h]>1.0001)return null;y+=g[h]}return Math.abs(y-1)>.001?null:[f,s(t,g),g]}},840:function(t,e,r){var n=r(3236),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * (view * (model * vec4(p, 1.0)));\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * (view * (model * vec4(position, 1.0)));\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * (view * (model * vec4(position, 1.0)));\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * (view * (model * vec4(position, 1.0)));\n  f_id        = id;\n  f_position  = position;\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * (view * (model * vec4(position, 1.0)));\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * (view * (model * vec4(position, 1.0)));\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},e.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},e.pickShader={vertex:u,fragment:h,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},e.pointPickShader={vertex:f,fragment:h,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},7201:function(t,e,r){"use strict";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(8406),l=r(6760),c=r(7608),u=r(9618),h=r(6729),f=r(7765),p=r(1888),d=r(840),m=r(7626),g=d.meshShader,y=d.wireShader,v=d.pointShader,x=d.pickShader,_=d.pointPickShader,b=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=m,this.edgeVAO=v,this.edgeCount=0,this.pointPositions=x,this.pointColors=b,this.pointUVs=T,this.pointSizes=k,this.pointIds=_,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var h=c[0];2===c.length&&(h=c[u]);for(var d=n[h][0],m=n[h][1],g=i[h],y=1-g,v=this.positions[d],x=this.positions[m],_=0;_<3;++_)o[s++]=g*v[_]+y*x[_]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=h({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],f=[],p=[],d=[],m=[],g=[],y=[],v=[],x=[],_=[],b=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,C=t.meshColor||[1,1,1,1],L=t.vertexUVs,I=t.vertexIntensity,P=t.cellUVs,z=t.cellIntensity,O=1/0,D=-1/0;if(!L&&!P)if(I)if(t.vertexIntensityBounds)O=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<I.length;++R){var F=I[R];O=Math.min(O,F),D=Math.max(D,F)}else if(z)if(t.cellIntensityBounds)O=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<z.length;++R)F=z[R],O=Math.min(O,F),D=Math.max(D,F);else for(R=0;R<n.length;++R)F=n[R][2],O=Math.min(O,F),D=Math.max(D,F);this.intensity=I||z||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.pickVertex=!(z||E);var B=t.pointSizes,N=t.pointSize||1;for(this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;y.push(j[0],j[1],j[2]),Y=S?S[W]:E?E[R]:C,this.opacityscale&&I?a.push(Y[0],Y[1],Y[2],this.opacity*A((I[W]-O)/(D-O),this.opacityscale)):3===Y.length?v.push(Y[0],Y[1],Y[2],this.opacity):(v.push(Y[0],Y[1],Y[2],Y[3]*this.opacity),Y[3]<1&&(this.hasAlpha=!0)),X=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],x.push(X[0],X[1]),B?_.push(B[W]):_.push(N),b.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Z=0;Z<3;++Z)if(isNaN(j[Z])||!isFinite(j[Z]))continue t}for(U=0;U<2;++U)j=n[W=G[U]],p.push(j[0],j[1],j[2]),Y=S?S[W]:E?E[R]:C,this.opacityscale&&I?a.push(Y[0],Y[1],Y[2],this.opacity*A((I[W]-O)/(D-O),this.opacityscale)):3===Y.length?d.push(Y[0],Y[1],Y[2],this.opacity):(d.push(Y[0],Y[1],Y[2],Y[3]*this.opacity),Y[3]<1&&(this.hasAlpha=!0)),X=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],m.push(X[0],X[1]),g.push(R);q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Z=0;Z<3;++Z)if(isNaN(j[Z])||!isFinite(j[Z]))continue t;for(U=0;U<3;++U){var W,Y,X,$;j=n[W=G[2-U]],i.push(j[0],j[1],j[2]),(Y=S?S[W]:E?E[R]:C)?this.opacityscale&&I?a.push(Y[0],Y[1],Y[2],this.opacity*A((I[W]-O)/(D-O),this.opacityscale)):3===Y.length?a.push(Y[0],Y[1],Y[2],this.opacity):(a.push(Y[0],Y[1],Y[2],Y[3]*this.opacity),Y[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),X=L?L[W]:I?[(I[W]-O)/(D-O),0]:P?P[R]:z?[(z[R]-O)/(D-O),0]:[(j[2]-O)/(D-O),0],c.push(X[0],X[1]),$=w?w[W]:T[R],l.push($[0],$[1],$[2]),f.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(y),this.pointColors.update(v),this.pointUVs.update(x),this.pointSizes.update(_),this.pointIds.update(new Uint32Array(b)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(f))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);for(l(u,s.view,s.model),l(u,s.projection,u),c(u,u),o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var h,f=u[15];for(o=0;o<3;++o)f+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/f}this.triangleCount>0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],h=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:h}}var f=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!f)return null;var p=f[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:f[1],index:r[f[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[f[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},t.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives")))throw new Error("derivatives not supported");var r=function(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=M(t),c=S(t),h=E(t),f=C(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),v=i(t),x=i(t),_=i(t),b=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:_,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),k=i(t),A=i(t),L=i(t),I=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:L,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:A,type:t.FLOAT,size:2}]),P=i(t),z=i(t),O=i(t),D=i(t),R=i(t),F=a(t,[{buffer:P,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:z,type:t.FLOAT,size:4},{buffer:O,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=new T(t,p,r,s,l,c,h,f,d,_,m,v,x,b,w,L,k,A,I,P,R,z,O,D,F,B,a(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},8120:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new o(t,n(e,[0,0,0,1,1,0,1,1]),i(e,a.boxVert,a.lineFrag))};var n=r(2762),i=r(9405),a=r(3603);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},1913:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new s(t,n(e),i(e,o.gridVert,o.gridFrag),i(e,o.tickVert,o.gridFrag))};var n=r(2762),i=r(9405),a=r(2478),o=r(3603);function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,h,f,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],h=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,f=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],y=a[m+2]-g,v=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*y/x,c[m]=2*(g-v)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var _=0;for(m=0;m<2;++m){h[0]=h[1]=0,h[m]=1,r.uniforms.dataAxis=h,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=f[m];var b=6*n[m].length;p[m]&&b&&i.drawArrays(i.TRIANGLES,_,b),_+=b}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,h=this.ticks,f=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,y=s.screenBox,v=y[2]-y[0],x=y[3]-y[1],_=m[2]-m[0],b=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=_/v,t[0]*=_/v,e[1]*=b/x,t[1]*=b/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,C=s.tickMarkWidth,L=s.tickMarkColor,I=6*h[0].length,P=Math.min(a.ge(h[0],(d[0]-p[0])/(p[2]-p[0]),l),h[0].length),z=Math.min(a.gt(h[0],(d[2]-p[0])/(p[2]-p[0]),l),h[0].length),O=0+6*P,D=6*Math.max(0,z-P),R=Math.min(a.ge(h[1],(d[1]-p[1])/(p[3]-p[1]),l),h[1].length),F=Math.min(a.gt(h[1],(d[3]-p[1])/(p[3]-p[1]),l),h[1].length),B=I+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/v-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/v,o[1]=C[1]*g/x,N&&(S.color=L[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,f.drawArrays(f.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/v-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=C[0]*g/v,o[1]=E[0]*g/x,D&&(S.color=L[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,f.drawArrays(f.TRIANGLES,O,D)),i[0]=2*(m[2]+E[3])/v-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/v,o[1]=C[3]*g/x,N&&(S.color=L[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,f.drawArrays(f.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/v-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=C[2]*g/v,o[1]=E[2]*g/x,D&&(S.color=L[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,f.drawArrays(f.TRIANGLES,O,D))}}(),d.update=(f=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],h=0;h<l.length;++h){var d=(l[h].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=f[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},4747:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new o(t,n(e,[-1,-1,-1,1,1,-1,1,1]),i(e,a.lineVert,a.lineFrag))};var n=r(2762),i=r(9405),a=r(3603);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},3603:function(t,e,r){"use strict";var n=r(3236),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);t.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},2142:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new l(t,n(e),i(e,s.textVert,s.textFrag))};var n=r(2762),i=r(9405),a=r(529),o=r(2478),s=r(3603);function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,h,f,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],h=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,f=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,y=e.tickAngle,v=e.labelEnable,x=e.labelPad,_=e.labelColor,b=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(f[2+t]-f[t])-1;var M=2/f[2+(1^t)]-f[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),v[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=_[t],r.uniforms.angle=b[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),v[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=_[t+2],r.uniforms.angle=b[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(f=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],h=a[o],m=a[o+2]-h,g=i[o],y=i[o+2]-g;p[o]=2*l/u*m/y,f[o]=2*(s-c)/u*m/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=f,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],h=[-1/0],f=l[o];for(e=0;e<f.length;++e){var p=f[e],d=p.x,m=p.text,g=p.font||"sans-serif",y=p.fontStyle||"normal",v=p.fontWeight||"normal",x=p.fontVariant||"normal";i=p.fontSize||12;for(var _=1/(c[o+2]-c[o]),b=c[o],w=m.split("\n"),T=0;T<w.length;T++)for(n=a(g,w[T],{fontStyle:y,fontWeight:v,fontVariant:x}).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-T*i*1.2,(d-b)*_);u.push(Math.floor(s.length/3)),h.push(d)}this.tickOffset[o]=u,this.tickX[o]=h}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{fontStyle:t.labelFontStyle[o],fontWeight:t.labelFontWeight[o],fontVariant:t.labelFontVariant[o],textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title,{fontStyle:t.titleFontStyle,fontWeight:t.titleFontWeight,fontVariant:t.titleFontVariant}).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},1850:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=new l(e,n(e,[e.drawingBufferWidth,e.drawingBufferHeight]));return r.grid=i(r),r.text=a(r),r.line=o(r),r.box=s(r),r.update(t),r};var n=r(3589),i=r(1913),a=r(2142),o=r(4747),s=r(8120);function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function h(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,p=this.zeroLineWidth;if(h[0]||h[1]){o.bind();for(var d=0;d<2;++d)if(h[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],f[d]):o.drawLine(e[0],m,e[2],m,p[d],f[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,y=this.borderLineWidth,v=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*y[1]*i,r[0],r[3]+.5*y[3]*i,y[1],v[1]),g[0]&&o.drawLine(r[0]-.5*y[0]*i,r[1],r[2]+.5*y[2]*i,r[1],y[0],v[0]),g[3]&&o.drawLine(r[2],r[1]-.5*y[1]*i,r[2],r[3]+.5*y[3]*i,y[3],v[3]),g[2]&&o.drawLine(r[0]-.5*y[0]*i,r[3],r[2]+.5*y[2]*i,r[3],y[2],v[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var h=c[u].pick(a,o,l);if(h)return h}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]),this.screenBox,this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(h),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],labelFontStyle:t.labelFontStyle||["normal","normal"],labelFontWeight:t.labelFontWeight||["normal","normal"],labelFontVariant:t.labelFontVariant||["normal","normal"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif",titleFontStyle:t.titleFontStyle||"normal",titleFontWeight:t.titleFontWeight||"normal",titleFontVariant:t.titleFontVariant||"normal"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},4437:function(t,e,r){"use strict";t.exports=function(t,e){t=t||document.body;var r=[.01,1/0];"distanceLimits"in(e=e||{})&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),"zoomMin"in e&&(r[0]=e.zoomMin),"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],h=0,f=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===f&&t.clientHeight===p;return f=t.clientWidth,p=t.clientHeight,a?!l:(h=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return h},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,f=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),y=!!(2&e),v=!!(4&e),x=1/t.clientHeight,_=x*(r-d._lastX),b=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!f&&!p&&!m||g&&!f&&!p&&m)&&c.rotate(A,w*k*_,-T*k*b,0),(l&&g&&!f&&!p&&!m||y||g&&f&&!p&&!m)&&c.pan(A,-d.translateSpeed*_*h,d.translateSpeed*b*h,0),u&&g&&!f&&!p&&!m||v||g&&!f&&p&&!m)){var M=-d.zoomSpeed*b/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,h*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,h*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(3025),i=r(6296),a=r(351),o=r(8512),s=r(24),l=r(7520)},799:function(t,e,r){var n=r(3236),i=r(9405),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);t.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},4100:function(t,e,r){"use strict";var n=r(4437),i=r(3837),a=r(5445),o=r(4449),s=r(3589),l=r(2260),c=r(7169),u=r(351),h=r(4772),f=r(4040),p=r(799),d=r(9216)({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return"boolean"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement("canvas"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error("webgl not supported");var v=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,_=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),b=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],C=[],L=[],I=[],P=!0,z=!0,O={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},D=(z=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:v,objects:E,shape:D,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:O,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,z=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",P=!0}}}function j(){for(var t=E.length,e=I.length,n=0;n<e;++n)L[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(L[o]+a<255){C[n]=o,i.setPickBase(L[o]+1),L[o]+=a;continue t}var l=s(r,D);C[n]=e,I.push(l),L.push(a),i.setPickBase(1),e+=1}else C[n]=-1}for(;e>0&&0===L[e-1];)L.pop(),I.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener("resize",N),F.update=function(t){F._stopped||(t=t||{},P=!0,z=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),C.push(-1),P=!0,z=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),C.pop(),P=!0,z=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener("resize",N),e.removeEventListener("webglcontextlost",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();for(_.dispose(),t=0;t<I.length;++t)I[t].dispose();b.dispose(),r=null,A=null,S=null,E=[]}},F._mouseRotating=!1,F._prevButtons=0,F.enableMouseListeners=function(){F.mouseListener=u(e,(function(t,e,r){if(!F._stopped){var n=I.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&F._prevButtons)F._mouseRotating=!0;else{F._mouseRotating&&(z=!0),F._mouseRotating=!1;for(var s=0;s<n;++s){var l=I[s].query(e,B[1]-r-1,F.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(C[c]===s){var h=u.pick(l);h&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=h.distance,x.dataPosition=h.position,x.dataCoordinate=h.dataCoordinate,x.data=h,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),P=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),P=!0),(o=o||x.object!==a)&&F.onselect&&F.onselect(x),1&t&&!(1&F._prevButtons)&&F.onclick&&F.onclick(x),F._prevButtons=t}}))},e.addEventListener("webglcontextlost",U);var V=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],q=[V[0].slice(),V[1].slice()];function H(){if(!U()){N();var t=F.camera.tick();O.view=F.camera.matrix,P=P||t,z=z||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,n=V[0],i=V[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(L=E[o]).pixelRatio=F.pixelRatio,L.axes=F.axes,P=P||!!L.dirty,z=z||!!L.dirty;var s=L.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=F.bounds;if(F.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var y=!1;for(p=0;p<3;++p)y=y||q[0][p]!==d[0][p]||q[1][p]!==d[1][p],q[0][p]=d[0][p],q[1][p]=d[1][p];if(z=z||y,P=P||y){if(y){var v=[0,0,0];for(o=0;o<3;++o)v[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:v}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;for(D[0]=T,D[1]=k,B[0]=0|Math.max(T/F.pixelRatio,1),B[1]=0|Math.max(k/F.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(f(i,-p,p,-1,1,l,c),n._ortho=!0):(h(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(F,w),o=0;o<e;++o)(L=E[o]).axesBounds=d,F.clipToBounds&&(L.clipBounds=d);x.object&&(F.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),z&&(z=!1,function(){if(!U()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=I.length,n=0;n<e;++n){var i=I[n];i.shape=B,i.begin();for(var a=0;a<t;++a)if(C[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(O))}i.end()}}}()),F.axesPixels=a(F.axes,O,T,k),F.onrender&&F.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),F.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;for(A.enable&&(M=M||A.isTransparent(),A.draw(O)),S.axes=A,x.object&&S.draw(O),r.disable(r.CULL_FACE),o=0;o<e;++o)(L=E[o]).axes=A,L.pixelRatio=F.pixelRatio,L.isOpaque&&L.isOpaque()&&L.draw(O),L.isTransparent&&L.isTransparent()&&(M=!0);if(M){for(_.shape=D,_.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(O),o=0;o<e;++o)(L=E[o]).isOpaque&&L.isOpaque()&&L.draw(O);for(r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(O),o=0;o<e;++o){var L;(L=E[o]).isTransparent&&L.isTransparent()&&L.drawTransparent(O)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),b.bind(),_.color[0].bind(0),b.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}for(P=!1,o=0;o<e;++o)E[o].dirty=!1}}}return F.enableMouseListeners(),function t(){F._stopped||F.contextLost||(H(),requestAnimationFrame(t))}(),F.redraw=function(){F._stopped||(P=!0,H())},F},createCamera:n}},6640:function(t,e,r){var n=r(3236);e.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),e.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),e.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),e.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},4696:function(t,e,r){"use strict";var n=r(9405),i=r(2762),a=r(1888),o=r(6640);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var h=n.getParameter(n.BLEND),f=n.getParameter(n.DITHER);return h&&!this.blend&&n.disable(n.BLEND),f&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),h&&!this.blend&&n.enable(n.BLEND),f&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},783:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],h=e[2],f=e[3],p=r[0],d=r[1],m=r[2],g=r[3];return(a=c*p+u*d+h*m+f*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*h+l*m,t[3]=s*f+l*g,t}},5964:function(t){"use strict";t.exports=function(t){return t||0===t?t.toString():""}},9366:function(t,e,r){"use strict";var n=r(4359);t.exports=function(t,e,r){var a=[e.style,e.weight,e.variant,e.family].join("_"),o=i[a];if(o||(o=i[a]={}),t in o)return o[t];var s={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e.family,fontStyle:e.style,fontWeight:e.weight,fontVariant:e.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},l=n(t,s);s.triangles=!1;var c,u,h=n(t,s);if(r&&1!==r){for(c=0;c<l.positions.length;++c)for(u=0;u<l.positions[c].length;++u)l.positions[c][u]/=r;for(c=0;c<h.positions.length;++c)for(u=0;u<h.positions[c].length;++u)h.positions[c][u]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],p=h.positions.length;for(c=0;c<p;++c){var d=h.positions[c];for(u=0;u<2;++u)f[0][u]=Math.min(f[0][u],d[u]),f[1][u]=Math.max(f[1][u],d[u])}return o[t]=[l,h,f]};var i={}},1283:function(t,e,r){var n=r(9405),i=r(3236),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * (view * (model * vec4(position, 1)));\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * (view * (model * vec4(dataPosition, 1)));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],h={vertex:a,fragment:l,attributes:u},f={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,h)},e.createOrtho=function(t){return y(t,f)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,m)},e.createPickProject=function(t){return y(t,g)}},8418:function(t,e,r){"use strict";var n=r(5219),i=r(2762),a=r(8116),o=r(1888),s=r(6760),l=r(1283),c=r(9366),u=r(5964),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],f=ArrayBuffer,p=DataView;function d(t){return Array.isArray(t)||function(t){return f.isView(t)&&!(t instanceof p)}(t)}function m(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function g(t,e,r,n){return m(n,n),m(n,n),m(n,n)}function y(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function x(t,e,r,n,i,a,o,s,l,c,u,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=h,this.points=[],this._selectResult=new y(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),h=i(e),f=i(e),p=i(e),d=i(e),m=new x(e,r,n,o,h,f,p,d,a(e,[{buffer:h,size:3,type:e.FLOAT},{buffer:f,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,c,u);return m.update(t),m};var _=x.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},_.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var b=[0,0],w=[0,0,0],T=[0,0,0],k=[0,0,0,1],A=[0,0,0,1],M=h.slice(),S=[0,0,0],E=[[0,0,0],[0,0,0]];function C(t){return t[0]=t[1]=t[2]=0,t}function L(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function I(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var P=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function z(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||h,u=r.view||h,f=r.projection||h,p=e.axesBounds,d=function(t){for(var e=E,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],b[0]=2/o.drawingBufferWidth,b[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=f,l.screenSize=b,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=d,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var y=M,v=0;v<16;++v)y[v]=0;for(v=0;v<4;++v)y[5*v]=1;y[5*m]=0,i[m]<0?y[12+m]=p[0][m]:y[12+m]=p[1][m],s(y,c,y),l.model=y;var x=(m+1)%3,_=(m+2)%3,P=C(w),z=C(T);P[x]=1,z[_]=1;var O=g(0,0,0,L(k,P)),D=g(0,0,0,L(A,z));if(Math.abs(O[1])>Math.abs(D[1])){var R=O;O=D,D=R,R=P,P=z,z=R;var F=x;x=_,_=F}O[0]<0&&(P[x]=-1),D[1]>0&&(z[_]=-1);var B=0,N=0;for(v=0;v<4;++v)B+=Math.pow(c[4*x+v],2),N+=Math.pow(c[4*_+v],2);P[x]/=Math.sqrt(B),z[_]/=Math.sqrt(N),l.axes[0]=P,l.axes[1]=z,l.fragClipBounds[0]=I(S,d[0],m,-1e8),l.fragClipBounds[1]=I(S,d[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var c=t.uniforms;c.model=n.model||h,c.view=n.view||h,c.projection=n.projection||h,b[0]=2/l.drawingBufferWidth,b[1]=2/l.drawingBufferHeight,c.screenSize=b,c.highlightId=r.highlightId,c.highlightScale=r.highlightScale,c.fragClipBounds=P,c.clipBounds=r.axes.bounds,c.opacity=r.opacity,c.pickGroup=r.pickId/255,c.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function O(t,e,r,i){var a;a=d(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="▼",o=!1),r||(r={});var s=r.family;d(s)&&(s=s[e]),s||(s="normal");var l=r.weight;d(l)&&(l=l[e]),l||(l="normal");var h=r.style;d(h)&&(h=h[e]),h||(h="normal");var f=r.variant;d(f)&&(f=f[e]),f||(f="normal");var p=c(a,{family:s,weight:l,style:h,variant:f},i);return{mesh:(p=c(a,r,i))[0],lines:p[1],bounds:p[2],visible:o}}_.draw=function(t){z(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},_.drawTransparent=function(t){z(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},_.drawPick=function(t){z(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},_.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(d(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(d(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){d(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l={family:t.font||"normal",style:t.fontStyle||"normal",weight:t.fontWeight||"normal",variant:t.fontVariant||"normal"},c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else for(i=[],a=[],n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1];var u=[1/0,1/0,1/0],h=[-1/0,-1/0,-1/0],f=t.glyph,p=t.color,m=t.size,g=t.angle,y=t.lineColor,x=-1,_=0,b=0,w=0;if(s.length){w=s.length;t:for(n=0;n<w;++n){for(var T=s[n],k=0;k<3;++k)if(isNaN(T[k])||!isFinite(T[k]))continue t;var A=(j=O(f,n,l,this.pixelRatio)).mesh,M=j.lines,S=j.bounds;_+=3*A.cells.length,b+=2*M.edges.length}}var E=_+b,C=o.mallocFloat(3*E),L=o.mallocFloat(4*E),I=o.mallocFloat(2*E),P=o.mallocUint32(E);if(E>0){var z=0,D=_,R=[0,0,0,1],F=[0,0,0,1],B=d(p)&&d(p[0]),N=d(y)&&d(y[0]);t:for(n=0;n<w;++n){for(x+=1,T=s[n],k=0;k<3;++k){if(isNaN(T[k])||!isFinite(T[k]))continue t;h[k]=Math.max(h[k],T[k]),u[k]=Math.min(u[k],T[k])}A=(j=O(f,n,l,this.pixelRatio)).mesh,M=j.lines,S=j.bounds;var j,U=j.visible;if(U)if(d(p)){if(3===(V=B?n<p.length?p[n]:[0,0,0,0]:p).length){for(k=0;k<3;++k)R[k]=V[k];R[3]=1}else if(4===V.length){for(k=0;k<4;++k)R[k]=V[k];!this.hasAlpha&&V[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];if(U)if(d(y)){var V;if(3===(V=N?n<y.length?y[n]:[0,0,0,0]:y).length){for(k=0;k<3;++k)F[k]=V[k];F[k]=1}else if(4===V.length){for(k=0;k<4;++k)F[k]=V[k];!this.hasAlpha&&V[3]<1&&(this.hasAlpha=!0)}}else F[0]=F[1]=F[2]=0,F[3]=1;else F=[1,1,1,0];var q=.5;U?d(m)?q=n<m.length?+m[n]:12:m?q=+m:this.useOrtho&&(q=12):q=0;var H=0;d(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var G=Math.cos(H),Z=Math.sin(H);for(T=s[n],k=0;k<3;++k)h[k]=Math.max(h[k],T[k]),u[k]=Math.min(u[k],T[k]);var W=i,Y=a;W=0,d(i)?W=n<i.length?i[n]:0:i&&(W=i),Y=0,d(a)?Y=n<a.length?a[n]:0:a&&(Y=a);var X=[W*=W>0?1-S[0][0]:W<0?1+S[1][0]:1,Y*=Y>0?1-S[0][1]:Y<0?1+S[1][1]:1],$=A.cells||[],J=A.positions||[];for(k=0;k<$.length;++k)for(var K=$[k],Q=0;Q<3;++Q){for(var tt=0;tt<3;++tt)C[3*z+tt]=T[tt];for(tt=0;tt<4;++tt)L[4*z+tt]=R[tt];P[z]=x;var et=J[K[Q]];I[2*z]=q*(G*et[0]-Z*et[1]+X[0]),I[2*z+1]=q*(Z*et[0]+G*et[1]+X[1]),z+=1}for($=M.edges,J=M.positions,k=0;k<$.length;++k)for(K=$[k],Q=0;Q<2;++Q){for(tt=0;tt<3;++tt)C[3*D+tt]=T[tt];for(tt=0;tt<4;++tt)L[4*D+tt]=F[tt];P[D]=x,et=J[K[Q]],I[2*D]=q*(G*et[0]-Z*et[1]+X[0]),I[2*D+1]=q*(Z*et[0]+G*et[1]+X[1]),D+=1}}}this.bounds=[u,h],this.points=s,this.pointCount=s.length,this.vertexCount=_,this.lineVertexCount=b,this.pointBuffer.update(C),this.colorBuffer.update(L),this.glyphBuffer.update(I),this.idBuffer.update(P),o.free(C),o.free(L),o.free(I),o.free(P)},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},4298:function(t,e,r){"use strict";var n=r(3236);e.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid 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coordinates[1], 0.5 * (position + 1.0)), abs(position));\n\n  vec4 clipPos = projection * (view * (model * vec4(vertexPosition, 1.0)));\n  vec2 clipOffset = (projection * (view * (model * vec4(color, 0.0)))).xy;\n  vec2 delta = weight * clipOffset * screenShape;\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n  gl_FragColor = fragColor;\n}"]);t.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},4449:function(t,e,r){"use strict";var n=r(2762),i=r(8116),a=r(1493);t.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var h=new s(t,l,c,u);return h.update(e),h};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],h=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var f=c,p=u,d=0;d<3;++d)i&&i[d]<0?(f[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(f[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);for(h[0]=e.drawingBufferWidth,h[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,f,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=h,d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},6740:function(t,e,r){var n=r(3236),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * (view * tubePosition);\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},7815:function(t,e,r){"use strict";var n=r(2931),i=r(9970),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};t.exports=function(t,e){var r=t.startingPositions,c=t.maxLength||1e3,u=t.tubeSize||1,h=t.absoluteTubeSize,f=t.gridFill||"+x+y+z",p={};-1!==f.indexOf("-x")&&(p.reversedX=!0),-1!==f.indexOf("-y")&&(p.reversedY=!0),-1!==f.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(f.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,l=t[0],c=t[1],u=t[2],h=a[0].length,f=a[1].length,p=a[2].length,d=o(a[0],l),m=o(a[1],c),g=o(a[2],u),y=d+1,v=m+1,x=g+1;if(d=s(d,0,h-1),y=s(y,0,h-1),m=s(m,0,f-1),v=s(v,0,f-1),g=s(g,0,p-1),x=s(x,0,p-1),d<0||m<0||g<0||y>h-1||v>f-1||x>p-1)return n.create();var _,b,w,T,k,A,M=a[0][d],S=a[0][y],E=a[1][m],C=a[1][v],L=a[2][g],I=(l-M)/(S-M),P=(c-E)/(C-E),z=(u-L)/(a[2][x]-L);switch(isFinite(I)||(I=.5),isFinite(P)||(P=.5),isFinite(z)||(z=.5),r.reversedX&&(d=h-1-d,y=h-1-y),r.reversedY&&(m=f-1-m,v=f-1-v),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=v*p,_=d*p*f,b=y*p*f;break;case 4:k=g,A=x,_=d*p,b=y*p,w=m*p*h,T=v*p*h;break;case 3:w=m,T=v,k=g*f,A=x*f,_=d*f*p,b=y*f*p;break;case 2:w=m,T=v,_=d*f,b=y*f,k=g*f*h,A=x*f*h;break;case 1:_=d,b=y,k=g*h,A=x*h,w=m*h*p,T=v*h*p;break;default:_=d,b=y,w=m*h,T=v*h,k=g*h*f,A=x*h*f}var O=i[_+w+k],D=i[_+w+A],R=i[_+T+k],F=i[_+T+A],B=i[b+w+k],N=i[b+w+A],j=i[b+T+k],U=i[b+T+A],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,O,B,I),n.lerp(q,D,N,I),n.lerp(H,R,j,I),n.lerp(G,F,U,I);var Z=n.create(),W=n.create();n.lerp(Z,V,H,P),n.lerp(W,q,G,P);var Y=n.create();return n.lerp(Y,Z,W,z),Y}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],y=e[0][0],v=e[0][1],x=e[0][2],_=e[1][0],b=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<y||e>_||r<v||r>b||n<x||n>w)},k=10*n.distance(e[0],e[1])/c,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,c=0;c<s;c++){var u=t[c],h=u[0],f=u[1],p=u[2];i[h]||(e.push(h),i[h]=!0),a[f]||(r.push(f),a[f]=!0),o[p]||(n.push(p),o[p]=!0)}var d=l(e),m=l(r),g=l(n),y=Math.min(d,m,g);return isFinite(y)?y:1}(r));for(var C=0;C<E;C++){var L=n.create();n.copy(L,r[C]);var I=[L],P=[],z=d(L),O=L;P.push(z);var D=[],R=m(L,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:I,velocities:P,divergences:D});for(var B=0;B<100*c&&I.length<c&&T(L);){B++;var N=n.clone(z),j=n.squaredLength(N);if(0===j)break;j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,L),z=d(N),n.squaredDistance(O,N)-A>-1e-4*A&&(I.push(N),O=N,P.push(z),R=m(N,z),F=n.length(R),isFinite(F)&&F>S&&(S=F),D.push(F)),L=N}}var U=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],h=[],f=[],p=[],d=[],m=0,g=0,y=i.create(),v=i.create(),x=0;x<o.length;x++){var _=o[x],b=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(b)/a,y=i.create(),n.copy(y,b),y[3]=w;for(var T=0;T<8;T++)p[T]=[_[0],_[1],_[2],T];if(f.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(f[T],p[T],p[k],p[k],f[k],f[T]),h.push(v,y,y,y,v,v),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=f;f=p,p=M;var S=v;v=y,y=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:h,vertexIntensity:d}}(t,r,a,o)})),h=[],f=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=h.length;for(h=h.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity),c=0;c<m.cells.length;c++){var y=m.cells[c],v=[];f.push(v);for(var x=0;x<y.length;x++)v.push(y[x]+g)}}return{positions:h,cells:f,vectors:p,vertexIntensity:d,colormap:e}}(g,t.colormap,S,M);return h?U.tubeScale=h:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var c=r(6740),u=r(6405).createMesh;t.exports.createTubeMesh=function(t,e){return u(t,e,{shaders:c,traceType:"streamtube"})}},990:function(t,e,r){var n=r(9405),i=r(3236),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));\n  vec4 worldPosition = (model * objectOffsetTranslation) * vec4(localCoordinate, 1.0);\n  vec4 clipPosition = projection * (view * worldPosition);\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color — in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  mat4 objectOffsetTranslation = mat4(1.0) + mat4(vec4(0), vec4(0), vec4(0), vec4(objectOffset, 0));\n  vec4 worldPosition = (model * objectOffsetTranslation) * vec4(dataCoordinate, 1.0);\n\n  vec4 clipPosition = projection * (view * worldPosition);\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},9499:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=v(e),n=_(e),s=x(e),l=b(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:w,offset:0},{buffer:c,size:3,stride:w,offset:16},{buffer:c,size:3,stride:w,offset:28}]),h=i(e),f=a(e,[{buffer:h,size:4,stride:20,offset:0},{buffer:h,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,h,f,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||"jet",g.update(y),g};var n=r(8828),i=r(2762),a=r(8116),o=r(7766),s=r(1888),l=r(6729),c=r(5298),u=r(9994),h=r(9618),f=r(3711),p=r(6760),d=r(7608),m=r(2478),g=r(6199),y=r(990),v=y.createShader,x=y.createContourShader,_=y.createPickShader,b=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,c,u,f,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=f,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var C=E.prototype;C.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:S,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},C.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},C.isOpaque=function(){return!this.isTransparent()},C.pickSlots=1,C.setPickBase=function(t){this.pickId=t};var L=[0,0,0],I={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function P(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||L,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=I.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=I.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return I.showSurface=o,I.showContour=s,I}var z={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},O=T.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=z;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=O;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=P(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var f=this._contourVAO;for(f.bind(),i=0;i<3;++i)for(h.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(h.uniforms.contourColor=this.highlightColor[i],h.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(h.uniforms.contourColor=this.contourColor[i],h.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(h.uniforms.height=this.contourLevels[i][o],f.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(h.uniforms.model=u.projections[i],h.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){h.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(h.uniforms.contourColor=this.highlightColor[o],h.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(h.uniforms.contourColor=this.contourColor[o],h.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(h.uniforms.height=this.contourLevels[o][m],f.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(f.unbind(),(f=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(h.uniforms.model=n.model,h.uniforms.clipBounds=n.clipBounds,h.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),h.uniforms.contourColor=this.dynamicColor[i],h.uniforms.contourTint=this.dynamicTint[i],h.uniforms.height=this.dynamicLevel[i],f.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(h.uniforms.model=u.projections[o],h.uniforms.clipBounds=u.clipBounds[o],f.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));f.unbind()}}C.draw=function(t){return R.call(this,t,!1)},C.drawTransparent=function(t){return R.call(this,t,!0)};var F={model:T,view:T,projection:T,inverseModel:T,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function B(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function N(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function j(t){if(Array.isArray(t)){if(Array.isArray(t))return[N(t[0]),N(t[1]),N(t[2])];var e=N(t);return[e.slice(),e.slice(),e.slice()]}}C.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=F;r.model=t.model||T,r.view=t.view||T,r.projection=t.projection||T,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=D;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=P(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},C.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var h=u?a:1-a,f=0;f<2;++f)for(var p=i+u,d=s+f,g=h*(f?l:1-l),y=0;y<3;++y)c[y]+=this._field[y].get(p,d)*g;for(var v=this._pickResult.level,x=0;x<3;++x)if(v[x]=m.le(this.contourLevels[x],c[x]),v[x]<0)this.contourLevels[x].length>0&&(v[x]=0);else if(v[x]<this.contourLevels[x].length-1){var _=this.contourLevels[x][v[x]],b=this.contourLevels[x][v[x]+1];Math.abs(_-c[x])>Math.abs(b-c[x])&&(v[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},C.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},C.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=B(t.contourWidth,Number)),"showContour"in t&&(this.showContour=B(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=B(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=j(t.contourColor)),"contourProject"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=j(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=h(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(y=0;y<2;++y)if(c.shape[y]!==a[y])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=h(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=h(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var v=this._field,x=h(s.mallocFloat(3*v[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),v[o],"mirror");var _=h(s.mallocFloat(3*v[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var b=x.get(0,o,y,0),w=x.get(0,o,y,1),T=x.get(1,o,y,0),A=x.get(1,o,y,1),M=x.get(2,o,y,0),S=x.get(2,o,y,1),E=T*S-A*M,C=M*w-S*b,L=b*A-w*T,I=Math.sqrt(E*E+C*C+L*L);I<1e-8?(I=Math.max(Math.abs(E),Math.abs(C),Math.abs(L)))<1e-8?(L=1,C=E=0,I=1):I=1/I:I=1/Math.sqrt(I),_.set(o,y,0,E*I),_.set(o,y,1,C*I),_.set(o,y,2,L*I)}s.free(x.data);var P=[1/0,1/0,1/0],z=[-1/0,-1/0,-1/0],O=1/0,D=-1/0,R=(a[0]-1)*(a[1]-1)*6,F=s.mallocFloat(n.nextPow2(10*R)),N=0,U=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+y+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var Z=o+k[H][0],W=y+k[H][1],Y=this._field[0].get(Z+1,W+1),X=this._field[1].get(Z+1,W+1);G=this._field[2].get(Z+1,W+1),E=_.get(Z+1,W+1,0),C=_.get(Z+1,W+1,1),L=_.get(Z+1,W+1,2),t.intensity&&($=t.intensity.get(Z,W));var $=t.intensity?t.intensity.get(Z,W):G+this.objectOffset[2];F[N++]=Z,F[N++]=W,F[N++]=Y,F[N++]=X,F[N++]=G,F[N++]=0,F[N++]=$,F[N++]=E,F[N++]=C,F[N++]=L,P[0]=Math.min(P[0],Y+this.objectOffset[0]),P[1]=Math.min(P[1],X+this.objectOffset[1]),P[2]=Math.min(P[2],G+this.objectOffset[2]),O=Math.min(O,$),z[0]=Math.max(z[0],Y+this.objectOffset[0]),z[1]=Math.max(z[1],X+this.objectOffset[1]),z[2]=Math.max(z[2],G+this.objectOffset[2]),D=Math.max(D,$),U+=1}}for(t.intensityBounds&&(O=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<N;o+=10)F[o]=(F[o]-O)/(D-O);this._vertexCount=U,this._coordinateBuffer.update(F.subarray(0,N)),s.freeFloat(F),s.free(_.data),this.bounds=[P,z],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===O&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[O,D]}if("levels"in t){var J=t.levels;for(J=Array.isArray(J[0])?J.slice():[[],[],J],o=0;o<3;++o)J[o]=J[o].slice(),J[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(y=0;y<J[o].length;++y)J[o][y]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(J[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<J[o].length;++y)if(J[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=J}if(r){v=this._field,a=this.shape;for(var K=[],Q=0;Q<3;++Q){var tt=this.contourLevels[Q],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=f(this._field[Q],tt[o]);et.push(K.length/5|0),U=0;t:for(y=0;y<it.cells.length;++y){var at=it.cells[y];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ht=0|Math.floor(ut),ft=ut-ht,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=(Q+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Z=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var yt=q?ft:1-ft;if(W=0|Math.min(Math.max(ht+q,0),a[1]),G=dt<2?this._field[mt].get(Z,W):(this.intensity.get(Z,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var vt=gt*yt;nt[dt]+=vt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)K.pop();U-=1}continue t}K.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var _t=s.mallocFloat(K.length);for(o=0;o<K.length;++o)_t[o]=K[o];this._contourBuffer.update(_t),s.freeFloat(_t)}},C.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},C.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],h=this._field[l],p=this._field[c],d=f(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var y=m[e],v=0;v<2;++v){var x=g[y[v]],_=+x[0],b=0|_,w=0|Math.min(b+1,i[0]),T=_-b,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,C=1-E,L=k*C,I=k*E,P=T*C,z=T*E,O=L*h.get(b,M)+I*h.get(b,S)+P*h.get(w,M)+z*h.get(w,S),D=L*p.get(b,M)+I*p.get(b,S)+P*p.get(w,M)+z*p.get(w,S);if(isNaN(O)||isNaN(D)){v&&(n-=1);break}a[2*n+0]=O,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},7766:function(t,e,r){"use strict";var n=r(9618),i=r(5298),a=r(1888);t.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");if(o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t),"number"==typeof arguments[1])return g(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return g(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=c(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new f(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error("gl-texture2d: Invalid texture size");var l=d(o,e.stride.slice()),c=0;"float32"===r?c=t.FLOAT:"float64"===r?(c=t.FLOAT,l=!1,r="float32"):"uint8"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r="uint8");var h,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");g=t.RGBA}}c!==t.FLOAT||t.getExtension("OES_texture_float")||(c=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)h=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var v=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,v,0);"float32"!==r&&"float64"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):u(x,e),h=p.subarray(0,y)}var _=m(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,c,h),l||a.free(p),new f(t,_,o[0],o[1],g,c)}(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof 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ve=function(t,e){this.type=t,this.value=e};ve.parse=function(t,e){if(2!==t.length)return e.error("'literal' expression requires exactly one argument, but found "+(t.length-1)+" instead.");if(!me(t[1]))return e.error("invalid value");var r=t[1],n=ge(r),i=e.expectedType;return"array"!==n.kind||0!==n.N||!i||"array"!==i.kind||"number"==typeof i.N&&0!==i.N||(n=i),new ve(n,r)},ve.prototype.evaluate=function(){return this.value},ve.prototype.eachChild=function(){},ve.prototype.outputDefined=function(){return!0},ve.prototype.serialize=function(){return"array"===this.type.kind||"object"===this.type.kind?["literal",this.value]:this.value instanceof ce?["rgba"].concat(this.value.toArray()):this.value instanceof fe?this.value.serialize():this.value};var xe=function(t){this.name="ExpressionEvaluationError",this.message=t};xe.prototype.toJSON=function(){return this.message};var 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e=("property"===t.expressionContext?dn:hn)(Vt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Bt(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Bt(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Ye(r))return[new Bt(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Ye(r))return[new Bt(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Xe(r,["zoom","feature-state"]))return[new Bt(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!We(r))return[new Bt(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function wn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ut(r))&&i.push(new Bt(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" 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An(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};Tn(t)||(t=En(t));var e=hn(t,kn);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:Sn(t)}}function Mn(t,e){return t<e?-1:t>e?1:0}function Sn(t){if(!Array.isArray(t))return!1;if("within"===t[0])return!0;for(var e=1;e<t.length;e++)if(Sn(t[e]))return!0;return!1}function En(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?Cn(t[1],t[2],"=="):"!="===r?Pn(Cn(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?Cn(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(En))):"all"===r?["all"].concat(t.slice(1).map(En)):"none"===r?["all"].concat(t.slice(1).map(En).map(Pn)):"in"===r?Ln(t[1],t.slice(2)):"!in"===r?Pn(Ln(t[1],t.slice(2))):"has"===r?In(t[1]):"!has"===r?Pn(In(t[1])):"within"!==r||t}function Cn(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function Ln(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(Mn)]]:["filter-in-small",t,["literal",e]]}}function In(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function Pn(t){return["!",t]}function zn(t){return Tn(Vt(t.value))?bn(jt({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):On(t)}function On(t){var e=t.value,r=t.key;if("array"!==Qr(e))return[new Bt(r,e,"array expected, "+Qr(e)+" found")];var n,i=t.styleSpec,a=[];if(e.length<1)return[new Bt(r,e,"filter array must have at least 1 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Bt(r,e,'filter array for "'+e[0]+'" operator must have 2 elements')):"string"!==n&&a.push(new Bt(r+"[1]",e[1],"string expected, "+n+" found"));break;case"within":n=Qr(e[1]),2!==e.length?a.push(new Bt(r,e,'filter array for "'+e[0]+'" operator must have 2 elements')):"object"!==n&&a.push(new Bt(r+"[1]",e[1],"object expected, "+n+" found"))}return a}function Dn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return Hn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Bt(r,a,'unknown property "'+o+'"')];if("string"===Qr(a)&&$r(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Bt(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var h=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&h.push(new Bt(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&tn(Vt(a))&&"identity"===Ut(a.type)&&h.push(new Bt(r,a,'"text-font" does not support identity functions'))),h.concat(Hn({key:t.key,value:a,valueSpec:u,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function Rn(t){return Dn(t,"paint")}function Fn(t){return Dn(t,"layout")}function Bn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Bt(n,r,'either "type" or "ref" is required'));var o,s=Ut(r.type),l=Ut(r.ref);if(r.id)for(var c=Ut(r.id),u=0;u<t.arrayIndex;u++){var h=i.layers[u];Ut(h.id)===c&&e.push(new Bt(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+h.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Bt(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Bt(n,r,'missing required property "source"'));return e=e.concat(yn({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return Hn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:zn,layout:function(t){return yn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return Fn(jt({layerType:s},t))}}})},paint:function(t){return yn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return Rn(jt({layerType:s},t))}}})}}})),e}function Nn(t){var e=t.value,r=t.key,n=Qr(e);return"string"!==n?[new Bt(r,e,"string expected, "+n+" found")]:[]}var jn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Qr(r))return Nn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,Nn({key:e+"."+i,value:r[i]}));return n}};function Un(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Bt(r,e,'"type" is required')];var a,o=Ut(e.type);switch(o){case"vector":case"raster":case"raster-dem":return yn({key:r,value:e,valueSpec:n["source_"+o.replace("-","_")],style:t.style,styleSpec:n,objectElementValidators:jn});case"geojson":if(a=yn({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:jn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],h="string"==typeof c?[c,["accumulated"],["get",s]]:c;a.push.apply(a,bn({key:r+"."+s+".map",value:u,expressionContext:"cluster-map"})),a.push.apply(a,bn({key:r+"."+s+".reduce",value:h,expressionContext:"cluster-reduce"}))}return a;case"video":return yn({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case"image":return yn({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case"canvas":return[new Bt(r,null,"Please use runtime APIs to add canvas sources, rather than including them in stylesheets.","source.canvas")];default:return wn({key:r+".type",value:e.type,valueSpec:{values:["vector","raster","raster-dem","geojson","video","image"]},style:i,styleSpec:n})}}function Vn(t){var e=t.value,r=t.styleSpec,n=r.light,i=t.style,a=[],o=Qr(e);if(void 0===e)return a;if("object"!==o)return a.concat([new Bt("light",e,"object expected, "+o+" found")]);for(var s in e){var l=s.match(/^(.*)-transition$/);a=l&&n[l[1]]&&n[l[1]].transition?a.concat(Hn({key:s,value:e[s],valueSpec:r.transition,style:i,styleSpec:r})):n[s]?a.concat(Hn({key:s,value:e[s],valueSpec:n[s],style:i,styleSpec:r})):a.concat([new Bt(s,e[s],'unknown property "'+s+'"')])}return a}var qn={"*":function(){return[]},array:vn,boolean:function(t){var e=t.value,r=t.key,n=Qr(e);return"boolean"!==n?[new Bt(r,e,"boolean expected, "+n+" found")]:[]},number:xn,color:function(t){var e=t.key,r=t.value,n=Qr(r);return"string"!==n?[new Bt(e,r,"color expected, "+n+" found")]:null===le(r)?[new Bt(e,r,'color 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Wn(t){return[].concat(t).sort((function(t,e){return t.line-e.line}))}function Yn(t){return function(){for(var e=[],r=arguments.length;r--;)e[r]=arguments[r];return Wn(t.apply(this,e))}}Zn.source=Yn(Un),Zn.light=Yn(Vn),Zn.layer=Yn(Bn),Zn.filter=Yn(zn),Zn.paintProperty=Yn(Rn),Zn.layoutProperty=Yn(Fn);var Xn=Zn,$n=Xn.light,Jn=Xn.paintProperty,Kn=Xn.layoutProperty;function Qn(t,e){var r=!1;if(e&&e.length)for(var n=0,i=e;n<i.length;n+=1){var a=i[n];t.fire(new Dt(new Error(a.message))),r=!0}return r}var ti=ri,ei=3;function ri(t,e,r){var n=this.cells=[];if(t instanceof ArrayBuffer){this.arrayBuffer=t;var i=new Int32Array(this.arrayBuffer);t=i[0],e=i[1],r=i[2],this.d=e+2*r;for(var a=0;a<this.d*this.d;a++){var o=i[ei+a],s=i[ei+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[ei+n.length],c=i[ei+n.length+1];this.keys=i.subarray(l,c),this.bboxes=i.subarray(c),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var u=0;u<this.d*this.d;u++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var h=r/e*t;this.min=-h,this.max=t+h}ri.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},ri.prototype._insertReadonly=function(){throw"Cannot insert into a GridIndex created from an ArrayBuffer."},ri.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},ri.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},ri.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var c=this.keys,u=this.bboxes,h=0;h<l.length;h++){var f=l[h];if(void 0===o[f]){var p=4*f;(s?s(u[p+0],u[p+1],u[p+2],u[p+3]):t<=u[p+2]&&e<=u[p+3]&&r>=u[p+0]&&n>=u[p+1])?(o[f]=!0,a.push(c[f])):o[f]=!1}}},ri.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),h=this._convertToCellCoord(n),f=l;f<=u;f++)for(var p=c;p<=h;p++){var d=this.d*p+f;if((!s||s(this._convertFromCellCoord(f),this._convertFromCellCoord(p),this._convertFromCellCoord(f+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},ri.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},ri.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},ri.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=ei+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new 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l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Qi);ra.prototype.bytesPerElement=4,oi("StructArrayLayout2i4",ra);var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return 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this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Qi);ca.prototype.bytesPerElement=12,oi("StructArrayLayout3f12",ca);var ua=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Qi);ua.prototype.bytesPerElement=4,oi("StructArrayLayout1ul4",ua);var ha=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new 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this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Qi);fa.prototype.bytesPerElement=12,oi("StructArrayLayout2i2i2i12",fa);var pa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Qi);pa.prototype.bytesPerElement=16,oi("StructArrayLayout2f1f2i16",pa);var da=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Qi);da.prototype.bytesPerElement=12,oi("StructArrayLayout2ub2f12",da);var ma=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return 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this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Qi);xa.prototype.bytesPerElement=6,oi("StructArrayLayout3i6",xa);var _a=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Qi);_a.prototype.bytesPerElement=8,oi("StructArrayLayout1ul2ui8",_a);var ba=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Qi);Ta.prototype.bytesPerElement=16,oi("StructArrayLayout4f16",Ta);var ka=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new a(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ki);ka.prototype.size=20;var Aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new 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this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ki);Ma.prototype.size=48;var Sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ma(this,t)},e}(ga);oi("PlacedSymbolArray",Sa);var Ea=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ki);Ea.prototype.size=68;var Ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ea(this,t)},e}(ya);oi("SymbolInstanceArray",Ca);var La=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(va);oi("GlyphOffsetArray",La);var Ia=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(xa);oi("SymbolLineVertexArray",Ia);var Pa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return 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Float32Array(16),ro=function(t){function e(e,r){t.call(this,e,r),this.current=eo}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t[12]!==this.current[12]||t[0]!==this.current[0])return this.current=t,void this.gl.uniformMatrix4fv(this.location,!1,t);for(var e=1;e<16;e++)if(t[e]!==this.current[e]){this.current=t,this.gl.uniformMatrix4fv(this.location,!1,t);break}},e}(Ya);function no(t){return[Ra(255*t.r,255*t.g),Ra(255*t.b,255*t.a)]}var io=function(t,e,r){this.value=t,this.uniformNames=e.map((function(t){return"u_"+t})),this.type=r};io.prototype.setUniform=function(t,e,r){t.set(r.constantOr(this.value))},io.prototype.getBinding=function(t,e,r){return"color"===this.type?new to(t,e):new $a(t,e)};var ao=function(t,e){this.uniformNames=e.map((function(t){return"u_"+t})),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1};ao.prototype.setConstantPatternPositions=function(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr},ao.prototype.setUniform=function(t,e,r,n){var i="u_pattern_to"===n?this.patternTo:"u_pattern_from"===n?this.patternFrom:"u_pixel_ratio_to"===n?this.pixelRatioTo:"u_pixel_ratio_from"===n?this.pixelRatioFrom:null;i&&t.set(i)},ao.prototype.getBinding=function(t,e,r){return"u_pattern"===r.substr(0,9)?new Qa(t,e):new $a(t,e)};var oo=function(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:"a_"+t,type:"Float32",components:"color"===r?2:1,offset:0}})),this.paintVertexArray=new n};oo.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.paintVertexArray.length,o=this.expression.evaluate(new Oi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)},oo.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)},oo.prototype._setPaintValue=function(t,e,r){if("color"===this.type)for(var n=no(r),i=t;i<e;i++)this.paintVertexArray.emplace(i,n[0],n[1]);else{for(var a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},oo.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},oo.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var so=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return"u_"+t+"_t"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:"a_"+t,type:"Float32",components:"color"===r?4:2,offset:0}})),this.paintVertexArray=new a};so.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new Oi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new Oi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},so.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},so.prototype._setPaintValue=function(t,e,r,n){if("color"===this.type)for(var i=no(r),a=no(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},so.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},so.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},so.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=h(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},so.prototype.getBinding=function(t,e,r){return new $a(t,e)};var lo=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};lo.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},lo.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},lo.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],c=n[o];if(s&&l&&c)for(var u=t;u<e;u++)this.zoomInPaintVertexArray.emplace(u,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(u,l.tl[0],l.tl[1],l.br[0],l.br[1],c.tl[0],c.tl[1],c.br[0],c.br[1],l.pixelRatio,c.pixelRatio)}},lo.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,Fa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,Fa.members,this.expression.isStateDependent))},lo.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var co=function(t,e,r){this.binders={},this._buffers=[];var n=[];for(var i in t.paint._values)if(r(i)){var a=t.paint.get(i);if(a instanceof Ui&&$r(a.property.specification)){var o=ho(i,t.type),s=a.value,l=a.property.specification.type,c=a.property.useIntegerZoom,u=a.property.specification["property-type"],h="cross-faded"===u||"cross-faded-data-driven"===u;if("constant"===s.kind)this.binders[i]=h?new ao(s.value,o):new io(s.value,o,l),n.push("/u_"+i);else if("source"===s.kind||h){var f=fo(i,l,"source");this.binders[i]=h?new lo(s,l,c,e,f,t.id):new oo(s,o,l,f),n.push("/a_"+i)}else{var p=fo(i,l,"composite");this.binders[i]=new so(s,o,l,c,e,p),n.push("/z_"+i)}}}this.cacheKey=n.sort().join("")};co.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof oo||e instanceof so?e.maxValue:0},co.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof oo||o instanceof so||o instanceof lo)&&o.populatePaintArray(t,e,r,n,i)}},co.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof ao&&n.setConstantPatternPositions(t,e)}},co.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var c=l[s],u=r.feature(c.index);for(var h in this.binders){var f=this.binders[h];if((f instanceof oo||f instanceof so||f instanceof lo)&&!0===f.expression.isStateDependent){var p=n.paint.get(h);f.expression=p.value,f.updatePaintArray(c.start,c.end,u,t[o],i),a=!0}}}return a},co.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof io||r instanceof ao)&&t.push.apply(t,r.uniformNames.map((function(t){return"#define HAS_UNIFORM_"+t})))}return t},co.prototype.getBinderAttributes=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof oo||r instanceof so)for(var n=0;n<r.paintVertexAttributes.length;n++)t.push(r.paintVertexAttributes[n].name);else if(r instanceof lo)for(var i=0;i<Fa.members.length;i++)t.push(Fa.members[i].name)}return t},co.prototype.getBinderUniforms=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof io||r instanceof ao||r instanceof so)for(var n=0,i=r.uniformNames;n<i.length;n+=1){var a=i[n];t.push(a)}}return t},co.prototype.getPaintVertexBuffers=function(){return this._buffers},co.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof io||i instanceof ao||i instanceof so)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},co.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,c=o.binding;this.binders[l].setUniform(c,n,r.get(l),s)}},co.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof lo){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof oo||r instanceof so)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},co.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof oo||r instanceof so||r instanceof lo)&&r.upload(t)}this.updatePaintBuffers()},co.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof oo||e instanceof so||e instanceof lo)&&e.destroy()}};var uo=function(t,e,r){void 0===r&&(r=function(){return!0}),this.programConfigurations={};for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.programConfigurations[a.id]=new co(a,e,r)}this.needsUpload=!1,this._featureMap=new qa,this._bufferOffset=0};function ho(t,e){return{"text-opacity":["opacity"],"icon-opacity":["opacity"],"text-color":["fill_color"],"icon-color":["fill_color"],"text-halo-color":["halo_color"],"icon-halo-color":["halo_color"],"text-halo-blur":["halo_blur"],"icon-halo-blur":["halo_blur"],"text-halo-width":["halo_width"],"icon-halo-width":["halo_width"],"line-gap-width":["gapwidth"],"line-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-extrusion-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"]}[t]||[t.replace(e+"-","").replace(/-/g,"_")]}function fo(t,e,r){var n={color:{source:oa,composite:Ta},number:{source:va,composite:oa}},i=function(t){return{"line-pattern":{source:sa,composite:sa},"fill-pattern":{source:sa,composite:sa},"fill-extrusion-pattern":{source:sa,composite:sa}}[t]}(t);return i&&i[r]||n[e][r]}uo.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},uo.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},uo.prototype.get=function(t){return this.programConfigurations[t]},uo.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},uo.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},oi("ConstantBinder",io),oi("CrossFadedConstantBinder",ao),oi("SourceExpressionBinder",oo),oi("CrossFadedCompositeBinder",lo),oi("CompositeExpressionBinder",so),oi("ProgramConfiguration",co,{omit:["_buffers"]}),oi("ProgramConfigurationSet",uo);var po=8192,mo=Math.pow(2,14)-1,go=-mo-1;function yo(t){for(var e=po/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a],s=Math.round(o.x*e),l=Math.round(o.y*e);o.x=h(s,go,mo),o.y=h(l,go,mo),(s<o.x||s>o.x+1||l<o.y||l>o.y+1)&&k("Geometry exceeds allowed extent, reduce your vector tile buffer size")}return r}function vo(t,e){return{type:t.type,id:t.id,properties:t.properties,geometry:e?yo(t):[]}}function xo(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var _o=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ra,this.indexArray=new ma,this.segments=new Da,this.programConfigurations=new uo(t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function bo(t,e){for(var r=0;r<t.length;r++)if(Lo(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Lo(t,e[n]))return!0;return!!Ao(t,e)}function wo(t,e,r){return!!Lo(t,e)||!!So(e,t,r)}function To(t,e){if(1===t.length)return Co(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Lo(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Co(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(Ao(t,e[o]))return!0;return!1}function ko(t,e,r){if(t.length>1){if(Ao(t,e))return!0;for(var n=0;n<e.length;n++)if(So(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(So(t[i],e,r))return!0;return!1}function Ao(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(Mo(n,i,e[a],e[a+1]))return!0;return!1}function Mo(t,e,r,n){return A(t,r,n)!==A(e,r,n)&&A(t,e,r)!==A(t,e,n)}function So(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Eo(t,e[i-1],e[i])<n)return!0;return!1}function Eo(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return i<0?t.distSqr(e):i>1?t.distSqr(r):t.distSqr(r.sub(e)._mult(i)._add(e))}function Co(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)n=r[s],i=r[l],n.y>e.y!=i.y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Lo(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function Io(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=A(t,e,r[0]);return a!==A(t,e,r[1])||a!==A(t,e,r[2])||a!==A(t,e,r[3])}function Po(t,e,r){var n=e.paint.get(t).value;return"constant"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function zo(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function Oo(t,e,r,n,i){if(!e[0]&&!e[1])return t;var o=a.convert(e)._mult(i);"viewport"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++){var c=t[l];s.push(c.sub(o))}return s}_o.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;"circle"===n.type&&(a=n.layout.get("circle-sort-key"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],c=l.feature,u=l.id,h=l.index,f=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d=vo(c,p);if(this.layers[0]._featureFilter.filter(new Oi(this.zoom),d,r)){var m=a?a.evaluate(d,{},r):void 0,g={id:u,properties:c.properties,type:c.type,sourceLayerIndex:f,index:h,geometry:p?d.geometry:yo(c),patterns:{},sortKey:m};i.push(g)}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var y=0,v=i;y<v.length;y+=1){var x=v[y],_=x,b=_.geometry,w=_.index,T=_.sourceLayerIndex,k=t[w].feature;this.addFeature(x,b,w,r),e.featureIndex.insert(k,b,w,T,this.index)}},_o.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},_o.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},_o.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},_o.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Oa),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},_o.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},_o.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],c=l.x,u=l.y;if(!(c<0||c>=po||u<0||u>=po)){var h=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),f=h.vertexLength;xo(this.layoutVertexArray,c,u,-1,-1),xo(this.layoutVertexArray,c,u,1,-1),xo(this.layoutVertexArray,c,u,1,1),xo(this.layoutVertexArray,c,u,-1,1),this.indexArray.emplaceBack(f,f+1,f+2),this.indexArray.emplaceBack(f,f+3,f+2),h.vertexLength+=4,h.primitiveLength+=2}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{},n)},oi("CircleBucket",_o,{omit:["layers"]});var Do=new Yi({"circle-sort-key":new Hi(Ft.layout_circle["circle-sort-key"])}),Ro={paint:new Yi({"circle-radius":new Hi(Ft.paint_circle["circle-radius"]),"circle-color":new Hi(Ft.paint_circle["circle-color"]),"circle-blur":new Hi(Ft.paint_circle["circle-blur"]),"circle-opacity":new Hi(Ft.paint_circle["circle-opacity"]),"circle-translate":new qi(Ft.paint_circle["circle-translate"]),"circle-translate-anchor":new qi(Ft.paint_circle["circle-translate-anchor"]),"circle-pitch-scale":new 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h=0;for(s=0;s<n.length;s+=3){var f=n[s]*r,p=n[s+1]*r,d=n[s+2]*r;h+=Math.abs((t[f]-t[d])*(t[p+1]-t[f+1])-(t[f]-t[p])*(t[d+1]-t[f+1]))}return 0===o&&0===h?0:Math.abs((h-o)/o)},ss.flatten=function(t){for(var e=t[0][0].length,r={vertices:[],holes:[],dimensions:e},n=0,i=0;i<t.length;i++){for(var a=0;a<t[i].length;a++)for(var o=0;o<e;o++)r.vertices.push(t[i][a][o]);i>0&&(n+=t[i-1].length,r.holes.push(n))}return r},as.default=os;var Us=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new ra,this.indexArray=new ma,this.indexArray2=new ba,this.programConfigurations=new uo(t.layers,t.zoom),this.segments=new Da,this.segments2=new Da,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return 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n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Qs(t,e,r){if(3===t){var n=new $s(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Js.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Zs(this._pbf,e,this.extent,this._keys,this._values)};var tl={VectorTile:function(t,e){this.layers=t.readFields(Qs,{},e)},VectorTileFeature:Zs,VectorTileLayer:$s},el=tl.VectorTileFeature.types,rl=Math.pow(2,13);function nl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*rl)+o,i*rl*2,a*rl*2,Math.round(s))}var il=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ia,this.indexArray=new ma,this.programConfigurations=new uo(t.layers,t.zoom),this.segments=new Da,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function al(t,e){return t.x===e.x&&(t.x<0||t.x>po)||t.y===e.y&&(t.y<0||t.y>po)}il.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=Ns("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,h=vo(o,u);if(this.layers[0]._featureFilter.filter(new Oi(this.zoom),h,r)){var f={id:s,sourceLayerIndex:c,index:l,geometry:u?h.geometry:yo(o),properties:o.properties,type:o.type,patterns:{}};this.hasPattern?this.features.push(js("fill-extrusion",this.layers,f,this.zoom,e)):this.addFeature(f,f.geometry,l,r,{}),e.featureIndex.insert(o,f.geometry,l,c,this.index,!0)}}},il.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},il.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},il.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},il.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},il.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Gs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},il.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},il.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Fs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1)l+=u[c].length;for(var h=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),f=0,p=s;f<p.length;f+=1){var d=p[f];if(0!==d.length&&!((P=d).every((function(t){return t.x<0}))||P.every((function(t){return t.x>po}))||P.every((function(t){return t.y<0}))||P.every((function(t){return t.y>po}))))for(var m=0,g=0;g<d.length;g++){var y=d[g];if(g>=1){var v=d[g-1];if(!al(y,v)){h.vertexLength+4>Da.MAX_VERTEX_ARRAY_LENGTH&&(h=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=y.sub(v)._perp()._unit(),_=v.dist(y);m+_>32768&&(m=0),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m),m+=_,nl(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),nl(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m);var b=h.vertexLength;this.indexArray.emplaceBack(b,b+2,b+1),this.indexArray.emplaceBack(b+1,b+2,b+3),h.vertexLength+=4,h.primitiveLength+=2}}}}if(h.vertexLength+l>Da.MAX_VERTEX_ARRAY_LENGTH&&(h=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===el[t.type]){for(var w=[],T=[],k=h.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var C=S[E];nl(this.layoutVertexArray,C.x,C.y,0,0,1,1,0),w.push(C.x),w.push(C.y)}}}for(var L=as(w,T),I=0;I<L.length;I+=3)this.indexArray.emplaceBack(k+L[I],k+L[I+2],k+L[I+1]);h.primitiveLength+=L.length/3,h.vertexLength+=l}}var P;this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},oi("FillExtrusionBucket",il,{omit:["layers","features"]});var ol={paint:new Yi({"fill-extrusion-opacity":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Hi(Ft["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new Gi(Ft["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Hi(Ft["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Hi(Ft["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},sl=function(t){function e(e){t.call(this,e,ol)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new il(t)},e.prototype.queryRadius=function(){return zo(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s,l){var c=Oo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),h=this.paint.get("fill-extrusion-base").evaluate(e,r),f=function(t,e,r,n){for(var i=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];qo(c,c,e),i.push(new a(c[0]/c[3],c[1]/c[3]))}return i}(c,l,0,0),p=function(t,e,r,n){for(var i=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,h=n[8]*r,f=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var y=[],v=[],x=0,_=g[m];x<_.length;x+=1){var b=_[x],w=b.x,T=b.y,k=n[0]*w+n[4]*T+n[12],A=n[1]*w+n[5]*T+n[13],M=n[2]*w+n[6]*T+n[14],S=n[3]*w+n[7]*T+n[15],E=M+c,C=S+u,L=k+h,I=A+f,P=M+p,z=S+d,O=new a((k+s)/C,(A+l)/C);O.z=E/C,y.push(O);var D=new a(L/z,I/z);D.z=P/z,v.push(D)}i.push(y),o.push(v)}return[i,o]}(n,h,u,l);return function(t,e,r){var n=1/0;To(r,e)&&(n=cl(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var 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e=this;this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((function(t){e.gradients[t.id]={}})),this.layoutVertexArray=new aa,this.layoutVertexArray2=new oa,this.indexArray=new ma,this.programConfigurations=new uo(t.layers,t.zoom),this.segments=new Da,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};ml.prototype.populate=function(t,e,r){this.hasPattern=Ns("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,h=s.sourceLayerIndex,f=this.layers[0]._featureFilter.needGeometry,p=vo(l,f);if(this.layers[0]._featureFilter.filter(new Oi(this.zoom),p,r)){var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:h,index:u,geometry:f?p.geometry:yo(l),patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var v=y[g],x=v,_=x.geometry,b=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=js("line",this.layers,v,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(v,_,b,r,{});var k=t[b].feature;e.featureIndex.insert(k,_,b,w,this.index)}},ml.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},ml.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},ml.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},ml.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},ml.prototype.upload=function(t){this.uploaded||(0!==this.layoutVertexArray2.length&&(this.layoutVertexBuffer2=t.createVertexBuffer(this.layoutVertexArray2,hl)),this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,ul),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},ml.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},ml.prototype.lineFeatureClips=function(t){if(t.properties&&t.properties.hasOwnProperty("mapbox_clip_start")&&t.properties.hasOwnProperty("mapbox_clip_end"))return{start:+t.properties.mapbox_clip_start,end:+t.properties.mapbox_clip_end}},ml.prototype.addFeature=function(t,e,r,n,i){var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit");this.lineClips=this.lineFeatureClips(t);for(var u=0,h=e;u<h.length;u+=1){var f=h[u];this.addLine(f,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},ml.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}for(var s="Polygon"===fl[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,h=this.overscaling<=16?15*po/(512*this.overscaling):0,f=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var y=c;y<l;y++)if(!(d=y===l-1?s?t[c+1]:void 0:t[y+1])||!t[y].equals(d)){g&&(m=g),u&&(p=u),u=t[y],g=d?d.sub(u)._unit()._perp():m;var v=(m=m||g).add(g);0===v.x&&0===v.y||v._unit();var x=m.x*g.x+m.y*g.y,_=v.x*g.x+v.y*g.y,b=0!==_?1/_:1/0,w=2*Math.sqrt(2-2*_),T=_<pl&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&y>c){var A=u.dist(p);if(A>2*h){var M=u.sub(u.sub(p)._mult(h/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,f),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(b<a?E="miter":b<=2&&(E="fakeround")),"miter"===E&&b>i&&(E="bevel"),"bevel"===E&&(b>2&&(E="flipbevel"),b<i&&(E="miter")),p&&this.updateDistance(p,u),"miter"===E)v._mult(b),this.addCurrentVertex(u,v,0,0,f);else if("flipbevel"===E){if(b>100)v=g.mult(-1);else{var C=b*m.add(g).mag()/m.sub(g).mag();v._perp()._mult(C*(k?-1:1))}this.addCurrentVertex(u,v,0,0,f),this.addCurrentVertex(u,v.mult(-1),0,0,f)}else if("bevel"===E||"fakeround"===E){var L=-Math.sqrt(b*b-1),I=k?L:0,P=k?0:L;if(p&&this.addCurrentVertex(u,m,I,P,f),"fakeround"===E)for(var z=Math.round(180*w/Math.PI/20),O=1;O<z;O++){var D=O/z;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,f)}d&&this.addCurrentVertex(u,g,-I,-P,f)}else if("butt"===E)this.addCurrentVertex(u,v,0,0,f);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,v,B,B,f)}else"round"===E&&(p&&(this.addCurrentVertex(u,m,0,0,f),this.addCurrentVertex(u,m,1,1,f,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,f,!0),this.addCurrentVertex(u,g,0,0,f)));if(T&&y<l-1){var N=u.dist(d);if(N>2*h){var j=u.add(d.sub(u)._mult(h/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,f),u=j}}}}},ml.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>dl/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},ml.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*(this.lineClips?this.scaledDistance*(dl-1):this.scaledDistance);if(this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6),this.lineClips){var u=(this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start);this.layoutVertexArray2.emplaceBack(u,this.lineClipsArray.length)}var h=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,h),o.primitiveLength++),i?this.e2=h:this.e1=h},ml.prototype.updateScaledDistance=function(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance},ml.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},oi("LineBucket",ml,{omit:["layers","patternFeatures"]});var gl=new Yi({"line-cap":new qi(Ft.layout_line["line-cap"]),"line-join":new Hi(Ft.layout_line["line-join"]),"line-miter-limit":new qi(Ft.layout_line["line-miter-limit"]),"line-round-limit":new qi(Ft.layout_line["line-round-limit"]),"line-sort-key":new Hi(Ft.layout_line["line-sort-key"])}),yl={paint:new Yi({"line-opacity":new Hi(Ft.paint_line["line-opacity"]),"line-color":new Hi(Ft.paint_line["line-color"]),"line-translate":new qi(Ft.paint_line["line-translate"]),"line-translate-anchor":new qi(Ft.paint_line["line-translate-anchor"]),"line-width":new Hi(Ft.paint_line["line-width"]),"line-gap-width":new Hi(Ft.paint_line["line-gap-width"]),"line-offset":new Hi(Ft.paint_line["line-offset"]),"line-blur":new Hi(Ft.paint_line["line-blur"]),"line-dasharray":new Zi(Ft.paint_line["line-dasharray"]),"line-pattern":new Gi(Ft.paint_line["line-pattern"]),"line-gradient":new Wi(Ft.paint_line["line-gradient"])}),layout:gl},vl=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new Oi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=p({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Hi),xl=new vl(yl.paint.properties["line-width"].specification);xl.useIntegerZoom=!0;var _l=function(t){function e(e){t.call(this,e,yl),this.gradientVersion=0}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){if("line-gradient"===t){var e=this._transitionablePaint._values["line-gradient"].value.expression;this.stepInterpolant=e._styleExpression.expression instanceof tr,this.gradientVersion=(this.gradientVersion+1)%l}},e.prototype.gradientExpression=function(){return this._transitionablePaint._values["line-gradient"].value.expression},e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r),this.paint._values["line-floorwidth"]=xl.possiblyEvaluate(this._transitioningPaint._values["line-width"].value,e)},e.prototype.createBucket=function(t){return new ml(t)},e.prototype.queryRadius=function(t){var e=t,r=bl(Po("line-width",this,e),Po("line-gap-width",this,e)),n=Po("line-offset",this,e);return r/2+Math.abs(n)+zo(this.paint.get("line-translate"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s){var l=Oo(t,this.paint.get("line-translate"),this.paint.get("line-translate-anchor"),o.angle,s),c=s/2*bl(this.paint.get("line-width").evaluate(e,r),this.paint.get("line-gap-width").evaluate(e,r)),u=this.paint.get("line-offset").evaluate(e,r);return u&&(n=function(t,e){for(var r=[],n=new a(0,0),i=0;i<t.length;i++){for(var o=t[i],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],h=o[l+1],f=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:h.sub(u)._unit()._perp(),d=f._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(Lo(t,i[a]))return!0;if(ko(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}($i);function bl(t,e){return e>0?e+2*t:t}var wl=ta([{name:"a_pos_offset",components:4,type:"Int16"},{name:"a_data",components:4,type:"Uint16"},{name:"a_pixeloffset",components:4,type:"Int16"}],4),Tl=ta([{name:"a_projected_pos",components:3,type:"Float32"}],4),kl=(ta([{name:"a_fade_opacity",components:1,type:"Uint32"}],4),ta([{name:"a_placed",components:2,type:"Uint8"},{name:"a_shift",components:2,type:"Float32"}])),Al=(ta([{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Int16",name:"x1"},{type:"Int16",name:"y1"},{type:"Int16",name:"x2"},{type:"Int16",name:"y2"},{type:"Uint32",name:"featureIndex"},{type:"Uint16",name:"sourceLayerIndex"},{type:"Uint16",name:"bucketIndex"}]),ta([{name:"a_pos",components:2,type:"Int16"},{name:"a_anchor_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"}],4)),Ml=ta([{name:"a_pos",components:2,type:"Float32"},{name:"a_radius",components:1,type:"Float32"},{name:"a_flags",components:2,type:"Int16"}],4);function Sl(t,e,r){return 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Kl=3;function Ql(t,e,r){1===t&&r.readMessage(tc,e)}function tc(t,e,r){if(3===t){var n=r.readMessage(ec,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new Jo({width:o+2*Kl,height:s+2*Kl},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function ec(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}var rc=Kl;function nc(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var h=u[c],f=o.length-1;f>=0;f--){var p=o[f];if(!(h.w>p.w||h.h>p.h)){if(h.x=p.x,h.y=p.y,l=Math.max(l,h.y+h.h),s=Math.max(s,h.x+h.w),h.w===p.w&&h.h===p.h){var d=o.pop();f<o.length&&(o[f]=d)}else 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Ko({width:o||1,height:s||1});for(var c in t){var u=t[c],h=r[c].paddedRect;Ko.copy(u.data,l,{x:0,y:0},{x:h.x+ic,y:h.y+ic},u.data)}for(var f in e){var p=e[f],d=n[f].paddedRect,m=d.x+ic,g=d.y+ic,y=p.data.width,v=p.data.height;Ko.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Ko.copy(p.data,l,{x:0,y:v-1},{x:m,y:g-1},{width:y,height:1}),Ko.copy(p.data,l,{x:0,y:0},{x:m,y:g+v},{width:y,height:1}),Ko.copy(p.data,l,{x:y-1,y:0},{x:m-1,y:g},{width:1,height:v}),Ko.copy(p.data,l,{x:0,y:0},{x:m+y,y:g},{width:1,height:v})}this.image=l,this.iconPositions=r,this.patternPositions=n};sc.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2*ic,h:i.data.height+2*ic};r.push(a),e[n]=new ac(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},sc.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},sc.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},oi("ImagePosition",ac),oi("ImageAtlas",sc);var lc={horizontal:1,vertical:2,horizontalOnly:3},cc=-17;var uc=function(){this.scale=1,this.fontStack="",this.imageName=null};uc.forText=function(t,e){var r=new uc;return r.scale=t||1,r.fontStack=e,r},uc.forImage=function(t){var e=new uc;return e.imageName=t,e};var hc=function(){this.text="",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function fc(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m){var g,y=hc.fromFeature(t,i);h===lc.vertical&&y.verticalizePunctuation();var v=zi.processBidirectionalText,x=zi.processStyledBidirectionalText;if(v&&1===y.sections.length){g=[];for(var _=0,b=v(y.toString(),_c(y,c,a,e,n,p,d));_<b.length;_+=1){var w=b[_],T=new hc;T.text=w,T.sections=y.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(y.text,y.sectionIndex,_c(y,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new hc;E.text=S[0],E.sectionIndex=S[1],E.sections=y.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(y,_c(y,c,a,e,n,p,d));var C=[],L={positionedLines:C,text:y.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:h,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,h){for(var f=0,p=cc,d=0,m=0,g="right"===s?1:"left"===s?0:.5,y=0,v=0,x=i;v<x.length;v+=1){var _=x[v];_.trim();var b=_.getMaxScale(),w=(b-1)*Cl,T={positionedGlyphs:[],lineOffset:0};t.positionedLines[y]=T;var k=T.positionedGlyphs,A=0;if(_.length()){for(var M=0;M<_.length();M++){var S=_.getSection(M),E=_.getSectionIndex(M),C=_.getCharCode(M),L=0,I=null,P=null,z=null,O=Cl,D=!(l===lc.horizontal||!u&&!mi(C)||u&&(pc[C]||yi(C)));if(S.imageName){var R=n[S.imageName];if(!R)continue;z=S.imageName,t.iconsInText=t.iconsInText||!0,P=R.paddedRect;var F=R.displaySize;S.scale=S.scale*Cl/h,I={width:F[0],height:F[1],left:ic,top:-rc,advance:D?F[1]:F[0]},L=w+(Cl-F[1]*S.scale),O=I.advance;var B=D?F[0]*S.scale-Cl*b:F[1]*S.scale-Cl*b;B>0&&B>A&&(A=B)}else{var N=r[S.fontStack],j=N&&N[C];if(j&&j.rect)P=j.rect,I=j.metrics;else{var U=e[S.fontStack],V=U&&U[C];if(!V)continue;I=V.metrics}L=(b-S.scale)*Cl}D?(t.verticalizable=!0,k.push({glyph:C,imageName:z,x:f,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),f+=O*S.scale+c):(k.push({glyph:C,imageName:z,x:f,y:p+L,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:I,rect:P}),f+=I.advance*S.scale+c)}if(0!==k.length){var q=f-c;d=Math.max(q,d),wc(k,0,k.length-1,g,A)}f=0;var H=a*b+A;T.lineOffset=Math.max(A,w),p+=H,m=Math.max(H,m),++y}else p+=a,++y}var G=p-cc,Z=bc(o),W=Z.horizontalAlign,Y=Z.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n-cc:(-n*l+.5)*o;for(var h=0,f=t;h<f.length;h+=1)for(var p=0,d=f[h].positionedGlyphs;p<d.length;p+=1){var m=d[p];m.x+=c,m.y+=u}})(t.positionedLines,g,W,Y,d,m,a,G,i.length),t.top+=-Y*G,t.bottom=t.top+G,t.left+=-W*d,t.right=t.left+d}(L,e,r,n,g,o,s,l,h,c,f,m),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(C)&&L}hc.fromFeature=function(t,e){for(var r=new hc,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},hc.prototype.length=function(){return this.text.length},hc.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},hc.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},hc.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},hc.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;n&&gi(n)&&!El[t[r+1]]||i&&gi(i)&&!El[t[r-1]]||!El[t[r]]?e+=t[r]:e+=El[t[r]]}return e}(this.text)},hc.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&pc[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&pc[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},hc.prototype.substring=function(t,e){var r=new hc;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},hc.prototype.toString=function(){return this.text},hc.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},hc.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(uc.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},hc.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(uc.forImage(e)),this.sectionIndex.push(this.sections.length-1)):k("Reached maximum number of images 6401")}else k("Can't add FormattedSection with an empty image.")},hc.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var pc={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},dc={};function mc(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*Cl/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function gc(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function yc(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function vc(t,e,r,n,i,a){for(var o=null,s=gc(e,r,i,a),l=0,c=n;l<c.length;l+=1){var u=c[l],h=gc(e-u.x,r,i,a)+u.badness;h<=s&&(o=u,s=h)}return{index:t,x:e,priorBreak:o,badness:s}}function xc(t){return t?xc(t.priorBreak).concat(t.index):[]}function _c(t,e,r,n,i,a,o){if("point"!==a)return[];if(!t)return[];for(var s=[],l=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=mc(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),c=t.text.indexOf("​")>=0,u=0,h=0;h<t.length();h++){var f=t.getSection(h),p=t.getCharCode(h);if(pc[p]||(u+=mc(p,f,n,i,e,o)),h<t.length()-1){var d=!((m=p)<11904||!(pi["Bopomofo Extended"](m)||pi.Bopomofo(m)||pi["CJK Compatibility Forms"](m)||pi["CJK Compatibility Ideographs"](m)||pi["CJK Compatibility"](m)||pi["CJK Radicals Supplement"](m)||pi["CJK Strokes"](m)||pi["CJK Symbols and Punctuation"](m)||pi["CJK Unified Ideographs Extension A"](m)||pi["CJK Unified Ideographs"](m)||pi["Enclosed CJK Letters and Months"](m)||pi["Halfwidth and Fullwidth Forms"](m)||pi.Hiragana(m)||pi["Ideographic Description Characters"](m)||pi["Kangxi Radicals"](m)||pi["Katakana Phonetic Extensions"](m)||pi.Katakana(m)||pi["Vertical Forms"](m)||pi["Yi Radicals"](m)||pi["Yi Syllables"](m)));(dc[p]||d||f.imageName)&&s.push(vc(h+1,u,l,s,yc(p,t.getCharCode(h+1),d&&c),!1))}}var m;return xc(vc(t.length(),u,l,s,0,!0))}function bc(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function wc(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Tc(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,h,f,p,d=e.left*a,m=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],h=i[0]+m+n[1]):h=(p=i[0]+(d+m-s.displaySize[0])/2)+s.displaySize[0];var g=e.top*a,y=e.bottom*a;return"height"===r||"both"===r?(u=i[1]+g-n[0],f=i[1]+y+n[2]):f=(u=i[1]+(g+y-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:u,right:h,bottom:f,left:p,collisionPadding:o}}dc[10]=!0,dc[32]=!0,dc[38]=!0,dc[40]=!0,dc[41]=!0,dc[43]=!0,dc[45]=!0,dc[47]=!0,dc[173]=!0,dc[183]=!0,dc[8203]=!0,dc[8208]=!0,dc[8211]=!0,dc[8231]=!0;var kc=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(a);oi("Anchor",kc);var Ac=128;function Mc(t,e){var r=e.expression;if("constant"===r.kind)return{kind:"constant",layoutSize:r.evaluate(new Oi(t+1))};if("source"===r.kind)return{kind:"source"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return"composite"===r.kind?{kind:"composite",minZoom:s,maxZoom:l,interpolationType:i}:{kind:"camera",minZoom:s,maxZoom:l,minSize:r.evaluate(new Oi(s)),maxSize:r.evaluate(new Oi(l)),interpolationType:i}}function Sc(t,e,r){var n=e.uSize,i=e.uSizeT,a=r.lowerSize,o=r.upperSize;return"source"===t.kind?a/Ac:"composite"===t.kind?er(a/Ac,o/Ac,i):n}function Ec(t,e){var r=0,n=0;if("constant"===t.kind)n=t.layoutSize;else if("source"!==t.kind){var i=t.interpolationType,a=t.minZoom,o=t.maxZoom,s=i?h(wr.interpolationFactor(i,e,a,o),0,1):0;"camera"===t.kind?n=er(t.minSize,t.maxSize,s):r=s}return{uSizeT:r,uSize:n}}var Cc=Object.freeze({__proto__:null,getSizeData:Mc,evaluateSizeForFeature:Sc,evaluateSizeForZoom:Ec,SIZE_PACK_FACTOR:Ac});function Lc(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],c=0;s<r/2;){var u=t[o-1],h=t[o],f=t[o+1];if(!f)return!1;var p=u.angleTo(h)-h.angleTo(f);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),c+=p;s-l[0].distance>n;)c-=l.shift().angleDelta;if(c>i)return!1;o++,s+=h.dist(f)}return!0}function Ic(t){for(var e=0,r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function Pc(t,e,r){return t?.6*e*r:0}function zc(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function Oc(t,e,r,n,i,a){for(var 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a(e,h.y+(f.y-h.y)*((e-h.x)/(f.x-h.x)))._round()),h.y<r&&f.y<r||(h.y<r?h=new a(h.x+(f.x-h.x)*((r-h.y)/(f.y-h.y)),r)._round():f.y<r&&(f=new a(h.x+(f.x-h.x)*((r-h.y)/(f.y-h.y)),r)._round()),h.x>=n&&f.x>=n||(h.x>=n?h=new a(n,h.y+(f.y-h.y)*((n-h.x)/(f.x-h.x)))._round():f.x>=n&&(f=new a(n,h.y+(f.y-h.y)*((n-h.x)/(f.x-h.x)))._round()),h.y>=i&&f.y>=i||(h.y>=i?h=new a(h.x+(f.x-h.x)*((i-h.y)/(f.y-h.y)),i)._round():f.y>=i&&(f=new a(h.x+(f.x-h.x)*((i-h.y)/(f.y-h.y)),i)._round()),c&&h.equals(c[c.length-1])||(c=[h],o.push(c)),c.push(f)))))}return o}var Bc=ic;function Nc(t,e,r,n){var i=[],o=t.image,s=o.pixelRatio,l=o.paddedRect.w-2*Bc,c=o.paddedRect.h-2*Bc,u=t.right-t.left,h=t.bottom-t.top,f=o.stretchX||[[0,l]],p=o.stretchY||[[0,c]],d=function(t,e){return t+e[1]-e[0]},m=f.reduce(d,0),g=p.reduce(d,0),y=l-m,v=c-g,x=0,_=m,b=0,w=g,T=0,k=y,A=0,M=v;if(o.content&&n){var S=o.content;x=jc(f,0,S[0]),b=jc(p,0,S[1]),_=jc(f,S[0],S[2]),w=jc(p,S[1],S[3]),T=S[0]-x,A=S[1]-b,k=S[2]-S[0]-_,M=S[3]-S[1]-w}var E=function(n,i,l,c){var f=Vc(n.stretch-x,_,u,t.left),p=qc(n.fixed-T,k,n.stretch,m),d=Vc(i.stretch-b,w,h,t.top),y=qc(i.fixed-A,M,i.stretch,g),v=Vc(l.stretch-x,_,u,t.left),S=qc(l.fixed-T,k,l.stretch,m),E=Vc(c.stretch-b,w,h,t.top),C=qc(c.fixed-A,M,c.stretch,g),L=new a(f,d),I=new a(v,d),P=new a(v,E),z=new a(f,E),O=new a(p/s,y/s),D=new a(S/s,C/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];L._matMult(N),I._matMult(N),z._matMult(N),P._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=i.stretch+i.fixed,q=c.stretch+c.fixed;return{tl:L,tr:I,bl:z,br:P,tex:{x:o.paddedRect.x+Bc+j,y:o.paddedRect.y+Bc+V,w:U-j,h:q-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:O,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/h,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var C=Uc(f,y,m),L=Uc(p,v,g),I=0;I<C.length-1;I++)for(var P=C[I],z=C[I+1],O=0;O<L.length-1;O++){var D=L[O],R=L[O+1];i.push(E(P,D,z,R))}else 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0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},uu.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,wl.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Tl.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,ou,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},uu.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},oi("SymbolBuffers",uu);var hu=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Da,this.collisionVertexArray=new da};hu.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,kl.members,!0)},hu.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},oi("CollisionBuffers",hu);var fu=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Bo([]),this.placementViewportMatrix=Bo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Mc(this.zoom,e["text-size"]),this.iconSizeData=Mc(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.canOverlap=r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement"),this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&this.canOverlap,"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return lc[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};fu.prototype.createArrays=function(){this.text=new uu(new uo(this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new uu(new uo(this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new La,this.lineVertexArray=new Ia,this.symbolInstances=new Ca},fu.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=El[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},fu.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof fe&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var h=e.iconDependencies,f=e.glyphDependencies,p=e.availableImages,d=new Oi(this.zoom),m=0,g=t;m<g.length;m+=1){var y=g[m],v=y.feature,x=y.id,_=y.index,b=y.sourceLayerIndex,w=n._featureFilter.needGeometry,T=vo(v,w);if(n._featureFilter.filter(d,T,r)){w||(T.geometry=yo(v));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=fe.factory(A);cu(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===Ii()||this.hasRTLText&&zi.isParsed())&&(k=Sl(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof pe?E:pe.fromString(E)}if(k||S){var C=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,L={id:x,text:k,icon:S,index:_,sourceLayerIndex:b,geometry:T.geometry,properties:v.properties,type:au[v.type],sortKey:C};if(this.features.push(L),S&&(h[S.name]=!0),k){var I=a.evaluate(T,{},r).join(","),P="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(lc.vertical)>=0;for(var z=0,O=k.sections;z<O.length;z+=1){var D=O[z];if(D.image)h[D.image.name]=!0;else{var R=di(k.toString()),F=D.fontStack||I,B=f[F]=f[F]||{};this.calculateGlyphDependencies(D.text,B,P,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],h=u.geometry,f=u.text?u.text.toString():null;if(f){var p=l(f,h),d=l(f,h,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,h),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(f,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,h):d in e?s(p,d,h):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},fu.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},fu.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},fu.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},fu.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},fu.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},fu.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},fu.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},fu.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,h){for(var f=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,f,this.canOverlap?a.sortKey:void 0),m=this.glyphOffsetArray.length,g=d.vertexLength,y=this.allowVerticalPlacement&&o===lc.vertical?Math.PI/2:0,v=a.text&&a.text.sections,x=0;x<e.length;x++){var _=e[x],b=_.tl,w=_.tr,T=_.bl,k=_.br,A=_.tex,M=_.pixelOffsetTL,S=_.pixelOffsetBR,E=_.minFontScaleX,C=_.minFontScaleY,L=_.glyphOffset,I=_.isSDF,P=_.sectionIndex,z=d.vertexLength,O=L[1];su(p,s.x,s.y,b.x,O+b.y,A.x,A.y,r,I,M.x,M.y,E,C),su(p,s.x,s.y,w.x,O+w.y,A.x+A.w,A.y,r,I,S.x,M.y,E,C),su(p,s.x,s.y,T.x,O+T.y,A.x,A.y+A.h,r,I,M.x,S.y,E,C),su(p,s.x,s.y,k.x,O+k.y,A.x+A.w,A.y+A.h,r,I,S.x,S.y,E,C),lu(t.dynamicLayoutVertexArray,s,y),f.emplaceBack(z,z+1,z+2),f.emplaceBack(z+1,z+2,z+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(L[0]),x!==e.length-1&&P===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},h,v&&v[P])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},fu.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},fu.prototype.addCollisionDebugVertices=function(t,e,r,n,i,o,s){var l=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),c=l.vertexLength,u=i.layoutVertexArray,h=i.collisionVertexArray,f=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,h,o,f,p,new a(t,e)),this._addCollisionDebugVertex(u,h,o,f,p,new a(r,e)),this._addCollisionDebugVertex(u,h,o,f,p,new a(r,n)),this._addCollisionDebugVertex(u,h,o,f,p,new a(t,n)),l.vertexLength+=4;var d=i.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},fu.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},fu.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new hu(fa,Al.members,ba),this.iconCollisionBox=new hu(fa,Al.members,ba);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},fu.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var h=t.get(u);c.textBox={x1:h.x1,y1:h.y1,x2:h.x2,y2:h.y2,anchorPointX:h.anchorPointX,anchorPointY:h.anchorPointY},c.textFeatureIndex=h.featureIndex;break}for(var f=n;f<i;f++){var p=t.get(f);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var y=t.get(g);c.verticalIconBox={x1:y.x1,y1:y.y1,x2:y.x2,y2:y.y2,anchorPointX:y.anchorPointX,anchorPointY:y.anchorPointY},c.verticalIconFeatureIndex=y.featureIndex;break}return c},fu.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},fu.prototype.hasTextData=function(){return this.text.segments.get().length>0},fu.prototype.hasIconData=function(){return this.icon.segments.get().length>0},fu.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},fu.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},fu.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},fu.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},fu.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},fu.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},fu.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},oi("SymbolBucket",fu,{omit:["layers","collisionBoxArray","features","compareText"]}),fu.MAX_GLYPHS=65535,fu.addDynamicAttributes=lu;var pu=new Yi({"symbol-placement":new qi(Ft.layout_symbol["symbol-placement"]),"symbol-spacing":new qi(Ft.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new qi(Ft.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Hi(Ft.layout_symbol["symbol-sort-key"]),"symbol-z-order":new qi(Ft.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new qi(Ft.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new qi(Ft.layout_symbol["icon-ignore-placement"]),"icon-optional":new qi(Ft.layout_symbol["icon-optional"]),"icon-rotation-alignment":new qi(Ft.layout_symbol["icon-rotation-alignment"]),"icon-size":new Hi(Ft.layout_symbol["icon-size"]),"icon-text-fit":new qi(Ft.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new qi(Ft.layout_symbol["icon-text-fit-padding"]),"icon-image":new Hi(Ft.layout_symbol["icon-image"]),"icon-rotate":new Hi(Ft.layout_symbol["icon-rotate"]),"icon-padding":new qi(Ft.layout_symbol["icon-padding"]),"icon-keep-upright":new qi(Ft.layout_symbol["icon-keep-upright"]),"icon-offset":new Hi(Ft.layout_symbol["icon-offset"]),"icon-anchor":new Hi(Ft.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new qi(Ft.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new qi(Ft.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new qi(Ft.layout_symbol["text-rotation-alignment"]),"text-field":new Hi(Ft.layout_symbol["text-field"]),"text-font":new Hi(Ft.layout_symbol["text-font"]),"text-size":new Hi(Ft.layout_symbol["text-size"]),"text-max-width":new Hi(Ft.layout_symbol["text-max-width"]),"text-line-height":new qi(Ft.layout_symbol["text-line-height"]),"text-letter-spacing":new Hi(Ft.layout_symbol["text-letter-spacing"]),"text-justify":new Hi(Ft.layout_symbol["text-justify"]),"text-radial-offset":new Hi(Ft.layout_symbol["text-radial-offset"]),"text-variable-anchor":new qi(Ft.layout_symbol["text-variable-anchor"]),"text-anchor":new Hi(Ft.layout_symbol["text-anchor"]),"text-max-angle":new qi(Ft.layout_symbol["text-max-angle"]),"text-writing-mode":new qi(Ft.layout_symbol["text-writing-mode"]),"text-rotate":new Hi(Ft.layout_symbol["text-rotate"]),"text-padding":new qi(Ft.layout_symbol["text-padding"]),"text-keep-upright":new qi(Ft.layout_symbol["text-keep-upright"]),"text-transform":new Hi(Ft.layout_symbol["text-transform"]),"text-offset":new Hi(Ft.layout_symbol["text-offset"]),"text-allow-overlap":new qi(Ft.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new qi(Ft.layout_symbol["text-ignore-placement"]),"text-optional":new qi(Ft.layout_symbol["text-optional"])}),du={paint:new Yi({"icon-opacity":new Hi(Ft.paint_symbol["icon-opacity"]),"icon-color":new Hi(Ft.paint_symbol["icon-color"]),"icon-halo-color":new Hi(Ft.paint_symbol["icon-halo-color"]),"icon-halo-width":new Hi(Ft.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Hi(Ft.paint_symbol["icon-halo-blur"]),"icon-translate":new qi(Ft.paint_symbol["icon-translate"]),"icon-translate-anchor":new qi(Ft.paint_symbol["icon-translate-anchor"]),"text-opacity":new Hi(Ft.paint_symbol["text-opacity"]),"text-color":new Hi(Ft.paint_symbol["text-color"],{runtimeType:Xt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Hi(Ft.paint_symbol["text-halo-color"]),"text-halo-width":new Hi(Ft.paint_symbol["text-halo-width"]),"text-halo-blur":new Hi(Ft.paint_symbol["text-halo-blur"]),"text-translate":new qi(Ft.paint_symbol["text-translate"]),"text-translate-anchor":new qi(Ft.paint_symbol["text-translate-anchor"])}),layout:pu},mu=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Gt,this.defaultValue=t};mu.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},mu.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},mu.prototype.outputDefined=function(){return!1},mu.prototype.serialize=function(){return null},oi("FormatSectionOverride",mu,{omit:["defaultValue"]});var gu=function(t){function e(e){t.call(this,e,du)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||un(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new fu(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=du.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new mu(a),s=new cn(o,a.property.specification);i="constant"===a.value.kind||"source"===a.value.kind?new fn("source",s):new pn("composite",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new Ui(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=du.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof fe)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ve&&ge(t.value)===Qt){var e=t.value;a(e.sections)}else t instanceof we?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}($i),yu={paint:new Yi({"background-color":new qi(Ft.paint_background["background-color"]),"background-pattern":new Zi(Ft.paint_background["background-pattern"]),"background-opacity":new qi(Ft.paint_background["background-opacity"])})},vu=function(t){function e(e){t.call(this,e,yu)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}($i),xu={paint:new Yi({"raster-opacity":new qi(Ft.paint_raster["raster-opacity"]),"raster-hue-rotate":new qi(Ft.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new qi(Ft.paint_raster["raster-brightness-min"]),"raster-brightness-max":new qi(Ft.paint_raster["raster-brightness-max"]),"raster-saturation":new qi(Ft.paint_raster["raster-saturation"]),"raster-contrast":new qi(Ft.paint_raster["raster-contrast"]),"raster-resampling":new qi(Ft.paint_raster["raster-resampling"]),"raster-fade-duration":new qi(Ft.paint_raster["raster-fade-duration"])})},_u=function(t){function e(e){t.call(this,e,xu)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}($i);var bu=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}($i),wu={circle:Go,heatmap:es,hillshade:ns,fill:Hs,"fill-extrusion":sl,line:_l,symbol:gu,background:vu,raster:_u};var Tu=s.HTMLImageElement,ku=s.HTMLCanvasElement,Au=s.HTMLVideoElement,Mu=s.ImageData,Su=s.ImageBitmap,Eu=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Eu.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Tu||t instanceof ku||t instanceof Au||t instanceof Mu||Su&&t instanceof Su?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Tu||t instanceof ku||t instanceof Au||t instanceof Mu||Su&&t instanceof Su?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Eu.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Eu.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Eu.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Cu=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Cu.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Cu.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Lu=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},v(["receive","process"],this),this.invoker=new Cu(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=S()?t:s};function Iu(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Lu.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=L(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:ui(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},Lu.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else S()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Lu.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Lu.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(hi(e.error)):n(null,hi(e.data)))}else{var i=!1,a=L(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?ui(e):null,data:ui(n,a)},a)}:function(t){i=!0},s=null,l=hi(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Lu.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Pu=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Pu.prototype.setNorthEast=function(t){return this._ne=t instanceof Ou?new Ou(t.lng,t.lat):Ou.convert(t),this},Pu.prototype.setSouthWest=function(t){return this._sw=t instanceof Ou?new Ou(t.lng,t.lat):Ou.convert(t),this},Pu.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Ou)e=t,r=t;else{if(!(t instanceof Pu)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Pu.convert(a))}var o=t;return this.extend(Ou.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Ou(e.lng,e.lat),this._ne=new Ou(r.lng,r.lat)),this},Pu.prototype.getCenter=function(){return new Ou((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Pu.prototype.getSouthWest=function(){return this._sw},Pu.prototype.getNorthEast=function(){return this._ne},Pu.prototype.getNorthWest=function(){return new Ou(this.getWest(),this.getNorth())},Pu.prototype.getSouthEast=function(){return new Ou(this.getEast(),this.getSouth())},Pu.prototype.getWest=function(){return this._sw.lng},Pu.prototype.getSouth=function(){return this._sw.lat},Pu.prototype.getEast=function(){return this._ne.lng},Pu.prototype.getNorth=function(){return this._ne.lat},Pu.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Pu.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Pu.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Pu.prototype.contains=function(t){var e=Ou.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Pu.convert=function(t){return!t||t instanceof Pu?t:new Pu(t)};var zu=6371008.8,Ou=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Ou.prototype.wrap=function(){return new Ou(f(this.lng,-180,180),this.lat)},Ou.prototype.toArray=function(){return[this.lng,this.lat]},Ou.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Ou.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return zu*Math.acos(Math.min(i,1))},Ou.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Pu(new Ou(this.lng-r,this.lat-e),new Ou(this.lng+r,this.lat+e))},Ou.convert=function(t){if(t instanceof Ou)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Ou(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Ou(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Du=2*Math.PI*zu;function Ru(t){return Du*Math.cos(t*Math.PI/180)}function Fu(t){return(180+t)/360}function Bu(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Nu(t,e){return t/Ru(e)}function ju(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Uu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Uu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Ou.convert(t);return new Uu(Fu(r.lng),Bu(r.lat),Nu(e,r.lat))},Uu.prototype.toLngLat=function(){return new Ou(360*this.x-180,ju(this.y))},Uu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Ru(ju(e));var t,e},Uu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Du*(t=ju(this.y),1/Math.cos(t*Math.PI/180));var t};var Vu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Gu(0,t,t,e,r)};Vu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Vu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Iu(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Iu(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},Vu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new a((t.x*e-this.x)*po,(t.y*e-this.y)*po)},Vu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var qu=function(t,e){this.wrap=t,this.canonical=e,this.key=Gu(t,e.z,e.z,e.x,e.y)},Hu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new Vu(r,+n,+i),this.key=Gu(e,t,r,n,i)};function Gu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Hu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Hu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Hu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Hu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Hu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Gu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Gu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Hu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Hu.prototype.children=function(t){if(this.overscaledZ>=t)return[new Hu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Hu(e,this.wrap,e,r,n),new Hu(e,this.wrap,e,r+1,n),new Hu(e,this.wrap,e,r,n+1),new Hu(e,this.wrap,e,r+1,n+1)]},Hu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Hu.prototype.wrapped=function(){return new Hu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Hu.prototype.unwrapTo=function(t){return new Hu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Hu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Hu.prototype.toUnwrapped=function(){return new qu(this.wrap,this.canonical)},Hu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},Hu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Uu(t.x-this.wrap,t.y))},oi("CanonicalTileID",Vu),oi("OverscaledTileID",Hu,{omit:["posMatrix"]});var Zu=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return k('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Zu.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Zu.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Zu.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},Zu.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Zu.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Zu.prototype.getPixels=function(){return new Ko({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Zu.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},oi("DEMData",Zu);var Wu=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Wu.prototype.encode=function(t){return this._stringToNumber[t]},Wu.prototype.decode=function(t){return this._numberToString[t]};var Yu=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},Xu={geometry:{configurable:!0}};Xu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Xu.geometry.set=function(t){this._geometry=t},Yu.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Yu.prototype,Xu);var $u=function(){this.state={},this.stateChanges={},this.deletedStates={}};$u.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},p(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},$u.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},$u.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=p({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},$u.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},$u.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),p(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var u=0,h=Object.keys(this.deletedStates[o][c]);u<h.length;u+=1){var f=h[u];delete this.state[o][c][f]}s[c]=this.state[o][c]}r[o]=r[o]||{},p(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t)t[d].setFeatureState(r,e)};var Ju=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new ti(po,16,0),this.grid3D=new ti(po,16,0),this.featureIndexArray=new za,this.promoteId=e};function Ku(t,e,r,n,i){return _(t,(function(t,a){var o=e instanceof Vi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Qu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function th(t,e){return e-t}Ju.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],h=0;h<c.length;h++){var f=c[h];u[0]=Math.min(u[0],f.x),u[1]=Math.min(u[1],f.y),u[2]=Math.max(u[2],f.x),u[3]=Math.max(u[3],f.y)}u[0]<po&&u[1]<po&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},Ju.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Pl(this.rawTileData)).layers,this.sourceLayerCoder=new Wu(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},Ju.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=po/t.tileSize/t.scale,l=An(o.filter),c=t.queryGeometry,u=t.queryPadding*s,h=Qu(c),f=this.grid.query(h.minX-u,h.minY-u,h.maxX+u,h.maxY+u),p=Qu(t.cameraQueryGeometry),d=0,m=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&i>=l.y)return!0}var c=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var u=0,h=c;u<h.length;u+=1)if(Lo(t,h[u]))return!0;for(var f=0;f<t.length-1;f++)if(Io(t[f],t[f+1],c))return!0;return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,i+u)}));d<m.length;d+=1){var g=m[d];f.push(g)}f.sort(th);for(var y,v={},x=function(a){var u=f[a];if(u!==y){y=u;var h=i.featureIndexArray.get(u),p=null;i.loadMatchingFeature(v,h.bucketIndex,h.sourceLayerIndex,h.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=yo(e)),r.queryIntersectsFeature(c,e,n,p,i.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<f.length;_++)x(_);return v},Ju.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var h=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,h)){var f=this.sourceLayerCoder.decode(r),d=this.vtLayers[f].feature(n);if(i.needGeometry){var m=vo(d,!0);if(!i.filter(new Oi(this.tileID.overscaledZ),m,this.tileID.canonical))return}else if(!i.filter(new Oi(this.tileID.overscaledZ),d))return;for(var g=this.getId(d,f),y=0;y<h.length;y++){var v=h[y];if(!(a&&a.indexOf(v)<0)){var x=s[v];if(x){var _={};void 0!==g&&c&&(_=c.getState(x.sourceLayer||"_geojsonTileLayer",g));var b=p({},l[v]);b.paint=Ku(b.paint,x.paint,d,_,o),b.layout=Ku(b.layout,x.layout,d,_,o);var w=!u||u(d,x,_);if(w){var T=new Yu(d,this.z,this.x,this.y,g);T.layer=b;var k=t[v];void 0===k&&(k=t[v]=[]),k.push({featureIndex:n,feature:T,intersectionZ:w})}}}}}},Ju.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=An(i),u=0,h=t;u<h.length;u+=1){var f=h[u];this.loadMatchingFeature(l,r,n,f,c,a,o,s,e)}return l},Ju.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Ju.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},oi("FeatureIndex",Ju,{omit:["rawTileData","sourceLayerCoder"]});var eh=function(t,e){this.tileID=t,this.uid=m(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};eh.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<N.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},eh.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},eh.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof fu){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof fu&&o.hasRTLText){this.hasRTLText=!0,zi.isLoading()||zi.isLoaded()||"deferred"!==Ii()||Pi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new Aa},eh.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},eh.prototype.getBucket=function(t){return this.buckets[t.id]},eh.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Eu(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Eu(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},eh.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},eh.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},eh.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=An(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,h={z:l,x:c,y:u},f=0;f<a.length;f++){var p=a.feature(f);if(o.needGeometry){var d=vo(p,!0);if(!o.filter(new Oi(this.tileID.overscaledZ),d,this.tileID.canonical))continue}else if(!o.filter(new Oi(this.tileID.overscaledZ),p))continue;var m=r.getId(p,i),g=new Yu(p,l,c,u,m);g.tile=h,t.push(g)}}},eh.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},eh.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},eh.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=E(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},eh.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},eh.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},eh.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},eh.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<N.now()},eh.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},eh.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=N.now()+t},eh.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},eh.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1)if(a[s[o]])return!0}return!1};var rh=s.performance,nh=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},rh.mark(this._marks.start)};nh.prototype.finish=function(){rh.mark(this._marks.end);var t=rh.getEntriesByName(this._marks.measure);return 0===t.length&&(rh.measure(this._marks.measure,this._marks.start,this._marks.end),t=rh.getEntriesByName(this._marks.measure),rh.clearMarks(this._marks.start),rh.clearMarks(this._marks.end),rh.clearMeasures(this._marks.measure)),t},t.Actor=Lu,t.AlphaImage=Jo,t.CanonicalTileID=Vu,t.CollisionBoxArray=Aa,t.Color=ce,t.DEMData=Zu,t.DataConstantProperty=qi,t.DictionaryCoder=Wu,t.EXTENT=po,t.ErrorEvent=Dt,t.EvaluationParameters=Oi,t.Event=Ot,t.Evented=Rt,t.FeatureIndex=Ju,t.FillBucket=Us,t.FillExtrusionBucket=il,t.ImageAtlas=sc,t.ImagePosition=ac,t.LineBucket=ml,t.LngLat=Ou,t.LngLatBounds=Pu,t.MercatorCoordinate=Uu,t.ONE_EM=Cl,t.OverscaledTileID=Hu,t.Point=a,t.Point$1=a,t.Properties=Yi,t.Protobuf=Pl,t.RGBAImage=Ko,t.RequestManager=Z,t.RequestPerformance=nh,t.ResourceType=bt,t.SegmentVector=Da,t.SourceFeatureState=$u,t.StructArrayLayout1ui2=wa,t.StructArrayLayout2f1f2i16=pa,t.StructArrayLayout2i4=ra,t.StructArrayLayout3ui6=ma,t.StructArrayLayout4i8=na,t.SymbolBucket=fu,t.Texture=Eu,t.Tile=eh,t.Transitionable=Fi,t.Uniform1f=$a,t.Uniform1i=Xa,t.Uniform2f=Ja,t.Uniform3f=Ka,t.Uniform4f=Qa,t.UniformColor=to,t.UniformMatrix4f=ro,t.UnwrappedTileID=qu,t.ValidationError=Bt,t.WritingMode=lc,t.ZoomHistory=fi,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=lu,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=c,t.bindAll=v,t.browser=N,t.cacheEntryPossiblyAdded=function(t){++xt>ft&&(t.getActor().send("enforceCacheSizeLimit",ht),xt=0)},t.clamp=h,t.clearTileCache=function(t){var e=s.caches.delete(ut);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Fc,t.clone=function(t){var e=new Fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=w,t.clone$2=function(t){var e=new Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=j,t.create=function(){var t=new Fo(16);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Fo(9);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Fo(4);return Fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=hn,t.createLayout=ta,t.createStyleLayer=function(t){return"custom"===t.type?new bu(t):new wu[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if("object"==typeof e&&null!==e&&null!==r){if("object"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=u,t.emitValidationErrors=Qn,t.endsWith=x,t.enforceCacheSizeLimit=function(t){dt(),rt&&rt.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Sc,t.evaluateSizeForZoom=Ec,t.evaluateVariableOffset=Kc,t.evented=Li,t.extend=p,t.featureFilter=An,t.filterObject=b,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=bc,t.getAnchorJustification=Qc,t.getArrayBuffer=Mt,t.getImage=It,t.getJSON=function(t,e){return At(p(t,{type:"json"}),e)},t.getRTLTextPluginStatus=Ii,t.getReferrer=Tt,t.getVideo=function(t,e){var r,n,i=s.document.createElement("video");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=s.document.createElement("source");r=t[a],n=void 0,(n=s.document.createElement("a")).href=r,n.protocol===s.document.location.protocol&&n.host===s.document.location.host||(i.crossOrigin="Anonymous"),o.src=t[a],i.appendChild(o)}return{cancel:function(){}}},t.identity=Bo,t.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],h=e[9],f=e[10],p=e[11],d=e[12],m=e[13],g=e[14],y=e[15],v=r*s-n*o,x=r*l-i*o,_=r*c-a*o,b=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-h*d,A=u*g-f*d,M=u*y-p*d,S=h*g-f*m,E=h*y-p*m,C=f*y-p*g,L=v*C-x*E+_*S+b*M-w*A+T*k;return L?(L=1/L,t[0]=(s*C-l*E+c*S)*L,t[1]=(i*E-n*C-a*S)*L,t[2]=(m*T-g*w+y*b)*L,t[3]=(f*w-h*T-p*b)*L,t[4]=(l*M-o*C-c*A)*L,t[5]=(r*C-i*M+a*A)*L,t[6]=(g*_-d*T-y*x)*L,t[7]=(u*T-f*_+p*x)*L,t[8]=(o*E-s*M+c*k)*L,t[9]=(n*M-r*E-a*k)*L,t[10]=(d*w-m*_+y*v)*L,t[11]=(h*_-u*w-p*v)*L,t[12]=(s*A-o*S-l*k)*L,t[13]=(r*S-n*A+i*k)*L,t[14]=(m*x-d*b-g*v)*L,t[15]=(u*b-h*x+f*v)*L,t):null},t.isChar=pi,t.isMapboxURL=W,t.keysDifference=function(t,e){var r=[];for(var n in t)n in e||r.push(n);return 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t;if(o<r||a>=n)return null;for(var l=[],c=0;c<t.length;c++){var u=t[c],h=u.geometry,f=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var m=[];if("Point"===f||"MultiPoint"===f)ct(h,m,r,n,i);else if("LineString"===f)ut(h,m,r,n,i,!1,s.lineMetrics);else if("MultiLineString"===f)ft(h,m,r,n,i,!1);else if("Polygon"===f)ft(h,m,r,n,i,!0);else if("MultiPolygon"===f)for(var g=0;g<h.length;g++){var y=[];ft(h[g],y,r,n,i,!0),y.length&&m.push(y)}if(m.length){if(s.lineMetrics&&"LineString"===f){for(g=0;g<m.length;g++)l.push(tt(u.id,f,m[g],u.tags));continue}"LineString"!==f&&"MultiLineString"!==f||(1===m.length?(f="LineString",m=m[0]):f="MultiLineString"),"Point"!==f&&"MultiPoint"!==f||(f=3===m.length?"Point":"MultiPoint"),l.push(tt(u.id,f,m,u.tags))}}}return l.length?l:null}function ct(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function ut(t,e,r,n,i,a,o){for(var s,l,c=ht(t),u=0===i?dt:mt,h=t.start,f=0;f<t.length-3;f+=3){var p=t[f],d=t[f+1],m=t[f+2],g=t[f+3],y=t[f+4],v=0===i?p:d,x=0===i?g:y,_=!1;o&&(s=Math.sqrt(Math.pow(p-g,2)+Math.pow(d-y,2))),v<r?x>r&&(l=u(c,p,d,g,y,r),o&&(c.start=h+s*l)):v>n?x<n&&(l=u(c,p,d,g,y,n),o&&(c.start=h+s*l)):pt(c,p,d,m),x<r&&v>=r&&(l=u(c,p,d,g,y,r),_=!0),x>n&&v<=n&&(l=u(c,p,d,g,y,n),_=!0),!a&&_&&(o&&(c.end=h+s*l),e.push(c),c=ht(t)),o&&(h+=s)}var b=t.length-3;p=t[b],d=t[b+1],m=t[b+2],(v=0===i?p:d)>=r&&v<=n&&pt(c,p,d,m),b=c.length-3,a&&b>=3&&(c[b]!==c[0]||c[b+1]!==c[1])&&pt(c,c[0],c[1],c[2]),c.length&&e.push(c)}function ht(t){var e=[];return e.size=t.size,e.start=t.start,e.end=t.end,e}function ft(t,e,r,n,i,a){for(var o=0;o<t.length;o++)ut(t[o],e,r,n,i,a,!1)}function pt(t,e,r,n){t.push(e),t.push(r),t.push(n)}function dt(t,e,r,n,i,a){var o=(a-e)/(n-e);return t.push(a),t.push(r+(i-r)*o),t.push(1),o}function mt(t,e,r,n,i,a){var o=(a-r)/(i-r);return t.push(e+(n-e)*o),t.push(a),t.push(1),o}function gt(t,e){for(var r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if("Point"===o||"MultiPoint"===o||"LineString"===o)i=yt(a.geometry,e);else if("MultiLineString"===o||"Polygon"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(yt(a.geometry[s],e))}else if("MultiPolygon"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(yt(a.geometry[s][c],e));i.push(l)}r.push(tt(a.id,o,i,a.tags))}return r}function yt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function vt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(xt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var h=[];for(i=0;i<c[n].length;i+=2)h.push(xt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(h)}}return t.transformed=!0,t}function xt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function _t(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,bt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,h=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),h>o.maxY&&(o.maxY=h)}return o}function bt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)wt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)wt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)wt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var h in u={},e.tags)u[h]=e.tags[h];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var f={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(f.id=e.id),t.features.push(f)}}function wt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function Tt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)rt(r,t.features[n],e,n);else"Feature"===t.type?rt(r,t,e):rt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=lt(t,1,-1-r,r,0,-1,2,e),a=lt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=lt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=gt(i,1).concat(n)),a&&(n=n.concat(gt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function kt(t,e,r){return 32*((1<<t)*r+e)+t}function At(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=b(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}V.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new j(s,$,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new j(s,$,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,h=l.range(W(r),Y(a),W(i),Y(n));u<h.length;u+=1){var f=h[u],p=l.points[f];c.push(p.numPoints?G(p):this.points[p.index])}return c},V.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],h=i.points[u];h.parentId===t&&s.push(h.numPoints?G(h):this.points[h.index])}if(0===s.length)throw new Error(n);return s},V.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},V.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},V.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},V.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},V.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?Z(l):this.points[l.index].properties},h=void 0;c?h=l.id:this.options.generateId?h=l.index:this.points[l.index].id&&(h=this.points[l.index].id),void 0!==h&&(u.id=h),a.features.push(u)}},V.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(+t,this.options.maxZoom+1))},V.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=n.minPoints,l=i/(a*Math.pow(2,e)),c=0;c<t.length;c++){var u=t[c];if(!(u.zoom<=e)){u.zoom=e;for(var h=this.trees[e+1],f=h.within(u.x,u.y,l),p=u.numPoints||1,d=p,m=0,g=f;m<g.length;m+=1){var y=g[m],v=h.points[y];v.zoom>e&&(d+=v.numPoints||1)}if(d>=s){for(var x=u.x*p,_=u.y*p,b=o&&p>1?this._map(u,!0):null,w=(c<<5)+(e+1)+this.points.length,T=0,k=f;T<k.length;T+=1){var A=k[T],M=h.points[A];if(!(M.zoom<=e)){M.zoom=e;var S=M.numPoints||1;x+=M.x*S,_+=M.y*S,M.parentId=w,o&&(b||(b=this._map(u,!0)),o(b,this._map(M)))}}u.parentId=w,r.push(q(x/d,_/d,w,d,b))}else if(r.push(u),d>1)for(var E=0,C=f;E<C.length;E+=1){var L=C[E],I=h.points[L];I.zoom<=e||(I.zoom=e,r.push(I))}}}return r},V.prototype._getOriginId=function(t){return t-this.points.length>>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,h=kt(e,r,n),f=this.tiles[h];if(!f&&(c>1&&console.time("creation"),f=this.tiles[h]=_t(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,f.numFeatures,f.numPoints,f.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(f.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||f.numPoints<=l.indexMaxPoints)continue;if(f.source=null,0!==t.length){c>1&&console.time("clipping");var m,g,y,v,x,_,b=.5*l.buffer/l.extent,w=.5-b,T=.5+b,k=1+b;m=g=y=v=null,x=lt(t,u,r-b,r+T,0,f.minX,f.maxX,l),_=lt(t,u,r+w,r+k,0,f.minX,f.maxX,l),t=null,x&&(m=lt(x,u,n-b,n+T,1,f.minY,f.maxY,l),g=lt(x,u,n+w,n+k,1,f.minY,f.maxY,l),x=null),_&&(y=lt(_,u,n-b,n+T,1,f.minY,f.maxY,l),v=lt(_,u,n+w,n+k,1,f.minY,f.maxY,l),_=null),c>1&&console.timeEnd("clipping"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(v||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=kt(t,e=(e%o+o)%o,r);if(this.tiles[s])return vt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,h=r;!l&&c>0;)c--,u=Math.floor(u/2),h=Math.floor(h/2),l=this.tiles[kt(c,u,h)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,h),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,h,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?vt(this.tiles[s],i):null):null};var Mt=function(e){function r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));h(o,!0);try{if(n.filter){var s=t.createExpression(n.filter,{type:"boolean","property-type":"data-driven",overridable:!1,transition:!1});if("error"===s.result)throw new Error(s.value.map((function(t){return t.key+": "+t.message})).join(", "));var l=o.features.filter((function(t){return s.value.evaluate({zoom:0},t)}));o={type:"FeatureCollection",features:l}}e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var h=u[c],f=n[h],p=f[0],d=f[1],m=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",h]]:p);i[h]=m.value,a[h]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new Tt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var c={};if(i){var u=i.finish();u&&(c.resourceTiming={},c.resourceTiming[n.source]=JSON.parse(JSON.stringify(u)))}r(null,c)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var St=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:Mt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return St.prototype.setReferrer=function(t,e){this.referrer=e},St.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in i)i[a].availableImages=e}r()},St.prototype.setLayers=function(t,e,r){this.getLayerIndex(t).replace(e),r()},St.prototype.updateLayers=function(t,e,r){this.getLayerIndex(t).update(e.layers,e.removedIds),r()},St.prototype.loadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).loadTile(e,r)},St.prototype.loadDEMTile=function(t,e,r){this.getDEMWorkerSource(t,e.source).loadTile(e,r)},St.prototype.reloadTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).reloadTile(e,r)},St.prototype.abortTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).abortTile(e,r)},St.prototype.removeTile=function(t,e,r){this.getWorkerSource(t,e.type,e.source).removeTile(e,r)},St.prototype.removeDEMTile=function(t,e){this.getDEMWorkerSource(t,e.source).removeTile(e)},St.prototype.removeSource=function(t,e,r){if(this.workerSources[t]&&this.workerSources[t][e.type]&&this.workerSources[t][e.type][e.source]){var n=this.workerSources[t][e.type][e.source];delete this.workerSources[t][e.type][e.source],void 0!==n.removeSource?n.removeSource(e,r):r()}},St.prototype.loadWorkerSource=function(t,e,r){try{this.self.importScripts(e.url),r()}catch(t){r(t.toString())}},St.prototype.syncRTLPluginState=function(e,r,n){try{t.plugin.setState(r);var i=t.plugin.getPluginURL();if(t.plugin.isLoaded()&&!t.plugin.isParsed()&&null!=i){this.self.importScripts(i);var a=t.plugin.isParsed();n(a?void 0:new Error("RTL Text Plugin failed to import scripts from "+i),a)}}catch(t){n(t.toString())}},St.prototype.getAvailableImages=function(t){var e=this.availableImages[t];return e||(e=[]),e},St.prototype.getLayerIndex=function(t){var e=this.layerIndexes[t];return e||(e=this.layerIndexes[t]=new n),e},St.prototype.getWorkerSource=function(t,e,r){var n=this;if(this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){var i={send:function(e,r,i){n.actor.send(e,r,i,t)}};this.workerSources[t][e][r]=new this.workerSourceTypes[e](i,this.getLayerIndex(t),this.getAvailableImages(t))}return this.workerSources[t][e][r]},St.prototype.getDEMWorkerSource=function(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new u),this.demWorkerSources[t][e]},St.prototype.enforceCacheSizeLimit=function(e,r){t.enforceCacheSizeLimit(r)},"undefined"!=typeof WorkerGlobalScope&&"undefined"!=typeof self&&self instanceof WorkerGlobalScope&&(self.worker=new St(self)),St})),n(0,(function(t){var e=t.createCommonjsModule((function(t){function e(t){return!r(t)}function r(t){return"undefined"!=typeof window&&"undefined"!=typeof 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e=t.getContext("2d");if(!e)return!1;var r=e.getImageData(0,0,1,1);return r&&r.width===t.width}()?(r=t&&t.failIfMajorPerformanceCaveat,void 0===n[r]&&(n[r]=function(t){var r=function(t){var r=document.createElement("canvas"),n=Object.create(e.webGLContextAttributes);return n.failIfMajorPerformanceCaveat=t,r.probablySupportsContext?r.probablySupportsContext("webgl",n)||r.probablySupportsContext("experimental-webgl",n):r.supportsContext?r.supportsContext("webgl",n)||r.supportsContext("experimental-webgl",n):r.getContext("webgl",n)||r.getContext("experimental-webgl",n)}(t);if(!r)return!1;var n=r.createShader(r.VERTEX_SHADER);return!(!n||r.isContextLost())&&(r.shaderSource(n,"void main() {}"),r.compileShader(n),!0===r.getShaderParameter(n,r.COMPILE_STATUS))}(r)),n[r]?void 0:"insufficient WebGL support"):"insufficient Canvas/getImageData support":"insufficient ArrayBuffer support":"insufficient Uint8ClampedArray support":"insufficient worker support":"insufficient JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support":"not a browser";var r}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document&&t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function h(t){var e=t.userImage;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf("MAC")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var f=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "stretchX" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "stretchY" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image "'+e+'" has invalid "content" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!(t&&(4!==t.length||t[0]<0||e.data.width<t[0]||t[1]<0||e.data.height<t[1]||t[2]<0||e.data.width<t[2]||t[3]<0||e.data.height<t[3]||t[2]<t[0]||t[3]<t[1]))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. 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x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var _=function(){this.specification=t.styleSpec.light.position};_.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},_.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var b=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new _,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w="-transition",T=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(b),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,w)?this._transitionable.setTransition(n.slice(0,-11),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),k=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};k.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},k.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},k.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),h=Math.abs(c-l.right),f=Math.min(u,h),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(f*f+m*m)}else p=n-Math.sqrt(f*f+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),h=Math.abs(c-l.right),f=Math.min(u,h),p=l.isDash?f:-f;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},k.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var A=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}A.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},A.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},A.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},A.Actor=t.Actor;var S=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};S.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},S.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var E=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setSourceProperty=function(t){this._tileJSONRequest&&this._tileJSONRequest.cancel(),t(),this.map.style.sourceCaches[this.id].clearTiles(),this.load()},r.prototype.setTiles=function(t){var e=this;return this.setSourceProperty((function(){e._options.tiles=t})),this},r.prototype.setUrl=function(t){var e=this;return this.setSourceProperty((function(){e.url=t,e._options.url=t})),this},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(C),I=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,minPoints:Math.max(2,n.clusterMinPoints||2),extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties,filter:n.filter},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),z=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,h=Math.max(c,u),f=Math.max(0,Math.floor(-Math.log(h)/Math.LN2)),p=Math.pow(2,f);return new t.CanonicalTileID(f,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(z),D=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(z),R={vector:E,raster:C,"raster-dem":L,geojson:I,video:O,image:z,canvas:D};function F(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function B(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(N);for(var c=[],u=0,h=l;u<h.length;u+=1){var f=h[u];c.push({wrappedTileID:f.tileID.wrapped().key,queryResults:f.tile.queryRenderedFeatures(e,r,t._state,f.queryGeometry,f.cameraQueryGeometry,f.scale,i,a,s,F(t.transform,f.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],h=l[c]=l[c]||{},f=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];h[m.featureIndex]||(h[m.featureIndex]=!0,f.push(m))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function N(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var j=function(t,e){this.max=t,this.onRemove=e,this.reset()};j.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},j.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var U=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};U.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},U.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},U.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var V={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},q=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};q.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},q.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},q.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},q.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[V[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},q.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var H=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};H.prototype.get=function(){return this.current},H.prototype.set=function(t){},H.prototype.getDefault=function(){return this.default},H.prototype.setDefault=function(){this.set(this.default)};var G=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(H),Z=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 1},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearDepth(t),this.current=t,this.dirty=!1)},e}(H),W=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearStencil(t),this.current=t,this.dirty=!1)},e}(H),Y=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[!0,!0,!0,!0]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),X=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.depthMask(t),this.current=t,this.dirty=!1)},e}(H),$=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 255},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.stencilMask(t),this.current=t,this.dirty=!1)},e}(H),J=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return{func:this.gl.ALWAYS,ref:0,mask:255}},e.prototype.set=function(t){var e=this.current;(t.func!==e.func||t.ref!==e.ref||t.mask!==e.mask||this.dirty)&&(this.gl.stencilFunc(t.func,t.ref,t.mask),this.current=t,this.dirty=!1)},e}(H),K=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[t.KEEP,t.KEEP,t.KEEP]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||this.dirty)&&(this.gl.stencilOp(t[0],t[1],t[2]),this.current=t,this.dirty=!1)},e}(H),Q=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.STENCIL_TEST):e.disable(e.STENCIL_TEST),this.current=t,this.dirty=!1}},e}(H),tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[0,1]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.depthRange(t[0],t[1]),this.current=t,this.dirty=!1)},e}(H),et=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.DEPTH_TEST):e.disable(e.DEPTH_TEST),this.current=t,this.dirty=!1}},e}(H),rt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.LESS},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.depthFunc(t),this.current=t,this.dirty=!1)},e}(H),nt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.BLEND):e.disable(e.BLEND),this.current=t,this.dirty=!1}},e}(H),it=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[t.ONE,t.ZERO]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.blendFunc(t[0],t[1]),this.current=t,this.dirty=!1)},e}(H),at=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(H),ot=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.FUNC_ADD},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.blendEquation(t),this.current=t,this.dirty=!1)},e}(H),st=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.CULL_FACE):e.disable(e.CULL_FACE),this.current=t,this.dirty=!1}},e}(H),lt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.BACK},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.cullFace(t),this.current=t,this.dirty=!1)},e}(H),ct=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.CCW},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.frontFace(t),this.current=t,this.dirty=!1)},e}(H),ut=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.useProgram(t),this.current=t,this.dirty=!1)},e}(H),ht=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.TEXTURE0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.activeTexture(t),this.current=t,this.dirty=!1)},e}(H),ft=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[0,0,t.drawingBufferWidth,t.drawingBufferHeight]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(H),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(H),mt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(H),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t,this.dirty=!1}},e}(H),yt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){var e=this.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t,this.dirty=!1},e}(H),vt=function(t){function e(e){t.call(this,e),this.vao=e.extVertexArrayObject}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){this.vao&&(t!==this.current||this.dirty)&&(this.vao.bindVertexArrayOES(t),this.current=t,this.dirty=!1)},e}(H),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 4},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(H),_t=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),wt=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e}(H),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setDirty=function(){this.dirty=!0},e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(wt),kt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(wt),At=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new Tt(t,a),n&&(this.depthAttachment=new kt(t,a))};At.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var St=7680,Et=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Et.disabled=new Et({func:519,mask:0},0,0,St,St,St);var Ct=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Ct.Replace=[1,0],Ct.disabled=new Ct(Ct.Replace,t.Color.transparent,[!1,!1,!1,!1]),Ct.unblended=new Ct(Ct.Replace,t.Color.transparent,[!0,!0,!0,!0]),Ct.alphaBlended=new Ct([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Lt=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Lt.disabled=new Lt(!1,1029,2305),Lt.backCCW=new Lt(!0,1029,2305);var It=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new G(this),this.clearDepth=new Z(this),this.clearStencil=new W(this),this.colorMask=new Y(this),this.depthMask=new X(this),this.stencilMask=new $(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new tt(this),this.depthTest=new et(this),this.depthFunc=new rt(this),this.blend=new nt(this),this.blendFunc=new it(this),this.blendColor=new at(this),this.blendEquation=new ot(this),this.cullFace=new st(this),this.cullFaceSide=new lt(this),this.frontFace=new ct(this),this.program=new ut(this),this.activeTexture=new ht(this),this.viewport=new ft(this),this.bindFramebuffer=new pt(this),this.bindRenderbuffer=new dt(this),this.bindTexture=new mt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new yt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new xt(this),this.pixelStoreUnpackPremultiplyAlpha=new _t(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query"),this.maxTextureSize=t.getParameter(t.MAX_TEXTURE_SIZE)};It.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},It.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},It.prototype.createIndexBuffer=function(t,e){return new U(this,t,e)},It.prototype.createVertexBuffer=function(t,e,r){return new q(this,t,e,r)},It.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},It.prototype.createFramebuffer=function(t,e,r){return new At(this,t,e,r)},It.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},It.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},It.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},It.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},It.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,Ct.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},It.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Pt=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new R[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new j(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(zt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(zt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Ot(this._source.type)){for(var c={},u={},h=0,f=Object.keys(l);h<f.length;h+=1){var p=f[h],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var y in this._retainLoadedChildren(u,a,s,l),c)l[y]||(this._coveredTiles[y]=!0,l[y]=c[y])}for(var v in l)this._tiles[v].clearFadeHold();for(var x=0,_=t.keysDifference(this._tiles,l);x<_.length;x+=1){var b=_[x],w=this._tiles[b];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(b)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],h=this._addTile(u);n[u.key]=u,h.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var f=0,p=t;f<p.length;f+=1){var d=p[f],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var v=d.children(this._source.maxzoom);if(n[v[0].key]&&n[v[1].key]&&n[v[2].key]&&n[v[3].key])continue}for(var x=m.wasRequested(),_=d.overscaledZ-1;_>=a;--_){var b=d.scaledTo(_);if(i[b.key])break;if(i[b.key]=!0,!(m=this.getTile(b))&&x&&(m=this._addTile(b)),m&&(n[b.key]=b,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),h=1/0,f=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var y=g[m];h=Math.min(h,y.x),f=Math.min(f,y.y),p=Math.max(p,y.x),d=Math.max(d,y.y)}for(var v=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,y=[s.getTilePoint(new t.MercatorCoordinate(h,f)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(y[0].x-g<t.EXTENT&&y[0].y-g<t.EXTENT&&y[1].x+g>=0&&y[1].y+g>=0){var v=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:v,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)v(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(Ot(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function zt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Ot(t){return"raster"===t||"image"===t||"video"===t}function Dt(){return new t.window.Worker(oa.workerUrl)}Pt.maxOverzooming=10,Pt.maxUnderzooming=3;var Rt="mapboxgl_preloaded_worker_pool",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Ft.workerCount;)this.workers.push(new Dt);return this.active[t]=!0,this.workers.slice()},Ft.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Ft.prototype.isPreloaded=function(){return!!this.active[Rt]},Ft.prototype.numActive=function(){return Object.keys(this.active).length};var Bt,Nt=Math.floor(t.browser.hardwareConcurrency/2);function jt(){return Bt||(Bt=new Ft),Bt}function Ut(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function Vt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Ut(t[n],e[t[n].ref]));return t}Ft.workerCount=Math.max(Math.min(Nt,6),1);var qt={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Ht(t,e,r){r.push({command:qt.addSource,args:[t,e[t]]})}function Gt(t,e,r){e.push({command:qt.removeSource,args:[t]}),r[t]=!0}function Zt(t,e,r,n){Gt(t,r,n),Ht(t,e,r)}function Wt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Xt(t){return t.id}function $t(t,e){return t[e.id]=e,t}function Jt(e,r){if(!e)return[{command:qt.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:qt.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:qt.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:qt.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:qt.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:qt.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:qt.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:qt.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:qt.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:qt.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Gt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Wt(e,r,a)?n.push({command:qt.setGeoJSONSourceData,args:[a,r[a].data]}):Zt(a,r,n,i)):Ht(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:qt.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,h=(e=e||[]).map(Xt),f=r.map(Xt),p=e.reduce($t,{}),d=r.reduce($t,{}),m=h.slice(),g=Object.create(null);for(i=0,a=0;i<h.length;i++)o=h[i],d.hasOwnProperty(o)?a++:(n.push({command:qt.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<f.length;i++)o=f[f.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:qt.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:qt.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<f.length;i++)if(s=p[o=f[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,qt.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,qt.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:qt.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:qt.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),qt.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:qt.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),qt.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:qt.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:qt.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:qt.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:qt.setStyle,args:[r]}]}return n}var Kt=function(t,e){this.reset(t,e)};Kt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Kt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function te(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function ee(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function re(e,r){var n=[e.x,e.y,0,1];pe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ne(t,e){return.5+t/e*.5}function ie(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),h=[256/n.width*2+1,256/n.height*2+1],f=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;f.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,y=0;y<d.length;y++){var v=d.get(y);if(v.hidden||v.writingMode===t.WritingMode.vertical&&!g)fe(v.numGlyphs,f);else{g=!1;var x=[v.anchorX,v.anchorY,0,1];if(t.transformMat4(x,x,r),ie(x,h)){var _=x[3],b=ne(n.transform.cameraToCenterDistance,_),w=t.evaluateSizeForFeature(c,u,v),T=s?w/b:w*b,k=new t.Point(v.anchorX,v.anchorY),A=re(k,a).point,M={},S=le(v,T,!1,l,r,a,o,e.glyphOffsetArray,p,f,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&le(v,T,!0,l,r,a,o,e.glyphOffsetArray,p,f,A,k,M,m).notEnoughRoom)&&fe(v.numGlyphs,f)}else fe(v.numGlyphs,f)}}i?e.text.dynamicLayoutVertexBuffer.updateData(f):e.icon.dynamicLayoutVertexBuffer.updateData(f)}function oe(t,e,r,n,i,a,o,s,l,c,u){var h=s.glyphStartIndex+s.numGlyphs,f=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(h-1),g=ue(t*d,r,n,i,a,o,s.segment,f,p,l,c,u);if(!g)return null;var y=ue(t*m,r,n,i,a,o,s.segment,f,p,l,c,u);return y?{first:g,last:y}:null}function se(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,c,u,h,f,p,d){var m,g=r/24,y=e.lineOffsetX*g,v=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,_=e.lineStartIndex,b=e.lineStartIndex+e.lineLength,w=oe(g,l,y,v,n,h,f,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(ue(g*l.getoffsetX(M),y,v,n,h,f,e.segment,_,b,c,o,p));m.push(w.last)}else{if(i&&!n){var S=re(f,a).point,E=e.lineStartIndex+e.segment+1,C=new t.Point(c.getx(E),c.gety(E)),L=re(C,a),I=L.signedDistanceFromCamera>0?L.point:ce(f,C,S,1,a),P=se(e.writingMode,S,I,d);if(P)return P}var z=ue(g*l.getoffsetX(e.glyphStartIndex),y,v,n,h,f,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!z)return{notEnoughRoom:!0};m=[z]}for(var O=0,D=m;O<D.length;O+=1){var R=D[O];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function ce(t,e,r,n,i){var a=re(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function ue(e,r,n,i,a,o,s,l,c,u,h,f){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,v=a,x=0,_=0,b=Math.abs(p),w=[];x+_<=b;){if((g+=d)<l||g>=c)return null;if(v=y,w.push(y),void 0===(y=f[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=re(T,h);if(k.signedDistanceFromCamera>0)y=f[g]=k.point;else{var A=g-d;y=ce(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,v,b-x+1,h)}}x+=_,_=v.dist(y)}var M=(b-x)/_,S=y.sub(v),E=S.mult(M)._add(v);E._add(S._unit()._perp()._mult(n*d));var C=m+Math.atan2(y.y-v.y,y.x-v.x);return w.push(E),{point:E,angle:C,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],h=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-h,y1:u-h,x2:c+h,y2:u+h})}return a?o.filter(a):o}var f={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,f,a),i?o.length>0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,h=0,f=c;h<f.length;h+=1){var p=f[h];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,y=0,v=m;y<v.length;y+=1){var x=v[y];if(!l.circle[x]){l.circle[x]=!0;var _=3*x;if(this._circleAndRectCollide(g[_],g[_+1],g[_+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var b=g[_],w=g[_+1],T=g[_+2];a.push({key:this.circleKeys[x],x1:b-T,y1:w-T,x2:b+T,y2:w+T})}}}},Qt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var h=this.bboxes,f=0,p=u;f<p.length;f+=1){var d=p[f];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,h[m+0],h[m+1],h[m+2],h[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var y=this.circles,v=0,x=g;v<x.length;v+=1){var _=x[v];if(!c.circle[_]){c.circle[_]=!0;var b=3*_;if(this._circlesCollide(y[b],y[b+1],y[b+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[_])))return a.push(!0),!0}}},Qt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),h=this._convertToYCellCoord(n),f=l;f<=u;f++)for(var p=c;p<=h;p++){var d=this.xCellCount*p+f;if(i.call(this,t,e,r,n,d,a,o,s))return}},Qt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Qt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Qt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var h=l-s,f=u-c;return h*h+f*f<=r*r};var he=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function fe(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(he,3*n)}}function pe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var de=100,me=function(t,e,r){void 0===e&&(e=new Qt(t.width+200,t.height+200,25)),void 0===r&&(r=new Qt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+de,this.screenBottomBoundary=t.height+de,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function ge(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}me.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},me.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,h,f,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=re(m,o),y=ne(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),v=(u?a/y:a*y)/t.ONE_EM,x=re(m,s).point,_=oe(v,i,r.lineOffsetX*v,r.lineOffsetY*v,!1,x,m,r,n,s,{}),b=!1,w=!1,T=!0;if(_){for(var k=.5*f*y+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Kt,E=_.first,C=_.last,L=[],I=E.path.length-1;I>=1;I--)L.push(E.path[I]);for(var P=1;P<C.path.length;P++)L.push(C.path[P]);var z=2.5*k;if(l){var O=L.map((function(t){return re(t,l)}));L=O.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:O.map((function(t){return t.point}))}var D=[];if(L.length>0){for(var R=L[0].clone(),F=L[0].clone(),B=1;B<L.length;B++)R.x=Math.min(R.x,L[B].x),R.y=Math.min(R.y,L[B].y),F.x=Math.max(F.x,L[B].x),F.y=Math.max(F.y,L[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[L]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([L],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/z)+1;for(var q=0;q<V;q++){var H=q/Math.max(V-1,1),G=S.lerp(H),Z=G.x+de,W=G.y+de;d.push(Z,W,k,0);var Y=Z-k,X=W-k,$=Z+k,J=W+k;if(T=T&&this.isOffscreen(Y,X,$,J),w=w||this.isInsideGrid(Y,X,$,J),!e&&this.grid.hitTestCircle(Z,W,k,h)&&(b=!0,!c))return{circles:[],offscreen:!1,collisionDetected:b}}}}return{circles:!c&&b||!w?[]:d,offscreen:T,collisionDetected:b}},me.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+de,c.y+de);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var h={},f={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]={}),!h[g.bucketInstanceId][g.featureIndex]){var y=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,y)&&(h[g.bucketInstanceId][g.featureIndex]=!0,void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]=[]),f[g.bucketInstanceId].push(g.featureIndex))}}return f},me.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},me.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},me.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return pe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+de,(-i[1]/i[3]+1)/2*this.transform.height+de),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},me.prototype.isOffscreen=function(t,e,r,n){return r<de||t>=this.screenRightBoundary||n<de||e>this.screenBottomBoundary},me.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},me.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var ye=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};ye.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var ve=function(t,e,r,n,i){this.text=new ye(t?t.text:null,e,r,i),this.icon=new ye(t?t.icon:null,e,n,i)};ve.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var xe=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},_e=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},be=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},we=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function Te(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function ke(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,h=e.anchorPointX,f=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:h,anchorPointY:f}}we.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var Ae=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new me(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new we(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Me(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}Ae.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,h=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),f="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=ge(n,1,this.transform.zoom),m=te(h,f,p,this.transform,d),g=null;if(f){var y=ee(h,f,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,y)}this.retainedQueryData[a.bucketInstanceId]=new be(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var v={bucket:a,layout:l,posMatrix:h,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,_=a.sortKeyRanges;x<_.length;x+=1){var b=_[x],w=b.sortKey,T=b.symbolInstanceStart,k=b.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:v})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:v})}},Ae.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d){var m,g=[h.textOffset0,h.textOffset1],y=Te(t,r,n,g,i),v=this.collisionIndex.placeCollisionBox(ke(e,y.x,y.y,a,o,this.transform.angle),u,s,l,c.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(ke(d,y.x,y.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return v.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[h.crossTileID]&&this.prevPlacement.placements[h.crossTileID]&&this.prevPlacement.placements[h.crossTileID].text&&(m=this.prevPlacement.variableOffsets[h.crossTileID].anchor),this.variableOffsets[h.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(f,t,h,p),f.allowVerticalPlacement&&(this.markUsedOrientation(f,p,h),this.placedOrientations[h.crossTileID]=p),{shift:y,placedGlyphBoxes:v}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,h=a.textPixelRatio,f=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get("text-optional"),y=s.get("icon-optional"),v=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),_="map"===s.get("text-rotation-alignment"),b="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=v&&(x||!o.hasIconData()||y),A=x&&(v||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(f)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,C={box:null,offscreen:null},L={box:null,offscreen:null},I=null,P=null,z=0,O=0,D=0;a.textFeatureIndex?z=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(z=e.featureIndex),a.verticalTextFeatureIndex&&(O=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(C=n(),L=C):C=r(),!(C&&C.box&&C.box.length));i+=1);else C=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,f={box:[],offscreen:!1},p=v?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],y=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,_,b,h,l,m,y,e,o,n,u);if(k&&(f=k.placedGlyphBoxes)&&f.box&&f.box.length){T=!0,E=k.shift;break}}return f};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=C&&C.box&&C.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),C&&(T=C.box,S=C.offscreen);var V=F(C&&C.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,v,h,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(C&&C.box&&C.box.length)}}if(T=(p=C)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Z=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),Y=e.collisionCircleDiameter;I=i.collisionIndex.placeCollisionCircles(v,G,o.lineVertexArray,o.glyphOffsetArray,Z,l,c,u,n,b,m.predicate,Y,W),T=v||I.circles.length>0&&!I.collisionDetected,S=S&&I.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?ke(t,E.x,E.y,_,b,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,h,l,m.predicate)};M=L&&L.box&&L.box.length&&a.verticalIconBox?(P=X(a.verticalIconBox)).box.length>0:(P=X(a.iconBox)).box.length>0,S=S&&P.offscreen}var $=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=y||0===e.numIconVertices;if($||J?J?$||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(L&&L.box&&O?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID)),M&&P&&i.collisionIndex.insertCollisionBox(P.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,m.ID),I&&(T&&i.collisionIndex.insertCollisionCircles(I.circles,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID),n)){var K=o.bucketInstanceId,Q=i.collisionCircleArrays[K];void 0===Q&&(Q=i.collisionCircleArrays[K]=new _e);for(var tt=0;tt<I.circles.length;tt+=4)Q.circles.push(I.circles[tt+0]),Q.circles.push(I.circles[tt+1]),Q.circles.push(I.circles[tt+2]),Q.circles.push(I.collisionDetected?1:0)}i.placements[e.crossTileID]=new xe(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var C=S[E];M(o.symbolInstances.get(C),o.collisionArrays[C])}else for(var L=e.symbolInstanceStart;L<e.symbolInstanceEnd;L++)M(o.symbolInstances.get(L),o.collisionArrays[L]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var I=this.collisionCircleArrays[o.bucketInstanceId];t.invert(I.invProjMatrix,l),I.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},Ae.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},Ae.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new ve(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new ve(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var h=i[u];if(!this.opacities[u]){var f=new ve(h,n,!1,!1);f.isHidden()||(this.opacities[u]=f,r=r||h.text.placed||h.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},Ae.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},Ae.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new ve(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),h="map"===a.get("text-pitch-alignment"),f="none"!==a.get("icon-text-fit"),p=new ve(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],y=i.opacities[m];g?y=o:y||(y=p,i.opacities[m]=y),r[m]=!0;var v=s>0||l>0,x=a.numIconVertices>0,_=i.placedOrientations[a.crossTileID],b=_===t.WritingMode.vertical,w=_===t.WritingMode.horizontal||_===t.WritingMode.horizontalOnly;if(v){var T=Oe(y.text),k=b?De:T;d(e.text,s,k);var A=w?De:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||b?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,_);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var C=Oe(y.icon),L=!(f&&a.verticalPlacedIconSymbolIndex&&b);if(a.placedIconSymbolIndex>=0){var I=L?C:De;d(e.icon,a.numIconVertices,I),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var P=L?De:C;d(e.icon,a.numVerticalIconVertices,P),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var z=e.collisionArrays[n];if(z){var O=new t.Point(0,0);if(z.textBox||z.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(O=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&O._rotate(h?i.transform.angle:-i.transform.angle)):D=!1}z.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||b,O.x,O.y),z.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||w,O.x,O.y)}var F=Boolean(!w&&z.verticalIconBox);z.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,f?O.x:0,f?O.y:0),z.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,f?O.x:0,f?O.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var y=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=y.invProjMatrix,e.placementViewportMatrix=y.viewportMatrix,e.collisionCircleArray=y.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},Ae.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},Ae.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},Ae.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},Ae.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Ce=Math.pow(2,17),Le=Math.pow(2,16),Ie=Math.pow(2,9),Pe=Math.pow(2,8),ze=Math.pow(2,1);function Oe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Ce+e*Le+r*Ie+e*Pe+r*ze+e}var De=0,Re=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var Fe=function(t,e,r,n,i,a,o){this.placement=new Ae(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Fe.prototype.isDone=function(){return this._done},Fe.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Ne.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=Be/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Ne.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var je=function(){this.maxCrossTileID=0};je.prototype.generate=function(){return++this.maxCrossTileID};var Ue=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Ue.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Ue.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var h=e.symbolInstances.get(u);h.crossTileID||(h.crossTileID=r.generate(),i[h.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Ne(t,e.symbolInstances,e.bucketInstanceId),!0},Ue.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Ue.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ve=function(){this.layerIndexes={},this.crossTileIDs=new je,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ve.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Ue);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ve.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var qe=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},He=t.pick(qt,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ge=t.pick(qt,["setCenter","setZoom","setBearing","setPitch"]),Ze=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),We=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new A(jt(),this),this.imageManager=new f,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new k(256,512),this.crossTileSymbolIndex=new Ve,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(Ze,!1)},r.prototype._load=function(e,r){if(!r||!qe(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=Vt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new T(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,h=l.x,f=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,y=l.content,v=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,v,{x:h,y:f},{x:0,y:0},{width:c,height:u}),r[s]={data:v,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}var l={};for(var c in this.sourceCaches){var u=this.sourceCaches[c];l[c]=u.used,u.used=!1}for(var h=0,f=this._order;h<f.length;h+=1){var p=f[h],d=this._layers[p];d.recalculate(e,this._availableImages),!d.isHidden(e.zoom)&&d.source&&(this.sourceCaches[d.source].used=!0)}for(var m in l){var g=this.sourceCaches[m];l[m]!==g.used&&g.fire(new t.Event("data",{sourceDataType:"visibility",dataType:"source",sourceId:m}))}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),qe(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=Vt(e.layers);var n=Jt(this.serialize(),e).filter((function(t){return!(t.command in Ge)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in He)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._afterImageUpdated(e)},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._afterImageUpdated(e)},r.prototype._afterImageUpdated=function(e){this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.dispatcher.broadcast("setImages",this._availableImages),this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,"sources."+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Pt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(qe(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is required to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,h=c;u<h.length;u+=1){var f=h[u];i.push(f)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id]<d)break;p.push(y),i.pop()}else for(var v=0,x=t;v<x.length;v+=1){var _=x[v][m];if(_)for(var b=0,w=_;b<w.length;b+=1){var T=w[b];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(B(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,h=Object.keys(l).map(Number);u<h.length;u+=1){var f=h[u];c.push(o[f])}c.sort(N);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,h=c;u<h.length;u+=1){var f=h[u];o.push(f)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var y in s)g(y);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&qe(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var h=u[c],f=this._layers[h];if("symbol"===f.type){if(!l[f.source]){var p=this.sourceCaches[f.source];l[f.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(f,l[f.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new Fe(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var y=g[m],v=this._layers[y];"symbol"===v.type&&this.placement.updateLayerOpacities(v,l[v.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);We.getSourceType=function(t){return R[t]},We.setSourceType=function(t,e){R[t]=e},We.registerForPluginStateChange=t.registerForPluginStateChange;var Ye=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Xe=br("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),$e=br("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Je=br("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ke=br("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Qe=br("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),tr=br("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),er=br("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),rr=br("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),nr=br("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),ir=br("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),ar=br("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),or=br("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),sr=br("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),lr=br("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),cr=br("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),ur=br("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),hr=br("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),fr=br("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),pr=br("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),dr=br("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),mr=br("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),gr=br("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),yr=br("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),vr=br("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),xr=br("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),_r=br("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function br(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n=e.match(/attribute ([\w]+) ([\w]+)/g),i=t.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),a=e.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,(function(t,e,r,n,i){return s[i]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nvarying "+r+" "+n+" "+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,n,i){var a="float"===n?"vec2":"vec4",o=i.match(/color/)?"color":a;return s[i]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nuniform lowp float u_"+i+"_t;\nattribute "+r+" "+a+" a_"+i+";\nvarying "+r+" "+n+" "+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"vec4"===o?"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+i+" = a_"+i+";\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+i+" = unpack_mix_"+o+"(a_"+i+", u_"+i+"_t);\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nuniform lowp float u_"+i+"_t;\nattribute "+r+" "+a+" a_"+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"vec4"===o?"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = a_"+i+";\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = unpack_mix_"+o+"(a_"+i+", u_"+i+"_t);\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n"})),staticAttributes:n,staticUniforms:o}}var wr=Object.freeze({__proto__:null,prelude:Xe,background:$e,backgroundPattern:Je,circle:Ke,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:cr,fillExtrusionPattern:ur,hillshadePrepare:hr,hillshade:fr,line:pr,lineGradient:dr,linePattern:mr,lineSDF:gr,raster:yr,symbolIcon:vr,symbolSDF:xr,symbolTextAndIcon:_r}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};function kr(t){for(var e=[],r=0;r<t.length;r++)if(null!==t[r]){var n=t[r].split(" ");e.push(n.pop())}return e}Tr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Tr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var h=l;h<s;h++)u.disableVertexAttribArray(h)}e.enableAttributes(u,t);for(var f=0,p=r;f<p.length;f+=1)p[f].enableAttributes(u,t);a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},Tr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var Ar=function(t,e,r,n,i,a){var o=t.gl;this.program=o.createProgram();for(var s=kr(r.staticAttributes),l=n?n.getBinderAttributes():[],c=s.concat(l),u=r.staticUniforms?kr(r.staticUniforms):[],h=n?n.getBinderUniforms():[],f=[],p=0,d=u.concat(h);p<d.length;p+=1){var m=d[p];f.indexOf(m)<0&&f.push(m)}var g=n?n.defines():[];a&&g.push("#define OVERDRAW_INSPECTOR;");var y=g.concat(Xe.fragmentSource,r.fragmentSource).join("\n"),v=g.concat(Xe.vertexSource,r.vertexSource).join("\n"),x=o.createShader(o.FRAGMENT_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(x,y),o.compileShader(x),o.attachShader(this.program,x);var _=o.createShader(o.VERTEX_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(_,v),o.compileShader(_),o.attachShader(this.program,_),this.attributes={};var b={};this.numAttributes=c.length;for(var w=0;w<this.numAttributes;w++)c[w]&&(o.bindAttribLocation(this.program,w,c[w]),this.attributes[c[w]]=w);o.linkProgram(this.program),o.deleteShader(_),o.deleteShader(x);for(var T=0;T<f.length;T++){var k=f[T];if(k&&!b[k]){var A=o.getUniformLocation(this.program,k);A&&(b[k]=A)}}this.fixedUniforms=i(t,b),this.binderUniforms=n?n.getUniforms(t,b):[]}}};function Mr(t,e,r){var n=1/ge(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}Ar.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m){var g,y=t.gl;if(!this.failedToCreate){for(var v in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[v].set(o[v]);p&&p.setUniforms(t,this.binderUniforms,h,{zoom:f});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],_=0,b=u.get();_<b.length;_+=1){var w=b[_],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Sr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},Er=function(e,r,n,i,a,o,s){return t.extend(Sr(e,r,n,i),Mr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Cr=function(t){return{u_matrix:t}},Lr=function(e,r,n,i){return t.extend(Cr(e),Mr(n,r,i))},Ir=function(t,e){return{u_matrix:t,u_world:e}},Pr=function(e,r,n,i,a){return t.extend(Lr(e,r,n,i),{u_world:a})},zr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+("map"===i.paint.get("circle-pitch-scale")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get("circle-translate"),i.paint.get("circle-translate-anchor")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Or=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Dr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Rr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Fr=function(t){return{u_matrix:t}},Br=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}},Nr=function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}},jr=function(e,r,n){var i=n.paint.get("hillshade-shadow-color"),a=n.paint.get("hillshade-highlight-color"),o=n.paint.get("hillshade-accent-color"),s=n.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===n.paint.get("hillshade-illumination-anchor")&&(s-=e.transform.angle);var l,c,u,h=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),h),u_image:0,u_latrange:(l=r.tileID,c=Math.pow(2,l.canonical.z),u=l.canonical.y,[new t.MercatorCoordinate(0,u/c).toLngLat().lat,new t.MercatorCoordinate(0,(u+1)/c).toLngLat().lat]),u_light:[n.paint.get("hillshade-exaggeration"),s],u_shadow:i,u_highlight:a,u_accent:o}},Ur=function(e,r){var n=r.stride,i=t.create();return t.ortho(i,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(i,i,[0,-t.EXTENT,0]),{u_matrix:i,u_image:1,u_dimension:[n,n],u_zoom:e.overscaledZ,u_unpack:r.getUnpackVector()}};var Vr=function(e,r,n){var i=e.transform;return{u_matrix:Wr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},qr=function(e,r,n,i){return t.extend(Vr(e,r,n),{u_image:0,u_image_height:i})},Hr=function(e,r,n,i){var a=e.transform,o=Zr(r,a);return{u_matrix:Wr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Gr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Zr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),h=s.getDash(i.to,c),f=u.width*a.fromScale,p=h.width*a.toScale;return t.extend(Vr(e,r,n),{u_patternscale_a:[l/f,-u.height/2],u_patternscale_b:[l/p,-h.height/2],u_sdfgamma:s.width/(256*Math.min(f,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:h.y,u_mix:a.t})};function Zr(t,e){return 1/ge(t,1,e.tileZoom)}function Wr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Yr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Xr(i.paint.get("raster-hue-rotate"))};var a,o};function Xr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var $r,Jr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Kr=function(e,r,n,i,a,o,s,l,c,u,h){var f=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(f._pitch)*f.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+h})},Qr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Kr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},tn=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},en=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,h=u*(n.tileID.canonical.x+n.tileID.wrap*c),f=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[h>>16,f>>16],u_pixel_coord_lower:[65535&h,65535&f]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},rn={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image),u_image_height:new t.Uniform1f(e,r.u_image_height)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function nn(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),h=[],f=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),y=g.getBucket(n);if(y){var v=m.posMatrix;0===a[0]&&0===a[1]||(v=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?y.textCollisionBox:y.iconCollisionBox,_=y.collisionCircleArray;if(_.length>0){var b=t.create(),w=v;t.mul(b,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(b,b,y.placementViewportMatrix),h.push({circleArray:_,circleOffset:p,transform:w,invTransform:b}),p=f+=_.length/4}x&&u.draw(l,c.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Lt.disabled,Or(v,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&h.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*f),k._trim();for(var A=0,M=0,S=h;M<S.length;M+=1)for(var E=S[M],C=0;C<E.circleArray.length/4;C++){var L=4*C,I=E.circleArray[L+0],P=E.circleArray[L+1],z=E.circleArray[L+2],O=E.circleArray[L+3];k.emplace(A++,I,P,z,O,0),k.emplace(A++,I,P,z,O,1),k.emplace(A++,I,P,z,O,2),k.emplace(A++,I,P,z,O,3)}(!$r||$r.length<2*f)&&($r=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(f));for(var D=l.createIndexBuffer($r,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=h;F<B.length;F+=1){var N=B[F],j=Dr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Lt.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var an=t.identity(new Float32Array(16));function on(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function sn(e,r,n,i,a,o,s,l,c,u,h){var f=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<f.length;g++){var y=f.get(g),v=e.allowVerticalPlacement&&!y.placedOrientation,x=y.hidden||!y.crossTileID||v?null:i[y.crossTileID];if(x){var _=new t.Point(y.anchorX,y.anchorY),b=re(_,n?l:s),w=ne(o.cameraToCenterDistance,b.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,y)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=on(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?re(_.add(M),s).point:b.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&y.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,C=0;C<y.numGlyphs;C++)t.addDynamicAttributes(p,S,E);h&&y.associatedIconIndex>=0&&(m[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else fe(y.numGlyphs,p)}if(h){d.clear();for(var L=e.icon.placedSymbolArray,I=0;I<L.length;I++){var P=L.get(I);if(P.hidden)fe(P.numGlyphs,d);else{var z=m[I];if(z)for(var O=0;O<P.numGlyphs;O++)t.addDynamicAttributes(d,z.shiftedAnchor,z.angle);else fe(P.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function ln(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function cn(e,r,n,i,a,o,s,l,c,u,h,f){for(var p=e.context,d=p.gl,m=e.transform,g="map"===l,y="map"===c,v=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!y&&!v,_=void 0!==n.layout.get("symbol-sort-key").constantOr(1),b=!1,w=e.depthModeForSublayer(0,Mt.ReadOnly),T=n.layout.get("text-variable-anchor"),k=[],A=0,M=i;A<M.length;A+=1){var S=M[A],E=r.getTile(S),C=E.getBucket(n);if(C){var L=a?C.text:C.icon;if(L&&L.segments.get().length){var I=L.programConfigurations.get(n.id),P=a||C.sdfIcons,z=a?C.textSizeData:C.iconSizeData,O=y||0!==m.pitch,D=e.useProgram(ln(P,a,C),I),R=t.evaluateSizeForZoom(z,m.zoom),F=void 0,B=[0,0],N=void 0,j=void 0,U=null,V=void 0;if(a){if(N=E.glyphAtlasTexture,j=d.LINEAR,F=E.glyphAtlasTexture.size,C.iconsInText){B=E.imageAtlasTexture.size,U=E.imageAtlasTexture;var q="composite"===z.kind||"camera"===z.kind;V=O||e.options.rotating||e.options.zooming||q?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get("icon-size").constantOr(0)||C.iconsNeedLinear;N=E.imageAtlasTexture,j=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,F=E.imageAtlasTexture.size}var G=ge(E,1,e.transform.zoom),Z=te(S.posMatrix,y,g,e.transform,G),W=ee(S.posMatrix,y,g,e.transform,G),Y=T&&C.hasTextData(),X="none"!==n.layout.get("icon-text-fit")&&Y&&C.hasIconData();v&&ae(C,S.posMatrix,e,a,Z,W,y,u);var $=e.translatePosMatrix(S.posMatrix,E,o,s),J=v||a&&T||X?an:Z,K=e.translatePosMatrix(W,E,o,s,!0),Q=P&&0!==n.paint.get(a?"text-halo-width":"icon-halo-width").constantOr(1),tt={program:D,buffers:L,uniformValues:P?C.iconsInText?Qr(z.kind,R,x,y,e,$,J,K,F,B):Kr(z.kind,R,x,y,e,$,J,K,a,F,!0):Jr(z.kind,R,x,y,e,$,J,K,a,F),atlasTexture:N,atlasTextureIcon:U,atlasInterpolation:j,atlasInterpolationIcon:V,isSDF:P,hasHalo:Q};if(_&&C.canOverlap){b=!0;for(var et=0,rt=L.segments.get();et<rt.length;et+=1){var nt=rt[et];k.push({segments:new t.SegmentVector([nt]),sortKey:nt.sortKey,state:tt})}}else k.push({segments:L.segments,sortKey:0,state:tt})}}}b&&k.sort((function(t,e){return t.sortKey-e.sortKey}));for(var it=0,at=k;it<at.length;it+=1){var ot=at[it],st=ot.state;if(p.activeTexture.set(d.TEXTURE0),st.atlasTexture.bind(st.atlasInterpolation,d.CLAMP_TO_EDGE),st.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),st.atlasTextureIcon&&st.atlasTextureIcon.bind(st.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),st.isSDF){var lt=st.uniformValues;st.hasHalo&&(lt.u_is_halo=1,un(st.buffers,ot.segments,n,e,st.program,w,h,f,lt)),lt.u_is_halo=0}un(st.buffers,ot.segments,n,e,st.program,w,h,f,st.uniformValues)}}function un(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Lt.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function hn(t,e,r,n,i,a,o){var s,l,c,u,h,f=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=f.LINES):(l=d?"fillPattern":"fill",s=f.TRIANGLES);for(var g=0,y=n;g<y.length;g+=1){var v=y[g],x=e.getTile(v);if(!d||x.patternsLoaded()){var _=x.getBucket(r);if(_){var b=_.programConfigurations.get(r.id),w=t.useProgram(l,b);d&&(t.context.activeTexture.set(f.TEXTURE0),x.imageAtlasTexture.bind(f.LINEAR,f.CLAMP_TO_EDGE),b.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&b.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(v.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=_.indexBuffer2,h=_.segments2;var E=[f.drawingBufferWidth,f.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Pr(S,t,m,x,E):Ir(S,E)}else u=_.indexBuffer,h=_.segments,c=d?Lr(S,t,m,x):Cr(S);w.draw(t.context,s,i,t.stencilModeForClipping(v),a,Lt.disabled,c,r.id,_.layoutVertexBuffer,u,h,r.paint,t.transform.zoom,b)}}}}function fn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),h=r.getCrossfadeParameters(),f=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),y=g.getBucket(r);if(y){var v=y.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",v);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),v.updatePaintBuffers(h));var _=c.constantOr(null);if(_&&g.imageAtlas){var b=g.imageAtlas,w=b.patternPositions[_.to.toString()],T=b.patternPositions[_.from.toString()];w&&T&&v.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?Er(k,t,A,f,m,h,g):Sr(k,t,A,f);x.draw(s,s.gl.TRIANGLES,i,a,o,Lt.backCCW,M,r.id,y.layoutVertexBuffer,y.indexBuffer,y.segments,r.paint,t.transform.zoom,v)}}}function pn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=jr(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Lt.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function dn(e,r,n,i,a,o){var s=e.context,l=s.gl,c=r.dem;if(c&&c.data){var u=c.dim,h=c.stride,f=c.getPixels();if(s.activeTexture.set(l.TEXTURE1),s.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var p=r.demTexture;p.update(f,{premultiply:!1}),p.bind(l.NEAREST,l.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(s,f,l.RGBA,{premultiply:!1}),r.demTexture.bind(l.NEAREST,l.CLAMP_TO_EDGE);s.activeTexture.set(l.TEXTURE0);var d=r.fbo;if(!d){var m=new t.Texture(s,{width:u,height:u,data:null},l.RGBA);m.bind(l.LINEAR,l.CLAMP_TO_EDGE),(d=r.fbo=s.createFramebuffer(u,u,!0)).colorAttachment.set(m.texture)}s.bindFramebuffer.set(d.framebuffer),s.viewport.set([0,0,u,u]),e.useProgram("hillshadePrepare").draw(s,l.TRIANGLES,i,a,o,Lt.disabled,Ur(r.tileID,c),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function mn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),h=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),f=!r||Math.abs(r.tileID.overscaledZ-h)>Math.abs(e.tileID.overscaledZ-h),p=f&&e.refreshedUponExpiration?1:t.clamp(f?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var gn=new t.Color(1,0,0,1),yn=new t.Color(0,1,0,1),vn=new t.Color(0,0,1,1),xn=new t.Color(1,0,1,1),_n=new t.Color(0,1,1,1);function bn(t){var e=t.transform.padding;wn(t,t.transform.height-(e.top||0),3,gn),wn(t,e.bottom||0,3,yn),Tn(t,e.left||0,3,vn),Tn(t,t.transform.width-(e.right||0),3,xn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;kn(t,e-a/2,r-i/2,a,i,n),kn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,_n)}function wn(t,e,r,n){kn(t,0,e+r/2,t.transform.width,r,n)}function Tn(t,e,r,n){kn(t,e-r/2,0,r,t.transform.height,n)}function kn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function An(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=Mt.disabled,c=Et.disabled,u=e.colorModeForRenderPass(),h="$debug";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Lt.disabled,Rr(o,t.Color.red),h,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,p=f&&f.byteLength||0,d=Math.floor(p/1024),m=r.getTile(n).tileSize,g=512/Math.min(m,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+" "+d+"kb"),s.draw(i,a.TRIANGLES,l,c,Ct.alphaBlended,Lt.disabled,Rr(o,t.Color.transparent,g),h,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var Mn={symbol:function(e,r,n,i,a){if("translucent"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get("text-variable-anchor")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c="map"===a,u="map"===o,h=0,f=e;h<f.length;h+=1){var p=f[h],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,y=t.evaluateSizeForZoom(g,l.zoom),v=ge(d,1,r.transform.zoom),x=te(p.posMatrix,u,c,r.transform,v),_="none"!==n.layout.get("icon-text-fit")&&m.hasIconData();if(y){var b=Math.pow(2,l.zoom-d.tileID.overscaledZ);sn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,b,y,_)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&cn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&cn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(nn(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),nn(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,h=e.depthModeForSublayer(0,Mt.ReadOnly),f=Et.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],y=r.getTile(g),v=y.getBucket(n);if(v){var x=v.programConfigurations.get(n.id),_={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:v.layoutVertexBuffer,indexBuffer:v.indexBuffer,uniformValues:zr(e,g,y,n)};if(l)for(var b=0,w=v.segments.get();b<w.length;b+=1){var T=w[b];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:_})}else d.push({segments:v.segments,sortKey:0,state:_})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,C=S.program,L=S.layoutVertexBuffer,I=S.indexBuffer,P=S.uniformValues,z=M.segments;C.draw(c,u.TRIANGLES,h,f,p,Lt.disabled,P,n.id,L,I,z,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Et.disabled,l=new Ct([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);(function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var h=r.getTile(u),f=h.getBucket(n);if(f){var p=f.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,Mt.disabled,s,l,Lt.disabled,Br(u.posMatrix,h,m,n.paint.get("heatmap-intensity")),n.id,f.layoutVertexBuffer,f.indexBuffer,f.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Lt.disabled,Nr(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1))for(var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),h=u.constantOr(1),f=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=h?"linePattern":c?"lineSDF":f?"lineGradient":"line",m=e.context,g=m.gl,y=!0,v=0,x=i;v<x.length;v+=1){var _=x[v],b=r.getTile(_);if(!h||b.patternsLoaded()){var w=b.getBucket(n);if(w){var T=w.programConfigurations.get(n.id),k=e.context.program.get(),A=e.useProgram(d,T),M=y||A.program!==k,S=u.constantOr(null);if(S&&b.imageAtlas){var E=b.imageAtlas,C=E.patternPositions[S.to.toString()],L=E.patternPositions[S.from.toString()];C&&L&&T.setConstantPatternPositions(C,L)}var I=h?Hr(e,b,n,p):c?Gr(e,b,n,c,p):f?qr(e,b,n,w.lineClipsArray.length):Vr(e,b,n);if(h)m.activeTexture.set(g.TEXTURE0),b.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),T.updatePaintBuffers(p);else if(c&&(M||e.lineAtlas.dirty))m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m);else if(f){var P=w.gradients[n.id],z=P.texture;if(n.gradientVersion!==P.version){var O=256;if(n.stepInterpolant){var D=r.getSource().maxzoom,R=_.canonical.z===D?Math.ceil(1<<e.transform.maxZoom-_.canonical.z):1,F=w.maxLineLength/t.EXTENT*1024*R;O=t.clamp(t.nextPowerOfTwo(F),256,m.maxTextureSize)}P.gradient=t.renderColorRamp({expression:n.gradientExpression(),evaluationKey:"lineProgress",resolution:O,image:P.gradient||void 0,clips:w.lineClipsArray}),P.texture?P.texture.update(P.gradient):P.texture=new t.Texture(m,P.gradient,g.RGBA),P.version=n.gradientVersion,z=P.texture}m.activeTexture.set(g.TEXTURE0),z.bind(n.stepInterpolant?g.NEAREST:g.LINEAR,g.CLAMP_TO_EDGE)}A.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Lt.disabled,I,n.id,w.layoutVertexBuffer,w.indexBuffer,w.segments,n.paint,e.transform.zoom,T,w.layoutVertexBuffer2),y=!1}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);hn(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var h=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,Mt.ReadOnly);hn(e,r,n,i,h,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))fn(t,e,r,n,a,Et.disabled,Ct.disabled),fn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();fn(t,e,r,n,a,Et.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=t.depthModeForSublayer(0,Mt.ReadOnly),o=t.colorModeForRenderPass(),s="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],l=s[0],c=0,u=s[1];c<u.length;c+=1){var h=u[c],f=e.getTile(h);f.needsHillshadePrepare&&"offscreen"===t.renderPass?dn(t,f,r,a,Et.disabled,o):"translucent"===t.renderPass&&pn(t,f,r,a,l[h.overscaledZ],o)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof z?[{},n]:t.stencilConfigForOverlap(n),u=c[0],h=c[1],f=h[h.length-1].overscaledZ,p=!t.options.moving,d=0,m=h;d<m.length;d+=1){var g=m[d],y=t.depthModeForSublayer(g.overscaledZ-f,1===r.paint.get("raster-opacity")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),v=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);v.registerFadeDuration(r.paint.get("raster-fade-duration"));var _=e.findLoadedParent(g,0),b=mn(v,_,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),v.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),_?(_.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,_.tileID.overscaledZ-v.tileID.overscaledZ),T=[v.tileID.canonical.x*w%1,v.tileID.canonical.y*w%1]):v.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Yr(x,T||[0,0],w||1,b,r);o instanceof z?s.draw(i,a.TRIANGLES,y,Et.disabled,l,Lt.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,y,u[g.overscaledZ],l,Lt.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var h=Et.disabled,f=t.depthModeForSublayer(0,"opaque"===u?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),y=0,v=m;y<v.length;y+=1){var x=v[y],_=t.transform.calculatePosMatrix(x.toUnwrapped()),b=c?en(_,i,t,c,{tileID:x,tileSize:l},g):tn(_,i,n);d.draw(a,o.TRIANGLES,f,h,p,Lt.disabled,b,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)An(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Et.disabled);var o="3d"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},Sn=function(t,e){this.context=new It(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Pt.maxUnderzooming+Pt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ve,this.gpuTimers={}};Sn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},Sn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ye.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ye.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ye.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Et({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},Sn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,Ct.disabled,Lt.disabled,Fr(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},Sn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Ct.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new Et({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),Ct.disabled,Lt.disabled,Fr(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},Sn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Et({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Et.disabled,e),n]},Sn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){var r=1/8;return new Ct([e.CONSTANT_COLOR,e.ONE],new t.Color(r,r,r,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?Ct.unblended:Ct.alphaBlended},Sn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},Sn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},Sn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},h={},f={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),h[p]=u[p].slice().reverse(),f[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass="offscreen";for(var y=0,v=i;y<v.length;y+=1){var x=v[y],_=this.style._layers[x];if(_.hasOffscreenPass()&&!_.isHidden(this.transform.zoom)){var b=h[_.source];("custom"===_.type||b.length)&&this.renderLayer(this,a[_.source],_,b)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?f:h)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&Mn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&bn(this),this.context.setDefault()},Sn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),Mn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},Sn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},Sn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},Sn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},Sn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},Sn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:ge(r,n[0],this.transform.zoom),a?n[1]:ge(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},Sn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},Sn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},Sn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Sn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new Ar(this.context,t,wr[t],e,rn[t],this._showOverdrawInspector)),this.cache[r]},Sn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Sn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Sn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Sn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var En=function(t,e){this.points=t,this.planes=e};En.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new En(a,o)};var Cn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Cn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Cn(n,i)},Cn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Cn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Cn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,h=0;h<e.points.length;h++){var f=e.points[h][l]-this.min[l];c=Math.min(c,f),u=Math.max(u,f)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var Ln=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};Ln.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Ln.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Ln.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Ln.prototype.clone=function(){return new Ln(this.top,this.bottom,this.left,this.right)},Ln.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var In=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Ln,this._posMatrixCache={},this._alignedPosMatrixCache={}},Pn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};In.prototype.clone=function(){var t=new In(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Pn.minZoom.get=function(){return this._minZoom},Pn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Pn.maxZoom.get=function(){return this._maxZoom},Pn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Pn.minPitch.get=function(){return this._minPitch},Pn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Pn.maxPitch.get=function(){return this._maxPitch},Pn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Pn.renderWorldCopies.get=function(){return this._renderWorldCopies},Pn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Pn.worldSize.get=function(){return this.tileSize*this.scale},Pn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Pn.size.get=function(){return new t.Point(this.width,this.height)},Pn.bearing.get=function(){return-this.angle/Math.PI*180},Pn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Pn.pitch.get=function(){return this._pitch/Math.PI*180},Pn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Pn.fov.get=function(){return this._fov/Math.PI*180},Pn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Pn.zoom.get=function(){return this._zoom},Pn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Pn.center.get=function(){return this._center},Pn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Pn.padding.get=function(){return this._edgeInsets.toJSON()},Pn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Pn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},In.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},In.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},In.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},In.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},In.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=En.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Cn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],h=[],f=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,y=m.y,v=m.fullyVisible;if(!v){var x=m.aabb.intersects(s);if(0===x)continue;v=2===x}var _=m.aabb.distanceX(o),b=m.aabb.distanceY(o),w=Math.max(Math.abs(_),Math.abs(b)),T=3+(1<<f-m.zoom)-2;if(m.zoom===f||w>T&&m.zoom>=l)h.push({tileID:new t.OverscaledTileID(m.zoom===f?p:m.zoom,m.wrap,m.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:v})}}return h.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},In.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Pn.unmodified.get=function(){return this._unmodified},In.prototype.zoomScale=function(t){return Math.pow(2,t)},In.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},In.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},In.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Pn.point.get=function(){return this.project(this.center)},In.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},In.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},In.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},In.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},In.prototype.coordinateLocation=function(t){return t.toLngLat()},In.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,h=n[2]/a,f=u===h?0:(0-u)/(h-u);return new t.MercatorCoordinate(t.number(o,s,f)/this.worldSize,t.number(l,c,f)/this.worldSize)},In.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},In.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},In.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},In.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},In.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},In.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},In.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var h=this.latRange;a=t.mercatorYfromLat(h[1])*this.worldSize,e=(o=t.mercatorYfromLat(h[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var f=this.lngRange;s=t.mercatorXfromLng(f[0])*this.worldSize,r=(l=t.mercatorXfromLng(f[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var y=p.x,v=c.x/2;y-v<s&&(n=s+v),y+v>l&&(n=l-v)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},In.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,h=new Float64Array(16);t.perspective(h,this._fov,this.width/this.height,u,c),h[8]=2*-r.x/this.width,h[9]=2*r.y/this.height,t.scale(h,h,[1,-1,1]),t.translate(h,h,[0,0,-this.cameraToCenterDistance]),t.rotateX(h,h,this._pitch),t.rotateZ(h,h,this.angle),t.translate(h,h,[-s,-l,0]),this.mercatorMatrix=t.scale([],h,[this.worldSize,this.worldSize,this.worldSize]),t.scale(h,h,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=h,this.invProjMatrix=t.invert([],this.projMatrix);var f=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*f+m*p,y=l-Math.round(l)+d*p+m*f,v=new Float64Array(h);if(t.translate(v,v,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=v,h=t.create(),t.scale(h,h,[this.width/2,-this.height/2,1]),t.translate(h,h,[1,-1,0]),this.labelPlaneMatrix=h,h=t.create(),t.scale(h,h,[1,-1,1]),t.translate(h,h,[-1,-1,0]),t.scale(h,h,[2/this.width,2/this.height,1]),this.glCoordMatrix=h,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(h=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=h,this._posMatrixCache={},this._alignedPosMatrixCache={}}},In.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},In.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},In.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(In.prototype,Pn);var zn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};zn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},zn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},zn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var h=this._hashName,f=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===h?(f=!0,e+"="+u):t})).filter((function(t){return t}));return f||p.push(h+"="+u),"#"+p.join("&")}return"#"+u},zn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},zn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},zn.prototype._updateHashUnthrottled=function(){var e=t.window.location.href.replace(/(#.+)?$/,this.getHashString());try{t.window.history.replaceState(t.window.history.state,null,e)}catch(t){}};var On={linearity:.3,easing:t.bezier(0,0,.3,1)},Dn=t.extend({deceleration:2500,maxSpeed:1400},On),Rn=t.extend({deceleration:20,maxSpeed:1400},On),Fn=t.extend({deceleration:1e3,maxSpeed:360},On),Bn=t.extend({deceleration:1e3,maxSpeed:90},On),Nn=function(t){this._map=t,this.clear()};function jn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Un(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Nn.prototype.clear=function(){this._inertiaBuffer=[]},Nn.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Nn.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Nn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Un(r.pan.mag(),o,t.extend({},Dn,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,jn(s,l)}if(r.zoom){var c=Un(r.zoom,o,Rn);s.zoom=this._map.transform.zoom+c.amount,jn(s,c)}if(r.bearing){var u=Un(r.bearing,o,Fn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),jn(s,u)}if(r.pitch){var h=Un(r.pitch,o,Bn);s.pitch=this._map.transform.pitch+h.amount,jn(s,h)}if(s.zoom||s.bearing){var f=void 0===r.pinchAround?r.around:r.pinchAround;s.around=f?this._map.unproject(f):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Vn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),qn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Hn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Gn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Gn.prototype.reset=function(){delete this._mousedownPos},Gn.prototype.wheel=function(t){return this._firePreventable(new Hn(t.type,this._map,t))},Gn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseup=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.dblclick=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseover=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.mouseout=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.touchstart=function(t){return this._firePreventable(new qn(t.type,this._map,t))},Gn.prototype.touchmove=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchend=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchcancel=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Zn=function(t){this._map=t};Zn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Zn.prototype.mousemove=function(t){this._map.fire(new Vn(t.type,this._map,t))},Zn.prototype.mousedown=function(){this._delayContextMenu=!0},Zn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Vn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Zn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Vn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},Zn.prototype.isEnabled=function(){return!0},Zn.prototype.isActive=function(){return!1},Zn.prototype.enable=function(){},Zn.prototype.disable=function(){};var Wn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Yn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Wn.prototype.isEnabled=function(){return!!this._enabled},Wn.prototype.isActive=function(){return!!this._active},Wn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Wn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Wn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Wn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Wn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Wn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Wn.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Wn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Xn=function(t){this.reset(),this.numTouches=t.numTouches};Xn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Xn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Yn(n,r)))},Xn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Yn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Xn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var $n=function(t){this.singleTap=new Xn(t),this.numTaps=t.numTaps,this.reset()};$n.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},$n.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},$n.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},$n.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new $n({numTouches:1,numTaps:2}),this._zoomOut=new $n({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn={};Kn[0]=1,Kn[2]=2;var Qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Qn.prototype._correctButton=function(t,e){return!1},Qn.prototype._move=function(t,e){return{}},Qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r)if(t.preventDefault(),function(t,e){var r=Kn[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton))this.reset();else if(this._moved||!(e.dist(r)<this._clickTolerance))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Qn.prototype.mouseupWindow=function(t){this._lastPoint&&r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Qn.prototype.enable=function(){this._enabled=!0},Qn.prototype.disable=function(){this._enabled=!1,this.reset()},Qn.prototype.isEnabled=function(){return this._enabled},Qn.prototype.isActive=function(){return this._active};var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Qn),ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ni=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};ni.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},ni.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},ni.prototype.touchmove=function(t,e,r){if(this._active&&!(r.length<this._minTouches))return t.preventDefault(),this._calculateTransform(t,e,r)},ni.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},ni.prototype.touchcancel=function(){this.reset()},ni.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Yn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var h=o.div(s);if(this._sum._add(h),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:h}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ii=function(){this.reset()};function ai(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}ii.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},ii.prototype._start=function(t){},ii.prototype._move=function(t,e,r){return{}},ii.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},ii.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=ai(r,e,i),s=ai(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},ii.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=ai(n,e,a),l=ai(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},ii.prototype.touchcancel=function(){this.reset()},ii.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},ii.prototype.disable=function(){this._enabled=!1,this.reset()},ii.prototype.isEnabled=function(){return this._enabled},ii.prototype.isActive=function(){return this._active};function oi(t,e){return Math.log(t/e)/Math.LN2}var si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(oi(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:oi(this._distance,r),pinchAround:e}},e}(ii);function li(t,e){return 180*t.angleWith(e)/Math.PI}var ci=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:li(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=li(t,this._startVector);return Math.abs(r)<e},e}(ii);function ui(t){return Math.abs(t.y)>Math.abs(t.x)}var hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ui(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ui(t)&&ui(e)&&a}},e}(ii),fi={panStep:100,bearingStep:15,pitchStep:10},pi=function(){var t=fi;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep,this._rotationDisabled=!1};function di(t){return t*(2-t)}pi.prototype.reset=function(){this._active=!1},pi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return this._rotationDisabled&&(n=0,i=0),{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:di,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active},pi.prototype.disableRotation=function(){this._rotationDisabled=!0},pi.prototype.enableRotation=function(){this._rotationDisabled=!1};var mi=4.000244140625,gi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll(["_onTimeout"],this)};gi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gi.prototype.isEnabled=function(){return!!this._enabled},gi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},gi.prototype.isZooming=function(){return!!this._zooming},gi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},gi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%mi==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},gi.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},gi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},gi.prototype.renderFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>mi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var h=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),f=c(h);o=t.number(l,s,f),h<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},gi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},gi.prototype.reset=function(){this._active=!1};var yi=function(t,e){this._clickZoom=t,this._tapZoom=e};yi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},yi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},yi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},yi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var vi=function(){this.reset()};vi.prototype.reset=function(){this._active=!1},vi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},vi.prototype.enable=function(){this._enabled=!0},vi.prototype.disable=function(){this._enabled=!1,this.reset()},vi.prototype.isEnabled=function(){return this._enabled},vi.prototype.isActive=function(){return this._active};var xi=function(){this._tap=new $n({numTouches:1,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},xi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},xi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},xi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var _i=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};_i.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},_i.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},_i.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},_i.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var bi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};bi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},bi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},bi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},bi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var wi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};wi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},wi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},wi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},wi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},wi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},wi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Ti=function(t){return t.zoom||t.drag||t.pitch||t.rotate},ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ai(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var Mi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Nn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!0}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};Mi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},Mi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Gn(e,t));var n=e.boxZoom=new Wn(e,t);this._add("boxZoom",n);var i=new Jn,a=new vi;e.doubleClickZoom=new yi(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new xi;this._add("tapDragZoom",o);var s=e.touchPitch=new hi;this._add("touchPitch",s);var l=new ei(t),c=new ri(t);e.dragRotate=new bi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new ti(t),h=new ni(t);e.dragPan=new _i(r,u,h),this._add("mousePan",u),this._add("touchPan",h,["touchZoom","touchRotate"]);var f=new ci,p=new si;e.touchZoomRotate=new wi(r,p,f,o),this._add("touchRotate",f,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new gi(e,this);this._add("scrollZoom",d,["mousePan"]);var m=e.keyboard=new pi;this._add("keyboard",m),this._add("blockableMapEvent",new Zn(e));for(var g=0,y=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<y.length;g+=1){var v=y[g];t.interactive&&t[v]&&e[v].enable(t[v])}},Mi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},Mi.prototype.stop=function(t){if(!this._updatingCamera){for(var e=0,r=this._handlers;e<r.length;e+=1)r[e].handler.reset();this._inertia.clear(),this._fireEvents({},{},t),this._changes=[]}},Mi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},Mi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Mi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Mi.prototype.isMoving=function(){return Boolean(Ti(this._eventsInProgress))||this.isZooming()},Mi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},Mi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},Mi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},Mi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var h=u[c],f=h.handlerName,p=h.handler,d=h.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,f)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,f,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[f]=p)}}var g={};for(var y in this._previousActiveHandlers)o[y]||(g[y]=n);this._previousActiveHandlers=o,(Object.keys(g).length||Ai(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||Ai(i))&&this._map._stop(!0),this._updatingCamera=!1;var v=i.cameraAnimation;v&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],v(this._map))}else this.stop(!0)},Mi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},Mi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},Mi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!Ai(t))return this._fireEvents(e,r,!0);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var h=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(h,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r,!0)},Mi.prototype._fireEvents=function(e,r,n){var i=this,a=Ti(this._eventsInProgress),o=Ti(e),s={};for(var l in e){var c=e[l].originalEvent;this._eventsInProgress[l]||(s[l+"start"]=c),this._eventsInProgress[l]=e[l]}for(var u in!a&&o&&this._fireEvent("movestart",o.originalEvent),s)this._fireEvent(u,s[u]);for(var h in o&&this._fireEvent("move",o.originalEvent),e){var f=e[h].originalEvent;this._fireEvent(h,f)}var p,d={};for(var m in this._eventsInProgress){var g=this._eventsInProgress[m],y=g.handlerName,v=g.originalEvent;this._handlersById[y].isActive()||(delete this._eventsInProgress[m],p=r[y]||v,d[m+"end"]=p)}for(var x in d)this._fireEvent(x,d[x]);var _=Ti(this._eventsInProgress);if(n&&(a||o)&&!_){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),w=function(t){return 0!==t&&-i._bearingSnap<t&&t<i._bearingSnap};b?(w(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:p})):(this._map.fire(new t.Event("moveend",{originalEvent:p})),w(this._map.getBearing())&&this._map.resetNorth()),this._updatingCamera=!1}},Mi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Mi.prototype._requestFrame=function(){var t=this;return this._map.triggerRepaint(),this._map._renderTaskQueue.add((function(e){delete t._frameId,t.handleEvent(new ki("renderFrame",{timeStamp:e})),t._applyChanges()}))},Mi.prototype._triggerRenderFrame=function(){void 0===this._frameId&&(this._frameId=this._requestFrame())};var Si=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){e=t.LngLatBounds.convert(e);var n=r&&r.bearing||0;return this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),n,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),h=c.rotate(-n*Math.PI/180),f=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(h.x,f.x),Math.max(h.y,f.y)),d=new t.Point(Math.min(h.x,f.x),Math.min(h.y,f.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,y=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(y<0||g<0)){var v=Math.min(s.scaleZoom(s.scale*Math.min(g,y)),i.maxZoom),x="number"==typeof i.offset.x?new t.Point(i.offset.x,i.offset.y):t.Point.convert(i.offset),_=(i.padding.left-i.padding.right)/2,b=(i.padding.top-i.padding.bottom)/2,w=new t.Point(_,b).rotate(n*Math.PI/180),T=x.add(w).mult(s.scale/s.zoomScale(v));return{center:s.unproject(c.add(u).div(2).sub(T)),zoom:v,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,h="pitch"in e?+e.pitch:s,f="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var y,v,x=i.project(m),_=i.project(g).sub(x),b=i.zoomScale(c-a);e.around&&(y=t.LngLat.convert(e.around),v=i.locationPoint(y));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||h!==s,this._padding=!i.isPaddingEqual(f),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,h,e)),n._padding&&(i.interpolatePadding(l,f,e),d=i.centerPoint.add(p)),y)i.setLocationAtPoint(y,v);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,b):Math.max(.5,b),w=Math.pow(g,1-e),T=i.unproject(x.add(_.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,h="bearing"in e?this._normalizeBearing(e.bearing,s):s,f="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),y=a.pointLocation(g),v=t.LngLat.convert(e.center||y);this._normalizeCenter(v);var x=a.project(y),_=a.project(v).sub(x),b=e.curve,w=Math.max(a.width,a.height),T=w/d,k=_.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);b=Math.sqrt(M/k*2)}var S=b*b;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function C(t){return(Math.exp(t)-Math.exp(-t))/2}function L(t){return(Math.exp(t)+Math.exp(-t))/2}var I=E(0),P=function(t){return L(I)/L(I+b*t)},z=function(t){return w*((L(I)*(C(e=I+b*t)/L(e))-C(I))/S)/k;var e},O=(E(1)-I)/b;if(Math.abs(k)<1e-6||!isFinite(O)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;O=Math.abs(Math.log(T/w))/b,z=function(){return 0},P=function(t){return Math.exp(D*b*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/b:+e.speed;e.duration=1e3*O/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==h,this._pitching=f!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*O,d=1/P(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,h,e)),n._pitching&&(a.pitch=t.number(l,f,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var y=1===e?v:a.unproject(x.add(_.mult(z(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop(!1)}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),Ei=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_toggleAttribution","_updateEditLink","_updateData","_updateCompact"],this)};Ei.prototype.getDefaultPosition=function(){return"bottom-right"},Ei.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._compactButton=r.create("button","mapboxgl-ctrl-attrib-button",this._container),this._compactButton.addEventListener("click",this._toggleAttribution),this._setElementTitle(this._compactButton,"ToggleAttribution"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),this._innerContainer.setAttribute("role","list"),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ei.prototype._setElementTitle=function(t,e){var r=this._map._getUIString("AttributionControl."+e);t.title=r,t.setAttribute("aria-label",r)},Ei.prototype._toggleAttribution=function(){this._container.classList.contains("mapboxgl-compact-show")?(this._container.classList.remove("mapboxgl-compact-show"),this._compactButton.setAttribute("aria-pressed","false")):(this._container.classList.add("mapboxgl-compact-show"),this._compactButton.setAttribute("aria-pressed","true"))},Ei.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow",this._setElementTitle(e,"MapFeedback")}},Ei.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"visibility"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},Ei.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},Ei.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact","mapboxgl-compact-show")};var Ci=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};Ci.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},Ci.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},Ci.prototype.getDefaultPosition=function(){return"bottom-left"},Ci.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},Ci.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Ci.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var Li=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Li.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Li.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},Li.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},Li.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ii={"AttributionControl.ToggleAttribution":"Toggle attribution","AttributionControl.MapFeedback":"Map feedback","FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},Pi=t.window.HTMLImageElement,zi=t.window.HTMLElement,Oi=t.window.ImageBitmap,Di=60,Ri={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:Di,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Fi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Ri,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>Di)throw new Error("maxPitch must be less than or equal to 60");var i=new In(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Li,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ii,e.locale),this._clickTolerance=e.clickTolerance,this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof zi))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_onMapScroll","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1),t.window.addEventListener("orientationchange",this._onWindowResize,!1)),this.handlers=new Mi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new zn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ei({customAttribution:e.customAttribution})),this.addControl(new Ci,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&(r=e.getDefaultPosition?e.getDefaultPosition():"top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.hasControl=function(t){return this._controls.indexOf(t)>-1},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?Di:t)>Di)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Vn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Vn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Vn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new We(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new We(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof Pi||Oi&&r instanceof Oi){var c=t.browser.getImageData(r),u=c.width,h=c.height,f=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:h},f),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof Pi||Oi&&r instanceof Oi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof Pi||Oi&&r instanceof Oi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map"),this._canvas.setAttribute("role","region");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)})),this._container.addEventListener("scroll",this._onMapScroll,!1)},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new Sn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype._onMapScroll=function(t){if(t.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var h=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:h,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var f=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(f);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}var p=this._sourcesDirty||this._styleDirty||this._placementDirty;return p||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new t.Event("idle")),!this._loaded||this._fullyLoaded||p||(this._fullyLoaded=!0),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("orientationchange",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext&&n.loseContext(),Bi(this._canvasContainer),Bi(this._controlContainer),Bi(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(Si);function Bi(t){t.parentNode&&t.parentNode.removeChild(t)}var Ni={showCompass:!0,showZoom:!0,visualizePitch:!1},ji=function(e){var n=this;this.options=t.extend({},Ni,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};ji.prototype._updateZoomButtons=function(){var t=this._map.getZoom(),e=t===this._map.getMaxZoom(),r=t===this._map.getMinZoom();this._zoomInButton.disabled=e,this._zoomOutButton.disabled=r,this._zoomInButton.setAttribute("aria-disabled",e.toString()),this._zoomOutButton.setAttribute("aria-disabled",r.toString())},ji.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},ji.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ui(this._map,this._compass,this.options.visualizePitch)),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},ji.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},ji.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ui=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new ei({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new ri({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Vi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ui.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ui.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ui.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ui.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ui.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ui.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ui.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ui.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ui.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ui.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ui.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var qi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Hi(t,e,r){var n=t.classList;for(var i in qi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Gi,Zi=function(e){function n(n,i){if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._scale=n&&n.scale||1,this._draggable=n&&n.draggable||!1,this._clickTolerance=n&&n.clickTolerance||0,this._isDragging=!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var a=r.createNS("http://www.w3.org/2000/svg","svg");a.setAttributeNS(null,"display","block"),a.setAttributeNS(null,"height","41px"),a.setAttributeNS(null,"width","27px"),a.setAttributeNS(null,"viewBox","0 0 27 41");var o=r.createNS("http://www.w3.org/2000/svg","g");o.setAttributeNS(null,"stroke","none"),o.setAttributeNS(null,"stroke-width","1"),o.setAttributeNS(null,"fill","none"),o.setAttributeNS(null,"fill-rule","evenodd");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"fill-rule","nonzero");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"transform","translate(3.0, 29.0)"),l.setAttributeNS(null,"fill","#000000");for(var c=0,u=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];c<u.length;c+=1){var h=u[c],f=r.createNS("http://www.w3.org/2000/svg","ellipse");f.setAttributeNS(null,"opacity","0.04"),f.setAttributeNS(null,"cx","10.5"),f.setAttributeNS(null,"cy","5.80029008"),f.setAttributeNS(null,"rx",h.rx),f.setAttributeNS(null,"ry",h.ry),l.appendChild(f)}var 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_=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),v.appendChild(x),v.appendChild(_),s.appendChild(l),s.appendChild(p),s.appendChild(m),s.appendChild(y),s.appendChild(v),a.appendChild(s),a.setAttributeNS(null,"height",41*this._scale+"px"),a.setAttributeNS(null,"width",27*this._scale+"px"),this._element.appendChild(a),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),Hi(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=13.5,r=Math.sqrt(Math.pow(e,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[r,-1*(24.6+r)],"bottom-right":[-r,-1*(24.6+r)],left:[e,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,qi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){if(!this._isDragging){var r=this._clickTolerance||this._map._clickTolerance;this._isDragging=e.point.dist(this._pointerdownPos)>=r}this._isDragging&&(this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag")))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Wi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Yi=0,Xi=!1,$i=function(e){function n(r){e.call(this),this.options=t.extend({},Wi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Gi?n(Gi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Gi="denied"!==t.state,n(Gi)})):(Gi=!!t.window.navigator.geolocation,n(Gi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Yi=0,Xi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Xi)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Zi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Zi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Yi--,Xi=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Yi>1?(e={maximumAge:6e5,timeout:0},Xi=!0):(e=this.options.positionOptions,Xi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ji={maxWidth:100,unit:"metric"},Ki=function(e){this.options=t.extend({},Ji,e),t.bindAll(["_onMove","setUnit"],this)};function Qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;l>5280?ta(e,n,l/5280,t._getUIString("ScaleControl.Miles")):ta(e,n,l,t._getUIString("ScaleControl.Feet"))}else r&&"nautical"===r.unit?ta(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles")):s>=1e3?ta(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):ta(e,n,s,t._getUIString("ScaleControl.Meters"))}function ta(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(""+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Ki.prototype.getDefaultPosition=function(){return"bottom-left"},Ki.prototype._onMove=function(){Qi(this._map,this._container,this.options)},Ki.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Ki.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Ki.prototype.setUnit=function(t){this.options.unit=t,Qi(this._map,this._container,this.options)};var ea=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};ea.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},ea.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},ea.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},ea.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},ea.prototype._isFullscreen=function(){return this._fullscreen},ea.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},ea.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var ra={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:"",maxWidth:"240px"},na=["a[href]","[tabindex]:not([tabindex='-1'])","[contenteditable]:not([contenteditable='false'])","button:not([disabled])","input:not([disabled])","select:not([disabled])","textarea:not([disabled])"].join(", "),ia=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(ra),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return 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this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=r.create("div","mapboxgl-popup-content",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.setOffset=function(t){return this.options.offset=t,this._update(),this},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close 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e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=aa(this.options.offset);if(!a){var s,l=this._container.offsetWidth,c=this._container.offsetHeight;s=i.y+o.bottom.y<c?["top"]:i.y>this._map.transform.height-c?["bottom"]:[],i.x<l/2?s.push("left"):i.x>this._map.transform.width-l/2&&s.push("right"),a=0===s.length?"bottom":s.join("-")}var u=i.add(o[a]).round();r.setTransform(this._container,qi[a]+" translate("+u.x+"px,"+u.y+"px)"),Hi(this._container,a,"popup")}},n.prototype._focusFirstElement=function(){if(this.options.focusAfterOpen&&this._container){var 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normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= 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miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n",frag:"\nprecision highp float;\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:"\nprecision highp float;\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n",frag:"\nprecision highp float;\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n",uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},g.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},g.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},g.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>g.precisionThreshold||e.scale[1]*e.viewport.height>g.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=g.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},g.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,u=this.gl;if(t.forEach((function(t,p){var y=e.passes[p];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=o(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),y||(e.passes[p]=y={id:p,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},g.defaults,t)),null!=t.thickness&&(y.thickness=parseFloat(t.thickness)),null!=t.opacity&&(y.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(y.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(y.overlay=!!t.overlay,p<g.maxLines&&(y.depth=2*(g.maxLines-1-p%g.maxLines)/g.maxLines-1)),null!=t.join&&(y.join=t.join),null!=t.hole&&(y.hole=t.hole),null!=t.fill&&(y.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(y.viewport=d(t.viewport)),y.viewport||(y.viewport=d([u.drawingBufferWidth,u.drawingBufferHeight])),null!=t.close&&(y.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var v,x;if(t.positions.x&&t.positions.y){var _=t.positions.x,b=t.positions.y;x=y.count=Math.max(_.length,b.length),v=new Float64Array(2*x);for(var w=0;w<x;w++)v[2*w]=_[w],v[2*w+1]=b[w]}else v=s(t.positions,"float64"),x=y.count=Math.floor(v.length/2);var T=y.bounds=i(v,2);if(y.fill){for(var k=[],A={},M=0,S=0,E=0,C=y.count;S<C;S++){var L=v[2*S],I=v[2*S+1];isNaN(L)||isNaN(I)||null==L||null==I?(L=v[2*M],I=v[2*M+1],A[S]=M):M=S,k[E++]=L,k[E++]=I}if(t.splitNull){y.count-1 in A||(A[y.count]=y.count-1);var P=Object.keys(A).map(Number).sort((function(t,e){return t-e})),z=[],O=0,D=null!=y.hole?y.hole[0]:null;if(null!=D){var R=m(P,(function(t){return t>=D}));(P=P.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*O,2*P[t]).concat(D?k.slice(2*D):[]),r=(y.hole||[]).map((function(e){return e-D+(P[t]-O)})),n=l(e,r);n=n.map((function(e){return e+O+(e+O<P[t]?0:D-P[t])})),z.push.apply(z,n),O=P[t]+1},B=0;B<P.length;B++)F(B);for(var N=0,j=z.length;N<j;N++)null!=A[z[N]]&&(z[N]=A[z[N]]);y.triangles=z}else{for(var U=l(k,y.hole||[]),V=0,q=U.length;V<q;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);y.triangles=U}}var H=new Float64Array(v);c(H,2,T);var G=new Float64Array(2*x+6);y.close?v[0]===v[2*x-2]&&v[1]===v[2*x-1]?(G[0]=H[2*x-4],G[1]=H[2*x-3]):(G[0]=H[2*x-2],G[1]=H[2*x-1]):(G[0]=H[0],G[1]=H[1]),G.set(H,2),y.close?v[0]===v[2*x-2]&&v[1]===v[2*x-1]?(G[2*x+2]=H[2],G[2*x+3]=H[3],y.count-=1):(G[2*x+2]=H[0],G[2*x+3]=H[1],G[2*x+4]=H[2],G[2*x+5]=H[3]):(G[2*x+2]=H[2*x-2],G[2*x+3]=H[2*x-1],G[2*x+4]=H[2*x-2],G[2*x+5]=H[2*x-1]);var Z=h(G);y.positionBuffer(Z);var W=f(G,Z);y.positionFractBuffer(W)}if(t.range?y.range=t.range:y.range||(y.range=y.bounds),(t.range||t.positions)&&y.count){var Y=y.bounds,X=Y[2]-Y[0],$=Y[3]-Y[1],J=y.range[2]-y.range[0],K=y.range[3]-y.range[1];y.scale=[X/J,$/K],y.translate=[-y.range[0]/J+Y[0]/J||0,-y.range[1]/K+Y[1]/K||0],y.scaleFract=f(y.scale),y.translateFract=f(y.translate)}if(t.dashes){var Q,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,Q=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];Q=new Uint8Array(tt*g.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*g.dashMult*.5;ot<st;++ot)Q[rt++]=nt;nt^=255}}y.dashLength=tt,y.dashTexture({channels:1,data:Q,width:Q.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=y.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ht=0;ht<lt;ht++){var ft=n(ct[ht],"uint8");ut.set(ft,4*ht)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);y.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[p]=null})),t.length<this.passes.length){for(var p=t.length;p<this.passes.length;p++){var y=this.passes[p];y&&(y.colorBuffer.destroy(),y.positionBuffer.destroy(),y.dashTexture.destroy())}this.passes.length=t.length}for(var v=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&v.push(this.passes[x]);return this.passes=v,this}},g.prototype.destroy=function(){return this.passes.forEach((function(t){t.colorBuffer.destroy(),t.positionBuffer.destroy(),t.dashTexture.destroy()})),this.passes.length=0,this}},62172:function(t,e,r){"use strict";function n(t,e){return function(t){if(Array.isArray(t))return t}(t)||function(t,e){var r=null==t?null:"undefined"!=typeof Symbol&&t[Symbol.iterator]||t["@@iterator"];if(null!=r){var n,i,a,o,s=[],l=!0,c=!1;try{if(a=(r=r.call(t)).next,0===e){if(Object(r)!==r)return;l=!1}else for(;!(l=(n=a.call(r)).done)&&(s.push(n.value),s.length!==e);l=!0);}catch(t){c=!0,i=t}finally{try{if(!l&&null!=r.return&&(o=r.return(),Object(o)!==o))return}finally{if(c)throw i}}return s}}(t,e)||i(t,e)||function(){throw new TypeError("Invalid attempt to destructure non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function i(t,e){if(t){if("string"==typeof t)return a(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return"Object"===r&&t.constructor&&(r=t.constructor.name),"Map"===r||"Set"===r?Array.from(t):"Arguments"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?a(t,e):void 0}}function a(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var o=r(162),s=r(78112),l=r(46762),c=r(27549),u=r(27976),h=r(76765),f=r(6807),p=r(79788),d=r(83473),m=r(39488),g=r(51498),y=r(5137),v=x;function x(t,e){var r=this;if(!(this instanceof x))return new x(t,e);"function"==typeof t?(e||(e={}),e.regl=t):(e=t,t=null),e&&e.length&&(e.positions=e);var n,i=(t=e.regl)._gl,a=[];this.tooManyColors=m,n=t.texture({data:new Uint8Array(1020),width:255,height:1,type:"uint8",format:"rgba",wrapS:"clamp",wrapT:"clamp",mag:"nearest",min:"nearest"}),u(this,{regl:t,gl:i,groups:[],markerCache:[null],markerTextures:[null],palette:a,paletteIds:{},paletteTexture:n,maxColors:255,maxSize:100,canvas:i.canvas}),this.update(e);var o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop("opacity"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context("pixelRatio"),scale:t.prop("scale"),scaleFract:t.prop("scaleFract"),translate:t.prop("translate"),translateFract:t.prop("translateFract"),markerTexture:t.prop("markerTexture"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=u({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\n// `invariant` effectively turns off optimizations for the position.\n// We need this because -fast-math on M1 Macs is re-ordering\n// floating point operations in a way that causes floating point\n// precision limits to put points in the wrong locations.\ninvariant gl_Position;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}\n"]),this.drawMarker=t(s);var l=u({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\n\n\tif (radius > 1.0 + delta) {\n\t\tdiscard;\n\t}\n\n\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\n\n\tfloat borderRadius = fragBorderRadius;\n\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\n\tvec4 color = mix(fragColor, fragBorderColor, ratio);\n\tcolor.a *= alpha * opacity;\n\tgl_FragColor = color;\n}\n"]),l.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\n// `invariant` effectively turns off optimizations for the position.\n// We need this because -fast-math on M1 Macs is re-ordering\n// floating point operations in a way that causes floating point\n// precision limits to put points in the wrong locations.\ninvariant gl_Position;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),m&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}x.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},x.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},x.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},x.prototype.drawItem=function(t,e){var r,n=this.groups,o=n[t];if("number"==typeof e&&(t=e,o=n[e],e=null),o&&o.count&&o.opacity){o.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,o,e));for(var s=[],l=1;l<o.activation.length;l++)o.activation[l]&&(!0===o.activation[l]||o.activation[l].data.length)&&s.push.apply(s,function(t){if(Array.isArray(t))return a(t)}(r=this.getMarkerDrawOptions(l,o,e))||function(t){if("undefined"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t["@@iterator"])return Array.from(t)}(r)||i(r)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}());s.length&&this.drawMarker(s)}},x.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;if(this.regl,!a)return r?[u({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[u({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var h=[],f=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var y=f.length;y--;){var v=n(f[y],2),x=v[0],_=v[1];h.push(u({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:_-x}))}return h},x.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,l=this.maxSize,h=this.maxColors,m=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=f(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=u({},x.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var v=0,_=0;if(p(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=x.defaults.size),v+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=x.defaults.borderSize),v+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=x.defaults.color),e=t.updateColor(e),_++,e},borderColor:function(e,r){return null==e&&(e=x.defaults.borderColor),e=t.updateColor(e),_++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,l=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=d(t,"float64");var u=e.count=Math.floor(t.length/2),h=e.bounds=u?s(t,2):null;if(r.range||e.range||(delete e.range,r.range=h),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||u>n)?e.tree=c(t,{bounds:h}):n&&n.length&&(e.tree=n),e.tree){var f={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(f):e.elements=o.elements(f)}var p=g.float32(t);return i({data:p,usage:"dynamic"}),a({data:g.fract32(t,p),usage:"dynamic"}),l({data:new Uint8Array(u),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var h={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](h):i[u]=o.buffer(h),i[u].data=a[u]}}else i[t.addMarker(e)]=!0;return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=g.fract(e.scale),e.translateFract=g.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),v){var b=n,w=b.count,T=b.size,k=b.borderSize,A=b.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/l),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/l);A({data:M,usage:"dynamic"})}if(_){var E,C=n,L=C.count,I=C.color,P=C.borderColor,z=C.colorBuffer;if(t.tooManyColors){if(I.length||P.length){E=new Uint8Array(8*L);for(var O=0;O<L;O++){var D=I[O];E[8*O]=m[4*D],E[8*O+1]=m[4*D+1],E[8*O+2]=m[4*D+2],E[8*O+3]=m[4*D+3];var R=P[O];E[8*O+4]=m[4*R],E[8*O+5]=m[4*R+1],E[8*O+6]=m[4*R+2],E[8*O+7]=m[4*R+3]}}}else if(I.length||P.length){E=new Uint8Array(4*L+2);for(var F=0;F<L;F++)null!=I[F]&&(E[4*F]=I[F]%h,E[4*F+1]=Math.floor(I[F]/h)),null!=P[F]&&(E[4*F+2]=P[F]%h,E[4*F+3]=Math.floor(P[F]/h))}z({data:E||new Uint8Array(0),type:"uint8",usage:"dynamic"})}return n}))}},x.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:"linear",min:"linear"})),a},x.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if("number"==typeof t[0]){var a=[];if(Array.isArray(t))for(var s=0;s<t.length;s+=4)a.push(t.slice(s,s+4));else for(var c=0;c<t.length;c+=4)a.push(t.subarray(c,c+4));t=a}for(var u=0;u<t.length;u++){var h=t[u];h=o(h,"uint8");var f=l(h,!1);if(null==e[f]){var p=r.length;e[f]=Math.floor(p/4),r[p]=h[0],r[p+1]=h[1],r[p+2]=h[2],r[p+3]=h[3]}i[u]=e[f]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},x.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},x.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=r(27976);t.exports=function(t,e){var r=new v(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},31239:function(t,e,r){"use strict";var n=r(62172),i=r(6807),a=r(78112),o=r(16494),s=r(27902),l=r(5137),c=r(83473);function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function h(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function f(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x,s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y,l.height),[a,n,o,i]}function p(t){if("number"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}t.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:"data items columns rows values dimensions samples x",snap:"snap cluster",size:"sizes size radius",color:"colors color fill fill-color fillColor",opacity:"opacity alpha transparency opaque",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",borderColor:"borderColors borderColor bordercolor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range ranges databox dataBox",viewport:"viewport viewBox viewbox",domain:"domain domains area areas",padding:"pad padding paddings pads margin margins",transpose:"transpose transposed",diagonal:"diagonal diag showDiagonal",upper:"upper up top upperhalf upperHalf showupperhalf showUpper showUpperHalf",lower:"lower low bottom lowerhalf lowerHalf showlowerhalf showLowerHalf showLower"}),s=this.traces[t]||(this.traces[t]={id:t,buffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),color:"black",marker:null,size:12,borderColor:"transparent",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(c(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var 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9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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n=r(27976);function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return 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this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day(),"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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e)if(r.evaluate(t))return!0;return!1}]]},"!":[ht,[ht],(t,[e])=>!e.evaluate(t)],"is-supported-script":[ht,[ut],(t,[e])=>{const r=t.globals&&t.globals.isSupportedScript;return!r||r(e.evaluate(t))}],upcase:[ut,[ut],(t,[e])=>e.evaluate(t).toUpperCase()],downcase:[ut,[ut],(t,[e])=>e.evaluate(t).toLowerCase()],concat:[ut,nn(dt),(t,e)=>e.map((e=>oe(e.evaluate(t)))).join("")],"resolved-locale":[ut,[mt],(t,[e])=>e.evaluate(t).resolvedLocale()]});class Tn{constructor(t,e){var r;this.expression=t,this._warningHistory={},this._evaluator=new de,this._defaultValue=e?"color"===(r=e).type&&mn(r.default)?new Xt(0,0,0,0):"color"===r.type?Xt.parse(r.default)||null:"padding"===r.type?Qt.parse(r.default)||null:"variableAnchorOffsetCollection"===r.type?ee.parse(r.default)||null:void 0===r.default?null:r.default:null,this._enumValues=e&&"enum"===e.type?e.values:null}evaluateWithoutErrorHandling(t,e,r,n,i,a){return this._evaluator.globals=t,this._evaluator.feature=e,this._evaluator.featureState=r,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a,this.expression.evaluate(this._evaluator)}evaluate(t,e,r,n,i,a){this._evaluator.globals=t,this._evaluator.feature=e||null,this._evaluator.featureState=r||null,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a||null;try{const t=this.expression.evaluate(this._evaluator);if(null==t||"number"==typeof t&&t!=t)return this._defaultValue;if(this._enumValues&&!(t in this._enumValues))throw new le(`Expected value to be one of ${Object.keys(this._enumValues).map((t=>JSON.stringify(t))).join(", ")}, but found ${JSON.stringify(t)} instead.`);return t}catch(t){return this._warningHistory[t.message]||(this._warningHistory[t.message]=!0,"undefined"!=typeof console&&console.warn(t.message)),this._defaultValue}}}function kn(t){return Array.isArray(t)&&t.length>0&&"string"==typeof t[0]&&t[0]in Kr}function An(t,e){const r=new me(Kr,an,[],e?function(t){const e={color:ft,string:ut,number:ct,enum:ut,boolean:ht,formatted:gt,padding:yt,resolvedImage:vt,variableAnchorOffsetCollection:xt};return"array"===t.type?_t(e[t.value]||dt,t.length):e[t.type]}(e):void 0),n=r.parse(t,void 0,void 0,void 0,e&&"string"===e.type?{typeAnnotation:"coerce"}:void 0);return n?cn(new Tn(n,e)):un(r.errors)}class Mn{constructor(t,e){this.kind=t,this._styleExpression=e,this.isStateDependent="constant"!==t&&!sn(e.expression)}evaluateWithoutErrorHandling(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)}evaluate(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)}}class Sn{constructor(t,e,r,n){this.kind=t,this.zoomStops=r,this._styleExpression=e,this.isStateDependent="camera"!==t&&!sn(e.expression),this.interpolationType=n}evaluateWithoutErrorHandling(t,e,r,n,i,a){return 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l={type:r.value,values:r.values};a.$version<7&&(l.function=r.function),"object"===dn(r.value)&&(l=r.value);let c=[];for(let r=0;r<e.length;r++)c=c.concat(s({array:e,arrayIndex:r,value:e[r],valueSpec:l,validateSpec:t.validateSpec,style:i,styleSpec:a,key:`${o}[${r}]`}));return c}function Yn(t){const e=t.key,r=t.value,n=t.valueSpec;let i=dn(r);return"number"===i&&r!=r&&(i="NaN"),"number"!==i?[new it(e,r,`number expected, ${i} found`)]:"minimum"in n&&r<n.minimum?[new it(e,r,`${r} is less than the minimum value ${n.minimum}`)]:"maximum"in n&&r>n.maximum?[new it(e,r,`${r} is greater than the maximum value ${n.maximum}`)]:[]}function Xn(t){const e=t.valueSpec,r=Hn(t.value.type);let n,i,a,o={};const s="categorical"!==r&&void 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this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))}}const zi={"Latin-1 Supplement":t=>t>=128&&t<=255,Arabic:t=>t>=1536&&t<=1791,"Arabic Supplement":t=>t>=1872&&t<=1919,"Arabic Extended-A":t=>t>=2208&&t<=2303,"Hangul Jamo":t=>t>=4352&&t<=4607,"Unified Canadian Aboriginal Syllabics":t=>t>=5120&&t<=5759,Khmer:t=>t>=6016&&t<=6143,"Unified Canadian Aboriginal Syllabics Extended":t=>t>=6320&&t<=6399,"General Punctuation":t=>t>=8192&&t<=8303,"Letterlike Symbols":t=>t>=8448&&t<=8527,"Number Forms":t=>t>=8528&&t<=8591,"Miscellaneous Technical":t=>t>=8960&&t<=9215,"Control Pictures":t=>t>=9216&&t<=9279,"Optical Character Recognition":t=>t>=9280&&t<=9311,"Enclosed Alphanumerics":t=>t>=9312&&t<=9471,"Geometric Shapes":t=>t>=9632&&t<=9727,"Miscellaneous Symbols":t=>t>=9728&&t<=9983,"Miscellaneous Symbols and Arrows":t=>t>=11008&&t<=11263,"CJK Radicals Supplement":t=>t>=11904&&t<=12031,"Kangxi Radicals":t=>t>=12032&&t<=12255,"Ideographic Description Characters":t=>t>=12272&&t<=12287,"CJK Symbols and Punctuation":t=>t>=12288&&t<=12351,Hiragana:t=>t>=12352&&t<=12447,Katakana:t=>t>=12448&&t<=12543,Bopomofo:t=>t>=12544&&t<=12591,"Hangul Compatibility Jamo":t=>t>=12592&&t<=12687,Kanbun:t=>t>=12688&&t<=12703,"Bopomofo Extended":t=>t>=12704&&t<=12735,"CJK Strokes":t=>t>=12736&&t<=12783,"Katakana Phonetic Extensions":t=>t>=12784&&t<=12799,"Enclosed CJK Letters and Months":t=>t>=12800&&t<=13055,"CJK Compatibility":t=>t>=13056&&t<=13311,"CJK Unified Ideographs Extension A":t=>t>=13312&&t<=19903,"Yijing Hexagram Symbols":t=>t>=19904&&t<=19967,"CJK Unified Ideographs":t=>t>=19968&&t<=40959,"Yi Syllables":t=>t>=40960&&t<=42127,"Yi Radicals":t=>t>=42128&&t<=42191,"Hangul Jamo Extended-A":t=>t>=43360&&t<=43391,"Hangul Syllables":t=>t>=44032&&t<=55215,"Hangul Jamo Extended-B":t=>t>=55216&&t<=55295,"Private Use Area":t=>t>=57344&&t<=63743,"CJK Compatibility Ideographs":t=>t>=63744&&t<=64255,"Arabic Presentation Forms-A":t=>t>=64336&&t<=65023,"Vertical Forms":t=>t>=65040&&t<=65055,"CJK Compatibility Forms":t=>t>=65072&&t<=65103,"Small Form Variants":t=>t>=65104&&t<=65135,"Arabic Presentation Forms-B":t=>t>=65136&&t<=65279,"Halfwidth and Fullwidth Forms":t=>t>=65280&&t<=65519};function Oi(t){for(const e of t)if(Fi(e.charCodeAt(0)))return!0;return!1}function Di(t){for(const e of t)if(!Ri(e.charCodeAt(0)))return!1;return!0}function Ri(t){return!(zi.Arabic(t)||zi["Arabic Supplement"](t)||zi["Arabic Extended-A"](t)||zi["Arabic Presentation Forms-A"](t)||zi["Arabic Presentation Forms-B"](t))}function Fi(t){return!(746!==t&&747!==t&&(t<4352||!(zi["Bopomofo Extended"](t)||zi.Bopomofo(t)||zi["CJK Compatibility Forms"](t)&&!(t>=65097&&t<=65103)||zi["CJK Compatibility Ideographs"](t)||zi["CJK Compatibility"](t)||zi["CJK Radicals Supplement"](t)||zi["CJK Strokes"](t)||!(!zi["CJK Symbols and Punctuation"](t)||t>=12296&&t<=12305||t>=12308&&t<=12319||12336===t)||zi["CJK Unified Ideographs Extension A"](t)||zi["CJK Unified Ideographs"](t)||zi["Enclosed CJK Letters and Months"](t)||zi["Hangul Compatibility Jamo"](t)||zi["Hangul Jamo Extended-A"](t)||zi["Hangul Jamo Extended-B"](t)||zi["Hangul Jamo"](t)||zi["Hangul Syllables"](t)||zi.Hiragana(t)||zi["Ideographic Description Characters"](t)||zi.Kanbun(t)||zi["Kangxi Radicals"](t)||zi["Katakana Phonetic Extensions"](t)||zi.Katakana(t)&&12540!==t||!(!zi["Halfwidth and Fullwidth Forms"](t)||65288===t||65289===t||65293===t||t>=65306&&t<=65310||65339===t||65341===t||65343===t||t>=65371&&t<=65503||65507===t||t>=65512&&t<=65519)||!(!zi["Small Form Variants"](t)||t>=65112&&t<=65118||t>=65123&&t<=65126)||zi["Unified Canadian Aboriginal Syllabics"](t)||zi["Unified Canadian Aboriginal Syllabics Extended"](t)||zi["Vertical Forms"](t)||zi["Yijing Hexagram Symbols"](t)||zi["Yi Syllables"](t)||zi["Yi Radicals"](t))))}function Bi(t){return!(Fi(t)||function(t){return!!(zi["Latin-1 Supplement"](t)&&(167===t||169===t||174===t||177===t||188===t||189===t||190===t||215===t||247===t)||zi["General Punctuation"](t)&&(8214===t||8224===t||8225===t||8240===t||8241===t||8251===t||8252===t||8258===t||8263===t||8264===t||8265===t||8273===t)||zi["Letterlike Symbols"](t)||zi["Number Forms"](t)||zi["Miscellaneous Technical"](t)&&(t>=8960&&t<=8967||t>=8972&&t<=8991||t>=8996&&t<=9e3||9003===t||t>=9085&&t<=9114||t>=9150&&t<=9165||9167===t||t>=9169&&t<=9179||t>=9186&&t<=9215)||zi["Control Pictures"](t)&&9251!==t||zi["Optical Character Recognition"](t)||zi["Enclosed Alphanumerics"](t)||zi["Geometric Shapes"](t)||zi["Miscellaneous Symbols"](t)&&!(t>=9754&&t<=9759)||zi["Miscellaneous Symbols and Arrows"](t)&&(t>=11026&&t<=11055||t>=11088&&t<=11097||t>=11192&&t<=11243)||zi["CJK Symbols and Punctuation"](t)||zi.Katakana(t)||zi["Private Use Area"](t)||zi["CJK Compatibility Forms"](t)||zi["Small Form Variants"](t)||zi["Halfwidth and Fullwidth Forms"](t)||8734===t||8756===t||8757===t||t>=9984&&t<=10087||t>=10102&&t<=10131||65532===t||65533===t)}(t))}function Ni(t){return zi.Arabic(t)||zi["Arabic Supplement"](t)||zi["Arabic Extended-A"](t)||zi["Arabic Presentation Forms-A"](t)||zi["Arabic Presentation Forms-B"](t)}function ji(t){return t>=1424&&t<=2303||zi["Arabic Presentation Forms-A"](t)||zi["Arabic Presentation Forms-B"](t)}function Ui(t,e){return!(!e&&ji(t)||t>=2304&&t<=3583||t>=3840&&t<=4255||zi.Khmer(t))}function Vi(t){for(const e of t)if(ji(e.charCodeAt(0)))return!0;return!1}const qi=new class{constructor(){this.applyArabicShaping=null,this.processBidirectionalText=null,this.processStyledBidirectionalText=null,this.pluginStatus="unavailable",this.pluginURL=null}setState(t){this.pluginStatus=t.pluginStatus,this.pluginURL=t.pluginURL}getState(){return{pluginStatus:this.pluginStatus,pluginURL:this.pluginURL}}setMethods(t){this.applyArabicShaping=t.applyArabicShaping,this.processBidirectionalText=t.processBidirectionalText,this.processStyledBidirectionalText=t.processStyledBidirectionalText}isParsed(){return null!=this.applyArabicShaping&&null!=this.processBidirectionalText&&null!=this.processStyledBidirectionalText}getPluginURL(){return this.pluginURL}getRTLTextPluginStatus(){return this.pluginStatus}};class Hi{constructor(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Pi,this.transition={})}isSupportedScript(t){return function(t,e){for(const r of t)if(!Ui(r.charCodeAt(0),e))return!1;return!0}(t,"loaded"===qi.getRTLTextPluginStatus())}crossFadingFactor(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)}getCrossfadeParameters(){const t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}}}class Gi{constructor(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(mn(t))return new Cn(t,e);if(kn(t)){const r=En(t,e);if("error"===r.result)throw new Error(r.value.map((t=>`${t.key}: ${t.message}`)).join(", "));return r.value}{let r=t;return"color"===e.type&&"string"==typeof t?r=Xt.parse(t):"padding"!==e.type||"number"!=typeof t&&!Array.isArray(t)?"variableAnchorOffsetCollection"===e.type&&Array.isArray(t)&&(r=ee.parse(t)):r=Qt.parse(t),{kind:"constant",evaluate:()=>r}}}(void 0===e?t.specification.default:e,t.specification)}isDataDriven(){return"source"===this.expression.kind||"composite"===this.expression.kind}possiblyEvaluate(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)}}class Zi{constructor(t){this.property=t,this.value=new Gi(t,void 0)}transitioned(t,e){return new Yi(this.property,this.value,e,y({},t.transition,this.transition),t.now)}untransitioned(){return new Yi(this.property,this.value,null,{},0)}}class Wi{constructor(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)}getValue(t){return b(this._values[t].value.value)}setValue(t,e){Object.prototype.hasOwnProperty.call(this._values,t)||(this._values[t]=new Zi(this._values[t].property)),this._values[t].value=new Gi(this._values[t].property,null===e?void 0:b(e))}getTransition(t){return b(this._values[t].transition)}setTransition(t,e){Object.prototype.hasOwnProperty.call(this._values,t)||(this._values[t]=new Zi(this._values[t].property)),this._values[t].transition=b(e)||void 0}serialize(){const t={};for(const e of Object.keys(this._values)){const r=this.getValue(e);void 0!==r&&(t[e]=r);const n=this.getTransition(e);void 0!==n&&(t[`${e}-transition`]=n)}return t}transitioned(t,e){const r=new Xi(this._properties);for(const n of Object.keys(this._values))r._values[n]=this._values[n].transitioned(t,e._values[n]);return r}untransitioned(){const t=new Xi(this._properties);for(const e of Object.keys(this._values))t._values[e]=this._values[e].untransitioned();return t}}class Yi{constructor(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)}possiblyEvaluate(t,e,r){const n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);{const o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;const e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}}return i}}class Xi{constructor(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)}possiblyEvaluate(t,e,r){const n=new Ki(this._properties);for(const i of Object.keys(this._values))n._values[i]=this._values[i].possiblyEvaluate(t,e,r);return n}hasTransition(){for(const t of Object.keys(this._values))if(this._values[t].prior)return!0;return!1}}class $i{constructor(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)}hasValue(t){return void 0!==this._values[t].value}getValue(t){return b(this._values[t].value)}setValue(t,e){this._values[t]=new Gi(this._values[t].property,null===e?void 0:b(e))}serialize(){const t={};for(const e of Object.keys(this._values)){const r=this.getValue(e);void 0!==r&&(t[e]=r)}return t}possiblyEvaluate(t,e,r){const n=new Ki(this._properties);for(const i of Object.keys(this._values))n._values[i]=this._values[i].possiblyEvaluate(t,e,r);return n}}class Ji{constructor(t,e,r){this.property=t,this.value=e,this.parameters=r}isConstant(){return"constant"===this.value.kind}constantOr(t){return"constant"===this.value.kind?this.value.value:t}evaluate(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)}}class Ki{constructor(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)}get(t){return this._values[t]}}class Qi{constructor(t){this.specification=t}possiblyEvaluate(t,e){if(t.isDataDriven())throw new Error("Value should not be data driven");return t.expression.evaluate(e)}interpolate(t,e,r){const n=this.specification.type,i=Pe[n];return i?i(t,e,r):t}}class ta{constructor(t,e){this.specification=t,this.overrides=e}possiblyEvaluate(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new Ji(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new Ji(this,t.expression,e)}interpolate(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Ji(this,{kind:"constant",value:void 0},t.parameters);const n=this.specification.type,i=Pe[n];if(i){const n=i(t.value.value,e.value.value,r);return new Ji(this,{kind:"constant",value:n},t.parameters)}return t}evaluate(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)}}class ea extends ta{possiblyEvaluate(t,e,r,n){if(void 0===t.value)return new Ji(this,{kind:"constant",value:void 0},e);if("constant"===t.expression.kind){const i=t.expression.evaluate(e,null,{},r,n),a="resolvedImage"===t.property.specification.type&&"string"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new Ji(this,{kind:"constant",value:o},e)}if("camera"===t.expression.kind){const r=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new Ji(this,{kind:"constant",value:r},e)}return new Ji(this,t.expression,e)}evaluate(t,e,r,n,i,a){if("source"===t.kind){const o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return"composite"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value}_calculate(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}}interpolate(t){return t}}class ra{constructor(t){this.specification=t}possiblyEvaluate(t,e,r,n){if(void 0!==t.value){if("constant"===t.expression.kind){const i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new Hi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Hi(Math.floor(e.zoom),e)),t.expression.evaluate(new Hi(Math.floor(e.zoom+1),e)),e)}}_calculate(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}}interpolate(t){return t}}class na{constructor(t){this.specification=t}possiblyEvaluate(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)}interpolate(){return!1}}class ia{constructor(t){this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[];for(const e in t){const r=t[e];r.specification.overridable&&this.overridableProperties.push(e);const n=this.defaultPropertyValues[e]=new Gi(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new Zi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}}}Mi("DataDrivenProperty",ta),Mi("DataConstantProperty",Qi),Mi("CrossFadedDataDrivenProperty",ea),Mi("CrossFadedProperty",ra),Mi("ColorRampProperty",na);const aa="-transition";class oa extends G{constructor(t,e){if(super(),this.id=t.id,this.type=t.type,this._featureFilter={filter:()=>!0,needGeometry:!1},"custom"!==t.type&&(this.metadata=t.metadata,this.minzoom=t.minzoom,this.maxzoom=t.maxzoom,"background"!==t.type&&(this.source=t.source,this.sourceLayer=t["source-layer"],this.filter=t.filter),e.layout&&(this._unevaluatedLayout=new $i(e.layout)),e.paint)){this._transitionablePaint=new Wi(e.paint);for(const e in t.paint)this.setPaintProperty(e,t.paint[e],{validate:!1});for(const e in t.layout)this.setLayoutProperty(e,t.layout[e],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Ki(e.paint)}}getCrossfadeParameters(){return this._crossfadeParameters}getLayoutProperty(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)}setLayoutProperty(t,e,r={}){if(null!=e){const n=`layers.${this.id}.layout.${t}`;if(this._validate(wi,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e}getPaintProperty(t){return t.endsWith(aa)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)}setPaintProperty(t,e,r={}){if(null!=e){const n=`layers.${this.id}.paint.${t}`;if(this._validate(bi,n,t,e,r))return!1}if(t.endsWith(aa))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;{const r=this._transitionablePaint._values[t],n="cross-faded-data-driven"===r.property.specification["property-type"],i=r.value.isDataDriven(),a=r.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);const o=this._transitionablePaint._values[t].value;return o.isDataDriven()||i||n||this._handleOverridablePaintPropertyUpdate(t,a,o)}}_handleSpecialPaintPropertyUpdate(t){}_handleOverridablePaintPropertyUpdate(t,e,r){return!1}isHidden(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility}updateTransitions(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculate(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)}serialize(){const t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),_(t,((t,e)=>!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)))}_validate(t,e,r,n,i={}){return(!i||!1!==i.validate)&&Ti(this,t.call(vi,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Z,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(const t in this.paint._values){const e=this.paint.get(t);if(e instanceof Ji&&hn(e.property.specification)&&("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent)return!0}return!1}}const sa={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class la{constructor(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class ca{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}}static deserialize(t){const e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(t){this.reserve(t),this.length=t}reserve(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);const e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}}_refreshViews(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")}}function ua(t,e=1){let r=0,n=0;return{members:t.map((t=>{const i=(s=t.type,sa[s].BYTES_PER_ELEMENT),a=r=ha(r,Math.max(e,i)),o=t.components||1;var s;return n=Math.max(n,i),r+=i*o,{name:t.name,type:t.type,components:o,offset:a}})),size:ha(r,Math.max(n,e)),alignment:e}}function ha(t,e){return Math.ceil(t/e)*e}class fa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t}}fa.prototype.bytesPerElement=4,Mi("StructArrayLayout2i4",fa);class pa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t}}pa.prototype.bytesPerElement=6,Mi("StructArrayLayout3i6",pa);class da extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n){const i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)}emplace(t,e,r,n,i){const a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t}}da.prototype.bytesPerElement=8,Mi("StructArrayLayout4i8",da);class ma extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t}}ma.prototype.bytesPerElement=12,Mi("StructArrayLayout2i4i12",ma);class ga extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t}}ga.prototype.bytesPerElement=8,Mi("StructArrayLayout2i4ub8",ga);class ya extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t}}ya.prototype.bytesPerElement=8,Mi("StructArrayLayout2f8",ya);class va extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c){const u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)}emplace(t,e,r,n,i,a,o,s,l,c,u){const h=10*t;return this.uint16[h+0]=e,this.uint16[h+1]=r,this.uint16[h+2]=n,this.uint16[h+3]=i,this.uint16[h+4]=a,this.uint16[h+5]=o,this.uint16[h+6]=s,this.uint16[h+7]=l,this.uint16[h+8]=c,this.uint16[h+9]=u,t}}va.prototype.bytesPerElement=20,Mi("StructArrayLayout10ui20",va);class xa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h){const f=this.length;return this.resize(f+1),this.emplace(f,t,e,r,n,i,a,o,s,l,c,u,h)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f){const p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=h,this.int16[p+11]=f,t}}xa.prototype.bytesPerElement=24,Mi("StructArrayLayout4i4ui4i24",xa);class _a extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t}}_a.prototype.bytesPerElement=12,Mi("StructArrayLayout3f12",_a);class ba extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.uint32[r+0]=e,t}}ba.prototype.bytesPerElement=4,Mi("StructArrayLayout1ul4",ba);class wa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l){const c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)}emplace(t,e,r,n,i,a,o,s,l,c){const u=10*t,h=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[h+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t}}wa.prototype.bytesPerElement=20,Mi("StructArrayLayout6i1ul2ui20",wa);class Ta extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t}}Ta.prototype.bytesPerElement=12,Mi("StructArrayLayout2i2i2i12",Ta);class ka extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i){const a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)}emplace(t,e,r,n,i,a){const o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t}}ka.prototype.bytesPerElement=16,Mi("StructArrayLayout2f1f2i16",ka);class Aa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=16*t,l=4*t,c=8*t;return this.uint8[s+0]=e,this.uint8[s+1]=r,this.float32[l+1]=n,this.float32[l+2]=i,this.int16[c+6]=a,this.int16[c+7]=o,t}}Aa.prototype.bytesPerElement=16,Mi("StructArrayLayout2ub2f2i16",Aa);class Ma extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t}}Ma.prototype.bytesPerElement=6,Mi("StructArrayLayout3ui6",Ma);class Sa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g){const y=this.length;return this.resize(y+1),this.emplace(y,t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y){const v=24*t,x=12*t,_=48*t;return this.int16[v+0]=e,this.int16[v+1]=r,this.uint16[v+2]=n,this.uint16[v+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[v+10]=l,this.uint16[v+11]=c,this.uint16[v+12]=u,this.float32[x+7]=h,this.float32[x+8]=f,this.uint8[_+36]=p,this.uint8[_+37]=d,this.uint8[_+38]=m,this.uint32[x+10]=g,this.int16[v+22]=y,t}}Sa.prototype.bytesPerElement=48,Mi("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Sa);class Ea extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S){const E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S,E){const C=32*t,L=16*t;return this.int16[C+0]=e,this.int16[C+1]=r,this.int16[C+2]=n,this.int16[C+3]=i,this.int16[C+4]=a,this.int16[C+5]=o,this.int16[C+6]=s,this.int16[C+7]=l,this.uint16[C+8]=c,this.uint16[C+9]=u,this.uint16[C+10]=h,this.uint16[C+11]=f,this.uint16[C+12]=p,this.uint16[C+13]=d,this.uint16[C+14]=m,this.uint16[C+15]=g,this.uint16[C+16]=y,this.uint16[C+17]=v,this.uint16[C+18]=x,this.uint16[C+19]=_,this.uint16[C+20]=b,this.uint16[C+21]=w,this.uint16[C+22]=T,this.uint32[L+12]=k,this.float32[L+13]=A,this.float32[L+14]=M,this.uint16[C+30]=S,this.uint16[C+31]=E,t}}Ea.prototype.bytesPerElement=64,Mi("StructArrayLayout8i15ui1ul2f2ui64",Ea);class Ca extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.float32[r+0]=e,t}}Ca.prototype.bytesPerElement=4,Mi("StructArrayLayout1f4",Ca);class La extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=6*t,a=3*t;return this.uint16[i+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,t}}La.prototype.bytesPerElement=12,Mi("StructArrayLayout1ui2f12",La);class Ia extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t}}Ia.prototype.bytesPerElement=8,Mi("StructArrayLayout1ul2ui8",Ia);class Pa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t}}Pa.prototype.bytesPerElement=4,Mi("StructArrayLayout2ui4",Pa);class za extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.uint16[r+0]=e,t}}za.prototype.bytesPerElement=2,Mi("StructArrayLayout1ui2",za);class Oa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n){const i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)}emplace(t,e,r,n,i){const a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t}}Oa.prototype.bytesPerElement=16,Mi("StructArrayLayout4f16",Oa);class Da extends la{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}}Da.prototype.size=20;class Ra extends wa{get(t){return new Da(this,t)}}Mi("CollisionBoxArray",Ra);class Fa extends la{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(t){this._structArray.uint8[this._pos1+37]=t}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(t){this._structArray.uint8[this._pos1+38]=t}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(t){this._structArray.uint32[this._pos4+10]=t}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}Fa.prototype.size=48;class Ba extends Sa{get(t){return new Fa(this,t)}}Mi("PlacedSymbolArray",Ba);class Na extends la{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(t){this._structArray.uint32[this._pos4+12]=t}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}Na.prototype.size=64;class ja extends Ea{get(t){return new Na(this,t)}}Mi("SymbolInstanceArray",ja);class Ua extends Ca{getoffsetX(t){return this.float32[1*t+0]}}Mi("GlyphOffsetArray",Ua);class Va extends pa{getx(t){return this.int16[3*t+0]}gety(t){return this.int16[3*t+1]}gettileUnitDistanceFromAnchor(t){return this.int16[3*t+2]}}Mi("SymbolLineVertexArray",Va);class qa extends la{get textAnchor(){return this._structArray.uint16[this._pos2+0]}get textOffset0(){return this._structArray.float32[this._pos4+1]}get textOffset1(){return this._structArray.float32[this._pos4+2]}}qa.prototype.size=12;class Ha extends La{get(t){return new qa(this,t)}}Mi("TextAnchorOffsetArray",Ha);class Ga extends la{get featureIndex(){return this._structArray.uint32[this._pos4+0]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+2]}get bucketIndex(){return this._structArray.uint16[this._pos2+3]}}Ga.prototype.size=8;class Za extends Ia{get(t){return new Ga(this,t)}}Mi("FeatureIndexArray",Za);class Wa extends fa{}class Ya extends fa{}class Xa extends fa{}class $a extends ma{}class Ja extends ga{}class Ka extends ya{}class Qa extends va{}class to extends xa{}class eo extends _a{}class ro extends ba{}class no extends Ta{}class io extends Aa{}class ao extends Ma{}class oo extends Pa{}const so=ua([{name:"a_pos",components:2,type:"Int16"}],4),{members:lo,size:co,alignment:uo}=so;class ho{constructor(t=[]){this.segments=t}prepareSegment(t,e,r,n){let i=this.segments[this.segments.length-1];return t>ho.MAX_VERTEX_ARRAY_LENGTH&&T(`Max vertices per segment is ${ho.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${t}`),(!i||i.vertexLength+t>ho.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 0!==n&&(i.sortKey=n),this.segments.push(i)),i}get(){return this.segments}destroy(){for(const t of this.segments)for(const e in t.vaos)t.vaos[e].destroy()}static simpleSegment(t,e,r,n){return new ho([{vertexOffset:t,primitiveOffset:e,vertexLength:r,primitiveLength:n,vaos:{},sortKey:0}])}}function fo(t,e){return 256*(t=m(Math.floor(t),0,255))+m(Math.floor(e),0,255)}ho.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Mi("SegmentVector",ho);const po=ua([{name:"a_pattern_from",components:4,type:"Uint16"},{name:"a_pattern_to",components:4,type:"Uint16"},{name:"a_pixel_ratio_from",components:1,type:"Uint16"},{name:"a_pixel_ratio_to",components:1,type:"Uint16"}]);var mo={exports:{}},go={exports:{}};!function(t){t.exports=function(t,e){var r,n,i,a,o,s,l,c;for(r=3&t.length,n=t.length-r,i=e,o=3432918353,s=461845907,c=0;c<n;)l=255&t.charCodeAt(c)|(255&t.charCodeAt(++c))<<8|(255&t.charCodeAt(++c))<<16|(255&t.charCodeAt(++c))<<24,++c,i=27492+(65535&(a=5*(65535&(i=(i^=l=(65535&(l=(l=(65535&l)*o+(((l>>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295)<<13|i>>>19))+((5*(i>>>16)&65535)<<16)&4294967295))+((58964+(a>>>16)&65535)<<16);switch(l=0,r){case 3:l^=(255&t.charCodeAt(c+2))<<16;case 2:l^=(255&t.charCodeAt(c+1))<<8;case 1:i^=l=(65535&(l=(l=(65535&(l^=255&t.charCodeAt(c)))*o+(((l>>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295}return i^=t.length,i=2246822507*(65535&(i^=i>>>16))+((2246822507*(i>>>16)&65535)<<16)&4294967295,i=3266489909*(65535&(i^=i>>>13))+((3266489909*(i>>>16)&65535)<<16)&4294967295,(i^=i>>>16)>>>0}}(go);var yo=go.exports,vo={exports:{}};!function(t){t.exports=function(t,e){for(var r,n=t.length,i=e^n,a=0;n>=4;)r=1540483477*(65535&(r=255&t.charCodeAt(a)|(255&t.charCodeAt(++a))<<8|(255&t.charCodeAt(++a))<<16|(255&t.charCodeAt(++a))<<24))+((1540483477*(r>>>16)&65535)<<16),i=1540483477*(65535&i)+((1540483477*(i>>>16)&65535)<<16)^(r=1540483477*(65535&(r^=r>>>24))+((1540483477*(r>>>16)&65535)<<16)),n-=4,++a;switch(n){case 3:i^=(255&t.charCodeAt(a+2))<<16;case 2:i^=(255&t.charCodeAt(a+1))<<8;case 1:i=1540483477*(65535&(i^=255&t.charCodeAt(a)))+((1540483477*(i>>>16)&65535)<<16)}return i=1540483477*(65535&(i^=i>>>13))+((1540483477*(i>>>16)&65535)<<16),(i^=i>>>15)>>>0}}(vo);var xo=yo,_o=vo.exports;mo.exports=xo,mo.exports.murmur3=xo,mo.exports.murmur2=_o;var bo=r(mo.exports);class wo{constructor(){this.ids=[],this.positions=[],this.indexed=!1}add(t,e,r,n){this.ids.push(To(t)),this.positions.push(e,r,n)}getPositions(t){if(!this.indexed)throw new Error("Trying to get index, but feature positions are not indexed");const e=To(t);let r=0,n=this.ids.length-1;for(;r<n;){const t=r+n>>1;this.ids[t]>=e?n=t:r=t+1}const i=[];for(;this.ids[r]===e;){const t=this.positions[3*r],e=this.positions[3*r+1],n=this.positions[3*r+2];i.push({index:t,start:e,end:n}),r++}return i}static serialize(t,e){const r=new Float64Array(t.ids),n=new Uint32Array(t.positions);return ko(r,n,0,r.length-1),e&&e.push(r.buffer,n.buffer),{ids:r,positions:n}}static deserialize(t){const e=new wo;return e.ids=t.ids,e.positions=t.positions,e.indexed=!0,e}}function To(t){const e=+t;return!isNaN(e)&&e<=Number.MAX_SAFE_INTEGER?e:bo(String(t))}function ko(t,e,r,n){for(;r<n;){const i=t[r+n>>1];let a=r-1,o=n+1;for(;;){do{a++}while(t[a]<i);do{o--}while(t[o]>i);if(a>=o)break;Ao(t,a,o),Ao(e,3*a,3*o),Ao(e,3*a+1,3*o+1),Ao(e,3*a+2,3*o+2)}o-r<n-o?(ko(t,e,r,o),r=o+1):(ko(t,e,o+1,n),n=o)}}function Ao(t,e,r){const n=t[e];t[e]=t[r],t[r]=n}Mi("FeaturePositionMap",wo);class Mo{constructor(t,e){this.gl=t.gl,this.location=e}}class So extends Mo{constructor(t,e){super(t,e),this.current=0}set(t){this.current!==t&&(this.current=t,this.gl.uniform1f(this.location,t))}}class Eo extends Mo{constructor(t,e){super(t,e),this.current=[0,0,0,0]}set(t){t[0]===this.current[0]&&t[1]===this.current[1]&&t[2]===this.current[2]&&t[3]===this.current[3]||(this.current=t,this.gl.uniform4f(this.location,t[0],t[1],t[2],t[3]))}}class Co extends Mo{constructor(t,e){super(t,e),this.current=Xt.transparent}set(t){t.r===this.current.r&&t.g===this.current.g&&t.b===this.current.b&&t.a===this.current.a||(this.current=t,this.gl.uniform4f(this.location,t.r,t.g,t.b,t.a))}}const Lo=new Float32Array(16);function Io(t){return[fo(255*t.r,255*t.g),fo(255*t.b,255*t.a)]}class Po{constructor(t,e,r){this.value=t,this.uniformNames=e.map((t=>`u_${t}`)),this.type=r}setUniform(t,e,r){t.set(r.constantOr(this.value))}getBinding(t,e,r){return"color"===this.type?new Co(t,e):new So(t,e)}}class zo{constructor(t,e){this.uniformNames=e.map((t=>`u_${t}`)),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1}setConstantPatternPositions(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr}setUniform(t,e,r,n){const i="u_pattern_to"===n?this.patternTo:"u_pattern_from"===n?this.patternFrom:"u_pixel_ratio_to"===n?this.pixelRatioTo:"u_pixel_ratio_from"===n?this.pixelRatioFrom:null;i&&t.set(i)}getBinding(t,e,r){return"u_pattern"===r.substr(0,9)?new Eo(t,e):new So(t,e)}}class Oo{constructor(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((t=>({name:`a_${t}`,type:"Float32",components:"color"===r?2:1,offset:0}))),this.paintVertexArray=new n}populatePaintArray(t,e,r,n,i){const a=this.paintVertexArray.length,o=this.expression.evaluate(new Hi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)}updatePaintArray(t,e,r,n){const i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)}_setPaintValue(t,e,r){if("color"===this.type){const n=Io(r);for(let r=t;r<e;r++)this.paintVertexArray.emplace(r,n[0],n[1])}else{for(let n=t;n<e;n++)this.paintVertexArray.emplace(n,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}}upload(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}}class Do{constructor(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((t=>`u_${t}_t`)),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((t=>({name:`a_${t}`,type:"Float32",components:"color"===r?4:2,offset:0}))),this.paintVertexArray=new a}populatePaintArray(t,e,r,n,i){const a=this.expression.evaluate(new Hi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new Hi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)}updatePaintArray(t,e,r,n){const i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)}_setPaintValue(t,e,r,n){if("color"===this.type){const i=Io(r),a=Io(n);for(let r=t;r<e;r++)this.paintVertexArray.emplace(r,i[0],i[1],a[0],a[1])}else{for(let i=t;i<e;i++)this.paintVertexArray.emplace(i,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}}upload(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))}destroy(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()}setUniform(t,e){const r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=m(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)}getBinding(t,e,r){return new So(t,e)}}class Ro{constructor(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i}populatePaintArray(t,e,r){const n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)}updatePaintArray(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)}_setPaintValues(t,e,r,n){if(!n||!r)return;const{min:i,mid:a,max:o}=r,s=n[i],l=n[a],c=n[o];if(s&&l&&c)for(let r=t;r<e;r++)this.zoomInPaintVertexArray.emplace(r,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(r,l.tl[0],l.tl[1],l.br[0],l.br[1],c.tl[0],c.tl[1],c.br[0],c.br[1],l.pixelRatio,c.pixelRatio)}upload(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,po.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,po.members,this.expression.isStateDependent))}destroy(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()}}class Fo{constructor(t,e,r){this.binders={},this._buffers=[];const n=[];for(const i in t.paint._values){if(!r(i))continue;const a=t.paint.get(i);if(!(a instanceof Ji&&hn(a.property.specification)))continue;const o=No(i,t.type),s=a.value,l=a.property.specification.type,c=a.property.useIntegerZoom,u=a.property.specification["property-type"],h="cross-faded"===u||"cross-faded-data-driven"===u;if("constant"===s.kind)this.binders[i]=h?new zo(s.value,o):new Po(s.value,o,l),n.push(`/u_${i}`);else if("source"===s.kind||h){const r=jo(i,l,"source");this.binders[i]=h?new Ro(s,l,c,e,r,t.id):new Oo(s,o,l,r),n.push(`/a_${i}`)}else{const t=jo(i,l,"composite");this.binders[i]=new Do(s,o,l,c,e,t),n.push(`/z_${i}`)}}this.cacheKey=n.sort().join("")}getMaxValue(t){const e=this.binders[t];return e instanceof Oo||e instanceof Do?e.maxValue:0}populatePaintArrays(t,e,r,n,i){for(const a in this.binders){const o=this.binders[a];(o instanceof Oo||o instanceof Do||o instanceof Ro)&&o.populatePaintArray(t,e,r,n,i)}}setConstantPatternPositions(t,e){for(const r in this.binders){const n=this.binders[r];n instanceof zo&&n.setConstantPatternPositions(t,e)}}updatePaintArrays(t,e,r,n,i){let a=!1;for(const o in t){const s=e.getPositions(o);for(const e of s){const s=r.feature(e.index);for(const r in this.binders){const l=this.binders[r];if((l instanceof Oo||l instanceof Do||l instanceof Ro)&&!0===l.expression.isStateDependent){const c=n.paint.get(r);l.expression=c.value,l.updatePaintArray(e.start,e.end,s,t[o],i),a=!0}}}}return a}defines(){const t=[];for(const e in this.binders){const r=this.binders[e];(r instanceof Po||r instanceof zo)&&t.push(...r.uniformNames.map((t=>`#define HAS_UNIFORM_${t}`)))}return t}getBinderAttributes(){const t=[];for(const e in this.binders){const r=this.binders[e];if(r instanceof Oo||r instanceof Do)for(let e=0;e<r.paintVertexAttributes.length;e++)t.push(r.paintVertexAttributes[e].name);else if(r instanceof Ro)for(let e=0;e<po.members.length;e++)t.push(po.members[e].name)}return t}getBinderUniforms(){const t=[];for(const e in this.binders){const r=this.binders[e];if(r instanceof Po||r instanceof zo||r instanceof Do)for(const e of r.uniformNames)t.push(e)}return t}getPaintVertexBuffers(){return this._buffers}getUniforms(t,e){const r=[];for(const n in this.binders){const i=this.binders[n];if(i instanceof Po||i instanceof zo||i instanceof Do)for(const a of i.uniformNames)if(e[a]){const o=i.getBinding(t,e[a],a);r.push({name:a,property:n,binding:o})}}return r}setUniforms(t,e,r,n){for(const{name:t,property:i,binding:a}of e)this.binders[i].setUniform(a,n,r.get(i),t)}updatePaintBuffers(t){this._buffers=[];for(const e in this.binders){const r=this.binders[e];if(t&&r instanceof Ro){const e=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;e&&this._buffers.push(e)}else(r instanceof Oo||r instanceof Do)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}}upload(t){for(const e in this.binders){const r=this.binders[e];(r instanceof Oo||r instanceof Do||r instanceof Ro)&&r.upload(t)}this.updatePaintBuffers()}destroy(){for(const t in this.binders){const e=this.binders[t];(e instanceof Oo||e instanceof Do||e instanceof Ro)&&e.destroy()}}}class Bo{constructor(t,e,r=(()=>!0)){this.programConfigurations={};for(const n of t)this.programConfigurations[n.id]=new Fo(n,e,r);this.needsUpload=!1,this._featureMap=new wo,this._bufferOffset=0}populatePaintArrays(t,e,r,n,i,a){for(const r in this.programConfigurations)this.programConfigurations[r].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0}updatePaintArrays(t,e,r,n){for(const i of r)this.needsUpload=this.programConfigurations[i.id].updatePaintArrays(t,this._featureMap,e,i,n)||this.needsUpload}get(t){return this.programConfigurations[t]}upload(t){if(this.needsUpload){for(const e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}}destroy(){for(const t in this.programConfigurations)this.programConfigurations[t].destroy()}}function No(t,e){return{"text-opacity":["opacity"],"icon-opacity":["opacity"],"text-color":["fill_color"],"icon-color":["fill_color"],"text-halo-color":["halo_color"],"icon-halo-color":["halo_color"],"text-halo-blur":["halo_blur"],"icon-halo-blur":["halo_blur"],"text-halo-width":["halo_width"],"icon-halo-width":["halo_width"],"line-gap-width":["gapwidth"],"line-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-extrusion-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"]}[t]||[t.replace(`${e}-`,"").replace(/-/g,"_")]}function jo(t,e,r){const n={color:{source:ya,composite:Oa},number:{source:Ca,composite:ya}},i=function(t){return{"line-pattern":{source:Qa,composite:Qa},"fill-pattern":{source:Qa,composite:Qa},"fill-extrusion-pattern":{source:Qa,composite:Qa}}[t]}(t);return i&&i[r]||n[e][r]}Mi("ConstantBinder",Po),Mi("CrossFadedConstantBinder",zo),Mi("SourceExpressionBinder",Oo),Mi("CrossFadedCompositeBinder",Ro),Mi("CompositeExpressionBinder",Do),Mi("ProgramConfiguration",Fo,{omit:["_buffers"]}),Mi("ProgramConfigurationSet",Bo);const Uo=8192,Vo=Math.pow(2,14)-1,qo=-Vo-1;function Ho(t){const e=Uo/t.extent,r=t.loadGeometry();for(let t=0;t<r.length;t++){const n=r[t];for(let t=0;t<n.length;t++){const r=n[t],i=Math.round(r.x*e),a=Math.round(r.y*e);r.x=m(i,qo,Vo),r.y=m(a,qo,Vo),(i<r.x||i>r.x+1||a<r.y||a>r.y+1)&&T("Geometry exceeds allowed extent, reduce your vector tile buffer size")}}return r}function Go(t,e){return{type:t.type,id:t.id,properties:t.properties,geometry:e?Ho(t):[]}}function Zo(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}class Wo{constructor(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((t=>t.id)),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ya,this.indexArray=new ao,this.segments=new ho,this.programConfigurations=new Bo(t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){const n=this.layers[0],i=[];let a=null,o=!1;"circle"===n.type&&(a=n.layout.get("circle-sort-key"),o=!a.isConstant());for(const{feature:e,id:n,index:s,sourceLayerIndex:l}of t){const t=this.layers[0]._featureFilter.needGeometry,c=Go(e,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),c,r))continue;const u=o?a.evaluate(c,{},r):void 0,h={id:n,properties:e.properties,type:e.type,sourceLayerIndex:l,index:s,geometry:t?c.geometry:Ho(e),patterns:{},sortKey:u};i.push(h)}o&&i.sort(((t,e)=>t.sortKey-e.sortKey));for(const n of i){const{geometry:i,index:a,sourceLayerIndex:o}=n,s=t[a].feature;this.addFeature(n,i,a,r),e.featureIndex.insert(s,i,a,o,this.index)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}isEmpty(){return 0===this.layoutVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,lo),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}addFeature(t,e,r,n){for(const r of e)for(const e of r){const r=e.x,n=e.y;if(r<0||r>=Uo||n<0||n>=Uo)continue;const i=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray,t.sortKey),a=i.vertexLength;Zo(this.layoutVertexArray,r,n,-1,-1),Zo(this.layoutVertexArray,r,n,1,-1),Zo(this.layoutVertexArray,r,n,1,1),Zo(this.layoutVertexArray,r,n,-1,1),this.indexArray.emplaceBack(a,a+1,a+2),this.indexArray.emplaceBack(a,a+3,a+2),i.vertexLength+=4,i.primitiveLength+=2}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,{},n)}}function Yo(t,e){for(let r=0;r<t.length;r++)if(ns(e,t[r]))return!0;for(let r=0;r<e.length;r++)if(ns(t,e[r]))return!0;return!!Ko(t,e)}function Xo(t,e,r){return!!ns(t,e)||!!ts(e,t,r)}function $o(t,e){if(1===t.length)return rs(e,t[0]);for(let r=0;r<e.length;r++){const n=e[r];for(let e=0;e<n.length;e++)if(ns(t,n[e]))return!0}for(let r=0;r<t.length;r++)if(rs(e,t[r]))return!0;for(let r=0;r<e.length;r++)if(Ko(t,e[r]))return!0;return!1}function Jo(t,e,r){if(t.length>1){if(Ko(t,e))return!0;for(let n=0;n<e.length;n++)if(ts(e[n],t,r))return!0}for(let 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ta(Z.paint_fill["fill-outline-color"]),"fill-translate":new Qi(Z.paint_fill["fill-translate"]),"fill-translate-anchor":new Qi(Z.paint_fill["fill-translate-anchor"]),"fill-pattern":new ea(Z.paint_fill["fill-pattern"])})},get layout(){return dl=dl||new ia({"fill-sort-key":new ta(Z.layout_fill["fill-sort-key"])})}};class yl extends oa{constructor(t){super(t,gl)}recalculate(t,e){super.recalculate(t,e);const r=this.paint._values["fill-outline-color"];"constant"===r.value.kind&&void 0===r.value.value&&(this.paint._values["fill-outline-color"]=this.paint._values["fill-color"])}createBucket(t){return new pl(t)}queryRadius(){return os(this.paint.get("fill-translate"))}queryIntersectsFeature(t,e,r,n,i,a,o){return $o(ss(t,this.paint.get("fill-translate"),this.paint.get("fill-translate-anchor"),a.angle,o),n)}isTileClipped(){return!0}}const vl=ua([{name:"a_pos",components:2,type:"Int16"},{name:"a_normal_ed",components:4,type:"Int16"}],4),xl=ua([{name:"a_centroid",components:2,type:"Int16"}],4),{members:_l,size:bl,alignment:wl}=vl;var Tl={},kl=n,Al=Ml;function Ml(t,e,r,n,i){this.properties={},this.extent=r,this.type=0,this._pbf=t,this._geometry=-1,this._keys=n,this._values=i,t.readFields(Sl,this,e)}function Sl(t,e,r){1==t?e.id=r.readVarint():2==t?function(t,e){for(var r=t.readVarint()+t.pos;t.pos<r;){var n=e._keys[t.readVarint()],i=e._values[t.readVarint()];e.properties[n]=i}}(r,e):3==t?e.type=r.readVarint():4==t&&(e._geometry=r.pos)}function El(t){for(var e,r,n=0,i=0,a=t.length,o=a-1;i<a;o=i++)e=t[i],n+=((r=t[o]).x-e.x)*(e.y+r.y);return n}Ml.types=["Unknown","Point","LineString","Polygon"],Ml.prototype.loadGeometry=function(){var t=this._pbf;t.pos=this._geometry;for(var e,r=t.readVarint()+t.pos,n=1,i=0,a=0,o=0,s=[];t.pos<r;){if(i<=0){var l=t.readVarint();n=7&l,i=l>>3}if(i--,1===n||2===n)a+=t.readSVarint(),o+=t.readSVarint(),1===n&&(e&&s.push(e),e=[]),e.push(new kl(a,o));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&s.push(e),s},Ml.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},Ml.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=Ml.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var h=[];for(n=0;n<l.length;n++)h[n]=l[n][0];u(l=h);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=El(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var f={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(f.id=this.id),f};var Cl=Al,Ll=Il;function Il(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Pl,this,e),this.length=this._features.length}function Pl(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}Il.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Cl(this._pbf,e,this.extent,this._keys,this._values)};var zl=Ll,Ol=function(t,e){this.layers=t.readFields(Dl,{},e)};function Dl(t,e,r){if(3===t){var n=new zl(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Tl.VectorTile=Ol,Tl.VectorTileFeature=Al,Tl.VectorTileLayer=Ll;const Rl=Tl.VectorTileFeature.types,Fl=Math.pow(2,13);function Bl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Fl)+o,i*Fl*2,a*Fl*2,Math.round(s))}class Nl{constructor(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((t=>t.id)),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new $a,this.centroidVertexArray=new Wa,this.indexArray=new ao,this.programConfigurations=new Bo(t.layers,t.zoom),this.segments=new ho,this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){this.features=[],this.hasPattern=hl("fill-extrusion",this.layers,e);for(const{feature:n,id:i,index:a,sourceLayerIndex:o}of t){const t=this.layers[0]._featureFilter.needGeometry,s=Go(n,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),s,r))continue;const l={id:i,sourceLayerIndex:o,index:a,geometry:t?s.geometry:Ho(n),properties:n.properties,type:n.type,patterns:{}};this.hasPattern?this.features.push(fl("fill-extrusion",this.layers,l,this.zoom,e)):this.addFeature(l,l.geometry,a,r,{}),e.featureIndex.insert(n,l.geometry,a,o,this.index,!0)}}addFeatures(t,e,r){for(const t of this.features){const{geometry:n}=t;this.addFeature(t,n,t.index,e,r)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}isEmpty(){return 0===this.layoutVertexArray.length&&0===this.centroidVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,_l),this.centroidVertexBuffer=t.createVertexBuffer(this.centroidVertexArray,xl.members,!0),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.centroidVertexBuffer.destroy())}addFeature(t,e,r,n,i){for(const r of br(e,500)){const e={x:0,y:0,vertexCount:0};let n=0;for(const t of r)n+=t.length;let i=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray);for(const t of r){if(0===t.length)continue;if(Ul(t))continue;let r=0;for(let n=0;n<t.length;n++){const a=t[n];if(n>=1){const o=t[n-1];if(!jl(a,o)){i.vertexLength+4>ho.MAX_VERTEX_ARRAY_LENGTH&&(i=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));const t=a.sub(o)._perp()._unit(),n=o.dist(a);r+n>32768&&(r=0),Bl(this.layoutVertexArray,a.x,a.y,t.x,t.y,0,0,r),Bl(this.layoutVertexArray,a.x,a.y,t.x,t.y,0,1,r),e.x+=2*a.x,e.y+=2*a.y,e.vertexCount+=2,r+=n,Bl(this.layoutVertexArray,o.x,o.y,t.x,t.y,0,0,r),Bl(this.layoutVertexArray,o.x,o.y,t.x,t.y,0,1,r),e.x+=2*o.x,e.y+=2*o.y,e.vertexCount+=2;const s=i.vertexLength;this.indexArray.emplaceBack(s,s+2,s+1),this.indexArray.emplaceBack(s+1,s+2,s+3),i.vertexLength+=4,i.primitiveLength+=2}}}}if(i.vertexLength+n>ho.MAX_VERTEX_ARRAY_LENGTH&&(i=this.segments.prepareSegment(n,this.layoutVertexArray,this.indexArray)),"Polygon"!==Rl[t.type])continue;const a=[],o=[],s=i.vertexLength;for(const t of r)if(0!==t.length){t!==r[0]&&o.push(a.length/2);for(let r=0;r<t.length;r++){const n=t[r];Bl(this.layoutVertexArray,n.x,n.y,0,0,1,1,0),e.x+=n.x,e.y+=n.y,e.vertexCount+=1,a.push(n.x),a.push(n.y)}}const l=js(a,o);for(let t=0;t<l.length;t+=3)this.indexArray.emplaceBack(s+l[t],s+l[t+2],s+l[t+1]);i.primitiveLength+=l.length/3,i.vertexLength+=n;for(let t=0;t<e.vertexCount;t++){const t=Math.floor(e.x/e.vertexCount),r=Math.floor(e.y/e.vertexCount);this.centroidVertexArray.emplaceBack(t,r)}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)}}function jl(t,e){return t.x===e.x&&(t.x<0||t.x>Uo)||t.y===e.y&&(t.y<0||t.y>Uo)}function Ul(t){return t.every((t=>t.x<0))||t.every((t=>t.x>Uo))||t.every((t=>t.y<0))||t.every((t=>t.y>Uo))}let Vl;Mi("FillExtrusionBucket",Nl,{omit:["layers","features"]});var ql={get paint(){return Vl=Vl||new ia({"fill-extrusion-opacity":new Qi(Z["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new ta(Z["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new Qi(Z["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new Qi(Z["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new ea(Z["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new ta(Z["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new ta(Z["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new Qi(Z["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})}};class Hl extends oa{constructor(t){super(t,ql)}createBucket(t){return new Nl(t)}queryRadius(){return os(this.paint.get("fill-extrusion-translate"))}is3D(){return!0}queryIntersectsFeature(t,e,r,n,i,o,s,l){const c=ss(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),h=this.paint.get("fill-extrusion-base").evaluate(e,r),f=function(t,e,r,n){const i=[];for(const r of t){const t=[r.x,r.y,n,1];vs(t,t,e),i.push(new a(t[0]/t[3],t[1]/t[3]))}return i}(c,l,0,0),p=function(t,e,r,n){const i=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,h=n[8]*r,f=n[9]*r,p=n[10]*r,d=n[11]*r;for(const e of t){const t=[],r=[];for(const i of e){const e=i.x,o=i.y,m=n[0]*e+n[4]*o+n[12],g=n[1]*e+n[5]*o+n[13],y=n[2]*e+n[6]*o+n[14],v=n[3]*e+n[7]*o+n[15],x=y+c,_=v+u,b=m+h,w=g+f,T=y+p,k=v+d,A=new a((m+s)/_,(g+l)/_);A.z=x/_,t.push(A);const M=new a(b/k,w/k);M.z=T/k,r.push(M)}i.push(t),o.push(r)}return[i,o]}(n,h,u,l);return function(t,e,r){let n=1/0;$o(r,e)&&(n=Zl(r,e[0]));for(let i=0;i<e.length;i++){const a=e[i],o=t[i];for(let t=0;t<a.length-1;t++){const e=a[t],i=a[t+1],s=o[t],l=[e,i,o[t+1],s,e];Yo(r,l)&&(n=Math.min(n,Zl(r,l)))}}return n!==1/0&&n}(p[0],p[1],f)}}function Gl(t,e){return t.x*e.x+t.y*e.y}function Zl(t,e){if(1===t.length){let r=0;const n=e[r++];let i;for(;!i||n.equals(i);)if(i=e[r++],!i)return 1/0;for(;r<e.length;r++){const a=e[r],o=t[0],s=i.sub(n),l=a.sub(n),c=o.sub(n),u=Gl(s,s),h=Gl(s,l),f=Gl(l,l),p=Gl(c,s),d=Gl(c,l),m=u*f-h*h,g=(f*p-h*d)/m,y=(u*d-h*p)/m,v=1-g-y,x=n.z*v+i.z*g+a.z*y;if(isFinite(x))return x}return 1/0}{let t=1/0;for(const r of e)t=Math.min(t,r.z);return t}}const Wl=ua([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4),{members:Yl,size:Xl,alignment:$l}=Wl,Jl=ua([{name:"a_uv_x",components:1,type:"Float32"},{name:"a_split_index",components:1,type:"Float32"}]),{members:Kl,size:Ql,alignment:tc}=Jl,ec=Tl.VectorTileFeature.types,rc=Math.cos(Math.PI/180*37.5),nc=Math.pow(2,14)/.5;class ic{constructor(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((t=>t.id)),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((t=>{this.gradients[t.id]={}})),this.layoutVertexArray=new Ja,this.layoutVertexArray2=new Ka,this.indexArray=new ao,this.programConfigurations=new Bo(t.layers,t.zoom),this.segments=new ho,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){this.hasPattern=hl("line",this.layers,e);const n=this.layers[0].layout.get("line-sort-key"),i=!n.isConstant(),a=[];for(const{feature:e,id:o,index:s,sourceLayerIndex:l}of t){const t=this.layers[0]._featureFilter.needGeometry,c=Go(e,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),c,r))continue;const u=i?n.evaluate(c,{},r):void 0,h={id:o,properties:e.properties,type:e.type,sourceLayerIndex:l,index:s,geometry:t?c.geometry:Ho(e),patterns:{},sortKey:u};a.push(h)}i&&a.sort(((t,e)=>t.sortKey-e.sortKey));for(const n of a){const{geometry:i,index:a,sourceLayerIndex:o}=n;if(this.hasPattern){const t=fl("line",this.layers,n,this.zoom,e);this.patternFeatures.push(t)}else this.addFeature(n,i,a,r,{});const s=t[a].feature;e.featureIndex.insert(s,i,a,o,this.index)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}addFeatures(t,e,r){for(const t of this.patternFeatures)this.addFeature(t,t.geometry,t.index,e,r)}isEmpty(){return 0===this.layoutVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(0!==this.layoutVertexArray2.length&&(this.layoutVertexBuffer2=t.createVertexBuffer(this.layoutVertexArray2,Kl)),this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Yl),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}lineFeatureClips(t){if(t.properties&&Object.prototype.hasOwnProperty.call(t.properties,"mapbox_clip_start")&&Object.prototype.hasOwnProperty.call(t.properties,"mapbox_clip_end"))return{start:+t.properties.mapbox_clip_start,end:+t.properties.mapbox_clip_end}}addFeature(t,e,r,n,i){const a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit");this.lineClips=this.lineFeatureClips(t);for(const r of e)this.addLine(r,t,o,s,l,c);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)}addLine(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(let e=0;e<t.length-1;e++)this.totalDistance+=t[e].dist(t[e+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}const o="Polygon"===ec[e.type];let s=t.length;for(;s>=2&&t[s-1].equals(t[s-2]);)s--;let l=0;for(;l<s-1&&t[l].equals(t[l+1]);)l++;if(s<(o?3:2))return;"bevel"===r&&(i=1.05);const c=this.overscaling<=16?15*Uo/(512*this.overscaling):0,u=this.segments.prepareSegment(10*s,this.layoutVertexArray,this.indexArray);let h,f,p,d,m;this.e1=this.e2=-1,o&&(h=t[s-2],m=t[l].sub(h)._unit()._perp());for(let e=l;e<s;e++){if(p=e===s-1?o?t[l+1]:void 0:t[e+1],p&&t[e].equals(p))continue;m&&(d=m),h&&(f=h),h=t[e],m=p?p.sub(h)._unit()._perp():d,d=d||m;let g=d.add(m);0===g.x&&0===g.y||g._unit();const y=d.x*m.x+d.y*m.y,v=g.x*m.x+g.y*m.y,x=0!==v?1/v:1/0,_=2*Math.sqrt(2-2*v),b=v<rc&&f&&p,w=d.x*m.y-d.y*m.x>0;if(b&&e>l){const t=h.dist(f);if(t>2*c){const e=h.sub(h.sub(f)._mult(c/t)._round());this.updateDistance(f,e),this.addCurrentVertex(e,d,0,0,u),f=e}}const T=f&&p;let k=T?r:o?"butt":n;if(T&&"round"===k&&(x<a?k="miter":x<=2&&(k="fakeround")),"miter"===k&&x>i&&(k="bevel"),"bevel"===k&&(x>2&&(k="flipbevel"),x<i&&(k="miter")),f&&this.updateDistance(f,h),"miter"===k)g._mult(x),this.addCurrentVertex(h,g,0,0,u);else if("flipbevel"===k){if(x>100)g=m.mult(-1);else{const t=x*d.add(m).mag()/d.sub(m).mag();g._perp()._mult(t*(w?-1:1))}this.addCurrentVertex(h,g,0,0,u),this.addCurrentVertex(h,g.mult(-1),0,0,u)}else if("bevel"===k||"fakeround"===k){const t=-Math.sqrt(x*x-1),e=w?t:0,r=w?0:t;if(f&&this.addCurrentVertex(h,d,e,r,u),"fakeround"===k){const t=Math.round(180*_/Math.PI/20);for(let e=1;e<t;e++){let r=e/t;if(.5!==r){const t=r-.5;r+=r*t*(r-1)*((1.0904+y*(y*(3.55645-1.43519*y)-3.2452))*t*t+(.848013+y*(.215638*y-1.06021)))}const n=m.sub(d)._mult(r)._add(d)._unit()._mult(w?-1:1);this.addHalfVertex(h,n.x,n.y,!1,w,0,u)}}p&&this.addCurrentVertex(h,m,-e,-r,u)}else if("butt"===k)this.addCurrentVertex(h,g,0,0,u);else if("square"===k){const t=f?1:-1;this.addCurrentVertex(h,g,t,t,u)}else"round"===k&&(f&&(this.addCurrentVertex(h,d,0,0,u),this.addCurrentVertex(h,d,1,1,u,!0)),p&&(this.addCurrentVertex(h,m,-1,-1,u,!0),this.addCurrentVertex(h,m,0,0,u)));if(b&&e<s-1){const t=h.dist(p);if(t>2*c){const e=h.add(p.sub(h)._mult(c/t)._round());this.updateDistance(h,e),this.addCurrentVertex(e,m,0,0,u),h=e}}}}addCurrentVertex(t,e,r,n,i,a=!1){const o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>nc/2&&0===this.totalDistance&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(t,e,r,n,i,a))}addHalfVertex({x:t,y:e},r,n,i,a,o,s){const l=.5*(this.lineClips?this.scaledDistance*(nc-1):this.scaledDistance);if(this.layoutVertexArray.emplaceBack((t<<1)+(i?1:0),(e<<1)+(a?1:0),Math.round(63*r)+128,Math.round(63*n)+128,1+(0===o?0:o<0?-1:1)|(63&l)<<2,l>>6),this.lineClips){const t=(this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start);this.layoutVertexArray2.emplaceBack(t,this.lineClipsArray.length)}const c=s.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,c),s.primitiveLength++),a?this.e2=c:this.e1=c}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(t,e){this.distance+=t.dist(e),this.updateScaledDistance()}}let ac;Mi("LineBucket",ic,{omit:["layers","patternFeatures"]});let oc;var sc={get paint(){return oc=oc||new ia({"line-opacity":new ta(Z.paint_line["line-opacity"]),"line-color":new ta(Z.paint_line["line-color"]),"line-translate":new Qi(Z.paint_line["line-translate"]),"line-translate-anchor":new Qi(Z.paint_line["line-translate-anchor"]),"line-width":new ta(Z.paint_line["line-width"]),"line-gap-width":new ta(Z.paint_line["line-gap-width"]),"line-offset":new ta(Z.paint_line["line-offset"]),"line-blur":new ta(Z.paint_line["line-blur"]),"line-dasharray":new ra(Z.paint_line["line-dasharray"]),"line-pattern":new ea(Z.paint_line["line-pattern"]),"line-gradient":new na(Z.paint_line["line-gradient"])})},get layout(){return ac=ac||new ia({"line-cap":new Qi(Z.layout_line["line-cap"]),"line-join":new ta(Z.layout_line["line-join"]),"line-miter-limit":new Qi(Z.layout_line["line-miter-limit"]),"line-round-limit":new Qi(Z.layout_line["line-round-limit"]),"line-sort-key":new ta(Z.layout_line["line-sort-key"])})}};class lc extends ta{possiblyEvaluate(t,e){return e=new Hi(Math.floor(e.zoom),{now:e.now,fadeDuration:e.fadeDuration,zoomHistory:e.zoomHistory,transition:e.transition}),super.possiblyEvaluate(t,e)}evaluate(t,e,r,n){return e=y({},e,{zoom:Math.floor(e.zoom)}),super.evaluate(t,e,r,n)}}let cc;class uc extends oa{constructor(t){super(t,sc),this.gradientVersion=0,cc||(cc=new lc(sc.paint.properties["line-width"].specification),cc.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(t){if("line-gradient"===t){const t=this.gradientExpression();!function(t){return void 0!==t._styleExpression}(t)?this.stepInterpolant=!1:this.stepInterpolant=t._styleExpression.expression instanceof Ae,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values["line-gradient"].value.expression}recalculate(t,e){super.recalculate(t,e),this.paint._values["line-floorwidth"]=cc.possiblyEvaluate(this._transitioningPaint._values["line-width"].value,t)}createBucket(t){return new ic(t)}queryRadius(t){const e=t,r=hc(as("line-width",this,e),as("line-gap-width",this,e)),n=as("line-offset",this,e);return r/2+Math.abs(n)+os(this.paint.get("line-translate"))}queryIntersectsFeature(t,e,r,n,i,o,s){const l=ss(t,this.paint.get("line-translate"),this.paint.get("line-translate-anchor"),o.angle,s),c=s/2*hc(this.paint.get("line-width").evaluate(e,r),this.paint.get("line-gap-width").evaluate(e,r)),u=this.paint.get("line-offset").evaluate(e,r);return u&&(n=function(t,e){const r=[];for(let n=0;n<t.length;n++){const i=t[n],o=[];for(let t=0;t<i.length;t++){const r=i[t-1],n=i[t],s=i[t+1],l=0===t?new a(0,0):n.sub(r)._unit()._perp(),c=t===i.length-1?new a(0,0):s.sub(n)._unit()._perp(),u=l._add(c)._unit(),h=u.x*c.x+u.y*c.y;0!==h&&u._mult(1/h),o.push(u._mult(e)._add(n))}r.push(o)}return r}(n,u*s)),function(t,e,r){for(let n=0;n<e.length;n++){const i=e[n];if(t.length>=3)for(let e=0;e<i.length;e++)if(ns(t,i[e]))return!0;if(Jo(t,i,r))return!0}return!1}(l,n,c)}isTileClipped(){return!0}}function hc(t,e){return e>0?e+2*t:t}const fc=ua([{name:"a_pos_offset",components:4,type:"Int16"},{name:"a_data",components:4,type:"Uint16"},{name:"a_pixeloffset",components:4,type:"Int16"}],4),pc=ua([{name:"a_projected_pos",components:3,type:"Float32"}],4);ua([{name:"a_fade_opacity",components:1,type:"Uint32"}],4);const dc=ua([{name:"a_placed",components:2,type:"Uint8"},{name:"a_shift",components:2,type:"Float32"},{name:"a_box_real",components:2,type:"Int16"}]);ua([{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Int16",name:"x1"},{type:"Int16",name:"y1"},{type:"Int16",name:"x2"},{type:"Int16",name:"y2"},{type:"Uint32",name:"featureIndex"},{type:"Uint16",name:"sourceLayerIndex"},{type:"Uint16",name:"bucketIndex"}]);const mc=ua([{name:"a_pos",components:2,type:"Int16"},{name:"a_anchor_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"}],4),gc=ua([{name:"a_pos",components:2,type:"Float32"},{name:"a_radius",components:1,type:"Float32"},{name:"a_flags",components:2,type:"Int16"}],4);function yc(t,e,r){return t.sections.forEach((t=>{t.text=function(t,e,r){const n=e.layout.get("text-transform").evaluate(r,{});return"uppercase"===n?t=t.toLocaleUpperCase():"lowercase"===n&&(t=t.toLocaleLowerCase()),qi.applyArabicShaping&&(t=qi.applyArabicShaping(t)),t}(t.text,e,r)})),t}ua([{name:"triangle",components:3,type:"Uint16"}]),ua([{type:"Int16",name:"anchorX"},{type:"Int16",name:"anchorY"},{type:"Uint16",name:"glyphStartIndex"},{type:"Uint16",name:"numGlyphs"},{type:"Uint32",name:"vertexStartIndex"},{type:"Uint32",name:"lineStartIndex"},{type:"Uint32",name:"lineLength"},{type:"Uint16",name:"segment"},{type:"Uint16",name:"lowerSize"},{type:"Uint16",name:"upperSize"},{type:"Float32",name:"lineOffsetX"},{type:"Float32",name:"lineOffsetY"},{type:"Uint8",name:"writingMode"},{type:"Uint8",name:"placedOrientation"},{type:"Uint8",name:"hidden"},{type:"Uint32",name:"crossTileID"},{type:"Int16",name:"associatedIconIndex"}]),ua([{type:"Int16",name:"anchorX"},{type:"Int16",name:"anchorY"},{type:"Int16",name:"rightJustifiedTextSymbolIndex"},{type:"Int16",name:"centerJustifiedTextSymbolIndex"},{type:"Int16",name:"leftJustifiedTextSymbolIndex"},{type:"Int16",name:"verticalPlacedTextSymbolIndex"},{type:"Int16",name:"placedIconSymbolIndex"},{type:"Int16",name:"verticalPlacedIconSymbolIndex"},{type:"Uint16",name:"key"},{type:"Uint16",name:"textBoxStartIndex"},{type:"Uint16",name:"textBoxEndIndex"},{type:"Uint16",name:"verticalTextBoxStartIndex"},{type:"Uint16",name:"verticalTextBoxEndIndex"},{type:"Uint16",name:"iconBoxStartIndex"},{type:"Uint16",name:"iconBoxEndIndex"},{type:"Uint16",name:"verticalIconBoxStartIndex"},{type:"Uint16",name:"verticalIconBoxEndIndex"},{type:"Uint16",name:"featureIndex"},{type:"Uint16",name:"numHorizontalGlyphVertices"},{type:"Uint16",name:"numVerticalGlyphVertices"},{type:"Uint16",name:"numIconVertices"},{type:"Uint16",name:"numVerticalIconVertices"},{type:"Uint16",name:"useRuntimeCollisionCircles"},{type:"Uint32",name:"crossTileID"},{type:"Float32",name:"textBoxScale"},{type:"Float32",name:"collisionCircleDiameter"},{type:"Uint16",name:"textAnchorOffsetStartIndex"},{type:"Uint16",name:"textAnchorOffsetEndIndex"}]),ua([{type:"Float32",name:"offsetX"}]),ua([{type:"Int16",name:"x"},{type:"Int16",name:"y"},{type:"Int16",name:"tileUnitDistanceFromAnchor"}]),ua([{type:"Uint16",name:"textAnchor"},{type:"Float32",components:2,name:"textOffset"}]);const vc={"!":"︕","#":"＃",$:"＄","%":"％","&":"＆","(":"︵",")":"︶","*":"＊","+":"＋",",":"︐","-":"︲",".":"・","/":"／",":":"︓",";":"︔","<":"︿","=":"＝",">":"﹀","?":"︖","@":"＠","[":"﹇","\\":"＼","]":"﹈","^":"＾",_:"︳","`":"｀","{":"︷","|":"―","}":"︸","~":"～","¢":"￠","£":"￡","¥":"￥","¦":"￤","¬":"￢","¯":"￣","–":"︲","—":"︱","‘":"﹃","’":"﹄","“":"﹁","”":"﹂","…":"︙","‧":"・","₩":"￦","、":"︑","。":"︒","〈":"︿","〉":"﹀","《":"︽","》":"︾","「":"﹁","」":"﹂","『":"﹃","』":"﹄","【":"︻","】":"︼","〔":"︹","〕":"︺","〖":"︗","〗":"︘","！":"︕","（":"︵","）":"︶","，":"︐","－":"︲","．":"・","：":"︓","；":"︔","＜":"︿","＞":"﹀","？":"︖","［":"﹇","］":"﹈","＿":"︳","｛":"︷","｜":"―","｝":"︸","｟":"︵","｠":"︶","｡":"︒","｢":"﹁","｣":"﹂"};var xc=24,_c=wc,bc={read:function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,h=r?i-1:0,f=r?-1:1,p=t[e+h];for(h+=f,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+h],h+=f,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+h],h+=f,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},write:function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,h=u>>1,f=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,m=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+h>=1?f/l:f*Math.pow(2,1-h))*l>=2&&(o++,l/=2),o+h>=u?(s=0,o=u):o+h>=1?(s=(e*l-1)*Math.pow(2,i),o+=h):(s=e*Math.pow(2,h-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*m}};function wc(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new Uint8Array(t||0),this.pos=0,this.type=0,this.length=this.buf.length}wc.Varint=0,wc.Fixed64=1,wc.Bytes=2,wc.Fixed32=5;var Tc=4294967296,kc=1/Tc,Ac="undefined"==typeof TextDecoder?null:new TextDecoder("utf-8");function Mc(t){return t.type===wc.Bytes?t.readVarint()+t.pos:t.pos+1}function Sc(t,e,r){return r?4294967296*e+(t>>>0):4294967296*(e>>>0)+(t>>>0)}function Ec(t,e,r){var n=e<=16383?1:e<=2097151?2:e<=268435455?3:Math.floor(Math.log(e)/(7*Math.LN2));r.realloc(n);for(var i=r.pos-1;i>=t;i--)r.buf[i+n]=r.buf[i]}function Cc(t,e){for(var r=0;r<t.length;r++)e.writeVarint(t[r])}function Lc(t,e){for(var r=0;r<t.length;r++)e.writeSVarint(t[r])}function Ic(t,e){for(var r=0;r<t.length;r++)e.writeFloat(t[r])}function Pc(t,e){for(var r=0;r<t.length;r++)e.writeDouble(t[r])}function zc(t,e){for(var r=0;r<t.length;r++)e.writeBoolean(t[r])}function Oc(t,e){for(var r=0;r<t.length;r++)e.writeFixed32(t[r])}function Dc(t,e){for(var r=0;r<t.length;r++)e.writeSFixed32(t[r])}function Rc(t,e){for(var r=0;r<t.length;r++)e.writeFixed64(t[r])}function Fc(t,e){for(var r=0;r<t.length;r++)e.writeSFixed64(t[r])}function Bc(t,e){return(t[e]|t[e+1]<<8|t[e+2]<<16)+16777216*t[e+3]}function Nc(t,e,r){t[r]=e,t[r+1]=e>>>8,t[r+2]=e>>>16,t[r+3]=e>>>24}function jc(t,e){return(t[e]|t[e+1]<<8|t[e+2]<<16)+(t[e+3]<<24)}wc.prototype={destroy:function(){this.buf=null},readFields:function(t,e,r){for(r=r||this.length;this.pos<r;){var n=this.readVarint(),i=n>>3,a=this.pos;this.type=7&n,t(i,e,this),this.pos===a&&this.skip(n)}return e},readMessage:function(t,e){return this.readFields(t,e,this.readVarint()+this.pos)},readFixed32:function(){var t=Bc(this.buf,this.pos);return this.pos+=4,t},readSFixed32:function(){var t=jc(this.buf,this.pos);return this.pos+=4,t},readFixed64:function(){var t=Bc(this.buf,this.pos)+Bc(this.buf,this.pos+4)*Tc;return this.pos+=8,t},readSFixed64:function(){var t=Bc(this.buf,this.pos)+jc(this.buf,this.pos+4)*Tc;return this.pos+=8,t},readFloat:function(){var t=bc.read(this.buf,this.pos,!0,23,4);return this.pos+=4,t},readDouble:function(){var t=bc.read(this.buf,this.pos,!0,52,8);return this.pos+=8,t},readVarint:function(t){var e,r,n=this.buf;return e=127&(r=n[this.pos++]),r<128?e:(e|=(127&(r=n[this.pos++]))<<7,r<128?e:(e|=(127&(r=n[this.pos++]))<<14,r<128?e:(e|=(127&(r=n[this.pos++]))<<21,r<128?e:function(t,e,r){var n,i,a=r.buf;if(n=(112&(i=a[r.pos++]))>>4,i<128)return Sc(t,n,e);if(n|=(127&(i=a[r.pos++]))<<3,i<128)return Sc(t,n,e);if(n|=(127&(i=a[r.pos++]))<<10,i<128)return Sc(t,n,e);if(n|=(127&(i=a[r.pos++]))<<17,i<128)return Sc(t,n,e);if(n|=(127&(i=a[r.pos++]))<<24,i<128)return Sc(t,n,e);if(n|=(1&(i=a[r.pos++]))<<31,i<128)return Sc(t,n,e);throw new Error("Expected varint not more than 10 bytes")}(e|=(15&(r=n[this.pos]))<<28,t,this))))},readVarint64:function(){return this.readVarint(!0)},readSVarint:function(){var t=this.readVarint();return t%2==1?(t+1)/-2:t/2},readBoolean:function(){return Boolean(this.readVarint())},readString:function(){var t=this.readVarint()+this.pos,e=this.pos;return this.pos=t,t-e>=12&&Ac?function(t,e,r){return Ac.decode(t.subarray(e,r))}(this.buf,e,t):function(t,e,r){for(var n="",i=e;i<r;){var a,o,s,l=t[i],c=null,u=l>239?4:l>223?3:l>191?2:1;if(i+u>r)break;1===u?l<128&&(c=l):2===u?128==(192&(a=t[i+1]))&&(c=(31&l)<<6|63&a)<=127&&(c=null):3===u?(a=t[i+1],o=t[i+2],128==(192&a)&&128==(192&o)&&((c=(15&l)<<12|(63&a)<<6|63&o)<=2047||c>=55296&&c<=57343)&&(c=null)):4===u&&(a=t[i+1],o=t[i+2],s=t[i+3],128==(192&a)&&128==(192&o)&&128==(192&s)&&((c=(15&l)<<18|(63&a)<<12|(63&o)<<6|63&s)<=65535||c>=1114112)&&(c=null)),null===c?(c=65533,u=1):c>65535&&(c-=65536,n+=String.fromCharCode(c>>>10&1023|55296),c=56320|1023&c),n+=String.fromCharCode(c),i+=u}return n}(this.buf,e,t)},readBytes:function(){var t=this.readVarint()+this.pos,e=this.buf.subarray(this.pos,t);return this.pos=t,e},readPackedVarint:function(t,e){if(this.type!==wc.Bytes)return t.push(this.readVarint(e));var r=Mc(this);for(t=t||[];this.pos<r;)t.push(this.readVarint(e));return t},readPackedSVarint:function(t){if(this.type!==wc.Bytes)return t.push(this.readSVarint());var e=Mc(this);for(t=t||[];this.pos<e;)t.push(this.readSVarint());return t},readPackedBoolean:function(t){if(this.type!==wc.Bytes)return t.push(this.readBoolean());var e=Mc(this);for(t=t||[];this.pos<e;)t.push(this.readBoolean());return t},readPackedFloat:function(t){if(this.type!==wc.Bytes)return t.push(this.readFloat());var e=Mc(this);for(t=t||[];this.pos<e;)t.push(this.readFloat());return t},readPackedDouble:function(t){if(this.type!==wc.Bytes)return t.push(this.readDouble());var e=Mc(this);for(t=t||[];this.pos<e;)t.push(this.readDouble());return t},readPackedFixed32:function(t){if(this.type!==wc.Bytes)return t.push(this.readFixed32());var e=Mc(this);for(t=t||[];this.pos<e;)t.push(this.readFixed32());return t},readPackedSFixed32:function(t){if(this.type!==wc.Bytes)return t.push(this.readSFixed32());var e=Mc(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==wc.Bytes)return t.push(this.readFixed64());var e=Mc(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==wc.Bytes)return t.push(this.readSFixed64());var e=Mc(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===wc.Varint)for(;this.buf[this.pos++]>127;);else if(e===wc.Bytes)this.pos=this.readVarint()+this.pos;else if(e===wc.Fixed32)this.pos+=4;else{if(e!==wc.Fixed64)throw new Error("Unimplemented type: "+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return this.length=this.pos,this.pos=0,this.buf.subarray(0,this.length)},writeFixed32:function(t){this.realloc(4),Nc(this.buf,t,this.pos),this.pos+=4},writeSFixed32:function(t){this.realloc(4),Nc(this.buf,t,this.pos),this.pos+=4},writeFixed64:function(t){this.realloc(8),Nc(this.buf,-1&t,this.pos),Nc(this.buf,Math.floor(t*kc),this.pos+4),this.pos+=8},writeSFixed64:function(t){this.realloc(8),Nc(this.buf,-1&t,this.pos),Nc(this.buf,Math.floor(t*kc),this.pos+4),this.pos+=8},writeVarint:function(t){(t=+t||0)>268435455||t<0?function(t,e){var r,n;if(t>=0?(r=t%4294967296|0,n=t/4294967296|0):(n=~(-t/4294967296),4294967295^(r=~(-t%4294967296))?r=r+1|0:(r=0,n=n+1|0)),t>=0x10000000000000000||t<-0x10000000000000000)throw new Error("Given varint doesn't fit into 10 bytes");e.realloc(10),function(t,e,r){r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos++]=127&t|128,t>>>=7,r.buf[r.pos]=127&t}(r,0,e),function(t,e){var r=(7&t)<<4;e.buf[e.pos++]|=r|((t>>>=3)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),t&&(e.buf[e.pos++]=127&t)))))}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&Ec(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),bc.write(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),bc.write(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&Ec(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,wc.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,Cc,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Lc,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,zc,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Ic,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,Pc,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,Oc,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,Dc,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,Rc,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,Fc,e)},writeBytesField:function(t,e){this.writeTag(t,wc.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,wc.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,wc.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,wc.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,wc.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,wc.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,wc.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,wc.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,wc.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,wc.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var Uc=r(_c);const Vc=3;function qc(t,e,r){1===t&&r.readMessage(Hc,e)}function Hc(t,e,r){if(3===t){const{id:t,bitmap:n,width:i,height:a,left:o,top:s,advance:l}=r.readMessage(Gc,{});e.push({id:t,bitmap:new Cs({width:i+2*Vc,height:a+2*Vc},n),metrics:{width:i,height:a,left:o,top:s,advance:l}})}}function Gc(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}const Zc=Vc;function Wc(t){let e=0,r=0;for(const n of t)e+=n.w*n.h,r=Math.max(r,n.w);t.sort(((t,e)=>e.h-t.h));const n=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}];let i=0,a=0;for(const e of t)for(let t=n.length-1;t>=0;t--){const r=n[t];if(!(e.w>r.w||e.h>r.h)){if(e.x=r.x,e.y=r.y,a=Math.max(a,e.y+e.h),i=Math.max(i,e.x+e.w),e.w===r.w&&e.h===r.h){const e=n.pop();t<n.length&&(n[t]=e)}else e.h===r.h?(r.x+=e.w,r.w-=e.w):e.w===r.w?(r.y+=e.h,r.h-=e.h):(n.push({x:r.x+e.w,y:r.y,w:r.w-e.w,h:e.h}),r.y+=e.h,r.h-=e.h);break}}return{w:i,h:a,fill:e/(i*a)||0}}const Yc=1;class Xc{constructor(t,{pixelRatio:e,version:r,stretchX:n,stretchY:i,content:a,textFitWidth:o,textFitHeight:s}){this.paddedRect=t,this.pixelRatio=e,this.stretchX=n,this.stretchY=i,this.content=a,this.version=r,this.textFitWidth=o,this.textFitHeight=s}get tl(){return[this.paddedRect.x+Yc,this.paddedRect.y+Yc]}get br(){return[this.paddedRect.x+this.paddedRect.w-Yc,this.paddedRect.y+this.paddedRect.h-Yc]}get tlbr(){return this.tl.concat(this.br)}get displaySize(){return[(this.paddedRect.w-2*Yc)/this.pixelRatio,(this.paddedRect.h-2*Yc)/this.pixelRatio]}}class $c{constructor(t,e){const r={},n={};this.haveRenderCallbacks=[];const i=[];this.addImages(t,r,i),this.addImages(e,n,i);const{w:a,h:o}=Wc(i),s=new Ls({width:a||1,height:o||1});for(const e in t){const n=t[e],i=r[e].paddedRect;Ls.copy(n.data,s,{x:0,y:0},{x:i.x+Yc,y:i.y+Yc},n.data)}for(const t in e){const r=e[t],i=n[t].paddedRect,a=i.x+Yc,o=i.y+Yc,l=r.data.width,c=r.data.height;Ls.copy(r.data,s,{x:0,y:0},{x:a,y:o},r.data),Ls.copy(r.data,s,{x:0,y:c-1},{x:a,y:o-1},{width:l,height:1}),Ls.copy(r.data,s,{x:0,y:0},{x:a,y:o+c},{width:l,height:1}),Ls.copy(r.data,s,{x:l-1,y:0},{x:a-1,y:o},{width:1,height:c}),Ls.copy(r.data,s,{x:0,y:0},{x:a+l,y:o},{width:1,height:c})}this.image=s,this.iconPositions=r,this.patternPositions=n}addImages(t,e,r){for(const n in t){const i=t[n],a={x:0,y:0,w:i.data.width+2*Yc,h:i.data.height+2*Yc};r.push(a),e[n]=new Xc(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}}patchUpdatedImages(t,e){t.dispatchRenderCallbacks(this.haveRenderCallbacks);for(const r in t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)}patchUpdatedImage(t,e,r){if(!t||!e)return;if(t.version===e.version)return;t.version=e.version;const[n,i]=t.tl;r.update(e.data,void 0,{x:n,y:i})}}var Jc;Mi("ImagePosition",Xc),Mi("ImageAtlas",$c),t.ai=void 0,(Jc=t.ai||(t.ai={}))[Jc.none=0]="none",Jc[Jc.horizontal=1]="horizontal",Jc[Jc.vertical=2]="vertical",Jc[Jc.horizontalOnly=3]="horizontalOnly";const Kc=-17;class Qc{constructor(){this.scale=1,this.fontStack="",this.imageName=null}static forText(t,e){const r=new Qc;return r.scale=t||1,r.fontStack=e,r}static forImage(t){const e=new Qc;return e.imageName=t,e}}class tu{constructor(){this.text="",this.sectionIndex=[],this.sections=[],this.imageSectionID=null}static fromFeature(t,e){const r=new tu;for(let n=0;n<t.sections.length;n++){const i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r}length(){return this.text.length}getSection(t){return this.sections[this.sectionIndex[t]]}getSectionIndex(t){return this.sectionIndex[t]}getCharCode(t){return this.text.charCodeAt(t)}verticalizePunctuation(){this.text=function(t){let e="";for(let r=0;r<t.length;r++){const n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;n&&Bi(n)&&!vc[t[r+1]]||i&&Bi(i)&&!vc[t[r-1]]||!vc[t[r]]?e+=t[r]:e+=vc[t[r]]}return e}(this.text)}trim(){let t=0;for(let e=0;e<this.text.length&&ru[this.text.charCodeAt(e)];e++)t++;let e=this.text.length;for(let r=this.text.length-1;r>=0&&r>=t&&ru[this.text.charCodeAt(r)];r--)e--;this.text=this.text.substring(t,e),this.sectionIndex=this.sectionIndex.slice(t,e)}substring(t,e){const r=new tu;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r}toString(){return this.text}getMaxScale(){return this.sectionIndex.reduce(((t,e)=>Math.max(t,this.sections[e].scale)),0)}addTextSection(t,e){this.text+=t.text,this.sections.push(Qc.forText(t.scale,t.fontStack||e));const r=this.sections.length-1;for(let e=0;e<t.text.length;++e)this.sectionIndex.push(r)}addImageSection(t){const e=t.image?t.image.name:"";if(0===e.length)return void T("Can't add FormattedSection with an empty image.");const r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(Qc.forImage(e)),this.sectionIndex.push(this.sections.length-1)):T("Reached maximum number of images 6401")}getNextImageSectionCharCode(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)}}function eu(e,r,n,i,a,o,s,l,c,u,h,f,p,d,m){const g=tu.fromFeature(e,a);let y;f===t.ai.vertical&&g.verticalizePunctuation();const{processBidirectionalText:v,processStyledBidirectionalText:x}=qi;if(v&&1===g.sections.length){y=[];const t=v(g.toString(),uu(g,u,o,r,i,d));for(const e of t){const t=new tu;t.text=e,t.sections=g.sections;for(let r=0;r<e.length;r++)t.sectionIndex.push(0);y.push(t)}}else if(x){y=[];const t=x(g.text,g.sectionIndex,uu(g,u,o,r,i,d));for(const e of t){const t=new tu;t.text=e[0],t.sectionIndex=e[1],t.sections=g.sections,y.push(t)}}else y=function(t,e){const r=[],n=t.text;let i=0;for(const n of e)r.push(t.substring(i,n)),i=n;return i<n.length&&r.push(t.substring(i,n.length)),r}(g,uu(g,u,o,r,i,d));const _=[],b={positionedLines:_,text:g.toString(),top:h[1],bottom:h[1],left:h[0],right:h[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(e,r,n,i,a,o,s,l,c,u,h,f){let p=0,d=Kc,m=0,g=0;const y="right"===l?1:"left"===l?0:.5;let v=0;for(const s of a){s.trim();const a=s.getMaxScale(),l=(a-1)*xc,x={positionedGlyphs:[],lineOffset:0};e.positionedLines[v]=x;const _=x.positionedGlyphs;let b=0;if(!s.length()){d+=o,++v;continue}for(let o=0;o<s.length();o++){const m=s.getSection(o),g=s.getSectionIndex(o),y=s.getCharCode(o);let v=0,x=null,w=null,T=null,k=xc;const A=!(c===t.ai.horizontal||!h&&!Fi(y)||h&&(ru[y]||Ni(y)));if(m.imageName){const t=i[m.imageName];if(!t)continue;T=m.imageName,e.iconsInText=e.iconsInText||!0,w=t.paddedRect;const r=t.displaySize;m.scale=m.scale*xc/f,x={width:r[0],height:r[1],left:Yc,top:-Zc,advance:A?r[1]:r[0]},v=l+(xc-r[1]*m.scale),k=x.advance;const n=A?r[0]*m.scale-xc*a:r[1]*m.scale-xc*a;n>0&&n>b&&(b=n)}else{const t=n[m.fontStack],e=t&&t[y];if(e&&e.rect)w=e.rect,x=e.metrics;else{const t=r[m.fontStack],e=t&&t[y];if(!e)continue;x=e.metrics}v=(a-m.scale)*xc}A?(e.verticalizable=!0,_.push({glyph:y,imageName:T,x:p,y:d+v,vertical:A,scale:m.scale,fontStack:m.fontStack,sectionIndex:g,metrics:x,rect:w}),p+=k*m.scale+u):(_.push({glyph:y,imageName:T,x:p,y:d+v,vertical:A,scale:m.scale,fontStack:m.fontStack,sectionIndex:g,metrics:x,rect:w}),p+=x.advance*m.scale+u)}if(0!==_.length){const t=p-u;m=Math.max(t,m),fu(_,0,_.length-1,y,b)}p=0;const w=o*a+b;x.lineOffset=Math.max(b,l),d+=w,g=Math.max(w,g),++v}const x=d-Kc,{horizontalAlign:_,verticalAlign:b}=hu(s);(function(t,e,r,n,i,a,o,s,l){const c=(e-r)*i;let u=0;u=a!==o?-s*n-Kc:(-n*l+.5)*o;for(const e of t)for(const t of e.positionedGlyphs)t.x+=c,t.y+=u})(e.positionedLines,y,_,b,m,g,o,x,a.length),e.top+=-b*x,e.bottom=e.top+x,e.left+=-_*m,e.right=e.left+m}(b,r,n,i,y,s,l,c,f,u,p,m),!function(t){for(const e of t)if(0!==e.positionedGlyphs.length)return!1;return!0}(_)&&b}const ru={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},nu={10:!0,32:!0,38:!0,41:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0,8231:!0},iu={40:!0};function au(t,e,r,n,i,a){if(e.imageName){const t=n[e.imageName];return t?t.displaySize[0]*e.scale*xc/a+i:0}{const n=r[e.fontStack],a=n&&n[t];return a?a.metrics.advance*e.scale+i:0}}function ou(t,e,r,n){const i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function su(t,e,r){let n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function lu(t,e,r,n,i,a){let o=null,s=ou(e,r,i,a);for(const t of n){const n=ou(e-t.x,r,i,a)+t.badness;n<=s&&(o=t,s=n)}return{index:t,x:e,priorBreak:o,badness:s}}function cu(t){return t?cu(t.priorBreak).concat(t.index):[]}function uu(t,e,r,n,i,a){if(!t)return[];const o=[],s=function(t,e,r,n,i,a){let o=0;for(let r=0;r<t.length();r++){const s=t.getSection(r);o+=au(t.getCharCode(r),s,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,a),l=t.text.indexOf("​")>=0;let c=0;for(let r=0;r<t.length();r++){const h=t.getSection(r),f=t.getCharCode(r);if(ru[f]||(c+=au(f,h,n,i,e,a)),r<t.length()-1){const e=!((u=f)<11904||!(zi["Bopomofo Extended"](u)||zi.Bopomofo(u)||zi["CJK Compatibility Forms"](u)||zi["CJK Compatibility Ideographs"](u)||zi["CJK Compatibility"](u)||zi["CJK Radicals Supplement"](u)||zi["CJK Strokes"](u)||zi["CJK Symbols and Punctuation"](u)||zi["CJK Unified Ideographs Extension A"](u)||zi["CJK Unified Ideographs"](u)||zi["Enclosed CJK Letters and Months"](u)||zi["Halfwidth and Fullwidth Forms"](u)||zi.Hiragana(u)||zi["Ideographic Description Characters"](u)||zi["Kangxi Radicals"](u)||zi["Katakana Phonetic Extensions"](u)||zi.Katakana(u)||zi["Vertical Forms"](u)||zi["Yi Radicals"](u)||zi["Yi Syllables"](u)));(nu[f]||e||h.imageName||r!==t.length()-2&&iu[t.getCharCode(r+1)])&&o.push(lu(r+1,c,s,o,su(f,t.getCharCode(r+1),e&&l),!1))}}var u;return cu(lu(t.length(),c,s,o,0,!0))}function hu(t){let e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function fu(t,e,r,n,i){if(!n&&!i)return;const a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n;for(let n=e;n<=r;n++)t[n].x-=s,t[n].y+=i}function pu(t,e,r){const{horizontalAlign:n,verticalAlign:i}=hu(r),a=e[0],o=e[1],s=a-t.displaySize[0]*n,l=s+t.displaySize[0],c=o-t.displaySize[1]*i;return{image:t,top:c,bottom:c+t.displaySize[1],left:s,right:l}}function du(t){var e,r;let n=t.left,i=t.top,a=t.right-n,o=t.bottom-i;const s=t.image.content[2]-t.image.content[0],l=t.image.content[3]-t.image.content[1],c=null!==(e=t.image.textFitWidth)&&void 0!==e?e:"stretchOrShrink",u=null!==(r=t.image.textFitHeight)&&void 0!==r?r:"stretchOrShrink",h=s/l;if("proportional"===u){if("stretchOnly"===c&&a/o<h||"proportional"===c){const t=Math.ceil(o*h);n*=t/a,a=t}}else if("proportional"===c&&"stretchOnly"===u&&0!==h&&a/o>h){const t=Math.ceil(a/h);i*=t/o,o=t}return{x1:n,y1:i,x2:n+a,y2:i+o}}function mu(t,e,r,n,i,a){const o=t.image;let s;if(o.content){const t=o.content,e=o.pixelRatio||1;s=[t[0]/e,t[1]/e,o.displaySize[0]-t[2]/e,o.displaySize[1]-t[3]/e]}const l=e.left*a,c=e.right*a;let u,h,f,p;"width"===r||"both"===r?(p=i[0]+l-n[3],h=i[0]+c+n[1]):(p=i[0]+(l+c-o.displaySize[0])/2,h=p+o.displaySize[0]);const d=e.top*a,m=e.bottom*a;return"height"===r||"both"===r?(u=i[1]+d-n[0],f=i[1]+m+n[2]):(u=i[1]+(d+m-o.displaySize[1])/2,f=u+o.displaySize[1]),{image:o,top:u,right:h,bottom:f,left:p,collisionPadding:s}}const gu=255,yu=128,vu=gu*yu;function xu(t,e){const{expression:r}=e;if("constant"===r.kind)return{kind:"constant",layoutSize:r.evaluate(new Hi(t+1))};if("source"===r.kind)return{kind:"source"};{const{zoomStops:e,interpolationType:n}=r;let i=0;for(;i<e.length&&e[i]<=t;)i++;i=Math.max(0,i-1);let a=i;for(;a<e.length&&e[a]<t+1;)a++;a=Math.min(e.length-1,a);const o=e[i],s=e[a];return"composite"===r.kind?{kind:"composite",minZoom:o,maxZoom:s,interpolationType:n}:{kind:"camera",minZoom:o,maxZoom:s,minSize:r.evaluate(new Hi(o)),maxSize:r.evaluate(new Hi(s)),interpolationType:n}}}function _u(t,e,r){let n="never";const i=t.get(e);return i?n=i:t.get(r)&&(n="always"),n}const bu=Tl.VectorTileFeature.types,wu=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function Tu(t,e,r,n,i,a,o,s,l,c,u,h,f){const p=s?Math.min(vu,Math.round(s[0])):0,d=s?Math.min(vu,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*h,256*f)}function ku(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Au(t){for(const e of t.sections)if(Vi(e.text))return!0;return!1}class Mu{constructor(t){this.layoutVertexArray=new to,this.indexArray=new ao,this.programConfigurations=t,this.segments=new ho,this.dynamicLayoutVertexArray=new eo,this.opacityVertexArray=new ro,this.hasVisibleVertices=!1,this.placedSymbolArray=new Ba}isEmpty(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length}upload(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,fc.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,pc.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,wu,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())}}Mi("SymbolBuffers",Mu);class Su{constructor(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new ho,this.collisionVertexArray=new io}upload(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,dc.members,!0)}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())}}Mi("CollisionBuffers",Su);class Eu{constructor(e){this.collisionBoxArray=e.collisionBoxArray,this.zoom=e.zoom,this.overscaling=e.overscaling,this.layers=e.layers,this.layerIds=this.layers.map((t=>t.id)),this.index=e.index,this.pixelRatio=e.pixelRatio,this.sourceLayerIndex=e.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ps([]),this.placementViewportMatrix=ps([]);const r=this.layers[0]._unevaluatedLayout._values;this.textSizeData=xu(this.zoom,r["text-size"]),this.iconSizeData=xu(this.zoom,r["icon-size"]);const n=this.layers[0].layout,i=n.get("symbol-sort-key"),a=n.get("symbol-z-order");this.canOverlap="never"!==_u(n,"text-overlap","text-allow-overlap")||"never"!==_u(n,"icon-overlap","icon-allow-overlap")||n.get("text-ignore-placement")||n.get("icon-ignore-placement"),this.sortFeaturesByKey="viewport-y"!==a&&!i.isConstant();const o="viewport-y"===a||"auto"===a&&!this.sortFeaturesByKey;this.sortFeaturesByY=o&&this.canOverlap,"point"===n.get("symbol-placement")&&(this.writingModes=n.get("text-writing-mode").map((e=>t.ai[e]))),this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id)),this.sourceID=e.sourceID}createArrays(){this.text=new Mu(new Bo(this.layers,this.zoom,(t=>/^text/.test(t)))),this.icon=new Mu(new Bo(this.layers,this.zoom,(t=>/^icon/.test(t)))),this.glyphOffsetArray=new Ua,this.lineVertexArray=new Va,this.symbolInstances=new ja,this.textAnchorOffsets=new Ha}calculateGlyphDependencies(t,e,r,n,i){for(let a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){const r=vc[t.charAt(a)];r&&(e[r.charCodeAt(0)]=!0)}}populate(e,r,n){const i=this.layers[0],a=i.layout,o=a.get("text-font"),s=a.get("text-field"),l=a.get("icon-image"),c=("constant"!==s.value.kind||s.value.value instanceof Kt&&!s.value.value.isEmpty()||s.value.value.toString().length>0)&&("constant"!==o.value.kind||o.value.value.length>0),u="constant"!==l.value.kind||!!l.value.value||Object.keys(l.parameters).length>0,h=a.get("symbol-sort-key");if(this.features=[],!c&&!u)return;const f=r.iconDependencies,p=r.glyphDependencies,d=r.availableImages,m=new Hi(this.zoom);for(const{feature:r,id:s,index:l,sourceLayerIndex:g}of e){const e=i._featureFilter.needGeometry,y=Go(r,e);if(!i._featureFilter.filter(m,y,n))continue;let v,x;if(e||(y.geometry=Ho(r)),c){const t=i.getValueAndResolveTokens("text-field",y,n,d),e=Kt.factory(t),r=this.hasRTLText=this.hasRTLText||Au(e);(!r||"unavailable"===qi.getRTLTextPluginStatus()||r&&qi.isParsed())&&(v=yc(e,i,y))}if(u){const t=i.getValueAndResolveTokens("icon-image",y,n,d);x=t instanceof re?t:re.fromString(t)}if(!v&&!x)continue;const _=this.sortFeaturesByKey?h.evaluate(y,{},n):void 0,b={id:s,text:v,icon:x,index:l,sourceLayerIndex:g,geometry:y.geometry,properties:r.properties,type:bu[r.type],sortKey:_};if(this.features.push(b),x&&(f[x.name]=!0),v){const e=o.evaluate(y,{},n).join(","),r="viewport"!==a.get("text-rotation-alignment")&&"point"!==a.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(t.ai.vertical)>=0;for(const t of v.sections)if(t.image)f[t.image.name]=!0;else{const n=Oi(v.toString()),i=t.fontStack||e,a=p[i]=p[i]||{};this.calculateGlyphDependencies(t.text,a,r,this.allowVerticalPlacement,n)}}}"line"===a.get("symbol-placement")&&(this.features=function(t){const e={},r={},n=[];let i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){const a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){const a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){const n=r?e[0][e[0].length-1]:e[0][0];return`${t}:${n.x}:${n.y}`}for(let c=0;c<t.length;c++){const u=t[c],h=u.geometry,f=u.text?u.text.toString():null;if(!f){a(c);continue}const p=l(f,h),d=l(f,h,!0);if(p in r&&d in e&&r[p]!==e[d]){const t=s(p,d,h),i=o(p,d,n[t].geometry);delete e[p],delete r[d],r[l(f,n[i].geometry,!0)]=i,n[t].geometry=null}else p in r?o(p,d,h):d in e?s(p,d,h):(a(c),e[p]=i-1,r[d]=i-1)}return n.filter((t=>t.geometry))}(this.features)),this.sortFeaturesByKey&&this.features.sort(((t,e)=>t.sortKey-e.sortKey))}update(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))}isEmpty(){return 0===this.symbolInstances.length&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(t,e){const r=this.lineVertexArray.length;if(void 0!==t.segment){let r=t.dist(e[t.segment+1]),n=t.dist(e[t.segment]);const i={};for(let n=t.segment+1;n<e.length;n++)i[n]={x:e[n].x,y:e[n].y,tileUnitDistanceFromAnchor:r},n<e.length-1&&(r+=e[n+1].dist(e[n]));for(let r=t.segment||0;r>=0;r--)i[r]={x:e[r].x,y:e[r].y,tileUnitDistanceFromAnchor:n},r>0&&(n+=e[r-1].dist(e[r]));for(let t=0;t<e.length;t++){const e=i[t];this.lineVertexArray.emplaceBack(e.x,e.y,e.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}}addSymbols(e,r,n,i,a,o,s,l,c,u,h,f){const p=e.indexArray,d=e.layoutVertexArray,m=e.segments.prepareSegment(4*r.length,d,p,this.canOverlap?o.sortKey:void 0),g=this.glyphOffsetArray.length,y=m.vertexLength,v=this.allowVerticalPlacement&&s===t.ai.vertical?Math.PI/2:0,x=o.text&&o.text.sections;for(let t=0;t<r.length;t++){const{tl:i,tr:a,bl:s,br:c,tex:u,pixelOffsetTL:h,pixelOffsetBR:g,minFontScaleX:y,minFontScaleY:_,glyphOffset:b,isSDF:w,sectionIndex:T}=r[t],k=m.vertexLength,A=b[1];Tu(d,l.x,l.y,i.x,A+i.y,u.x,u.y,n,w,h.x,h.y,y,_),Tu(d,l.x,l.y,a.x,A+a.y,u.x+u.w,u.y,n,w,g.x,h.y,y,_),Tu(d,l.x,l.y,s.x,A+s.y,u.x,u.y+u.h,n,w,h.x,g.y,y,_),Tu(d,l.x,l.y,c.x,A+c.y,u.x+u.w,u.y+u.h,n,w,g.x,g.y,y,_),ku(e.dynamicLayoutVertexArray,l,v),p.emplaceBack(k,k+1,k+2),p.emplaceBack(k+1,k+2,k+3),m.vertexLength+=4,m.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(b[0]),t!==r.length-1&&T===r[t+1].sectionIndex||e.programConfigurations.populatePaintArrays(d.length,o,o.index,{},f,x&&x[T])}e.placedSymbolArray.emplaceBack(l.x,l.y,g,this.glyphOffsetArray.length-g,y,c,u,l.segment,n?n[0]:0,n?n[1]:0,i[0],i[1],s,0,!1,0,h)}_addCollisionDebugVertex(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))}addCollisionDebugVertices(t,e,r,n,i,o,s){const l=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),c=l.vertexLength,u=i.layoutVertexArray,h=i.collisionVertexArray,f=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,h,o,f,p,new a(t,e)),this._addCollisionDebugVertex(u,h,o,f,p,new a(r,e)),this._addCollisionDebugVertex(u,h,o,f,p,new a(r,n)),this._addCollisionDebugVertex(u,h,o,f,p,new a(t,n)),l.vertexLength+=4;const d=i.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4}addDebugCollisionBoxes(t,e,r,n){for(let i=t;i<e;i++){const t=this.collisionBoxArray.get(i),e=t.x1,a=t.y1,o=t.x2,s=t.y2;this.addCollisionDebugVertices(e,a,o,s,n?this.textCollisionBox:this.iconCollisionBox,t.anchorPoint,r)}}generateCollisionDebugBuffers(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Su(no,mc.members,oo),this.iconCollisionBox=new Su(no,mc.members,oo);for(let t=0;t<this.symbolInstances.length;t++){const e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}}_deserializeCollisionBoxesForSymbol(t,e,r,n,i,a,o,s,l){const c={};for(let n=e;n<r;n++){const e=t.get(n);c.textBox={x1:e.x1,y1:e.y1,x2:e.x2,y2:e.y2,anchorPointX:e.anchorPointX,anchorPointY:e.anchorPointY},c.textFeatureIndex=e.featureIndex;break}for(let e=n;e<i;e++){const r=t.get(e);c.verticalTextBox={x1:r.x1,y1:r.y1,x2:r.x2,y2:r.y2,anchorPointX:r.anchorPointX,anchorPointY:r.anchorPointY},c.verticalTextFeatureIndex=r.featureIndex;break}for(let e=a;e<o;e++){const r=t.get(e);c.iconBox={x1:r.x1,y1:r.y1,x2:r.x2,y2:r.y2,anchorPointX:r.anchorPointX,anchorPointY:r.anchorPointY},c.iconFeatureIndex=r.featureIndex;break}for(let e=s;e<l;e++){const r=t.get(e);c.verticalIconBox={x1:r.x1,y1:r.y1,x2:r.x2,y2:r.y2,anchorPointX:r.anchorPointX,anchorPointY:r.anchorPointY},c.verticalIconFeatureIndex=r.featureIndex;break}return c}deserializeCollisionBoxes(t){this.collisionArrays=[];for(let e=0;e<this.symbolInstances.length;e++){const r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}}hasTextData(){return this.text.segments.get().length>0}hasIconData(){return this.icon.segments.get().length>0}hasDebugData(){return this.textCollisionBox&&this.iconCollisionBox}hasTextCollisionBoxData(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0}hasIconCollisionBoxData(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0}addIndicesForPlacedSymbol(t,e){const r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs;for(let e=r.vertexStartIndex;e<n;e+=4)t.indexArray.emplaceBack(e,e+1,e+2),t.indexArray.emplaceBack(e+1,e+2,e+3)}getSortedSymbolIndexes(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;const e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[];for(let t=0;t<this.symbolInstances.length;++t){a.push(t);const o=this.symbolInstances.get(t);n.push(0|Math.round(e*o.anchorX+r*o.anchorY)),i.push(o.featureIndex)}return a.sort(((t,e)=>n[t]-n[e]||i[e]-i[t])),a}addToSortKeyRanges(t,e){const r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})}sortFeatures(t){if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(const t of this.symbolInstanceIndexes){const e=this.symbolInstances.get(t);this.featureSortOrder.push(e.featureIndex),[e.rightJustifiedTextSymbolIndex,e.centerJustifiedTextSymbolIndex,e.leftJustifiedTextSymbolIndex].forEach(((t,e,r)=>{t>=0&&r.indexOf(t)===e&&this.addIndicesForPlacedSymbol(this.text,t)})),e.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,e.verticalPlacedTextSymbolIndex),e.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,e.placedIconSymbolIndex),e.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,e.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let Cu;Mi("SymbolBucket",Eu,{omit:["layers","collisionBoxArray","features","compareText"]}),Eu.MAX_GLYPHS=65535,Eu.addDynamicAttributes=ku;let Lu;var Iu={get paint(){return Lu=Lu||new ia({"icon-opacity":new ta(Z.paint_symbol["icon-opacity"]),"icon-color":new ta(Z.paint_symbol["icon-color"]),"icon-halo-color":new ta(Z.paint_symbol["icon-halo-color"]),"icon-halo-width":new ta(Z.paint_symbol["icon-halo-width"]),"icon-halo-blur":new ta(Z.paint_symbol["icon-halo-blur"]),"icon-translate":new Qi(Z.paint_symbol["icon-translate"]),"icon-translate-anchor":new Qi(Z.paint_symbol["icon-translate-anchor"]),"text-opacity":new ta(Z.paint_symbol["text-opacity"]),"text-color":new ta(Z.paint_symbol["text-color"],{runtimeType:ft,getOverride:t=>t.textColor,hasOverride:t=>!!t.textColor}),"text-halo-color":new ta(Z.paint_symbol["text-halo-color"]),"text-halo-width":new ta(Z.paint_symbol["text-halo-width"]),"text-halo-blur":new ta(Z.paint_symbol["text-halo-blur"]),"text-translate":new Qi(Z.paint_symbol["text-translate"]),"text-translate-anchor":new Qi(Z.paint_symbol["text-translate-anchor"])})},get layout(){return Cu=Cu||new ia({"symbol-placement":new Qi(Z.layout_symbol["symbol-placement"]),"symbol-spacing":new Qi(Z.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new Qi(Z.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new ta(Z.layout_symbol["symbol-sort-key"]),"symbol-z-order":new Qi(Z.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new Qi(Z.layout_symbol["icon-allow-overlap"]),"icon-overlap":new Qi(Z.layout_symbol["icon-overlap"]),"icon-ignore-placement":new Qi(Z.layout_symbol["icon-ignore-placement"]),"icon-optional":new Qi(Z.layout_symbol["icon-optional"]),"icon-rotation-alignment":new Qi(Z.layout_symbol["icon-rotation-alignment"]),"icon-size":new ta(Z.layout_symbol["icon-size"]),"icon-text-fit":new Qi(Z.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new Qi(Z.layout_symbol["icon-text-fit-padding"]),"icon-image":new ta(Z.layout_symbol["icon-image"]),"icon-rotate":new ta(Z.layout_symbol["icon-rotate"]),"icon-padding":new ta(Z.layout_symbol["icon-padding"]),"icon-keep-upright":new Qi(Z.layout_symbol["icon-keep-upright"]),"icon-offset":new ta(Z.layout_symbol["icon-offset"]),"icon-anchor":new ta(Z.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new Qi(Z.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new Qi(Z.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new Qi(Z.layout_symbol["text-rotation-alignment"]),"text-field":new ta(Z.layout_symbol["text-field"]),"text-font":new ta(Z.layout_symbol["text-font"]),"text-size":new ta(Z.layout_symbol["text-size"]),"text-max-width":new ta(Z.layout_symbol["text-max-width"]),"text-line-height":new Qi(Z.layout_symbol["text-line-height"]),"text-letter-spacing":new ta(Z.layout_symbol["text-letter-spacing"]),"text-justify":new ta(Z.layout_symbol["text-justify"]),"text-radial-offset":new ta(Z.layout_symbol["text-radial-offset"]),"text-variable-anchor":new Qi(Z.layout_symbol["text-variable-anchor"]),"text-variable-anchor-offset":new ta(Z.layout_symbol["text-variable-anchor-offset"]),"text-anchor":new ta(Z.layout_symbol["text-anchor"]),"text-max-angle":new Qi(Z.layout_symbol["text-max-angle"]),"text-writing-mode":new Qi(Z.layout_symbol["text-writing-mode"]),"text-rotate":new ta(Z.layout_symbol["text-rotate"]),"text-padding":new Qi(Z.layout_symbol["text-padding"]),"text-keep-upright":new Qi(Z.layout_symbol["text-keep-upright"]),"text-transform":new ta(Z.layout_symbol["text-transform"]),"text-offset":new ta(Z.layout_symbol["text-offset"]),"text-allow-overlap":new Qi(Z.layout_symbol["text-allow-overlap"]),"text-overlap":new Qi(Z.layout_symbol["text-overlap"]),"text-ignore-placement":new Qi(Z.layout_symbol["text-ignore-placement"]),"text-optional":new Qi(Z.layout_symbol["text-optional"])})}};class Pu{constructor(t){if(void 0===t.property.overrides)throw new Error("overrides must be provided to instantiate FormatSectionOverride class");this.type=t.property.overrides?t.property.overrides.runtimeType:lt,this.defaultValue=t}evaluate(t){if(t.formattedSection){const e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default}eachChild(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}Mi("FormatSectionOverride",Pu,{omit:["defaultValue"]});class zu extends oa{constructor(t){super(t,Iu)}recalculate(t,e){if(super.recalculate(t,e),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]="map"===this.layout.get("text-rotation-alignment")?"map":"viewport"),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){const t=this.layout.get("text-writing-mode");if(t){const e=[];for(const r of t)e.indexOf(r)<0&&e.push(r);this.layout._values["text-writing-mode"]=e}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()}getValueAndResolveTokens(t,e,r,n){const i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||kn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,((e,r)=>t&&r in t?String(t[r]):""))}(e.properties,i)}createBucket(t){return new Eu(t)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error("Should take a different path in FeatureIndex")}_setPaintOverrides(){for(const t of Iu.paint.overridableProperties){if(!zu.hasPaintOverride(this.layout,t))continue;const e=this.paint.get(t),r=new Pu(e),n=new Tn(r,e.property.specification);let i=null;i="constant"===e.value.kind||"source"===e.value.kind?new Mn("source",n):new Sn("composite",n,e.value.zoomStops),this.paint._values[t]=new Ji(e.property,i,e.parameters)}}_handleOverridablePaintPropertyUpdate(t,e,r){return!(!this.layout||e.isDataDriven()||r.isDataDriven())&&zu.hasPaintOverride(this.layout,t)}static hasPaintOverride(t,e){const r=t.get("text-field"),n=Iu.paint.properties[e];let i=!1;const a=t=>{for(const e of t)if(n.overrides&&n.overrides.hasOverride(e))return void(i=!0)};if("constant"===r.value.kind&&r.value.value instanceof Kt)a(r.value.value.sections);else if("source"===r.value.kind){const t=e=>{if(!i)if(e instanceof se&&ae(e.value)===gt){const t=e.value;a(t.sections)}else e instanceof We?a(e.sections):e.eachChild(t)},e=r.value;e._styleExpression&&t(e._styleExpression.expression)}return i}}let Ou;var Du={get paint(){return Ou=Ou||new ia({"background-color":new Qi(Z.paint_background["background-color"]),"background-pattern":new ra(Z.paint_background["background-pattern"]),"background-opacity":new Qi(Z.paint_background["background-opacity"])})}};class Ru extends oa{constructor(t){super(t,Du)}}let Fu;var Bu={get paint(){return Fu=Fu||new ia({"raster-opacity":new Qi(Z.paint_raster["raster-opacity"]),"raster-hue-rotate":new Qi(Z.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new Qi(Z.paint_raster["raster-brightness-min"]),"raster-brightness-max":new Qi(Z.paint_raster["raster-brightness-max"]),"raster-saturation":new Qi(Z.paint_raster["raster-saturation"]),"raster-contrast":new Qi(Z.paint_raster["raster-contrast"]),"raster-resampling":new Qi(Z.paint_raster["raster-resampling"]),"raster-fade-duration":new Qi(Z.paint_raster["raster-fade-duration"])})}};class Nu extends oa{constructor(t){super(t,Bu)}}class ju extends oa{constructor(t){super(t,{}),this.onAdd=t=>{this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},this.onRemove=t=>{this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},this.implementation=t}is3D(){return"3d"===this.implementation.renderingMode}hasOffscreenPass(){return void 0!==this.implementation.prerender}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error("Custom layers cannot be serialized")}}class Uu{constructor(t){this._methodToThrottle=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._methodToThrottle()})}trigger(){this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((()=>{this._triggered=!1,this._methodToThrottle()}),0))}remove(){delete this._channel,this._methodToThrottle=()=>{}}}const Vu=6371008.8;class qu{constructor(t,e){if(isNaN(t)||isNaN(e))throw new Error(`Invalid LngLat object: (${t}, ${e})`);if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")}wrap(){return new qu(g(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(t){const e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Vu*Math.acos(Math.min(i,1))}static convert(t){if(t instanceof qu)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new qu(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new qu(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")}}const Hu=2*Math.PI*Vu;function Gu(t){return Hu*Math.cos(t*Math.PI/180)}function Zu(t){return(180+t)/360}function Wu(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Yu(t,e){return t/Gu(e)}function Xu(t){const e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}class $u{constructor(t,e,r=0){this.x=+t,this.y=+e,this.z=+r}static fromLngLat(t,e=0){const r=qu.convert(t);return new $u(Zu(r.lng),Wu(r.lat),Yu(e,r.lat))}toLngLat(){return new qu(360*this.x-180,Xu(this.y))}toAltitude(){return t=this.z,e=this.y,t*Gu(Xu(e));var t,e}meterInMercatorCoordinateUnits(){return 1/Hu*(t=Xu(this.y),1/Math.cos(t*Math.PI/180));var t}}function Ju(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}class Ku{constructor(t,e,r){if(t<0||t>25||r<0||r>=Math.pow(2,t)||e<0||e>=Math.pow(2,t))throw new Error(`x=${e}, y=${r}, z=${t} outside of bounds. 0<=x<${Math.pow(2,t)}, 0<=y<${Math.pow(2,t)} 0<=z<=25 `);this.z=t,this.x=e,this.y=r,this.key=eh(0,t,t,e,r)}equals(t){return this.z===t.z&&this.x===t.x&&this.y===t.y}url(t,e,r){const n=(a=this.x,o=this.y,s=this.z,l=Ju(256*a,256*(o=Math.pow(2,s)-o-1),s),c=Ju(256*(a+1),256*(o+1),s),l[0]+","+l[1]+","+c[0]+","+c[1]),i=function(t,e,r){let n,i="";for(let a=t;a>0;a--)n=1<<a-1,i+=(e&n?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);var a,o,s,l,c;return t[(this.x+this.y)%t.length].replace(/{prefix}/g,(this.x%16).toString(16)+(this.y%16).toString(16)).replace(/{z}/g,String(this.z)).replace(/{x}/g,String(this.x)).replace(/{y}/g,String("tms"===r?Math.pow(2,this.z)-this.y-1:this.y)).replace(/{ratio}/g,e>1?"@2x":"").replace(/{quadkey}/g,i).replace(/{bbox-epsg-3857}/g,n)}isChildOf(t){const e=this.z-t.z;return e>0&&t.x===this.x>>e&&t.y===this.y>>e}getTilePoint(t){const e=Math.pow(2,this.z);return new a((t.x*e-this.x)*Uo,(t.y*e-this.y)*Uo)}toString(){return`${this.z}/${this.x}/${this.y}`}}class Qu{constructor(t,e){this.wrap=t,this.canonical=e,this.key=eh(t,e.z,e.z,e.x,e.y)}}class th{constructor(t,e,r,n,i){if(t<r)throw new Error(`overscaledZ should be >= z; overscaledZ = ${t}; z = ${r}`);this.overscaledZ=t,this.wrap=e,this.canonical=new Ku(r,+n,+i),this.key=eh(e,t,r,n,i)}clone(){return new th(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)}scaledTo(t){if(t>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${t}; overscaledZ = ${this.overscaledZ}`);const e=this.canonical.z-t;return t>this.canonical.z?new th(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new th(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)}calculateScaledKey(t,e){if(t>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${t}; overscaledZ = ${this.overscaledZ}`);const r=this.canonical.z-t;return t>this.canonical.z?eh(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):eh(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)}isChildOf(t){if(t.wrap!==this.wrap)return!1;const e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e}children(t){if(this.overscaledZ>=t)return[new th(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];const e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new th(e,this.wrap,e,r,n),new th(e,this.wrap,e,r+1,n),new th(e,this.wrap,e,r,n+1),new th(e,this.wrap,e,r+1,n+1)]}isLessThan(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))}wrapped(){return new th(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)}unwrapTo(t){return new th(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)}overscaleFactor(){return Math.pow(2,this.overscaledZ-this.canonical.z)}toUnwrapped(){return new Qu(this.wrap,this.canonical)}toString(){return`${this.overscaledZ}/${this.canonical.x}/${this.canonical.y}`}getTilePoint(t){return this.canonical.getTilePoint(new $u(t.x-this.wrap,t.y))}}function eh(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);const a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Mi("CanonicalTileID",Ku),Mi("OverscaledTileID",th,{omit:["posMatrix"]});class rh{constructor(t,e,r,n=1,i=1,a=1,o=0){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&!["mapbox","terrarium","custom"].includes(r))return void T(`"${r}" is not a valid encoding type. Valid types include "mapbox", "terrarium" and "custom".`);this.stride=e.height;const s=this.dim=e.height-2;switch(this.data=new Uint32Array(e.data.buffer),r){case"terrarium":this.redFactor=256,this.greenFactor=1,this.blueFactor=1/256,this.baseShift=32768;break;case"custom":this.redFactor=n,this.greenFactor=i,this.blueFactor=a,this.baseShift=o;break;default:this.redFactor=6553.6,this.greenFactor=25.6,this.blueFactor=.1,this.baseShift=1e4}for(let t=0;t<s;t++)this.data[this._idx(-1,t)]=this.data[this._idx(0,t)],this.data[this._idx(s,t)]=this.data[this._idx(s-1,t)],this.data[this._idx(t,-1)]=this.data[this._idx(t,0)],this.data[this._idx(t,s)]=this.data[this._idx(t,s-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(s,-1)]=this.data[this._idx(s-1,0)],this.data[this._idx(-1,s)]=this.data[this._idx(0,s-1)],this.data[this._idx(s,s)]=this.data[this._idx(s-1,s-1)],this.min=Number.MAX_SAFE_INTEGER,this.max=Number.MIN_SAFE_INTEGER;for(let t=0;t<s;t++)for(let e=0;e<s;e++){const r=this.get(t,e);r>this.max&&(this.max=r),r<this.min&&(this.min=r)}}get(t,e){const r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return this.unpack(r[n],r[n+1],r[n+2])}getUnpackVector(){return[this.redFactor,this.greenFactor,this.blueFactor,this.baseShift]}_idx(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)}unpack(t,e,r){return t*this.redFactor+e*this.greenFactor+r*this.blueFactor-this.baseShift}getPixels(){return new Ls({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");let n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}const s=-e*this.dim,l=-r*this.dim;for(let e=a;e<o;e++)for(let r=n;r<i;r++)this.data[this._idx(r,e)]=t.data[this._idx(r+s,e+l)]}}Mi("DEMData",rh);class nh{constructor(t){this._stringToNumber={},this._numberToString=[];for(let e=0;e<t.length;e++){const r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}}encode(t){return this._stringToNumber[t]}decode(t){if(t>=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${t} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[t]}}class ih{constructor(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i}get geometry(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry}set geometry(t){this._geometry=t}toJSON(){const t={geometry:this.geometry};for(const e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t}}class ah{constructor(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new ki(Uo,16,0),this.grid3D=new ki(Uo,16,0),this.featureIndexArray=new Za,this.promoteId=e}insert(t,e,r,n,i,a){const o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);const s=a?this.grid3D:this.grid;for(let t=0;t<e.length;t++){const r=e[t],n=[1/0,1/0,-1/0,-1/0];for(let t=0;t<r.length;t++){const e=r[t];n[0]=Math.min(n[0],e.x),n[1]=Math.min(n[1],e.y),n[2]=Math.max(n[2],e.x),n[3]=Math.max(n[3],e.y)}n[0]<Uo&&n[1]<Uo&&n[2]>=0&&n[3]>=0&&s.insert(o,n[0],n[1],n[2],n[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=new Tl.VectorTile(new Uc(this.rawTileData)).layers,this.sourceLayerCoder=new nh(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers}query(t,e,r,n){this.loadVTLayers();const i=t.params||{},o=Uo/t.tileSize/t.scale,s=zn(i.filter),l=t.queryGeometry,c=t.queryPadding*o,u=sh(l),h=this.grid.query(u.minX-c,u.minY-c,u.maxX+c,u.maxY+c),f=sh(t.cameraQueryGeometry),p=this.grid3D.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c,((e,r,n,i)=>function(t,e,r,n,i){for(const a of t)if(e<=a.x&&r<=a.y&&n>=a.x&&i>=a.y)return!0;const o=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(const e of o)if(ns(t,e))return!0;for(let e=0;e<t.length-1;e++)if(is(t[e],t[e+1],o))return!0;return!1}(t.cameraQueryGeometry,e-c,r-c,n+c,i+c)));for(const t of p)h.push(t);h.sort(lh);const d={};let m;for(let a=0;a<h.length;a++){const c=h[a];if(c===m)continue;m=c;const u=this.featureIndexArray.get(c);let f=null;this.loadMatchingFeature(d,u.bucketIndex,u.sourceLayerIndex,u.featureIndex,s,i.layers,i.availableImages,e,r,n,((e,r,n)=>(f||(f=Ho(e)),r.queryIntersectsFeature(l,e,n,f,this.z,t.transform,o,t.pixelPosMatrix))))}return d}loadMatchingFeature(t,e,r,n,i,a,o,s,l,c,u){const h=this.bucketLayerIDs[e];if(a&&!function(t,e){for(let r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,h))return;const f=this.sourceLayerCoder.decode(r),p=this.vtLayers[f].feature(n);if(i.needGeometry){const t=Go(p,!0);if(!i.filter(new Hi(this.tileID.overscaledZ),t,this.tileID.canonical))return}else if(!i.filter(new Hi(this.tileID.overscaledZ),p))return;const d=this.getId(p,f);for(let e=0;e<h.length;e++){const r=h[e];if(a&&a.indexOf(r)<0)continue;const i=s[r];if(!i)continue;let f={};d&&c&&(f=c.getState(i.sourceLayer||"_geojsonTileLayer",d));const m=y({},l[r]);m.paint=oh(m.paint,i.paint,p,f,o),m.layout=oh(m.layout,i.layout,p,f,o);const g=!u||u(p,i,f);if(!g)continue;const v=new ih(p,this.z,this.x,this.y,d);v.layer=m;let x=t[r];void 0===x&&(x=t[r]=[]),x.push({featureIndex:n,feature:v,intersectionZ:g})}}lookupSymbolFeatures(t,e,r,n,i,a,o,s){const l={};this.loadVTLayers();const c=zn(i);for(const i of t)this.loadMatchingFeature(l,r,n,i,c,a,o,s,e);return l}hasLayer(t){for(const e of this.bucketLayerIDs)for(const r of e)if(t===r)return!0;return!1}getId(t,e){let r=t.id;if(this.promoteId){const n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];r=t.properties[n],"boolean"==typeof r&&(r=Number(r))}return r}}function oh(t,e,r,n,i){return x(t,((t,a)=>{const o=e instanceof Ki?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function sh(t){let e=1/0,r=1/0,n=-1/0,i=-1/0;for(const a of t)e=Math.min(e,a.x),r=Math.min(r,a.y),n=Math.max(n,a.x),i=Math.max(i,a.y);return{minX:e,minY:r,maxX:n,maxY:i}}function lh(t,e){return e-t}function ch(t,e,r,n,i){const o=[];for(let s=0;s<t.length;s++){const l=t[s];let c;for(let t=0;t<l.length-1;t++){let s=l[t],u=l[t+1];s.x<e&&u.x<e||(s.x<e?s=new a(e,s.y+(u.y-s.y)*((e-s.x)/(u.x-s.x)))._round():u.x<e&&(u=new a(e,s.y+(u.y-s.y)*((e-s.x)/(u.x-s.x)))._round()),s.y<r&&u.y<r||(s.y<r?s=new a(s.x+(u.x-s.x)*((r-s.y)/(u.y-s.y)),r)._round():u.y<r&&(u=new a(s.x+(u.x-s.x)*((r-s.y)/(u.y-s.y)),r)._round()),s.x>=n&&u.x>=n||(s.x>=n?s=new a(n,s.y+(u.y-s.y)*((n-s.x)/(u.x-s.x)))._round():u.x>=n&&(u=new a(n,s.y+(u.y-s.y)*((n-s.x)/(u.x-s.x)))._round()),s.y>=i&&u.y>=i||(s.y>=i?s=new a(s.x+(u.x-s.x)*((i-s.y)/(u.y-s.y)),i)._round():u.y>=i&&(u=new a(s.x+(u.x-s.x)*((i-s.y)/(u.y-s.y)),i)._round()),c&&s.equals(c[c.length-1])||(c=[s],o.push(c)),c.push(u)))))}}return o}Mi("FeatureIndex",ah,{omit:["rawTileData","sourceLayerCoder"]});class uh extends a{constructor(t,e,r,n){super(t,e),this.angle=r,void 0!==n&&(this.segment=n)}clone(){return new uh(this.x,this.y,this.angle,this.segment)}}function hh(t,e,r,n,i){if(void 0===e.segment||0===r)return!0;let a=e,o=e.segment+1,s=0;for(;s>-r/2;){if(o--,o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;const l=[];let c=0;for(;s<r/2;){const e=t[o-1],r=t[o],a=t[o+1];if(!a)return!1;let u=e.angleTo(r)-r.angleTo(a);for(u=Math.abs((u+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:u}),c+=u;s-l[0].distance>n;)c-=l.shift().angleDelta;if(c>i)return!1;o++,s+=r.dist(a)}return!0}function fh(t){let e=0;for(let r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function ph(t,e,r){return t?.6*e*r:0}function dh(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function mh(t,e,r,n,i,a){const o=ph(r,i,a),s=dh(r,n)*a;let l=0;const c=fh(t)/2;for(let r=0;r<t.length-1;r++){const n=t[r],i=t[r+1],a=n.dist(i);if(l+a>c){const u=(c-l)/a,h=Pe.number(n.x,i.x,u),f=Pe.number(n.y,i.y,u),p=new uh(h,f,i.angleTo(n),r);return p._round(),!o||hh(t,p,s,o,e)?p:void 0}l+=a}}function gh(t,e,r,n,i,a,o,s,l){const c=ph(n,a,o),u=dh(n,i),h=u*o,f=0===t[0].x||t[0].x===l||0===t[0].y||t[0].y===l;return e-h<e/4&&(e=h+e/4),yh(t,f?e/2*s%e:(u/2+2*a)*o*s%e,e,c,r,h,f,!1,l)}function yh(t,e,r,n,i,a,o,s,l){const c=a/2,u=fh(t);let h=0,f=e-r,p=[];for(let e=0;e<t.length-1;e++){const o=t[e],s=t[e+1],d=o.dist(s),m=s.angleTo(o);for(;f+r<h+d;){f+=r;const g=(f-h)/d,y=Pe.number(o.x,s.x,g),v=Pe.number(o.y,s.y,g);if(y>=0&&y<l&&v>=0&&v<l&&f-c>=0&&f+c<=u){const r=new uh(y,v,m,e);r._round(),n&&!hh(t,r,a,n,i)||p.push(r)}}h+=d}return s||p.length||o||(p=yh(t,h/2,r,n,i,a,o,!0,l)),p}Mi("Anchor",uh);const vh=Yc;function xh(t,e,r,n){const i=[],o=t.image,s=o.pixelRatio,l=o.paddedRect.w-2*vh,c=o.paddedRect.h-2*vh;let u={x1:t.left,y1:t.top,x2:t.right,y2:t.bottom};const 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t.S(e.tileID.overscaledZ,e.tileID.wrap,e.tileID.canonical.z,e.tileID.canonical.x,e.tileID.canonical.y),this.uid=e.uid,this.zoom=e.zoom,this.pixelRatio=e.pixelRatio,this.tileSize=e.tileSize,this.source=e.source,this.overscaling=this.tileID.overscaleFactor(),this.showCollisionBoxes=e.showCollisionBoxes,this.collectResourceTiming=!!e.collectResourceTiming,this.returnDependencies=!!e.returnDependencies,this.promoteId=e.promoteId,this.inFlightDependencies=[]}parse(e,n,a,o){return t._(this,void 0,void 0,(function*(){this.status="parsing",this.data=e,this.collisionBoxArray=new t.a5;const s=new t.bl(Object.keys(e.layers).sort()),l=new t.bm(this.tileID,this.promoteId);l.bucketLayerIDs=[];const c={},u={featureIndex:l,iconDependencies:{},patternDependencies:{},glyphDependencies:{},availableImages:a},h=n.familiesBySource[this.source];for(const r in h){const n=e.layers[r];if(!n)continue;1===n.version&&t.w(`Vector tile source "${this.source}" layer "${r}" does not use vector tile spec v2 and therefore may have some rendering errors.`);const o=s.encode(r),f=[];for(let t=0;t<n.length;t++){const e=n.feature(t),i=l.getId(e,r);f.push({feature:e,id:i,index:t,sourceLayerIndex:o})}for(const e of h[r]){const r=e[0];r.source!==this.source&&t.w(`layer.source = ${r.source} does not equal this.source = ${this.source}`),r.minzoom&&this.zoom<Math.floor(r.minzoom)||r.maxzoom&&this.zoom>=r.maxzoom||"none"!==r.visibility&&(i(e,this.zoom,a),(c[r.id]=r.createBucket({index:l.bucketLayerIDs.length,layers:e,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:o,sourceID:this.source})).populate(f,u,this.tileID.canonical),l.bucketLayerIDs.push(e.map((t=>t.id))))}}const f=t.aG(u.glyphDependencies,(t=>Object.keys(t).map(Number)));this.inFlightDependencies.forEach((t=>null==t?void 0:t.abort())),this.inFlightDependencies=[];let p=Promise.resolve({});if(Object.keys(f).length){const t=new AbortController;this.inFlightDependencies.push(t),p=o.sendAsync({type:"GG",data:{stacks:f,source:this.source,tileID:this.tileID,type:"glyphs"}},t)}const d=Object.keys(u.iconDependencies);let m=Promise.resolve({});if(d.length){const t=new AbortController;this.inFlightDependencies.push(t),m=o.sendAsync({type:"GI",data:{icons:d,source:this.source,tileID:this.tileID,type:"icons"}},t)}const g=Object.keys(u.patternDependencies);let y=Promise.resolve({});if(g.length){const t=new AbortController;this.inFlightDependencies.push(t),y=o.sendAsync({type:"GI",data:{icons:g,source:this.source,tileID:this.tileID,type:"patterns"}},t)}const[v,x,_]=yield Promise.all([p,m,y]),b=new r(v),w=new t.bn(x,_);for(const e in c){const r=c[e];r instanceof t.a6?(i(r.layers,this.zoom,a),t.bo({bucket:r,glyphMap:v,glyphPositions:b.positions,imageMap:x,imagePositions:w.iconPositions,showCollisionBoxes:this.showCollisionBoxes,canonical:this.tileID.canonical})):r.hasPattern&&(r instanceof t.bp||r instanceof t.bq||r instanceof t.br)&&(i(r.layers,this.zoom,a),r.addFeatures(u,this.tileID.canonical,w.patternPositions))}return this.status="done",{buckets:Object.values(c).filter((t=>!t.isEmpty())),featureIndex:l,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:b.image,imageAtlas:w,glyphMap:this.returnDependencies?v:null,iconMap:this.returnDependencies?x:null,glyphPositions:this.returnDependencies?b.positions:null}}))}}function i(e,r,n){const i=new t.a9(r);for(const t of e)t.recalculate(i,n)}class a{constructor(t,e,r){this.actor=t,this.layerIndex=e,this.availableImages=r,this.fetching={},this.loading={},this.loaded={}}loadVectorTile(e,r){return t._(this,void 0,void 0,(function*(){const n=yield t.l(e.request,r);try{return{vectorTile:new t.bs.VectorTile(new t.bt(n.data)),rawData:n.data,cacheControl:n.cacheControl,expires:n.expires}}catch(t){const r=new Uint8Array(n.data),i=31===r[0]&&139===r[1];let a=`Unable to parse the tile at ${e.request.url}, `;throw a+=i?"please make sure the data is not gzipped and that you have configured the relevant header in the server":`got error: ${t.message}`,new Error(a)}}))}loadTile(e){return t._(this,void 0,void 0,(function*(){const r=e.uid,i=!!(e&&e.request&&e.request.collectResourceTiming)&&new t.bu(e.request),a=new n(e);this.loading[r]=a;const o=new AbortController;a.abort=o;try{const n=yield this.loadVectorTile(e,o);if(delete this.loading[r],!n)return null;const s=n.rawData,l={};n.expires&&(l.expires=n.expires),n.cacheControl&&(l.cacheControl=n.cacheControl);const c={};if(i){const t=i.finish();t&&(c.resourceTiming=JSON.parse(JSON.stringify(t)))}a.vectorTile=n.vectorTile;const u=a.parse(n.vectorTile,this.layerIndex,this.availableImages,this.actor);this.loaded[r]=a,this.fetching[r]={rawTileData:s,cacheControl:l,resourceTiming:c};try{const e=yield u;return t.e({rawTileData:s.slice(0)},e,l,c)}finally{delete this.fetching[r]}}catch(t){throw delete this.loading[r],a.status="done",this.loaded[r]=a,t}}))}reloadTile(e){return t._(this,void 0,void 0,(function*(){const r=e.uid;if(!this.loaded||!this.loaded[r])throw new Error("Should not be trying to reload a tile that was never loaded or has been removed");const n=this.loaded[r];if(n.showCollisionBoxes=e.showCollisionBoxes,"parsing"===n.status){const e=yield n.parse(n.vectorTile,this.layerIndex,this.availableImages,this.actor);let i;if(this.fetching[r]){const{rawTileData:n,cacheControl:a,resourceTiming:o}=this.fetching[r];delete this.fetching[r],i=t.e({rawTileData:n.slice(0)},e,a,o)}else i=e;return i}if("done"===n.status&&n.vectorTile)return n.parse(n.vectorTile,this.layerIndex,this.availableImages,this.actor)}))}abortTile(e){return t._(this,void 0,void 0,(function*(){const t=this.loading,r=e.uid;t&&t[r]&&t[r].abort&&(t[r].abort.abort(),delete t[r])}))}removeTile(e){return t._(this,void 0,void 0,(function*(){this.loaded&&this.loaded[e.uid]&&delete this.loaded[e.uid]}))}}class o{constructor(){this.loaded={}}loadTile(e){return t._(this,void 0,void 0,(function*(){const{uid:r,encoding:n,rawImageData:i,redFactor:a,greenFactor:o,blueFactor:s,baseShift:l}=e,c=i.width+2,u=i.height+2,h=t.b(i)?new t.R({width:c,height:u},yield t.bv(i,-1,-1,c,u)):i,f=new t.bw(r,h,n,a,o,s,l);return this.loaded=this.loaded||{},this.loaded[r]=f,f}))}removeTile(t){const e=this.loaded,r=t.uid;e&&e[r]&&delete e[r]}}var s=function t(e,r){var n,i=e&&e.type;if("FeatureCollection"===i)for(n=0;n<e.features.length;n++)t(e.features[n],r);else if("GeometryCollection"===i)for(n=0;n<e.geometries.length;n++)t(e.geometries[n],r);else if("Feature"===i)t(e.geometry,r);else if("Polygon"===i)l(e.coordinates,r);else if("MultiPolygon"===i)for(n=0;n<e.coordinates.length;n++)l(e.coordinates[n],r);return e};function l(t,e){if(0!==t.length){c(t[0],e);for(var r=1;r<t.length;r++)c(t[r],!e)}}function c(t,e){for(var r=0,n=0,i=0,a=t.length,o=a-1;i<a;o=i++){var s=(t[i][0]-t[o][0])*(t[o][1]+t[i][1]),l=r+s;n+=Math.abs(r)>=Math.abs(s)?r-l+s:s-l+r,r=l}r+n>=0!=!!e&&t.reverse()}var u=t.bx(s);const h=t.bs.VectorTileFeature.prototype.toGeoJSON;let f=class{constructor(e){this._feature=e,this.extent=t.X,this.type=e.type,this.properties=e.tags,"id"in e&&!isNaN(e.id)&&(this.id=parseInt(e.id,10))}loadGeometry(){if(1===this._feature.type){const e=[];for(const r of this._feature.geometry)e.push([new t.P(r[0],r[1])]);return e}{const e=[];for(const r of this._feature.geometry){const n=[];for(const e of r)n.push(new t.P(e[0],e[1]));e.push(n)}return e}}toGeoJSON(t,e,r){return h.call(this,t,e,r)}},p=class{constructor(e){this.layers={_geojsonTileLayer:this},this.name="_geojsonTileLayer",this.extent=t.X,this.length=e.length,this._features=e}feature(t){return new f(this._features[t])}};var d={exports:{}},m=t.by,g=t.bs.VectorTileFeature,y=v;function v(t,e){this.options=e||{},this.features=t,this.length=t.length}function x(t,e){this.id="number"==typeof t.id?t.id:void 0,this.type=t.type,this.rawGeometry=1===t.type?[t.geometry]:t.geometry,this.properties=t.tags,this.extent=e||4096}v.prototype.feature=function(t){return new x(this.features[t],this.options.extent)},x.prototype.loadGeometry=function(){var t=this.rawGeometry;this.geometry=[];for(var e=0;e<t.length;e++){for(var r=t[e],n=[],i=0;i<r.length;i++)n.push(new m(r[i][0],r[i][1]));this.geometry.push(n)}return this.geometry},x.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var t=this.geometry,e=1/0,r=-1/0,n=1/0,i=-1/0,a=0;a<t.length;a++)for(var o=t[a],s=0;s<o.length;s++){var l=o[s];e=Math.min(e,l.x),r=Math.max(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.y)}return[e,n,r,i]},x.prototype.toGeoJSON=g.prototype.toGeoJSON;var _=t.bz,b=y;function w(t){var e=new _;return function(t,e){for(var r in t.layers)e.writeMessage(3,T,t.layers[r])}(t,e),e.finish()}function T(t,e){var r;e.writeVarintField(15,t.version||1),e.writeStringField(1,t.name||""),e.writeVarintField(5,t.extent||4096);var n={keys:[],values:[],keycache:{},valuecache:{}};for(r=0;r<t.length;r++)n.feature=t.feature(r),e.writeMessage(2,k,n);var i=n.keys;for(r=0;r<i.length;r++)e.writeStringField(3,i[r]);var a=n.values;for(r=0;r<a.length;r++)e.writeMessage(4,C,a[r])}function k(t,e){var r=t.feature;void 0!==r.id&&e.writeVarintField(1,r.id),e.writeMessage(2,A,t),e.writeVarintField(3,r.type),e.writeMessage(4,E,r)}function A(t,e){var r=t.feature,n=t.keys,i=t.values,a=t.keycache,o=t.valuecache;for(var s in r.properties){var l=r.properties[s],c=a[s];if(null!==l){void 0===c&&(n.push(s),c=n.length-1,a[s]=c),e.writeVarint(c);var u=typeof l;"string"!==u&&"boolean"!==u&&"number"!==u&&(l=JSON.stringify(l));var h=u+":"+l,f=o[h];void 0===f&&(i.push(l),f=i.length-1,o[h]=f),e.writeVarint(f)}}}function M(t,e){return(e<<3)+(7&t)}function S(t){return t<<1^t>>31}function E(t,e){for(var r=t.loadGeometry(),n=t.type,i=0,a=0,o=r.length,s=0;s<o;s++){var l=r[s],c=1;1===n&&(c=l.length),e.writeVarint(M(1,c));for(var u=3===n?l.length-1:l.length,h=0;h<u;h++){1===h&&1!==n&&e.writeVarint(M(2,u-1));var f=l[h].x-i,p=l[h].y-a;e.writeVarint(S(f)),e.writeVarint(S(p)),i+=f,a+=p}3===n&&e.writeVarint(M(7,1))}}function C(t,e){var r=typeof t;"string"===r?e.writeStringField(1,t):"boolean"===r?e.writeBooleanField(7,t):"number"===r&&(t%1!=0?e.writeDoubleField(3,t):t<0?e.writeSVarintField(6,t):e.writeVarintField(5,t))}d.exports=w,d.exports.fromVectorTileJs=w,d.exports.fromGeojsonVt=function(t,e){e=e||{};var r={};for(var n in t)r[n]=new b(t[n].features,e),r[n].name=n,r[n].version=e.version,r[n].extent=e.extent;return w({layers:r})},d.exports.GeoJSONWrapper=b;var L=d.exports,I=t.bx(L);const P={minZoom:0,maxZoom:16,minPoints:2,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:t=>t},z=Math.fround||(O=new Float32Array(1),t=>(O[0]=+t,O[0]));var O;const D=3,R=5,F=6;class B{constructor(t){this.options=Object.assign(Object.create(P),t),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[]}load(t){const{log:e,minZoom:r,maxZoom:n}=this.options;e&&console.time("total time");const i=`prepare ${t.length} points`;e&&console.time(i),this.points=t;const a=[];for(let e=0;e<t.length;e++){const r=t[e];if(!r.geometry)continue;const[n,i]=r.geometry.coordinates,o=z(U(n)),s=z(V(i));a.push(o,s,1/0,e,-1,1),this.options.reduce&&a.push(0)}let o=this.trees[n+1]=this._createTree(a);e&&console.timeEnd(i);for(let t=n;t>=r;t--){const r=+Date.now();o=this.trees[t]=this._createTree(this._cluster(o,t)),e&&console.log("z%d: %d clusters in %dms",t,o.numItems,+Date.now()-r)}return e&&console.timeEnd("total time"),this}getClusters(t,e){let r=((t[0]+180)%360+360)%360-180;const n=Math.max(-90,Math.min(90,t[1]));let i=180===t[2]?180:((t[2]+180)%360+360)%360-180;const a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){const t=this.getClusters([r,n,180,a],e),o=this.getClusters([-180,n,i,a],e);return t.concat(o)}const o=this.trees[this._limitZoom(e)],s=o.range(U(r),V(a),U(i),V(n)),l=o.data,c=[];for(const t of s){const e=this.stride*t;c.push(l[e+R]>1?N(l,e,this.clusterProps):this.points[l[e+D]])}return c}getChildren(t){const e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);const a=i.data;if(e*this.stride>=a.length)throw new Error(n);const o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=a[e*this.stride],l=a[e*this.stride+1],c=i.within(s,l,o),u=[];for(const e of c){const r=e*this.stride;a[r+4]===t&&u.push(a[r+R]>1?N(a,r,this.clusterProps):this.points[a[r+D]])}if(0===u.length)throw new Error(n);return u}getLeaves(t,e,r){e=e||10,r=r||0;const n=[];return this._appendLeaves(n,t,e,r,0),n}getTile(t,e,r){const n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),{extent:a,radius:o}=this.options,s=o/a,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.data,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.data,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.data,-1,r,i,u),u.features.length?u:null}getClusterExpansionZoom(t){let e=this._getOriginZoom(t)-1;for(;e<=this.options.maxZoom;){const r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e}_appendLeaves(t,e,r,n,i){const a=this.getChildren(e);for(const e of a){const a=e.properties;if(a&&a.cluster?i+a.point_count<=n?i+=a.point_count:i=this._appendLeaves(t,a.cluster_id,r,n,i):i<n?i++:t.push(e),t.length===r)break}return i}_createTree(e){const r=new t.aw(e.length/this.stride|0,this.options.nodeSize,Float32Array);for(let t=0;t<e.length;t+=this.stride)r.add(e[t],e[t+1]);return r.finish(),r.data=e,r}_addTileFeatures(t,e,r,n,i,a){for(const o of 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t=n.finish();t&&(r.resourceTiming={},r.resourceTiming[e.source]=JSON.parse(JSON.stringify(t)))}return r}catch(e){if(delete this._pendingRequest,t.bA(e))return{abandoned:!0};throw e}var i,a}))}getData(){return t._(this,void 0,void 0,(function*(){return this._pendingData}))}reloadTile(t){const e=this.loaded,r=t.uid;return e&&e[r]?super.reloadTile(t):this.loadTile(t)}loadAndProcessGeoJSON(e,r){return t._(this,void 0,void 0,(function*(){let n=yield this.loadGeoJSON(e,r);if(delete this._pendingRequest,"object"!=typeof n)throw new Error(`Input data given to '${e.source}' is not a valid GeoJSON object.`);if(u(n,!0),e.filter){const r=t.bB(e.filter,{type:"boolean","property-type":"data-driven",overridable:!1,transition:!1});if("error"===r.result)throw new Error(r.value.map((t=>`${t.key}: ${t.message}`)).join(", "));const i=n.features.filter((t=>r.value.evaluate({zoom:0},t)));n={type:"FeatureCollection",features:i}}return n}))}loadGeoJSON(e,r){return t._(this,void 0,void 0,(function*(){const{promoteId:n}=e;if(e.request){const i=yield t.h(e.request,r);return this._dataUpdateable=xt(i.data,n)?_t(i.data,n):void 0,i.data}if("string"==typeof e.data)try{const t=JSON.parse(e.data);return this._dataUpdateable=xt(t,n)?_t(t,n):void 0,t}catch(t){throw new Error(`Input data given to '${e.source}' is not a valid GeoJSON object.`)}if(!e.dataDiff)throw new Error(`Input data given to '${e.source}' is not a valid GeoJSON object.`);if(!this._dataUpdateable)throw new Error(`Cannot update existing geojson data in ${e.source}`);return function(t,e,r){var n,i,a,o;if(e.removeAll&&t.clear(),e.remove)for(const r of e.remove)t.delete(r);if(e.add)for(const n of e.add){const e=vt(n,r);null!=e&&t.set(e,n)}if(e.update)for(const r of e.update){let e=t.get(r.id);if(null==e)continue;const s=r.newGeometry||r.removeAllProperties,l=!r.removeAllProperties&&((null===(n=r.removeProperties)||void 0===n?void 0:n.length)>0||(null===(i=r.addOrUpdateProperties)||void 0===i?void 0:i.length)>0);if((s||l)&&(e=Object.assign({},e),t.set(r.id,e),l&&(e.properties=Object.assign({},e.properties))),r.newGeometry&&(e.geometry=r.newGeometry),r.removeAllProperties)e.properties={};else if((null===(a=r.removeProperties)||void 0===a?void 0:a.length)>0)for(const t of r.removeProperties)Object.prototype.hasOwnProperty.call(e.properties,t)&&delete e.properties[t];if((null===(o=r.addOrUpdateProperties)||void 0===o?void 0:o.length)>0)for(const{key:t,value:n}of r.addOrUpdateProperties)e.properties[t]=n}}(this._dataUpdateable,e.dataDiff,n),{type:"FeatureCollection",features:Array.from(this._dataUpdateable.values())}}))}removeSource(e){return t._(this,void 0,void 0,(function*(){this._pendingRequest&&this._pendingRequest.abort()}))}getClusterExpansionZoom(t){return this._geoJSONIndex.getClusterExpansionZoom(t.clusterId)}getClusterChildren(t){return this._geoJSONIndex.getChildren(t.clusterId)}getClusterLeaves(t){return this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset)}}class wt{constructor(e){this.self=e,this.actor=new t.F(e),this.layerIndexes={},this.availableImages={},this.workerSources={},this.demWorkerSources={},this.externalWorkerSourceTypes={},this.self.registerWorkerSource=(t,e)=>{if(this.externalWorkerSourceTypes[t])throw new Error(`Worker source with name "${t}" already registered.`);this.externalWorkerSourceTypes[t]=e},this.self.addProtocol=t.bh,this.self.removeProtocol=t.bi,this.self.registerRTLTextPlugin=e=>{if(t.bC.isParsed())throw new Error("RTL text plugin already registered.");t.bC.setMethods(e)},this.actor.registerMessageHandler("LDT",((t,e)=>this._getDEMWorkerSource(t,e.source).loadTile(e))),this.actor.registerMessageHandler("RDT",((e,r)=>t._(this,void 0,void 0,(function*(){this._getDEMWorkerSource(e,r.source).removeTile(r)})))),this.actor.registerMessageHandler("GCEZ",((e,r)=>t._(this,void 0,void 0,(function*(){return this._getWorkerSource(e,r.type,r.source).getClusterExpansionZoom(r)})))),this.actor.registerMessageHandler("GCC",((e,r)=>t._(this,void 0,void 0,(function*(){return this._getWorkerSource(e,r.type,r.source).getClusterChildren(r)})))),this.actor.registerMessageHandler("GCL",((e,r)=>t._(this,void 0,void 0,(function*(){return this._getWorkerSource(e,r.type,r.source).getClusterLeaves(r)})))),this.actor.registerMessageHandler("LD",((t,e)=>this._getWorkerSource(t,e.type,e.source).loadData(e))),this.actor.registerMessageHandler("GD",((t,e)=>this._getWorkerSource(t,e.type,e.source).getData())),this.actor.registerMessageHandler("LT",((t,e)=>this._getWorkerSource(t,e.type,e.source).loadTile(e))),this.actor.registerMessageHandler("RT",((t,e)=>this._getWorkerSource(t,e.type,e.source).reloadTile(e))),this.actor.registerMessageHandler("AT",((t,e)=>this._getWorkerSource(t,e.type,e.source).abortTile(e))),this.actor.registerMessageHandler("RMT",((t,e)=>this._getWorkerSource(t,e.type,e.source).removeTile(e))),this.actor.registerMessageHandler("RS",((e,r)=>t._(this,void 0,void 0,(function*(){if(!this.workerSources[e]||!this.workerSources[e][r.type]||!this.workerSources[e][r.type][r.source])return;const t=this.workerSources[e][r.type][r.source];delete this.workerSources[e][r.type][r.source],void 0!==t.removeSource&&t.removeSource(r)})))),this.actor.registerMessageHandler("RM",(e=>t._(this,void 0,void 0,(function*(){delete this.layerIndexes[e],delete this.availableImages[e],delete this.workerSources[e],delete this.demWorkerSources[e]})))),this.actor.registerMessageHandler("SR",((e,r)=>t._(this,void 0,void 0,(function*(){this.referrer=r})))),this.actor.registerMessageHandler("SRPS",((t,e)=>this._syncRTLPluginState(t,e))),this.actor.registerMessageHandler("IS",((e,r)=>t._(this,void 0,void 0,(function*(){this.self.importScripts(r)})))),this.actor.registerMessageHandler("SI",((t,e)=>this._setImages(t,e))),this.actor.registerMessageHandler("UL",((e,r)=>t._(this,void 0,void 0,(function*(){this._getLayerIndex(e).update(r.layers,r.removedIds)})))),this.actor.registerMessageHandler("SL",((e,r)=>t._(this,void 0,void 0,(function*(){this._getLayerIndex(e).replace(r)}))))}_setImages(e,r){return t._(this,void 0,void 0,(function*(){this.availableImages[e]=r;for(const t in this.workerSources[e]){const n=this.workerSources[e][t];for(const t in n)n[t].availableImages=r}}))}_syncRTLPluginState(e,r){return t._(this,void 0,void 0,(function*(){if(t.bC.isParsed())return t.bC.getState();if("loading"!==r.pluginStatus)return t.bC.setState(r),r;const e=r.pluginURL;if(this.self.importScripts(e),t.bC.isParsed()){const r={pluginStatus:"loaded",pluginURL:e};return t.bC.setState(r),r}throw t.bC.setState({pluginStatus:"error",pluginURL:""}),new Error(`RTL Text Plugin failed to import scripts from ${e}`)}))}_getAvailableImages(t){let e=this.availableImages[t];return e||(e=[]),e}_getLayerIndex(t){let r=this.layerIndexes[t];return r||(r=this.layerIndexes[t]=new e),r}_getWorkerSource(t,e,r){if(this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){const n={sendAsync:(e,r)=>(e.targetMapId=t,this.actor.sendAsync(e,r))};switch(e){case"vector":this.workerSources[t][e][r]=new a(n,this._getLayerIndex(t),this._getAvailableImages(t));break;case"geojson":this.workerSources[t][e][r]=new bt(n,this._getLayerIndex(t),this._getAvailableImages(t));break;default:this.workerSources[t][e][r]=new this.externalWorkerSourceTypes[e](n,this._getLayerIndex(t),this._getAvailableImages(t))}}return this.workerSources[t][e][r]}_getDEMWorkerSource(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new o),this.demWorkerSources[t][e]}}return t.i(self)&&(self.worker=new wt(self)),wt})),r("index",0,(function(t,e){var r="4.5.2";let n,i;const a={now:"undefined"!=typeof performance&&performance&&performance.now?performance.now.bind(performance):Date.now.bind(Date),frameAsync(t){return new Promise(((r,n)=>{const i=requestAnimationFrame(r);t.signal.addEventListener("abort",(()=>{cancelAnimationFrame(i),n(e.c())}))}))},getImageData(t,e=0){return this.getImageCanvasContext(t).getImageData(-e,-e,t.width+2*e,t.height+2*e)},getImageCanvasContext(t){const e=window.document.createElement("canvas"),r=e.getContext("2d",{willReadFrequently:!0});if(!r)throw new Error("failed to create canvas 2d context");return e.width=t.width,e.height=t.height,r.drawImage(t,0,0,t.width,t.height),r},resolveURL(t){return n||(n=document.createElement("a")),n.href=t,n.href},hardwareConcurrency:"undefined"!=typeof navigator&&navigator.hardwareConcurrency||4,get prefersReducedMotion(){return!!matchMedia&&(null==i&&(i=matchMedia("(prefers-reduced-motion: reduce)")),i.matches)}};class o{static testProp(t){if(!o.docStyle)return t[0];for(let e=0;e<t.length;e++)if(t[e]in o.docStyle)return t[e];return t[0]}static create(t,e,r){const n=window.document.createElement(t);return void 0!==e&&(n.className=e),r&&r.appendChild(n),n}static createNS(t,e){return window.document.createElementNS(t,e)}static disableDrag(){o.docStyle&&o.selectProp&&(o.userSelect=o.docStyle[o.selectProp],o.docStyle[o.selectProp]="none")}static enableDrag(){o.docStyle&&o.selectProp&&(o.docStyle[o.selectProp]=o.userSelect)}static setTransform(t,e){t.style[o.transformProp]=e}static addEventListener(t,e,r,n={}){"passive"in n?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)}static removeEventListener(t,e,r,n={}){"passive"in n?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)}static suppressClickInternal(t){t.preventDefault(),t.stopPropagation(),window.removeEventListener("click",o.suppressClickInternal,!0)}static suppressClick(){window.addEventListener("click",o.suppressClickInternal,!0),window.setTimeout((()=>{window.removeEventListener("click",o.suppressClickInternal,!0)}),0)}static getScale(t){const e=t.getBoundingClientRect();return{x:e.width/t.offsetWidth||1,y:e.height/t.offsetHeight||1,boundingClientRect:e}}static getPoint(t,r,n){const i=r.boundingClientRect;return new e.P((n.clientX-i.left)/r.x-t.clientLeft,(n.clientY-i.top)/r.y-t.clientTop)}static mousePos(t,e){const r=o.getScale(t);return o.getPoint(t,r,e)}static touchPos(t,e){const r=[],n=o.getScale(t);for(let i=0;i<e.length;i++)r.push(o.getPoint(t,n,e[i]));return r}static mouseButton(t){return t.button}static remove(t){t.parentNode&&t.parentNode.removeChild(t)}}o.docStyle="undefined"!=typeof window&&window.document&&window.document.documentElement.style,o.selectProp=o.testProp(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]),o.transformProp=o.testProp(["transform","WebkitTransform"]);const s={supported:!1,testSupport:function(t){!u&&c&&(h?f(t):l=t)}};let l,c,u=!1,h=!1;function f(t){const e=t.createTexture();t.bindTexture(t.TEXTURE_2D,e);try{if(t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,c),t.isContextLost())return;s.supported=!0}catch(t){}t.deleteTexture(e),u=!0}var p;"undefined"!=typeof document&&(c=document.createElement("img"),c.onload=()=>{l&&f(l),l=null,h=!0},c.onerror=()=>{u=!0,l=null},c.src="data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA="),function(t){let r,n,i,a;t.resetRequestQueue=()=>{r=[],n=0,i=0,a={}},t.addThrottleControl=t=>{const e=i++;return a[e]=t,e},t.removeThrottleControl=t=>{delete a[t],l()};t.getImage=(t,n,i=!0)=>new Promise(((a,o)=>{s.supported&&(t.headers||(t.headers={}),t.headers.accept="image/webp,*/*"),e.e(t,{type:"image"});const c={abortController:n,requestParameters:t,supportImageRefresh:i,state:"queued",onError:t=>{o(t)},onSuccess:t=>{a(t)}};r.push(c),l()}));const o=t=>e._(this,void 0,void 0,(function*(){t.state="running";const{requestParameters:r,supportImageRefresh:i,onError:a,onSuccess:o,abortController:s}=t,u=!1===i&&!e.i(self)&&!e.g(r.url)&&(!r.headers||Object.keys(r.headers).reduce(((t,e)=>t&&"accept"===e),!0));n++;const h=u?c(r,s):e.m(r,s);try{const r=yield h;delete t.abortController,t.state="completed",r.data instanceof HTMLImageElement||e.b(r.data)?o(r):r.data&&o({data:yield(f=r.data,"function"==typeof createImageBitmap?e.d(f):e.f(f)),cacheControl:r.cacheControl,expires:r.expires})}catch(e){delete t.abortController,a(e)}finally{n--,l()}var f})),l=()=>{const t=(()=>{for(const t of Object.keys(a))if(a[t]())return!0;return!1})()?e.a.MAX_PARALLEL_IMAGE_REQUESTS_PER_FRAME:e.a.MAX_PARALLEL_IMAGE_REQUESTS;for(let e=n;e<t&&r.length>0;e++){const t=r.shift();t.abortController.signal.aborted?e--:o(t)}},c=(t,r)=>new Promise(((n,i)=>{const a=new Image,o=t.url,s=t.credentials;s&&"include"===s?a.crossOrigin="use-credentials":(s&&"same-origin"===s||!e.s(o))&&(a.crossOrigin="anonymous"),r.signal.addEventListener("abort",(()=>{a.src="",i(e.c())})),a.fetchPriority="high",a.onload=()=>{a.onerror=a.onload=null,n({data:a})},a.onerror=()=>{a.onerror=a.onload=null,r.signal.aborted||i(new Error("Could not load image. Please make sure to use a supported image type such as PNG or JPEG. Note that SVGs are not supported."))},a.src=o}))}(p||(p={})),p.resetRequestQueue();class d{constructor(t){this._transformRequestFn=t}transformRequest(t,e){return this._transformRequestFn&&this._transformRequestFn(t,e)||{url:t}}setTransformRequest(t){this._transformRequestFn=t}}function m(t){var r=new e.A(3);return r[0]=t[0],r[1]=t[1],r[2]=t[2],r}var g,y=function(t,e,r){return t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t};g=new e.A(3),e.A!=Float32Array&&(g[0]=0,g[1]=0,g[2]=0);var v=function(t){var e=t[0],r=t[1];return e*e+r*r};function x(t){const e=[];if("string"==typeof t)e.push({id:"default",url:t});else if(t&&t.length>0){const r=[];for(const{id:n,url:i}of t){const t=`${n}${i}`;-1===r.indexOf(t)&&(r.push(t),e.push({id:n,url:i}))}}return e}function _(t,e,r){const n=t.split("?");return n[0]+=`${e}${r}`,n.join("?")}function b(t,r,n,i){return e._(this,void 0,void 0,(function*(){const o=x(t),s=n>1?"@2x":"",l={},c={};for(const{id:t,url:n}of o){const a=r.transformRequest(_(n,s,".json"),"SpriteJSON");l[t]=e.h(a,i);const o=r.transformRequest(_(n,s,".png"),"SpriteImage");c[t]=p.getImage(o,i)}return yield Promise.all([...Object.values(l),...Object.values(c)]),function(t,r){return e._(this,void 0,void 0,(function*(){const e={};for(const n in t){e[n]={};const i=a.getImageCanvasContext((yield r[n]).data),o=(yield t[n]).data;for(const t in o){const{width:r,height:a,x:s,y:l,sdf:c,pixelRatio:u,stretchX:h,stretchY:f,content:p,textFitWidth:d,textFitHeight:m}=o[t],g={width:r,height:a,x:s,y:l,context:i};e[n][t]={data:null,pixelRatio:u,sdf:c,stretchX:h,stretchY:f,content:p,textFitWidth:d,textFitHeight:m,spriteData:g}}}return e}))}(l,c)}))}!function(){var t=new e.A(2);e.A!=Float32Array&&(t[0]=0,t[1]=0)}();class w{constructor(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)}update(t,r,n){const{width:i,height:a}=t,o=!(this.size&&this.size[0]===i&&this.size[1]===a||n),{context:s}=this,{gl:l}=s;if(this.useMipmap=Boolean(r&&r.useMipmap),l.bindTexture(l.TEXTURE_2D,this.texture),s.pixelStoreUnpackFlipY.set(!1),s.pixelStoreUnpack.set(1),s.pixelStoreUnpackPremultiplyAlpha.set(this.format===l.RGBA&&(!r||!1!==r.premultiply)),o)this.size=[i,a],t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof HTMLVideoElement||t instanceof ImageData||e.b(t)?l.texImage2D(l.TEXTURE_2D,0,this.format,this.format,l.UNSIGNED_BYTE,t):l.texImage2D(l.TEXTURE_2D,0,this.format,i,a,0,this.format,l.UNSIGNED_BYTE,t.data);else{const{x:r,y:o}=n||{x:0,y:0};t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof HTMLVideoElement||t instanceof ImageData||e.b(t)?l.texSubImage2D(l.TEXTURE_2D,0,r,o,l.RGBA,l.UNSIGNED_BYTE,t):l.texSubImage2D(l.TEXTURE_2D,0,r,o,i,a,l.RGBA,l.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&l.generateMipmap(l.TEXTURE_2D)}bind(t,e,r){const{context:n}=this,{gl:i}=n;i.bindTexture(i.TEXTURE_2D,this.texture),r!==i.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=i.LINEAR),t!==this.filter&&(i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,e),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,e),this.wrap=e)}isSizePowerOfTwo(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0}destroy(){const{gl:t}=this.context;t.deleteTexture(this.texture),this.texture=null}}function T(t){const{userImage:e}=t;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}class k extends e.E{constructor(){super(),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new e.R({width:1,height:1}),this.dirty=!0}isLoaded(){return this.loaded}setLoaded(t){if(this.loaded!==t&&(this.loaded=t,t)){for(const{ids:t,promiseResolve:e}of this.requestors)e(this._getImagesForIds(t));this.requestors=[]}}getImage(t){const r=this.images[t];if(r&&!r.data&&r.spriteData){const t=r.spriteData;r.data=new e.R({width:t.width,height:t.height},t.context.getImageData(t.x,t.y,t.width,t.height).data),r.spriteData=null}return r}addImage(t,e){if(this.images[t])throw new Error(`Image id ${t} already exist, use updateImage instead`);this._validate(t,e)&&(this.images[t]=e)}_validate(t,r){let n=!0;const i=r.data||r.spriteData;return this._validateStretch(r.stretchX,i&&i.width)||(this.fire(new e.j(new Error(`Image "${t}" has invalid "stretchX" value`))),n=!1),this._validateStretch(r.stretchY,i&&i.height)||(this.fire(new e.j(new Error(`Image "${t}" has invalid "stretchY" value`))),n=!1),this._validateContent(r.content,r)||(this.fire(new e.j(new Error(`Image "${t}" has invalid "content" value`))),n=!1),n}_validateStretch(t,e){if(!t)return!0;let r=0;for(const n of t){if(n[0]<r||n[1]<n[0]||e<n[1])return!1;r=n[1]}return!0}_validateContent(t,e){if(!t)return!0;if(4!==t.length)return!1;const r=e.spriteData,n=r&&r.width||e.data.width,i=r&&r.height||e.data.height;return!(t[0]<0||n<t[0]||t[1]<0||i<t[1]||t[2]<0||n<t[2]||t[3]<0||i<t[3]||t[2]<t[0]||t[3]<t[1])}updateImage(t,e,r=!0){const n=this.getImage(t);if(r&&(n.data.width!==e.data.width||n.data.height!==e.data.height))throw new Error(`size mismatch between old image (${n.data.width}x${n.data.height}) and new image (${e.data.width}x${e.data.height}).`);e.version=n.version+1,this.images[t]=e,this.updatedImages[t]=!0}removeImage(t){const e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()}listImages(){return Object.keys(this.images)}getImages(t){return new Promise(((e,r)=>{let n=!0;if(!this.isLoaded())for(const e of t)this.images[e]||(n=!1);this.isLoaded()||n?e(this._getImagesForIds(t)):this.requestors.push({ids:t,promiseResolve:e})}))}_getImagesForIds(t){const r={};for(const n of t){let t=this.getImage(n);t||(this.fire(new e.k("styleimagemissing",{id:n})),t=this.getImage(n)),t?r[n]={data:t.data.clone(),pixelRatio:t.pixelRatio,sdf:t.sdf,version:t.version,stretchX:t.stretchX,stretchY:t.stretchY,content:t.content,textFitWidth:t.textFitWidth,textFitHeight:t.textFitHeight,hasRenderCallback:Boolean(t.userImage&&t.userImage.render)}:e.w(`Image "${n}" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.`)}return r}getPixelSize(){const{width:t,height:e}=this.atlasImage;return{width:t,height:e}}getPattern(t){const r=this.patterns[t],n=this.getImage(t);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{const r={w:n.data.width+2,h:n.data.height+2,x:0,y:0},i=new e.I(r,n);this.patterns[t]={bin:r,position:i}}return this._updatePatternAtlas(),this.patterns[t].position}bind(t){const e=t.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new w(t,this.atlasImage,e.RGBA),this.atlasTexture.bind(e.LINEAR,e.CLAMP_TO_EDGE)}_updatePatternAtlas(){const t=[];for(const e in this.patterns)t.push(this.patterns[e].bin);const{w:r,h:n}=e.p(t),i=this.atlasImage;i.resize({width:r||1,height:n||1});for(const t in this.patterns){const{bin:r}=this.patterns[t],n=r.x+1,a=r.y+1,o=this.getImage(t).data,s=o.width,l=o.height;e.R.copy(o,i,{x:0,y:0},{x:n,y:a},{width:s,height:l}),e.R.copy(o,i,{x:0,y:l-1},{x:n,y:a-1},{width:s,height:1}),e.R.copy(o,i,{x:0,y:0},{x:n,y:a+l},{width:s,height:1}),e.R.copy(o,i,{x:s-1,y:0},{x:n-1,y:a},{width:1,height:l}),e.R.copy(o,i,{x:0,y:0},{x:n+s,y:a},{width:1,height:l})}this.dirty=!0}beginFrame(){this.callbackDispatchedThisFrame={}}dispatchRenderCallbacks(t){for(const r of t){if(this.callbackDispatchedThisFrame[r])continue;this.callbackDispatchedThisFrame[r]=!0;const t=this.getImage(r);t||e.w(`Image with ID: "${r}" was not found`),T(t)&&this.updateImage(r,t)}}}const A=1e20;function M(t,e,r,n,i,a,o,s,l){for(let c=e;c<e+n;c++)S(t,r*a+c,a,i,o,s,l);for(let c=r;c<r+i;c++)S(t,c*a+e,1,n,o,s,l)}function S(t,e,r,n,i,a,o){a[0]=0,o[0]=-A,o[1]=A,i[0]=t[e];for(let s=1,l=0,c=0;s<n;s++){i[s]=t[e+s*r];const n=s*s;do{const t=a[l];c=(i[s]-i[t]+n-t*t)/(s-t)/2}while(c<=o[l]&&--l>-1);l++,a[l]=s,o[l]=c,o[l+1]=A}for(let s=0,l=0;s<n;s++){for(;o[l+1]<s;)l++;const n=a[l],c=s-n;t[e+s*r]=i[n]+c*c}}class E{constructor(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}}setURL(t){this.url=t}getGlyphs(t){return e._(this,void 0,void 0,(function*(){const e=[];for(const r in t)for(const n of t[r])e.push(this._getAndCacheGlyphsPromise(r,n));const r=yield Promise.all(e),n={};for(const{stack:t,id:e,glyph:i}of r)n[t]||(n[t]={}),n[t][e]=i&&{id:i.id,bitmap:i.bitmap.clone(),metrics:i.metrics};return n}))}_getAndCacheGlyphsPromise(t,r){return e._(this,void 0,void 0,(function*(){let e=this.entries[t];e||(e=this.entries[t]={glyphs:{},requests:{},ranges:{}});let n=e.glyphs[r];if(void 0!==n)return{stack:t,id:r,glyph:n};if(n=this._tinySDF(e,t,r),n)return e.glyphs[r]=n,{stack:t,id:r,glyph:n};const i=Math.floor(r/256);if(256*i>65535)throw new Error("glyphs > 65535 not supported");if(e.ranges[i])return{stack:t,id:r,glyph:n};if(!this.url)throw new Error("glyphsUrl is not set");if(!e.requests[i]){const r=E.loadGlyphRange(t,i,this.url,this.requestManager);e.requests[i]=r}const a=yield e.requests[i];for(const t in a)this._doesCharSupportLocalGlyph(+t)||(e.glyphs[+t]=a[+t]);return e.ranges[i]=!0,{stack:t,id:r,glyph:a[r]||null}}))}_doesCharSupportLocalGlyph(t){return!!this.localIdeographFontFamily&&(e.u["CJK Unified Ideographs"](t)||e.u["Hangul Syllables"](t)||e.u.Hiragana(t)||e.u.Katakana(t))}_tinySDF(t,r,n){const i=this.localIdeographFontFamily;if(!i)return;if(!this._doesCharSupportLocalGlyph(n))return;let a=t.tinySDF;if(!a){let e="400";/bold/i.test(r)?e="900":/medium/i.test(r)?e="500":/light/i.test(r)&&(e="200"),a=t.tinySDF=new E.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:i,fontWeight:e})}const o=a.draw(String.fromCharCode(n));return{id:n,bitmap:new e.o({width:o.width||60,height:o.height||60},o.data),metrics:{width:o.glyphWidth/2||24,height:o.glyphHeight/2||24,left:o.glyphLeft/2+.5||0,top:o.glyphTop/2-27.5||-8,advance:o.glyphAdvance/2||24,isDoubleResolution:!0}}}}E.loadGlyphRange=function(t,r,n,i){return e._(this,void 0,void 0,(function*(){const a=256*r,o=a+255,s=i.transformRequest(n.replace("{fontstack}",t).replace("{range}",`${a}-${o}`),"Glyphs"),l=yield e.l(s,new AbortController);if(!l||!l.data)throw new Error(`Could not load glyph range. range: ${r}, ${a}-${o}`);const c={};for(const t of e.n(l.data))c[t.id]=t;return c}))},E.TinySDF=class{constructor({fontSize:t=24,buffer:e=3,radius:r=8,cutoff:n=.25,fontFamily:i="sans-serif",fontWeight:a="normal",fontStyle:o="normal"}={}){this.buffer=e,this.cutoff=n,this.radius=r;const s=this.size=t+4*e,l=this._createCanvas(s),c=this.ctx=l.getContext("2d",{willReadFrequently:!0});c.font=`${o} ${a} ${t}px ${i}`,c.textBaseline="alphabetic",c.textAlign="left",c.fillStyle="black",this.gridOuter=new Float64Array(s*s),this.gridInner=new Float64Array(s*s),this.f=new Float64Array(s),this.z=new Float64Array(s+1),this.v=new Uint16Array(s)}_createCanvas(t){const e=document.createElement("canvas");return e.width=e.height=t,e}draw(t){const{width:e,actualBoundingBoxAscent:r,actualBoundingBoxDescent:n,actualBoundingBoxLeft:i,actualBoundingBoxRight:a}=this.ctx.measureText(t),o=Math.ceil(r),s=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(a-i))),l=Math.min(this.size-this.buffer,o+Math.ceil(n)),c=s+2*this.buffer,u=l+2*this.buffer,h=Math.max(c*u,0),f=new Uint8ClampedArray(h),p={data:f,width:c,height:u,glyphWidth:s,glyphHeight:l,glyphTop:o,glyphLeft:0,glyphAdvance:e};if(0===s||0===l)return p;const{ctx:d,buffer:m,gridInner:g,gridOuter:y}=this;d.clearRect(m,m,s,l),d.fillText(t,m,m+o);const v=d.getImageData(m,m,s,l);y.fill(A,0,h),g.fill(0,0,h);for(let t=0;t<l;t++)for(let e=0;e<s;e++){const r=v.data[4*(t*s+e)+3]/255;if(0===r)continue;const n=(t+m)*c+e+m;if(1===r)y[n]=0,g[n]=A;else{const t=.5-r;y[n]=t>0?t*t:0,g[n]=t<0?t*t:0}}M(y,0,0,c,u,c,this.f,this.v,this.z),M(g,m,m,s,l,c,this.f,this.v,this.z);for(let t=0;t<h;t++){const e=Math.sqrt(y[t])-Math.sqrt(g[t]);f[t]=Math.round(255-255*(e/this.radius+this.cutoff))}return p}};class C{constructor(){this.specification=e.v.light.position}possiblyEvaluate(t,r){return e.y(t.expression.evaluate(r))}interpolate(t,r,n){return{x:e.z.number(t.x,r.x,n),y:e.z.number(t.y,r.y,n),z:e.z.number(t.z,r.z,n)}}}const L="-transition";let I;class P extends e.E{constructor(t){super(),I=I||new e.q({anchor:new e.D(e.v.light.anchor),position:new C,color:new e.D(e.v.light.color),intensity:new e.D(e.v.light.intensity)}),this._transitionable=new e.T(I),this.setLight(t),this._transitioning=this._transitionable.untransitioned()}getLight(){return this._transitionable.serialize()}setLight(t,r={}){if(!this._validate(e.r,t,r))for(const e in t){const r=t[e];e.endsWith(L)?this._transitionable.setTransition(e.slice(0,-11),r):this._transitionable.setValue(e,r)}}updateTransitions(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(t){this.properties=this._transitioning.possiblyEvaluate(t)}_validate(t,r,n){return(!n||!1!==n.validate)&&e.t(this,t.call(e.x,{value:r,style:{glyphs:!0,sprite:!0},styleSpec:e.v}))}}const z=new e.q({"sky-color":new e.D(e.v.sky["sky-color"]),"horizon-color":new e.D(e.v.sky["horizon-color"]),"fog-color":new e.D(e.v.sky["fog-color"]),"fog-ground-blend":new e.D(e.v.sky["fog-ground-blend"]),"horizon-fog-blend":new e.D(e.v.sky["horizon-fog-blend"]),"sky-horizon-blend":new e.D(e.v.sky["sky-horizon-blend"]),"atmosphere-blend":new e.D(e.v.sky["atmosphere-blend"])}),O="-transition";class D extends e.E{constructor(t){super(),this._transitionable=new e.T(z),this.setSky(t),this._transitioning=this._transitionable.untransitioned()}setSky(t,r={}){if(!this._validate(e.B,t,r))for(const e in t){const r=t[e];e.endsWith(O)?this._transitionable.setTransition(e.slice(0,-11),r):this._transitionable.setValue(e,r)}}getSky(){return this._transitionable.serialize()}updateTransitions(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)}hasTransition(){return this._transitioning.hasTransition()}recalculate(t){this.properties=this._transitioning.possiblyEvaluate(t)}_validate(t,r,n={}){return!1!==(null==n?void 0:n.validate)&&e.t(this,t.call(e.x,e.e({value:r,style:{glyphs:!0,sprite:!0},styleSpec:e.v})))}calculateFogBlendOpacity(t){return t<60?0:t<70?(t-60)/10:1}}class R{constructor(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}}getDash(t,e){const r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]}getDashRanges(t,e,r){const n=[];let i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});let s=t[0];for(let e=1;e<t.length;e++){o=!o;const l=t[e];i=s*r,s+=l,a=s*r,n.push({left:i,right:a,isDash:o,zeroLength:0===l})}return n}addRoundDash(t,e,r){const n=e/2;for(let e=-r;e<=r;e++){const i=this.nextRow+r+e,a=this.width*i;let o=0,s=t[o];for(let i=0;i<this.width;i++){i/s.right>1&&(s=t[++o]);const l=Math.abs(i-s.left),c=Math.abs(i-s.right),u=Math.min(l,c);let h;const f=e/r*(n+1);if(s.isDash){const t=n-Math.abs(f);h=Math.sqrt(u*u+t*t)}else h=n-Math.sqrt(u*u+f*f);this.data[a+i]=Math.max(0,Math.min(255,h+128))}}}addRegularDash(t){for(let e=t.length-1;e>=0;--e){const r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}const e=t[0],r=t[t.length-1];e.isDash===r.isDash&&(e.left=r.left-this.width,r.right=e.right+this.width);const n=this.width*this.nextRow;let i=0,a=t[i];for(let e=0;e<this.width;e++){e/a.right>1&&(a=t[++i]);const r=Math.abs(e-a.left),o=Math.abs(e-a.right),s=Math.min(r,o),l=a.isDash?s:-s;this.data[n+e]=Math.max(0,Math.min(255,l+128))}}addDash(t,r){const n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return e.w("LineAtlas out of space"),null;let a=0;for(let e=0;e<t.length;e++)a+=t[e];if(0!==a){const e=this.width/a,i=this.getDashRanges(t,this.width,e);r?this.addRoundDash(i,e,n):this.addRegularDash(i)}const o={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,o}bind(t){const e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))}}const F="maplibre_preloaded_worker_pool";class B{constructor(){this.active={}}acquire(t){if(!this.workers)for(this.workers=[];this.workers.length<B.workerCount;)this.workers.push(new Worker(e.a.WORKER_URL));return this.active[t]=!0,this.workers.slice()}release(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((t=>{t.terminate()})),this.workers=null)}isPreloaded(){return!!this.active[F]}numActive(){return Object.keys(this.active).length}}const N=Math.floor(a.hardwareConcurrency/2);let j,U;function V(){return j||(j=new B),j}B.workerCount=e.C(globalThis)?Math.max(Math.min(N,3),1):1;class q{constructor(t,r){this.workerPool=t,this.actors=[],this.currentActor=0,this.id=r;const n=this.workerPool.acquire(r);for(let t=0;t<n.length;t++){const i=n[t],a=new e.F(i,r);a.name=`Worker ${t}`,this.actors.push(a)}if(!this.actors.length)throw new Error("No actors found")}broadcast(t,e){const r=[];for(const n of this.actors)r.push(n.sendAsync({type:t,data:e}));return Promise.all(r)}getActor(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]}remove(t=!0){this.actors.forEach((t=>{t.remove()})),this.actors=[],t&&this.workerPool.release(this.id)}registerMessageHandler(t,e){for(const r of this.actors)r.registerMessageHandler(t,e)}}function H(){return U||(U=new q(V(),e.G),U.registerMessageHandler("GR",((t,r,n)=>e.m(r,n)))),U}function G(t,r){const n=e.H();return e.J(n,n,[1,1,0]),e.K(n,n,[.5*t.width,.5*t.height,1]),e.L(n,n,t.calculatePosMatrix(r.toUnwrapped()))}function Z(t,e,r,n,i,a){const o=function(t,e,r){if(t)for(const n of t){const t=e[n];if(t&&t.source===r&&"fill-extrusion"===t.type)return!0}else for(const t in e){const n=e[t];if(n.source===r&&"fill-extrusion"===n.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(W);const c=[];for(const n of l)c.push({wrappedTileID:n.tileID.wrapped().key,queryResults:n.tile.queryRenderedFeatures(e,r,t._state,n.queryGeometry,n.cameraQueryGeometry,n.scale,i,a,s,G(t.transform,n.tileID))});const u=function(t){const e={},r={};for(const n of t){const t=n.queryResults,i=n.wrappedTileID,a=r[i]=r[i]||{};for(const r in t){const n=t[r],i=a[r]=a[r]||{},o=e[r]=e[r]||[];for(const t of n)i[t.featureIndex]||(i[t.featureIndex]=!0,o.push(t))}}return e}(c);for(const e in u)u[e].forEach((e=>{const r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return u}function W(t,e){const r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}function Y(t,r,n){return e._(this,void 0,void 0,(function*(){let i=t;if(t.url?i=(yield e.h(r.transformRequest(t.url,"Source"),n)).data:yield a.frameAsync(n),!i)return null;const o=e.M(e.e(i,t),["tiles","minzoom","maxzoom","attribution","bounds","scheme","tileSize","encoding"]);return"vector_layers"in i&&i.vector_layers&&(o.vectorLayerIds=i.vector_layers.map((t=>t.id))),o}))}class X{constructor(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):Array.isArray(t)&&(4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1])))}setNorthEast(t){return this._ne=t instanceof e.N?new e.N(t.lng,t.lat):e.N.convert(t),this}setSouthWest(t){return this._sw=t instanceof e.N?new e.N(t.lng,t.lat):e.N.convert(t),this}extend(t){const r=this._sw,n=this._ne;let i,a;if(t instanceof e.N)i=t,a=t;else{if(!(t instanceof X)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){const e=t;return this.extend(X.convert(e))}{const r=t;return this.extend(e.N.convert(r))}}return t&&("lng"in t||"lon"in t)&&"lat"in t?this.extend(e.N.convert(t)):this}if(i=t._sw,a=t._ne,!i||!a)return this}return r||n?(r.lng=Math.min(i.lng,r.lng),r.lat=Math.min(i.lat,r.lat),n.lng=Math.max(a.lng,n.lng),n.lat=Math.max(a.lat,n.lat)):(this._sw=new e.N(i.lng,i.lat),this._ne=new e.N(a.lng,a.lat)),this}getCenter(){return new e.N((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new e.N(this.getWest(),this.getNorth())}getSouthEast(){return new e.N(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(t){const{lng:r,lat:n}=e.N.convert(t),i=this._sw.lat<=n&&n<=this._ne.lat;let a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a}static convert(t){return t instanceof X?t:t?new X(t):t}static fromLngLat(t,r=0){const n=360*r/40075017,i=n/Math.cos(Math.PI/180*t.lat);return new X(new e.N(t.lng-i,t.lat-n),new e.N(t.lng+i,t.lat+n))}}class ${constructor(t,e,r){this.bounds=X.convert(this.validateBounds(t)),this.minzoom=e||0,this.maxzoom=r||24}validateBounds(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]}contains(t){const r=Math.pow(2,t.z),n=Math.floor(e.O(this.bounds.getWest())*r),i=Math.floor(e.Q(this.bounds.getNorth())*r),a=Math.ceil(e.O(this.bounds.getEast())*r),o=Math.ceil(e.Q(this.bounds.getSouth())*r);return t.x>=n&&t.x<a&&t.y>=i&&t.y<o}}class J extends e.E{constructor(t,r,n,i){if(super(),this.id=t,this.dispatcher=n,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,e.e(this,e.M(r,["url","scheme","tileSize","promoteId"])),this._options=e.e({type:"vector"},r),this._collectResourceTiming=r.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(i)}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new e.k("dataloading",{dataType:"source"})),this._tileJSONRequest=new AbortController;try{const t=yield Y(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,this.map.style.sourceCaches[this.id].clearTiles(),t&&(e.e(this,t),t.bounds&&(this.tileBounds=new $(t.bounds,this.minzoom,this.maxzoom)),this.fire(new e.k("data",{dataType:"source",sourceDataType:"metadata"})),this.fire(new e.k("data",{dataType:"source",sourceDataType:"content"})))}catch(t){this._tileJSONRequest=null,this.fire(new e.j(t))}}))}loaded(){return this._loaded}hasTile(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)}onAdd(t){this.map=t,this.load()}setSourceProperty(t){this._tileJSONRequest&&this._tileJSONRequest.abort(),t(),this.load()}setTiles(t){return this.setSourceProperty((()=>{this._options.tiles=t})),this}setUrl(t){return this.setSourceProperty((()=>{this.url=t,this._options.url=t})),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}serialize(){return e.e({},this._options)}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),r={request:this.map._requestManager.transformRequest(e,"Tile"),uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,tileSize:this.tileSize*t.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};r.request.collectResourceTiming=this._collectResourceTiming;let n="RT";if(t.actor&&"expired"!==t.state){if("loading"===t.state)return new Promise(((e,r)=>{t.reloadPromise={resolve:e,reject:r}}))}else t.actor=this.dispatcher.getActor(),n="LT";t.abortController=new AbortController;try{const e=yield t.actor.sendAsync({type:n,data:r},t.abortController);if(delete t.abortController,t.aborted)return;this._afterTileLoadWorkerResponse(t,e)}catch(e){if(delete t.abortController,t.aborted)return;if(e&&404!==e.status)throw e;this._afterTileLoadWorkerResponse(t,null)}}))}_afterTileLoadWorkerResponse(t,e){if(e&&e.resourceTiming&&(t.resourceTiming=e.resourceTiming),e&&this.map._refreshExpiredTiles&&t.setExpiryData(e),t.loadVectorData(e,this.map.painter),t.reloadPromise){const e=t.reloadPromise;t.reloadPromise=null,this.loadTile(t).then(e.resolve).catch(e.reject)}}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController),t.actor&&(yield t.actor.sendAsync({type:"AT",data:{uid:t.uid,type:this.type,source:this.id}}))}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.unloadVectorData(),t.actor&&(yield t.actor.sendAsync({type:"RMT",data:{uid:t.uid,type:this.type,source:this.id}}))}))}hasTransition(){return!1}}class K extends e.E{constructor(t,r,n,i){super(),this.id=t,this.dispatcher=n,this.setEventedParent(i),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=e.e({type:"raster"},r),e.e(this,e.M(r,["url","scheme","tileSize"]))}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new e.k("dataloading",{dataType:"source"})),this._tileJSONRequest=new AbortController;try{const t=yield Y(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,t&&(e.e(this,t),t.bounds&&(this.tileBounds=new $(t.bounds,this.minzoom,this.maxzoom)),this.fire(new e.k("data",{dataType:"source",sourceDataType:"metadata"})),this.fire(new e.k("data",{dataType:"source",sourceDataType:"content"})))}catch(t){this._tileJSONRequest=null,this.fire(new e.j(t))}}))}loaded(){return this._loaded}onAdd(t){this.map=t,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}setSourceProperty(t){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null),t(),this.load()}setTiles(t){return this.setSourceProperty((()=>{this._options.tiles=t})),this}setUrl(t){return this.setSourceProperty((()=>{this.url=t,this._options.url=t})),this}serialize(){return e.e({},this._options)}hasTile(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);t.abortController=new AbortController;try{const r=yield p.getImage(this.map._requestManager.transformRequest(e,"Tile"),t.abortController,this.map._refreshExpiredTiles);if(delete t.abortController,t.aborted)return void(t.state="unloaded");if(r&&r.data){this.map._refreshExpiredTiles&&r.cacheControl&&r.expires&&t.setExpiryData({cacheControl:r.cacheControl,expires:r.expires});const e=this.map.painter.context,n=e.gl,i=r.data;t.texture=this.map.painter.getTileTexture(i.width),t.texture?t.texture.update(i,{useMipmap:!0}):(t.texture=new w(e,i,n.RGBA,{useMipmap:!0}),t.texture.bind(n.LINEAR,n.CLAMP_TO_EDGE,n.LINEAR_MIPMAP_NEAREST)),t.state="loaded"}}catch(e){if(delete t.abortController,t.aborted)t.state="unloaded";else if(e)throw t.state="errored",e}}))}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController)}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.texture&&this.map.painter.saveTileTexture(t.texture)}))}hasTransition(){return!1}}class Q extends K{constructor(t,r,n,i){super(t,r,n,i),this.type="raster-dem",this.maxzoom=22,this._options=e.e({type:"raster-dem"},r),this.encoding=r.encoding||"mapbox",this.redFactor=r.redFactor,this.greenFactor=r.greenFactor,this.blueFactor=r.blueFactor,this.baseShift=r.baseShift}loadTile(t){return e._(this,void 0,void 0,(function*(){const r=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),n=this.map._requestManager.transformRequest(r,"Tile");t.neighboringTiles=this._getNeighboringTiles(t.tileID),t.abortController=new AbortController;try{const r=yield p.getImage(n,t.abortController,this.map._refreshExpiredTiles);if(delete t.abortController,t.aborted)return void(t.state="unloaded");if(r&&r.data){const n=r.data;this.map._refreshExpiredTiles&&r.cacheControl&&r.expires&&t.setExpiryData({cacheControl:r.cacheControl,expires:r.expires});const i=e.b(n)&&e.U()?n:yield this.readImageNow(n),a={type:this.type,uid:t.uid,source:this.id,rawImageData:i,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};if(!t.actor||"expired"===t.state){t.actor=this.dispatcher.getActor();const e=yield t.actor.sendAsync({type:"LDT",data:a});t.dem=e,t.needsHillshadePrepare=!0,t.needsTerrainPrepare=!0,t.state="loaded"}}}catch(e){if(delete t.abortController,t.aborted)t.state="unloaded";else if(e)throw t.state="errored",e}}))}readImageNow(t){return e._(this,void 0,void 0,(function*(){if("undefined"!=typeof VideoFrame&&e.V()){const r=t.width+2,n=t.height+2;try{return new e.R({width:r,height:n},yield e.W(t,-1,-1,r,n))}catch(t){}}return a.getImageData(t,1)}))}_getNeighboringTiles(t){const r=t.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?t.wrap-1:t.wrap,o=(r.x+1+n)%n,s=r.x+1===n?t.wrap+1:t.wrap,l={};return l[new e.S(t.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new e.S(t.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new e.S(t.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new e.S(t.overscaledZ,t.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new e.S(t.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new e.S(t.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new e.S(t.overscaledZ,t.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new e.S(t.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&(yield t.actor.sendAsync({type:"RDT",data:{type:this.type,uid:t.uid,source:this.id}}))}))}}class tt extends e.E{constructor(t,r,n,i){super(),this.id=t,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._pendingLoads=0,this.actor=n.getActor(),this.setEventedParent(i),this._data=r.data,this._options=e.e({},r),this._collectResourceTiming=r.collectResourceTiming,void 0!==r.maxzoom&&(this.maxzoom=r.maxzoom),r.type&&(this.type=r.type),r.attribution&&(this.attribution=r.attribution),this.promoteId=r.promoteId;const a=e.X/this.tileSize;this.workerOptions=e.e({source:this.id,cluster:r.cluster||!1,geojsonVtOptions:{buffer:(void 0!==r.buffer?r.buffer:128)*a,tolerance:(void 0!==r.tolerance?r.tolerance:.375)*a,extent:e.X,maxZoom:this.maxzoom,lineMetrics:r.lineMetrics||!1,generateId:r.generateId||!1},superclusterOptions:{maxZoom:void 0!==r.clusterMaxZoom?r.clusterMaxZoom:this.maxzoom-1,minPoints:Math.max(2,r.clusterMinPoints||2),extent:e.X,radius:(r.clusterRadius||50)*a,log:!1,generateId:r.generateId||!1},clusterProperties:r.clusterProperties,filter:r.filter},r.workerOptions),"string"==typeof this.promoteId&&(this.workerOptions.promoteId=this.promoteId)}load(){return e._(this,void 0,void 0,(function*(){yield this._updateWorkerData()}))}onAdd(t){this.map=t,this.load()}setData(t){return this._data=t,this._updateWorkerData(),this}updateData(t){return this._updateWorkerData(t),this}getData(){return e._(this,void 0,void 0,(function*(){const t=e.e({type:this.type},this.workerOptions);return this.actor.sendAsync({type:"GD",data:t})}))}setClusterOptions(t){return this.workerOptions.cluster=t.cluster,t&&(void 0!==t.clusterRadius&&(this.workerOptions.superclusterOptions.radius=t.clusterRadius),void 0!==t.clusterMaxZoom&&(this.workerOptions.superclusterOptions.maxZoom=t.clusterMaxZoom)),this._updateWorkerData(),this}getClusterExpansionZoom(t){return this.actor.sendAsync({type:"GCEZ",data:{type:this.type,clusterId:t,source:this.id}})}getClusterChildren(t){return this.actor.sendAsync({type:"GCC",data:{type:this.type,clusterId:t,source:this.id}})}getClusterLeaves(t,e,r){return this.actor.sendAsync({type:"GCL",data:{type:this.type,source:this.id,clusterId:t,limit:e,offset:r}})}_updateWorkerData(t){return e._(this,void 0,void 0,(function*(){const r=e.e({type:this.type},this.workerOptions);t?r.dataDiff=t:"string"==typeof this._data?(r.request=this.map._requestManager.transformRequest(a.resolveURL(this._data),"Source"),r.request.collectResourceTiming=this._collectResourceTiming):r.data=JSON.stringify(this._data),this._pendingLoads++,this.fire(new e.k("dataloading",{dataType:"source"}));try{const t=yield this.actor.sendAsync({type:"LD",data:r});if(this._pendingLoads--,this._removed||t.abandoned)return void this.fire(new e.k("dataabort",{dataType:"source"}));let n=null;t.resourceTiming&&t.resourceTiming[this.id]&&(n=t.resourceTiming[this.id].slice(0));const i={dataType:"source"};this._collectResourceTiming&&n&&n.length>0&&e.e(i,{resourceTiming:n}),this.fire(new e.k("data",Object.assign(Object.assign({},i),{sourceDataType:"metadata"}))),this.fire(new e.k("data",Object.assign(Object.assign({},i),{sourceDataType:"content"})))}catch(t){if(this._pendingLoads--,this._removed)return void this.fire(new e.k("dataabort",{dataType:"source"}));this.fire(new e.j(t))}}))}loaded(){return 0===this._pendingLoads}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.actor?"RT":"LT";t.actor=this.actor;const r={type:this.type,uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};t.abortController=new AbortController;const n=yield this.actor.sendAsync({type:e,data:r},t.abortController);delete t.abortController,t.unloadVectorData(),t.aborted||t.loadVectorData(n,this.map.painter,"RT"===e)}))}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController),t.aborted=!0}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.unloadVectorData(),yield this.actor.sendAsync({type:"RMT",data:{uid:t.uid,type:this.type,source:this.id}})}))}onRemove(){this._removed=!0,this.actor.sendAsync({type:"RS",data:{type:this.type,source:this.id}})}serialize(){return e.e({},this._options,{type:this.type,data:this._data})}hasTransition(){return!1}}var et=e.Y([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]);class rt extends e.E{constructor(t,e,r,n){super(),this.id=t,this.dispatcher=r,this.coordinates=e.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(n),this.options=e}load(t){return e._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new e.k("dataloading",{dataType:"source"})),this.url=this.options.url,this._request=new AbortController;try{const e=yield p.getImage(this.map._requestManager.transformRequest(this.url,"Image"),this._request);this._request=null,this._loaded=!0,e&&e.data&&(this.image=e.data,t&&(this.coordinates=t),this._finishLoading())}catch(t){this._request=null,this._loaded=!0,this.fire(new e.j(t))}}))}loaded(){return this._loaded}updateImage(t){return t.url?(this._request&&(this._request.abort(),this._request=null),this.options.url=t.url,this.load(t.coordinates).finally((()=>{this.texture=null})),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new e.k("data",{dataType:"source",sourceDataType:"metadata"})))}onAdd(t){this.map=t,this.load()}onRemove(){this._request&&(this._request.abort(),this._request=null)}setCoordinates(t){this.coordinates=t;const r=t.map(e.Z.fromLngLat);this.tileID=function(t){let r=1/0,n=1/0,i=-1/0,a=-1/0;for(const e of t)r=Math.min(r,e.x),n=Math.min(n,e.y),i=Math.max(i,e.x),a=Math.max(a,e.y);const o=i-r,s=a-n,l=Math.max(o,s),c=Math.max(0,Math.floor(-Math.log(l)/Math.LN2)),u=Math.pow(2,c);return new e.a1(c,Math.floor((r+i)/2*u),Math.floor((n+a)/2*u))}(r),this.minzoom=this.maxzoom=this.tileID.z;const n=r.map((t=>this.tileID.getTilePoint(t)._round()));return this._boundsArray=new e.$,this._boundsArray.emplaceBack(n[0].x,n[0].y,0,0),this._boundsArray.emplaceBack(n[1].x,n[1].y,e.X,0),this._boundsArray.emplaceBack(n[3].x,n[3].y,0,e.X),this._boundsArray.emplaceBack(n[2].x,n[2].y,e.X,e.X),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new e.k("data",{dataType:"source",sourceDataType:"content"})),this}prepare(){if(0===Object.keys(this.tiles).length||!this.image)return;const t=this.map.painter.context,r=t.gl;this.boundsBuffer||(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture||(this.texture=new w(t,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE));let n=!1;for(const t in this.tiles){const e=this.tiles[t];"loaded"!==e.state&&(e.state="loaded",e.texture=this.texture,n=!0)}n&&this.fire(new e.k("data",{dataType:"source",sourceDataType:"idle",sourceId:this.id}))}loadTile(t){return e._(this,void 0,void 0,(function*(){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={}):t.state="errored"}))}serialize(){return{type:"image",url:this.options.url,coordinates:this.coordinates}}hasTransition(){return!1}}class nt extends rt{constructor(t,e,r,n){super(t,e,r,n),this.roundZoom=!0,this.type="video",this.options=e}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!1;const t=this.options;this.urls=[];for(const e of t.urls)this.urls.push(this.map._requestManager.transformRequest(e,"Source").url);try{const t=yield e.a3(this.urls);if(this._loaded=!0,!t)return;this.video=t,this.video.loop=!0,this.video.addEventListener("playing",(()=>{this.map.triggerRepaint()})),this.map&&this.video.play(),this._finishLoading()}catch(t){this.fire(new e.j(t))}}))}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(t){if(this.video){const r=this.video.seekable;t<r.start(0)||t>r.end(0)?this.fire(new e.j(new e.a2(`sources.${this.id}`,null,`Playback for this video can be set only between the ${r.start(0)} and ${r.end(0)}-second mark.`))):this.video.currentTime=t}}getVideo(){return this.video}onAdd(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}prepare(){if(0===Object.keys(this.tiles).length||this.video.readyState<2)return;const t=this.map.painter.context,r=t.gl;this.boundsBuffer||(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new w(t,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE));let n=!1;for(const t in this.tiles){const e=this.tiles[t];"loaded"!==e.state&&(e.state="loaded",e.texture=this.texture,n=!0)}n&&this.fire(new e.k("data",{dataType:"source",sourceDataType:"idle",sourceId:this.id}))}serialize(){return{type:"video",urls:this.urls,coordinates:this.coordinates}}hasTransition(){return this.video&&!this.video.paused}}class it extends rt{constructor(t,r,n,i){super(t,r,n,i),r.coordinates?Array.isArray(r.coordinates)&&4===r.coordinates.length&&!r.coordinates.some((t=>!Array.isArray(t)||2!==t.length||t.some((t=>"number"!=typeof t))))||this.fire(new e.j(new e.a2(`sources.${t}`,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new e.j(new e.a2(`sources.${t}`,null,'missing required property "coordinates"'))),r.animate&&"boolean"!=typeof r.animate&&this.fire(new e.j(new e.a2(`sources.${t}`,null,'optional "animate" property must be a boolean value'))),r.canvas?"string"==typeof r.canvas||r.canvas instanceof HTMLCanvasElement||this.fire(new e.j(new e.a2(`sources.${t}`,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new e.j(new e.a2(`sources.${t}`,null,'missing required property "canvas"'))),this.options=r,this.animate=void 0===r.animate||r.animate}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new e.j(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())}))}getCanvas(){return this.canvas}onAdd(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}prepare(){let t=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,t=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,t=!0),this._hasInvalidDimensions())return;if(0===Object.keys(this.tiles).length)return;const r=this.map.painter.context,n=r.gl;this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture?(t||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new w(r,this.canvas,n.RGBA,{premultiply:!0});let i=!1;for(const t in this.tiles){const e=this.tiles[t];"loaded"!==e.state&&(e.state="loaded",e.texture=this.texture,i=!0)}i&&this.fire(new e.k("data",{dataType:"source",sourceDataType:"idle",sourceId:this.id}))}serialize(){return{type:"canvas",coordinates:this.coordinates}}hasTransition(){return this._playing}_hasInvalidDimensions(){for(const t of[this.canvas.width,this.canvas.height])if(isNaN(t)||t<=0)return!0;return!1}}const at={},ot=t=>{switch(t){case"geojson":return tt;case"image":return rt;case"raster":return K;case"raster-dem":return Q;case"vector":return J;case"video":return nt;case"canvas":return it}return at[t]};const st="RTLPluginLoaded";class lt extends e.E{constructor(){super(...arguments),this.status="unavailable",this.url=null,this.dispatcher=H()}_syncState(t){return this.status=t,this.dispatcher.broadcast("SRPS",{pluginStatus:t,pluginURL:this.url}).catch((t=>{throw this.status="error",t}))}getRTLTextPluginStatus(){return this.status}clearRTLTextPlugin(){this.status="unavailable",this.url=null}setRTLTextPlugin(t){return e._(this,arguments,void 0,(function*(t,e=!1){if(this.url)throw new Error("setRTLTextPlugin cannot be called multiple times.");if(this.url=a.resolveURL(t),!this.url)throw new Error(`requested url ${t} is invalid`);if("unavailable"===this.status){if(!e)return this._requestImport();this.status="deferred",this._syncState(this.status)}else if("requested"===this.status)return this._requestImport()}))}_requestImport(){return e._(this,void 0,void 0,(function*(){yield this._syncState("loading"),this.status="loaded",this.fire(new e.k(st))}))}lazyLoad(){"unavailable"===this.status?this.status="requested":"deferred"===this.status&&this._requestImport()}}let ct=null;function ut(){return ct||(ct=new lt),ct}class ht{constructor(t,r){this.timeAdded=0,this.fadeEndTime=0,this.tileID=t,this.uid=e.a4(),this.uses=0,this.tileSize=r,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttCoords={},this.expiredRequestCount=0,this.state="loading"}registerFadeDuration(t){const e=t+this.timeAdded;e<this.fadeEndTime||(this.fadeEndTime=e)}wasRequested(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state}clearTextures(t){this.demTexture&&t.saveTileTexture(this.demTexture),this.demTexture=null}loadVectorData(t,r,n){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){const r={};if(!e)return r;for(const n of t){const t=n.layerIds.map((t=>e.getLayer(t))).filter(Boolean);if(0!==t.length){n.layers=t,n.stateDependentLayerIds&&(n.stateDependentLayers=n.stateDependentLayerIds.map((e=>t.filter((t=>t.id===e))[0])));for(const e of t)r[e.id]=n}}return r}(t.buckets,r.style),this.hasSymbolBuckets=!1;for(const t in this.buckets){const r=this.buckets[t];if(r instanceof e.a6){if(this.hasSymbolBuckets=!0,!n)break;r.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(const t in this.buckets){const r=this.buckets[t];if(r instanceof e.a6&&r.hasRTLText){this.hasRTLText=!0,ut().lazyLoad();break}}this.queryPadding=0;for(const t in this.buckets){const e=this.buckets[t];this.queryPadding=Math.max(this.queryPadding,r.style.getLayer(t).queryRadius(e))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new e.a5}unloadVectorData(){for(const t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"}getBucket(t){return this.buckets[t.id]}upload(t){for(const e in this.buckets){const r=this.buckets[e];r.uploadPending()&&r.upload(t)}const e=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new w(t,this.imageAtlas.image,e.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new w(t,this.glyphAtlasImage,e.ALPHA),this.glyphAtlasImage=null)}prepare(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)}queryRenderedFeatures(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}}querySourceFeatures(t,r){const n=this.latestFeatureIndex;if(!n||!n.rawTileData)return;const i=n.loadVTLayers(),a=r&&r.sourceLayer?r.sourceLayer:"",o=i._geojsonTileLayer||i[a];if(!o)return;const s=e.a7(r&&r.filter),{z:l,x:c,y:u}=this.tileID.canonical,h={z:l,x:c,y:u};for(let r=0;r<o.length;r++){const i=o.feature(r);if(s.needGeometry){const t=e.a8(i,!0);if(!s.filter(new e.a9(this.tileID.overscaledZ),t,this.tileID.canonical))continue}else if(!s.filter(new e.a9(this.tileID.overscaledZ),i))continue;const f=n.getId(i,a),p=new e.aa(i,l,c,u,f);p.tile=h,t.push(p)}}hasData(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state}patternsLoaded(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length}setExpiryData(t){const r=this.expirationTime;if(t.cacheControl){const r=e.ab(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){const t=Date.now();let e=!1;if(this.expirationTime>t)e=!1;else if(r)if(this.expirationTime<r)e=!0;else{const n=this.expirationTime-r;n?this.expirationTime=t+Math.max(n,3e4):e=!0}else e=!0;e?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}}getExpiryTimeout(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)}setFeatureState(t,e){if(!this.latestFeatureIndex||!this.latestFeatureIndex.rawTileData||0===Object.keys(t).length)return;const r=this.latestFeatureIndex.loadVTLayers();for(const n in this.buckets){if(!e.style.hasLayer(n))continue;const i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(!o||!s||0===Object.keys(s).length)continue;i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});const l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}holdingForFade(){return void 0!==this.symbolFadeHoldUntil}symbolFadeFinished(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<a.now()}clearFadeHold(){this.symbolFadeHoldUntil=void 0}setHoldDuration(t){this.symbolFadeHoldUntil=a.now()+t}setDependencies(t,e){const r={};for(const t of e)r[t]=!0;this.dependencies[t]=r}hasDependency(t,e){for(const r of t){const t=this.dependencies[r];if(t)for(const r of e)if(t[r])return!0}return!1}}class ft{constructor(t,e){this.max=t,this.onRemove=e,this.reset()}reset(){for(const t in this.data)for(const e of this.data[t])e.timeout&&clearTimeout(e.timeout),this.onRemove(e.value);return this.data={},this.order=[],this}add(t,e,r){const n=t.wrapped().key;void 0===this.data[n]&&(this.data[n]=[]);const i={value:e,timeout:void 0};if(void 0!==r&&(i.timeout=setTimeout((()=>{this.remove(t,i)}),r)),this.data[n].push(i),this.order.push(n),this.order.length>this.max){const t=this._getAndRemoveByKey(this.order[0]);t&&this.onRemove(t)}return this}has(t){return t.wrapped().key in this.data}getAndRemove(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null}_getAndRemoveByKey(t){const e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value}getByKey(t){const e=this.data[t];return e?e[0].value:null}get(t){return this.has(t)?this.data[t.wrapped().key][0].value:null}remove(t,e){if(!this.has(t))return this;const r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this}setMaxSize(t){for(this.max=t;this.order.length>this.max;){const t=this._getAndRemoveByKey(this.order[0]);t&&this.onRemove(t)}return this}filter(t){const e=[];for(const r in this.data)for(const n of this.data[r])t(n.value)||e.push(n);for(const t of e)this.remove(t.value.tileID,t)}}class pt{constructor(){this.state={},this.stateChanges={},this.deletedStates={}}updateState(t,r,n){const i=String(r);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][i]=this.stateChanges[t][i]||{},e.e(this.stateChanges[t][i],n),null===this.deletedStates[t]){this.deletedStates[t]={};for(const e in this.state[t])e!==i&&(this.deletedStates[t][e]=null)}else if(this.deletedStates[t]&&null===this.deletedStates[t][i]){this.deletedStates[t][i]={};for(const e in this.state[t][i])n[e]||(this.deletedStates[t][i][e]=null)}else for(const e in n)this.deletedStates[t]&&this.deletedStates[t][i]&&null===this.deletedStates[t][i][e]&&delete this.deletedStates[t][i][e]}removeFeatureState(t,e,r){if(null===this.deletedStates[t])return;const n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}getState(t,r){const n=String(r),i=this.state[t]||{},a=this.stateChanges[t]||{},o=e.e({},i[n],a[n]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){const e=this.deletedStates[t][r];if(null===e)return{};for(const t in e)delete o[t]}return o}initializeTileState(t,e){t.setFeatureState(this.state,e)}coalesceChanges(t,r){const n={};for(const t in this.stateChanges){this.state[t]=this.state[t]||{};const r={};for(const n in this.stateChanges[t])this.state[t][n]||(this.state[t][n]={}),e.e(this.state[t][n],this.stateChanges[t][n]),r[n]=this.state[t][n];n[t]=r}for(const t in this.deletedStates){this.state[t]=this.state[t]||{};const r={};if(null===this.deletedStates[t])for(const e in this.state[t])r[e]={},this.state[t][e]={};else for(const e in this.deletedStates[t]){if(null===this.deletedStates[t][e])this.state[t][e]={};else for(const r of Object.keys(this.deletedStates[t][e]))delete this.state[t][e][r];r[e]=this.state[t][e]}n[t]=n[t]||{},e.e(n[t],r)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(n).length)for(const e in t)t[e].setFeatureState(n,r)}}class dt extends e.E{constructor(t,e,r){super(),this.id=t,this.dispatcher=r,this.on("data",(t=>this._dataHandler(t))),this.on("dataloading",(()=>{this._sourceErrored=!1})),this.on("error",(()=>{this._sourceErrored=this._source.loaded()})),this._source=((t,e,r,n)=>{const i=new(ot(e.type))(t,e,r,n);if(i.id!==t)throw new Error(`Expected Source id to be ${t} instead of ${i.id}`);return i})(t,e,r,this),this._tiles={},this._cache=new ft(0,(t=>this._unloadTile(t))),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new pt,this._didEmitContent=!1,this._updated=!1}onAdd(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._maxTileCacheZoomLevels=t?t._maxTileCacheZoomLevels:null,this._source&&this._source.onAdd&&this._source.onAdd(t)}onRemove(t){this.clearTiles(),this._source&&this._source.onRemove&&this._source.onRemove(t)}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;if(!(void 0===this.used&&void 0===this.usedForTerrain||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(const t in this._tiles){const e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0}getSource(){return this._source}pause(){this._paused=!0}resume(){if(!this._paused)return;const t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(t,r,n){return e._(this,void 0,void 0,(function*(){try{yield this._source.loadTile(t),this._tileLoaded(t,r,n)}catch(r){t.state="errored",404!==r.status?this._source.fire(new e.j(r,{tile:t})):this.update(this.transform,this.terrain)}}))}_unloadTile(t){this._source.unloadTile&&this._source.unloadTile(t)}_abortTile(t){this._source.abortTile&&this._source.abortTile(t),this._source.fire(new e.k("dataabort",{tile:t,coord:t.tileID,dataType:"source"}))}serialize(){return this._source.serialize()}prepare(t){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(const e in this._tiles){const r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}}getIds(){return Object.values(this._tiles).map((t=>t.tileID)).sort(mt).map((t=>t.key))}getRenderableIds(t){const r=[];for(const e in this._tiles)this._isIdRenderable(e,t)&&r.push(this._tiles[e]);return t?r.sort(((t,r)=>{const n=t.tileID,i=r.tileID,a=new e.P(n.canonical.x,n.canonical.y)._rotate(this.transform.angle),o=new e.P(i.canonical.x,i.canonical.y)._rotate(this.transform.angle);return n.overscaledZ-i.overscaledZ||o.y-a.y||o.x-a.x})).map((t=>t.tileID.key)):r.map((t=>t.tileID)).sort(mt).map((t=>t.key))}hasRenderableParent(t){const e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)}_isIdRenderable(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())}reload(){if(this._paused)this._shouldReloadOnResume=!0;else{this._cache.reset();for(const t in this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")}}_reloadTile(t,r){return e._(this,void 0,void 0,(function*(){const e=this._tiles[t];e&&("loading"!==e.state&&(e.state=r),yield this._loadTile(e,t,r))}))}_tileLoaded(t,r,n){t.timeAdded=a.now(),"expired"===n&&(t.refreshedUponExpiration=!0),this._setTileReloadTimer(r,t),"raster-dem"===this.getSource().type&&t.dem&&this._backfillDEM(t),this._state.initializeTileState(t,this.map?this.map.painter:null),t.aborted||this._source.fire(new e.k("data",{dataType:"source",tile:t,coord:t.tileID}))}_backfillDEM(t){const e=this.getRenderableIds();for(let n=0;n<e.length;n++){const i=e[n];if(t.neighboringTiles&&t.neighboringTiles[i]){const e=this.getTileByID(i);r(t,e),r(e,t)}}function r(t,e){t.needsHillshadePrepare=!0,t.needsTerrainPrepare=!0;let r=e.tileID.canonical.x-t.tileID.canonical.x;const n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}}getTile(t){return this.getTileByID(t.key)}getTileByID(t){return this._tiles[t]}_retainLoadedChildren(t,e,r,n){for(const i in this._tiles){let a=this._tiles[i];if(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)continue;let o=a.tileID;for(;a&&a.tileID.overscaledZ>e+1;){const t=a.tileID.scaledTo(a.tileID.overscaledZ-1);a=this._tiles[t.key],a&&a.hasData()&&(o=t)}let s=o;for(;s.overscaledZ>e;)if(s=s.scaledTo(s.overscaledZ-1),t[s.key]){n[o.key]=o;break}}}findLoadedParent(t,e){if(t.key in this._loadedParentTiles){const r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(let r=t.overscaledZ-1;r>=e;r--){const e=t.scaledTo(r),n=this._getLoadedTile(e);if(n)return n}}findLoadedSibling(t){return this._getLoadedTile(t)}_getLoadedTile(t){const e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)}updateCacheSize(t){const r=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),n=null===this._maxTileCacheZoomLevels?e.a.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels,i=Math.floor(r*n),a="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,i):i;this._cache.setMaxSize(a)}handleWrapJump(t){const e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){const t={};for(const e in this._tiles){const n=this._tiles[e];n.tileID=n.tileID.unwrapTo(n.tileID.wrap+r),t[n.tileID.key]=n}this._tiles=t;for(const t in this._timers)clearTimeout(this._timers[t]),delete this._timers[t];for(const t in this._tiles){const e=this._tiles[t];this._setTileReloadTimer(t,e)}}}_updateCoveredAndRetainedTiles(t,e,r,n,i,o){const s={},l={},c=Object.keys(t),u=a.now();for(const r of c){const n=t[r],i=this._tiles[r];if(!i||0!==i.fadeEndTime&&i.fadeEndTime<=u)continue;const a=this.findLoadedParent(n,e),o=this.findLoadedSibling(n),c=a||o||null;c&&(this._addTile(c.tileID),s[c.tileID.key]=c.tileID),l[r]=n}this._retainLoadedChildren(l,n,r,t);for(const e in s)t[e]||(this._coveredTiles[e]=!0,t[e]=s[e]);if(o){const e={},r={};for(const t of i)this._tiles[t.key].hasData()?e[t.key]=t:r[t.key]=t;for(const n in r){const i=r[n].children(this._source.maxzoom);this._tiles[i[0].key]&&this._tiles[i[1].key]&&this._tiles[i[2].key]&&this._tiles[i[3].key]&&(e[i[0].key]=t[i[0].key]=i[0],e[i[1].key]=t[i[1].key]=i[1],e[i[2].key]=t[i[2].key]=i[2],e[i[3].key]=t[i[3].key]=i[3],delete r[n])}for(const n in r){const i=r[n],a=this.findLoadedParent(i,this._source.minzoom),o=this.findLoadedSibling(i),s=a||o||null;if(s){e[s.tileID.key]=t[s.tileID.key]=s.tileID;for(const t in e)e[t].isChildOf(s.tileID)&&delete e[t]}}for(const t in this._tiles)e[t]||(this._coveredTiles[t]=!0)}}update(t,r){if(!this._sourceLoaded||this._paused)return;let n;this.transform=t,this.terrain=r,this.updateCacheSize(t),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used||this.usedForTerrain?this._source.tileID?n=t.getVisibleUnwrappedCoordinates(this._source.tileID).map((t=>new e.S(t.canonical.z,t.wrap,t.canonical.z,t.canonical.x,t.canonical.y))):(n=t.coveringTiles({tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:r}),this._source.hasTile&&(n=n.filter((t=>this._source.hasTile(t))))):n=[];const i=t.coveringZoomLevel(this._source),a=Math.max(i-dt.maxOverzooming,this._source.minzoom),o=Math.max(i+dt.maxUnderzooming,this._source.minzoom);if(this.usedForTerrain){const t={};for(const e of n)if(e.canonical.z>this._source.minzoom){const r=e.scaledTo(e.canonical.z-1);t[r.key]=r;const n=e.scaledTo(Math.max(this._source.minzoom,Math.min(e.canonical.z,5)));t[n.key]=n}n=n.concat(Object.values(t))}const s=0===n.length&&!this._updated&&this._didEmitContent;this._updated=!0,s&&this.fire(new e.k("data",{sourceDataType:"idle",dataType:"source",sourceId:this.id}));const l=this._updateRetainedTiles(n,i);gt(this._source.type)&&this._updateCoveredAndRetainedTiles(l,a,o,i,n,r);for(const t in l)this._tiles[t].clearFadeHold();const c=e.ac(this._tiles,l);for(const t of c){const e=this._tiles[t];e.hasSymbolBuckets&&!e.holdingForFade()?e.setHoldDuration(this.map._fadeDuration):e.hasSymbolBuckets&&!e.symbolFadeFinished()||this._removeTile(t)}this._updateLoadedParentTileCache(),this._updateLoadedSiblingTileCache()}releaseSymbolFadeTiles(){for(const t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)}_updateRetainedTiles(t,e){var r;const n={},i={},a=Math.max(e-dt.maxOverzooming,this._source.minzoom),o=Math.max(e+dt.maxUnderzooming,this._source.minzoom),s={};for(const r of t){const t=this._addTile(r);n[r.key]=r,t.hasData()||e<this._source.maxzoom&&(s[r.key]=r)}this._retainLoadedChildren(s,e,o,n);for(const o of t){let t=this._tiles[o.key];if(t.hasData())continue;if(e+1>this._source.maxzoom){const t=o.children(this._source.maxzoom)[0],e=this.getTile(t);if(e&&e.hasData()){n[t.key]=t;continue}}else{const t=o.children(this._source.maxzoom);if(n[t[0].key]&&n[t[1].key]&&n[t[2].key]&&n[t[3].key])continue}let s=t.wasRequested();for(let e=o.overscaledZ-1;e>=a;--e){const a=o.scaledTo(e);if(i[a.key])break;if(i[a.key]=!0,t=this.getTile(a),!t&&s&&(t=this._addTile(a)),t){const e=t.hasData();if((e||!(null===(r=this.map)||void 0===r?void 0:r.cancelPendingTileRequestsWhileZooming)||s)&&(n[a.key]=a),s=t.wasRequested(),e)break}}}return n}_updateLoadedParentTileCache(){this._loadedParentTiles={};for(const t in this._tiles){const e=[];let r,n=this._tiles[t].tileID;for(;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);const t=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(t),r)break;n=t}for(const t of e)this._loadedParentTiles[t]=r}}_updateLoadedSiblingTileCache(){this._loadedSiblingTiles={};for(const t in this._tiles){const e=this._tiles[t].tileID,r=this._getLoadedTile(e);this._loadedSiblingTiles[e.key]=r}}_addTile(t){let r=this._tiles[t.key];if(r)return r;r=this._cache.getAndRemove(t),r&&(this._setTileReloadTimer(t.key,r),r.tileID=t,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[t.key]&&(clearTimeout(this._cacheTimers[t.key]),delete this._cacheTimers[t.key],this._setTileReloadTimer(t.key,r)));const n=r;return r||(r=new ht(t,this._source.tileSize*t.overscaleFactor()),this._loadTile(r,t.key,r.state)),r.uses++,this._tiles[t.key]=r,n||this._source.fire(new e.k("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r}_setTileReloadTimer(t,e){t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);const r=e.getExpiryTimeout();r&&(this._timers[t]=setTimeout((()=>{this._reloadTile(t,"expired"),delete this._timers[t]}),r))}_removeTile(t){const e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))}_dataHandler(t){const e=t.sourceDataType;"source"===t.dataType&&"metadata"===e&&(this._sourceLoaded=!0),this._sourceLoaded&&!this._paused&&"source"===t.dataType&&"content"===e&&(this.reload(),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0)}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(const t in this._tiles)this._removeTile(t);this._cache.reset()}tilesIn(t,r,n){const i=[],a=this.transform;if(!a)return i;const o=n?a.getCameraQueryGeometry(t):t,s=t.map((t=>a.pointCoordinate(t,this.terrain))),l=o.map((t=>a.pointCoordinate(t,this.terrain))),c=this.getIds();let u=1/0,h=1/0,f=-1/0,p=-1/0;for(const t of l)u=Math.min(u,t.x),h=Math.min(h,t.y),f=Math.max(f,t.x),p=Math.max(p,t.y);for(let t=0;t<c.length;t++){const n=this._tiles[c[t]];if(n.holdingForFade())continue;const o=n.tileID,d=Math.pow(2,a.zoom-n.tileID.overscaledZ),m=r*n.queryPadding*e.X/n.tileSize/d,g=[o.getTilePoint(new e.Z(u,h)),o.getTilePoint(new e.Z(f,p))];if(g[0].x-m<e.X&&g[0].y-m<e.X&&g[1].x+m>=0&&g[1].y+m>=0){const t=s.map((t=>o.getTilePoint(t))),e=l.map((t=>o.getTilePoint(t)));i.push({tile:n,tileID:o,queryGeometry:t,cameraQueryGeometry:e,scale:d})}}return i}getVisibleCoordinates(t){const e=this.getRenderableIds(t).map((t=>this._tiles[t].tileID));for(const t of e)t.posMatrix=this.transform.calculatePosMatrix(t.toUnwrapped());return e}hasTransition(){if(this._source.hasTransition())return!0;if(gt(this._source.type)){const t=a.now();for(const e in this._tiles)if(this._tiles[e].fadeEndTime>=t)return!0}return!1}setFeatureState(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)}removeFeatureState(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)}getFeatureState(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)}setDependencies(t,e,r){const n=this._tiles[t];n&&n.setDependencies(e,r)}reloadTilesForDependencies(t,e){for(const r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading");this._cache.filter((r=>!r.hasDependency(t,e)))}}function mt(t,e){const r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function gt(t){return"raster"===t||"image"===t||"video"===t}dt.maxOverzooming=10,dt.maxUnderzooming=3;class yt{constructor(t,e){this.reset(t,e)}reset(t,e){this.points=t||[],this._distances=[0];for(let t=1;t<this.points.length;t++)this._distances[t]=this._distances[t-1]+this.points[t].dist(this.points[t-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding}lerp(t){if(1===this.points.length)return this.points[0];t=e.ad(t,0,1);let r=1,n=this._distances[r];const i=t*this.paddedLength+this.padding;for(;n<i&&r<this._distances.length;)n=this._distances[++r];const a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))}}function vt(t,e){let r=!0;return"always"===t||"never"!==t&&"never"!==e||(r=!1),r}class xt{constructor(t,e,r){const n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(let t=0;t<this.xCellCount*this.yCellCount;t++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0}keysLength(){return this.boxKeys.length+this.circleKeys.length}insert(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)}insertCircle(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)}_insertBoxCell(t,e,r,n,i,a){this.boxCells[i].push(a)}_insertCircleCell(t,e,r,n,i,a){this.circleCells[i].push(a)}_query(t,e,r,n,i,a,o){if(r<0||t>this.width||n<0||e>this.height)return[];const s=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return[{key:null,x1:t,y1:e,x2:r,y2:n}];for(let t=0;t<this.boxKeys.length;t++)s.push({key:this.boxKeys[t],x1:this.bboxes[4*t],y1:this.bboxes[4*t+1],x2:this.bboxes[4*t+2],y2:this.bboxes[4*t+3]});for(let t=0;t<this.circleKeys.length;t++){const e=this.circles[3*t],r=this.circles[3*t+1],n=this.circles[3*t+2];s.push({key:this.circleKeys[t],x1:e-n,y1:r-n,x2:e+n,y2:r+n})}}else{const l={hitTest:i,overlapMode:a,seenUids:{box:{},circle:{}}};this._forEachCell(t,e,r,n,this._queryCell,s,l,o)}return s}query(t,e,r,n){return this._query(t,e,r,n,!1,null)}hitTest(t,e,r,n,i,a){return this._query(t,e,r,n,!0,i,a).length>0}hitTestCircle(t,e,r,n,i){const a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!1;const c=[],u={hitTest:!0,overlapMode:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),c.length>0}_queryCell(t,e,r,n,i,a,o,s){const{seenUids:l,hitTest:c,overlapMode:u}=o,h=this.boxCells[i];if(null!==h){const i=this.bboxes;for(const o of h)if(!l.box[o]){l.box[o]=!0;const h=4*o,f=this.boxKeys[o];if(t<=i[h+2]&&e<=i[h+3]&&r>=i[h+0]&&n>=i[h+1]&&(!s||s(f))&&(!c||!vt(u,f.overlapMode))&&(a.push({key:f,x1:i[h],y1:i[h+1],x2:i[h+2],y2:i[h+3]}),c))return!0}}const f=this.circleCells[i];if(null!==f){const i=this.circles;for(const o of f)if(!l.circle[o]){l.circle[o]=!0;const h=3*o,f=this.circleKeys[o];if(this._circleAndRectCollide(i[h],i[h+1],i[h+2],t,e,r,n)&&(!s||s(f))&&(!c||!vt(u,f.overlapMode))){const t=i[h],e=i[h+1],r=i[h+2];if(a.push({key:f,x1:t-r,y1:e-r,x2:t+r,y2:e+r}),c)return!0}}}return!1}_queryCellCircle(t,e,r,n,i,a,o,s){const{circle:l,seenUids:c,overlapMode:u}=o,h=this.boxCells[i];if(null!==h){const t=this.bboxes;for(const e of h)if(!c.box[e]){c.box[e]=!0;const r=4*e,n=this.boxKeys[e];if(this._circleAndRectCollide(l.x,l.y,l.radius,t[r+0],t[r+1],t[r+2],t[r+3])&&(!s||s(n))&&!vt(u,n.overlapMode))return a.push(!0),!0}}const f=this.circleCells[i];if(null!==f){const t=this.circles;for(const e of f)if(!c.circle[e]){c.circle[e]=!0;const r=3*e,n=this.circleKeys[e];if(this._circlesCollide(t[r],t[r+1],t[r+2],l.x,l.y,l.radius)&&(!s||s(n))&&!vt(u,n.overlapMode))return a.push(!0),!0}}}_forEachCell(t,e,r,n,i,a,o,s){const l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),h=this._convertToYCellCoord(n);for(let f=l;f<=u;f++)for(let l=c;l<=h;l++){const c=this.xCellCount*l+f;if(i.call(this,t,e,r,n,c,a,o,s))return}}_convertToXCellCoord(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))}_convertToYCellCoord(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))}_circlesCollide(t,e,r,n,i,a){const o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s}_circleAndRectCollide(t,e,r,n,i,a,o){const s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;const c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;const h=l-s,f=u-c;return h*h+f*f<=r*r}}function _t(t,r,n,i,a){const o=e.H();return r?(e.K(o,o,[1/a,1/a,1]),n||e.ae(o,o,i.angle)):e.L(o,i.labelPlaneMatrix,t),o}function bt(t,r,n,i,a){if(r){const r=e.af(t);return e.K(r,r,[a,a,1]),n||e.ae(r,r,-i.angle),r}return i.glCoordMatrix}function wt(t,r,n){let i;n?(i=[t.x,t.y,n(t.x,t.y),1],e.ag(i,i,r)):(i=[t.x,t.y,0,1],function(t,e,r){const n=e[0],i=e[1];t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15]}(i,i,r));const a=i[3];return{point:new e.P(i[0]/a,i[1]/a),signedDistanceFromCamera:a,isOccluded:!1}}function Tt(t,e){return.5+t/e*.5}function kt(t,e){return t.x>=-e[0]&&t.x<=e[0]&&t.y>=-e[1]&&t.y<=e[1]}function At(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m){const g=i?t.textSizeData:t.iconSizeData,y=e.ah(g,n.transform.zoom),v=[256/n.width*2+1,256/n.height*2+1],x=i?t.text.dynamicLayoutVertexArray:t.icon.dynamicLayoutVertexArray;x.clear();const _=t.lineVertexArray,b=i?t.text.placedSymbolArray:t.icon.placedSymbolArray,w=n.transform.width/n.transform.height;let T=!1;for(let i=0;i<b.length;i++){const k=b.get(i);if(k.hidden||k.writingMode===e.ai.vertical&&!T){Rt(k.numGlyphs,x);continue}T=!1;const A=wt(new e.P(k.anchorX,k.anchorY),r,m);if(!kt(A.point,v)){Rt(k.numGlyphs,x);continue}const M=A.signedDistanceFromCamera,S=Tt(n.transform.cameraToCenterDistance,M),E=e.aj(g,y,k),C=s?E/S:E*S,L={getElevation:m,labelPlaneMatrix:a,lineVertexArray:_,pitchWithMap:s,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},projection:u,tileAnchorPoint:new e.P(k.anchorX,k.anchorY),unwrappedTileID:h,width:f,height:p,translation:d},I=Et(L,k,C,!1,l,r,o,t.glyphOffsetArray,x,w,c);T=I.useVertical,(I.notEnoughRoom||T||I.needsFlipping&&Et(L,k,C,!0,l,r,o,t.glyphOffsetArray,x,w,c).notEnoughRoom)&&Rt(k.numGlyphs,x)}i?t.text.dynamicLayoutVertexBuffer.updateData(x):t.icon.dynamicLayoutVertexBuffer.updateData(x)}function Mt(t,e,r,n,i,a,o,s){const l=a.glyphStartIndex+a.numGlyphs,c=a.lineStartIndex,u=a.lineStartIndex+a.lineLength,h=e.getoffsetX(a.glyphStartIndex),f=e.getoffsetX(l-1),p=Ot(t*h,r,n,i,a.segment,c,u,s,o);if(!p)return null;const d=Ot(t*f,r,n,i,a.segment,c,u,s,o);return d?s.projectionCache.anyProjectionOccluded?null:{first:p,last:d}:null}function St(t,r,n,i){return t===e.ai.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(t===e.ai.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function Et(t,r,n,i,a,o,s,l,c,u,h){const f=n/24,p=r.lineOffsetX*f,d=r.lineOffsetY*f;let m;if(r.numGlyphs>1){const e=r.glyphStartIndex+r.numGlyphs,n=r.lineStartIndex,o=r.lineStartIndex+r.lineLength,c=Mt(f,l,p,d,i,r,h,t);if(!c)return{notEnoughRoom:!0};const g=wt(c.first.point,s,t.getElevation).point,y=wt(c.last.point,s,t.getElevation).point;if(a&&!i){const t=St(r.writingMode,g,y,u);if(t)return t}m=[c.first];for(let a=r.glyphStartIndex+1;a<e-1;a++)m.push(Ot(f*l.getoffsetX(a),p,d,i,r.segment,n,o,t,h));m.push(c.last)}else{if(a&&!i){const n=wt(t.tileAnchorPoint,o,t.getElevation).point,i=r.lineStartIndex+r.segment+1,a=new e.P(t.lineVertexArray.getx(i),t.lineVertexArray.gety(i)),s=wt(a,o,t.getElevation),l=s.signedDistanceFromCamera>0?s.point:function(t,e,r,n,i,a){return Ct(t,e,r,n,i,a)}(t.tileAnchorPoint,a,n,1,o,t),c=St(r.writingMode,n,l,u);if(c)return c}const n=Ot(f*l.getoffsetX(r.glyphStartIndex),p,d,i,r.segment,r.lineStartIndex,r.lineStartIndex+r.lineLength,t,h);if(!n||t.projectionCache.anyProjectionOccluded)return{notEnoughRoom:!0};m=[n]}for(const t of m)e.ak(c,t.point,t.angle);return{}}function Ct(t,e,r,n,i,a){const o=t.add(t.sub(e)._unit()),s=void 0!==i?wt(o,i,a.getElevation).point:It(o.x,o.y,a).point,l=r.sub(s);return r.add(l._mult(n/l.mag()))}function Lt(t,r,n){const i=r.projectionCache;if(i.projections[t])return i.projections[t];const a=new e.P(r.lineVertexArray.getx(t),r.lineVertexArray.gety(t)),o=It(a.x,a.y,r);if(o.signedDistanceFromCamera>0)return i.projections[t]=o.point,i.anyProjectionOccluded=i.anyProjectionOccluded||o.isOccluded,o.point;const s=t-n.direction,l=0===n.distanceFromAnchor?r.tileAnchorPoint:new e.P(r.lineVertexArray.getx(s),r.lineVertexArray.gety(s)),c=n.absOffsetX-n.distanceFromAnchor+1;return function(t,e,r,n,i){return Ct(t,e,r,n,void 0,i)}(l,a,n.previousVertex,c,r)}function It(t,r,n){const i=t+n.translation[0],a=r+n.translation[1];let o;return!n.pitchWithMap&&n.projection.useSpecialProjectionForSymbols?(o=n.projection.projectTileCoordinates(i,a,n.unwrappedTileID,n.getElevation),o.point.x=(.5*o.point.x+.5)*n.width,o.point.y=(.5*-o.point.y+.5)*n.height):(o=wt(new e.P(i,a),n.labelPlaneMatrix,n.getElevation),o.isOccluded=!1),o}function Pt(t,e,r){return t._unit()._perp()._mult(e*r)}function zt(t,r,n,i,a,o,s,l,c){if(l.projectionCache.offsets[t])return l.projectionCache.offsets[t];const u=n.add(r);if(t+c.direction<i||t+c.direction>=a)return l.projectionCache.offsets[t]=u,u;const h=Lt(t+c.direction,l,c),f=Pt(h.sub(n),s,c.direction),p=n.add(f),d=h.add(f);return l.projectionCache.offsets[t]=e.al(o,u,p,d)||u,l.projectionCache.offsets[t]}function Ot(t,e,r,n,i,a,o,s,l){const c=n?t-e:t+e;let u=c>0?1:-1,h=0;n&&(u*=-1,h=Math.PI),u<0&&(h+=Math.PI);let f,p=u>0?a+i:a+i+1;s.projectionCache.cachedAnchorPoint?f=s.projectionCache.cachedAnchorPoint:(f=It(s.tileAnchorPoint.x,s.tileAnchorPoint.y,s).point,s.projectionCache.cachedAnchorPoint=f);let d,m,g=f,y=f,v=0,x=0;const _=Math.abs(c),b=[];let w;for(;v+x<=_;){if(p+=u,p<a||p>=o)return null;v+=x,y=g,m=d;const t={absOffsetX:_,direction:u,distanceFromAnchor:v,previousVertex:y};if(g=Lt(p,s,t),0===r)b.push(y),w=g.sub(y);else{let e;const n=g.sub(y);e=0===n.mag()?Pt(Lt(p+u,s,t).sub(g),r,u):Pt(n,r,u),m||(m=y.add(e)),d=zt(p,e,g,a,o,m,r,s,t),b.push(m),w=d.sub(m)}x=w.mag()}const T=(_-v)/x,k=w._mult(T)._add(m||y),A=h+Math.atan2(g.y-y.y,g.x-y.x);return b.push(k),{point:k,angle:l?A:0,path:b}}const Dt=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function Rt(t,e){for(let r=0;r<t;r++){const t=e.length;e.resize(t+4),e.float32.set(Dt,3*t)}}const Ft=100;class Bt{constructor(t,e,r=new xt(t.width+200,t.height+200,25),n=new xt(t.width+200,t.height+200,25)){this.transform=t,this.mapProjection=e,this.grid=r,this.ignoredGrid=n,this.pitchFactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+Ft,this.screenBottomBoundary=t.height+Ft,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200,this.perspectiveRatioCutoff=.6}placeCollisionBox(t,e,r,n,i,a,o,s,l,c,u){const h=t.anchorPointX+s[0],f=t.anchorPointY+s[1],p=this.projectAndGetPerspectiveRatio(n,h,f,i,c),d=this._projectCollisionBox(t,r,n,i,a,o,s,p,c,u),[m,g,y,v]=d.box;return this.mapProjection.useSpecialProjectionForSymbols&&(a?d.allPointsOccluded:this.mapProjection.isOccluded(h,f,i))||p.perspectiveRatio<this.perspectiveRatioCutoff||!this.isInsideGrid(m,g,y,v)||"always"!==e&&this.grid.hitTest(m,g,y,v,e,l)?{box:[m,g,y,v],placeable:!1,offscreen:!1}:{box:[m,g,y,v],placeable:!0,offscreen:this.isOffscreen(m,g,y,v)}}placeCollisionCircles(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g){const y=[],v=new e.P(r.anchorX,r.anchorY),x=this.getPerspectiveRatio(o,v.x,v.y,s,g),_=(h?a/x:a*x)/e.aq,b=r.lineOffsetX*_,w=r.lineOffsetY*_,T={getElevation:g,labelPlaneMatrix:l,lineVertexArray:n,pitchWithMap:h,projectionCache:{projections:{},offsets:{},cachedAnchorPoint:void 0,anyProjectionOccluded:!1},projection:this.mapProjection,tileAnchorPoint:v,unwrappedTileID:s,width:this.transform.width,height:this.transform.height,translation:m},k=Mt(_,i,b,w,!1,r,!1,T);let A=!1,M=!1,S=!0;if(k){const r=.5*p*x+d,n=new e.P(-100,-100),i=new e.P(this.screenRightBoundary,this.screenBottomBoundary),a=new yt,o=k.first,s=k.last;let l=[];for(let t=o.path.length-1;t>=1;t--)l.push(o.path[t]);for(let t=1;t<s.path.length;t++)l.push(s.path[t]);const h=2.5*r;if(c){const t=this.projectPathToScreenSpace(l,T,c);l=t.some((t=>t.signedDistanceFromCamera<=0))?[]:t.map((t=>t.point))}let m=[];if(l.length>0){const t=l[0].clone(),r=l[0].clone();for(let e=1;e<l.length;e++)t.x=Math.min(t.x,l[e].x),t.y=Math.min(t.y,l[e].y),r.x=Math.max(r.x,l[e].x),r.y=Math.max(r.y,l[e].y);m=t.x>=n.x&&r.x<=i.x&&t.y>=n.y&&r.y<=i.y?[l]:r.x<n.x||t.x>i.x||r.y<n.y||t.y>i.y?[]:e.am([l],n.x,n.y,i.x,i.y)}for(const e of m){a.reset(e,.25*r);let n=0;n=a.length<=.5*r?1:Math.ceil(a.paddedLength/h)+1;for(let e=0;e<n;e++){const i=e/Math.max(n-1,1),o=a.lerp(i),s=o.x+Ft,l=o.y+Ft;y.push(s,l,r,0);const c=s-r,h=l-r,p=s+r,d=l+r;if(S=S&&this.isOffscreen(c,h,p,d),M=M||this.isInsideGrid(c,h,p,d),"always"!==t&&this.grid.hitTestCircle(s,l,r,t,f)&&(A=!0,!u))return{circles:[],offscreen:!1,collisionDetected:A}}}}return{circles:!u&&A||!M||x<this.perspectiveRatioCutoff?[]:y,offscreen:S,collisionDetected:A}}projectPathToScreenSpace(t,e,r){return t.map((t=>wt(t,r,e.getElevation)))}queryRenderedSymbols(t){if(0===t.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};const r=[];let n=1/0,i=1/0,a=-1/0,o=-1/0;for(const s of t){const t=new e.P(s.x+Ft,s.y+Ft);n=Math.min(n,t.x),i=Math.min(i,t.y),a=Math.max(a,t.x),o=Math.max(o,t.y),r.push(t)}const s=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o)),l={},c={};for(const t of s){const n=t.key;if(void 0===l[n.bucketInstanceId]&&(l[n.bucketInstanceId]={}),l[n.bucketInstanceId][n.featureIndex])continue;const i=[new e.P(t.x1,t.y1),new e.P(t.x2,t.y1),new e.P(t.x2,t.y2),new e.P(t.x1,t.y2)];e.an(r,i)&&(l[n.bucketInstanceId][n.featureIndex]=!0,void 0===c[n.bucketInstanceId]&&(c[n.bucketInstanceId]=[]),c[n.bucketInstanceId].push(n.featureIndex))}return c}insertCollisionBox(t,e,r,n,i,a){const o={bucketInstanceId:n,featureIndex:i,collisionGroupID:a,overlapMode:e};(r?this.ignoredGrid:this.grid).insert(o,t[0],t[1],t[2],t[3])}insertCollisionCircles(t,e,r,n,i,a){const o=r?this.ignoredGrid:this.grid,s={bucketInstanceId:n,featureIndex:i,collisionGroupID:a,overlapMode:e};for(let e=0;e<t.length;e+=4)o.insertCircle(s,t[e],t[e+1],t[e+2])}projectAndGetPerspectiveRatio(t,r,n,i,a){const o=this.mapProjection.useSpecialProjectionForSymbols?this.mapProjection.projectTileCoordinates(r,n,i,a):wt(new e.P(r,n),t,a);return{point:new e.P((o.point.x+1)/2*this.transform.width+Ft,(1-o.point.y)/2*this.transform.height+Ft),perspectiveRatio:.5+this.transform.cameraToCenterDistance/o.signedDistanceFromCamera*.5,isOccluded:o.isOccluded,signedDistanceFromCamera:o.signedDistanceFromCamera}}getPerspectiveRatio(t,r,n,i,a){const o=this.mapProjection.useSpecialProjectionForSymbols?this.mapProjection.projectTileCoordinates(r,n,i,a):wt(new e.P(r,n),t,a);return.5+this.transform.cameraToCenterDistance/o.signedDistanceFromCamera*.5}isOffscreen(t,e,r,n){return r<Ft||t>=this.screenRightBoundary||n<Ft||e>this.screenBottomBoundary}isInsideGrid(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary}getViewportMatrix(){const t=e.ao([]);return e.J(t,t,[-100,-100,0]),t}_projectCollisionBox(t,r,n,i,a,o,s,l,c,u){const h=r*l.perspectiveRatio;let f=new e.P(1,0),p=new e.P(0,1);const d=new e.P(t.anchorPointX+s[0],t.anchorPointY+s[1]);if(o&&!a){const t=this.projectAndGetPerspectiveRatio(n,d.x+1,d.y,i,c).point.sub(l.point).unit(),r=Math.atan(t.y/t.x)+(t.x<0?Math.PI:0),a=Math.sin(r),o=Math.cos(r);f=new e.P(o,a),p=new e.P(-a,o)}else if(!o&&a){const t=-this.transform.angle,r=Math.sin(t),n=Math.cos(t);f=new e.P(n,r),p=new e.P(-r,n)}let m=l.point,g=h;if(a){m=d;const t=this.transform.zoom-Math.floor(this.transform.zoom);if(g=Math.pow(2,-t),g*=this.mapProjection.getPitchedTextCorrection(this.transform,d,i),!u){const t=l.signedDistanceFromCamera/this.transform.cameraToCenterDistance;g*=e.ad(.5+.5*t,0,4)}}u&&(m=m.add(f.mult(u.x*g)).add(p.mult(u.y*g)));const y=t.x1*g,v=t.x2*g,x=(y+v)/2,_=t.y1*g,b=t.y2*g,w=(_+b)/2,T=[{offsetX:y,offsetY:_},{offsetX:x,offsetY:_},{offsetX:v,offsetY:_},{offsetX:v,offsetY:w},{offsetX:v,offsetY:b},{offsetX:x,offsetY:b},{offsetX:y,offsetY:b},{offsetX:y,offsetY:w}];let k=[];for(const{offsetX:t,offsetY:r}of T)k.push(new e.P(m.x+f.x*t+p.x*r,m.y+f.y*t+p.y*r));let A=!1;if(a){const t=k.map((t=>this.projectAndGetPerspectiveRatio(n,t.x,t.y,i,c)));A=t.some((t=>!t.isOccluded)),k=t.map((t=>t.point))}else A=!0;return{box:e.ap(k),allPointsOccluded:!A}}}function Nt(t,r,n){return r*(e.X/(t.tileSize*Math.pow(2,n-t.tileID.overscaledZ)))}class jt{constructor(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r}isHidden(){return 0===this.opacity&&!this.placed}}class Ut{constructor(t,e,r,n,i){this.text=new jt(t?t.text:null,e,r,i),this.icon=new jt(t?t.icon:null,e,n,i)}isHidden(){return this.text.isHidden()&&this.icon.isHidden()}}class Vt{constructor(t,e,r){this.text=t,this.icon=e,this.skipFade=r}}class qt{constructor(){this.invProjMatrix=e.H(),this.viewportMatrix=e.H(),this.circles=[]}}class Ht{constructor(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i}}class Gt{constructor(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}}get(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){const e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:t=>t.collisionGroupID===e}}return this.collisionGroups[t]}}function Zt(t,r,n,i,a){const{horizontalAlign:o,verticalAlign:s}=e.av(t),l=-(o-.5)*r,c=-(s-.5)*n;return new e.P(l+i[0]*a,c+i[1]*a)}class Wt{constructor(t,e,r,n,i,a){this.transform=t.clone(),this.terrain=r,this.collisionIndex=new Bt(this.transform,e),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=n,this.retainedQueryData={},this.collisionGroups=new Gt(i),this.collisionCircleArrays={},this.collisionBoxArrays=new Map,this.prevPlacement=a,a&&(a.prevPlacement=void 0),this.placedOrientations={}}_getTerrainElevationFunc(t){const e=this.terrain;return e?(r,n)=>e.getElevation(t,r,n):null}getBucketParts(t,r,n,i){const a=n.getBucket(r),o=n.latestFeatureIndex;if(!a||!o||r.id!==a.layerIds[0])return;const s=n.collisionBoxArray,l=a.layers[0].layout,c=a.layers[0].paint,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),h=n.tileSize/e.X,f=n.tileID.toUnwrapped(),p=this.transform.calculatePosMatrix(f),d="map"===l.get("text-pitch-alignment"),m="map"===l.get("text-rotation-alignment"),g=Nt(n,1,this.transform.zoom),y=this.collisionIndex.mapProjection.translatePosition(this.transform,n,c.get("text-translate"),c.get("text-translate-anchor")),v=this.collisionIndex.mapProjection.translatePosition(this.transform,n,c.get("icon-translate"),c.get("icon-translate-anchor")),x=_t(p,d,m,this.transform,g);let _=null;if(d){const t=bt(p,d,m,this.transform,g);_=e.L([],this.transform.labelPlaneMatrix,t)}this.retainedQueryData[a.bucketInstanceId]=new Ht(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);const b={bucket:a,layout:l,translationText:y,translationIcon:v,posMatrix:p,unwrappedTileID:f,textLabelPlaneMatrix:x,labelToScreenMatrix:_,scale:u,textPixelRatio:h,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:e.ah(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(const e of a.sortKeyRanges){const{sortKey:r,symbolInstanceStart:n,symbolInstanceEnd:i}=e;t.push({sortKey:r,symbolInstanceStart:n,symbolInstanceEnd:i,parameters:b})}else t.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:b})}attemptAnchorPlacement(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x){const _=e.ar[t.textAnchor],b=[t.textOffset0,t.textOffset1],w=Zt(_,n,i,b,a),T=this.collisionIndex.placeCollisionBox(r,f,l,c,u,s,o,g,h.predicate,x,w);if((!v||this.collisionIndex.placeCollisionBox(v,f,l,c,u,s,o,y,h.predicate,x,w).placeable)&&T.placeable){let t;if(this.prevPlacement&&this.prevPlacement.variableOffsets[p.crossTileID]&&this.prevPlacement.placements[p.crossTileID]&&this.prevPlacement.placements[p.crossTileID].text&&(t=this.prevPlacement.variableOffsets[p.crossTileID].anchor),0===p.crossTileID)throw new Error("symbolInstance.crossTileID can't be 0");return this.variableOffsets[p.crossTileID]={textOffset:b,width:n,height:i,anchor:_,textBoxScale:a,prevAnchor:t},this.markUsedJustification(d,_,p,m),d.allowVerticalPlacement&&(this.markUsedOrientation(d,m,p),this.placedOrientations[p.crossTileID]=m),{shift:w,placedGlyphBoxes:T}}}placeLayerBucketPart(t,r,n){const{bucket:i,layout:a,translationText:o,translationIcon:s,posMatrix:l,unwrappedTileID:c,textLabelPlaneMatrix:u,labelToScreenMatrix:h,textPixelRatio:f,holdingForFade:p,collisionBoxArray:d,partiallyEvaluatedTextSize:m,collisionGroup:g}=t.parameters,y=a.get("text-optional"),v=a.get("icon-optional"),x=e.as(a,"text-overlap","text-allow-overlap"),_="always"===x,b=e.as(a,"icon-overlap","icon-allow-overlap"),w="always"===b,T="map"===a.get("text-rotation-alignment"),k="map"===a.get("text-pitch-alignment"),A="none"!==a.get("icon-text-fit"),M="viewport-y"===a.get("symbol-z-order"),S=_&&(w||!i.hasIconData()||v),E=w&&(_||!i.hasTextData()||y);!i.collisionArrays&&d&&i.deserializeCollisionBoxes(d);const C=this.retainedQueryData[i.bucketInstanceId].tileID,L=this._getTerrainElevationFunc(C),I=(t,d,w)=>{var M,C;if(r[t.crossTileID])return;if(p)return void(this.placements[t.crossTileID]=new Vt(!1,!1,!1));let I=!1,P=!1,z=!0,O=null,D={box:null,placeable:!1,offscreen:null},R={box:null,placeable:!1,offscreen:null},F=null,B=null,N=null,j=0,U=0,V=0;d.textFeatureIndex?j=d.textFeatureIndex:t.useRuntimeCollisionCircles&&(j=t.featureIndex),d.verticalTextFeatureIndex&&(U=d.verticalTextFeatureIndex);const q=d.textBox;if(q){const r=r=>{let n=e.ai.horizontal;if(i.allowVerticalPlacement&&!r&&this.prevPlacement){const e=this.prevPlacement.placedOrientations[t.crossTileID];e&&(this.placedOrientations[t.crossTileID]=e,n=e,this.markUsedOrientation(i,n,t))}return n},a=(r,n)=>{if(i.allowVerticalPlacement&&t.numVerticalGlyphVertices>0&&d.verticalTextBox){for(const t of i.writingModes)if(t===e.ai.vertical?(D=n(),R=D):D=r(),D&&D.placeable)break}else D=r()},u=t.textAnchorOffsetStartIndex,h=t.textAnchorOffsetEndIndex;if(h===u){const n=(e,r)=>{const n=this.collisionIndex.placeCollisionBox(e,x,f,l,c,k,T,o,g.predicate,L);return n&&n.placeable&&(this.markUsedOrientation(i,r,t),this.placedOrientations[t.crossTileID]=r),n};a((()=>n(q,e.ai.horizontal)),(()=>{const r=d.verticalTextBox;return i.allowVerticalPlacement&&t.numVerticalGlyphVertices>0&&r?n(r,e.ai.vertical):{box:null,offscreen:null}})),r(D&&D.placeable)}else{let p=e.ar[null===(C=null===(M=this.prevPlacement)||void 0===M?void 0:M.variableOffsets[t.crossTileID])||void 0===C?void 0:C.anchor];const m=(r,a,d)=>{const m=r.x2-r.x1,y=r.y2-r.y1,v=t.textBoxScale,_=A&&"never"===b?a:null;let w=null,M="never"===x?1:2,S="never";p&&M++;for(let e=0;e<M;e++){for(let e=u;e<h;e++){const n=i.textAnchorOffsets.get(e);if(p&&n.textAnchor!==p)continue;const a=this.attemptAnchorPlacement(n,r,m,y,v,T,k,f,l,c,g,S,t,i,d,o,s,_,L);if(a&&(w=a.placedGlyphBoxes,w&&w.placeable))return I=!0,O=a.shift,w}p?p=null:S=x}return n&&!w&&(w={box:this.collisionIndex.placeCollisionBox(q,"always",f,l,c,k,T,o,g.predicate,L,new e.P(0,0)).box,offscreen:!1,placeable:!1}),w};a((()=>m(q,d.iconBox,e.ai.horizontal)),(()=>{const r=d.verticalTextBox,n=D&&D.placeable;return i.allowVerticalPlacement&&!n&&t.numVerticalGlyphVertices>0&&r?m(r,d.verticalIconBox,e.ai.vertical):{box:null,occluded:!0,offscreen:null}})),D&&(I=D.placeable,z=D.offscreen);const y=r(D&&D.placeable);if(!I&&this.prevPlacement){const e=this.prevPlacement.variableOffsets[t.crossTileID];e&&(this.variableOffsets[t.crossTileID]=e,this.markUsedJustification(i,e.anchor,t,y))}}}if(F=D,I=F&&F.placeable,z=F&&F.offscreen,t.useRuntimeCollisionCircles){const r=i.text.placedSymbolArray.get(t.centerJustifiedTextSymbolIndex),s=e.aj(i.textSizeData,m,r),f=a.get("text-padding"),p=t.collisionCircleDiameter;B=this.collisionIndex.placeCollisionCircles(x,r,i.lineVertexArray,i.glyphOffsetArray,s,l,c,u,h,n,k,g.predicate,p,f,o,L),B.circles.length&&B.collisionDetected&&!n&&e.w("Collisions detected, but collision boxes are not shown"),I=_||B.circles.length>0&&!B.collisionDetected,z=z&&B.offscreen}if(d.iconFeatureIndex&&(V=d.iconFeatureIndex),d.iconBox){const t=t=>this.collisionIndex.placeCollisionBox(t,b,f,l,c,k,T,s,g.predicate,L,A&&O?O:void 0);R&&R.placeable&&d.verticalIconBox?(N=t(d.verticalIconBox),P=N.placeable):(N=t(d.iconBox),P=N.placeable),z=z&&N.offscreen}const H=y||0===t.numHorizontalGlyphVertices&&0===t.numVerticalGlyphVertices,G=v||0===t.numIconVertices;H||G?G?H||(P=P&&I):I=P&&I:P=I=P&&I;const Z=I&&F.placeable,W=P&&N.placeable;if(Z&&(R&&R.placeable&&U?this.collisionIndex.insertCollisionBox(F.box,x,a.get("text-ignore-placement"),i.bucketInstanceId,U,g.ID):this.collisionIndex.insertCollisionBox(F.box,x,a.get("text-ignore-placement"),i.bucketInstanceId,j,g.ID)),W&&this.collisionIndex.insertCollisionBox(N.box,b,a.get("icon-ignore-placement"),i.bucketInstanceId,V,g.ID),B&&I&&this.collisionIndex.insertCollisionCircles(B.circles,x,a.get("text-ignore-placement"),i.bucketInstanceId,j,g.ID),n&&this.storeCollisionData(i.bucketInstanceId,w,d,F,N,B),0===t.crossTileID)throw new Error("symbolInstance.crossTileID can't be 0");if(0===i.bucketInstanceId)throw new Error("bucket.bucketInstanceId can't be 0");this.placements[t.crossTileID]=new Vt(I||S,P||E,z||i.justReloaded),r[t.crossTileID]=!0};if(M){if(0!==t.symbolInstanceStart)throw new Error("bucket.bucketInstanceId should be 0");const e=i.getSortedSymbolIndexes(this.transform.angle);for(let t=e.length-1;t>=0;--t){const r=e[t];I(i.symbolInstances.get(r),i.collisionArrays[r],r)}}else for(let e=t.symbolInstanceStart;e<t.symbolInstanceEnd;e++)I(i.symbolInstances.get(e),i.collisionArrays[e],e);if(n&&i.bucketInstanceId in this.collisionCircleArrays){const t=this.collisionCircleArrays[i.bucketInstanceId];e.at(t.invProjMatrix,l),t.viewportMatrix=this.collisionIndex.getViewportMatrix()}i.justReloaded=!1}storeCollisionData(t,e,r,n,i,a){if(r.textBox||r.iconBox){let a,o;this.collisionBoxArrays.has(t)?a=this.collisionBoxArrays.get(t):(a=new Map,this.collisionBoxArrays.set(t,a)),a.has(e)?o=a.get(e):(o={text:null,icon:null},a.set(e,o)),r.textBox&&(o.text=n.box),r.iconBox&&(o.icon=i.box)}if(a){let e=this.collisionCircleArrays[t];void 0===e&&(e=this.collisionCircleArrays[t]=new qt);for(let t=0;t<a.circles.length;t+=4)e.circles.push(a.circles[t+0]),e.circles.push(a.circles[t+1]),e.circles.push(a.circles[t+2]),e.circles.push(a.collisionDetected?1:0)}}markUsedJustification(t,r,n,i){const a={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};let o;o=i===e.ai.vertical?n.verticalPlacedTextSymbolIndex:a[e.au(r)];const s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];for(const e of s)e>=0&&(t.text.placedSymbolArray.get(e).crossTileID=o>=0&&e!==o?0:n.crossTileID)}markUsedOrientation(t,r,n){const i=r===e.ai.horizontal||r===e.ai.horizontalOnly?r:0,a=r===e.ai.vertical?r:0,o=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];for(const e of o)t.text.placedSymbolArray.get(e).placedOrientation=i;n.verticalPlacedTextSymbolIndex&&(t.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)}commit(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;const e=this.prevPlacement;let r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;const n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(const t in this.placements){const e=this.placements[t],a=i[t];a?(this.opacities[t]=new Ut(a,n,e.text,e.icon),r=r||e.text!==a.text.placed||e.icon!==a.icon.placed):(this.opacities[t]=new Ut(null,n,e.text,e.icon,e.skipFade),r=r||e.text||e.icon)}for(const t in i){const e=i[t];if(!this.opacities[t]){const i=new Ut(e,n,!1,!1);i.isHidden()||(this.opacities[t]=i,r=r||e.text.placed||e.icon.placed)}}for(const t in a)this.variableOffsets[t]||!this.opacities[t]||this.opacities[t].isHidden()||(this.variableOffsets[t]=a[t]);for(const t in o)this.placedOrientations[t]||!this.opacities[t]||this.opacities[t].isHidden()||(this.placedOrientations[t]=o[t]);if(e&&void 0===e.lastPlacementChangeTime)throw new Error("Last placement time for previous placement is not defined");r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)}updateLayerOpacities(t,e){const r={};for(const n of e){const e=n.getBucket(t);e&&n.latestFeatureIndex&&t.id===e.layerIds[0]&&this.updateBucketOpacities(e,n.tileID,r,n.collisionBoxArray)}}updateBucketOpacities(t,r,n,i){t.hasTextData()&&(t.text.opacityVertexArray.clear(),t.text.hasVisibleVertices=!1),t.hasIconData()&&(t.icon.opacityVertexArray.clear(),t.icon.hasVisibleVertices=!1),t.hasIconCollisionBoxData()&&t.iconCollisionBox.collisionVertexArray.clear(),t.hasTextCollisionBoxData()&&t.textCollisionBox.collisionVertexArray.clear();const a=t.layers[0],o=a.layout,s=new Ut(null,0,!1,!1,!0),l=o.get("text-allow-overlap"),c=o.get("icon-allow-overlap"),u=a._unevaluatedLayout.hasValue("text-variable-anchor")||a._unevaluatedLayout.hasValue("text-variable-anchor-offset"),h="map"===o.get("text-rotation-alignment"),f="map"===o.get("text-pitch-alignment"),p="none"!==o.get("icon-text-fit"),d=new Ut(null,0,l&&(c||!t.hasIconData()||o.get("icon-optional")),c&&(l||!t.hasTextData()||o.get("text-optional")),!0);!t.collisionArrays&&i&&(t.hasIconCollisionBoxData()||t.hasTextCollisionBoxData())&&t.deserializeCollisionBoxes(i);const m=(t,e,r)=>{for(let n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r);t.hasVisibleVertices=t.hasVisibleVertices||r!==ne},g=this.collisionBoxArrays.get(t.bucketInstanceId);for(let r=0;r<t.symbolInstances.length;r++){const i=t.symbolInstances.get(r),{numHorizontalGlyphVertices:a,numVerticalGlyphVertices:o,crossTileID:l}=i,c=n[l];let y=this.opacities[l];c?y=s:y||(y=d,this.opacities[l]=y),n[l]=!0;const v=a>0||o>0,x=i.numIconVertices>0,_=this.placedOrientations[i.crossTileID],b=_===e.ai.vertical,w=_===e.ai.horizontal||_===e.ai.horizontalOnly;if(v){const e=re(y.text),r=b?ne:e;m(t.text,a,r);const n=w?ne:e;m(t.text,o,n);const s=y.text.isHidden();[i.rightJustifiedTextSymbolIndex,i.centerJustifiedTextSymbolIndex,i.leftJustifiedTextSymbolIndex].forEach((e=>{e>=0&&(t.text.placedSymbolArray.get(e).hidden=s||b?1:0)})),i.verticalPlacedTextSymbolIndex>=0&&(t.text.placedSymbolArray.get(i.verticalPlacedTextSymbolIndex).hidden=s||w?1:0);const l=this.variableOffsets[i.crossTileID];l&&this.markUsedJustification(t,l.anchor,i,_);const c=this.placedOrientations[i.crossTileID];c&&(this.markUsedJustification(t,"left",i,c),this.markUsedOrientation(t,c,i))}if(x){const e=re(y.icon),r=!(p&&i.verticalPlacedIconSymbolIndex&&b);if(i.placedIconSymbolIndex>=0){const n=r?e:ne;m(t.icon,i.numIconVertices,n),t.icon.placedSymbolArray.get(i.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(i.verticalPlacedIconSymbolIndex>=0){const n=r?ne:e;m(t.icon,i.numVerticalIconVertices,n),t.icon.placedSymbolArray.get(i.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}const T=g&&g.has(r)?g.get(r):{text:null,icon:null};if(t.hasIconCollisionBoxData()||t.hasTextCollisionBoxData()){const n=t.collisionArrays[r];if(n){let r=new e.P(0,0);if(n.textBox||n.verticalTextBox){let e=!0;if(u){const t=this.variableOffsets[l];t?(r=Zt(t.anchor,t.width,t.height,t.textOffset,t.textBoxScale),h&&r._rotate(f?this.transform.angle:-this.transform.angle)):e=!1}if(n.textBox||n.verticalTextBox){let i;n.textBox&&(i=b),n.verticalTextBox&&(i=w),Yt(t.textCollisionBox.collisionVertexArray,y.text.placed,!e||i,T.text,r.x,r.y)}}if(n.iconBox||n.verticalIconBox){const e=Boolean(!w&&n.verticalIconBox);let i;n.iconBox&&(i=e),n.verticalIconBox&&(i=!e),Yt(t.iconCollisionBox.collisionVertexArray,y.icon.placed,i,T.icon,p?r.x:0,p?r.y:0)}}}}if(t.sortFeatures(this.transform.angle),this.retainedQueryData[t.bucketInstanceId]&&(this.retainedQueryData[t.bucketInstanceId].featureSortOrder=t.featureSortOrder),t.hasTextData()&&t.text.opacityVertexBuffer&&t.text.opacityVertexBuffer.updateData(t.text.opacityVertexArray),t.hasIconData()&&t.icon.opacityVertexBuffer&&t.icon.opacityVertexBuffer.updateData(t.icon.opacityVertexArray),t.hasIconCollisionBoxData()&&t.iconCollisionBox.collisionVertexBuffer&&t.iconCollisionBox.collisionVertexBuffer.updateData(t.iconCollisionBox.collisionVertexArray),t.hasTextCollisionBoxData()&&t.textCollisionBox.collisionVertexBuffer&&t.textCollisionBox.collisionVertexBuffer.updateData(t.textCollisionBox.collisionVertexArray),t.text.opacityVertexArray.length!==t.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${t.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${t.text.layoutVertexArray.length}) / 4`);if(t.icon.opacityVertexArray.length!==t.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${t.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${t.icon.layoutVertexArray.length}) / 4`);if(t.bucketInstanceId in this.collisionCircleArrays){const e=this.collisionCircleArrays[t.bucketInstanceId];t.placementInvProjMatrix=e.invProjMatrix,t.placementViewportMatrix=e.viewportMatrix,t.collisionCircleArray=e.circles,delete this.collisionCircleArrays[t.bucketInstanceId]}}symbolFadeChange(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(t){return Math.max(0,(this.transform.zoom-t)/1.5)}hasTransitions(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration}stillRecent(t,e){const r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t}setStale(){this.stale=!0}}function Yt(t,e,r,n,i,a){n&&0!==n.length||(n=[0,0,0,0]);const o=n[0]-Ft,s=n[1]-Ft,l=n[2]-Ft,c=n[3]-Ft;t.emplaceBack(e?1:0,r?1:0,i||0,a||0,o,s),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,l,s),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,l,c),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,o,c)}const Xt=Math.pow(2,25),$t=Math.pow(2,24),Jt=Math.pow(2,17),Kt=Math.pow(2,16),Qt=Math.pow(2,9),te=Math.pow(2,8),ee=Math.pow(2,1);function re(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;const e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Xt+e*$t+r*Jt+e*Kt+r*Qt+e*te+r*ee+e}const ne=0;function ie(){return{isOccluded(t,e,r){return!1},getPitchedTextCorrection(t,e,r){return 1},get useSpecialProjectionForSymbols(){return!1},projectTileCoordinates(t,e,r,n){throw new Error("Not implemented.")},translatePosition(t,e,r,n){return function(t,e,r,n,i=!1){if(!r[0]&&!r[1])return[0,0];const a=i?"map"===n?t.angle:0:"viewport"===n?-t.angle:0;if(a){const t=Math.sin(a),e=Math.cos(a);r=[r[0]*e-r[1]*t,r[0]*t+r[1]*e]}return[i?r[0]:Nt(e,r[0],t.zoom),i?r[1]:Nt(e,r[1],t.zoom)]}(t,e,r,n)},getCircleRadiusCorrection(t){return 1}}}class ae{constructor(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&!t.layout.get("symbol-sort-key").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(t,e,r,n,i){const a=this._bucketParts;for(;this._currentTileIndex<t.length;){const r=t[this._currentTileIndex];if(e.getBucketParts(a,n,r,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort(((t,e)=>t.sortKey-e.sortKey)));this._currentPartIndex<a.length;){const t=a[this._currentPartIndex];if(e.placeLayerBucketPart(t,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1}}class oe{constructor(t,e,r,n,i,a,o,s){this.placement=new Wt(t,ie(),e,a,o,s),this._currentPlacementIndex=r.length-1,this._forceFullPlacement=n,this._showCollisionBoxes=i,this._done=!1}isDone(){return this._done}continuePlacement(t,e,r){const n=a.now(),i=()=>!this._forceFullPlacement&&a.now()-n>2;for(;this._currentPlacementIndex>=0;){const n=e[t[this._currentPlacementIndex]],a=this.placement.collisionIndex.transform.zoom;if("symbol"===n.type&&(!n.minzoom||n.minzoom<=a)&&(!n.maxzoom||n.maxzoom>a)){if(this._inProgressLayer||(this._inProgressLayer=new ae(n)),this._inProgressLayer.continuePlacement(r[n.source],this.placement,this._showCollisionBoxes,n,i))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(t){return this.placement.commit(t),this.placement}}const se=512/e.X/2;class le{constructor(t,r,n){this.tileID=t,this.bucketInstanceId=n,this._symbolsByKey={};const i=new Map;for(let t=0;t<r.length;t++){const e=r.get(t),n=e.key,a=i.get(n);a?a.push(e):i.set(n,[e])}for(const[t,r]of i){const n={positions:r.map((t=>({x:Math.floor(t.anchorX*se),y:Math.floor(t.anchorY*se)}))),crossTileIDs:r.map((t=>t.crossTileID))};if(n.positions.length>128){const t=new e.aw(n.positions.length,16,Uint16Array);for(const{x:e,y:r}of n.positions)t.add(e,r);t.finish(),delete n.positions,n.index=t}this._symbolsByKey[t]=n}}getScaledCoordinates(t,r){const{x:n,y:i,z:a}=this.tileID.canonical,{x:o,y:s,z:l}=r.canonical,c=l-a,u=se/Math.pow(2,c),h=(o*e.X+t.anchorX)*u,f=(s*e.X+t.anchorY)*u,p=n*e.X*se,d=i*e.X*se;return{x:Math.floor(h-p),y:Math.floor(f-d)}}findMatches(t,e,r){const n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z);for(let i=0;i<t.length;i++){const a=t.get(i);if(a.crossTileID)continue;const o=this._symbolsByKey[a.key];if(!o)continue;const s=this.getScaledCoordinates(a,e);if(o.index){const t=o.index.range(s.x-n,s.y-n,s.x+n,s.y+n).sort();for(const e of t){const t=o.crossTileIDs[e];if(!r[t]){r[t]=!0,a.crossTileID=t;break}}}else if(o.positions)for(let t=0;t<o.positions.length;t++){const e=o.positions[t],i=o.crossTileIDs[t];if(Math.abs(e.x-s.x)<=n&&Math.abs(e.y-s.y)<=n&&!r[i]){r[i]=!0,a.crossTileID=i;break}}}}getCrossTileIDsLists(){return Object.values(this._symbolsByKey).map((({crossTileIDs:t})=>t))}}class ce{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class ue{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(t){const e=Math.round((t-this.lng)/360);if(0!==e)for(const t in this.indexes){const r=this.indexes[t],n={};for(const t in r){const i=r[t];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+e),n[i.tileID.key]=i}this.indexes[t]=n}this.lng=t}addBucket(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(let t=0;t<e.symbolInstances.length;t++)e.symbolInstances.get(t).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});const n=this.usedCrossTileIDs[t.overscaledZ];for(const r in this.indexes){const i=this.indexes[r];if(Number(r)>t.overscaledZ)for(const r in i){const a=i[r];a.tileID.isChildOf(t)&&a.findMatches(e.symbolInstances,t,n)}else{const a=i[t.scaledTo(Number(r)).key];a&&a.findMatches(e.symbolInstances,t,n)}}for(let t=0;t<e.symbolInstances.length;t++){const i=e.symbolInstances.get(t);i.crossTileID||(i.crossTileID=r.generate(),n[i.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new le(t,e.symbolInstances,e.bucketInstanceId),!0}removeBucketCrossTileIDs(t,e){for(const r of e.getCrossTileIDsLists())for(const e of r)delete this.usedCrossTileIDs[t][e]}removeStaleBuckets(t){let e=!1;for(const r in this.indexes){const n=this.indexes[r];for(const i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e}}class he{constructor(){this.layerIndexes={},this.crossTileIDs=new ce,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}}addLayer(t,e,r){let n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new ue);let i=!1;const a={};n.handleWrapJump(r);for(const r of e){const e=r.getBucket(t);e&&t.id===e.layerIds[0]&&(e.bucketInstanceId||(e.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(r.tileID,e,this.crossTileIDs)&&(i=!0),a[e.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i}pruneUnusedLayers(t){const e={};t.forEach((t=>{e[t]=!0}));for(const t in this.layerIndexes)e[t]||delete this.layerIndexes[t]}}const fe=(t,r)=>e.t(t,r&&r.filter((t=>"source.canvas"!==t.identifier))),pe=e.ax();class de extends e.E{constructor(t,r={}){super(),this._rtlPluginLoaded=()=>{for(const t in this.sourceCaches){const e=this.sourceCaches[t].getSource().type;"vector"!==e&&"geojson"!==e||this.sourceCaches[t].reload()}},this.map=t,this.dispatcher=new q(V(),t._getMapId()),this.dispatcher.registerMessageHandler("GG",((t,e)=>this.getGlyphs(t,e))),this.dispatcher.registerMessageHandler("GI",((t,e)=>this.getImages(t,e))),this.imageManager=new k,this.imageManager.setEventedParent(this),this.glyphManager=new E(t._requestManager,r.localIdeographFontFamily),this.lineAtlas=new R(256,512),this.crossTileSymbolIndex=new he,this._spritesImagesIds={},this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new e.ay,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("SR",e.az()),ut().on(st,this._rtlPluginLoaded),this.on("data",(t=>{if("source"!==t.dataType||"metadata"!==t.sourceDataType)return;const e=this.sourceCaches[t.sourceId];if(!e)return;const r=e.getSource();if(r&&r.vectorLayerIds)for(const t in this._layers){const e=this._layers[t];e.source===r.id&&this._validateLayer(e)}}))}loadURL(t,r={},n){this.fire(new e.k("dataloading",{dataType:"style"})),r.validate="boolean"!=typeof r.validate||r.validate;const i=this.map._requestManager.transformRequest(t,"Style");this._loadStyleRequest=new AbortController;const a=this._loadStyleRequest;e.h(i,this._loadStyleRequest).then((t=>{this._loadStyleRequest=null,this._load(t.data,r,n)})).catch((t=>{this._loadStyleRequest=null,t&&!a.signal.aborted&&this.fire(new e.j(t))}))}loadJSON(t,r={},n){this.fire(new e.k("dataloading",{dataType:"style"})),this._frameRequest=new AbortController,a.frameAsync(this._frameRequest).then((()=>{this._frameRequest=null,r.validate=!1!==r.validate,this._load(t,r,n)})).catch((()=>{}))}loadEmpty(){this.fire(new e.k("dataloading",{dataType:"style"})),this._load(pe,{validate:!1})}_load(t,r,n){var i;const a=r.transformStyle?r.transformStyle(n,t):t;if(!r.validate||!fe(this,e.x(a))){this._loaded=!0,this.stylesheet=a;for(const t in a.sources)this.addSource(t,a.sources[t],{validate:!1});a.sprite?this._loadSprite(a.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(a.glyphs),this._createLayers(),this.light=new P(this.stylesheet.light),this.sky=new D(this.stylesheet.sky),this.map.setTerrain(null!==(i=this.stylesheet.terrain)&&void 0!==i?i:null),this.fire(new e.k("data",{dataType:"style"})),this.fire(new e.k("style.load"))}}_createLayers(){const t=e.aA(this.stylesheet.layers);this.dispatcher.broadcast("SL",t),this._order=t.map((t=>t.id)),this._layers={},this._serializedLayers=null;for(const r of t){const t=e.aB(r);t.setEventedParent(this,{layer:{id:r.id}}),this._layers[r.id]=t}}_loadSprite(t,r=!1,n=void 0){let i;this.imageManager.setLoaded(!1),this._spriteRequest=new AbortController,b(t,this.map._requestManager,this.map.getPixelRatio(),this._spriteRequest).then((t=>{if(this._spriteRequest=null,t)for(const e in t){this._spritesImagesIds[e]=[];const n=this._spritesImagesIds[e]?this._spritesImagesIds[e].filter((e=>!(e in t))):[];for(const t of n)this.imageManager.removeImage(t),this._changedImages[t]=!0;for(const n in t[e]){const i="default"===e?n:`${e}:${n}`;this._spritesImagesIds[e].push(i),i in this.imageManager.images?this.imageManager.updateImage(i,t[e][n],!1):this.imageManager.addImage(i,t[e][n]),r&&(this._changedImages[i]=!0)}}})).catch((t=>{this._spriteRequest=null,i=t,this.fire(new e.j(i))})).finally((()=>{this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),r&&(this._changed=!0),this.dispatcher.broadcast("SI",this._availableImages),this.fire(new e.k("data",{dataType:"style"})),n&&n(i)}))}_unloadSprite(){for(const t of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(t),this._changedImages[t]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast("SI",this._availableImages),this.fire(new e.k("data",{dataType:"style"}))}_validateLayer(t){const r=this.sourceCaches[t.source];if(!r)return;const n=t.sourceLayer;if(!n)return;const i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new e.j(new Error(`Source layer "${n}" does not exist on source "${i.id}" as specified by style layer "${t.id}".`)))}loaded(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(const t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()}_serializeByIds(t){const e=this._serializedAllLayers();if(!t||0===t.length)return Object.values(e);const r=[];for(const n of t)e[n]&&r.push(e[n]);return r}_serializedAllLayers(){let t=this._serializedLayers;if(t)return t;t=this._serializedLayers={};const e=Object.keys(this._layers);for(const r of e){const e=this._layers[r];"custom"!==e.type&&(t[r]=e.serialize())}return t}hasTransitions(){if(this.light&&this.light.hasTransition())return!0;if(this.sky&&this.sky.hasTransition())return!0;for(const t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(const t in this._layers)if(this._layers[t].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error("Style is not done loading.")}update(t){if(!this._loaded)return;const r=this._changed;if(r){const e=Object.keys(this._updatedLayers),r=Object.keys(this._removedLayers);(e.length||r.length)&&this._updateWorkerLayers(e,r);for(const t in this._updatedSources){const e=this._updatedSources[t];if("reload"===e)this._reloadSource(t);else{if("clear"!==e)throw new Error(`Invalid action ${e}`);this._clearSource(t)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(const e in this._updatedPaintProps)this._layers[e].updateTransitions(t);this.light.updateTransitions(t),this.sky.updateTransitions(t),this._resetUpdates()}const n={};for(const t in this.sourceCaches){const e=this.sourceCaches[t];n[t]=e.used,e.used=!1}for(const e of this._order){const r=this._layers[e];r.recalculate(t,this._availableImages),!r.isHidden(t.zoom)&&r.source&&(this.sourceCaches[r.source].used=!0)}for(const t in n){const r=this.sourceCaches[t];!!n[t]!=!!r.used&&r.fire(new e.k("data",{sourceDataType:"visibility",dataType:"source",sourceId:t}))}this.light.recalculate(t),this.sky.recalculate(t),this.z=t.zoom,r&&this.fire(new e.k("data",{dataType:"style"}))}_updateTilesForChangedImages(){const t=Object.keys(this._changedImages);if(t.length){for(const e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(const t in this.sourceCaches)this.sourceCaches[t].reloadTilesForDependencies(["glyphs"],[""]);this._glyphsDidChange=!1}}_updateWorkerLayers(t,e){this.dispatcher.broadcast("UL",{layers:this._serializeByIds(t),removedIds:e})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(t,r={}){var n;this._checkLoaded();const i=this.serialize();if(t=r.transformStyle?r.transformStyle(i,t):t,(null===(n=r.validate)||void 0===n||n)&&fe(this,e.x(t)))return!1;(t=e.aC(t)).layers=e.aA(t.layers);const a=e.aD(i,t),o=this._getOperationsToPerform(a);if(o.unimplemented.length>0)throw new Error(`Unimplemented: ${o.unimplemented.join(", ")}.`);if(0===o.operations.length)return!1;for(const t of o.operations)t();return this.stylesheet=t,this._serializedLayers=null,!0}_getOperationsToPerform(t){const e=[],r=[];for(const n of t)switch(n.command){case"setCenter":case"setZoom":case"setBearing":case"setPitch":continue;case"addLayer":e.push((()=>this.addLayer.apply(this,n.args)));break;case"removeLayer":e.push((()=>this.removeLayer.apply(this,n.args)));break;case"setPaintProperty":e.push((()=>this.setPaintProperty.apply(this,n.args)));break;case"setLayoutProperty":e.push((()=>this.setLayoutProperty.apply(this,n.args)));break;case"setFilter":e.push((()=>this.setFilter.apply(this,n.args)));break;case"addSource":e.push((()=>this.addSource.apply(this,n.args)));break;case"removeSource":e.push((()=>this.removeSource.apply(this,n.args)));break;case"setLayerZoomRange":e.push((()=>this.setLayerZoomRange.apply(this,n.args)));break;case"setLight":e.push((()=>this.setLight.apply(this,n.args)));break;case"setGeoJSONSourceData":e.push((()=>this.setGeoJSONSourceData.apply(this,n.args)));break;case"setGlyphs":e.push((()=>this.setGlyphs.apply(this,n.args)));break;case"setSprite":e.push((()=>this.setSprite.apply(this,n.args)));break;case"setSky":e.push((()=>this.setSky.apply(this,n.args)));break;case"setTerrain":e.push((()=>this.map.setTerrain.apply(this,n.args)));break;case"setTransition":e.push((()=>{}));break;default:r.push(n.command)}return{operations:e,unimplemented:r}}addImage(t,r){if(this.getImage(t))return this.fire(new e.j(new Error(`An image named "${t}" already exists.`)));this.imageManager.addImage(t,r),this._afterImageUpdated(t)}updateImage(t,e){this.imageManager.updateImage(t,e)}getImage(t){return this.imageManager.getImage(t)}removeImage(t){if(!this.getImage(t))return this.fire(new e.j(new Error(`An image named "${t}" does not exist.`)));this.imageManager.removeImage(t),this._afterImageUpdated(t)}_afterImageUpdated(t){this._availableImages=this.imageManager.listImages(),this._changedImages[t]=!0,this._changed=!0,this.dispatcher.broadcast("SI",this._availableImages),this.fire(new e.k("data",{dataType:"style"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(t,r,n={}){if(this._checkLoaded(),void 0!==this.sourceCaches[t])throw new Error(`Source "${t}" already exists.`);if(!r.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(r).join(", ")}.`);if(["vector","raster","geojson","video","image"].indexOf(r.type)>=0&&this._validate(e.x.source,`sources.${t}`,r,null,n))return;this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);const i=this.sourceCaches[t]=new dt(t,r,this.dispatcher);i.style=this,i.setEventedParent(this,(()=>({isSourceLoaded:i.loaded(),source:i.serialize(),sourceId:t}))),i.onAdd(this.map),this._changed=!0}removeSource(t){if(this._checkLoaded(),void 0===this.sourceCaches[t])throw new Error("There is no source with this ID");for(const r in this._layers)if(this._layers[r].source===t)return this.fire(new e.j(new Error(`Source "${t}" cannot be removed while layer "${r}" is using it.`)));const r=this.sourceCaches[t];delete this.sourceCaches[t],delete this._updatedSources[t],r.fire(new e.k("data",{sourceDataType:"metadata",dataType:"source",sourceId:t})),r.setEventedParent(null),r.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(t,e){if(this._checkLoaded(),void 0===this.sourceCaches[t])throw new Error(`There is no source with this ID=${t}`);const r=this.sourceCaches[t].getSource();if("geojson"!==r.type)throw new Error(`geojsonSource.type is ${r.type}, which is !== 'geojson`);r.setData(e),this._changed=!0}getSource(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()}addLayer(t,r,n={}){this._checkLoaded();const i=t.id;if(this.getLayer(i))return void this.fire(new e.j(new Error(`Layer "${i}" already exists on this map.`)));let a;if("custom"===t.type){if(fe(this,e.aE(t)))return;a=e.aB(t)}else{if("source"in t&&"object"==typeof t.source&&(this.addSource(i,t.source),t=e.aC(t),t=e.e(t,{source:i})),this._validate(e.x.layer,`layers.${i}`,t,{arrayIndex:-1},n))return;a=e.aB(t),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}})}const o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new e.j(new Error(`Cannot add layer "${i}" before non-existing layer "${r}".`)));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){const t=this._removedLayers[i];delete this._removedLayers[i],t.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}moveLayer(t,r){if(this._checkLoaded(),this._changed=!0,!this._layers[t])return void this.fire(new e.j(new Error(`The layer '${t}' does not exist in the map's style and cannot be moved.`)));if(t===r)return;const n=this._order.indexOf(t);this._order.splice(n,1);const i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new e.j(new Error(`Cannot move layer "${t}" before non-existing layer "${r}".`))):(this._order.splice(i,0,t),this._layerOrderChanged=!0)}removeLayer(t){this._checkLoaded();const r=this._layers[t];if(!r)return void this.fire(new e.j(new Error(`Cannot remove non-existing layer "${t}".`)));r.setEventedParent(null);const n=this._order.indexOf(t);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[t]=r,delete this._layers[t],this._serializedLayers&&delete this._serializedLayers[t],delete this._updatedLayers[t],delete this._updatedPaintProps[t],r.onRemove&&r.onRemove(this.map)}getLayer(t){return this._layers[t]}getLayersOrder(){return[...this._order]}hasLayer(t){return t in this._layers}setLayerZoomRange(t,r,n){this._checkLoaded();const i=this.getLayer(t);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new e.j(new Error(`Cannot set the zoom range of non-existing layer "${t}".`)))}setFilter(t,r,n={}){this._checkLoaded();const i=this.getLayer(t);if(i){if(!e.aF(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(e.x.filter,`layers.${i.id}.filter`,r,null,n)||(i.filter=e.aC(r),this._updateLayer(i)))}else this.fire(new e.j(new Error(`Cannot filter non-existing layer "${t}".`)))}getFilter(t){return e.aC(this.getLayer(t).filter)}setLayoutProperty(t,r,n,i={}){this._checkLoaded();const a=this.getLayer(t);a?e.aF(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new e.j(new Error(`Cannot style non-existing layer "${t}".`)))}getLayoutProperty(t,r){const n=this.getLayer(t);if(n)return n.getLayoutProperty(r);this.fire(new e.j(new Error(`Cannot get style of non-existing layer "${t}".`)))}setPaintProperty(t,r,n,i={}){this._checkLoaded();const a=this.getLayer(t);a?e.aF(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[t]=!0,this._serializedLayers=null):this.fire(new e.j(new Error(`Cannot style non-existing layer "${t}".`)))}getPaintProperty(t,e){return this.getLayer(t).getPaintProperty(e)}setFeatureState(t,r){this._checkLoaded();const n=t.source,i=t.sourceLayer,a=this.sourceCaches[n];if(void 0===a)return void this.fire(new e.j(new Error(`The source '${n}' does not exist in the map's style.`)));const o=a.getSource().type;"geojson"===o&&i?this.fire(new e.j(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===t.id&&this.fire(new e.j(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,t.id,r)):this.fire(new e.j(new Error("The sourceLayer parameter must be provided for vector source types.")))}removeFeatureState(t,r){this._checkLoaded();const n=t.source,i=this.sourceCaches[n];if(void 0===i)return void this.fire(new e.j(new Error(`The source '${n}' does not exist in the map's style.`)));const a=i.getSource().type,o="vector"===a?t.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof t.id&&"number"!=typeof t.id?this.fire(new e.j(new Error("A feature id is required to remove its specific state property."))):i.removeFeatureState(o,t.id,r):this.fire(new e.j(new Error("The sourceLayer parameter must be provided for vector source types.")))}getFeatureState(t){this._checkLoaded();const r=t.source,n=t.sourceLayer,i=this.sourceCaches[r];if(void 0!==i)return"vector"!==i.getSource().type||n?(void 0===t.id&&this.fire(new e.j(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,t.id)):void this.fire(new e.j(new Error("The sourceLayer parameter must be provided for vector source types.")));this.fire(new e.j(new Error(`The source '${r}' does not exist in the map's style.`)))}getTransition(){return e.e({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)}serialize(){if(!this._loaded)return;const t=e.aG(this.sourceCaches,(t=>t.serialize())),r=this._serializeByIds(this._order),n=this.map.getTerrain()||void 0,i=this.stylesheet;return e.aH({version:i.version,name:i.name,metadata:i.metadata,light:i.light,sky:i.sky,center:i.center,zoom:i.zoom,bearing:i.bearing,pitch:i.pitch,sprite:i.sprite,glyphs:i.glyphs,transition:i.transition,sources:t,layers:r,terrain:n},(t=>void 0!==t))}_updateLayer(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(t){const e=t=>"fill-extrusion"===this._layers[t].type,r={},n=[];for(let i=this._order.length-1;i>=0;i--){const a=this._order[i];if(e(a)){r[a]=i;for(const e of t){const t=e[a];if(t)for(const e of t)n.push(e)}}}n.sort(((t,e)=>e.intersectionZ-t.intersectionZ));const i=[];for(let a=this._order.length-1;a>=0;a--){const o=this._order[a];if(e(o))for(let t=n.length-1;t>=0;t--){const e=n[t].feature;if(r[e.layer.id]<a)break;i.push(e),n.pop()}else for(const e of t){const t=e[o];if(t)for(const e of t)i.push(e.feature)}}return i}queryRenderedFeatures(t,r,n){r&&r.filter&&this._validate(e.x.filter,"queryRenderedFeatures.filter",r.filter,null,r);const i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new e.j(new Error("parameters.layers must be an Array."))),[];for(const t of r.layers){const r=this._layers[t];if(!r)return this.fire(new e.j(new Error(`The layer '${t}' does not exist in the map's style and cannot be queried for features.`))),[];i[r.source]=!0}}const a=[];r.availableImages=this._availableImages;const o=this._serializedAllLayers();for(const e in this.sourceCaches)r.layers&&!i[e]||a.push(Z(this.sourceCaches[e],this._layers,o,t,r,n));return this.placement&&a.push(function(t,e,r,n,i,a,o){const s={},l=a.queryRenderedSymbols(n),c=[];for(const t of Object.keys(l).map(Number))c.push(o[t]);c.sort(W);for(const r of c){const n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(const t in n){const e=s[t]=s[t]||[],i=n[t];i.sort(((t,e)=>{const n=r.featureSortOrder;if(n){const r=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-r}return e.featureIndex-t.featureIndex}));for(const t of i)e.push(t)}}for(const e in s)s[e].forEach((n=>{const i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}));return s}(this._layers,o,this.sourceCaches,t,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(a)}querySourceFeatures(t,r){r&&r.filter&&this._validate(e.x.filter,"querySourceFeatures.filter",r.filter,null,r);const n=this.sourceCaches[t];return n?function(t,e){const r=t.getRenderableIds().map((e=>t.getTileByID(e))),n=[],i={};for(let t=0;t<r.length;t++){const a=r[t],o=a.tileID.canonical.key;i[o]||(i[o]=!0,a.querySourceFeatures(n,e))}return n}(n,r):[]}getLight(){return this.light.getLight()}setLight(t,r={}){this._checkLoaded();const n=this.light.getLight();let i=!1;for(const r in t)if(!e.aF(t[r],n[r])){i=!0;break}if(!i)return;const o={now:a.now(),transition:e.e({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(t,r),this.light.updateTransitions(o)}getSky(){var t;return null===(t=this.stylesheet)||void 0===t?void 0:t.sky}setSky(t,r={}){const n=this.sky.getSky();let i=!1;t||n&&(i=!0);for(const r in t)if(!e.aF(t[r],n[r])){i=!0;break}if(!i)return;const o={now:a.now(),transition:e.e({duration:300,delay:0},this.stylesheet.transition)};this.stylesheet.sky=t,this.sky.setSky(t,r),this.sky.updateTransitions(o)}_validate(t,r,n,i,a={}){return(!a||!1!==a.validate)&&fe(this,t.call(e.x,e.e({key:r,style:this.serialize(),value:n,styleSpec:e.v},i)))}_remove(t=!0){this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._loadStyleRequest&&(this._loadStyleRequest.abort(),this._loadStyleRequest=null),this._spriteRequest&&(this._spriteRequest.abort(),this._spriteRequest=null),ut().off(st,this._rtlPluginLoaded);for(const t in this._layers)this._layers[t].setEventedParent(null);for(const t in this.sourceCaches){const e=this.sourceCaches[t];e.setEventedParent(null),e.onRemove(this.map)}this.imageManager.setEventedParent(null),this.setEventedParent(null),t&&this.dispatcher.broadcast("RM",void 0),this.dispatcher.remove(t)}_clearSource(t){this.sourceCaches[t].clearTiles()}_reloadSource(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()}_updateSources(t){for(const e in this.sourceCaches)this.sourceCaches[e].update(t,this.map.terrain)}_generateCollisionBoxes(){for(const t in this.sourceCaches)this._reloadSource(t)}_updatePlacement(t,e,r,n,i=!1){let o=!1,s=!1;const l={};for(const e of this._order){const r=this._layers[e];if("symbol"!==r.type)continue;if(!l[r.source]){const t=this.sourceCaches[r.source];l[r.source]=t.getRenderableIds(!0).map((e=>t.getTileByID(e))).sort(((t,e)=>e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)))}const n=this.crossTileSymbolIndex.addLayer(r,l[r.source],t.center.lng);o=o||n}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((i=i||this._layerOrderChanged||0===r)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(a.now(),t.zoom))&&(this.pauseablePlacement=new oe(t,this.map.terrain,this._order,i,e,r,n,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(a.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(const t of this._order){const e=this._layers[t];"symbol"===e.type&&this.placement.updateLayerOpacities(e,l[e.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(a.now())}_releaseSymbolFadeTiles(){for(const t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()}getImages(t,r){return e._(this,void 0,void 0,(function*(){const t=yield this.imageManager.getImages(r.icons);this._updateTilesForChangedImages();const e=this.sourceCaches[r.source];return e&&e.setDependencies(r.tileID.key,r.type,r.icons),t}))}getGlyphs(t,r){return e._(this,void 0,void 0,(function*(){const t=yield this.glyphManager.getGlyphs(r.stacks),e=this.sourceCaches[r.source];return e&&e.setDependencies(r.tileID.key,r.type,[""]),t}))}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(t,r={}){this._checkLoaded(),t&&this._validate(e.x.glyphs,"glyphs",t,null,r)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=t,this.glyphManager.entries={},this.glyphManager.setURL(t))}addSprite(t,r,n={},i){this._checkLoaded();const a=[{id:t,url:r}],o=[...x(this.stylesheet.sprite),...a];this._validate(e.x.sprite,"sprite",o,null,n)||(this.stylesheet.sprite=o,this._loadSprite(a,!0,i))}removeSprite(t){this._checkLoaded();const r=x(this.stylesheet.sprite);if(r.find((e=>e.id===t))){if(this._spritesImagesIds[t])for(const e of this._spritesImagesIds[t])this.imageManager.removeImage(e),this._changedImages[e]=!0;r.splice(r.findIndex((e=>e.id===t)),1),this.stylesheet.sprite=r.length>0?r:void 0,delete this._spritesImagesIds[t],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast("SI",this._availableImages),this.fire(new e.k("data",{dataType:"style"}))}else this.fire(new e.j(new Error(`Sprite "${t}" doesn't exists on this map.`)))}getSprite(){return x(this.stylesheet.sprite)}setSprite(t,r={},n){this._checkLoaded(),t&&this._validate(e.x.sprite,"sprite",t,null,r)||(this.stylesheet.sprite=t,t?this._loadSprite(t,!0,n):(this._unloadSprite(),n&&n(null)))}}var me=e.Y([{name:"a_pos",type:"Int16",components:2}]);const ge={prelude:ye("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\n","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\n#ifdef TERRAIN3D\nuniform sampler2D u_terrain;uniform float u_terrain_dim;uniform mat4 u_terrain_matrix;uniform vec4 u_terrain_unpack;uniform float u_terrain_exaggeration;uniform highp sampler2D u_depth;\n#endif\nconst highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitShifts=vec4(1.)/bitSh;highp float unpack(highp vec4 color) {return dot(color,bitShifts);}highp float depthOpacity(vec3 frag) {\n#ifdef TERRAIN3D\nhighp float d=unpack(texture2D(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));\n#else\nreturn 1.0;\n#endif\n}float calculate_visibility(vec4 pos) {\n#ifdef TERRAIN3D\nvec3 frag=pos.xyz/pos.w;highp float d=depthOpacity(frag);if (d > 0.95) return 1.0;return (d+depthOpacity(frag+vec3(0.0,0.01,0.0)))/2.0;\n#else\nreturn 1.0;\n#endif\n}float ele(vec2 pos) {\n#ifdef TERRAIN3D\nvec4 rgb=(texture2D(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;\n#else\nreturn 0.0;\n#endif\n}float get_elevation(vec2 pos) {\n#ifdef TERRAIN3D\nvec2 coord=(u_terrain_matrix*vec4(pos,0.0,1.0)).xy*u_terrain_dim+1.0;vec2 f=fract(coord);vec2 c=(floor(coord)+0.5)/(u_terrain_dim+2.0);float d=1.0/(u_terrain_dim+2.0);float tl=ele(c);float tr=ele(c+vec2(d,0.0));float bl=ele(c+vec2(0.0,d));float br=ele(c+vec2(d,d));float elevation=mix(mix(tl,tr,f.x),mix(bl,br,f.x),f.y);return elevation*u_terrain_exaggeration;\n#else\nreturn 0.0;\n#endif\n}"),background:ye("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),backgroundPattern:ye("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),circle:ye("varying vec3 v_data;varying float v_visibility;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;varying float v_visibility;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);float ele=get_elevation(circle_center);v_visibility=calculate_visibility(u_matrix*vec4(circle_center,ele,1.0));if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,ele,1);} else {gl_Position=u_matrix*vec4(circle_center,ele,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),clippingMask:ye("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),heatmap:ye("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),heatmapTexture:ye("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),collisionBox:ye("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_anchor_pos;attribute vec2 a_placed;attribute vec2 a_box_real;uniform mat4 u_matrix;uniform vec2 u_pixel_extrude_scale;varying float v_placed;varying float v_notUsed;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}void main() {gl_Position=projectTileWithElevation(a_anchor_pos,get_elevation(a_anchor_pos));gl_Position.xy=((a_box_real+0.5)*u_pixel_extrude_scale*2.0-1.0)*vec2(1.0,-1.0)*gl_Position.w;if (gl_Position.z/gl_Position.w < 1.1) {gl_Position.z=0.5;}v_placed=a_placed.x;v_notUsed=a_placed.y;}"),collisionCircle:ye("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),debug:ye("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,get_elevation(a_pos),1);}"),fill:ye("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),fillOutline:ye("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),fillOutlinePattern:ye("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),fillPattern:ye("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),fillExtrusion:ye("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n#ifdef TERRAIN3D\nattribute vec2 a_centroid;\n#endif\nvarying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;\n#ifdef TERRAIN3D\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\n#else\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n#endif\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),fillExtrusionPattern:ye("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\n#ifdef TERRAIN3D\nattribute vec2 a_centroid;\n#endif\nvarying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;\n#ifdef TERRAIN3D\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\n#else\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\n#endif\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),hillshadePrepare:ye("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),hillshade:ye("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),line:ye("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_width2=vec2(outset,inset);}"),lineGradient:ye("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_width2=vec2(outset,inset);}"),linePattern:ye("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),lineSDF:ye("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\n#ifdef TERRAIN3D\nv_gamma_scale=1.0;\n#else\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\n#endif\nv_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),raster:ye("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),symbolIcon:ye("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_tex;varying float v_fade_opacity;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}gl_Position=finalPos;v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));}"),symbolSDF:ye("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);inner_edge=inner_edge+gamma*gamma_scale;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);if (u_is_halo) {lowp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha=min(smoothstep(halo_edge-gamma_scaled,halo_edge+gamma_scaled,dist),1.0-alpha);}gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_data0;varying vec3 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),symbolTextAndIcon:ye("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec4 v_data0;varying vec4 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}"),terrain:ye("uniform sampler2D u_texture;uniform vec4 u_fog_color;uniform vec4 u_horizon_color;uniform float u_fog_ground_blend;uniform float u_fog_ground_blend_opacity;uniform float u_horizon_fog_blend;varying vec2 v_texture_pos;varying float v_fog_depth;const float gamma=2.2;vec4 gammaToLinear(vec4 color) {return pow(color,vec4(gamma));}vec4 linearToGamma(vec4 color) {return pow(color,vec4(1.0/gamma));}void main() {vec4 surface_color=texture2D(u_texture,v_texture_pos);if (v_fog_depth > u_fog_ground_blend) {vec4 surface_color_linear=gammaToLinear(surface_color);float blend_color=smoothstep(0.0,1.0,max((v_fog_depth-u_horizon_fog_blend)/(1.0-u_horizon_fog_blend),0.0));vec4 fog_horizon_color_linear=mix(gammaToLinear(u_fog_color),gammaToLinear(u_horizon_color),blend_color);float factor_fog=max(v_fog_depth-u_fog_ground_blend,0.0)/(1.0-u_fog_ground_blend);gl_FragColor=linearToGamma(mix(surface_color_linear,fog_horizon_color_linear,pow(factor_fog,2.0)*u_fog_ground_blend_opacity));} else {gl_FragColor=surface_color;}}","attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform mat4 u_fog_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;varying float v_fog_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);vec4 pos=u_fog_matrix*vec4(a_pos3d.xy,ele,1.0);v_fog_depth=pos.z/pos.w*0.5+0.5;}"),terrainDepth:ye("varying float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {gl_FragColor=pack(v_depth);}","attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying float v_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);v_depth=gl_Position.z/gl_Position.w;}"),terrainCoords:ye("precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;varying vec2 v_texture_pos;void main() {vec4 rgba=texture2D(u_texture,v_texture_pos);gl_FragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}","attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);}"),sky:ye("uniform vec4 u_sky_color;uniform vec4 u_horizon_color;uniform float u_horizon;uniform float u_sky_horizon_blend;void main() {float y=gl_FragCoord.y;if (y > u_horizon) {float blend=y-u_horizon;if (blend < u_sky_horizon_blend) {gl_FragColor=mix(u_sky_color,u_horizon_color,pow(1.0-blend/u_sky_horizon_blend,2.0));} else {gl_FragColor=u_sky_color;}}}","attribute vec2 a_pos;void main() {gl_Position=vec4(a_pos,1.0,1.0);}")};function ye(t,e){const r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n=e.match(/attribute ([\w]+) ([\w]+)/g),i=t.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),a=e.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,((t,e,r,n,i)=>(s[i]=!0,"define"===e?`\n#ifndef HAS_UNIFORM_u_${i}\nvarying ${r} ${n} ${i};\n#else\nuniform ${r} ${n} u_${i};\n#endif\n`:`\n#ifdef HAS_UNIFORM_u_${i}\n    ${r} ${n} ${i} = u_${i};\n#endif\n`))),vertexSource:e=e.replace(r,((t,e,r,n,i)=>{const a="float"===n?"vec2":"vec4",o=i.match(/color/)?"color":a;return s[i]?"define"===e?`\n#ifndef HAS_UNIFORM_u_${i}\nuniform lowp float u_${i}_t;\nattribute ${r} ${a} a_${i};\nvarying ${r} ${n} ${i};\n#else\nuniform ${r} ${n} u_${i};\n#endif\n`:"vec4"===o?`\n#ifndef HAS_UNIFORM_u_${i}\n    ${i} = a_${i};\n#else\n    ${r} ${n} ${i} = u_${i};\n#endif\n`:`\n#ifndef HAS_UNIFORM_u_${i}\n    ${i} = unpack_mix_${o}(a_${i}, u_${i}_t);\n#else\n    ${r} ${n} ${i} = u_${i};\n#endif\n`:"define"===e?`\n#ifndef HAS_UNIFORM_u_${i}\nuniform lowp float u_${i}_t;\nattribute ${r} ${a} a_${i};\n#else\nuniform ${r} ${n} u_${i};\n#endif\n`:"vec4"===o?`\n#ifndef HAS_UNIFORM_u_${i}\n    ${r} ${n} ${i} = a_${i};\n#else\n    ${r} ${n} ${i} = u_${i};\n#endif\n`:`\n#ifndef HAS_UNIFORM_u_${i}\n    ${r} ${n} ${i} = unpack_mix_${o}(a_${i}, u_${i}_t);\n#else\n    ${r} ${n} ${i} = u_${i};\n#endif\n`})),staticAttributes:n,staticUniforms:o}}class ve{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(t,e,r,n,i,a,o,s,l){this.context=t;let c=this.boundPaintVertexBuffers.length!==n.length;for(let t=0;!c&&t<n.length;t++)this.boundPaintVertexBuffers[t]!==n[t]&&(c=!0);!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||c||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s||this.boundDynamicVertexBuffer3!==l?this.freshBind(e,r,n,i,a,o,s,l):(t.bindVertexArray.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind(),l&&l.bind())}freshBind(t,e,r,n,i,a,o,s){const l=t.numAttributes,c=this.context,u=c.gl;this.vao&&this.destroy(),this.vao=c.createVertexArray(),c.bindVertexArray.set(this.vao),this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o,this.boundDynamicVertexBuffer3=s,e.enableAttributes(u,t);for(const e of r)e.enableAttributes(u,t);a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),s&&s.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(const e of r)e.bind(),e.setVertexAttribPointers(u,t,i);a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),s&&(s.bind(),s.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l}destroy(){this.vao&&(this.context.deleteVertexArray(this.vao),this.vao=null)}}const xe=(t,r,n,i,a)=>({u_matrix:t,u_texture:0,u_ele_delta:r,u_fog_matrix:n,u_fog_color:i?i.properties.get("fog-color"):e.aN.white,u_fog_ground_blend:i?i.properties.get("fog-ground-blend"):1,u_fog_ground_blend_opacity:i?i.calculateFogBlendOpacity(a):0,u_horizon_color:i?i.properties.get("horizon-color"):e.aN.white,u_horizon_fog_blend:i?i.properties.get("horizon-fog-blend"):1});function _e(t){const e=[];for(let r=0;r<t.length;r++){if(null===t[r])continue;const n=t[r].split(" ");e.push(n.pop())}return e}class be{constructor(t,r,n,i,a,o){const s=t.gl;this.program=s.createProgram();const l=_e(r.staticAttributes),c=n?n.getBinderAttributes():[],u=l.concat(c),h=ge.prelude.staticUniforms?_e(ge.prelude.staticUniforms):[],f=r.staticUniforms?_e(r.staticUniforms):[],p=n?n.getBinderUniforms():[],d=h.concat(f).concat(p),m=[];for(const t of d)m.indexOf(t)<0&&m.push(t);const g=n?n.defines():[];a&&g.push("#define OVERDRAW_INSPECTOR;"),o&&g.push("#define TERRAIN3D;");const y=g.concat(ge.prelude.fragmentSource,r.fragmentSource).join("\n"),v=g.concat(ge.prelude.vertexSource,r.vertexSource).join("\n"),x=s.createShader(s.FRAGMENT_SHADER);if(s.isContextLost())return void(this.failedToCreate=!0);if(s.shaderSource(x,y),s.compileShader(x),!s.getShaderParameter(x,s.COMPILE_STATUS))throw new Error(`Could not compile fragment shader: ${s.getShaderInfoLog(x)}`);s.attachShader(this.program,x);const _=s.createShader(s.VERTEX_SHADER);if(s.isContextLost())return void(this.failedToCreate=!0);if(s.shaderSource(_,v),s.compileShader(_),!s.getShaderParameter(_,s.COMPILE_STATUS))throw new Error(`Could not compile vertex shader: ${s.getShaderInfoLog(_)}`);s.attachShader(this.program,_),this.attributes={};const b={};this.numAttributes=u.length;for(let t=0;t<this.numAttributes;t++)u[t]&&(s.bindAttribLocation(this.program,t,u[t]),this.attributes[u[t]]=t);if(s.linkProgram(this.program),!s.getProgramParameter(this.program,s.LINK_STATUS))throw new Error(`Program failed to link: 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e.aI(t,r.u_image),u_tex_y_a:new e.aJ(t,r.u_tex_y_a),u_tex_y_b:new e.aJ(t,r.u_tex_y_b),u_mix:new e.aJ(t,r.u_mix)}),raster:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_tl_parent:new e.aP(t,r.u_tl_parent),u_scale_parent:new e.aJ(t,r.u_scale_parent),u_buffer_scale:new e.aJ(t,r.u_buffer_scale),u_fade_t:new e.aJ(t,r.u_fade_t),u_opacity:new e.aJ(t,r.u_opacity),u_image0:new e.aI(t,r.u_image0),u_image1:new e.aI(t,r.u_image1),u_brightness_low:new e.aJ(t,r.u_brightness_low),u_brightness_high:new e.aJ(t,r.u_brightness_high),u_saturation_factor:new e.aJ(t,r.u_saturation_factor),u_contrast_factor:new e.aJ(t,r.u_contrast_factor),u_spin_weights:new e.aO(t,r.u_spin_weights)}),symbolIcon:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texture:new e.aI(t,r.u_texture),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),symbolSDF:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texture:new e.aI(t,r.u_texture),u_gamma_scale:new e.aJ(t,r.u_gamma_scale),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_is_halo:new e.aI(t,r.u_is_halo),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),symbolTextAndIcon:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texsize_icon:new e.aP(t,r.u_texsize_icon),u_texture:new e.aI(t,r.u_texture),u_texture_icon:new e.aI(t,r.u_texture_icon),u_gamma_scale:new e.aJ(t,r.u_gamma_scale),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_is_halo:new e.aI(t,r.u_is_halo),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),background:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_opacity:new e.aJ(t,r.u_opacity),u_color:new e.aM(t,r.u_color)}),backgroundPattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_opacity:new e.aJ(t,r.u_opacity),u_image:new e.aI(t,r.u_image),u_pattern_tl_a:new e.aP(t,r.u_pattern_tl_a),u_pattern_br_a:new e.aP(t,r.u_pattern_br_a),u_pattern_tl_b:new e.aP(t,r.u_pattern_tl_b),u_pattern_br_b:new e.aP(t,r.u_pattern_br_b),u_texsize:new e.aP(t,r.u_texsize),u_mix:new e.aJ(t,r.u_mix),u_pattern_size_a:new e.aP(t,r.u_pattern_size_a),u_pattern_size_b:new e.aP(t,r.u_pattern_size_b),u_scale_a:new e.aJ(t,r.u_scale_a),u_scale_b:new e.aJ(t,r.u_scale_b),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_tile_units_to_pixels:new e.aJ(t,r.u_tile_units_to_pixels)}),terrain:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_texture:new e.aI(t,r.u_texture),u_ele_delta:new e.aJ(t,r.u_ele_delta),u_fog_matrix:new e.aK(t,r.u_fog_matrix),u_fog_color:new e.aM(t,r.u_fog_color),u_fog_ground_blend:new e.aJ(t,r.u_fog_ground_blend),u_fog_ground_blend_opacity:new e.aJ(t,r.u_fog_ground_blend_opacity),u_horizon_color:new e.aM(t,r.u_horizon_color),u_horizon_fog_blend:new e.aJ(t,r.u_horizon_fog_blend)}),terrainDepth:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ele_delta:new e.aJ(t,r.u_ele_delta)}),terrainCoords:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_texture:new e.aI(t,r.u_texture),u_terrain_coords_id:new e.aJ(t,r.u_terrain_coords_id),u_ele_delta:new e.aJ(t,r.u_ele_delta)}),sky:(t,r)=>({u_sky_color:new e.aM(t,r.u_sky_color),u_horizon_color:new e.aM(t,r.u_horizon_color),u_horizon:new e.aJ(t,r.u_horizon),u_sky_horizon_blend:new e.aJ(t,r.u_sky_horizon_blend)})};class Qe{constructor(t,e,r){this.context=t;const n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer}bind(){this.context.bindElementBuffer.set(this.buffer)}updateData(t){const e=this.context.gl;if(!this.dynamicDraw)throw new Error("Attempted to update data while not in dynamic mode.");this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)}destroy(){const t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)}}const tr={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"};class er{constructor(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;const i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(t){if(t.length!==this.length)throw new Error(`Length of new data is ${t.length}, which doesn't match current length of ${this.length}`);const e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)}enableAttributes(t,e){for(let r=0;r<this.attributes.length;r++){const n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}}setVertexAttribPointers(t,e,r){for(let n=0;n<this.attributes.length;n++){const i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[tr[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}}destroy(){const t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)}}const rr=new WeakMap;function nr(t){var e;if(rr.has(t))return rr.get(t);{const r=null===(e=t.getParameter(t.VERSION))||void 0===e?void 0:e.startsWith("WebGL 2.0");return rr.set(t,r),r}}class ir{constructor(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1}get(){return this.current}set(t){}getDefault(){return this.default}setDefault(){this.set(this.default)}}class ar extends ir{getDefault(){return e.aN.transparent}set(t){const e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)}}class or extends ir{getDefault(){return 1}set(t){(t!==this.current||this.dirty)&&(this.gl.clearDepth(t),this.current=t,this.dirty=!1)}}class sr extends ir{getDefault(){return 0}set(t){(t!==this.current||this.dirty)&&(this.gl.clearStencil(t),this.current=t,this.dirty=!1)}}class lr extends ir{getDefault(){return[!0,!0,!0,!0]}set(t){const e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)}}class cr extends ir{getDefault(){return!0}set(t){(t!==this.current||this.dirty)&&(this.gl.depthMask(t),this.current=t,this.dirty=!1)}}class ur extends ir{getDefault(){return 255}set(t){(t!==this.current||this.dirty)&&(this.gl.stencilMask(t),this.current=t,this.dirty=!1)}}class hr extends ir{getDefault(){return{func:this.gl.ALWAYS,ref:0,mask:255}}set(t){const e=this.current;(t.func!==e.func||t.ref!==e.ref||t.mask!==e.mask||this.dirty)&&(this.gl.stencilFunc(t.func,t.ref,t.mask),this.current=t,this.dirty=!1)}}class fr extends ir{getDefault(){const t=this.gl;return[t.KEEP,t.KEEP,t.KEEP]}set(t){const e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||this.dirty)&&(this.gl.stencilOp(t[0],t[1],t[2]),this.current=t,this.dirty=!1)}}class pr extends ir{getDefault(){return!1}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;t?e.enable(e.STENCIL_TEST):e.disable(e.STENCIL_TEST),this.current=t,this.dirty=!1}}class dr extends ir{getDefault(){return[0,1]}set(t){const e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.depthRange(t[0],t[1]),this.current=t,this.dirty=!1)}}class mr extends ir{getDefault(){return!1}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;t?e.enable(e.DEPTH_TEST):e.disable(e.DEPTH_TEST),this.current=t,this.dirty=!1}}class gr extends ir{getDefault(){return this.gl.LESS}set(t){(t!==this.current||this.dirty)&&(this.gl.depthFunc(t),this.current=t,this.dirty=!1)}}class yr extends ir{getDefault(){return!1}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;t?e.enable(e.BLEND):e.disable(e.BLEND),this.current=t,this.dirty=!1}}class vr extends ir{getDefault(){const t=this.gl;return[t.ONE,t.ZERO]}set(t){const e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.blendFunc(t[0],t[1]),this.current=t,this.dirty=!1)}}class xr extends ir{getDefault(){return e.aN.transparent}set(t){const e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)}}class _r extends ir{getDefault(){return this.gl.FUNC_ADD}set(t){(t!==this.current||this.dirty)&&(this.gl.blendEquation(t),this.current=t,this.dirty=!1)}}class br extends ir{getDefault(){return!1}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;t?e.enable(e.CULL_FACE):e.disable(e.CULL_FACE),this.current=t,this.dirty=!1}}class wr extends ir{getDefault(){return this.gl.BACK}set(t){(t!==this.current||this.dirty)&&(this.gl.cullFace(t),this.current=t,this.dirty=!1)}}class Tr extends ir{getDefault(){return this.gl.CCW}set(t){(t!==this.current||this.dirty)&&(this.gl.frontFace(t),this.current=t,this.dirty=!1)}}class kr extends ir{getDefault(){return null}set(t){(t!==this.current||this.dirty)&&(this.gl.useProgram(t),this.current=t,this.dirty=!1)}}class Ar extends ir{getDefault(){return this.gl.TEXTURE0}set(t){(t!==this.current||this.dirty)&&(this.gl.activeTexture(t),this.current=t,this.dirty=!1)}}class Mr extends ir{getDefault(){const t=this.gl;return[0,0,t.drawingBufferWidth,t.drawingBufferHeight]}set(t){const e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)}}class Sr extends ir{getDefault(){return null}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}}class Er extends ir{getDefault(){return null}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}}class Cr extends ir{getDefault(){return null}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}}class Lr extends ir{getDefault(){return null}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t,this.dirty=!1}}class Ir extends ir{getDefault(){return null}set(t){const e=this.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t,this.dirty=!1}}class Pr extends ir{getDefault(){return null}set(t){var e;if(t===this.current&&!this.dirty)return;const r=this.gl;nr(r)?r.bindVertexArray(t):null===(e=r.getExtension("OES_vertex_array_object"))||void 0===e||e.bindVertexArrayOES(t),this.current=t,this.dirty=!1}}class zr extends ir{getDefault(){return 4}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}}class Or extends ir{getDefault(){return!1}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}}class Dr extends ir{getDefault(){return!1}set(t){if(t===this.current&&!this.dirty)return;const e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}}class Rr extends ir{constructor(t,e){super(t),this.context=t,this.parent=e}getDefault(){return null}}class Fr extends Rr{setDirty(){this.dirty=!0}set(t){if(t===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);const e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}}class Br extends Rr{set(t){if(t===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);const e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}}class Nr extends Rr{set(t){if(t===this.current&&!this.dirty)return;this.context.bindFramebuffer.set(this.parent);const e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_STENCIL_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}}class jr{constructor(t,e,r,n,i){this.context=t,this.width=e,this.height=r;const a=t.gl,o=this.framebuffer=a.createFramebuffer();if(this.colorAttachment=new Fr(t,o),n)this.depthAttachment=i?new Nr(t,o):new Br(t,o);else if(i)throw new Error("Stencil cannot be set without depth");if(a.checkFramebufferStatus(a.FRAMEBUFFER)!==a.FRAMEBUFFER_COMPLETE)throw new Error("Framebuffer is not complete")}destroy(){const t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){const e=this.depthAttachment.get();e&&t.deleteRenderbuffer(e)}t.deleteFramebuffer(this.framebuffer)}}class Ur{constructor(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r}}Ur.Replace=[1,0],Ur.disabled=new Ur(Ur.Replace,e.aN.transparent,[!1,!1,!1,!1]),Ur.unblended=new Ur(Ur.Replace,e.aN.transparent,[!0,!0,!0,!0]),Ur.alphaBlended=new Ur([1,771],e.aN.transparent,[!0,!0,!0,!0]);class Vr{constructor(t){var e,r;if(this.gl=t,this.clearColor=new ar(this),this.clearDepth=new or(this),this.clearStencil=new sr(this),this.colorMask=new lr(this),this.depthMask=new cr(this),this.stencilMask=new ur(this),this.stencilFunc=new hr(this),this.stencilOp=new fr(this),this.stencilTest=new pr(this),this.depthRange=new dr(this),this.depthTest=new mr(this),this.depthFunc=new gr(this),this.blend=new yr(this),this.blendFunc=new vr(this),this.blendColor=new xr(this),this.blendEquation=new _r(this),this.cullFace=new br(this),this.cullFaceSide=new wr(this),this.frontFace=new Tr(this),this.program=new kr(this),this.activeTexture=new Ar(this),this.viewport=new Mr(this),this.bindFramebuffer=new Sr(this),this.bindRenderbuffer=new Er(this),this.bindTexture=new Cr(this),this.bindVertexBuffer=new Lr(this),this.bindElementBuffer=new Ir(this),this.bindVertexArray=new Pr(this),this.pixelStoreUnpack=new zr(this),this.pixelStoreUnpackPremultiplyAlpha=new Or(this),this.pixelStoreUnpackFlipY=new Dr(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.maxTextureSize=t.getParameter(t.MAX_TEXTURE_SIZE),nr(t)){this.HALF_FLOAT=t.HALF_FLOAT;const n=t.getExtension("EXT_color_buffer_half_float");this.RGBA16F=null!==(e=t.RGBA16F)&&void 0!==e?e:null==n?void 0:n.RGBA16F_EXT,this.RGB16F=null!==(r=t.RGB16F)&&void 0!==r?r:null==n?void 0:n.RGB16F_EXT,t.getExtension("EXT_color_buffer_float")}else{t.getExtension("EXT_color_buffer_half_float"),t.getExtension("OES_texture_half_float_linear");const e=t.getExtension("OES_texture_half_float");this.HALF_FLOAT=null==e?void 0:e.HALF_FLOAT_OES}}setDefault(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()}setDirty(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.bindVertexArray.dirty=!0,this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0}createIndexBuffer(t,e){return new Qe(this,t,e)}createVertexBuffer(t,e,r){return new er(this,t,e,r)}createRenderbuffer(t,e,r){const n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i}createFramebuffer(t,e,r,n){return new jr(this,t,e,r,n)}clear({color:t,depth:e,stencil:r}){const n=this.gl;let i=0;t&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(t),this.colorMask.set([!0,!0,!0,!0])),void 0!==e&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(e),this.depthMask.set(!0)),void 0!==r&&(i|=n.STENCIL_BUFFER_BIT,this.clearStencil.set(r),this.stencilMask.set(255)),n.clear(i)}setCullFace(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))}setDepthMode(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)}setStencilMode(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)}setColorMode(t){e.aF(t.blendFunction,Ur.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(t.blendFunction),this.blendColor.set(t.blendColor)),this.colorMask.set(t.mask)}createVertexArray(){var t;return nr(this.gl)?this.gl.createVertexArray():null===(t=this.gl.getExtension("OES_vertex_array_object"))||void 0===t?void 0:t.createVertexArrayOES()}deleteVertexArray(t){var e;return nr(this.gl)?this.gl.deleteVertexArray(t):null===(e=this.gl.getExtension("OES_vertex_array_object"))||void 0===e?void 0:e.deleteVertexArrayOES(t)}unbindVAO(){this.bindVertexArray.set(null)}}class qr{constructor(t,e,r){this.func=t,this.mask=e,this.range=r}}qr.ReadOnly=!1,qr.ReadWrite=!0,qr.disabled=new qr(519,qr.ReadOnly,[0,1]);const Hr=7680;class Gr{constructor(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a}}Gr.disabled=new Gr({func:519,mask:0},0,0,Hr,Hr,Hr);class Zr{constructor(t,e,r){this.enable=t,this.mode=e,this.frontFace=r}}let Wr;function Yr(t,r,n,i,a){const o=t.context,s=o.gl,l=t.useProgram("collisionBox"),c=[];let u=0,h=0;for(let f=0;f<i.length;f++){const p=i[f],d=r.getTile(p).getBucket(n);if(!d)continue;const m=a?d.textCollisionBox:d.iconCollisionBox,g=d.collisionCircleArray;if(g.length>0){const r=e.H();e.aR(r,d.placementInvProjMatrix,t.transform.glCoordMatrix),e.aR(r,r,d.placementViewportMatrix),c.push({circleArray:g,circleOffset:h,transform:p.posMatrix,invTransform:r,coord:p}),u+=g.length/4,h=u}m&&l.draw(o,s.LINES,qr.disabled,Gr.disabled,t.colorModeForRenderPass(),Zr.disabled,Le(t.transform,p.posMatrix),t.style.map.terrain&&t.style.map.terrain.getTerrainData(p),n.id,m.layoutVertexBuffer,m.indexBuffer,m.segments,null,t.transform.zoom,null,null,m.collisionVertexBuffer)}if(!a||!c.length)return;const f=t.useProgram("collisionCircle"),p=new e.aS;p.resize(4*u),p._trim();let d=0;for(const t of c)for(let e=0;e<t.circleArray.length/4;e++){const r=4*e,n=t.circleArray[r+0],i=t.circleArray[r+1],a=t.circleArray[r+2],o=t.circleArray[r+3];p.emplace(d++,n,i,a,o,0),p.emplace(d++,n,i,a,o,1),p.emplace(d++,n,i,a,o,2),p.emplace(d++,n,i,a,o,3)}(!Wr||Wr.length<2*u)&&(Wr=function(t){const r=2*t,n=new e.aU;n.resize(r),n._trim();for(let t=0;t<r;t++){const e=6*t;n.uint16[e+0]=4*t+0,n.uint16[e+1]=4*t+1,n.uint16[e+2]=4*t+2,n.uint16[e+3]=4*t+2,n.uint16[e+4]=4*t+3,n.uint16[e+5]=4*t+0}return n}(u));const m=o.createIndexBuffer(Wr,!0),g=o.createVertexBuffer(p,e.aT.members,!0);for(const r of c){const i=Ie(r.transform,r.invTransform,t.transform);f.draw(o,s.TRIANGLES,qr.disabled,Gr.disabled,t.colorModeForRenderPass(),Zr.disabled,i,t.style.map.terrain&&t.style.map.terrain.getTerrainData(r.coord),n.id,g,m,e.a0.simpleSegment(0,2*r.circleOffset,r.circleArray.length,r.circleArray.length/2),null,t.transform.zoom,null,null,null)}g.destroy(),m.destroy()}Zr.disabled=new Zr(!1,1029,2305),Zr.backCCW=new Zr(!0,1029,2305);const Xr=e.ao(new Float32Array(16));function $r(t,r,n,i,a){if("translucent"!==t.renderPass)return;const o=Gr.disabled,s=t.colorModeForRenderPass();(n._unevaluatedLayout.hasValue("text-variable-anchor")||n._unevaluatedLayout.hasValue("text-variable-anchor-offset"))&&function(t,r,n,i,a,o,s,l,c){const u=r.transform,h=ie(),f="map"===a,p="map"===o;for(const a of t){const t=i.getTile(a),o=t.getBucket(n);if(!o||!o.text||!o.text.segments.get().length)continue;const d=o.textSizeData,m=e.ah(d,u.zoom),g=Nt(t,1,r.transform.zoom),y=_t(a.posMatrix,p,f,r.transform,g),v="none"!==n.layout.get("icon-text-fit")&&o.hasIconData();if(m){const e=Math.pow(2,u.zoom-t.tileID.overscaledZ),n=r.style.map.terrain?(t,e)=>r.style.map.terrain.getElevation(a,t,e):null,i=h.translatePosition(u,t,s,l);Qr(o,f,p,c,u,y,a.posMatrix,e,m,v,h,i,a.toUnwrapped(),n)}}}(i,t,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&en(t,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&en(t,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Yr(t,r,n,i,!0),Yr(t,r,n,i,!1))}function Jr(t,r,n,i,a,o){const{horizontalAlign:s,verticalAlign:l}=e.av(t),c=-(s-.5)*r,u=-(l-.5)*n;return new e.P((c/a+i[0])*o,(u/a+i[1])*o)}function Kr(t,r,n,i,a,o){const s=r.tileAnchorPoint.add(new e.P(r.translation[0],r.translation[1]));if(r.pitchWithMap){let t=i.mult(o);return n||(t=t.rotate(-a)),wt(s.add(t),r.labelPlaneMatrix,r.getElevation).point}if(n){const e=It(r.tileAnchorPoint.x+1,r.tileAnchorPoint.y,r).point.sub(t),n=Math.atan(e.y/e.x)+(e.x<0?Math.PI:0);return t.add(i.rotate(n))}return t.add(i)}function Qr(t,r,n,i,a,o,s,l,c,u,h,f,p,d){const m=t.text.placedSymbolArray,g=t.text.dynamicLayoutVertexArray,y=t.icon.dynamicLayoutVertexArray,v={};g.clear();for(let y=0;y<m.length;y++){const x=m.get(y),_=t.allowVerticalPlacement&&!x.placedOrientation,b=x.hidden||!x.crossTileID||_?null:i[x.crossTileID];if(b){const i=new e.P(x.anchorX,x.anchorY),m={getElevation:d,width:a.width,height:a.height,labelPlaneMatrix:o,lineVertexArray:null,pitchWithMap:n,projection:h,projectionCache:null,tileAnchorPoint:i,translation:f,unwrappedTileID:p},y=n?wt(i,s,d):It(i.x,i.y,m),_=Tt(a.cameraToCenterDistance,y.signedDistanceFromCamera);let w=e.aj(t.textSizeData,c,x)*_/e.aq;n&&(w*=t.tilePixelRatio/l);const{width:T,height:k,anchor:A,textOffset:M,textBoxScale:S}=b,E=Jr(A,T,k,M,S,w),C=h.getPitchedTextCorrection(a,i.add(new e.P(f[0],f[1])),p),L=Kr(y.point,m,r,E,a.angle,C),I=t.allowVerticalPlacement&&x.placedOrientation===e.ai.vertical?Math.PI/2:0;for(let t=0;t<x.numGlyphs;t++)e.ak(g,L,I);u&&x.associatedIconIndex>=0&&(v[x.associatedIconIndex]={shiftedAnchor:L,angle:I})}else Rt(x.numGlyphs,g)}if(u){y.clear();const r=t.icon.placedSymbolArray;for(let t=0;t<r.length;t++){const n=r.get(t);if(n.hidden)Rt(n.numGlyphs,y);else{const r=v[t];if(r)for(let t=0;t<n.numGlyphs;t++)e.ak(y,r.shiftedAnchor,r.angle);else Rt(n.numGlyphs,y)}}t.icon.dynamicLayoutVertexBuffer.updateData(y)}t.text.dynamicLayoutVertexBuffer.updateData(g)}function tn(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function en(t,r,n,i,a,o,s,l,c,u,h,f){const p=t.context,d=p.gl,m=t.transform,g=ie(),y="map"===l,v="map"===c,x="viewport"!==l&&"point"!==n.layout.get("symbol-placement"),_=y&&!v&&!x,b=!v&&x,w=!n.layout.get("symbol-sort-key").isConstant();let T=!1;const k=t.depthModeForSublayer(0,qr.ReadOnly),A=n._unevaluatedLayout.hasValue("text-variable-anchor")||n._unevaluatedLayout.hasValue("text-variable-anchor-offset"),M=[],S=g.getCircleRadiusCorrection(m);for(const l of i){const i=r.getTile(l),c=i.getBucket(n);if(!c)continue;const h=a?c.text:c.icon;if(!h||!h.segments.get().length||!h.hasVisibleVertices)continue;const f=h.programConfigurations.get(n.id),p=a||c.sdfIcons,k=a?c.textSizeData:c.iconSizeData,E=v||0!==m.pitch,C=t.useProgram(tn(p,a,c),f),L=e.ah(k,m.zoom),I=t.style.map.terrain&&t.style.map.terrain.getTerrainData(l);let P,z,O,D,R=[0,0],F=null;if(a){if(z=i.glyphAtlasTexture,O=d.LINEAR,P=i.glyphAtlasTexture.size,c.iconsInText){R=i.imageAtlasTexture.size,F=i.imageAtlasTexture;const e="composite"===k.kind||"camera"===k.kind;D=E||t.options.rotating||t.options.zooming||e?d.LINEAR:d.NEAREST}}else{const e=1!==n.layout.get("icon-size").constantOr(0)||c.iconsNeedLinear;z=i.imageAtlasTexture,O=p||t.options.rotating||t.options.zooming||e||E?d.LINEAR:d.NEAREST,P=i.imageAtlasTexture.size}const B=Nt(i,1,t.transform.zoom),N=b?l.posMatrix:Xr,j=_t(N,v,y,t.transform,B),U=bt(N,v,y,t.transform,B),V=bt(l.posMatrix,v,y,t.transform,B),q=g.translatePosition(t.transform,i,o,s),H=A&&c.hasTextData(),G="none"!==n.layout.get("icon-text-fit")&&H&&c.hasIconData();if(x){const e=t.style.map.terrain?(e,r)=>t.style.map.terrain.getElevation(l,e,r):null,r="map"===n.layout.get("text-rotation-alignment");At(c,l.posMatrix,t,a,j,V,v,u,r,g,l.toUnwrapped(),m.width,m.height,q,e)}const Z=l.posMatrix,W=a&&A||G,Y=x||W?Xr:j,X=U,$=p&&0!==n.paint.get(a?"text-halo-width":"icon-halo-width").constantOr(1);let J;J=p?c.iconsInText?Xe(k.kind,L,_,v,x,W,t,Z,Y,X,q,P,R,S):Ye(k.kind,L,_,v,x,W,t,Z,Y,X,q,a,P,!0,S):We(k.kind,L,_,v,x,W,t,Z,Y,X,q,a,P,S);const K={program:C,buffers:h,uniformValues:J,atlasTexture:z,atlasTextureIcon:F,atlasInterpolation:O,atlasInterpolationIcon:D,isSDF:p,hasHalo:$};if(w&&c.canOverlap){T=!0;const t=h.segments.get();for(const r of t)M.push({segments:new e.a0([r]),sortKey:r.sortKey,state:K,terrainData:I})}else M.push({segments:h.segments,sortKey:0,state:K,terrainData:I})}T&&M.sort(((t,e)=>t.sortKey-e.sortKey));for(const e of M){const r=e.state;if(p.activeTexture.set(d.TEXTURE0),r.atlasTexture.bind(r.atlasInterpolation,d.CLAMP_TO_EDGE),r.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),r.atlasTextureIcon&&r.atlasTextureIcon.bind(r.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),r.isSDF){const i=r.uniformValues;r.hasHalo&&(i.u_is_halo=1,rn(r.buffers,e.segments,n,t,r.program,k,h,f,i,e.terrainData)),i.u_is_halo=0}rn(r.buffers,e.segments,n,t,r.program,k,h,f,r.uniformValues,e.terrainData)}}function rn(t,e,r,n,i,a,o,s,l,c){const u=n.context,h=u.gl;i.draw(u,h.TRIANGLES,a,o,s,Zr.disabled,l,c,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function nn(t,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===t.renderPass){const a=t.context,o=a.gl,s=Gr.disabled,l=new Ur([o.ONE,o.ONE],e.aN.transparent,[!0,!0,!0,!0]);(function(t,e,r){const n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);let i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{const a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1,!1),function(t,e,r,n){var i,a;const o=t.gl,s=null!==(i=t.HALF_FLOAT)&&void 0!==i?i:o.UNSIGNED_BYTE,l=null!==(a=t.RGBA16F)&&void 0!==a?a:o.RGBA;o.texImage2D(o.TEXTURE_2D,0,l,e.width/4,e.height/4,0,o.RGBA,s,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,t,n),a.clear({color:e.aN.transparent});for(let e=0;e<i.length;e++){const c=i[e];if(r.hasRenderableParent(c))continue;const u=r.getTile(c),h=u.getBucket(n);if(!h)continue;const f=h.programConfigurations.get(n.id),p=t.useProgram("heatmap",f),{zoom:d}=t.transform;p.draw(a,o.TRIANGLES,qr.disabled,s,l,Zr.disabled,Oe(c.posMatrix,u,d,n.paint.get("heatmap-intensity")),null,n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,t.transform.zoom,f)}a.viewport.set([0,0,t.width,t.height])}else"translucent"===t.renderPass&&(t.context.setColorMode(t.colorModeForRenderPass()),function(t,e){const r=t.context,n=r.gl,i=e.heatmapFbo;if(!i)return;r.activeTexture.set(n.TEXTURE0),n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),r.activeTexture.set(n.TEXTURE1);let a=e.colorRampTexture;a||(a=e.colorRampTexture=new w(r,e.colorRamp,n.RGBA)),a.bind(n.LINEAR,n.CLAMP_TO_EDGE),t.useProgram("heatmapTexture").draw(r,n.TRIANGLES,qr.disabled,Gr.disabled,t.colorModeForRenderPass(),Zr.disabled,De(t,e,0,1),null,e.id,t.viewportBuffer,t.quadTriangleIndexBuffer,t.viewportSegments,e.paint,t.transform.zoom)}(t,n))}function an(t,e,r,n,i){if(!r||!n||!n.imageAtlas)return;const a=n.imageAtlas.patternPositions;let o=a[r.to.toString()],s=a[r.from.toString()];if(!o&&s&&(o=s),!s&&o&&(s=o),!o||!s){const t=i.getPaintProperty(e);o=a[t],s=a[t]}o&&s&&t.setConstantPatternPositions(o,s)}function on(t,e,r,n,i,a,o){const s=t.context.gl,l="fill-pattern",c=r.paint.get(l),u=c&&c.constantOr(1),h=r.getCrossfadeParameters();let f,p,d,m,g;o?(p=u&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",f=s.LINES):(p=u?"fillPattern":"fill",f=s.TRIANGLES);const y=c.constantOr(null);for(const c of n){const n=e.getTile(c);if(u&&!n.patternsLoaded())continue;const v=n.getBucket(r);if(!v)continue;const x=v.programConfigurations.get(r.id),_=t.useProgram(p,x),b=t.style.map.terrain&&t.style.map.terrain.getTerrainData(c);u&&(t.context.activeTexture.set(s.TEXTURE0),n.imageAtlasTexture.bind(s.LINEAR,s.CLAMP_TO_EDGE),x.updatePaintBuffers(h)),an(x,l,y,n,r);const w=b?c:null,T=w?w.posMatrix:c.posMatrix,k=t.translatePosMatrix(T,n,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){m=v.indexBuffer2,g=v.segments2;const e=[s.drawingBufferWidth,s.drawingBufferHeight];d="fillOutlinePattern"===p&&u?Ee(k,t,h,n,e):Se(k,e)}else m=v.indexBuffer,g=v.segments,d=u?Me(k,t,h,n):Ae(k);_.draw(t.context,f,i,t.stencilModeForClipping(c),a,Zr.disabled,d,b,r.id,v.layoutVertexBuffer,m,g,r.paint,t.transform.zoom,x)}}function sn(t,e,r,n,i,a,o){const s=t.context,l=s.gl,c="fill-extrusion-pattern",u=r.paint.get(c),h=u.constantOr(1),f=r.getCrossfadeParameters(),p=r.paint.get("fill-extrusion-opacity"),d=u.constantOr(null);for(const u of n){const n=e.getTile(u),m=n.getBucket(r);if(!m)continue;const g=t.style.map.terrain&&t.style.map.terrain.getTerrainData(u),y=m.programConfigurations.get(r.id),v=t.useProgram(h?"fillExtrusionPattern":"fillExtrusion",y);h&&(t.context.activeTexture.set(l.TEXTURE0),n.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f)),an(y,c,d,n,r);const x=t.translatePosMatrix(u.posMatrix,n,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),_=r.paint.get("fill-extrusion-vertical-gradient"),b=h?ke(x,t,_,p,u,f,n):Te(x,t,_,p);v.draw(s,s.gl.TRIANGLES,i,a,o,Zr.backCCW,b,g,r.id,m.layoutVertexBuffer,m.indexBuffer,m.segments,r.paint,t.transform.zoom,y,t.style.map.terrain&&m.centroidVertexBuffer)}}function ln(t,e,r,n,i,a,o){const s=t.context,l=s.gl,c=r.fbo;if(!c)return;const u=t.useProgram("hillshade"),h=t.style.map.terrain&&t.style.map.terrain.getTerrainData(e);s.activeTexture.set(l.TEXTURE0),l.bindTexture(l.TEXTURE_2D,c.colorAttachment.get());const f=h?e:null;u.draw(s,l.TRIANGLES,i,a,o,Zr.disabled,Re(t,r,n,f),h,n.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}function cn(t,e,r,n,i,a){const o=t.context,s=o.gl,l=e.dem;if(l&&l.data){const c=l.dim,u=l.stride,h=l.getPixels();if(o.activeTexture.set(s.TEXTURE1),o.pixelStoreUnpackPremultiplyAlpha.set(!1),e.demTexture=e.demTexture||t.getTileTexture(u),e.demTexture){const t=e.demTexture;t.update(h,{premultiply:!1}),t.bind(s.NEAREST,s.CLAMP_TO_EDGE)}else e.demTexture=new w(o,h,s.RGBA,{premultiply:!1}),e.demTexture.bind(s.NEAREST,s.CLAMP_TO_EDGE);o.activeTexture.set(s.TEXTURE0);let f=e.fbo;if(!f){const t=new w(o,{width:c,height:c,data:null},s.RGBA);t.bind(s.LINEAR,s.CLAMP_TO_EDGE),f=e.fbo=o.createFramebuffer(c,c,!0,!1),f.colorAttachment.set(t.texture)}o.bindFramebuffer.set(f.framebuffer),o.viewport.set([0,0,c,c]),t.useProgram("hillshadePrepare").draw(o,s.TRIANGLES,n,i,a,Zr.disabled,Fe(e.tileID,l),null,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments),e.needsHillshadePrepare=!1}}function un(t,r,n,i,o,s){const l=i.paint.get("raster-fade-duration");if(!s&&l>0){const i=a.now(),s=(i-t.timeAdded)/l,c=r?(i-r.timeAdded)/l:-1,u=n.getSource(),h=o.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),f=!r||Math.abs(r.tileID.overscaledZ-h)>Math.abs(t.tileID.overscaledZ-h),p=f&&t.refreshedUponExpiration?1:e.ad(f?s:1-c,0,1);return t.refreshedUponExpiration&&s>=1&&(t.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}const hn=new e.aN(1,0,0,1),fn=new e.aN(0,1,0,1),pn=new e.aN(0,0,1,1),dn=new e.aN(1,0,1,1),mn=new e.aN(0,1,1,1);function gn(t){const e=t.transform.padding;yn(t,t.transform.height-(e.top||0),3,hn),yn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,pn),vn(t,t.transform.width-(e.right||0),3,dn);const r=t.transform.centerPoint;!function(t,e,r,n){const i=20,a=2;xn(t,e-a/2,r-i/2,a,i,n),xn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,mn)}function yn(t,e,r,n){xn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){xn(t,e-r/2,0,r,t.transform.height,n)}function xn(t,e,r,n,i,a){const o=t.context,s=o.gl;s.enable(s.SCISSOR_TEST),s.scissor(e*t.pixelRatio,r*t.pixelRatio,n*t.pixelRatio,i*t.pixelRatio),o.clear({color:a}),s.disable(s.SCISSOR_TEST)}function _n(t,r,n){const i=t.context,a=i.gl,o=n.posMatrix,s=t.useProgram("debug"),l=qr.disabled,c=Gr.disabled,u=t.colorModeForRenderPass(),h="$debug",f=t.style.map.terrain&&t.style.map.terrain.getTerrainData(n);i.activeTexture.set(a.TEXTURE0);const p=r.getTileByID(n.key).latestRawTileData,d=p&&p.byteLength||0,m=Math.floor(d/1024),g=r.getTile(n).tileSize,y=512/Math.min(g,512)*(n.overscaledZ/t.transform.zoom)*.5;let v=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(v+=` => ${n.overscaledZ}`),function(t,e){t.initDebugOverlayCanvas();const r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(t,`${v} ${m}kB`),s.draw(i,a.TRIANGLES,l,c,Ur.alphaBlended,Zr.disabled,Pe(o,e.aN.transparent,y),null,h,t.debugBuffer,t.quadTriangleIndexBuffer,t.debugSegments),s.draw(i,a.LINE_STRIP,l,c,u,Zr.disabled,Pe(o,e.aN.red),f,h,t.debugBuffer,t.tileBorderIndexBuffer,t.debugSegments)}function bn(t,e,r){const n=t.context,i=n.gl,a=t.colorModeForRenderPass(),o=new qr(i.LEQUAL,qr.ReadWrite,t.depthRangeFor3D),s=t.useProgram("terrain"),l=e.getTerrainMesh();n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height]);for(const c of r){const r=t.renderToTexture.getTexture(c),u=e.getTerrainData(c.tileID);n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,r.texture);const h=t.transform.calculatePosMatrix(c.tileID.toUnwrapped()),f=e.getMeshFrameDelta(t.transform.zoom),p=t.transform.calculateFogMatrix(c.tileID.toUnwrapped()),d=xe(h,f,p,t.style.sky,t.transform.pitch);s.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,d,u,"terrain",l.vertexBuffer,l.indexBuffer,l.segments)}}class wn{constructor(t,e,r){this.vertexBuffer=t,this.indexBuffer=e,this.segments=r}destroy(){this.vertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.vertexBuffer=null,this.indexBuffer=null,this.segments=null}}class Tn{constructor(t,r){this.context=new Vr(t),this.transform=r,this._tileTextures={},this.terrainFacilitator={dirty:!0,matrix:e.ao(new Float64Array(16)),renderTime:0},this.setup(),this.numSublayers=dt.maxUnderzooming+dt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new he}resize(t,e,r){if(this.width=Math.floor(t*r),this.height=Math.floor(e*r),this.pixelRatio=r,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(const t of this.style._order)this.style._layers[t].resize()}setup(){const t=this.context,r=new e.aX;r.emplaceBack(0,0),r.emplaceBack(e.X,0),r.emplaceBack(0,e.X),r.emplaceBack(e.X,e.X),this.tileExtentBuffer=t.createVertexBuffer(r,me.members),this.tileExtentSegments=e.a0.simpleSegment(0,0,4,2);const n=new e.aX;n.emplaceBack(0,0),n.emplaceBack(e.X,0),n.emplaceBack(0,e.X),n.emplaceBack(e.X,e.X),this.debugBuffer=t.createVertexBuffer(n,me.members),this.debugSegments=e.a0.simpleSegment(0,0,4,5);const i=new e.$;i.emplaceBack(0,0,0,0),i.emplaceBack(e.X,0,e.X,0),i.emplaceBack(0,e.X,0,e.X),i.emplaceBack(e.X,e.X,e.X,e.X),this.rasterBoundsBuffer=t.createVertexBuffer(i,et.members),this.rasterBoundsSegments=e.a0.simpleSegment(0,0,4,2);const a=new e.aX;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=t.createVertexBuffer(a,me.members),this.viewportSegments=e.a0.simpleSegment(0,0,4,2);const o=new e.aZ;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=t.createIndexBuffer(o);const s=new e.aY;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=t.createIndexBuffer(s);const l=this.context.gl;this.stencilClearMode=new Gr({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)}clearStencil(){const t=this.context,r=t.gl;this.nextStencilID=1,this.currentStencilSource=void 0;const n=e.H();e.aQ(n,0,this.width,this.height,0,0,1),e.K(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(t,r.TRIANGLES,qr.disabled,this.stencilClearMode,Ur.disabled,Zr.disabled,ze(n),null,"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(t,e){if(this.currentStencilSource===t.source||!t.isTileClipped()||!e||!e.length)return;this.currentStencilSource=t.source;const r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Ur.disabled),r.setDepthMode(qr.disabled);const i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(const t of e){const e=this._tileClippingMaskIDs[t.key]=this.nextStencilID++,a=this.style.map.terrain&&this.style.map.terrain.getTerrainData(t);i.draw(r,n.TRIANGLES,qr.disabled,new Gr({func:n.ALWAYS,mask:0},e,255,n.KEEP,n.KEEP,n.REPLACE),Ur.disabled,Zr.disabled,ze(t.posMatrix),a,"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();const t=this.nextStencilID++,e=this.context.gl;return new Gr({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)}stencilModeForClipping(t){const e=this.context.gl;return new Gr({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)}stencilConfigForOverlap(t){const e=this.context.gl,r=t.sort(((t,e)=>e.overscaledZ-t.overscaledZ)),n=r[r.length-1].overscaledZ,i=r[0].overscaledZ-n+1;if(i>1){this.currentStencilSource=void 0,this.nextStencilID+i>256&&this.clearStencil();const t={};for(let r=0;r<i;r++)t[r+n]=new Gr({func:e.GEQUAL,mask:255},r+this.nextStencilID,255,e.KEEP,e.KEEP,e.REPLACE);return this.nextStencilID+=i,[t,r]}return[{[n]:Gr.disabled},r]}colorModeForRenderPass(){const t=this.context.gl;if(this._showOverdrawInspector){const r=1/8;return new Ur([t.CONSTANT_COLOR,t.ONE],new e.aN(r,r,r,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?Ur.unblended:Ur.alphaBlended}depthModeForSublayer(t,e,r){if(!this.opaquePassEnabledForLayer())return qr.disabled;const n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new qr(r||this.context.gl.LEQUAL,e,[n,n])}opaquePassEnabledForLayer(){return this.currentLayer<this.opaquePassCutoff}render(t,r){var n;this.style=t,this.options=r,this.lineAtlas=t.lineAtlas,this.imageManager=t.imageManager,this.glyphManager=t.glyphManager,this.symbolFadeChange=t.placement.symbolFadeChange(a.now()),this.imageManager.beginFrame();const i=this.style._order,o=this.style.sourceCaches,s={},l={},c={};for(const t in o){const e=o[t];e.used&&e.prepare(this.context),s[t]=e.getVisibleCoordinates(),l[t]=s[t].slice().reverse(),c[t]=e.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(let t=0;t<i.length;t++){const e=i[t];if(this.style._layers[e].is3D()){this.opaquePassCutoff=t;break}}this.maybeDrawDepthAndCoords(!1),this.renderToTexture&&(this.renderToTexture.prepareForRender(this.style,this.transform.zoom),this.opaquePassCutoff=0),this.renderPass="offscreen";for(const t of i){const e=this.style._layers[t];if(!e.hasOffscreenPass()||e.isHidden(this.transform.zoom))continue;const r=l[e.source];("custom"===e.type||r.length)&&this.renderLayer(this,o[e.source],e,r)}if(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?e.aN.black:e.aN.transparent,depth:1}),this.clearStencil(),(null===(n=this.style.stylesheet)||void 0===n?void 0:n.sky)&&function(t,r){const n=t.context,i=n.gl,a=((t,e,r)=>({u_sky_color:t.properties.get("sky-color"),u_horizon_color:t.properties.get("horizon-color"),u_horizon:(e.height/2+e.getHorizon())*r,u_sky_horizon_blend:t.properties.get("sky-horizon-blend")*e.height/2*r}))(r,t.style.map.transform,t.pixelRatio),o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=Gr.disabled,l=t.colorModeForRenderPass(),c=t.useProgram("sky");if(!r.mesh){const t=new e.aX;t.emplaceBack(-1,-1),t.emplaceBack(1,-1),t.emplaceBack(1,1),t.emplaceBack(-1,1);const i=new e.aY;i.emplaceBack(0,1,2),i.emplaceBack(0,2,3),r.mesh=new wn(n.createVertexBuffer(t,me.members),n.createIndexBuffer(i),e.a0.simpleSegment(0,0,t.length,i.length))}c.draw(n,i.TRIANGLES,o,s,l,Zr.disabled,a,void 0,"sky",r.mesh.vertexBuffer,r.mesh.indexBuffer,r.mesh.segments)}(this,this.style.sky),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(t._order.length+2)*this.numSublayers*this.depthEpsilon],!this.renderToTexture)for(this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){const t=this.style._layers[i[this.currentLayer]],e=o[t.source],r=s[t.source];this._renderTileClippingMasks(t,r),this.renderLayer(this,e,t,r)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){const t=this.style._layers[i[this.currentLayer]],e=o[t.source];if(this.renderToTexture&&this.renderToTexture.renderLayer(t))continue;const r=("symbol"===t.type?c:l)[t.source];this._renderTileClippingMasks(t,s[t.source]),this.renderLayer(this,e,t,r)}if(this.options.showTileBoundaries){const t=function(t,e){let r=null;const n=Object.values(t._layers).flatMap((r=>r.source&&!r.isHidden(e)?[t.sourceCaches[r.source]]:[])),i=n.filter((t=>"vector"===t.getSource().type)),a=n.filter((t=>"vector"!==t.getSource().type)),o=t=>{(!r||r.getSource().maxzoom<t.getSource().maxzoom)&&(r=t)};return i.forEach((t=>o(t))),r||a.forEach((t=>o(t))),r}(this.style,this.transform.zoom);t&&function(t,e,r){for(let n=0;n<r.length;n++)_n(t,e,r[n])}(this,t,t.getVisibleCoordinates())}this.options.showPadding&&gn(this),this.context.setDefault()}maybeDrawDepthAndCoords(t){if(!this.style||!this.style.map||!this.style.map.terrain)return;const r=this.terrainFacilitator.matrix,n=this.transform.modelViewProjectionMatrix;let i=this.terrainFacilitator.dirty;i||(i=t?!e.a_(r,n):!e.a$(r,n)),i||(i=this.style.map.terrain.sourceCache.tilesAfterTime(this.terrainFacilitator.renderTime).length>0),i&&(e.b0(r,n),this.terrainFacilitator.renderTime=Date.now(),this.terrainFacilitator.dirty=!1,function(t,r){const n=t.context,i=n.gl,a=Ur.unblended,o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=r.getTerrainMesh(),l=r.sourceCache.getRenderableTiles(),c=t.useProgram("terrainDepth");n.bindFramebuffer.set(r.getFramebuffer("depth").framebuffer),n.viewport.set([0,0,t.width/devicePixelRatio,t.height/devicePixelRatio]),n.clear({color:e.aN.transparent,depth:1});for(const e of l){const l=r.getTerrainData(e.tileID),u={u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped()),u_ele_delta:r.getMeshFrameDelta(t.transform.zoom)};c.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,u,l,"terrain",s.vertexBuffer,s.indexBuffer,s.segments)}n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height])}(this,this.style.map.terrain),function(t,r){const n=t.context,i=n.gl,a=Ur.unblended,o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=r.getTerrainMesh(),l=r.getCoordsTexture(),c=r.sourceCache.getRenderableTiles(),u=t.useProgram("terrainCoords");n.bindFramebuffer.set(r.getFramebuffer("coords").framebuffer),n.viewport.set([0,0,t.width/devicePixelRatio,t.height/devicePixelRatio]),n.clear({color:e.aN.transparent,depth:1}),r.coordsIndex=[];for(const e of c){const c=r.getTerrainData(e.tileID);n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,l.texture);const h={u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped()),u_terrain_coords_id:(255-r.coordsIndex.length)/255,u_texture:0,u_ele_delta:r.getMeshFrameDelta(t.transform.zoom)};u.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,h,c,"terrain",s.vertexBuffer,s.indexBuffer,s.segments),r.coordsIndex.push(e.tileID.key)}n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height])}(this,this.style.map.terrain))}renderLayer(t,r,n,i){if(!n.isHidden(this.transform.zoom)&&("background"===n.type||"custom"===n.type||(i||[]).length))switch(this.id=n.id,n.type){case"symbol":$r(t,r,n,i,this.style.placement.variableOffsets);break;case"circle":!function(t,r,n,i){if("translucent"!==t.renderPass)return;const a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=!n.layout.get("circle-sort-key").isConstant();if(0===a.constantOr(1)&&(0===o.constantOr(1)||0===s.constantOr(1)))return;const c=t.context,u=c.gl,h=t.depthModeForSublayer(0,qr.ReadOnly),f=Gr.disabled,p=t.colorModeForRenderPass(),d=[];for(let a=0;a<i.length;a++){const o=i[a],s=r.getTile(o),c=s.getBucket(n);if(!c)continue;const u=c.programConfigurations.get(n.id),h=t.useProgram("circle",u),f=c.layoutVertexBuffer,p=c.indexBuffer,m=t.style.map.terrain&&t.style.map.terrain.getTerrainData(o),g={programConfiguration:u,program:h,layoutVertexBuffer:f,indexBuffer:p,uniformValues:Ce(t,o,s,n),terrainData:m};if(l){const t=c.segments.get();for(const r of t)d.push({segments:new e.a0([r]),sortKey:r.sortKey,state:g})}else d.push({segments:c.segments,sortKey:0,state:g})}l&&d.sort(((t,e)=>t.sortKey-e.sortKey));for(const e of d){const{programConfiguration:r,program:i,layoutVertexBuffer:a,indexBuffer:o,uniformValues:s,terrainData:l}=e.state,d=e.segments;i.draw(c,u.TRIANGLES,h,f,p,Zr.disabled,s,l,n.id,a,o,d,n.paint,t.transform.zoom,r)}}(t,r,n,i);break;case"heatmap":nn(t,r,n,i);break;case"line":!function(t,r,n,i){if("translucent"!==t.renderPass)return;const a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0===a.constantOr(1)||0===o.constantOr(1))return;const s=t.depthModeForSublayer(0,qr.ReadOnly),l=t.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),h=u.constantOr(1),f=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=h?"linePattern":c?"lineSDF":f?"lineGradient":"line",m=t.context,g=m.gl;let y=!0;for(const a of i){const i=r.getTile(a);if(h&&!i.patternsLoaded())continue;const o=i.getBucket(n);if(!o)continue;const v=o.programConfigurations.get(n.id),x=t.context.program.get(),_=t.useProgram(d,v),b=y||_.program!==x,T=t.style.map.terrain&&t.style.map.terrain.getTerrainData(a),k=u.constantOr(null);if(k&&i.imageAtlas){const t=i.imageAtlas,e=t.patternPositions[k.to.toString()],r=t.patternPositions[k.from.toString()];e&&r&&v.setConstantPatternPositions(e,r)}const A=T?a:null,M=h?Ue(t,i,n,p,A):c?Ve(t,i,n,c,p,A):f?je(t,i,n,o.lineClipsArray.length,A):Ne(t,i,n,A);if(h)m.activeTexture.set(g.TEXTURE0),i.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),v.updatePaintBuffers(p);else if(c&&(b||t.lineAtlas.dirty))m.activeTexture.set(g.TEXTURE0),t.lineAtlas.bind(m);else if(f){const i=o.gradients[n.id];let s=i.texture;if(n.gradientVersion!==i.version){let l=256;if(n.stepInterpolant){const n=r.getSource().maxzoom,i=a.canonical.z===n?Math.ceil(1<<t.transform.maxZoom-a.canonical.z):1,s=o.maxLineLength/e.X*1024*i;l=e.ad(e.aV(s),256,m.maxTextureSize)}i.gradient=e.aW({expression:n.gradientExpression(),evaluationKey:"lineProgress",resolution:l,image:i.gradient||void 0,clips:o.lineClipsArray}),i.texture?i.texture.update(i.gradient):i.texture=new w(m,i.gradient,g.RGBA),i.version=n.gradientVersion,s=i.texture}m.activeTexture.set(g.TEXTURE0),s.bind(n.stepInterpolant?g.NEAREST:g.LINEAR,g.CLAMP_TO_EDGE)}_.draw(m,g.TRIANGLES,s,t.stencilModeForClipping(a),l,Zr.disabled,M,T,n.id,o.layoutVertexBuffer,o.indexBuffer,o.segments,n.paint,t.transform.zoom,v,o.layoutVertexBuffer2),y=!1}}(t,r,n,i);break;case"fill":!function(t,r,n,i){const a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0===o.constantOr(1))return;const s=t.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=t.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(e.aN.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(t.renderPass===c){const e=t.depthModeForSublayer(1,"opaque"===t.renderPass?qr.ReadWrite:qr.ReadOnly);on(t,r,n,i,e,s,!1)}if("translucent"===t.renderPass&&n.paint.get("fill-antialias")){const e=t.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,qr.ReadOnly);on(t,r,n,i,e,s,!0)}}(t,r,n,i);break;case"fill-extrusion":!function(t,e,r,n){const i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){const a=new qr(t.context.gl.LEQUAL,qr.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))sn(t,e,r,n,a,Gr.disabled,Ur.disabled),sn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{const i=t.colorModeForRenderPass();sn(t,e,r,n,a,Gr.disabled,i)}}}(t,r,n,i);break;case"hillshade":!function(t,e,r,n){if("offscreen"!==t.renderPass&&"translucent"!==t.renderPass)return;const i=t.context,a=t.depthModeForSublayer(0,qr.ReadOnly),o=t.colorModeForRenderPass(),[s,l]="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n];for(const n of l){const i=e.getTile(n);void 0!==i.needsHillshadePrepare&&i.needsHillshadePrepare&&"offscreen"===t.renderPass?cn(t,i,r,a,Gr.disabled,o):"translucent"===t.renderPass&&ln(t,n,i,r,a,s[n.overscaledZ],o)}i.viewport.set([0,0,t.width,t.height])}(t,r,n,i);break;case"raster":!function(t,e,r,n){if("translucent"!==t.renderPass)return;if(0===r.paint.get("raster-opacity"))return;if(!n.length)return;const i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),[c,u]=o instanceof rt?[{},n]:t.stencilConfigForOverlap(n),h=u[u.length-1].overscaledZ,f=!t.options.moving;for(const n of u){const u=t.depthModeForSublayer(n.overscaledZ-h,1===r.paint.get("raster-opacity")?qr.ReadWrite:qr.ReadOnly,a.LESS),p=e.getTile(n);p.registerFadeDuration(r.paint.get("raster-fade-duration"));const d=e.findLoadedParent(n,0),m=e.findLoadedSibling(n),g=un(p,d||m||null,e,r,t.transform,t.style.map.terrain);let y,v;const x="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),p.texture.bind(x,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),d?(d.texture.bind(x,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),y=Math.pow(2,d.tileID.overscaledZ-p.tileID.overscaledZ),v=[p.tileID.canonical.x*y%1,p.tileID.canonical.y*y%1]):p.texture.bind(x,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),p.texture.useMipmap&&i.extTextureFilterAnisotropic&&t.transform.pitch>20&&a.texParameterf(a.TEXTURE_2D,i.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,i.extTextureFilterAnisotropicMax);const _=t.style.map.terrain&&t.style.map.terrain.getTerrainData(n),b=_?n:null,w=b?b.posMatrix:t.transform.calculatePosMatrix(n.toUnwrapped(),f),T=Ge(w,v||[0,0],y||1,g,r);o instanceof rt?s.draw(i,a.TRIANGLES,u,Gr.disabled,l,Zr.disabled,T,_,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,u,c[n.overscaledZ],l,Zr.disabled,T,_,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}(t,r,n,i);break;case"background":!function(t,e,r,n){const i=r.paint.get("background-color"),a=r.paint.get("background-opacity");if(0===a)return;const o=t.context,s=o.gl,l=t.transform,c=l.tileSize,u=r.paint.get("background-pattern");if(t.isPatternMissing(u))return;const h=!u&&1===i.a&&1===a&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass!==h)return;const f=Gr.disabled,p=t.depthModeForSublayer(0,"opaque"===h?qr.ReadWrite:qr.ReadOnly),d=t.colorModeForRenderPass(),m=t.useProgram(u?"backgroundPattern":"background"),g=n||l.coveringTiles({tileSize:c,terrain:t.style.map.terrain});u&&(o.activeTexture.set(s.TEXTURE0),t.imageManager.bind(t.context));const y=r.getCrossfadeParameters();for(const e of g){const l=n?e.posMatrix:t.transform.calculatePosMatrix(e.toUnwrapped()),h=u?Je(l,a,t,u,{tileID:e,tileSize:c},y):$e(l,a,i),g=t.style.map.terrain&&t.style.map.terrain.getTerrainData(e);m.draw(o,s.TRIANGLES,p,f,d,Zr.disabled,h,g,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}(t,0,n,i);break;case"custom":!function(t,e,r){const n=t.context,i=r.implementation;if("offscreen"===t.renderPass){const e=i.prerender;e&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),e.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Gr.disabled);const e="3d"===i.renderingMode?new qr(t.context.gl.LEQUAL,qr.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,qr.ReadOnly);n.setDepthMode(e),i.render(n.gl,t.transform.customLayerMatrix(),{farZ:t.transform.farZ,nearZ:t.transform.nearZ,fov:t.transform._fov,modelViewProjectionMatrix:t.transform.modelViewProjectionMatrix,projectionMatrix:t.transform.projectionMatrix}),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}(t,0,n)}}translatePosMatrix(t,r,n,i,a){if(!n[0]&&!n[1])return t;const o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){const t=Math.sin(o),e=Math.cos(o);n=[n[0]*e-n[1]*t,n[0]*t+n[1]*e]}const s=[a?n[0]:Nt(r,n[0],this.transform.zoom),a?n[1]:Nt(r,n[1],this.transform.zoom),0],l=new Float32Array(16);return e.J(l,t,s),l}saveTileTexture(t){const e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]}getTileTexture(t){const e=this._tileTextures[t];return e&&e.length>0?e.pop():null}isPatternMissing(t){if(!t)return!1;if(!t.from||!t.to)return!0;const e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r}useProgram(t,e){this.cache=this.cache||{};const r=t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"")+(this.style.map.terrain?"/terrain":"");return this.cache[r]||(this.cache[r]=new be(this.context,ge[t],e,Ke[t],this._showOverdrawInspector,this.style.map.terrain)),this.cache[r]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){const t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)}initDebugOverlayCanvas(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;const t=this.context.gl;this.debugOverlayTexture=new w(this.context,this.debugOverlayCanvas,t.RGBA)}}destroy(){this.debugOverlayTexture&&this.debugOverlayTexture.destroy()}overLimit(){const{drawingBufferWidth:t,drawingBufferHeight:e}=this.context.gl;return this.width!==t||this.height!==e}}class kn{constructor(t,e){this.points=t,this.planes=e}static fromInvProjectionMatrix(t,r,n){const i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((n=>{const a=1/(n=e.ag([],n,t))[3]/r*i;return e.b1(n,n,[a,a,1/n[3],a])})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((t=>{const e=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t}([],function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t}([],y([],a[t[0]],a[t[1]]),y([],a[t[2]],a[t[1]]))),r=(n=e,i=a[t[1]],-(n[0]*i[0]+n[1]*i[1]+n[2]*i[2]));var n,i;return e.concat(r)}));return new kn(a,o)}}class An{constructor(t,e){this.min=t,this.max=e,this.center=function(t,e,r){return t[0]=e[0]*r,t[1]=e[1]*r,t[2]=e[2]*r,t}([],function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t}([],this.min,this.max),.5)}quadrant(t){const e=[t%2==0,t<2],r=m(this.min),n=m(this.max);for(let t=0;t<e.length;t++)r[t]=e[t]?this.min[t]:this.center[t],n[t]=e[t]?this.center[t]:this.max[t];return n[2]=this.max[2],new An(r,n)}distanceX(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]}distanceY(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]}intersects(t){const r=[[this.min[0],this.min[1],this.min[2],1],[this.max[0],this.min[1],this.min[2],1],[this.max[0],this.max[1],this.min[2],1],[this.min[0],this.max[1],this.min[2],1],[this.min[0],this.min[1],this.max[2],1],[this.max[0],this.min[1],this.max[2],1],[this.max[0],this.max[1],this.max[2],1],[this.min[0],this.max[1],this.max[2],1]];let n=!0;for(let i=0;i<t.planes.length;i++){const a=t.planes[i];let o=0;for(let t=0;t<r.length;t++)e.b2(a,r[t])>=0&&o++;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(let e=0;e<3;e++){let r=Number.MAX_VALUE,n=-Number.MAX_VALUE;for(let i=0;i<t.points.length;i++){const a=t.points[i][e]-this.min[e];r=Math.min(r,a),n=Math.max(n,a)}if(n<0||r>this.max[e]-this.min[e])return 0}return 1}}class Mn{constructor(t=0,e=0,r=0,n=0){if(isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n}interpolate(t,r,n){return null!=r.top&&null!=t.top&&(this.top=e.z.number(t.top,r.top,n)),null!=r.bottom&&null!=t.bottom&&(this.bottom=e.z.number(t.bottom,r.bottom,n)),null!=r.left&&null!=t.left&&(this.left=e.z.number(t.left,r.left,n)),null!=r.right&&null!=t.right&&(this.right=e.z.number(t.right,r.right,n)),this}getCenter(t,r){const n=e.ad((this.left+t-this.right)/2,0,t),i=e.ad((this.top+r-this.bottom)/2,0,r);return new e.P(n,i)}equals(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right}clone(){return new Mn(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}const Sn=85.051129;class En{constructor(t,r,n,i,a){this.tileSize=512,this._renderWorldCopies=void 0===a||!!a,this._minZoom=t||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new e.N(0,0),this._elevation=0,this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Mn,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={},this.minElevationForCurrentTile=0}clone(){const t=new En(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.apply(this),t}apply(t){this.tileSize=t.tileSize,this.latRange=t.latRange,this.width=t.width,this.height=t.height,this._center=t._center,this._elevation=t._elevation,this.minElevationForCurrentTile=t.minElevationForCurrentTile,this.zoom=t.zoom,this.angle=t.angle,this._fov=t._fov,this._pitch=t._pitch,this._unmodified=t._unmodified,this._edgeInsets=t._edgeInsets.clone(),this._calcMatrices()}get minZoom(){return this._minZoom}set minZoom(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))}get maxZoom(){return this._maxZoom}set maxZoom(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))}get minPitch(){return this._minPitch}set minPitch(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))}get maxPitch(){return this._maxPitch}set maxPitch(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))}get renderWorldCopies(){return this._renderWorldCopies}set renderWorldCopies(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t}get worldSize(){return this.tileSize*this.scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new e.P(this.width,this.height)}get bearing(){return-this.angle/Math.PI*180}set bearing(t){const r=-e.b3(t,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=function(){var t=new e.A(4);return e.A!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t}(),function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=Math.sin(r),l=Math.cos(r);t[0]=n*l+a*s,t[1]=i*l+o*s,t[2]=n*-s+a*l,t[3]=i*-s+o*l}(this.rotationMatrix,this.rotationMatrix,this.angle))}get pitch(){return this._pitch/Math.PI*180}set pitch(t){const r=e.ad(t,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())}get fov(){return this._fov/Math.PI*180}set fov(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())}get zoom(){return this._zoom}set zoom(t){const e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.tileZoom=Math.max(0,Math.floor(e)),this.scale=this.zoomScale(e),this._constrain(),this._calcMatrices())}get center(){return this._center}set center(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())}get elevation(){return this._elevation}set elevation(t){t!==this._elevation&&(this._elevation=t,this._constrain(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}set padding(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this.width,this.height)}isPaddingEqual(t){return this._edgeInsets.equals(t)}interpolatePadding(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()}coveringZoomLevel(t){const e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)}getVisibleUnwrappedCoordinates(t){const r=[new e.b4(0,t)];if(this._renderWorldCopies){const n=this.pointCoordinate(new e.P(0,0)),i=this.pointCoordinate(new e.P(this.width,0)),a=this.pointCoordinate(new e.P(this.width,this.height)),o=this.pointCoordinate(new e.P(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=1;for(let n=s-c;n<=l+c;n++)0!==n&&r.push(new e.b4(n,t))}return r}coveringTiles(t){var r,n;let i=this.coveringZoomLevel(t);const a=i;if(void 0!==t.minzoom&&i<t.minzoom)return[];void 0!==t.maxzoom&&i>t.maxzoom&&(i=t.maxzoom);const o=this.pointCoordinate(this.getCameraPoint()),s=e.Z.fromLngLat(this.center),l=Math.pow(2,i),c=[l*o.x,l*o.y,0],u=[l*s.x,l*s.y,0],h=kn.fromInvProjectionMatrix(this.invModelViewProjectionMatrix,this.worldSize,i);let f=t.minzoom||0;!t.terrain&&this.pitch<=60&&this._edgeInsets.top<.1&&(f=i);const p=t.terrain?2/Math.min(this.tileSize,t.tileSize)*this.tileSize:3,d=t=>({aabb:new An([t*l,0,0],[(t+1)*l,l,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}),m=[],g=[],y=i,x=t.reparseOverscaled?a:i;if(this._renderWorldCopies)for(let t=1;t<=3;t++)m.push(d(-t)),m.push(d(t));for(m.push(d(0));m.length>0;){const i=m.pop(),a=i.x,o=i.y;let s=i.fullyVisible;if(!s){const t=i.aabb.intersects(h);if(0===t)continue;s=2===t}const l=t.terrain?c:u,d=i.aabb.distanceX(l),_=i.aabb.distanceY(l),b=Math.max(Math.abs(d),Math.abs(_)),w=p+(1<<y-i.zoom)-2;if(i.zoom===y||b>w&&i.zoom>=f){const t=y-i.zoom,r=c[0]-.5-(a<<t),n=c[1]-.5-(o<<t);g.push({tileID:new e.S(i.zoom===y?x:i.zoom,i.wrap,i.zoom,a,o),distanceSq:v([u[0]-.5-a,u[1]-.5-o]),tileDistanceToCamera:Math.sqrt(r*r+n*n)})}else for(let l=0;l<4;l++){const c=(a<<1)+l%2,u=(o<<1)+(l>>1),h=i.zoom+1;let f=i.aabb.quadrant(l);if(t.terrain){const a=new e.S(h,i.wrap,h,c,u),o=t.terrain.getMinMaxElevation(a),s=null!==(r=o.minElevation)&&void 0!==r?r:this.elevation,l=null!==(n=o.maxElevation)&&void 0!==n?n:this.elevation;f=new An([f.min[0],f.min[1],s],[f.max[0],f.max[1],l])}m.push({aabb:f,zoom:h,x:c,y:u,wrap:i.wrap,fullyVisible:s})}}return g.sort(((t,e)=>t.distanceSq-e.distanceSq)).map((t=>t.tileID))}resize(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()}get unmodified(){return this._unmodified}zoomScale(t){return Math.pow(2,t)}scaleZoom(t){return Math.log(t)/Math.LN2}project(t){const r=e.ad(t.lat,-85.051129,Sn);return new e.P(e.O(t.lng)*this.worldSize,e.Q(r)*this.worldSize)}unproject(t){return new e.Z(t.x/this.worldSize,t.y/this.worldSize).toLngLat()}get point(){return this.project(this.center)}getCameraPosition(){return{lngLat:this.pointLocation(this.getCameraPoint()),altitude:Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter+this.elevation}}recalculateZoom(t){const r=this.elevation,n=Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter,i=this.pointLocation(this.centerPoint,t),a=t.getElevationForLngLatZoom(i,this.tileZoom);if(!(this.elevation-a))return;const o=n+r-a,s=Math.cos(this._pitch)*this.cameraToCenterDistance/o/e.b5(1,i.lat)/this.tileSize,l=this.scaleZoom(s);this._elevation=a,this._center=i,this.zoom=l}setLocationAtPoint(t,r){const n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(t),o=new e.Z(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())}locationPoint(t,e){return e?this.coordinatePoint(this.locationCoordinate(t),e.getElevationForLngLatZoom(t,this.tileZoom),this.pixelMatrix3D):this.coordinatePoint(this.locationCoordinate(t))}pointLocation(t,e){return this.coordinateLocation(this.pointCoordinate(t,e))}locationCoordinate(t){return e.Z.fromLngLat(t)}coordinateLocation(t){return t&&t.toLngLat()}pointCoordinate(t,r){if(r){const e=r.pointCoordinate(t);if(null!=e)return e}const n=[t.x,t.y,0,1],i=[t.x,t.y,1,1];e.ag(n,n,this.pixelMatrixInverse),e.ag(i,i,this.pixelMatrixInverse);const a=n[3],o=i[3],s=n[0]/a,l=i[0]/o,c=n[1]/a,u=i[1]/o,h=n[2]/a,f=i[2]/o,p=h===f?0:(0-h)/(f-h);return new e.Z(e.z.number(s,l,p)/this.worldSize,e.z.number(c,u,p)/this.worldSize)}coordinatePoint(t,r=0,n=this.pixelMatrix){const i=[t.x*this.worldSize,t.y*this.worldSize,r,1];return e.ag(i,i,n),new e.P(i[0]/i[3],i[1]/i[3])}getBounds(){const t=Math.max(0,this.height/2-this.getHorizon());return(new X).extend(this.pointLocation(new e.P(0,t))).extend(this.pointLocation(new e.P(this.width,t))).extend(this.pointLocation(new e.P(this.width,this.height))).extend(this.pointLocation(new e.P(0,this.height)))}getMaxBounds(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new X([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null}getHorizon(){return Math.tan(Math.PI/2-this._pitch)*this.cameraToCenterDistance*.85}setMaxBounds(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-85.051129,Sn])}calculateTileMatrix(t){const r=t.canonical,n=this.worldSize/this.zoomScale(r.z),i=r.x+Math.pow(2,r.z)*t.wrap,a=e.ao(new Float64Array(16));return e.J(a,a,[i*n,r.y*n,0]),e.K(a,a,[n/e.X,n/e.X,1]),a}calculatePosMatrix(t,r=!1){const n=t.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];const a=this.calculateTileMatrix(t);return e.L(a,r?this.alignedModelViewProjectionMatrix:this.modelViewProjectionMatrix,a),i[n]=new Float32Array(a),i[n]}calculateFogMatrix(t){const r=t.key,n=this._fogMatrixCache;if(n[r])return n[r];const i=this.calculateTileMatrix(t);return e.L(i,this.fogMatrix,i),n[r]=new Float32Array(i),n[r]}customLayerMatrix(){return this.mercatorMatrix.slice()}getConstrained(t,r){r=e.ad(+r,this.minZoom,this.maxZoom);const n={center:new e.N(t.lng,t.lat),zoom:r};let i=this.lngRange;if(!this._renderWorldCopies&&null===i){const t=180-1e-10;i=[-t,t]}const a=this.tileSize*this.zoomScale(n.zoom);let o=0,s=a,l=0,c=a,u=0,h=0;const{x:f,y:p}=this.size;if(this.latRange){const t=this.latRange;o=e.Q(t[1])*a,s=e.Q(t[0])*a,s-o<p&&(u=p/(s-o))}i&&(l=e.b3(e.O(i[0])*a,0,a),c=e.b3(e.O(i[1])*a,0,a),c<l&&(c+=a),c-l<f&&(h=f/(c-l)));const{x:d,y:m}=this.project.call({worldSize:a},t);let g,y;const v=Math.max(h||0,u||0);if(v){const t=new e.P(h?(c+l)/2:d,u?(s+o)/2:m);return n.center=this.unproject.call({worldSize:a},t).wrap(),n.zoom+=this.scaleZoom(v),n}if(this.latRange){const t=p/2;m-t<o&&(y=o+t),m+t>s&&(y=s-t)}if(i){const t=(l+c)/2;let r=d;this._renderWorldCopies&&(r=e.b3(d,t-a/2,t+a/2));const n=f/2;r-n<l&&(g=l+n),r+n>c&&(g=c-n)}if(void 0!==g||void 0!==y){const t=new e.P(null!=g?g:d,null!=y?y:m);n.center=this.unproject.call({worldSize:a},t).wrap()}return n}_constrain(){if(!this.center||!this.width||!this.height||this._constraining)return;this._constraining=!0;const t=this._unmodified,{center:e,zoom:r}=this.getConstrained(this.center,this.zoom);this.center=e,this.zoom=r,this._unmodified=t,this._constraining=!1}_calcMatrices(){if(!this.height)return;const t=this._fov/2,r=this.centerOffset,n=this.point.x,i=this.point.y;this.cameraToCenterDistance=.5/Math.tan(t)*this.height,this._pixelPerMeter=e.b5(1,this.center.lat)*this.worldSize;let a=e.ao(new Float64Array(16));e.K(a,a,[this.width/2,-this.height/2,1]),e.J(a,a,[1,-1,0]),this.labelPlaneMatrix=a,a=e.ao(new Float64Array(16)),e.K(a,a,[1,-1,1]),e.J(a,a,[-1,-1,0]),e.K(a,a,[2/this.width,2/this.height,1]),this.glCoordMatrix=a;const o=this.cameraToCenterDistance+this._elevation*this._pixelPerMeter/Math.cos(this._pitch),s=Math.min(this.elevation,this.minElevationForCurrentTile),l=o-s*this._pixelPerMeter/Math.cos(this._pitch),c=s<0?l:o,u=Math.PI/2+this._pitch,h=this._fov*(.5+r.y/this.height),f=Math.sin(h)*c/Math.sin(e.ad(Math.PI-u-h,.01,Math.PI-.01)),p=this.getHorizon(),d=2*Math.atan(p/this.cameraToCenterDistance)*(.5+r.y/(2*p)),m=Math.sin(d)*c/Math.sin(e.ad(Math.PI-u-d,.01,Math.PI-.01)),g=Math.min(f,m);this.farZ=1.01*(Math.cos(Math.PI/2-this._pitch)*g+c),this.nearZ=this.height/50,a=new Float64Array(16),e.b6(a,this._fov,this.width/this.height,this.nearZ,this.farZ),a[8]=2*-r.x/this.width,a[9]=2*r.y/this.height,this.projectionMatrix=e.af(a),e.K(a,a,[1,-1,1]),e.J(a,a,[0,0,-this.cameraToCenterDistance]),e.b7(a,a,this._pitch),e.ae(a,a,this.angle),e.J(a,a,[-n,-i,0]),this.mercatorMatrix=e.K([],a,[this.worldSize,this.worldSize,this.worldSize]),e.K(a,a,[1,1,this._pixelPerMeter]),this.pixelMatrix=e.L(new Float64Array(16),this.labelPlaneMatrix,a),e.J(a,a,[0,0,-this.elevation]),this.modelViewProjectionMatrix=a,this.invModelViewProjectionMatrix=e.at([],a),this.fogMatrix=new Float64Array(16),e.b6(this.fogMatrix,this._fov,this.width/this.height,o,this.farZ),this.fogMatrix[8]=2*-r.x/this.width,this.fogMatrix[9]=2*r.y/this.height,e.K(this.fogMatrix,this.fogMatrix,[1,-1,1]),e.J(this.fogMatrix,this.fogMatrix,[0,0,-this.cameraToCenterDistance]),e.b7(this.fogMatrix,this.fogMatrix,this._pitch),e.ae(this.fogMatrix,this.fogMatrix,this.angle),e.J(this.fogMatrix,this.fogMatrix,[-n,-i,0]),e.K(this.fogMatrix,this.fogMatrix,[1,1,this._pixelPerMeter]),e.J(this.fogMatrix,this.fogMatrix,[0,0,-this.elevation]),this.pixelMatrix3D=e.L(new Float64Array(16),this.labelPlaneMatrix,a);const y=this.width%2/2,v=this.height%2/2,x=Math.cos(this.angle),_=Math.sin(this.angle),b=n-Math.round(n)+x*y+_*v,w=i-Math.round(i)+x*v+_*y,T=new Float64Array(a);if(e.J(T,T,[b>.5?b-1:b,w>.5?w-1:w,0]),this.alignedModelViewProjectionMatrix=T,a=e.at(new Float64Array(16),this.pixelMatrix),!a)throw new Error("failed to invert matrix");this.pixelMatrixInverse=a,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={}}maxPitchScaleFactor(){if(!this.pixelMatrixInverse)return 1;const t=this.pointCoordinate(new e.P(0,0)),r=[t.x*this.worldSize,t.y*this.worldSize,0,1];return e.ag(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance}getCameraPoint(){const t=this._pitch,r=Math.tan(t)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new e.P(0,r))}getCameraQueryGeometry(t){const r=this.getCameraPoint();if(1===t.length)return[t[0],r];{let n=r.x,i=r.y,a=r.x,o=r.y;for(const e of t)n=Math.min(n,e.x),i=Math.min(i,e.y),a=Math.max(a,e.x),o=Math.max(o,e.y);return[new e.P(n,i),new e.P(a,i),new e.P(a,o),new e.P(n,o),new e.P(n,i)]}}lngLatToCameraDepth(t,r){const n=this.locationCoordinate(t),i=[n.x*this.worldSize,n.y*this.worldSize,r,1];return e.ag(i,i,this.modelViewProjectionMatrix),i[2]/i[3]}}function Cn(t,e){let r,n=!1,i=null,a=null;const o=()=>{i=null,n&&(t.apply(a,r),i=setTimeout(o,e),n=!1)};return(...t)=>(n=!0,a=this,r=t,i||o(),i)}class Ln{constructor(t){this._getCurrentHash=()=>{const t=window.location.hash.replace("#","");if(this._hashName){let e;return t.split("&").map((t=>t.split("="))).forEach((t=>{t[0]===this._hashName&&(e=t)})),(e&&e[1]||"").split("/")}return t.split("/")},this._onHashChange=()=>{const t=this._getCurrentHash();if(t.length>=3&&!t.some((t=>isNaN(t)))){const e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},this._updateHashUnthrottled=()=>{const t=window.location.href.replace(/(#.+)?$/,this.getHashString());window.history.replaceState(window.history.state,null,t)},this._removeHash=()=>{const t=this._getCurrentHash();if(0===t.length)return;const e=t.join("/");let r=e;r.split("&").length>0&&(r=r.split("&")[0]),this._hashName&&(r=`${this._hashName}=${e}`);let n=window.location.hash.replace(r,"");n.startsWith("#&")?n=n.slice(0,1)+n.slice(2):"#"===n&&(n="");let i=window.location.href.replace(/(#.+)?$/,n);i=i.replace("&&","&"),window.history.replaceState(window.history.state,null,i)},this._updateHash=Cn(this._updateHashUnthrottled,300),this._hashName=t&&encodeURIComponent(t)}addTo(t){return this._map=t,addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this}remove(){return removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),this._removeHash(),delete this._map,this}getHashString(t){const e=this._map.getCenter(),r=Math.round(100*this._map.getZoom())/100,n=Math.ceil((r*Math.LN2+Math.log(512/360/.5))/Math.LN10),i=Math.pow(10,n),a=Math.round(e.lng*i)/i,o=Math.round(e.lat*i)/i,s=this._map.getBearing(),l=this._map.getPitch();let c="";if(c+=t?`/${a}/${o}/${r}`:`${r}/${o}/${a}`,(s||l)&&(c+="/"+Math.round(10*s)/10),l&&(c+=`/${Math.round(l)}`),this._hashName){const t=this._hashName;let e=!1;const r=window.location.hash.slice(1).split("&").map((r=>{const n=r.split("=")[0];return n===t?(e=!0,`${n}=${c}`):r})).filter((t=>t));return e||r.push(`${t}=${c}`),`#${r.join("&")}`}return`#${c}`}}const In={linearity:.3,easing:e.b8(0,0,.3,1)},Pn=e.e({deceleration:2500,maxSpeed:1400},In),zn=e.e({deceleration:20,maxSpeed:1400},In),On=e.e({deceleration:1e3,maxSpeed:360},In),Dn=e.e({deceleration:1e3,maxSpeed:90},In);class Rn{constructor(t){this._map=t,this.clear()}clear(){this._inertiaBuffer=[]}record(t){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:a.now(),settings:t})}_drainInertiaBuffer(){const t=this._inertiaBuffer,e=a.now();for(;t.length>0&&e-t[0].time>160;)t.shift()}_onMoveEnd(t){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;const r={zoom:0,bearing:0,pitch:0,pan:new e.P(0,0),pinchAround:void 0,around:void 0};for(const{settings:t}of this._inertiaBuffer)r.zoom+=t.zoomDelta||0,r.bearing+=t.bearingDelta||0,r.pitch+=t.pitchDelta||0,t.panDelta&&r.pan._add(t.panDelta),t.around&&(r.around=t.around),t.pinchAround&&(r.pinchAround=t.pinchAround);const n=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,i={};if(r.pan.mag()){const a=Bn(r.pan.mag(),n,e.e({},Pn,t||{}));i.offset=r.pan.mult(a.amount/r.pan.mag()),i.center=this._map.transform.center,Fn(i,a)}if(r.zoom){const t=Bn(r.zoom,n,zn);i.zoom=this._map.transform.zoom+t.amount,Fn(i,t)}if(r.bearing){const t=Bn(r.bearing,n,On);i.bearing=this._map.transform.bearing+e.ad(t.amount,-179,179),Fn(i,t)}if(r.pitch){const t=Bn(r.pitch,n,Dn);i.pitch=this._map.transform.pitch+t.amount,Fn(i,t)}if(i.zoom||i.bearing){const t=void 0===r.pinchAround?r.around:r.pinchAround;i.around=t?this._map.unproject(t):this._map.getCenter()}return this.clear(),e.e(i,{noMoveStart:!0})}}function Fn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Bn(t,r,n){const{maxSpeed:i,linearity:a,deceleration:o}=n,s=e.ad(t*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}class Nn extends e.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(t,r,n,i={}){const a=o.mousePos(r.getCanvas(),n),s=r.unproject(a);super(t,e.e({point:a,lngLat:s,originalEvent:n},i)),this._defaultPrevented=!1,this.target=r}}class jn extends e.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(t,r,n){const i="touchend"===t?n.changedTouches:n.touches,a=o.touchPos(r.getCanvasContainer(),i),s=a.map((t=>r.unproject(t))),l=a.reduce(((t,e,r,n)=>t.add(e.div(n.length))),new e.P(0,0));super(t,{points:a,point:l,lngLats:s,lngLat:r.unproject(l),originalEvent:n}),this._defaultPrevented=!1}}class Un extends e.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(t,e,r){super(t,{originalEvent:r}),this._defaultPrevented=!1}}class Vn{constructor(t,e){this._map=t,this._clickTolerance=e.clickTolerance}reset(){delete this._mousedownPos}wheel(t){return this._firePreventable(new Un(t.type,this._map,t))}mousedown(t,e){return this._mousedownPos=e,this._firePreventable(new Nn(t.type,this._map,t))}mouseup(t){this._map.fire(new Nn(t.type,this._map,t))}click(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Nn(t.type,this._map,t))}dblclick(t){return this._firePreventable(new Nn(t.type,this._map,t))}mouseover(t){this._map.fire(new Nn(t.type,this._map,t))}mouseout(t){this._map.fire(new Nn(t.type,this._map,t))}touchstart(t){return this._firePreventable(new jn(t.type,this._map,t))}touchmove(t){this._map.fire(new jn(t.type,this._map,t))}touchend(t){this._map.fire(new jn(t.type,this._map,t))}touchcancel(t){this._map.fire(new jn(t.type,this._map,t))}_firePreventable(t){if(this._map.fire(t),t.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class qn{constructor(t){this._map=t}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(t){this._map.fire(new Nn(t.type,this._map,t))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Nn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(t){this._delayContextMenu?this._contextMenuEvent=t:this._ignoreContextMenu||this._map.fire(new Nn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Hn{constructor(t){this._map=t}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(t){return this.transform.pointLocation(e.P.convert(t),this._map.terrain)}}class Gn{constructor(t,e){this._map=t,this._tr=new Hn(t),this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(o.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)}mousemoveWindow(t,e){if(!this._active)return;const r=e;if(this._lastPos.equals(r)||!this._box&&r.dist(this._startPos)<this._clickTolerance)return;const n=this._startPos;this._lastPos=r,this._box||(this._box=o.create("div","maplibregl-boxzoom",this._container),this._container.classList.add("maplibregl-crosshair"),this._fireEvent("boxzoomstart",t));const i=Math.min(n.x,r.x),a=Math.max(n.x,r.x),s=Math.min(n.y,r.y),l=Math.max(n.y,r.y);o.setTransform(this._box,`translate(${i}px,${s}px)`),this._box.style.width=a-i+"px",this._box.style.height=l-s+"px"}mouseupWindow(t,r){if(!this._active)return;if(0!==t.button)return;const n=this._startPos,i=r;if(this.reset(),o.suppressClick(),n.x!==i.x||n.y!==i.y)return this._map.fire(new e.k("boxzoomend",{originalEvent:t})),{cameraAnimation:t=>t.fitScreenCoordinates(n,i,this._tr.bearing,{linear:!0})};this._fireEvent("boxzoomcancel",t)}keydown(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))}reset(){this._active=!1,this._container.classList.remove("maplibregl-crosshair"),this._box&&(o.remove(this._box),this._box=null),o.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(t,r){return this._map.fire(new e.k(t,{originalEvent:r}))}}function Zn(t,e){if(t.length!==e.length)throw new Error(`The number of touches and points are not equal - touches ${t.length}, points ${e.length}`);const r={};for(let n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}class Wn{constructor(t){this.reset(),this.numTouches=t.numTouches}reset(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1}touchstart(t,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=t.timeStamp),n.length===this.numTouches&&(this.centroid=function(t){const r=new e.P(0,0);for(const e of t)r._add(e);return r.div(t.length)}(r),this.touches=Zn(n,r)))}touchmove(t,e,r){if(this.aborted||!this.centroid)return;const n=Zn(r,e);for(const t in this.touches){const e=this.touches[t],r=n[t];(!r||r.dist(e)>30)&&(this.aborted=!0)}}touchend(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){const t=!this.aborted&&this.centroid;if(this.reset(),t)return t}}}class Yn{constructor(t){this.singleTap=new Wn(t),this.numTaps=t.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(t,e,r){this.singleTap.touchstart(t,e,r)}touchmove(t,e,r){this.singleTap.touchmove(t,e,r)}touchend(t,e,r){const n=this.singleTap.touchend(t,e,r);if(n){const e=t.timeStamp-this.lastTime<500,r=!this.lastTap||this.lastTap.dist(n)<30;if(e&&r||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}}}class Xn{constructor(t){this._tr=new Hn(t),this._zoomIn=new Yn({numTouches:1,numTaps:2}),this._zoomOut=new Yn({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)}touchmove(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)}touchend(t,e,r){const n=this._zoomIn.touchend(t,e,r),i=this._zoomOut.touchend(t,e,r),a=this._tr;return n?(this._active=!0,t.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:e=>e.easeTo({duration:300,zoom:a.zoom+1,around:a.unproject(n)},{originalEvent:t})}):i?(this._active=!0,t.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:e=>e.easeTo({duration:300,zoom:a.zoom-1,around:a.unproject(i)},{originalEvent:t})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class $n{constructor(t){this._enabled=!!t.enable,this._moveStateManager=t.moveStateManager,this._clickTolerance=t.clickTolerance||1,this._moveFunction=t.move,this._activateOnStart=!!t.activateOnStart,t.assignEvents(this),this.reset()}reset(t){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(t)}_move(...t){const e=this._moveFunction(...t);if(e.bearingDelta||e.pitchDelta||e.around||e.panDelta)return this._active=!0,e}dragStart(t,e){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(t)&&(this._moveStateManager.startMove(t),this._lastPoint=e.length?e[0]:e,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(t,e){if(!this.isEnabled())return;const r=this._lastPoint;if(!r)return;if(t.preventDefault(),!this._moveStateManager.isValidMoveEvent(t))return void this.reset(t);const n=e.length?e[0]:e;return!this._moved&&n.dist(r)<this._clickTolerance?void 0:(this._moved=!0,this._lastPoint=n,this._move(r,n))}dragEnd(t){this.isEnabled()&&this._lastPoint&&this._moveStateManager.isValidEndEvent(t)&&(this._moved&&o.suppressClick(),this.reset(t))}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}getClickTolerance(){return this._clickTolerance}}const Jn={0:1,2:2};class Kn{constructor(t){this._correctEvent=t.checkCorrectEvent}startMove(t){const e=o.mouseButton(t);this._eventButton=e}endMove(t){delete this._eventButton}isValidStartEvent(t){return this._correctEvent(t)}isValidMoveEvent(t){return!function(t,e){const r=Jn[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton)}isValidEndEvent(t){return o.mouseButton(t)===this._eventButton}}class Qn{constructor(){this._firstTouch=void 0}_isOneFingerTouch(t){return 1===t.targetTouches.length}_isSameTouchEvent(t){return t.targetTouches[0].identifier===this._firstTouch}startMove(t){const e=t.targetTouches[0].identifier;this._firstTouch=e}endMove(t){delete this._firstTouch}isValidStartEvent(t){return this._isOneFingerTouch(t)}isValidMoveEvent(t){return this._isOneFingerTouch(t)&&this._isSameTouchEvent(t)}isValidEndEvent(t){return this._isOneFingerTouch(t)&&this._isSameTouchEvent(t)}}const ti=t=>{t.mousedown=t.dragStart,t.mousemoveWindow=t.dragMove,t.mouseup=t.dragEnd,t.contextmenu=t=>{t.preventDefault()}},ei=({enable:t,clickTolerance:e,bearingDegreesPerPixelMoved:r=.8})=>{const n=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&t.ctrlKey||2===o.mouseButton(t)});return new $n({clickTolerance:e,move:(t,e)=>({bearingDelta:(e.x-t.x)*r}),moveStateManager:n,enable:t,assignEvents:ti})},ri=({enable:t,clickTolerance:e,pitchDegreesPerPixelMoved:r=-.5})=>{const n=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&t.ctrlKey||2===o.mouseButton(t)});return new $n({clickTolerance:e,move:(t,e)=>({pitchDelta:(e.y-t.y)*r}),moveStateManager:n,enable:t,assignEvents:ti})};class ni{constructor(t,e){this._clickTolerance=t.clickTolerance||1,this._map=e,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new e.P(0,0)}_shouldBePrevented(t){return t<(this._map.cooperativeGestures.isEnabled()?2:1)}touchstart(t,e,r){return this._calculateTransform(t,e,r)}touchmove(t,e,r){if(this._active){if(!this._shouldBePrevented(r.length))return t.preventDefault(),this._calculateTransform(t,e,r);this._map.cooperativeGestures.notifyGestureBlocked("touch_pan",t)}}touchend(t,e,r){this._calculateTransform(t,e,r),this._active&&this._shouldBePrevented(r.length)&&this.reset()}touchcancel(){this.reset()}_calculateTransform(t,r,n){n.length>0&&(this._active=!0);const i=Zn(n,r),a=new e.P(0,0),o=new e.P(0,0);let s=0;for(const t in i){const e=i[t],r=this._touches[t];r&&(a._add(e),o._add(e.sub(r)),s++,i[t]=e)}if(this._touches=i,this._shouldBePrevented(s)||!o.mag())return;const l=o.div(s);return this._sum._add(l),this._sum.mag()<this._clickTolerance?void 0:{around:a.div(s),panDelta:l}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class ii{constructor(){this.reset()}reset(){this._active=!1,delete this._firstTwoTouches}touchstart(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))}touchmove(t,e,r){if(!this._firstTwoTouches)return;t.preventDefault();const[n,i]=this._firstTwoTouches,a=ai(r,e,n),o=ai(r,e,i);if(!a||!o)return;const s=this._aroundCenter?null:a.add(o).div(2);return this._move([a,o],s,t)}touchend(t,e,r){if(!this._firstTwoTouches)return;const[n,i]=this._firstTwoTouches,a=ai(r,e,n),s=ai(r,e,i);a&&s||(this._active&&o.suppressClick(),this.reset())}touchcancel(){this.reset()}enable(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around}disable(){this._enabled=!1,this.reset()}isEnabled(){return!!this._enabled}isActive(){return!!this._active}}function ai(t,e,r){for(let n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}function oi(t,e){return Math.log(t/e)/Math.LN2}class si extends ii{reset(){super.reset(),delete this._distance,delete this._startDistance}_start(t){this._startDistance=this._distance=t[0].dist(t[1])}_move(t,e){const r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(oi(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:oi(this._distance,r),pinchAround:e}}}function li(t,e){return 180*t.angleWith(e)/Math.PI}class ci extends ii{reset(){super.reset(),delete this._minDiameter,delete this._startVector,delete this._vector}_start(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])}_move(t,e,r){const n=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:li(this._vector,n),pinchAround:e}}_isBelowThreshold(t){this._minDiameter=Math.min(this._minDiameter,t.mag());const e=25/(Math.PI*this._minDiameter)*360,r=li(t,this._startVector);return Math.abs(r)<e}}function ui(t){return Math.abs(t.y)>Math.abs(t.x)}class hi extends ii{constructor(t){super(),this._currentTouchCount=0,this._map=t}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(t,e,r){super.touchstart(t,e,r),this._currentTouchCount=r.length}_start(t){this._lastPoints=t,ui(t[0].sub(t[1]))&&(this._valid=!1)}_move(t,e,r){if(this._map.cooperativeGestures.isEnabled()&&this._currentTouchCount<3)return;const n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid?(this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}):void 0}gestureBeginsVertically(t,e,r){if(void 0!==this._valid)return this._valid;const n=t.mag()>=2,i=e.mag()>=2;if(!n&&!i)return;if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;const a=t.y>0==e.y>0;return ui(t)&&ui(e)&&a}}const fi={panStep:100,bearingStep:15,pitchStep:10};class pi{constructor(t){this._tr=new Hn(t);const e=fi;this._panStep=e.panStep,this._bearingStep=e.bearingStep,this._pitchStep=e.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(t){if(t.altKey||t.ctrlKey||t.metaKey)return;let e=0,r=0,n=0,i=0,a=0;switch(t.keyCode){case 61:case 107:case 171:case 187:e=1;break;case 189:case 109:case 173:e=-1;break;case 37:t.shiftKey?r=-1:(t.preventDefault(),i=-1);break;case 39:t.shiftKey?r=1:(t.preventDefault(),i=1);break;case 38:t.shiftKey?n=1:(t.preventDefault(),a=-1);break;case 40:t.shiftKey?n=-1:(t.preventDefault(),a=1);break;default:return}return this._rotationDisabled&&(r=0,n=0),{cameraAnimation:o=>{const s=this._tr;o.easeTo({duration:300,easeId:"keyboardHandler",easing:di,zoom:e?Math.round(s.zoom)+e*(t.shiftKey?2:1):s.zoom,bearing:s.bearing+r*this._bearingStep,pitch:s.pitch+n*this._pitchStep,offset:[-i*this._panStep,-a*this._panStep],center:s.center},{originalEvent:t})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function di(t){return t*(2-t)}const mi=4.000244140625;class gi{constructor(t,e){this._onTimeout=t=>{this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},this._map=t,this._tr=new Hn(t),this._triggerRenderFrame=e,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222}setZoomRate(t){this._defaultZoomRate=t}setWheelZoomRate(t){this._wheelZoomRate=t}isEnabled(){return!!this._enabled}isActive(){return!!this._active||void 0!==this._finishTimeout}isZooming(){return!!this._zooming}enable(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around)}disable(){this.isEnabled()&&(this._enabled=!1)}_shouldBePrevented(t){return!!this._map.cooperativeGestures.isEnabled()&&!(t.ctrlKey||this._map.cooperativeGestures.isBypassed(t))}wheel(t){if(!this.isEnabled())return;if(this._shouldBePrevented(t))return void this._map.cooperativeGestures.notifyGestureBlocked("wheel_zoom",t);let e=t.deltaMode===WheelEvent.DOM_DELTA_LINE?40*t.deltaY:t.deltaY;const r=a.now(),n=r-(this._lastWheelEventTime||0);this._lastWheelEventTime=r,0!==e&&e%mi==0?this._type="wheel":0!==e&&Math.abs(e)<4?this._type="trackpad":n>400?(this._type=null,this._lastValue=e,this._timeout=setTimeout(this._onTimeout,40,t)):this._type||(this._type=Math.abs(n*e)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,e+=this._lastValue)),t.shiftKey&&e&&(e/=4),this._type&&(this._lastWheelEvent=t,this._delta-=e,this._active||this._start(t)),t.preventDefault()}_start(t){if(!this._delta)return;this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);const r=o.mousePos(this._map.getCanvas(),t),n=this._tr;r.y>n.transform.height/2-n.transform.getHorizon()?this._around=e.N.convert(this._aroundCenter?n.center:n.unproject(r)):this._around=e.N.convert(n.center),this._aroundPoint=n.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._triggerRenderFrame())}renderFrame(){if(!this._frameId)return;if(this._frameId=null,!this.isActive())return;const t=this._tr.transform;if(0!==this._delta){const e="wheel"===this._type&&Math.abs(this._delta)>mi?this._wheelZoomRate:this._defaultZoomRate;let r=2/(1+Math.exp(-Math.abs(this._delta*e)));this._delta<0&&0!==r&&(r=1/r);const n="number"==typeof this._targetZoom?t.zoomScale(this._targetZoom):t.scale;this._targetZoom=Math.min(t.maxZoom,Math.max(t.minZoom,t.scaleZoom(n*r))),"wheel"===this._type&&(this._startZoom=t.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}const r="number"==typeof this._targetZoom?this._targetZoom:t.zoom,n=this._startZoom,i=this._easing;let o,s=!1;const l=a.now()-this._lastWheelEventTime;if("wheel"===this._type&&n&&i&&l){const t=Math.min(l/200,1),a=i(t);o=e.z.number(n,r,a),t<1?this._frameId||(this._frameId=!0):s=!0}else o=r,s=!0;return this._active=!0,s&&(this._active=!1,this._finishTimeout=setTimeout((()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!s,zoomDelta:o-t.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(t){let r=e.b9;if(this._prevEase){const t=this._prevEase,n=(a.now()-t.start)/t.duration,i=t.easing(n+.01)-t.easing(n),o=.27/Math.sqrt(i*i+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=e.b8(o,s,.25,1)}return this._prevEase={start:a.now(),duration:t,easing:r},r}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class yi{constructor(t,e){this._clickZoom=t,this._tapZoom=e}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class vi{constructor(t){this._tr=new Hn(t),this.reset()}reset(){this._active=!1}dblclick(t,e){return t.preventDefault(),{cameraAnimation:r=>{r.easeTo({duration:300,zoom:this._tr.zoom+(t.shiftKey?-1:1),around:this._tr.unproject(e)},{originalEvent:t})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class xi{constructor(){this._tap=new Yn({numTouches:1,numTaps:1}),this.reset()}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(t,e,r){if(!this._swipePoint)if(this._tapTime){const n=e[0],i=t.timeStamp-this._tapTime<500,a=this._tapPoint.dist(n)<30;i&&a?r.length>0&&(this._swipePoint=n,this._swipeTouch=r[0].identifier):this.reset()}else this._tap.touchstart(t,e,r)}touchmove(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;const n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)}touchend(t,e,r){if(this._tapTime)this._swipePoint&&0===r.length&&this.reset();else{const n=this._tap.touchend(t,e,r);n&&(this._tapTime=t.timeStamp,this._tapPoint=n)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class _i{constructor(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r}enable(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("maplibregl-touch-drag-pan")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("maplibregl-touch-drag-pan")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class bi{constructor(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()}}class wi{constructor(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0}enable(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("maplibregl-touch-zoom-rotate")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("maplibregl-touch-zoom-rotate")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}class Ti{constructor(t,e){this._bypassKey=-1!==navigator.userAgent.indexOf("Mac")?"metaKey":"ctrlKey",this._map=t,this._options=e,this._enabled=!1}isActive(){return!1}reset(){}_setupUI(){if(this._container)return;const t=this._map.getCanvasContainer();t.classList.add("maplibregl-cooperative-gestures"),this._container=o.create("div","maplibregl-cooperative-gesture-screen",t);let e=this._map._getUIString("CooperativeGesturesHandler.WindowsHelpText");"metaKey"===this._bypassKey&&(e=this._map._getUIString("CooperativeGesturesHandler.MacHelpText"));const r=this._map._getUIString("CooperativeGesturesHandler.MobileHelpText"),n=document.createElement("div");n.className="maplibregl-desktop-message",n.textContent=e,this._container.appendChild(n);const i=document.createElement("div");i.className="maplibregl-mobile-message",i.textContent=r,this._container.appendChild(i),this._container.setAttribute("aria-hidden","true")}_destroyUI(){this._container&&(o.remove(this._container),this._map.getCanvasContainer().classList.remove("maplibregl-cooperative-gestures")),delete this._container}enable(){this._setupUI(),this._enabled=!0}disable(){this._enabled=!1,this._destroyUI()}isEnabled(){return this._enabled}isBypassed(t){return t[this._bypassKey]}notifyGestureBlocked(t,r){this._enabled&&(this._map.fire(new e.k("cooperativegestureprevented",{gestureType:t,originalEvent:r})),this._container.classList.add("maplibregl-show"),setTimeout((()=>{this._container.classList.remove("maplibregl-show")}),100))}}const ki=t=>t.zoom||t.drag||t.pitch||t.rotate;class Ai extends e.k{}function Mi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}class Si{constructor(t,e){this.handleWindowEvent=t=>{this.handleEvent(t,`${t.type}Window`)},this.handleEvent=(t,e)=>{if("blur"===t.type)return void this.stop(!0);this._updatingCamera=!0;const r="renderFrame"===t.type?void 0:t,n={needsRenderFrame:!1},i={},a={},s=t.touches,l=s?this._getMapTouches(s):void 0,c=l?o.touchPos(this._map.getCanvas(),l):o.mousePos(this._map.getCanvas(),t);for(const{handlerName:o,handler:s,allowed:u}of this._handlers){if(!s.isEnabled())continue;let h;this._blockedByActive(a,u,o)?s.reset():s[e||t.type]&&(h=s[e||t.type](t,c,l),this.mergeHandlerResult(n,i,h,o,r),h&&h.needsRenderFrame&&this._triggerRenderFrame()),(h||s.isActive())&&(a[o]=s)}const u={};for(const t in this._previousActiveHandlers)a[t]||(u[t]=r);this._previousActiveHandlers=a,(Object.keys(u).length||Mi(n))&&(this._changes.push([n,i,u]),this._triggerRenderFrame()),(Object.keys(a).length||Mi(n))&&this._map._stop(!0),this._updatingCamera=!1;const{cameraAnimation:h}=n;h&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],h(this._map))},this._map=t,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Rn(t),this._bearingSnap=e.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(e);const r=this._el;this._listeners=[[r,"touchstart",{passive:!0}],[r,"touchmove",{passive:!1}],[r,"touchend",void 0],[r,"touchcancel",void 0],[r,"mousedown",void 0],[r,"mousemove",void 0],[r,"mouseup",void 0],[document,"mousemove",{capture:!0}],[document,"mouseup",void 0],[r,"mouseover",void 0],[r,"mouseout",void 0],[r,"dblclick",void 0],[r,"click",void 0],[r,"keydown",{capture:!1}],[r,"keyup",void 0],[r,"wheel",{passive:!1}],[r,"contextmenu",void 0],[window,"blur",void 0]];for(const[t,e,r]of this._listeners)o.addEventListener(t,e,t===document?this.handleWindowEvent:this.handleEvent,r)}destroy(){for(const[t,e,r]of this._listeners)o.removeEventListener(t,e,t===document?this.handleWindowEvent:this.handleEvent,r)}_addDefaultHandlers(t){const e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Vn(e,t));const n=e.boxZoom=new Gn(e,t);this._add("boxZoom",n),t.interactive&&t.boxZoom&&n.enable();const i=e.cooperativeGestures=new Ti(e,t.cooperativeGestures);this._add("cooperativeGestures",i),t.cooperativeGestures&&i.enable();const a=new Xn(e),s=new vi(e);e.doubleClickZoom=new yi(s,a),this._add("tapZoom",a),this._add("clickZoom",s),t.interactive&&t.doubleClickZoom&&e.doubleClickZoom.enable();const l=new xi;this._add("tapDragZoom",l);const c=e.touchPitch=new hi(e);this._add("touchPitch",c),t.interactive&&t.touchPitch&&e.touchPitch.enable(t.touchPitch);const u=ei(t),h=ri(t);e.dragRotate=new bi(t,u,h),this._add("mouseRotate",u,["mousePitch"]),this._add("mousePitch",h,["mouseRotate"]),t.interactive&&t.dragRotate&&e.dragRotate.enable();const f=(({enable:t,clickTolerance:e})=>{const r=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&!t.ctrlKey});return new $n({clickTolerance:e,move:(t,e)=>({around:e,panDelta:e.sub(t)}),activateOnStart:!0,moveStateManager:r,enable:t,assignEvents:ti})})(t),p=new ni(t,e);e.dragPan=new _i(r,f,p),this._add("mousePan",f),this._add("touchPan",p,["touchZoom","touchRotate"]),t.interactive&&t.dragPan&&e.dragPan.enable(t.dragPan);const d=new ci,m=new si;e.touchZoomRotate=new wi(r,m,d,l),this._add("touchRotate",d,["touchPan","touchZoom"]),this._add("touchZoom",m,["touchPan","touchRotate"]),t.interactive&&t.touchZoomRotate&&e.touchZoomRotate.enable(t.touchZoomRotate);const g=e.scrollZoom=new gi(e,(()=>this._triggerRenderFrame()));this._add("scrollZoom",g,["mousePan"]),t.interactive&&t.scrollZoom&&e.scrollZoom.enable(t.scrollZoom);const y=e.keyboard=new pi(e);this._add("keyboard",y),t.interactive&&t.keyboard&&e.keyboard.enable(),this._add("blockableMapEvent",new qn(e))}_add(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e}stop(t){if(!this._updatingCamera){for(const{handler:t}of this._handlers)t.reset();this._inertia.clear(),this._fireEvents({},{},t),this._changes=[]}}isActive(){for(const{handler:t}of this._handlers)if(t.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return Boolean(ki(this._eventsInProgress))||this.isZooming()}_blockedByActive(t,e,r){for(const n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1}_getMapTouches(t){const e=[];for(const r of t){const t=r.target;this._el.contains(t)&&e.push(r)}return e}mergeHandlerResult(t,r,n,i,a){if(!n)return;e.e(t,n);const o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}_applyChanges(){const t={},r={},n={};for(const[i,a,o]of this._changes)i.panDelta&&(t.panDelta=(t.panDelta||new e.P(0,0))._add(i.panDelta)),i.zoomDelta&&(t.zoomDelta=(t.zoomDelta||0)+i.zoomDelta),i.bearingDelta&&(t.bearingDelta=(t.bearingDelta||0)+i.bearingDelta),i.pitchDelta&&(t.pitchDelta=(t.pitchDelta||0)+i.pitchDelta),void 0!==i.around&&(t.around=i.around),void 0!==i.pinchAround&&(t.pinchAround=i.pinchAround),i.noInertia&&(t.noInertia=i.noInertia),e.e(r,a),e.e(n,o);this._updateMapTransform(t,r,n),this._changes=[]}_updateMapTransform(t,e,r){const n=this._map,i=n._getTransformForUpdate(),a=n.terrain;if(!(Mi(t)||a&&this._terrainMovement))return this._fireEvents(e,r,!0);let{panDelta:o,zoomDelta:s,bearingDelta:l,pitchDelta:c,around:u,pinchAround:h}=t;void 0!==h&&(u=h),n._stop(!0),u=u||n.transform.centerPoint;const f=i.pointLocation(o?u.sub(o):u);l&&(i.bearing+=l),c&&(i.pitch+=c),s&&(i.zoom+=s),a?this._terrainMovement||!e.drag&&!e.zoom?e.drag&&this._terrainMovement?i.center=i.pointLocation(i.centerPoint.sub(o)):i.setLocationAtPoint(f,u):(this._terrainMovement=!0,this._map._elevationFreeze=!0,i.setLocationAtPoint(f,u)):i.setLocationAtPoint(f,u),n._applyUpdatedTransform(i),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r,!0)}_fireEvents(t,r,n){const i=ki(this._eventsInProgress),o=ki(t),s={};for(const e in t){const{originalEvent:r}=t[e];this._eventsInProgress[e]||(s[`${e}start`]=r),this._eventsInProgress[e]=t[e]}!i&&o&&this._fireEvent("movestart",o.originalEvent);for(const t in s)this._fireEvent(t,s[t]);o&&this._fireEvent("move",o.originalEvent);for(const e in t){const{originalEvent:r}=t[e];this._fireEvent(e,r)}const l={};let c;for(const t in this._eventsInProgress){const{handlerName:e,originalEvent:n}=this._eventsInProgress[t];this._handlersById[e].isActive()||(delete this._eventsInProgress[t],c=r[e]||n,l[`${t}end`]=c)}for(const t in l)this._fireEvent(t,l[t]);const u=ki(this._eventsInProgress),h=(i||o)&&!u;if(h&&this._terrainMovement){this._map._elevationFreeze=!1,this._terrainMovement=!1;const t=this._map._getTransformForUpdate();t.recalculateZoom(this._map.terrain),this._map._applyUpdatedTransform(t)}if(n&&h){this._updatingCamera=!0;const t=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),r=t=>0!==t&&-this._bearingSnap<t&&t<this._bearingSnap;!t||!t.essential&&a.prefersReducedMotion?(this._map.fire(new e.k("moveend",{originalEvent:c})),r(this._map.getBearing())&&this._map.resetNorth()):(r(t.bearing||this._map.getBearing())&&(t.bearing=0),t.freezeElevation=!0,this._map.easeTo(t,{originalEvent:c})),this._updatingCamera=!1}}_fireEvent(t,r){this._map.fire(new e.k(t,r?{originalEvent:r}:{}))}_requestFrame(){return this._map.triggerRepaint(),this._map._renderTaskQueue.add((t=>{delete this._frameId,this.handleEvent(new Ai("renderFrame",{timeStamp:t})),this._applyChanges()}))}_triggerRenderFrame(){void 0===this._frameId&&(this._frameId=this._requestFrame())}}class Ei extends e.E{constructor(t,e){super(),this._renderFrameCallback=()=>{const t=Math.min((a.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(t)),t<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=t,this._bearingSnap=e.bearingSnap,this.on("moveend",(()=>{delete this._requestedCameraState}))}getCenter(){return new e.N(this.transform.center.lng,this.transform.center.lat)}setCenter(t,e){return this.jumpTo({center:t},e)}panBy(t,r,n){return t=e.P.convert(t).mult(-1),this.panTo(this.transform.center,e.e({offset:t},r),n)}panTo(t,r,n){return this.easeTo(e.e({center:t},r),n)}getZoom(){return this.transform.zoom}setZoom(t,e){return this.jumpTo({zoom:t},e),this}zoomTo(t,r,n){return this.easeTo(e.e({zoom:t},r),n)}zoomIn(t,e){return this.zoomTo(this.getZoom()+1,t,e),this}zoomOut(t,e){return this.zoomTo(this.getZoom()-1,t,e),this}getBearing(){return this.transform.bearing}setBearing(t,e){return this.jumpTo({bearing:t},e),this}getPadding(){return this.transform.padding}setPadding(t,e){return this.jumpTo({padding:t},e),this}rotateTo(t,r,n){return this.easeTo(e.e({bearing:t},r),n)}resetNorth(t,r){return this.rotateTo(0,e.e({duration:1e3},t),r),this}resetNorthPitch(t,r){return this.easeTo(e.e({bearing:0,pitch:0,duration:1e3},t),r),this}snapToNorth(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this}getPitch(){return this.transform.pitch}setPitch(t,e){return this.jumpTo({pitch:t},e),this}cameraForBounds(t,e){t=X.convert(t);const r=e&&e.bearing||0;return this._cameraForBoxAndBearing(t.getNorthWest(),t.getSouthEast(),r,e)}_cameraForBoxAndBearing(t,r,n,i){const a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=e.e({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){const t=i.padding;i.padding={top:t,bottom:t,right:t,left:t}}i.padding=e.e(a,i.padding);const o=this.transform,s=o.padding,l=new X(t,r),c=o.project(l.getNorthWest()),u=o.project(l.getNorthEast()),h=o.project(l.getSouthEast()),f=o.project(l.getSouthWest()),p=e.ba(-n),d=c.rotate(p),m=u.rotate(p),g=h.rotate(p),y=f.rotate(p),v=new e.P(Math.max(d.x,m.x,y.x,g.x),Math.max(d.y,m.y,y.y,g.y)),x=new e.P(Math.min(d.x,m.x,y.x,g.x),Math.min(d.y,m.y,y.y,g.y)),_=v.sub(x),b=(o.width-(s.left+s.right+i.padding.left+i.padding.right))/_.x,w=(o.height-(s.top+s.bottom+i.padding.top+i.padding.bottom))/_.y;if(w<0||b<0)return void e.w("Map cannot fit within canvas with the given bounds, padding, and/or offset.");const T=Math.min(o.scaleZoom(o.scale*Math.min(b,w)),i.maxZoom),k=e.P.convert(i.offset),A=(i.padding.left-i.padding.right)/2,M=(i.padding.top-i.padding.bottom)/2,S=new e.P(A,M).rotate(e.ba(n)),E=k.add(S).mult(o.scale/o.zoomScale(T));return{center:o.unproject(c.add(h).div(2).sub(E)),zoom:T,bearing:n}}fitBounds(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)}fitScreenCoordinates(t,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(e.P.convert(t)),this.transform.pointLocation(e.P.convert(r)),n,i),i,a)}_fitInternal(t,r,n){return t?(delete(r=e.e(t,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this}jumpTo(t,r){this.stop();const n=this._getTransformForUpdate();let i=!1,a=!1,o=!1;return"zoom"in t&&n.zoom!==+t.zoom&&(i=!0,n.zoom=+t.zoom),void 0!==t.center&&(n.center=e.N.convert(t.center)),"bearing"in t&&n.bearing!==+t.bearing&&(a=!0,n.bearing=+t.bearing),"pitch"in t&&n.pitch!==+t.pitch&&(o=!0,n.pitch=+t.pitch),null==t.padding||n.isPaddingEqual(t.padding)||(n.padding=t.padding),this._applyUpdatedTransform(n),this.fire(new e.k("movestart",r)).fire(new e.k("move",r)),i&&this.fire(new e.k("zoomstart",r)).fire(new e.k("zoom",r)).fire(new e.k("zoomend",r)),a&&this.fire(new e.k("rotatestart",r)).fire(new e.k("rotate",r)).fire(new e.k("rotateend",r)),o&&this.fire(new e.k("pitchstart",r)).fire(new e.k("pitch",r)).fire(new e.k("pitchend",r)),this.fire(new e.k("moveend",r))}calculateCameraOptionsFromTo(t,r,n,i=0){const a=e.Z.fromLngLat(t,r),o=e.Z.fromLngLat(n,i),s=o.x-a.x,l=o.y-a.y,c=o.z-a.z,u=Math.hypot(s,l,c);if(0===u)throw new Error("Can't calculate camera options with same From and To");const h=Math.hypot(s,l),f=this.transform.scaleZoom(this.transform.cameraToCenterDistance/u/this.transform.tileSize),p=180*Math.atan2(s,-l)/Math.PI;let d=180*Math.acos(h/u)/Math.PI;return d=c<0?90-d:90+d,{center:o.toLngLat(),zoom:f,pitch:d,bearing:p}}easeTo(t,r){var n;this._stop(!1,t.easeId),(!1===(t=e.e({offset:[0,0],duration:500,easing:e.b9},t)).animate||!t.essential&&a.prefersReducedMotion)&&(t.duration=0);const i=this._getTransformForUpdate(),o=i.zoom,s=i.bearing,l=i.pitch,c=i.padding,u="bearing"in t?this._normalizeBearing(t.bearing,s):s,h="pitch"in t?+t.pitch:l,f="padding"in t?t.padding:i.padding,p=e.P.convert(t.offset);let d=i.centerPoint.add(p);const m=i.pointLocation(d),{center:g,zoom:y}=i.getConstrained(e.N.convert(t.center||m),null!==(n=t.zoom)&&void 0!==n?n:o);this._normalizeCenter(g,i);const v=i.project(m),x=i.project(g).sub(v),_=i.zoomScale(y-o);let b,w;t.around&&(b=e.N.convert(t.around),w=i.locationPoint(b));const T={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||y!==o,this._rotating=this._rotating||s!==u,this._pitching=this._pitching||h!==l,this._padding=!i.isPaddingEqual(f),this._easeId=t.easeId,this._prepareEase(r,t.noMoveStart,T),this.terrain&&this._prepareElevation(g),this._ease((n=>{if(this._zooming&&(i.zoom=e.z.number(o,y,n)),this._rotating&&(i.bearing=e.z.number(s,u,n)),this._pitching&&(i.pitch=e.z.number(l,h,n)),this._padding&&(i.interpolatePadding(c,f,n),d=i.centerPoint.add(p)),this.terrain&&!t.freezeElevation&&this._updateElevation(n),b)i.setLocationAtPoint(b,w);else{const t=i.zoomScale(i.zoom-o),e=y>o?Math.min(2,_):Math.max(.5,_),r=Math.pow(e,1-n),a=i.unproject(v.add(x.mult(n*r)).mult(t));i.setLocationAtPoint(i.renderWorldCopies?a.wrap():a,d)}this._applyUpdatedTransform(i),this._fireMoveEvents(r)}),(e=>{this.terrain&&t.freezeElevation&&this._finalizeElevation(),this._afterEase(r,e)}),t),this}_prepareEase(t,r,n={}){this._moving=!0,r||n.moving||this.fire(new e.k("movestart",t)),this._zooming&&!n.zooming&&this.fire(new e.k("zoomstart",t)),this._rotating&&!n.rotating&&this.fire(new e.k("rotatestart",t)),this._pitching&&!n.pitching&&this.fire(new e.k("pitchstart",t))}_prepareElevation(t){this._elevationCenter=t,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(t,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(t){this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);const r=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(t<1&&r!==this._elevationTarget){const e=this._elevationTarget-this._elevationStart,n=(r-(e*t+this._elevationStart))/(1-t);this._elevationStart+=t*(e-n),this._elevationTarget=r}this.transform.elevation=e.z.number(this._elevationStart,this._elevationTarget,t)}_finalizeElevation(){this._elevationFreeze=!1,this.transform.recalculateZoom(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate||this.terrain?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_elevateCameraIfInsideTerrain(t){const e=t.getCameraPosition(),r=this.terrain.getElevationForLngLatZoom(e.lngLat,t.zoom);if(e.altitude<r){const n=this.calculateCameraOptionsFromTo(e.lngLat,r,t.center,t.elevation);return{pitch:n.pitch,zoom:n.zoom}}return{}}_applyUpdatedTransform(t){const e=[];if(this.terrain&&e.push((t=>this._elevateCameraIfInsideTerrain(t))),this.transformCameraUpdate&&e.push((t=>this.transformCameraUpdate(t))),!e.length)return;const r=t.clone();for(const t of e){const e=r.clone(),{center:n,zoom:i,pitch:a,bearing:o,elevation:s}=t(e);n&&(e.center=n),void 0!==i&&(e.zoom=i),void 0!==a&&(e.pitch=a),void 0!==o&&(e.bearing=o),void 0!==s&&(e.elevation=s),r.apply(e)}this.transform.apply(r)}_fireMoveEvents(t){this.fire(new e.k("move",t)),this._zooming&&this.fire(new e.k("zoom",t)),this._rotating&&this.fire(new e.k("rotate",t)),this._pitching&&this.fire(new e.k("pitch",t))}_afterEase(t,r){if(this._easeId&&r&&this._easeId===r)return;delete this._easeId;const n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new e.k("zoomend",t)),i&&this.fire(new e.k("rotateend",t)),a&&this.fire(new e.k("pitchend",t)),this.fire(new e.k("moveend",t))}flyTo(t,r){var n;if(!t.essential&&a.prefersReducedMotion){const n=e.M(t,["center","zoom","bearing","pitch","around"]);return this.jumpTo(n,r)}this.stop(),t=e.e({offset:[0,0],speed:1.2,curve:1.42,easing:e.b9},t);const i=this._getTransformForUpdate(),o=i.zoom,s=i.bearing,l=i.pitch,c=i.padding,u="bearing"in t?this._normalizeBearing(t.bearing,s):s,h="pitch"in t?+t.pitch:l,f="padding"in t?t.padding:i.padding,p=e.P.convert(t.offset);let d=i.centerPoint.add(p);const m=i.pointLocation(d),{center:g,zoom:y}=i.getConstrained(e.N.convert(t.center||m),null!==(n=t.zoom)&&void 0!==n?n:o);this._normalizeCenter(g,i);const v=i.zoomScale(y-o),x=i.project(m),_=i.project(g).sub(x);let b=t.curve;const w=Math.max(i.width,i.height),T=w/v,k=_.mag();if("minZoom"in t){const r=e.ad(Math.min(t.minZoom,o,y),i.minZoom,i.maxZoom),n=w/i.zoomScale(r-o);b=Math.sqrt(n/k*2)}const A=b*b;function M(t){const e=(T*T-w*w+(t?-1:1)*A*A*k*k)/(2*(t?T:w)*A*k);return Math.log(Math.sqrt(e*e+1)-e)}function S(t){return(Math.exp(t)-Math.exp(-t))/2}function E(t){return(Math.exp(t)+Math.exp(-t))/2}const C=M(!1);let L=function(t){return E(C)/E(C+b*t)},I=function(t){return w*((E(C)*(S(e=C+b*t)/E(e))-S(C))/A)/k;var e},P=(M(!0)-C)/b;if(Math.abs(k)<1e-6||!isFinite(P)){if(Math.abs(w-T)<1e-6)return this.easeTo(t,r);const e=T<w?-1:1;P=Math.abs(Math.log(T/w))/b,I=()=>0,L=t=>Math.exp(e*b*t)}if("duration"in t)t.duration=+t.duration;else{const e="screenSpeed"in t?+t.screenSpeed/b:+t.speed;t.duration=1e3*P/e}return t.maxDuration&&t.duration>t.maxDuration&&(t.duration=0),this._zooming=!0,this._rotating=s!==u,this._pitching=h!==l,this._padding=!i.isPaddingEqual(f),this._prepareEase(r,!1),this.terrain&&this._prepareElevation(g),this._ease((n=>{const a=n*P,m=1/L(a);i.zoom=1===n?y:o+i.scaleZoom(m),this._rotating&&(i.bearing=e.z.number(s,u,n)),this._pitching&&(i.pitch=e.z.number(l,h,n)),this._padding&&(i.interpolatePadding(c,f,n),d=i.centerPoint.add(p)),this.terrain&&!t.freezeElevation&&this._updateElevation(n);const v=1===n?g:i.unproject(x.add(_.mult(I(a))).mult(m));i.setLocationAtPoint(i.renderWorldCopies?v.wrap():v,d),this._applyUpdatedTransform(i),this._fireMoveEvents(r)}),(()=>{this.terrain&&t.freezeElevation&&this._finalizeElevation(),this._afterEase(r)}),t),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(t,e){var r;if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){const t=this._onEaseEnd;delete this._onEaseEnd,t.call(this,e)}return t||null===(r=this.handlers)||void 0===r||r.stop(!1),this}_ease(t,e,r){!1===r.animate||0===r.duration?(t(1),e()):(this._easeStart=a.now(),this._easeOptions=r,this._onEaseFrame=t,this._onEaseEnd=e,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(t,r){t=e.b3(t,-180,180);const n=Math.abs(t-r);return Math.abs(t-360-r)<n&&(t-=360),Math.abs(t+360-r)<n&&(t+=360),t}_normalizeCenter(t,e){if(!e.renderWorldCopies||e.lngRange)return;const r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}queryTerrainElevation(t){return this.terrain?this.terrain.getElevationForLngLatZoom(e.N.convert(t),this.transform.tileZoom)-this.transform.elevation:null}}const Ci={compact:!0,customAttribution:'<a href="https://maplibre.org/" target="_blank">MapLibre</a>'};class Li{constructor(t=Ci){this._toggleAttribution=()=>{this._container.classList.contains("maplibregl-compact")&&(this._container.classList.contains("maplibregl-compact-show")?(this._container.setAttribute("open",""),this._container.classList.remove("maplibregl-compact-show")):(this._container.classList.add("maplibregl-compact-show"),this._container.removeAttribute("open")))},this._updateData=t=>{!t||"metadata"!==t.sourceDataType&&"visibility"!==t.sourceDataType&&"style"!==t.dataType&&"terrain"!==t.type||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?!1===this._compact?this._container.setAttribute("open",""):this._container.classList.contains("maplibregl-compact")||this._container.classList.contains("maplibregl-attrib-empty")||(this._container.setAttribute("open",""),this._container.classList.add("maplibregl-compact","maplibregl-compact-show")):(this._container.setAttribute("open",""),this._container.classList.contains("maplibregl-compact")&&this._container.classList.remove("maplibregl-compact","maplibregl-compact-show"))},this._updateCompactMinimize=()=>{this._container.classList.contains("maplibregl-compact")&&this._container.classList.contains("maplibregl-compact-show")&&this._container.classList.remove("maplibregl-compact-show")},this.options=t}getDefaultPosition(){return"bottom-right"}onAdd(t){return this._map=t,this._compact=this.options.compact,this._container=o.create("details","maplibregl-ctrl maplibregl-ctrl-attrib"),this._compactButton=o.create("summary","maplibregl-ctrl-attrib-button",this._container),this._compactButton.addEventListener("click",this._toggleAttribution),this._setElementTitle(this._compactButton,"ToggleAttribution"),this._innerContainer=o.create("div","maplibregl-ctrl-attrib-inner",this._container),this._updateAttributions(),this._updateCompact(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("terrain",this._updateData),this._map.on("resize",this._updateCompact),this._map.on("drag",this._updateCompactMinimize),this._container}onRemove(){o.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("terrain",this._updateData),this._map.off("resize",this._updateCompact),this._map.off("drag",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(t,e){const r=this._map._getUIString(`AttributionControl.${e}`);t.title=r,t.setAttribute("aria-label",r)}_updateAttributions(){if(!this._map.style)return;let t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((t=>"string"!=typeof t?"":t))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){const t=this._map.style.stylesheet;this.styleOwner=t.owner,this.styleId=t.id}const e=this._map.style.sourceCaches;for(const r in e){const n=e[r];if(n.used||n.usedForTerrain){const e=n.getSource();e.attribution&&t.indexOf(e.attribution)<0&&t.push(e.attribution)}}t=t.filter((t=>String(t).trim())),t.sort(((t,e)=>t.length-e.length)),t=t.filter(((e,r)=>{for(let n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}));const r=t.join(" | ");r!==this._attribHTML&&(this._attribHTML=r,t.length?(this._innerContainer.innerHTML=r,this._container.classList.remove("maplibregl-attrib-empty")):this._container.classList.add("maplibregl-attrib-empty"),this._updateCompact(),this._editLink=null)}}class Ii{constructor(t={}){this._updateCompact=()=>{const t=this._container.children;if(t.length){const e=t[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?!1!==this._compact&&e.classList.add("maplibregl-compact"):e.classList.remove("maplibregl-compact")}},this.options=t}getDefaultPosition(){return"bottom-left"}onAdd(t){this._map=t,this._compact=this.options&&this.options.compact,this._container=o.create("div","maplibregl-ctrl");const e=o.create("a","maplibregl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://maplibre.org/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="block",this._map.on("resize",this._updateCompact),this._updateCompact(),this._container}onRemove(){o.remove(this._container),this._map.off("resize",this._updateCompact),this._map=void 0,this._compact=void 0}}class Pi{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(t){const e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e}remove(t){const e=this._currentlyRunning,r=e?this._queue.concat(e):this._queue;for(const e of r)if(e.id===t)return void(e.cancelled=!0)}run(t=0){if(this._currentlyRunning)throw new Error("Attempting to run(), but is already running.");const e=this._currentlyRunning=this._queue;this._queue=[];for(const r of e)if(!r.cancelled&&(r.callback(t),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}var zi=e.Y([{name:"a_pos3d",type:"Int16",components:3}]);class Oi extends e.E{constructor(t){super(),this.sourceCache=t,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.deltaZoom=1,t.usedForTerrain=!0,t.tileSize=this.tileSize*2**this.deltaZoom}destruct(){this.sourceCache.usedForTerrain=!1,this.sourceCache.tileSize=null}update(t,r){this.sourceCache.update(t,r),this._renderableTilesKeys=[];const n={};for(const i of t.coveringTiles({tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:r}))n[i.key]=!0,this._renderableTilesKeys.push(i.key),this._tiles[i.key]||(i.posMatrix=new Float64Array(16),e.aQ(i.posMatrix,0,e.X,0,e.X,0,1),this._tiles[i.key]=new ht(i,this.tileSize));for(const t in this._tiles)n[t]||delete this._tiles[t]}freeRtt(t){for(const e in this._tiles){const r=this._tiles[e];(!t||r.tileID.equals(t)||r.tileID.isChildOf(t)||t.isChildOf(r.tileID))&&(r.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map((t=>this.getTileByID(t)))}getTileByID(t){return this._tiles[t]}getTerrainCoords(t){const r={};for(const n of this._renderableTilesKeys){const i=this._tiles[n].tileID;if(i.canonical.equals(t.canonical)){const i=t.clone();i.posMatrix=new Float64Array(16),e.aQ(i.posMatrix,0,e.X,0,e.X,0,1),r[n]=i}else if(i.canonical.isChildOf(t.canonical)){const a=t.clone();a.posMatrix=new Float64Array(16);const o=i.canonical.z-t.canonical.z,s=i.canonical.x-(i.canonical.x>>o<<o),l=i.canonical.y-(i.canonical.y>>o<<o),c=e.X>>o;e.aQ(a.posMatrix,0,c,0,c,0,1),e.J(a.posMatrix,a.posMatrix,[-s*c,-l*c,0]),r[n]=a}else if(t.canonical.isChildOf(i.canonical)){const a=t.clone();a.posMatrix=new Float64Array(16);const o=t.canonical.z-i.canonical.z,s=t.canonical.x-(t.canonical.x>>o<<o),l=t.canonical.y-(t.canonical.y>>o<<o),c=e.X>>o;e.aQ(a.posMatrix,0,e.X,0,e.X,0,1),e.J(a.posMatrix,a.posMatrix,[s*c,l*c,0]),e.K(a.posMatrix,a.posMatrix,[1/2**o,1/2**o,0]),r[n]=a}}return r}getSourceTile(t,e){const r=this.sourceCache._source;let n=t.overscaledZ-this.deltaZoom;if(n>r.maxzoom&&(n=r.maxzoom),n<r.minzoom)return null;this._sourceTileCache[t.key]||(this._sourceTileCache[t.key]=t.scaledTo(n).key);let i=this.sourceCache.getTileByID(this._sourceTileCache[t.key]);if((!i||!i.dem)&&e)for(;n>=r.minzoom&&(!i||!i.dem);)i=this.sourceCache.getTileByID(t.scaledTo(n--).key);return i}tilesAfterTime(t=Date.now()){return Object.values(this._tiles).filter((e=>e.timeAdded>=t))}}class Di{constructor(t,e,r){this.painter=t,this.sourceCache=new Oi(e),this.options=r,this.exaggeration="number"==typeof r.exaggeration?r.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}getDEMElevation(t,r,n,i=e.X){var a;if(!(r>=0&&r<i&&n>=0&&n<i))return 0;const o=this.getTerrainData(t),s=null===(a=o.tile)||void 0===a?void 0:a.dem;if(!s)return 0;const l=function(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t}([],[r/i*e.X,n/i*e.X],o.u_terrain_matrix),c=[l[0]*s.dim,l[1]*s.dim],u=Math.floor(c[0]),h=Math.floor(c[1]),f=c[0]-u,p=c[1]-h;return s.get(u,h)*(1-f)*(1-p)+s.get(u+1,h)*f*(1-p)+s.get(u,h+1)*(1-f)*p+s.get(u+1,h+1)*f*p}getElevationForLngLatZoom(t,r){const{tileID:n,mercatorX:i,mercatorY:a}=this._getOverscaledTileIDFromLngLatZoom(t,r);return this.getElevation(n,i%e.X,a%e.X,e.X)}getElevation(t,r,n,i=e.X){return this.getDEMElevation(t,r,n,i)*this.exaggeration}getTerrainData(t){if(!this._emptyDemTexture){const t=this.painter.context,r=new e.R({width:1,height:1},new Uint8Array(4));this._emptyDepthTexture=new w(t,r,t.gl.RGBA,{premultiply:!1}),this._emptyDemUnpack=[0,0,0,0],this._emptyDemTexture=new w(t,new e.R({width:1,height:1}),t.gl.RGBA,{premultiply:!1}),this._emptyDemTexture.bind(t.gl.NEAREST,t.gl.CLAMP_TO_EDGE),this._emptyDemMatrix=e.ao([])}const r=this.sourceCache.getSourceTile(t,!0);if(r&&r.dem&&(!r.demTexture||r.needsTerrainPrepare)){const t=this.painter.context;r.demTexture=this.painter.getTileTexture(r.dem.stride),r.demTexture?r.demTexture.update(r.dem.getPixels(),{premultiply:!1}):r.demTexture=new w(t,r.dem.getPixels(),t.gl.RGBA,{premultiply:!1}),r.demTexture.bind(t.gl.NEAREST,t.gl.CLAMP_TO_EDGE),r.needsTerrainPrepare=!1}const n=r&&r+r.tileID.key+t.key;if(n&&!this._demMatrixCache[n]){const n=this.sourceCache.sourceCache._source.maxzoom;let i=t.canonical.z-r.tileID.canonical.z;t.overscaledZ>t.canonical.z&&(t.canonical.z>=n?i=t.canonical.z-n:e.w("cannot calculate elevation if elevation maxzoom > source.maxzoom"));const a=t.canonical.x-(t.canonical.x>>i<<i),o=t.canonical.y-(t.canonical.y>>i<<i),s=e.bb(new Float64Array(16),[1/(e.X<<i),1/(e.X<<i),0]);e.J(s,s,[a*e.X,o*e.X,0]),this._demMatrixCache[t.key]={matrix:s,coord:t}}return{u_depth:2,u_terrain:3,u_terrain_dim:r&&r.dem&&r.dem.dim||1,u_terrain_matrix:n?this._demMatrixCache[t.key].matrix:this._emptyDemMatrix,u_terrain_unpack:r&&r.dem&&r.dem.getUnpackVector()||this._emptyDemUnpack,u_terrain_exaggeration:this.exaggeration,texture:(r&&r.demTexture||this._emptyDemTexture).texture,depthTexture:(this._fboDepthTexture||this._emptyDepthTexture).texture,tile:r}}getFramebuffer(t){const e=this.painter,r=e.width/devicePixelRatio,n=e.height/devicePixelRatio;return!this._fbo||this._fbo.width===r&&this._fbo.height===n||(this._fbo.destroy(),this._fboCoordsTexture.destroy(),this._fboDepthTexture.destroy(),delete this._fbo,delete this._fboDepthTexture,delete this._fboCoordsTexture),this._fboCoordsTexture||(this._fboCoordsTexture=new w(e.context,{width:r,height:n,data:null},e.context.gl.RGBA,{premultiply:!1}),this._fboCoordsTexture.bind(e.context.gl.NEAREST,e.context.gl.CLAMP_TO_EDGE)),this._fboDepthTexture||(this._fboDepthTexture=new w(e.context,{width:r,height:n,data:null},e.context.gl.RGBA,{premultiply:!1}),this._fboDepthTexture.bind(e.context.gl.NEAREST,e.context.gl.CLAMP_TO_EDGE)),this._fbo||(this._fbo=e.context.createFramebuffer(r,n,!0,!1),this._fbo.depthAttachment.set(e.context.createRenderbuffer(e.context.gl.DEPTH_COMPONENT16,r,n))),this._fbo.colorAttachment.set("coords"===t?this._fboCoordsTexture.texture:this._fboDepthTexture.texture),this._fbo}getCoordsTexture(){const t=this.painter.context;if(this._coordsTexture)return this._coordsTexture;const r=new Uint8Array(this._coordsTextureSize*this._coordsTextureSize*4);for(let t=0,e=0;t<this._coordsTextureSize;t++)for(let n=0;n<this._coordsTextureSize;n++,e+=4)r[e+0]=255&n,r[e+1]=255&t,r[e+2]=n>>8<<4|t>>8,r[e+3]=0;const n=new e.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(r.buffer)),i=new w(t,n,t.gl.RGBA,{premultiply:!1});return i.bind(t.gl.NEAREST,t.gl.CLAMP_TO_EDGE),this._coordsTexture=i,i}pointCoordinate(t){this.painter.maybeDrawDepthAndCoords(!0);const r=new Uint8Array(4),n=this.painter.context,i=n.gl,a=Math.round(t.x*this.painter.pixelRatio/devicePixelRatio),o=Math.round(t.y*this.painter.pixelRatio/devicePixelRatio),s=Math.round(this.painter.height/devicePixelRatio);n.bindFramebuffer.set(this.getFramebuffer("coords").framebuffer),i.readPixels(a,s-o-1,1,1,i.RGBA,i.UNSIGNED_BYTE,r),n.bindFramebuffer.set(null);const l=r[0]+(r[2]>>4<<8),c=r[1]+((15&r[2])<<8),u=this.coordsIndex[255-r[3]],h=u&&this.sourceCache.getTileByID(u);if(!h)return null;const f=this._coordsTextureSize,p=(1<<h.tileID.canonical.z)*f;return new e.Z((h.tileID.canonical.x*f+l)/p+h.tileID.wrap,(h.tileID.canonical.y*f+c)/p,this.getElevation(h.tileID,l,c,f))}depthAtPoint(t){const e=new Uint8Array(4),r=this.painter.context,n=r.gl;return r.bindFramebuffer.set(this.getFramebuffer("depth").framebuffer),n.readPixels(t.x,this.painter.height/devicePixelRatio-t.y-1,1,1,n.RGBA,n.UNSIGNED_BYTE,e),r.bindFramebuffer.set(null),(e[0]/16777216+e[1]/65536+e[2]/256+e[3])/256}getTerrainMesh(){if(this._mesh)return this._mesh;const t=this.painter.context,r=new e.bc,n=new e.aY,i=this.meshSize,a=e.X/i,o=i*i;for(let t=0;t<=i;t++)for(let e=0;e<=i;e++)r.emplaceBack(e*a,t*a,0);for(let t=0;t<o;t+=i+1)for(let e=0;e<i;e++)n.emplaceBack(e+t,i+e+t+1,i+e+t+2),n.emplaceBack(e+t,i+e+t+2,e+t+1);const s=r.length,l=s+2*(i+1);for(const t of[0,1])for(let n=0;n<=i;n++)for(const i of[0,1])r.emplaceBack(n*a,t*e.X,i);for(let t=0;t<2*i;t+=2)n.emplaceBack(l+t,l+t+1,l+t+3),n.emplaceBack(l+t,l+t+3,l+t+2),n.emplaceBack(s+t,s+t+3,s+t+1),n.emplaceBack(s+t,s+t+2,s+t+3);const c=r.length,u=c+2*(i+1);for(const t of[0,1])for(let n=0;n<=i;n++)for(const i of[0,1])r.emplaceBack(t*e.X,n*a,i);for(let t=0;t<2*i;t+=2)n.emplaceBack(c+t,c+t+1,c+t+3),n.emplaceBack(c+t,c+t+3,c+t+2),n.emplaceBack(u+t,u+t+3,u+t+1),n.emplaceBack(u+t,u+t+2,u+t+3);return this._mesh=new wn(t.createVertexBuffer(r,zi.members),t.createIndexBuffer(n),e.a0.simpleSegment(0,0,r.length,n.length)),this._mesh}getMeshFrameDelta(t){return 2*Math.PI*e.bd/Math.pow(2,t)/5}getMinTileElevationForLngLatZoom(t,e){var r;const{tileID:n}=this._getOverscaledTileIDFromLngLatZoom(t,e);return null!==(r=this.getMinMaxElevation(n).minElevation)&&void 0!==r?r:0}getMinMaxElevation(t){const e=this.getTerrainData(t).tile,r={minElevation:null,maxElevation:null};return e&&e.dem&&(r.minElevation=e.dem.min*this.exaggeration,r.maxElevation=e.dem.max*this.exaggeration),r}_getOverscaledTileIDFromLngLatZoom(t,r){const n=e.Z.fromLngLat(t.wrap()),i=(1<<r)*e.X,a=n.x*i,o=n.y*i,s=Math.floor(a/e.X),l=Math.floor(o/e.X);return{tileID:new e.S(r,0,r,s,l),mercatorX:a,mercatorY:o}}}class Ri{constructor(t,e,r){this._context=t,this._size=e,this._tileSize=r,this._objects=[],this._recentlyUsed=[],this._stamp=0}destruct(){for(const t of this._objects)t.texture.destroy(),t.fbo.destroy()}_createObject(t){const e=this._context.createFramebuffer(this._tileSize,this._tileSize,!0,!0),r=new w(this._context,{width:this._tileSize,height:this._tileSize,data:null},this._context.gl.RGBA);return r.bind(this._context.gl.LINEAR,this._context.gl.CLAMP_TO_EDGE),e.depthAttachment.set(this._context.createRenderbuffer(this._context.gl.DEPTH_STENCIL,this._tileSize,this._tileSize)),e.colorAttachment.set(r.texture),{id:t,fbo:e,texture:r,stamp:-1,inUse:!1}}getObjectForId(t){return this._objects[t]}useObject(t){t.inUse=!0,this._recentlyUsed=this._recentlyUsed.filter((e=>t.id!==e)),this._recentlyUsed.push(t.id)}stampObject(t){t.stamp=++this._stamp}getOrCreateFreeObject(){for(const t of this._recentlyUsed)if(!this._objects[t].inUse)return this._objects[t];if(this._objects.length>=this._size)throw new Error("No free RenderPool available, call freeAllObjects() required!");const t=this._createObject(this._objects.length);return this._objects.push(t),t}freeObject(t){t.inUse=!1}freeAllObjects(){for(const t of this._objects)this.freeObject(t)}isFull(){return!(this._objects.length<this._size)&&!1===this._objects.some((t=>!t.inUse))}}const Fi={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0};class Bi{constructor(t,e){this.painter=t,this.terrain=e,this.pool=new Ri(t.context,30,e.sourceCache.tileSize*e.qualityFactor)}destruct(){this.pool.destruct()}getTexture(t){return this.pool.getObjectForId(t.rtt[this._stacks.length-1].id).texture}prepareForRender(t,e){this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.sourceCache.getRenderableTiles(),this._renderableLayerIds=t._order.filter((r=>!t._layers[r].isHidden(e))),this._coordsDescendingInv={};for(const e in t.sourceCaches){this._coordsDescendingInv[e]={};const r=t.sourceCaches[e].getVisibleCoordinates();for(const t of r){const r=this.terrain.sourceCache.getTerrainCoords(t);for(const t in r)this._coordsDescendingInv[e][t]||(this._coordsDescendingInv[e][t]=[]),this._coordsDescendingInv[e][t].push(r[t])}}this._coordsDescendingInvStr={};for(const e of t._order){const r=t._layers[e],n=r.source;if(Fi[r.type]&&!this._coordsDescendingInvStr[n]){this._coordsDescendingInvStr[n]={};for(const t in this._coordsDescendingInv[n])this._coordsDescendingInvStr[n][t]=this._coordsDescendingInv[n][t].map((t=>t.key)).sort().join()}}for(const t of this._renderableTiles)for(const e in this._coordsDescendingInvStr){const r=this._coordsDescendingInvStr[e][t.tileID.key];r&&r!==t.rttCoords[e]&&(t.rtt=[])}}renderLayer(t){if(t.isHidden(this.painter.transform.zoom))return!1;const r=t.type,n=this.painter,i=this._renderableLayerIds[this._renderableLayerIds.length-1]===t.id;if(Fi[r]&&(this._prevType&&Fi[this._prevType]||this._stacks.push([]),this._prevType=r,this._stacks[this._stacks.length-1].push(t.id),!i))return!0;if(Fi[this._prevType]||Fi[r]&&i){this._prevType=r;const t=this._stacks.length-1,i=this._stacks[t]||[];for(const r of this._renderableTiles){if(this.pool.isFull()&&(bn(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(r),r.rtt[t]){const e=this.pool.getObjectForId(r.rtt[t].id);if(e.stamp===r.rtt[t].stamp){this.pool.useObject(e);continue}}const a=this.pool.getOrCreateFreeObject();this.pool.useObject(a),this.pool.stampObject(a),r.rtt[t]={id:a.id,stamp:a.stamp},n.context.bindFramebuffer.set(a.fbo.framebuffer),n.context.clear({color:e.aN.transparent,stencil:0}),n.currentStencilSource=void 0;for(let t=0;t<i.length;t++){const e=n.style._layers[i[t]],o=e.source?this._coordsDescendingInv[e.source][r.tileID.key]:[r.tileID];n.context.viewport.set([0,0,a.fbo.width,a.fbo.height]),n._renderTileClippingMasks(e,o),n.renderLayer(n,n.style.sourceCaches[e.source],e,o),e.source&&(r.rttCoords[e.source]=this._coordsDescendingInvStr[e.source][r.tileID.key])}}return bn(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects(),Fi[r]}return!1}}const Ni={"AttributionControl.ToggleAttribution":"Toggle attribution","AttributionControl.MapFeedback":"Map feedback","FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"MapLibre logo","Map.Title":"Map","Marker.Title":"Map marker","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","Popup.Close":"Close popup","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm","TerrainControl.Enable":"Enable terrain","TerrainControl.Disable":"Disable terrain","CooperativeGesturesHandler.WindowsHelpText":"Use Ctrl + scroll to zoom the map","CooperativeGesturesHandler.MacHelpText":"Use ⌘ + scroll to zoom the map","CooperativeGesturesHandler.MobileHelpText":"Use two fingers to move the map"},ji=r,Ui={hash:!1,interactive:!0,bearingSnap:7,attributionControl:Ci,maplibreLogo:!1,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,refreshExpiredTiles:!0,scrollZoom:!0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,cooperativeGestures:!1,trackResize:!0,center:[0,0],zoom:0,bearing:0,pitch:0,renderWorldCopies:!0,maxTileCacheSize:null,maxTileCacheZoomLevels:e.a.MAX_TILE_CACHE_ZOOM_LEVELS,transformRequest:null,transformCameraUpdate:null,fadeDuration:300,crossSourceCollisions:!0,clickTolerance:3,localIdeographFontFamily:"sans-serif",pitchWithRotate:!0,validateStyle:!0,maxCanvasSize:[4096,4096],cancelPendingTileRequestsWhileZooming:!0};const Vi=t=>{t.touchstart=t.dragStart,t.touchmoveWindow=t.dragMove,t.touchend=t.dragEnd},qi={showCompass:!0,showZoom:!0,visualizePitch:!1};class Hi{constructor(t,r,n=!1){this.mousedown=t=>{this.startMouse(e.e({},t,{ctrlKey:!0,preventDefault:()=>t.preventDefault()}),o.mousePos(this.element,t)),o.addEventListener(window,"mousemove",this.mousemove),o.addEventListener(window,"mouseup",this.mouseup)},this.mousemove=t=>{this.moveMouse(t,o.mousePos(this.element,t))},this.mouseup=t=>{this.mouseRotate.dragEnd(t),this.mousePitch&&this.mousePitch.dragEnd(t),this.offTemp()},this.touchstart=t=>{1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=o.touchPos(this.element,t.targetTouches)[0],this.startTouch(t,this._startPos),o.addEventListener(window,"touchmove",this.touchmove,{passive:!1}),o.addEventListener(window,"touchend",this.touchend))},this.touchmove=t=>{1!==t.targetTouches.length?this.reset():(this._lastPos=o.touchPos(this.element,t.targetTouches)[0],this.moveTouch(t,this._lastPos))},this.touchend=t=>{0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),delete this._startPos,delete this._lastPos,this.offTemp()},this.reset=()=>{this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),this.touchRotate.reset(),this.touchPitch&&this.touchPitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10;const i=t.dragRotate._mouseRotate.getClickTolerance(),a=t.dragRotate._mousePitch.getClickTolerance();this.element=r,this.mouseRotate=ei({clickTolerance:i,enable:!0}),this.touchRotate=(({enable:t,clickTolerance:e,bearingDegreesPerPixelMoved:r=.8})=>{const n=new Qn;return new $n({clickTolerance:e,move:(t,e)=>({bearingDelta:(e.x-t.x)*r}),moveStateManager:n,enable:t,assignEvents:Vi})})({clickTolerance:i,enable:!0}),this.map=t,n&&(this.mousePitch=ri({clickTolerance:a,enable:!0}),this.touchPitch=(({enable:t,clickTolerance:e,pitchDegreesPerPixelMoved:r=-.5})=>{const n=new Qn;return new $n({clickTolerance:e,move:(t,e)=>({pitchDelta:(e.y-t.y)*r}),moveStateManager:n,enable:t,assignEvents:Vi})})({clickTolerance:a,enable:!0})),o.addEventListener(r,"mousedown",this.mousedown),o.addEventListener(r,"touchstart",this.touchstart,{passive:!1}),o.addEventListener(r,"touchcancel",this.reset)}startMouse(t,e){this.mouseRotate.dragStart(t,e),this.mousePitch&&this.mousePitch.dragStart(t,e),o.disableDrag()}startTouch(t,e){this.touchRotate.dragStart(t,e),this.touchPitch&&this.touchPitch.dragStart(t,e),o.disableDrag()}moveMouse(t,e){const r=this.map,{bearingDelta:n}=this.mouseRotate.dragMove(t,e)||{};if(n&&r.setBearing(r.getBearing()+n),this.mousePitch){const{pitchDelta:n}=this.mousePitch.dragMove(t,e)||{};n&&r.setPitch(r.getPitch()+n)}}moveTouch(t,e){const r=this.map,{bearingDelta:n}=this.touchRotate.dragMove(t,e)||{};if(n&&r.setBearing(r.getBearing()+n),this.touchPitch){const{pitchDelta:n}=this.touchPitch.dragMove(t,e)||{};n&&r.setPitch(r.getPitch()+n)}}off(){const t=this.element;o.removeEventListener(t,"mousedown",this.mousedown),o.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),o.removeEventListener(window,"touchmove",this.touchmove,{passive:!1}),o.removeEventListener(window,"touchend",this.touchend),o.removeEventListener(t,"touchcancel",this.reset),this.offTemp()}offTemp(){o.enableDrag(),o.removeEventListener(window,"mousemove",this.mousemove),o.removeEventListener(window,"mouseup",this.mouseup),o.removeEventListener(window,"touchmove",this.touchmove,{passive:!1}),o.removeEventListener(window,"touchend",this.touchend)}}let Gi;function Zi(t,r,n){const i=new e.N(t.lng,t.lat);if(t=new e.N(t.lng,t.lat),r){const i=new e.N(t.lng-360,t.lat),a=new e.N(t.lng+360,t.lat),o=n.locationPoint(t).distSqr(r);n.locationPoint(i).distSqr(r)<o?t=i:n.locationPoint(a).distSqr(r)<o&&(t=a)}for(;Math.abs(t.lng-n.center.lng)>180;){const e=n.locationPoint(t);if(e.x>=0&&e.y>=0&&e.x<=n.width&&e.y<=n.height)break;t.lng>n.center.lng?t.lng-=360:t.lng+=360}return t.lng!==i.lng&&n.locationPoint(t).y>n.height/2-n.getHorizon()?t:i}const Wi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Yi(t,e,r){const n=t.classList;for(const t in Wi)n.remove(`maplibregl-${r}-anchor-${t}`);n.add(`maplibregl-${r}-anchor-${e}`)}class Xi extends e.E{constructor(t){if(super(),this._onKeyPress=t=>{const e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},this._onMapClick=t=>{const e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},this._update=t=>{var e;if(!this._map)return;const r=this._map.loaded()&&!this._map.isMoving();("terrain"===(null==t?void 0:t.type)||"render"===(null==t?void 0:t.type)&&!r)&&this._map.once("render",this._update),this._map.transform.renderWorldCopies?this._lngLat=Zi(this._lngLat,this._flatPos,this._map.transform):this._lngLat=null===(e=this._lngLat)||void 0===e?void 0:e.wrap(),this._flatPos=this._pos=this._map.project(this._lngLat)._add(this._offset),this._map.terrain&&(this._flatPos=this._map.transform.locationPoint(this._lngLat)._add(this._offset));let n="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?n=`rotateZ(${this._rotation}deg)`:"map"===this._rotationAlignment&&(n=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let i="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?i="rotateX(0deg)":"map"===this._pitchAlignment&&(i=`rotateX(${this._map.getPitch()}deg)`),this._subpixelPositioning||t&&"moveend"!==t.type||(this._pos=this._pos.round()),o.setTransform(this._element,`${Wi[this._anchor]} translate(${this._pos.x}px, ${this._pos.y}px) ${i} ${n}`),a.frameAsync(new AbortController).then((()=>{this._updateOpacity(t&&"moveend"===t.type)})).catch((()=>{}))},this._onMove=t=>{if(!this._isDragging){const e=this._clickTolerance||this._map._clickTolerance;this._isDragging=t.point.dist(this._pointerdownPos)>=e}this._isDragging&&(this._pos=t.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new e.k("dragstart"))),this.fire(new e.k("drag")))},this._onUp=()=>{this._element.style.pointerEvents="auto",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new e.k("dragend")),this._state="inactive"},this._addDragHandler=t=>{this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},this._anchor=t&&t.anchor||"center",this._color=t&&t.color||"#3FB1CE",this._scale=t&&t.scale||1,this._draggable=t&&t.draggable||!1,this._clickTolerance=t&&t.clickTolerance||0,this._subpixelPositioning=t&&t.subpixelPositioning||!1,this._isDragging=!1,this._state="inactive",this._rotation=t&&t.rotation||0,this._rotationAlignment=t&&t.rotationAlignment||"auto",this._pitchAlignment=t&&t.pitchAlignment&&"auto"!==t.pitchAlignment?t.pitchAlignment:this._rotationAlignment,this.setOpacity(),this.setOpacity(null==t?void 0:t.opacity,null==t?void 0:t.opacityWhenCovered),t&&t.element)this._element=t.element,this._offset=e.P.convert(t&&t.offset||[0,0]);else{this._defaultMarker=!0,this._element=o.create("div");const r=o.createNS("http://www.w3.org/2000/svg","svg"),n=41,i=27;r.setAttributeNS(null,"display","block"),r.setAttributeNS(null,"height",`${n}px`),r.setAttributeNS(null,"width",`${i}px`),r.setAttributeNS(null,"viewBox",`0 0 ${i} ${n}`);const a=o.createNS("http://www.w3.org/2000/svg","g");a.setAttributeNS(null,"stroke","none"),a.setAttributeNS(null,"stroke-width","1"),a.setAttributeNS(null,"fill","none"),a.setAttributeNS(null,"fill-rule","evenodd");const s=o.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"fill-rule","nonzero");const l=o.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"transform","translate(3.0, 29.0)"),l.setAttributeNS(null,"fill","#000000");const c=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];for(const t of c){const e=o.createNS("http://www.w3.org/2000/svg","ellipse");e.setAttributeNS(null,"opacity","0.04"),e.setAttributeNS(null,"cx","10.5"),e.setAttributeNS(null,"cy","5.80029008"),e.setAttributeNS(null,"rx",t.rx),e.setAttributeNS(null,"ry",t.ry),l.appendChild(e)}const u=o.createNS("http://www.w3.org/2000/svg","g");u.setAttributeNS(null,"fill",this._color);const h=o.createNS("http://www.w3.org/2000/svg","path");h.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),u.appendChild(h);const 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m=o.createNS("http://www.w3.org/2000/svg","g");m.setAttributeNS(null,"transform","translate(8.0, 8.0)");const g=o.createNS("http://www.w3.org/2000/svg","circle");g.setAttributeNS(null,"fill","#000000"),g.setAttributeNS(null,"opacity","0.25"),g.setAttributeNS(null,"cx","5.5"),g.setAttributeNS(null,"cy","5.5"),g.setAttributeNS(null,"r","5.4999962");const y=o.createNS("http://www.w3.org/2000/svg","circle");y.setAttributeNS(null,"fill","#FFFFFF"),y.setAttributeNS(null,"cx","5.5"),y.setAttributeNS(null,"cy","5.5"),y.setAttributeNS(null,"r","5.4999962"),m.appendChild(g),m.appendChild(y),s.appendChild(l),s.appendChild(u),s.appendChild(f),s.appendChild(d),s.appendChild(m),r.appendChild(s),r.setAttributeNS(null,"height",n*this._scale+"px"),r.setAttributeNS(null,"width",i*this._scale+"px"),this._element.appendChild(r),this._offset=e.P.convert(t&&t.offset||[0,-14])}if(this._element.classList.add("maplibregl-marker"),this._element.addEventListener("dragstart",(t=>{t.preventDefault()})),this._element.addEventListener("mousedown",(t=>{t.preventDefault()})),Yi(this._element,this._anchor,"marker"),t&&t.className)for(const e of t.className.split(" "))this._element.classList.add(e);this._popup=null}addTo(t){return this.remove(),this._map=t,this._element.setAttribute("aria-label",t._getUIString("Marker.Title")),t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),t.on("terrain",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this}remove(){return this._opacityTimeout&&(clearTimeout(this._opacityTimeout),delete this._opacityTimeout),this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("terrain",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),o.remove(this._element),this._popup&&this._popup.remove(),this}getLngLat(){return this._lngLat}setLngLat(t){return this._lngLat=e.N.convert(t),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){const e=38.1,r=13.5,n=Math.abs(r)/Math.SQRT2;t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-e],"bottom-left":[n,-1*(e-r+n)],"bottom-right":[-n,-1*(e-r+n)],left:[r,-1*(e-r)],right:[-r,-1*(e-r)]}:this._offset}this._popup=t,this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this}setSubpixelPositioning(t){return this._subpixelPositioning=t,this}getPopup(){return this._popup}togglePopup(){const t=this._popup;return this._element.style.opacity===this._opacityWhenCovered?this:t?(t.isOpen()?t.remove():(t.setLngLat(this._lngLat),t.addTo(this._map)),this):this}_updateOpacity(t=!1){var r,n;if(!(null===(r=this._map)||void 0===r?void 0:r.terrain))return void(this._element.style.opacity!==this._opacity&&(this._element.style.opacity=this._opacity));if(t)this._opacityTimeout=null;else{if(this._opacityTimeout)return;this._opacityTimeout=setTimeout((()=>{this._opacityTimeout=null}),100)}const i=this._map,a=i.terrain.depthAtPoint(this._pos),o=i.terrain.getElevationForLngLatZoom(this._lngLat,i.transform.tileZoom);if(i.transform.lngLatToCameraDepth(this._lngLat,o)-a<.006)return void(this._element.style.opacity=this._opacity);const s=-this._offset.y/i.transform._pixelPerMeter,l=Math.sin(i.getPitch()*Math.PI/180)*s,c=i.terrain.depthAtPoint(new e.P(this._pos.x,this._pos.y-this._offset.y)),u=i.transform.lngLatToCameraDepth(this._lngLat,o+l)-c>.006;(null===(n=this._popup)||void 0===n?void 0:n.isOpen())&&u&&this._popup.remove(),this._element.style.opacity=u?this._opacityWhenCovered:this._opacity}getOffset(){return this._offset}setOffset(t){return this._offset=e.P.convert(t),this._update(),this}addClassName(t){this._element.classList.add(t)}removeClassName(t){this._element.classList.remove(t)}toggleClassName(t){return this._element.classList.toggle(t)}setDraggable(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(t){return this._rotation=t||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(t){return this._rotationAlignment=t||"auto",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}setOpacity(t,e){return void 0===t&&void 0===e&&(this._opacity="1",this._opacityWhenCovered="0.2"),void 0!==t&&(this._opacity=t),void 0!==e&&(this._opacityWhenCovered=e),this._map&&this._updateOpacity(!0),this}}const $i={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};let Ji=0,Ki=!1;class Qi extends e.E{constructor(t){super(),this._onSuccess=t=>{if(this._map){if(this._isOutOfMapMaxBounds(t))return this._setErrorState(),this.fire(new e.k("outofmaxbounds",t)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=t,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-background");break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(t),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(t),this.options.showUserLocation&&this._dotElement.classList.remove("maplibregl-user-location-dot-stale"),this.fire(new e.k("geolocate",t)),this._finish()}},this._updateCamera=t=>{const r=new e.N(t.coords.longitude,t.coords.latitude),n=t.coords.accuracy,i=this._map.getBearing(),a=e.e({bearing:i},this.options.fitBoundsOptions),o=X.fromLngLat(r,n);this._map.fitBounds(o,a,{geolocateSource:!0})},this._updateMarker=t=>{if(t){const r=new e.N(t.coords.longitude,t.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=t.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onZoom=()=>{this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},this._onError=t=>{if(this._map){if(this.options.trackUserLocation)if(1===t.code){this._watchState="OFF",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;const t=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=t,this._geolocateButton.setAttribute("aria-label",t),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===t.code&&Ki)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("maplibregl-user-location-dot-stale"),this.fire(new e.k("error",t)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._setupUI=()=>{this._map&&(this._container.addEventListener("contextmenu",(t=>t.preventDefault())),this._geolocateButton=o.create("button","maplibregl-ctrl-geolocate",this._container),o.create("span","maplibregl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden","true"),this._geolocateButton.type="button",this._geolocateButton.disabled=!0)},this._finishSetupUI=t=>{if(this._map){if(!1===t){e.w("Geolocation support is not available so the GeolocateControl will be disabled.");const t=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=t,this._geolocateButton.setAttribute("aria-label",t)}else{const t=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.disabled=!1,this._geolocateButton.title=t,this._geolocateButton.setAttribute("aria-label",t)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=o.create("div","maplibregl-user-location-dot"),this._userLocationDotMarker=new Xi({element:this._dotElement}),this._circleElement=o.create("div","maplibregl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Xi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",(()=>this.trigger())),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(t=>{const r=t.originalEvent&&"resize"===t.originalEvent.type;t.geolocateSource||"ACTIVE_LOCK"!==this._watchState||r||(this._watchState="BACKGROUND",this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this.fire(new e.k("trackuserlocationend")),this.fire(new e.k("userlocationlostfocus")))}))}},this.options=e.e({},$i,t)}onAdd(t){return this._map=t,this._container=o.create("div","maplibregl-ctrl maplibregl-ctrl-group"),this._setupUI(),function(){return e._(this,arguments,void 0,(function*(t=!1){if(void 0!==Gi&&!t)return Gi;if(void 0===window.navigator.permissions)return Gi=!!window.navigator.geolocation,Gi;try{const t=yield window.navigator.permissions.query({name:"geolocation"});Gi="denied"!==t.state}catch(t){Gi=!!window.navigator.geolocation}return Gi}))}().then((t=>this._finishSetupUI(t))),this._container}onRemove(){void 0!==this._geolocationWatchID&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),o.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ji=0,Ki=!1}_isOutOfMapMaxBounds(t){const e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())}_setErrorState(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-waiting");break;case"ACTIVE_ERROR":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadius(){const t=this._map.getBounds(),e=t.getSouthEast(),r=t.getNorthEast(),n=e.distanceTo(r),i=this._map._container.clientHeight,a=Math.ceil(this._accuracy/(n/i)*2);this._circleElement.style.width=`${a}px`,this._circleElement.style.height=`${a}px`}trigger(){if(!this._setup)return e.w("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new e.k("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ji--,Ki=!1,this._watchState="OFF",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background-error"),this.fire(new e.k("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new e.k("trackuserlocationstart")),this.fire(new e.k("userlocationfocus"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-active");break;case"OFF":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){let t;this._geolocateButton.classList.add("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),Ji++,Ji>1?(t={maximumAge:6e5,timeout:0},Ki=!0):(t=this.options.positionOptions,Ki=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,t)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("maplibregl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)}}const ta={maxWidth:100,unit:"metric"};function ea(t,e,r){const n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){const r=3.2808*s;r>5280?ra(e,n,r/5280,t._getUIString("ScaleControl.Miles")):ra(e,n,r,t._getUIString("ScaleControl.Feet"))}else r&&"nautical"===r.unit?ra(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles")):s>=1e3?ra(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):ra(e,n,s,t._getUIString("ScaleControl.Meters"))}function ra(t,e,r,n){const i=function(t){const e=Math.pow(10,`${Math.floor(t)}`.length-1);let r=t/e;return r=r>=10?10:r>=5?5:r>=3?3:r>=2?2:r>=1?1:function(t){const e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(r),e*r}(r),a=i/r;t.style.width=e*a+"px",t.innerHTML=`${i}&nbsp;${n}`}class na extends e.E{constructor(t={}){super(),this._onFullscreenChange=()=>{var t;let e=window.document.fullscreenElement||window.document.mozFullScreenElement||window.document.webkitFullscreenElement||window.document.msFullscreenElement;for(;null===(t=null==e?void 0:e.shadowRoot)||void 0===t?void 0:t.fullscreenElement;)e=e.shadowRoot.fullscreenElement;e===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,t&&t.container&&(t.container instanceof HTMLElement?this._container=t.container:e.w("Full screen control 'container' must be a DOM element.")),"onfullscreenchange"in document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in document&&(this._fullscreenchange="MSFullscreenChange")}onAdd(t){return this._map=t,this._container||(this._container=this._map.getContainer()),this._controlContainer=o.create("div","maplibregl-ctrl maplibregl-ctrl-group"),this._setupUI(),this._controlContainer}onRemove(){o.remove(this._controlContainer),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){const t=this._fullscreenButton=o.create("button","maplibregl-ctrl-fullscreen",this._controlContainer);o.create("span","maplibregl-ctrl-icon",t).setAttribute("aria-hidden","true"),t.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){const t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t}_getTitle(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")}_isFullscreen(){return this._fullscreen}_handleFullscreenChange(){this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("maplibregl-ctrl-shrink"),this._fullscreenButton.classList.toggle("maplibregl-ctrl-fullscreen"),this._updateTitle(),this._fullscreen?(this.fire(new e.k("fullscreenstart")),this._prevCooperativeGesturesEnabled=this._map.cooperativeGestures.isEnabled(),this._map.cooperativeGestures.disable()):(this.fire(new e.k("fullscreenend")),this._prevCooperativeGesturesEnabled&&this._map.cooperativeGestures.enable())}_exitFullscreen(){window.document.exitFullscreen?window.document.exitFullscreen():window.document.mozCancelFullScreen?window.document.mozCancelFullScreen():window.document.msExitFullscreen?window.document.msExitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle("maplibregl-pseudo-fullscreen"),this._handleFullscreenChange(),this._map.resize()}}const ia={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:"",maxWidth:"240px",subpixelPositioning:!1},aa=["a[href]","[tabindex]:not([tabindex='-1'])","[contenteditable]:not([contenteditable='false'])","button:not([disabled])","input:not([disabled])","select:not([disabled])","textarea:not([disabled])"].join(", ");class oa extends e.E{constructor(t){super(),this.remove=()=>(this._content&&o.remove(this._content),this._container&&(o.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),this._map._canvasContainer.classList.remove("maplibregl-track-pointer"),delete this._map,this.fire(new e.k("close"))),this),this._onMouseUp=t=>{this._update(t.point)},this._onMouseMove=t=>{this._update(t.point)},this._onDrag=t=>{this._update(t.point)},this._update=t=>{var e;const r=this._lngLat||this._trackPointer;if(!this._map||!r||!this._content)return;if(!this._container){if(this._container=o.create("div","maplibregl-popup",this._map.getContainer()),this._tip=o.create("div","maplibregl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className)for(const t of this.options.className.split(" "))this._container.classList.add(t);this._closeButton&&this._closeButton.setAttribute("aria-label",this._map._getUIString("Popup.Close")),this._trackPointer&&this._container.classList.add("maplibregl-popup-track-pointer")}if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer?this._lngLat=Zi(this._lngLat,this._flatPos,this._map.transform):this._lngLat=null===(e=this._lngLat)||void 0===e?void 0:e.wrap(),this._trackPointer&&!t)return;const n=this._flatPos=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat);this._map.terrain&&(this._flatPos=this._trackPointer&&t?t:this._map.transform.locationPoint(this._lngLat));let i=this.options.anchor;const a=sa(this.options.offset);if(!i){const t=this._container.offsetWidth,e=this._container.offsetHeight;let r;r=n.y+a.bottom.y<e?["top"]:n.y>this._map.transform.height-e?["bottom"]:[],n.x<t/2?r.push("left"):n.x>this._map.transform.width-t/2&&r.push("right"),i=0===r.length?"bottom":r.join("-")}let s=n.add(a[i]);this.options.subpixelPositioning||(s=s.round()),o.setTransform(this._container,`${Wi[i]} translate(${s.x}px,${s.y}px)`),Yi(this._container,i,"popup")},this._onClose=()=>{this.remove()},this.options=e.e(Object.create(ia),t)}addTo(t){return this._map&&this.remove(),this._map=t,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("maplibregl-popup-track-pointer"),this._map._canvasContainer.classList.add("maplibregl-track-pointer")):this._map.on("move",this._update),this.fire(new e.k("open")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(t){return this._lngLat=e.N.convert(t),this._pos=null,this._flatPos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("maplibregl-popup-track-pointer"),this._map._canvasContainer.classList.remove("maplibregl-track-pointer")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._flatPos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("maplibregl-popup-track-pointer"),this._map._canvasContainer.classList.add("maplibregl-track-pointer")),this}getElement(){return this._container}setText(t){return this.setDOMContent(document.createTextNode(t))}setHTML(t){const e=document.createDocumentFragment(),r=document.createElement("body");let n;for(r.innerHTML=t;n=r.firstChild,n;)e.appendChild(n);return this.setDOMContent(e)}getMaxWidth(){var t;return null===(t=this._container)||void 0===t?void 0:t.style.maxWidth}setMaxWidth(t){return this.options.maxWidth=t,this._update(),this}setDOMContent(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=o.create("div","maplibregl-popup-content",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(t){return this._container&&this._container.classList.add(t),this}removeClassName(t){return this._container&&this._container.classList.remove(t),this}setOffset(t){return this.options.offset=t,this._update(),this}toggleClassName(t){if(this._container)return this._container.classList.toggle(t)}setSubpixelPositioning(t){this.options.subpixelPositioning=t}_createCloseButton(){this.options.closeButton&&(this._closeButton=o.create("button","maplibregl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;const t=this._container.querySelector(aa);t&&t.focus()}}function sa(t){if(t){if("number"==typeof t){const r=Math.round(Math.abs(t)/Math.SQRT2);return{center:new e.P(0,0),top:new e.P(0,t),"top-left":new e.P(r,r),"top-right":new e.P(-r,r),bottom:new e.P(0,-t),"bottom-left":new e.P(r,-r),"bottom-right":new e.P(-r,-r),left:new e.P(t,0),right:new e.P(-t,0)}}if(t instanceof e.P||Array.isArray(t)){const r=e.P.convert(t);return{center:r,top:r,"top-left":r,"top-right":r,bottom:r,"bottom-left":r,"bottom-right":r,left:r,right:r}}return{center:e.P.convert(t.center||[0,0]),top:e.P.convert(t.top||[0,0]),"top-left":e.P.convert(t["top-left"]||[0,0]),"top-right":e.P.convert(t["top-right"]||[0,0]),bottom:e.P.convert(t.bottom||[0,0]),"bottom-left":e.P.convert(t["bottom-left"]||[0,0]),"bottom-right":e.P.convert(t["bottom-right"]||[0,0]),left:e.P.convert(t.left||[0,0]),right:e.P.convert(t.right||[0,0])}}return sa(new e.P(0,0))}const la=r;t.AJAXError=e.bg,t.Evented=e.E,t.LngLat=e.N,t.MercatorCoordinate=e.Z,t.Point=e.P,t.addProtocol=e.bh,t.config=e.a,t.removeProtocol=e.bi,t.AttributionControl=Li,t.BoxZoomHandler=Gn,t.CanvasSource=it,t.CooperativeGesturesHandler=Ti,t.DoubleClickZoomHandler=yi,t.DragPanHandler=_i,t.DragRotateHandler=bi,t.EdgeInsets=Mn,t.FullscreenControl=na,t.GeoJSONSource=tt,t.GeolocateControl=Qi,t.Hash=Ln,t.ImageSource=rt,t.KeyboardHandler=pi,t.LngLatBounds=X,t.LogoControl=Ii,t.Map=class extends Ei{constructor(t){e.be.mark(e.bf.create);const r=Object.assign(Object.assign({},Ui),t);if(null!=r.minZoom&&null!=r.maxZoom&&r.minZoom>r.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=r.minPitch&&null!=r.maxPitch&&r.minPitch>r.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=r.minPitch&&r.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=r.maxPitch&&r.maxPitch>85)throw new Error("maxPitch must be less than or equal to 85");if(super(new En(r.minZoom,r.maxZoom,r.minPitch,r.maxPitch,r.renderWorldCopies),{bearingSnap:r.bearingSnap}),this._idleTriggered=!1,this._crossFadingFactor=1,this._renderTaskQueue=new Pi,this._controls=[],this._mapId=e.a4(),this._contextLost=t=>{t.preventDefault(),this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this.fire(new e.k("webglcontextlost",{originalEvent:t}))},this._contextRestored=t=>{this._setupPainter(),this.resize(),this._update(),this.fire(new e.k("webglcontextrestored",{originalEvent:t}))},this._onMapScroll=t=>{if(t.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=r.interactive,this._maxTileCacheSize=r.maxTileCacheSize,this._maxTileCacheZoomLevels=r.maxTileCacheZoomLevels,this._failIfMajorPerformanceCaveat=!0===r.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=!0===r.preserveDrawingBuffer,this._antialias=!0===r.antialias,this._trackResize=!0===r.trackResize,this._bearingSnap=r.bearingSnap,this._refreshExpiredTiles=!0===r.refreshExpiredTiles,this._fadeDuration=r.fadeDuration,this._crossSourceCollisions=!0===r.crossSourceCollisions,this._collectResourceTiming=!0===r.collectResourceTiming,this._locale=Object.assign(Object.assign({},Ni),r.locale),this._clickTolerance=r.clickTolerance,this._overridePixelRatio=r.pixelRatio,this._maxCanvasSize=r.maxCanvasSize,this.transformCameraUpdate=r.transformCameraUpdate,this.cancelPendingTileRequestsWhileZooming=!0===r.cancelPendingTileRequestsWhileZooming,this._imageQueueHandle=p.addThrottleControl((()=>this.isMoving())),this._requestManager=new d(r.transformRequest),"string"==typeof r.container){if(this._container=document.getElementById(r.container),!this._container)throw new Error(`Container '${r.container}' not found.`)}else{if(!(r.container instanceof HTMLElement))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=r.container}if(r.maxBounds&&this.setMaxBounds(r.maxBounds),this._setupContainer(),this._setupPainter(),this.on("move",(()=>this._update(!1))).on("moveend",(()=>this._update(!1))).on("zoom",(()=>this._update(!0))).on("terrain",(()=>{this.painter.terrainFacilitator.dirty=!0,this._update(!0)})).once("idle",(()=>{this._idleTriggered=!0})),"undefined"!=typeof window){addEventListener("online",this._onWindowOnline,!1);let t=!1;const e=Cn((t=>{this._trackResize&&!this._removed&&this.resize(t)._update()}),50);this._resizeObserver=new ResizeObserver((r=>{t?e(r):t=!0})),this._resizeObserver.observe(this._container)}this.handlers=new Si(this,r);const n="string"==typeof r.hash&&r.hash||void 0;this._hash=r.hash&&new Ln(n).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:r.center,zoom:r.zoom,bearing:r.bearing,pitch:r.pitch}),r.bounds&&(this.resize(),this.fitBounds(r.bounds,e.e({},r.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=r.localIdeographFontFamily,this._validateStyle=r.validateStyle,r.style&&this.setStyle(r.style,{localIdeographFontFamily:r.localIdeographFontFamily}),r.attributionControl&&this.addControl(new Li("boolean"==typeof r.attributionControl?void 0:r.attributionControl)),r.maplibreLogo&&this.addControl(new Ii,r.logoPosition),this.on("style.load",(()=>{this.transform.unmodified&&this.jumpTo(this.style.stylesheet)})),this.on("data",(t=>{this._update("style"===t.dataType),this.fire(new e.k(`${t.dataType}data`,t))})),this.on("dataloading",(t=>{this.fire(new e.k(`${t.dataType}dataloading`,t))})),this.on("dataabort",(t=>{this.fire(new e.k("sourcedataabort",t))}))}_getMapId(){return this._mapId}addControl(t,r){if(void 0===r&&(r=t.getDefaultPosition?t.getDefaultPosition():"top-right"),!t||!t.onAdd)return this.fire(new e.j(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));const n=t.onAdd(this);this._controls.push(t);const i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this}removeControl(t){if(!t||!t.onRemove)return this.fire(new e.j(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));const r=this._controls.indexOf(t);return r>-1&&this._controls.splice(r,1),t.onRemove(this),this}hasControl(t){return this._controls.indexOf(t)>-1}calculateCameraOptionsFromTo(t,e,r,n){return null==n&&this.terrain&&(n=this.terrain.getElevationForLngLatZoom(r,this.transform.tileZoom)),super.calculateCameraOptionsFromTo(t,e,r,n)}resize(t){var r;const n=this._containerDimensions(),i=n[0],a=n[1],o=this._getClampedPixelRatio(i,a);if(this._resizeCanvas(i,a,o),this.painter.resize(i,a,o),this.painter.overLimit()){const t=this.painter.context.gl;this._maxCanvasSize=[t.drawingBufferWidth,t.drawingBufferHeight];const e=this._getClampedPixelRatio(i,a);this._resizeCanvas(i,a,e),this.painter.resize(i,a,e)}this.transform.resize(i,a),null===(r=this._requestedCameraState)||void 0===r||r.resize(i,a);const s=!this._moving;return s&&(this.stop(),this.fire(new e.k("movestart",t)).fire(new e.k("move",t))),this.fire(new e.k("resize",t)),s&&this.fire(new e.k("moveend",t)),this}_getClampedPixelRatio(t,e){const{0:r,1:n}=this._maxCanvasSize,i=this.getPixelRatio(),a=t*i,o=e*i,s=a>r?r/a:1,l=o>n?n/o:1;return Math.min(s,l)*i}getPixelRatio(){var t;return null!==(t=this._overridePixelRatio)&&void 0!==t?t:devicePixelRatio}setPixelRatio(t){this._overridePixelRatio=t,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(t){return this.transform.setMaxBounds(X.convert(t)),this._update()}setMinZoom(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")}getMinZoom(){return this.transform.minZoom}setMaxZoom(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")}getMinPitch(){return this.transform.minPitch}setMaxPitch(t){if((t=null==t?60:t)>85)throw new Error("maxPitch must be less than or equal to 85");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")}getMaxPitch(){return this.transform.maxPitch}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(t){return this.transform.renderWorldCopies=t,this._update()}project(t){return this.transform.locationPoint(e.N.convert(t),this.style&&this.terrain)}unproject(t){return this.transform.pointLocation(e.P.convert(t),this.terrain)}isMoving(){var t;return this._moving||(null===(t=this.handlers)||void 0===t?void 0:t.isMoving())}isZooming(){var t;return this._zooming||(null===(t=this.handlers)||void 0===t?void 0:t.isZooming())}isRotating(){var t;return this._rotating||(null===(t=this.handlers)||void 0===t?void 0:t.isRotating())}_createDelegatedListener(t,e,r){if("mouseenter"===t||"mouseover"===t){let n=!1;const i=i=>{const a=this.getLayer(e)?this.queryRenderedFeatures(i.point,{layers:[e]}):[];a.length?n||(n=!0,r.call(this,new Nn(t,this,i.originalEvent,{features:a}))):n=!1};return{layer:e,listener:r,delegates:{mousemove:i,mouseout:()=>{n=!1}}}}if("mouseleave"===t||"mouseout"===t){let n=!1;const i=i=>{(this.getLayer(e)?this.queryRenderedFeatures(i.point,{layers:[e]}):[]).length?n=!0:n&&(n=!1,r.call(this,new Nn(t,this,i.originalEvent)))},a=e=>{n&&(n=!1,r.call(this,new Nn(t,this,e.originalEvent)))};return{layer:e,listener:r,delegates:{mousemove:i,mouseout:a}}}{const n=t=>{const n=this.getLayer(e)?this.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(this,t),delete t.features)};return{layer:e,listener:r,delegates:{[t]:n}}}}on(t,e,r){if(void 0===r)return super.on(t,e);const n=this._createDelegatedListener(t,e,r);this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(n);for(const t in n.delegates)this.on(t,n.delegates[t]);return this}once(t,e,r){if(void 0===r)return super.once(t,e);const n=this._createDelegatedListener(t,e,r);for(const t in n.delegates)this.once(t,n.delegates[t]);return this}off(t,e,r){if(void 0===r)return super.off(t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&(n=>{const i=n[t];for(let t=0;t<i.length;t++){const n=i[t];if(n.layer===e&&n.listener===r){for(const t in n.delegates)this.off(t,n.delegates[t]);return i.splice(t,1),this}}})(this._delegatedListeners),this}queryRenderedFeatures(t,r){if(!this.style)return[];let n;const i=t instanceof e.P||Array.isArray(t),a=i?t:[[0,0],[this.transform.width,this.transform.height]];if(r=r||(i?{}:t)||{},a instanceof e.P||"number"==typeof a[0])n=[e.P.convert(a)];else{const t=e.P.convert(a[0]),r=e.P.convert(a[1]);n=[t,new e.P(r.x,t.y),r,new e.P(t.x,r.y),t]}return this.style.queryRenderedFeatures(n,r,this.transform)}querySourceFeatures(t,e){return this.style.querySourceFeatures(t,e)}setStyle(t,r){return!1!==(r=e.e({},{localIdeographFontFamily:this._localIdeographFontFamily,validate:this._validateStyle},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&t?(this._diffStyle(t,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(t,r))}setTransformRequest(t){return this._requestManager.setTransformRequest(t),this}_getUIString(t){const e=this._locale[t];if(null==e)throw new Error(`Missing UI string '${t}'`);return e}_updateStyle(t,e){if(e.transformStyle&&this.style&&!this.style._loaded)return void this.style.once("style.load",(()=>this._updateStyle(t,e)));const r=this.style&&e.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!t)),t?(this.style=new de(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t,e,r):this.style.loadJSON(t,e,r),this):(delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new de(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(t,r){if("string"==typeof t){const n=t,i=this._requestManager.transformRequest(n,"Style");e.h(i,new AbortController).then((t=>{this._updateDiff(t.data,r)})).catch((t=>{t&&this.fire(new e.j(t))}))}else"object"==typeof t&&this._updateDiff(t,r)}_updateDiff(t,r){try{this.style.setState(t,r)&&this._update(!0)}catch(n){e.w(`Unable to perform style diff: ${n.message||n.error||n}.  Rebuilding the style from scratch.`),this._updateStyle(t,r)}}getStyle(){if(this.style)return this.style.serialize()}isStyleLoaded(){return this.style?this.style.loaded():e.w("There is no style added to the map.")}addSource(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)}isSourceLoaded(t){const r=this.style&&this.style.sourceCaches[t];if(void 0!==r)return r.loaded();this.fire(new e.j(new Error(`There is no source with ID '${t}'`)))}setTerrain(t){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off("data",this._terrainDataCallback),t){const r=this.style.sourceCaches[t.source];if(!r)throw new Error(`cannot load terrain, because there exists no source with ID: ${t.source}`);null===this.terrain&&r.reload();for(const r in this.style._layers){const n=this.style._layers[r];"hillshade"===n.type&&n.source===t.source&&e.w("You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.")}this.terrain=new Di(this.painter,r,t),this.painter.renderToTexture=new Bi(this.painter,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._terrainDataCallback=e=>{"style"===e.dataType?this.terrain.sourceCache.freeRtt():"source"===e.dataType&&e.tile&&(e.sourceId!==t.source||this._elevationFreeze||(this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.terrain.sourceCache.freeRtt(e.tile.tileID))},this.style.on("data",this._terrainDataCallback)}else this.terrain&&this.terrain.sourceCache.destruct(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform.minElevationForCurrentTile=0,this.transform.elevation=0;return this.fire(new e.k("terrain",{terrain:t})),this}getTerrain(){var t,e;return null!==(e=null===(t=this.terrain)||void 0===t?void 0:t.options)&&void 0!==e?e:null}areTilesLoaded(){const t=this.style&&this.style.sourceCaches;for(const e in t){const r=t[e]._tiles;for(const t in r){const e=r[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}}return!0}removeSource(t){return this.style.removeSource(t),this._update(!0)}getSource(t){return this.style.getSource(t)}addImage(t,r,n={}){const{pixelRatio:i=1,sdf:o=!1,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h}=n;this._lazyInitEmptyStyle();if(!(r instanceof HTMLImageElement||e.b(r))){if(void 0===r.width||void 0===r.height)return this.fire(new e.j(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));{const{width:n,height:a,data:f}=r,p=r;return this.style.addImage(t,{data:new e.R({width:n,height:a},new Uint8Array(f)),pixelRatio:i,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h,sdf:o,version:0,userImage:p}),p.onAdd&&p.onAdd(this,t),this}}{const{width:n,height:f,data:p}=a.getImageData(r);this.style.addImage(t,{data:new e.R({width:n,height:f},p),pixelRatio:i,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h,sdf:o,version:0})}}updateImage(t,r){const n=this.style.getImage(t);if(!n)return this.fire(new e.j(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));const i=r instanceof HTMLImageElement||e.b(r)?a.getImageData(r):r,{width:o,height:s,data:l}=i;if(void 0===o||void 0===s)return this.fire(new e.j(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(o!==n.data.width||s!==n.data.height)return this.fire(new e.j(new Error("The width and height of the updated image must be that same as the previous version of the image")));const c=!(r instanceof HTMLImageElement||e.b(r));return n.data.replace(l,c),this.style.updateImage(t,n),this}getImage(t){return this.style.getImage(t)}hasImage(t){return t?!!this.style.getImage(t):(this.fire(new e.j(new Error("Missing required image id"))),!1)}removeImage(t){this.style.removeImage(t)}loadImage(t){return p.getImage(this._requestManager.transformRequest(t,"Image"),new AbortController)}listImages(){return this.style.listImages()}addLayer(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)}moveLayer(t,e){return this.style.moveLayer(t,e),this._update(!0)}removeLayer(t){return this.style.removeLayer(t),this._update(!0)}getLayer(t){return this.style.getLayer(t)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)}setFilter(t,e,r={}){return this.style.setFilter(t,e,r),this._update(!0)}getFilter(t){return this.style.getFilter(t)}setPaintProperty(t,e,r,n={}){return this.style.setPaintProperty(t,e,r,n),this._update(!0)}getPaintProperty(t,e){return this.style.getPaintProperty(t,e)}setLayoutProperty(t,e,r,n={}){return this.style.setLayoutProperty(t,e,r,n),this._update(!0)}getLayoutProperty(t,e){return this.style.getLayoutProperty(t,e)}setGlyphs(t,e={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(t,e),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(t,e,r={}){return this._lazyInitEmptyStyle(),this.style.addSprite(t,e,r,(t=>{t||this._update(!0)})),this}removeSprite(t){return this._lazyInitEmptyStyle(),this.style.removeSprite(t),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(t,e={}){return this._lazyInitEmptyStyle(),this.style.setSprite(t,e,(t=>{t||this._update(!0)})),this}setLight(t,e={}){return this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)}getLight(){return this.style.getLight()}setSky(t){return this._lazyInitEmptyStyle(),this.style.setSky(t),this._update(!0)}getSky(){return this.style.getSky()}setFeatureState(t,e){return this.style.setFeatureState(t,e),this._update()}removeFeatureState(t,e){return this.style.removeFeatureState(t,e),this._update()}getFeatureState(t){return this.style.getFeatureState(t)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]}_setupContainer(){const t=this._container;t.classList.add("maplibregl-map");const e=this._canvasContainer=o.create("div","maplibregl-canvas-container",t);this._interactive&&e.classList.add("maplibregl-interactive"),this._canvas=o.create("canvas","maplibregl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex",this._interactive?"0":"-1"),this._canvas.setAttribute("aria-label",this._getUIString("Map.Title")),this._canvas.setAttribute("role","region");const r=this._containerDimensions(),n=this._getClampedPixelRatio(r[0],r[1]);this._resizeCanvas(r[0],r[1],n);const i=this._controlContainer=o.create("div","maplibregl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((t=>{a[t]=o.create("div",`maplibregl-ctrl-${t} `,i)})),this._container.addEventListener("scroll",this._onMapScroll,!1)}_resizeCanvas(t,e,r){this._canvas.width=Math.floor(r*t),this._canvas.height=Math.floor(r*e),this._canvas.style.width=`${t}px`,this._canvas.style.height=`${e}px`}_setupPainter(){const t={alpha:!0,stencil:!0,depth:!0,failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1};let e=null;this._canvas.addEventListener("webglcontextcreationerror",(r=>{e={requestedAttributes:t},r&&(e.statusMessage=r.statusMessage,e.type=r.type)}),{once:!0});const r=this._canvas.getContext("webgl2",t)||this._canvas.getContext("webgl",t);if(!r){const t="Failed to initialize WebGL";throw e?(e.message=t,new Error(JSON.stringify(e))):new Error(t)}this.painter=new Tn(r,this.transform),s.testSupport(r)}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(t){return this.style&&this.style._loaded?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(t){return this._update(),this._renderTaskQueue.add(t)}_cancelRenderFrame(t){this._renderTaskQueue.remove(t)}_render(t){const r=this._idleTriggered?this._fadeDuration:0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(t),this._removed)return;let n=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;const t=this.transform.zoom,i=a.now();this.style.zoomHistory.update(t,i);const o=new e.a9(t,{now:i,fadeDuration:r,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),s=o.crossFadingFactor();1===s&&s===this._crossFadingFactor||(n=!0,this._crossFadingFactor=s),this.style.update(o)}this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.sourceCache.update(this.transform,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._elevationFreeze||(this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform.minElevationForCurrentTile=0,this.transform.elevation=0),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,r,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:r,showPadding:this.showPadding}),this.fire(new e.k("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,e.be.mark(e.bf.load),this.fire(new e.k("load"))),this.style&&(this.style.hasTransitions()||n)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();const i=this._sourcesDirty||this._styleDirty||this._placementDirty;return i||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new e.k("idle")),!this._loaded||this._fullyLoaded||i||(this._fullyLoaded=!0,e.be.mark(e.bf.fullLoad)),this}redraw(){return this.style&&(this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._render(0)),this}remove(){var t;this._hash&&this._hash.remove();for(const t of this._controls)t.onRemove(this);this._controls=[],this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),"undefined"!=typeof window&&removeEventListener("online",this._onWindowOnline,!1),p.removeThrottleControl(this._imageQueueHandle),null===(t=this._resizeObserver)||void 0===t||t.disconnect();const r=this.painter.context.gl.getExtension("WEBGL_lose_context");(null==r?void 0:r.loseContext)&&r.loseContext(),this._canvas.removeEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.removeEventListener("webglcontextlost",this._contextLost,!1),o.remove(this._canvasContainer),o.remove(this._controlContainer),this._container.classList.remove("maplibregl-map"),e.be.clearMetrics(),this._removed=!0,this.fire(new e.k("remove"))}triggerRepaint(){this.style&&!this._frameRequest&&(this._frameRequest=new AbortController,a.frameAsync(this._frameRequest).then((t=>{e.be.frame(t),this._frameRequest=null,this._render(t)})).catch((()=>{})))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())}get showPadding(){return!!this._showPadding}set showPadding(t){this._showPadding!==t&&(this._showPadding=t,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())}get repaint(){return!!this._repaint}set repaint(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(t){this._vertices=t,this._update()}get version(){return ji}getCameraTargetElevation(){return this.transform.elevation}},t.MapMouseEvent=Nn,t.MapTouchEvent=jn,t.MapWheelEvent=Un,t.Marker=Xi,t.NavigationControl=class{constructor(t){this._updateZoomButtons=()=>{const t=this._map.getZoom(),e=t===this._map.getMaxZoom(),r=t===this._map.getMinZoom();this._zoomInButton.disabled=e,this._zoomOutButton.disabled=r,this._zoomInButton.setAttribute("aria-disabled",e.toString()),this._zoomOutButton.setAttribute("aria-disabled",r.toString())},this._rotateCompassArrow=()=>{const t=this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${this._map.transform.angle*(180/Math.PI)}deg)`:`rotate(${this._map.transform.angle*(180/Math.PI)}deg)`;this._compassIcon.style.transform=t},this._setButtonTitle=(t,e)=>{const r=this._map._getUIString(`NavigationControl.${e}`);t.title=r,t.setAttribute("aria-label",r)},this.options=e.e({},qi,t),this._container=o.create("div","maplibregl-ctrl maplibregl-ctrl-group"),this._container.addEventListener("contextmenu",(t=>t.preventDefault())),this.options.showZoom&&(this._zoomInButton=this._createButton("maplibregl-ctrl-zoom-in",(t=>this._map.zoomIn({},{originalEvent:t}))),o.create("span","maplibregl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden","true"),this._zoomOutButton=this._createButton("maplibregl-ctrl-zoom-out",(t=>this._map.zoomOut({},{originalEvent:t}))),o.create("span","maplibregl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden","true")),this.options.showCompass&&(this._compass=this._createButton("maplibregl-ctrl-compass",(t=>{this.options.visualizePitch?this._map.resetNorthPitch({},{originalEvent:t}):this._map.resetNorth({},{originalEvent:t})})),this._compassIcon=o.create("span","maplibregl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden","true"))}onAdd(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Hi(this._map,this._compass,this.options.visualizePitch)),this._container}onRemove(){o.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map}_createButton(t,e){const r=o.create("button",t,this._container);return r.type="button",r.addEventListener("click",e),r}},t.Popup=oa,t.RasterDEMTileSource=Q,t.RasterTileSource=K,t.ScaleControl=class{constructor(t){this._onMove=()=>{ea(this._map,this._container,this.options)},this.setUnit=t=>{this.options.unit=t,ea(this._map,this._container,this.options)},this.options=Object.assign(Object.assign({},ta),t)}getDefaultPosition(){return"bottom-left"}onAdd(t){return this._map=t,this._container=o.create("div","maplibregl-ctrl maplibregl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container}onRemove(){o.remove(this._container),this._map.off("move",this._onMove),this._map=void 0}},t.ScrollZoomHandler=gi,t.Style=de,t.TerrainControl=class{constructor(t){this._toggleTerrain=()=>{this._map.getTerrain()?this._map.setTerrain(null):this._map.setTerrain(this.options),this._updateTerrainIcon()},this._updateTerrainIcon=()=>{this._terrainButton.classList.remove("maplibregl-ctrl-terrain"),this._terrainButton.classList.remove("maplibregl-ctrl-terrain-enabled"),this._map.terrain?(this._terrainButton.classList.add("maplibregl-ctrl-terrain-enabled"),this._terrainButton.title=this._map._getUIString("TerrainControl.Disable")):(this._terrainButton.classList.add("maplibregl-ctrl-terrain"),this._terrainButton.title=this._map._getUIString("TerrainControl.Enable"))},this.options=t}onAdd(t){return this._map=t,this._container=o.create("div","maplibregl-ctrl maplibregl-ctrl-group"),this._terrainButton=o.create("button","maplibregl-ctrl-terrain",this._container),o.create("span","maplibregl-ctrl-icon",this._terrainButton).setAttribute("aria-hidden","true"),this._terrainButton.type="button",this._terrainButton.addEventListener("click",this._toggleTerrain),this._updateTerrainIcon(),this._map.on("terrain",this._updateTerrainIcon),this._container}onRemove(){o.remove(this._container),this._map.off("terrain",this._updateTerrainIcon),this._map=void 0}},t.TwoFingersTouchPitchHandler=hi,t.TwoFingersTouchRotateHandler=ci,t.TwoFingersTouchZoomHandler=si,t.TwoFingersTouchZoomRotateHandler=wi,t.VectorTileSource=J,t.VideoSource=nt,t.addSourceType=(t,r)=>e._(void 0,void 0,void 0,(function*(){if(ot(t))throw new Error(`A source type called "${t}" already exists.`);((t,e)=>{at[t]=e})(t,r)})),t.clearPrewarmedResources=function(){const t=j;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(F),j=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))},t.getMaxParallelImageRequests=function(){return e.a.MAX_PARALLEL_IMAGE_REQUESTS},t.getRTLTextPluginStatus=function(){return ut().getRTLTextPluginStatus()},t.getVersion=function(){return la},t.getWorkerCount=function(){return B.workerCount},t.getWorkerUrl=function(){return e.a.WORKER_URL},t.importScriptInWorkers=function(t){return H().broadcast("IS",t)},t.prewarm=function(){V().acquire(F)},t.setMaxParallelImageRequests=function(t){e.a.MAX_PARALLEL_IMAGE_REQUESTS=t},t.setRTLTextPlugin=function(t,e){return ut().setRTLTextPlugin(t,e)},t.setWorkerCount=function(t){B.workerCount=t},t.setWorkerUrl=function(t){e.a.WORKER_URL=t}})),t}()},88640:function(t,e,r){"use strict";function n(t,e,r){t.prototype=e.prototype=r,r.constructor=t}function i(t,e){var r=Object.create(t.prototype);for(var n in e)r[n]=e[n];return r}function a(){}r.d(e,{GW:function(){return K},Dj:function(){return 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From 12b5b75c9dcd0a0b0f9ee7ef9569ca737241716f Mon Sep 17 00:00:00 2001
From: Shristi Rawat <shristirawat68@gmail.com>
Date: Mon, 7 Oct 2024 19:26:02 +0530
Subject: [PATCH 11/76] Matched feature names in model prediction

---
 .../Stock_Price_Prediction-checkpoint.ipynb   | 4681 ++++++++++++
 Stock_Price_Prediction.ipynb                  | 6398 ++++++++++++-----
 2 files changed, 9307 insertions(+), 1772 deletions(-)
 create mode 100644 .ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb

diff --git a/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb b/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb
new file mode 100644
index 0000000..afca730
--- /dev/null
+++ b/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb
@@ -0,0 +1,4681 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "qCDSjVhXLr_Z"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense,LSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "Sc4id6VxL8BS",
+    "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01-01-1996</td>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-01-1996</td>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>03-01-1996</td>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>04-01-1996</td>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>05-01-1996</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date       Open       High        Low      Close  Adj Close  \\\n",
+       "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+       "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+       "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+       "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+       "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+       "\n",
+       "       Volume  \n",
+       "0  43733533.0  \n",
+       "1  56167280.0  \n",
+       "2  68296318.0  \n",
+       "3  86073880.0  \n",
+       "4  76613039.0  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "id": "7LaYGXsfN-8y"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop the 'Date' and 'Adj Close' columns\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "pqbTBdnBOKJc",
+    "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Open       High        Low      Close      Volume\n",
+       "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
+       "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
+       "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
+       "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
+       "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "dydEPoNeM6eN"
+   },
+   "outputs": [],
+   "source": [
+    "# Handle missing values\n",
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "OQ3cGqgTMBwt"
+   },
+   "outputs": [],
+   "source": [
+    "# Select features and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "id": "9Oz-bwJOMEWD"
+   },
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "id": "ugapDyXODtn3"
+   },
+   "outputs": [],
+   "source": [
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "997ZEgibCZIO",
+    "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5659, 4)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bmtt76RuCeyG",
+    "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1415, 4)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "CeJkUJ92Ciqd",
+    "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5659,)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7HGC7VuTCjWc",
+    "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1415,)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to evaluate and print RMSE, MAE, and MAPE\n",
+    "def evaluate_model(model, X_test, y_test):\n",
+    "    predictions = model.predict(X_test)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+    "    mae = mean_absolute_error(y_test, predictions)\n",
+    "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+    "\n",
+    "    print(f\"RMSE: {rmse}\")\n",
+    "    print(f\"MAE: {mae}\")\n",
+    "    print(f\"MAPE: {mape}\\n\")\n",
+    "    \n",
+    "    return rmse, mae, mape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = {\n",
+    "    \"Model\": [],\n",
+    "    \"RMSE\": [],\n",
+    "    \"MAE\": [],\n",
+    "    \"MAPE\": []\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c6Ek8jRlO2_I"
+   },
+   "source": [
+    "## 1. LINEAR REGRESSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "id": "RdZ1SpzdMHAJ"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a linear regression model\n",
+    "model1 = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mPM035IzMY04",
+    "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5286    257.350006\n",
+       "3408    129.464996\n",
+       "5477    279.350006\n",
+       "6906    588.500000\n",
+       "530      21.644367\n",
+       "Name: Close, dtype: float64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "qBhQ9HbYMI3d",
+    "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-12 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-12 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-12 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-12 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-12 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-12 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model1.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "id": "X269co2kMS4z"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 1.6881364651923558\n",
+      "MAE: 0.9433353486266928\n",
+      "MAPE: 0.006085435968276741\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Linear Regressor\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GxtMzlg-gR2P"
+   },
+   "source": [
+    "## 2. SVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "id": "0xQewd7QWTtq"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an SVR model\n",
+    "model2 = SVR()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "22SaCsQmfhgP",
+    "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-13 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-13 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-13 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-13 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-13 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-13 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-13 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-13 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-13 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-13 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-13 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-13 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-13 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-13 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVR()"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model2.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "id": "OQ1nL4oYfkAC"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 17.574809673127547\n",
+      "MAE: 6.278157692070486\n",
+      "MAPE: 0.09040265035344064\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"SVR\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hcIfVMWdgcKt"
+   },
+   "source": [
+    "## 3. Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "id": "f7raXT_hf2ij"
+   },
+   "outputs": [],
+   "source": [
+    "model3 = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "fF002Yepgk55",
+    "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-14 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-14 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-14 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-14 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-14 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-14 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-14 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-14 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-14 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-14 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-14 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-14 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-14 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model3.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "id": "8nRU_pzEgnCt"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.2053909891328036\n",
+      "MAE: 1.2608162799481166\n",
+      "MAPE: 0.008015308194076972\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Random Forest\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mZsLwLivhLGH"
+   },
+   "source": [
+    "## 4. Gradient Boosting Models (GBM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "id": "TI8idoxOg6jF"
+   },
+   "outputs": [],
+   "source": [
+    "model4 = GradientBoostingRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "2gpbDxshhexj",
+    "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-15 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-15 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-15 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-15 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-15 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-15 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-15 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-15 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-15 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-15 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-15 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-15 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-15 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GradientBoostingRegressor()"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model4.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "id": "Jj9DXdUPhh9V"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.6985863368468084\n",
+      "MAE: 1.692542658558929\n",
+      "MAPE: 0.011883244132236716\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"GBM\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d8nSGoyuh9dx"
+   },
+   "source": [
+    "## 5. Extreme Gradient Boosting (XGBoost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "id": "DyhhdlZAhx94"
+   },
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "# Create an XGBoost model\n",
+    "model5 = xgb.XGBRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "RAIwxIp5iH9Z",
+    "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-16 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-16 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-16 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-16 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-16 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-16 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-16 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-16 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-16 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model5.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.733930065274145\n",
+      "MAE: 1.502457380471909\n",
+      "MAPE: 0.010026410639661481\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"XGBoost\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "A_J776rtiovq"
+   },
+   "source": [
+    "## 6. AdaBoostRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "id": "HNq66cXRiYPJ"
+   },
+   "outputs": [],
+   "source": [
+    "model6 = AdaBoostRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "P0oB5wjQivBr",
+    "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-17 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-17 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-17 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-17 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-17 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-17 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-17 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-17 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-17 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-17 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-17 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-17 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-17 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-17 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "AdaBoostRegressor()"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model6.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "id": "Bf1m5ukOi2VM"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 9.175482477551942\n",
+      "MAE: 7.527617905792734\n",
+      "MAPE: 0.1858930099598583\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Q9DzOt3CkWFX"
+   },
+   "source": [
+    "## 7. Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "id": "23DZ2biSjF9a"
+   },
+   "outputs": [],
+   "source": [
+    "model7 = DecisionTreeRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "6mQEQf-ykc9F",
+    "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-18 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-18 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-18 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-18 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-18 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-18 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-18 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-18 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-18 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeRegressor()"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model7.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "id": "BFJ9q_tvkgRC"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 3.12966540689625\n",
+      "MAE: 1.6497286032971983\n",
+      "MAPE: 0.010286427942970355\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Decision Tree\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LH-B-Xd6k5UD"
+   },
+   "source": [
+    "## 8. KNeighborsRegressor(KNN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "id": "JVDSed7yktFY"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a KNN model\n",
+    "model8 = KNeighborsRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "9fn64o-ZlBka",
+    "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-19 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-19 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-19 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-19 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-19 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-19 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-19 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-19 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsRegressor()"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model8.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "id": "hbfbbjcSlDn7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 3.0274590148039873\n",
+      "MAE: 1.7525904376439672\n",
+      "MAPE: 0.013668115353592272\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"KNN\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "X5XtlzMXljps"
+   },
+   "source": [
+    "## 9. Artificial Neural Networks (ANN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an ANN model\n",
+    "model9 = Sequential()\n",
+    "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+    "model9.add(Dense(16, activation='relu'))\n",
+    "model9.add(Dense(1, activation='linear'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "id": "ZIf94WLMlv04"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model9.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "FX5DTKqslxWf",
+    "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1a508bc2c90>"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OVW2qpNsmGVq",
+    "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
+      "RMSE: 2.801001091255311\n",
+      "MAE: 1.7605365826618848\n",
+      "MAPE: 0.0126215060590655\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"ANN\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vjSMQNcOnFPJ"
+   },
+   "source": [
+    "## 10. LSTM(Long Short term Memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "id": "uACvajfImrbB"
+   },
+   "outputs": [],
+   "source": [
+    "# Reshape the input data for LSTM\n",
+    "n_features = X_train_scaled.shape[1]\n",
+    "n_steps = 10\n",
+    "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+    "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+    "\n",
+    "# Reshape the input data\n",
+    "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+    "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+    "model.add(Dense(1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "id": "YpSfHu6gov35"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0vHjcluaoxzP",
+    "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1a508dda390>"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"LSTM\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a DataFrame for metrics\n",
+    "metrics_df = pd.DataFrame(metrics)\n",
+    "\n",
+    "# Plot RMSE, MAE, and MAPE for each model\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# RMSE Plot\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
+    "plt.xlabel('RMSE')\n",
+    "plt.title('RMSE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MAE Plot\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n",
+    "plt.xlabel('MAE')\n",
+    "plt.title('MAE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MAPE Plot\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
+    "plt.xlabel('MAPE')\n",
+    "plt.title('MAPE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using of LightGBM and CatBoost For Optimizing the model accuracy and time complexity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import lightgbm as lgb\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "\n",
+    "# Function to train and evaluate a model\n",
+    "def train_and_evaluate_model(model, X_train, X_test, y_train, y_test):\n",
+    "    model.fit(X_train, y_train)\n",
+    "    pred = model.predict(X_test)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, pred))\n",
+    "    mae = mean_absolute_error(y_test, pred)\n",
+    "    mape = mean_absolute_percentage_error(y_test, pred)\n",
+    "    accuracy = accuracy_score(y_test > pred, y_test > pred.round())\n",
+    "    precision = precision_score(y_test > pred, y_test > pred.round())\n",
+    "    confusion = confusion_matrix(y_test > pred, y_test > pred.round())\n",
+    "    recall = recall_score(y_test > pred, y_test > pred.round())\n",
+    "    f1 = f1_score(y_test > pred, y_test > pred.round())\n",
+    "    return rmse, mae, mape, accuracy, precision, confusion, recall, f1\n",
+    "\n",
+    "# Train and evaluate LightGBM model for from this directly print accuracy \n",
+    "model_lightgbm = lgb.LGBMRegressor()\n",
+    "metrics_lightgbm = train_and_evaluate_model(model_lightgbm, X_train, X_test, y_train, y_test)\n",
+    "print(\"LightGBM Metrics:\", metrics_lightgbm)\n",
+    "\n",
+    "# Train and evaluate CatBoost model\n",
+    "model_catboost = CatBoostRegressor(verbose=0)\n",
+    "metrics_catboost = train_and_evaluate_model(model_catboost, X_train, X_test, y_train, y_test)\n",
+    "print(\"CatBoost Metrics:\", metrics_catboost)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Stock_Price_Prediction.ipynb b/Stock_Price_Prediction.ipynb
index b6205f9..01ad953 100644
--- a/Stock_Price_Prediction.ipynb
+++ b/Stock_Price_Prediction.ipynb
@@ -1,1848 +1,4702 @@
 {
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "qCDSjVhXLr_Z"
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "from sklearn.linear_model import LinearRegression\n",
-        "from sklearn.svm import SVR\n",
-        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor\n",
-        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
-        "from sklearn.neighbors import KNeighborsRegressor\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Dense,LSTM"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "SOQbXSiB-g5G",
-        "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
-          ]
-        }
-      ],
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')\n",
-        "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "Sc4id6VxL8BS",
-        "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-d89bb189-3861-4a83-b0ea-1a19c2115c37\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Date</th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Adj Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>01-01-1996</td>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>12.409931</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>02-01-1996</td>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>12.014931</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>03-01-1996</td>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>11.694577</td>\n",
-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>04-01-1996</td>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>11.654142</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>05-01-1996</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>11.588827</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d89bb189-3861-4a83-b0ea-1a19c2115c37')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-d89bb189-3861-4a83-b0ea-1a19c2115c37');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-2188052a-064d-46d7-8fd2-d03b4cf1736e\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2188052a-064d-46d7-8fd2-d03b4cf1736e')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-2188052a-064d-46d7-8fd2-d03b4cf1736e button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "         Date       Open       High        Low      Close  Adj Close  \\\n",
-              "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
-              "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
-              "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
-              "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
-              "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
-              "\n",
-              "       Volume  \n",
-              "0  43733533.0  \n",
-              "1  56167280.0  \n",
-              "2  68296318.0  \n",
-              "3  86073880.0  \n",
-              "4  76613039.0  "
-            ]
-          },
-          "execution_count": 23,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Load the dataset\n",
-        "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {
-        "id": "7LaYGXsfN-8y"
-      },
-      "outputs": [],
-      "source": [
-        "# Drop the 'Date' and 'Adj Close' columns\n",
-        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "pqbTBdnBOKJc",
-        "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "  <div id=\"df-7696e222-c2c8-45d3-99ad-84f50b7bfd98\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Open</th>\n",
-              "      <th>High</th>\n",
-              "      <th>Low</th>\n",
-              "      <th>Close</th>\n",
-              "      <th>Volume</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>18.691147</td>\n",
-              "      <td>18.978922</td>\n",
-              "      <td>18.540184</td>\n",
-              "      <td>18.823240</td>\n",
-              "      <td>43733533.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>18.894005</td>\n",
-              "      <td>18.964767</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>18.224106</td>\n",
-              "      <td>56167280.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>18.327892</td>\n",
-              "      <td>18.568489</td>\n",
-              "      <td>17.643839</td>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>68296318.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>17.502312</td>\n",
-              "      <td>17.832542</td>\n",
-              "      <td>17.223972</td>\n",
-              "      <td>17.676863</td>\n",
-              "      <td>86073880.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>17.738192</td>\n",
-              "      <td>17.785366</td>\n",
-              "      <td>17.459852</td>\n",
-              "      <td>17.577793</td>\n",
-              "      <td>76613039.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7696e222-c2c8-45d3-99ad-84f50b7bfd98')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-7696e222-c2c8-45d3-99ad-84f50b7bfd98');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-f7486739-4b0f-423e-900b-6a7c4fdde864\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7486739-4b0f-423e-900b-6a7c4fdde864')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-f7486739-4b0f-423e-900b-6a7c4fdde864 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
-            ],
-            "text/plain": [
-              "        Open       High        Low      Close      Volume\n",
-              "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
-              "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
-              "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
-              "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
-              "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
-            ]
-          },
-          "execution_count": 25,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {
-        "id": "dydEPoNeM6eN"
-      },
-      "outputs": [],
-      "source": [
-        "# Handle missing values\n",
-        "imputer = SimpleImputer(strategy='mean')\n",
-        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {
-        "id": "OQ3cGqgTMBwt"
-      },
-      "outputs": [],
-      "source": [
-        "# Select features and target variable\n",
-        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
-        "y = df['Close']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {
-        "id": "9Oz-bwJOMEWD"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "ugapDyXODtn3"
-      },
-      "outputs": [],
-      "source": [
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "997ZEgibCZIO",
-        "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(5659, 4)"
-            ]
-          },
-          "execution_count": 29,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "X_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bmtt76RuCeyG",
-        "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(1415, 4)"
-            ]
-          },
-          "execution_count": 30,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "X_test.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CeJkUJ92Ciqd",
-        "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(5659,)"
-            ]
-          },
-          "execution_count": 31,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "y_train.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7HGC7VuTCjWc",
-        "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(1415,)"
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "y_test.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Function to evaluate and print RMSE, MAE, and MAPE\n",
-        "def evaluate_model(model, X_test, y_test):\n",
-        "    predictions = model.predict(X_test)\n",
-        "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
-        "    mae = mean_absolute_error(y_test, predictions)\n",
-        "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
-        "\n",
-        "    print(f\"RMSE: {rmse}\")\n",
-        "    print(f\"MAE: {mae}\")\n",
-        "    print(f\"MAPE: {mape}\\n\")\n",
-        "    \n",
-        "    return rmse, mae, mape\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "metrics = {\n",
-        "    \"Model\": [],\n",
-        "    \"RMSE\": [],\n",
-        "    \"MAE\": [],\n",
-        "    \"MAPE\": []\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c6Ek8jRlO2_I"
-      },
-      "source": [
-        "## 1. LINEAR REGRESSION"
-      ]
-    },
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "id": "qCDSjVhXLr_Z"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense,LSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "id": "RdZ1SpzdMHAJ"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a linear regression model\n",
-        "model1 = LinearRegression()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running in local system\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    import google.colab\n",
+    "    In_colab=True\n",
+    "except:\n",
+    "    In_colab=False\n",
+    "\n",
+    "if(In_colab):\n",
+    "    print(\"Running in google colab\")\n",
+    "    from google.colab import drive\n",
+    "    drive.mount('/content/drive')\n",
+    "    df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')\n",
+    "else:\n",
+    "    print(\"Running in local system\")\n",
+    "    path=r'C:\\Users\\SHRISTI\\OneDrive\\Desktop\\GitHub\\Stock-Price-Prediction\\Data\\SBIN.csv'\n",
+    "    df=pd.read_csv(path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
     },
+    "id": "Sc4id6VxL8BS",
+    "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mPM035IzMY04",
-        "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "5286    257.350006\n",
-              "3408    129.464996\n",
-              "5477    279.350006\n",
-              "6906    588.500000\n",
-              "530      21.644367\n",
-              "Name: Close, dtype: float64"
-            ]
-          },
-          "execution_count": 34,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01-01-1996</td>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-01-1996</td>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>03-01-1996</td>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>04-01-1996</td>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>05-01-1996</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "y_train.head()"
+      "text/plain": [
+       "         Date       Open       High        Low      Close  Adj Close  \\\n",
+       "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+       "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+       "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+       "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+       "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+       "\n",
+       "       Volume  \n",
+       "0  43733533.0  \n",
+       "1  56167280.0  \n",
+       "2  68296318.0  \n",
+       "3  86073880.0  \n",
+       "4  76613039.0  "
       ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "id": "7LaYGXsfN-8y"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop the 'Date' and 'Adj Close' columns\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
     },
+    "id": "pqbTBdnBOKJc",
+    "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "qBhQ9HbYMI3d",
-        "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
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-            ],
-            "text/plain": [
-              "LinearRegression()"
-            ]
-          },
-          "execution_count": 35,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "# Train the model\n",
-        "model1.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 36,
-      "metadata": {
-        "id": "X269co2kMS4z"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Linear Regressor\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GxtMzlg-gR2P"
-      },
-      "source": [
-        "## 2. SVR"
+      "text/plain": [
+       "        Open       High        Low      Close      Volume\n",
+       "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
+       "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
+       "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
+       "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
+       "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
       ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "dydEPoNeM6eN"
+   },
+   "outputs": [],
+   "source": [
+    "# Handle missing values\n",
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "OQ3cGqgTMBwt"
+   },
+   "outputs": [],
+   "source": [
+    "# Select features and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "id": "9Oz-bwJOMEWD"
+   },
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "id": "ugapDyXODtn3"
+   },
+   "outputs": [],
+   "source": [
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "997ZEgibCZIO",
+    "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 40,
-      "metadata": {
-        "id": "0xQewd7QWTtq"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an SVR model\n",
-        "model2 = SVR()"
+     "data": {
+      "text/plain": [
+       "(5659, 4)"
       ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "bmtt76RuCeyG",
+    "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 42,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "22SaCsQmfhgP",
-        "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "SVR()"
-            ]
-          },
-          "execution_count": 42,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model2.fit(X_train, y_train)"
+     "data": {
+      "text/plain": [
+       "(1415, 4)"
       ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "CeJkUJ92Ciqd",
+    "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 43,
-      "metadata": {
-        "id": "OQ1nL4oYfkAC"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"SVR\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
+     "data": {
+      "text/plain": [
+       "(5659,)"
       ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "7HGC7VuTCjWc",
+    "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hcIfVMWdgcKt"
-      },
-      "source": [
-        "## 3. Random Forest"
+     "data": {
+      "text/plain": [
+       "(1415,)"
       ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to evaluate and print RMSE, MAE, and MAPE\n",
+    "def evaluate_model(model, X_test, y_test):\n",
+    "    predictions = model.predict(X_test)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+    "    mae = mean_absolute_error(y_test, predictions)\n",
+    "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+    "\n",
+    "    print(f\"RMSE: {rmse}\")\n",
+    "    print(f\"MAE: {mae}\")\n",
+    "    print(f\"MAPE: {mape}\\n\")\n",
+    "    \n",
+    "    return rmse, mae, mape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = {\n",
+    "    \"Model\": [],\n",
+    "    \"RMSE\": [],\n",
+    "    \"MAE\": [],\n",
+    "    \"MAPE\": []\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c6Ek8jRlO2_I"
+   },
+   "source": [
+    "## 1. LINEAR REGRESSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "id": "RdZ1SpzdMHAJ"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a linear regression model\n",
+    "model1 = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "mPM035IzMY04",
+    "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 46,
-      "metadata": {
-        "id": "f7raXT_hf2ij"
-      },
-      "outputs": [],
-      "source": [
-        "model3 = RandomForestRegressor()"
+     "data": {
+      "text/plain": [
+       "5286    257.350006\n",
+       "3408    129.464996\n",
+       "5477    279.350006\n",
+       "6906    588.500000\n",
+       "530      21.644367\n",
+       "Name: Close, dtype: float64"
       ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
     },
+    "id": "qBhQ9HbYMI3d",
+    "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 48,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "fF002Yepgk55",
-        "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "RandomForestRegressor()"
-            ]
-          },
-          "execution_count": 48,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-20 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-20 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-20 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-20 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-20 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-20 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-20 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-20 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
       ],
-      "source": [
-        "# Train the model\n",
-        "model3.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "metadata": {
-        "id": "8nRU_pzEgnCt"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Random Forest\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
+      "text/plain": [
+       "LinearRegression()"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mZsLwLivhLGH"
-      },
-      "source": [
-        "## 4. Gradient Boosting Models (GBM)"
-      ]
-    },
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model1.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "id": "X269co2kMS4z"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 52,
-      "metadata": {
-        "id": "TI8idoxOg6jF"
-      },
-      "outputs": [],
-      "source": [
-        "model4 = GradientBoostingRegressor()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 1.6881364651923558\n",
+      "MAE: 0.9433353486266928\n",
+      "MAPE: 0.006085435968276741\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Linear Regressor\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GxtMzlg-gR2P"
+   },
+   "source": [
+    "## 2. SVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "id": "0xQewd7QWTtq"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an SVR model\n",
+    "model2 = SVR()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
     },
+    "id": "22SaCsQmfhgP",
+    "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 54,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "2gpbDxshhexj",
-        "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ]
-          },
-          "execution_count": 54,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-21 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-21 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-21 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-21 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-21 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-21 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-21 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-21 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-21 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
       ],
-      "source": [
-        "# Train the model\n",
-        "model4.fit(X_train, y_train)"
+      "text/plain": [
+       "SVR()"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 55,
-      "metadata": {
-        "id": "Jj9DXdUPhh9V"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"GBM\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d8nSGoyuh9dx"
-      },
-      "source": [
-        "## 5. Extreme Gradient Boosting (XGBoost)"
-      ]
-    },
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model2.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "id": "OQ1nL4oYfkAC"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 58,
-      "metadata": {
-        "id": "DyhhdlZAhx94"
-      },
-      "outputs": [],
-      "source": [
-        "import xgboost as xgb\n",
-        "# Create an XGBoost model\n",
-        "model5 = xgb.XGBRegressor()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 17.574809673127547\n",
+      "MAE: 6.278157692070486\n",
+      "MAPE: 0.09040265035344064\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"SVR\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hcIfVMWdgcKt"
+   },
+   "source": [
+    "## 3. Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "id": "f7raXT_hf2ij"
+   },
+   "outputs": [],
+   "source": [
+    "model3 = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
     },
+    "id": "fF002Yepgk55",
+    "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 60,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "RAIwxIp5iH9Z",
-        "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-              "             colsample_bylevel=None, colsample_bynode=None,\n",
-              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
-              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
-              "             gamma=None, grow_policy=None, importance_type=None,\n",
-              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
-              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
-              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
-              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
-              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-              "             num_parallel_tree=None, random_state=None, ...)"
-            ]
-          },
-          "execution_count": 60,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-22 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-22 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-22 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-22 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-22 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-22 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-22 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-22 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-22 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
       ],
-      "source": [
-        "# Train the model\n",
-        "model5.fit(X_train, y_train)"
+      "text/plain": [
+       "RandomForestRegressor()"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"XGBoost\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "A_J776rtiovq"
-      },
-      "source": [
-        "## 6. AdaBoostRegressor"
-      ]
-    },
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model3.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "id": "8nRU_pzEgnCt"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 64,
-      "metadata": {
-        "id": "HNq66cXRiYPJ"
-      },
-      "outputs": [],
-      "source": [
-        "model6 = AdaBoostRegressor()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.2053909891328036\n",
+      "MAE: 1.2608162799481166\n",
+      "MAPE: 0.008015308194076972\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Random Forest\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mZsLwLivhLGH"
+   },
+   "source": [
+    "## 4. Gradient Boosting Models (GBM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "id": "TI8idoxOg6jF"
+   },
+   "outputs": [],
+   "source": [
+    "model4 = GradientBoostingRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
     },
+    "id": "2gpbDxshhexj",
+    "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 66,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "P0oB5wjQivBr",
-        "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "AdaBoostRegressor()"
-            ]
-          },
-          "execution_count": 66,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-23 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-23 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-23 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-23 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-23 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-23 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-23 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-23 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-23 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" checked><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
       ],
-      "source": [
-        "# Train the model\n",
-        "model6.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 67,
-      "metadata": {
-        "id": "Bf1m5ukOi2VM"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Q9DzOt3CkWFX"
-      },
-      "source": [
-        "## 7. Decision Tree"
+      "text/plain": [
+       "GradientBoostingRegressor()"
       ]
-    },
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model4.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "id": "Jj9DXdUPhh9V"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 70,
-      "metadata": {
-        "id": "23DZ2biSjF9a"
-      },
-      "outputs": [],
-      "source": [
-        "model7 = DecisionTreeRegressor()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.6985863368468084\n",
+      "MAE: 1.692542658558929\n",
+      "MAPE: 0.011883244132236716\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"GBM\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d8nSGoyuh9dx"
+   },
+   "source": [
+    "## 5. Extreme Gradient Boosting (XGBoost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "id": "DyhhdlZAhx94"
+   },
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "# Create an XGBoost model\n",
+    "model5 = xgb.XGBRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
     },
+    "id": "RAIwxIp5iH9Z",
+    "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 72,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "6mQEQf-ykc9F",
-        "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "DecisionTreeRegressor()"
-            ]
-          },
-          "execution_count": 72,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-24 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-24 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-24 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-24 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-24 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-24 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-24 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-24 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-24 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" checked><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
       ],
-      "source": [
-        "# Train the model\n",
-        "model7.fit(X_train, y_train)"
+      "text/plain": [
+       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 73,
-      "metadata": {
-        "id": "BFJ9q_tvkgRC"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"Decision Tree\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LH-B-Xd6k5UD"
-      },
-      "source": [
-        "## 8. KNeighborsRegressor(KNN)"
-      ]
-    },
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model5.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 76,
-      "metadata": {
-        "id": "JVDSed7yktFY"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a KNN model\n",
-        "model8 = KNeighborsRegressor()"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.733930065274145\n",
+      "MAE: 1.502457380471909\n",
+      "MAPE: 0.010026410639661481\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"XGBoost\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "A_J776rtiovq"
+   },
+   "source": [
+    "## 6. AdaBoostRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "id": "HNq66cXRiYPJ"
+   },
+   "outputs": [],
+   "source": [
+    "model6 = AdaBoostRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
     },
+    "id": "P0oB5wjQivBr",
+    "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 78,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 74
-        },
-        "id": "9fn64o-ZlBka",
-        "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
-            ],
-            "text/plain": [
-              "KNeighborsRegressor()"
-            ]
-          },
-          "execution_count": 78,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-25 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-25 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-25 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-25 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-25 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-25 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-25 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-25 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-25 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" checked><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
       ],
-      "source": [
-        "# Train the model\n",
-        "model8.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 79,
-      "metadata": {
-        "id": "hbfbbjcSlDn7"
-      },
-      "outputs": [],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"KNN\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
+      "text/plain": [
+       "AdaBoostRegressor()"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "X5XtlzMXljps"
-      },
-      "source": [
-        "## 9. Artificial Neural Networks (ANN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 84,
-      "metadata": {
-        "id": "vd1fDjQiltP4"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an ANN model\n",
-        "model9 = Sequential()\n",
-        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
-        "model9.add(Dense(16, activation='relu'))\n",
-        "model9.add(Dense(1, activation='linear'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 85,
-      "metadata": {
-        "id": "ZIf94WLMlv04"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model9.compile(loss='mean_squared_error', optimizer='adam')"
-      ]
-    },
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model6.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "id": "Bf1m5ukOi2VM"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 86,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "FX5DTKqslxWf",
-        "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca68076d540>"
-            ]
-          },
-          "execution_count": 86,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 9.175482477551942\n",
+      "MAE: 7.527617905792734\n",
+      "MAPE: 0.1858930099598583\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Q9DzOt3CkWFX"
+   },
+   "source": [
+    "## 7. Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "id": "23DZ2biSjF9a"
+   },
+   "outputs": [],
+   "source": [
+    "model7 = DecisionTreeRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
     },
+    "id": "6mQEQf-ykc9F",
+    "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 87,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OVW2qpNsmGVq",
-        "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "45/45 [==============================] - 0s 1ms/step\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-26 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-26 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-26 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-26 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-26 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-26 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-26 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-26 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-26 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-26 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-26 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-26 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-26 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-26 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
       ],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"ANN\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vjSMQNcOnFPJ"
-      },
-      "source": [
-        "## 10. LSTM(Long Short term Memory)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 92,
-      "metadata": {
-        "id": "uACvajfImrbB"
-      },
-      "outputs": [],
-      "source": [
-        "# Reshape the input data for LSTM\n",
-        "n_features = X_train_scaled.shape[1]\n",
-        "n_steps = 10\n",
-        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
-        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
-        "\n",
-        "# Reshape the input data\n",
-        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
-        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 93,
-      "metadata": {
-        "id": "r066pVYpnXH5"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an LSTM model\n",
-        "model = Sequential()\n",
-        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
-        "model.add(Dense(1))\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 94,
-      "metadata": {
-        "id": "YpSfHu6gov35"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+      "text/plain": [
+       "DecisionTreeRegressor()"
       ]
-    },
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model7.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "id": "BFJ9q_tvkgRC"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 95,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0vHjcluaoxzP",
-        "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.History at 0x7ca680459cc0>"
-            ]
-          },
-          "execution_count": 95,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Train the model\n",
-        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 3.12966540689625\n",
+      "MAE: 1.6497286032971983\n",
+      "MAPE: 0.010286427942970355\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Decision Tree\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LH-B-Xd6k5UD"
+   },
+   "source": [
+    "## 8. KNeighborsRegressor(KNN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "id": "JVDSed7yktFY"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a KNN model\n",
+    "model8 = KNeighborsRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
     },
+    "id": "9fn64o-ZlBka",
+    "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 96,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "gEE06_TjozYv",
-        "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "44/44 [==============================] - 0s 4ms/step\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-27 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-27 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-27 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-27 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-27 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-27 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-27 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-27 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-27 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-27 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-27 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-27 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-27 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-27 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-27\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" checked><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
       ],
-      "source": [
-        "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
-        "metrics[\"Model\"].append(\"LSTM\")\n",
-        "metrics[\"RMSE\"].append(rmse)\n",
-        "metrics[\"MAE\"].append(mae)\n",
-        "metrics[\"MAPE\"].append(mape)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Create a DataFrame for metrics\n",
-        "metrics_df = pd.DataFrame(metrics)\n",
-        "\n",
-        "# Plot RMSE, MAE, and MAPE for each model\n",
-        "plt.figure(figsize=(15, 5))\n",
-        "\n",
-        "# RMSE Plot\n",
-        "plt.subplot(1, 3, 1)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
-        "plt.xlabel('RMSE')\n",
-        "plt.title('RMSE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# MAE Plot\n",
-        "plt.subplot(1, 3, 2)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n",
-        "plt.xlabel('MAE')\n",
-        "plt.title('MAE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
+      "text/plain": [
+       "KNeighborsRegressor()"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# MAPE Plot\n",
-        "plt.subplot(1, 3, 3)\n",
-        "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
-        "plt.xlabel('MAPE')\n",
-        "plt.title('MAPE for Different Models')\n",
-        "plt.tight_layout()\n",
-        "plt.show()"
-      ]
-    },
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model8.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "id": "hbfbbjcSlDn7"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Using of LightGBM and CatBoost For Optimizing the model accuracy and time complexity"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 3.0274590148039873\n",
+      "MAE: 1.7525904376439672\n",
+      "MAPE: 0.013668115353592272\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"KNN\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "X5XtlzMXljps"
+   },
+   "source": [
+    "## 9. Artificial Neural Networks (ANN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an ANN model\n",
+    "model9 = Sequential()\n",
+    "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+    "model9.add(Dense(16, activation='relu'))\n",
+    "model9.add(Dense(1, activation='linear'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "id": "ZIf94WLMlv04"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model9.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "FX5DTKqslxWf",
+    "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Import necessary libraries\n",
-        "import lightgbm as lgb\n",
-        "from catboost import CatBoostRegressor\n",
-        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
-        "\n",
-        "# Function to train and evaluate a model\n",
-        "def train_and_evaluate_model(model, X_train, X_test, y_train, y_test):\n",
-        "    model.fit(X_train, y_train)\n",
-        "    pred = model.predict(X_test)\n",
-        "    rmse = np.sqrt(mean_squared_error(y_test, pred))\n",
-        "    mae = mean_absolute_error(y_test, pred)\n",
-        "    mape = mean_absolute_percentage_error(y_test, pred)\n",
-        "    accuracy = accuracy_score(y_test > pred, y_test > pred.round())\n",
-        "    precision = precision_score(y_test > pred, y_test > pred.round())\n",
-        "    confusion = confusion_matrix(y_test > pred, y_test > pred.round())\n",
-        "    recall = recall_score(y_test > pred, y_test > pred.round())\n",
-        "    f1 = f1_score(y_test > pred, y_test > pred.round())\n",
-        "    return rmse, mae, mape, accuracy, precision, confusion, recall, f1\n",
-        "\n",
-        "# Train and evaluate LightGBM model for from this directly print accuracy \n",
-        "model_lightgbm = lgb.LGBMRegressor()\n",
-        "metrics_lightgbm = train_and_evaluate_model(model_lightgbm, X_train, X_test, y_train, y_test)\n",
-        "print(\"LightGBM Metrics:\", metrics_lightgbm)\n",
-        "\n",
-        "# Train and evaluate CatBoost model\n",
-        "model_catboost = CatBoostRegressor(verbose=0)\n",
-        "metrics_catboost = train_and_evaluate_model(model_catboost, X_train, X_test, y_train, y_test)\n",
-        "print(\"CatBoost Metrics:\", metrics_catboost)"
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1a508bc2c90>"
       ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "OVW2qpNsmGVq",
+    "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
+      "RMSE: 2.801001091255311\n",
+      "MAE: 1.7605365826618848\n",
+      "MAPE: 0.0126215060590655\n",
+      "\n"
+     ]
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"ANN\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vjSMQNcOnFPJ"
+   },
+   "source": [
+    "## 10. LSTM(Long Short term Memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "id": "uACvajfImrbB"
+   },
+   "outputs": [],
+   "source": [
+    "# Reshape the input data for LSTM\n",
+    "n_features = X_train_scaled.shape[1]\n",
+    "n_steps = 10\n",
+    "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+    "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+    "\n",
+    "# Reshape the input data\n",
+    "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+    "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+    "model.add(Dense(1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "id": "YpSfHu6gov35"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
     "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
+     "base_uri": "https://localhost:8080/"
     },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
+    "id": "0vHjcluaoxzP",
+    "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1a508dda390>"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"LSTM\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a DataFrame for metrics\n",
+    "metrics_df = pd.DataFrame(metrics)\n",
+    "\n",
+    "# Plot RMSE, MAE, and MAPE for each model\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# RMSE Plot\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
+    "plt.xlabel('RMSE')\n",
+    "plt.title('RMSE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MAE Plot\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n",
+    "plt.xlabel('MAE')\n",
+    "plt.title('MAE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MAPE Plot\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
+    "plt.xlabel('MAPE')\n",
+    "plt.title('MAPE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using of LightGBM and CatBoost For Optimizing the model accuracy and time complexity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import lightgbm as lgb\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "\n",
+    "# Function to train and evaluate a model\n",
+    "def train_and_evaluate_model(model, X_train, X_test, y_train, y_test):\n",
+    "    model.fit(X_train, y_train)\n",
+    "    pred = model.predict(X_test)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, pred))\n",
+    "    mae = mean_absolute_error(y_test, pred)\n",
+    "    mape = mean_absolute_percentage_error(y_test, pred)\n",
+    "    accuracy = accuracy_score(y_test > pred, y_test > pred.round())\n",
+    "    precision = precision_score(y_test > pred, y_test > pred.round())\n",
+    "    confusion = confusion_matrix(y_test > pred, y_test > pred.round())\n",
+    "    recall = recall_score(y_test > pred, y_test > pred.round())\n",
+    "    f1 = f1_score(y_test > pred, y_test > pred.round())\n",
+    "    return rmse, mae, mape, accuracy, precision, confusion, recall, f1\n",
+    "\n",
+    "# Train and evaluate LightGBM model for from this directly print accuracy \n",
+    "model_lightgbm = lgb.LGBMRegressor()\n",
+    "metrics_lightgbm = train_and_evaluate_model(model_lightgbm, X_train, X_test, y_train, y_test)\n",
+    "print(\"LightGBM Metrics:\", metrics_lightgbm)\n",
+    "\n",
+    "# Train and evaluate CatBoost model\n",
+    "model_catboost = CatBoostRegressor(verbose=0)\n",
+    "metrics_catboost = train_and_evaluate_model(model_catboost, X_train, X_test, y_train, y_test)\n",
+    "print(\"CatBoost Metrics:\", metrics_catboost)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 0
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }

From fd013b1f1d1d2e55ba3e6b894869142b6aa26684 Mon Sep 17 00:00:00 2001
From: Ayushi Sha <124066968+AYUSHI-SHA@users.noreply.github.com>
Date: Mon, 7 Oct 2024 20:05:44 +0530
Subject: [PATCH 12/76] Updated graphs

---
 README.md                                       |   7 +++----
 .../085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg   | Bin 0 -> 34062 bytes
 .../6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg   | Bin 0 -> 32177 bytes
 .../f23e9194-72de-438d-bd69-744667680d3e.jpeg   | Bin 0 -> 31498 bytes
 4 files changed, 3 insertions(+), 4 deletions(-)
 create mode 100644 images/085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg
 create mode 100644 images/6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg
 create mode 100644 images/f23e9194-72de-438d-bd69-744667680d3e.jpeg

diff --git a/README.md b/README.md
index 50519e6..5e8c229 100644
--- a/README.md
+++ b/README.md
@@ -54,16 +54,15 @@ The sequence of all the algorithms used is as follows:
 10. LSTM(Long Short term Memory)
 
 The **Root Mean Square Error (RMSE)** of all the following 10 Regression Algorithms is provided below: 
-
-![image](https://github.com/rohitinu6/Stock-Price-Prediction/assets/113301503/5c3d986f-ef0f-453e-8f5a-e43193489174)
+![image](images\f23e9194-72de-438d-bd69-744667680d3e.jpeg)
 
 The **Mean Absolute Error (MAE)** of all the following 10 Regression Algorithms is provided below: 
 
-![image](https://github.com/rohitinu6/Stock-Price-Prediction/assets/113301503/50b9a8ae-72c6-4927-8356-18af1f1cacfb)
+![image](images\085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg)
 
 The **Mean Absolute Percentage Error (MAPE)** of all the following 10 Regression Algorithms is provided below: 
 
-![image](https://github.com/rohitinu6/Stock-Price-Prediction/assets/113301503/4ddab02c-6fa4-414e-b14b-6642dbe6183b)
+![image](images\6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg)
 
 
 
diff --git a/images/085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg b/images/085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg
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HcmV?d00001


From b70ad23435fc5c4ef67e2ca5deaca02f14ba8da4 Mon Sep 17 00:00:00 2001
From: Shraddha <shradulsha@gmail.com>
Date: Mon, 7 Oct 2024 23:51:56 +0530
Subject: [PATCH 13/76] Create greetings.yml

action triggered on pull request and issue creation
---
 .github/workflows/greetings.yml | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 .github/workflows/greetings.yml

diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml
new file mode 100644
index 0000000..d4f4cca
--- /dev/null
+++ b/.github/workflows/greetings.yml
@@ -0,0 +1,16 @@
+name: Greetings
+
+on: [pull_request_target, issues]
+
+jobs:
+  greeting:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+    - uses: actions/first-interaction@v1
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        issue-message: "Ensure the issue is not similar or previously being worked on.Thanks for your time"
+        pr-message: "Ensure the PR matches the requirements mentioned in the Contribution guide. The maintainer might get in touch to enusre quality. Thanks for your time"

From 9510a4502d1b8c5c77a68374374dcbfb147a47c0 Mon Sep 17 00:00:00 2001
From: Shraddha <shradulsha@gmail.com>
Date: Tue, 8 Oct 2024 00:09:00 +0530
Subject: [PATCH 14/76] Create dependencies.yml

---
 .github/workflows/dependencies.yml | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 .github/workflows/dependencies.yml

diff --git a/.github/workflows/dependencies.yml b/.github/workflows/dependencies.yml
new file mode 100644
index 0000000..65825f4
--- /dev/null
+++ b/.github/workflows/dependencies.yml
@@ -0,0 +1,29 @@
+name: Install Dependencies
+
+on:
+    workflow_dispatch
+  # push:
+  #   branches:
+  #     - main
+  #     - develop  
+  # pull_request:  
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4 
+    - name: Set up Python 3.x  
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.x'
+
+    - name: Install dependencies from requirements.txt
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+
+    - name: Verify installation 
+      run: |
+        pip list  # List installed packages

From 3f90d6b18ef0faa20fb0ac70e4f7c37740f75d6f Mon Sep 17 00:00:00 2001
From: Shraddha <shradulsha@gmail.com>
Date: Tue, 8 Oct 2024 00:18:36 +0530
Subject: [PATCH 15/76] Update dependencies.yml

---
 .github/workflows/dependencies.yml | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/dependencies.yml b/.github/workflows/dependencies.yml
index 65825f4..c6adb38 100644
--- a/.github/workflows/dependencies.yml
+++ b/.github/workflows/dependencies.yml
@@ -1,12 +1,11 @@
 name: Install Dependencies
 
 on:
-    workflow_dispatch
-  # push:
-  #   branches:
-  #     - main
-  #     - develop  
-  # pull_request:  
+  push:
+    branches:
+      - main
+      - develop  
+  pull_request:  
 
 jobs:
   build:
@@ -19,7 +18,7 @@ jobs:
       with:
         python-version: '3.x'
 
-    - name: Install dependencies from requirements.txt
+    - name: dependencies installation
       run: |
         python -m pip install --upgrade pip
         pip install -r requirements.txt

From 0beaa2c6c2bfd9eb324754d11c9cea4919b25ce0 Mon Sep 17 00:00:00 2001
From: Shraddha <shradulsha@gmail.com>
Date: Tue, 8 Oct 2024 00:20:37 +0530
Subject: [PATCH 16/76] Create contributioncheck.yml

---
 .github/workflows/contributioncheck.yml | 31 +++++++++++++++++++++++++
 1 file changed, 31 insertions(+)
 create mode 100644 .github/workflows/contributioncheck.yml

diff --git a/.github/workflows/contributioncheck.yml b/.github/workflows/contributioncheck.yml
new file mode 100644
index 0000000..9fa722c
--- /dev/null
+++ b/.github/workflows/contributioncheck.yml
@@ -0,0 +1,31 @@
+name: PR Contribution Rules Check
+
+on:
+    workflow_dispatch:
+  # pull_request:
+  #   types: [opened, edited, synchronize]  # Trigger on PR creation or update
+
+jobs:
+  check-contribution-rules:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout the repository
+      uses: actions/checkout@v4
+
+    - name: Check PR Description for Contribution Rules
+      run: |
+        # Check for issue reference
+        if ! grep -q "Issue Reference:" "$GITHUB_EVENT_PATH"; then
+          echo "PR does not reference an issue."
+          exit 1
+        fi
+
+        # Check for description of changes
+        if ! grep -q "Description of changes:" "$GITHUB_EVENT_PATH"; then
+          echo "PR does not contain a description of changes."
+          exit 1
+        fi
+
+    - name: Success message
+      run: echo "PR follows contribution rules."

From c96378f012527e283b2bfde4572a39a087c172fb Mon Sep 17 00:00:00 2001
From: Shraddha <shradulsha@gmail.com>
Date: Tue, 8 Oct 2024 00:31:17 +0530
Subject: [PATCH 17/76] Create PR_TEMPLATE.md

---
 .github/ISSUE_TEMPLATE/PR_TEMPLATE.md | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/PR_TEMPLATE.md

diff --git a/.github/ISSUE_TEMPLATE/PR_TEMPLATE.md b/.github/ISSUE_TEMPLATE/PR_TEMPLATE.md
new file mode 100644
index 0000000..e76bf49
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/PR_TEMPLATE.md
@@ -0,0 +1,26 @@
+## Issue Reference Number:
+<!-- Please mention the issue number this PR addresses (e.g., Resolves #123) -->
+
+## Description of Changes:
+<!-- Please provide a summary of the changes this PR introduces. Include all relevant context -->
+
+## Type of Change:
+<!-- Please delete options that are not relevant -->
+- [ ] Bug fix
+- [ ] New ML model
+- [ ] Feature engineering
+- [ ] Data analysis
+- [ ] Documentation update
+- [ ] Code refactor
+- [ ] Other (please specify in detail):
+
+## Checklist:
+<!-- Please check off the following, if applicable -->
+- [ ] My code follows the style guidelines of this project
+- [ ] I have performed a self-review of my code
+- [ ] I have made sure to comment on my code, particularly in hard-to-understand areas
+- [ ] My changes generate no new warnings or errors
+- [ ] Unit tests pass locally with my changes
+
+## Notes for Testings your implementation:
+<!-- Describe how the changes or the model you added can be tested for accuracy. Include any setup or dependencies that the maintainer or reviewer might need to install -->

From a5949e9481f2404e2f887734529501b1eee6ac68 Mon Sep 17 00:00:00 2001
From: Shraddha <shradulsha@gmail.com>
Date: Tue, 8 Oct 2024 00:32:46 +0530
Subject: [PATCH 18/76] Rename .github/ISSUE_TEMPLATE/PR_TEMPLATE.md to
 .github/PR_TEMPLATE.md

---
 .github/{ISSUE_TEMPLATE => }/PR_TEMPLATE.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename .github/{ISSUE_TEMPLATE => }/PR_TEMPLATE.md (100%)

diff --git a/.github/ISSUE_TEMPLATE/PR_TEMPLATE.md b/.github/PR_TEMPLATE.md
similarity index 100%
rename from .github/ISSUE_TEMPLATE/PR_TEMPLATE.md
rename to .github/PR_TEMPLATE.md

From b2a64b4c73e59139081e93c92398313eef86806a Mon Sep 17 00:00:00 2001
From: Shraddha <shradulsha@gmail.com>
Date: Tue, 8 Oct 2024 00:35:12 +0530
Subject: [PATCH 19/76] Update PR_TEMPLATE.md

---
 .github/PR_TEMPLATE.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/PR_TEMPLATE.md b/.github/PR_TEMPLATE.md
index e76bf49..44b905c 100644
--- a/.github/PR_TEMPLATE.md
+++ b/.github/PR_TEMPLATE.md
@@ -22,5 +22,5 @@
 - [ ] My changes generate no new warnings or errors
 - [ ] Unit tests pass locally with my changes
 
-## Notes for Testings your implementation:
+## Notes for Testing your implementation:
 <!-- Describe how the changes or the model you added can be tested for accuracy. Include any setup or dependencies that the maintainer or reviewer might need to install -->

From 9545421b57c4afbeea3564e805d279e57c656ee0 Mon Sep 17 00:00:00 2001
From: Shraddha <shradulsha@gmail.com>
Date: Tue, 8 Oct 2024 00:40:40 +0530
Subject: [PATCH 20/76] Update contributioncheck.yml

---
 .github/workflows/contributioncheck.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/contributioncheck.yml b/.github/workflows/contributioncheck.yml
index 9fa722c..a96b3bf 100644
--- a/.github/workflows/contributioncheck.yml
+++ b/.github/workflows/contributioncheck.yml
@@ -1,9 +1,8 @@
 name: PR Contribution Rules Check
 
 on:
-    workflow_dispatch:
-  # pull_request:
-  #   types: [opened, edited, synchronize]  # Trigger on PR creation or update
+  pull_request:
+    types: [opened, edited, synchronize]
 
 jobs:
   check-contribution-rules:

From be3a837b38ec669bd071b8a00fa56210d31bd04a Mon Sep 17 00:00:00 2001
From: Pankaj Kumar Goyal <140732455+Pankaj4152@users.noreply.github.com>
Date: Tue, 8 Oct 2024 00:46:59 +0530
Subject: [PATCH 21/76] indicator graph improved

---
 .../Stock_Price_Prediction_REMOTE_20502.ipynb | 20 ++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/Python File/Stock_Price_Prediction_REMOTE_20502.ipynb b/Python File/Stock_Price_Prediction_REMOTE_20502.ipynb
index c08a8c8..60d2fc6 100644
--- a/Python File/Stock_Price_Prediction_REMOTE_20502.ipynb	
+++ b/Python File/Stock_Price_Prediction_REMOTE_20502.ipynb	
@@ -324,10 +324,10 @@
       ],
       "source": [
         "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
-        "\n",
-        "ax1.plot(df[\"SMA_10\"], label = \"SMA_10\", color='Red', linewidth=1, alpha=0.8)\n",
-        "ax1.plot(df[\"SMA_50\"], label = \"SMA_50\", color='Green', linewidth=1, alpha=0.8)\n",
-        "ax1.plot(df[\"Close\"], label = \"Close\", color='Blue', linewidth=2)\n",
+        "days = int(input(\"of how many past days you want to see graph: \"))\n",
+        "ax1.plot(df[\"SMA_10\"][-days:], label = \"SMA_10\", color='Red', linewidth=1, alpha=0.8)\n",
+        "ax1.plot(df[\"SMA_50\"][-days:], label = \"SMA_50\", color='Green', linewidth=1, alpha=0.8)\n",
+        "ax1.plot(df[\"Close\"][-days:], label = \"Close\", color='Blue', linewidth=2)\n",
         "\n",
         "plt.legend()\n",
         "plt.show()"
@@ -397,14 +397,15 @@
         "# Create subplots\n",
         "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})\n",
         "\n",
+        "days = int(input(\"of how many past days you want to see graph: \"))\n",
         "# price graph\n",
-        "ax1.plot(df['Close'], label='Close Price', color='blue')\n",
+        "ax1.plot(df['Close'][-days:], label='Close Price', color='blue')\n",
         "ax1.set_title('Stock Price')\n",
         "ax1.set_ylabel('Price')\n",
         "ax1.legend()\n",
         "\n",
         "# rsi graph\n",
-        "ax2.plot(df['RSI'], label='RSI', color='orange')\n",
+        "ax2.plot(df['RSI'][-days:], label='RSI', color='orange')\n",
         "ax2.axhline(70, color='red', linestyle='--')  # Overbought line\n",
         "ax2.axhline(30, color='green', linestyle='--')  # Oversold line\n",
         "ax2.set_title('Relative Strength Index (RSI)')\n",
@@ -725,14 +726,15 @@
         "fig, ax = plt.subplots(2, 1, figsize=(10, 8))\n",
         "\n",
         "\n",
+        "days = int(input(\"of how many past days you want to see graph: \"))\n",
         "# Plot stock price on first subplot (bold)\n",
-        "ax[0].plot( df['Close'], label='Close Price', color='blue', linewidth=3)\n",
+        "ax[0].plot( df['Close'][-days:], label='Close Price', color='blue', linewidth=3)\n",
         "ax[0].set_title('Stock Price')\n",
         "ax[0].set_ylabel('Price')\n",
         "\n",
         "# Plot MACD and Signal line on second subplot\n",
-        "ax[1].plot( df['MACD'], label='MACD', color='green', linewidth=2)\n",
-        "ax[1].plot( df['Signal_Line'], label='Signal Line', color='red', linestyle='--', alpha=0.7)\n",
+        "ax[1].plot( df['MACD'][-days:], label='MACD', color='green', linewidth=2)\n",
+        "ax[1].plot( df['Signal_Line'][-days:], label='Signal Line', color='red', linestyle='--', alpha=0.7)\n",
         "ax[1].set_title('MACD')\n",
         "ax[1].set_ylabel('MACD Value')\n",
         "\n",

From 761c33db2851d42570763493b36bc431b2d54c04 Mon Sep 17 00:00:00 2001
From: Pankaj Kumar Goyal <140732455+Pankaj4152@users.noreply.github.com>
Date: Tue, 8 Oct 2024 02:13:38 +0530
Subject: [PATCH 22/76] Combined file

---
 stock_market(complete).ipynb | 2509 ++++++++++++++++++++++++++++++++++
 1 file changed, 2509 insertions(+)
 create mode 100644 stock_market(complete).ipynb

diff --git a/stock_market(complete).ipynb b/stock_market(complete).ipynb
new file mode 100644
index 0000000..52417b4
--- /dev/null
+++ b/stock_market(complete).ipynb
@@ -0,0 +1,2509 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 105,
+      "metadata": {
+        "id": "Rqr4Dq5vWXmV"
+      },
+      "outputs": [],
+      "source": [
+        "# importing modules\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+        "\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "\n",
+        "from sklearn.svm import SVR\n",
+        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense,LSTM\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BigUM4vtWZdc"
+      },
+      "outputs": [],
+      "source": [
+        "# Load data\n",
+        "df = pd.read_csv(\"D:\\Pankaj\\GSSOC\\Stock Market\\SBIN.csv\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 185,
+      "metadata": {
+        "id": "RqOe6KPNWs8Q"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vAcMar5WXELR"
+      },
+      "source": [
+        "## Data Analysis and Visualization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "vkDxEavVXHqv",
+        "outputId": "279f15e7-d2d2-442a-c162-e7db9780f2c7"
+      },
+      "outputs": [],
+      "source": [
+        "# Display the first 5 rows of the dataframe\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Vx3DYtfdXJTU",
+        "outputId": "18c93f2d-5232-4e80-dfcf-e7d748901c37"
+      },
+      "outputs": [],
+      "source": [
+        "# Display information about the dataframe\n",
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "SXdvitYIXKKL",
+        "outputId": "1a2c42d0-4f6e-4180-8ffe-7a40ef4ddabf"
+      },
+      "outputs": [],
+      "source": [
+        "# Display summary statistics of the dataframe\n",
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 303
+        },
+        "id": "8rddiK_nXLNi",
+        "outputId": "4b65dd80-db84-45c8-ef16-2133bc25c68b"
+      },
+      "outputs": [],
+      "source": [
+        "# Display the number of missing values in each column\n",
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I1khhoMECh4y"
+      },
+      "source": [
+        "### Detailed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 111,
+      "metadata": {
+        "id": "HpS7l8oSCf6y"
+      },
+      "outputs": [],
+      "source": [
+        "# Function to create scatter plots\n",
+        "def create_scatter_plot(x_data, y_data, size_data=None, title=None, x_label=None, y_label=None, figsize=(15,7)):\n",
+        "    plt.figure(figsize=figsize)\n",
+        "    sns.scatterplot(x=x_data, y=y_data, size=size_data)\n",
+        "    if title:\n",
+        "        plt.title(title)\n",
+        "    if x_label:\n",
+        "        plt.xlabel(x_label)\n",
+        "    if y_label:\n",
+        "        plt.ylabel(y_label)\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 112,
+      "metadata": {
+        "id": "oVEjj8ZMCnDx"
+      },
+      "outputs": [],
+      "source": [
+        "# Function to create line plots\n",
+        "def create_line_plot(x_data, y_data, title=None, x_label=None, y_label=None, figsize=(15,7)):\n",
+        "    plt.figure(figsize=figsize)\n",
+        "    sns.lineplot(x=x_data, y=y_data)\n",
+        "    if title:\n",
+        "        plt.title(title)\n",
+        "    if x_label:\n",
+        "        plt.xlabel(x_label)\n",
+        "    if y_label:\n",
+        "        plt.ylabel(y_label)\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the relationship between 'Date' and 'Volume'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 769
+        },
+        "id": "-n8vOGhdCmer",
+        "outputId": "ea5057f1-9493-43de-ec65-7a017134d171"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the relationship between 'Date' and 'Volume'\n",
+        "sns.scatterplot(x = df['Date'], y = df['Volume'])\n",
+        "sns.kdeplot(x = df['Date'], y = df['Volume'])\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FYvz9YaZDeDp"
+      },
+      "source": [
+        "Early 1990s spike: There is a high trading volume around the mid-1990s, peaking at over 4 × 10^8 (400 million trades).\n",
+        "Post-1996 to 2020: The volume significantly decreases, showing lower levels of activity with some fluctuations and minor peaks, particularly after 2016.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the price variation over time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "q3QfzWquCmjk"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the price variation over time\n",
+        "create_line_plot(df['Date'], df['High'] - df['Low'],\n",
+        "                 title='Price Variation Over Time', x_label='Date', y_label='Price Variation')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mYGFClShDgsj"
+      },
+      "source": [
+        "#### Stable period until 2004:\n",
+        "The price variation was relatively low and stable until around 2003-2004, generally staying under 10 units.\n",
+        "#### 2004-2008 increase:\n",
+        "Starting from 2004, price variations gradually began to increase, peaking sharply just before the 2008 financial crisis. Some spikes went beyond 40 units.\n",
+        "#### 2008-2020 fluctuations:\n",
+        "After the 2008 peak, price variation showed continuous fluctuations with noticeable peaks, though they were more frequent post-2016.\n",
+        "#### 2020 onward:\n",
+        "The recent period (2020-2024) shows significant and frequent fluctuations, with peaks reaching 60+ units, possibly influenced by events like the COVID-19 pandemic and other global factors."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the relationship between 'Adj Close' and 'Volume'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XKsek--aCmoC"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the relationship between 'Adj Close' and 'Volume'\n",
+        "create_line_plot(df['Adj Close'], df['Volume'],\n",
+        "                 title='Adjusted Close vs Volume',\n",
+        "                 x_label='Adjusted Close',\n",
+        "                 y_label='Volume')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "omVoWiC-DjJX"
+      },
+      "source": [
+        "The overall trend seems to be somewhat volatile, with periods of upward and downward movement."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the relationship between 'Open' and 'Adj Close'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "W5TY59ikCv78"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the relationship between 'Open' and 'Adj Close'\n",
+        "create_scatter_plot(df['Open'], df['Adj Close'], size_data=df['Volume'],\n",
+        "                    title='Open vs Adjusted Close', x_label='Open', y_label='Adj Close')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q_BiuXvnDlfJ"
+      },
+      "source": [
+        "The scatter points generally show an upward trend, indicating a positive correlation between the opening and closing prices. This means that, in general, when the stock opens higher, it tends to close higher as well."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the relationship between 'Close' and 'Adj Close'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CD0LiK3VCwiU"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the relationship between 'Close' and 'Adj Close'\n",
+        "create_scatter_plot(df['Close'], df['Adj Close'], size_data=df['Volume'],\n",
+        "                    title='Close vs Adjusted Close', x_label='Close', y_label='Adj Close')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3TgT8XWtDnpy"
+      },
+      "source": [
+        "The scatter points show a very strong upward trend, indicating a strong positive correlation between the closing price and the adjusted closing price. This means that, in general, when the stock closes higher, the adjusted closing price is also higher."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the relationship between 'Open' and 'Close'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bgq_AQ-nCwnA"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the relationship between 'Open' and 'Close'\n",
+        "create_scatter_plot(df['Open'], df['Close'], size_data=df['Volume'],\n",
+        "                    title='Open vs Close', x_label='Open', y_label='Close')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the relationship between 'Open - Close' and 'High - Low'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Zqsy16wWCwrC"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the relationship between 'Open - Close' and 'High - Low'\n",
+        "create_line_plot(df['Open'] - df['Close'], df['High'] - df['Low'],\n",
+        "                 title='Stock Variation vs Price Variation',\n",
+        "                 x_label='Stock Variation (Open - Close)',\n",
+        "                 y_label='Price Variation (High - Low)')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PbUZdfH4Dru0"
+      },
+      "source": [
+        "#### This infers for stable or less price difference volume of stock trading is higher\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the effect of price differences on volume"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NAT3Sy3hC2a3"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the effect of price differences on volume\n",
+        "plt.figure(figsize = (20,10))\n",
+        "sns.scatterplot(x=df['Open'] - df['Close'], y=df['High'] - df['Low'], hue=df['Volume'], palette='viridis', size=df['Volume'], sizes=(20, 200), alpha=0.6)\n",
+        "plt.title('Effect of Price Differences on Volume')\n",
+        "plt.xlabel('Open - Close')\n",
+        "plt.ylabel('High - Low')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the effect of price differences on adjusted close"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B8zeVZt7C2fT"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the effect of price differences on adjusted close\n",
+        "plt.figure(figsize = (20,10))\n",
+        "sns.lineplot(x = df['Open']-df['Close'], y = df['High'] - df['Low'], hue = df['Adj Close'])\n",
+        "plt.title('Effect of Price Differences on Adjusted Close')\n",
+        "plt.xlabel('Open - Close')\n",
+        "plt.ylabel('High - Low')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Plot the relationship between 'Open - Close' and 'Adj Close'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EpjnJ3bJC2jq"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the relationship between 'Open - Close' and 'Adj Close'\n",
+        "plt.figure(figsize = (20,10))\n",
+        "sns.lineplot(x = df['Open']-df['Close'], y = df['Adj Close'])\n",
+        "plt.xlabel('Open - Close')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a pair plot of stock features"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "omOa3x81C2uS"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a pair plot of stock features\n",
+        "subset = df[['Open', 'Close', 'High', 'Low', 'Volume']]\n",
+        "sns.pairplot(subset)\n",
+        "plt.suptitle('Pair Plot of Stock Features', y=1.02)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a 3D scatter plot of 'Open', 'Close', and 'Volume'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gNjDWptzC2zP"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a 3D scatter plot of 'Open', 'Close', and 'Volume'\n",
+        "from mpl_toolkits.mplot3d import Axes3D\n",
+        "\n",
+        "fig = plt.figure(figsize=(12, 8))\n",
+        "ax = fig.add_subplot(111, projection='3d')\n",
+        "ax.scatter(df['Open']- df['Close'], df['High'] - df['Low'],df['Volume'], c='r', marker='o')\n",
+        "ax.set_xlabel('Open Price')\n",
+        "ax.set_ylabel('Close Price')\n",
+        "ax.set_zlabel('Volume')\n",
+        "plt.title('3D Scatter Plot of Open, Close, and Volume')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Calculate the correlation matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Soovqm1IC24P"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate the correlation matrix\n",
+        "correlation_matrix = df.corr(numeric_only = True)\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', square=True, cbar_kws={\"shrink\": .8})\n",
+        "\n",
+        "plt.title('Correlation Heatmap')\n",
+        "plt.show()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d2g_ypkeDvdO"
+      },
+      "source": [
+        "The high correlation between 'Open,' 'High,' 'Low,' 'Close,' and 'Adj Close' shows these features are highly interdependent and tend to move together in the same direction.\n",
+        "The negative correlation of 'Volume' with price-related features suggests that increased trading volume does not necessarily coincide with an increase in stock prices."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### More Charts"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 126,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from plotly.offline import plot\n",
+        "import plotly.graph_objs as go"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "A line chart is a simple and effective way to visualize the closing price of the stock over time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the closing price over time\n",
+        "plt.plot(df['Date'], df['Close'])\n",
+        "plt.xlabel('Date')\n",
+        "plt.ylabel('Closing Price')\n",
+        "plt.title('SBIN Stock Price Over Time')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "A candlestick chart is a more detailed way to visualize the stock price, showing the high, low, open, and close prices for each day"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the candlestick chart\n",
+        "fig = go.Figure(data=[go.Candlestick(x=df['Date'],\n",
+        "                open=df['Open'],\n",
+        "                high=df['High'],\n",
+        "                low=df['Low'],\n",
+        "                close=df['Close'])])\n",
+        "plot(fig, filename='candlestick_chart')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python312\\site-packages\\plotly\\offline\\offline.py:557: UserWarning:\n",
+        "\n",
+        "Your filename `candlestick_chart` didn't end with .html. Adding .html to the end of your file.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "'candlestick_chart.html'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the daily volume\n",
+        "plt.bar(df['Date'], df['Volume'])\n",
+        "plt.xlabel('Date')\n",
+        "plt.ylabel('Volume')\n",
+        "plt.title('SBIN Daily Volume')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the scatter plot of closing price vs volume\n",
+        "sns.scatterplot(x=df['Close'], y=df['Volume'])\n",
+        "plt.xlabel('Closing Price')\n",
+        "plt.ylabel('Volume')\n",
+        "plt.title('Relationship between SBIN Closing Price and Volume')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the histogram of closing prices\n",
+        "plt.hist(df['Close'], bins=50)\n",
+        "plt.xlabel('Closing Price')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.title('Distribution of SBIN Closing Prices')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "A moving average chart is a type of chart that shows the average value of a stock's price over a certain period of time. It is used to smooth out the fluctuations in the price and to identify trends."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the moving average chart\n",
+        "df['MA_50'] = df['Close'].rolling(window=50).mean()\n",
+        "df['MA_200'] = df['Close'].rolling(window=200).mean()\n",
+        "\n",
+        "plt.plot(df['Date'], df['Close'], label='Close')\n",
+        "plt.plot(df['Date'], df['MA_50'], label='MA 50')\n",
+        "plt.plot(df['Date'], df['MA_200'], label='MA 200')\n",
+        "plt.xlabel('Date')\n",
+        "plt.ylabel('Price')\n",
+        "plt.title('SBIN Stock Price with Moving Averages')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This chart shows the closing price of the stock (blue line) along with its 20-day moving average (red line) and two standard deviations plotted above (green line) and below (orange line) it. The Bollinger Bands are used to identify volatility in the stock price"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the Bollinger Bands chart\n",
+        "df['MA_20'] = df['Close'].rolling(window=20).mean()\n",
+        "df['Upper_BB'] = df['MA_20'] + 2*df['Close'].rolling(window=20).std()\n",
+        "df['Lower_BB'] = df['MA_20'] - 2*df['Close'].rolling(window=20).std()\n",
+        "\n",
+        "plt.plot(df['Date'], df['Close'], label='Close')\n",
+        "plt.plot(df['Date'], df['Upper_BB'], label='Upper BB')\n",
+        "plt.plot(df['Date'], df['Lower_BB'], label='Lower BB')\n",
+        "plt.xlabel('Date')\n",
+        "plt.ylabel('Price')\n",
+        "plt.title('SBIN Stock Price with Bollinger Bands')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        " This chart shows the RSI of the stock (blue line) along with two horizontal lines at 30 (red line) and 70 (green line). The RSI is used to identify overbought (above 70) and oversold (below 30) conditions.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the RSI chart\n",
+        "delta = df['Close'].diff(1)\n",
+        "up, down = delta.copy(), delta.copy()\n",
+        "up[up < 0] = 0\n",
+        "down[down > 0] = 0\n",
+        "roll_up = up.rolling(window=14).mean()\n",
+        "roll_down = down.rolling(window=14).mean().abs()\n",
+        "RS = roll_up / roll_down\n",
+        "RSI = 100.0 - (100.0 / (1.0 + RS))\n",
+        "\n",
+        "plt.plot(df['Date'], RSI, label='RSI')\n",
+        "plt.axhline(y=30, color='red', linestyle='--')\n",
+        "plt.axhline(y=70, color='green', linestyle='--')\n",
+        "plt.xlabel('Date')\n",
+        "plt.ylabel('RSI')\n",
+        "plt.title('SBIN Stock Price with RSI')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This chart shows the correlation between the open, high, low, close, and volume of the stock. The correlation is measured on a scale of -1 to 1, where 1 means perfect positive correlation and -1 means perfect negative correlation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the heatmap\n",
+        "corr_matrix = df[['Open', 'High', 'Low', 'Close', 'Volume']].corr()\n",
+        "plt.imshow(corr_matrix, cmap='hot', interpolation='nearest')\n",
+        "plt.title('Correlation Matrix')\n",
+        "plt.colorbar()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D6ssCbaNXem6"
+      },
+      "source": [
+        "## Feature Engineering"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WWb3qGu1Xa30"
+      },
+      "source": [
+        "### Handle missing values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "id": "yi9UnG0BXtGG"
+      },
+      "outputs": [],
+      "source": [
+        "# Drop unnecessary columns('Date' and 'Adj Close')\n",
+        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "id": "_QwN3imwXZGZ"
+      },
+      "outputs": [],
+      "source": [
+        "imputer = SimpleImputer(strategy='mean')\n",
+        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 195,
+      "metadata": {
+        "id": "WYCTE93pXhyC"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cYIyc2QpXvoM"
+      },
+      "source": [
+        "### Adding Indicators"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IUH2Nt70Xy-X"
+      },
+      "source": [
+        "#### SMA"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PzQGu8JnX8Bk"
+      },
+      "source": [
+        "Its the avg of stock price over a specific time period\n",
+        "\n",
+        "SMA = (sum of closing price os past n days) / n\n",
+        "\n",
+        "It helps identify trends by filtering out shortterm fluctuations\n",
+        "\n",
+        "Price above SMA indicate Uptrend and price below SMA indicate lowertrend"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "CFGQWjNTX4Mf"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate the Simple Moving Average (SMA)\n",
+        "df[\"SMA_10\"] = df[\"Close\"].rolling(window=10).mean()\n",
+        "df[\"SMA_50\"] = df[\"Close\"].rolling(window=50).mean()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LoX_CWbbX-tN"
+      },
+      "outputs": [],
+      "source": [
+        "# Drop rows with missing values\n",
+        "df.dropna(subset=['SMA_10', 'SMA_50'], inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jaU2IGH_YcIe"
+      },
+      "source": [
+        "##### SMA Graph"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Plot the SMA graph\n",
+        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
+        "days = int(input(\"of how many past days you want to see graph: \"))\n",
+        "ax1.plot(df[\"SMA_10\"][-days:], label = \"SMA_10\", color='Red', linewidth=1, alpha=0.8)\n",
+        "ax1.plot(df[\"SMA_50\"][-days:], label = \"SMA_50\", color='Green', linewidth=1, alpha=0.8)\n",
+        "ax1.plot(df[\"Close\"][-days:], label = \"Close\", color='Blue', linewidth=2)\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qCxKwtnTX1Ck"
+      },
+      "source": [
+        "#### RSI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bbNsRzqmYiJi"
+      },
+      "source": [
+        "It is a momentum indicator used to measure the speed and change of price movements. It ranges from 0 to 100 and helps identify whether a stock is overbought or oversold.  \n",
+        "\n",
+        "RSI > 70: Overbought  \n",
+        "RSI < 30: Oversold"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "lTkIdld1YaxN"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate the Relative Strength Index (RSI)\n",
+        "delta = df['Close'].diff(1)\n",
+        "\n",
+        "gain = delta.where(delta > 0, 0)\n",
+        "loss = -delta.where(delta < 0, 0)\n",
+        "\n",
+        "avg_gain = gain.rolling(window=14).mean()\n",
+        "avg_loss = loss.rolling(window=14).mean()\n",
+        "\n",
+        "rs = avg_gain / avg_loss  # Relative Strength\n",
+        "df['RSI'] = 100 - (100 / (1 + rs))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Drop rows with missing values\n",
+        "df.dropna(subset=['RSI'], inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bW10tgm0YlSM"
+      },
+      "source": [
+        "##### RSI Graph"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "dqR2l5r-YngD",
+        "outputId": "07d7a94a-4fd6-4f87-cf37-54b6980fa5cb"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the RSI graph\n",
+        "\n",
+        "# Create subplots\n",
+        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})\n",
+        "\n",
+        "days = int(input(\"of how many past days you want to see graph: \"))\n",
+        "# price graph\n",
+        "ax1.plot(df['Close'][-days:], label='Close Price', color='blue')\n",
+        "ax1.set_title('Stock Price')\n",
+        "ax1.set_ylabel('Price')\n",
+        "ax1.legend()\n",
+        "\n",
+        "# rsi graph\n",
+        "ax2.plot(df['RSI'][-days:], label='RSI', color='orange')\n",
+        "ax2.axhline(70, color='red', linestyle='--')  # Overbought line\n",
+        "ax2.axhline(30, color='green', linestyle='--')  # Oversold line\n",
+        "ax2.set_title('Relative Strength Index (RSI)')\n",
+        "ax2.set_ylabel('RSI')\n",
+        "ax2.legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LaU9_eV9X2Zc"
+      },
+      "source": [
+        "#### MACD"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 78,
+      "metadata": {
+        "id": "4j9soKAXXvZX"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate the Moving Average Convergence Divergence (MACD)\n",
+        "# Calculate the short-term and long-term EMAs\n",
+        "df['EMA_12'] = df['Close'].ewm(span=12, adjust=False).mean()\n",
+        "df['EMA_26'] = df['Close'].ewm(span=26, adjust=False).mean()\n",
+        "\n",
+        "# Calculate MACD and Signal line\n",
+        "df['MACD'] = df['EMA_12'] - df['EMA_26']  # MACD line\n",
+        "df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()  # Signal line"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vJU2F-TiYrSB"
+      },
+      "source": [
+        "##### MACD Graph"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "BV8z1ge1Ysl3",
+        "outputId": "9ddf6a13-ee48-45ea-f6f4-e361d1decc11"
+      },
+      "outputs": [],
+      "source": [
+        "# Plot the MACD\n",
+        "fig, ax = plt.subplots(2, 1, figsize=(10, 8))\n",
+        "\n",
+        "\n",
+        "days = int(input(\"of how many past days you want to see graph: \"))\n",
+        "# Plot stock price on first subplot (bold)\n",
+        "ax[0].plot( df['Close'][-days:], label='Close Price', color='blue', linewidth=3)\n",
+        "ax[0].set_title('Stock Price')\n",
+        "ax[0].set_ylabel('Price')\n",
+        "\n",
+        "# Plot MACD and Signal line on second subplot\n",
+        "ax[1].plot( df['MACD'][-days:], label='MACD', color='green', linewidth=2)\n",
+        "ax[1].plot( df['Signal_Line'][-days:], label='Signal Line', color='red', linestyle='--', alpha=0.7)\n",
+        "ax[1].set_title('MACD')\n",
+        "ax[1].set_ylabel('MACD Value')\n",
+        "\n",
+        "# Show legends\n",
+        "ax[0].legend()\n",
+        "ax[1].legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VC-x8kPuY_FU"
+      },
+      "source": [
+        "#### Corrrelations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 425
+        },
+        "id": "I7pi-rfLZBGf",
+        "outputId": "549af121-3ef6-476f-e359-2e3ce2cbb166"
+      },
+      "outputs": [],
+      "source": [
+        "corr = df.corr()\n",
+        "corr"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 746
+        },
+        "id": "1OGAC3uxZCxG",
+        "outputId": "9d7ad372-d66d-4c2a-b72a-717179294401"
+      },
+      "outputs": [],
+      "source": [
+        "plt.figure(figsize=(10,8))\n",
+        "sns.heatmap(corr, annot=True, linewidths=0.5)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 204,
+      "metadata": {
+        "id": "GvIj_HpsZJ26"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v8aLHujGZQu_"
+      },
+      "source": [
+        "## Model Training Preperation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "FV2-jJCSZQaA"
+      },
+      "outputs": [],
+      "source": [
+        "# Select features and target variable\n",
+        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+        "y = df['Close']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "MStjwvxqZV1e"
+      },
+      "outputs": [],
+      "source": [
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "adMP-Zn1ZW7s"
+      },
+      "outputs": [],
+      "source": [
+        "# Scale the features using Min-Max scaling\n",
+        "scaler = MinMaxScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7nfzuX5hZZtj",
+        "outputId": "6e0f44fa-598b-482f-db74-449179691b30"
+      },
+      "outputs": [],
+      "source": [
+        "X_train.shape, X_test.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "avggYFQCZiF8"
+      },
+      "source": [
+        "## Model Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i63wO9AVZkjf"
+      },
+      "source": [
+        "#### 1. LINEAR REGRESSION"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 209,
+      "metadata": {
+        "id": "9AJm7PhjZZ9p"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a linear regression model\n",
+        "model1 = LinearRegression()\n",
+        "\n",
+        "# Train the model\n",
+        "model1.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions on the test set\n",
+        "pred1 = model1.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 210,
+      "metadata": {
+        "id": "lYpraDj7Zwqr"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
+        "mae1 = mean_absolute_error(y_test, pred1)\n",
+        "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
+        "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
+        "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
+        "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
+        "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
+        "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xOAnEaKoZxuY",
+        "outputId": "202a1add-cae6-4637-8106-735d3f958007"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse1)\n",
+        "print(\"MAE:\", mae1)\n",
+        "print(\"MAPE:\", mape1)\n",
+        "print(\"Accuracy:\", accuracy1)\n",
+        "print(\"Precision:\", precision1)\n",
+        "print(\"Confusion Matrix:\\n\", confusion1)\n",
+        "print(\"Recall:\", recall1)\n",
+        "print(\"F1 Score:\", f11)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HxCNbk0JafKE"
+      },
+      "source": [
+        "#### 2. SVR"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### With Grid Search"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "XxOnG95gZ0NR"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an SVR model\n",
+        "model2 = SVR()\n",
+        "param_grid = {'C':[0.1, 1], 'epsilon':[0.01, 0.1, 0.5], 'kernel':['sigmoid']}\n",
+        "GV_SVR = GridSearchCV(model2, param_grid = param_grid, scoring = 'accuracy', n_jobs = -1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "heZ6MwXaalFT",
+        "outputId": "732d3ac8-014f-47fb-f1af-e3f30afbb037"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "GV_SVR.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred2 = GV_SVR.predict(X_test)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "984H1ej2apPP"
+      },
+      "outputs": [],
+      "source": [
+        "GV_SVR.best_params_"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### Without Grid Search"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#fitting without grid search\n",
+        "model2.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred2_1 = model2.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "id": "ErnLBNW6apM4"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
+        "mae2 = mean_absolute_error(y_test, pred2)\n",
+        "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
+        "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
+        "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
+        "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
+        "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
+        "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "82Gd1kSwapKR",
+        "outputId": "1dc4a6a9-f0ad-4b96-eeaa-9682f4912767"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse2)\n",
+        "print(\"MAE:\", mae2)\n",
+        "print(\"MAPE:\", mape2)\n",
+        "print(\"Accuracy:\", accuracy2)\n",
+        "print(\"Precision:\", precision2)\n",
+        "print(\"Confusion Matrix:\\n\", confusion2)\n",
+        "print(\"Recall:\", recall2)\n",
+        "print(\"F1 Score:\", f12)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GsVk_xFZauR6"
+      },
+      "source": [
+        "#### 3. Random Forest"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 217,
+      "metadata": {
+        "id": "YwerJPNxapGt"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "# Create a Random Forest model\n",
+        "model3 = RandomForestRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "fspXTQOXapBl",
+        "outputId": "16e51cc3-390d-4dad-8a04-68b48104ac0b"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "model3.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 219,
+      "metadata": {
+        "id": "Hu0iAgE9ao-_"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred3 = model3.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 220,
+      "metadata": {
+        "id": "WTu2YAoQao6Q"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
+        "mae3 = mean_absolute_error(y_test, pred3)\n",
+        "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
+        "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
+        "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
+        "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
+        "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
+        "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9_7z9JcRao4C",
+        "outputId": "9b6dbe5a-de58-4519-d12b-afd1783b5024"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse3)\n",
+        "print(\"MAE:\", mae3)\n",
+        "print(\"MAPE:\", mape3)\n",
+        "print(\"Accuracy:\", accuracy3)\n",
+        "print(\"Precision:\", precision3)\n",
+        "print(\"Confusion Matrix:\\n\", confusion3)\n",
+        "print(\"Recall:\", recall3)\n",
+        "print(\"F1 Score:\", f13)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kg80FkpDa4Am"
+      },
+      "source": [
+        "#### 4. Gradient Boosting Models (GBM)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 222,
+      "metadata": {
+        "id": "D75bIlqqao1t"
+      },
+      "outputs": [],
+      "source": [
+        "model4 = GradientBoostingRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "uUHqBGsTa9TV",
+        "outputId": "4694a027-32b2-4c7b-c9cc-ee84e6c4b425"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "model4.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 224,
+      "metadata": {
+        "id": "T7XyO3qIa9QU"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred4 = model4.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 225,
+      "metadata": {
+        "id": "j2DCsz23a9M3"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
+        "mae4 = mean_absolute_error(y_test, pred4)\n",
+        "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
+        "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
+        "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
+        "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
+        "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
+        "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tl4jTkC9a9Je",
+        "outputId": "4d2f1ee3-2886-4176-e9dc-2062b3890377"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse4)\n",
+        "print(\"MAE:\", mae4)\n",
+        "print(\"MAPE:\", mape4)\n",
+        "print(\"Accuracy:\", accuracy4)\n",
+        "print(\"Precision:\", precision4)\n",
+        "print(\"Confusion Matrix:\\n\", confusion4)\n",
+        "print(\"Recall:\", recall4)\n",
+        "print(\"F1 Score:\", f14)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aDoDcLUdbCq6"
+      },
+      "source": [
+        "#### 5. Extreme Gradient Boosting (XGBoost)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 227,
+      "metadata": {
+        "id": "rmgm3VADa9Gb"
+      },
+      "outputs": [],
+      "source": [
+        "import xgboost as xgb\n",
+        "# Create an XGBoost model\n",
+        "model5 = xgb.XGBRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        },
+        "id": "txadd-U3a9AP",
+        "outputId": "0a5d9230-a87f-486b-cda4-197face55486"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "model5.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 229,
+      "metadata": {
+        "id": "xYWVMZJ5a89t"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred5 = model5.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 230,
+      "metadata": {
+        "id": "qeDxXt9pa87W"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
+        "mae5 = mean_absolute_error(y_test, pred5)\n",
+        "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
+        "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
+        "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
+        "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
+        "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
+        "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TdY1rRy_a83-",
+        "outputId": "d43b90b2-d098-402f-fbf7-46130f5310e3"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse5)\n",
+        "print(\"MAE:\", mae5)\n",
+        "print(\"MAPE:\", mape5)\n",
+        "print(\"Accuracy:\", accuracy5)\n",
+        "print(\"Precision:\", precision5)\n",
+        "print(\"Confusion Matrix:\\n\", confusion5)\n",
+        "print(\"Recall:\", recall5)\n",
+        "print(\"F1 Score:\", f15)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EbxPon-VbMGE"
+      },
+      "source": [
+        "#### 6. AdaBoostRegressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 232,
+      "metadata": {
+        "id": "Xze9G-tUa802"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import AdaBoostRegressor\n",
+        "# Create an AdaBoost model\n",
+        "model6 = AdaBoostRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "DEmXpAV8a8wC",
+        "outputId": "5cadcb29-13ce-45f0-cfe1-f9e21a24866b"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "model6.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 234,
+      "metadata": {
+        "id": "KvI77shea8tk"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred6 = model6.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 235,
+      "metadata": {
+        "id": "catCNPwla8rL"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
+        "mae6 = mean_absolute_error(y_test, pred6)\n",
+        "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
+        "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
+        "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
+        "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
+        "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
+        "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wpUqeumZa8oZ",
+        "outputId": "db2f5904-a96b-46d9-fbc3-ec519d5b7c7a"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse6)\n",
+        "print(\"MAE:\", mae6)\n",
+        "print(\"MAPE:\", mape6)\n",
+        "print(\"Accuracy:\", accuracy6)\n",
+        "print(\"Precision:\", precision6)\n",
+        "print(\"Confusion Matrix:\\n\", confusion6)\n",
+        "print(\"Recall:\", recall6)\n",
+        "print(\"F1 Score:\", f16)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ubml65zDbUZu"
+      },
+      "source": [
+        "#### 7. Decision Tree"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 237,
+      "metadata": {
+        "id": "qCvEszOKbVGV"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "# Create a Decision Tree model\n",
+        "model7 = DecisionTreeRegressor()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "5lYl0STGbWw8",
+        "outputId": "9aabd48a-73aa-4737-e243-ae4eb5899765"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "model7.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 239,
+      "metadata": {
+        "id": "Wu34WtF-bXy0"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred7 = model7.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 240,
+      "metadata": {
+        "id": "H0NdEFHXbYuI"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
+        "mae7 = mean_absolute_error(y_test, pred7)\n",
+        "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
+        "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
+        "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
+        "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
+        "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
+        "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wq6aws7ZbZft",
+        "outputId": "3f0b27ba-3257-458b-b5d3-abba03fa5b47"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse7)\n",
+        "print(\"MAE:\", mae7)\n",
+        "print(\"MAPE:\", mape7)\n",
+        "print(\"Accuracy:\", accuracy7)\n",
+        "print(\"Precision:\", precision7)\n",
+        "print(\"Confusion Matrix:\\n\", confusion7)\n",
+        "print(\"Recall:\", recall7)\n",
+        "print(\"F1 Score:\", f17)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5OX62RD6bb-V"
+      },
+      "source": [
+        "#### 8. KNeighborsRegressor(KNN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "id": "5kz2DGKMbaem"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a KNN model\n",
+        "model8 = KNeighborsRegressor()\n",
+        "param_grid = {'n_neighbors':[3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]}\n",
+        "GV_KNN = GridSearchCV(model8, param_grid, cv=5, scoring='neg_mean_squared_error')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "PFdqC4oXbeg9",
+        "outputId": "b06c9cd8-c45c-4a8b-bec3-17fe23c6dc6d"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "model8.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "id": "_Rv2V1W8bfhp"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred8 = model8.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "id": "K_QgGMDybgj8"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
+        "mae8 = mean_absolute_error(y_test, pred8)\n",
+        "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
+        "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
+        "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
+        "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
+        "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
+        "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PKv6I2oCbha3",
+        "outputId": "ef250676-44d3-47ff-f7bd-ade1697c4789"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse8)\n",
+        "print(\"MAE:\", mae8)\n",
+        "print(\"MAPE:\", mape8)\n",
+        "print(\"Accuracy:\", accuracy8)\n",
+        "print(\"Precision:\", precision8)\n",
+        "print(\"Confusion Matrix:\\n\", confusion8)\n",
+        "print(\"Recall:\", recall8)\n",
+        "print(\"F1 Score:\", f18)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Grid Search parameters\n",
+        "GV_KNN.fit(X_train, y_train)\n",
+        "pred8_1 = GV_KNN.predict(X_test)\n",
+        "GV_KNN.best_estimator_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "results = GV_KNN.cv_results_\n",
+        "mse = -results['mean_test_score']\n",
+        "k_values = results['param_n_neighbors'].data\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(k_values, mse, marker='o', linestyle='-')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##### KNN Hyperparameter Tuning"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import cross_val_score\n",
+        "\n",
+        "# KNN Hyperparameter Tuning\n",
+        "def knn_hyperparameter_tuning(X_train, y_train):\n",
+        "    k_values = range(1, 31)  # Example range for k\n",
+        "    mse_values = []\n",
+        "    \n",
+        "    for k in k_values:\n",
+        "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
+        "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
+        "        mse_values.append(mse)\n",
+        "    \n",
+        "    return k_values, mse_values\n",
+        "\n",
+        "# Perform KNN Hyperparameter Tuning\n",
+        "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
+        "\n",
+        "# Plotting the results\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
+        "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
+        "plt.xlabel('Number of Neighbors (k)')\n",
+        "plt.ylabel('Mean Squared Error (MSE)')\n",
+        "plt.xticks(k_values)  # Show all k values on x-axis\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-yoFias0bkhT"
+      },
+      "source": [
+        "#### 9. Artificial Neural Networks (ANN)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oj1tv3-Sbm4M",
+        "outputId": "0d547cbc-d317-400a-a304-1bc283287926"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an ANN model\n",
+        "model9 = Sequential()\n",
+        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+        "model9.add(Dense(16, activation='relu'))\n",
+        "model9.add(Dense(1, activation='linear'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Llqm4lw3bnxu"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model9.compile(loss='mean_squared_error', optimizer='adam')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8gzrlwUTbpG7",
+        "outputId": "c3b2372d-3881-4568-efd3-59ffe8bce6f3"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cI6QW1utbp-J",
+        "outputId": "1c8b27b2-3380-40a1-ac68-17a68092f650"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "pred9 = model9.predict(X_test_scaled).flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 252,
+      "metadata": {
+        "id": "-qDeEJUgbrUq"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
+        "mae9 = mean_absolute_error(y_test, pred9)\n",
+        "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
+        "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
+        "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
+        "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
+        "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
+        "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dEhvx1BTbvXO",
+        "outputId": "af73599a-ae1e-4d56-9288-8193e4aaad2b"
+      },
+      "outputs": [],
+      "source": [
+        "# Print the evaluation metrics\n",
+        "print(\"RMSE:\", rmse9)\n",
+        "print(\"MAE:\", mae9)\n",
+        "print(\"MAPE:\", mape9)\n",
+        "print(\"Accuracy:\", accuracy9)\n",
+        "print(\"Precision:\", precision9)\n",
+        "print(\"Confusion Matrix:\\n\", confusion9)\n",
+        "print(\"Recall:\", recall9)\n",
+        "print(\"F1 Score:\", f19)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qosFFsxrbyX7"
+      },
+      "source": [
+        "#### 10. LSTM(Long Short term Memory)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 255,
+      "metadata": {
+        "id": "3QajxGzXbxM4"
+      },
+      "outputs": [],
+      "source": [
+        "# Reshape the input data for LSTM\n",
+        "n_features = X_train_scaled.shape[1]\n",
+        "n_steps = 10\n",
+        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+        "\n",
+        "# Reshape the input data\n",
+        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gNRiL_B0bxJ3",
+        "outputId": "045b5c85-f8ef-4fc4-bf38-1c253d886c82"
+      },
+      "outputs": [],
+      "source": [
+        "# Create an LSTM model\n",
+        "model = Sequential()\n",
+        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+        "model.add(Dense(1))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 257,
+      "metadata": {
+        "id": "DsndIHIQbxHV"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile the model\n",
+        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SzhA1fh9bxEz"
+      },
+      "outputs": [],
+      "source": [
+        "# Train the model\n",
+        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AQXh8a46bxCx"
+      },
+      "outputs": [],
+      "source": [
+        "# Make predictions on the test set\n",
+        "y_pred = model.predict(X_test_reshaped).flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iIXfZ2eubxAD"
+      },
+      "outputs": [],
+      "source": [
+        "# Calculate evaluation metrics\n",
+        "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
+        "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
+        "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
+        "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+        "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9Gjh33nNbw-O"
+      },
+      "outputs": [],
+      "source": [
+        "# Print evaluation metrics\n",
+        "print(\"RMSE:\", rmse10)\n",
+        "print(\"MAE:\", mae10)\n",
+        "print(\"MAPE:\", mape10)\n",
+        "print(\"Accuracy:\", accuracy10)\n",
+        "print(\"Precision:\", precision10)\n",
+        "print(\"Recall:\", recall10)\n",
+        "print(\"F1 Score:\", f110)\n",
+        "print(\"Confusion Matrix:\\n\", confusion10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l6QVMqW-bw66"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Model Performance Graphs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6r0qumqybw4g"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Accuracy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-hFs2mnDbwGQ"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
+        "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n",
+        "\n",
+        "# List of corresponding labels for each accuracy\n",
+        "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, accuracies, color='blue')\n",
+        "plt.xlabel('Accuracy Variables')\n",
+        "plt.ylabel('Accuracy Values')\n",
+        "plt.title('Bar Graph of Accuracies')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### RMSE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1tBYk4qmcEMk"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
+        "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n",
+        "\n",
+        "# List of corresponding labels for each RMSE value\n",
+        "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, rmse_values, color='green')\n",
+        "plt.xlabel('RMSE Variables')\n",
+        "plt.ylabel('RMSE Values')\n",
+        "plt.title('Bar Graph of RMSE')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### MAE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BS0oljCZcFhM"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of MAE values from mae1 to mae10\n",
+        "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n",
+        "\n",
+        "# List of corresponding labels for each MAE value\n",
+        "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, mae_values, color='orange')\n",
+        "plt.xlabel('MAE Variables')\n",
+        "plt.ylabel('MAE Values')\n",
+        "plt.title('Bar Graph of MAE')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### MAPE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9woaoeNVcGx5"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of MAPE values from mape1 to mape10\n",
+        "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n",
+        "\n",
+        "# List of corresponding labels for each MAPE value\n",
+        "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, mape_values, color='purple')\n",
+        "plt.xlabel('MAPE Variables')\n",
+        "plt.ylabel('MAPE Values')\n",
+        "plt.title('Bar Graph of MAPE')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Precision"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bbRSmd3tcIIX"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of precision values from precision1 to precision10\n",
+        "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
+        "\n",
+        "# List of corresponding labels for each precision value\n",
+        "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, precision_values, color='red')\n",
+        "plt.xlabel('Precision Variables')\n",
+        "plt.ylabel('Precision Values')\n",
+        "plt.title('Bar Graph of Precision')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Recall"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tDNk7iuWcJpS"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Assuming you have a list of recall values from recall1 to recall10\n",
+        "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
+        "\n",
+        "# List of corresponding labels for each recall value\n",
+        "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n",
+        "\n",
+        "# Plotting the bar graph\n",
+        "plt.bar(labels, recall_values, color='cyan')\n",
+        "plt.xlabel('Recall Variables')\n",
+        "plt.ylabel('Recall Values')\n",
+        "plt.title('Bar Graph of Recall')\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 6d4cd9105fcb3cb917d6b9baa20d39c89b36a27d Mon Sep 17 00:00:00 2001
From: kundana29 <149463648+kundana29@users.noreply.github.com>
Date: Tue, 8 Oct 2024 19:27:21 +0530
Subject: [PATCH 23/76] Implemented Buy/Sell Recommendation System with K-Means
 Clustering and Silhouette Score Evaluation

- Added K-Means clustering to classify stock price data into Buy, Sell, and Hold signals based on technical indicators such as Returns, Moving Averages (MA_50 and
  MA_200), and Volatility.
- Integrated a backtesting system to simulate trades based on the generated buy/sell signals and calculated final portfolio value.
- Evaluated the clustering quality using the Silhouette Score and optimized the number of clusters using the Elbow Method.
- Enhanced the feature set by calculating additional indicators like MACD and incorporated them into the clustering model.
- Plotted the buy/sell signals on historical stock data to visually validate the system's performance.
---
 buy_sell_recommendation_system.ipynb | 198 +++++++++++++++++++++++++++
 1 file changed, 198 insertions(+)
 create mode 100644 buy_sell_recommendation_system.ipynb

diff --git a/buy_sell_recommendation_system.ipynb b/buy_sell_recommendation_system.ipynb
new file mode 100644
index 0000000..7e1a9ce
--- /dev/null
+++ b/buy_sell_recommendation_system.ipynb
@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Buy/Sell Recommendation System using K-Means Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import necessary libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.metrics import silhouette_score\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load stock data from CSV\n",
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')\n",
+    "stock_data['Date'] = pd.to_datetime(stock_data['Date'], format='%d-%m-%Y')\n",
+    "stock_data.set_index('Date', inplace=True)\n",
+    "\n",
+    "# ## Preprocess the Data to Add Technical Indicators\n",
+    "def calculate_technical_indicators(stock_data):\n",
+    "    # Forward fill any NA values before calculating percentage change\n",
+    "    stock_data['Returns'] = stock_data['Close'].ffill().pct_change()\n",
+    "    \n",
+    "    # Calculate moving averages\n",
+    "    stock_data['MA_50'] = stock_data['Close'].rolling(window=50).mean()\n",
+    "    stock_data['MA_200'] = stock_data['Close'].rolling(window=200).mean()\n",
+    "    \n",
+    "    # Calculate volatility (standard deviation of returns)\n",
+    "    stock_data['Volatility'] = stock_data['Returns'].rolling(window=50).std()\n",
+    "    \n",
+    "    # Drop NaN values resulting from rolling windows\n",
+    "    stock_data.dropna(inplace=True)\n",
+    "    \n",
+    "    return stock_data\n",
+    "\n",
+    "# Apply the preprocessing\n",
+    "stock_data = calculate_technical_indicators(stock_data)\n",
+    "\n",
+    "# ## Feature Set for Clustering\n",
+    "features = stock_data[['Returns', 'MA_50', 'MA_200', 'Volatility']]\n",
+    "\n",
+    "# ## Apply K-Means Clustering\n",
+    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
+    "stock_data['Cluster'] = kmeans.fit_predict(features)\n",
+    "\n",
+    "# ## Define 'Buy', 'Sell', 'Hold' Clusters\n",
+    "cluster_map = {0: 'Hold', 1: 'Buy', 2: 'Sell'}\n",
+    "stock_data['Signal'] = stock_data['Cluster'].map(cluster_map)\n",
+    "\n",
+    "# ## Plot the Buy/Sell Signals on Stock Price\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "plt.plot(stock_data['Close'], label='Close Price', color='blue')\n",
+    "buy_signals = stock_data[stock_data['Signal'] == 'Buy']\n",
+    "sell_signals = stock_data[stock_data['Signal'] == 'Sell']\n",
+    "plt.scatter(buy_signals.index, buy_signals['Close'], label='Buy Signal', marker='^', color='green')\n",
+    "plt.scatter(sell_signals.index, sell_signals['Close'], label='Sell Signal', marker='v', color='red')\n",
+    "plt.title('Stock Price with Buy/Sell Signals')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Stock Price')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Silhouette Score: 0.7111919059346077\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ## Calculate Silhouette Score\n",
+    "silhouette_avg = silhouette_score(features, stock_data['Cluster'])\n",
+    "print(f'Silhouette Score: {silhouette_avg}')\n",
+    "\n",
+    "# ## Elbow Method to Find Optimal Number of Clusters\n",
+    "sse = []\n",
+    "k_range = range(1, 10)\n",
+    "for k in k_range:\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(features)\n",
+    "    sse.append(kmeans.inertia_)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_range, sse, marker='o')\n",
+    "plt.title('Elbow Method for Optimal K')\n",
+    "plt.xlabel('Number of Clusters')\n",
+    "plt.ylabel('Sum of Squared Distances (SSE)')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bought at 429.950012, positions = 232.5851778322546\n",
+      "Final Portfolio Value: 168682.40022284264\n",
+      "Net Profit/Loss: 68682.40022284264\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ## Backtest the Strategy\n",
+    "initial_balance = 100000  # Assume you start with $100,000\n",
+    "balance = initial_balance\n",
+    "positions = 0\n",
+    "stock_price_at_last_signal = 0  # To store the price of stock at the last signal\n",
+    "\n",
+    "for i in range(1, len(stock_data)):\n",
+    "    signal = stock_data['Signal'].iloc[i]\n",
+    "    price = stock_data['Close'].iloc[i]\n",
+    "\n",
+    "    if signal == 'Buy' and positions == 0:\n",
+    "        # Buy as much stock as possible\n",
+    "        positions = balance / price\n",
+    "        stock_price_at_last_signal = price\n",
+    "        print(f'Bought at {price}, positions = {positions}')\n",
+    "        balance = 0  # All money invested\n",
+    "\n",
+    "    elif signal == 'Sell' and positions > 0:\n",
+    "        # Sell all positions\n",
+    "        balance = positions * price\n",
+    "        positions = 0\n",
+    "        profit = (price - stock_price_at_last_signal) * (balance / price)\n",
+    "        print(f'Sold at {price}, balance = {balance}, profit = {profit}')\n",
+    "\n",
+    "# ## Final Portfolio Value (Including Any Remaining Stock)\n",
+    "final_portfolio_value = balance + positions * stock_data['Close'].iloc[-1]\n",
+    "print(f'Final Portfolio Value: {final_portfolio_value}')\n",
+    "print(f'Net Profit/Loss: {final_portfolio_value - initial_balance}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 40a39b47a3e437afc47e13ce12b4cbec77febafe Mon Sep 17 00:00:00 2001
From: DarshAgrawal14 <agrawaldarsh.14@gmail.com>
Date: Tue, 8 Oct 2024 20:53:11 +0530
Subject: [PATCH 24/76] Created arima model

stages of training arima :
- intially just on date and close
- combining with lstm
- updating data to include days , lags and other features and include while training

Current RMSE : 4.67
---
 ARIMA/ARIMA_V2.ipynb |  337 ++++++++++++++
 ARIMA/README.md      |   75 +++
 ARIMA/hybrid.ipynb   | 1028 ++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 1440 insertions(+)
 create mode 100644 ARIMA/ARIMA_V2.ipynb
 create mode 100644 ARIMA/README.md
 create mode 100644 ARIMA/hybrid.ipynb

diff --git a/ARIMA/ARIMA_V2.ipynb b/ARIMA/ARIMA_V2.ipynb
new file mode 100644
index 0000000..bd5fe49
--- /dev/null
+++ b/ARIMA/ARIMA_V2.ipynb
@@ -0,0 +1,337 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Intial code\n",
+    "for reference purposes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pandas as pd\n",
+    "# import numpy as np\n",
+    "# from sklearn.metrics import mean_squared_error\n",
+    "# from sklearn.preprocessing import StandardScaler\n",
+    "# from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "# from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
+    "# import warnings\n",
+    "\n",
+    "# # Ignore convergence warnings\n",
+    "# warnings.simplefilter(\"ignore\")\n",
+    "\n",
+    "# # Load dataset with parsed dates\n",
+    "# data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# # Set the index to the Date column\n",
+    "# data.set_index('Date', inplace=True)\n",
+    "# # data = data.asfreq('D')\n",
+    "# # Feature Engineering: Add day of week and month\n",
+    "# data['day_of_week'] = data.index.dayofweek\n",
+    "# data['month'] = data.index.month\n",
+    "\n",
+    "# # Add lagged value of the Close price and moving averages\n",
+    "# data['lagged_close'] = data['Close'].shift(1)  \n",
+    "# data['moving_avg_3'] = data['Close'].rolling(window=3).mean()\n",
+    "# data['moving_avg_7'] = data['Close'].rolling(window=7).mean()  # New: 7-day moving average for long-term trend\n",
+    "\n",
+    "# # Add Volume as a feature (scaling might help)\n",
+    "# data['volume'] = data['Volume']\n",
+    "\n",
+    "# # Drop rows with NaN values\n",
+    "# data.dropna(inplace=True)\n",
+    "\n",
+    "# # Standardize the features (important for scaling)\n",
+    "# scaler = StandardScaler()\n",
+    "# exog_features = ['day_of_week', 'month', 'lagged_close', 'moving_avg_3', 'moving_avg_7', 'volume']\n",
+    "# data[exog_features] = scaler.fit_transform(data[exog_features])\n",
+    "\n",
+    "# # Split the data into training and testing sets\n",
+    "# train_size = int(len(data) * 0.8)\n",
+    "# train, test = data.iloc[:train_size], data.iloc[train_size:]\n",
+    "\n",
+    "# # Tune SARIMAX hyperparameters (ARIMA order (p, d, q))\n",
+    "# order = (2, 1, 2)  # Consider using AIC/BIC for finding optimal order\n",
+    "# seasonal_order = (1, 1, 1, 12)  # Adding seasonality with monthly frequency\n",
+    "\n",
+    "# # Fit the SARIMAX model\n",
+    "# try:\n",
+    "#     model = SARIMAX(train['Close'], \n",
+    "#                     exog=train[exog_features],\n",
+    "#                     order=order,\n",
+    "#                     seasonal_order=seasonal_order)\n",
+    "#     model_fit = model.fit(disp=False)\n",
+    "# except ConvergenceWarning as e:\n",
+    "#     print(f\"Convergence warning: {e}\")\n",
+    "# except Exception as e:\n",
+    "#     print(f\"Error: {e}\")\n",
+    "\n",
+    "# # Forecasting\n",
+    "# forecast = model_fit.forecast(steps=len(test), exog=test[exog_features])\n",
+    "\n",
+    "# # Calculate RMSE for forecast\n",
+    "# rmse_arimax = np.sqrt(mean_squared_error(test['Close'], forecast))\n",
+    "# print(f\"Improved ARIMAX Model RMSE: {rmse_arimax}\")\n",
+    "\n",
+    "# test_prices = [i for i in test['Close']]\n",
+    "# # Check residuals diagnostics (optional)\n",
+    "# residuals = test_prices - forecast\n",
+    "# print(\"Mean of residuals:\", residuals.mean())\n",
+    "# print(\"Standard deviation of residuals:\", residuals.std())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### immporting necessary libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Udating features to dataset for proper time-series analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Ignore convergence warnings\n",
+    "warnings.simplefilter(\"ignore\", ConvergenceWarning)\n",
+    "\n",
+    "# Load training dataset with parsed dates\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# Set the index to the Date column\n",
+    "train_data.set_index('Date', inplace=True)\n",
+    "\n",
+    "# Feature Engineering: Add day of week and month\n",
+    "train_data['day_of_week'] = train_data.index.dayofweek\n",
+    "train_data['month'] = train_data.index.month\n",
+    "\n",
+    "# Add lagged value of the Close price and moving averages\n",
+    "train_data['lagged_close'] = train_data['Close'].shift(1)\n",
+    "train_data['moving_avg_3'] = train_data['Close'].rolling(window=3).mean()\n",
+    "train_data['moving_avg_7'] = train_data['Close'].rolling(window=7).mean()\n",
+    "\n",
+    "# Add Volume as a feature (scaling might help)\n",
+    "train_data['volume'] = train_data['Volume']\n",
+    "\n",
+    "# Drop rows with NaN values after applying the rolling window and lagging\n",
+    "train_data.dropna(inplace=True)\n",
+    "\n",
+    "# Standardize the features\n",
+    "scaler = StandardScaler()\n",
+    "exog_features = ['day_of_week', 'month', 'lagged_close', 'moving_avg_3', 'moving_avg_7', 'volume']\n",
+    "train_data[exog_features] = scaler.fit_transform(train_data[exog_features])\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "train_size = int(len(train_data) * 0.8)\n",
+    "train, validation = train_data.iloc[:train_size], train_data.iloc[train_size:]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training and savinng model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model and scaler saved successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the SARIMAX model\n",
+    "order = (2, 1, 2)\n",
+    "seasonal_order = (1, 1, 1, 12)\n",
+    "\n",
+    "model = SARIMAX(train['Close'], exog=train[exog_features], order=order, seasonal_order=seasonal_order)\n",
+    "model_fit = model.fit(disp=False)\n",
+    "\n",
+    "# Save the model to a file using pickle\n",
+    "with open('sarimax_model.pkl', 'wb') as f:\n",
+    "    pickle.dump(model_fit, f)\n",
+    "\n",
+    "# Optionally save the scaler as well\n",
+    "with open('scaler.pkl', 'wb') as f:\n",
+    "    pickle.dump(scaler, f)\n",
+    "\n",
+    "print(\"Model and scaler saved successfully.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading saved model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the model and scaler from the files\n",
+    "with open('sarimax_model.pkl', 'rb') as f:\n",
+    "    loaded_model = pickle.load(f)\n",
+    "\n",
+    "with open('scaler.pkl', 'rb') as f:\n",
+    "    loaded_scaler = pickle.load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading and processing Test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the test dataset\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# Set the index to the Date column\n",
+    "test_data.set_index('Date', inplace=True)\n",
+    "\n",
+    "# Apply the same feature engineering on the test data\n",
+    "test_data['day_of_week'] = test_data.index.dayofweek\n",
+    "test_data['month'] = test_data.index.month\n",
+    "test_data['lagged_close'] = test_data['Close'].shift(1)\n",
+    "test_data['moving_avg_3'] = test_data['Close'].rolling(window=3).mean()\n",
+    "test_data['moving_avg_7'] = test_data['Close'].rolling(window=7).mean()\n",
+    "\n",
+    "# Add Volume as a feature\n",
+    "test_data['volume'] = test_data['Volume']\n",
+    "\n",
+    "# Drop rows with NaN values\n",
+    "test_data.dropna(inplace=True)\n",
+    "\n",
+    "# Standardize the features in the test dataset using the loaded scaler\n",
+    "test_data[exog_features] = loaded_scaler.transform(test_data[exog_features])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predicting share prices using model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+      "  return get_prediction_index(\n",
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
+      "  return get_prediction_index(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Data RMSE: 4.673693537736142\n",
+      "Mean of residuals: 0.311316834051805\n",
+      "Standard deviation of residuals: 4.664969245987366\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Forecasting on the test data using the loaded model\n",
+    "forecast_test = loaded_model.forecast(steps=len(test_data), exog=test_data[exog_features])\n",
+    "\n",
+    "# Calculate RMSE for forecast\n",
+    "rmse_test = np.sqrt(mean_squared_error(test_data['Close'], forecast_test))\n",
+    "print(f\"Test Data RMSE: {rmse_test}\")\n",
+    "\n",
+    "# Check residuals diagnostics (optional)\n",
+    "test_prices = test_data['Close'].values\n",
+    "residuals_test = test_prices - forecast_test\n",
+    "print(\"Mean of residuals:\", residuals_test.mean())\n",
+    "print(\"Standard deviation of residuals:\", residuals_test.std())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ARIMA/README.md b/ARIMA/README.md
new file mode 100644
index 0000000..1b56446
--- /dev/null
+++ b/ARIMA/README.md
@@ -0,0 +1,75 @@
+# ARIMA Model Development for Time Series Forecasting
+
+## Overview
+
+This notebook contains the code and resources for developing an ARIMA model to forecast time series data. The model uses historical data to make predictions on future values, specifically focusing on stock prices.
+
+## Table of Contents
+
+- [Database Changes](#database-changes)
+- [ARIMA Model Development Process](#arima-model-development-process)
+- [Usage](#usage)
+
+## Database Changes
+
+To facilitate the development of the ARIMA model, the following changes were made to the database:
+
+1. **New Features Added**:
+
+   - **Day of Week**: Extracted from the date to capture weekly seasonality.
+   - **Month**: Extracted from the date to capture monthly trends.
+   - **Lagged Close Price**: Included the previous day's closing price as a feature for better prediction accuracy.
+   - **Moving Averages**: Added 3-day and 7-day moving averages to smooth the data and identify trends.
+   - **Volume**: Included volume as a feature to understand its impact on stock prices.
+
+2. **Data Preprocessing**:
+   - Rows with missing values were removed after feature engineering to ensure model accuracy.
+   - Features were standardized using `StandardScaler` to improve model performance.
+
+## ARIMA Model Development Process
+
+The following steps outline the process for developing the ARIMA model:
+
+1. **Data Loading**:
+
+   - Load historical stock price data from a CSV file, parsing dates correctly.
+
+2. **Feature Engineering**:
+
+   - Extract features like day of the week, month, lagged prices, moving averages, and volume.
+   - Drop any rows with NaN values resulting from feature engineering.
+
+3. **Data Splitting**:
+
+   - Split the dataset into training and validation sets (80% training, 20% validation).
+
+4. **Model Specification**:
+
+   - Specify the order of the ARIMA model (p, d, q) based on prior analysis or domain knowledge.
+   - Define seasonal orders if applicable.
+
+5. **Model Fitting**:
+
+   - Fit the SARIMAX model using the training dataset, including exogenous features.
+
+6. **Model Saving**:
+
+   - Save the trained model and scaler using `pickle` for later use in forecasting.
+
+7. **Model Testing**:
+
+   - Load the saved model and scaler.
+   - Preprocess the test dataset in the same way as the training dataset.
+   - Make predictions using the test dataset and evaluate model performance using RMSE (Root Mean Square Error).
+
+8. **Residual Analysis**:
+   - Analyze the residuals of the model to check for any patterns that may indicate model inadequacy.
+
+## Usage
+
+1. **Install Dependencies**:
+   Make sure you have the required packages installed. You can do this using `pip`:
+
+   ```bash
+   pip install pandas numpy scikit-learn statsmodels
+   ```
diff --git a/ARIMA/hybrid.ipynb b/ARIMA/hybrid.ipynb
new file mode 100644
index 0000000..491551d
--- /dev/null
+++ b/ARIMA/hybrid.ipynb
@@ -0,0 +1,1028 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.62 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.16 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.22 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.84 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.24 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.17 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.30 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.28 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.38 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.56 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.97 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.44 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.13 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.51 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 11.362 seconds\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'numpy.ndarray' object has no attribute 'values'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 71\u001b[0m\n\u001b[0;32m     68\u001b[0m custom_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../Data/SBI Train data.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     69\u001b[0m close_prices \u001b[38;5;241m=\u001b[39m custom_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m---> 71\u001b[0m prediction \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclose_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHybrid model prediction for next day closing price: $\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprediction\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Cell \u001b[1;32mIn[2], line 34\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(data, time_steps)\u001b[0m\n\u001b[0;32m     31\u001b[0m arima_results \u001b[38;5;241m=\u001b[39m arima_model\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Get ARIMA residuals\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m arima_residuals \u001b[38;5;241m=\u001b[39m df \u001b[38;5;241m-\u001b[39m \u001b[43marima_results\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfittedvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     36\u001b[0m \u001b[38;5;66;03m# Prepare data for LSTM\u001b[39;00m\n\u001b[0;32m     37\u001b[0m scaler \u001b[38;5;241m=\u001b[39m MinMaxScaler()\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'values'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(data, pd.DataFrame):\n",
+    "        df = data.values\n",
+    "    else:\n",
+    "        df = np.array(data)\n",
+    "    \n",
+    "    df = df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = df - arima_results.fittedvalues.values.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make hybrid prediction\n",
+    "    last_60_days = residuals_scaled[-60:]\n",
+    "    X_test = np.array([last_60_days])\n",
+    "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "\n",
+    "    lstm_prediction = lstm_model.predict(X_test)\n",
+    "    lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "    arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "    hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "\n",
+    "    return hybrid_prediction[0]\n",
+    "\n",
+    "# Example usage with custom data\n",
+    "# Assuming you have a CSV file named 'my_stock_data.csv' with a 'Close' column\n",
+    "custom_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "close_prices = custom_data['Close']\n",
+    "\n",
+    "prediction = hybrid_model(close_prices)\n",
+    "print(f\"Hybrid model prediction for next day closing price: ${prediction:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=1.22 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.09 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.55 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.46 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.06 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=1.17 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.24 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.18 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.31 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.17 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.31 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.39 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.08 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.57 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.97 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.07 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.45 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.55 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 13.125 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n",
+      "Hybrid model prediction for next day closing price: $245.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(data, pd.Series):\n",
+    "        df = data.values\n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        df = data.values\n",
+    "    else:\n",
+    "        df = np.array(data)\n",
+    "    \n",
+    "    df = df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make hybrid prediction\n",
+    "    last_60_days = residuals_scaled[-60:]\n",
+    "    X_test = np.array([last_60_days])\n",
+    "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "\n",
+    "    lstm_prediction = lstm_model.predict(X_test)\n",
+    "    lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "    arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "    hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "\n",
+    "    return hybrid_prediction[0]\n",
+    "\n",
+    "# Example usage with custom data\n",
+    "custom_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "close_prices = custom_data['Close']\n",
+    "\n",
+    "prediction = hybrid_model(close_prices)\n",
+    "print(f\"Hybrid model prediction for next day closing price: ${prediction:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.62 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.15 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.21 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.03 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.85 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.21 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.25 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.96 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.21 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.32 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.29 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.43 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.58 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=1.01 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.44 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.15 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.52 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 11.613 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'ARIMA' object has no attribute 'append'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 88\u001b[0m\n\u001b[0;32m     85\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m---> 88\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m     91\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
+      "Cell \u001b[1;32mIn[4], line 76\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps)\u001b[0m\n\u001b[0;32m     73\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model\u001b[39;00m\n\u001b[1;32m---> 76\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'ARIMA' object has no attribute 'append'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(train_data, test_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    elif isinstance(train_data, pd.DataFrame):\n",
+    "        train_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# You can also calculate percentage error\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.63 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.17 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.24 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.93 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.23 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.30 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.22 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.30 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.31 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.42 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.10 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.61 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=1.00 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.05 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.45 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.54 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 11.839 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'ARIMA' object has no attribute 'append'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 88\u001b[0m\n\u001b[0;32m     85\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m---> 88\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m     91\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
+      "Cell \u001b[1;32mIn[5], line 76\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps)\u001b[0m\n\u001b[0;32m     73\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model\u001b[39;00m\n\u001b[1;32m---> 76\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'ARIMA' object has no attribute 'append'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(train_data, test_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    elif isinstance(train_data, pd.DataFrame):\n",
+    "        train_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# You can also calculate percentage error\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.59 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.06 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.16 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.25 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.85 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.28 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=2.09 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.29 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.33 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.23 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.30 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.38 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.57 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.98 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.42 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.50 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 11.823 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n",
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'saved_model\\\\lstm_model.h5\\\\arima_model.pkl'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[6], line 122\u001b[0m\n\u001b[0;32m    119\u001b[0m save_model(arima_results, lstm_model, scaler, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msaved_model\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    121\u001b[0m \u001b[38;5;66;03m# Later, load the model and make predictions\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m loaded_arima, loaded_lstm, loaded_scaler \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msaved_model\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    123\u001b[0m predictions \u001b[38;5;241m=\u001b[39m make_predictions(loaded_arima, loaded_lstm, loaded_scaler, test_close_prices)\n\u001b[0;32m    125\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[6], line 77\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(folder_path)\u001b[0m\n\u001b[0;32m     74\u001b[0m arima_results \u001b[38;5;241m=\u001b[39m joblib\u001b[38;5;241m.\u001b[39mload(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124marima_model.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m     76\u001b[0m \u001b[38;5;66;03m# Load LSTM model\u001b[39;00m\n\u001b[1;32m---> 77\u001b[0m lstm_model \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlstm_model.h5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;66;03m# Load scaler\u001b[39;00m\n\u001b[0;32m     80\u001b[0m scaler \u001b[38;5;241m=\u001b[39m joblib\u001b[38;5;241m.\u001b[39mload(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscaler.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
+      "Cell \u001b[1;32mIn[6], line 74\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(folder_path)\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_model\u001b[39m(folder_path):\n\u001b[0;32m     73\u001b[0m     \u001b[38;5;66;03m# Load ARIMA model\u001b[39;00m\n\u001b[1;32m---> 74\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43mjoblib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43marima_model.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     76\u001b[0m     \u001b[38;5;66;03m# Load LSTM model\u001b[39;00m\n\u001b[0;32m     77\u001b[0m     lstm_model \u001b[38;5;241m=\u001b[39m load_model(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlstm_model.h5\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\joblib\\numpy_pickle.py:579\u001b[0m, in \u001b[0;36mload\u001b[1;34m(filename, mmap_mode)\u001b[0m\n\u001b[0;32m    577\u001b[0m         obj \u001b[38;5;241m=\u001b[39m _unpickle(fobj)\n\u001b[0;32m    578\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 579\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m    580\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m _read_fileobject(f, filename, mmap_mode) \u001b[38;5;28;01mas\u001b[39;00m fobj:\n\u001b[0;32m    581\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fobj, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    582\u001b[0m                 \u001b[38;5;66;03m# if the returned file object is a string, this means we\u001b[39;00m\n\u001b[0;32m    583\u001b[0m                 \u001b[38;5;66;03m# try to load a pickle file generated with an version of\u001b[39;00m\n\u001b[0;32m    584\u001b[0m                 \u001b[38;5;66;03m# Joblib so we load it with joblib compatibility function.\u001b[39;00m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_model\\\\lstm_model.h5\\\\arima_model.pkl'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential, load_model\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "import joblib\n",
+    "import os\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def create_hybrid_model(train_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    elif isinstance(train_data, pd.DataFrame):\n",
+    "        train_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    return arima_results, lstm_model, scaler\n",
+    "\n",
+    "def save_model(arima_results, lstm_model, scaler, folder_path):\n",
+    "    if not os.path.exists(folder_path):\n",
+    "        os.makedirs(folder_path)\n",
+    "    \n",
+    "    # Save ARIMA model\n",
+    "    joblib.dump(arima_results, os.path.join(folder_path, 'arima_model.pkl'))\n",
+    "    \n",
+    "    # Save LSTM model\n",
+    "    lstm_model.save(os.path.join(folder_path, 'lstm_model.h5'))\n",
+    "    \n",
+    "    # Save scaler\n",
+    "    joblib.dump(scaler, os.path.join(folder_path, 'scaler.pkl'))\n",
+    "\n",
+    "def load_model(folder_path):\n",
+    "    # Load ARIMA model\n",
+    "    arima_results = joblib.load(os.path.join(folder_path, 'arima_model.pkl'))\n",
+    "    \n",
+    "    # Load LSTM model\n",
+    "    lstm_model = load_model(os.path.join(folder_path, 'lstm_model.h5'))\n",
+    "    \n",
+    "    # Load scaler\n",
+    "    scaler = joblib.load(os.path.join(folder_path, 'scaler.pkl'))\n",
+    "    \n",
+    "    return arima_results, lstm_model, scaler\n",
+    "\n",
+    "def make_predictions(arima_results, lstm_model, scaler, test_data, time_steps=60):\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(test_data[i:i+time_steps])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_results.append(test_data[i])\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Example usage\n",
+    "if __name__ == \"__main__\":\n",
+    "    # Load data\n",
+    "    train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "    test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "    train_close_prices = train_data['Close']\n",
+    "    test_close_prices = test_data['Close']\n",
+    "\n",
+    "    # Create and save the model\n",
+    "    arima_results, lstm_model, scaler = create_hybrid_model(train_close_prices)\n",
+    "    save_model(arima_results, lstm_model, scaler, 'saved_model')\n",
+    "\n",
+    "    # Later, load the model and make predictions\n",
+    "    loaded_arima, loaded_lstm, loaded_scaler = load_model('saved_model')\n",
+    "    predictions = make_predictions(loaded_arima, loaded_lstm, loaded_scaler, test_close_prices)\n",
+    "\n",
+    "    # Calculate accuracy metrics\n",
+    "    mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "    rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "    mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "\n",
+    "    print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "    print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "    print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "    # Plot results\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    plt.figure(figsize=(12,6))\n",
+    "    plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "    plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "    plt.title('Actual vs Predicted Stock Prices')\n",
+    "    plt.xlabel('Date')\n",
+    "    plt.ylabel('Price')\n",
+    "    plt.legend()\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000002DD2831BCE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000002DD2831BCE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n"
+     ]
+    },
+    {
+     "ename": "UnboundLocalError",
+     "evalue": "cannot access local variable 'arima_model' where it is not associated with a value",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 107\u001b[0m\n\u001b[0;32m    104\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m    106\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m--> 107\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    109\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m    110\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
+      "Cell \u001b[1;32mIn[9], line 95\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps, model_dir)\u001b[0m\n\u001b[0;32m     92\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model with test data\u001b[39;00m\n\u001b[1;32m---> 95\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241m.\u001b[39mappend(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
+      "\u001b[1;31mUnboundLocalError\u001b[0m: cannot access local variable 'arima_model' where it is not associated with a value"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential, load_model\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "import joblib  # For saving the ARIMA model\n",
+    "import os\n",
+    "\n",
+    "# Data preparation\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "# Hybrid ARIMA-LSTM Model\n",
+    "def hybrid_model(train_data, test_data, time_steps=60, model_dir='./model'):\n",
+    "    # Ensure data is a numpy array\n",
+    "    train_df = np.array(train_data).reshape(-1, 1)\n",
+    "\n",
+    "    # Create a directory to save models if it doesn't exist\n",
+    "    if not os.path.exists(model_dir):\n",
+    "        os.makedirs(model_dir)\n",
+    "\n",
+    "    # ARIMA Model\n",
+    "    arima_model_path = os.path.join(model_dir, 'arima_model.pkl')\n",
+    "    if not os.path.exists(arima_model_path):\n",
+    "        model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                                d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                                error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "        arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "        arima_results = arima_model.fit()\n",
+    "        # Save ARIMA model\n",
+    "        joblib.dump(arima_results, arima_model_path)\n",
+    "    else:\n",
+    "        arima_results = joblib.load(arima_model_path)\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM Model\n",
+    "    lstm_model_path = os.path.join(model_dir, 'lstm_model.keras')  # Updated file extension\n",
+    "    if not os.path.exists(lstm_model_path):\n",
+    "        lstm_model = Sequential([\n",
+    "            LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "            LSTM(units=50),\n",
+    "            Dense(units=1)\n",
+    "        ])\n",
+    "        lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "        # Early stopping and model checkpoint\n",
+    "        early_stopping = EarlyStopping(monitor='loss', patience=10, restore_best_weights=True)\n",
+    "        model_checkpoint = ModelCheckpoint(lstm_model_path, save_best_only=True, monitor='loss')\n",
+    "\n",
+    "        # Train LSTM model\n",
+    "        lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=1, callbacks=[early_stopping, model_checkpoint])\n",
+    "\n",
+    "    else:\n",
+    "        lstm_model = load_model(lstm_model_path)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model with test data\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# Calculate Mean Absolute Percentage Error (MAPE)\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 567de0c188b88e9dc2b2b222273ade9747470eb1 Mon Sep 17 00:00:00 2001
From: Pankaj Kumar Goyal <140732455+Pankaj4152@users.noreply.github.com>
Date: Wed, 9 Oct 2024 22:23:28 +0530
Subject: [PATCH 25/76] requirements.txt

---
 requirements.txt | 53 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 requirements.txt

diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..3de8465
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,53 @@
+absl-py==2.1.0
+astunparse==1.6.3
+certifi==2024.8.30
+charset-normalizer==3.4.0
+contourpy==1.3.0
+cycler==0.12.1
+flatbuffers==24.3.25
+fonttools==4.54.1
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.66.2
+h5py==3.12.1
+idna==3.10
+joblib==1.4.2
+keras==3.6.0
+kiwisolver==1.4.7
+libclang==18.1.1
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.1
+matplotlib==3.9.2
+mdurl==0.1.2
+ml-dtypes==0.4.1
+namex==0.0.8
+numpy==1.26.4
+opt_einsum==3.4.0
+optree==0.13.0
+packaging==24.1
+pandas==2.2.3
+pillow==10.4.0
+protobuf==4.25.5
+Pygments==2.18.0
+pyparsing==3.1.4
+python-dateutil==2.9.0.post0
+pytz==2024.2
+requests==2.32.3
+rich==13.9.2
+scikit-learn==1.5.2
+scipy==1.14.1
+seaborn==0.13.2
+setuptools==75.1.0
+six==1.16.0
+tensorboard==2.17.1
+tensorboard-data-server==0.7.2
+tensorflow==2.17.0
+termcolor==2.5.0
+threadpoolctl==3.5.0
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3
+Werkzeug==3.0.4
+wheel==0.44.0
+wrapt==1.16.0
\ No newline at end of file

From f244cc774c2923ee1f1b4119ee281992809b2873 Mon Sep 17 00:00:00 2001
From: DarshAgrawal14 <agrawaldarsh.14@gmail.com>
Date: Fri, 11 Oct 2024 23:25:14 +0530
Subject: [PATCH 26/76] added requirements.txt

---
 requirements.txt | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 requirements.txt

diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..30db39a
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+pandas==2.1.1
+numpy==1.26.0
+scikit-learn==1.3.0
+statsmodels==0.14.0
+pickle-mixin==1.0.2
\ No newline at end of file

From 0d92d4270f5a26c94c0248c36b2488ae5dcf4a03 Mon Sep 17 00:00:00 2001
From: Shristi Rawat <shristirawat68@gmail.com>
Date: Mon, 14 Oct 2024 17:04:08 +0530
Subject: [PATCH 27/76] Categorical variables to numeric

---
 .../stock_market(complete)-checkpoint.ipynb   |  2811 +++++
 candlestick_chart.html                        |    14 +
 stock_market(complete).ipynb                  | 10293 ++++++++++++----
 3 files changed, 10629 insertions(+), 2489 deletions(-)
 create mode 100644 .ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
 create mode 100644 candlestick_chart.html

diff --git a/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb b/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
new file mode 100644
index 0000000..0cc81a2
--- /dev/null
+++ b/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
@@ -0,0 +1,2811 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "Rqr4Dq5vWXmV"
+   },
+   "outputs": [],
+   "source": [
+    "# importing modules\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense,LSTM\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "id": "BigUM4vtWZdc"
+   },
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "df = pd.read_csv(\"SBIN.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RqOe6KPNWs8Q"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vAcMar5WXELR"
+   },
+   "source": [
+    "## Data Analysis and Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "vkDxEavVXHqv",
+    "outputId": "279f15e7-d2d2-442a-c162-e7db9780f2c7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01-01-1996</td>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-01-1996</td>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>03-01-1996</td>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>04-01-1996</td>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>05-01-1996</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date       Open       High        Low      Close  Adj Close  \\\n",
+       "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+       "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+       "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+       "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+       "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+       "\n",
+       "       Volume  \n",
+       "0  43733533.0  \n",
+       "1  56167280.0  \n",
+       "2  68296318.0  \n",
+       "3  86073880.0  \n",
+       "4  76613039.0  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display the first 5 rows of the dataframe\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Vx3DYtfdXJTU",
+    "outputId": "18c93f2d-5232-4e80-dfcf-e7d748901c37"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7074 entries, 0 to 7073\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Date       7074 non-null   object \n",
+      " 1   Open       7065 non-null   float64\n",
+      " 2   High       7065 non-null   float64\n",
+      " 3   Low        7065 non-null   float64\n",
+      " 4   Close      7065 non-null   float64\n",
+      " 5   Adj Close  7065 non-null   float64\n",
+      " 6   Volume     7065 non-null   float64\n",
+      "dtypes: float64(6), object(1)\n",
+      "memory usage: 387.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display information about the dataframe\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "SXdvitYIXKKL",
+    "outputId": "1a2c42d0-4f6e-4180-8ffe-7a40ef4ddabf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7.065000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>180.682841</td>\n",
+       "      <td>183.085167</td>\n",
+       "      <td>177.998209</td>\n",
+       "      <td>180.448294</td>\n",
+       "      <td>166.021712</td>\n",
+       "      <td>3.130217e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>154.773229</td>\n",
+       "      <td>156.345078</td>\n",
+       "      <td>152.980516</td>\n",
+       "      <td>154.630549</td>\n",
+       "      <td>152.903249</td>\n",
+       "      <td>3.462744e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>13.478195</td>\n",
+       "      <td>13.935802</td>\n",
+       "      <td>13.214009</td>\n",
+       "      <td>13.346102</td>\n",
+       "      <td>9.531410</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>28.423565</td>\n",
+       "      <td>28.824560</td>\n",
+       "      <td>28.022570</td>\n",
+       "      <td>28.456589</td>\n",
+       "      <td>19.854374</td>\n",
+       "      <td>1.299123e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>173.100006</td>\n",
+       "      <td>176.500000</td>\n",
+       "      <td>170.250000</td>\n",
+       "      <td>172.925003</td>\n",
+       "      <td>152.411270</td>\n",
+       "      <td>2.064292e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>265.500000</td>\n",
+       "      <td>268.899994</td>\n",
+       "      <td>261.299988</td>\n",
+       "      <td>265.174988</td>\n",
+       "      <td>245.764954</td>\n",
+       "      <td>3.651478e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>703.650024</td>\n",
+       "      <td>728.349976</td>\n",
+       "      <td>694.200012</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>4.469483e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Open         High          Low        Close    Adj Close  \\\n",
+       "count  7065.000000  7065.000000  7065.000000  7065.000000  7065.000000   \n",
+       "mean    180.682841   183.085167   177.998209   180.448294   166.021712   \n",
+       "std     154.773229   156.345078   152.980516   154.630549   152.903249   \n",
+       "min      13.478195    13.935802    13.214009    13.346102     9.531410   \n",
+       "25%      28.423565    28.824560    28.022570    28.456589    19.854374   \n",
+       "50%     173.100006   176.500000   170.250000   172.925003   152.411270   \n",
+       "75%     265.500000   268.899994   261.299988   265.174988   245.764954   \n",
+       "max     703.650024   728.349976   694.200012   725.250000   725.250000   \n",
+       "\n",
+       "             Volume  \n",
+       "count  7.065000e+03  \n",
+       "mean   3.130217e+07  \n",
+       "std    3.462744e+07  \n",
+       "min    0.000000e+00  \n",
+       "25%    1.299123e+07  \n",
+       "50%    2.064292e+07  \n",
+       "75%    3.651478e+07  \n",
+       "max    4.469483e+08  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display summary statistics of the dataframe\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 303
+    },
+    "id": "8rddiK_nXLNi",
+    "outputId": "4b65dd80-db84-45c8-ef16-2133bc25c68b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date         0\n",
+       "Open         9\n",
+       "High         9\n",
+       "Low          9\n",
+       "Close        9\n",
+       "Adj Close    9\n",
+       "Volume       9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display the number of missing values in each column\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I1khhoMECh4y"
+   },
+   "source": [
+    "### Detailed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "HpS7l8oSCf6y"
+   },
+   "outputs": [],
+   "source": [
+    "# Function to create scatter plots\n",
+    "def create_scatter_plot(x_data, y_data, size_data=None, title=None, x_label=None, y_label=None, figsize=(15,7)):\n",
+    "    plt.figure(figsize=figsize)\n",
+    "    sns.scatterplot(x=x_data, y=y_data, size=size_data)\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    if x_label:\n",
+    "        plt.xlabel(x_label)\n",
+    "    if y_label:\n",
+    "        plt.ylabel(y_label)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "oVEjj8ZMCnDx"
+   },
+   "outputs": [],
+   "source": [
+    "# Function to create line plots\n",
+    "def create_line_plot(x_data, y_data, title=None, x_label=None, y_label=None, figsize=(15,7)):\n",
+    "    plt.figure(figsize=figsize)\n",
+    "    sns.lineplot(x=x_data, y=y_data)\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    if x_label:\n",
+    "        plt.xlabel(x_label)\n",
+    "    if y_label:\n",
+    "        plt.ylabel(y_label)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Date' and 'Volume'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 769
+    },
+    "id": "-n8vOGhdCmer",
+    "outputId": "ea5057f1-9493-43de-ec65-7a017134d171"
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "The x variable is categorical, but one of ['numeric', 'datetime'] is required",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[28], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Plot the relationship between 'Date' and 'Volume'\u001b[39;00m\n\u001b[0;32m      2\u001b[0m sns\u001b[38;5;241m.\u001b[39mscatterplot(x \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate\u001b[39m\u001b[38;5;124m'\u001b[39m], y \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVolume\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m----> 3\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkdeplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDate\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mVolume\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
+      "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\seaborn\\distributions.py:1679\u001b[0m, in \u001b[0;36mkdeplot\u001b[1;34m(data, x, y, hue, weights, palette, hue_order, hue_norm, color, fill, multiple, common_norm, common_grid, cumulative, bw_method, bw_adjust, warn_singular, log_scale, levels, thresh, gridsize, cut, clip, legend, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[0;32m   1676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1677\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 1679\u001b[0m \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_attach\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallowed_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnumeric\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdatetime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_scale\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1681\u001b[0m method \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mfill_between \u001b[38;5;28;01mif\u001b[39;00m fill \u001b[38;5;28;01melse\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mplot\n\u001b[0;32m   1682\u001b[0m color \u001b[38;5;241m=\u001b[39m _default_color(method, hue, color, kwargs)\n",
+      "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\seaborn\\_base.py:1084\u001b[0m, in \u001b[0;36mVectorPlotter._attach\u001b[1;34m(self, obj, allowed_types, log_scale)\u001b[0m\n\u001b[0;32m   1079\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m var_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m allowed_types:\n\u001b[0;32m   1080\u001b[0m         err \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1081\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m variable is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but one of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1082\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mallowed_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is required\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1083\u001b[0m         )\n\u001b[1;32m-> 1084\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(err)\n\u001b[0;32m   1086\u001b[0m \u001b[38;5;66;03m# -- Get axis objects for each row in plot_data for type conversions and scaling\u001b[39;00m\n\u001b[0;32m   1088\u001b[0m facet_dim \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcol\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrow\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n",
+      "\u001b[1;31mTypeError\u001b[0m: The x variable is categorical, but one of ['numeric', 'datetime'] is required"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Date' and 'Volume'\n",
+    "sns.scatterplot(x = df['Date'], y = df['Volume'])\n",
+    "sns.kdeplot(x = df['Date'], y = df['Volume'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FYvz9YaZDeDp"
+   },
+   "source": [
+    "Early 1990s spike: There is a high trading volume around the mid-1990s, peaking at over 4 × 10^8 (400 million trades).\n",
+    "Post-1996 to 2020: The volume significantly decreases, showing lower levels of activity with some fluctuations and minor peaks, particularly after 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the price variation over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "q3QfzWquCmjk"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the price variation over time\n",
+    "create_line_plot(df['Date'], df['High'] - df['Low'],\n",
+    "                 title='Price Variation Over Time', x_label='Date', y_label='Price Variation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mYGFClShDgsj"
+   },
+   "source": [
+    "#### Stable period until 2004:\n",
+    "The price variation was relatively low and stable until around 2003-2004, generally staying under 10 units.\n",
+    "#### 2004-2008 increase:\n",
+    "Starting from 2004, price variations gradually began to increase, peaking sharply just before the 2008 financial crisis. Some spikes went beyond 40 units.\n",
+    "#### 2008-2020 fluctuations:\n",
+    "After the 2008 peak, price variation showed continuous fluctuations with noticeable peaks, though they were more frequent post-2016.\n",
+    "#### 2020 onward:\n",
+    "The recent period (2020-2024) shows significant and frequent fluctuations, with peaks reaching 60+ units, possibly influenced by events like the COVID-19 pandemic and other global factors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Adj Close' and 'Volume'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "XKsek--aCmoC"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the relationship between 'Adj Close' and 'Volume'\n",
+    "create_line_plot(df['Adj Close'], df['Volume'],\n",
+    "                 title='Adjusted Close vs Volume',\n",
+    "                 x_label='Adjusted Close',\n",
+    "                 y_label='Volume')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "omVoWiC-DjJX"
+   },
+   "source": [
+    "The overall trend seems to be somewhat volatile, with periods of upward and downward movement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Open' and 'Adj Close'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "W5TY59ikCv78"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the relationship between 'Open' and 'Adj Close'\n",
+    "create_scatter_plot(df['Open'], df['Adj Close'], size_data=df['Volume'],\n",
+    "                    title='Open vs Adjusted Close', x_label='Open', y_label='Adj Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "q_BiuXvnDlfJ"
+   },
+   "source": [
+    "The scatter points generally show an upward trend, indicating a positive correlation between the opening and closing prices. This means that, in general, when the stock opens higher, it tends to close higher as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Close' and 'Adj Close'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CD0LiK3VCwiU"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the relationship between 'Close' and 'Adj Close'\n",
+    "create_scatter_plot(df['Close'], df['Adj Close'], size_data=df['Volume'],\n",
+    "                    title='Close vs Adjusted Close', x_label='Close', y_label='Adj Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3TgT8XWtDnpy"
+   },
+   "source": [
+    "The scatter points show a very strong upward trend, indicating a strong positive correlation between the closing price and the adjusted closing price. This means that, in general, when the stock closes higher, the adjusted closing price is also higher."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Open' and 'Close'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bgq_AQ-nCwnA"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the relationship between 'Open' and 'Close'\n",
+    "create_scatter_plot(df['Open'], df['Close'], size_data=df['Volume'],\n",
+    "                    title='Open vs Close', x_label='Open', y_label='Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Open - Close' and 'High - Low'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Zqsy16wWCwrC"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the relationship between 'Open - Close' and 'High - Low'\n",
+    "create_line_plot(df['Open'] - df['Close'], df['High'] - df['Low'],\n",
+    "                 title='Stock Variation vs Price Variation',\n",
+    "                 x_label='Stock Variation (Open - Close)',\n",
+    "                 y_label='Price Variation (High - Low)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PbUZdfH4Dru0"
+   },
+   "source": [
+    "#### This infers for stable or less price difference volume of stock trading is higher\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the effect of price differences on volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NAT3Sy3hC2a3"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the effect of price differences on volume\n",
+    "plt.figure(figsize = (20,10))\n",
+    "sns.scatterplot(x=df['Open'] - df['Close'], y=df['High'] - df['Low'], hue=df['Volume'], palette='viridis', size=df['Volume'], sizes=(20, 200), alpha=0.6)\n",
+    "plt.title('Effect of Price Differences on Volume')\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.ylabel('High - Low')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the effect of price differences on adjusted close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "B8zeVZt7C2fT"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the effect of price differences on adjusted close\n",
+    "plt.figure(figsize = (20,10))\n",
+    "sns.lineplot(x = df['Open']-df['Close'], y = df['High'] - df['Low'], hue = df['Adj Close'])\n",
+    "plt.title('Effect of Price Differences on Adjusted Close')\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.ylabel('High - Low')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Open - Close' and 'Adj Close'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EpjnJ3bJC2jq"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the relationship between 'Open - Close' and 'Adj Close'\n",
+    "plt.figure(figsize = (20,10))\n",
+    "sns.lineplot(x = df['Open']-df['Close'], y = df['Adj Close'])\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a pair plot of stock features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "omOa3x81C2uS"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a pair plot of stock features\n",
+    "subset = df[['Open', 'Close', 'High', 'Low', 'Volume']]\n",
+    "sns.pairplot(subset)\n",
+    "plt.suptitle('Pair Plot of Stock Features', y=1.02)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a 3D scatter plot of 'Open', 'Close', and 'Volume'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "gNjDWptzC2zP"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a 3D scatter plot of 'Open', 'Close', and 'Volume'\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "fig = plt.figure(figsize=(12, 8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.scatter(df['Open']- df['Close'], df['High'] - df['Low'],df['Volume'], c='r', marker='o')\n",
+    "ax.set_xlabel('Open Price')\n",
+    "ax.set_ylabel('Close Price')\n",
+    "ax.set_zlabel('Volume')\n",
+    "plt.title('3D Scatter Plot of Open, Close, and Volume')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the correlation matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Soovqm1IC24P"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the correlation matrix\n",
+    "correlation_matrix = df.corr(numeric_only = True)\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', square=True, cbar_kws={\"shrink\": .8})\n",
+    "\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d2g_ypkeDvdO"
+   },
+   "source": [
+    "The high correlation between 'Open,' 'High,' 'Low,' 'Close,' and 'Adj Close' shows these features are highly interdependent and tend to move together in the same direction.\n",
+    "The negative correlation of 'Volume' with price-related features suggests that increased trading volume does not necessarily coincide with an increase in stock prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### More Charts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotly.offline import plot\n",
+    "import plotly.graph_objs as go"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A line chart is a simple and effective way to visualize the closing price of the stock over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the closing price over time\n",
+    "plt.plot(df['Date'], df['Close'])\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.title('SBIN Stock Price Over Time')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A candlestick chart is a more detailed way to visualize the stock price, showing the high, low, open, and close prices for each day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the candlestick chart\n",
+    "fig = go.Figure(data=[go.Candlestick(x=df['Date'],\n",
+    "                open=df['Open'],\n",
+    "                high=df['High'],\n",
+    "                low=df['Low'],\n",
+    "                close=df['Close'])])\n",
+    "plot(fig, filename='candlestick_chart')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python312\\site-packages\\plotly\\offline\\offline.py:557: UserWarning:\n",
+    "\n",
+    "Your filename `candlestick_chart` didn't end with .html. Adding .html to the end of your file.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "'candlestick_chart.html'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the daily volume\n",
+    "plt.bar(df['Date'], df['Volume'])\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Volume')\n",
+    "plt.title('SBIN Daily Volume')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the scatter plot of closing price vs volume\n",
+    "sns.scatterplot(x=df['Close'], y=df['Volume'])\n",
+    "plt.xlabel('Closing Price')\n",
+    "plt.ylabel('Volume')\n",
+    "plt.title('Relationship between SBIN Closing Price and Volume')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the histogram of closing prices\n",
+    "plt.hist(df['Close'], bins=50)\n",
+    "plt.xlabel('Closing Price')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Distribution of SBIN Closing Prices')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A moving average chart is a type of chart that shows the average value of a stock's price over a certain period of time. It is used to smooth out the fluctuations in the price and to identify trends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the moving average chart\n",
+    "df['MA_50'] = df['Close'].rolling(window=50).mean()\n",
+    "df['MA_200'] = df['Close'].rolling(window=200).mean()\n",
+    "\n",
+    "plt.plot(df['Date'], df['Close'], label='Close')\n",
+    "plt.plot(df['Date'], df['MA_50'], label='MA 50')\n",
+    "plt.plot(df['Date'], df['MA_200'], label='MA 200')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.title('SBIN Stock Price with Moving Averages')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This chart shows the closing price of the stock (blue line) along with its 20-day moving average (red line) and two standard deviations plotted above (green line) and below (orange line) it. The Bollinger Bands are used to identify volatility in the stock price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the Bollinger Bands chart\n",
+    "df['MA_20'] = df['Close'].rolling(window=20).mean()\n",
+    "df['Upper_BB'] = df['MA_20'] + 2*df['Close'].rolling(window=20).std()\n",
+    "df['Lower_BB'] = df['MA_20'] - 2*df['Close'].rolling(window=20).std()\n",
+    "\n",
+    "plt.plot(df['Date'], df['Close'], label='Close')\n",
+    "plt.plot(df['Date'], df['Upper_BB'], label='Upper BB')\n",
+    "plt.plot(df['Date'], df['Lower_BB'], label='Lower BB')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.title('SBIN Stock Price with Bollinger Bands')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " This chart shows the RSI of the stock (blue line) along with two horizontal lines at 30 (red line) and 70 (green line). The RSI is used to identify overbought (above 70) and oversold (below 30) conditions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the RSI chart\n",
+    "delta = df['Close'].diff(1)\n",
+    "up, down = delta.copy(), delta.copy()\n",
+    "up[up < 0] = 0\n",
+    "down[down > 0] = 0\n",
+    "roll_up = up.rolling(window=14).mean()\n",
+    "roll_down = down.rolling(window=14).mean().abs()\n",
+    "RS = roll_up / roll_down\n",
+    "RSI = 100.0 - (100.0 / (1.0 + RS))\n",
+    "\n",
+    "plt.plot(df['Date'], RSI, label='RSI')\n",
+    "plt.axhline(y=30, color='red', linestyle='--')\n",
+    "plt.axhline(y=70, color='green', linestyle='--')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('RSI')\n",
+    "plt.title('SBIN Stock Price with RSI')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This chart shows the correlation between the open, high, low, close, and volume of the stock. The correlation is measured on a scale of -1 to 1, where 1 means perfect positive correlation and -1 means perfect negative correlation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the heatmap\n",
+    "corr_matrix = df[['Open', 'High', 'Low', 'Close', 'Volume']].corr()\n",
+    "plt.imshow(corr_matrix, cmap='hot', interpolation='nearest')\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D6ssCbaNXem6"
+   },
+   "source": [
+    "## Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WWb3qGu1Xa30"
+   },
+   "source": [
+    "### Handle missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "id": "yi9UnG0BXtGG"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop unnecessary columns('Date' and 'Adj Close')\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "id": "_QwN3imwXZGZ"
+   },
+   "outputs": [],
+   "source": [
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "id": "WYCTE93pXhyC"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cYIyc2QpXvoM"
+   },
+   "source": [
+    "### Adding Indicators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IUH2Nt70Xy-X"
+   },
+   "source": [
+    "#### SMA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PzQGu8JnX8Bk"
+   },
+   "source": [
+    "Its the avg of stock price over a specific time period\n",
+    "\n",
+    "SMA = (sum of closing price os past n days) / n\n",
+    "\n",
+    "It helps identify trends by filtering out shortterm fluctuations\n",
+    "\n",
+    "Price above SMA indicate Uptrend and price below SMA indicate lowertrend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "id": "CFGQWjNTX4Mf"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the Simple Moving Average (SMA)\n",
+    "df[\"SMA_10\"] = df[\"Close\"].rolling(window=10).mean()\n",
+    "df[\"SMA_50\"] = df[\"Close\"].rolling(window=50).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "LoX_CWbbX-tN"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop rows with missing values\n",
+    "df.dropna(subset=['SMA_10', 'SMA_50'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jaU2IGH_YcIe"
+   },
+   "source": [
+    "##### SMA Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the SMA graph\n",
+    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
+    "days = int(input(\"of how many past days you want to see graph: \"))\n",
+    "ax1.plot(df[\"SMA_10\"][-days:], label = \"SMA_10\", color='Red', linewidth=1, alpha=0.8)\n",
+    "ax1.plot(df[\"SMA_50\"][-days:], label = \"SMA_50\", color='Green', linewidth=1, alpha=0.8)\n",
+    "ax1.plot(df[\"Close\"][-days:], label = \"Close\", color='Blue', linewidth=2)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qCxKwtnTX1Ck"
+   },
+   "source": [
+    "#### RSI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bbNsRzqmYiJi"
+   },
+   "source": [
+    "It is a momentum indicator used to measure the speed and change of price movements. It ranges from 0 to 100 and helps identify whether a stock is overbought or oversold.  \n",
+    "\n",
+    "RSI > 70: Overbought  \n",
+    "RSI < 30: Oversold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "id": "lTkIdld1YaxN"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the Relative Strength Index (RSI)\n",
+    "delta = df['Close'].diff(1)\n",
+    "\n",
+    "gain = delta.where(delta > 0, 0)\n",
+    "loss = -delta.where(delta < 0, 0)\n",
+    "\n",
+    "avg_gain = gain.rolling(window=14).mean()\n",
+    "avg_loss = loss.rolling(window=14).mean()\n",
+    "\n",
+    "rs = avg_gain / avg_loss  # Relative Strength\n",
+    "df['RSI'] = 100 - (100 / (1 + rs))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop rows with missing values\n",
+    "df.dropna(subset=['RSI'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bW10tgm0YlSM"
+   },
+   "source": [
+    "##### RSI Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 807
+    },
+    "id": "dqR2l5r-YngD",
+    "outputId": "07d7a94a-4fd6-4f87-cf37-54b6980fa5cb"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the RSI graph\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})\n",
+    "\n",
+    "days = int(input(\"of how many past days you want to see graph: \"))\n",
+    "# price graph\n",
+    "ax1.plot(df['Close'][-days:], label='Close Price', color='blue')\n",
+    "ax1.set_title('Stock Price')\n",
+    "ax1.set_ylabel('Price')\n",
+    "ax1.legend()\n",
+    "\n",
+    "# rsi graph\n",
+    "ax2.plot(df['RSI'][-days:], label='RSI', color='orange')\n",
+    "ax2.axhline(70, color='red', linestyle='--')  # Overbought line\n",
+    "ax2.axhline(30, color='green', linestyle='--')  # Oversold line\n",
+    "ax2.set_title('Relative Strength Index (RSI)')\n",
+    "ax2.set_ylabel('RSI')\n",
+    "ax2.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LaU9_eV9X2Zc"
+   },
+   "source": [
+    "#### MACD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "id": "4j9soKAXXvZX"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the Moving Average Convergence Divergence (MACD)\n",
+    "# Calculate the short-term and long-term EMAs\n",
+    "df['EMA_12'] = df['Close'].ewm(span=12, adjust=False).mean()\n",
+    "df['EMA_26'] = df['Close'].ewm(span=26, adjust=False).mean()\n",
+    "\n",
+    "# Calculate MACD and Signal line\n",
+    "df['MACD'] = df['EMA_12'] - df['EMA_26']  # MACD line\n",
+    "df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()  # Signal line"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vJU2F-TiYrSB"
+   },
+   "source": [
+    "##### MACD Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 807
+    },
+    "id": "BV8z1ge1Ysl3",
+    "outputId": "9ddf6a13-ee48-45ea-f6f4-e361d1decc11"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the MACD\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(10, 8))\n",
+    "\n",
+    "\n",
+    "days = int(input(\"of how many past days you want to see graph: \"))\n",
+    "# Plot stock price on first subplot (bold)\n",
+    "ax[0].plot( df['Close'][-days:], label='Close Price', color='blue', linewidth=3)\n",
+    "ax[0].set_title('Stock Price')\n",
+    "ax[0].set_ylabel('Price')\n",
+    "\n",
+    "# Plot MACD and Signal line on second subplot\n",
+    "ax[1].plot( df['MACD'][-days:], label='MACD', color='green', linewidth=2)\n",
+    "ax[1].plot( df['Signal_Line'][-days:], label='Signal Line', color='red', linestyle='--', alpha=0.7)\n",
+    "ax[1].set_title('MACD')\n",
+    "ax[1].set_ylabel('MACD Value')\n",
+    "\n",
+    "# Show legends\n",
+    "ax[0].legend()\n",
+    "ax[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VC-x8kPuY_FU"
+   },
+   "source": [
+    "#### Corrrelations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 425
+    },
+    "id": "I7pi-rfLZBGf",
+    "outputId": "549af121-3ef6-476f-e359-2e3ce2cbb166"
+   },
+   "outputs": [],
+   "source": [
+    "corr = df.corr()\n",
+    "corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 746
+    },
+    "id": "1OGAC3uxZCxG",
+    "outputId": "9d7ad372-d66d-4c2a-b72a-717179294401"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,8))\n",
+    "sns.heatmap(corr, annot=True, linewidths=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "id": "GvIj_HpsZJ26"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "v8aLHujGZQu_"
+   },
+   "source": [
+    "## Model Training Preperation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "FV2-jJCSZQaA"
+   },
+   "outputs": [],
+   "source": [
+    "# Select features and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "id": "MStjwvxqZV1e"
+   },
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "id": "adMP-Zn1ZW7s"
+   },
+   "outputs": [],
+   "source": [
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7nfzuX5hZZtj",
+    "outputId": "6e0f44fa-598b-482f-db74-449179691b30"
+   },
+   "outputs": [],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "avggYFQCZiF8"
+   },
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i63wO9AVZkjf"
+   },
+   "source": [
+    "#### 1. LINEAR REGRESSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "id": "9AJm7PhjZZ9p"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a linear regression model\n",
+    "model1 = LinearRegression()\n",
+    "\n",
+    "# Train the model\n",
+    "model1.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "pred1 = model1.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "id": "lYpraDj7Zwqr"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
+    "mae1 = mean_absolute_error(y_test, pred1)\n",
+    "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
+    "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
+    "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
+    "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
+    "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
+    "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xOAnEaKoZxuY",
+    "outputId": "202a1add-cae6-4637-8106-735d3f958007"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse1)\n",
+    "print(\"MAE:\", mae1)\n",
+    "print(\"MAPE:\", mape1)\n",
+    "print(\"Accuracy:\", accuracy1)\n",
+    "print(\"Precision:\", precision1)\n",
+    "print(\"Confusion Matrix:\\n\", confusion1)\n",
+    "print(\"Recall:\", recall1)\n",
+    "print(\"F1 Score:\", f11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HxCNbk0JafKE"
+   },
+   "source": [
+    "#### 2. SVR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### With Grid Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "id": "XxOnG95gZ0NR"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an SVR model\n",
+    "model2 = SVR()\n",
+    "param_grid = {'C':[0.1, 1], 'epsilon':[0.01, 0.1, 0.5], 'kernel':['sigmoid']}\n",
+    "GV_SVR = GridSearchCV(model2, param_grid = param_grid, scoring = 'accuracy', n_jobs = -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "heZ6MwXaalFT",
+    "outputId": "732d3ac8-014f-47fb-f1af-e3f30afbb037"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "GV_SVR.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred2 = GV_SVR.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "984H1ej2apPP"
+   },
+   "outputs": [],
+   "source": [
+    "GV_SVR.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Without Grid Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fitting without grid search\n",
+    "model2.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred2_1 = model2.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "id": "ErnLBNW6apM4"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
+    "mae2 = mean_absolute_error(y_test, pred2)\n",
+    "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
+    "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
+    "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
+    "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
+    "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
+    "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "82Gd1kSwapKR",
+    "outputId": "1dc4a6a9-f0ad-4b96-eeaa-9682f4912767"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse2)\n",
+    "print(\"MAE:\", mae2)\n",
+    "print(\"MAPE:\", mape2)\n",
+    "print(\"Accuracy:\", accuracy2)\n",
+    "print(\"Precision:\", precision2)\n",
+    "print(\"Confusion Matrix:\\n\", confusion2)\n",
+    "print(\"Recall:\", recall2)\n",
+    "print(\"F1 Score:\", f12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GsVk_xFZauR6"
+   },
+   "source": [
+    "#### 3. Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {
+    "id": "YwerJPNxapGt"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "# Create a Random Forest model\n",
+    "model3 = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "fspXTQOXapBl",
+    "outputId": "16e51cc3-390d-4dad-8a04-68b48104ac0b"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model3.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {
+    "id": "Hu0iAgE9ao-_"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred3 = model3.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {
+    "id": "WTu2YAoQao6Q"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
+    "mae3 = mean_absolute_error(y_test, pred3)\n",
+    "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
+    "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
+    "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
+    "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
+    "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
+    "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9_7z9JcRao4C",
+    "outputId": "9b6dbe5a-de58-4519-d12b-afd1783b5024"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse3)\n",
+    "print(\"MAE:\", mae3)\n",
+    "print(\"MAPE:\", mape3)\n",
+    "print(\"Accuracy:\", accuracy3)\n",
+    "print(\"Precision:\", precision3)\n",
+    "print(\"Confusion Matrix:\\n\", confusion3)\n",
+    "print(\"Recall:\", recall3)\n",
+    "print(\"F1 Score:\", f13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kg80FkpDa4Am"
+   },
+   "source": [
+    "#### 4. Gradient Boosting Models (GBM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {
+    "id": "D75bIlqqao1t"
+   },
+   "outputs": [],
+   "source": [
+    "model4 = GradientBoostingRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "uUHqBGsTa9TV",
+    "outputId": "4694a027-32b2-4c7b-c9cc-ee84e6c4b425"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model4.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {
+    "id": "T7XyO3qIa9QU"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred4 = model4.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "id": "j2DCsz23a9M3"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
+    "mae4 = mean_absolute_error(y_test, pred4)\n",
+    "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
+    "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
+    "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
+    "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
+    "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
+    "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tl4jTkC9a9Je",
+    "outputId": "4d2f1ee3-2886-4176-e9dc-2062b3890377"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse4)\n",
+    "print(\"MAE:\", mae4)\n",
+    "print(\"MAPE:\", mape4)\n",
+    "print(\"Accuracy:\", accuracy4)\n",
+    "print(\"Precision:\", precision4)\n",
+    "print(\"Confusion Matrix:\\n\", confusion4)\n",
+    "print(\"Recall:\", recall4)\n",
+    "print(\"F1 Score:\", f14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aDoDcLUdbCq6"
+   },
+   "source": [
+    "#### 5. Extreme Gradient Boosting (XGBoost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "id": "rmgm3VADa9Gb"
+   },
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "# Create an XGBoost model\n",
+    "model5 = xgb.XGBRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 253
+    },
+    "id": "txadd-U3a9AP",
+    "outputId": "0a5d9230-a87f-486b-cda4-197face55486"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model5.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "id": "xYWVMZJ5a89t"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred5 = model5.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {
+    "id": "qeDxXt9pa87W"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
+    "mae5 = mean_absolute_error(y_test, pred5)\n",
+    "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
+    "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
+    "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
+    "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
+    "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
+    "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TdY1rRy_a83-",
+    "outputId": "d43b90b2-d098-402f-fbf7-46130f5310e3"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse5)\n",
+    "print(\"MAE:\", mae5)\n",
+    "print(\"MAPE:\", mape5)\n",
+    "print(\"Accuracy:\", accuracy5)\n",
+    "print(\"Precision:\", precision5)\n",
+    "print(\"Confusion Matrix:\\n\", confusion5)\n",
+    "print(\"Recall:\", recall5)\n",
+    "print(\"F1 Score:\", f15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EbxPon-VbMGE"
+   },
+   "source": [
+    "#### 6. AdaBoostRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {
+    "id": "Xze9G-tUa802"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import AdaBoostRegressor\n",
+    "# Create an AdaBoost model\n",
+    "model6 = AdaBoostRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "DEmXpAV8a8wC",
+    "outputId": "5cadcb29-13ce-45f0-cfe1-f9e21a24866b"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model6.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {
+    "id": "KvI77shea8tk"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred6 = model6.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {
+    "id": "catCNPwla8rL"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
+    "mae6 = mean_absolute_error(y_test, pred6)\n",
+    "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
+    "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
+    "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
+    "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
+    "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
+    "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wpUqeumZa8oZ",
+    "outputId": "db2f5904-a96b-46d9-fbc3-ec519d5b7c7a"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse6)\n",
+    "print(\"MAE:\", mae6)\n",
+    "print(\"MAPE:\", mape6)\n",
+    "print(\"Accuracy:\", accuracy6)\n",
+    "print(\"Precision:\", precision6)\n",
+    "print(\"Confusion Matrix:\\n\", confusion6)\n",
+    "print(\"Recall:\", recall6)\n",
+    "print(\"F1 Score:\", f16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ubml65zDbUZu"
+   },
+   "source": [
+    "#### 7. Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {
+    "id": "qCvEszOKbVGV"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "# Create a Decision Tree model\n",
+    "model7 = DecisionTreeRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "5lYl0STGbWw8",
+    "outputId": "9aabd48a-73aa-4737-e243-ae4eb5899765"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model7.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {
+    "id": "Wu34WtF-bXy0"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred7 = model7.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {
+    "id": "H0NdEFHXbYuI"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
+    "mae7 = mean_absolute_error(y_test, pred7)\n",
+    "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
+    "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
+    "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
+    "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
+    "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
+    "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wq6aws7ZbZft",
+    "outputId": "3f0b27ba-3257-458b-b5d3-abba03fa5b47"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse7)\n",
+    "print(\"MAE:\", mae7)\n",
+    "print(\"MAPE:\", mape7)\n",
+    "print(\"Accuracy:\", accuracy7)\n",
+    "print(\"Precision:\", precision7)\n",
+    "print(\"Confusion Matrix:\\n\", confusion7)\n",
+    "print(\"Recall:\", recall7)\n",
+    "print(\"F1 Score:\", f17)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5OX62RD6bb-V"
+   },
+   "source": [
+    "#### 8. KNeighborsRegressor(KNN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "id": "5kz2DGKMbaem"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a KNN model\n",
+    "model8 = KNeighborsRegressor()\n",
+    "param_grid = {'n_neighbors':[3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]}\n",
+    "GV_KNN = GridSearchCV(model8, param_grid, cv=5, scoring='neg_mean_squared_error')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "PFdqC4oXbeg9",
+    "outputId": "b06c9cd8-c45c-4a8b-bec3-17fe23c6dc6d"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model8.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "id": "_Rv2V1W8bfhp"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred8 = model8.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "id": "K_QgGMDybgj8"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
+    "mae8 = mean_absolute_error(y_test, pred8)\n",
+    "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
+    "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
+    "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
+    "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
+    "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
+    "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "PKv6I2oCbha3",
+    "outputId": "ef250676-44d3-47ff-f7bd-ade1697c4789"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse8)\n",
+    "print(\"MAE:\", mae8)\n",
+    "print(\"MAPE:\", mape8)\n",
+    "print(\"Accuracy:\", accuracy8)\n",
+    "print(\"Precision:\", precision8)\n",
+    "print(\"Confusion Matrix:\\n\", confusion8)\n",
+    "print(\"Recall:\", recall8)\n",
+    "print(\"F1 Score:\", f18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Grid Search parameters\n",
+    "GV_KNN.fit(X_train, y_train)\n",
+    "pred8_1 = GV_KNN.predict(X_test)\n",
+    "GV_KNN.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "results = GV_KNN.cv_results_\n",
+    "mse = -results['mean_test_score']\n",
+    "k_values = results['param_n_neighbors'].data\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_values, mse, marker='o', linestyle='-')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### KNN Hyperparameter Tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# KNN Hyperparameter Tuning\n",
+    "def knn_hyperparameter_tuning(X_train, y_train):\n",
+    "    k_values = range(1, 31)  # Example range for k\n",
+    "    mse_values = []\n",
+    "    \n",
+    "    for k in k_values:\n",
+    "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
+    "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
+    "        mse_values.append(mse)\n",
+    "    \n",
+    "    return k_values, mse_values\n",
+    "\n",
+    "# Perform KNN Hyperparameter Tuning\n",
+    "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
+    "\n",
+    "# Plotting the results\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
+    "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
+    "plt.xlabel('Number of Neighbors (k)')\n",
+    "plt.ylabel('Mean Squared Error (MSE)')\n",
+    "plt.xticks(k_values)  # Show all k values on x-axis\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yoFias0bkhT"
+   },
+   "source": [
+    "#### 9. Artificial Neural Networks (ANN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "oj1tv3-Sbm4M",
+    "outputId": "0d547cbc-d317-400a-a304-1bc283287926"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an ANN model\n",
+    "model9 = Sequential()\n",
+    "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+    "model9.add(Dense(16, activation='relu'))\n",
+    "model9.add(Dense(1, activation='linear'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Llqm4lw3bnxu"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model9.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8gzrlwUTbpG7",
+    "outputId": "c3b2372d-3881-4568-efd3-59ffe8bce6f3"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cI6QW1utbp-J",
+    "outputId": "1c8b27b2-3380-40a1-ac68-17a68092f650"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred9 = model9.predict(X_test_scaled).flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {
+    "id": "-qDeEJUgbrUq"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
+    "mae9 = mean_absolute_error(y_test, pred9)\n",
+    "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
+    "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
+    "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
+    "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
+    "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
+    "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dEhvx1BTbvXO",
+    "outputId": "af73599a-ae1e-4d56-9288-8193e4aaad2b"
+   },
+   "outputs": [],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse9)\n",
+    "print(\"MAE:\", mae9)\n",
+    "print(\"MAPE:\", mape9)\n",
+    "print(\"Accuracy:\", accuracy9)\n",
+    "print(\"Precision:\", precision9)\n",
+    "print(\"Confusion Matrix:\\n\", confusion9)\n",
+    "print(\"Recall:\", recall9)\n",
+    "print(\"F1 Score:\", f19)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qosFFsxrbyX7"
+   },
+   "source": [
+    "#### 10. LSTM(Long Short term Memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {
+    "id": "3QajxGzXbxM4"
+   },
+   "outputs": [],
+   "source": [
+    "# Reshape the input data for LSTM\n",
+    "n_features = X_train_scaled.shape[1]\n",
+    "n_steps = 10\n",
+    "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+    "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+    "\n",
+    "# Reshape the input data\n",
+    "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+    "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "gNRiL_B0bxJ3",
+    "outputId": "045b5c85-f8ef-4fc4-bf38-1c253d886c82"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+    "model.add(Dense(1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {
+    "id": "DsndIHIQbxHV"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SzhA1fh9bxEz"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "AQXh8a46bxCx"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "y_pred = model.predict(X_test_reshaped).flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "iIXfZ2eubxAD"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
+    "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
+    "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
+    "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+    "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+    "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+    "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+    "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9Gjh33nNbw-O"
+   },
+   "outputs": [],
+   "source": [
+    "# Print evaluation metrics\n",
+    "print(\"RMSE:\", rmse10)\n",
+    "print(\"MAE:\", mae10)\n",
+    "print(\"MAPE:\", mape10)\n",
+    "print(\"Accuracy:\", accuracy10)\n",
+    "print(\"Precision:\", precision10)\n",
+    "print(\"Recall:\", recall10)\n",
+    "print(\"F1 Score:\", f110)\n",
+    "print(\"Confusion Matrix:\\n\", confusion10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "l6QVMqW-bw66"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Performance Graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6r0qumqybw4g"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-hFs2mnDbwGQ"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
+    "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n",
+    "\n",
+    "# List of corresponding labels for each accuracy\n",
+    "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, accuracies, color='blue')\n",
+    "plt.xlabel('Accuracy Variables')\n",
+    "plt.ylabel('Accuracy Values')\n",
+    "plt.title('Bar Graph of Accuracies')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### RMSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "1tBYk4qmcEMk"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
+    "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n",
+    "\n",
+    "# List of corresponding labels for each RMSE value\n",
+    "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, rmse_values, color='green')\n",
+    "plt.xlabel('RMSE Variables')\n",
+    "plt.ylabel('RMSE Values')\n",
+    "plt.title('Bar Graph of RMSE')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BS0oljCZcFhM"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of MAE values from mae1 to mae10\n",
+    "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n",
+    "\n",
+    "# List of corresponding labels for each MAE value\n",
+    "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, mae_values, color='orange')\n",
+    "plt.xlabel('MAE Variables')\n",
+    "plt.ylabel('MAE Values')\n",
+    "plt.title('Bar Graph of MAE')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MAPE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9woaoeNVcGx5"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of MAPE values from mape1 to mape10\n",
+    "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n",
+    "\n",
+    "# List of corresponding labels for each MAPE value\n",
+    "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, mape_values, color='purple')\n",
+    "plt.xlabel('MAPE Variables')\n",
+    "plt.ylabel('MAPE Values')\n",
+    "plt.title('Bar Graph of MAPE')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bbRSmd3tcIIX"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of precision values from precision1 to precision10\n",
+    "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
+    "\n",
+    "# List of corresponding labels for each precision value\n",
+    "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, precision_values, color='red')\n",
+    "plt.xlabel('Precision Variables')\n",
+    "plt.ylabel('Precision Values')\n",
+    "plt.title('Bar Graph of Precision')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tDNk7iuWcJpS"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of recall values from recall1 to recall10\n",
+    "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
+    "\n",
+    "# List of corresponding labels for each recall value\n",
+    "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, recall_values, color='cyan')\n",
+    "plt.xlabel('Recall Variables')\n",
+    "plt.ylabel('Recall Values')\n",
+    "plt.title('Bar Graph of Recall')\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/candlestick_chart.html b/candlestick_chart.html
new file mode 100644
index 0000000..14f6af6
--- /dev/null
+++ b/candlestick_chart.html
@@ -0,0 +1,14 @@
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+<head><meta charset="utf-8" /></head>
+<body>
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l,u,c=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<c.length;h++)l=c[h],u="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(u+t,s)}}},45216:function(t,e,r){"use strict";var n=r(3400),i=r(54460),a=r(51272),o=r(87192),s=r(13916);function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),u=a("clicktoshow");if(l||u){o(t,e,r,a);for(var c=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var v=f[d],g=i.coerceRef(t,e,p,v,"","paper");if("paper"!==g&&i.getFromId(p,g)._annIndices.push(e._index),i.coercePosition(e,p,a,g,v,.5),c){var y="a"+v,m=i.coerceRef(t,e,p,y,"pixel",["pixel","paper"]);"pixel"!==m&&m!==g&&(m=e[y]="pixel");var x="pixel"===m?h[d]:.4;i.coercePosition(e,p,a,m,y,x)}a(v+"anchor"),a(v+"shift")}if(n.noneOrAll(t,e,["x","y"]),c&&n.noneOrAll(t,e,["ax","ay"]),u){var b=a("xclick"),_=a("yclick");e._xclick=void 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r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color,fontWeight:n.weight,fontStyle:n.style,fontVariant:n.variant},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(u.stroke,e.bordercolor).call(u.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var 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n=R.select(".annotation-text-math-group"),f=!n.empty(),v=c.bBox((f?n:G).node()),b=v.width,_=v.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),q=Math.round(D+2*N);function H(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var Y=!1,X=["x","y"],Z=0;Z<X.length;Z++){var K,J,$,Q,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:y,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=q*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ut=e[et+"anchor"],ct=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(Y=!0):Y=!0),K=it._offset+it.r2p(e[et]),Q=.5}else{var dt="domain"===ht;"x"===et?($=e[et],K=dt?it._offset+it._length*$:K=T.l+T.w*$):($=1-e[et],K=dt?it._offset+it._length*$:K=T.t+T.h*$),Q=e.showarrow?.5:$}if(e.showarrow){ft.head=K;var vt=e["a"+et];if(tt=ot*H(.5,e.xanchor)-st*H(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);"domain"===gt?("y"===et&&(vt=1-vt),ft.tail=it._offset+it._length*vt):"paper"===gt?"y"===et?(vt=1-vt,ft.tail=T.t+T.h*vt):ft.tail=T.l+T.w*vt:ft.tail=it._offset+it.r2p(vt),J=tt}else ft.tail=K+vt,J=tt+vt;ft.text=ft.tail+tt;var yt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,yt-1)),"pixel"===nt){var mt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-yt;mt>0?(ft.tail+=mt,ft.text+=mt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ct,ft.head+=ct}else J=tt=lt*H(Q,ut),ft.text=K+tt;ft.text+=ct,tt+=ct,J+=ct,e["_"+et+"padplus"]=lt/2+J,e["_"+et+"padminus"]=lt/2-J,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(Y)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(c.setClipUrl,U?L:null,t);else{var wt=N+_t-v.top,Tt=N+bt-v.left;G.call(h.positionText,Tt,wt).call(c.setClipUrl,U?L:null,t)}V.select("rect").call(c.setRect,N,N,A,D),j.call(c.setRect,F/2,F/2,B-F,q-F),R.call(c.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-q/2)),I.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){O.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,v=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,v,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=v-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,q=e.arrowcolor,H=e.arrowside,G=O.append("g").style({opacity:u.opacity(q)}).classed("annotation-arrow-g",!0),W=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(u.stroke,u.rgb(q));if(g(W,H,e),k.annotationPosition&&W.node().parentNode&&!a){var Y=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));Y+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var K,J,$=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-Y)+","+(p-X),transform:s(Y,X)}).style("stroke-width",V+6+"px").call(u.stroke,"rgba(0,0,0,0)").call(u.fill,"rgba(0,0,0,0)");d.init({element:$.node(),gd:t,prepFn:function(){var t=c.getTranslate(R);K=t.x,J=t.y,y&&y.autorange&&M(y._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(K,J),i=n[0]+t,a=n[1]+r;R.call(c.setTranslate,i,a),S("x",m(y,t,"x",T,e)),S("y",m(x,r,"y",T,e)),e.axref===e.xref&&S("ax",m(y,t,"ax",T,e)),e.ayref===e.yref&&S("ay",m(x,r,"ay",T,e)),G.attr("transform",s(t,r)),I.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};e.showarrow&&At(0,0),z&&d.init({element:R.node(),gd:t,prepFn:function(){kt=I.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",m(y,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",m(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(y)i=m(y,t,"x",T,e);else{var l=e._xsize/T.w,u=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(u+t/T.w,l,0,1,e.xanchor)}if(x)o=m(x,r,"y",T,e);else{var 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o,c,f,h,p=t.node(),d=a[r.arrowhead||0],v=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),y=(r.arrowwidth||1)*(r.startarrowsize||1),m=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=v.backoff*y+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},c={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-c.x,T=o.y-c.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);c.x+=k,c.y+=A,t.attr({x2:c.x,y2:c.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var O=p.getPointAtLength(E),I=p.getPointAtLength(E-.1);h=Math.atan2(O.y-I.y,O.x-I.x),c=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,c){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:u(a.x,a.y)+l(180*o/Math.PI)+s(c)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}m&&D(v,o,f,y),x&&D(d,c,h,g)}},79180:function(t,e,r){"use strict";var n=r(23816),i=r(42300);t.exports={moduleType:"component",name:"annotations",layoutAttributes:r(13916),supplyLayoutDefaults:r(45216),includeBasePlot:r(36632)("annotations"),calcAutorange:r(90272),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:r(26828)}},45899:function(t,e,r){"use strict";var n=r(13916),i=r(67824).overrideAll,a=r(31780).templatedArray;t.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},42456:function(t,e,r){"use strict";var n=r(3400),i=r(54460);function a(t,e){var 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t=n.select(this);t.append("rect").call(d.fill,d.addOpacity(c,.8)),t.append("text").classed("name",!0),t.append("path").style("stroke-width","1px"),t.append("text").classed("nums",!0).call(p.font,{weight:A,style:M,variant:L,family:T,size:k})})),At.exit().remove(),At.each((function(t){var e=n.select(this).attr("transform",""),o=t.color;Array.isArray(o)&&(o=o[t.eventData[0].pointNumber]);var 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A=v._isShape;(v.showlegend||v._dfltShowLegend&&!(v._module&&v._module.attributes&&v._module.attributes.showlegend&&!1===v._module.attributes.showlegend.dflt))&&(x++,v.showlegend&&(b=!0,(!A&&n.traceIs(v,"pie-like")||!0===v._input.showlegend)&&x++),i.coerceFont(g,"legendgrouptitle.font",m)),(!A&&n.traceIs(v,"bar")&&"stack"===r.barmode||-1!==["tonextx","tonexty"].indexOf(v.fill))&&(_=u.isGrouped({traceorder:_})?"grouped+reversed":"reversed"),void 0!==v.legendgroup&&""!==v.legendgroup&&(_=u.isReversed({traceorder:_})?"reversed+grouped":"grouped")}var M=i.coerce(e,r,l,"showlegend",b&&x>("legend"===t?1:0));if(!1===M&&(r[t]=void 0),(!1!==M||f.uirevision)&&(p("uirevision",r.uirevision),!1!==M)){p("borderwidth");var 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V,q=i.ensureSingle(N,"g","scrollbox"),H=h.title;h._titleWidth=0,h._titleHeight=0,H.text?((V=i.ensureSingle(q,"text",w+"titletext")).attr("text-anchor","start").call(u.font,H.font).text(H.text),L(V,q,t,h,_)):q.selectAll("."+w+"titletext").remove();var G=i.ensureSingle(N,"rect","scrollbar",(function(t){t.attr(p.scrollBarEnterAttrs).call(c.fill,p.scrollBarColor)})),W=q.selectAll("g.groups").data(S);W.enter().append("g").attr("class","groups"),W.exit().remove();var Y=W.selectAll("g.traces").data(i.identity);Y.enter().append("g").attr("class","traces"),Y.exit().remove(),Y.style("opacity",(function(t){var e=t[0].trace;return o.traceIs(e,"pie-like")?-1!==B.indexOf(t[0].label)?.5:1:"legendonly"===e.visible?.5:1})).each((function(){n.select(this).call(M,t,h)})).call(x,t,h).each((function(){T||n.select(this).call(E,t,w)})),i.syncOrAsync([a.previousPromises,function(){return function(t,e,r,i){var a=t._fullLayout,o=O(i);i||(i=a[o]);var 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S=k[a][o].trace.legendrank;M>S&&(M=S)}k[a][0]._groupMinRank=M,k[a][0]._preGroupSort=a}var E=function(t,e){return t.trace.legendrank-e.trace.legendrank||t._preSort-e._preSort};for(k.forEach((function(t,e){t[0]._preGroupSort=e})),k.sort((function(t,e){return t[0]._groupMinRank-e[0]._groupMinRank||t[0]._preGroupSort-e[0]._preGroupSort})),a=0;a<k.length;a++){k[a].forEach((function(t,e){t._preSort=e})),k[a].sort(E);var L=k[a][0].trace,C=null;for(o=0;o<k[a].length;o++){var P=k[a][o].trace.legendgrouptitle;if(P&&P.text){C=P,s&&(P.font=e._groupTitleFont);break}}if(u&&k[a].reverse(),C){var O=!1;for(o=0;o<k[a].length;o++)if(n.traceIs(k[a][o].trace,"pie-like")){O=!0;break}k[a].unshift({i:-1,groupTitle:C,noClick:O,trace:{showlegend:L.showlegend,legendgroup:L.legendgroup,visible:"toggleitem"===e.groupclick||L.visible}})}for(o=0;o<k[a].length;o++)k[a][o]=[k[a][o]]}return e._lgroupsLength=k.length,e._maxNameLength=v,k}},33048:function(t,e,r){"use strict";var n=r(24040),i=r(3400),a=i.pushUnique,o=!0;t.exports=function(t,e,r){var s=e._fullLayout;if(!e._dragged&&!e._editing){var l,u=s.legend.itemclick,c=s.legend.itemdoubleclick,f=s.legend.groupclick;if(1===r&&"toggle"===u&&"toggleothers"===c&&o&&e.data&&e._context.showTips?(i.notifier(i._(e,"Double-click on legend to isolate one trace"),"long"),o=!1):o=!1,1===r?l=u:2===r&&(l=c),l){var h="togglegroup"===f,p=s.hiddenlabels?s.hiddenlabels.slice():[],d=t.data()[0][0];if(!d.groupTitle||!d.noClick){var v=e._fullData,g=(s.shapes||[]).filter((function(t){return t.showlegend})),y=v.concat(g),m=d.trace;m._isShape&&(m=m._fullInput);var x,b,_,w,T,k=m.legendgroup,A={},M=[],S=[],E=[],L=(s.shapes||[]).map((function(t){return t._input})),C=!1,P=m.legend,O=m._fullInput;if(O&&O._isShape||!n.traceIs(m,"pie-like")){var I,z=k&&k.length,D=[];if(z)for(x=0;x<y.length;x++)(I=y[x]).visible&&I.legendgroup===k&&D.push(x);if("toggle"===l){var 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save")},attr:"resetLastSave",icon:o.movie,click:p},c.hoverClosest3d={name:"hoverClosest3d",_cat:"hoverclosest",title:function(t){return u(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);n.call("_guiRelayout",t,r)}},c.zoomInGeo={name:"zoomInGeo",_cat:"zoomin",title:function(t){return u(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:v},c.zoomOutGeo={name:"zoomOutGeo",_cat:"zoomout",title:function(t){return u(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:v},c.resetGeo={name:"resetGeo",_cat:"reset",title:function(t){return u(t,"Reset")},attr:"reset",val:null,icon:o.autoscale,click:v},c.hoverClosestGeo={name:"hoverClosestGeo",_cat:"hoverclosest",title:function(t){return u(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:y},c.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return u(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:y},c.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return u(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:y},c.resetViewSankey={name:"resetSankeyGroup",title:function(t){return u(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},c.toggleHover={name:"toggleHover",title:function(t){return u(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call("_guiRelayout",t,r)}},c.resetViews={name:"resetViews",title:function(t){return u(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},c.toggleSpikelines={name:"toggleSpikelines",title:function(t){return u(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},c.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return u(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},c.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return u(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:m},c.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return u(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:m}},76052:function(t,e,r){"use strict";var n=r(44248),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var e=t.name,r=(t._cat||t.name).toLowerCase();-1===s.indexOf(e)&&s.push(e),-1===s.indexOf(r)&&s.push(r)}}(n[t])})),s.sort(),t.exports={DRAW_MODES:a,backButtons:o,foreButtons:s}},90824:function(t,e,r){"use strict";var n=r(3400),i=r(76308),a=r(31780),o=r(66540);t.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,"modebar");function l(t,e){return n.coerce(r,s,o,t,e)}l("orientation"),l("bgcolor",i.addOpacity(e.paper_bgcolor,.5));var u=i.contrast(i.rgb(e.modebar.bgcolor));l("color",i.addOpacity(u,.3)),l("activecolor",i.addOpacity(u,.7)),l("uirevision",e.uirevision),l("add"),l("remove")}},45460:function(t,e,r){"use strict";t.exports={moduleType:"component",name:"modebar",layoutAttributes:r(66540),supplyLayoutDefaults:r(90824),manage:r(18816)}},18816:function(t,e,r){"use strict";var n=r(79811),i=r(43028),a=r(24040),o=r(10624).isUnifiedHover,s=r(66400),l=r(44248),u=r(76052).DRAW_MODES,c=r(3400).extendDeep;t.exports=function(t){var e=t._fullLayout,r=t._context,f=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error(["*modeBarButtonsToRemove* configuration options","must be an array."].join(" "));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error(["*modeBarButtonsToAdd* configuration options","must be an array."].join(" "));var h,p=r.modeBarButtons;h=Array.isArray(p)&&p.length?function(t){for(var e=c([],t),r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++){var a=n[i];if("string"==typeof a){if(void 0===l[a])throw new Error(["*modeBarButtons* configuration options","invalid button name"].join(" "));e[r][i]=l[a]}}return e}(p):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function c(t,e){if("string"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;"string"==typeof f&&(f=[f]);var h=e.modebar.remove;"string"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(c(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(c(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),v=e._has("cartesian"),g=e._has("gl3d"),y=e._has("geo"),m=e._has("pie"),x=e._has("funnelarea"),b=e._has("gl2d"),_=e._has("ternary"),w=e._has("mapbox"),T=e._has("polar"),k=e._has("smith"),A=e._has("sankey"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,u=0;u<d.length;u++){var c=d[u].toLowerCase();if(c===a||c===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=["toImage"];s.showEditInChartStudio?C.push("editInChartStudio"):s.showSendToCloud&&C.push("sendDataToCloud"),L(C);var P=[],O=[],I=[],z=[];(v||b||m||x||_)+y+g+w+T+k>1?(O=["toggleHover"],I=["resetViews"]):y?(P=["zoomInGeo","zoomOutGeo"],O=["hoverClosestGeo"],I=["resetGeo"]):g?(O=["hoverClosest3d"],I=["resetCameraDefault3d","resetCameraLastSave3d"]):w?(P=["zoomInMapbox","zoomOutMapbox"],O=["toggleHover"],I=["resetViewMapbox"]):b?O=["hoverClosestGl2d"]:m?O=["hoverClosestPie"]:A?(O=["hoverClosestCartesian","hoverCompareCartesian"],I=["resetViewSankey"]):O=["toggleHover"],v&&(O=["toggleSpikelines","hoverClosestCartesian","hoverCompareCartesian"]),(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],"noHover"))return!1;return!0}(r)||S)&&(O=[]),!v&&!b||M||(P=["zoomIn2d","zoomOut2d","autoScale2d"],"resetViews"!==I[0]&&(I=["resetScale2d"])),g?z=["zoom3d","pan3d","orbitRotation","tableRotation"]:(v||b)&&!M||_?z=["zoom2d","pan2d"]:w||y?z=["pan2d"]:T&&(z=["zoom2d"]),function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,"scatter-like")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,"box-violin")&&"all"!==n.boxpoints&&"all"!==n.points||(e=!0))}return e}(r)&&z.push("select2d","lasso2d");var D=[],R=function(t){-1===D.indexOf(t)&&-1!==O.indexOf(t)&&D.push(t)};if(Array.isArray(p)){for(var F=[],B=0;B<p.length;B++){var N=p[B];"string"==typeof N?(N=N.toLowerCase(),-1!==u.indexOf(N)?(e._has("mapbox")||e._has("cartesian"))&&z.push(N):"togglespikelines"===N?R("toggleSpikelines"):"togglehover"===N?R("toggleHover"):"hovercompare"===N?R("hoverCompareCartesian"):"hoverclosest"===N?(R("hoverClosestCartesian"),R("hoverClosestGeo"),R("hoverClosest3d"),R("hoverClosestGl2d"),R("hoverClosestPie")):"v1hovermode"===N&&(R("toggleHover"),R("hoverClosestCartesian"),R("hoverCompareCartesian"),R("hoverClosestGeo"),R("hoverClosest3d"),R("hoverClosestGl2d"),R("hoverClosestPie"))):F.push(N)}p=F}return L(z),L(P.concat(I)),L(D),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(E,p)}(t),f?f.update(t,h):e._modeBar=s(t,h)}else f&&(f.destroy(),delete e._modeBar)}},66400:function(t,e,r){"use strict";var n=r(33428),i=r(38248),a=r(3400),o=r(9224),s=r(25788).version,l=new DOMParser;function u(t){this.container=t.container,this.element=document.createElement("div"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=u.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i="modebar-"+n._uid;this.element.setAttribute("id",i),this._uid=i,this.element.className="modebar","hover"===r.displayModeBar&&(this.element.className+=" modebar--hover ease-bg"),"v"===n.modebar.orientation&&(this.element.className+=" vertical",e=e.reverse());var o=n.modebar,s="hover"===r.displayModeBar?".js-plotly-plot .plotly:hover 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Error("must provide button 'name' in button config");if(-1!==e.buttonsNames.indexOf(n))throw new Error("button name '"+n+"' is taken");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},c.createGroup=function(){var t=document.createElement("div");return t.className="modebar-group",t},c.createButton=function(t){var e=this,r=document.createElement("a");r.setAttribute("rel","tooltip"),r.className="modebar-btn";var i=t.title;void 0===i?i=t.name:"function"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute("data-title",i),void 0!==t.attr&&r.setAttribute("data-attr",t.attr);var a=t.val;if(void 0!==a&&("function"==typeof a&&(a=a(this.graphInfo)),r.setAttribute("data-val",a)),"function"!=typeof t.click)throw new Error("must provide button 'click' function in button 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e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute("data-attr"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute("data-val")||!0,o=t.getAttribute("data-attr"),s="true"===t.getAttribute("data-toggle"),l=n.select(t);if(s)o===r&&l.classed("active",!l.classed("active"));else{var u=null===o?o:a.nestedProperty(e,o).get();l.classed("active",u===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement("a");return e.href="https://plotly.com/",e.target="_blank",e.setAttribute("data-title",a._(this.graphInfo,"Produced with Plotly.js")+" (v"+s+")"),e.className="modebar-btn plotlyjsicon 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n=r(25376),i=r(22548),a=(0,r(31780).templatedArray)("button",{visible:{valType:"boolean",dflt:!0,editType:"plot"},step:{valType:"enumerated",values:["month","year","day","hour","minute","second","all"],dflt:"month",editType:"plot"},stepmode:{valType:"enumerated",values:["backward","todate"],dflt:"backward",editType:"plot"},count:{valType:"number",min:0,dflt:1,editType:"plot"},label:{valType:"string",editType:"plot"},editType:"plot"});t.exports={visible:{valType:"boolean",editType:"plot"},buttons:a,x:{valType:"number",min:-2,max:3,editType:"plot"},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"left",editType:"plot"},y:{valType:"number",min:-2,max:3,editType:"plot"},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"bottom",editType:"plot"},font:n({editType:"plot"}),bgcolor:{valType:"color",dflt:i.lightLine,editType:"plot"},activecolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:i.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"}},85984:function(t){"use 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g=d("bgcolor");d("activecolor",i.contrast(g,l.lightAmount,l.darkAmount)),d("bordercolor"),d("borderwidth")}}},50216:function(t,e,r){"use strict";var n=r(33428),i=r(24040),a=r(7316),o=r(76308),s=r(43616),l=r(3400),u=l.strTranslate,c=r(72736),f=r(79811),h=r(84284),p=h.LINE_SPACING,d=h.FROM_TL,v=h.FROM_BR,g=r(85984),y=r(48040);function m(t){return t._id}function x(t,e,r){var n=l.ensureSingle(t,"rect","selector-rect",(function(t){t.attr("shape-rendering","crispEdges")}));n.attr({rx:g.rx,ry:g.ry}),n.call(o.stroke,e.bordercolor).call(o.fill,function(t,e){return e._isActive||e._isHovered?t.activecolor:t.bgcolor}(e,r)).style("stroke-width",e.borderwidth+"px")}function b(t,e,r,n){var i,a;l.ensureSingle(t,"text","selector-text",(function(t){t.attr("text-anchor","middle")})).call(s.font,e.font).text((i=r,a=n._fullLayout._meta,i.label?a?l.templateString(i.label,a):i.label:"all"===i.step?"all":i.count+i.step.charAt(0))).call((function(t){c.convertToTspans(t,n)}))}t.exports=function(t){var e=t._fullLayout._infolayer.selectAll(".rangeselector").data(function(t){for(var e=f.list(t,"x",!0),r=[],n=0;n<e.length;n++){var i=e[n];i.rangeselector&&i.rangeselector.visible&&r.push(i)}return r}(t),m);e.enter().append("g").classed("rangeselector",!0),e.exit().remove(),e.style({cursor:"pointer","pointer-events":"all"}),e.each((function(e){var r=n.select(this),o=e,f=o.rangeselector,h=r.selectAll("g.button").data(l.filterVisible(f.buttons));h.enter().append("g").classed("button",!0),h.exit().remove(),h.each((function(e){var r=n.select(this),a=y(o,e);e._isActive=function(t,e,r){if("all"===e.step)return!0===t.autorange;var n=Object.keys(r);return t.range[0]===r[n[0]]&&t.range[1]===r[n[1]]}(o,e,a),r.call(x,f,e),r.call(b,f,e,t),r.on("click",(function(){t._dragged||i.call("_guiRelayout",t,a)})),r.on("mouseover",(function(){e._isHovered=!0,r.call(x,f,e)})),r.on("mouseout",(function(){e._isHovered=!1,r.call(x,f,e)}))})),function(t,e,r,i,o){var f=0,h=0,y=r.borderwidth;e.each((function(){var 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r=t._name,a={};if("all"===e.step)a[r+".autorange"]=!0;else{var o=function(t,e){var r,a=t.range,o=new Date(t.r2l(a[1])),s=e.step,l=n["utc"+i(s)],u=e.count;switch(e.stepmode){case"backward":r=t.l2r(+l.offset(o,-u));break;case"todate":var c=l.offset(o,-u);r=t.l2r(+l.ceil(c))}return[r,a[1]]}(t,e);a[r+".range[0]"]=o[0],a[r+".range[1]"]=o[1]}return a}},41152:function(t,e,r){"use strict";t.exports={moduleType:"component",name:"rangeselector",schema:{subplots:{xaxis:{rangeselector:r(26680)}}},layoutAttributes:r(26680),handleDefaults:r(22148),draw:r(50216)}},11200:function(t,e,r){"use strict";var n=r(22548);t.exports={bgcolor:{valType:"color",dflt:n.background,editType:"plot"},bordercolor:{valType:"color",dflt:n.defaultLine,editType:"plot"},borderwidth:{valType:"integer",dflt:0,min:0,editType:"plot"},autorange:{valType:"boolean",dflt:!0,editType:"calc",impliedEdits:{"range[0]":void 0,"range[1]":void 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n=r(3400),i=r(31780),a=r(79811),o=r(11200),s=r(10936);t.exports=function(t,e,r){var l=t[r],u=e[r];if(l.rangeslider||e._requestRangeslider[u._id]){n.isPlainObject(l.rangeslider)||(l.rangeslider={});var c,f,h=l.rangeslider,p=i.newContainer(u,"rangeslider");if(_("visible")){_("bgcolor",e.plot_bgcolor),_("bordercolor"),_("borderwidth"),_("thickness"),_("autorange",!u.isValidRange(h.range)),_("range");var d=e._subplots;if(d)for(var v=d.cartesian.filter((function(t){return t.substr(0,t.indexOf("y"))===a.name2id(r)})).map((function(t){return t.substr(t.indexOf("y"),t.length)})),g=n.simpleMap(v,a.id2name),y=0;y<g.length;y++){var m=g[y];c=h[m]||{},f=i.newContainer(p,m,"yaxis");var x,b=e[m];c.range&&b.isValidRange(c.range)&&(x="fixed"),"match"!==w("rangemode",x)&&w("range",b.range.slice())}p._input=h}}function _(t,e){return n.coerce(h,p,o,t,e)}function w(t,e){return n.coerce(c,f,s,t,e)}}},20060:function(t,e,r){"use strict";var n=r(33428),i=r(24040),a=r(7316),o=r(3400),s=o.strTranslate,l=r(43616),u=r(76308),c=r(81668),f=r(57952),h=r(79811),p=r(86476),d=r(93972),v=r(74636);function g(t){return"number"==typeof t.clientX?t.clientX:t.touches&&t.touches.length>0?t.touches[0].clientX:0}function y(t,e,r,n){var i=o.ensureSingle(t,"rect",v.bgClassName,(function(t){t.attr({x:0,y:0,"shape-rendering":"crispEdges"})})),a=n.borderwidth%2==0?n.borderwidth:n.borderwidth-1,c=-n._offsetShift,f=l.crispRound(e,n.borderwidth);i.attr({width:n._width+a,height:n._height+a,transform:s(c,c),"stroke-width":f}).call(u.stroke,n.bordercolor).call(u.fill,n.bgcolor)}function m(t,e,r,n){var i=e._fullLayout;o.ensureSingleById(i._topdefs,"clipPath",n._clipId,(function(t){t.append("rect").attr({x:0,y:0})})).select("rect").attr({width:n._width,height:n._height})}function x(t,e,r,i){var s,u=e.calcdata,c=t.selectAll("g."+v.rangePlotClassName).data(r._subplotsWith,o.identity);c.enter().append("g").attr("class",(function(t){return 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b(t,e,r,n,i){o.ensureSingle(t,"rect",v.maskMinClassName,(function(t){t.attr({x:0,y:0,"shape-rendering":"crispEdges"})})).attr("height",n._height).call(u.fill,v.maskColor),o.ensureSingle(t,"rect",v.maskMaxClassName,(function(t){t.attr({y:0,"shape-rendering":"crispEdges"})})).attr("height",n._height).call(u.fill,v.maskColor),"match"!==i.rangemode&&(o.ensureSingle(t,"rect",v.maskMinOppAxisClassName,(function(t){t.attr({y:0,"shape-rendering":"crispEdges"})})).attr("width",n._width).call(u.fill,v.maskOppAxisColor),o.ensureSingle(t,"rect",v.maskMaxOppAxisClassName,(function(t){t.attr({y:0,"shape-rendering":"crispEdges"})})).attr("width",n._width).style("border-top",v.maskOppBorder).call(u.fill,v.maskOppAxisColor))}function _(t,e,r,n){e._context.staticPlot||o.ensureSingle(t,"rect",v.slideBoxClassName,(function(t){t.attr({y:0,cursor:v.slideBoxCursor,"shape-rendering":"crispEdges"})})).attr({height:n._height,fill:v.slideBoxFill})}function w(t,e,r,n){var i=o.ensureSingle(t,"g",v.grabberMinClassName),a=o.ensureSingle(t,"g",v.grabberMaxClassName),s={x:0,width:v.handleWidth,rx:v.handleRadius,fill:u.background,stroke:u.defaultLine,"stroke-width":v.handleStrokeWidth,"shape-rendering":"crispEdges"},l={y:Math.round(n._height/4),height:Math.round(n._height/2)};o.ensureSingle(i,"rect",v.handleMinClassName,(function(t){t.attr(s)})).attr(l),o.ensureSingle(a,"rect",v.handleMaxClassName,(function(t){t.attr(s)})).attr(l);var c={width:v.grabAreaWidth,x:0,y:0,fill:v.grabAreaFill,cursor:e._context.staticPlot?void 0:v.grabAreaCursor};o.ensureSingle(i,"rect",v.grabAreaMinClassName,(function(t){t.attr(c)})).attr("height",n._height),o.ensureSingle(a,"rect",v.grabAreaMaxClassName,(function(t){t.attr(c)})).attr("height",n._height)}t.exports=function(t){for(var e=t._fullLayout,r=e._rangeSliderData,a=0;a<r.length;a++){var l=r[a][v.name];l._clipId=l._id+"-"+e._uid}var u=e._infolayer.selectAll("g."+v.containerClassName).data(r,(function(t){return t._name}));u.exit().each((function(t){var r=t[v.name];e._topdefs.select("#"+r._clipId).remove()})).remove(),0!==r.length&&(u.enter().append("g").classed(v.containerClassName,!0).attr("pointer-events","all"),u.each((function(r){var a=n.select(this),l=r[v.name],u=e[h.id2name(r.anchor)],f=l[h.id2name(r.anchor)];if(l.range){var T,k=o.simpleMap(l.range,r.r2l),A=o.simpleMap(r.range,r.r2l);T=A[0]<A[1]?[Math.min(k[0],A[0]),Math.max(k[1],A[1])]:[Math.max(k[0],A[0]),Math.min(k[1],A[1])],l.range=l._input.range=o.simpleMap(T,r.l2r)}r.cleanRange("rangeslider.range");var M=e._size,S=r.domain;l._width=M.w*(S[1]-S[0]);var E=Math.round(M.l+M.w*S[0]),L=Math.round(M.t+M.h*(1-r._counterDomainMin)+("bottom"===r.side?r._depth:0)+l._offsetShift+v.extraPad);a.attr("transform",s(E,L)),l._rl=o.simpleMap(l.range,r.r2l);var C=l._rl[0],P=l._rl[1],O=P-C;if(l.p2d=function(t){return t/l._width*O+C},l.d2p=function(t){return(t-C)/O*l._width},r.rangebreaks){var I=r.locateBreaks(C,P);if(I.length){var z,D,R=0;for(z=0;z<I.length;z++)R+=(D=I[z]).max-D.min;var F=l._width/(P-C-R),B=[-F*C];for(z=0;z<I.length;z++)D=I[z],B.push(B[B.length-1]-F*(D.max-D.min));for(l.d2p=function(t){for(var e=B[0],r=0;r<I.length;r++){var n=I[r];if(t>=n.max)e=B[r+1];else if(t<n.min)break}return e+F*t},z=0;z<I.length;z++)(D=I[z]).pmin=l.d2p(D.min),D.pmax=l.d2p(D.max);l.p2d=function(t){for(var e=B[0],r=0;r<I.length;r++){var n=I[r];if(t>=n.pmax)e=B[r+1];else if(t<n.pmin)break}return(t-e)/F}}}if("match"!==f.rangemode){var N=u.r2l(f.range[0]),j=u.r2l(f.range[1])-N;l.d2pOppAxis=function(t){return(t-N)/j*l._height}}a.call(y,t,r,l).call(m,t,r,l).call(x,t,r,l).call(b,t,r,l,f).call(_,t,r,l).call(w,t,r,l),function(t,e,r,a){if(!e._context.staticPlot){var s=t.select("rect."+v.slideBoxClassName).node(),l=t.select("rect."+v.grabAreaMinClassName).node(),u=t.select("rect."+v.grabAreaMaxClassName).node();t.on("mousedown",c),t.on("touchstart",c)}function c(){var c=n.event,f=c.target,h=g(c),v=h-t.node().getBoundingClientRect().left,y=a.d2p(r._rl[0]),m=a.d2p(r._rl[1]),x=p.coverSlip();function b(t){var c,p,b,_=+g(t)-h;switch(f){case s:if(b="ew-resize",y+_>r._length||m+_<0)return;c=y+_,p=m+_;break;case l:if(b="col-resize",y+_>r._length)return;c=y+_,p=m;break;case u:if(b="col-resize",m+_<0)return;c=y,p=m+_;break;default:b="ew-resize",c=v,p=v+_}if(p<c){var w=p;p=c,c=w}a._pixelMin=c,a._pixelMax=p,d(n.select(x),b),function(t,e,r,n){function a(t){return r.l2r(o.constrain(t,n._rl[0],n._rl[1]))}var s=a(n.p2d(n._pixelMin)),l=a(n.p2d(n._pixelMax));window.requestAnimationFrame((function(){i.call("_guiRelayout",e,r._name+".range",[s,l])}))}(0,e,r,a)}function _(){x.removeEventListener("mousemove",b),x.removeEventListener("mouseup",_),this.removeEventListener("touchmove",b),this.removeEventListener("touchend",_),o.removeElement(x)}this.addEventListener("touchmove",b),this.addEventListener("touchend",_),x.addEventListener("mousemove",b),x.addEventListener("mouseup",_)}}(a,t,r,l),function(t,e,r,n,i,a){var l=v.handleWidth/2;function u(t){return o.constrain(t,0,n._width)}function c(t){return o.constrain(t,0,n._height)}function f(t){return o.constrain(t,-l,n._width+l)}var h=u(n.d2p(r._rl[0])),p=u(n.d2p(r._rl[1]));if(t.select("rect."+v.slideBoxClassName).attr("x",h).attr("width",p-h),t.select("rect."+v.maskMinClassName).attr("width",h),t.select("rect."+v.maskMaxClassName).attr("x",p).attr("width",n._width-p),"match"!==a.rangemode){var d=n._height-c(n.d2pOppAxis(i._rl[1])),g=n._height-c(n.d2pOppAxis(i._rl[0]));t.select("rect."+v.maskMinOppAxisClassName).attr("x",h).attr("height",d).attr("width",p-h),t.select("rect."+v.maskMaxOppAxisClassName).attr("x",h).attr("y",g).attr("height",n._height-g).attr("width",p-h),t.select("rect."+v.slideBoxClassName).attr("y",d).attr("height",g-d)}var y=.5,m=Math.round(f(h-l))-y,x=Math.round(f(p-l))+y;t.select("g."+v.grabberMinClassName).attr("transform",s(m,y)),t.select("g."+v.grabberMaxClassName).attr("transform",s(x,y))}(a,0,r,l,u,f),"bottom"===r.side&&c.draw(t,r._id+"title",{propContainer:r,propName:r._name+".title",placeholder:e._dfltTitle.x,attributes:{x:r._offset+r._length/2,y:L+l._height+l._offsetShift+10+1.5*r.title.font.size,"text-anchor":"middle"}})})))}},97944:function(t,e,r){"use strict";var n=r(79811),i=r(72736),a=r(74636),o=r(84284).LINE_SPACING,s=a.name;function l(t){var e=t&&t[s];return e&&e.visible}e.isVisible=l,e.makeData=function(t){var e=n.list({_fullLayout:t},"x",!0),r=t.margin,i=[];if(!t._has("gl2d"))for(var a=0;a<e.length;a++){var o=e[a];if(l(o)){i.push(o);var u=o[s];u._id=s+o._id,u._height=(t.height-r.b-r.t)*u.thickness,u._offsetShift=Math.floor(u.borderwidth/2)}}t._rangeSliderData=i},e.autoMarginOpts=function(t,e){var r=t._fullLayout,n=e[s],l=e._id.charAt(0),u=0,c=0;return"bottom"===e.side&&(u=e._depth,e.title.text!==r._dfltTitle[l]&&(c=1.5*e.title.font.size+10+n._offsetShift,c+=(e.title.text.match(i.BR_TAG_ALL)||[]).length*e.title.font.size*o)),{x:0,y:e._counterDomainMin,l:0,r:0,t:0,b:n._height+u+Math.max(r.margin.b,c),pad:a.extraPad+2*n._offsetShift}}},49692:function(t,e,r){"use strict";var n=r(3400),i=r(11200),a=r(10936),o=r(97944);t.exports={moduleType:"component",name:"rangeslider",schema:{subplots:{xaxis:{rangeslider:n.extendFlat({},i,{yaxis:a})}}},layoutAttributes:r(11200),handleDefaults:r(94040),calcAutorange:r(26652),draw:r(20060),isVisible:o.isVisible,makeData:o.makeData,autoMarginOpts:o.autoMarginOpts}},10936:function(t){"use strict";t.exports={_isSubplotObj:!0,rangemode:{valType:"enumerated",values:["auto","fixed","match"],dflt:"match",editType:"calc"},range:{valType:"info_array",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},editType:"calc"}},93956:function(t,e,r){"use strict";var n=r(13916),i=r(52904).line,a=r(98192).u,o=r(92880).extendFlat,s=r(67824).overrideAll,l=r(31780).templatedArray;r(36208),t.exports=s(l("selection",{type:{valType:"enumerated",values:["rect","path"]},xref:o({},n.xref,{}),yref:o({},n.yref,{}),x0:{valType:"any"},x1:{valType:"any"},y0:{valType:"any"},y1:{valType:"any"},path:{valType:"string",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:.7,editType:"arraydraw"},line:{color:i.color,width:o({},i.width,{min:1,dflt:1}),dash:o({},a,{dflt:"dot"})}}),"arraydraw","from-root")},83280:function(t){"use strict";t.exports={BENDPX:1.5,MINSELECT:12,SELECTDELAY:100,SELECTID:"-select"}},74224:function(t,e,r){"use strict";var n=r(3400),i=r(54460),a=r(51272),o=r(93956),s=r(65152);function l(t,e,r){function a(r,i){return n.coerce(t,e,o,r,i)}var l=a("path"),u="path"!==a("type",l?"path":"rect");u&&delete e.path,a("opacity"),a("line.color"),a("line.width"),a("line.dash");for(var c=["x","y"],f=0;f<2;f++){var h,p,d,v=c[f],g={_fullLayout:r},y=i.coerceRef(t,e,g,v);if((h=i.getFromId(g,y))._selectionIndices.push(e._index),d=s.rangeToShapePosition(h),p=s.shapePositionToRange(h),u){var m=v+"0",x=v+"1",b=t[m],_=t[x];t[m]=p(t[m],!0),t[x]=p(t[x],!0),i.coercePosition(e,g,a,y,m),i.coercePosition(e,g,a,y,x);var w=e[m],T=e[x];void 0!==w&&void 0!==T&&(e[m]=d(w),e[x]=d(T),t[m]=b,t[x]=_)}}u&&n.noneOrAll(t,e,["x0","x1","y0","y1"])}t.exports=function(t,e){a(t,e,{name:"selections",handleItemDefaults:l});for(var r=e.selections,n=0;n<r.length;n++){var i=r[n];i&&void 0===i.path&&(void 0!==i.x0&&void 0!==i.x1&&void 0!==i.y0&&void 0!==i.y1||(e.selections[n]=null))}}},23640:function(t,e,r){"use strict";var n=r(9856).readPaths,i=r(55496),a=r(1936).clearOutlineControllers,o=r(76308),s=r(43616),l=r(31780).arrayEditor,u=r(65152),c=u.getPathString;function f(t){var e=t._fullLayout;for(var r in a(t),e._selectionLayer.selectAll("path").remove(),e._plots){var 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n=r(98192).u,i=r(92880).extendFlat;t.exports={newselection:{mode:{valType:"enumerated",values:["immediate","gradual"],dflt:"immediate",editType:"none"},line:{color:{valType:"color",editType:"none"},width:{valType:"number",min:1,dflt:1,editType:"none"},dash:i({},n,{dflt:"dot",editType:"none"}),editType:"none"},editType:"none"},activeselection:{fillcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"none"},opacity:{valType:"number",min:0,max:1,dflt:.5,editType:"none"},editType:"none"}}},81004:function(t){"use strict";t.exports=function(t,e,r){r("newselection.mode"),r("newselection.line.width")&&(r("newselection.line.color"),r("newselection.line.dash")),r("activeselection.fillcolor"),r("activeselection.opacity")}},5968:function(t,e,r){"use strict";var n=r(72760).selectMode,i=r(1936).clearOutline,a=r(9856),o=a.readPaths,s=a.writePaths,l=a.fixDatesForPaths;t.exports=function(t,e){if(t.length){var r=t[0][0];if(r){var 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c=l[u],f=c.data,h=c.fullData,p=c.pointIndex,d=c.pointIndices;d?([].push.apply(f.selectedpoints,d),i._fullInput!==i&&[].push.apply(h.selectedpoints,d)):(f.selectedpoints.push(p),i._fullInput!==i&&h.selectedpoints.push(p))}}else for(n=0;n<e.length;n++)delete(i=e[n].cd[0].trace).selectedpoints,delete i._input.selectedpoints,i._fullInput!==i&&delete i._fullInput.selectedpoints;!function(t,e){for(var r=!1,n=0;n<e.length;n++){var i=e[n],o=i.cd;a.traceIs(o[0].trace,"regl")&&(r=!0);var s=i._module,l=s.styleOnSelect||s.style;l&&(l(t,o,o[0].node3),o[0].nodeRangePlot3&&l(t,o,o[0].nodeRangePlot3))}r&&(P(t),O(t))}(t,e)}function Q(t,e,r){for(var i=(r?n.difference:n.union)({regions:t},{regions:[e]}).regions.reverse(),a=0;a<i.length;a++){var o=i[a];o.subtract=st(o,i.slice(0,a))}return i}function tt(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=u(t[i],n,r);return t}function et(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var 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v="pixel"===h.xsizemode?h.xanchor:h.x0,g="pixel"===h.xsizemode?h.xanchor:h.x1;(f=c(u=i.getFromId(t,h.xref),v,g,h.path,a.paramIsX))&&(h._extremes[u._id]=i.findExtremes(u,f,s(h)))}if("paper"!==h.yref&&"domain"!==d){var y="pixel"===h.ysizemode?h.yanchor:h.y0,m="pixel"===h.ysizemode?h.yanchor:h.y1;(f=c(u=i.getFromId(t,h.yref),y,m,h.path,a.paramIsY))&&(h._extremes[u._id]=i.findExtremes(u,f,l(h)))}}}},85448:function(t){"use strict";t.exports={segmentRE:/[MLHVQCTSZ][^MLHVQCTSZ]*/g,paramRE:/[^\s,]+/g,paramIsX:{M:{0:!0,drawn:0},L:{0:!0,drawn:0},H:{0:!0,drawn:0},V:{},Q:{0:!0,2:!0,drawn:2},C:{0:!0,2:!0,4:!0,drawn:4},T:{0:!0,drawn:0},S:{0:!0,2:!0,drawn:2},Z:{}},paramIsY:{M:{1:!0,drawn:1},L:{1:!0,drawn:1},H:{},V:{0:!0,drawn:0},Q:{1:!0,3:!0,drawn:3},C:{1:!0,3:!0,5:!0,drawn:5},T:{1:!0,drawn:1},S:{1:!0,3:!0,drawn:5},Z:{}},numParams:{M:2,L:2,H:1,V:1,Q:4,C:6,T:2,S:4,Z:0}}},43712:function(t,e,r){"use strict";var n=r(3400),i=r(54460),a=r(51272),o=r(46056),s=r(65152);function l(t,e,r){function 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n,i=t[0][0],o=e.gd,p=i.getAttribute("d"),g=o._fullLayout.newshape,y=e.plotinfo,w=e.isActiveShape,T=y.xaxis,k=y.yaxis,A=!!y.domain||!y.xaxis,M=!!y.domain||!y.yaxis,S=a(r),E=m(p,o,y,w),L={editable:!0,visible:g.visible,name:g.name,showlegend:g.showlegend,legend:g.legend,legendwidth:g.legendwidth,legendgroup:g.legendgroup,legendgrouptitle:{text:g.legendgrouptitle.text,font:g.legendgrouptitle.font},legendrank:g.legendrank,label:g.label,xref:A?"paper":T._id,yref:M?"paper":k._id,layer:g.layer,opacity:g.opacity,line:{color:g.line.color,width:g.line.width,dash:g.line.dash}};if(S||(L.fillcolor=g.fillcolor,L.fillrule=g.fillrule),1===E.length&&(n=E[0]),n&&5===n.length&&"drawrect"===r)L.type="rect",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[2][1],L.y1=n[2][2];else if(n&&"drawline"===r)L.type="line",L.x0=n[0][1],L.y0=n[0][2],L.x1=n[1][1],L.y1=n[1][2];else if(n&&"drawcircle"===r){L.type="circle";var C=n[s][1],P=n[l][1],O=n[u][1],I=n[c][1],z=n[s][2],D=n[l][2],R=n[u][2],F=n[c][2],B=y.xaxis&&("date"===y.xaxis.type||"log"===y.xaxis.type),N=y.yaxis&&("date"===y.yaxis.type||"log"===y.yaxis.type);B&&(C=v(y.xaxis,C),P=v(y.xaxis,P),O=v(y.xaxis,O),I=v(y.xaxis,I)),N&&(z=v(y.yaxis,z),D=v(y.yaxis,D),R=v(y.yaxis,R),F=v(y.yaxis,F));var j=(P+I)/2,U=(z+R)/2,V=b({x0:j,y0:U,x1:j+(I-P+O-C)/2*f,y1:U+(F-D+R-z)/2*h});B&&(V.x0=d(y.xaxis,V.x0),V.x1=d(y.xaxis,V.x1)),N&&(V.y0=d(y.yaxis,V.y0),V.y1=d(y.yaxis,V.y1)),L.x0=V.x0,L.y0=V.y0,L.x1=V.x1,L.y1=V.y1}else L.type="path",T&&k&&_(E,T,k),L.path=x(E),n=null;return L}t.exports={newShapes:function(t,e){if(t.length&&t[0][0]){var r=e.gd,n=e.isActiveShape,a=e.dragmode,o=(r.layout||{}).shapes||[];if(!i(a)&&void 0!==n){var s=r._fullLayout._activeShapeIndex;if(s<o.length)switch(r._fullLayout.shapes[s].type){case"rect":a="drawrect";break;case"circle":a="drawcircle";break;case"line":a="drawline";break;case"path":var l=o[s].path||"";a="Z"===l[l.length-1]?"drawclosedpath":"drawopenpath"}}var u=w(t,e,a);g(r);for(var c=e.editHelpers,f=(c||{}).modifyItem,h=[],p=0;p<o.length;p++){var d=r._fullLayout.shapes[p];if(h[p]=d._input,void 0!==n&&p===r._fullLayout._activeShapeIndex){var v=u;switch(d.type){case"line":case"rect":case"circle":f("x0",v.x0),f("x1",v.x1),f("y0",v.y0),f("y1",v.y1);break;case"path":f("path",v.path)}}}return void 0===n?(h.push(u),h):c?c.getUpdateObj():{}}},createShapeObj:w}},1936:function(t){"use strict";t.exports={clearOutlineControllers:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(".outline-controllers").remove()},clearOutline:function(t){var e=t._fullLayout._zoomlayer;e&&e.selectAll(".select-outline").remove(),t._fullLayout._outlining=!1}}},65152:function(t,e,r){"use strict";var n=r(85448),i=r(3400),a=r(54460);e.rangeToShapePosition=function(t){return"log"===t.type?t.r2d:function(t){return 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r=n.select(this),i=t._promises.length;r.call(u.positionText,p.xFn(e),p.yFn(e)).call(h.font,{family:e.font,size:e.fontSize,color:e.fontColor,weight:e.fontWeight,style:e.fontStyle,variant:e.fontVariant}).text(e.text).call(u.convertToTspans,t),t._promises[i]?y.push(t._promises.pop().then((function(){m(r,d)}))):m(r,d)})),Ft(e,[F]),g.exit().remove(),r.repositionOnUpdate&&g.each((function(t){n.select(this).select("text").call(u.positionText,p.xFn(t),p.yFn(t))})),e._adjustTickLabelsOverflow=function(){var r=e.ticklabeloverflow;if(r&&"allow"!==r){var i=-1!==r.indexOf("hide"),o="x"===e._id.charAt(0),l=0,u=o?t._fullLayout.width:t._fullLayout.height;if(-1!==r.indexOf("domain")){var c=s.simpleMap(e.range,e.r2l);l=e.l2p(c[0])+e._offset,u=e.l2p(c[1])+e._offset}var f=Math.min(l,u),p=Math.max(l,u),d=e.side,v=1/0,y=-1/0;for(var m in g.each((function(t){var r=n.select(this);if(r.select(".text-math-group").empty()){var a=h.bBox(r.node()),s=0;o?(a.right>p||a.left<f)&&(s=1):(a.bottom>p||a.top+(e.tickangle?0:t.fontSize/4)<f)&&(s=1);var l=r.select("text");s?i&&l.style("opacity",0):(l.style("opacity",1),v="bottom"===d||"right"===d?Math.min(v,o?a.top:a.left):-1/0,y="top"===d||"left"===d?Math.max(y,o?a.bottom:a.right):1/0)}})),a._plots){var x=a._plots[m];if(e._id===x.xaxis._id||e._id===x.yaxis._id){var b=o?x.yaxis:x.xaxis;b&&(b["_visibleLabelMin_"+e._id]=v,b["_visibleLabelMax_"+e._id]=y)}}}},e._hideCounterAxisInsideTickLabels=function(t){var r="x"===e._id.charAt(0),i=[];for(var o in a._plots){var s=a._plots[o];e._id!==s.xaxis._id&&e._id!==s.yaxis._id||i.push(r?s.yaxis:s.xaxis)}i.forEach((function(r,i){r&&Rt(r)&&(t||[I,D,z,R,F]).forEach((function(t){var o="tick"===t.K&&"text"===t.L&&"period"===e.ticklabelmode,s=a._plots[e._mainSubplot];(t.K===I.K?s.zerolinelayer.selectAll("."+e._id+"zl"):t.K===D.K?s.minorGridlayer.selectAll("."+e._id):t.K===z.K?s.gridlayer.selectAll("."+e._id):s[e._id.charAt(0)+"axislayer"]).each((function(){var a=n.select(this);t.L&&(a=a.selectAll(t.L)),a.each((function(a){var s=e.l2p(o?Tt(a):a.x)+e._offset,l=n.select(this);s<e["_visibleLabelMax_"+r._id]&&s>e["_visibleLabelMin_"+r._id]?l.style("display","none"):"tick"!==t.K||i||l.style("display",null)}))}))}))}))},m(g,v+1?v:d);var x=null;e._selections&&(e._selections[c]=g);var b=[function(){return y.length&&Promise.all(y)}];e.automargin&&a._redrawFromAutoMarginCount&&90===v?(x=v,b.push((function(){m(g,v)}))):b.push((function(){if(m(g,d),f.length&&e.autotickangles&&("log"!==e.type||"D"!==String(e.dtick).charAt(0))){x=e.autotickangles[0];var t,n=0,i=[],a=1;g.each((function(t){n=Math.max(n,t.fontSize);var r=e.l2p(t.x),o=St(this),s=h.bBox(o.node());a=Math.max(a,u.lineCount(o)),i.push({top:0,bottom:10,height:10,left:r-s.width/2,right:r+s.width/2+2,width:s.width+2})}));var o=("boundaries"===e.tickson||e.showdividers)&&!r.secondary,l=f.length,c=Math.abs((f[l-1].x-f[0].x)*e._m)/(l-1),p=o?c/2:c,v=o?e.ticklen:1.25*n*a,y=p/Math.sqrt(Math.pow(p,2)+Math.pow(v,2)),b=e.autotickangles.map((function(t){return t*Math.PI/180})),_=b.find((function(t){return Math.abs(Math.cos(t))<=y}));void 0===_&&(_=b.reduce((function(t,e){return Math.abs(Math.cos(t))<Math.abs(Math.cos(e))?t:e}),b[0]));var w=_*(180/Math.PI);if(o){var T=2;for(e.ticks&&(T+=e.tickwidth/2),t=0;t<i.length;t++){var k=f[t].xbnd,A=i[t];if(null!==k[0]&&A.left-e.l2p(k[0])<T||null!==k[1]&&e.l2p(k[1])-A.right<T){x=w;break}}}else{var M=e.ticklabelposition||"",S=function(t){return-1!==M.indexOf(t)},E=S("top"),L=S("left"),C=S("right"),P=S("bottom")||L||E||C?(e.tickwidth||0)+6:0;for(t=0;t<i.length-1;t++)if(s.bBoxIntersect(i[t],i[t+1],P)){x=w;break}}x&&m(g,x)}})),e._tickAngles&&b.push((function(){e._tickAngles[c]=null===x?i(d)?d:0:x}));var _=function(){var t=0,r=0;return g.each((function(n,i){var a,o=St(this);o.select(".text-math-group").empty()&&(e._vals[i]&&(a=e._vals[i].bb||h.bBox(o.node()),e._vals[i].bb=a),t=Math.max(t,a.width),r=Math.max(r,a.height))})),{labelsMaxW:t,labelsMaxH:r}},w=e._anchorAxis;if(w&&(w.autorange||w.insiderange)&&Rt(e)&&!X(a,e._id)&&(a._insideTickLabelsUpdaterange||(a._insideTickLabelsUpdaterange={}),w.autorange&&(a._insideTickLabelsUpdaterange[w._name+".autorange"]=w.autorange,b.push(_)),w.insiderange)){var T=_(),k="y"===e._id.charAt(0)?T.labelsMaxW:T.labelsMaxH;k+=6,"inside"===e.ticklabelposition&&(k+=e.ticklen||0);var A="right"===e.side||"top"===e.side?1:-1,M=1===A?1:0,S=1===A?0:1,E=[];E[S]=w.range[S];var L=w.range,C=w.r2p(L[M]),P=w.r2p(L[S]),O=a._insideTickLabelsUpdaterange[w._name+".range"];if(O){var B=w.r2p(O[M]),N=w.r2p(O[S]),j=A*("y"===e._id.charAt(0)?1:-1);j*C<j*B&&(C=B,E[M]=L[M]=O[M]),j*P>j*N&&(P=N,E[S]=L[S]=O[S])}var U=Math.abs(P-C);U-k>0?k*=1+k/(U-=k):k=0,"y"!==e._id.charAt(0)&&(k=-k),E[M]=w.p2r(w.r2p(L[M])+A*k),"min"===w.autorange||"max reversed"===w.autorange?(E[0]=null,w._rangeInitial0=void 0,w._rangeInitial1=void 0):"max"!==w.autorange&&"min reversed"!==w.autorange||(E[1]=null,w._rangeInitial0=void 0,w._rangeInitial1=void 0),a._insideTickLabelsUpdaterange[w._name+".range"]=E}var q=s.syncOrAsync(b);return q&&q.then&&t._promises.push(q),q},H.getPxPosition=function(t,e){var r,n=t._fullLayout._size,i=e._id.charAt(0),a=e.side;return"free"!==e.anchor?r=e._anchorAxis:"x"===i?r={_offset:n.t+(1-(e.position||0))*n.h,_length:0}:"y"===i&&(r={_offset:n.l+(e.position||0)*n.w+e._shift,_length:0}),"top"===a||"left"===a?r._offset:"bottom"===a||"right"===a?r._offset+r._length:void 0},H.shouldShowZeroLine=function(t,e,r){var n=s.simpleMap(e.range,e.r2l);return n[0]*n[1]<=0&&e.zeroline&&("linear"===e.type||"-"===e.type)&&!(e.rangebreaks&&e.maskBreaks(0)===O)&&(Mt(e,0)||!function(t,e,r,n){var i=r._mainAxis;if(i){var a=t._fullLayout,o=e._id.charAt(0),s=H.counterLetter(e._id),l=e._offset+(Math.abs(n[0])<Math.abs(n[1])==("x"===o)?0:e._length),u=a._plots[r._mainSubplot];if(!(u.mainplotinfo||u).overlays.length)return p(r);for(var c=H.list(t,s),f=0;f<c.length;f++){var h=c[f];if(h._mainAxis===i&&p(h))return!0}}function p(t){if(!t.showline||!t.linewidth)return!1;var r=Math.max((t.linewidth+e.zerolinewidth)/2,1);function n(t){return"number"==typeof 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a=[],o=t._fullData[e[r]].xaxis,s=t._fullData[e[r]].yaxis;if(o&&s){for(n=0;n<i.length;n++)-1===i[n].x.indexOf(o)&&-1===i[n].y.indexOf(s)||a.push(n);if(a.length){var l,u=i[a[0]];if(a.length>1)for(n=1;n<a.length;n++)l=i[a[n]],Pt(u.x,l.x),Pt(u.y,l.y);Pt(u.x,[o]),Pt(u.y,[s])}else i.push({x:[o],y:[s]})}}return i}(t,e),n=0;n<r.length;n++)Ot(t,r[n].x,r[n].y)}},52976:function(t,e,r){"use strict";var n=r(38248),i=r(3400),a=r(39032).BADNUM,o=i.isArrayOrTypedArray,s=i.isDateTime,l=i.cleanNumber,u=Math.round;function c(t,e){return e?n(t):"number"==typeof t}function f(t){return Math.max(1,(t-1)/1e3)}t.exports=function(t,e,r){var i=t,h=r.noMultiCategory;if(o(i)&&!i.length)return"-";if(!h&&function(t){return o(t[0])&&o(t[1])}(i))return"multicategory";if(h&&Array.isArray(i[0])){for(var p=[],d=0;d<i.length;d++)if(o(i[d]))for(var v=0;v<i[d].length;v++)p.push(i[d][v]);i=p}if(function(t,e){for(var r=t.length,i=f(r),a=0,o=0,l={},c=0;c<r;c+=i){var h=t[u(c)],p=String(h);l[p]||(l[p]=1,s(h,e)&&a++,n(h)&&o++)}return a>2*o}(i,e))return"date";var g="strict"!==r.autotypenumbers;return function(t,e){for(var r=t.length,n=f(r),i=0,o=0,s={},c=0;c<r;c+=n){var h=t[u(c)],p=String(h);if(!s[p]){s[p]=1;var d=typeof h;"boolean"===d?o++:(e?l(h)!==a:"number"===d)?i++:"string"===d&&o++}}return o>2*i}(i,g)?"category":function(t,e){for(var r=t.length,n=0;n<r;n++)if(c(t[n],e))return!0;return!1}(i,g)?"linear":"-"}},28336:function(t,e,r){"use strict";var n=r(38248),i=r(24040),a=r(3400),o=r(31780),s=r(51272),l=r(94724),u=r(26332),c=r(25404),f=r(95936),h=r(42568),p=r(22416),d=r(42136),v=r(96312),g=r(78344),y=r(33816).WEEKDAY_PATTERN,m=r(33816).HOUR_PATTERN;function x(t,e,r){function i(r,n){return a.coerce(t,e,l.rangebreaks,r,n)}if(i("enabled")){var o=i("bounds");if(o&&o.length>=2){var s,u,c="";if(2===o.length)for(s=0;s<2;s++)if(u=_(o[s])){c=y;break}var f=i("pattern",c);if(f===y)for(s=0;s<2;s++)(u=_(o[s]))&&(e.bounds[s]=o[s]=u-1);if(f)for(s=0;s<2;s++)switch(u=o[s],f){case y:if(!n(u))return void(e.enabled=!1);if((u=+u)!==Math.floor(u)||u<0||u>=7)return void(e.enabled=!1);e.bounds[s]=o[s]=u;break;case m:if(!n(u))return void(e.enabled=!1);if((u=+u)<0||u>24)return void(e.enabled=!1);e.bounds[s]=o[s]=u}if(!1===r.autorange){var h=r.range;if(h[0]<h[1]){if(o[0]<h[0]&&o[1]>h[1])return void(e.enabled=!1)}else if(o[0]>h[0]&&o[1]<h[1])return void(e.enabled=!1)}}else{var p=i("values");if(!p||!p.length)return void(e.enabled=!1);i("dvalue")}}}t.exports=function(t,e,r,n,m){var b,_=n.letter,w=n.font||{},T=n.splomStash||{},k=r("visible",!n.visibleDflt),A=e._template||{},M=e.type||A.type||"-";"date"===M&&(i.getComponentMethod("calendars","handleDefaults")(t,e,"calendar",n.calendar),n.noTicklabelmode||(b=r("ticklabelmode")));var S="";n.noTicklabelposition&&"multicategory"!==M||(S=a.coerce(t,e,{ticklabelposition:{valType:"enumerated",dflt:"outside",values:"period"===b?["outside","inside"]:"x"===_?["outside","inside","outside left","inside left","outside right","inside right"]:["outside","inside","outside top","inside top","outside bottom","inside bottom"]}},"ticklabelposition")),n.noTicklabeloverflow||r("ticklabeloverflow",-1!==S.indexOf("inside")?"hide past domain":"category"===M||"multicategory"===M?"allow":"hide past div"),g(e,m),v(t,e,r,n),p(t,e,r,n),"category"===M||n.noHover||r("hoverformat");var E=r("color"),L=E!==l.color.dflt?E:w.color,C=T.label||m._dfltTitle[_];if(h(t,e,r,M,n),!k)return e;r("title.text",C),a.coerceFont(r,"title.font",{family:w.family,weight:w.weight,style:w.style,variant:w.variant,size:a.bigFont(w.size),color:L}),u(t,e,r,M);var P=n.hasMinor;if(P&&(o.newContainer(e,"minor"),u(t,e,r,M,{isMinor:!0})),f(t,e,r,M,n),c(t,e,r,n),P){var O=n.isMinor;n.isMinor=!0,c(t,e,r,n),n.isMinor=O}d(t,e,r,{dfltColor:E,bgColor:n.bgColor,showGrid:n.showGrid,hasMinor:P,attributes:l}),!P||e.minor.ticks||e.minor.showgrid||delete e.minor,(e.showline||e.ticks)&&r("mirror");var I,z="multicategory"===M;if(n.noTickson||"category"!==M&&!z||!e.ticks&&!e.showgrid||(z&&(I="boundaries"),"boundaries"===r("tickson",I)&&delete e.ticklabelposition),z&&r("showdividers")&&(r("dividercolor"),r("dividerwidth")),"date"===M)if(s(t,e,{name:"rangebreaks",inclusionAttr:"enabled",handleItemDefaults:x}),e.rangebreaks.length){for(var D=0;D<e.rangebreaks.length;D++)if(e.rangebreaks[D].pattern===y){e._hasDayOfWeekBreaks=!0;break}if(g(e,m),m._has("scattergl")||m._has("splom"))for(var R=0;R<n.data.length;R++){var F=n.data[R];"scattergl"!==F.type&&"splom"!==F.type||(F.visible=!1,a.warn(F.type+" traces do not work on axes with rangebreaks. 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u.boxEnabled?(a=u.boxStart[0]!==u.boxEnd[0],s=u.boxStart[1]!==u.boxEnd[1],a||s?(a&&(g(0,u.boxStart[0],u.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,u.boxStart[1],u.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),u.boxEnabled=!1,u.boxInited=!1):u.boxInited&&(u.boxInited=!1);break;case"pan":u.boxEnabled=!1,u.boxInited=!1,e?(u.panning||(u.dragStart[0]=n,u.dragStart[1]=i),Math.abs(u.dragStart[0]-n)<d&&(n=u.dragStart[0]),Math.abs(u.dragStart[1]-i)<d&&(i=u.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),u.panning=!0,u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations()):u.panning&&(u.panning=!1,t.relayoutCallback())}u.lastPos[0]=n,u.lastPos[1]=i}return u.mouseListener=n(e,f),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){f(0,u.lastPos[0],u.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),u.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=u.lastPos[0],s=u.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),u.lastInputTime=Date.now(),c(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),u}},92568:function(t,e,r){"use strict";var n=r(54460),i=r(43080);function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,u,c,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;u=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!u,l=this.hasAxisInAltrPos(e,r)&&!u,a=r.mirror||!1,c=u?-1!==String(a).indexOf("all"):!!a,f=u?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||c)&&(this.borderLineEnable[h]=r.showline),(l||c)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=1.5,n=e.title.font.size,i=e.showticklabels;return"xaxis"===t?"top"===e.side?n*(r+(i?1:0))-10:n*(r+(i?.5:0))-10:"yaxis"===t?"right"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},t.exports=function(t){return new a(t)}},39952:function(t,e,r){"use strict";var n=r(67824).overrideAll,i=r(17188),a=r(64859),o=r(9616),s=r(33816),l=r(57952),u=r(65460),c=r(84888).op;e.name="gl2d",e.attr=["xaxis","yaxis"],e.idRoot=["x","y"],e.idRegex=s.idRegex,e.attrRegex=s.attrRegex,e.attributes=r(26720),e.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},e.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),e.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:u.hoverlabel},"plot","nested"),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=c(r,"gl2d",o),u=s._scene2d;void 0===u&&(u=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=u),u.plot(l,t.calcdata,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];s._scene2d&&0===c(t,"gl2d",o).length&&(s._scene2d.destroy(),delete n._plots[o])}l.clean.apply(this,arguments)},e.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++)e._plots[r[n]]._scene2d.updateFx(e.dragmode)}},17188:function(t,e,r){"use strict";var n,i,a=r(24040),o=r(54460),s=r(93024),l=r(67792).gl_plot2d,u=r(67792).gl_spikes2d,c=r(67792).gl_select_box,f=r(5408),h=r(92568),p=r(2428),d=r(16576),v=r(71888),g=v.enforce,y=v.clean,m=r(19280).doAutoRange,x=r(72760),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=r(33816).SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=u(this.glplot),this.selectBox=c(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}t.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var u=0;u<4;++u){var c=a[4*(r*o+l)+u];a[4*(r*o+l)+u]=a[4*(r*s+l)+u],a[4*(r*s+l)+u]=c}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d",{willReadFrequently:!0}),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var u=n[r._name];u.range=s,u.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};y(s,this.xaxis),y(s,this.yaxis);var l,u,c=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[c.l+f[0]*c.w,c.b+h[0]*c.h,i-c.r-(1-f[1])*c.w,a-c.t-(1-h[1])*c.h],this.mouseContainer.style.width=c.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=c.h*(h[1]-h[0])+"px",this.mouseContainer.height=c.h*(h[1]-h[0]),this.mouseContainer.style.left=c.l+f[0]*c.w+"px",this.mouseContainer.style.top=c.t+(1-h[1])*c.h+"px",u=0;u<2;++u)(l=this[w[u]])._length=o.viewBox[u+2]-o.viewBox[u],m(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var u=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],c=0;c<2;c++)e.boxStart[c]===e.boxEnd[c]&&(u[c]=t.dataBox[c],u[c+2]=t.dataBox[c+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var v=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),v.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[v.trace.index]||{},y=v.pointIndex,m=s.castHoverinfo(g,i,y);if(m&&"all"!==m){var x=m.split("+");-1===x.indexOf("x")&&(v.traceCoord[0]=void 0),-1===x.indexOf("y")&&(v.traceCoord[1]=void 0),-1===x.indexOf("z")&&(v.traceCoord[2]=void 0),-1===x.indexOf("text")&&(v.textLabel=void 0),-1===x.indexOf("name")&&(v.name=void 0)}s.loneHover({x:v.screenCoord[0],y:v.screenCoord[1],xLabel:this.hoverFormatter("xaxis",v.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",v.traceCoord[1]),zLabel:v.traceCoord[2],text:v.textLabel,name:v.name,color:s.castHoverOption(g,y,"bgcolor")||v.color,borderColor:s.castHoverOption(g,y,"bordercolor"),fontFamily:s.castHoverOption(g,y,"font.family"),fontSize:s.castHoverOption(g,y,"font.size"),fontColor:s.castHoverOption(g,y,"font.color"),nameLength:s.castHoverOption(g,y,"namelength"),textAlign:s.castHoverOption(g,y,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},12536:function(t,e,r){"use strict";var n=r(67824).overrideAll,i=r(65460),a=r(98432),o=r(84888).op,s=r(3400),l=r(9616),u="gl3d",c="scene";e.name=u,e.attr=c,e.idRoot=c,e.idRegex=e.attrRegex=s.counterRegex("scene"),e.attributes=r(6636),e.layoutAttributes=r(346),e.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),e.supplyLayoutDefaults=r(5208),e.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots[u],i=0;i<n.length;i++){var s=n[i],l=o(r,u,s),c=e[s],f=c.camera,h=c._scene;h||(h=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:f},e),c._scene=h),h.viewInitial||(h.viewInitial={up:{x:f.up.x,y:f.up.y,z:f.up.z},eye:{x:f.eye.x,y:f.eye.y,z:f.eye.z},center:{x:f.center.x,y:f.center.y,z:f.center.z}}),h.plot(l,e,t.layout)}},e.clean=function(t,e,r,n){for(var i=n._subplots[u]||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},e.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},e.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),c+e}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[u],n=0;n<r.length;n++)e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}},6636:function(t){"use strict";t.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},86140:function(t,e,r){"use strict";var n=r(76308),i=r(94724),a=r(92880).extendFlat,o=r(67824).overrideAll;t.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 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204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},64380:function(t,e,r){"use strict";var n=r(49760).mix,i=r(3400),a=r(31780),o=r(86140),s=r(14944),l=r(28336),u=["xaxis","yaxis","zaxis"];t.exports=function(t,e,r){var c,f;function h(t,e){return i.coerce(c,f,o,t,e)}for(var p=0;p<u.length;p++){var d=u[p];c=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(c,f,h,r),l(c,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noAutotickangles:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,noInsiderange:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,72.72727272727273).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},44728:function(t,e,r){"use strict";var n=r(43080),i=r(3400),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickFontWeight=["normal","normal","normal","normal"],this.tickFontStyle=["normal","normal","normal","normal"],this.tickFontVariant=["normal","normal","normal","normal"],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelFontWeight=["normal","normal","normal","normal"],this.labelFontStyle=["normal","normal","normal","normal"],this.labelFontVariant=["normal","normal","normal","normal"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=this,o=0;o<3;++o){var s=e[a[o]];s.visible?(r.labels[o]=t._meta?i.templateString(s.title.text,t._meta):s.title.text,"font"in s.title&&(s.title.font.color&&(r.labelColor[o]=n(s.title.font.color)),s.title.font.family&&(r.labelFont[o]=s.title.font.family),s.title.font.size&&(r.labelSize[o]=s.title.font.size),s.title.font.weight&&(r.labelFontWeight[o]=s.title.font.weight),s.title.font.style&&(r.labelFontStyle[o]=s.title.font.style),s.title.font.variant&&(r.labelFontVariant[o]=s.title.font.variant)),"showline"in s&&(r.lineEnable[o]=s.showline),"linecolor"in s&&(r.lineColor[o]=n(s.linecolor)),"linewidth"in s&&(r.lineWidth[o]=s.linewidth),"showgrid"in s&&(r.gridEnable[o]=s.showgrid),"gridcolor"in s&&(r.gridColor[o]=n(s.gridcolor)),"gridwidth"in s&&(r.gridWidth[o]=s.gridwidth),"log"===s.type?r.zeroEnable[o]=!1:"zeroline"in s&&(r.zeroEnable[o]=s.zeroline),"zerolinecolor"in s&&(r.zeroLineColor[o]=n(s.zerolinecolor)),"zerolinewidth"in s&&(r.zeroLineWidth[o]=s.zerolinewidth),"ticks"in s&&s.ticks?r.lineTickEnable[o]=!0:r.lineTickEnable[o]=!1,"ticklen"in s&&(r.lineTickLength[o]=r._defaultLineTickLength[o]=s.ticklen),"tickcolor"in s&&(r.lineTickColor[o]=n(s.tickcolor)),"tickwidth"in s&&(r.lineTickWidth[o]=s.tickwidth),"tickangle"in s&&(r.tickAngle[o]="auto"===s.tickangle?-3600:Math.PI*-s.tickangle/180),"showticklabels"in s&&(r.tickEnable[o]=s.showticklabels),"tickfont"in s&&(s.tickfont.color&&(r.tickColor[o]=n(s.tickfont.color)),s.tickfont.family&&(r.tickFont[o]=s.tickfont.family),s.tickfont.size&&(r.tickSize[o]=s.tickfont.size),s.tickfont.weight&&(r.tickFontWeight[o]=s.tickfont.weight),s.tickfont.style&&(r.tickFontStyle[o]=s.tickfont.style),s.tickfont.variant&&(r.tickFontVariant[o]=s.tickfont.variant)),"mirror"in s?-1!==["ticks","all","allticks"].indexOf(s.mirror)?(r.lineTickMirror[o]=!0,r.lineMirror[o]=!0):!0===s.mirror?(r.lineTickMirror[o]=!1,r.lineMirror[o]=!0):(r.lineTickMirror[o]=!1,r.lineMirror[o]=!1):r.lineMirror[o]=!1,"showbackground"in s&&!1!==s.showbackground?(r.backgroundEnable[o]=!0,r.backgroundColor[o]=n(s.backgroundcolor)):r.backgroundEnable[o]=!1):(r.tickEnable[o]=!1,r.labelEnable[o]=!1,r.lineEnable[o]=!1,r.lineTickEnable[o]=!1,r.gridEnable[o]=!1,r.zeroEnable[o]=!1,r.backgroundEnable[o]=!1)}},t.exports=function(t,e){var r=new o;return r.merge(t,e),r}},5208:function(t,e,r){"use strict";var n=r(3400),i=r(76308),a=r(24040),o=r(168),s=r(64380),l=r(346),u=r(84888).op,c="gl3d";function f(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),f=["up","center","eye"],h=0;h<f.length;h++)r("camera."+f[h]+".x"),r("camera."+f[h]+".y"),r("camera."+f[h]+".z");r("camera.projection.type");var p=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),d=r("aspectmode",p?"manual":"auto");p||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===d&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var v=u(n.fullData,c,n.id);s(t,e,{font:n.font,scene:n.id,data:v,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var g=n.getDfltFromLayout("dragmode");if(!1!==g&&!g)if(g="orbit",t.camera&&t.camera.up){var y=t.camera.up.x,m=t.camera.up.y,x=t.camera.up.z;0!==x&&(y&&m&&x?x/Math.sqrt(y*y+m*m+x*x)>.999&&(g="turntable"):g="turntable")}else g="turntable";r("dragmode",g),r("hovermode",n.getDfltFromLayout("hovermode"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},346:function(t,e,r){"use strict";var n=r(86140),i=r(86968).u,a=r(92880).extendFlat,o=r(3400).counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}t.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},9020:function(t,e,r){"use strict";var n=r(43080),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},87152:function(t,e,r){"use strict";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if("auto"===c.tickmode){c.tickmode="linear";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[u],"date"===c.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[u]=p,c.tickmode=f}}for(e.ticks=l,u=0;u<3;++u)for(o[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]),d=0;d<2;++d)e.bounds[d][u]=t.glplot.bounds[d][u];t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=r(54460),i=r(3400),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},94424:function(t){"use strict";function e(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}t.exports=function(t,r){return e(t.projection,e(t.view,e(t.model,[r[0],r[1],r[2],1])))}},98432:function(t,e,r){"use strict";var n,i,a=r(67792).gl_plot3d,o=a.createCamera,s=a.createScene,l=r(5408),u=r(89184),c=r(24040),f=r(3400),h=f.preserveDrawingBuffer(),p=r(54460),d=r(93024),v=r(43080),g=r(16576),y=r(94424),m=r(44728),x=r(9020),b=r(87152),_=r(19280).applyAutorangeOptions,w=!1;function T(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=m(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=c.getComponentMethod("annotations3d","convert"),this.drawAnnotations=c.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var k=T.prototype;k.prepareOptions=function(){var t=this,e={canvas:t.canvas,gl:t.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:t.camera,pixelRatio:t.pixelRatio};if(t.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");e.gl=i,e.canvas=n}return e};var A=!0;k.tryCreatePlot=function(){var t=this,e=t.prepareOptions(),r=!0;try{t.glplot=s(e)}catch(n){if(t.staticMode||!A||h)r=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=e.glOptions.preserveDrawingBuffer=!0,t.glplot=s(e)}catch(t){h=e.glOptions.preserveDrawingBuffer=!1,r=!1}}}return 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t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("touchstart",(function(){w=!0})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!u&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},k.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var 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e,r,s=this,u=s.isSmith,c=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,v=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,P=s.radialAxis,O=E.clampTiny,I=E.findXYatLength,z=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(f,"path","maindrag",!1===t.dragmode?"none":"crosshair");n.select(F).attr("d",s.pathSubplot()).attr("transform",l(T,k)),F.onmousemove=function(t){m.hover(c,t,s.id),c._fullLayout._lasthover=F,c._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){c._dragging||y.unhover(c,t)};var B,N,j,U,V,q,H,G,W,Y={element:F,gd:c,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function K(t,e){return Math.atan2(M-e,t-A)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function $(t,e){if(0===t)return s.pathSector(2*D);var 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e=f.c2l(t.pos+p,!0),a=f.l2p(e-s)+v,o=f.l2p(e+l)+v,b=h?(a+o)/2:f.l2p(e)+v,_=r.whiskerwidth,w=h?a*_+(1-_)*b:f.l2p(e-d)+v,T=h?o*_+(1-_)*b:f.l2p(e+d)+v,k=f.l2p(e-s*x)+v,A=f.l2p(e+l*x)+v,M="sd"===r.sizemode,S=c.c2p(M?t.mean-t.sd:t.q1,!0),E=M?c.c2p(t.mean+t.sd,!0):c.c2p(t.q3,!0),L=i.constrain(M?c.c2p(t.mean,!0):c.c2p(t.med,!0),Math.min(S,E)+1,Math.max(S,E)-1),C=void 0===t.lf||!1===r.boxpoints||M,P=c.c2p(C?t.min:t.lf,!0),O=c.c2p(C?t.max:t.uf,!0),I=c.c2p(t.ln,!0),z=c.c2p(t.un,!0);u?n.select(this).attr("d","M"+L+","+k+"V"+A+"M"+S+","+a+"V"+o+(m?"H"+I+"L"+L+","+A+"L"+z+","+o:"")+"H"+E+"V"+a+(m?"H"+z+"L"+L+","+k+"L"+I+","+a:"")+"Z"+(y?"M"+S+","+b+"H"+P+"M"+E+","+b+"H"+O+(0===g?"":"M"+P+","+w+"V"+T+"M"+O+","+w+"V"+T):"")):n.select(this).attr("d","M"+k+","+L+"H"+A+"M"+a+","+S+"H"+o+(m?"V"+I+"L"+A+","+L+"L"+o+","+z:"")+"V"+E+"H"+a+(m?"V"+z+"L"+k+","+L+"L"+a+","+I:"")+"Z"+(y?"M"+b+","+S+"V"+P+"M"+b+","+E+"V"+O+(0===g?"":"M"+w+","+P+"H"+T+"M"+w+","+O+"H"+T):""))}))}function s(t,e,r,n){var o=e.x,s=e.y,l=n.bdPos,u=n.bPos,c=r.boxpoints||r.points;i.seedPseudoRandom();var f=t.selectAll("g.points").data(c?function(t){return t.forEach((function(t){t.t=n,t.trace=r})),t}:[]);f.enter().append("g").attr("class","points"),f.exit().remove();var h=f.selectAll("path").data((function(t){var e,n,a=t.pts2,o=Math.max((t.max-t.min)/10,t.q3-t.q1),s=1e-9*o,f=.01*o,h=[],p=0;if(r.jitter){if(0===o)for(p=1,h=new Array(a.length),e=0;e<a.length;e++)h[e]=1;else for(e=0;e<a.length;e++){var d=Math.max(0,e-5),v=a[d].v,g=Math.min(a.length-1,e+5),y=a[g].v;"all"!==c&&(a[e].v<t.lf?y=Math.min(y,t.lf):v=Math.max(v,t.uf));var m=Math.sqrt(f*(g-d)/(y-v+s))||0;m=i.constrain(Math.abs(m),0,1),h.push(m),p=Math.max(m,p)}n=2*r.jitter/(p||1)}for(e=0;e<a.length;e++){var x=a[e],b=x.v,_=r.jitter?n*h[e]*(i.pseudoRandom()-.5):0,w=t.pos+u+l*(r.pointpos+_);"h"===r.orientation?(x.y=w,x.x=b):(x.x=w,x.y=b),"suspectedoutliers"===c&&b<t.uo&&b>t.lo&&(x.so=!0)}return a}));h.enter().append("path").classed("point",!0),h.exit().remove(),h.call(a.translatePoints,o,s)}function l(t,e,r,a){var o,s,l=e.val,u=e.pos,c=!!u.rangebreaks,f=a.bPos,h=a.bPosPxOffset||0,p=r.boxmean||(r.meanline||{}).visible;Array.isArray(a.bdPos)?(o=a.bdPos[0],s=a.bdPos[1]):(o=a.bdPos,s=a.bdPos);var d=t.selectAll("path.mean").data("box"===r.type&&r.boxmean||"violin"===r.type&&r.box.visible&&r.meanline.visible?i.identity:[]);d.enter().append("path").attr("class","mean").style({fill:"none","vector-effect":"non-scaling-stroke"}),d.exit().remove(),d.each((function(t){var e=u.c2l(t.pos+f,!0),i=u.l2p(e-o)+h,a=u.l2p(e+s)+h,d=c?(i+a)/2:u.l2p(e)+h,v=l.c2p(t.mean,!0),g=l.c2p(t.mean-t.sd,!0),y=l.c2p(t.mean+t.sd,!0);"h"===r.orientation?n.select(this).attr("d","M"+v+","+i+"V"+a+("sd"===p?"m0,0L"+g+","+d+"L"+v+","+i+"L"+y+","+d+"Z":"")):n.select(this).attr("d","M"+i+","+v+"H"+a+("sd"===p?"m0,0L"+d+","+g+"L"+i+","+v+"L"+d+","+y+"Z":""))}))}t.exports={plot:function(t,e,r,a){var u=t._context.staticPlot,c=e.xaxis,f=e.yaxis;i.makeTraceGroups(a,r,"trace boxes").each((function(t){var e,r,i=n.select(this),a=t[0],h=a.t,p=a.trace;h.wdPos=h.bdPos*p.whiskerwidth,!0!==p.visible||h.empty?i.remove():("h"===p.orientation?(e=f,r=c):(e=c,r=f),o(i,{pos:e,val:r},p,h,u),s(i,{x:c,y:f},p,h),l(i,{pos:e,val:r},p,h))}))},plotBoxAndWhiskers:o,plotPoints:s,plotBoxMean:l}},8264:function(t){"use strict";t.exports=function(t,e){var r,n,i=t.cd,a=t.xaxis,o=t.yaxis,s=[];if(!1===e)for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts||[]).length;n++)i[r].pts[n].selected=0;else for(r=0;r<i.length;r++)for(n=0;n<(i[r].pts||[]).length;n++){var l=i[r].pts[n],u=a.c2p(l.x),c=o.c2p(l.y);e.contains([u,c],null,l.i,t)?(s.push({pointNumber:l.i,x:a.c2d(l.x),y:o.c2d(l.y)}),l.selected=1):l.selected=0}return s}},25776:function(t,e,r){"use strict";var n=r(33428),i=r(76308),a=r(43616);t.exports={style:function(t,e,r){var o=r||n.select(t).selectAll("g.trace.boxes");o.style("opacity",(function(t){return 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n=t._fullLayout,i=o.extendFlat({type:"linear",ticks:"outside",range:r,showline:!0},e),a={type:"linear",_id:"x"+e._id},s={letter:"x",font:n.font,noAutotickangles:!0,noHover:!0,noTickson:!0};function l(t,e){return o.coerce(i,a,y,t,e)}return v(i,a,l,s,n),g(i,a,l,s),a}function A(t,e,r){return[Math.min(e/t.width,r/t.height),t,e+"x"+r]}function M(t,e,r,i){var a=document.createElementNS("http://www.w3.org/2000/svg","text"),o=n.select(a);return o.text(t).attr("x",0).attr("y",0).attr("text-anchor",r).attr("data-unformatted",t).call(p.convertToTspans,i).call(f.font,e),f.bBox(o.node())}function S(t,e,r,n,i,a){var s="_cache"+e;t[s]&&t[s].key===i||(t[s]={key:i,value:r});var l=o.aggNums(a,null,[t[s].value,n],2);return t[s].value=l,l}t.exports=function(t,e,r,v){var g,y=t._fullLayout;w(r)&&v&&(g=v()),o.makeTraceGroups(y._indicatorlayer,e,"trace").each((function(e){var v,E,L,C,P,O=e[0].trace,I=n.select(this),z=O._hasGauge,D=O._isAngular,R=O._isBullet,F=O.domain,B={w:y._size.w*(F.x[1]-F.x[0]),h:y._size.h*(F.y[1]-F.y[0]),l:y._size.l+y._size.w*F.x[0],r:y._size.r+y._size.w*(1-F.x[1]),t:y._size.t+y._size.h*(1-F.y[1]),b:y._size.b+y._size.h*F.y[0]},N=B.l+B.w/2,j=B.t+B.h/2,U=Math.min(B.w/2,B.h),V=h.innerRadius*U,q=O.align||"center";if(E=j,z){if(D&&(v=N,E=j+U/2,L=function(t){return function(t,e){return[e/Math.sqrt(t.width/2*(t.width/2)+t.height*t.height),t,e]}(t,.9*V)}),R){var H=h.bulletPadding,G=1-h.bulletNumberDomainSize+H;v=B.l+(G+(1-G)*b[q])*B.w,L=function(t){return A(t,(h.bulletNumberDomainSize-H)*B.w,B.h)}}}else v=B.l+b[q]*B.w,L=function(t){return A(t,B.w,B.h)};!function(t,e,r,i){var u,c,h,v=r[0].trace,g=i.numbersX,y=i.numbersY,T=v.align||"center",A=x[T],E=i.transitionOpts,L=i.onComplete,C=o.ensureSingle(e,"g","numbers"),P=[];v._hasNumber&&P.push("number"),v._hasDelta&&(P.push("delta"),"left"===v.delta.position&&P.reverse());var O=C.selectAll("text").data(P);function I(e,r,n,i){if(!e.match("s")||n>=0==i>=0||r(n).slice(-1).match(_)||r(i).slice(-1).match(_))return r;var a=e.slice().replace("s","f").replace(/\d+/,(function(t){return parseInt(t)-1})),o=k(t,{tickformat:a});return function(t){return Math.abs(t)<1?d.tickText(o,t).text:r(t)}}O.enter().append("text"),O.attr("text-anchor",(function(){return A})).attr("class",(function(t){return t})).attr("x",null).attr("y",null).attr("dx",null).attr("dy",null),O.exit().remove();var z,D=v.mode+v.align;if(v._hasDelta&&(z=function(){var e=k(t,{tickformat:v.delta.valueformat},v._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=v.delta.suffix,s=v.delta.prefix,l=function(t){return v.delta.relative?t.relativeDelta:t.delta},u=function(t,e){return 0===t||"number"!=typeof t||isNaN(t)?"-":(t>0?v.delta.increasing.symbol:v.delta.decreasing.symbol)+s+e(t)+o},h=function(t){return t.delta>=0?v.delta.increasing.color:v.delta.decreasing.color};void 0===v._deltaLastValue&&(v._deltaLastValue=l(r[0]));var g=C.select("text.delta");function y(){g.text(u(l(r[0]),i)).call(m.fill,h(r[0])).call(p.convertToTspans,t)}return g.call(f.font,v.delta.font).call(m.fill,h({delta:v._deltaLastValue})),w(E)?g.transition().duration(E.duration).ease(E.easing).tween("text",(function(){var t=n.select(this),e=l(r[0]),o=v._deltaLastValue,s=I(v.delta.valueformat,i,o,e),c=a(o,e);return v._deltaLastValue=e,function(e){t.text(u(c(e),s)),t.call(m.fill,h({delta:c(e)}))}})).each("end",(function(){y(),L&&L()})).each("interrupt",(function(){y(),L&&L()})):y(),c=M(u(l(r[0]),i),v.delta.font,A,t),g}(),D+=v.delta.position+v.delta.font.size+v.delta.font.family+v.delta.valueformat,D+=v.delta.increasing.symbol+v.delta.decreasing.symbol,h=c),v._hasNumber&&(function(){var e=k(t,{tickformat:v.number.valueformat},v._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=v.number.suffix,s=v.number.prefix,l=C.select("text.number");function c(){var e="number"==typeof r[0].y?s+i(r[0].y)+o:"-";l.text(e).call(f.font,v.number.font).call(p.convertToTspans,t)}w(E)?l.transition().duration(E.duration).ease(E.easing).each("end",(function(){c(),L&&L()})).each("interrupt",(function(){c(),L&&L()})).attrTween("text",(function(){var t=n.select(this),e=a(r[0].lastY,r[0].y);v._lastValue=r[0].y;var l=I(v.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(s+l(e(r))+o)}})):c(),u=M(s+i(r[0].y)+o,v.number.font,A,t)}(),D+=v.number.font.size+v.number.font.family+v.number.valueformat+v.number.suffix+v.number.prefix,h=u),v._hasDelta&&v._hasNumber){var R,F,B=[(u.left+u.right)/2,(u.top+u.bottom)/2],N=[(c.left+c.right)/2,(c.top+c.bottom)/2],j=.75*v.delta.font.size;"left"===v.delta.position&&(R=S(v,"deltaPos",0,-1*(u.width*b[v.align]+c.width*(1-b[v.align])+j),D,Math.min),F=B[1]-N[1],h={width:u.width+c.width+j,height:Math.max(u.height,c.height),left:c.left+R,right:u.right,top:Math.min(u.top,c.top+F),bottom:Math.max(u.bottom,c.bottom+F)}),"right"===v.delta.position&&(R=S(v,"deltaPos",0,u.width*(1-b[v.align])+c.width*b[v.align]+j,D,Math.max),F=B[1]-N[1],h={width:u.width+c.width+j,height:Math.max(u.height,c.height),left:u.left,right:c.right+R,top:Math.min(u.top,c.top+F),bottom:Math.max(u.bottom,c.bottom+F)}),"bottom"===v.delta.position&&(R=null,F=c.height,h={width:Math.max(u.width,c.width),height:u.height+c.height,left:Math.min(u.left,c.left),right:Math.max(u.right,c.right),top:u.bottom-u.height,bottom:u.bottom+c.height}),"top"===v.delta.position&&(R=null,F=u.top,h={width:Math.max(u.width,c.width),height:u.height+c.height,left:Math.min(u.left,c.left),right:Math.max(u.right,c.right),top:u.bottom-u.height-c.height,bottom:u.bottom}),z.attr({dx:R,dy:F})}(v._hasNumber||v._hasDelta)&&C.attr("transform",(function(){var t=i.numbersScaler(h);D+=t[2];var e,r=S(v,"numbersScale",1,t[0],D,Math.min);v._scaleNumbers||(r=1),e=v._isAngular?y-r*h.bottom:y-r*(h.top+h.bottom)/2,v._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(v,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,I,e,{numbersX:v,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:O.gauge.axis.range,color:O.gauge.bgcolor,line:{color:O.gauge.bordercolor,width:0},thickness:1},P={range:O.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:O.gauge.bordercolor,width:O.gauge.borderwidth},thickness:1});var W=I.selectAll("g.angular").data(D?e:[]);W.exit().remove();var Y=I.selectAll("g.angularaxis").data(D?e:[]);Y.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,v=a.size,g=a.radius,y=a.innerRadius,m=a.gaugeBg,x=a.gaugeOutline,b=[v.l+v.w/2,v.t+v.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((y+g)/2-t/2*(g-y)).outerRadius((y+g)/2+t/2*(g-y)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var O=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},I={},z=d.makeLabelFns(o,0).labelStandoff;I.xFn=function(t){var e=O(t);return Math.cos(e)*z},I.yFn=function(t){var e=O(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*c)},I.anchorFn=function(t){var e=O(t),r=Math.cos(e);return Math.abs(r)<.1?"middle":r>0?"start":"end"},I.heightFn=function(t,e,r){var n=O(t);return-.5*(1+Math.sin(n))*r};var D=function(t){return l(b[0]+g*Math.cos(t),b[1]-g*Math.sin(t))};f=function(t){return D(O(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h="inside"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:"M"+h*R+",0h"+h*o.ticklen,transFn:function(t){var e=O(t);return D(e)+"rotate("+-u(e)+")"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:I})}var F=[m].concat(p.gauge.steps),B=_.selectAll("g.bg-arc").data(F);B.enter().append("g").classed("bg-arc",!0).append("path"),B.select("path").call(P).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll("g.value-arc").data([p.gauge.bar]);j.enter().append("g").classed("value-arc",!0).append("path");var U,V,q,H=j.select("path");w(M)?(H.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(U=N,V=L(r[0].lastY),q=L(r[0].y),function(){var t=i(V,q);return function(e){return U.endAngle(t(e))()}})),p._lastValue=r[0].y):H.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z"),H.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var W=_.selectAll("g.threshold-arc").data(F);W.enter().append("g").classed("threshold-arc",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove();var Y=_.selectAll("g.gauge-outline").data([x]);Y.enter().append("g").classed("gauge-outline",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:W,layer:Y,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=I.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=I.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,u,c=r[0].trace,f=n.gauge,p=n.layer,v=n.gaugeBg,g=n.gaugeOutline,y=n.size,x=c.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(y.l,y.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=y.h,M=c.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(c._hasNumber||c._hasDelta?1-h.bulletNumberDomainSize:1);function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}(i=k(t,c.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],u=y.t+y.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,u,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,u)}));var C=[v].concat(c.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var O=f.selectAll("g.value-bullet").data([c.gauge.bar]);O.enter().append("g").classed("value-bullet",!0).append("rect"),O.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?O.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(c.gauge.axis.range[1],r[0].y)))):O.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(c.gauge.axis.range[1],r[0].y))):0),O.exit().remove();var I=r.filter((function(){return c.gauge.threshold.value||0===c.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(I);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(c.gauge.threshold.value)).attr("x2",i.c2p(c.gauge.threshold.value)).attr("y1",(1-c.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-c.gauge.threshold.thickness)/2)*A).call(m.stroke,c.gauge.threshold.line.color).style("stroke-width",c.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var K=I.selectAll("text.title").data(e);K.exit().remove(),K.enter().append("text").classed("title",!0),K.attr("text-anchor",(function(){return R?x.right:x[O.title.align]})).text(O.title.text).call(f.font,O.title.font).call(p.convertToTspans,t),K.attr("transform",(function(){var t,e=B.l+B.w*b[O.title.align],r=h.titlePadding,n=f.bBox(K.node());return z?(D&&(t=O.gauge.axis.visible?f.bBox(Y.node()).top-r-n.bottom:B.t+B.h/2-U/2-n.bottom-r),R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)):t=O._numbersTop-r-n.bottom,l(e,t)}))}))}},50048:function(t,e,r){"use strict";var n=r(49084),i=r(29736).axisHoverFormat,a=r(21776).Ks,o=r(52948),s=r(45464),l=r(92880).extendFlat,u=r(67824).overrideAll,c=t.exports=u(l({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:a(),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),valuehoverformat:i("value",1),showlegend:l({},s.showlegend,{dflt:!1})},n("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),"calc","nested");c.flatshading.dflt=!0,c.lighting.facenormalsepsilon.dflt=0,c.x.editType=c.y.editType=c.z.editType=c.value.editType="calc+clearAxisTypes",c.transforms=void 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c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.data=null,this.showContour=!1}var f=c.prototype;f.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],o=this.data._Ys.length,s=this.data._Zs.length,l=u(r,this.data._Xs).id,c=u(n,this.data._Ys).id,f=u(i,this.data._Zs).id,h=t.index=f+s*c+s*o*l;t.traceCoordinate=[this.data._meshX[h],this.data._meshY[h],this.data._meshZ[h],this.data._value[h]];var p=this.data.hovertext||this.data.text;return a(p)&&void 0!==p[h]?t.textLabel=p[h]:p&&(t.textLabel=p),!0}},f.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=p(t);var 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S=[];for(b=0;b<M.length;b++)S.push(M[b][x]);d=p[x%p.length],v+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(m.color)?T=w=m.color[_%m.color.length]:w=m.color,{color:y(w),rawColor:T}),L=S+"-"+E.rawColor;void 0===A[L]&&(A[L]=f(S,E.color,E.rawColor)),h(A[L],x,d)}var C,P=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,v)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<P.length;b++){var O=P[b].containerInd,I=g[b].inds[x],z=P[b].categories;if(void 0===z[I]){var D=e.dimensions[O]._categoryarray[I],R=e.dimensions[O]._ticktext[I];z[I]={dimensionInd:b,categoryInd:C=I,categoryValue:D,displayInd:C,categoryLabel:R,valueInds:[],count:0,dragY:null}}c(z[I],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(P,A,v))}},76671:function(t,e,r){"use strict";var n=r(3400),i=r(94288).hasColorscale,a=r(27260),o=r(86968).Q,s=r(51272),l=r(72140),u=r(26284),c=r(38116).isTypedArraySpec;function f(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r("values"),a=r("visible");if(i&&i.length||(a=e.visible=!1),a){r("label"),r("displayindex",e._index);var o,s=t.categoryarray,u=n.isArrayOrTypedArray(s)&&s.length>0||c(s);u&&(o="array");var f=r("categoryorder",o);"array"===f?(r("categoryarray"),r("ticktext")):(delete t.categoryarray,delete t.ticktext),u||"array"!==f||(e.categoryorder="trace")}}t.exports=function(t,e,r,c){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:"dimensions",handleItemDefaults:f}),d=function(t,e,r,o,s){s("line.shape"),s("line.hovertemplate");var l=s("line.color",o.colorway[0]);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 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a=e._context.staticPlot,o=t.map(F.bind(0,e,r)),c=i.selectAll("g.parcatslayer").data([null]);c.enter().append("g").attr("class","parcatslayer").style("pointer-events",a?"none":"all");var h=c.selectAll("g.trace.parcats").data(o,p),m=h.enter().append("g").attr("class","trace parcats");h.attr("transform",(function(t){return l(t.x,t.y)})),m.append("g").attr("class","paths");var x=h.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);x.attr("fill",(function(t){return t.model.color}));var w=x.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(w),x.attr("d",(function(t){return t.svgD})),w.empty()||x.sort(v),x.exit().remove(),x.on("mouseover",g).on("mouseout",y).on("click",b),m.append("g").attr("class","dimensions");var A=h.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);A.enter().append("g").attr("class","dimension"),A.attr("transform",(function(t){return l(t.x,0)})),A.exit().remove();var M=A.selectAll("g.category").data((function(t){return t.categories}),p),S=M.enter().append("g").attr("class","category");M.attr("transform",(function(t){return l(0,t.y)})),S.append("rect").attr("class","catrect").attr("pointer-events","none"),M.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(S);var E=M.selectAll("rect.bandrect").data((function(t){return t.bands}),p);E.each((function(){s.raiseToTop(this)})),E.attr("fill",(function(t){return t.color}));var z=E.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);E.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(z),E.exit().remove(),S.append("text").attr("class","catlabel").attr("pointer-events","none");var D=e._fullLayout.paper_bgcolor;M.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(D)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){u.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),S.append("text").attr("class","dimlabel"),M.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){u.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",O).on("dragend",I)),h.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),h.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function v(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=m(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,u=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),v=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=u&&u<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],y=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+y.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=c.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+v.left,y:_-d.top+v.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function y(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(v),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function m(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=m(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return c.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,m(t))}))}));var u={};u[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:u})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,m(t))}));var u={};u[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(u.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:u})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),u=l.node().getBoundingClientRect(),c=l.datum(),f=c.parcatsViewModel,h=f.model.dimensions[c.model.dimensionInd],p=f.trace,d=u.top+u.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=u.left,a="left"):(i=u.left+u.width,a="right");var v=c.model.count,g=c.model.categoryLabel,y=v/c.parcatsViewModel.model.count,m={countLabel:v,categoryLabel:g,probabilityLabel:y.toFixed(3)},x=[];-1!==c.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",m.countLabel].join(" ")),-1!==c.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+m.categoryLabel+"):",m.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:m,eventData:[{data:p._input,fullData:p,count:v,category:g,probability:y}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron,u=this;"color"===l?(function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(u),S(u,"plotly_hover",n.event)):(function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(u),M(u,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")&&("category"===l?e=E(r,a,u):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),u=n.select(r).datum(),f=u.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,v=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,y=u.parcatsViewModel.model.count,m=0;u.categoryViewModel.bands.forEach((function(t){t.color===u.color&&(m+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===u.color&&(b+=t.model.count)}));var _=m/y,w=m/b,T=m/x,k={countLabel:m,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color ∩ "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=c.mostReadable(u.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(v-e.top),text:M,color:u.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:y,probability:_,categorycount:x,colorcount:b,bandcolorcount:m}]}}(r,a,u):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,u)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r}))}}function C(t){var e=t.parcatsViewModel;e.dragDimension||(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(v),-1!==e.hoverinfoItems.indexOf("skip"))||("color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event))}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,u=a.categories,c=u[l-1],f=u[l+1];void 0!==c&&s<c.y+c.height/2&&(o.model.displayInd=c.model.displayInd,c.model.displayInd=l),void 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n=r(3400),i=r(94288).hasColorscale,a=r(27260),o=r(86968).Q,s=r(51272),l=r(54460),u=r(82296),c=r(71864),f=r(30140).maxDimensionCount,h=r(26284);function p(t,e,r,i){function a(r,i){return n.coerce(t,e,u.dimensions,r,i)}var o=a("values"),s=a("visible");if(o&&o.length||(s=e.visible=!1),s){a("label"),a("tickvals"),a("ticktext"),a("tickformat");var f=a("range");e._ax={_id:"y",type:"linear",showexponent:"all",exponentformat:"B",range:f},l.setConvert(e._ax,i.layout),a("multiselect");var h=a("constraintrange");h&&(e.constraintrange=c.cleanRanges(h,e))}}t.exports=function(t,e,r,l){function c(r,i){return n.coerce(t,e,u,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log("parcoords traces support up to "+f+" dimensions at the moment"),d.splice(f));var v=s(t,e,{name:"dimensions",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s("line.color",r);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return 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a=y(e),o=a.trace,l=m.convertTypedArray(a.lineColor),u=o.line,c={color:s(o.unselected.line.color),opacity:o.unselected.line.opacity},f=p.extractOpts(u),h=f.reversescale?p.flipScale(a.cscale):a.cscale,d=o.domain,v=o.dimensions,g=t.width,b=o.labelangle,_=o.labelside,w=o.labelfont,T=o.tickfont,A=o.rangefont,M=i.extendDeepNoArrays({},u,{color:l.map(n.scale.linear().domain(k({values:l,range:[f.min,f.max],_length:o._length}))),blockLineCount:x.blockLineCount,canvasOverdrag:x.overdrag*x.canvasPixelRatio}),S=Math.floor(g*(d.x[1]-d.x[0])),L=Math.floor(t.height*(d.y[1]-d.y[0])),C=t.margin||{l:80,r:80,t:100,b:80},P=S,O=L;return{key:r,colCount:v.filter(m.isVisible).length,dimensions:v,tickDistance:x.tickDistance,unitToColor:E(h),lines:M,deselectedLines:c,labelAngle:b,labelSide:_,labelFont:w,tickFont:T,rangeFont:A,layoutWidth:g,layoutHeight:t.height,domain:d,translateX:d.x[0]*g,translateY:t.height-d.y[1]*t.height,pad:C,canvasWidth:P*x.canvasPixelRatio+2*M.canvasOverdrag,canvasHeight:O*x.canvasPixelRatio,width:P,height:O,canvasPixelRatio:x.canvasPixelRatio}}function 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D=P.map(O).sort(I),R=0;R<P.length;R++)P[R]=D[R].val,C[R]=D[R].text;break}}else P=void 0;return E=m.convertTypedArray(E),{key:_,label:s.label,tickFormat:s.tickformat,tickvals:P,ticktext:C,ordinal:m.isOrdinal(s),multiselect:s.multiselect,xIndex:u,crossfilterDimensionIndex:u,visibleIndex:s._index,height:l,values:E,paddedUnitValues:E.map(g),unitTickvals:P&&P.map(g),xScale:f,x:f(u),canvasX:f(u)*c,unitToPaddedPx:p,domainScale:M(l,x.verticalPadding,s,P,C),ordinalScale:S(s),parent:d,model:r,brush:b.makeBrush(t,T,A,(function(){t.linePickActive(!1)}),(function(){var e=d;e.focusLayer&&e.focusLayer.render(e.panels,!0);var r=L(e);!t.contextShown()&&r?(e.contextLayer&&e.contextLayer.render(e.panels,!0),t.contextShown(!0)):t.contextShown()&&!r&&(e.contextLayer&&e.contextLayer.render(e.panels,!0,!0),t.contextShown(!1))}),(function(r){if(d.focusLayer.render(d.panels,!0),d.pickLayer&&d.pickLayer.render(d.panels,!0),t.linePickActive(!0),e&&e.filterChanged){var n=g.invert,a=r.map((function(t){return 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r=0;r<t[e].length;r++)for(var n=t[e][r].trace,i=n.dimensions,a=0;a<i.length;a++){var o=i[a].values,s=i[a]._ax;s&&(s.range?s.range=A(s.range[0],s.range[1]):s.range=T(o,n._length),s.dtick||(s.dtick=.01*(Math.abs(s.range[1]-s.range[0])||1)),s.tickformat=i[a].tickformat,l.calcTicks(s),s.cleanRange())}}(e);var M,S,E=(M=!0,S=!1,{linePickActive:function(t){return arguments.length?M=!!t:M},contextShown:function(t){return arguments.length?S=!!t:S}}),F=e.filter((function(t){return y(t).trace.visible})).map(C.bind(0,r)).map(P.bind(0,E,a));d.each((function(t,e){return i.extendFlat(t,F[e])}));var B=d.selectAll(".gl-canvas").each((function(t){t.viewModel=F[0],t.viewModel.plotGlPixelRatio=w,t.viewModel.paperColor=k,t.model=t.viewModel?t.viewModel.model:null})),N=null;B.filter((function(t){return t.pick})).style("pointer-events",o?"none":"auto").on("mousemove",(function(t){if(E.linePickActive()&&t.lineLayer&&a&&a.hover){var e=n.event,r=this.width,i=this.height,o=n.mouse(this),s=o[0],l=o[1];if(s<0||l<0||s>=r||l>=i)return;var u=t.lineLayer.readPixel(s,i-1-l),c=0!==u[3],f=c?u[2]+256*(u[1]+256*u[0]):null,h={x:s,y:l,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:f};f!==N&&(c?a.hover(h):a.unhover&&a.unhover(h),N=f)}})),B.style("opacity",(function(t){return t.pick?0:1})),p.style("background","rgba(255, 255, 255, 0)");var j=p.selectAll("."+x.cn.parcoords).data(F,v);j.exit().remove(),j.enter().append("g").classed(x.cn.parcoords,!0).style("shape-rendering","crispEdges").style("pointer-events","none"),j.attr("transform",(function(t){return c(t.model.translateX,t.model.translateY)}));var U=j.selectAll("."+x.cn.parcoordsControlView).data(g,v);U.enter().append("g").classed(x.cn.parcoordsControlView,!0),U.attr("transform",(function(t){return c(t.model.pad.l,t.model.pad.t)}));var V=U.selectAll("."+x.cn.yAxis).data((function(t){return t.dimensions}),v);V.enter().append("g").classed(x.cn.yAxis,!0),U.each((function(t){z(V,t,w)})),B.each((function(t){if(t.viewModel){!t.lineLayer||a?t.lineLayer=_(this,t):t.lineLayer.update(t),(t.key||0===t.key)&&(t.viewModel[t.key]=t.lineLayer);var e=!t.context||a;t.lineLayer.render(t.viewModel.panels,e)}})),V.attr("transform",(function(t){return c(t.xScale(t.xIndex),0)})),V.call(n.behavior.drag().origin((function(t){return t})).on("drag",(function(t){var e=t.parent;E.linePickActive(!1),t.x=Math.max(-x.overdrag,Math.min(t.model.width+x.overdrag,n.event.x)),t.canvasX=t.x*t.model.canvasPixelRatio,V.sort((function(t,e){return t.x-e.x})).each((function(e,r){e.xIndex=r,e.x=t===e?e.x:e.xScale(e.xIndex),e.canvasX=e.x*e.model.canvasPixelRatio})),z(V,e,w),V.filter((function(e){return 0!==Math.abs(t.xIndex-e.xIndex)})).attr("transform",(function(t){return c(t.xScale(t.xIndex),0)})),n.select(this).attr("transform",c(t.x,0)),V.each((function(r,n,i){i===t.parent.key&&(e.dimensions[n]=r)})),e.contextLayer&&e.contextLayer.render(e.panels,!1,!L(e)),e.focusLayer.render&&e.focusLayer.render(e.panels)})).on("dragend",(function(t){var e=t.parent;t.x=t.xScale(t.xIndex),t.canvasX=t.x*t.model.canvasPixelRatio,z(V,e,w),n.select(this).attr("transform",(function(t){return c(t.x,0)})),e.contextLayer&&e.contextLayer.render(e.panels,!1,!L(e)),e.focusLayer&&e.focusLayer.render(e.panels),e.pickLayer&&e.pickLayer.render(e.panels,!0),E.linePickActive(!0),a&&a.axesMoved&&a.axesMoved(e.key,e.dimensions.map((function(t){return t.crossfilterDimensionIndex})))}))),V.exit().remove();var q=V.selectAll("."+x.cn.axisOverlays).data(g,v);q.enter().append("g").classed(x.cn.axisOverlays,!0),q.selectAll("."+x.cn.axis).remove();var H=q.selectAll("."+x.cn.axis).data(g,v);H.enter().append("g").classed(x.cn.axis,!0),H.each((function(t){var 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K=Y.selectAll("."+x.cn.axisExtentBottom).data(g,v);K.enter().append("g").classed(x.cn.axisExtentBottom,!0),K.attr("transform",(function(t){return c(0,t.model.height+x.axisExtentOffset)}));var J=K.selectAll("."+x.cn.axisExtentBottomText).data(g,v);J.enter().append("text").classed(x.cn.axisExtentBottomText,!0).attr("dy","0.75em").call(O),J.text((function(t){return R(t,!1)})).each((function(t){h.font(n.select(this),t.model.rangeFont)})),b.ensureAxisBrush(q,k,t)}},24196:function(t,e,r){"use strict";var n=r(36336),i=r(5048),a=r(95724).isVisible,o={};function s(t,e,r){var n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}(t.exports=function(t,e){var r=t._fullLayout;if(i(t,[],o)){var l={},u={},c={},f={},h=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=f[r]=n._fullInput.index;l[r]=t.data[i].dimensions,u[r]=t.data[i].dimensions.slice()})),n(t,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{filterChanged:function(e,n,i){var a=u[e][n],o=i.map((function(t){return t.slice()})),s="dimensions["+n+"].constraintrange",l=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===l[s]){var h=a.constraintrange;l[s]=h||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit("plotly_restyle",[d,[f[e]]])},hover:function(e){t.emit("plotly_hover",e)},unhover:function(e){t.emit("plotly_unhover",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return s(t,e,r)-s(t,e,n)}}(r,u[e].filter(a));l[e].sort(n),u[e].filter((function(t){return!a(t)})).sort((function(t){return u[e].indexOf(t)})).forEach((function(t){l[e].splice(l[e].indexOf(t),1),l[e].splice(u[e].indexOf(t),0,t)})),t.emit("plotly_restyle",[{dimensions:[l[e]]},[f[e]]])}})}}).reglPrecompiled=o},74996:function(t,e,r){"use strict";var n=r(45464),i=r(86968).u,a=r(25376),o=r(22548),s=r(21776).Ks,l=r(21776).Gw,u=r(92880).extendFlat,c=r(98192).c,f=a({editType:"plot",arrayOk:!0,colorEditType:"plot"});t.exports={labels:{valType:"data_array",editType:"calc"},label0:{valType:"number",dflt:0,editType:"calc"},dlabel:{valType:"number",dflt:1,editType:"calc"},values:{valType:"data_array",editType:"calc"},marker:{colors:{valType:"data_array",editType:"calc"},line:{color:{valType:"color",dflt:o.defaultLine,arrayOk:!0,editType:"style"},width:{valType:"number",min:0,dflt:0,arrayOk:!0,editType:"style"},editType:"calc"},pattern:c,editType:"calc"},text:{valType:"data_array",editType:"plot"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"style"},scalegroup:{valType:"string",dflt:"",editType:"calc"},textinfo:{valType:"flaglist",flags:["label","text","value","percent"],extras:["none"],editType:"calc"},hoverinfo:u({},n.hoverinfo,{flags:["label","text","value","percent","name"]}),hovertemplate:s({},{keys:["label","color","value","percent","text"]}),texttemplate:l({editType:"plot"},{keys:["label","color","value","percent","text"]}),textposition:{valType:"enumerated",values:["inside","outside","auto","none"],dflt:"auto",arrayOk:!0,editType:"plot"},textfont:u({},f,{}),insidetextorientation:{valType:"enumerated",values:["horizontal","radial","tangential","auto"],dflt:"auto",editType:"plot"},insidetextfont:u({},f,{}),outsidetextfont:u({},f,{}),automargin:{valType:"boolean",dflt:!1,editType:"plot"},title:{text:{valType:"string",dflt:"",editType:"plot"},font:u({},f,{}),position:{valType:"enumerated",values:["top left","top center","top right","middle center","bottom left","bottom center","bottom right"],editType:"plot"},editType:"plot"},domain:i({name:"pie",trace:!0,editType:"calc"}),hole:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},sort:{valType:"boolean",dflt:!0,editType:"calc"},direction:{valType:"enumerated",values:["clockwise","counterclockwise"],dflt:"counterclockwise",editType:"calc"},rotation:{valType:"angle",dflt:0,editType:"calc"},pull:{valType:"number",min:0,max:1,dflt:0,arrayOk:!0,editType:"calc"},_deprecated:{title:{valType:"string",dflt:"",editType:"calc"},titlefont:u({},f,{}),titleposition:{valType:"enumerated",values:["top left","top center","top right","middle center","bottom left","bottom center","bottom right"],editType:"calc"}}}},80036:function(t,e,r){"use strict";var n=r(7316);e.name="pie",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},45768:function(t,e,r){"use strict";var n=r(38248),i=r(49760),a=r(76308),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}t.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],u=e.labels,c=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(u=new Array(h),r=0;r<h;r++)u[r]=String(e.label0+r*e.dlabel);var d={},v=s(o["_"+e.type+"colormap"]),g=0,y=!1;for(r=0;r<h;r++){var m,x,b;if(p){if(m=f[r],!n(m))continue;m=+m}else m=1;void 0!==(x=u[r])&&""!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=m),a.push({v:m,label:x,color:v(c[r],x),i:r,pts:[r],hidden:b})):(y=!0,(i=a[_]).v+=m,i.pts.push(r),i.hidden||(g+=m),!1===i.color&&c[r]&&(i.color=v(c[r],x)))}return a=a.filter((function(t){return t.v>=0})),("funnelarea"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r="pie");var n=t._fullLayout,i=t.calcdata,a=n[r+"colorway"],s=n["_"+r+"colormap"];n["extend"+r+"colors"]&&(a=l(a,o));for(var u=0,c=0;c<i.length;c++){var f=i[c];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[u%a.length],u++))}}},makePullColorFn:s,generateExtendedColors:l}},74174:function(t,e,r){"use strict";var n=r(38248),i=r(3400),a=r(74996),o=r(86968).Q,s=r(31508).handleText,l=r(3400).coercePattern;function u(t,e){var r=i.isArrayOrTypedArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var u=e[l];if(n(u)&&u>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function c(t,e,r,n,i){n("marker.line.width")&&n("marker.line.color",i?void 0:r.paper_bgcolor);var a=n("marker.colors");l(n,"marker.pattern",a),t.marker&&!e.marker.pattern.fgcolor&&(e.marker.pattern.fgcolor=t.marker.colors),e.marker.pattern.bgcolor||(e.marker.pattern.bgcolor=r.paper_bgcolor)}t.exports={handleLabelsAndValues:u,handleMarkerDefaults:c,supplyDefaults:function(t,e,r,n){function l(r,n){return i.coerce(t,e,a,r,n)}var f=u(l("labels"),l("values")),h=f.len;if(e._hasLabels=f.hasLabels,e._hasValues=f.hasValues,!e._hasLabels&&e._hasValues&&(l("label0"),l("dlabel")),h){e._length=h,c(t,e,n,l,!0),l("scalegroup");var p,d=l("text"),v=l("texttemplate");if(v||(p=l("textinfo",i.isArrayOrTypedArray(d)?"text+percent":"percent")),l("hovertext"),l("hovertemplate"),v||p&&"none"!==p){var g=l("textposition");s(t,e,n,l,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||"auto"===g||"outside"===g)&&l("automargin"),("inside"===g||"auto"===g||Array.isArray(g))&&l("insidetextorientation")}else"none"===p&&l("textposition","none");o(e,n,l);var y=l("hole");if(l("title.text")){var m=l("title.position",y?"middle center":"top center");y||"middle center"!==m||(e.title.position="top center"),i.coerceFont(l,"title.font",n.font)}l("sort"),l("direction"),l("rotation"),l("pull")}else e.visible=!1}}},53644:function(t,e,r){"use strict";var n=r(10624).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},21552:function(t,e,r){"use strict";var n=r(43616),i=r(76308);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},69656:function(t,e,r){"use strict";var n=r(3400);function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(n.isArrayOrTypedArray(t))for(var r=0;r<e.length;r++){var i=t[e[r]];if(i||0===i||""===i)return i}},e.castOption=function(t,r){return n.isArrayOrTypedArray(t)?e.getFirstFilled(t,r):t||void 0},e.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},75792:function(t,e,r){"use strict";t.exports={attributes:r(74996),supplyDefaults:r(74174).supplyDefaults,supplyLayoutDefaults:r(90248),layoutAttributes:r(85204),calc:r(45768).calc,crossTraceCalc:r(45768).crossTraceCalc,plot:r(37820).plot,style:r(22152),styleOne:r(10528),moduleType:"trace",name:"pie",basePlotModule:r(80036),categories:["pie-like","pie","showLegend"],meta:{}}},85204:function(t){"use strict";t.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},90248:function(t,e,r){"use strict";var n=r(3400),i=r(85204);t.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},37820:function(t,e,r){"use strict";var n=r(33428),i=r(7316),a=r(93024),o=r(76308),s=r(43616),l=r(3400),u=l.strScale,c=l.strTranslate,f=r(72736),h=r(82744),p=h.recordMinTextSize,d=h.clearMinTextSize,v=r(78048).TEXTPAD,g=r(69656),y=r(53644),m=r(3400).isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,u=i.trace,c="funnelarea"===u.type;"_hasHoverLabel"in u||(u._hasHoverLabel=!1),"_hasHoverEvent"in u||(u._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[u.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:u._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),v=s+t.pxmid[1]*(1-p),m=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,m),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,m),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:u,x0:d-p*i.r,x1:d+p*i.r,y:v,_x0:c?o+t.TL[0]:d-p*i.r,_x1:c?o+t.TR[0]:d+p*i.r,_y0:c?s+t.TL[1]:v-p*i.r,_y1:c?s+t.BL[1]:v+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],u._hasHoverLabel=!0}u._hasHoverEvent=!0,e.emit("plotly_hover",{points:[y(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[u.index],o=n.select(this).datum();u._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[y(o,i)],event:n.event}),u._hasHoverEvent=!1),u._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),u._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[u.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size,s=g.castOption(t.insidetextfont.weight,e.pts)||g.castOption(t.textfont.weight,e.pts)||r.weight,l=g.castOption(t.insidetextfont.style,e.pts)||g.castOption(t.textfont.style,e.pts)||r.style,u=g.castOption(t.insidetextfont.variant,e.pts)||g.castOption(t.textfont.variant,e.pts)||r.variant;return{color:n||o.contrast(e.color),family:i,size:a,weight:s,style:l,variant:u}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),u=s.bBox(o.node(),!0);r.titleBox={width:u.width,height:u.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,u=e.midangle,c=r.trace.insidetextorientation,f="horizontal"===c,h="tangential"===c,p="radial"===c,d="auto"===c,v=[];if(!d){var g,y=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),u=s<l?s:l;(a="tan"===i?k(t,n,o,u,0):T(t,n,o,u,Math.PI/2)).textPosAngle=r,v.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)y(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)y(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)y(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)y(Math.PI*(g+.5),"rad")}}if(s||d||f){var m=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/m,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;v.push(a)}(d||p)&&((a=T(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a)),(d||h)&&((a=k(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a));for(var x=0,b=0,_=0;_<v.length;_++){var w=v[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return v[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=O(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?c*=2:-1!==a.title.position.indexOf("right")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function O(t){var e,r=t.pull;if(!r)return 0;if(l.isArrayOrTypedArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function I(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var u=s/2,c=l/2;"funnelarea"!==a.type||a.scalegroup||(c/=a.aspectratio),i.r=Math.min(u,c)/(1+O(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var u,c;i.aspectratio>1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if((i=(r=t[n][0]).trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,u=o.split("+"),c=function(t){return-1!==u.indexOf(t)},f=c("label"),h=c("text"),p=c("value"),d=c("percent"),v=n.separators;if(s=f?[e.label]:[],h){var y=g.getFirstFilled(i.text,e.pts);m(y)&&s.push(y)}p&&s.push(g.formatPieValue(e.v,v)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,v)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(m(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d("pie",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,"trace").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,u="px0",c="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,u="px1",c="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[u]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,n=z(a,s+=l*r.v/2),r.stopangle=s,r[c]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr("stroke-linejoin","round"),d.each((function(){var m=n.select(this).selectAll("g.slice").data(e);m.enter().append("g").classed("slice",!0),m.exit().remove();var _=[[[],[]],[[],[]]],T=!1;m.each((function(i,o){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,_[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var u=v.cx,c=v.cy,h=n.select(this),d=h.selectAll("path.surface").data([i]);if(d.enter().append("path").classed("surface",!0).style({"pointer-events":r?"none":"all"}),h.call(x,t,e),y.pull){var m=+g.castOption(y.pull,i.pts)||0;m>0&&(u+=m*i.pxmid[0],c+=m*i.pxmid[1])}i.cxFinal=u,i.cyFinal=c;var k=y.hole;if(i.v===v.vTotal){var A="M"+(u+i.px0[0])+","+(c+i.px0[1])+P(i.px0,i.pxmid,!0,1)+P(i.pxmid,i.px0,!0,1)+"Z";k?d.attr("d","M"+(u+k*i.px0[0])+","+(c+k*i.px0[1])+P(i.px0,i.pxmid,!1,k)+P(i.pxmid,i.px0,!1,k)+"Z"+A):d.attr("d",A)}else{var M=P(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr("d","M"+(u+k*i.px1[0])+","+(c+k*i.px1[1])+P(i.px1,i.px0,!1,k)+"l"+S*i.px0[0]+","+S*i.px0[1]+M+"Z")}else d.attr("d","M"+u+","+c+"l"+i.px0[0]+","+i.px0[1]+M+"Z")}D(t,i,v);var E=g.castOption(y.textposition,i.pts),C=h.selectAll("g.slicetext").data(i.text&&"none"!==E?[0]:[]);C.enter().append("g").classed("slicetext",!0),C.exit().remove(),C.each((function(){var r=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===E?function(t,e,r){return{color:g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,family:g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,size:g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size,weight:g.castOption(t.outsidetextfont.weight,e.pts)||g.castOption(t.textfont.weight,e.pts)||r.weight,style:g.castOption(t.outsidetextfont.style,e.pts)||g.castOption(t.textfont.style,e.pts)||r.style,variant:g.castOption(t.outsidetextfont.variant,e.pts)||g.castOption(t.textfont.variant,e.pts)||r.variant}}(y,i,a.font):b(y,i,a.font));r.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var d,m=s.bBox(r.node());if("outside"===E)d=L(m,i);else if(d=w(m,i,v),"auto"===E&&d.scale<1){var 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l=0;for(T=0;T<r.sourceLinks.length;T++)l+=r.sourceLinks[T].value;for(T=0;T<r.sourceLinks.length;T++)(e=r.sourceLinks[T]).concentrationOut=e.value/l;var c=0;for(T=0;T<r.targetLinks.length;T++)c+=r.targetLinks[T].value;for(T=0;T<r.targetLinks.length;T++)(e=r.targetLinks[T]).concenrationIn=e.value/c}}(),a.node.x.length&&a.node.y.length){for(w=0;w<Math.min(a.node.x.length,a.node.y.length,A.nodes.length);w++)if(a.node.x[w]&&a.node.y[w]){var C=[a.node.x[w]*g,a.node.y[w]*y];A.nodes[w].x0=C[0]-d/2,A.nodes[w].x1=C[0]+d/2;var P=A.nodes[w].y1-A.nodes[w].y0;A.nodes[w].y0=C[1]-P/2,A.nodes[w].y1=C[1]+P/2}"snap"===a.arrangement&&function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(w=0;w<n.length;w++){var s=t[n[w].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(x=A.nodes).forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return 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e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:a,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:c.tinyRGB(n),tinyColorAlpha:n.getAlpha(),tinyColorHoverHue:c.tinyRGB(i),tinyColorHoverAlpha:i.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,linkArrowLength:t.linkArrowLength,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){var e=t.linkArrowLength;if(t.link.circular)return function(t,e){var r=t.width/2,n=t.circularPathData;return"top"===t.circularLinkType?"M "+(n.targetX-e)+" "+(n.targetY+r)+" L"+(n.rightInnerExtent-e)+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r-e)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r-e)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 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n=t.link.source.x1,a=t.link.target.x0-e,o=i(n,a),s=o(.5),l=o(.5),u=t.link.y0-t.link.width/2,c=t.link.y0+t.link.width/2,f=t.link.y1-t.link.width/2,h=t.link.y1+t.link.width/2,p="M"+n+","+u,d="C"+s+","+u+" "+l+","+f+" "+a+","+f,v="C"+l+","+h+" "+s+","+c+" "+n+","+c,g=e>0?"L"+(a+e)+","+(f+t.link.width/2):"";return p+d+(g+="L"+a+","+h)+v+"Z"}}function S(t,e){var r=u(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s="node_"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:c.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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b=u<2||i[0]!==i[u-1]||o[0]!==o[u-1];b&&("tozerox"===y||"tonextx"===y&&(p||"h"===d))?m.tozero=!0:(e.error_y||{}).visible||"tonexty"!==y&&"tozeroy"!==y&&(l.hasMarkers(e)||l.hasText(e))||(m.padded=!1,m.ppad=0),b&&("tozeroy"===y||"tonexty"===y&&(p||"v"===d))?x.tozero=!0:"tonextx"!==y&&"tozerox"!==y||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,m)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var u=s.makeCalcdata(t.marker,"size"),c=new Array(e),f=0;f<e;f++)c[f]=r(u[f]);return c}return r(n.size)}}function d(t,e){var r=v(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function v(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var i=t.stackgroup;if(i){var 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n?(s.color=a(o.color,"uint8"),s.color[3]=0,s.borderColor=a(o.color,"uint8")):(s.color=a(o.color,"uint8"),s.borderColor=a(o.line.color,"uint8")),s.opacity=e.opacity*o.opacity,s.marker=L({mx:o.symbol,ma:o.angle},e);var S,E=p(e,1);if(y||m){var C,P=s.sizes=new Array(i),O=s.borderSizes=new Array(i),I=0;if(y){for(r=0;r<i;r++)P[r]=E(o.size[r]),I+=P[r];C=I/i}else for(S=E(o.size),r=0;r<i;r++)P[r]=S;if(m)for(r=0;r<i;r++)O[r]=o.line.width[r];else for(S=o.line.width,r=0;r<i;r++)O[r]=S;s.sizeAvg=C}else s.size=E(o&&o.size||10),s.borderSizes=E(o.line.width);return s}function _(t,e,r){var n=e.marker,i={};return r?(r.marker&&r.marker.symbol?i=b(0,s.extendFlat({},n,r.marker)):r.marker&&(r.marker.size&&(i.size=r.marker.size),r.marker.color&&(i.colors=r.marker.color),void 0!==r.marker.opacity&&(i.opacity=r.marker.opacity)),i):i}function w(t,e,r){var n={};if(!r)return n;if(r.textfont){var i={opacity:1,text:e.text,texttemplate:e.texttemplate,textposition:e.textposition,textfont:s.extendFlat({},e.textfont)};r.textfont&&s.extendFlat(i.textfont,r.textfont),n=x(t,i)}return n}function T(t,e,r){var n={capSize:2*e.width*r,lineWidth:e.thickness*r,color:e.color};return e.copy_ystyle&&(n=t.error_y),n}var k=v.SYMBOL_SDF_SIZE,A=v.SYMBOL_SIZE,M=v.SYMBOL_STROKE,S={},E=u.symbolFuncs[0](.05*A);function L(t,e){var r,n,a=t.mx;if("circle"===a)return null;var o=u.symbolNumber(a),s=u.symbolFuncs[o%100],l=!!u.symbolNoDot[o%100],c=!!u.symbolNoFill[o%100],f=d.isDotSymbol(a);if(t.ma&&(a+="_"+t.ma),S[a])return S[a];var h=u.getMarkerAngle(t,e);return r=f&&!l?s(1.1*A,h)+E:s(A,h),n=i(r,{w:k,h:k,viewBox:[-A,-A,A,A],stroke:c?M:-M}),S[a]=n,n||null}t.exports={style:function(t,e){var r,n={marker:void 0,markerSel:void 0,markerUnsel:void 0,line:void 0,fill:void 0,errorX:void 0,errorY:void 0,text:void 0,textSel:void 0,textUnsel:void 0},i=t._context.plotGlPixelRatio;if(!0!==e.visible)return n;if(h.hasText(e)&&(n.text=x(t,e),n.textSel=w(t,e,e.selected),n.textUnsel=w(t,e,e.unselected)),h.hasMarkers(e)&&(n.marker=b(0,e),n.markerSel=_(0,e,e.selected),n.markerUnsel=_(0,e,e.unselected),!e.unselected&&l(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=g*a[r]}if(h.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(v.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=T(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=T(e,e.error_y,i)),e.fill&&"none"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:b,markerSelection:_,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(h.hasLines(e)&&o)if("hv"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if("hvh"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if("vhv"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if("vh"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var u=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){u=!0;break}var c=u||n.length>v.TOO_MANY_POINTS||h.hasMarkers(e)?"rect":"round";if(u&&e.connectgaps){var f=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=f,n[i+1]=p):(f=n[i],p=n[i+1])}return{join:c,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod("errorbars","makeComputeError"),l=c.getFromId(t,e.xaxis,"x"),u=c.getFromId(t,e.yaxis,"y"),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e["error_"+a];if(o&&o.visible&&("linear"===i.type||"log"===i.type)){for(var l=s(o),u={x:0,y:1}[a],c={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,v=-1/0,g=0,y=0;g<f;g++,y+=4){var m=t[g];if(n(m)){var x=r[2*g+u],b=l(m,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=m-_,k=m+w;p[y+c[0]]=x-i.c2l(T),p[y+c[1]]=i.c2l(k)-x,p[y+c[2]]=0,p[y+c[3]]=0,d=Math.min(d,m-_),v=Math.max(v,m+w)}}}h[a]={positions:r,errors:p,_bnds:[d,v]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(h.hasMarkers(e)){var s=r.font,u=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var f=n.sizes?n.sizes[i]:n.size,p=l(s)?s[i].size:s.size,d=l(u)?u.length>1?u[i]:u[0]:u,v=l(c)?c.length>1?c[i]:c[0]:c,g=y[d],m=y[v],x=f?f/.8+1:0,b=-m*x-.5*m;o.offset[i]=[g*x/p,b/p]}}return o}}},80220:function(t,e,r){"use strict";var n=r(3400),i=r(24040),a=r(80088),o=r(2876),s=r(88200),l=r(43028),u=r(43980),c=r(31147),f=r(74428),h=r(66828),p=r(70840),d=r(124);t.exports=function(t,e,r,v){function g(r,i){return n.coerce(t,e,o,r,i)}var y=!!t.marker&&a.isOpenSymbol(t.marker.symbol),m=l.isBubble(t),x=u(t,e,v,g);if(x){c(t,e,v,g),g("xhoverformat"),g("yhoverformat");var b=x<s.PTS_LINESONLY?"lines+markers":"lines";g("text"),g("hovertext"),g("hovertemplate"),g("mode",b),l.hasMarkers(e)&&(f(t,e,r,v,g,{noAngleRef:!0,noStandOff:!0}),g("marker.line.width",y||m?1:0)),l.hasLines(e)&&(g("connectgaps"),h(t,e,r,v,g),g("line.shape")),l.hasText(e)&&(g("texttemplate"),d(t,e,v,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g("fill"),"none"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},26768:function(t,e,r){"use strict";var n=r(3400),i=r(76308),a=r(13448).DESELECTDIM;t.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,u=l._scene,c=l.index,f=u.selectBatch[c],h=u.unselectBatch[c],p=u.textOptions[c],d=u.textSelectedOptions[c]||{},v=u.textUnselectedOptions[c]||{},g=n.extendFlat({},p);if(f.length||h.length){var y=d.color,m=v.color,x=p.color,b=n.isArrayOrTypedArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=y||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=m||(y?_:i.addOpacity(_,a))}}u.glText[c].update(g)}}},99396:function(t,e,r){"use strict";var n=r(76688);t.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},80088:function(t,e,r){"use strict";var n=r(67072);e.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},e.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},41272:function(t,e,r){"use strict";var n=r(24040),i=r(3400),a=r(44928);function o(t,e,r,o){var s=t.xa,l=t.ya,u=t.distance,c=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=i.isArrayOrTypedArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=i.isArrayOrTypedArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family,h.tw=Array.isArray(p.weight)?p.weight[f]:p.weight,h.ty=Array.isArray(p.style)?p.style[f]:p.style,h.tv=Array.isArray(p.variant)?p.variant[f]:p.variant);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.ma=i.isArrayOrTypedArray(d.angle)?d.angle[f]:d.angle,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var v=d&&d.line;v&&(h.mlc=Array.isArray(v.color)?v.color[f]:v.color,h.mlw=i.isArrayOrTypedArray(v.width)?v.width[f]:v.width);var g=d&&d.gradient;g&&"none"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var y=s.c2p(h.x,!0),m=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:y-x,x1:y+x,xLabelVal:k?k[f]:h.x,y0:m-x,y1:m+x,yLabelVal:A?A[f]:h.y,cd:T,distance:u,spikeDistance:c,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}t.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,u,c,f,h,p,d,v=t.cd,g=v[0].t,y=v[0].trace,m=t.xa,x=t.ya,b=g.x,_=g.y,w=m.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=m.p2c(w-k),M=m.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i="x"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if("x"===n){var C=!!y.xperiodalignment,P=!!y.yperiodalignment;for(c=0;c<i.length;c++){if(l=b[a=i[c]],f=Math.abs(m.c2p(l)-w),C){var O=m.c2p(y._xStarts[a]),I=m.c2p(y._xEnds[a]);f=w>=Math.min(O,I)&&w<=Math.max(O,I)?0:1/0}if(f<L){if(L=f,u=_[a],h=x.c2p(u)-T,P){var z=x.c2p(y._yStarts[a]),D=x.c2p(y._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[c]}}}else for(c=i.length-1;c>-1;c--)l=b[a=i[c]],u=_[a],f=m.c2p(l)-w,h=x.c2p(u)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,y)]},calcHover:o}},38983:function(t,e,r){"use strict";var n=r(64628);n.plot=r(89876),t.exports=n},89876:function(t,e,r){"use strict";var n=r(38540),i=r(13472),a=r(24544),o=r(23352),s=r(3400),l=r(72760).selectMode,u=r(5048),c=r(43028),f=r(14328),h=r(26768).styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,u=i.r*n,c=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-u-(1-e.domain[1])*f,o-c-(1-r.domain[1])*h]}(t.exports=function(t,e,r){if(r.length){var v,g,y=t._fullLayout,m=e._scene,x=e.xaxis,b=e.yaxis;if(m)if(u(t,["ANGLE_instanced_arrays","OES_element_index_uint"],p)){var _=m.count,w=y._glcanvas.data()[0].regl;if(f(t,e,r),m.dirty){if(!m.line2d&&!m.error2d||m.scatter2d||m.fill2d||m.glText||w.clear({}),!0===m.error2d&&(m.error2d=a(w)),!0===m.line2d&&(m.line2d=i(w)),!0===m.scatter2d&&(m.scatter2d=n(w)),!0===m.fill2d&&(m.fill2d=i(w)),!0===m.glText)for(m.glText=new Array(_),v=0;v<_;v++)m.glText[v]=new o(w);if(m.glText){if(_>m.glText.length){var 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f,h,p=[],d=u&&u.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if("toself"===s.fill||"tonext"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var v=s._nexttrace;if(v){var g=m.lineOptions[e+1];if(g){var y=g.positions;if("tonexty"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=v.fillcolor}}}}if(s._prevtrace&&"tonext"===s._prevtrace.fill){var 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n=r(21776).Ks,i=r(21776).Gw,a=r(98304),o=r(6096),s=r(52904),l=r(5232),u=r(45464),c=r(49084),f=r(92880).extendFlat,h=r(67824).overrideAll,p=r(5232),d=o.line,v=o.marker;t.exports=h({lon:o.lon,lat:o.lat,cluster:{enabled:{valType:"boolean"},maxzoom:f({},p.layers.maxzoom,{}),step:{valType:"number",arrayOk:!0,dflt:-1,min:-1},size:{valType:"number",arrayOk:!0,dflt:20,min:0},color:{valType:"color",arrayOk:!0},opacity:f({},v.opacity,{dflt:1})},mode:f({},s.mode,{dflt:"markers"}),text:f({},s.text,{}),texttemplate:i({editType:"plot"},{keys:["lat","lon","text"]}),hovertext:f({},s.hovertext,{}),line:{color:d.color,width:d.width},connectgaps:s.connectgaps,marker:f({symbol:{valType:"string",dflt:"circle",arrayOk:!0},angle:{valType:"number",dflt:"auto",arrayOk:!0},allowoverlap:{valType:"boolean",dflt:!1},opacity:v.opacity,size:v.size,sizeref:v.sizeref,sizemin:v.sizemin,sizemode:v.sizemode},c("marker")),fill:o.fill,fillcolor:a(),textfont:l.layers.symbol.textfont,textposition:l.layers.symbol.textposition,below:{valType:"string"},selected:{marker:s.selected.marker},unselected:{marker:s.unselected.marker},hoverinfo:f({},u.hoverinfo,{flags:["lon","lat","text","name"]}),hovertemplate:n()},"calc","nested")},59392:function(t,e,r){"use 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v=e.marker;"circle"!==v.symbol&&(n.isArrayOrTypedArray(v.size)&&(v.size=v.size[0]),n.isArrayOrTypedArray(v.color)&&(v.color=v.color[0]))}i.hasLines(e)&&(o(t,e,r,f,h,{noDash:!0}),h("connectgaps"));var g=p("cluster.maxzoom"),y=p("cluster.step"),m=p("cluster.color",e.marker&&e.marker.color||r),x=p("cluster.size"),b=p("cluster.opacity");(h("cluster.enabled",!1!==g||!1!==y||!1!==m||!1!==x||!1!==b)||i.hasText(e))&&s(t,e,f,h,{noSelect:!0,noFontVariant:!0,font:{family:-1!==c.indexOf(f.font.family)?f.font.family:"Open Sans Regular",weight:f.font.weight,style:f.font.style,size:f.font.size,color:f.font.color}}),h("fill"),"none"!==e.fill&&l(t,e,r,h),n.coerceSelectionMarkerOpacity(e,h)}else e.visible=!1}},37920:function(t){"use strict";t.exports=function(t,e){return t.lon=e.lon,t.lat=e.lat,t}},11960:function(t,e,r){"use strict";var n=r(54460);t.exports=function(t,e,r){var i={},a=r[e.subplot]._subplot.mockAxis,o=t.lonlat;return 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l=s("u"),u=s("v"),c=s("w"),f=s("x"),h=s("y"),p=s("z");l&&l.length&&u&&u.length&&c&&c.length&&f&&f.length&&h&&h.length&&p&&p.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null):e.visible=!1}},15436:function(t,e,r){"use strict";t.exports={moduleType:"trace",name:"streamtube",basePlotModule:r(12536),categories:["gl3d","showLegend"],attributes:r(90167),supplyDefaults:r(54304),colorbar:{min:"cmin",max:"cmax"},calc:r(3832).calc,plot:r(25668),eventData:function(t,e){return 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e=function(t){var e,r=F[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},R)if(t.parent)if(Z){var i=t.x1>Z?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,$(t));else e.x0=e.x1=V;else e.x0=e.x1=V;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),u=a(e.x0,t.x0),f=a(e.x1,t.x1),h=a(e.transform.scale,n.scale),p=a(e.transform.rotate,n.rotate),d=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,v=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=u(t),i=f(t),a=function(t){return v(Math.pow(t,d))}(t),s={pxmid:G(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return c(A.type,n,y),{transform:{targetX:Y(s),targetY:X(s),scale:h(t),rotate:p(t),rCenter:a}}}}(t);return function(t){return E(e(t),M)}})):w.attr("transform",E(i,M))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}e.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,u=!r,c=!s.uniformtext.mode&&b.hasTransition(r);f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),c?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(".trace"),"sunburst")),u&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,u=n[0],c=t.data.data,f=u.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var v,g=o.split("+"),y=function(t){return-1!==g.indexOf(t)},m=[];if(y("label")&&c.label&&m.push(c.label),c.hasOwnProperty("v")&&y("value")&&m.push(b.formatValue(c.v,l)),!h){y("current path")&&m.push(b.getPath(t.data));var x=0;y("percent parent")&&x++,y("percent entry")&&x++,y("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){v=b.formatPercent(w,l),_&&(v+=" of "+t),m.push(v)};y("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),y("percent entry")&&(w=d/b.getValue(e),T("entry")),y("percent root")&&(w=d/b.getValue(f),T("root"))}}return y("text")&&(v=s.castOption(r,c.i,"text"),s.isValidTextValue(v)&&m.push(v)),m.join("<br>")}var k=s.castOption(r,c.i,"texttemplate");if(!k)return"";var A={};c.label&&(A.label=c.label),c.hasOwnProperty("v")&&(A.value=c.v,A.valueLabel=b.formatValue(c.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),c.hasOwnProperty("color")&&(A.color=c.color);var M=s.castOption(r,c.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,c.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},85676:function(t,e,r){"use strict";var n=r(33428),i=r(76308),a=r(3400),o=r(82744).resizeText,s=r(60404);function l(t,e,r,n){var o=e.data.data,l=!e.children,u=o.i,c=a.castOption(r,u,"marker.line.color")||i.defaultLine,f=a.castOption(r,u,"marker.line.width")||0;t.call(s,e,r,n).style("stroke-width",f).call(i.stroke,c).style("opacity",l?r.leaf.opacity:null)}t.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(e){var r=n.select(this),i=e[0].trace;r.style("opacity",i.opacity),r.selectAll("path.surface").each((function(e){n.select(this).call(l,e,i,t)}))}))},styleOne:l}},16716:function(t,e,r){"use strict";var n=r(76308),i=r(49084),a=r(29736).axisHoverFormat,o=r(21776).Ks,s=r(45464),l=r(92880).extendFlat,u=r(67824).overrideAll;function c(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=t.exports=u(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or 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n=r(47128);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},79164:function(t,e,r){"use strict";var n=r(67792).gl_surface3d,i=r(67792).ndarray,a=r(67792).ndarray_linear_interpolate.d2,o=r(70448),s=r(11240),l=r(3400).isArrayOrTypedArray,u=r(33040).parseColorScale,c=r(43080),f=r(8932).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){null!=t.dataCoordinate[a]&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var o=this.data.hovertext||this.data.text;return l(o)&&o[i]&&void 0!==o[i][n]?t.textLabel=o[i][n]:t.textLabel=o||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function v(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(v(t,n))}return e}function y(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function m(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var u=0;u<s;u++)r(i,[l,u],n),t.set(l,u,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=m(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=y(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,u=1+a+1,c=i(new Float32Array(l*u),[l,u]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(c,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,c,f),t[h]=p}},p.setContourLevels=function(){var 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bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(y,K)|0,i=(i=i+Math.imul(y,J)|0)+Math.imul(m,K)|0,a=a+Math.imul(m,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,Y)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,K)|0,i=(i=i+Math.imul(b,J)|0)+Math.imul(_,K)|0,a=a+Math.imul(_,J)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(I,U)|0,a=Math.imul(I,V),n=n+Math.imul(L,H)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,H)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,Y)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,K)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(k,K)|0,a=a+Math.imul(k,J)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0;var Tt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(O,H)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(I,H)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(L,Y)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,Y)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,K)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(S,K)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ut)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ut)|0;var kt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(O,Y)|0,i=(i=i+Math.imul(O,X)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,X)|0,n=n+Math.imul(L,K)|0,i=(i=i+Math.imul(L,J)|0)+Math.imul(C,K)|0,a=a+Math.imul(C,J)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ut)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(v,ft)|0,a=a+Math.imul(v,ht)|0;var At=(u+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,vt)|0)+Math.imul(h,dt)|0))<<13)|0;u=((a=a+Math.imul(h,vt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,Y)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(O,K)|0,i=(i=i+Math.imul(O,J)|0)+Math.imul(I,K)|0,a=a+Math.imul(I,J)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(y,ft)|0,i=(i=i+Math.imul(y,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(u+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,vt)|0)+Math.imul(v,dt)|0))<<13)|0;u=((a=a+Math.imul(v,vt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,Y),i=(i=Math.imul(B,X))+Math.imul(N,Y)|0,a=Math.imul(N,X),n=n+Math.imul(D,K)|0,i=(i=i+Math.imul(D,J)|0)+Math.imul(R,K)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ut)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(u+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,vt)|0)+Math.imul(m,dt)|0))<<13)|0;u=((a=a+Math.imul(m,vt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,K),i=(i=Math.imul(B,J))+Math.imul(N,K)|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(u+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,vt)|0)+Math.imul(_,dt)|0))<<13)|0;u=((a=a+Math.imul(_,vt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(u+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,vt)|0)+Math.imul(k,dt)|0))<<13)|0;u=((a=a+Math.imul(k,vt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ut)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,vt)|0)+Math.imul(S,dt)|0))<<13)|0;u=((a=a+Math.imul(S,vt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(O,ft)|0,i=(i=i+Math.imul(O,ht)|0)+Math.imul(I,ft)|0,a=a+Math.imul(I,ht)|0;var Pt=(u+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,vt)|0)+Math.imul(C,dt)|0))<<13)|0;u=((a=a+Math.imul(C,vt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ut))+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Ot=(u+(n=n+Math.imul(O,dt)|0)|0)+((8191&(i=(i=i+Math.imul(O,vt)|0)+Math.imul(I,dt)|0))<<13)|0;u=((a=a+Math.imul(I,vt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var It=(u+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,vt)|0)+Math.imul(R,dt)|0))<<13)|0;u=((a=a+Math.imul(R,vt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var zt=(u+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,vt))+Math.imul(N,dt)|0))<<13)|0;return u=((a=Math.imul(N,vt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=yt,l[2]=mt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=It,l[18]=zt,0!==u&&(l[19]=u,r.length++),r};function v(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),u=Math.max(0,a-t.length+1);u<=l;u++){var c=a-u,f=(0|t.words[c])*(0|e.words[u]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):v(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),u=Math.sin(2*Math.PI/s),c=0;c<i;c+=s)for(var f=l,h=u,p=0;p<o;p++){var d=r[c+p],v=n[c+p],g=r[c+p+o],y=n[c+p+o],m=f*g-h*y;y=f*y+h*g,g=m,r[c+p]=d+g,n[c+p]=v+y,r[c+p+o]=d-g,n[c+p+o]=v-y,p!==s&&(m=l*f-u*h,h=l*h+u*f,f=m)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),u=new Array(n),c=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,u,n),this.transform(o,a,s,l,n,i),this.transform(u,a,c,f,n,i);for(var p=0;p<n;p++){var d=s[p]*c[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*c[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),v(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var u=0;u<o;u++)l.words[u]=this.words[u];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,u=0;u<this.length;u++)this.words[u]=this.words[u+o];else this.words[0]=0,this.length=1;var c=0;for(u=this.length-1;u>=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r||!(this.words[r]&i))},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u<s.length;u++)s.words[u]=0}var c=n.clone()._ishlnsubmul(i,1,l);0===c.negative&&(n=c,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,v=1;0==(r.words[0]&v)&&d<26;++d,v<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction context"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,"redAdd works only with red numbers"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,"redSub works only with red numbers"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,"redISub works only with red numbers"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,"redShl works only with red numbers"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){m.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function b(){m.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function _(){m.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function w(){m.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function T(t){if("string"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),"modulus must be greater than 1"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(x,m),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i<n;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];for(e.words[e.length++]=a&r,i=10;i<t.length;i++){var o=0|t.words[i];t.words[i-10]=(o&r)<<4|a>>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,m),i(_,m),i(w,m),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return 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f=o[o.length-2];for(f.left===s?f.left=null:f.right===s&&(f.right=null),o.pop(),l=0;l<o.length;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(s.left||s.right){for(s.left?h(s,s.left):s.right&&h(s,s.right),s._color=1,l=0;l<o.length-1;++l)o[l]._count--;return new a(this.tree._compare,o[0])}if(1===o.length)return new a(this.tree._compare,null);for(l=0;l<o.length;++l)o[l]._count--;var p=o[o.length-2];return function(t){for(var e,a,o,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((a=t[l-1]).left===e){if((o=a.right).right&&0===o.right._color)return s=(o=a.right=r(o)).right=r(o.right),a.right=o.left,o.left=a,o.right=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),void(t[l-1]=o);if(o.left&&0===o.left._color)return s=(o=a.right=r(o)).left=r(o.left),a.right=s.left,o.left=s.right,s.left=a,s.right=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).left===a?u.left=s:u.right=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.right=n(0,o));a.right=n(0,o);continue}o=r(o),a.right=o.left,o.left=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((o=a.left).left&&0===o.left._color)return s=(o=a.left=r(o)).left=r(o.left),a.left=o.right,o.right=a,o.left=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),void(t[l-1]=o);if(o.right&&0===o.right._color)return s=(o=a.left=r(o)).right=r(o.right),a.left=s.right,o.right=s.left,s.right=a,s.left=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).right===a?u.right=s:u.left=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.left=n(0,o));a.left=n(0,o);continue}var u;o=r(o),a.left=o.right,o.right=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),t[l-1]=o,t[l]=a,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(o),p.left===s?p.left=null:p.right=null,new a(this.tree._compare,o[0])},Object.defineProperty(f,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(f,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(f,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),f.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),f.update=function(t){var r=this._stack;if(0===r.length)throw new Error("Can't update empty node!");var n=new Array(r.length),i=r[r.length-1];n[n.length-1]=new e(i._color,i.key,t,i.left,i.right,i._count);for(var o=r.length-2;o>=0;--o)(i=r[o]).left===r[o+1]?n[o]=new e(i._color,i.key,i.value,n[o+1],i.right,i._count):n[o]=new e(i._color,i.key,i.value,i.left,n[o+1],i._count);return new a(this.tree._compare,n[0])},f.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},3837:function(t,e,r){"use strict";t.exports=function(t,e){var r=new c(t);return r.update(e),r};var n=r(4935),i=r(501),a=r(5304),o=r(6429),s=r(6444),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function u(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function c(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickFontStyle=["normal","normal","normal"],this.tickFontWeight=["normal","normal","normal"],this.tickFontVariant=["normal","normal","normal"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["sans-serif","sans-serif","sans-serif"],this.labelFontStyle=["normal","normal","normal"],this.labelFontWeight=["normal","normal","normal"],this.labelFontVariant=["normal","normal","normal"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=c.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),c=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t)for(r=t.ticks,c=!0,this.autoTicks=!1,p=0;p<3;++p)this.tickSpacing[p]=0;else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,c=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),c=!0),c){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?c=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(c=!0),l("tickFontStyle")&&(c=!0),l("tickFontWeight")&&(c=!0),l("tickFontVariant")&&(c=!0),a("tickSize"),a("tickAngle"),a("tickPad"),u("tickColor");var v=l("labels");l("labelFont")&&(v=!0),l("labelFontStyle")&&(v=!0),l("labelFontWeight")&&(v=!0),l("labelFontVariant")&&(v=!0),o("labelEnable"),a("labelSize"),a("labelPad"),u("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),u("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),u("lineTickColor"),o("gridEnable"),a("gridWidth"),u("gridColor"),o("zeroEnable"),u("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),u("backgroundColor");var g=[{family:this.labelFont[0],style:this.labelFontStyle[0],weight:this.labelFontWeight[0],variant:this.labelFontVariant[0]},{family:this.labelFont[1],style:this.labelFontStyle[1],weight:this.labelFontWeight[1],variant:this.labelFontVariant[1]},{family:this.labelFont[2],style:this.labelFontStyle[2],weight:this.labelFontWeight[2],variant:this.labelFontVariant[2]}],y=[{family:this.tickFont[0],style:this.tickFontStyle[0],weight:this.tickFontWeight[0],variant:this.tickFontVariant[0]},{family:this.tickFont[1],style:this.tickFontStyle[1],weight:this.tickFontWeight[1],variant:this.tickFontVariant[1]},{family:this.tickFont[2],style:this.tickFontStyle[2],weight:this.tickFontWeight[2],variant:this.tickFontVariant[2]}];this._text?this._text&&(v||c)&&this._text.update(this.bounds,this.labels,g,this.ticks,y):this._text=n(this.gl,this.bounds,this.labels,g,this.ticks,y),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,p=o,d=l;u&1<<c&&(f=s,h=a,p=l,d=o),f[c]=r[0][c],h[c]=r[1][c],i[c]>0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}var v=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var y=[0,0,0],m=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),f=c.cubeEdges,h=c.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E,L,C=v;for(A=0;A<3;++A)this.backgroundEnable[A]?C[A]=h[A]:C[A]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var P=[0,0,0];h[A]>0?P[A]=a[1][A]:P[A]=a[0][A];for(var O=0;O<2;++O){var I=(A+1+O)%3,z=(A+1+(1^O))%3;this.gridEnable[I]&&this._lines.drawGrid(I,z,this.bounds,P,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(O=0;O<2;++O)I=(A+1+O)%3,z=(A+1+(1^O))%3,this.zeroEnable[z]&&Math.min(a[0][z],a[1][z])<=0&&Math.max(a[0][z],a[1][z])>=0&&this._lines.drawZero(I,z,this.bounds,P,this.zeroLineColor[z],this.zeroLineWidth[z]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var D=u(y,M[A].primalMinor),R=u(m,M[A].mirrorMinor),F=this.lineTickLength;for(O=0;O<3;++O){var B=k/r[5*O];D[O]*=F[O]*B,R[O]*=F[O]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,D,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(L=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,q=u(x,M[A].primalOffset);for(O=0;O<3;++O)this.lineTickEnable[A]&&(q[O]+=k*U[O]*Math.max(this.lineTickLength[O],0)/r[5*O]);var H=[0,0,0];if(H[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,E=1,"auto"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(""+S[0]),L=[0,0,0],j(A,U,V),O=0;O<3;++O)q[O]+=k*U[O]*this.tickPad[O]/r[5*O];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,L,S)}if(this.labelEnable[A]){for(E=0,L=[0,0,0],this.labels[A].length>4&&(N(A),E=1),"auto"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(""+S[0]),O=0;O<3;++O)q[O]+=k*U[O]*this.labelPad[O]/r[5*O];q[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],L,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},5304:function(t,e,r){"use strict";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[u]=d;for(var v=-1;v<=1;v+=2)f[c]=v,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=u;u=c,c=g}var y=n(t,new Float32Array(e)),m=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],m),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,y,x,b)};var n=r(2762),i=r(8116),a=r(1879).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},6429:function(t,e,r){"use strict";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var m=0,x=0;x<2;++x){c[2]=a[x][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[m],c,s),m+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)u[x][k]=l[x][k]/T;p&&(u[x][2]*=-1),T<0&&(w<0||u[x][2]<u[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);o(u[I],u[P],u[O],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(I=C<<A)+(C<<M)+(1-C<<S),O=I+(1-C<<M)+(C<<S);var 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r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],u=s[0]/s[3]-n,c=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(u*h-c*f)}return o}var v=[1,1,1],g=[0,0,0],y={cubeEdges:v,axis:g}},501:function(t,e,r){"use strict";t.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var v=+r[f][d].x;o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h,h=o.length/3|0;for(var y=0;y<r[f].length;++y)v=+r[f][y].x,o.push(v,0,1,v,1,1,v,0,-1,v,0,-1,v,1,1,v,1,-1);g=o.length/3|0,u[f]=h,c[f]=g-h}var m=n(t,new Float32Array(o)),x=i(t,[{buffer:m,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,m,x,b,l,s,c,u)};var n=r(2762),i=r(8116),a=r(1879).n,o=[0,0,0],s=[0,0,0],l=[0,0,0],u=[0,0,0],c=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,c[0]=this.gl.drawingBufferWidth,c[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=c,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,c=h(u,r);c[t]+=e[0][t],this.shader.uniforms.offset=c,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var c=f(s);c[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=c;var p=h(u,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var v=f(l);v[t]=1,this.shader.uniforms.screenAxis=v,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var c=h(u,n);c[t]+=r[0][t],this.shader.uniforms.offset=c;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},1879:function(t,e,r){"use strict";var n=r(3236),i=r(9405),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);e.n=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =     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HALF_PI :\n                                    0.0;\n}\n\nfloat roundTo(float a, float b) {\n  return float(b * floor((a + 0.5 * b) / b));\n}\n\nfloat look_round_n_directions(float a, int n) {\n  float b = positive_angle(a);\n  float div = TWO_PI / float(n);\n  float c = roundTo(b, div);\n  return look_upwards(c);\n}\n\nfloat applyAlignOption(float rawAngle, float delta) {\n  return\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\n                    rawAngle;                // otherwise return back raw input angle\n}\n\nbool isAxisTitle = (axis.x == 0.0) &&\n                   (axis.y == 0.0) &&\n                   (axis.z == 0.0);\n\nvoid main() {\n  //Compute world offset\n  float axisDistance = position.z;\n  vec3 dataPosition = axisDistance * axis + offset;\n\n  float beta = angle; // i.e. user defined attributes for each tick\n\n  float axisAngle;\n  float clipAngle;\n  float flip;\n\n  if (enableAlign) {\n    axisAngle = (isAxisTitle) ? HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);e.Q=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);e.bg=function(t){return i(t,u,c,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},4935:function(t,e,r){"use 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g={family:n[d][v].font||i[d].family,style:i[d].fontStyle||i[d].style,weight:i[d].fontWeight||i[d].weight,variant:i[d].fontVariant||i[d].variant};s(n[d][v].x,n[d][v].text,g,n[d][v].fontSize||12,1.25,p)}c[d]=(a.length/3|0)-l[d]}this.buffer.update(a),this.tickOffset=l,this.tickCount=c,this.labelOffset=f,this.labelCount=h},f.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},f.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},f.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}},6444:function(t,e){"use 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h=e.model||u,p=e.view||u,y=e.projection||u,m=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,y,x,m)).axis;o(c,p,h),o(c,y,c);for(var _=v,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(c,c));s(c,c);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=m?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var O=d(g,c,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(O[C])))}}}}return _};var n=r(5033),i=r(5202),a=r(6429),o=r(6760),s=r(5665),l=r(5352),u=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,u=0;u<3;++u)s[u]=o[u]=r[u];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var c=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(c*c+f*f)}return t}var v=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},2762:function(t,e,r){"use strict";var n=r(1888),i=r(5298),a=r(9618),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function u(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER&&(r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16"),r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,"uint16"):c(t,"float32"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},6405:function(t,e,r){"use strict";var n=r(2931);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,p=null,d=null,v=[],g=1/0,y=!1,m="raw"===t.coneSizemode,x=0;x<r.length;x++){var b=r[x];s=Math.min(b[0],s),l=Math.max(b[0],l),u=Math.min(b[1],u),c=Math.max(b[1],c),f=Math.min(b[2],f),h=Math.max(b[2],h);var _=i[x];if(n.length(_)>o&&(o=n.length(_)),x&&!m){var w=2*n.distance(p,b)/(n.length(d)+n.length(_));w?(g=Math.min(g,w),y=!1):y=!0}y||(p=b,d=_),v.push(_)}var T=[s,u,f],k=[l,c,h];e&&(e[0]=T,e[1]=k),0===o&&(o=1);var A=1/o;isFinite(g)||(g=1),a.vectorScale=g;var M=t.coneSize||(m?1:.5);t.absoluteConeSize&&(M=t.absoluteConeSize*A),a.coneScale=M,x=0;for(var S=0;x<r.length;x++)for(var E=(b=r[x])[0],L=b[1],C=b[2],P=v[x],O=n.length(P)*A,I=0;I<8;I++){a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.positions.push([E,L,C,S++]),a.vectors.push(P),a.vectors.push(P),a.vectors.push(P),a.vectors.push(P),a.vectors.push(P),a.vectors.push(P),a.vertexIntensity.push(O,O,O),a.vertexIntensity.push(O,O,O);var z=a.positions.length;a.cells.push([z-6,z-5,z-4],[z-3,z-2,z-1])}return a};var i=r(614);t.exports.createMesh=r(9060),t.exports.createConeMesh=function(e,r){return t.exports.createMesh(e,r,{shaders:i,traceType:"cone"})}},9060:function(t,e,r){"use strict";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(6760),l=r(7608),u=r(9618),c=r(6729),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,u,c){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=u,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=c,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=c({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,v=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],v=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var y=p[g];d=Math.min(d,y),v=Math.max(v,y)}else for(g=0;g<n.length;++g)y=n[g][2],d=Math.min(d,y),v=Math.max(v,y);for(this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],g=0;g<n.length;++g)for(var m=n[g],x=0;x<3;++x)!isNaN(m[x])&&isFinite(m[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],m[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],m[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];if(3===_.length){for(x=0;x<3;++x){m=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(m[w])||!isFinite(m[w]))continue t}for(x=0;x<3;++x){var T;m=n[T=_[2-x]],a.push(m[0],m[1],m[2],m[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(v-d),0]:[(m[2]-d)/(v-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var u={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};u.inverseModel=l(u.inverseModel,u.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(s(c,u.view,u.model),s(c,u.projection,c),l(c,c),o=0;o<3;++o)u.eyePosition[o]=c[12+o]/c[15];var h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];u.lightPosition[o]=p/h}if(this.triangleCount>0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),c=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),f=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));f.generateMipmap(),f.minFilter=t.LINEAR_MIPMAP_LINEAR,f.magFilter=t.LINEAR;var p=i(t),d=i(t),v=i(t),g=i(t),y=i(t),m=new h(t,f,l,c,p,d,y,v,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||"cone");return m.update(e),m}},614:function(t,e,r){var n=r(3236),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},737:function(t){t.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},5171:function(t,e,r){var n=r(737);t.exports=function(t){return n[t]}},9165:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var u=new s(e,r,o,l);return u.update(t),u};var n=r(2762),i=r(8116),a=r(3436),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function u(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],u=n[15],c=(t._ortho?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*u)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*c,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var c=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=c[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var c=r[l],h=0;h<3;++h)if(isNaN(c[h])||!isFinite(c[h]))continue t;var p,d=n[l],v=e[s];if(Array.isArray(v[0])&&(v=e[l]),3===v.length?v=[v[0],v[1],v[2],1]:4===v.length&&(v=[v[0],v[1],v[2],v[3]],!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0)),!isNaN(d[0][s])&&!isNaN(d[1][s]))d[0][s]<0&&((p=c.slice())[s]+=d[0][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s)),d[1][s]>0&&((p=c.slice())[s]+=d[1][s],i.push(c[0],c[1],c[2],v[0],v[1],v[2],v[3],0,0,0,p[0],p[1],p[2],v[0],v[1],v[2],v[3],0,0,0),u(this.bounds,p),o+=2+f(i,p,v,s))}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},3436:function(t,e,r){"use strict";var n=r(3236),i=r(9405),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);t.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},2260:function(t,e,r){"use strict";var n=r(7766);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension("WEBGL_draw_buffers");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,u),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),"number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var c=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>c||r<0||r>c)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!u)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var v=!0;"depth"in n&&(v=!!n.depth);var g=!1;return"stencil"in n&&(g=!!n.stencil),new d(t,e,r,h,f,v,g,u)};var i,a,o,s,l=null;function u(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function c(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var v=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return v._shape[0]},set:function(t){return v.width=t}},1:{get:function(){return v._shape[1]},set:function(t){return v.height=t}}}),this._shapeVector=g,function(t){var e=u(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,v=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var y=0;y<o;++y)t.color[y]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+y);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var m=r.getExtension("WEBGL_depth_texture");m?d?t.depth=h(r,i,a,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):v&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):v?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;y<t.color.length;++y)t.color[y].dispose(),t.color[y]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),c(r,e),f(x)}c(r,e)}(this)}var v=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),c(n,a),f(s)),c(n,a)}}Object.defineProperties(v,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),v.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},v.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},2992:function(t,e,r){var n=r(3387).sprintf,i=r(5171),a=r(1848),o=r(1085);t.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var u=n("Error compiling %s shader %s:\n",l,s),c=n("%s%s",u,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var v=parseInt(d.split(":")[2]);if(isNaN(v))throw new Error(n("Could not parse error: %s",d));h[v]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if((h[p+3]||h[p+2]||h[p+1])&&(u+=g[p]+"\n",h[p+1])){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),u+=n("^^^ %s\n\n",y)}return{long:u.trim(),short:c.trim()}}},2510:function(t,e,r){"use strict";t.exports=function(t,e){var r=t.gl,n=new u(t,o(r,l.vertex,l.fragment),o(r,l.pickVertex,l.pickFragment),s(r),s(r),s(r),s(r));return n.update(e),t.addObject(n),n};var n=r(2478),i=r(7762),a=r(1888),o=r(9405),s=r(2762),l=r(6768);function u(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var c,f=u.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(c=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],u=a[3]-a[1];c[0]=2*o/l,c[4]=2*s/u,c[6]=2*(r[0]-a[0])/l-1,c[7]=2*(r[1]-a[1])/u-1,e.bind();var f=e.uniforms;f.viewTransform=c,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,u=a[2]-a[0],c=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*u/f,t[4]=2*c/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,O;l?(P=x*(o[C]-c),O=x*(o[C+1]-c)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-c):x*(o[C]-(o[C]-o[C-1])/2-c),O=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-c):x*(o[C]+(o[C]-o[C-1])/2-c));for(var I=0;I<E;++I){var z,D;l?(z=m*(r[I]-u),D=m*(r[I+1]-u)):(z=I<b-1?m*(r[I]-(r[I+1]-r[I])/2-u):m*(r[I]-(r[I]-r[I-1])/2-u),D=I<b-1?m*(r[I]+(r[I+1]-r[I])/2-u):m*(r[I]+(r[I]-r[I-1])/2-u));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],q=s[l?(C+V)*b+(I+U):C*b+I],H=n.le(d,q);if(H<0)F=v[0],B=v[1],N=v[2],j=v[3];else if(H===g-1)F=v[4*g-4],B=v[4*g-3],N=v[4*g-2],j=v[4*g-1];else{var G=(q-d[H])/(d[H+1]-d[H]),W=1-G,Y=4*H,X=4*(H+1);F=W*v[Y]+G*v[X],B=W*v[Y+1]+G*v[X+1],N=W*v[Y+2]+G*v[X+2],j=W*v[Y+3]+G*v[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*O,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+I,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},6768:function(t,e,r){"use strict";var n=r(3236);t.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},7319:function(t,e,r){var n=r(3236),i=r(9405),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},5714:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(2762),i=r(8116),a=r(7766),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(2478),u=r(9618),c=r(7319),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:v(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:v(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,c=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],v=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var y,m,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);c+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],m=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=m=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===m.length&&(m=[m[0],m[1],m[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(v)?v.length>e-1?v[e-1]:v.length>0?v[v.length-1]:[0,0,0,1]:v;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3]);c+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,m[0],m[1],m[2],m[3]),c+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=c,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=u(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},m.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},m.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=0,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2]),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),u=1-a,c=[0,0,0],f=0;f<3;++f)c[f]=u*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,c,h,this.points[h])}},1903:function(t){t.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},6864:function(t){t.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},9921:function(t){t.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],u=t[8],c=t[9],f=t[10],h=t[11],p=t[12],d=t[13],v=t[14],g=t[15];return(e*o-r*a)*(f*g-h*v)-(e*s-n*a)*(c*g-h*d)+(e*l-i*a)*(c*v-f*d)+(r*s-n*o)*(u*g-h*p)-(r*l-i*o)*(u*v-f*p)+(n*l-i*s)*(u*d-c*p)}},7399:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,u=r*o,c=n*o,f=n*s,h=i*o,p=i*s,d=i*l,v=a*o,g=a*s,y=a*l;return t[0]=1-f-d,t[1]=c+y,t[2]=h-g,t[3]=0,t[4]=c-y,t[5]=1-u-d,t[6]=p+v,t[7]=0,t[8]=h+g,t[9]=p-v,t[10]=1-u-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},6743:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,u=a+a,c=n*s,f=n*l,h=n*u,p=i*l,d=i*u,v=a*u,g=o*s,y=o*l,m=o*u;return t[0]=1-(p+v),t[1]=f+m,t[2]=h-y,t[3]=0,t[4]=f-m,t[5]=1-(c+v),t[6]=d+g,t[7]=0,t[8]=h+y,t[9]=d-g,t[10]=1-(c+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},7894:function(t){t.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},7608:function(t){t.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=e[9],h=e[10],p=e[11],d=e[12],v=e[13],g=e[14],y=e[15],m=r*s-n*o,x=r*l-i*o,b=r*u-a*o,_=n*l-i*s,w=n*u-a*s,T=i*u-a*l,k=c*v-f*d,A=c*g-h*d,M=c*y-p*d,S=f*g-h*v,E=f*y-p*v,L=h*y-p*g,C=m*L-x*E+b*S+_*M-w*A+T*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(v*T-g*w+y*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-u*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-y*x)*C,t[7]=(c*T-h*b+p*x)*C,t[8]=(o*E-s*M+u*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-v*b+y*m)*C,t[11]=(f*b-c*w-p*m)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(v*x-d*_-g*m)*C,t[15]=(c*_-f*x+h*m)*C,t):null}},6582:function(t,e,r){var n=r(7894);t.exports=function(t,e,r,i){var a,o,s,l,u,c,f,h,p,d,v=e[0],g=e[1],y=e[2],m=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];return Math.abs(v-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(y-T)<1e-6?n(t):(f=v-_,h=g-w,p=y-T,a=x*(p*=d=1/Math.sqrt(f*f+h*h+p*p))-b*(h*=d),o=b*(f*=d)-m*p,s=m*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0),l=h*s-p*o,u=p*a-f*s,c=f*o-h*a,(d=Math.sqrt(l*l+u*u+c*c))?(l*=d=1/d,u*=d,c*=d):(l=0,u=0,c=0),t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=u,t[6]=h,t[7]=0,t[8]=s,t[9]=c,t[10]=p,t[11]=0,t[12]=-(a*v+o*g+s*y),t[13]=-(l*v+u*g+c*y),t[14]=-(f*v+h*g+p*y),t[15]=1,t)}},6760:function(t){t.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],u=e[6],c=e[7],f=e[8],h=e[9],p=e[10],d=e[11],v=e[12],g=e[13],y=e[14],m=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*v,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*u+_*p+w*y,t[3]=x*o+b*c+_*d+w*m,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*v,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*u+_*p+w*y,t[7]=x*o+b*c+_*d+w*m,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*v,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*u+_*p+w*y,t[11]=x*o+b*c+_*d+w*m,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*v,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*u+_*p+w*y,t[15]=x*o+b*c+_*d+w*m,t}},4040:function(t){t.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t}},4772:function(t){t.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},6079:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);return Math.abs(P)<1e-6?null:(E*=P=1/P,L*=P,C*=P,i=Math.sin(r),o=1-(a=Math.cos(r)),s=e[0],l=e[1],u=e[2],c=e[3],f=e[4],h=e[5],p=e[6],d=e[7],v=e[8],g=e[9],y=e[10],m=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+v*_,t[1]=l*x+h*b+g*_,t[2]=u*x+p*b+y*_,t[3]=c*x+d*b+m*_,t[4]=s*w+f*T+v*k,t[5]=l*w+h*T+g*k,t[6]=u*w+p*T+y*k,t[7]=c*w+d*T+m*k,t[8]=s*A+f*M+v*S,t[9]=l*A+h*M+g*S,t[10]=u*A+p*M+y*S,t[11]=c*A+d*M+m*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)}},5567:function(t){t.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],u=e[8],c=e[9],f=e[10],h=e[11];return 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view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},e.pointShader={vertex:l,fragment:u,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},e.pickShader={vertex:c,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},e.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},7201:function(t,e,r){"use strict";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(8406),l=r(6760),u=r(7608),c=r(9618),f=r(6729),h=r(7765),p=r(1888),d=r(840),v=r(7626),g=d.meshShader,y=d.wireShader,m=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=v,this.edgeVAO=m,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var u=r[l],c=0;c<2;++c){var f=u[0];2===u.length&&(f=u[c]);for(var d=n[f][0],v=n[f][1],g=i[f],y=1-g,m=this.positions[d],x=this.positions[v],b=0;b<3;++b)o[s++]=g*m[b]+y*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return c(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],u=[],h=[],p=[],d=[],v=[],g=[],y=[],m=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,O=t.cellUVs,I=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!O)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(I)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<I.length;++R)F=I[R],z=Math.min(z,F),D=Math.max(D,F);else for(R=0;R<n.length;++R)F=n[R][2],z=Math.min(z,F),D=Math.max(D,F);this.intensity=P||I||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.pickVertex=!(I||E);var B=t.pointSizes,N=t.pointSize||1;for(this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,q=0,H=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[Y=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;y.push(j[0],j[1],j[2]),X=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-z)/(D-z),this.opacityscale)):3===X.length?m.push(X[0],X[1],X[2],this.opacity):(m.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[Y]:P?[(P[Y]-z)/(D-z),0]:O?O[R]:I?[(I[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[Y]):b.push(N),_.push(R),H+=1;break;case 2:for(U=0;U<2;++U){j=n[Y=G[U]];for(var W=0;W<3;++W)if(isNaN(j[W])||!isFinite(j[W]))continue t}for(U=0;U<2;++U)j=n[Y=G[U]],p.push(j[0],j[1],j[2]),X=S?S[Y]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[Y]:P?[(P[Y]-z)/(D-z),0]:O?O[R]:I?[(I[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],v.push(Z[0],Z[1]),g.push(R);q+=1;break;case 3:for(U=0;U<3;++U)for(j=n[Y=G[U]],W=0;W<3;++W)if(isNaN(j[W])||!isFinite(j[W]))continue t;for(U=0;U<3;++U){var Y,X,Z,K;j=n[Y=G[2-U]],i.push(j[0],j[1],j[2]),(X=S?S[Y]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[Y]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[Y]:P?[(P[Y]-z)/(D-z),0]:O?O[R]:I?[(I[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],u.push(Z[0],Z[1]),K=w?w[Y]:T[R],l.push(K[0],K[1],K[2]),h.push(R)}V+=1}}this.pointCount=H,this.edgeCount=q,this.triangleCount=V,this.pointPositions.update(y),this.pointColors.update(m),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(u),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=u(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var c=new Array(16);for(l(c,s.view,s.model),l(c,s.projection,c),u(c,c),o=0;o<3;++o)s.eyePosition[o]=c[12+o]/c[15];var f,h=c[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*c[4*o+3];for(o=0;o<3;++o){for(var p=c[12+o],d=0;d<3;++d)p+=c[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],u=this.positions[r[1]],c=this.positions[r[2]],f=[(l[0]+u[0]+c[0])/3,(l[1]+u[1]+c[1])/3,(l[2]+u[2]+c[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=v(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},t.exports=function(t,e){if(1===arguments.length&&(t=(e=t).gl),!(t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives")))throw new Error("derivatives not supported");var r=function(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}(t),s=function(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}(t),l=M(t),u=S(t),f=E(t),h=L(t),p=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),v=i(t),m=i(t),x=i(t),b=i(t),_=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:b,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:v,type:t.FLOAT,size:4},{buffer:m,type:t.FLOAT,size:2},{buffer:x,type:t.FLOAT,size:3}]),w=i(t),k=i(t),A=i(t),C=i(t),P=a(t,[{buffer:w,type:t.FLOAT,size:3},{buffer:C,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:k,type:t.FLOAT,size:4},{buffer:A,type:t.FLOAT,size:2}]),O=i(t),I=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:O,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:I,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=new T(t,p,r,s,l,u,f,h,d,b,v,m,x,_,w,C,k,A,P,O,R,I,z,D,F,B,a(t,[{buffer:B,type:t.FLOAT,size:3}]));return N.update(e),N}},8120:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new o(t,n(e,[0,0,0,1,1,0,1,1]),i(e,a.boxVert,a.lineFrag))};var n=r(2762),i=r(9405),a=r(3603);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,u=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},1913:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new s(t,n(e),i(e,o.gridVert,o.gridFrag),i(e,o.tickVert,o.gridFrag))};var n=r(2762),i=r(9405),a=r(2478),o=r(3603);function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var u,c,f,h,p,d=s.prototype;d.draw=(u=[0,0],c=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,v=0;v<2;++v){var g=a[v],y=a[v+2]-g,m=.5*(o[v+2]+o[v]),x=o[v+2]-o[v];c[v]=2*y/x,u[v]=2*(g-m)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=u,r.uniforms.dataScale=c;var b=0;for(v=0;v<2;++v){f[0]=f[1]=0,f[v]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[v]/(s[v+2]-s[v])*d,r.uniforms.color=h[v];var _=6*n[v].length;p[v]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,u=this.vbo,c=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,v=s.viewBox,g=s.pixelRatio,y=s.screenBox,m=y[2]-y[0],x=y[3]-y[1],b=v[2]-v[0],_=v[3]-v[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/m,t[0]*=b/m,e[1]*=_/x,t[1]*=_/x,c.bind(),u.bind(),c.attributes.dataCoord.pointer();var S=c.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,O=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),I=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*O,D=6*Math.max(0,I-O),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(v[0]-E[1])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[1]*g/m,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[1]-E[0])/x-1,o[0]=L[0]*g/m,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(v[2]+E[3])/m-1,i[1]=(v[3]+v[1])/x-1,o[0]=E[3]*g/m,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(v[2]+v[0])/m-1,i[1]=2*(v[3]+E[2])/x-1,o[0]=L[2]*g/m,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],u=r[o],c=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-u)/(c-u);s.push(d);for(var v=0;v<6;++v)n[i++]=d,n[i++]=h[v],n[i++]=p[v]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},4747:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new o(t,n(e,[-1,-1,-1,1,1,-1,1,1]),i(e,a.lineVert,a.lineFrag))};var n=r(2762),i=r(9405),a=r(3603);function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,u=o.prototype;u.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},u.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,u=this.shader,c=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,u.uniforms.start=s,u.uniforms.end=l,u.uniforms.width=i*o.pixelRatio,u.uniforms.color=a,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},3603:function(t,e,r){"use strict";var n=r(3236),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);t.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},2142:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl;return new l(t,n(e),i(e,s.textVert,s.textFrag))};var n=r(2762),i=r(9405),a=r(529),o=r(2478),s=r(3603);function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var u,c,f,h,p,d,v=l.prototype;v.drawTicks=(u=[0,0],c=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,v=e.tickPad,g=e.tickColor,y=e.tickAngle,m=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);u[0]=u[1]=0,u[t]=1,c[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];c[1^t]=M*s[1^t]-1,d[t]&&(c[1^t]-=M*p*v[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t]&&T&&(c[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),c[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(c[1^t]+=M*p*v[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t+2]&&T&&(c[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),v.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),v.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,u=.5*(n[o+2]+n[o]),c=n[o+2]-n[o],f=a[o],v=a[o+2]-f,g=i[o],y=i[o+2]-g;p[o]=2*l/c*v/y,h[o]=2*(s-u)/c*v/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),v.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,u=t.bounds;for(o=0;o<2;++o){var c=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,v=p.text,g=p.font||"sans-serif",y=p.fontStyle||"normal",m=p.fontWeight||"normal",x=p.fontVariant||"normal";i=p.fontSize||12;for(var b=1/(u[o+2]-u[o]),_=u[o],w=v.split("\n"),T=0;T<w.length;T++)for(n=a(g,w[T],{fontStyle:y,fontWeight:m,fontVariant:x}).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-T*i*1.2,(d-_)*b);c.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=c,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{fontStyle:t.labelFontStyle[o],fontWeight:t.labelFontWeight[o],fontVariant:t.labelFontVariant[o],textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title,{fontStyle:t.titleFontStyle,fontWeight:t.titleFontWeight,fontVariant:t.titleFontVariant}).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},v.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},1850:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=new l(e,n(e,[e.drawingBufferWidth,e.drawingBufferHeight]));return r.grid=i(r),r.text=a(r),r.line=o(r),r.box=s(r),r.update(t),r};var n=r(3589),i=r(1913),a=r(2142),o=r(4747),s=r(8120);function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var u=l.prototype;function c(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}u.setDirty=function(){this.dirty=this.pickDirty=!0},u.setOverlayDirty=function(){this.dirty=!0},u.nextDepthValue=function(){return this._depthCounter++/65536},u.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var v=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(v,e[1],v,e[3],p[d],h[d]):o.drawLine(e[0],v,e[2],v,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,y=this.borderLineWidth,m=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*y[1]*i,r[0],r[3]+.5*y[3]*i,y[1],m[1]),g[0]&&o.drawLine(r[0]-.5*y[0]*i,r[1],r[2]+.5*y[2]*i,r[1],y[0],m[0]),g[3]&&o.drawLine(r[2],r[1]-.5*y[1]*i,r[2],r[3]+.5*y[3]*i,y[3],m[3]),g[2]&&o.drawLine(r[0]-.5*y[0]*i,r[3],r[2]+.5*y[2]*i,r[3],y[2],m[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},u.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},u.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var f=u[c].pick(a,o,l);if(f)return f}return null}},u.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},u.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},u.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},u.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]),this.screenBox,this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=c(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=c(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=c(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=c(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=c(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=c(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],labelFontStyle:t.labelFontStyle||["normal","normal"],labelFontWeight:t.labelFontWeight||["normal","normal"],labelFontVariant:t.labelFontVariant||["normal","normal"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif",titleFontStyle:t.titleFontStyle||"normal",titleFontWeight:t.titleFontWeight||"normal",titleFontVariant:t.titleFontVariant||"normal"}),this.static=!!t.static,this.setDirty()},u.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},u.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},u.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},u.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},u.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},4437:function(t,e,r){"use strict";t.exports=function(t,e){t=t||document.body;var r=[.01,1/0];"distanceLimits"in(e=e||{})&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),"zoomMin"in e&&(r[0]=e.zoomMin),"zoomMax"in e&&(r[1]=e.zoomMax);var u=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),c=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:u,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:u.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;u.idle(e-r),u.recalcMatrix(i),u.flush(e-(100+2*r));for(var a=!0,o=u.computedMatrix,s=0;s<16;++s)a=a&&c[s]===o[s],c[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(u.computedRadius[0]),!0)},lookAt:function(t,e,r){u.lookAt(u.lastT(),t,e,r)},rotate:function(t,e,r){u.rotate(u.lastT(),t,e,r)},pan:function(t,e,r){u.pan(u.lastT(),t,e,r)},translate:function(t,e,r){u.translate(u.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return u.computedMatrix},set:function(t){return u.setMatrix(u.lastT(),t),u.computedMatrix},enumerable:!0},mode:{get:function(){return u.getMode()},set:function(t){var e=u.computedUp.slice(),r=u.computedEye.slice(),i=u.computedCenter.slice();if(u.setMode(t),"turntable"===t){var a=n();u._active.lookAt(a,r,i,e),u._active.lookAt(a+500,r,i,[0,0,1]),u._active.flush(a)}return u.getMode()},enumerable:!0},center:{get:function(){return u.computedCenter},set:function(t){return u.lookAt(u.lastT(),null,t),u.computedCenter},enumerable:!0},eye:{get:function(){return u.computedEye},set:function(t){return u.lookAt(u.lastT(),t),u.computedEye},enumerable:!0},up:{get:function(){return u.computedUp},set:function(t){return u.lookAt(u.lastT(),null,null,t),u.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return u.setDistance(u.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return u.getDistanceLimits(r)},set:function(t){return u.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,c="zoom"===o,h=!!a.control,p=!!a.alt,v=!!a.shift,g=!!(1&e),y=!!(2&e),m=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!v||g&&!h&&!p&&v)&&u.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!v||y||g&&h&&!p&&!v)&&u.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),c&&g&&!h&&!p&&!v||m||g&&!h&&p&&!v)){var M=-d.zoomSpeed*_/window.innerHeight*(A-u.lastT())*100;u.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))u.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(3025),i=r(6296),a=r(351),o=r(8512),s=r(24),l=r(7520)},799:function(t,e,r){var n=r(3236),i=r(9405),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);t.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},4100:function(t,e,r){"use strict";var n=r(4437),i=r(3837),a=r(5445),o=r(4449),s=r(3589),l=r(2260),u=r(7169),c=r(351),f=r(4772),h=r(4040),p=r(799),d=r(9216)({tablet:!0,featureDetect:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return"boolean"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement("canvas"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error("webgl not supported");var m=t.bounds||[[-10,-10,-10],[10,10,10]],x=new v,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],O=!0,I=!0,z={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},D=(I=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:m,objects:E,shape:D,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:z,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",O=!0}}}function j(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,D);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener("resize",N),F.update=function(t){F._stopped||(t=t||{},O=!0,I=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),L.push(-1),O=!0,I=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),O=!0,I=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener("resize",N),e.removeEventListener("webglcontextlost",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();for(b.dispose(),t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},F._mouseRotating=!1,F._prevButtons=0,F.enableMouseListeners=function(){F.mouseListener=c(e,(function(t,e,r){if(!F._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&F._prevButtons)F._mouseRotating=!0;else{F._mouseRotating&&(I=!0),F._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,B[1]-r-1,F.pickRadius);if(l){if(l.distance>x.distance)continue;for(var u=0;u<i;++u){var c=E[u];if(L[u]===s){var f=c.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=c,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),O=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),O=!0),(o=o||x.object!==a)&&F.onselect&&F.onselect(x),1&t&&!(1&F._prevButtons)&&F.onclick&&F.onclick(x),F._prevButtons=t}}))},e.addEventListener("webglcontextlost",U);var V=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],q=[V[0].slice(),V[1].slice()];function H(){if(!U()){N();var t=F.camera.tick();z.view=F.camera.matrix,O=O||t,I=I||t,A.pixelRatio=F.pixelRatio,S.pixelRatio=F.pixelRatio;var e=E.length,n=V[0],i=V[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=F.pixelRatio,C.axes=F.axes,O=O||!!C.dirty,I=I||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],c=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],c[p])}var d=F.bounds;if(F.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var v=.05*(i[p]-n[p]);n[p]=n[p]-v,i[p]=i[p]+v}d[0][p]=n[p],d[1][p]=i[p]}var y=!1;for(p=0;p<3;++p)y=y||q[0][p]!==d[0][p]||q[1][p]!==d[1][p],q[0][p]=d[0][p],q[1][p]=d[1][p];if(I=I||y,O=O||y){if(y){var m=[0,0,0];for(o=0;o<3;++o)m[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:m}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;for(D[0]=T,D[1]=k,B[0]=0|Math.max(T/F.pixelRatio,1),B[1]=0|Math.max(k/F.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,u=t.zFar,c=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,u),n._ortho=!0):(f(i,c,p,l,u),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var v=0;for(d=0;d<3;++d)v=Math.max(v,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/v,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(F,w),o=0;o<e;++o)(C=E[o]).axesBounds=d,F.clipToBounds&&(C.clipBounds=d);x.object&&(F.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),I&&(I=!1,function(){if(!U()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=B,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(z))}i.end()}}}()),F.axesPixels=a(F.axes,z,T,k),F.onrender&&F.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),F.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;for(A.enable&&(M=M||A.isTransparent(),A.draw(z)),S.axes=A,x.object&&S.draw(z),r.disable(r.CULL_FACE),o=0;o<e;++o)(C=E[o]).axes=A,C.pixelRatio=F.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(z),C.isTransparent&&C.isTransparent()&&(M=!0);if(M){for(b.shape=D,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(z),o=0;o<e;++o)(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(z);for(r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(z),o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(z)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,u(r),r.disable(r.BLEND)}for(O=!1,o=0;o<e;++o)E[o].dirty=!1}}}return F.enableMouseListeners(),function t(){F._stopped||F.contextLost||(H(),requestAnimationFrame(t))}(),F.redraw=function(){F._stopped||(O=!0,H())},F},createCamera:n}},6640:function(t,e,r){var n=r(3236);e.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),e.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),e.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),e.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},4696:function(t,e,r){"use strict";var n=r(9405),i=r(2762),a=r(1888),o=r(6640);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,u,c=s.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),u=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)u[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(u),i||a.free(l),o||a.free(u),this.pointCount=n,this.pickOffset=0},c.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],u=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),c=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=c<5,r.uniforms.pointSize=c,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(u[0]=255&t,u[1]=t>>8&255,u[2]=t>>16&255,u[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=u,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},783:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],p=r[0],d=r[1],v=r[2],g=r[3];return(a=u*p+c*d+f*v+h*g)<0&&(a=-a,p=-p,d=-d,v=-v,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*p,t[1]=s*c+l*d,t[2]=s*f+l*v,t[3]=s*h+l*g,t}},5964:function(t){"use strict";t.exports=function(t){return t||0===t?t.toString():""}},9366:function(t,e,r){"use strict";var n=r(4359);t.exports=function(t,e,r){var a=[e.style,e.weight,e.variant,e.family].join("_"),o=i[a];if(o||(o=i[a]={}),t in o)return o[t];var s={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e.family,fontStyle:e.style,fontWeight:e.weight,fontVariant:e.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},l=n(t,s);s.triangles=!1;var u,c,f=n(t,s);if(r&&1!==r){for(u=0;u<l.positions.length;++u)for(c=0;c<l.positions[u].length;++c)l.positions[u][c]/=r;for(u=0;u<f.positions.length;++u)for(c=0;c<f.positions[u].length;++c)f.positions[u][c]/=r}var h=[[1/0,1/0],[-1/0,-1/0]],p=f.positions.length;for(u=0;u<p;++u){var d=f.positions[u];for(c=0;c<2;++c)h[0][c]=Math.min(h[0][c],d[c]),h[1][c]=Math.max(h[1][c],d[c])}return o[t]=[l,f,h]};var i={}},1283:function(t,e,r){var n=r(9405),i=r(3236),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),u=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),c=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:a,fragment:u,attributes:c},v={vertex:o,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,f)},e.createOrtho=function(t){return y(t,h)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,v)},e.createPickProject=function(t){return y(t,g)}},8418:function(t,e,r){"use strict";var n=r(5219),i=r(2762),a=r(8116),o=r(1888),s=r(6760),l=r(1283),u=r(9366),c=r(5964),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=new g(e,r,n,o,f,h,p,d,a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,u,c);return v.update(t),v};var y=g.prototype;y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},y.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var m=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function C(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,d=e.axesBounds,v=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=m,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=v,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var y=T,L=0;L<16;++L)y[L]=0;for(L=0;L<4;++L)y[5*L]=1;y[5*g]=0,i[g]<0?y[12+g]=d[0][g]:y[12+g]=d[1][g],s(y,u,y),l.model=y;var C=(g+1)%3,P=(g+2)%3,O=M(x),I=M(b);O[C]=1,I[P]=1;var z=p(0,0,0,S(_,O)),D=p(0,0,0,S(w,I));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=O,O=I,I=R;var F=C;C=P,P=F}z[0]<0&&(O[C]=-1),D[1]>0&&(I[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(u[4*C+L],2),N+=Math.pow(u[4*P+L],2);O[C]/=Math.sqrt(B),I[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=I,l.fragClipBounds[0]=E(k,v[0],g,-1e8),l.fragClipBounds[1]=E(k,v[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,u.screenSize=m,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=L,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=c(a);var o=!0;n(a)&&(a="▼",o=!1),r||(r={});var s=r.family;Array.isArray(s)&&(s=s[e]),s||(s="normal");var l=r.weight;Array.isArray(l)&&(l=l[e]),l||(l="normal");var f=r.style;Array.isArray(f)&&(f=f[e]),f||(f="normal");var h=r.variant;Array.isArray(h)&&(h=h[e]),h||(h="normal");var p=u(a,{family:s,weight:l,style:f,variant:h},i);return{mesh:(p=u(a,r,i))[0],lines:p[1],bounds:p[2],visible:o}}y.draw=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},y.drawTransparent=function(t){C(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},y.drawPick=function(t){C(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},y.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},y.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){Array.isArray(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l={family:t.font||"normal",style:t.fontStyle||"normal",weight:t.fontWeight||"normal",variant:t.fontVariant||"normal"},u=t.alignment||[0,0];if(2===u.length)i=u[0],a=u[1];else for(i=[],a=[],n=0;n<u.length;++n)i[n]=u[n][0],a[n]=u[n][1];var c=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,y=t.lineColor,m=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),O=o.mallocUint32(S);if(S>0){var I=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(y)&&Array.isArray(y[0]);t:for(n=0;n<_;++n){for(m+=1,w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(y)){var U;if(3===(U=B?n<y.length?y[n]:[0,0,0,0]:y).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var q=0;Array.isArray(g)?q=n<g.length?+g[n]:0:g&&(q=+g);var H=Math.cos(q),G=Math.sin(q);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T]);var W=i,Y=a;W=0,Array.isArray(i)?W=n<i.length?i[n]:0:i&&(W=i),Y=0,Array.isArray(a)?Y=n<a.length?a[n]:0:a&&(Y=a);var X=[W*=W>0?1-M[0][0]:W<0?1+M[1][0]:1,Y*=Y>0?1-M[0][1]:Y<0?1+M[1][1]:1],Z=k.cells||[],K=k.positions||[];for(T=0;T<Z.length;++T)for(var J=Z[T],$=0;$<3;++$){for(var Q=0;Q<3;++Q)E[3*I+Q]=w[Q];for(Q=0;Q<4;++Q)L[4*I+Q]=D[Q];O[I]=m;var tt=K[J[$]];C[2*I]=V*(H*tt[0]-G*tt[1]+X[0]),C[2*I+1]=V*(G*tt[0]+H*tt[1]+X[1]),I+=1}for(Z=A.edges,K=A.positions,T=0;T<Z.length;++T)for(J=Z[T],$=0;$<2;++$){for(Q=0;Q<3;++Q)E[3*z+Q]=w[Q];for(Q=0;Q<4;++Q)L[4*z+Q]=R[Q];O[z]=m,tt=K[J[$]],C[2*z]=V*(H*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+H*tt[1]+X[1]),z+=1}}}this.bounds=[c,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(O),o.free(E),o.free(L),o.free(C),o.free(O)},y.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},4298:function(t,e,r){"use strict";var n=r(3236);e.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),e.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},3161:function(t,e,r){"use strict";var n=r(9405),i=r(2762),a=r(4298);function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}t.exports=function(t,e){var r=t.gl,s=new o(t,i(r,[0,0,0,1,1,0,1,1]),n(r,a.boxVertex,a.boxFragment));return s.update(e),t.addOverlay(s),s};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,f=(e[0]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],h=(e[1]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1],p=(e[2]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],d=(e[3]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1];if(f=Math.max(f,u[0]),h=Math.max(h,u[1]),p=Math.min(p,u[2]),d=Math.min(d,u[3]),!(p<f||d<h)){o.bind();var v=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,v,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,v,g,i),o.drawBox(p,h,v,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var y=r*c;o.drawBox(f-y,h-y,p+y,h+y,a),o.drawBox(f-y,d-y,p+y,d+y,a),o.drawBox(f-y,h-y,f+y,d+y,a),o.drawBox(p-y,h-y,p+y,d+y,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},3589:function(t,e,r){"use strict";t.exports=function(t,e){var r=e[0],a=e[1];return new l(t,n(t,r,a,{}),i.mallocUint8(r*a*4))};var n=r(2260),i=r(1888),a=r(9618),o=r(8828).nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var u=l.prototype;Object.defineProperty(u,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),u.begin=function(){var t=this.gl;this.shape,t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},u.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},u.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,"number"!=typeof r&&(r=1);var i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),u=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||u<=l)return null;var c=[o-i,u-l],f=a(this.buffer,[c[0],c[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var u=0;u<s;u++){var c=t.get(l,u,0),f=t.get(l,u,1),h=t.get(l,u,2),p=t.get(l,u,3);if(c<255||f<255||h<255||p<255){var d=e-l,v=r-u,g=d*d+v*v;g<n&&(n=g,i=l,a=u)}}return[i,a,n]}(f.hi(c[0],c[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},u.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},9405:function(t,e,r){"use strict";var n=r(3327),i=r(8731),a=r(216),o=r(5091),s=r(2145),l=r(8866);function 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t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},6740:function(t,e,r){var n=r(3236),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},7815:function(t,e,r){"use strict";var n=r(2931),i=r(9970),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},s=function(t,e,r){return t<e?e:t>r?r:t},l=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};t.exports=function(t,e){var r=t.startingPositions,u=t.maxLength||1e3,c=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,l=t[0],u=t[1],c=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=o(a[0],l),v=o(a[1],u),g=o(a[2],c),y=d+1,m=v+1,x=g+1;if(d=s(d,0,f-1),y=s(y,0,f-1),v=s(v,0,h-1),m=s(m,0,h-1),g=s(g,0,p-1),x=s(x,0,p-1),d<0||v<0||g<0||y>f-1||m>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][y],E=a[1][v],L=a[1][m],C=a[2][g],P=(l-M)/(S-M),O=(u-E)/(L-E),I=(c-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(O)||(O=.5),isFinite(I)||(I=.5),r.reversedX&&(d=f-1-d,y=f-1-y),r.reversedY&&(v=h-1-v,m=h-1-m),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=v*p,T=m*p,b=d*p*h,_=y*p*h;break;case 4:k=g,A=x,b=d*p,_=y*p,w=v*p*f,T=m*p*f;break;case 3:w=v,T=m,k=g*h,A=x*h,b=d*h*p,_=y*h*p;break;case 2:w=v,T=m,b=d*h,_=y*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=y,k=g*f,A=x*f,w=v*f*p,T=m*f*p;break;default:b=d,_=y,w=v*f,T=m*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(q,D,N,P),n.lerp(H,R,j,P),n.lerp(G,F,U,P);var W=n.create(),Y=n.create();n.lerp(W,V,H,O),n.lerp(Y,q,G,O);var X=n.create();return n.lerp(X,W,Y,I),X}(e,t,p)},v=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],y=e[0][0],m=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<y||e>b||r<m||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/u,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,u=0;u<s;u++){var c=t[u],f=c[0],h=c[1],p=c[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=l(e),v=l(r),g=l(n),y=Math.min(d,v,g);return isFinite(y)?y:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],O=[],I=d(C),z=C;O.push(I);var D=[],R=v(C,I),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:O,divergences:D});for(var B=0;B<100*u&&P.length<u&&T(C);){B++;var N=n.clone(I),j=n.squaredLength(N);if(0===j)break;j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),I=d(N),n.squaredDistance(z,N)-A>-1e-4*A&&(P.push(N),z=N,O.push(I),R=v(N,I),F=n.length(R),isFinite(F)&&F>S&&(S=F),D.push(F)),C=N}}var U=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,u=0;u<l.length;u++)o=Math.max(o,n.length(l[u]));var c=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,u=[],c=[],f=[],h=[],p=[],d=[],v=0,g=0,y=i.create(),m=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,y=i.create(),n.copy(y,_),y[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;u.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(m,y,y,y,m,m),d.push(v,g,g,g,v,v);var A=u.length;c.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=m;m=y,y=S;var E=v;v=g,g=E}return{positions:u,cells:c,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<c.length;s++){var v=c[s],g=f.length;for(f=f.concat(v.positions),p=p.concat(v.vectors),d=d.concat(v.vertexIntensity),u=0;u<v.cells.length;u++){var y=v.cells[u],m=[];h.push(m);for(var x=0;x<y.length;x++)m.push(y[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}}(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*c*M/S),U};var u=r(6740),c=r(6405).createMesh;t.exports.createTubeMesh=function(t,e){return c(t,e,{shaders:u,traceType:"streamtube"})}},990:function(t,e,r){var n=r(9405),i=r(3236),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color — in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},9499:function(t,e,r){"use strict";t.exports=function(t){var e=t.gl,r=m(e),n=b(e),s=x(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:w,offset:0},{buffer:u,size:3,stride:w,offset:16},{buffer:u,size:3,stride:w,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),v=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);v.minFilter=e.LINEAR,v.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,v,s,l,f,h,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||"jet",g.update(y),g};var n=r(8828),i=r(2762),a=r(8116),o=r(7766),s=r(1888),l=r(6729),u=r(5298),c=r(9994),f=r(9618),h=r(3711),p=r(6760),d=r(7608),v=r(2478),g=r(6199),y=r(990),m=y.createShader,x=y.createContourShader,b=y.createPickShader,_=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,u,c,h,p,d,v,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=v,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var L=E.prototype;L.genColormap=function(t,e){var r=!1,n=c([l({colormap:t,nshades:S,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return u.divseq(n,255),this.hasAlphaScale=r,n},L.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},L.isOpaque=function(){return!this.isTransparent()},L.pickSlots=1,L.setPickBase=function(t){this.pickId=t};var C=[0,0,0],P={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function O(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||C,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=P.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var u=P.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return P.showSurface=o,P.showContour=s,P}var I={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},z=T.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=I;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=z;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=O(n,this);if(c.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=c.projections[i],f.uniforms.clipBounds=c.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=A[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var v=0;v<this.contourLevels[o].length;++v)v===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==v&&v-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][v]&&(f.uniforms.height=this.contourLevels[o][v],h.draw(r.LINES,this._contourCounts[o][v],this._contourOffsets[o][v]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=A[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=c.projections[o],f.uniforms.clipBounds=c.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}L.draw=function(t){return R.call(this,t,!1)},L.drawTransparent=function(t){return R.call(this,t,!0)};var F={model:T,view:T,projection:T,inverseModel:T,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function B(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function N(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function j(t){if(Array.isArray(t)){if(Array.isArray(t))return[N(t[0]),N(t[1]),N(t[2])];var e=N(t);return[e.slice(),e.slice(),e.slice()]}}L.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=F;r.model=t.model||T,r.view=t.view||T,r.projection=t.projection||T,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=D;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=O(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=A[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=A[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(s.uniforms.height=this.contourLevels[a][u],l.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}l.unbind()}},L.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var p=i+c,d=s+h,g=f*(h?l:1-l),y=0;y<3;++y)u[y]+=this._field[y].get(p,d)*g;for(var m=this._pickResult.level,x=0;x<3;++x)if(m[x]=v.le(this.contourLevels[x],u[x]),m[x]<0)this.contourLevels[x].length>0&&(m[x]=0);else if(m[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][m[x]],_=this.contourLevels[x][m[x]+1];Math.abs(b-u[x])>Math.abs(_-u[x])&&(m[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},L.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},L.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=B(t.contourWidth,Number)),"showContour"in t&&(this.showContour=B(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=B(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=j(t.contourColor)),"contourProject"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=j(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var u=l[o];for(y=0;y<2;++y)if(u.shape[y]!==a[y])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],u)}}else if(t.ticks){var c=t.ticks;if(!Array.isArray(c)||2!==c.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=c[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var m=this._field,x=f(s.mallocFloat(3*m[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),m[o],"mirror");var b=f(s.mallocFloat(3*m[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var _=x.get(0,o,y,0),w=x.get(0,o,y,1),T=x.get(1,o,y,0),A=x.get(1,o,y,1),M=x.get(2,o,y,0),S=x.get(2,o,y,1),E=T*S-A*M,L=M*w-S*_,C=_*A-w*T,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,y,0,E*P),b.set(o,y,1,L*P),b.set(o,y,2,C*P)}s.free(x.data);var O=[1/0,1/0,1/0],I=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,R=(a[0]-1)*(a[1]-1)*6,F=s.mallocFloat(n.nextPow2(10*R)),N=0,U=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var V=0;V<2;++V)for(var q=0;q<2;++q)for(var H=0;H<3;++H){var G=this._field[H].get(1+o+V,1+y+q);if(isNaN(G)||!isFinite(G))continue t}for(H=0;H<6;++H){var W=o+k[H][0],Y=y+k[H][1],X=this._field[0].get(W+1,Y+1),Z=this._field[1].get(W+1,Y+1);G=this._field[2].get(W+1,Y+1),E=b.get(W+1,Y+1,0),L=b.get(W+1,Y+1,1),C=b.get(W+1,Y+1,2),t.intensity&&(K=t.intensity.get(W,Y));var K=t.intensity?t.intensity.get(W,Y):G+this.objectOffset[2];F[N++]=W,F[N++]=Y,F[N++]=X,F[N++]=Z,F[N++]=G,F[N++]=0,F[N++]=K,F[N++]=E,F[N++]=L,F[N++]=C,O[0]=Math.min(O[0],X+this.objectOffset[0]),O[1]=Math.min(O[1],Z+this.objectOffset[1]),O[2]=Math.min(O[2],G+this.objectOffset[2]),z=Math.min(z,K),I[0]=Math.max(I[0],X+this.objectOffset[0]),I[1]=Math.max(I[1],Z+this.objectOffset[1]),I[2]=Math.max(I[2],G+this.objectOffset[2]),D=Math.max(D,K),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<N;o+=10)F[o]=(F[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(F.subarray(0,N)),s.freeFloat(F),s.free(b.data),this.bounds=[O,I],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var J=t.levels;for(J=Array.isArray(J[0])?J.slice():[[],[],J],o=0;o<3;++o)J[o]=J[o].slice(),J[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(y=0;y<J[o].length;++y)J[o][y]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(J[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<J[o].length;++y)if(J[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=J}if(r){m=this._field,a=this.shape;for(var $=[],Q=0;Q<3;++Q){var tt=this.contourLevels[Q],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[Q],tt[o]);et.push($.length/5|0),U=0;t:for(y=0;y<it.cells.length;++y){var at=it.cells[y];for(H=0;H<2;++H){var ot=it.positions[at[H]],st=ot[0],lt=0|Math.floor(st),ut=st-lt,ct=ot[1],ft=0|Math.floor(ct),ht=ct-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var vt=(Q+dt+1)%3;for(V=0;V<2;++V){var gt=V?ut:1-ut;for(W=0|Math.min(Math.max(lt+V,0),a[0]),q=0;q<2;++q){var yt=q?ht:1-ht;if(Y=0|Math.min(Math.max(ft+q,0),a[1]),G=dt<2?this._field[vt].get(W,Y):(this.intensity.get(W,Y)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var mt=gt*yt;nt[dt]+=mt*G}}}if(pt){if(H>0){for(var xt=0;xt<5;++xt)$.pop();U-=1}continue t}$.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var bt=s.mallocFloat($.length);for(o=0;o<$.length;++o)bt[o]=$[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},L.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},L.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],p=this._field[u],d=h(c,r[o]),v=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<v.length;++e)for(var y=v[e],m=0;m<2;++m){var x=g[y[m]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,O=T*L,I=T*E,z=C*f.get(_,M)+P*f.get(_,S)+O*f.get(w,M)+I*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+O*p.get(w,M)+I*p.get(w,S);if(isNaN(z)||isNaN(D)){m&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},7766:function(t,e,r){"use strict";var n=r(9618),i=r(5298),a=r(1888);t.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");if(o||function(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}(t),"number"==typeof arguments[1])return g(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return g(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return function(t,e,r,n,i,a){var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new h(t,o,r,n,i,a)}(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return function(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error("gl-texture2d: Invalid texture size");var l=d(o,e.stride.slice()),u=0;"float32"===r?u=t.FLOAT:"float64"===r?(u=t.FLOAT,l=!1,r="float32"):"uint8"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r="uint8");var f,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");g=t.RGBA}}u!==t.FLOAT||t.getExtension("OES_texture_float")||(u=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)f=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var m=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,m,0);"float32"!==r&&"float64"!==r||u!==t.UNSIGNED_BYTE?i.assign(x,e):c(x,e),f=p.subarray(0,y)}var b=v(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,u,f),l||a.free(p),new h(t,b,o[0],o[1],g,u)}(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var c=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function h(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=h.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function v(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function g(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=v(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new h(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension("OES_texture_float_linear")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error("gl-texture2d: Unknown filter mode "+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension("OES_texture_float_linear")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error("gl-texture2d: Unknown filter mode "+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension("EXT_texture_filter_anisotropic");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error("gl-texture2d: Unknown wrap mode "+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error("gl-texture2d: Unknown wrap mode "+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error("gl-texture2d: Must specify wrap mode for rows and columns");for(var e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error("gl-texture2d: Unknown wrap mode "+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return this._shapeVector},set:function(t){if(Array.isArray(t)){if(2!==t.length)throw new Error("gl-texture2d: Invalid texture shape")}else t=[0|t,0|t];return f(this,0|t[0],0|t[1]),[0|t[0],0|t[1]]}},width:{get:function(){return this._shape[0]},set:function(t){return f(this,t|=0,this._shape[1]),t}},height:{get:function(){return this._shape[1]},set:function(t){return t|=0,f(this,this._shape[0],t),t}}}),p.bind=function(t){var e=this.gl;return void 0!==t&&e.activeTexture(e.TEXTURE0+(0|t)),e.bindTexture(e.TEXTURE_2D,this.handle),void 0!==t?0|t:e.getParameter(e.ACTIVE_TEXTURE)-e.TEXTURE0},p.dispose=function(){this.gl.deleteTexture(this.handle)},p.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var t=Math.min(this._shape[0],this._shape[1]),e=0;t>0;++e,t>>>=1)this._mipLevels.indexOf(e)<0&&this._mipLevels.push(e)},p.setPixels=function(t,e,r,o){var s=this.gl;this.bind(),Array.isArray(e)?(o=r,r=0|e[1],e=0|e[0]):(e=e||0,r=r||0),o=o||0;var 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e=("property"===t.expressionContext?dn:fn)(Vt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Bt(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Bt(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Xe(r))return[new Bt(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Xe(r))return[new Bt(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Ze(r,["zoom","feature-state"]))return[new Bt(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!Ye(r))return[new Bt(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function wn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ut(r))&&i.push(new Bt(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" 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An(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};Tn(t)||(t=En(t));var e=fn(t,kn);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:Sn(t)}}function Mn(t,e){return t<e?-1:t>e?1:0}function Sn(t){if(!Array.isArray(t))return!1;if("within"===t[0])return!0;for(var e=1;e<t.length;e++)if(Sn(t[e]))return!0;return!1}function En(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?Ln(t[1],t[2],"=="):"!="===r?On(Ln(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?Ln(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(En))):"all"===r?["all"].concat(t.slice(1).map(En)):"none"===r?["all"].concat(t.slice(1).map(En).map(On)):"in"===r?Cn(t[1],t.slice(2)):"!in"===r?On(Cn(t[1],t.slice(2))):"has"===r?Pn(t[1]):"!has"===r?On(Pn(t[1])):"within"!==r||t}function Ln(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function Cn(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(Mn)]]:["filter-in-small",t,["literal",e]]}}function Pn(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function On(t){return["!",t]}function In(t){return Tn(Vt(t.value))?_n(jt({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):zn(t)}function zn(t){var e=t.value,r=t.key;if("array"!==Qr(e))return[new Bt(r,e,"array expected, "+Qr(e)+" found")];var n,i=t.styleSpec,a=[];if(e.length<1)return[new Bt(r,e,"filter array must have at least 1 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Bt(r,e,'filter array for "'+e[0]+'" operator must have 2 elements')):"string"!==n&&a.push(new Bt(r+"[1]",e[1],"string expected, "+n+" found"));break;case"within":n=Qr(e[1]),2!==e.length?a.push(new Bt(r,e,'filter array for "'+e[0]+'" operator must have 2 elements')):"object"!==n&&a.push(new Bt(r+"[1]",e[1],"object expected, "+n+" found"))}return a}function Dn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return Hn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var u,c=t.valueSpec||s[o];if(!c)return[new Bt(r,a,'unknown property "'+o+'"')];if("string"===Qr(a)&&Kr(c)&&!c.tokens&&(u=/^{([^}]+)}$/.exec(a)))return[new Bt(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(u[1])+" }`.")];var f=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&f.push(new Bt(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&tn(Vt(a))&&"identity"===Ut(a.type)&&f.push(new Bt(r,a,'"text-font" does not support identity functions'))),f.concat(Hn({key:t.key,value:a,valueSpec:c,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function Rn(t){return Dn(t,"paint")}function Fn(t){return Dn(t,"layout")}function Bn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Bt(n,r,'either "type" or "ref" is required'));var o,s=Ut(r.type),l=Ut(r.ref);if(r.id)for(var u=Ut(r.id),c=0;c<t.arrayIndex;c++){var f=i.layers[c];Ut(f.id)===u&&e.push(new Bt(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+f.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Bt(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Bt(n,r,'missing required property "source"'));return e=e.concat(yn({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return Hn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:In,layout:function(t){return yn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return Fn(jt({layerType:s},t))}}})},paint:function(t){return yn({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return Rn(jt({layerType:s},t))}}})}}})),e}function Nn(t){var e=t.value,r=t.key,n=Qr(e);return"string"!==n?[new Bt(r,e,"string expected, "+n+" found")]:[]}var jn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Qr(r))return Nn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,Nn({key:e+"."+i,value:r[i]}));return 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Yn(t){return[].concat(t).sort((function(t,e){return t.line-e.line}))}function Xn(t){return function(){for(var e=[],r=arguments.length;r--;)e[r]=arguments[r];return Yn(t.apply(this,e))}}Wn.source=Xn(Un),Wn.light=Xn(Vn),Wn.layer=Xn(Bn),Wn.filter=Xn(In),Wn.paintProperty=Xn(Rn),Wn.layoutProperty=Xn(Fn);var Zn=Wn,Kn=Zn.light,Jn=Zn.paintProperty,$n=Zn.layoutProperty;function Qn(t,e){var r=!1;if(e&&e.length)for(var n=0,i=e;n<i.length;n+=1){var a=i[n];t.fire(new Dt(new Error(a.message))),r=!0}return r}var ti=ri,ei=3;function ri(t,e,r){var n=this.cells=[];if(t instanceof ArrayBuffer){this.arrayBuffer=t;var i=new Int32Array(this.arrayBuffer);t=i[0],e=i[1],r=i[2],this.d=e+2*r;for(var a=0;a<this.d*this.d;a++){var o=i[ei+a],s=i[ei+a+1];n.push(o===s?null:i.subarray(o,s))}var l=i[ei+n.length],u=i[ei+n.length+1];this.keys=i.subarray(l,u),this.bboxes=i.subarray(u),this.insert=this._insertReadonly}else{this.d=e+2*r;for(var c=0;c<this.d*this.d;c++)n.push([]);this.keys=[],this.bboxes=[]}this.n=e,this.extent=t,this.padding=r,this.scale=e/t,this.uid=0;var f=r/e*t;this.min=-f,this.max=t+f}ri.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertCell,this.uid++),this.keys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},ri.prototype._insertReadonly=function(){throw"Cannot insert into a GridIndex created from an ArrayBuffer."},ri.prototype._insertCell=function(t,e,r,n,i,a){this.cells[i].push(a)},ri.prototype.query=function(t,e,r,n,i){var a=this.min,o=this.max;if(t<=a&&e<=a&&o<=r&&o<=n&&!i)return Array.prototype.slice.call(this.keys);var s=[];return this._forEachCell(t,e,r,n,this._queryCell,s,{},i),s},ri.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=this.cells[i];if(null!==l)for(var u=this.keys,c=this.bboxes,f=0;f<l.length;f++){var h=l[f];if(void 0===o[h]){var p=4*h;(s?s(c[p+0],c[p+1],c[p+2],c[p+3]):t<=c[p+2]&&e<=c[p+3]&&r>=c[p+0]&&n>=c[p+1])?(o[h]=!0,a.push(u[h])):o[h]=!1}}},ri.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),u=this._convertToCellCoord(e),c=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},ri.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},ri.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},ri.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=ei+this.cells.length+1+1,r=0,n=0;n<this.cells.length;n++)r+=this.cells[n].length;var i=new Int32Array(e+r+this.keys.length+this.bboxes.length);i[0]=this.extent,i[1]=this.n,i[2]=this.padding;for(var a=e,o=0;o<t.length;o++){var s=t[o];i[ei+o]=a,i.set(s,a),a+=s.length}return i[ei+t.length]=a,i.set(this.keys,a),a+=this.keys.length,i[ei+t.length+1]=a,i.set(this.bboxes,a),a+=this.bboxes.length,i.buffer};var ni=s.ImageData,ii=s.ImageBitmap,ai={};function oi(t,e,r){void 0===r&&(r={}),Object.defineProperty(e,"_classRegistryKey",{value:t,writeable:!1}),ai[t]={klass:e,omit:r.omit||[],shallow:r.shallow||[]}}for(var si in oi("Object",Object),ti.serialize=function(t,e){var r=t.toArrayBuffer();return e&&e.push(r),{buffer:r}},ti.deserialize=function(t){return new ti(t.buffer)},oi("Grid",ti),oi("Color",ue),oi("Error",Error),oi("ResolvedImage",pe),oi("StylePropertyFunction",vn),oi("StyleExpression",un,{omit:["_evaluator"]}),oi("ZoomDependentExpression",pn),oi("ZoomConstantExpression",hn),oi("CompoundExpression",Ee,{omit:["_evaluate"]}),qr)qr[si]._classRegistryKey||oi("Expression_"+si,qr[si]);function li(t){return t&&"undefined"!=typeof ArrayBuffer&&(t instanceof ArrayBuffer||t.constructor&&"ArrayBuffer"===t.constructor.name)}function ui(t){return ii&&t instanceof ii}function ci(t,e){if(null==t||"boolean"==typeof t||"number"==typeof t||"string"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp)return t;if(li(t)||ui(t))return e&&e.push(t),t;if(ArrayBuffer.isView(t)){var r=t;return e&&e.push(r.buffer),r}if(t instanceof ni)return e&&e.push(t.data.buffer),t;if(Array.isArray(t)){for(var n=[],i=0,a=t;i<a.length;i+=1){var o=a[i];n.push(ci(o,e))}return n}if("object"==typeof t){var s=t.constructor,l=s._classRegistryKey;if(!l)throw new Error("can't serialize object of unregistered class");var u=s.serialize?s.serialize(t,e):{};if(!s.serialize){for(var c in t)if(t.hasOwnProperty(c)&&!(ai[l].omit.indexOf(c)>=0)){var f=t[c];u[c]=ai[l].shallow.indexOf(c)>=0?f:ci(f,e)}t instanceof Error&&(u.message=t.message)}if(u.$name)throw new Error("$name property is reserved for worker serialization logic.");return"Object"!==l&&(u.$name=l),u}throw new Error("can't serialize object of type "+typeof t)}function fi(t){if(null==t||"boolean"==typeof t||"number"==typeof t||"string"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||li(t)||ui(t)||ArrayBuffer.isView(t)||t instanceof ni)return t;if(Array.isArray(t))return t.map(fi);if("object"==typeof t){var e=t.$name||"Object",r=ai[e].klass;if(!r)throw new Error("can't deserialize unregistered class "+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i<a.length;i+=1){var o=a[i];if("$name"!==o){var s=t[o];n[o]=ai[e].shallow.indexOf(o)>=0?s:fi(s)}}return n}throw new Error("can't deserialize object of type "+typeof t)}var hi=function(){this.first=!0};hi.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom<r&&(this.lastIntegerZoom=r,this.lastIntegerZoomTime=e),t!==this.lastZoom&&(this.lastZoom=t,this.lastFloorZoom=r,!0))};var pi={"Latin-1 Supplement":function(t){return t>=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},"Arabic Supplement":function(t){return t>=1872&&t<=1919},"Arabic Extended-A":function(t){return t>=2208&&t<=2303},"Hangul Jamo":function(t){return t>=4352&&t<=4607},"Unified Canadian Aboriginal Syllabics":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},"Unified Canadian Aboriginal Syllabics Extended":function(t){return t>=6320&&t<=6399},"General Punctuation":function(t){return t>=8192&&t<=8303},"Letterlike Symbols":function(t){return t>=8448&&t<=8527},"Number Forms":function(t){return t>=8528&&t<=8591},"Miscellaneous Technical":function(t){return t>=8960&&t<=9215},"Control Pictures":function(t){return t>=9216&&t<=9279},"Optical Character Recognition":function(t){return t>=9280&&t<=9311},"Enclosed Alphanumerics":function(t){return t>=9312&&t<=9471},"Geometric Shapes":function(t){return t>=9632&&t<=9727},"Miscellaneous Symbols":function(t){return t>=9728&&t<=9983},"Miscellaneous Symbols and Arrows":function(t){return t>=11008&&t<=11263},"CJK Radicals Supplement":function(t){return t>=11904&&t<=12031},"Kangxi Radicals":function(t){return t>=12032&&t<=12255},"Ideographic Description Characters":function(t){return t>=12272&&t<=12287},"CJK Symbols and 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t>=43360&&t<=43391},"Hangul Syllables":function(t){return t>=44032&&t<=55215},"Hangul Jamo Extended-B":function(t){return t>=55216&&t<=55295},"Private Use Area":function(t){return t>=57344&&t<=63743},"CJK Compatibility Ideographs":function(t){return t>=63744&&t<=64255},"Arabic Presentation Forms-A":function(t){return t>=64336&&t<=65023},"Vertical Forms":function(t){return t>=65040&&t<=65055},"CJK Compatibility Forms":function(t){return t>=65072&&t<=65103},"Small Form Variants":function(t){return t>=65104&&t<=65135},"Arabic Presentation Forms-B":function(t){return t>=65136&&t<=65279},"Halfwidth and Fullwidth Forms":function(t){return t>=65280&&t<=65519}};function di(t){for(var e=0,r=t;e<r.length;e+=1)if(vi(r[e].charCodeAt(0)))return!0;return!1}function vi(t){return!(746!==t&&747!==t&&(t<4352||!(pi["Bopomofo Extended"](t)||pi.Bopomofo(t)||pi["CJK Compatibility Forms"](t)&&!(t>=65097&&t<=65103)||pi["CJK Compatibility Ideographs"](t)||pi["CJK Compatibility"](t)||pi["CJK Radicals 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Syllables"](t)||pi["Yi Radicals"](t))))}function gi(t){return!(vi(t)||function(t){return!!(pi["Latin-1 Supplement"](t)&&(167===t||169===t||174===t||177===t||188===t||189===t||190===t||215===t||247===t)||pi["General Punctuation"](t)&&(8214===t||8224===t||8225===t||8240===t||8241===t||8251===t||8252===t||8258===t||8263===t||8264===t||8265===t||8273===t)||pi["Letterlike Symbols"](t)||pi["Number Forms"](t)||pi["Miscellaneous Technical"](t)&&(t>=8960&&t<=8967||t>=8972&&t<=8991||t>=8996&&t<=9e3||9003===t||t>=9085&&t<=9114||t>=9150&&t<=9165||9167===t||t>=9169&&t<=9179||t>=9186&&t<=9215)||pi["Control Pictures"](t)&&9251!==t||pi["Optical Character Recognition"](t)||pi["Enclosed Alphanumerics"](t)||pi["Geometric Shapes"](t)||pi["Miscellaneous Symbols"](t)&&!(t>=9754&&t<=9759)||pi["Miscellaneous Symbols and Arrows"](t)&&(t>=11026&&t<=11055||t>=11088&&t<=11097||t>=11192&&t<=11243)||pi["CJK Symbols and Punctuation"](t)||pi.Katakana(t)||pi["Private Use Area"](t)||pi["CJK Compatibility Forms"](t)||pi["Small Form Variants"](t)||pi["Halfwidth and Fullwidth Forms"](t)||8734===t||8756===t||8757===t||t>=9984&&t<=10087||t>=10102&&t<=10131||65532===t||65533===t)}(t))}function yi(t){return pi.Arabic(t)||pi["Arabic Supplement"](t)||pi["Arabic Extended-A"](t)||pi["Arabic Presentation Forms-A"](t)||pi["Arabic Presentation Forms-B"](t)}function mi(t){return t>=1424&&t<=2303||pi["Arabic Presentation Forms-A"](t)||pi["Arabic Presentation Forms-B"](t)}function xi(t,e){return!(!e&&mi(t)||t>=2304&&t<=3583||t>=3840&&t<=4255||pi.Khmer(t))}function bi(t){for(var e=0,r=t;e<r.length;e+=1)if(mi(r[e].charCodeAt(0)))return!0;return!1}var _i="deferred",wi="loading",Ti="loaded",ki="error",Ai=null,Mi="unavailable",Si=null,Ei=function(t){t&&"string"==typeof t&&t.indexOf("NetworkError")>-1&&(Mi=ki),Ai&&Ai(t)};function Li(){Ci.fire(new zt("pluginStateChange",{pluginStatus:Mi,pluginURL:Si}))}var Ci=new Rt,Pi=function(){return Mi},Oi=function(){if(Mi!==_i||!Si)throw new Error("rtl-text-plugin cannot 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0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},zi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var Di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(tn(t))return new vn(t,e);if(cn(t)){var r=dn(t,e);if("error"===r.result)throw new Error(r.value.map((function(t){return t.key+": "+t.message})).join(", "));return r.value}var n=t;return"string"==typeof t&&"color"===e.type&&(n=ue.parse(t)),{kind:"constant",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};Di.prototype.isDataDriven=function(){return"source"===this.expression.kind||"composite"===this.expression.kind},Di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var Ri=function(t){this.property=t,this.value=new Di(t,void 0)};Ri.prototype.transitioned=function(t,e){return new Bi(this.property,this.value,e,p({},t.transition,this.transition),t.now)},Ri.prototype.untransitioned=function(){return new Bi(this.property,this.value,null,{},0)};var Fi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};Fi.prototype.getValue=function(t){return w(this._values[t].value.value)},Fi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ri(this._values[t].property)),this._values[t].value=new Di(this._values[t].property,null===e?void 0:w(e))},Fi.prototype.getTransition=function(t){return w(this._values[t].transition)},Fi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new Ri(this._values[t].property)),this._values[t].transition=w(e)||void 0},Fi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var 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o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var Ni=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Ni.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},Ni.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var ji=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};ji.prototype.getValue=function(t){return w(this._values[t].value)},ji.prototype.setValue=function(t,e){this._values[t]=new Di(this._values[t].property,null===e?void 0:w(e))},ji.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},ji.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var Ui=function(t,e,r){this.property=t,this.value=e,this.parameters=r};Ui.prototype.isConstant=function(){return"constant"===this.value.kind},Ui.prototype.constantOr=function(t){return"constant"===this.value.kind?this.value.value:t},Ui.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var Vi=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};Vi.prototype.get=function(t){return this._values[t]};var qi=function(t){this.specification=t};qi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},qi.prototype.interpolate=function(t,e,r){var n=rr[this.specification.type];return n?n(t,e,r):t};var Hi=function(t,e){this.specification=t,this.overrides=e};Hi.prototype.possiblyEvaluate=function(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new Ui(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new Ui(this,t.expression,e)},Hi.prototype.interpolate=function(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Ui(this,{kind:"constant",value:void 0},t.parameters);var n=rr[this.specification.type];return n?new Ui(this,{kind:"constant",value:n(t.value.value,e.value.value,r)},t.parameters):t},Hi.prototype.evaluate=function(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 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this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".layout."+t;if(this._validate($n,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return x(t,Zi)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".paint."+t;if(this._validate(Jn,n,t,e,r))return!1}if(x(t,Zi))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;var i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),_(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Qn(this,t.call(Zn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Ft,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof 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e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Qi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Qi.prototype.clear=function(){this.length=0},Qi.prototype.resize=function(t){this.reserve(t),this.length=t},Qi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Qi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var ra=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Qi);ra.prototype.bytesPerElement=4,oi("StructArrayLayout2i4",ra);var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return 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this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Qi);ua.prototype.bytesPerElement=12,oi("StructArrayLayout3f12",ua);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Qi);ca.prototype.bytesPerElement=4,oi("StructArrayLayout1ul4",ca);var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new 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this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Qi);ha.prototype.bytesPerElement=12,oi("StructArrayLayout2i2i2i12",ha);var pa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Qi);pa.prototype.bytesPerElement=16,oi("StructArrayLayout2f1f2i16",pa);var da=function(t){function 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t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Qi);Ta.prototype.bytesPerElement=16,oi("StructArrayLayout4f16",Ta);var ka=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new a(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}($i);ka.prototype.size=20;var Aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new 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this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}($i);Ma.prototype.size=48;var Sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ma(this,t)},e}(ga);oi("PlacedSymbolArray",Sa);var Ea=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return 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this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}($i);Ea.prototype.size=68;var La=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new Ea(this,t)},e}(ya);oi("SymbolInstanceArray",La);var Ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(ma);oi("GlyphOffsetArray",Ca);var Pa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(xa);oi("SymbolLineVertexArray",Pa);var Oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return 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Float32Array(16),ro=function(t){function e(e,r){t.call(this,e,r),this.current=eo}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t[12]!==this.current[12]||t[0]!==this.current[0])return this.current=t,void this.gl.uniformMatrix4fv(this.location,!1,t);for(var e=1;e<16;e++)if(t[e]!==this.current[e]){this.current=t,this.gl.uniformMatrix4fv(this.location,!1,t);break}},e}(Xa);function no(t){return[Ra(255*t.r,255*t.g),Ra(255*t.b,255*t.a)]}var io=function(t,e,r){this.value=t,this.uniformNames=e.map((function(t){return"u_"+t})),this.type=r};io.prototype.setUniform=function(t,e,r){t.set(r.constantOr(this.value))},io.prototype.getBinding=function(t,e,r){return"color"===this.type?new to(t,e):new Ka(t,e)};var ao=function(t,e){this.uniformNames=e.map((function(t){return"u_"+t})),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1};ao.prototype.setConstantPatternPositions=function(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr},ao.prototype.setUniform=function(t,e,r,n){var i="u_pattern_to"===n?this.patternTo:"u_pattern_from"===n?this.patternFrom:"u_pixel_ratio_to"===n?this.pixelRatioTo:"u_pixel_ratio_from"===n?this.pixelRatioFrom:null;i&&t.set(i)},ao.prototype.getBinding=function(t,e,r){return"u_pattern"===r.substr(0,9)?new Qa(t,e):new Ka(t,e)};var oo=function(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:"a_"+t,type:"Float32",components:"color"===r?2:1,offset:0}})),this.paintVertexArray=new n};oo.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.paintVertexArray.length,o=this.expression.evaluate(new zi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)},oo.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)},oo.prototype._setPaintValue=function(t,e,r){if("color"===this.type)for(var n=no(r),i=t;i<e;i++)this.paintVertexArray.emplace(i,n[0],n[1]);else{for(var a=t;a<e;a++)this.paintVertexArray.emplace(a,r);this.maxValue=Math.max(this.maxValue,Math.abs(r))}},oo.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},oo.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()};var so=function(t,e,r,n,i,a){this.expression=t,this.uniformNames=e.map((function(t){return"u_"+t+"_t"})),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((function(t){return{name:"a_"+t,type:"Float32",components:"color"===r?4:2,offset:0}})),this.paintVertexArray=new a};so.prototype.populatePaintArray=function(t,e,r,n,i){var a=this.expression.evaluate(new zi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new zi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)},so.prototype.updatePaintArray=function(t,e,r,n){var i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)},so.prototype._setPaintValue=function(t,e,r,n){if("color"===this.type)for(var i=no(r),a=no(n),o=t;o<e;o++)this.paintVertexArray.emplace(o,i[0],i[1],a[0],a[1]);else{for(var s=t;s<e;s++)this.paintVertexArray.emplace(s,r,n);this.maxValue=Math.max(this.maxValue,Math.abs(r),Math.abs(n))}},so.prototype.upload=function(t){this.paintVertexArray&&this.paintVertexArray.arrayBuffer&&(this.paintVertexBuffer&&this.paintVertexBuffer.buffer?this.paintVertexBuffer.updateData(this.paintVertexArray):this.paintVertexBuffer=t.createVertexBuffer(this.paintVertexArray,this.paintVertexAttributes,this.expression.isStateDependent))},so.prototype.destroy=function(){this.paintVertexBuffer&&this.paintVertexBuffer.destroy()},so.prototype.setUniform=function(t,e){var r=this.useIntegerZoom?Math.floor(e.zoom):e.zoom,n=f(this.expression.interpolationFactor(r,this.zoom,this.zoom+1),0,1);t.set(n)},so.prototype.getBinding=function(t,e,r){return new Ka(t,e)};var lo=function(t,e,r,n,i,a){this.expression=t,this.type=e,this.useIntegerZoom=r,this.zoom=n,this.layerId=a,this.zoomInPaintVertexArray=new i,this.zoomOutPaintVertexArray=new i};lo.prototype.populatePaintArray=function(t,e,r){var n=this.zoomInPaintVertexArray.length;this.zoomInPaintVertexArray.resize(t),this.zoomOutPaintVertexArray.resize(t),this._setPaintValues(n,t,e.patterns&&e.patterns[this.layerId],r)},lo.prototype.updatePaintArray=function(t,e,r,n,i){this._setPaintValues(t,e,r.patterns&&r.patterns[this.layerId],i)},lo.prototype._setPaintValues=function(t,e,r,n){if(n&&r){var i=r.min,a=r.mid,o=r.max,s=n[i],l=n[a],u=n[o];if(s&&l&&u)for(var c=t;c<e;c++)this.zoomInPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],s.tl[0],s.tl[1],s.br[0],s.br[1],l.pixelRatio,s.pixelRatio),this.zoomOutPaintVertexArray.emplace(c,l.tl[0],l.tl[1],l.br[0],l.br[1],u.tl[0],u.tl[1],u.br[0],u.br[1],l.pixelRatio,u.pixelRatio)}},lo.prototype.upload=function(t){this.zoomInPaintVertexArray&&this.zoomInPaintVertexArray.arrayBuffer&&this.zoomOutPaintVertexArray&&this.zoomOutPaintVertexArray.arrayBuffer&&(this.zoomInPaintVertexBuffer=t.createVertexBuffer(this.zoomInPaintVertexArray,Fa.members,this.expression.isStateDependent),this.zoomOutPaintVertexBuffer=t.createVertexBuffer(this.zoomOutPaintVertexArray,Fa.members,this.expression.isStateDependent))},lo.prototype.destroy=function(){this.zoomOutPaintVertexBuffer&&this.zoomOutPaintVertexBuffer.destroy(),this.zoomInPaintVertexBuffer&&this.zoomInPaintVertexBuffer.destroy()};var uo=function(t,e,r){this.binders={},this._buffers=[];var n=[];for(var i in t.paint._values)if(r(i)){var a=t.paint.get(i);if(a instanceof Ui&&Kr(a.property.specification)){var o=fo(i,t.type),s=a.value,l=a.property.specification.type,u=a.property.useIntegerZoom,c=a.property.specification["property-type"],f="cross-faded"===c||"cross-faded-data-driven"===c;if("constant"===s.kind)this.binders[i]=f?new ao(s.value,o):new io(s.value,o,l),n.push("/u_"+i);else if("source"===s.kind||f){var h=ho(i,l,"source");this.binders[i]=f?new lo(s,l,u,e,h,t.id):new oo(s,o,l,h),n.push("/a_"+i)}else{var p=ho(i,l,"composite");this.binders[i]=new so(s,o,l,u,e,p),n.push("/z_"+i)}}}this.cacheKey=n.sort().join("")};uo.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof oo||e instanceof so?e.maxValue:0},uo.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof oo||o instanceof so||o instanceof lo)&&o.populatePaintArray(t,e,r,n,i)}},uo.prototype.setConstantPatternPositions=function(t,e){for(var r in this.binders){var n=this.binders[r];n instanceof ao&&n.setConstantPatternPositions(t,e)}},uo.prototype.updatePaintArrays=function(t,e,r,n,i){var a=!1;for(var o in t)for(var s=0,l=e.getPositions(o);s<l.length;s+=1){var u=l[s],c=r.feature(u.index);for(var f in this.binders){var h=this.binders[f];if((h instanceof oo||h instanceof so||h instanceof lo)&&!0===h.expression.isStateDependent){var p=n.paint.get(f);h.expression=p.value,h.updatePaintArray(u.start,u.end,c,t[o],i),a=!0}}}return a},uo.prototype.defines=function(){var t=[];for(var e in this.binders){var r=this.binders[e];(r instanceof io||r instanceof ao)&&t.push.apply(t,r.uniformNames.map((function(t){return"#define HAS_UNIFORM_"+t})))}return t},uo.prototype.getBinderAttributes=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof oo||r instanceof so)for(var n=0;n<r.paintVertexAttributes.length;n++)t.push(r.paintVertexAttributes[n].name);else if(r instanceof lo)for(var i=0;i<Fa.members.length;i++)t.push(Fa.members[i].name)}return t},uo.prototype.getBinderUniforms=function(){var t=[];for(var e in this.binders){var r=this.binders[e];if(r instanceof io||r instanceof ao||r instanceof so)for(var n=0,i=r.uniformNames;n<i.length;n+=1){var a=i[n];t.push(a)}}return t},uo.prototype.getPaintVertexBuffers=function(){return this._buffers},uo.prototype.getUniforms=function(t,e){var r=[];for(var n in this.binders){var i=this.binders[n];if(i instanceof io||i instanceof ao||i instanceof so)for(var a=0,o=i.uniformNames;a<o.length;a+=1){var s=o[a];if(e[s]){var l=i.getBinding(t,e[s],s);r.push({name:s,property:n,binding:l})}}}return r},uo.prototype.setUniforms=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.name,l=o.property,u=o.binding;this.binders[l].setUniform(u,n,r.get(l),s)}},uo.prototype.updatePaintBuffers=function(t){for(var e in this._buffers=[],this.binders){var r=this.binders[e];if(t&&r instanceof lo){var n=2===t.fromScale?r.zoomInPaintVertexBuffer:r.zoomOutPaintVertexBuffer;n&&this._buffers.push(n)}else(r instanceof oo||r instanceof so)&&r.paintVertexBuffer&&this._buffers.push(r.paintVertexBuffer)}},uo.prototype.upload=function(t){for(var e in this.binders){var r=this.binders[e];(r instanceof oo||r instanceof so||r instanceof lo)&&r.upload(t)}this.updatePaintBuffers()},uo.prototype.destroy=function(){for(var t in this.binders){var e=this.binders[t];(e instanceof oo||e instanceof so||e instanceof lo)&&e.destroy()}};var co=function(t,e,r){void 0===r&&(r=function(){return!0}),this.programConfigurations={};for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.programConfigurations[a.id]=new uo(a,e,r)}this.needsUpload=!1,this._featureMap=new qa,this._bufferOffset=0};function fo(t,e){return{"text-opacity":["opacity"],"icon-opacity":["opacity"],"text-color":["fill_color"],"icon-color":["fill_color"],"text-halo-color":["halo_color"],"icon-halo-color":["halo_color"],"text-halo-blur":["halo_blur"],"icon-halo-blur":["halo_blur"],"text-halo-width":["halo_width"],"icon-halo-width":["halo_width"],"line-gap-width":["gapwidth"],"line-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"],"fill-extrusion-pattern":["pattern_to","pattern_from","pixel_ratio_to","pixel_ratio_from"]}[t]||[t.replace(e+"-","").replace(/-/g,"_")]}function ho(t,e,r){var n={color:{source:oa,composite:Ta},number:{source:ma,composite:oa}},i=function(t){return{"line-pattern":{source:sa,composite:sa},"fill-pattern":{source:sa,composite:sa},"fill-extrusion-pattern":{source:sa,composite:sa}}[t]}(t);return i&&i[r]||n[e][r]}co.prototype.populatePaintArrays=function(t,e,r,n,i,a){for(var o in this.programConfigurations)this.programConfigurations[o].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0},co.prototype.updatePaintArrays=function(t,e,r,n){for(var i=0,a=r;i<a.length;i+=1){var o=a[i];this.needsUpload=this.programConfigurations[o.id].updatePaintArrays(t,this._featureMap,e,o,n)||this.needsUpload}},co.prototype.get=function(t){return this.programConfigurations[t]},co.prototype.upload=function(t){if(this.needsUpload){for(var e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}},co.prototype.destroy=function(){for(var t in this.programConfigurations)this.programConfigurations[t].destroy()},oi("ConstantBinder",io),oi("CrossFadedConstantBinder",ao),oi("SourceExpressionBinder",oo),oi("CrossFadedCompositeBinder",lo),oi("CompositeExpressionBinder",so),oi("ProgramConfiguration",uo,{omit:["_buffers"]}),oi("ProgramConfigurationSet",co);var po=8192,vo=Math.pow(2,14)-1,go=-vo-1;function yo(t){for(var e=po/t.extent,r=t.loadGeometry(),n=0;n<r.length;n++)for(var i=r[n],a=0;a<i.length;a++){var o=i[a],s=Math.round(o.x*e),l=Math.round(o.y*e);o.x=f(s,go,vo),o.y=f(l,go,vo),(s<o.x||s>o.x+1||l<o.y||l>o.y+1)&&k("Geometry exceeds allowed extent, reduce your vector tile buffer size")}return r}function mo(t,e){return{type:t.type,id:t.id,properties:t.properties,geometry:e?yo(t):[]}}function xo(t,e,r,n,i){t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)}var bo=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ra,this.indexArray=new va,this.segments=new Da,this.programConfigurations=new co(t.layers,t.zoom),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function _o(t,e){for(var r=0;r<t.length;r++)if(Co(e,t[r]))return!0;for(var n=0;n<e.length;n++)if(Co(t,e[n]))return!0;return!!Ao(t,e)}function wo(t,e,r){return!!Co(t,e)||!!So(e,t,r)}function To(t,e){if(1===t.length)return Lo(e,t[0]);for(var r=0;r<e.length;r++)for(var n=e[r],i=0;i<n.length;i++)if(Co(t,n[i]))return!0;for(var a=0;a<t.length;a++)if(Lo(e,t[a]))return!0;for(var o=0;o<e.length;o++)if(Ao(t,e[o]))return!0;return!1}function ko(t,e,r){if(t.length>1){if(Ao(t,e))return!0;for(var n=0;n<e.length;n++)if(So(e[n],t,r))return!0}for(var i=0;i<t.length;i++)if(So(t[i],e,r))return!0;return!1}function Ao(t,e){if(0===t.length||0===e.length)return!1;for(var r=0;r<t.length-1;r++)for(var n=t[r],i=t[r+1],a=0;a<e.length-1;a++)if(Mo(n,i,e[a],e[a+1]))return!0;return!1}function Mo(t,e,r,n){return A(t,r,n)!==A(e,r,n)&&A(t,e,r)!==A(t,e,n)}function So(t,e,r){var n=r*r;if(1===e.length)return t.distSqr(e[0])<n;for(var i=1;i<e.length;i++)if(Eo(t,e[i-1],e[i])<n)return!0;return!1}function Eo(t,e,r){var n=e.distSqr(r);if(0===n)return t.distSqr(e);var i=((t.x-e.x)*(r.x-e.x)+(t.y-e.y)*(r.y-e.y))/n;return i<0?t.distSqr(e):i>1?t.distSqr(r):t.distSqr(r.sub(e)._mult(i)._add(e))}function Lo(t,e){for(var r,n,i,a=!1,o=0;o<t.length;o++)for(var s=0,l=(r=t[o]).length-1;s<r.length;l=s++)n=r[s],i=r[l],n.y>e.y!=i.y>e.y&&e.x<(i.x-n.x)*(e.y-n.y)/(i.y-n.y)+n.x&&(a=!a);return a}function Co(t,e){for(var r=!1,n=0,i=t.length-1;n<t.length;i=n++){var a=t[n],o=t[i];a.y>e.y!=o.y>e.y&&e.x<(o.x-a.x)*(e.y-a.y)/(o.y-a.y)+a.x&&(r=!r)}return r}function Po(t,e,r){var n=r[0],i=r[2];if(t.x<n.x&&e.x<n.x||t.x>i.x&&e.x>i.x||t.y<n.y&&e.y<n.y||t.y>i.y&&e.y>i.y)return!1;var a=A(t,e,r[0]);return a!==A(t,e,r[1])||a!==A(t,e,r[2])||a!==A(t,e,r[3])}function Oo(t,e,r){var n=e.paint.get(t).value;return"constant"===n.kind?n.value:r.programConfigurations.get(e.id).getMaxValue(t)}function Io(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function zo(t,e,r,n,i){if(!e[0]&&!e[1])return t;var o=a.convert(e)._mult(i);"viewport"===r&&o._rotate(-n);for(var s=[],l=0;l<t.length;l++){var u=t[l];s.push(u.sub(o))}return s}bo.prototype.populate=function(t,e,r){var n=this.layers[0],i=[],a=null;"circle"===n.type&&(a=n.layout.get("circle-sort-key"));for(var o=0,s=t;o<s.length;o+=1){var l=s[o],u=l.feature,c=l.id,f=l.index,h=l.sourceLayerIndex,p=this.layers[0]._featureFilter.needGeometry,d=mo(u,p);if(this.layers[0]._featureFilter.filter(new zi(this.zoom),d,r)){var v=a?a.evaluate(d,{},r):void 0,g={id:c,properties:u.properties,type:u.type,sourceLayerIndex:h,index:f,geometry:p?d.geometry:yo(u),patterns:{},sortKey:v};i.push(g)}}a&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var y=0,m=i;y<m.length;y+=1){var x=m[y],b=x,_=b.geometry,w=b.index,T=b.sourceLayerIndex,k=t[w].feature;this.addFeature(x,_,w,r),e.featureIndex.insert(k,_,w,T,this.index)}},bo.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},bo.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},bo.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},bo.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,za),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},bo.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},bo.prototype.addFeature=function(t,e,r,n){for(var i=0,a=e;i<a.length;i+=1)for(var o=0,s=a[i];o<s.length;o+=1){var l=s[o],u=l.x,c=l.y;if(!(u<0||u>=po||c<0||c>=po)){var 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t.id}))};Us.prototype.populate=function(t,e,r){this.hasPattern=Ns("fill",this.layers,e);for(var n=this.layers[0].layout.get("fill-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,u=s.id,c=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p=mo(l,h);if(this.layers[0]._featureFilter.filter(new zi(this.zoom),p,r)){var d=n?n.evaluate(p,{},r,e.availableImages):void 0,v={id:u,properties:l.properties,type:l.type,sourceLayerIndex:f,index:c,geometry:h?p.geometry:yo(l),patterns:{},sortKey:d};i.push(v)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var m=y[g],x=m,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=js("fill",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},Us.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Us.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},Us.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Us.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Us.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,is),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.indexBuffer2=t.createIndexBuffer(this.indexArray2)),this.programConfigurations.upload(t),this.uploaded=!0},Us.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.indexBuffer2.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.segments2.destroy())},Us.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Fs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,u=0,c=s;u<c.length;u+=1)l+=c[u].length;for(var f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray),h=f.vertexLength,p=[],d=[],v=0,g=s;v<g.length;v+=1){var y=g[v];if(0!==y.length){y!==s[0]&&d.push(p.length/2);var 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u=t.readVarint();n=7&u,i=u>>3}if(i--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new a(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},Ys.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,u=-1/0;t.pos<e;){if(n<=0){var c=t.readVarint();r=7&c,n=c>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>u&&(u=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,u]},Ys.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),u=Ys.types[this.type];function c(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];c(l=f);break;case 2:for(n=0;n<l.length;n++)c(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=Zs(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}return r&&i.push(r),i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)c(l[n][i])}1===l.length?l=l[0]:u="Multi"+u;var h={type:"Feature",geometry:{type:u,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var Ks=Js;function Js(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields($s,this,e),this.length=this._features.length}function $s(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){for(var e=null,r=t.readVarint()+t.pos;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Qs(t,e,r){if(3===t){var n=new Ks(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Js.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Ws(this._pbf,e,this.extent,this._keys,this._values)};var tl={VectorTile:function(t,e){this.layers=t.readFields(Qs,{},e)},VectorTileFeature:Ws,VectorTileLayer:Ks},el=tl.VectorTileFeature.types,rl=Math.pow(2,13);function nl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*rl)+o,i*rl*2,a*rl*2,Math.round(s))}var il=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new ia,this.indexArray=new va,this.programConfigurations=new co(t.layers,t.zoom),this.segments=new Da,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function al(t,e){return t.x===e.x&&(t.x<0||t.x>po)||t.y===e.y&&(t.y<0||t.y>po)}il.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=Ns("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,u=a.sourceLayerIndex,c=this.layers[0]._featureFilter.needGeometry,f=mo(o,c);if(this.layers[0]._featureFilter.filter(new zi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:u,index:l,geometry:c?f.geometry:yo(o),properties:o.properties,type:o.type,patterns:{}};this.hasPattern?this.features.push(js("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,u,this.index,!0)}}},il.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},il.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},il.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},il.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},il.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Gs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},il.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},il.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=Fs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,u=0,c=s;u<c.length;u+=1)l+=c[u].length;for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!((O=d).every((function(t){return t.x<0}))||O.every((function(t){return t.x>po}))||O.every((function(t){return t.y<0}))||O.every((function(t){return t.y>po}))))for(var v=0,g=0;g<d.length;g++){var y=d[g];if(g>=1){var m=d[g-1];if(!al(y,m)){f.vertexLength+4>Da.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=y.sub(m)._perp()._unit(),b=m.dist(y);v+b>32768&&(v=0),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,v),v+=b,nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,0,v),nl(this.layoutVertexArray,m.x,m.y,x.x,x.y,0,1,v);var 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Hi(Ft["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new Gi(Ft["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Hi(Ft["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Hi(Ft["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new qi(Ft["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},sl=function(t){function e(e){t.call(this,e,ol)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new il(t)},e.prototype.queryRadius=function(){return Io(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s,l){var u=zo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),c=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var i=[],o=0,s=t;o<s.length;o+=1){var l=s[o],u=[l.x,l.y,n,1];qo(u,u,e),i.push(new a(u[0]/u[3],u[1]/u[3]))}return i}(u,l,0,0),p=function(t,e,r,n){for(var i=[],o=[],s=n[8]*e,l=n[9]*e,u=n[10]*e,c=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,v=0,g=t;v<g.length;v+=1){for(var y=[],m=[],x=0,b=g[v];x<b.length;x+=1){var _=b[x],w=_.x,T=_.y,k=n[0]*w+n[4]*T+n[12],A=n[1]*w+n[5]*T+n[13],M=n[2]*w+n[6]*T+n[14],S=n[3]*w+n[7]*T+n[15],E=M+u,L=S+c,C=k+f,P=A+h,O=M+p,I=S+d,z=new a((k+s)/L,(A+l)/L);z.z=E/L,y.push(z);var D=new a(C/I,P/I);D.z=O/I,m.push(D)}i.push(y),o.push(m)}return[i,o]}(n,f,c,l);return function(t,e,r){var n=1/0;To(r,e)&&(n=ul(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],u=a[s+1],c=o[s],f=[l,u,o[s+1],c,l];_o(r,f)&&(n=Math.min(n,ul(r,f)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ki);function ll(t,e){return t.x*e.x+t.y*e.y}function ul(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),u=o.sub(i),c=ll(s,s),f=ll(s,l),h=ll(l,l),p=ll(u,s),d=ll(u,l),v=c*h-f*f,g=(h*p-f*d)/v,y=(c*d-f*p)/v,m=1-g-y,x=i.z*m+r.z*g+a.z*y;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var cl=ta([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,fl=ta([{name:"a_uv_x",components:1,type:"Float32"},{name:"a_split_index",components:1,type:"Float32"}]).members,hl=tl.VectorTileFeature.types,pl=Math.cos(Math.PI/180*37.5),dl=Math.pow(2,14)/.5,vl=function(t){var e=this;this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((function(t){e.gradients[t.id]={}})),this.layoutVertexArray=new aa,this.layoutVertexArray2=new oa,this.indexArray=new va,this.programConfigurations=new co(t.layers,t.zoom),this.segments=new Da,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};vl.prototype.populate=function(t,e,r){this.hasPattern=Ns("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,u=s.id,c=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p=mo(l,h);if(this.layers[0]._featureFilter.filter(new zi(this.zoom),p,r)){var d=n?n.evaluate(p,{},r):void 0,v={id:u,properties:l.properties,type:l.type,sourceLayerIndex:f,index:c,geometry:h?p.geometry:yo(l),patterns:{},sortKey:d};i.push(v)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,y=i;g<y.length;g+=1){var m=y[g],x=m,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=js("line",this.layers,m,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(m,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},vl.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},vl.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},vl.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},vl.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},vl.prototype.upload=function(t){this.uploaded||(0!==this.layoutVertexArray2.length&&(this.layoutVertexBuffer2=t.createVertexBuffer(this.layoutVertexArray2,fl)),this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,cl),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},vl.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},vl.prototype.lineFeatureClips=function(t){if(t.properties&&t.properties.hasOwnProperty("mapbox_clip_start")&&t.properties.hasOwnProperty("mapbox_clip_end"))return{start:+t.properties.mapbox_clip_start,end:+t.properties.mapbox_clip_end}},vl.prototype.addFeature=function(t,e,r,n,i){var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),u=a.get("line-round-limit");this.lineClips=this.lineFeatureClips(t);for(var c=0,f=e;c<f.length;c+=1){var h=f[c];this.addLine(h,t,o,s,l,u)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},vl.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance(),this.maxLineLength=Math.max(this.maxLineLength,this.totalDistance)}for(var s="Polygon"===hl[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var u=0;u<l-1&&t[u].equals(t[u+1]);)u++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var c,f=this.overscaling<=16?15*po/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,v=void 0,g=void 0;this.e1=this.e2=-1,s&&(c=t[l-2],g=t[u].sub(c)._unit()._perp());for(var y=u;y<l;y++)if(!(d=y===l-1?s?t[u+1]:void 0:t[y+1])||!t[y].equals(d)){g&&(v=g),c&&(p=c),c=t[y],g=d?d.sub(c)._unit()._perp():v;var m=(v=v||g).add(g);0===m.x&&0===m.y||m._unit();var x=v.x*g.x+v.y*g.y,b=m.x*g.x+m.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<pl&&p&&d,k=v.x*g.y-v.y*g.x>0;if(T&&y>u){var A=c.dist(p);if(A>2*f){var M=c.sub(c.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,v,0,0,h),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(_<a?E="miter":_<=2&&(E="fakeround")),"miter"===E&&_>i&&(E="bevel"),"bevel"===E&&(_>2&&(E="flipbevel"),_<i&&(E="miter")),p&&this.updateDistance(p,c),"miter"===E)m._mult(_),this.addCurrentVertex(c,m,0,0,h);else if("flipbevel"===E){if(_>100)m=g.mult(-1);else{var L=_*v.add(g).mag()/v.sub(g).mag();m._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(c,m,0,0,h),this.addCurrentVertex(c,m.mult(-1),0,0,h)}else if("bevel"===E||"fakeround"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,O=k?0:C;if(p&&this.addCurrentVertex(c,v,P,O,h),"fakeround"===E)for(var I=Math.round(180*w/Math.PI/20),z=1;z<I;z++){var D=z/I;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(v)._mult(D)._add(v)._unit()._mult(k?-1:1);this.addHalfVertex(c,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(c,g,-P,-O,h)}else if("butt"===E)this.addCurrentVertex(c,m,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(c,m,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(c,v,0,0,h),this.addCurrentVertex(c,v,1,1,h,!0)),d&&(this.addCurrentVertex(c,g,-1,-1,h,!0),this.addCurrentVertex(c,g,0,0,h)));if(T&&y<l-1){var N=c.dist(d);if(N>2*f){var j=c.add(d.sub(c)._mult(f/N)._round());this.updateDistance(c,j),this.addCurrentVertex(j,g,0,0,h),c=j}}}}},vl.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,u=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,u,a,!0,-n,i),this.distance>dl/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},vl.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,u=.5*(this.lineClips?this.scaledDistance*(dl-1):this.scaledDistance);if(this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&u)<<2,u>>6),this.lineClips){var c=(this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start);this.layoutVertexArray2.emplaceBack(c,this.lineClipsArray.length)}var f=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,f),o.primitiveLength++),i?this.e2=f:this.e1=f},vl.prototype.updateScaledDistance=function(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance},vl.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},oi("LineBucket",vl,{omit:["layers","patternFeatures"]});var gl=new Xi({"line-cap":new qi(Ft.layout_line["line-cap"]),"line-join":new Hi(Ft.layout_line["line-join"]),"line-miter-limit":new qi(Ft.layout_line["line-miter-limit"]),"line-round-limit":new qi(Ft.layout_line["line-round-limit"]),"line-sort-key":new Hi(Ft.layout_line["line-sort-key"])}),yl={paint:new Xi({"line-opacity":new Hi(Ft.paint_line["line-opacity"]),"line-color":new Hi(Ft.paint_line["line-color"]),"line-translate":new qi(Ft.paint_line["line-translate"]),"line-translate-anchor":new qi(Ft.paint_line["line-translate-anchor"]),"line-width":new Hi(Ft.paint_line["line-width"]),"line-gap-width":new Hi(Ft.paint_line["line-gap-width"]),"line-offset":new Hi(Ft.paint_line["line-offset"]),"line-blur":new Hi(Ft.paint_line["line-blur"]),"line-dasharray":new Wi(Ft.paint_line["line-dasharray"]),"line-pattern":new Gi(Ft.paint_line["line-pattern"]),"line-gradient":new Yi(Ft.paint_line["line-gradient"])}),layout:gl},ml=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new zi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=p({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Hi),xl=new ml(yl.paint.properties["line-width"].specification);xl.useIntegerZoom=!0;var bl=function(t){function e(e){t.call(this,e,yl),this.gradientVersion=0}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){if("line-gradient"===t){var e=this._transitionablePaint._values["line-gradient"].value.expression;this.stepInterpolant=e._styleExpression.expression instanceof tr,this.gradientVersion=(this.gradientVersion+1)%l}},e.prototype.gradientExpression=function(){return this._transitionablePaint._values["line-gradient"].value.expression},e.prototype.recalculate=function(e,r){t.prototype.recalculate.call(this,e,r),this.paint._values["line-floorwidth"]=xl.possiblyEvaluate(this._transitioningPaint._values["line-width"].value,e)},e.prototype.createBucket=function(t){return new vl(t)},e.prototype.queryRadius=function(t){var e=t,r=_l(Oo("line-width",this,e),Oo("line-gap-width",this,e)),n=Oo("line-offset",this,e);return r/2+Math.abs(n)+Io(this.paint.get("line-translate"))},e.prototype.queryIntersectsFeature=function(t,e,r,n,i,o,s){var l=zo(t,this.paint.get("line-translate"),this.paint.get("line-translate-anchor"),o.angle,s),u=s/2*_l(this.paint.get("line-width").evaluate(e,r),this.paint.get("line-gap-width").evaluate(e,r)),c=this.paint.get("line-offset").evaluate(e,r);return c&&(n=function(t,e){for(var r=[],n=new a(0,0),i=0;i<t.length;i++){for(var o=t[i],s=[],l=0;l<o.length;l++){var u=o[l-1],c=o[l],f=o[l+1],h=0===l?n:c.sub(u)._unit()._perp(),p=l===o.length-1?n:f.sub(c)._unit()._perp(),d=h._add(p)._unit(),v=d.x*p.x+d.y*p.y;d._mult(1/v),s.push(d._mult(e)._add(c))}r.push(s)}return r}(n,c*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(Co(t,i[a]))return!0;if(ko(t,i,r))return!0}return!1}(l,n,u)},e.prototype.isTileClipped=function(){return!0},e}(Ki);function _l(t,e){return e>0?e+2*t:t}var wl=ta([{name:"a_pos_offset",components:4,type:"Int16"},{name:"a_data",components:4,type:"Uint16"},{name:"a_pixeloffset",components:4,type:"Int16"}],4),Tl=ta([{name:"a_projected_pos",components:3,type:"Float32"}],4),kl=(ta([{name:"a_fade_opacity",components:1,type:"Uint32"}],4),ta([{name:"a_placed",components:2,type:"Uint8"},{name:"a_shift",components:2,type:"Float32"}])),Al=(ta([{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Int16",name:"x1"},{type:"Int16",name:"y1"},{type:"Int16",name:"x2"},{type:"Int16",name:"y2"},{type:"Uint32",name:"featureIndex"},{type:"Uint16",name:"sourceLayerIndex"},{type:"Uint16",name:"bucketIndex"}]),ta([{name:"a_pos",components:2,type:"Int16"},{name:"a_anchor_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"}],4)),Ml=ta([{name:"a_pos",components:2,type:"Float32"},{name:"a_radius",components:1,type:"Float32"},{name:"a_flags",components:2,type:"Int16"}],4);function Sl(t,e,r){return 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$o({width:o||1,height:s||1});for(var u in t){var c=t[u],f=r[u].paddedRect;$o.copy(c.data,l,{x:0,y:0},{x:f.x+iu,y:f.y+iu},c.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,v=d.x+iu,g=d.y+iu,y=p.data.width,m=p.data.height;$o.copy(p.data,l,{x:0,y:0},{x:v,y:g},p.data),$o.copy(p.data,l,{x:0,y:m-1},{x:v,y:g-1},{width:y,height:1}),$o.copy(p.data,l,{x:0,y:0},{x:v,y:g+m},{width:y,height:1}),$o.copy(p.data,l,{x:y-1,y:0},{x:v-1,y:g},{width:1,height:m}),$o.copy(p.data,l,{x:0,y:0},{x:v+y,y:g},{width:1,height:m})}this.image=l,this.iconPositions=r,this.patternPositions=n};su.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2*iu,h:i.data.height+2*iu};r.push(a),e[n]=new au(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},su.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},su.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},oi("ImagePosition",au),oi("ImageAtlas",su);var lu={horizontal:1,vertical:2,horizontalOnly:3},uu=-17;var cu=function(){this.scale=1,this.fontStack="",this.imageName=null};cu.forText=function(t,e){var r=new cu;return r.scale=t||1,r.fontStack=e,r},cu.forImage=function(t){var e=new cu;return e.imageName=t,e};var fu=function(){this.text="",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function hu(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=fu.fromFeature(t,i);f===lu.vertical&&y.verticalizePunctuation();var m=Ii.processBidirectionalText,x=Ii.processStyledBidirectionalText;if(m&&1===y.sections.length){g=[];for(var b=0,_=m(y.toString(),bu(y,u,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new fu;T.text=w,T.sections=y.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(y.text,y.sectionIndex,bu(y,u,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new fu;E.text=S[0],E.sectionIndex=S[1],E.sections=y.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(y,bu(y,u,a,e,n,p,d));var L=[],C={positionedLines:L,text:y.toString(),top:c[1],bottom:c[1],left:c[0],right:c[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=0,p=uu,d=0,v=0,g="right"===s?1:"left"===s?0:.5,y=0,m=0,x=i;m<x.length;m+=1){var b=x[m];b.trim();var _=b.getMaxScale(),w=(_-1)*Ll,T={positionedGlyphs:[],lineOffset:0};t.positionedLines[y]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,O=null,I=null,z=Ll,D=!(l===lu.horizontal||!c&&!vi(L)||c&&(pu[L]||yi(L)));if(S.imageName){var R=n[S.imageName];if(!R)continue;I=S.imageName,t.iconsInText=t.iconsInText||!0,O=R.paddedRect;var F=R.displaySize;S.scale=S.scale*Ll/f,P={width:F[0],height:F[1],left:iu,top:-ru,advance:D?F[1]:F[0]},C=w+(Ll-F[1]*S.scale),z=P.advance;var B=D?F[0]*S.scale-Ll*_:F[1]*S.scale-Ll*_;B>0&&B>A&&(A=B)}else{var N=r[S.fontStack],j=N&&N[L];if(j&&j.rect)O=j.rect,P=j.metrics;else{var U=e[S.fontStack],V=U&&U[L];if(!V)continue;P=V.metrics}C=(_-S.scale)*Ll}D?(t.verticalizable=!0,k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=z*S.scale+u):(k.push({glyph:L,imageName:I,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:O}),h+=P.advance*S.scale+u)}if(0!==k.length){var q=h-u;d=Math.max(q,d),wu(k,0,k.length-1,g,A)}h=0;var H=a*_+A;T.lineOffset=Math.max(A,w),p+=H,v=Math.max(H,v),++y}else p+=a,++y}var G=p-uu,W=_u(o),Y=W.horizontalAlign,X=W.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var u=(e-r)*i,c=0;c=a!==o?-s*n-uu:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=0,d=h[f].positionedGlyphs;p<d.length;p+=1){var v=d[p];v.x+=u,v.y+=c}})(t.positionedLines,g,Y,X,d,v,a,G,i.length),t.top+=-X*G,t.bottom=t.top+G,t.left+=-Y*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,u,h,v),!function(t){for(var e=0,r=t;e<r.length;e+=1)if(0!==r[e].positionedGlyphs.length)return!1;return!0}(L)&&C}fu.fromFeature=function(t,e){for(var r=new fu,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},fu.prototype.length=function(){return this.text.length},fu.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},fu.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},fu.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},fu.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;n&&gi(n)&&!El[t[r+1]]||i&&gi(i)&&!El[t[r-1]]||!El[t[r]]?e+=t[r]:e+=El[t[r]]}return e}(this.text)},fu.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&pu[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&pu[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},fu.prototype.substring=function(t,e){var r=new fu;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},fu.prototype.toString=function(){return this.text},fu.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},fu.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(cu.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},fu.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(cu.forImage(e)),this.sectionIndex.push(this.sections.length-1)):k("Reached maximum number of images 6401")}else k("Can't add FormattedSection with an empty image.")},fu.prototype.getNextImageSectionCharCode=function(){return this.imageSectionID?this.imageSectionID>=63743?null:++this.imageSectionID:(this.imageSectionID=57344,this.imageSectionID)};var pu={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},du={};function vu(t,e,r,n,i,a){if(e.imageName){var o=n[e.imageName];return o?o.displaySize[0]*e.scale*Ll/a+i:0}var s=r[e.fontStack],l=s&&s[t];return l?l.metrics.advance*e.scale+i:0}function gu(t,e,r,n){var i=Math.pow(t-e,2);return n?t<e?i/2:2*i:i+Math.abs(r)*r}function yu(t,e,r){var n=0;return 10===t&&(n-=1e4),r&&(n+=150),40!==t&&65288!==t||(n+=50),41!==e&&65289!==e||(n+=50),n}function mu(t,e,r,n,i,a){for(var o=null,s=gu(e,r,i,a),l=0,u=n;l<u.length;l+=1){var c=u[l],f=gu(e-c.x,r,i,a)+c.badness;f<=s&&(o=c,s=f)}return{index:t,x:e,priorBreak:o,badness:s}}function xu(t){return t?xu(t.priorBreak).concat(t.index):[]}function bu(t,e,r,n,i,a,o){if("point"!==a)return[];if(!t)return[];for(var s=[],l=function(t,e,r,n,i,a){for(var o=0,s=0;s<t.length();s++){var l=t.getSection(s);o+=vu(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("​")>=0,c=0,f=0;f<t.length();f++){var h=t.getSection(f),p=t.getCharCode(f);if(pu[p]||(c+=vu(p,h,n,i,e,o)),f<t.length()-1){var d=!((v=p)<11904||!(pi["Bopomofo Extended"](v)||pi.Bopomofo(v)||pi["CJK Compatibility Forms"](v)||pi["CJK Compatibility Ideographs"](v)||pi["CJK 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a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Tu(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,u=s.pixelRatio||1;o=[l[0]/u,l[1]/u,s.displaySize[0]-l[2]/u,s.displaySize[1]-l[3]/u]}var c,f,h,p,d=e.left*a,v=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+v+n[1]):f=(p=i[0]+(d+v-s.displaySize[0])/2)+s.displaySize[0];var g=e.top*a,y=e.bottom*a;return"height"===r||"both"===r?(c=i[1]+g-n[0],h=i[1]+y+n[2]):h=(c=i[1]+(g+y-s.displaySize[1])/2)+s.displaySize[1],{image:s,top:c,right:f,bottom:h,left:p,collisionPadding:o}}du[10]=!0,du[32]=!0,du[38]=!0,du[40]=!0,du[41]=!0,du[43]=!0,du[45]=!0,du[47]=!0,du[173]=!0,du[183]=!0,du[8203]=!0,du[8208]=!0,du[8211]=!0,du[8231]=!0;var ku=function(t){function e(e,r,n,i){t.call(this,e,r),this.angle=n,void 0!==i&&(this.segment=i)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.clone=function(){return new e(this.x,this.y,this.angle,this.segment)},e}(a);oi("Anchor",ku);var Au=128;function Mu(t,e){var r=e.expression;if("constant"===r.kind)return{kind:"constant",layoutSize:r.evaluate(new zi(t+1))};if("source"===r.kind)return{kind:"source"};for(var n=r.zoomStops,i=r.interpolationType,a=0;a<n.length&&n[a]<=t;)a++;for(var o=a=Math.max(0,a-1);o<n.length&&n[o]<t+1;)o++;o=Math.min(n.length-1,o);var s=n[a],l=n[o];return"composite"===r.kind?{kind:"composite",minZoom:s,maxZoom:l,interpolationType:i}:{kind:"camera",minZoom:s,maxZoom:l,minSize:r.evaluate(new zi(s)),maxSize:r.evaluate(new zi(l)),interpolationType:i}}function Su(t,e,r){var n=e.uSize,i=e.uSizeT,a=r.lowerSize,o=r.upperSize;return"source"===t.kind?a/Au:"composite"===t.kind?er(a/Au,o/Au,i):n}function Eu(t,e){var r=0,n=0;if("constant"===t.kind)n=t.layoutSize;else if("source"!==t.kind){var i=t.interpolationType,a=t.minZoom,o=t.maxZoom,s=i?f(wr.interpolationFactor(i,e,a,o),0,1):0;"camera"===t.kind?n=er(t.minSize,t.maxSize,s):r=s}return{uSizeT:r,uSize:n}}var Lu=Object.freeze({__proto__:null,getSizeData:Mu,evaluateSizeForFeature:Su,evaluateSizeForZoom:Eu,SIZE_PACK_FACTOR:Au});function Cu(t,e,r,n,i){if(void 0===e.segment)return!0;for(var a=e,o=e.segment+1,s=0;s>-r/2;){if(--o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;for(var l=[],u=0;s<r/2;){var c=t[o-1],f=t[o],h=t[o+1];if(!h)return!1;var p=c.angleTo(f)-f.angleTo(h);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),u+=p;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;o++,s+=f.dist(h)}return!0}function Pu(t){for(var e=0,r=0;r<t.length-1;r++)e+=t[r].dist(t[r+1]);return e}function Ou(t,e,r){return t?.6*e*r:0}function Iu(t,e){return Math.max(t?t.right-t.left:0,e?e.right-e.left:0)}function zu(t,e,r,n,i,a){for(var o=Ou(r,i,a),s=Iu(r,n)*a,l=0,u=Pu(t)/2,c=0;c<t.length-1;c++){var f=t[c],h=t[c+1],p=f.dist(h);if(l+p>u){var d=(u-l)/p,v=er(f.x,h.x,d),g=er(f.y,h.y,d),y=new ku(v,g,h.angleTo(f),c);return y._round(),!o||Cu(t,y,s,o,e)?y:void 0}l+=p}}function Du(t,e,r,n,i,a,o,s,l){var u=Ou(n,a,o),c=Iu(n,i),f=c*o,h=0===t[0].x||t[0].x===l||0===t[0].y||t[0].y===l;return e-f<e/4&&(e=f+e/4),Ru(t,h?e/2*s%e:(c/2+2*a)*o*s%e,e,u,r,f,h,!1,l)}function Ru(t,e,r,n,i,a,o,s,l){for(var u=a/2,c=Pu(t),f=0,h=e-r,p=[],d=0;d<t.length-1;d++){for(var v=t[d],g=t[d+1],y=v.dist(g),m=g.angleTo(v);h+r<f+y;){var x=((h+=r)-f)/y,b=er(v.x,g.x,x),_=er(v.y,g.y,x);if(b>=0&&b<l&&_>=0&&_<l&&h-u>=0&&h+u<=c){var w=new ku(b,_,m,d);w._round(),n&&!Cu(t,w,a,n,i)||p.push(w)}}f+=y}return s||p.length||o||(p=Ru(t,f/2,r,n,i,a,o,!0,l)),p}function Fu(t,e,r,n,i){for(var o=[],s=0;s<t.length;s++)for(var l=t[s],u=void 0,c=0;c<l.length-1;c++){var f=l[c],h=l[c+1];f.x<e&&h.x<e||(f.x<e?f=new a(e,f.y+(h.y-f.y)*((e-f.x)/(h.x-f.x)))._round():h.x<e&&(h=new a(e,f.y+(h.y-f.y)*((e-f.x)/(h.x-f.x)))._round()),f.y<r&&h.y<r||(f.y<r?f=new a(f.x+(h.x-f.x)*((r-f.y)/(h.y-f.y)),r)._round():h.y<r&&(h=new a(f.x+(h.x-f.x)*((r-f.y)/(h.y-f.y)),r)._round()),f.x>=n&&h.x>=n||(f.x>=n?f=new a(n,f.y+(h.y-f.y)*((n-f.x)/(h.x-f.x)))._round():h.x>=n&&(h=new a(n,f.y+(h.y-f.y)*((n-f.x)/(h.x-f.x)))._round()),f.y>=i&&h.y>=i||(f.y>=i?f=new a(f.x+(h.x-f.x)*((i-f.y)/(h.y-f.y)),i)._round():h.y>=i&&(h=new a(f.x+(h.x-f.x)*((i-f.y)/(h.y-f.y)),i)._round()),u&&f.equals(u[u.length-1])||(u=[f],o.push(u)),u.push(h)))))}return o}var Bu=iu;function Nu(t,e,r,n){var i=[],o=t.image,s=o.pixelRatio,l=o.paddedRect.w-2*Bu,u=o.paddedRect.h-2*Bu,c=t.right-t.left,f=t.bottom-t.top,h=o.stretchX||[[0,l]],p=o.stretchY||[[0,u]],d=function(t,e){return t+e[1]-e[0]},v=h.reduce(d,0),g=p.reduce(d,0),y=l-v,m=u-g,x=0,b=v,_=0,w=g,T=0,k=y,A=0,M=m;if(o.content&&n){var S=o.content;x=ju(h,0,S[0]),_=ju(p,0,S[1]),b=ju(h,S[0],S[2]),w=ju(p,S[1],S[3]),T=S[0]-x,A=S[1]-_,k=S[2]-S[0]-b,M=S[3]-S[1]-w}var E=function(n,i,l,u){var h=Vu(n.stretch-x,b,c,t.left),p=qu(n.fixed-T,k,n.stretch,v),d=Vu(i.stretch-_,w,f,t.top),y=qu(i.fixed-A,M,i.stretch,g),m=Vu(l.stretch-x,b,c,t.left),S=qu(l.fixed-T,k,l.stretch,v),E=Vu(u.stretch-_,w,f,t.top),L=qu(u.fixed-A,M,u.stretch,g),C=new a(h,d),P=new a(m,d),O=new a(m,E),I=new a(h,E),z=new a(p/s,y/s),D=new a(S/s,L/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),I._matMult(N),O._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=i.stretch+i.fixed,q=u.stretch+u.fixed;return{tl:C,tr:P,bl:I,br:O,tex:{x:o.paddedRect.x+Bu+j,y:o.paddedRect.y+Bu+V,w:U-j,h:q-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/c,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=Uu(h,y,v),C=Uu(p,m,g),P=0;P<L.length-1;P++)for(var O=L[P],I=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];i.push(E(O,D,I,R))}else 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a(m,g),k=c*Math.PI/180;b._rotate(k),_._rotate(k),w._rotate(k),T._rotate(k),y=Math.min(b.x,_.x,w.x,T.x),m=Math.max(b.x,_.x,w.x,T.x),v=Math.min(b.y,_.y,w.y,T.y),g=Math.max(b.y,_.y,w.y,T.y)}t.emplaceBack(e.x,e.y,y,v,m,g,r,n,i)}this.boxEndIndex=t.length},Gu=function(t,e){if(void 0===t&&(t=[]),void 0===e&&(e=Wu),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function Wu(t,e){return t<e?-1:t>e?1:0}function Yu(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,i=1/0,o=-1/0,s=-1/0,l=t[0],u=0;u<l.length;u++){var c=l[u];(!u||c.x<n)&&(n=c.x),(!u||c.y<i)&&(i=c.y),(!u||c.x>o)&&(o=c.x),(!u||c.y>s)&&(s=c.y)}var f=o-n,h=s-i,p=Math.min(f,h),d=p/2,v=new Gu([],Xu);if(0===p)return new a(n,i);for(var g=n;g<o;g+=p)for(var y=i;y<s;y+=p)v.push(new Zu(g+d,y+d,d,t));for(var m=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],u=i[s],c=l.x*u.y-u.x*l.y;r+=(l.x+u.x)*c,n+=(l.y+u.y)*c,e+=3*c}return new Zu(r/e,n/e,0,t)}(t),x=v.length;v.length;){var b=v.pop();(b.d>m.d||!m.d)&&(m=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-m.d<=e||(d=b.h/2,v.push(new Zu(b.p.x-d,b.p.y-d,d,t)),v.push(new Zu(b.p.x+d,b.p.y-d,d,t)),v.push(new Zu(b.p.x-d,b.p.y+d,d,t)),v.push(new Zu(b.p.x+d,b.p.y+d,d,t)),x+=4)}return r&&(console.log("num probes: "+x),console.log("best distance: "+m.d)),m.p}function Xu(t,e){return e.max-t.max}function Zu(t,e,r,n){this.p=new a(t,e),this.h=r,this.d=function(t,e){for(var r=!1,n=1/0,i=0;i<e.length;i++)for(var a=e[i],o=0,s=a.length,l=s-1;o<s;l=o++){var u=a[o],c=a[l];u.y>t.y!=c.y>t.y&&t.x<(c.x-u.x)*(t.y-u.y)/(c.y-u.y)+u.x&&(r=!r),n=Math.min(n,Eo(t,u,c))}return(r?1:-1)*Math.sqrt(n)}(this.p,n),this.max=this.d+this.h*Math.SQRT2}Gu.prototype.push=function(t){this.data.push(t),this.length++,this._up(this.length-1)},Gu.prototype.pop=function(){if(0!==this.length){var t=this.data[0],e=this.data.pop();return this.length--,this.length>0&&(this.data[0]=e,this._down(0)),t}},Gu.prototype.peek=function(){return this.data[0]},Gu.prototype._up=function(t){for(var e=this.data,r=this.compare,n=e[t];t>0;){var i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n},Gu.prototype._down=function(t){for(var e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var Ku=7,Ju=Number.POSITIVE_INFINITY;function $u(t,e){return e[1]!==Ju?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case"top-right":case"top-left":case"top":i=r-Ku;break;case"bottom-right":case"bottom-left":case"bottom":i=-r+Ku}switch(t){case"top-right":case"bottom-right":case"right":n=-e;break;case"top-left":case"bottom-left":case"left":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case"top-right":case"top-left":n=i-Ku;break;case"bottom-right":case"bottom-left":n=-i+Ku;break;case"bottom":n=-e+Ku;break;case"top":n=e-Ku}switch(t){case"top-right":case"bottom-right":r=-i;break;case"top-left":case"bottom-left":r=i;break;case"left":r=e;break;case"right":r=-e}return[r,n]}(t,e[0])}function Qu(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}var tc=255,ec=tc*Au;function rc(t,e,r,n,i,o,s,l,u,c,f,h,p,d,v){var g=function(t,e,r,n,i,o,s,l){for(var u=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,c=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,v=p.positionedGlyphs;d<v.length;d+=1){var g=v[d];if(g.rect){var y=g.rect||{},m=ru+1,x=!0,b=1,_=0,w=(i||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=(g.scale-1)*Ll,A=(Ll-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,b=M.pixelRatio,m=iu/b}var S=i?[g.x+T,g.y]:[0,0],E=i?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-m)*g.scale-T+E[0],P=(-g.metrics.top-m)*g.scale+E[1],O=C+y.w*g.scale/b,I=P+y.h*g.scale/b,z=new a(C,P),D=new a(O,P),R=new a(C,I),F=new a(O,I);if(w){var B=new a(-T,T-uu),N=-Math.PI/2,j=Ll/2-T,U=g.imageName?j:0,V=new a(5-uu-j,-U),q=new(Function.prototype.bind.apply(a,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(q),D._rotateAround(N,B)._add(V)._add(q),R._rotateAround(N,B)._add(V)._add(q),F._rotateAround(N,B)._add(V)._add(q)}if(u){var H=Math.sin(u),G=Math.cos(u),W=[G,-H,H,G];z._matMult(W),D._matMult(W),R._matMult(W),F._matMult(W)}var Y=new a(0,0),X=new a(0,0);c.push({tl:z,tr:D,bl:R,br:F,tex:y,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:Y,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return c}(0,r,l,i,o,s,n,t.allowVerticalPlacement),y=t.textSizeData,m=null;"source"===y.kind?(m=[Au*i.layout.get("text-size").evaluate(s,{})])[0]>ec&&k(t.layerIds[0]+': Value for "text-size" is >= '+tc+'. 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Reduce your "text-size".'),t.addSymbols(t.text,g,m,l,o,s,c,e,u.lineStartIndex,u.lineLength,p,v);for(var x=0,b=f;x<b.length;x+=1)h[b[x]]=t.text.placedSymbolArray.length-1;return 4*g.length}function nc(t){for(var e in t)return t[e];return null}function ic(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var ac=tl.VectorTileFeature.types,oc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function sc(t,e,r,n,i,a,o,s,l,u,c,f,h){var p=s?Math.min(ec,Math.round(s[0])):0,d=s?Math.min(ec,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*u,16*c,256*f,256*h)}function lc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function uc(t){for(var e=0,r=t.sections;e<r.length;e+=1)if(bi(r[e].text))return!0;return!1}var cc=function(t){this.layoutVertexArray=new la,this.indexArray=new va,this.programConfigurations=t,this.segments=new Da,this.dynamicLayoutVertexArray=new ua,this.opacityVertexArray=new ca,this.placedSymbolArray=new Sa};cc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},cc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,wl.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Tl.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,oc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},cc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},oi("SymbolBuffers",cc);var fc=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new Da,this.collisionVertexArray=new da};fc.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,kl.members,!0)},fc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},oi("CollisionBuffers",fc);var hc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=Bo([]),this.placementViewportMatrix=Bo([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Mu(this.zoom,e["text-size"]),this.iconSizeData=Mu(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.canOverlap=r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement"),this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&this.canOverlap,"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return lu[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};hc.prototype.createArrays=function(){this.text=new cc(new co(this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new cc(new co(this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new Ca,this.lineVertexArray=new Pa,this.symbolInstances=new La},hc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=El[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},hc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof he&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),u="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,c=i.get("symbol-sort-key");if(this.features=[],l||u){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new zi(this.zoom),v=0,g=t;v<g.length;v+=1){var y=g[v],m=y.feature,x=y.id,b=y.index,_=y.sourceLayerIndex,w=n._featureFilter.needGeometry,T=mo(m,w);if(n._featureFilter.filter(d,T,r)){w||(T.geometry=yo(m));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=he.factory(A);uc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===Pi()||this.hasRTLText&&Ii.isParsed())&&(k=Sl(M,n,T))}var S=void 0;if(u){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof pe?E:pe.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?c.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:T.geometry,properties:m.properties,type:ac[m.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),O="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(lu.vertical)>=0;for(var I=0,z=k.sections;I<z.length;I+=1){var D=z[I];if(D.image)f[D.image.name]=!0;else{var R=di(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,O,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var u=0;u<t.length;u++){var c=t[u],f=c.geometry,h=c.text?c.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var v=s(p,d,f),g=o(p,d,n[v].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[v].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(u),e[p]=i-1,r[d]=i-1)}else a(u)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},hc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},hc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},hc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},hc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},hc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},hc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},hc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var u=a[l];this.lineVertexArray.emplaceBack(u.x,u.y,u.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},hc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,u,c,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,this.canOverlap?a.sortKey:void 0),v=this.glyphOffsetArray.length,g=d.vertexLength,y=this.allowVerticalPlacement&&o===lu.vertical?Math.PI/2:0,m=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,O=b.sectionIndex,I=d.vertexLength,z=C[1];sc(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),sc(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),sc(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),sc(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),lc(t.dynamicLayoutVertexArray,s,y),h.emplaceBack(I,I+1,I+2),h.emplaceBack(I+1,I+2,I+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&O===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,m&&m[O])}t.placedSymbolArray.emplaceBack(s.x,s.y,v,this.glyphOffsetArray.length-v,g,l,u,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,c)},hc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},hc.prototype.addCollisionDebugVertices=function(t,e,r,n,i,o,s){var l=i.segments.prepareSegment(4,i.layoutVertexArray,i.indexArray),u=l.vertexLength,c=i.layoutVertexArray,f=i.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(c,f,o,h,p,new a(t,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,e)),this._addCollisionDebugVertex(c,f,o,h,p,new a(r,n)),this._addCollisionDebugVertex(c,f,o,h,p,new a(t,n)),l.vertexLength+=4;var d=i.indexArray;d.emplaceBack(u,u+1),d.emplaceBack(u+1,u+2),d.emplaceBack(u+2,u+3),d.emplaceBack(u+3,u),l.primitiveLength+=4},hc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,u=a.y2;this.addCollisionDebugVertices(o,s,l,u,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},hc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new fc(ha,Al.members,_a),this.iconCollisionBox=new fc(ha,Al.members,_a);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},hc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var u={},c=e;c<r;c++){var f=t.get(c);u.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},u.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);u.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},u.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var v=t.get(d);u.iconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},u.iconFeatureIndex=v.featureIndex;break}for(var g=s;g<l;g++){var y=t.get(g);u.verticalIconBox={x1:y.x1,y1:y.y1,x2:y.x2,y2:y.y2,anchorPointX:y.anchorPointX,anchorPointY:y.anchorPointY},u.verticalIconFeatureIndex=y.featureIndex;break}return u},hc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},hc.prototype.hasTextData=function(){return this.text.segments.get().length>0},hc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},hc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},hc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},hc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},hc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},hc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},hc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},hc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},oi("SymbolBucket",hc,{omit:["layers","collisionBoxArray","features","compareText"]}),hc.MAX_GLYPHS=65535,hc.addDynamicAttributes=lc;var pc=new Xi({"symbol-placement":new qi(Ft.layout_symbol["symbol-placement"]),"symbol-spacing":new qi(Ft.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new qi(Ft.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Hi(Ft.layout_symbol["symbol-sort-key"]),"symbol-z-order":new qi(Ft.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new qi(Ft.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new qi(Ft.layout_symbol["icon-ignore-placement"]),"icon-optional":new qi(Ft.layout_symbol["icon-optional"]),"icon-rotation-alignment":new qi(Ft.layout_symbol["icon-rotation-alignment"]),"icon-size":new Hi(Ft.layout_symbol["icon-size"]),"icon-text-fit":new qi(Ft.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new qi(Ft.layout_symbol["icon-text-fit-padding"]),"icon-image":new Hi(Ft.layout_symbol["icon-image"]),"icon-rotate":new Hi(Ft.layout_symbol["icon-rotate"]),"icon-padding":new qi(Ft.layout_symbol["icon-padding"]),"icon-keep-upright":new qi(Ft.layout_symbol["icon-keep-upright"]),"icon-offset":new Hi(Ft.layout_symbol["icon-offset"]),"icon-anchor":new Hi(Ft.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new qi(Ft.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new qi(Ft.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new qi(Ft.layout_symbol["text-rotation-alignment"]),"text-field":new Hi(Ft.layout_symbol["text-field"]),"text-font":new Hi(Ft.layout_symbol["text-font"]),"text-size":new Hi(Ft.layout_symbol["text-size"]),"text-max-width":new Hi(Ft.layout_symbol["text-max-width"]),"text-line-height":new qi(Ft.layout_symbol["text-line-height"]),"text-letter-spacing":new Hi(Ft.layout_symbol["text-letter-spacing"]),"text-justify":new Hi(Ft.layout_symbol["text-justify"]),"text-radial-offset":new Hi(Ft.layout_symbol["text-radial-offset"]),"text-variable-anchor":new qi(Ft.layout_symbol["text-variable-anchor"]),"text-anchor":new Hi(Ft.layout_symbol["text-anchor"]),"text-max-angle":new qi(Ft.layout_symbol["text-max-angle"]),"text-writing-mode":new qi(Ft.layout_symbol["text-writing-mode"]),"text-rotate":new Hi(Ft.layout_symbol["text-rotate"]),"text-padding":new qi(Ft.layout_symbol["text-padding"]),"text-keep-upright":new qi(Ft.layout_symbol["text-keep-upright"]),"text-transform":new Hi(Ft.layout_symbol["text-transform"]),"text-offset":new Hi(Ft.layout_symbol["text-offset"]),"text-allow-overlap":new qi(Ft.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new qi(Ft.layout_symbol["text-ignore-placement"]),"text-optional":new qi(Ft.layout_symbol["text-optional"])}),dc={paint:new Xi({"icon-opacity":new Hi(Ft.paint_symbol["icon-opacity"]),"icon-color":new Hi(Ft.paint_symbol["icon-color"]),"icon-halo-color":new Hi(Ft.paint_symbol["icon-halo-color"]),"icon-halo-width":new Hi(Ft.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Hi(Ft.paint_symbol["icon-halo-blur"]),"icon-translate":new qi(Ft.paint_symbol["icon-translate"]),"icon-translate-anchor":new qi(Ft.paint_symbol["icon-translate-anchor"]),"text-opacity":new Hi(Ft.paint_symbol["text-opacity"]),"text-color":new Hi(Ft.paint_symbol["text-color"],{runtimeType:Zt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Hi(Ft.paint_symbol["text-halo-color"]),"text-halo-width":new Hi(Ft.paint_symbol["text-halo-width"]),"text-halo-blur":new Hi(Ft.paint_symbol["text-halo-blur"]),"text-translate":new qi(Ft.paint_symbol["text-translate"]),"text-translate-anchor":new qi(Ft.paint_symbol["text-translate-anchor"])}),layout:pc},vc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Gt,this.defaultValue=t};vc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},vc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},vc.prototype.outputDefined=function(){return!1},vc.prototype.serialize=function(){return null},oi("FormatSectionOverride",vc,{omit:["defaultValue"]});var gc=function(t){function e(e){t.call(this,e,dc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||cn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new hc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=dc.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i,a=this.paint.get(n),o=new vc(a),s=new un(o,a.property.specification);i="constant"===a.value.kind||"source"===a.value.kind?new hn("source",s):new pn("composite",s,a.value.zoomStops,a.value._interpolationType),this.paint._values[n]=new Ui(a.property,i,a.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=dc.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof he)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof me&&ge(t.value)===Qt){var e=t.value;a(e.sections)}else t instanceof we?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ki),yc={paint:new Xi({"background-color":new qi(Ft.paint_background["background-color"]),"background-pattern":new Wi(Ft.paint_background["background-pattern"]),"background-opacity":new qi(Ft.paint_background["background-opacity"])})},mc=function(t){function e(e){t.call(this,e,yc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ki),xc={paint:new Xi({"raster-opacity":new qi(Ft.paint_raster["raster-opacity"]),"raster-hue-rotate":new qi(Ft.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new qi(Ft.paint_raster["raster-brightness-min"]),"raster-brightness-max":new qi(Ft.paint_raster["raster-brightness-max"]),"raster-saturation":new qi(Ft.paint_raster["raster-saturation"]),"raster-contrast":new qi(Ft.paint_raster["raster-contrast"]),"raster-resampling":new qi(Ft.paint_raster["raster-resampling"]),"raster-fade-duration":new qi(Ft.paint_raster["raster-fade-duration"])})},bc=function(t){function e(e){t.call(this,e,xc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ki);var _c=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ki),wc={circle:Go,heatmap:es,hillshade:ns,fill:Hs,"fill-extrusion":sl,line:bl,symbol:gc,background:mc,raster:bc};var Tc=s.HTMLImageElement,kc=s.HTMLCanvasElement,Ac=s.HTMLVideoElement,Mc=s.ImageData,Sc=s.ImageBitmap,Ec=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Ec.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Tc||t instanceof kc||t instanceof Ac||t instanceof Mc||Sc&&t instanceof Sc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},u=l.x,c=l.y;t instanceof Tc||t instanceof kc||t instanceof Ac||t instanceof Mc||Sc&&t instanceof Sc?s.texSubImage2D(s.TEXTURE_2D,0,u,c,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,u,c,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Ec.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Ec.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Ec.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Lc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Lc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Lc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var Cc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Lc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=S()?t:s};function Pc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}Cc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=C(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:ci(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else S()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(fi(e.error)):n(null,fi(e.data)))}else{var i=!1,a=C(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?ci(e):null,data:ci(n,a)},a)}:function(t){i=!0},s=null,l=fi(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var u=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,u[0],l.source)[u[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Oc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Oc.prototype.setNorthEast=function(t){return this._ne=t instanceof zc?new zc(t.lng,t.lat):zc.convert(t),this},Oc.prototype.setSouthWest=function(t){return this._sw=t instanceof zc?new zc(t.lng,t.lat):zc.convert(t),this},Oc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof zc)e=t,r=t;else{if(!(t instanceof Oc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Oc.convert(a))}var o=t;return this.extend(zc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new zc(e.lng,e.lat),this._ne=new zc(r.lng,r.lat)),this},Oc.prototype.getCenter=function(){return new zc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Oc.prototype.getSouthWest=function(){return this._sw},Oc.prototype.getNorthEast=function(){return this._ne},Oc.prototype.getNorthWest=function(){return new zc(this.getWest(),this.getNorth())},Oc.prototype.getSouthEast=function(){return new zc(this.getEast(),this.getSouth())},Oc.prototype.getWest=function(){return this._sw.lng},Oc.prototype.getSouth=function(){return this._sw.lat},Oc.prototype.getEast=function(){return this._ne.lng},Oc.prototype.getNorth=function(){return this._ne.lat},Oc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Oc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Oc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Oc.prototype.contains=function(t){var e=zc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Oc.convert=function(t){return!t||t instanceof Oc?t:new Oc(t)};var Ic=6371008.8,zc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};zc.prototype.wrap=function(){return new zc(h(this.lng,-180,180),this.lat)},zc.prototype.toArray=function(){return[this.lng,this.lat]},zc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},zc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Ic*Math.acos(Math.min(i,1))},zc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Oc(new zc(this.lng-r,this.lat-e),new zc(this.lng+r,this.lat+e))},zc.convert=function(t){if(t instanceof zc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new zc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new zc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Dc=2*Math.PI*Ic;function Rc(t){return Dc*Math.cos(t*Math.PI/180)}function Fc(t){return(180+t)/360}function Bc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Nc(t,e){return t/Rc(e)}function jc(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Uc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Uc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=zc.convert(t);return new Uc(Fc(r.lng),Bc(r.lat),Nc(e,r.lat))},Uc.prototype.toLngLat=function(){return new zc(360*this.x-180,jc(this.y))},Uc.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Rc(jc(e));var t,e},Uc.prototype.meterInMercatorCoordinateUnits=function(){return 1/Dc*(t=jc(this.y),1/Math.cos(t*Math.PI/180));var t};var Vc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Gc(0,t,t,e,r)};Vc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Vc.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Pc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Pc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},Vc.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new a((t.x*e-this.x)*po,(t.y*e-this.y)*po)},Vc.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var qc=function(t,e){this.wrap=t,this.canonical=e,this.key=Gc(t,e.z,e.z,e.x,e.y)},Hc=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new Vc(r,+n,+i),this.key=Gc(e,t,r,n,i)};function Gc(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}Hc.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},Hc.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new Hc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Hc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Hc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Gc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Gc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Hc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},Hc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Hc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Hc(e,this.wrap,e,r,n),new Hc(e,this.wrap,e,r+1,n),new Hc(e,this.wrap,e,r,n+1),new Hc(e,this.wrap,e,r+1,n+1)]},Hc.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},Hc.prototype.wrapped=function(){return new Hc(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},Hc.prototype.unwrapTo=function(t){return new Hc(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},Hc.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},Hc.prototype.toUnwrapped=function(){return new qc(this.wrap,this.canonical)},Hc.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},Hc.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new Uc(t.x-this.wrap,t.y))},oi("CanonicalTileID",Vc),oi("OverscaledTileID",Hc,{omit:["posMatrix"]});var Wc=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return k('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};Wc.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},Wc.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},Wc.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},Wc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Wc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Wc.prototype.getPixels=function(){return new $o({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Wc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,u=a;u<o;u++)for(var c=n;c<i;c++)this.data[this._idx(c,u)]=t.data[this._idx(c+s,u+l)]},oi("DEMData",Wc);var Yc=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};Yc.prototype.encode=function(t){return this._stringToNumber[t]},Yc.prototype.decode=function(t){return this._numberToString[t]};var Xc=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},Zc={geometry:{configurable:!0}};Zc.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},Zc.geometry.set=function(t){this._geometry=t},Xc.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(Xc.prototype,Zc);var Kc=function(){this.state={},this.stateChanges={},this.deletedStates={}};Kc.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},p(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r)this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]},Kc.prototype.removeFeatureState=function(t,e,r){if(null!==this.deletedStates[t]){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}},Kc.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=p({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},Kc.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},Kc.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),p(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var u in this.deletedStates[o]){if(null===this.deletedStates[o][u])this.state[o][u]={};else for(var c=0,f=Object.keys(this.deletedStates[o][u]);c<f.length;c+=1){var h=f[c];delete this.state[o][u][h]}s[u]=this.state[o][u]}r[o]=r[o]||{},p(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t)t[d].setFeatureState(r,e)};var Jc=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new ti(po,16,0),this.grid3D=new ti(po,16,0),this.featureIndexArray=new Ia,this.promoteId=e};function $c(t,e,r,n,i){return b(t,(function(t,a){var o=e instanceof Vi?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function Qc(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function tf(t,e){return e-t}Jc.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var u=e[l],c=[1/0,1/0,-1/0,-1/0],f=0;f<u.length;f++){var h=u[f];c[0]=Math.min(c[0],h.x),c[1]=Math.min(c[1],h.y),c[2]=Math.max(c[2],h.x),c[3]=Math.max(c[3],h.y)}c[0]<po&&c[1]<po&&c[2]>=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Jc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Ol(this.rawTileData)).layers,this.sourceLayerCoder=new Yc(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},Jc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=po/t.tileSize/t.scale,l=An(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Qc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Qc(t.cameraQueryGeometry),d=0,v=this.grid3D.query(p.minX-c,p.minY-c,p.maxX+c,p.maxY+c,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&i>=l.y)return!0}var u=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var c=0,f=u;c<f.length;c+=1)if(Co(t,f[c]))return!0;for(var h=0;h<t.length-1;h++)if(Po(t[h],t[h+1],u))return!0;return!1}(t.cameraQueryGeometry,e-c,r-c,n+c,i+c)}));d<v.length;d+=1){var g=v[d];h.push(g)}h.sort(tf);for(var y,m={},x=function(a){var c=h[a];if(c!==y){y=c;var f=i.featureIndexArray.get(c),p=null;i.loadMatchingFeature(m,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=yo(e)),r.queryIntersectsFeature(u,e,n,p,i.z,t.transform,s,t.pixelPosMatrix)}))}},b=0;b<h.length;b++)x(b);return m},Jc.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,u,c){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),d=this.vtLayers[h].feature(n);if(i.needGeometry){var v=mo(d,!0);if(!i.filter(new zi(this.tileID.overscaledZ),v,this.tileID.canonical))return}else if(!i.filter(new zi(this.tileID.overscaledZ),d))return;for(var g=this.getId(d,h),y=0;y<f.length;y++){var m=f[y];if(!(a&&a.indexOf(m)<0)){var x=s[m];if(x){var b={};void 0!==g&&u&&(b=u.getState(x.sourceLayer||"_geojsonTileLayer",g));var _=p({},l[m]);_.paint=$c(_.paint,x.paint,d,b,o),_.layout=$c(_.layout,x.layout,d,b,o);var w=!c||c(d,x,b);if(w){var T=new Xc(d,this.z,this.x,this.y,g);T.layer=_;var k=t[m];void 0===k&&(k=t[m]=[]),k.push({featureIndex:n,feature:T,intersectionZ:w})}}}}}},Jc.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var u=An(i),c=0,f=t;c<f.length;c+=1){var h=f[c];this.loadMatchingFeature(l,r,n,h,u,a,o,s,e)}return l},Jc.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1)if(t===i[n])return!0;return!1},Jc.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},oi("FeatureIndex",Jc,{omit:["rawTileData","sourceLayerCoder"]});var ef=function(t,e){this.tileID=t,this.uid=v(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};ef.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<N.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},ef.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},ef.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof hc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof hc&&o.hasRTLText){this.hasRTLText=!0,Ii.isLoading()||Ii.isLoaded()||"deferred"!==Pi()||Oi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new Aa},ef.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},ef.prototype.getBucket=function(t){return this.buckets[t.id]},ef.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Ec(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Ec(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},ef.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},ef.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,u){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:u,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},ef.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=An(e&&e.filter),s=this.tileID.canonical,l=s.z,u=s.x,c=s.y,f={z:l,x:u,y:c},h=0;h<a.length;h++){var p=a.feature(h);if(o.needGeometry){var d=mo(p,!0);if(!o.filter(new zi(this.tileID.overscaledZ),d,this.tileID.canonical))continue}else if(!o.filter(new zi(this.tileID.overscaledZ),p))continue;var v=r.getId(p,i),g=new Xc(p,l,u,c,v);g.tile=f,t.push(g)}}},ef.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},ef.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},ef.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=E(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},ef.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},ef.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},ef.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},ef.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<N.now()},ef.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},ef.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=N.now()+t},ef.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1)r[i[n]]=!0;this.dependencies[t]=r},ef.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1)if(a[s[o]])return!0}return!1};var rf=s.performance,nf=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},rf.mark(this._marks.start)};nf.prototype.finish=function(){rf.mark(this._marks.end);var t=rf.getEntriesByName(this._marks.measure);return 0===t.length&&(rf.measure(this._marks.measure,this._marks.start,this._marks.end),t=rf.getEntriesByName(this._marks.measure),rf.clearMarks(this._marks.start),rf.clearMarks(this._marks.end),rf.clearMeasures(this._marks.measure)),t},t.Actor=Cc,t.AlphaImage=Jo,t.CanonicalTileID=Vc,t.CollisionBoxArray=Aa,t.Color=ue,t.DEMData=Wc,t.DataConstantProperty=qi,t.DictionaryCoder=Yc,t.EXTENT=po,t.ErrorEvent=Dt,t.EvaluationParameters=zi,t.Event=zt,t.Evented=Rt,t.FeatureIndex=Jc,t.FillBucket=Us,t.FillExtrusionBucket=il,t.ImageAtlas=su,t.ImagePosition=au,t.LineBucket=vl,t.LngLat=zc,t.LngLatBounds=Oc,t.MercatorCoordinate=Uc,t.ONE_EM=Ll,t.OverscaledTileID=Hc,t.Point=a,t.Point$1=a,t.Properties=Xi,t.Protobuf=Ol,t.RGBAImage=$o,t.RequestManager=W,t.RequestPerformance=nf,t.ResourceType=_t,t.SegmentVector=Da,t.SourceFeatureState=Kc,t.StructArrayLayout1ui2=wa,t.StructArrayLayout2f1f2i16=pa,t.StructArrayLayout2i4=ra,t.StructArrayLayout3ui6=va,t.StructArrayLayout4i8=na,t.SymbolBucket=hc,t.Texture=Ec,t.Tile=ef,t.Transitionable=Fi,t.Uniform1f=Ka,t.Uniform1i=Za,t.Uniform2f=Ja,t.Uniform3f=$a,t.Uniform4f=Qa,t.UniformColor=to,t.UniformMatrix4f=ro,t.UnwrappedTileID=qc,t.ValidationError=Bt,t.WritingMode=lu,t.ZoomHistory=hi,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=lc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=u,t.bindAll=m,t.browser=N,t.cacheEntryPossiblyAdded=function(t){++xt>ht&&(t.getActor().send("enforceCacheSizeLimit",ft),xt=0)},t.clamp=f,t.clearTileCache=function(t){var e=s.caches.delete(ct);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Fu,t.clone=function(t){var e=new Fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=w,t.clone$2=function(t){var e=new Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=j,t.create=function(){var t=new Fo(16);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Fo(9);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Fo(4);return Fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=fn,t.createLayout=ta,t.createStyleLayer=function(t){return"custom"===t.type?new _c(t):new wc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n<e.length;n++)if(!t(e[n],r[n]))return!1;return!0}if("object"==typeof e&&null!==e&&null!==r){if("object"!=typeof r)return!1;if(Object.keys(e).length!==Object.keys(r).length)return!1;for(var i in e)if(!t(e[i],r[i]))return!1;return!0}return e===r},t.dot=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]},t.dot$1=function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},t.ease=c,t.emitValidationErrors=Qn,t.endsWith=x,t.enforceCacheSizeLimit=function(t){dt(),rt&&rt.then((function(e){e.keys().then((function(r){for(var n=0;n<r.length-t;n++)e.delete(r[n])}))}))},t.evaluateSizeForFeature=Su,t.evaluateSizeForZoom=Eu,t.evaluateVariableOffset=$u,t.evented=Ci,t.extend=p,t.featureFilter=An,t.filterObject=_,t.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},t.getAnchorAlignment=_u,t.getAnchorJustification=Qu,t.getArrayBuffer=Mt,t.getImage=Pt,t.getJSON=function(t,e){return At(p(t,{type:"json"}),e)},t.getRTLTextPluginStatus=Pi,t.getReferrer=Tt,t.getVideo=function(t,e){var r,n,i=s.document.createElement("video");i.muted=!0,i.onloadstart=function(){e(null,i)};for(var a=0;a<t.length;a++){var o=s.document.createElement("source");r=t[a],n=void 0,(n=s.document.createElement("a")).href=r,n.protocol===s.document.location.protocol&&n.host===s.document.location.host||(i.crossOrigin="Anonymous"),o.src=t[a],i.appendChild(o)}return{cancel:function(){}}},t.identity=Bo,t.invert=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],u=e[7],c=e[8],f=e[9],h=e[10],p=e[11],d=e[12],v=e[13],g=e[14],y=e[15],m=r*s-n*o,x=r*l-i*o,b=r*u-a*o,_=n*l-i*s,w=n*u-a*s,T=i*u-a*l,k=c*v-f*d,A=c*g-h*d,M=c*y-p*d,S=f*g-h*v,E=f*y-p*v,L=h*y-p*g,C=m*L-x*E+b*S+_*M-w*A+T*k;return C?(C=1/C,t[0]=(s*L-l*E+u*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(v*T-g*w+y*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-u*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-y*x)*C,t[7]=(c*T-h*b+p*x)*C,t[8]=(o*E-s*M+u*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-v*b+y*m)*C,t[11]=(f*b-c*w-p*m)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(v*x-d*_-g*m)*C,t[15]=(c*_-f*x+h*m)*C,t):null},t.isChar=pi,t.isMapboxURL=Y,t.keysDifference=function(t,e){var r=[];for(var n in t)n in e||r.push(n);return r},t.makeRequest=At,t.mapObject=b,t.mercatorXfromLng=Fc,t.mercatorYfromLat=Bc,t.mercatorZfromAltitude=Nc,t.mul=jo,t.multiply=No,t.mvt=tl,t.nextPowerOfTwo=function(t){return t<=1?1:Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))},t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=er,t.offscreenCanvasSupported=bt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t},t.parseGlyphPBF=function(t){return new Ol(t).readFields(Ql,[])},t.pbf=Ol,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=po/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,u=t.layers[0]._unevaluatedLayout._values,c={};if("composite"===t.textSizeData.kind){var 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g=d.toString(),_=l.get("text-letter-spacing").evaluate(a,{},o)*Ll,A=function(t){for(var e=0,r=t;e<r.length;e+=1)if(n=r[e].charCodeAt(0),pi.Arabic(n)||pi["Arabic Supplement"](n)||pi["Arabic Extended-A"](n)||pi["Arabic Presentation Forms-A"](n)||pi["Arabic Presentation Forms-B"](n))return!1;var n;return!0}(g)?_:0,M=l.get("text-anchor").evaluate(a,{},o),S=l.get("text-variable-anchor");if(!S){var E=l.get("text-radial-offset").evaluate(a,{},o);v=E?$u(M,[E*Ll,Ju]):l.get("text-offset").evaluate(a,{},o).map((function(t){return t*Ll}))}var L=m?"center":l.get("text-justify").evaluate(a,{},o),C=l.get("symbol-placement"),P="point"===C?l.get("text-max-width").evaluate(a,{},o)*Ll:0,O=function(){t.allowVerticalPlacement&&di(g)&&(p.vertical=hu(d,e,r,i,s,P,y,M,"left",A,v,lu.vertical,!0,C,f,u))};if(!m&&S){for(var I="auto"===L?S.map((function(t){return Qu(t)})):[L],z=!1,D=0;D<I.length;D++){var R=I[D];if(!p.horizontal[R])if(z)p.horizontal[R]=p.horizontal[0];else{var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if("Point"===o||"MultiPoint"===o||"LineString"===o)i=yt(a.geometry,e);else if("MultiLineString"===o||"Polygon"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(yt(a.geometry[s],e))}else if("MultiPolygon"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],u=0;u<a.geometry[s].length;u++)l.push(yt(a.geometry[s][u],e));i.push(l)}r.push(tt(a.id,o,i,a.tags))}return r}function yt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function mt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],u=l.geometry,c=l.type;if(l.geometry=[],1===c)for(n=0;n<u.length;n+=2)l.geometry.push(xt(u[n],u[n+1],e,a,o,s));else for(n=0;n<u.length;n++){var f=[];for(i=0;i<u[n].length;i+=2)f.push(xt(u[n][i],u[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function xt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function bt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,_t(o,t[s],a,i);var l=t[s].minX,u=t[s].minY,c=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),u<o.minY&&(o.minY=u),c>o.maxX&&(o.maxX=c),f>o.maxY&&(o.maxY=f)}return o}function _t(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)wt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)wt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var u=i[l];for(s=0;s<u.length;s++)wt(o,u[s],t,r,!0,0===s)}if(o.length){var c=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in c={},e.tags)c[f]=e.tags[f];c.mapbox_clip_start=i.start/i.size,c.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:c};null!==e.id&&(h.id=e.id),t.features.push(h)}}function wt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function Tt(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)rt(r,t.features[n],e,n);else"Feature"===t.type?rt(r,t,e):rt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=lt(t,1,-1-r,r,0,-1,2,e),a=lt(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=lt(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=gt(i,1).concat(n)),a&&(n=n.concat(gt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function kt(t,e,r){return 32*((1<<t)*r+e)+t}function At(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}V.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new j(s,K,J,a,Float32Array),r&&console.timeEnd(o);for(var u=i;u>=n;u--){var c=+Date.now();s=this._cluster(s,u),this.trees[u]=new j(s,K,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",u,s.length,+Date.now()-c)}return r&&console.timeEnd("total time"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],u=[],c=0,f=l.range(Y(r),X(a),Y(i),X(n));c<f.length;c+=1){var h=f[c],p=l.points[h];u.push(p.numPoints?G(p):this.points[p.index])}return u},V.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,u=i.within(a.x,a.y,o);l<u.length;l+=1){var c=u[l],f=i.points[c];f.parentId===t&&s.push(f.numPoints?G(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},V.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},V.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,u=(r+1+s)/i,c={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,u),n.points,e,r,i,c),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,u),n.points,i,r,i,c),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,u),n.points,-1,r,i,c),c.features.length?c:null},V.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},V.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},V.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],u=l.numPoints,c={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:u?W(l):this.points[l.index].properties},f=void 0;u?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(c.id=f),a.features.push(c)}},V.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(+t,this.options.maxZoom+1))},V.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=n.minPoints,l=i/(a*Math.pow(2,e)),u=0;u<t.length;u++){var c=t[u];if(!(c.zoom<=e)){c.zoom=e;for(var f=this.trees[e+1],h=f.within(c.x,c.y,l),p=c.numPoints||1,d=p,v=0,g=h;v<g.length;v+=1){var y=g[v],m=f.points[y];m.zoom>e&&(d+=m.numPoints||1)}if(d>=s){for(var x=c.x*p,b=c.y*p,_=o&&p>1?this._map(c,!0):null,w=(u<<5)+(e+1)+this.points.length,T=0,k=h;T<k.length;T+=1){var A=k[T],M=f.points[A];if(!(M.zoom<=e)){M.zoom=e;var S=M.numPoints||1;x+=M.x*S,b+=M.y*S,M.parentId=w,o&&(_||(_=this._map(c,!0)),o(_,this._map(M)))}}c.parentId=w,r.push(q(x/d,b/d,w,d,_))}else if(r.push(c),d>1)for(var E=0,L=h;E<L.length;E+=1){var C=L[E],P=f.points[C];P.zoom<=e||(P.zoom=e,r.push(P))}}}return r},V.prototype._getOriginId=function(t){return t-this.points.length>>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?Z({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?Z({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,u=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var c=1<<e,f=kt(e,r,n),h=this.tiles[f];if(!h&&(u>1&&console.time("creation"),h=this.tiles[f]=bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),u)){u>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){u>1&&console.time("clipping");var v,g,y,m,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;v=g=y=m=null,x=lt(t,c,r-_,r+T,0,h.minX,h.maxX,l),b=lt(t,c,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(v=lt(x,c,n-_,n+T,1,h.minY,h.maxY,l),g=lt(x,c,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(y=lt(b,c,n-_,n+T,1,h.minY,h.maxY,l),m=lt(b,c,n+w,n+k,1,h.minY,h.maxY,l),b=null),u>1&&console.timeEnd("clipping"),s.push(v||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(m||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=kt(t,e=(e%o+o)%o,r);if(this.tiles[s])return mt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,u=t,c=e,f=r;!l&&u>0;)u--,c=Math.floor(c/2),f=Math.floor(f/2),l=this.tiles[kt(u,c,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",u,c,f),a>1&&console.time("drilling down"),this.splitTile(l.source,u,c,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?mt(this.tiles[s],i):null):null};var Mt=function(e){function r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{if(n.filter){var s=t.createExpression(n.filter,{type:"boolean","property-type":"data-driven",overridable:!1,transition:!1});if("error"===s.result)throw new Error(s.value.map((function(t){return t.key+": "+t.message})).join(", "));var l=o.features.filter((function(t){return s.value.evaluate({zoom:0},t)}));o={type:"FeatureCollection",features:l}}e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),u=0,c=l;u<c.length;u+=1){var f=c[u],h=n[f],p=h[0],d=h[1],v=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=v.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new Tt(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var u={};if(i){var c=i.finish();c&&(u.resourceTiming={},u.resourceTiming[n.source]=JSON.parse(JSON.stringify(c)))}r(null,u)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var St=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:Mt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return St.prototype.setReferrer=function(t,e){this.referrer=e},St.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. 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x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1)(0,o[i])(t,e);delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,u=o.glyph;(n[s]||(n[s]={}))[l]=u&&{id:u.id,bitmap:u.bitmap.clone(),metrics:u.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w="-transition",T=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,w)?this._transitionable.setTransition(n.slice(0,-11),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),k=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};k.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},k.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var u=t[l];i=s*r,a=(s+=u)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===u})}return n},k.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=void 0,d=i/r*(n+1);if(l.isDash){var v=n-Math.abs(d);p=Math.sqrt(h*h+v*v)}else p=n-Math.sqrt(h*h+d*d);this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],u=0;u<this.width;u++){u/l.right>1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=l.isDash?h:-h;this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var u={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,u},k.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var A=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function M(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}A.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},A.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},A.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},A.Actor=t.Actor;var S=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};S.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},S.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var E=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setSourceProperty=function(t){this._tileJSONRequest&&this._tileJSONRequest.cancel(),t(),this.map.style.sourceCaches[this.id].clearTiles(),this.load()},r.prototype.setTiles=function(t){var e=this;return this.setSourceProperty((function(){e._options.tiles=t})),this},r.prototype.setUrl=function(t){var e=this;return this.setSourceProperty((function(){e.url=t,e._options.url=t})),this},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=M(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new S(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),C=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(L),P=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,minPoints:Math.max(2,n.clusterMinPoints||2),extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties,filter:n.filter},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),O=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var u=i-r,c=a-n,f=Math.max(u,c),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var z=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),D=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),R={vector:E,raster:L,"raster-dem":C,geojson:P,video:z,image:I,canvas:D};function F(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function B(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(N);for(var u=[],c=0,f=l;c<f.length;c+=1){var h=f[c];u.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,F(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var u in o)for(var c=o[u],f=l[u]=l[u]||{},h=e[u]=e[u]||[],p=0,d=c;p<d.length;p+=1){var v=d[p];f[v.featureIndex]||(f[v.featureIndex]=!0,h.push(v))}}return e}(u);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function N(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var j=function(t,e){this.max=t,this.onRemove=e,this.reset()};j.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},j.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var U=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};U.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},U.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},U.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var V={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},q=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};q.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},q.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},q.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],i=e.attributes[n.name];void 0!==i&&t.enableVertexAttribArray(i)}},q.prototype.setVertexAttribPointers=function(t,e,r){for(var n=0;n<this.attributes.length;n++){var i=this.attributes[n],a=e.attributes[i.name];void 0!==a&&t.vertexAttribPointer(a,i.components,t[V[i.type]],!1,this.itemSize,i.offset+this.itemSize*(r||0))}},q.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var H=function(t){this.gl=t.gl,this.default=this.getDefault(),this.current=this.default,this.dirty=!1};H.prototype.get=function(){return this.current},H.prototype.set=function(t){},H.prototype.getDefault=function(){return this.default},H.prototype.setDefault=function(){this.set(this.default)};var G=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.clearColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(H),W=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 1},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearDepth(t),this.current=t,this.dirty=!1)},e}(H),Y=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.clearStencil(t),this.current=t,this.dirty=!1)},e}(H),X=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return[!0,!0,!0,!0]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.colorMask(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),Z=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.depthMask(t),this.current=t,this.dirty=!1)},e}(H),K=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 255},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.stencilMask(t),this.current=t,this.dirty=!1)},e}(H),J=function(t){function e(){t.apply(this,arguments)}return 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t=this.gl;return[t.ONE,t.ZERO]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||this.dirty)&&(this.gl.blendFunc(t[0],t[1]),this.current=t,this.dirty=!1)},e}(H),at=function(e){function r(){e.apply(this,arguments)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getDefault=function(){return t.Color.transparent},r.prototype.set=function(t){var e=this.current;(t.r!==e.r||t.g!==e.g||t.b!==e.b||t.a!==e.a||this.dirty)&&(this.gl.blendColor(t.r,t.g,t.b,t.a),this.current=t,this.dirty=!1)},r}(H),ot=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.FUNC_ADD},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.blendEquation(t),this.current=t,this.dirty=!1)},e}(H),st=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;t?e.enable(e.CULL_FACE):e.disable(e.CULL_FACE),this.current=t,this.dirty=!1}},e}(H),lt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.BACK},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.cullFace(t),this.current=t,this.dirty=!1)},e}(H),ut=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.CCW},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.frontFace(t),this.current=t,this.dirty=!1)},e}(H),ct=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.useProgram(t),this.current=t,this.dirty=!1)},e}(H),ft=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return this.gl.TEXTURE0},e.prototype.set=function(t){(t!==this.current||this.dirty)&&(this.gl.activeTexture(t),this.current=t,this.dirty=!1)},e}(H),ht=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){var t=this.gl;return[0,0,t.drawingBufferWidth,t.drawingBufferHeight]},e.prototype.set=function(t){var e=this.current;(t[0]!==e[0]||t[1]!==e[1]||t[2]!==e[2]||t[3]!==e[3]||this.dirty)&&(this.gl.viewport(t[0],t[1],t[2],t[3]),this.current=t,this.dirty=!1)},e}(H),pt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindFramebuffer(e.FRAMEBUFFER,t),this.current=t,this.dirty=!1}},e}(H),dt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindRenderbuffer(e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(H),vt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindTexture(e.TEXTURE_2D,t),this.current=t,this.dirty=!1}},e}(H),gt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.bindBuffer(e.ARRAY_BUFFER,t),this.current=t,this.dirty=!1}},e}(H),yt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){var e=this.gl;e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,t),this.current=t,this.dirty=!1},e}(H),mt=function(t){function e(e){t.call(this,e),this.vao=e.extVertexArrayObject}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e.prototype.set=function(t){this.vao&&(t!==this.current||this.dirty)&&(this.vao.bindVertexArrayOES(t),this.current=t,this.dirty=!1)},e}(H),xt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return 4},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_ALIGNMENT,t),this.current=t,this.dirty=!1}},e}(H),bt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),_t=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return!1},e.prototype.set=function(t){if(t!==this.current||this.dirty){var e=this.gl;e.pixelStorei(e.UNPACK_FLIP_Y_WEBGL,t),this.current=t,this.dirty=!1}},e}(H),wt=function(t){function e(e,r){t.call(this,e),this.context=e,this.parent=r}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getDefault=function(){return null},e}(H),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.setDirty=function(){this.dirty=!0},e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(wt),kt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(wt),At=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new Tt(t,a),n&&(this.depthAttachment=new kt(t,a))};At.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var Mt=function(t,e,r){this.func=t,this.mask=e,this.range=r};Mt.ReadOnly=!1,Mt.ReadWrite=!0,Mt.disabled=new Mt(519,Mt.ReadOnly,[0,1]);var St=7680,Et=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Et.disabled=new Et({func:519,mask:0},0,0,St,St,St);var Lt=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};Lt.Replace=[1,0],Lt.disabled=new Lt(Lt.Replace,t.Color.transparent,[!1,!1,!1,!1]),Lt.unblended=new Lt(Lt.Replace,t.Color.transparent,[!0,!0,!0,!0]),Lt.alphaBlended=new Lt([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Ct=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Ct.disabled=new Ct(!1,1029,2305),Ct.backCCW=new Ct(!0,1029,2305);var Pt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new G(this),this.clearDepth=new W(this),this.clearStencil=new Y(this),this.colorMask=new X(this),this.depthMask=new Z(this),this.stencilMask=new K(this),this.stencilFunc=new J(this),this.stencilOp=new $(this),this.stencilTest=new Q(this),this.depthRange=new tt(this),this.depthTest=new et(this),this.depthFunc=new rt(this),this.blend=new nt(this),this.blendFunc=new it(this),this.blendColor=new at(this),this.blendEquation=new ot(this),this.cullFace=new st(this),this.cullFaceSide=new lt(this),this.frontFace=new ut(this),this.program=new ct(this),this.activeTexture=new ft(this),this.viewport=new ht(this),this.bindFramebuffer=new pt(this),this.bindRenderbuffer=new dt(this),this.bindTexture=new vt(this),this.bindVertexBuffer=new gt(this),this.bindElementBuffer=new yt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new mt(this),this.pixelStoreUnpack=new xt(this),this.pixelStoreUnpackPremultiplyAlpha=new bt(this),this.pixelStoreUnpackFlipY=new _t(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query"),this.maxTextureSize=t.getParameter(t.MAX_TEXTURE_SIZE)};Pt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Pt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Pt.prototype.createIndexBuffer=function(t,e){return new U(this,t,e)},Pt.prototype.createVertexBuffer=function(t,e,r){return new q(this,t,e,r)},Pt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Pt.prototype.createFramebuffer=function(t,e,r){return new At(this,t,e,r)},Pt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Pt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Pt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Pt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Pt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,Lt.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Pt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ot=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new R[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new j(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(It).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(zt(this._source.type)){for(var u={},c={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],v=this._tiles[p];if(v&&!(v.fadeEndTime&&v.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),u[g.tileID.key]=g.tileID),c[p]=d}}for(var y in this._retainLoadedChildren(c,a,s,l),u)l[y]||(this._coveredTiles[y]=!0,l[y]=u[y])}for(var m in l)this._tiles[m].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,u=t;l<u.length;l+=1){var c=u[l],f=this._addTile(c);n[c.key]=c,f.hasData()||e<this._source.maxzoom&&(s[c.key]=c)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],v=this._tiles[d.key];if(!v.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var m=d.children(this._source.maxzoom);if(n[m[0].key]&&n[m[1].key]&&n[m[2].key]&&n[m[3].key])continue}for(var x=v.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(v=this.getTile(_))&&x&&(v=this._addTile(_)),v&&(n[_.key]=_,x=v.wasRequested(),v.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),u=s.map((function(t){return o.pointCoordinate(t)})),c=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,v=0,g=u;v<g.length;v+=1){var y=g[v];f=Math.min(f,y.x),h=Math.min(h,y.y),p=Math.max(p,y.x),d=Math.max(d,y.y)}for(var m=function(e){var n=i._tiles[c[e]];if(!n.holdingForFade()){var s=n.tileID,v=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/v,y=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(y[0].x-g<t.EXTENT&&y[0].y-g<t.EXTENT&&y[1].x+g>=0&&y[1].y+g>=0){var m=l.map((function(t){return s.getTilePoint(t)})),x=u.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:m,cameraQueryGeometry:x,scale:v})}}},x=0;x<c.length;x++)m(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(zt(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function zt(t){return"raster"===t||"image"===t||"video"===t}function Dt(){return new t.window.Worker(oa.workerUrl)}Ot.maxOverzooming=10,Ot.maxUnderzooming=3;var Rt="mapboxgl_preloaded_worker_pool",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Ft.workerCount;)this.workers.push(new Dt);return this.active[t]=!0,this.workers.slice()},Ft.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Ft.prototype.isPreloaded=function(){return!!this.active[Rt]},Ft.prototype.numActive=function(){return Object.keys(this.active).length};var Bt,Nt=Math.floor(t.browser.hardwareConcurrency/2);function jt(){return Bt||(Bt=new Ft),Bt}function Ut(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function Vt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Ut(t[n],e[t[n].ref]));return t}Ft.workerCount=Math.max(Math.min(Nt,6),1);var qt={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Ht(t,e,r){r.push({command:qt.addSource,args:[t,e[t]]})}function Gt(t,e,r){e.push({command:qt.removeSource,args:[t]}),r[t]=!0}function Wt(t,e,r,n){Gt(t,r,n),Ht(t,e,r)}function Yt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Xt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Zt(t){return t.id}function Kt(t,e){return t[e.id]=e,t}function Jt(e,r){if(!e)return[{command:qt.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:qt.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:qt.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:qt.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:qt.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:qt.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:qt.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:qt.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:qt.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:qt.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Gt(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Yt(e,r,a)?n.push({command:qt.setGeoJSONSourceData,args:[a,r[a].data]}):Wt(a,r,n,i)):Ht(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:qt.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,u,c,f=(e=e||[]).map(Zt),h=r.map(Zt),p=e.reduce(Kt,{}),d=r.reduce(Kt,{}),v=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:qt.removeLayer,args:[o]}),v.splice(v.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],v[v.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:qt.removeLayer,args:[o]}),v.splice(v.lastIndexOf(o,v.length-a),1)):a++,u=v[v.length-i],n.push({command:qt.addLayer,args:[d[o],u]}),v.splice(v.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(c in Xt(s.layout,l.layout,n,o,null,qt.setLayoutProperty),Xt(s.paint,l.paint,n,o,null,qt.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:qt.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:qt.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(c)&&"layout"!==c&&"paint"!==c&&"filter"!==c&&"metadata"!==c&&"minzoom"!==c&&"maxzoom"!==c&&(0===c.indexOf("paint.")?Xt(s[c],l[c],n,o,c.slice(6),qt.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:qt.setLayerProperty,args:[o,c,l[c]]}));for(c in l)l.hasOwnProperty(c)&&!s.hasOwnProperty(c)&&"layout"!==c&&"paint"!==c&&"filter"!==c&&"metadata"!==c&&"minzoom"!==c&&"maxzoom"!==c&&(0===c.indexOf("paint.")?Xt(s[c],l[c],n,o,c.slice(6),qt.setPaintProperty):t.deepEqual(s[c],l[c])||n.push({command:qt.setLayerProperty,args:[o,c,l[c]]}))}else n.push({command:qt.removeLayer,args:[o]}),u=v[v.lastIndexOf(o)+1],n.push({command:qt.addLayer,args:[l,u]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:qt.setStyle,args:[r]}]}return n}var $t=function(t,e){this.reset(t,e)};$t.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},$t.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function te(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function ee(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function re(e,r){var n=[e.x,e.y,0,1];pe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ne(t,e){return.5+t/e*.5}function ie(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var u=i?e.textSizeData:e.iconSizeData,c=t.evaluateSizeForZoom(u,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,v=n.transform.width/n.transform.height,g=!1,y=0;y<d.length;y++){var m=d.get(y);if(m.hidden||m.writingMode===t.WritingMode.vertical&&!g)he(m.numGlyphs,h);else{g=!1;var x=[m.anchorX,m.anchorY,0,1];if(t.transformMat4(x,x,r),ie(x,f)){var b=x[3],_=ne(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(u,c,m),T=s?w/_:w*_,k=new t.Point(m.anchorX,m.anchorY),A=re(k,a).point,M={},S=le(m,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&le(m,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,v).notEnoughRoom)&&he(m.numGlyphs,h)}else he(m.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function oe(t,e,r,n,i,a,o,s,l,u,c){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),v=e.getoffsetX(f-1),g=ce(t*d,r,n,i,a,o,s.segment,h,p,l,u,c);if(!g)return null;var y=ce(t*v,r,n,i,a,o,s.segment,h,p,l,u,c);return y?{first:g,last:y}:null}function se(e,r,n,i){return e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=r/24,y=e.lineOffsetX*g,m=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=oe(g,l,y,m,n,f,h,e,u,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}v=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)v.push(ce(g*l.getoffsetX(M),y,m,n,f,h,e.segment,b,_,u,o,p));v.push(w.last)}else{if(i&&!n){var S=re(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(u.getx(E),u.gety(E)),C=re(L,a),P=C.signedDistanceFromCamera>0?C.point:ue(h,L,S,1,a),O=se(e.writingMode,S,P,d);if(O)return O}var I=ce(g*l.getoffsetX(e.glyphStartIndex),y,m,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,u,o,p);if(!I)return{notEnoughRoom:!0};v=[I]}for(var z=0,D=v;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(c,R.point,R.angle)}return{}}function ue(t,e,r,n,i){var a=re(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function ce(e,r,n,i,a,o,s,l,u,c,f,h){var p=i?e-r:e+r,d=p>0?1:-1,v=0;i&&(d*=-1,v=Math.PI),d<0&&(v+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,m=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=u)return null;if(m=y,w.push(y),void 0===(y=h[g])){var T=new t.Point(c.getx(g),c.gety(g)),k=re(T,f);if(k.signedDistanceFromCamera>0)y=h[g]=k.point;else{var A=g-d;y=ue(0===x?o:new t.Point(c.getx(A),c.gety(A)),T,m,_-x+1,f)}}x+=b,b=m.dist(y)}var M=(_-x)/b,S=y.sub(m),E=S.mult(M)._add(m);E._add(S._unit()._perp()._mult(n*d));var L=v+Math.atan2(y.y-m.y,y.x-m.x);return w.push(E),{point:E,angle:L,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var u=this.circles[3*l],c=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:u-f,y1:c-f,x2:u+f,y2:c+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var u=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,u,c,i),n?u.length>0:u},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,u=this.boxCells[i];if(null!==u)for(var c=this.bboxes,f=0,h=u;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=c[d+2]&&e<=c[d+3]&&r>=c[d+0]&&n>=c[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:c[d],y1:c[d+1],x2:c[d+2],y2:c[d+3]})}}}var v=this.circleCells[i];if(null!==v)for(var g=this.circles,y=0,m=v;y<m.length;y+=1){var x=m[y];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Qt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,u=o.seenUids,c=this.boxCells[i];if(null!==c)for(var f=this.bboxes,h=0,p=c;h<p.length;h+=1){var d=p[h];if(!u.box[d]){u.box[d]=!0;var v=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[v+0],f[v+1],f[v+2],f[v+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var y=this.circles,m=0,x=g;m<x.length;m+=1){var b=x[m];if(!u.circle[b]){u.circle[b]=!0;var _=3*b;if(this._circlesCollide(y[_],y[_+1],y[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Qt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),u=this._convertToYCellCoord(e),c=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Qt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Qt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Qt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var u=(o-i)/2,c=Math.abs(e-(i+u));if(c>u+r)return!1;if(l<=s||c<=u)return!0;var f=l-s,h=c-u;return f*f+h*h<=r*r};var fe=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function he(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(fe,3*n)}}function pe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var de=100,ve=function(t,e,r){void 0===e&&(e=new Qt(t.width+200,t.height+200,25)),void 0===r&&(r=new Qt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+de,this.screenBottomBoundary=t.height+de,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function ge(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}ve.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,u=t.x2*o+a.point.x,c=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,u,c)||!e&&this.grid.hitTest(s,l,u,c,i)?{box:[],offscreen:!1}:{box:[s,l,u,c],offscreen:this.isOffscreen(s,l,u,c)}},ve.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,u,c,f,h,p){var d=[],v=new t.Point(r.anchorX,r.anchorY),g=re(v,o),y=ne(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),m=(c?a/y:a*y)/t.ONE_EM,x=re(v,s).point,b=oe(m,i,r.lineOffsetX*m,r.lineOffsetY*m,!1,x,v,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*y+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new $t,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var O=1;O<L.path.length;O++)C.push(L.path[O]);var I=2.5*k;if(l){var z=C.map((function(t){return re(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/I)+1;for(var q=0;q<V;q++){var H=q/Math.max(V-1,1),G=S.lerp(H),W=G.x+de,Y=G.y+de;d.push(W,Y,k,0);var X=W-k,Z=Y-k,K=W+k,J=Y+k;if(T=T&&this.isOffscreen(X,Z,K,J),w=w||this.isInsideGrid(X,Z,K,J),!e&&this.grid.hitTestCircle(W,Y,k,f)&&(_=!0,!u))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!u&&_||!w?[]:d,offscreen:T,collisionDetected:_}},ve.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var u=l[s],c=new t.Point(u.x+de,u.y+de);n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y),r.push(c)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var v=d[p],g=v.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var y=[new t.Point(v.x1,v.y1),new t.Point(v.x2,v.y1),new t.Point(v.x2,v.y2),new t.Point(v.x1,v.y2)];t.polygonIntersectsPolygon(r,y)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},ve.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},ve.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},ve.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return pe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+de,(-i[1]/i[3]+1)/2*this.transform.height+de),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},ve.prototype.isOffscreen=function(t,e,r,n){return r<de||t>=this.screenRightBoundary||n<de||e>this.screenBottomBoundary},ve.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},ve.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var ye=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};ye.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new ye(t?t.text:null,e,r,i),this.icon=new ye(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var xe=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},be=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},_e=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},we=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function Te(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point(s+u[0]*a,l+u[1]*a)}function ke(e,r,n,i,a,o){var s=e.x1,l=e.x2,u=e.y1,c=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:u+p.y,x2:l+p.x,y2:c+p.y,anchorPointX:f,anchorPointY:h}}we.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var Ae=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new ve(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new we(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Me(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}Ae.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),c=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=ge(n,1,this.transform.zoom),v=te(f,h,p,this.transform,d),g=null;if(h){var y=ee(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,y)}this.retainedQueryData[a.bucketInstanceId]=new _e(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var m={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:v,labelToScreenMatrix:g,scale:u,textPixelRatio:c,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:m})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:m})}},Ae.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=[f.textOffset0,f.textOffset1],y=Te(t,r,n,g,i),m=this.collisionIndex.placeCollisionBox(ke(e,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate);if(!d||0!==this.collisionIndex.placeCollisionBox(ke(d,y.x,y.y,a,o,this.transform.angle),c,s,l,u.predicate).box.length)return m.box.length>0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(v=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:v},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:y,placedGlyphBoxes:m}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,u=a.textLabelPlaneMatrix,c=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,v=a.collisionGroup,g=s.get("text-optional"),y=s.get("icon-optional"),m=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=m&&(x||!o.hasIconData()||y),A=x&&(m||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,O=null,I=0,z=0,D=0;a.textFeatureIndex?I=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(I=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length&&(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),!(L&&L.box&&L.box.length));i+=1);else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,u=e.textBoxScale,c=w&&!x?r:null,h={box:[],offscreen:!1},p=m?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],y=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,m,f,l,v.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),W=t.evaluateSizeForFeature(o.textSizeData,d,G),Y=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,W,l,u,c,n,_,v.predicate,X,Y),T=m||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?ke(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,v.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(O=Z(a.verticalIconBox)).box.length>0:(O=Z(a.iconBox)).box.length>0,S=S&&O.offscreen}var K=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=y||0===e.numIconVertices;if(K||J?J?K||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,v.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,I,v.ID)),M&&O&&i.collisionIndex.insertCollisionBox(O.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,v.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,I,v.ID),n)){var $=o.bucketInstanceId,Q=i.collisionCircleArrays[$];void 0===Q&&(Q=i.collisionCircleArrays[$]=new be);for(var tt=0;tt<P.circles.length;tt+=4)Q.circles.push(P.circles[tt+0]),Q.circles.push(P.circles[tt+1]),Q.circles.push(P.circles[tt+2]),Q.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new xe(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},Ae.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var u=l[s];u>=0&&(e.text.placedSymbolArray.get(u).crossTileID=a>=0&&u!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},Ae.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],u=i[s];u?(this.opacities[s]=new me(u,n,l.text,l.icon),r=r||l.text!==u.text.placed||l.icon!==u.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var c in i){var f=i[c];if(!this.opacities[c]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[c]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},Ae.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},Ae.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),u=a.get("text-variable-anchor"),c="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},v=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,v=a.crossTileID,g=r[v],y=i.opacities[v];g?y=o:y||(y=p,i.opacities[v]=y),r[v]=!0;var m=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(m){var T=ze(y.text),k=_?De:T;d(e.text,s,k);var A=w?De:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=ze(y.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:De;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var O=C?De:L;d(e.icon,a.numVerticalIconVertices,O),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var I=e.collisionArrays[n];if(I){var z=new t.Point(0,0);if(I.textBox||I.verticalTextBox){var D=!0;if(u){var R=i.variableOffsets[v];R?(z=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),c&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}I.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||_,z.x,z.y),I.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&I.verticalIconBox);I.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,h?z.x:0,h?z.y:0),I.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)v(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var y=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=y.invProjMatrix,e.placementViewportMatrix=y.viewportMatrix,e.collisionCircleArray=y.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},Ae.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},Ae.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},Ae.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},Ae.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Le=Math.pow(2,17),Ce=Math.pow(2,16),Pe=Math.pow(2,9),Oe=Math.pow(2,8),Ie=Math.pow(2,1);function ze(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Le+e*Ce+r*Pe+e*Oe+r*Ie+e}var De=0,Re=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var Fe=function(t,e,r,n,i,a,o){this.placement=new Ae(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};Fe.prototype.isDone=function(){return this._done},Fe.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Ne.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=Be/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Ne.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,u=o;l<u.length;l+=1){var c=u[l];if(Math.abs(c.coord.x-s.x)<=n&&Math.abs(c.coord.y-s.y)<=n&&!r[c.crossTileID]){r[c.crossTileID]=!0,a.crossTileID=c.crossTileID;break}}}}};var je=function(){this.maxCrossTileID=0};je.prototype.generate=function(){return++this.maxCrossTileID};var Ue=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Ue.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Ue.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++)e.symbolInstances.get(n).crossTileID=0;this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var u=o[t.scaledTo(Number(a)).key];u&&u.findMatches(e.symbolInstances,t,i)}}for(var c=0;c<e.symbolInstances.length;c++){var f=e.symbolInstances.get(c);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Ne(t,e.symbolInstances,e.bucketInstanceId),!0},Ue.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Ue.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ve=function(){this.layerIndexes={},this.crossTileIDs=new je,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ve.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Ue);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],u=l.getBucket(t);u&&t.id===u.layerIds[0]&&(u.bucketInstanceId||(u.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,u,this.crossTileIDs)&&(i=!0),a[u.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ve.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var qe=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},He=t.pick(qt,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ge=t.pick(qt,["setCenter","setZoom","setBearing","setPitch"]),We=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),Ye=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new A(jt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new k(256,512),this.crossTileSymbolIndex=new Ve,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if(t.triggerPluginCompletionEvent(e),r&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(We,!1)},r.prototype._load=function(e,r){if(!r||!qe(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=Vt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new T(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,c())})),u=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){u=null,o||(o=t,a=e,c())}));function c(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],u=l.width,c=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,v=l.stretchX,g=l.stretchY,y=l.content,m=new t.RGBAImage({width:u,height:c});t.RGBAImage.copy(e,m,{x:f,y:h},{x:0,y:0},{width:u,height:c}),r[s]={data:m,pixelRatio:d,sdf:p,stretchX:v,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),u&&(u.cancel(),u=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}var l={};for(var u in this.sourceCaches){var c=this.sourceCaches[u];l[u]=c.used,c.used=!1}for(var f=0,h=this._order;f<h.length;f+=1){var p=h[f],d=this._layers[p];d.recalculate(e,this._availableImages),!d.isHidden(e.zoom)&&d.source&&(this.sourceCaches[d.source].used=!0)}for(var v in l){var g=this.sourceCaches[v];l[v]!==g.used&&g.fire(new t.Event("data",{sourceDataType:"visibility",dataType:"source",sourceId:v}))}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),qe(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=Vt(e.layers);var n=Jt(this.serialize(),e).filter((function(t){return!(t.command in Ge)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in He)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._afterImageUpdated(e)},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._afterImageUpdated(e)},r.prototype._afterImageUpdated=function(e){this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.dispatcher.broadcast("setImages",this._availableImages),this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,"sources."+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ot(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(qe(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is required to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var u=l[s][o];if(u)for(var c=0,f=u;c<f.length;c+=1){var h=f[c];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var v=this._order[d];if(r(v))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id]<d)break;p.push(y),i.pop()}else for(var m=0,x=t;m<x.length;m+=1){var b=x[m][v];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var u=[];for(var c in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[c]||u.push(B(this.sourceCaches[c],this._layers,this._serializedLayers,e,r,n));return this.placement&&u.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),u=[],c=0,f=Object.keys(l).map(Number);c<f.length;c+=1){var h=f[c];u.push(o[h])}u.sort(N);for(var p=function(){var r=v[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],u=n[a];u.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var c=0,f=u;c<f.length;c+=1){var h=f[c];o.push(h)}}},d=0,v=u;d<v.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var y in s)g(y);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(u)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&qe(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers)this._layers[e].setEventedParent(null);for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},u=0,c=this._order;u<c.length;u+=1){var f=c[u],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new Fe(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var v=0,g=this._order;v<g.length;v+=1){var y=g[v],m=this._layers[y];"symbol"===m.type&&this.placement.updateLayerOpacities(m,l[m.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);Ye.getSourceType=function(t){return R[t]},Ye.setSourceType=function(t,e){R[t]=e},Ye.registerForPluginStateChange=t.registerForPluginStateChange;var Xe=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ze=_r("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),Ke=_r("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Je=_r("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),$e=_r("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Qe=_r("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),tr=_r("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),er=_r("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),rr=_r("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),nr=_r("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),ir=_r("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),ar=_r("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),or=_r("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),sr=_r("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),lr=_r("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),ur=_r("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),cr=_r("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),fr=_r("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),hr=_r("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),pr=_r("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),dr=_r("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),vr=_r("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),gr=_r("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),yr=_r("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=_r("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),xr=_r("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),br=_r("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function _r(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n=e.match(/attribute ([\w]+) ([\w]+)/g),i=t.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),a=e.match(/uniform ([\w]+) ([\w]+)([\s]*)([\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,(function(t,e,r,n,i){return s[i]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nvarying "+r+" "+n+" "+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,n,i){var a="float"===n?"vec2":"vec4",o=i.match(/color/)?"color":a;return s[i]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nuniform lowp float u_"+i+"_t;\nattribute "+r+" "+a+" a_"+i+";\nvarying "+r+" "+n+" "+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"vec4"===o?"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+i+" = a_"+i+";\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+i+" = unpack_mix_"+o+"(a_"+i+", u_"+i+"_t);\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+i+"\nuniform lowp float u_"+i+"_t;\nattribute "+r+" "+a+" a_"+i+";\n#else\nuniform "+r+" "+n+" u_"+i+";\n#endif\n":"vec4"===o?"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = a_"+i+";\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+i+"\n    "+r+" "+n+" "+i+" = unpack_mix_"+o+"(a_"+i+", u_"+i+"_t);\n#else\n    "+r+" "+n+" "+i+" = u_"+i+";\n#endif\n"})),staticAttributes:n,staticUniforms:o}}var wr=Object.freeze({__proto__:null,prelude:Ze,background:Ke,backgroundPattern:Je,circle:$e,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:ur,fillExtrusionPattern:cr,hillshadePrepare:fr,hillshade:hr,line:pr,lineGradient:dr,linePattern:vr,lineSDF:gr,raster:yr,symbolIcon:mr,symbolSDF:xr,symbolTextAndIcon:br}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};function kr(t){for(var e=[],r=0;r<t.length;r++)if(null!==t[r]){var n=t[r].split(" ");e.push(n.pop())}return e}Tr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,u=0;!l&&u<n.length;u++)this.boundPaintVertexBuffers[u]!==n[u]&&(l=!0);var c=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||c?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},Tr.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,u=this.context,c=u.gl;if(u.extVertexArrayObject)this.vao&&this.destroy(),this.vao=u.extVertexArrayObject.createVertexArrayOES(),u.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=u.currentNumAttributes||0;for(var f=l;f<s;f++)c.disableVertexAttribArray(f)}e.enableAttributes(c,t);for(var h=0,p=r;h<p.length;h+=1)p[h].enableAttributes(c,t);a&&a.enableAttributes(c,t),o&&o.enableAttributes(c,t),e.bind(),e.setVertexAttribPointers(c,t,i);for(var d=0,v=r;d<v.length;d+=1){var g=v[d];g.bind(),g.setVertexAttribPointers(c,t,i)}a&&(a.bind(),a.setVertexAttribPointers(c,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(c,t,i)),u.currentNumAttributes=l},Tr.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var Ar=function(t,e,r,n,i,a){var o=t.gl;this.program=o.createProgram();for(var s=kr(r.staticAttributes),l=n?n.getBinderAttributes():[],u=s.concat(l),c=r.staticUniforms?kr(r.staticUniforms):[],f=n?n.getBinderUniforms():[],h=[],p=0,d=c.concat(f);p<d.length;p+=1){var v=d[p];h.indexOf(v)<0&&h.push(v)}var g=n?n.defines():[];a&&g.push("#define OVERDRAW_INSPECTOR;");var y=g.concat(Ze.fragmentSource,r.fragmentSource).join("\n"),m=g.concat(Ze.vertexSource,r.vertexSource).join("\n"),x=o.createShader(o.FRAGMENT_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(x,y),o.compileShader(x),o.attachShader(this.program,x);var b=o.createShader(o.VERTEX_SHADER);if(o.isContextLost())this.failedToCreate=!0;else{o.shaderSource(b,m),o.compileShader(b),o.attachShader(this.program,b),this.attributes={};var _={};this.numAttributes=u.length;for(var w=0;w<this.numAttributes;w++)u[w]&&(o.bindAttribLocation(this.program,w,u[w]),this.attributes[u[w]]=w);o.linkProgram(this.program),o.deleteShader(b),o.deleteShader(x);for(var T=0;T<h.length;T++){var k=h[T];if(k&&!_[k]){var A=o.getUniformLocation(this.program,k);A&&(_[k]=A)}}this.fixedUniforms=i(t,_),this.binderUniforms=n?n.getUniforms(t,_):[]}}};function Mr(t,e,r){var n=1/ge(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}Ar.prototype.draw=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=t.gl;if(!this.failedToCreate){for(var m in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[m].set(o[m]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],b=0,_=c.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],u,w.vertexOffset,d,v),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Sr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var u=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[u.r,u.g,u.b],u_vertical_gradient:+n,u_opacity:i}},Er=function(e,r,n,i,a,o,s){return t.extend(Sr(e,r,n,i),Mr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Lr=function(t){return{u_matrix:t}},Cr=function(e,r,n,i){return t.extend(Lr(e),Mr(n,r,i))},Pr=function(t,e){return{u_matrix:t,u_world:e}},Or=function(e,r,n,i,a){return t.extend(Cr(e,r,n,i),{u_world:a})},Ir=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+("map"===i.paint.get("circle-pitch-scale")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get("circle-translate"),i.paint.get("circle-translate-anchor")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},zr=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Dr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Rr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Fr=function(t){return{u_matrix:t}},Br=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}},Nr=function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}},jr=function(e,r,n){var i=n.paint.get("hillshade-shadow-color"),a=n.paint.get("hillshade-highlight-color"),o=n.paint.get("hillshade-accent-color"),s=n.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===n.paint.get("hillshade-illumination-anchor")&&(s-=e.transform.angle);var l,u,c,f=!e.options.moving;return{u_matrix:e.transform.calculatePosMatrix(r.tileID.toUnwrapped(),f),u_image:0,u_latrange:(l=r.tileID,u=Math.pow(2,l.canonical.z),c=l.canonical.y,[new t.MercatorCoordinate(0,c/u).toLngLat().lat,new t.MercatorCoordinate(0,(c+1)/u).toLngLat().lat]),u_light:[n.paint.get("hillshade-exaggeration"),s],u_shadow:i,u_highlight:a,u_accent:o}},Ur=function(e,r){var n=r.stride,i=t.create();return t.ortho(i,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(i,i,[0,-t.EXTENT,0]),{u_matrix:i,u_image:1,u_dimension:[n,n],u_zoom:e.overscaledZ,u_unpack:r.getUnpackVector()}};var Vr=function(e,r,n){var i=e.transform;return{u_matrix:Yr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},qr=function(e,r,n,i){return t.extend(Vr(e,r,n),{u_image:0,u_image_height:i})},Hr=function(e,r,n,i){var a=e.transform,o=Wr(r,a);return{u_matrix:Yr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Gr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Wr(r,o),u="round"===n.layout.get("line-cap"),c=s.getDash(i.from,u),f=s.getDash(i.to,u),h=c.width*a.fromScale,p=f.width*a.toScale;return t.extend(Vr(e,r,n),{u_patternscale_a:[l/h,-c.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:f.y,u_mix:a.t})};function Wr(t,e){return 1/ge(t,1,e.tileZoom)}function Yr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Xr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Zr(i.paint.get("raster-hue-rotate"))};var a,o};function Zr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Kr,Jr=function(t,e,r,n,i,a,o,s,l,u){var c=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:c.cameraToCenterDistance,u_pitch:c.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:c.width/c.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:u,u_texture:0}},$r=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,u,c),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Qr=function(e,r,n,i,a,o,s,l,u,c){return t.extend($r(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},tn=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},en=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,u=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/u,f=c*(n.tileID.canonical.x+n.tileID.wrap*u),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},rn={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image),u_image_height:new t.Uniform1f(e,r.u_image_height)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function nn(e,r,n,i,a,o,s){for(var l=e.context,u=l.gl,c=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var v=i[d],g=r.getTile(v),y=g.getBucket(n);if(y){var m=v.posMatrix;0===a[0]&&0===a[1]||(m=e.translatePosMatrix(v.posMatrix,g,a,o));var x=s?y.textCollisionBox:y.iconCollisionBox,b=y.collisionCircleArray;if(b.length>0){var _=t.create(),w=m;t.mul(_,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,y.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&c.draw(l,u.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,zr(m,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],O=E.circleArray[C+1],I=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,O,I,z,0),k.emplace(A++,P,O,I,z,1),k.emplace(A++,P,O,I,z,2),k.emplace(A++,P,O,I,z,3)}(!Kr||Kr.length<2*h)&&(Kr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(Kr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Dr(N.transform,N.invTransform,e.transform);T.draw(l,u.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var an=t.identity(new Float32Array(16));function on(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,u=-(s.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point((l/a+c[0])*o,(u/a+c[1])*o)}function sn(e,r,n,i,a,o,s,l,u,c,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,v={};p.clear();for(var g=0;g<h.length;g++){var y=h.get(g),m=e.allowVerticalPlacement&&!y.placedOrientation,x=y.hidden||!y.crossTileID||m?null:i[y.crossTileID];if(x){var b=new t.Point(y.anchorX,y.anchorY),_=re(b,n?l:s),w=ne(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,c,y)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/u);for(var k=x.width,A=x.height,M=on(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?re(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&y.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<y.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&y.associatedIconIndex>=0&&(v[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else he(y.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var O=C.get(P);if(O.hidden)he(O.numGlyphs,d);else{var I=v[P];if(I)for(var z=0;z<O.numGlyphs;z++)t.addDynamicAttributes(d,I.shiftedAnchor,I.angle);else he(O.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function ln(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function un(e,r,n,i,a,o,s,l,u,c,f,h){for(var p=e.context,d=p.gl,v=e.transform,g="map"===l,y="map"===u,m=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!y&&!m,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=!1,w=e.depthModeForSublayer(0,Mt.ReadOnly),T=n.layout.get("text-variable-anchor"),k=[],A=0,M=i;A<M.length;A+=1){var S=M[A],E=r.getTile(S),L=E.getBucket(n);if(L){var C=a?L.text:L.icon;if(C&&C.segments.get().length){var P=C.programConfigurations.get(n.id),O=a||L.sdfIcons,I=a?L.textSizeData:L.iconSizeData,z=y||0!==v.pitch,D=e.useProgram(ln(O,a,L),P),R=t.evaluateSizeForZoom(I,v.zoom),F=void 0,B=[0,0],N=void 0,j=void 0,U=null,V=void 0;if(a){if(N=E.glyphAtlasTexture,j=d.LINEAR,F=E.glyphAtlasTexture.size,L.iconsInText){B=E.imageAtlasTexture.size,U=E.imageAtlasTexture;var q="composite"===I.kind||"camera"===I.kind;V=z||e.options.rotating||e.options.zooming||q?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get("icon-size").constantOr(0)||L.iconsNeedLinear;N=E.imageAtlasTexture,j=O||e.options.rotating||e.options.zooming||H||z?d.LINEAR:d.NEAREST,F=E.imageAtlasTexture.size}var G=ge(E,1,e.transform.zoom),W=te(S.posMatrix,y,g,e.transform,G),Y=ee(S.posMatrix,y,g,e.transform,G),X=T&&L.hasTextData(),Z="none"!==n.layout.get("icon-text-fit")&&X&&L.hasIconData();m&&ae(L,S.posMatrix,e,a,W,Y,y,c);var K=e.translatePosMatrix(S.posMatrix,E,o,s),J=m||a&&T||Z?an:W,$=e.translatePosMatrix(Y,E,o,s,!0),Q=O&&0!==n.paint.get(a?"text-halo-width":"icon-halo-width").constantOr(1),tt={program:D,buffers:C,uniformValues:O?L.iconsInText?Qr(I.kind,R,x,y,e,K,J,$,F,B):$r(I.kind,R,x,y,e,K,J,$,a,F,!0):Jr(I.kind,R,x,y,e,K,J,$,a,F),atlasTexture:N,atlasTextureIcon:U,atlasInterpolation:j,atlasInterpolationIcon:V,isSDF:O,hasHalo:Q};if(b&&L.canOverlap){_=!0;for(var et=0,rt=C.segments.get();et<rt.length;et+=1){var nt=rt[et];k.push({segments:new t.SegmentVector([nt]),sortKey:nt.sortKey,state:tt})}}else k.push({segments:C.segments,sortKey:0,state:tt})}}}_&&k.sort((function(t,e){return t.sortKey-e.sortKey}));for(var it=0,at=k;it<at.length;it+=1){var ot=at[it],st=ot.state;if(p.activeTexture.set(d.TEXTURE0),st.atlasTexture.bind(st.atlasInterpolation,d.CLAMP_TO_EDGE),st.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),st.atlasTextureIcon&&st.atlasTextureIcon.bind(st.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),st.isSDF){var lt=st.uniformValues;st.hasHalo&&(lt.u_is_halo=1,cn(st.buffers,ot.segments,n,e,st.program,w,f,h,lt)),lt.u_is_halo=0}cn(st.buffers,ot.segments,n,e,st.program,w,f,h,st.uniformValues)}}function cn(t,e,r,n,i,a,o,s,l){var u=n.context,c=u.gl;i.draw(u,c.TRIANGLES,a,o,s,Ct.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function fn(t,e,r,n,i,a,o){var s,l,u,c,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),v=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,y=n;g<y.length;g+=1){var m=y[g],x=e.getTile(m);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(v));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(m.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){c=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];u="fillOutlinePattern"===l&&d?Or(S,t,v,x,E):Pr(S,E)}else c=b.indexBuffer,f=b.segments,u=d?Cr(S,t,v,x):Lr(S);w.draw(t.context,s,i,t.stencilModeForClipping(m),a,Ct.disabled,u,r.id,b.layoutVertexBuffer,c,f,r.paint,t.transform.zoom,_)}}}}function hn(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,u=r.paint.get("fill-extrusion-pattern"),c=u.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var v=d[p],g=e.getTile(v),y=g.getBucket(r);if(y){var m=y.programConfigurations.get(r.id),x=t.useProgram(c?"fillExtrusionPattern":"fillExtrusion",m);c&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),m.updatePaintBuffers(f));var b=u.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&m.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(v.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=c?Er(k,t,A,h,v,f,g):Sr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Ct.backCCW,M,r.id,y.layoutVertexBuffer,y.indexBuffer,y.segments,r.paint,t.transform.zoom,m)}}}function pn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var u=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var c=jr(t,e,r);u.draw(o,s.TRIANGLES,n,i,a,Ct.disabled,c,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function dn(e,r,n,i,a,o){var s=e.context,l=s.gl,u=r.dem;if(u&&u.data){var c=u.dim,f=u.stride,h=u.getPixels();if(s.activeTexture.set(l.TEXTURE1),s.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(f),r.demTexture){var p=r.demTexture;p.update(h,{premultiply:!1}),p.bind(l.NEAREST,l.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(s,h,l.RGBA,{premultiply:!1}),r.demTexture.bind(l.NEAREST,l.CLAMP_TO_EDGE);s.activeTexture.set(l.TEXTURE0);var d=r.fbo;if(!d){var v=new t.Texture(s,{width:c,height:c,data:null},l.RGBA);v.bind(l.LINEAR,l.CLAMP_TO_EDGE),(d=r.fbo=s.createFramebuffer(c,c,!0)).colorAttachment.set(v.texture)}s.bindFramebuffer.set(d.framebuffer),s.viewport.set([0,0,c,c]),e.useProgram("hillshadePrepare").draw(s,l.TRIANGLES,i,a,o,Ct.disabled,Ur(r.tileID,u),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function vn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,u=r?(s-r.timeAdded)/o:-1,c=n.getSource(),f=a.coveringZoomLevel({tileSize:c.tileSize,roundZoom:c.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-u,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var gn=new t.Color(1,0,0,1),yn=new t.Color(0,1,0,1),mn=new t.Color(0,0,1,1),xn=new t.Color(1,0,1,1),bn=new t.Color(0,1,1,1);function _n(t){var e=t.transform.padding;wn(t,t.transform.height-(e.top||0),3,gn),wn(t,e.bottom||0,3,yn),Tn(t,e.left||0,3,mn),Tn(t,t.transform.width-(e.right||0),3,xn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;kn(t,e-a/2,r-i/2,a,i,n),kn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,bn)}function wn(t,e,r,n){kn(t,0,e+r/2,t.transform.width,r,n)}function Tn(t,e,r,n){kn(t,e-r/2,0,r,t.transform.height,n)}function kn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function An(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=Mt.disabled,u=Et.disabled,c=e.colorModeForRenderPass(),f="$debug";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,u,c,Ct.disabled,Rr(o,t.Color.red),f,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,p=h&&h.byteLength||0,d=Math.floor(p/1024),v=r.getTile(n).tileSize,g=512/Math.min(v,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+" "+d+"kb"),s.draw(i,a.TRIANGLES,l,u,Lt.alphaBlended,Ct.disabled,Rr(o,t.Color.transparent,g),f,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var Mn={symbol:function(e,r,n,i,a){if("translucent"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get("text-variable-anchor")&&function(e,r,n,i,a,o,s){for(var l=r.transform,u="map"===a,c="map"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),v=d.getBucket(n);if(v&&v.text&&v.text.segments.get().length){var g=v.textSizeData,y=t.evaluateSizeForZoom(g,l.zoom),m=ge(d,1,r.transform.zoom),x=te(p.posMatrix,c,u,r.transform,m),b="none"!==n.layout.get("icon-text-fit")&&v.hasIconData();if(y){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);sn(v,u,c,s,t.symbolSize,l,x,p.posMatrix,_,y,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&un(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&un(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(nn(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),nn(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var u=e.context,c=u.gl,f=e.depthModeForSublayer(0,Mt.ReadOnly),h=Et.disabled,p=e.colorModeForRenderPass(),d=[],v=0;v<i.length;v++){var g=i[v],y=r.getTile(g),m=y.getBucket(n);if(m){var x=m.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:m.layoutVertexBuffer,indexBuffer:m.indexBuffer,uniformValues:Ir(e,g,y,n)};if(l)for(var _=0,w=m.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:m.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,O=S.uniformValues,I=M.segments;L.draw(u,c.TRIANGLES,f,h,p,Ct.disabled,O,n.id,C,P,I,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Et.disabled,l=new Lt([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);(function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,e,n),a.clear({color:t.Color.transparent});for(var u=0;u<i.length;u++){var c=i[u];if(!r.hasRenderableParent(c)){var f=r.getTile(c),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),v=e.transform.zoom;d.draw(a,o.TRIANGLES,Mt.disabled,s,l,Ct.disabled,Br(c.posMatrix,f,v,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(a){n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA)),o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Nr(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1))for(var s=e.depthModeForSublayer(0,Mt.ReadOnly),l=e.colorModeForRenderPass(),u=n.paint.get("line-dasharray"),c=n.paint.get("line-pattern"),f=c.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":u?"lineSDF":h?"lineGradient":"line",v=e.context,g=v.gl,y=!0,m=0,x=i;m<x.length;m+=1){var b=x[m],_=r.getTile(b);if(!f||_.patternsLoaded()){var w=_.getBucket(n);if(w){var T=w.programConfigurations.get(n.id),k=e.context.program.get(),A=e.useProgram(d,T),M=y||A.program!==k,S=c.constantOr(null);if(S&&_.imageAtlas){var E=_.imageAtlas,L=E.patternPositions[S.to.toString()],C=E.patternPositions[S.from.toString()];L&&C&&T.setConstantPatternPositions(L,C)}var P=f?Hr(e,_,n,p):u?Gr(e,_,n,u,p):h?qr(e,_,n,w.lineClipsArray.length):Vr(e,_,n);if(f)v.activeTexture.set(g.TEXTURE0),_.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),T.updatePaintBuffers(p);else if(u&&(M||e.lineAtlas.dirty))v.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(v);else if(h){var O=w.gradients[n.id],I=O.texture;if(n.gradientVersion!==O.version){var z=256;if(n.stepInterpolant){var D=r.getSource().maxzoom,R=b.canonical.z===D?Math.ceil(1<<e.transform.maxZoom-b.canonical.z):1,F=w.maxLineLength/t.EXTENT*1024*R;z=t.clamp(t.nextPowerOfTwo(F),256,v.maxTextureSize)}O.gradient=t.renderColorRamp({expression:n.gradientExpression(),evaluationKey:"lineProgress",resolution:z,image:O.gradient||void 0,clips:w.lineClipsArray}),O.texture?O.texture.update(O.gradient):O.texture=new t.Texture(v,O.gradient,g.RGBA),O.version=n.gradientVersion,I=O.texture}v.activeTexture.set(g.TEXTURE0),I.bind(n.stepInterpolant?g.NEAREST:g.LINEAR,g.CLAMP_TO_EDGE)}A.draw(v,g.TRIANGLES,s,e.stencilModeForClipping(b),l,Ct.disabled,P,n.id,w.layoutVertexBuffer,w.indexBuffer,w.segments,n.paint,e.transform.zoom,T,w.layoutVertexBuffer2),y=!1}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),u=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===u){var c=e.depthModeForSublayer(1,"opaque"===e.renderPass?Mt.ReadWrite:Mt.ReadOnly);fn(e,r,n,i,c,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,Mt.ReadOnly);fn(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))hn(t,e,r,n,a,Et.disabled,Lt.disabled),hn(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();hn(t,e,r,n,a,Et.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=t.depthModeForSublayer(0,Mt.ReadOnly),o=t.colorModeForRenderPass(),s="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],l=s[0],u=0,c=s[1];u<c.length;u+=1){var f=c[u],h=e.getTile(f);h.needsHillshadePrepare&&"offscreen"===t.renderPass?dn(t,h,r,a,Et.disabled,o):"translucent"===t.renderPass&&pn(t,h,r,a,l[f.overscaledZ],o)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),u=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),c=u[0],f=u[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,v=f;d<v.length;d+=1){var g=v[d],y=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?Mt.ReadWrite:Mt.ReadOnly,a.LESS),m=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);m.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=vn(m,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-m.tileID.overscaledZ),T=[m.tileID.canonical.x*w%1,m.tileID.canonical.y*w%1]):m.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Xr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,y,Et.disabled,l,Ct.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,y,c[g.overscaledZ],l,Ct.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,u=r.paint.get("background-pattern");if(!t.isPatternMissing(u)){var c=!u&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===c){var f=Et.disabled,h=t.depthModeForSublayer(0,"opaque"===c?Mt.ReadWrite:Mt.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(u?"backgroundPattern":"background"),v=s.coveringTiles({tileSize:l});u&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),y=0,m=v;y<m.length;y+=1){var x=m[y],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=u?en(b,i,t,u,{tileID:x,tileSize:l},g):tn(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Ct.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)An(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Et.disabled);var o="3d"===i.renderingMode?new Mt(t.context.gl.LEQUAL,Mt.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,Mt.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},Sn=function(t,e){this.context=new Pt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ot.maxUnderzooming+Ot.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ve,this.gpuTimers={}};Sn.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},Sn.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Xe.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Xe.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,O.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Xe.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Et({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},Sn.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,Mt.disabled,this.stencilClearMode,Lt.disabled,Ct.disabled,Fr(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},Sn.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Lt.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,Mt.disabled,new Et({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),Lt.disabled,Ct.disabled,Fr(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},Sn.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Et({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Et.disabled,e),n]},Sn.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){var r=1/8;return new Lt([e.CONSTANT_COLOR,e.ONE],new t.Color(r,r,r,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?Lt.unblended:Lt.alphaBlended},Sn.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return Mt.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new Mt(r||this.context.gl.LEQUAL,e,[n,n])},Sn.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},Sn.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,u,c={},f={},h={};for(var p in a){var d=a[p];c[p]=d.getVisibleCoordinates(),f[p]=c[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var v=0;v<i.length;v++){var g=i[v];if(this.style._layers[g].is3D()){this.opaquePassCutoff=v;break}}this.renderPass="offscreen";for(var y=0,m=i;y<m.length;y+=1){var x=m[y],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=c[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,c[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(u&&u.id)&&(u=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<u.getSource().maxzoom)&&(l=u))})),l&&Mn.debug(this,l,l.getVisibleCoordinates())),this.options.showPadding&&_n(this),this.context.setDefault()},Sn.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),Mn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},Sn.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},Sn.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},Sn.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},Sn.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},Sn.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var u=[a?n[0]:ge(r,n[0],this.transform.zoom),a?n[1]:ge(r,n[1],this.transform.zoom),0],c=new Float32Array(16);return t.translate(c,e,u),c},Sn.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},Sn.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},Sn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Sn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new Ar(this.context,t,wr[t],e,rn[t],this._showOverdrawInspector)),this.cache[r]},Sn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Sn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Sn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Sn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var En=function(t,e){this.points=t,this.planes=e};En.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new En(a,o)};var Ln=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Ln.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Ln(n,i)},Ln.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Ln.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Ln.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var u=Number.MAX_VALUE,c=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];u=Math.min(u,h),c=Math.max(c,h)}if(c<0||u>this.max[l]-this.min[l])return 0}return 1};var Cn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};Cn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Cn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Cn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Cn.prototype.clone=function(){return new Cn(this.top,this.bottom,this.left,this.right)},Cn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Pn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Cn,this._posMatrixCache={},this._alignedPosMatrixCache={}},On={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Pn.prototype.clone=function(){var t=new Pn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},On.minZoom.get=function(){return this._minZoom},On.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},On.maxZoom.get=function(){return this._maxZoom},On.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},On.minPitch.get=function(){return this._minPitch},On.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},On.maxPitch.get=function(){return this._maxPitch},On.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},On.renderWorldCopies.get=function(){return this._renderWorldCopies},On.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},On.worldSize.get=function(){return this.tileSize*this.scale},On.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},On.size.get=function(){return new t.Point(this.width,this.height)},On.bearing.get=function(){return-this.angle/Math.PI*180},On.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},On.pitch.get=function(){return this._pitch/Math.PI*180},On.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},On.fov.get=function(){return this._fov/Math.PI*180},On.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},On.zoom.get=function(){return this._zoom},On.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},On.center.get=function(){return this._center},On.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},On.padding.get=function(){return this._edgeInsets.toJSON()},On.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},On.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Pn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Pn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Pn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},Pn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),u=s-1;u<=l+1;u++)0!==u&&r.push(new t.UnwrappedTileID(u,e));return r},Pn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=En.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var u=function(t){return{aabb:new Ln([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},c=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)c.push(u(-d)),c.push(u(d));for(c.push(u(0));c.length>0;){var v=c.pop(),g=v.x,y=v.y,m=v.fullyVisible;if(!m){var x=v.aabb.intersects(s);if(0===x)continue;m=2===x}var b=v.aabb.distanceX(o),_=v.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-v.zoom)-2;if(v.zoom===h||w>T&&v.zoom>=l)f.push({tileID:new t.OverscaledTileID(v.zoom===h?p:v.zoom,v.wrap,v.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);c.push({aabb:v.aabb.quadrant(k),zoom:v.zoom+1,x:A,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},Pn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},On.unmodified.get=function(){return this._unmodified},Pn.prototype.zoomScale=function(t){return Math.pow(2,t)},Pn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Pn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},Pn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},On.point.get=function(){return this.project(this.center)},Pn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},Pn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Pn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Pn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},Pn.prototype.coordinateLocation=function(t){return t.toLngLat()},Pn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,u=n[1]/a,c=r[2]/i,f=n[2]/a,h=c===f?0:(0-c)/(f-c);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,u,h)/this.worldSize)},Pn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},Pn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},Pn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},Pn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},Pn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Pn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Pn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,u=this.size,c=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<u.y?u.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<u.x?u.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=c,void(this._constraining=!1);if(this.latRange){var v=p.y,g=u.y/2;v-g<a&&(i=a+g),v+g>o&&(i=o-g)}if(this.lngRange){var y=p.x,m=u.x/2;y-m<s&&(n=s+m),y+m>l&&(n=l-m)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=c,this._constraining=!1}},Pn.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,u=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),c=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,c,u),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),v=Math.sin(this.angle),g=s-Math.round(s)+d*h+v*p,y=l-Math.round(l)+d*p+v*h,m=new Float64Array(f);if(t.translate(m,m,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=m,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Pn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Pn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},Pn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var u=l[s];n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(Pn.prototype,On);var In=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};In.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},In.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},In.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),u=this._map.getPitch(),c="";if(c+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||u)&&(c+="/"+Math.round(10*l)/10),u&&(c+="/"+Math.round(u)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+c):t})).filter((function(t){return t}));return h||p.push(f+"="+c),"#"+p.join("&")}return"#"+c},In.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},In.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},In.prototype._updateHashUnthrottled=function(){var e=t.window.location.href.replace(/(#.+)?$/,this.getHashString());try{t.window.history.replaceState(t.window.history.state,null,e)}catch(t){}};var zn={linearity:.3,easing:t.bezier(0,0,.3,1)},Dn=t.extend({deceleration:2500,maxSpeed:1400},zn),Rn=t.extend({deceleration:20,maxSpeed:1400},zn),Fn=t.extend({deceleration:1e3,maxSpeed:360},zn),Bn=t.extend({deceleration:1e3,maxSpeed:90},zn),Nn=function(t){this._map=t,this.clear()};function jn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Un(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}Nn.prototype.clear=function(){this._inertiaBuffer=[]},Nn.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},Nn.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},Nn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Un(r.pan.mag(),o,t.extend({},Dn,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,jn(s,l)}if(r.zoom){var u=Un(r.zoom,o,Rn);s.zoom=this._map.transform.zoom+u.amount,jn(s,u)}if(r.bearing){var c=Un(r.bearing,o,Fn);s.bearing=this._map.transform.bearing+t.clamp(c.amount,-179,179),jn(s,c)}if(r.pitch){var f=Un(r.pitch,o,Bn);s.pitch=this._map.transform.pitch+f.amount,jn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Vn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),qn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),u=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),c=i.unproject(u);e.call(this,n,{points:s,point:u,lngLats:l,lngLat:c,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Hn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Gn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Gn.prototype.reset=function(){delete this._mousedownPos},Gn.prototype.wheel=function(t){return this._firePreventable(new Hn(t.type,this._map,t))},Gn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseup=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.dblclick=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseover=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.mouseout=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.touchstart=function(t){return this._firePreventable(new qn(t.type,this._map,t))},Gn.prototype.touchmove=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchend=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchcancel=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Wn=function(t){this._map=t};Wn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Wn.prototype.mousemove=function(t){this._map.fire(new Vn(t.type,this._map,t))},Wn.prototype.mousedown=function(){this._delayContextMenu=!0},Wn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Vn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Wn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Vn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},Wn.prototype.isEnabled=function(){return!0},Wn.prototype.isActive=function(){return!1},Wn.prototype.enable=function(){},Wn.prototype.disable=function(){};var Yn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Xn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Yn.prototype.isEnabled=function(){return!!this._enabled},Yn.prototype.isActive=function(){return!!this._active},Yn.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Yn.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Yn.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Yn.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Yn.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Yn.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Yn.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Yn.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Zn=function(t){this.reset(),this.numTouches=t.numTouches};Zn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Zn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Xn(n,r)))},Zn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Xn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Zn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Kn=function(t){this.singleTap=new Zn(t),this.numTaps=t.numTaps,this.reset()};Kn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Kn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Kn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Kn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new Kn({numTouches:1,numTaps:2}),this._zoomOut=new Kn({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var $n={};$n[0]=1,$n[2]=2;var Qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Qn.prototype._correctButton=function(t,e){return!1},Qn.prototype._move=function(t,e){return{}},Qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r)if(t.preventDefault(),function(t,e){var r=$n[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton))this.reset();else if(this._moved||!(e.dist(r)<this._clickTolerance))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Qn.prototype.mouseupWindow=function(t){this._lastPoint&&r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Qn.prototype.enable=function(){this._enabled=!0},Qn.prototype.disable=function(){this._enabled=!1,this.reset()},Qn.prototype.isEnabled=function(){return this._enabled},Qn.prototype.isActive=function(){return this._active};var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Qn),ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Qn),ni=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};ni.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},ni.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},ni.prototype.touchmove=function(t,e,r){if(this._active&&!(r.length<this._minTouches))return t.preventDefault(),this._calculateTransform(t,e,r)},ni.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},ni.prototype.touchcancel=function(){this.reset()},ni.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Xn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var u=i[l],c=this._touches[l];c&&(a._add(u),o._add(u.sub(c)),s++,i[l]=u)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ii=function(){this.reset()};function ai(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}ii.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},ii.prototype._start=function(t){},ii.prototype._move=function(t,e,r){return{}},ii.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},ii.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=ai(r,e,i),s=ai(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},ii.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=ai(n,e,a),l=ai(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},ii.prototype.touchcancel=function(){this.reset()},ii.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},ii.prototype.disable=function(){this._enabled=!1,this.reset()},ii.prototype.isEnabled=function(){return this._enabled},ii.prototype.isActive=function(){return this._active};function oi(t,e){return Math.log(t/e)/Math.LN2}var si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs(oi(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:oi(this._distance,r),pinchAround:e}},e}(ii);function li(t,e){return 180*t.angleWith(e)/Math.PI}var ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:li(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=li(t,this._startVector);return Math.abs(r)<e},e}(ii);function ci(t){return Math.abs(t.y)>Math.abs(t.x)}var fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ci(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ci(t)&&ci(e)&&a}},e}(ii),hi={panStep:100,bearingStep:15,pitchStep:10},pi=function(){var t=hi;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep,this._rotationDisabled=!1};function di(t){return t*(2-t)}pi.prototype.reset=function(){this._active=!1},pi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return this._rotationDisabled&&(n=0,i=0),{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:di,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active},pi.prototype.disableRotation=function(){this._rotationDisabled=!0},pi.prototype.enableRotation=function(){this._rotationDisabled=!1};var vi=4.000244140625,gi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll(["_onTimeout"],this)};gi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gi.prototype.isEnabled=function(){return!!this._enabled},gi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},gi.prototype.isZooming=function(){return!!this._zooming},gi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},gi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%vi==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},gi.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},gi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},gi.prototype.renderFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>vi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,u=this._easing,c=!1;if("wheel"===this._type&&l&&u){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=u(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):c=!0}else o=s,c=!0;return this._active=!0,c&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!c,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},gi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},gi.prototype.reset=function(){this._active=!1};var yi=function(t,e){this._clickZoom=t,this._tapZoom=e};yi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},yi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},yi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},yi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var mi=function(){this.reset()};mi.prototype.reset=function(){this._active=!1},mi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},mi.prototype.enable=function(){this._enabled=!0},mi.prototype.disable=function(){this._enabled=!1,this.reset()},mi.prototype.isEnabled=function(){return this._enabled},mi.prototype.isActive=function(){return this._active};var xi=function(){this._tap=new Kn({numTouches:1,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},xi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},xi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},xi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var bi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};bi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},bi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},bi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},bi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var _i=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};_i.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},_i.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},_i.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},_i.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var wi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};wi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},wi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},wi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},wi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},wi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},wi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Ti=function(t){return t.zoom||t.drag||t.pitch||t.rotate},ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ai(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var Mi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Nn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!0}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],u=s[1],c=s[2];r.addEventListener(l,u,l===t.window.document?this.handleWindowEvent:this.handleEvent,c)}};Mi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},Mi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Gn(e,t));var n=e.boxZoom=new Yn(e,t);this._add("boxZoom",n);var i=new Jn,a=new mi;e.doubleClickZoom=new yi(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new xi;this._add("tapDragZoom",o);var s=e.touchPitch=new fi;this._add("touchPitch",s);var l=new ei(t),u=new ri(t);e.dragRotate=new _i(t,l,u),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",u,["mouseRotate"]);var c=new ti(t),f=new ni(t);e.dragPan=new bi(r,c,f),this._add("mousePan",c),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ui,p=new si;e.touchZoomRotate=new wi(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new gi(e,this);this._add("scrollZoom",d,["mousePan"]);var v=e.keyboard=new pi;this._add("keyboard",v),this._add("blockableMapEvent",new Wn(e));for(var g=0,y=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<y.length;g+=1){var m=y[g];t.interactive&&t[m]&&e[m].enable(t[m])}},Mi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},Mi.prototype.stop=function(t){if(!this._updatingCamera){for(var e=0,r=this._handlers;e<r.length;e+=1)r[e].handler.reset();this._inertia.clear(),this._fireEvents({},{},t),this._changes=[]}},Mi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1)if(e[t].handler.isActive())return!0;return!1},Mi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},Mi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},Mi.prototype.isMoving=function(){return Boolean(Ti(this._eventsInProgress))||this.isZooming()},Mi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},Mi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},Mi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},Mi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),u=0,c=this._handlers;u<c.length;u+=1){var f=c[u],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var v=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(v=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,v,h,n),v&&v.needsRenderFrame&&this._triggerRenderFrame()),(v||p.isActive())&&(o[h]=p)}}var g={};for(var y in this._previousActiveHandlers)o[y]||(g[y]=n);this._previousActiveHandlers=o,(Object.keys(g).length||Ai(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||Ai(i))&&this._map._stop(!0),this._updatingCamera=!1;var m=i.cameraAnimation;m&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],m(this._map))}else this.stop(!0)},Mi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},Mi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],u=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,u)}this._updateMapTransform(e,r,n),this._changes=[]},Mi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!Ai(t))return this._fireEvents(e,r,!0);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,u=t.around,c=t.pinchAround;void 0!==c&&(u=c),n._stop(!0),u=u||n.transform.centerPoint;var f=i.pointLocation(a?u.sub(a):u);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,u),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r,!0)},Mi.prototype._fireEvents=function(e,r,n){var i=this,a=Ti(this._eventsInProgress),o=Ti(e),s={};for(var l in e){var u=e[l].originalEvent;this._eventsInProgress[l]||(s[l+"start"]=u),this._eventsInProgress[l]=e[l]}for(var c in!a&&o&&this._fireEvent("movestart",o.originalEvent),s)this._fireEvent(c,s[c]);for(var f in o&&this._fireEvent("move",o.originalEvent),e){var h=e[f].originalEvent;this._fireEvent(f,h)}var p,d={};for(var v in this._eventsInProgress){var g=this._eventsInProgress[v],y=g.handlerName,m=g.originalEvent;this._handlersById[y].isActive()||(delete this._eventsInProgress[v],p=r[y]||m,d[v+"end"]=p)}for(var x in d)this._fireEvent(x,d[x]);var b=Ti(this._eventsInProgress);if(n&&(a||o)&&!b){this._updatingCamera=!0;var _=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),w=function(t){return 0!==t&&-i._bearingSnap<t&&t<i._bearingSnap};_?(w(_.bearing||this._map.getBearing())&&(_.bearing=0),this._map.easeTo(_,{originalEvent:p})):(this._map.fire(new t.Event("moveend",{originalEvent:p})),w(this._map.getBearing())&&this._map.resetNorth()),this._updatingCamera=!1}},Mi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},Mi.prototype._requestFrame=function(){var t=this;return this._map.triggerRepaint(),this._map._renderTaskQueue.add((function(e){delete t._frameId,t.handleEvent(new ki("renderFrame",{timeStamp:e})),t._applyChanges()}))},Mi.prototype._triggerRenderFrame=function(){void 0===this._frameId&&(this._frameId=this._requestFrame())};var Si=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){e=t.LngLatBounds.convert(e);var n=r&&r.bearing||0;return this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),n,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,u=s.project(t.LngLat.convert(e)),c=s.project(t.LngLat.convert(r)),f=u.rotate(-n*Math.PI/180),h=c.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),v=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/v.x,y=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/v.y;if(!(y<0||g<0)){var m=Math.min(s.scaleZoom(s.scale*Math.min(g,y)),i.maxZoom),x="number"==typeof i.offset.x?new t.Point(i.offset.x,i.offset.y):t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(b,_).rotate(n*Math.PI/180),T=x.add(w).mult(s.scale/s.zoomScale(m));return{center:s.unproject(u.add(c).div(2).sub(T)),zoom:m,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),u="zoom"in e?+e.zoom:a,c="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),v=i.pointLocation(d),g=t.LngLat.convert(e.center||v);this._normalizeCenter(g);var y,m,x=i.project(v),b=i.project(g).sub(x),_=i.zoomScale(u-a);e.around&&(y=t.LngLat.convert(e.around),m=i.locationPoint(y));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||u!==a,this._rotating=this._rotating||o!==c,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,u,e)),n._rotating&&(i.bearing=t.number(o,c,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),y)i.setLocationAtPoint(y,m);else{var v=i.zoomScale(i.zoom-a),g=u>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(v));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),u=this.getPadding(),c="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(c-o),v=t.Point.convert(e.offset),g=a.centerPoint.add(v),y=a.pointLocation(g),m=t.LngLat.convert(e.center||y);this._normalizeCenter(m);var x=a.project(y),b=a.project(m).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),O=function(t){return C(P)/C(P+_*t)},I=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,I=function(){return 0},O=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/O(i);a.zoom=1===e?c:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(u,p,e),g=a.centerPoint.add(v));var y=1===e?m:a.unproject(x.add(b.mult(I(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop(!1)}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),Ei=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_toggleAttribution","_updateEditLink","_updateData","_updateCompact"],this)};Ei.prototype.getDefaultPosition=function(){return"bottom-right"},Ei.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._compactButton=r.create("button","mapboxgl-ctrl-attrib-button",this._container),this._compactButton.addEventListener("click",this._toggleAttribution),this._setElementTitle(this._compactButton,"ToggleAttribution"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),this._innerContainer.setAttribute("role","list"),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ei.prototype._setElementTitle=function(t,e){var r=this._map._getUIString("AttributionControl."+e);t.title=r,t.setAttribute("aria-label",r)},Ei.prototype._toggleAttribution=function(){this._container.classList.contains("mapboxgl-compact-show")?(this._container.classList.remove("mapboxgl-compact-show"),this._compactButton.setAttribute("aria-pressed","false")):(this._container.classList.add("mapboxgl-compact-show"),this._compactButton.setAttribute("aria-pressed","true"))},Ei.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow",this._setElementTitle(e,"MapFeedback")}},Ei.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"visibility"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},Ei.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},Ei.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact","mapboxgl-compact-show")};var Li=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};Li.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},Li.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},Li.prototype.getDefaultPosition=function(){return"bottom-left"},Li.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},Li.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Li.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var Ci=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Ci.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Ci.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},Ci.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},Ci.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Pi={"AttributionControl.ToggleAttribution":"Toggle attribution","AttributionControl.MapFeedback":"Map feedback","FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},Oi=t.window.HTMLImageElement,Ii=t.window.HTMLElement,zi=t.window.ImageBitmap,Di=60,Ri={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:Di,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Fi=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Ri,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>Di)throw new Error("maxPitch must be less than or equal to 60");var i=new Pn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Ci,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Pi,e.locale),this._clickTolerance=e.clickTolerance,this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ii))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_onMapScroll","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1),t.window.addEventListener("orientationchange",this._onWindowResize,!1)),this.handlers=new Mi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new In(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ei({customAttribution:e.customAttribution})),this.addControl(new Li,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&(r=e.getDefaultPosition?e.getDefaultPosition():"top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.hasControl=function(t){return this._controls.indexOf(t)>-1},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?Di:t)>Di)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Vn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Vn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Vn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new Ye(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new Ye(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof Oi||zi&&r instanceof zi){var u=t.browser.getImageData(r),c=u.width,f=u.height,h=u.data;this.style.addImage(e,{data:new t.RGBAImage({width:c,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,v=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(v)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof Oi||zi&&r instanceof zi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof Oi||zi&&r instanceof zi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map"),this._canvas.setAttribute("role","region");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)})),this._container.addEventListener("scroll",this._onMapScroll,!1)},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new Sn(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype._onMapScroll=function(t){if(t.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var u=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),c=u.crossFadingFactor();1===c&&c===this._crossFadingFactor||(o=!0,this._crossFadingFactor=c),this.style.update(u)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}var p=this._sourcesDirty||this._styleDirty||this._placementDirty;return p||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new t.Event("idle")),!this._loaded||this._fullyLoaded||p||(this._fullyLoaded=!0),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1)r[e].onRemove(this);this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("orientationchange",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext&&n.loseContext(),Bi(this._canvasContainer),Bi(this._controlContainer),Bi(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(Si);function Bi(t){t.parentNode&&t.parentNode.removeChild(t)}var Ni={showCompass:!0,showZoom:!0,visualizePitch:!1},ji=function(e){var n=this;this.options=t.extend({},Ni,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};ji.prototype._updateZoomButtons=function(){var t=this._map.getZoom(),e=t===this._map.getMaxZoom(),r=t===this._map.getMinZoom();this._zoomInButton.disabled=e,this._zoomOutButton.disabled=r,this._zoomInButton.setAttribute("aria-disabled",e.toString()),this._zoomOutButton.setAttribute("aria-disabled",r.toString())},ji.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},ji.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ui(this._map,this._compass,this.options.visualizePitch)),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},ji.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},ji.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ui=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new ei({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new ri({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Vi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ui.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ui.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ui.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ui.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ui.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ui.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ui.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ui.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ui.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ui.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ui.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var qi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Hi(t,e,r){var n=t.classList;for(var i in qi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Gi,Wi=function(e){function n(n,i){if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._scale=n&&n.scale||1,this._draggable=n&&n.draggable||!1,this._clickTolerance=n&&n.clickTolerance||0,this._isDragging=!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var a=r.createNS("http://www.w3.org/2000/svg","svg");a.setAttributeNS(null,"display","block"),a.setAttributeNS(null,"height","41px"),a.setAttributeNS(null,"width","27px"),a.setAttributeNS(null,"viewBox","0 0 27 41");var o=r.createNS("http://www.w3.org/2000/svg","g");o.setAttributeNS(null,"stroke","none"),o.setAttributeNS(null,"stroke-width","1"),o.setAttributeNS(null,"fill","none"),o.setAttributeNS(null,"fill-rule","evenodd");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"fill-rule","nonzero");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"transform","translate(3.0, 29.0)"),l.setAttributeNS(null,"fill","#000000");for(var u=0,c=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<c.length;u+=1){var f=c[u],h=r.createNS("http://www.w3.org/2000/svg","ellipse");h.setAttributeNS(null,"opacity","0.04"),h.setAttributeNS(null,"cx","10.5"),h.setAttributeNS(null,"cy","5.80029008"),h.setAttributeNS(null,"rx",f.rx),h.setAttributeNS(null,"ry",f.ry),l.appendChild(h)}var 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b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#FFFFFF"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962"),m.appendChild(x),m.appendChild(b),s.appendChild(l),s.appendChild(p),s.appendChild(v),s.appendChild(y),s.appendChild(m),a.appendChild(s),a.setAttributeNS(null,"height",41*this._scale+"px"),a.setAttributeNS(null,"width",27*this._scale+"px"),this._element.appendChild(a),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),Hi(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=13.5,r=Math.sqrt(Math.pow(e,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[r,-1*(24.6+r)],"bottom-right":[-r,-1*(24.6+r)],left:[e,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,qi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){if(!this._isDragging){var r=this._clickTolerance||this._map._clickTolerance;this._isDragging=e.point.dist(this._pointerdownPos)>=r}this._isDragging&&(this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag")))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Yi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Xi=0,Zi=!1,Ki=function(e){function n(r){e.call(this),this.options=t.extend({},Yi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Gi?n(Gi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Gi="denied"!==t.state,n(Gi)})):(Gi=!!t.window.navigator.geolocation,n(Gi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Xi=0,Zi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Zi)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Wi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Wi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Xi--,Zi=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Xi>1?(e={maximumAge:6e5,timeout:0},Zi=!0):(e=this.options.positionOptions,Zi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ji={maxWidth:100,unit:"metric"},$i=function(e){this.options=t.extend({},Ji,e),t.bindAll(["_onMove","setUnit"],this)};function Qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;l>5280?ta(e,n,l/5280,t._getUIString("ScaleControl.Miles")):ta(e,n,l,t._getUIString("ScaleControl.Feet"))}else r&&"nautical"===r.unit?ta(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles")):s>=1e3?ta(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):ta(e,n,s,t._getUIString("ScaleControl.Meters"))}function ta(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(""+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}$i.prototype.getDefaultPosition=function(){return"bottom-left"},$i.prototype._onMove=function(){Qi(this._map,this._container,this.options)},$i.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},$i.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},$i.prototype.setUnit=function(t){this.options.unit=t,Qi(this._map,this._container,this.options)};var ea=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};ea.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},ea.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},ea.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},ea.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},ea.prototype._isFullscreen=function(){return 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u(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return c.length<=0?{type:"Polygon",coordinates:[]}:1==c.length?{type:"Polygon",coordinates:c[0]}:{type:"MultiPolygon",coordinates:c}}};t.exports=e},72200:function(t,e,r){var n=r(48088);t.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return i=t.isStart,a=t.pt,o=r,s=n.isStart,l=n.pt,u=n.other.pt,(0!==(c=e.pointsCompare(a,l))?c:e.pointsSame(o,u)?0:i!==s?i?1:-1:e.pointAboveOrOnLine(o,s?l:u,s?u:l)?1:-1)<0;var i,a,o,s,l,u,c}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function u(i,o){var s=n.create();function u(t){return s.findTransition((function(r){var n,i,a,o,s,l;return n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,(e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function c(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,u=a.start,c=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,u,c);if(!1===f){if(!e.pointsCollinear(o,s,u))return!1;if(e.pointsSame(o,c)||e.pointsSame(s,u))return!1;var h=e.pointsSame(o,u),p=e.pointsSame(s,c);if(h&&p)return n;var d=!h&&e.pointBetween(o,u,c),v=!p&&e.pointBetween(s,u,c);if(h)return v?l(n,s):l(t,c),n;d&&(p||(v?l(n,s):l(t,c)),l(n,o))}else 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y,m,x=g();if(x)t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(x.seg.myFill.above=!x.seg.myFill.above):x.seg.otherFill=h.seg.myFill,r&&r.segmentUpdate(x.seg),h.other.remove(),h.remove();if(a.getHead()!==h){r&&r.rewind(h.seg);continue}t?(m=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=v?v.seg.myFill.above:i,h.seg.myFill.above=m?!h.seg.myFill.below:h.seg.myFill.below):null===h.seg.otherFill&&(y=v?h.primary===v.primary?v.seg.otherFill.above:v.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:y,below:y}),r&&r.status(h.seg,!!d&&d.seg,!!v&&v.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error("PolyBool: Zero-length segment detected; your epsilon is probably too small or too large");if(s.exists(b.prev)&&s.exists(b.next)&&c(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var 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a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],u=o[0],c=o[1];e.pointsCollinear(l,s,u)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,u,c)&&(r&&r.chainRemoveHead(i,u),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},82368:function(t){function e(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var r={union:function(t,r){return e(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],r)},intersect:function(t,r){return e(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],r)},difference:function(t,r){return e(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],r)},differenceRev:function(t,r){return e(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],r)},xor:function(t,r){return e(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],r)}};t.exports=r},9696:function(t,e,r){"use 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r=new Error(t);return r.code=e,r}function r(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function n(t,r,n){this.input=t.subarray(r,n),this.start=r;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw e("invalid TIFF signature","EBADDATA");this.big_endian="M"===i[0]}n.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},n.prototype.read_uint16=function(t){var r=this.input;if(t+2>r.length)throw e("unexpected EOF","EBADDATA");return this.big_endian?256*r[t]+r[t+1]:r[t]+256*r[t+1]},n.prototype.read_uint32=function(t){var r=this.input;if(t+4>r.length)throw e("unexpected EOF","EBADDATA");return this.big_endian?16777216*r[t]+65536*r[t+1]+256*r[t+2]+r[t+3]:r[t]+256*r[t+1]+65536*r[t+2]+16777216*r[t+3]},n.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},n.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},n.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return this.read_uint32(e);case 9:return 0|this.read_uint32(e);default:return null}},n.prototype.scan_ifd=function(t,n,i){var a=this.read_uint16(n);n+=2;for(var o=0;o<a;o++){var s=this.read_uint16(n),l=this.read_uint16(n+2),u=this.read_uint32(n+4),c=this.exif_format_length(l),f=u*c,h=f<=4?n+8:this.read_uint32(n+8),p=!1;if(h+f>this.input.length)throw e("unexpected EOF","EBADDATA");for(var d=[],v=h,g=0;g<u;g++,v+=c){var 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t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=f(e.scale),e.translateFract=f(e.translate),t},viewport:function(t){var e;return Array.isArray(t)?e={x:t[0],y:t[1],width:t[2]-t[0],height:t[3]-t[1]}:t?(e={x:t.x||t.left||0,y:t.y||t.top||0},t.right?e.width=t.right-e.x:e.width=t.w||t.width||0,t.bottom?e.height=t.bottom-e.y:e.height=t.h||t.height||0):e={x:0,y:0,width:y.drawingBufferWidth,height:y.drawingBufferHeight},e}}]),c):c})),e||r){var h=x.reduce((function(t,e,r){return t+(e?e.count:0)}),0),g=new Float64Array(2*h),_=new Uint8Array(4*h),w=new Float32Array(4*h);x.forEach((function(t,e){if(t){var r=t.positions,n=t.count,i=t.offset,a=t.color,o=t.errors;n&&(_.set(a,4*i),w.set(o,4*i),g.set(r,2*i))}}));var T=c(g);u(T);var k=f(g,T);p(k),d(_),v(w)}}}function k(){u.destroy(),p.destroy(),d.destroy(),v.destroy(),g.destroy()}};var h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},13472:function(t,e,r){"use strict";var n=r(72160),i=r(76752),a=r(50896),o=r(55616),s=r(47520),l=r(28912),u=r(71152),c=r(37816),f=c.float32,h=c.fract32,p=r(60463),d=r(51160),v=r(10272);function g(t,e){if(!(this instanceof g))return new g(t,e);if("function"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=g.shaders.has(t)?g.shaders.get(t):g.shaders.set(t,g.createShaders(t)).get(t),this.update(e)}t.exports=g,g.dashMult=2,g.maxPatternLength=256,g.precisionThreshold=3e6,g.maxPoints=1e4,g.maxLines=2048,g.shaders=new p,g.createShaders=function(t){var e,r=t.buffer({usage:"static",type:"float",data:[0,1,0,0,1,1,1,0]}),n={primitive:"triangle strip",instances:t.prop("count"),count:4,offset:0,uniforms:{miterMode:function(t,e){return"round"===e.join?2:1},miterLimit:t.prop("miterLimit"),scale:t.prop("scale"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),thickness:t.prop("thickness"),dashTexture:t.prop("dashTexture"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),dashLength:t.prop("dashLength"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop("depth")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:"add",alpha:"add"},func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport")},i=t(a({vert:"\nprecision highp float;\n\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\nattribute vec4 color;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\n\t// the order is important\n\treturn position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n}\n\nvoid main() {\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineOffset = lineTop * 2. - 1.;\n\n\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\n\ttangent = normalize(diff * scale * viewport.zw);\n\tvec2 normal = vec2(-tangent.y, tangent.x);\n\n\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\n\t\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\n\n\t\t+ thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n",frag:"\nprecision highp float;\n\nuniform float dashLength, pixelRatio, thickness, opacity, id;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:"back"},vert:"\nprecision highp float;\n\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\nattribute vec4 aColor, bColor;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, translate;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\nuniform float miterLimit, miterMode;\n\nvarying vec4 fragColor;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 tangent;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n",frag:"\nprecision highp float;\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * 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fragColor;\n}\n",uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},g.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},g.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},g.prototype.draw=function(){for(var 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gl_PointSize;\n}\n"]),v&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}x.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},x.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},x.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},x.prototype.drawItem=function(t,e){var r,n=this.groups,o=n[t];if("number"==typeof e&&(t=e,o=n[e],e=null),o&&o.count&&o.opacity){o.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,o,e));for(var s=[],l=1;l<o.activation.length;l++)o.activation[l]&&(!0===o.activation[l]||o.activation[l].data.length)&&s.push.apply(s,function(t){if(Array.isArray(t))return a(t)}(r=this.getMarkerDrawOptions(l,o,e))||function(t){if("undefined"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t["@@iterator"])return Array.from(t)}(r)||i(r)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}());s.length&&this.drawMarker(s)}},x.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,u=e.count;if(this.regl,!a)return r?[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[c({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var f=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new 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n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var u=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(u)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(u,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var u=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,u)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 0===this.NEPALI_CALENDAR_DATA[r]&&(this.NEPALI_CALENDAR_DATA[r]=e)},NEPALI_CALENDAR_DATA:{1970:[18,31,31,32,31,31,31,30,29,30,29,30,30],1971:[18,31,31,32,31,32,30,30,29,30,29,30,30],1972:[17,31,32,31,32,31,30,30,30,29,29,30,30],1973:[19,30,32,31,32,31,30,30,30,29,30,29,31],1974:[19,31,31,32,30,31,31,30,29,30,29,30,30],1975:[18,31,31,32,32,30,31,30,29,30,29,30,30],1976:[17,31,32,31,32,31,30,30,30,29,29,30,31],1977:[18,31,32,31,32,31,31,29,30,29,30,29,31],1978:[18,31,31,32,31,31,31,30,29,30,29,30,30],1979:[18,31,31,32,32,31,30,30,29,30,29,30,30],1980:[17,31,32,31,32,31,30,30,30,29,29,30,31],1981:[18,31,31,31,32,31,31,29,30,30,29,30,30],1982:[18,31,31,32,31,31,31,30,29,30,29,30,30],1983:[18,31,31,32,32,31,30,30,29,30,29,30,30],1984:[17,31,32,31,32,31,30,30,30,29,29,30,31],1985:[18,31,31,31,32,31,31,29,30,30,29,30,30],1986:[18,31,31,32,31,31,31,30,29,30,29,30,30],1987:[18,31,32,31,32,31,30,30,29,30,29,30,30],1988:[17,31,32,31,32,31,30,30,30,29,29,30,31],1989:[18,31,31,31,32,31,31,30,29,30,29,30,30],1990:[18,31,31,32,31,31,31,30,29,30,29,30,30],1991:[18,31,32,31,32,31,30,30,29,30,29,30,30],1992:[17,31,32,31,32,31,30,30,30,29,30,29,31],1993:[18,31,31,31,32,31,31,30,29,30,29,30,30],1994:[18,31,31,32,31,31,31,30,29,30,29,30,30],1995:[17,31,32,31,32,31,30,30,30,29,29,30,30],1996:[17,31,32,31,32,31,30,30,30,29,30,29,31],1997:[18,31,31,32,31,31,31,30,29,30,29,30,30],1998:[18,31,31,32,31,31,31,30,29,30,29,30,30],1999:[17,31,32,31,32,31,30,30,30,29,29,30,31],2e3:[17,30,32,31,32,31,30,30,30,29,30,29,31],2001:[18,31,31,32,31,31,31,30,29,30,29,30,30],2002:[18,31,31,32,32,31,30,30,29,30,29,30,30],2003:[17,31,32,31,32,31,30,30,30,29,29,30,31],2004:[17,30,32,31,32,31,30,30,30,29,30,29,31],2005:[18,31,31,32,31,31,31,30,29,30,29,30,30],2006:[18,31,31,32,32,31,30,30,29,30,29,30,30],2007:[17,31,32,31,32,31,30,30,30,29,29,30,31],2008:[17,31,31,31,32,31,31,29,30,30,29,29,31],2009:[18,31,31,32,31,31,31,30,29,30,29,30,30],2010:[18,31,31,32,32,31,30,30,29,30,29,30,30],2011:[17,31,32,31,32,31,30,30,30,29,29,30,31],2012:[17,31,31,31,32,31,31,29,30,30,29,30,30],2013:[18,31,31,32,31,31,31,30,29,30,29,30,30],2014:[18,31,31,32,32,31,30,30,29,30,29,30,30],2015:[17,31,32,31,32,31,30,30,30,29,29,30,31],2016:[17,31,31,31,32,31,31,29,30,30,29,30,30],2017:[18,31,31,32,31,31,31,30,29,30,29,30,30],2018:[18,31,32,31,32,31,30,30,29,30,29,30,30],2019:[17,31,32,31,32,31,30,30,30,29,30,29,31],2020:[17,31,31,31,32,31,31,30,29,30,29,30,30],2021:[18,31,31,32,31,31,31,30,29,30,29,30,30],2022:[17,31,32,31,32,31,30,30,30,29,29,30,30],2023:[17,31,32,31,32,31,30,30,30,29,30,29,31],2024:[17,31,31,31,32,31,31,30,29,30,29,30,30],2025:[18,31,31,32,31,31,31,30,29,30,29,30,30],2026:[17,31,32,31,32,31,30,30,30,29,29,30,31],2027:[17,30,32,31,32,31,30,30,30,29,30,29,31],2028:[17,31,31,32,31,31,31,30,29,30,29,30,30],2029:[18,31,31,32,31,32,30,30,29,30,29,30,30],2030:[17,31,32,31,32,31,30,30,30,30,30,30,31],2031:[17,31,32,31,32,31,31,31,31,31,31,31,31],2032:[17,32,32,32,32,32,32,32,32,32,32,32,32],2033:[18,31,31,32,32,31,30,30,29,30,29,30,30],2034:[17,31,32,31,32,31,30,30,30,29,29,30,31],2035:[17,30,32,31,32,31,31,29,30,30,29,29,31],2036:[17,31,31,32,31,31,31,30,29,30,29,30,30],2037:[18,31,31,32,32,31,30,30,29,30,29,30,30],2038:[17,31,32,31,32,31,30,30,30,29,29,30,31],2039:[17,31,31,31,32,31,31,29,30,30,29,30,30],2040:[17,31,31,32,31,31,31,30,29,30,29,30,30],2041:[18,31,31,32,32,31,30,30,29,30,29,30,30],2042:[17,31,32,31,32,31,30,30,30,29,29,30,31],2043:[17,31,31,31,32,31,31,29,30,30,29,30,30],2044:[17,31,31,32,31,31,31,30,29,30,29,30,30],2045:[18,31,32,31,32,31,30,30,29,30,29,30,30],2046:[17,31,32,31,32,31,30,30,30,29,29,30,31],2047:[17,31,31,31,32,31,31,30,29,30,29,30,30],2048:[17,31,31,32,31,31,31,30,29,30,29,30,30],2049:[17,31,32,31,32,31,30,30,30,29,29,30,30],2050:[17,31,32,31,32,31,30,30,30,29,30,29,31],2051:[17,31,31,31,32,31,31,30,29,30,29,30,30],2052:[17,31,31,32,31,31,31,30,29,30,29,30,30],2053:[17,31,32,31,32,31,30,30,30,29,29,30,30],2054:[17,31,32,31,32,31,30,30,30,29,30,29,31],2055:[17,31,31,32,31,31,31,30,29,30,30,29,30],2056:[17,31,31,32,31,32,30,30,29,30,29,30,30],2057:[17,31,32,31,32,31,30,30,30,29,29,30,31],2058:[17,30,32,31,32,31,30,30,30,29,30,29,31],2059:[17,31,31,32,31,31,31,30,29,30,29,30,30],2060:[17,31,31,32,32,31,30,30,29,30,29,30,30],2061:[17,31,32,31,32,31,30,30,30,29,29,30,31],2062:[17,30,32,31,32,31,31,29,30,29,30,29,31],2063:[17,31,31,32,31,31,31,30,29,30,29,30,30],2064:[17,31,31,32,32,31,30,30,29,30,29,30,30],2065:[17,31,32,31,32,31,30,30,30,29,29,30,31],2066:[17,31,31,31,32,31,31,29,30,30,29,29,31],2067:[17,31,31,32,31,31,31,30,29,30,29,30,30],2068:[17,31,31,32,32,31,30,30,29,30,29,30,30],2069:[17,31,32,31,32,31,30,30,30,29,29,30,31],2070:[17,31,31,31,32,31,31,29,30,30,29,30,30],2071:[17,31,31,32,31,31,31,30,29,30,29,30,30],2072:[17,31,32,31,32,31,30,30,29,30,29,30,30],2073:[17,31,32,31,32,31,30,30,30,29,29,30,31],2074:[17,31,31,31,32,31,31,30,29,30,29,30,30],2075:[17,31,31,32,31,31,31,30,29,30,29,30,30],2076:[16,31,32,31,32,31,30,30,30,29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n=r(38700),i=r(50896);function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Persian",jdEpoch:1948320.5,daysPerMonth:[31,31,31,31,31,31,30,30,30,30,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Persian",epochs:["BP","AP"],monthNames:["Farvardin","Ordibehesht","Khordad","Tir","Mordad","Shahrivar","Mehr","Aban","Azar","Day","Bahman","Esfand"],monthNamesShort:["Far","Ord","Kho","Tir","Mor","Sha","Meh","Aba","Aza","Day","Bah","Esf"],dayNames:["Yekshambe","Doshambe","Seshambe","Chæharshambe","Panjshambe","Jom'e","Shambe"],dayNamesShort:["Yek","Do","Se","Chæ","Panj","Jom","Sha"],dayNamesMin:["Ye","Do","Se","Ch","Pa","Jo","Sh"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var u=t-this.toJD(l,1,1)+1,c=u<=186?Math.ceil(u/31):Math.ceil((u-6)/30),f=t-this.toJD(l,c,1)+1;return this.newDate(l,c,f)}}),n.calendars.persian=a,n.calendars.jalali=a},51456:function(t,e,r){var n=r(38700),i=r(50896),a=n.instance();function o(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}o.prototype=new 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t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},4592:function(t,e,r){var n=r(38700),i=r(50896),a=n.instance();function o(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}o.prototype=new n.baseCalendar,i(o.prototype,{name:"Thai",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Thai",epochs:["BBE","BE"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(e.year()),a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 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n=r(50896);function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,u.local.invalidMonth||u.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,u.local.invalidDate||u.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day(),"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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diff --git a/stock_market(complete).ipynb b/stock_market(complete).ipynb
index 52417b4..d3fe033 100644
--- a/stock_market(complete).ipynb
+++ b/stock_market(complete).ipynb
@@ -1,2509 +1,7824 @@
 {
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 105,
-      "metadata": {
-        "id": "Rqr4Dq5vWXmV"
-      },
-      "outputs": [],
-      "source": [
-        "# importing modules\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.impute import SimpleImputer\n",
-        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
-        "from sklearn.linear_model import LinearRegression\n",
-        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
-        "\n",
-        "from sklearn.model_selection import GridSearchCV\n",
-        "\n",
-        "from sklearn.svm import SVR\n",
-        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
-        "from sklearn.tree import DecisionTreeRegressor\n",
-        "from sklearn.neighbors import KNeighborsRegressor\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Dense,LSTM\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BigUM4vtWZdc"
-      },
-      "outputs": [],
-      "source": [
-        "# Load data\n",
-        "df = pd.read_csv(\"D:\\Pankaj\\GSSOC\\Stock Market\\SBIN.csv\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 185,
-      "metadata": {
-        "id": "RqOe6KPNWs8Q"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vAcMar5WXELR"
-      },
-      "source": [
-        "## Data Analysis and Visualization"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "vkDxEavVXHqv",
-        "outputId": "279f15e7-d2d2-442a-c162-e7db9780f2c7"
-      },
-      "outputs": [],
-      "source": [
-        "# Display the first 5 rows of the dataframe\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Vx3DYtfdXJTU",
-        "outputId": "18c93f2d-5232-4e80-dfcf-e7d748901c37"
-      },
-      "outputs": [],
-      "source": [
-        "# Display information about the dataframe\n",
-        "df.info()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 300
-        },
-        "id": "SXdvitYIXKKL",
-        "outputId": "1a2c42d0-4f6e-4180-8ffe-7a40ef4ddabf"
-      },
-      "outputs": [],
-      "source": [
-        "# Display summary statistics of the dataframe\n",
-        "df.describe()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 303
-        },
-        "id": "8rddiK_nXLNi",
-        "outputId": "4b65dd80-db84-45c8-ef16-2133bc25c68b"
-      },
-      "outputs": [],
-      "source": [
-        "# Display the number of missing values in each column\n",
-        "df.isnull().sum()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "I1khhoMECh4y"
-      },
-      "source": [
-        "### Detailed"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 111,
-      "metadata": {
-        "id": "HpS7l8oSCf6y"
-      },
-      "outputs": [],
-      "source": [
-        "# Function to create scatter plots\n",
-        "def create_scatter_plot(x_data, y_data, size_data=None, title=None, x_label=None, y_label=None, figsize=(15,7)):\n",
-        "    plt.figure(figsize=figsize)\n",
-        "    sns.scatterplot(x=x_data, y=y_data, size=size_data)\n",
-        "    if title:\n",
-        "        plt.title(title)\n",
-        "    if x_label:\n",
-        "        plt.xlabel(x_label)\n",
-        "    if y_label:\n",
-        "        plt.ylabel(y_label)\n",
-        "    plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 112,
-      "metadata": {
-        "id": "oVEjj8ZMCnDx"
-      },
-      "outputs": [],
-      "source": [
-        "# Function to create line plots\n",
-        "def create_line_plot(x_data, y_data, title=None, x_label=None, y_label=None, figsize=(15,7)):\n",
-        "    plt.figure(figsize=figsize)\n",
-        "    sns.lineplot(x=x_data, y=y_data)\n",
-        "    if title:\n",
-        "        plt.title(title)\n",
-        "    if x_label:\n",
-        "        plt.xlabel(x_label)\n",
-        "    if y_label:\n",
-        "        plt.ylabel(y_label)\n",
-        "    plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the relationship between 'Date' and 'Volume'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 769
-        },
-        "id": "-n8vOGhdCmer",
-        "outputId": "ea5057f1-9493-43de-ec65-7a017134d171"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the relationship between 'Date' and 'Volume'\n",
-        "sns.scatterplot(x = df['Date'], y = df['Volume'])\n",
-        "sns.kdeplot(x = df['Date'], y = df['Volume'])\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FYvz9YaZDeDp"
-      },
-      "source": [
-        "Early 1990s spike: There is a high trading volume around the mid-1990s, peaking at over 4 × 10^8 (400 million trades).\n",
-        "Post-1996 to 2020: The volume significantly decreases, showing lower levels of activity with some fluctuations and minor peaks, particularly after 2016.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the price variation over time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "q3QfzWquCmjk"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the price variation over time\n",
-        "create_line_plot(df['Date'], df['High'] - df['Low'],\n",
-        "                 title='Price Variation Over Time', x_label='Date', y_label='Price Variation')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mYGFClShDgsj"
-      },
-      "source": [
-        "#### Stable period until 2004:\n",
-        "The price variation was relatively low and stable until around 2003-2004, generally staying under 10 units.\n",
-        "#### 2004-2008 increase:\n",
-        "Starting from 2004, price variations gradually began to increase, peaking sharply just before the 2008 financial crisis. Some spikes went beyond 40 units.\n",
-        "#### 2008-2020 fluctuations:\n",
-        "After the 2008 peak, price variation showed continuous fluctuations with noticeable peaks, though they were more frequent post-2016.\n",
-        "#### 2020 onward:\n",
-        "The recent period (2020-2024) shows significant and frequent fluctuations, with peaks reaching 60+ units, possibly influenced by events like the COVID-19 pandemic and other global factors."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the relationship between 'Adj Close' and 'Volume'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "XKsek--aCmoC"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the relationship between 'Adj Close' and 'Volume'\n",
-        "create_line_plot(df['Adj Close'], df['Volume'],\n",
-        "                 title='Adjusted Close vs Volume',\n",
-        "                 x_label='Adjusted Close',\n",
-        "                 y_label='Volume')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "omVoWiC-DjJX"
-      },
-      "source": [
-        "The overall trend seems to be somewhat volatile, with periods of upward and downward movement."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the relationship between 'Open' and 'Adj Close'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "W5TY59ikCv78"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the relationship between 'Open' and 'Adj Close'\n",
-        "create_scatter_plot(df['Open'], df['Adj Close'], size_data=df['Volume'],\n",
-        "                    title='Open vs Adjusted Close', x_label='Open', y_label='Adj Close')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "q_BiuXvnDlfJ"
-      },
-      "source": [
-        "The scatter points generally show an upward trend, indicating a positive correlation between the opening and closing prices. This means that, in general, when the stock opens higher, it tends to close higher as well."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the relationship between 'Close' and 'Adj Close'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "CD0LiK3VCwiU"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the relationship between 'Close' and 'Adj Close'\n",
-        "create_scatter_plot(df['Close'], df['Adj Close'], size_data=df['Volume'],\n",
-        "                    title='Close vs Adjusted Close', x_label='Close', y_label='Adj Close')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3TgT8XWtDnpy"
-      },
-      "source": [
-        "The scatter points show a very strong upward trend, indicating a strong positive correlation between the closing price and the adjusted closing price. This means that, in general, when the stock closes higher, the adjusted closing price is also higher."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the relationship between 'Open' and 'Close'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bgq_AQ-nCwnA"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the relationship between 'Open' and 'Close'\n",
-        "create_scatter_plot(df['Open'], df['Close'], size_data=df['Volume'],\n",
-        "                    title='Open vs Close', x_label='Open', y_label='Close')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the relationship between 'Open - Close' and 'High - Low'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Zqsy16wWCwrC"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the relationship between 'Open - Close' and 'High - Low'\n",
-        "create_line_plot(df['Open'] - df['Close'], df['High'] - df['Low'],\n",
-        "                 title='Stock Variation vs Price Variation',\n",
-        "                 x_label='Stock Variation (Open - Close)',\n",
-        "                 y_label='Price Variation (High - Low)')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PbUZdfH4Dru0"
-      },
-      "source": [
-        "#### This infers for stable or less price difference volume of stock trading is higher\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the effect of price differences on volume"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "NAT3Sy3hC2a3"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the effect of price differences on volume\n",
-        "plt.figure(figsize = (20,10))\n",
-        "sns.scatterplot(x=df['Open'] - df['Close'], y=df['High'] - df['Low'], hue=df['Volume'], palette='viridis', size=df['Volume'], sizes=(20, 200), alpha=0.6)\n",
-        "plt.title('Effect of Price Differences on Volume')\n",
-        "plt.xlabel('Open - Close')\n",
-        "plt.ylabel('High - Low')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the effect of price differences on adjusted close"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "B8zeVZt7C2fT"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the effect of price differences on adjusted close\n",
-        "plt.figure(figsize = (20,10))\n",
-        "sns.lineplot(x = df['Open']-df['Close'], y = df['High'] - df['Low'], hue = df['Adj Close'])\n",
-        "plt.title('Effect of Price Differences on Adjusted Close')\n",
-        "plt.xlabel('Open - Close')\n",
-        "plt.ylabel('High - Low')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plot the relationship between 'Open - Close' and 'Adj Close'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "EpjnJ3bJC2jq"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the relationship between 'Open - Close' and 'Adj Close'\n",
-        "plt.figure(figsize = (20,10))\n",
-        "sns.lineplot(x = df['Open']-df['Close'], y = df['Adj Close'])\n",
-        "plt.xlabel('Open - Close')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Create a pair plot of stock features"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "omOa3x81C2uS"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a pair plot of stock features\n",
-        "subset = df[['Open', 'Close', 'High', 'Low', 'Volume']]\n",
-        "sns.pairplot(subset)\n",
-        "plt.suptitle('Pair Plot of Stock Features', y=1.02)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Create a 3D scatter plot of 'Open', 'Close', and 'Volume'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gNjDWptzC2zP"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a 3D scatter plot of 'Open', 'Close', and 'Volume'\n",
-        "from mpl_toolkits.mplot3d import Axes3D\n",
-        "\n",
-        "fig = plt.figure(figsize=(12, 8))\n",
-        "ax = fig.add_subplot(111, projection='3d')\n",
-        "ax.scatter(df['Open']- df['Close'], df['High'] - df['Low'],df['Volume'], c='r', marker='o')\n",
-        "ax.set_xlabel('Open Price')\n",
-        "ax.set_ylabel('Close Price')\n",
-        "ax.set_zlabel('Volume')\n",
-        "plt.title('3D Scatter Plot of Open, Close, and Volume')\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Calculate the correlation matrix"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Soovqm1IC24P"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate the correlation matrix\n",
-        "correlation_matrix = df.corr(numeric_only = True)\n",
-        "plt.figure(figsize=(12, 8))\n",
-        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', square=True, cbar_kws={\"shrink\": .8})\n",
-        "\n",
-        "plt.title('Correlation Heatmap')\n",
-        "plt.show()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d2g_ypkeDvdO"
-      },
-      "source": [
-        "The high correlation between 'Open,' 'High,' 'Low,' 'Close,' and 'Adj Close' shows these features are highly interdependent and tend to move together in the same direction.\n",
-        "The negative correlation of 'Volume' with price-related features suggests that increased trading volume does not necessarily coincide with an increase in stock prices."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### More Charts"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 126,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from plotly.offline import plot\n",
-        "import plotly.graph_objs as go"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A line chart is a simple and effective way to visualize the closing price of the stock over time"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the closing price over time\n",
-        "plt.plot(df['Date'], df['Close'])\n",
-        "plt.xlabel('Date')\n",
-        "plt.ylabel('Closing Price')\n",
-        "plt.title('SBIN Stock Price Over Time')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A candlestick chart is a more detailed way to visualize the stock price, showing the high, low, open, and close prices for each day"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the candlestick chart\n",
-        "fig = go.Figure(data=[go.Candlestick(x=df['Date'],\n",
-        "                open=df['Open'],\n",
-        "                high=df['High'],\n",
-        "                low=df['Low'],\n",
-        "                close=df['Close'])])\n",
-        "plot(fig, filename='candlestick_chart')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python312\\site-packages\\plotly\\offline\\offline.py:557: UserWarning:\n",
-        "\n",
-        "Your filename `candlestick_chart` didn't end with .html. Adding .html to the end of your file.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "'candlestick_chart.html'"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the daily volume\n",
-        "plt.bar(df['Date'], df['Volume'])\n",
-        "plt.xlabel('Date')\n",
-        "plt.ylabel('Volume')\n",
-        "plt.title('SBIN Daily Volume')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the scatter plot of closing price vs volume\n",
-        "sns.scatterplot(x=df['Close'], y=df['Volume'])\n",
-        "plt.xlabel('Closing Price')\n",
-        "plt.ylabel('Volume')\n",
-        "plt.title('Relationship between SBIN Closing Price and Volume')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the histogram of closing prices\n",
-        "plt.hist(df['Close'], bins=50)\n",
-        "plt.xlabel('Closing Price')\n",
-        "plt.ylabel('Frequency')\n",
-        "plt.title('Distribution of SBIN Closing Prices')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A moving average chart is a type of chart that shows the average value of a stock's price over a certain period of time. It is used to smooth out the fluctuations in the price and to identify trends."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the moving average chart\n",
-        "df['MA_50'] = df['Close'].rolling(window=50).mean()\n",
-        "df['MA_200'] = df['Close'].rolling(window=200).mean()\n",
-        "\n",
-        "plt.plot(df['Date'], df['Close'], label='Close')\n",
-        "plt.plot(df['Date'], df['MA_50'], label='MA 50')\n",
-        "plt.plot(df['Date'], df['MA_200'], label='MA 200')\n",
-        "plt.xlabel('Date')\n",
-        "plt.ylabel('Price')\n",
-        "plt.title('SBIN Stock Price with Moving Averages')\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This chart shows the closing price of the stock (blue line) along with its 20-day moving average (red line) and two standard deviations plotted above (green line) and below (orange line) it. The Bollinger Bands are used to identify volatility in the stock price"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the Bollinger Bands chart\n",
-        "df['MA_20'] = df['Close'].rolling(window=20).mean()\n",
-        "df['Upper_BB'] = df['MA_20'] + 2*df['Close'].rolling(window=20).std()\n",
-        "df['Lower_BB'] = df['MA_20'] - 2*df['Close'].rolling(window=20).std()\n",
-        "\n",
-        "plt.plot(df['Date'], df['Close'], label='Close')\n",
-        "plt.plot(df['Date'], df['Upper_BB'], label='Upper BB')\n",
-        "plt.plot(df['Date'], df['Lower_BB'], label='Lower BB')\n",
-        "plt.xlabel('Date')\n",
-        "plt.ylabel('Price')\n",
-        "plt.title('SBIN Stock Price with Bollinger Bands')\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        " This chart shows the RSI of the stock (blue line) along with two horizontal lines at 30 (red line) and 70 (green line). The RSI is used to identify overbought (above 70) and oversold (below 30) conditions.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the RSI chart\n",
-        "delta = df['Close'].diff(1)\n",
-        "up, down = delta.copy(), delta.copy()\n",
-        "up[up < 0] = 0\n",
-        "down[down > 0] = 0\n",
-        "roll_up = up.rolling(window=14).mean()\n",
-        "roll_down = down.rolling(window=14).mean().abs()\n",
-        "RS = roll_up / roll_down\n",
-        "RSI = 100.0 - (100.0 / (1.0 + RS))\n",
-        "\n",
-        "plt.plot(df['Date'], RSI, label='RSI')\n",
-        "plt.axhline(y=30, color='red', linestyle='--')\n",
-        "plt.axhline(y=70, color='green', linestyle='--')\n",
-        "plt.xlabel('Date')\n",
-        "plt.ylabel('RSI')\n",
-        "plt.title('SBIN Stock Price with RSI')\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This chart shows the correlation between the open, high, low, close, and volume of the stock. The correlation is measured on a scale of -1 to 1, where 1 means perfect positive correlation and -1 means perfect negative correlation."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the heatmap\n",
-        "corr_matrix = df[['Open', 'High', 'Low', 'Close', 'Volume']].corr()\n",
-        "plt.imshow(corr_matrix, cmap='hot', interpolation='nearest')\n",
-        "plt.title('Correlation Matrix')\n",
-        "plt.colorbar()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "D6ssCbaNXem6"
-      },
-      "source": [
-        "## Feature Engineering"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WWb3qGu1Xa30"
-      },
-      "source": [
-        "### Handle missing values"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 71,
-      "metadata": {
-        "id": "yi9UnG0BXtGG"
-      },
-      "outputs": [],
-      "source": [
-        "# Drop unnecessary columns('Date' and 'Adj Close')\n",
-        "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 72,
-      "metadata": {
-        "id": "_QwN3imwXZGZ"
-      },
-      "outputs": [],
-      "source": [
-        "imputer = SimpleImputer(strategy='mean')\n",
-        "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 195,
-      "metadata": {
-        "id": "WYCTE93pXhyC"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cYIyc2QpXvoM"
-      },
-      "source": [
-        "### Adding Indicators"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IUH2Nt70Xy-X"
-      },
-      "source": [
-        "#### SMA"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PzQGu8JnX8Bk"
-      },
-      "source": [
-        "Its the avg of stock price over a specific time period\n",
-        "\n",
-        "SMA = (sum of closing price os past n days) / n\n",
-        "\n",
-        "It helps identify trends by filtering out shortterm fluctuations\n",
-        "\n",
-        "Price above SMA indicate Uptrend and price below SMA indicate lowertrend"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 73,
-      "metadata": {
-        "id": "CFGQWjNTX4Mf"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate the Simple Moving Average (SMA)\n",
-        "df[\"SMA_10\"] = df[\"Close\"].rolling(window=10).mean()\n",
-        "df[\"SMA_50\"] = df[\"Close\"].rolling(window=50).mean()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "LoX_CWbbX-tN"
-      },
-      "outputs": [],
-      "source": [
-        "# Drop rows with missing values\n",
-        "df.dropna(subset=['SMA_10', 'SMA_50'], inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jaU2IGH_YcIe"
-      },
-      "source": [
-        "##### SMA Graph"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Plot the SMA graph\n",
-        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
-        "days = int(input(\"of how many past days you want to see graph: \"))\n",
-        "ax1.plot(df[\"SMA_10\"][-days:], label = \"SMA_10\", color='Red', linewidth=1, alpha=0.8)\n",
-        "ax1.plot(df[\"SMA_50\"][-days:], label = \"SMA_50\", color='Green', linewidth=1, alpha=0.8)\n",
-        "ax1.plot(df[\"Close\"][-days:], label = \"Close\", color='Blue', linewidth=2)\n",
-        "\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qCxKwtnTX1Ck"
-      },
-      "source": [
-        "#### RSI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bbNsRzqmYiJi"
-      },
-      "source": [
-        "It is a momentum indicator used to measure the speed and change of price movements. It ranges from 0 to 100 and helps identify whether a stock is overbought or oversold.  \n",
-        "\n",
-        "RSI > 70: Overbought  \n",
-        "RSI < 30: Oversold"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 76,
-      "metadata": {
-        "id": "lTkIdld1YaxN"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate the Relative Strength Index (RSI)\n",
-        "delta = df['Close'].diff(1)\n",
-        "\n",
-        "gain = delta.where(delta > 0, 0)\n",
-        "loss = -delta.where(delta < 0, 0)\n",
-        "\n",
-        "avg_gain = gain.rolling(window=14).mean()\n",
-        "avg_loss = loss.rolling(window=14).mean()\n",
-        "\n",
-        "rs = avg_gain / avg_loss  # Relative Strength\n",
-        "df['RSI'] = 100 - (100 / (1 + rs))\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Drop rows with missing values\n",
-        "df.dropna(subset=['RSI'], inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bW10tgm0YlSM"
-      },
-      "source": [
-        "##### RSI Graph"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 807
-        },
-        "id": "dqR2l5r-YngD",
-        "outputId": "07d7a94a-4fd6-4f87-cf37-54b6980fa5cb"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the RSI graph\n",
-        "\n",
-        "# Create subplots\n",
-        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})\n",
-        "\n",
-        "days = int(input(\"of how many past days you want to see graph: \"))\n",
-        "# price graph\n",
-        "ax1.plot(df['Close'][-days:], label='Close Price', color='blue')\n",
-        "ax1.set_title('Stock Price')\n",
-        "ax1.set_ylabel('Price')\n",
-        "ax1.legend()\n",
-        "\n",
-        "# rsi graph\n",
-        "ax2.plot(df['RSI'][-days:], label='RSI', color='orange')\n",
-        "ax2.axhline(70, color='red', linestyle='--')  # Overbought line\n",
-        "ax2.axhline(30, color='green', linestyle='--')  # Oversold line\n",
-        "ax2.set_title('Relative Strength Index (RSI)')\n",
-        "ax2.set_ylabel('RSI')\n",
-        "ax2.legend()\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "LaU9_eV9X2Zc"
-      },
-      "source": [
-        "#### MACD"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 78,
-      "metadata": {
-        "id": "4j9soKAXXvZX"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate the Moving Average Convergence Divergence (MACD)\n",
-        "# Calculate the short-term and long-term EMAs\n",
-        "df['EMA_12'] = df['Close'].ewm(span=12, adjust=False).mean()\n",
-        "df['EMA_26'] = df['Close'].ewm(span=26, adjust=False).mean()\n",
-        "\n",
-        "# Calculate MACD and Signal line\n",
-        "df['MACD'] = df['EMA_12'] - df['EMA_26']  # MACD line\n",
-        "df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()  # Signal line"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vJU2F-TiYrSB"
-      },
-      "source": [
-        "##### MACD Graph"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 807
-        },
-        "id": "BV8z1ge1Ysl3",
-        "outputId": "9ddf6a13-ee48-45ea-f6f4-e361d1decc11"
-      },
-      "outputs": [],
-      "source": [
-        "# Plot the MACD\n",
-        "fig, ax = plt.subplots(2, 1, figsize=(10, 8))\n",
-        "\n",
-        "\n",
-        "days = int(input(\"of how many past days you want to see graph: \"))\n",
-        "# Plot stock price on first subplot (bold)\n",
-        "ax[0].plot( df['Close'][-days:], label='Close Price', color='blue', linewidth=3)\n",
-        "ax[0].set_title('Stock Price')\n",
-        "ax[0].set_ylabel('Price')\n",
-        "\n",
-        "# Plot MACD and Signal line on second subplot\n",
-        "ax[1].plot( df['MACD'][-days:], label='MACD', color='green', linewidth=2)\n",
-        "ax[1].plot( df['Signal_Line'][-days:], label='Signal Line', color='red', linestyle='--', alpha=0.7)\n",
-        "ax[1].set_title('MACD')\n",
-        "ax[1].set_ylabel('MACD Value')\n",
-        "\n",
-        "# Show legends\n",
-        "ax[0].legend()\n",
-        "ax[1].legend()\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VC-x8kPuY_FU"
-      },
-      "source": [
-        "#### Corrrelations"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 425
-        },
-        "id": "I7pi-rfLZBGf",
-        "outputId": "549af121-3ef6-476f-e359-2e3ce2cbb166"
-      },
-      "outputs": [],
-      "source": [
-        "corr = df.corr()\n",
-        "corr"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 746
-        },
-        "id": "1OGAC3uxZCxG",
-        "outputId": "9d7ad372-d66d-4c2a-b72a-717179294401"
-      },
-      "outputs": [],
-      "source": [
-        "plt.figure(figsize=(10,8))\n",
-        "sns.heatmap(corr, annot=True, linewidths=0.5)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 204,
-      "metadata": {
-        "id": "GvIj_HpsZJ26"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "v8aLHujGZQu_"
-      },
-      "source": [
-        "## Model Training Preperation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {
-        "id": "FV2-jJCSZQaA"
-      },
-      "outputs": [],
-      "source": [
-        "# Select features and target variable\n",
-        "X = df[['Open', 'High', 'Low', 'Volume']]\n",
-        "y = df['Close']"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {
-        "id": "MStjwvxqZV1e"
-      },
-      "outputs": [],
-      "source": [
-        "# Split the data into training and testing sets\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 34,
-      "metadata": {
-        "id": "adMP-Zn1ZW7s"
-      },
-      "outputs": [],
-      "source": [
-        "# Scale the features using Min-Max scaling\n",
-        "scaler = MinMaxScaler()\n",
-        "X_train_scaled = scaler.fit_transform(X_train)\n",
-        "X_test_scaled = scaler.transform(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7nfzuX5hZZtj",
-        "outputId": "6e0f44fa-598b-482f-db74-449179691b30"
-      },
-      "outputs": [],
-      "source": [
-        "X_train.shape, X_test.shape"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "avggYFQCZiF8"
-      },
-      "source": [
-        "## Model Training"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "i63wO9AVZkjf"
-      },
-      "source": [
-        "#### 1. LINEAR REGRESSION"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 209,
-      "metadata": {
-        "id": "9AJm7PhjZZ9p"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a linear regression model\n",
-        "model1 = LinearRegression()\n",
-        "\n",
-        "# Train the model\n",
-        "model1.fit(X_train, y_train)\n",
-        "\n",
-        "# Make predictions on the test set\n",
-        "pred1 = model1.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 210,
-      "metadata": {
-        "id": "lYpraDj7Zwqr"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
-        "mae1 = mean_absolute_error(y_test, pred1)\n",
-        "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
-        "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
-        "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
-        "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
-        "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
-        "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "xOAnEaKoZxuY",
-        "outputId": "202a1add-cae6-4637-8106-735d3f958007"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse1)\n",
-        "print(\"MAE:\", mae1)\n",
-        "print(\"MAPE:\", mape1)\n",
-        "print(\"Accuracy:\", accuracy1)\n",
-        "print(\"Precision:\", precision1)\n",
-        "print(\"Confusion Matrix:\\n\", confusion1)\n",
-        "print(\"Recall:\", recall1)\n",
-        "print(\"F1 Score:\", f11)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HxCNbk0JafKE"
-      },
-      "source": [
-        "#### 2. SVR"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "##### With Grid Search"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 41,
-      "metadata": {
-        "id": "XxOnG95gZ0NR"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an SVR model\n",
-        "model2 = SVR()\n",
-        "param_grid = {'C':[0.1, 1], 'epsilon':[0.01, 0.1, 0.5], 'kernel':['sigmoid']}\n",
-        "GV_SVR = GridSearchCV(model2, param_grid = param_grid, scoring = 'accuracy', n_jobs = -1)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        },
-        "id": "heZ6MwXaalFT",
-        "outputId": "732d3ac8-014f-47fb-f1af-e3f30afbb037"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "GV_SVR.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 46,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred2 = GV_SVR.predict(X_test)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "984H1ej2apPP"
-      },
-      "outputs": [],
-      "source": [
-        "GV_SVR.best_params_"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "##### Without Grid Search"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#fitting without grid search\n",
-        "model2.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 45,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred2_1 = model2.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 47,
-      "metadata": {
-        "id": "ErnLBNW6apM4"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
-        "mae2 = mean_absolute_error(y_test, pred2)\n",
-        "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
-        "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
-        "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
-        "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
-        "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
-        "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "82Gd1kSwapKR",
-        "outputId": "1dc4a6a9-f0ad-4b96-eeaa-9682f4912767"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse2)\n",
-        "print(\"MAE:\", mae2)\n",
-        "print(\"MAPE:\", mape2)\n",
-        "print(\"Accuracy:\", accuracy2)\n",
-        "print(\"Precision:\", precision2)\n",
-        "print(\"Confusion Matrix:\\n\", confusion2)\n",
-        "print(\"Recall:\", recall2)\n",
-        "print(\"F1 Score:\", f12)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GsVk_xFZauR6"
-      },
-      "source": [
-        "#### 3. Random Forest"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 217,
-      "metadata": {
-        "id": "YwerJPNxapGt"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.ensemble import RandomForestRegressor\n",
-        "# Create a Random Forest model\n",
-        "model3 = RandomForestRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        },
-        "id": "fspXTQOXapBl",
-        "outputId": "16e51cc3-390d-4dad-8a04-68b48104ac0b"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "model3.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 219,
-      "metadata": {
-        "id": "Hu0iAgE9ao-_"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred3 = model3.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 220,
-      "metadata": {
-        "id": "WTu2YAoQao6Q"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
-        "mae3 = mean_absolute_error(y_test, pred3)\n",
-        "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
-        "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
-        "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
-        "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
-        "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
-        "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "9_7z9JcRao4C",
-        "outputId": "9b6dbe5a-de58-4519-d12b-afd1783b5024"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse3)\n",
-        "print(\"MAE:\", mae3)\n",
-        "print(\"MAPE:\", mape3)\n",
-        "print(\"Accuracy:\", accuracy3)\n",
-        "print(\"Precision:\", precision3)\n",
-        "print(\"Confusion Matrix:\\n\", confusion3)\n",
-        "print(\"Recall:\", recall3)\n",
-        "print(\"F1 Score:\", f13)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kg80FkpDa4Am"
-      },
-      "source": [
-        "#### 4. Gradient Boosting Models (GBM)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 222,
-      "metadata": {
-        "id": "D75bIlqqao1t"
-      },
-      "outputs": [],
-      "source": [
-        "model4 = GradientBoostingRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        },
-        "id": "uUHqBGsTa9TV",
-        "outputId": "4694a027-32b2-4c7b-c9cc-ee84e6c4b425"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "model4.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 224,
-      "metadata": {
-        "id": "T7XyO3qIa9QU"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred4 = model4.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 225,
-      "metadata": {
-        "id": "j2DCsz23a9M3"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
-        "mae4 = mean_absolute_error(y_test, pred4)\n",
-        "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
-        "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
-        "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
-        "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
-        "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
-        "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "tl4jTkC9a9Je",
-        "outputId": "4d2f1ee3-2886-4176-e9dc-2062b3890377"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse4)\n",
-        "print(\"MAE:\", mae4)\n",
-        "print(\"MAPE:\", mape4)\n",
-        "print(\"Accuracy:\", accuracy4)\n",
-        "print(\"Precision:\", precision4)\n",
-        "print(\"Confusion Matrix:\\n\", confusion4)\n",
-        "print(\"Recall:\", recall4)\n",
-        "print(\"F1 Score:\", f14)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aDoDcLUdbCq6"
-      },
-      "source": [
-        "#### 5. Extreme Gradient Boosting (XGBoost)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 227,
-      "metadata": {
-        "id": "rmgm3VADa9Gb"
-      },
-      "outputs": [],
-      "source": [
-        "import xgboost as xgb\n",
-        "# Create an XGBoost model\n",
-        "model5 = xgb.XGBRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 253
-        },
-        "id": "txadd-U3a9AP",
-        "outputId": "0a5d9230-a87f-486b-cda4-197face55486"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "model5.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 229,
-      "metadata": {
-        "id": "xYWVMZJ5a89t"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred5 = model5.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 230,
-      "metadata": {
-        "id": "qeDxXt9pa87W"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
-        "mae5 = mean_absolute_error(y_test, pred5)\n",
-        "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
-        "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
-        "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
-        "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
-        "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
-        "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "TdY1rRy_a83-",
-        "outputId": "d43b90b2-d098-402f-fbf7-46130f5310e3"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse5)\n",
-        "print(\"MAE:\", mae5)\n",
-        "print(\"MAPE:\", mape5)\n",
-        "print(\"Accuracy:\", accuracy5)\n",
-        "print(\"Precision:\", precision5)\n",
-        "print(\"Confusion Matrix:\\n\", confusion5)\n",
-        "print(\"Recall:\", recall5)\n",
-        "print(\"F1 Score:\", f15)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EbxPon-VbMGE"
-      },
-      "source": [
-        "#### 6. AdaBoostRegressor"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 232,
-      "metadata": {
-        "id": "Xze9G-tUa802"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.ensemble import AdaBoostRegressor\n",
-        "# Create an AdaBoost model\n",
-        "model6 = AdaBoostRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        },
-        "id": "DEmXpAV8a8wC",
-        "outputId": "5cadcb29-13ce-45f0-cfe1-f9e21a24866b"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "model6.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 234,
-      "metadata": {
-        "id": "KvI77shea8tk"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred6 = model6.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 235,
-      "metadata": {
-        "id": "catCNPwla8rL"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
-        "mae6 = mean_absolute_error(y_test, pred6)\n",
-        "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
-        "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
-        "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
-        "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
-        "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
-        "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "wpUqeumZa8oZ",
-        "outputId": "db2f5904-a96b-46d9-fbc3-ec519d5b7c7a"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse6)\n",
-        "print(\"MAE:\", mae6)\n",
-        "print(\"MAPE:\", mape6)\n",
-        "print(\"Accuracy:\", accuracy6)\n",
-        "print(\"Precision:\", precision6)\n",
-        "print(\"Confusion Matrix:\\n\", confusion6)\n",
-        "print(\"Recall:\", recall6)\n",
-        "print(\"F1 Score:\", f16)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ubml65zDbUZu"
-      },
-      "source": [
-        "#### 7. Decision Tree"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 237,
-      "metadata": {
-        "id": "qCvEszOKbVGV"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.tree import DecisionTreeRegressor\n",
-        "# Create a Decision Tree model\n",
-        "model7 = DecisionTreeRegressor()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        },
-        "id": "5lYl0STGbWw8",
-        "outputId": "9aabd48a-73aa-4737-e243-ae4eb5899765"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "model7.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 239,
-      "metadata": {
-        "id": "Wu34WtF-bXy0"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred7 = model7.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 240,
-      "metadata": {
-        "id": "H0NdEFHXbYuI"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
-        "mae7 = mean_absolute_error(y_test, pred7)\n",
-        "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
-        "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
-        "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
-        "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
-        "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
-        "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Wq6aws7ZbZft",
-        "outputId": "3f0b27ba-3257-458b-b5d3-abba03fa5b47"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse7)\n",
-        "print(\"MAE:\", mae7)\n",
-        "print(\"MAPE:\", mape7)\n",
-        "print(\"Accuracy:\", accuracy7)\n",
-        "print(\"Precision:\", precision7)\n",
-        "print(\"Confusion Matrix:\\n\", confusion7)\n",
-        "print(\"Recall:\", recall7)\n",
-        "print(\"F1 Score:\", f17)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5OX62RD6bb-V"
-      },
-      "source": [
-        "#### 8. KNeighborsRegressor(KNN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 49,
-      "metadata": {
-        "id": "5kz2DGKMbaem"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a KNN model\n",
-        "model8 = KNeighborsRegressor()\n",
-        "param_grid = {'n_neighbors':[3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]}\n",
-        "GV_KNN = GridSearchCV(model8, param_grid, cv=5, scoring='neg_mean_squared_error')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 80
-        },
-        "id": "PFdqC4oXbeg9",
-        "outputId": "b06c9cd8-c45c-4a8b-bec3-17fe23c6dc6d"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "model8.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 51,
-      "metadata": {
-        "id": "_Rv2V1W8bfhp"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred8 = model8.predict(X_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 52,
-      "metadata": {
-        "id": "K_QgGMDybgj8"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
-        "mae8 = mean_absolute_error(y_test, pred8)\n",
-        "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
-        "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
-        "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
-        "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
-        "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
-        "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "PKv6I2oCbha3",
-        "outputId": "ef250676-44d3-47ff-f7bd-ade1697c4789"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse8)\n",
-        "print(\"MAE:\", mae8)\n",
-        "print(\"MAPE:\", mape8)\n",
-        "print(\"Accuracy:\", accuracy8)\n",
-        "print(\"Precision:\", precision8)\n",
-        "print(\"Confusion Matrix:\\n\", confusion8)\n",
-        "print(\"Recall:\", recall8)\n",
-        "print(\"F1 Score:\", f18)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Grid Search parameters\n",
-        "GV_KNN.fit(X_train, y_train)\n",
-        "pred8_1 = GV_KNN.predict(X_test)\n",
-        "GV_KNN.best_estimator_"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "results = GV_KNN.cv_results_\n",
-        "mse = -results['mean_test_score']\n",
-        "k_values = results['param_n_neighbors'].data\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "plt.plot(k_values, mse, marker='o', linestyle='-')\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "##### KNN Hyperparameter Tuning"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import cross_val_score\n",
-        "\n",
-        "# KNN Hyperparameter Tuning\n",
-        "def knn_hyperparameter_tuning(X_train, y_train):\n",
-        "    k_values = range(1, 31)  # Example range for k\n",
-        "    mse_values = []\n",
-        "    \n",
-        "    for k in k_values:\n",
-        "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
-        "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
-        "        mse_values.append(mse)\n",
-        "    \n",
-        "    return k_values, mse_values\n",
-        "\n",
-        "# Perform KNN Hyperparameter Tuning\n",
-        "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
-        "\n",
-        "# Plotting the results\n",
-        "plt.figure(figsize=(10, 6))\n",
-        "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
-        "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
-        "plt.xlabel('Number of Neighbors (k)')\n",
-        "plt.ylabel('Mean Squared Error (MSE)')\n",
-        "plt.xticks(k_values)  # Show all k values on x-axis\n",
-        "plt.grid()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "-yoFias0bkhT"
-      },
-      "source": [
-        "#### 9. Artificial Neural Networks (ANN)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "oj1tv3-Sbm4M",
-        "outputId": "0d547cbc-d317-400a-a304-1bc283287926"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an ANN model\n",
-        "model9 = Sequential()\n",
-        "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
-        "model9.add(Dense(16, activation='relu'))\n",
-        "model9.add(Dense(1, activation='linear'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Llqm4lw3bnxu"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model9.compile(loss='mean_squared_error', optimizer='adam')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8gzrlwUTbpG7",
-        "outputId": "c3b2372d-3881-4568-efd3-59ffe8bce6f3"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cI6QW1utbp-J",
-        "outputId": "1c8b27b2-3380-40a1-ac68-17a68092f650"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "pred9 = model9.predict(X_test_scaled).flatten()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 252,
-      "metadata": {
-        "id": "-qDeEJUgbrUq"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
-        "mae9 = mean_absolute_error(y_test, pred9)\n",
-        "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
-        "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
-        "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
-        "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
-        "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
-        "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dEhvx1BTbvXO",
-        "outputId": "af73599a-ae1e-4d56-9288-8193e4aaad2b"
-      },
-      "outputs": [],
-      "source": [
-        "# Print the evaluation metrics\n",
-        "print(\"RMSE:\", rmse9)\n",
-        "print(\"MAE:\", mae9)\n",
-        "print(\"MAPE:\", mape9)\n",
-        "print(\"Accuracy:\", accuracy9)\n",
-        "print(\"Precision:\", precision9)\n",
-        "print(\"Confusion Matrix:\\n\", confusion9)\n",
-        "print(\"Recall:\", recall9)\n",
-        "print(\"F1 Score:\", f19)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qosFFsxrbyX7"
-      },
-      "source": [
-        "#### 10. LSTM(Long Short term Memory)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 255,
-      "metadata": {
-        "id": "3QajxGzXbxM4"
-      },
-      "outputs": [],
-      "source": [
-        "# Reshape the input data for LSTM\n",
-        "n_features = X_train_scaled.shape[1]\n",
-        "n_steps = 10\n",
-        "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
-        "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
-        "\n",
-        "# Reshape the input data\n",
-        "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
-        "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "gNRiL_B0bxJ3",
-        "outputId": "045b5c85-f8ef-4fc4-bf38-1c253d886c82"
-      },
-      "outputs": [],
-      "source": [
-        "# Create an LSTM model\n",
-        "model = Sequential()\n",
-        "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
-        "model.add(Dense(1))\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 257,
-      "metadata": {
-        "id": "DsndIHIQbxHV"
-      },
-      "outputs": [],
-      "source": [
-        "# Compile the model\n",
-        "model.compile(loss='mean_squared_error', optimizer='adam')\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "SzhA1fh9bxEz"
-      },
-      "outputs": [],
-      "source": [
-        "# Train the model\n",
-        "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "AQXh8a46bxCx"
-      },
-      "outputs": [],
-      "source": [
-        "# Make predictions on the test set\n",
-        "y_pred = model.predict(X_test_reshaped).flatten()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "iIXfZ2eubxAD"
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate evaluation metrics\n",
-        "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
-        "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
-        "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
-        "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
-        "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9Gjh33nNbw-O"
-      },
-      "outputs": [],
-      "source": [
-        "# Print evaluation metrics\n",
-        "print(\"RMSE:\", rmse10)\n",
-        "print(\"MAE:\", mae10)\n",
-        "print(\"MAPE:\", mape10)\n",
-        "print(\"Accuracy:\", accuracy10)\n",
-        "print(\"Precision:\", precision10)\n",
-        "print(\"Recall:\", recall10)\n",
-        "print(\"F1 Score:\", f110)\n",
-        "print(\"Confusion Matrix:\\n\", confusion10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "l6QVMqW-bw66"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Model Performance Graphs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6r0qumqybw4g"
-      },
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Accuracy"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "-hFs2mnDbwGQ"
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
-        "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n",
-        "\n",
-        "# List of corresponding labels for each accuracy\n",
-        "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, accuracies, color='blue')\n",
-        "plt.xlabel('Accuracy Variables')\n",
-        "plt.ylabel('Accuracy Values')\n",
-        "plt.title('Bar Graph of Accuracies')\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### RMSE"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "1tBYk4qmcEMk"
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
-        "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n",
-        "\n",
-        "# List of corresponding labels for each RMSE value\n",
-        "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, rmse_values, color='green')\n",
-        "plt.xlabel('RMSE Variables')\n",
-        "plt.ylabel('RMSE Values')\n",
-        "plt.title('Bar Graph of RMSE')\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### MAE"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "BS0oljCZcFhM"
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of MAE values from mae1 to mae10\n",
-        "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n",
-        "\n",
-        "# List of corresponding labels for each MAE value\n",
-        "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, mae_values, color='orange')\n",
-        "plt.xlabel('MAE Variables')\n",
-        "plt.ylabel('MAE Values')\n",
-        "plt.title('Bar Graph of MAE')\n",
-        "plt.show()\n"
-      ]
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "id": "Rqr4Dq5vWXmV"
+   },
+   "outputs": [],
+   "source": [
+    "# importing modules\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense,LSTM\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "id": "BigUM4vtWZdc"
+   },
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "df = pd.read_csv(\"SBIN.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RqOe6KPNWs8Q"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vAcMar5WXELR"
+   },
+   "source": [
+    "## Data Analysis and Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "vkDxEavVXHqv",
+    "outputId": "279f15e7-d2d2-442a-c162-e7db9780f2c7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01-01-1996</td>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-01-1996</td>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>03-01-1996</td>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>04-01-1996</td>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>05-01-1996</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date       Open       High        Low      Close  Adj Close  \\\n",
+       "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+       "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+       "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+       "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+       "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+       "\n",
+       "       Volume  \n",
+       "0  43733533.0  \n",
+       "1  56167280.0  \n",
+       "2  68296318.0  \n",
+       "3  86073880.0  \n",
+       "4  76613039.0  "
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display the first 5 rows of the dataframe\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Vx3DYtfdXJTU",
+    "outputId": "18c93f2d-5232-4e80-dfcf-e7d748901c37"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7074 entries, 0 to 7073\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Date       7074 non-null   object \n",
+      " 1   Open       7065 non-null   float64\n",
+      " 2   High       7065 non-null   float64\n",
+      " 3   Low        7065 non-null   float64\n",
+      " 4   Close      7065 non-null   float64\n",
+      " 5   Adj Close  7065 non-null   float64\n",
+      " 6   Volume     7065 non-null   float64\n",
+      "dtypes: float64(6), object(1)\n",
+      "memory usage: 387.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display information about the dataframe\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "SXdvitYIXKKL",
+    "outputId": "1a2c42d0-4f6e-4180-8ffe-7a40ef4ddabf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7.065000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>180.682841</td>\n",
+       "      <td>183.085167</td>\n",
+       "      <td>177.998209</td>\n",
+       "      <td>180.448294</td>\n",
+       "      <td>166.021712</td>\n",
+       "      <td>3.130217e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>154.773229</td>\n",
+       "      <td>156.345078</td>\n",
+       "      <td>152.980516</td>\n",
+       "      <td>154.630549</td>\n",
+       "      <td>152.903249</td>\n",
+       "      <td>3.462744e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>13.478195</td>\n",
+       "      <td>13.935802</td>\n",
+       "      <td>13.214009</td>\n",
+       "      <td>13.346102</td>\n",
+       "      <td>9.531410</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>28.423565</td>\n",
+       "      <td>28.824560</td>\n",
+       "      <td>28.022570</td>\n",
+       "      <td>28.456589</td>\n",
+       "      <td>19.854374</td>\n",
+       "      <td>1.299123e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>173.100006</td>\n",
+       "      <td>176.500000</td>\n",
+       "      <td>170.250000</td>\n",
+       "      <td>172.925003</td>\n",
+       "      <td>152.411270</td>\n",
+       "      <td>2.064292e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>265.500000</td>\n",
+       "      <td>268.899994</td>\n",
+       "      <td>261.299988</td>\n",
+       "      <td>265.174988</td>\n",
+       "      <td>245.764954</td>\n",
+       "      <td>3.651478e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>703.650024</td>\n",
+       "      <td>728.349976</td>\n",
+       "      <td>694.200012</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>4.469483e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Open         High          Low        Close    Adj Close  \\\n",
+       "count  7065.000000  7065.000000  7065.000000  7065.000000  7065.000000   \n",
+       "mean    180.682841   183.085167   177.998209   180.448294   166.021712   \n",
+       "std     154.773229   156.345078   152.980516   154.630549   152.903249   \n",
+       "min      13.478195    13.935802    13.214009    13.346102     9.531410   \n",
+       "25%      28.423565    28.824560    28.022570    28.456589    19.854374   \n",
+       "50%     173.100006   176.500000   170.250000   172.925003   152.411270   \n",
+       "75%     265.500000   268.899994   261.299988   265.174988   245.764954   \n",
+       "max     703.650024   728.349976   694.200012   725.250000   725.250000   \n",
+       "\n",
+       "             Volume  \n",
+       "count  7.065000e+03  \n",
+       "mean   3.130217e+07  \n",
+       "std    3.462744e+07  \n",
+       "min    0.000000e+00  \n",
+       "25%    1.299123e+07  \n",
+       "50%    2.064292e+07  \n",
+       "75%    3.651478e+07  \n",
+       "max    4.469483e+08  "
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display summary statistics of the dataframe\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 303
+    },
+    "id": "8rddiK_nXLNi",
+    "outputId": "4b65dd80-db84-45c8-ef16-2133bc25c68b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date         0\n",
+       "Open         9\n",
+       "High         9\n",
+       "Low          9\n",
+       "Close        9\n",
+       "Adj Close    9\n",
+       "Volume       9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display the number of missing values in each column\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I1khhoMECh4y"
+   },
+   "source": [
+    "### Detailed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "id": "HpS7l8oSCf6y"
+   },
+   "outputs": [],
+   "source": [
+    "# Function to create scatter plots\n",
+    "def create_scatter_plot(x_data, y_data, size_data=None, title=None, x_label=None, y_label=None, figsize=(15,7)):\n",
+    "    plt.figure(figsize=figsize)\n",
+    "    sns.scatterplot(x=x_data, y=y_data, size=size_data)\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    if x_label:\n",
+    "        plt.xlabel(x_label)\n",
+    "    if y_label:\n",
+    "        plt.ylabel(y_label)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "id": "oVEjj8ZMCnDx"
+   },
+   "outputs": [],
+   "source": [
+    "# Function to create line plots\n",
+    "def create_line_plot(x_data, y_data, title=None, x_label=None, y_label=None, figsize=(15,7)):\n",
+    "    plt.figure(figsize=figsize)\n",
+    "    sns.lineplot(x=x_data, y=y_data)\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    if x_label:\n",
+    "        plt.xlabel(x_label)\n",
+    "    if y_label:\n",
+    "        plt.ylabel(y_label)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Date' and 'Volume'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[   0  222  442 ... 1422 1657 1894]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le = LabelEncoder()\n",
+    "encoded=le.fit_transform(df['Date'])\n",
+    "print(encoded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "      <td>222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "      <td>442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "      <td>678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "      <td>914</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Open       High        Low      Close  Adj Close      Volume  Date\n",
+       "0  18.691147  18.978922  18.540184  18.823240  12.409931  43733533.0     0\n",
+       "1  18.894005  18.964767  17.738192  18.224106  12.014931  56167280.0   222\n",
+       "2  18.327892  18.568489  17.643839  17.738192  11.694577  68296318.0   442\n",
+       "3  17.502312  17.832542  17.223972  17.676863  11.654142  86073880.0   678\n",
+       "4  17.738192  17.785366  17.459852  17.577793  11.588827  76613039.0   914"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(\"Date\",axis=1,inplace=True)\n",
+    "df[\"Date\"]=encoded\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 769
+    },
+    "id": "-n8vOGhdCmer",
+    "outputId": "ea5057f1-9493-43de-ec65-7a017134d171"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Date' and 'Volume'\n",
+    "sns.scatterplot(x = df['Date'], y = df['Volume'])\n",
+    "sns.kdeplot(x = df['Date'], y = df['Volume'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FYvz9YaZDeDp"
+   },
+   "source": [
+    "Early 1990s spike: There is a high trading volume around the mid-1990s, peaking at over 4 × 10^8 (400 million trades).\n",
+    "Post-1996 to 2020: The volume significantly decreases, showing lower levels of activity with some fluctuations and minor peaks, particularly after 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the price variation over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "id": "q3QfzWquCmjk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the price variation over time\n",
+    "create_line_plot(df['Date'], df['High'] - df['Low'],\n",
+    "                 title='Price Variation Over Time', x_label='Date', y_label='Price Variation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mYGFClShDgsj"
+   },
+   "source": [
+    "#### Stable period until 2004:\n",
+    "The price variation was relatively low and stable until around 2003-2004, generally staying under 10 units.\n",
+    "#### 2004-2008 increase:\n",
+    "Starting from 2004, price variations gradually began to increase, peaking sharply just before the 2008 financial crisis. Some spikes went beyond 40 units.\n",
+    "#### 2008-2020 fluctuations:\n",
+    "After the 2008 peak, price variation showed continuous fluctuations with noticeable peaks, though they were more frequent post-2016.\n",
+    "#### 2020 onward:\n",
+    "The recent period (2020-2024) shows significant and frequent fluctuations, with peaks reaching 60+ units, possibly influenced by events like the COVID-19 pandemic and other global factors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Adj Close' and 'Volume'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "id": "XKsek--aCmoC"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Adj Close' and 'Volume'\n",
+    "create_line_plot(df['Adj Close'], df['Volume'],\n",
+    "                 title='Adjusted Close vs Volume',\n",
+    "                 x_label='Adjusted Close',\n",
+    "                 y_label='Volume')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "omVoWiC-DjJX"
+   },
+   "source": [
+    "The overall trend seems to be somewhat volatile, with periods of upward and downward movement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Open' and 'Adj Close'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "id": "W5TY59ikCv78"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Open' and 'Adj Close'\n",
+    "create_scatter_plot(df['Open'], df['Adj Close'], size_data=df['Volume'],\n",
+    "                    title='Open vs Adjusted Close', x_label='Open', y_label='Adj Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "q_BiuXvnDlfJ"
+   },
+   "source": [
+    "The scatter points generally show an upward trend, indicating a positive correlation between the opening and closing prices. This means that, in general, when the stock opens higher, it tends to close higher as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Close' and 'Adj Close'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "id": "CD0LiK3VCwiU"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Close' and 'Adj Close'\n",
+    "create_scatter_plot(df['Close'], df['Adj Close'], size_data=df['Volume'],\n",
+    "                    title='Close vs Adjusted Close', x_label='Close', y_label='Adj Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3TgT8XWtDnpy"
+   },
+   "source": [
+    "The scatter points show a very strong upward trend, indicating a strong positive correlation between the closing price and the adjusted closing price. This means that, in general, when the stock closes higher, the adjusted closing price is also higher."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Open' and 'Close'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "id": "bgq_AQ-nCwnA"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Open' and 'Close'\n",
+    "create_scatter_plot(df['Open'], df['Close'], size_data=df['Volume'],\n",
+    "                    title='Open vs Close', x_label='Open', y_label='Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Open - Close' and 'High - Low'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "id": "Zqsy16wWCwrC"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cdtttWLFiBc6dO4eVK1ciICAAM2fONB/zySefYNKkSXjnnXesbttWMtHWr/GTTz7BnXfeieeee87q8tLSUgQHB9t0juZMSbBz5861SEoXFhY69GeKiIjIk7DSjIiIyI20lowBLrQomqqZgLYrS6ZMmYLDhw9jz549VpevWbMGgiCYK7yuueYaNDY2Wm0GbM+TTz6J5cuX4+mnn8bjjz9u021MMV999dX4/PPP8dZbb0Emk2HOnDnm603JiubJkvfee8/m+2hNa+cVRbHVjZ/2VOlMmTLFnOiztGbNGvj6+pq3TzrCbbfdBm9vb6xevRqrV69GXFwcrrzyyjaP79u3L5588kkMGTKkxePfFWz5mZo6dSpEUURBQQFGjRrV4mPIkCE23df8+fOh0+nw0ksv4ccff8Stt94KX19f8/WCILT4mcrOzm7RPmuv1s67YcMGFBQUWF1ma0UhAHPr6yeffGJ1+c6dO3HkyJEWyyCIiIjINqw0IyIiciNXXXUVevTogWnTpqF///7Q6/XYt28fXnnlFfj7++OBBx4wHztkyBB88cUXWLt2LZKSkuDt7Y0hQ4bgwQcfxJo1a3Ddddfh2WefRXx8PDZs2IC3334b9957L/r27QvAkJBZtWoV7rnnHhw7dgyXXXYZ9Ho9srKyMGDAANx6660t4nvggQfg7++Pu+++G7W1tXj99ddtqtCZP38+NmzYgA8++ABXXXWV1TywtLQ0hISE4J577sEzzzwDpVKJTz/9FPv37+/U9/KKK66ASqXCbbfdhkceeQSNjY145513UFFR0eLYIUOG4Ntvv8U777yDkSNHQiaTYdSoUa2e95lnnsEPP/yAyy67DE8//TRCQ0Px6aefYsOGDVi2bBmCgoI6Fbel4OBg3HjjjVi9ejUqKyvx8MMPW1VwZWdn469//StmzpyJPn36QKVS4ffff0d2djYee+wxh8VhK1t+psaPH4+7774bd911F3bt2oVLL70Ufn5+OHfuHP744w8MGTIE995770Xva9SoURg6dCiWL18OURStWjMBQ3LuX//6F5555hlMnDgRx44dw7PPPovExMR2N65ezNSpU7F69Wr0798fQ4cOxe7du/HSSy+1qBBLTk6Gj48PPv30UwwYMAD+/v6IjY21Snyb9OvXD3fffTfeeOMNyGQyXHPNNebtmT179sSDDz7Y4XiJiIg8mqRrCIiIiMih1q5dK86aNUvs06eP6O/vLyqVSrFXr17iHXfcIR4+fNjq2FOnTolXXnmlGBAQIAKw2v54+vRpcdasWWJYWJioVCrFfv36iS+99JKo0+msztHQ0CA+/fTTYp8+fUSVSiWGhYWJkydPFtPT083HwLg909Lnn38uKhQK8a677mpxztZoNBoxKipKBCB++eWXLa5PT08XU1NTRV9fXzEiIkJcsGCBuGfPnhbbB+fMmSP6+fm1eh+tbc9cv369mJKSInp7e4txcXHi3//+d/Gnn34SAYibN282H1deXi7edNNNYnBwsCgIgtVmQzTbnimKonjgwAFx2rRpYlBQkKhSqcSUlJQWWxJN2zO/+uorq8tt2choaePGjeZtqs03QxYXF4tz584V+/fvL/r5+Yn+/v7i0KFDxddee03UarXtnte0wfH8+fPtHjdx4kRx0KBBbV7XfIOrLT9ToiiKK1euFMeOHSv6+fmJPj4+YnJysnjnnXeKu3btajceS//9739FAOLAgQNbXKdWq8WHH35YjIuLE729vcURI0aI//vf/1r8nJgej5deeqnFOVp7rCoqKsT58+eLkZGRoq+vr3jJJZeI27dvb/V78fnnn4v9+/cXlUql1c9R8+2ZomjY7vriiy+Kffv2FZVKpRgeHi7efvvtYn5+vtVxbT0eF9seS0RE5IkEURTFLs/UERERERERERERuTDONCMiIiIiIiIiImqGSTMiIiIiIiIiIqJmmDQjIiIiIiIiIiJqhkkzIiIiIiIiIiKiZpg0IyIiIiIiIiIiaoZJMyIiIiIiIiIiomYUUgfgbHq9HoWFhQgICIAgCFKHQ0REREREREREEhJFETU1NYiNjYVM1nY9mdsnzQoLC9GzZ0+pwyAiIiIiIiIiIheSn5+PHj16tHm92yfNAgICABi+EYGBgRJHQ0REREREREREUqqurkbPnj3NOaO2uH3SzNSSGRgYyKQZEREREREREREBwEXHeHERABERERERERERUTNMmhERERERERERETXDpBkREREREREREVEzTJoRERERERERERE1w6QZERERERERERFRM0yaERERERERERERNcOkGRERERERERERUTNMmhERERERERERETXDpBkREREREREREVEzTJoRERERERERERE1w6QZERERERERERFRM0yaERERERERERERNcOkGRERERERERERUTNMmhERERERERERETXDpBkREREREREREVEzTJoRERERERERERE1w6QZERERERERERFRM0yaERERERERERERNcOkGRERERERERERUTNMmhERERERERERUQuiKEIURanDkAyTZkRERERERERE1EL22SqMfe43PPm/A1KHIgkmzYiIiIiIiIiIqIX0nDKU1KhRVKWWOhRJMGlGREREREREREQtZOSWAQDSksMkjkQaTJoREREREREREZGVJp0eu06VAwBSmTQjIiIiIiIiIiICss9Wol6jQ4ivEv2iAqQORxJMmhERERERERERkZX0k4bWzHFJYZDJBImjkQaTZkREREREREREZMXT55kBTJoREREREREREZEFtVaH3acrAHjuPDOASTMiIiIiIiIiIrKw90wl1Fo9IgK8kBzhL3U4kmHSjIiIiIiIiIiIzNJzLswzEwTPnGcGMGlGREREREREREQWMnM4zwxg0oycoKaxCS//cgyHC6ulDoWIiIiIiIiI7NCg0WFvvnGeWRKTZkQOtWJ7Ht7cfBIz301HhjE7TURERERERESub/fpCjTpRMQEeSM+zFfqcCTFpBk5lCiK+GF/IQCgTqPDnFU78NuRYomjIiIiIiIiIiJbpOeUAjBszfTkeWYAk2bkYIfPVSO3tA5eChkm94+ERqvHoo9343tjIo2IiIiIiIiIXFdGrqFjzNNbMwEmzcjB1u8/BwC4rF8k3rtjJG4YFgutXsQDX+zFZ1lnJI6OiIiIiIiIiNpSq9Yi+2wVAEOlmadj0owcRhRF/JBtqCiblhILpVyGV28ehtlje0EUgX+sO4D3t+VIHCURERERERERtWbnqXLo9CJ6hvqgR4hnzzMDmDQjB9qXX4mzFQ3wVckxuX8kAEAmE/DvGwbjnonJAIDnfjyKVzcegyiKUoZKRERERERERM2YlvmlJYVLHIlrYNKMHOaHbENr5uUDouCjkpsvFwQBj13TH49c3Q8A8PrvJ/HP9Yeh1zNxRkREREREROQqTEkztmYaMGlGDqHXi9hgTJpNHRrT6jGLJ/XGv6YPAgCsTj+FR77Jhlan77IYiYiIiIiIiKh1VQ1NOFTIeWaWmDQjh9h1ugJF1Y0I8FZgYr+INo+7IzUBr96cArlMwNe7z+L/Pt8LtVbXhZESERERERERUXM78sqhF4GkcD9EBXpLHY5LYNKMHMK0AODKgdHwUsjbPXbGiB54a9YIqOQy/HSwCAvX7EaDhokzIiIiIiIiIqmk55QCYJWZJSbNqNO0Oj1+PGBozZyW0nprZnNXD47Gh3NHwUcpx7bj53HnyixUNzY5M0wiIiIiIiIiagPnmbXEpBl1WlZeOUprNQjxVWJ8b9s3bEzoE4FPFoxBgLcCO09VYNaKTJTVqp0YKRERERERERE1V16nwdGiGgDAuCQmzUyYNKNOW7/f0Jp59eBoKOX2/UiNjA/FF3ePQ5ifCgcLqnHL+5koqmp0RphERERERERE1IrMXEOVWd8of4T7e0kcjetg0ow6RaPV4+dDRQCAaUNjO3SOQbFB+PKeVMQEeeNkSS1mvpeOM2X1jgyTiIiIiIiIiNpgas1MS7a9e8wTMGlGnfLnyVJU1jch3N8LYztRwpkc4Y+v7klFQpgv8ssbcNO76TheXOPASImIiIiIiIioNRnGSjO2Zlpj0ow6Zb1xa+Z1Q6IhlwmdOlePEF98eU8q+kUFoKRGjZvfy8D+/EoHRElERERERERErSmpacTJkloIAjAuKVTqcFwKk2bUYY1NOmw6VAwAmJrSsdbM5iIDvLF20Tik9AxGZX0TZn+QZe6tJiIiIiIiIiLHMrVmDogORLCvSuJoXAuTZtRhW4+fR41ai5ggb4zsFeKw8wb7qvDpgrFITQpDrVqLOSt3YPPREoedn4iIiIiIiIgMTIUqaclszWyOSTPqsB+yzwEArhsSA1knWzOb8/dSYNVdo3H5gEiotXosXLMLPxhbQYmIiIiIiIjIMUyVZqlMmrXApBl1SL1Gi18PG1ozpzmoNbM5b6Uc79w+EtenxEKrF3H/53uxducZp9wXERERERERkacprGzAqbJ6yARgdCLnmTXHpBl1yO9HS9DQpEOvUF8M7RHktPtRymV47ZZhuG1ML+hF4NFvDuCD7blOuz8iIiIiIiIiT2GqMhsSF4RAb6XE0bgeJs2oQ9bvN7RKTh0aA0FwbGtmc3KZgOduHIxFlyYBAP694Qhe23Qcoig69X6JiIiIiIiI3FlGrqk1M1ziSFwTk2Zkt5rGJmw+dh4AMHWoc1ozmxMEAY9d0x8PX9kXAPDf307g3xuOMHFGRERERERE1EGcZ9Y+Js3IbpsOF0Oj1SMpwg8DYgK67H4FQcBfJ/fB0mkDAQAf/pGHx745AJ2eiTMiIiIiIiIie+SX16OgsgEKmYBR8SFSh+OSmDQju5m2Zk4bGuv01szWzB2fiJdnpkAmAGt35eP+L/ZCo9V3eRxERERERERE3VV6TikAIKVnMPy8FBJH45qYNCO7VNZrsO24oTVzWkqMZHHcNLIH3po1Akq5gA3Z53D3x7vQoNFJFg8RERERERFRd2JqzUxja2abmDQju/xyqAhavYj+0QHoHdl1rZmtuWZIDD6YMxreShm2HDuPOat2oKaxSdKYiIiIiIiIiFydKIoXlgAkMWnWFibNyC7m1syUrlkAcDET+0bg4/ljEeClwI68csz+IAvldRqpwyIiIiIiIiJyWXmldSiuVkMll2EE55m1SfKkWUFBAW6//XaEhYXB19cXw4YNw+7du83Xi6KIpUuXIjY2Fj4+Ppg0aRIOHTokYcSeq7RWjT9PGnqepw6VrjWzudEJofj87nEI9VMh+2wVbnkvA8XVjVKHRUREREREROSS0o2tmSPig+GtlEscjeuSNGlWUVGB8ePHQ6lU4qeffsLhw4fxyiuvIDg42HzMsmXL8Oqrr+LNN9/Ezp07ER0djSuuuAI1NTXSBe6hfjpYBL0IDO0RhPgwP6nDsTI4LghfLhqH6EBvnCipxcx3M5BfXi91WEREREREREQu50JrZrjEkbg2SZNmL774Inr27IlVq1ZhzJgxSEhIwJQpU5CcnAzAUGW2fPlyPPHEE5gxYwYGDx6Mjz76CPX19fjss89aPadarUZ1dbXVBznG+v2FAAxbM11R78gAfHVPKuLDfHGmvB43vZuOE8VMrhIRERERERGZiKKILFPSjEsA2iVp0uz777/HqFGjMHPmTERGRmL48OFYsWKF+fq8vDwUFRXhyiuvNF/m5eWFiRMnIj09vdVzPv/88wgKCjJ/9OzZ0+lfhycoqmrEzlPlAIDrXKg1s7meob74alEq+kb5o7hajZvfy8CBs1VSh0VERERERETkEk6U1KK0VgNvpQwpPYOkDselSZo0y83NxTvvvIM+ffrgl19+wT333IP7778fa9asAQAUFRUBAKKioqxuFxUVZb6uuccffxxVVVXmj/z8fOd+ER5iw4FzEEVgZHwIYoN9pA6nXZGB3lh7dyqG9ghCRX0TZq3IxI68cqnDIiIiIiIiIpJcunFW+eiEUHgpOM+sPZImzfR6PUaMGIHnnnsOw4cPx6JFi7Bw4UK88847VscJgmD1/6IotrjMxMvLC4GBgVYf1Hk/ZJtaM123ysxSiJ8Kny4Yi7GJoahRa3HnyixsOVYidVhEREREREREkjLNMxuXxNbMi5E0aRYTE4OBAwdaXTZgwACcOXMGABAdHQ0ALarKSkpKWlSfkfPkl9dj75lKCAJw7ZDukTQDgABvJT6aNwaT+0eisUmPhWt24ccD56QOi4iIiIiIiEgSer2IzFxDJxbnmV2cpEmz8ePH49ixY1aXHT9+HPHx8QCAxMREREdHY9OmTebrNRoNtm7dirS0tC6N1ZNtMCaaxiWGITLQW+Jo7OOtlOPd20di6tAYNOlE/PWzPfhqF1t2iYiIiIiIyPMcPleNqoYm+KnkGBLHeWYXI2nS7MEHH0RmZiaee+45nDx5Ep999hnef/993HfffQAMbZlLlizBc889h3Xr1uHgwYOYO3cufH19MWvWLClD9yimrZlTU7pPlZkllUKG/946HLeO7gm9CPz962ys+jNP6rCIiIiIiIiIulSmsTVzTGIolHJJU0LdgkLKOx89ejTWrVuHxx9/HM8++ywSExOxfPlyzJ4923zMI488goaGBixevBgVFRUYO3YsNm7ciICAAAkj9xx5pXU4VFgNuUzANYO7Z9IMAOQyAc/PGAJ/LwU++CMP/1x/GDWNWvzf5N5tzscjIiIiIiIicicZOYakGVszbSOIoihKHYQzVVdXIygoCFVVVVwK0AFv/HYCr2w6jkv7RmDNvDFSh9Npoiji9d9O4rVfjwMAFk5IxD+uHcDEGREREREREbk1rU6P4c9uQo1ai/V/vQRDenhue6atuSLW4lG71nezrZkXIwgCHri8D56ealhAsWJ7Hv6x7gB0erfOHRMREREREZGHO1hYjRq1FoHeCgyMZVGRLZg0ozYdK6rB8eJaqOQyXDkoWupwHGreJYlY9pehkAnA5zvy8cAXe9Gk00sdFhEREREREZFTmFozxyaFQS5jt5UtmDSjNv1grDK7tG8EgnyUEkfjeDeP7ok3bhsBpVzAD9nnsOjj3Whs0kkdFhEREREREZHDZRiXAKQmcZ6ZrZg0o1aJoogfss8BAKZ1062ZtrhuaAxW3DkK3koZfj9agrmrdqBWrZU6LCIiIiIiIiKH0Wj12HWqHACXANiDSTNq1aHCauSV1sFLIcOUAVFSh+NUk/pFYs28sfD3UiAztxyzV2Siok4jdVhEREREREREDpF9thL1Gh1CfJXoFxUgdTjdBpNm1CrTAoApAyLh76WQOBrnG5MYis8WjkWIrxL7z1bh1vczUVLdKHVYRERERERERJ1mmmeWmhwGGeeZ2YxJM2pBFEX8sN/Qmjl1aKzE0XSdoT2C8eWiVEQGeOFYcQ1mvpeB/PJ6qcMiIiIiIiIi6hTOM+sYJs2ohb35lSiobICfSo7L+kVKHU6X6hMVgK/vSUPPUB+cLqvHze9l4GRJrdRhEREREREREXVIY5MOu09XAOA8M3sxaUYtmKrMLh8YBR+VXOJoul6vMF98tSgNfSL9ca6qEbe8l4GDBVVSh0VERERERERkt71nKqHW6hER4IXkCH+pw+lWmDQjK3q9iA0HDPPMpnlQa2Zz0UHeWLsoFUPiglBWp8FtKzLNm0aIiIiIiIiIugvL1kxB4DwzezBpRlZ2nipHcbUaAd4KTOgbLnU4kgr1U+HThWMxJiEUNY1a3PHhDmw7fl7qsIiIiIiIiIhslmmxBIDsw6QZWTFtzbx6UDS8FJ7XmtlcoLcSH80bg4l9I9DQpMOCj3bh54PnpA6LiIiIiIiI6KIaNDrszTfOM+MSALsxaUZmWp0ePx0oAgBMTfHc1szmfFRyrLhzFK4bEgONTo/Fn+7BN7vPSh0WERERERERUbt2nS5Hk05ETJA34sN8pQ6n22HSjMwycstQVqdBiK8SaSzbtKJSyPD6bcNx86ge0IvAQ1/tx5qMU1KHRURERERERNSmDIvWTM4zsx+TZmRm2pp5zZAYKOX80WhOLhPwwoyhmDc+EQDw9HeH8NbmkxBFUeLIiIiIiIiIiFqyXAJA9mNmhAAAGq0ePxlndU0dGiNxNK5LJhPw1NQBeGBKHwDAS78cwws/H2XijIiIiIiIiFxKrVqL7LNVALgEoKOYNCMAwB8nz6O6UYuIAC+MTeQvU3sEQcCDV/TFk9cNAAC8tzUXT/zvIHR6Js6IiIiIiIjINezMK4dOL6JnqA96hHCeWUcwaUYAgPXG1szrhsRALmOfsy0WTEjCCzOGQBCAz7LO4G9f7kOTTi91WERERERERETm1sy0pHCJI+m+mDQjNDbpsOlwMQBgWgpbM+1x65heeP3W4VDIBHy3rxD3frIbjU06qcMiIiIiIiIiD2e5BIA6hkkzwpZj51Gr1iI2yBvDe4ZIHU63My0lFu/fORJeChl+PVKCeat3ok6tlTosIiIiIiIi8lBV9U04VMh5Zp3FpBlhfXYhAGBqSixkbM3skMn9o/DRvDHwU8mRnlOG2R9kobJeI3VYRERERERE5IGy8sqgF4GkCD9EBXpLHU63xaSZh6vXaPH7kRIA3JrZWeOSwvDZwnEI9lViX34lbn0/EyU1jVKHRURERERERB7GNM8sNYlVZp3BpJmH+/VICRqadIgP88WQuCCpw+n2UnoGY+3dqYgI8MLRohrc/G4GzlbUSx0WEREREREReRDOM3MMJs083A/7ja2ZQ2MgCGzNdIR+0QH4+p5U9Ajxwamyetz8bgZyz9dKHRYRERERERF5gLJaNY4W1QAwdERRxzFp5sGqG5uw5dh5AMDUobESR+Ne4sP88NU9qUiO8ENhVSNufi8DhwurpQ6LiIiIiIiI3FxWXjkAoF9UAML9vSSOpntj0syDbTpUDI1Oj96R/ugfHSB1OG4nJsgHXy5KxaDYQJTWanDr+xnYfbpC6rCIiIiIiIjIjbE103GYNPNg5q2ZbM10mjB/L3x+9ziMTghBdaMWd3yYhT9OlEodFhEREREREbkp0xIAtmZ2HpNmHqqiTmNO3rA107kCvZVYM28sJvQJR71Gh3mrd2LjoSKpwyIiIiIiIiI3U1LdiJMltRAEYFxSqNThdHtMmnmoXw4VQasXMSAmEL0j/aUOx+35qOT4YM4oXD0oGhqdHvd+ugfr9p6VOiwiIiIiIiJyI6Yqs4ExgQj2VUkcTffHpJmHMrVmTkuJkTgSz+GlkOPNWcPxlxE9oNOLeHDtfnyceVrqsIiIiIiIiMhNZBqTZqlszXQIJs080PkatXkw4NQhbM3sSgq5DC/dNBRz0xIAAE/97yDe3nJS2qCIiIiIiIjILXAJgGMxaeaBfjp4DnoRSOkZjF5hvlKH43FkMgHPTBuI/5vcGwCw7OdjePHnoxBFUeLIiIiIiIiIqLsqrGzAqbJ6yARgdCLnmTkCk2Ye6If95wAA04ayNVMqgiDgoSv74fFr+gMA3tmSg6e/OwS9nokzIiIiIiIisp+pymxIj2AEeisljsY9MGnmYc5VNWDHqXIAwLVDmDST2qKJyfjPjYMhCMDHmafx0Ff7odXppQ6LiIiIiIiIupkMzjNzOCbNPMyGbEOV2eiEEMQG+0gcDQHA7LHxWH7LMMhlAtbtLcC9n+5BY5NO6rCIiIiIiIiomxBFkfPMnIBJMw+z3pg0mzqUCwBcyfRhcXjv9pFQKWTYdLgYCz7ahXqNVuqwiIiIiIiIqBvIL29AQWUDFDIBo+JDpA7HbTBp5kHyy+uxP78SMgG4Zki01OFQM5cPjMLqu0bDTyXHHydLcfsHWahqaJI6LCIiIiIiInJxGbmlAIBhPYPh56WQOBr3waSZB1mfXQgAGJcUhsgAb4mjodakJYfjkwVjEeSjxJ4zlbj1/UyU1qqlDouIiIiIiIhcGFsznYNJMw9i3pqZwtZMVza8VwjWLhqHcH8vHDlXjZvfzUBhZYPUYREREREREZELEkWRSwCchEkzD5FzvhaHz1VDIRNw9SC2Zrq6/tGB+OqeVMQF+yC3tA4z381AXmmd1GERERERERGRi8ktrUNxtRoquQwjOM/MoZg08xCmKrNL+oQjxE8lcTRki8RwP3x1TyqSwv1QUNmAme9m4Mi5aqnDIiIiIiIiIhdias0cER8Mb6Vc4mjcC5NmHkAURfM8M27N7F5ig33w5T2pGBgTiNJaNW55LwN7z1RIHRYRERERERG5iAutmeESR+J+mDTzAMeKa3CypBYquQxXDoqSOhyyU7i/Fz6/exxGxoegulGL2R9kIT2nVOqwiIiIiIiISGKiKCKTSwCchkkzD2BqzZzYLwKB3kqJo6GOCPJR4uP5YzChTzjqNTrMXbUTvx4uljosIiIiIiIiktDx4lqU1WngrZQhpWeQ1OG4HSbN3Jx1a2aMxNFQZ/iqFPhgzihcOTAKGq0eiz7Zje/2FUgdFhEREREREUkkw9iFNDohFF4KzjNzNCbN3NzBgmqcLquHt1KGywewNbO781LI8fbsEZgxPA46vYgla/fh06zTUodFREREREREEjDNMxuXxNZMZ2DSzM2Zqsym9I+Cn5dC4mjIERRyGV6emYI7xsVDFIEn1h3Ee1tzpA6LiIiIiIiIupBeLyIztxwA55k5C5NmbkwURWzINswzm5bC1kx3IpMJeHb6ICyelAwAeP6no3j5l2MQRVHiyIiIiIiIiKgrHD5XjaqGJvip5BgSx3lmzsCkmRvbc6YSBZUN8FPJMalfpNThkIMJgoBHru6PR6/uDwB4c/NJ/HP9Yej1TJwRERERERG5u0xja+aYxFAo5UzvOAO/q25s/X5Da+aVg6LhreRAQHd176Rk/OuGwRAEYHX6Kfz962xodXqpwyIiIiIiIiInysgxJM3Ymuk8TJq5KZ1exI8HDK2Z3Jrp/u4YF4/Xbh4GuUzAN3vO4q+f7YVaq5M6LCIiIiIiInICrU6PrDzjPLOkcImjcV9MmrmpHXnlKKlRI9BbgQl9IqQOh7rADcPj8M7sEVDJZfj5UBEWfLQL9Rqt1GERERERERGRgx0srEatWotAbwUGxgZKHY7bYtLMTf1g3Jp59eBoqBR8mD3FlYOisequ0fBVybH9RCnu/HAHqhqapA6LiIiIiIiIHMjUmjk2KQxymSBxNO6L2RQ3pNXp8dPBIgDA1KGxEkdDXW1873B8PH8sAr0V2HW6ArNWZKKsVi11WEREREREROQgGcYlAKlJnGfmTEyauaH0nDKU12kQ6qdCGgcCeqSR8SH44u5UhPurcKiwGje/l4FzVQ1Sh0VERERERESdpNHqsdM0z4yv+Z2KSTM3ZNqaec3gaCi4dtZjDYwNxJeLUhEb5I2c83W46Z0MnCqtkzosIiIiIiIi6oTss5VoaNIh1E+FflEBUofj1phRcTNqrQ6/HDK0Zk5LYWump0uK8MdX96YhMdwPBZUNmPleBo4V1UgdFhEREREREXWQaZ7ZuKRQyDjPzKmYNHMz24+XorpRi8gAL4xOCJU6HHIBccE++HJRKvpHB+B8jRq3vJ+BffmVUodFREREREREHcB5Zl2HSTM3Y9qaed3QGG7QILOIAC+svTsVw3sFo7K+CbNXZJrfnSAiIiIiIqLuobFJh12nKwBwnllXYNLMjTQ26bDpcDEAbs2kloJ8lfhk/likJYehTqPD3FU78PvRYqnDIiIiIiIiIhvtPVMJjVaPiAAvJEf4Sx2O22PSzI1sPlqCOo0OccE+GNErWOpwyAX5eSmwcu5oXD4gCmqtHnev2W1eHEFERERERESuzbI1UxDYXeZsTJq5kR+yzwEApg6N4S8PtclbKcc7t4/A9GGx0OpF3P/FXny+44zUYREREREREdFFZBrH7LA1s2swaeYm6tRa/GZstePWTLoYpVyG124ehtlje0EUgce/PYAV23KlDouIiIiIiIja0KDRYW++cZ4ZlwB0CUmTZkuXLoUgCFYf0dHR5utFUcTSpUsRGxsLHx8fTJo0CYcOHZIwYtf165FiNDbpkRDmi0GxgVKHQ92ATCbg3zcMxqKJSQCA//x4BK9uOg5RFCWOjIiIiIiIiJrbdbocTToRsUHeiA/zlTocjyB5pdmgQYNw7tw588eBAwfM1y1btgyvvvoq3nzzTezcuRPR0dG44oorUFNTI2HErmn9flNrZixbM8lmgiDg8WsG4O9X9QMAvP7bCTz7w2Ho9UycERERERERuZIMY2vmuGTOM+sqkifNFAoFoqOjzR8REREADFVmy5cvxxNPPIEZM2Zg8ODB+Oijj1BfX4/PPvtM4qhdS1VDE7YdPw+ArZnUMfdd1hvPTh8EAFj15yk8+k02dEycERERERERuQzLJQDUNSRPmp04cQKxsbFITEzErbfeitxcw1ylvLw8FBUV4corrzQf6+XlhYkTJyI9Pb3N86nValRXV1t9uLuNh4qg0enRJ9If/aIDpA6Huqk7UxPw6s0pkMsEfLX7LP7v8z3QaPVSh0VEREREROTxatVaZJ+tAsAlAF1J0qTZ2LFjsWbNGvzyyy9YsWIFioqKkJaWhrKyMhQVFQEAoqKirG4TFRVlvq41zz//PIKCgswfPXv2dOrX4ApMWzNZZUadNWNED7w1awRUchl+PFCEhWt2oUGjkzosIiIiIiIij7Yzrxw6vYheob7oEcJ5Zl1F0qTZNddcg7/85S8YMmQILr/8cmzYsAEA8NFHH5mPad6nK4piu727jz/+OKqqqswf+fn5zgneRZTXafDHyVIAwNShMRJHQ+7g6sHR+HDuKPgo5dh6/DzmrNyB6sYmqcMiIiIiIiLyWGzNlIbk7ZmW/Pz8MGTIEJw4ccK8RbN5VVlJSUmL6jNLXl5eCAwMtPpwZz8fLIJOL2JQbCCSIvylDofcxIQ+Efh4/hgEeCuw41Q5Zq3IRHmdRuqwiIiIiIiIPJJpCQBbM7uWSyXN1Go1jhw5gpiYGCQmJiI6OhqbNm0yX6/RaLB161akpaVJGKVr+SG7EIBhayaRI41KCMXnC8chzE+FgwXVuOW9DBRVNUodFhERERERkUepqm/CwULOM5OCpEmzhx9+GFu3bkVeXh6ysrJw0003obq6GnPmzIEgCFiyZAmee+45rFu3DgcPHsTcuXPh6+uLWbNmSRm2yyipaUSmsUSTrZnkDIPjgrB2USpigrxxoqQWM99Lx5myeqnDIiIiIiIi8hhZeWUQRSApwg9Rgd5Sh+NRJE2anT17Frfddhv69euHGTNmQKVSITMzE/Hx8QCARx55BEuWLMHixYsxatQoFBQUYOPGjQgI4IZIAPjpQBH0IjCsZzB6hnIQIDlH70h/fHVPKhLCfJFf3oCb3k3H8eIaqcMiIiIiIiLyCJxnJh1BFEVR6iCcqbq6GkFBQaiqqnK7+WY3vZOOXacr8OR1A7BgQpLU4ZCbK6lpxB0f7MCx4hqE+Crx0bwxGNojWOqwiIiIiIiI3NrVy7fhaFEN3pw1nKOZHMTWXJFLzTQj2xVWNmDX6QoIAueZUdeIDPDG2kXjkNIzGBX1TZi1IgtZxnc8iIiIiIiIyPHKatU4WmTo9BnHSrMux6RZN7Uh+xwAYHR8KKKD2NNMXSPYV4VPF4zFuKRQ1Kq1uHPlDmw+ViJ1WERERERERG4pK68cANAvKgDh/l4SR+N5mDTrpkxbM6elcAEAdS1/LwVW3zUGU/pHQq3V4+41u8xJXCIiIiIiInKcjBzjPDNuzZQEk2bd0OmyOuw/WwWZAFw9mEkz6nreSjnevWMkpqXEokkn4v8+34Mvd+ZLHRYREREREZFbSc8pBcDWTKkwadYN/WCs6klLDkdEAMszSRpKuQzLbxmG28b0gl4EHvkmGx/+kSd1WERERERERG6hpLoROefrIAjAuKRQqcPxSEyadUOmpNnUoawyI2nJZQKeu3Ew7r7UsL31Xz8cxvJfj8PNl/ISERERERE5XYZx8drAmEAE+6okjsYzMWnWzZwsqcWRc9VQyARcPTha6nCIIAgCHr+mPx66oi8AYPmvJ/DvDUeYOCMiIiIiIuqETGPSLJWtmZJRSB0A2Uej1WNy/0goZAIzzeQyBEHA/03pA39vBf65/jA+/CMPdWot/nPjEMhlgtThERERERERdTvpXAIgOSbNupmBsYFYOXc09HpW8ZDruWt8Ivy9FHj0m2x8sTMftWotXr15GFQKFrUSERERERHZqrCyAafL6iGXCRiTyHlmUuEr2W5KxuodclEzR/XEW7NGQCkX8EP2OSz6eBcam3RSh0VERERERNRtZBirzAbHBSHAWylxNJ6LSTMicrhrhsTggzmj4a2UYfOx85izcgdqGpukDouIiIiIiKhbyOA8M5fApBkROcXEvhFYM28sArwUyMorx+wPslBRp5E6LCIiIiIiIpcmiqK50ozzzKTFpBkROc2YxFB8fvc4hPqpkH22Cre8n4GS6kapwyIiIiIiInJZ+eUNKKhsgEImYHRCiNTheDQmzYjIqQbHBeHLReMQFeiF48W1uOndDOSX10sdFhERERERkUvKyC0FAAzrGQxfFfc3SolJMyJyut6RAfj6njT0CvXFmfJ6zHw3AydLaqQOi4iIiIiIyOWwNdN1MGlGRF2iZ6gvvronFX2j/FFU3Yib38vEwYIqqcMiIiIiIiJyGaIoIj2HSwBcBZNmRNRlogK9sfbuVAztEYTyOg1uez8TO0+VSx0WERERERGRS8gtrUNJjRoqhQwj4jnPTGpMmhFRlwrxU+HTBWMxJjEUNWot7vgwC1uPn5c6LCIiIiIiIsmZWjNH9AqGt1IucTTEpBkRdbkAbyXWzBuDy/pFoLFJjwUf7cRPB85JHRYREREREZGkMnJNrZnhEkdCAJNmRCQRb6Uc790xCtcNjUGTTsR9n+3B17vPSh0WERERERGRJERRRCaXALgUJs2ISDIqhQyv3zoct47uCb0IPPzVfqz+M0/qsIiIiIiIiLrc8eJalNVp4K2UYVjPYKnDITBpRkQSk8sEPD9jCBZckggAWLr+MN747QREUZQ4MiIiIiIioq6TkVMKABidEAqVgukaV8BHgYgkJwgCnrhuAB68vC8A4JVNx/H8T0eZOCMiIiIiIo9hmmc2Lomtma6CSTMicgmCIOCBy/vgqakDAQDvb8vFP9YdhE7PxBkREREREbk3vV5EZm45AM4zcyVMmhGRS5l/SSKW/WUoZALw+Y4zWLJ2H5p0eqnDIiIiIiIicprD56pR1dAEfy8FhsYFSR0OGTFpRkQu5+bRPfHGbSOglAtYv78Q93y8G41NOqnDIiIiIiIicopMY2vm6IQQKORM1bgKPhJE5JKuGxqD9+8cBS+FDL8dLcFdq3aiVq2VOiwiIiIiIiKHS88xJM3YmulamDQjIpd1Wb9IrJk3Bv5eCmTklmH2B1morNdIHRYREREREZHDaHV67MgzzjNLCpc4GrLEpBkRubSxSWH4bOFYhPgqsT+/Ere8l4mS6kapwyIiIiIiInKIg4XVqFVrEeitwMDYQKnDIQsdSpo1NTUhPz8fx44dQ3l5uaNjIiKyMrRHMNYuSkVkgBeOFddg5nsZyC+vlzosIiIiIiKiTsswtmaOTQqDXCZIHA1ZsjlpVltbi/feew+TJk1CUFAQEhISMHDgQERERCA+Ph4LFy7Ezp07nRkrEXmwvlEB+PqeNPQM9cHpsnrc/F4Gcs7XSh0WERERERFRp6TnlAIAUpM4z8zV2JQ0e+2115CQkIAVK1Zg8uTJ+Pbbb7Fv3z4cO3YMGRkZeOaZZ6DVanHFFVfg6quvxokTJ5wdNxF5oF5hvvhqURp6R/rjXFUjbn43A4cKq6QOi4iIiIiIqEM0Wj12naoAwCUArkgQRVG82EEzZ87E008/jSFDhrR7nFqtxocffgiVSoUFCxY4LMjOqK6uRlBQEKqqqhAYyN5gIndQXqfBnJU7cKCgCgHeCqy+azRGxodKHRYREREREZFddp0qx03vZiDUT4VdT1wOGdszu4StuSKbkmbdGZNmRO6purEJ81fvxM5TFfBRyvH+nSMxoU+E1GERERERERHZ7I3fTuCVTcdx7ZBovD17pNTheAxbc0V2LwLYtGkT6us5gJuIpBXorcSaeWMxsW8EGpp0mL96F34+WCR1WERERERERDZLNy4B4Dwz12R30uwvf/kLQkJCkJaWhscffxy//PILams5jJuIup6PSo4Vd47CtUOiodHpcd9ne/DtnrNSh0VERERERHRRjU067D7DeWauzO6kWUVFBbZs2YLrr78ee/fuxcyZMxEaGopx48bhsccec0aMRERtUilkeP3W4Zg5sgd0ehF/+3I/Ps44JXVYRERERERE7dp7phIarR4RAV5IjvCXOhxqRadnmh08eBAvv/wyPv30U+j1euh0OkfF5hCcaUbkGfR6Ef/acBir/jwFAPj7Vf1w32W9pQ2KiIiIiIioDa9uOo7XfzuB61Ni8fptw6UOx6PYmitS2HviI0eOYOvWrdiyZQu2bt0KnU6HSy65BK+88gomTpzYqaCJiDpKJhPw9NSBCPBW4vXfTuClX46hplGLR6/uB0HgBhoiIiIiInItGTmlANia6crsTpoNGjQIERERWLJkCZ566ikMGjTIGXEREdlNEAT87Yq+CPBS4D8/HsG7W3NQ09iEf00fzNXNRERERETkMho0OuzLrwQApDFp5rLsnml2//33Iy4uDkuXLsW8efPw6KOP4qeffuIyACJyGQsvTcLzM4ZAEIBPs87gwS/3oUmnlzosIiIiIiIiAMCu0+Vo0omIDfJGr1BfqcOhNtidNFu+fDn27NmD4uJiPPnkk9DpdHj66acRHh6OcePGOSNGIiK73TamF/5763AoZAK+21eIez/Zg8Ym15q5SEREREREnikjpwwAMC45jONkXJjdSTMTvV4PrVYLjUYDtVqNpqYmnDp1yoGhERF1zvUpsXj/zpHwUsjw65FizP9oJ+rUWqnDIiIiIiIiD5duTJqlJrE105XZnTR74IEHkJKSgsjISCxatAiFhYW4++67sX//fhQVFTkjRiKiDpvcPwqr7xoDP5Ucf54sw+0fZqGqvknqsIiIiIiIyEPVqrU4UFAFgEsAXJ3diwAKCgqwcOFCTJo0CYMHD3ZGTEREDpWaHIZPF47D3FU7sPdMJW55PwMfzx+LiAAvqUMjIiIiIiIPszOvHDq9iF6hvugRwnlmrszupNnXX3/tjDiIiJxqWM9grL07Fbd/mIWjRTW4+b0MfLJgLOKCfaQOjYiIiIiIPEhGLlszu4sOzTTLycnB//3f/+Hyyy/HFVdcgfvvvx85OTmOjo2IyKH6RQfgq0WpiAv2QV5pHWa+k47c89z8S0REREREXSc9pxQAWzO7A7uTZr/88gsGDhyIHTt2YOjQoRg8eDCysrIwaNAgbNq0yRkxEhE5TEK4H76+NxXJEX4orGrEze9l4HBhtdRhERERERGRB6iqb8Ih4+sPJs1cnyCKomjPDYYPH46rrroKL7zwgtXljz32GDZu3Ig9e/Y4NMDOqq6uRlBQEKqqqhAYGCh1OETkIspq1bhz5Q4cKqxGoLcCq+eNwYheIVKHRRbOlNWjskGDoT2CpQ6FiIiIiMghNh4qwt0f70ZShB9+f2iS1OF4LFtzRXZXmh05cgTz589vcfm8efNw+PBhe09HRCSJMH8vfLZwHEbFh6C6UYvbP8jCnydLpQ6LLNy5Mgsz3k7H+Rq11KEQERERETlEeg7nmXUndifNIiIisG/fvhaX79u3D5GRkY6IiYioSwT5KLFm/hhM6BOOeo0Od63aiY2HiqQOi4wKqxqh1YvIr6iXOhQiIiIiIofINC0BYGtmt2D39syFCxfi7rvvRm5uLtLS0iAIAv744w+8+OKLeOihh5wRIxGR0/iqFPhgzig88Pk+/HyoCPd+ugevzEzBDcPjpA7No4miCI1WDwAoq9VIHA0RERERUeeV1apxtKgGADCOlWbdgt1Js6eeegoBAQF45ZVX8PjjjwMAYmNjsXTpUjzwwAMOD5CIyNm8FHK8OWs4HvkmG9/uKcCDX+5DrVqL28fFSx2ax1IbE2aA4ckFEREREVF3l5VXDgDoFxWAcH8viaMhW9jdnikIAh588EGcPXsWVVVVqKqqwtmzZ7FgwQJs27bNGTESETmdQi7DyzelYE5qPEQRePJ/B/HOlhypw/JYlkmzUibNiIiIiMgNpOcYZiizNbP7sDtpZikgIAABAQEAgJMnT+Kyyy5zSFBERFKQyQQsvX4Q/npZbwDAiz8fxbKfj8LOJcPkAGqtzvx5KdsziYiIiMgNZBiXALA1s/voVNKMiMjdCIKAh6/qh8ev6Q8AeHtLDp75/hD0eibOupK6yaI9s45JMyIiIiLq3kqqG5Fzvg6CAIxLCpU6HLIRk2ZERK1YNDEZ/7lxMAQBWJNxGg9/tR9anf7iNySH4EwzIiIiInInGcatmQNjAhHsq5I4GrIVk2ZERG2YPTYey28ZBrlMwLd7C7D40z1WbYPkPNbtmUyaEREREVH3ZmrNTGVrZrdi8/bM77//vt3r8/LyOh0MEZGrmT4sDn4qBRZ/tgcbDxdjwUe78N4dI+Grsnv5MNnButKM7ZlERERE1L2ZKs24BKB7sflV3w033HDRYwRB6EwsREQu6fKBUVg9dzQWrNmF7SdKcceHO7By7mgE+SilDs1tWc40K6/XQKcXIZfxbwwRERERdT+FlQ04XVYPuUzAmETOM+tObG7P1Ov1F/3Q6di2RETuKa13OD5dMBZBPkrsPl2B297PZNugE1m2Z4oiUFHPajMiIiIi6p5MrZmD44IQ4M033rsTzjQjIrLR8F4h+OLucQj398Lhc9W4+b0MFFY2SB2WW7JszwTYoklERERE3Vc655l1W0yaERHZYUBMIL66JxVxwT7IPV+Hme9mIK+0Tuqw3E7zpBmr+oiIiIioOxJFEZmcZ9ZtMWlGRGSnxHA/fHlPKpLC/VBQ2YCZ72bgaFG11GG5FXWTdbs/k2ZERERE1B3llzegoLIBCpmA0QkhUodDdnKZpNnzzz8PQRCwZMkS82WiKGLp0qWIjY2Fj48PJk2ahEOHDkkXJBGRUVywD9YuSsWAmECU1qpxy3uZ2HumQuqw3IaU7ZlanR43vv0nHvl6f5fdJxERERG5p4zcUgDAsJ7B8FXZvIuRXIRLJM127tyJ999/H0OHDrW6fNmyZXj11Vfx5ptvYufOnYiOjsYVV1yBmpoaiSIlIrogIsALXywchxG9glHV0ITbP8hCek6p1GG5hRZJs7quqzTLzC3H3jOV+HLX2S67TyIiIiJyT+Z5ZmzN7JY6lTRbvHgxSks79wKxtrYWs2fPxooVKxAScqFUURRFLF++HE888QRmzJiBwYMH46OPPkJ9fT0+++yzTt0nEZGjBPkq8fH8sbikdzjqNDrMXbUTvx4uljqsbs9yeybQtZVmIsQuuy8iIiIicl+iKJo3ZzJp1j11Kmn2ySefoLq6c3N87rvvPlx33XW4/PLLrS7Py8tDUVERrrzySvNlXl5emDhxItLT09s8n1qtRnV1tdUHEZEz+Xkp8MGcUbhyYBQ0Wj3u+WQ3vttXIHVY3Zq6yVBpppQLALp2pplMELrsvoiIiIjIfeWW1qGkRg2VQoYRvTjPrDvqVNJMFDv3bvwXX3yBPXv24Pnnn29xXVFREQAgKirK6vKoqCjzda15/vnnERQUZP7o2bNnp2IkIrKFt1KOt2ePwI3D46DVi1iydh8+yzojdVjdlqk9MybIBwBQ2oWVZkyZEREREZEjmKrMRvQKhrdSLnE01BGSzTTLz8/HAw88gE8++QTe3t5tHic0e8dfFMUWl1l6/PHHUVVVZf7Iz893WMxERO1RyGV4ZWYKbh/XC6II/GPdAby3NUfqsLolU3tmbLDh70NXzjSz/BvT2TeHiIiIiMhzmVszk8IljoQ6qlOrGzozkH/37t0oKSnByJEjzZfpdDps27YNb775Jo4dOwbAUHEWExNjPqakpKRF9ZklLy8veHl5dTguIqLOkMkE/Gv6YAR4K/HOlhw8/9NR1Kq1+NsVfdtN+JM1U6VZrLHSrCtnmsksHia9CMj5sBERERGRnURRRGauIWmW1pvzzLorySrNpkyZggMHDmDfvn3mj1GjRmH27NnYt28fkpKSEB0djU2bNplvo9FosHXrVqSlpUkVNhHRRQmCgEev7o9Hru4HAHjj95P45/rD0OtZtWQr00yz2GBD0qxeo0O9Rtsl9y2zyJrp+JgRERERUQccL65FWZ0GPko5UnoESx0OdVCnKs06IyAgAIMHD7a6zM/PD2FhYebLlyxZgueeew59+vRBnz598Nxzz8HX1xezZs2SImQiIrssntQbAd5KPP3dQaxOP4VatRYv/mUo5DKWLl2MqT0z1E8FlUIGjVaPsloNfEOd/2fL8tHRsz2TiIiIiDogI6cUADAqIQQqhWT1StRJkiXNbPHII4+goaEBixcvRkVFBcaOHYuNGzciICBA6tCIiGxyx7h4+HvJ8fBX2fh691mMTgjBLaN7SR2WyzO1Z3or5Yjw90JBZQNKa9XoGerr9Pu2bKNlpRkRERERdUS6cZ7ZuCS2ZnZnLpU027Jli9X/C4KApUuXYunSpZLEQ0TkCDcO74GCiga8vPE4vtlTwKSZDUxJMy+FDGH+KhRUNnTZXDPLQkAdK82IiIiIyE56vYisvHIAQFoyk2bdGWsEiYi6wIwRPSAIwI68chRWNkgdjstTNxnaM72UMoT5qQB03QZNmUWlGefQEREREZG9Dp+rRlVDE/y9FBgSFyR1ONQJdlea1dXV4YUXXsBvv/2GkpIS6PV6q+tzc3MdFhwRkbuIDfbBmIRQZOWVY/3+QiyamCx1SC7tQqWZHGH+ho3IpV1UaWa55JTtmURERERkrwxja+bohBAo5KxV6s7sTpotWLAAW7duxR133IGYmBir2S9ERNS26cPikJVXjv/tY9LsYizbM8PNSbOuqTSzxPZMIiIiIrJXRq4haZbK1sxuz+6k2U8//YQNGzZg/PjxzoiHiMhtXTskGs98fxBHzlXjeHEN+kZxqUlbTNszDUkzY3tmF1WaWebJmhVTExERERG1S6vTY4d5nlm4xNFQZ9ldJxgSEoLQ0FBnxEJE5NaCfVWY2DcSAPD9vkKJo3Ft6iZjpZlSjjD/rp1pZllbxkozIiIiIrLHwcJq1Kq1CPRWYEBMoNThUCfZnTT717/+haeffhr19fXOiIeIyK1NHxYLAPhufwFEJmTaZLU908/QntlVlWZ6i8eFiwCIiIiIyB7pOaUAgLFJYZDLOM6qu7OpPXP48OFWs8tOnjyJqKgoJCQkQKlUWh27Z88ex0ZIRORGLh8QBT+VHPnlDdhzphIj40OkDsklWbZnmirNumoRgGUuk4sAiIiIiMgepiUAqUmcZ+YObEqa3XDDDU4Og4jIM/io5LhqUDS+3VuA7/cVMGnWBnOlmVIOfy/Dn6ryOjV0erEL3rG7kChjeyYRERER2Uqj1WPXqQoAQFpvJs3cgU1Js2eeecbZcRAReYzrh8Xi270F+CH7HJ6cOhBKrqG2IooiNBbtmUE+hopmvQhU1msQZtym6bz7v/A52zOJiIiIyFbZZyvR0KRDqJ8KfSO59Msd8JUaEVEXu6R3OML8VCir0+DPk6VSh+NyTFVmgCFpppTLEOxrSJyV1Tm/RdMyT8ZKMyIiIiKyVbqxNXNcUihknGfmFjq8PbP5R1hYGOLi4jBx4kSsWrXKGbESEbkFhVyGqUNjAHCLZmusk2ZyAECYn2mumfM3aFouaOBMMyIiIiKyFeeZuR+7k2ZPP/00ZDIZrrvuOvzzn//E0qVLcd1110Emk+G+++5D3759ce+992LFihXOiJeIyC1cPywOAPDLoSI0aHQSR+NaTEsABAFQyg3v0JlaMrtig6Zlmkyvb/MwIiIiIiKzxiYddp8xzDNLTQ6XOBpyFJtmmln6448/8O9//xv33HOP1eXvvfceNm7ciG+++QZDhw7F66+/joULFzosUCIidzKiVzB6hvogv7wBvx4pxrSUWKlDchnqpgvzzEybmyOMSbOuqDTTi1wEQERERNSekyU1qGpowsj4UKlDcRl7z1RCo9UjIsALyRF+UodDDmJ3pdkvv/yCyy+/vMXlU6ZMwS+//AIAuPbaa5Gbm9v56IiI3JQgCJieYqg2+44tmlbMmzONrZkAEOZvaM/sikozy1IztmcSERERWdPrRcxakYWb3s1A9tlKqcNxGRk5hlnFqUlh5jd+qfuzO2kWGhqK9evXt7h8/fr1CA01ZJnr6uoQEMBNEURE7Zk+zFBdtvV4CSrruyAZ1E2Y2jO9FBf+RIX5Gdsz67pgppnF53pWmhERERFZOV5Sg5IaNUQReHtzjtThuIyMXOM8s2TOM3MndrdnPvXUU7j33nuxefNmjBkzBoIgYMeOHfjxxx/x7rvvAgA2bdqEiRMnOjxYIiJ30icqAANiAnHkXDV+PFCEWWN7SR2SSzBXmiktkmb+pkUAXTDTjJVmRERERG3adarC/PnPh4pworgGfaI8u2imQaPDvvxKAEAak2Zuxe5Ks4ULF2Lr1q3w8/PDt99+i6+//hq+vr7YunUr5s+fDwB46KGHsHbtWocHS0Tkbm4wVpt9t69A4khch6aV9sxw8yKArp1ppmfSjIiIiMjK7tOGpJlcZmhBfGcrq812nS5Hk05EbJA3eoX6Sh0OOZDdlWYAMH78eIwfP97RsRAReZxpKbF4/qejyMorR2FlA2KDfaQOSXIXZppdeF8nvCsrzSw+5yIAIiIiImu7TpcDAJZM6YNXNh3Hd/sK8eDlfdHTg5NF6TmG1sxxyZxn5m5sqjSrrq62+ry9DyIisl1ssA/GJBrmQa7fz4UAAKBuamWmWRdWmomW2zNZaUZERERkVlLdiPzyBggCMGd8Ai7pHQ6dXsT72zx7EWCGMWmWmsTWTHdjU9IsJCQEJSUlAIDg4GCEhIS0+DBdTkRE9rlhGLdoWmpve2adRocGjc6p989FAERERESt22VszewXFYBAbyUWX5YMAFi7Kx8lNY1ShtYmnV7EvvxKp70ZWqvW4kBBFQAuAXBHNrVn/v777+bNmJs3b3ZqQEREnuaawdF45vuDOHyumoNU0foigAAvBVRyGTQ6Pcrq1Oihcl75v3WlmdPuhoiIiKjbMS0BGJVgKJhJTQrDiF7B2HOmEh/+kYfHrxkgZXit+iTzNJ75/hD+dkVf3D+lj8PPvzOvHDq9iF6hvugR4rktqu7KpqSZ5SZMbsUkInKsED8VJvaNwK9HSvDdvkI8fFU/qUOSlFrbsj1TEASE+6tQWNWI0lqNU5+QcHsmERERUet2G+eZjYo3FNUIgoD7LuuN+R/twicZp3HvxGQE+6qkDLGFbcfPAwC+31/olKRZek4pALZmuiubFwFkZ2fbdNzQoUM7HAwRkaeaPizOkDTbX4CHruzr0QNE1U0t2zMBw1yzwqpGp881s0yasT2TiIiIyKBBo8OhQsMc85HxF0YzTe4fif7RAThaVIOP0k/jgcsdn5jqKFEUsf+soXXyZEkt8svrHb6wICPXMM8srTeTZu7I5qTZsGHDIAiCuW3F9ILOso1FEATodM6dNUNE5I4uHxAFX5Uc+eUN2HOm0uqJiKdpbXsmcGGuWZmTN2jquQiAiIiIqIV9+ZXQ6kVEBXqhR8iFje+CIGDxZb1x/+d7sSo9DwsmJMLPy+ZUg1MVVTei1OIN19+PlmBOWoLDzl9V32ROJLLSzD3ZtAgAAPLy8pCbm2v+r4+PDzZv3oy8vDzzR26uZ2/MICLqKB+VHFcNigYAfL+vQOJopGVuz1Q2S5r5GTZoltY5udLM4nNWmhEREREZWLZmNu+KuG5IDBLCfFFZ34TPd5yRIrxWZRurzEx+O1ri0PNn5ZVBFIGkCD9EBno79NzkGmxOmsXHx5s/EhISIAgCevToYXV5fHy8M2MlInJr04fFAgB+yD4HrQdPoG9teyYAhAd0TaUZZ5oRERERtWTanNlaR4RcJuCeiYZNmu9vyzW/CSq17LOVAC7EnJlbhnqN1mHnT88xtGayysx92Zw0IyIi5xrfOxxhfiqU1Wnwx8lSqcORzIWZZtZ/osJNlWZOnmlmWWvGpBkRERERoNeL2HPaenNmczeOiEN0oDdKatT4ZrdrdE6YKs1uHB6HHiE+0Gj1+PNkmcPOn2maZ5Yc7rBzkmth0oyIyEUo5TJcNzQGAPD9vkKJo5HOhe2ZzRcBdNVMM8vPmTQjIiIiOlFSi+pGLXyUcgyICWz1GC+FHAsvTQIAvLs1R/LOCVEUcaDAkDRL6RGMyf0jARjmmjlCWa0aR4tqAADjkkIdck5yPZ1KmnnydjciImeYPiwOAPDLoSI0aFyjrL2rmdszm8808++aSjPr9kyn3hURERFRt7DLOM9seK9gKOVtpxFuG9MToX4qnCmvx4YD57oqvFbllzegsr4JKrkM/aIDcJkxabblWInVQsOOysw1fE/6RQWYn6eS+7F5pcXw4cOtkmQNDQ2YNm0aVCqV1XF79uxxXHRERB5mRK9g9AjxwdmKBvx6pBjTUmKlDqnLtbk9089YaVbn5Jlmlu2ZrDQjIhs16fQor9Mg1E/V7gtKIqLuaPcpY2vmRTa8+6oUmDc+AS9vPI63Np/EtKGxkMmkKbbZb5xnNiAmACqFDKlJYfBRynGuqhFHztVgYGzrFXO2ysg1jFNJTeY8M3dmc9LshhtusPr/6dOnOzoWIiKPJwgCpg+LxVubc/DdvkLPTJo1td6eGRFgeAevvE4DvV502hMwyzyZnjPNiMhGWp2Ikmo1Ar2VUMovfjwRUXdiXgKQcPE2xDtSE/Du1lwcL67Fr0eKcaVxQ3xXM7VmDu0RDADwVsoxvncYfj1Sgt+PFnc+aWZaAsCkmVuzOWn2zDPPODMOIiIyumFYHN7anIOtx0tQWa9BsK/q4jdyI21VmoUYvw86vYjKhiaE+jnn+2I5x4yLAIiIiMjTldQ04kx5PQTB0J55MUE+StyRGo93tuTgrS05uGJglCSjnfbnVwIAhvQIMl92Wf9IY9KsBH+d3KfD5y6pbkTO+ToIAjAukUkzd8bacSIiF9MnKgADYgLRpBPx44EiqcPpcuZFAM1mmqkUMgT5KAEYBq92BS4CICIiIk9nas3sFxWAQG+lTbeZNz4RXgoZ9udXIj3HcdsqbaXXizhorjSzSJr1M8w125tfifJOjPzIMG7NHBgTiCBf274n1D3ZlDS7+uqrkZ6eftHjampq8OKLL+Ktt97qdGBERJ5s+jBDW+Z3+1xjXXdXulBp1rK/ybRBs9SJGzRFbs8kIiIiMjO1Zo5KaH+emaWIAC/cOronAOCtzSedEld7ckvrUKfRwUcpR+8If/PlscE+6B8dAFEEth7v+BZNc2tmEqvM3J1NSbOZM2fi5ptvxoABA/Doo4/iq6++wp9//ondu3fj119/xeuvv46bb74ZMTEx2Lt3L66//npnx01E5NauN84yy8orR2Flg8TRdC11U+vtmQAQ7meYa1ZW57xKM+v2TKfdDXWB5b8ex9XLt6GmsUnqUIiIiLotc9Is/uLzzCwtvDQJCpmA9Jwy7DlT4YzQ2pRtXAIwKDYQimbLWaYMMFSb/X70fIfPb6o0S+vNpJm7sylpNn/+fOTm5uKpp57C0aNHsWjRIkyYMAGjR4/GVVddhRUrVqBXr17YvXs3vvjiC/Ts2dPZcRMRubXYYB+MSTQ8MVm/v1DiaLqWuT2ztaRZgHGDJivNyAbLfz2Bo0U1+DjztNShEBER2e3zHWeQ9vxvOF5cI1kMDRodDhnbHEdeZHNmcz1CfHHD8DgAwNubcxweW3uyz1ovAbA0ub8habb1WAm0HXiHtKCyAafL6iGXCRhtw2IE6t5snmmmUqkwa9YsfPfddygvL0dFRQUKCwvR2NiIAwcO4OWXX0a/fv2cGSsRkUe50KLpaUkzY6VZK+vnwoyVZqVOnGlmmSbjIgD3oNGyZJCIiLqfHw+cQ2FVI7afKJUshv1nK6HVi4gK9EKPEB+7b3/vpGQIAvDrkWIcLap2QoStM1WaWc4zMxnWMwQhvkpUN2qx+7T9FXCm1szBcUEIsHHGG3VfHV4EEBQUhOjoaCiV/CEhInKGawfHQCkXcPhcNU5I+A5jV2treybQVTPNuD3T3bBgkIiIuqOqBsN4gYpODKzvrN0WrZkd2YCZHOGPawfHAOi6ajOtTo9DhYYEXWtJM7lMwMS+EQCA34/ZP9eM88w8C7dnEhG5qBA/lfkPuidVm6mb2m7PDPM3zjRzZqUZ2zPdDh9FIiLqjirqDcmyMgmTZrtOlQOwvzXT0r2TkgEAP2QX4lRpnUPias/x4lqotXoEeCmQEObX6jGXGVs0Nx+1L2kmiiIyTfPMkpk08wRMmhERubDrhxnmQHy3v8CqAsqdtdeeGWGsNHPmk0cRrDRzOx7yu0NERO6lsl7aSjO9XrxQaWbH5szmBscFYVK/COhF4L1tzq82O1BQab5fmaz16riJfSMglwk4XlyLsxX1Np87v7wBBZUNUMqFTn1PqPtg0oyIyIVdMSAKvio58ssbsOdMpdThOJ0oihdpz+zaSjMdky1ugY8iERF1N1qdHjWNWgBAuURJs5Pna1HdqIWPUo4BMYGdOtd9l/UGAHy9+yyKqhodEV6bzEsAerZszTQJ9lVhZC9D0suearP0HMN8uZQewfBVKToRJXUXTJoREbkwH5UcVw2KBgB8v69A4micT2OxwUjVWtLMz/kzzSyLy/SsNHMLzH0SEVF3Y5pnBgDl9dIkzXadMlSZDesZDKW8c6mD0QmhGJMQiiadiBXbcx0RXpvMSbO44HaPM7Vo/mZH0izD2JqZytZMj9Hhn3yNRoOzZ8/izJkzVh9ERORY1xu3aP6Qfa5Da7G7E7XFlsP2Ks1q1Vo0GmefOZp1e6ZT7oK6mMhaMyIi6mYqLZNmElWa7TptmGfmqDbExZcZZpt9lnXGaV+TWqszb+lsbQmApcnGpFlGThkaNBd/XimK4oUlAEyaeQy7k2YnTpzAhAkT4OPjg/j4eCQmJiIxMREJCQlITEx0RoxERB7tkt7hCPNToaxOgz9OSrdyvCuomywqzVp5RzPQW2G+3FlzzbgIwP2wYJCIiLob0zwzw+caSeasmuaZdWYJgKWJfSMwOC4QDU06rP4zzyHnbO7ouRo06USE+CrRI8Sn3WP7RvkjLtgHaq3e3HbZntzSOpTUqKFSyDCiF+eZeQq7k2Zz586FTCbDDz/8gN27d2PPnj3Ys2cP9u7diz179jgjRiIij6aUy3DdUMOq7u/dfIumWnthc2Zra80FQUCYaRmAk+aaWT4l5SIA98DcJxERdTeVFi2ZetG6XbMrnK9R43RZPQQBGOGgpJkgCLhvkmG22er0U6hpdPzXlF1gbM3sEdzqc8nm8ZiqzX63oUUz3VhlNqJXMLxbWVhF7snuyXX79u3D7t270b9/f2fEQ0RErZg+LBZrMk7jl0NFaNDo4KNyzz/U7S0BMAnzV+FcVSPKnDTXzHJLKRcBuAe2ZxIROVZRVSNW/pmHO8bFo2eor9ThuCXLSjPA0KIZapzt2hV2G1sz+0UFINBb6bDzXjUoGskRfsg5X4dPs87gnonJDjs3AGTnVwK4eGumyeT+kfg48zQ2Hy2BKIrtJtoyTa2ZSeGdjpO6D7srzQYOHIjSUvduDyIicjUjeoWgR4gP6jQ6/HqkWOpwnMbUnunVzrt3YX6GuWbnnVVpxkUA7ocPIxGRQy36eBfe35aLW9/PlDoUt1XZ0DJp1pVMSwAc1ZppIpMJuNdYbfbB9jyHz6g9YKw0GxJnW9IsNTkM3koZCqsacay4ps3jRFFEpnEJQFpvzjPzJHYnzV588UU88sgj2LJlC8rKylBdXW31QUREjicIAqYbFwJ858YtmpbtmW250J7ZBZVmTJq5BT6KRESOtd+4nbCgskHiSNxXZbONmV2eNDPOM3PUEgBL04fFIi7YB6W1any5K99h563XaHHcmPhK6Rls0228lXKkJRsqx3470naL5vHiWpTVaeCjlCOlh23nJvdgd9Ls8ssvR2ZmJqZMmYLIyEiEhIQgJCQEwcHBCAnhMDwiImeZPiwOALD1eEmLJ1Luwpb2zAjjBs0umWnG9ky3IPJxJCKibqa19syu0tikw6FCQ2J0VHyow8+vlMuwaGISAOC9rbloctC68sOF1dCLQGSAF6ICvW2+3WXGuWab25lrZloUMCohBKp2nqeS+7F7ptnmzZudEQcREV1E36gA9I8OwNGiGvx4oAizxvaSOiSHu5A0a6c901Rp5qQnj3q2Z7od5syIiKi7ad6eWdGFb5juz69Ek05EZIDXRTdQdtTNo3ri9d9OoKCyAd/tK8RNI3t0+pzZZy8sAbDH5P6ReArAnjMVqKjTIKSV2XEZxnlm45LYmulp7E6aTZw40RlxEBGRDW4YHocXfjqK7/YVuGfSzDjXwkvZTnumcaZZqdNmmlkuAnDKXVAXY+6TiMix5DKBIwyczNRVEBnghZIatdPGUrTGsjXzYhsoO8pbKcf8S5Lw4s9H8faWk7hxeBzkss7dV/bZSgC2LwEwiQv2Mb8xve3EeXN3h4lOLyIrz7AYIS2ZSTNP06G6wsrKSrzyyitYsGABFi5ciNdeew1VVVWOjo2IiJqZlmKYa7bjVDkK3XCOiK3bMwGgtAuePLLSjIiIqCW5kxIpdIGpPTMpwg9A11aa7T5tWgLg+NZMS7eP64VAbwVyz9dh46GiTp8vu8BUaWZf0gy40KL5eystmkfOVaOqoQn+XgqbFwyQ+7A7abZr1y4kJyfjtddeQ3l5OUpLS/Hqq68iOTkZe/bscUaMRERkFBfsgzEJoRBFYP1+91sIYEt7ZriTZ5rpuQjA7YhcBUBE5FAyjnRyusoGQ5IsKcIfgPPGUjSn14vmpNkoB2/ObC7AW4m5aQkAgLe2nOzUDNLqxibknq8DYH97JmBo0QSArcfPQ9tsxpqpNXN0QggUcv7wexq7H/EHH3wQ119/PU6dOoVvv/0W69atQ15eHqZOnYolS5Y4IUQiIrI0fbj7btG0ZXumKWlWXqdxSiWY5fM1LgJwD3wYiYgci5VmzldZZ6w0CzdWmnVR0iznfC2qGprgo5RjYGyg0+9v7vhE+CjlOFhQjW0nSjt8noPGKrMeIT4IbWUm2cUM7xmMIB8lKuubsDe/0uq6jFxD0sy0ZZM8S4cqzR599FEoFBfGoSkUCjzyyCPYtWuXQ4MjIqKWrh0cA4VMwOFz1ThhXKvtLtRNxkozZduVZqYnQlq9iOrGpjaP6yjL/ArbM90Dt2cSETmWrJOzp6h9TTo9atRaABfaM7tqe6ZpnllKzyAou6CqKtRPZZ7T+9bvJzt8ngtLADrWPqmQyzCpXwQA6xZNrU6PHcZ5ZqmcZ+aR7P4tCAwMxJkzZ1pcnp+fj4CAAIcERUREbQvxU2FiX8MfdXerNrNlpplKIUOgt+GNG2fMNWOlmfvho0hE5FidHdhO7au22JyZENbFSbNTptZM584zs7RwQhKUcgE7TpWbE1T2OmBMmg2JC+5wHKYWzc0WSbMDBVWoVWsR6K3AgBjnV96R67E7aXbLLbdg/vz5WLt2LfLz83H27Fl88cUXWLBgAW677TZnxEhERM1MH27Y6vPd/gK3qqKxpT0TuNCi6YwNmpxp5n7c6FeEiMglKJg0c6oK4xKAQG8FIgIMz3kamnRo0Oicft+7TxuSViMTnDvPzFJ0kDduGtkDAPD2lo5Vm+03bs5M6WClGQBM7BsBmQAcLapBgXHhlqk1c2xSGJPFHsrupNnLL7+MGTNm4M4770RCQgLi4+Mxd+5c3HTTTXjxxRedESMRETVz+YBI+KrkyC9vaDF3oTuzZREAcGGDprPXr+uZbXELXARARORYMs40c6oq4xKAYF8V/L0UUBnbJMudvEHzfI0ap8rqIQjAiF5dlzQDgEWXJkMmAFuOnTfPJ7NVeZ0GZysMSa5BndhuGeyrMn/dpmoz0xKANLZmeiy7k2YqlQr//e9/UVFRgX379mHv3r0oLy/Ha6+9Bi8vL2fESEREzfiqFLhyYBQA4Lu9BRJH4zgXZprZVmlWVuf4SjORlWZuh7lPIiLHYsWNc1UaK82CfZUQBAEhfkoAQLmT3yw0bc3sGxmAIB+lU++ruYRwP0wdalh29c6WHLtue8CYZEsK9+t03JdZtGhqtHpzuyrnmXmuDk/28/X1xZAhQzB06FD4+vo6MiYiIrLB1YOjAQC7z1RIGsfPB8/hzd9POCTBZGt7pqnSzNkzzfT6to+j7oM5MyIix2LSzLkqzEkzw/OdUD/j5nAnV5pJ0ZppafFlyQCAHw+eQ875Wptvl23suhjSidZMkykDDEmzP3NKkZVXhoYmHUL9VOgbyfntnkpx8UOAGTNmYPXq1QgMDMSMGTPaPfbbb791SGBERNS+HiGGNyyKqx1fbWUrURTx6DcHUNXQhOQIf1wzJKZT59PY2p7p58yZZhc+5yIA9+BOc/+IiFwBk2bOVWlMjgUbq6ZCTZVmTqiwt2TanDkqXpqkWf/oQFw+IAq/HinGu1ty8NLMFJtul11g2pwZ3OkY+kUFIDbIG4VVjXht03EAwLikUG6M9WA2VZoFBQVBMPatBwYGIigoqM0PIiLqGlGB3gAMiSOtTpqSqNJaDaqMG55Wp5/q9Pls2Z4JAOHmmWZOaM8E2zOJiIjaI+dMM6cyPbcK9jUlzYyVZnVNbd6msxqbdOZZYl25ObM5U7XZur0F5mH8F5NtXAIw1AGVZoIgmFs095wxnDc1ObzT56Xuy6ZKs1WrVpk/X716tbNiISIiO4T5qaCQCdDqRZTWahAd5N3lMeSV1pk/z8orx+HCagyM7fg6bnN7pq0zzZzdnskKJbfg6IdRFEW8vPEYBsUG4dpOVlcSEXVHrLpxror6C4sAACDU1/mVZtlnq9CkExER4IWeoT5Ou5+LGdErBGnJYUjPKcP7W3Pwz+mD2z2+uLoRxdVqyARgUCeeg1qa3D8Sn2adMf9/ahLnmXkyu2eaTZ48GZWVlS0ur66uxuTJkx0RExER2UAmExBpXENeVN0oSQx5pdbzJj7qZLWZ7dszTYsAnJE0Y6WZu3F00mzLsfN4a3MOFn+6x7EnJiLqJhRMmjmVeRGAT/NKM+fNNNtlnGc2Kj7E3GUmlfsu6w0A+GJnPs7XtJ8ozD5rqI7rExkAX5VNNUEXlZYcbu56iAjwQnKEn0POS92T3UmzLVu2QKNp+cva2NiI7du323Wud955B0OHDkVgYCACAwORmpqKn376yXy9KIpYunQpYmNj4ePjg0mTJuHQoUP2hkxE5LYijS2axRIlzXKNlWZDjOu9/7evABWdeEJn3p5p8yIAZ7RnXsCkmXtwdMVgSY00v29ERK5CxvZMp7LcnglYzjRzXtJst3FL5EiJ5plZSksOQ0rPYKi1eqz8M6/dYw8YWzMdsQTAxEclR5pxW2ZqUpjkSUSSls1Js+zsbGRnZwMADh8+bP7/7Oxs7N27Fx9++CHi4uLsuvMePXrghRdewK5du7Br1y5MnjwZ06dPNyfGli1bhldffRVvvvkmdu7ciejoaFxxxRWoqamx636IiNxVVKDhnccSiZJmp4xJsxkj4jA4LhBqrR5f7Mzv8Pls3Z4ZbnzHtaZRi8YmXavHlNdpcKiwyu4Y2J7pfhz9KAro2JPnqvomHC6sdnA0RERdj4sAnKuywZAcC2m+PdNJSTO9XjRvYx+VIN08MxNBEHDfJMNss48zTptnvLXGtAQgxYFJM8BQ7Ta0RxDmX5Lo0PNS92Nz/eKwYcMgCAIEQWi1DdPHxwdvvPGGXXc+bdo0q///z3/+g3feeQeZmZkYOHAgli9fjieeeMK8sfOjjz5CVFQUPvvsMyxatMiu+yIickdR5kozaTZommaaJUX4Y05qAv7+dTY+zjiFhRMSoZDbXcx8oT1T2X57ZqCPAkq5gCadiPI6DWKDW87eWPTxLuw8VYFf/zYRvSP9bY6BiwDcj8Nznx18rZj6wm+o1+jwzb1pLvFOPhFRR3GmmXOZKs2CjJVmIU6uNMstrUVlfRO8lTKHzQXrrMsHRKFfVACOFdfg44xT+OvkPi2OEUXR3J45xAGbMy2NSgjF93+9xKHnpO7J5lc0eXl5yMnJgSiK2LFjB/Ly8swfBQUFqK6uxrx58zociE6nwxdffIG6ujqkpqYiLy8PRUVFuPLKK83HeHl5YeLEiUhPT2/zPGq1GtXV1VYfRETuKkrC9kydXsSpsnoAQFK4H6alxCLUT4XCqkZsOlzcoXPauj1TEASE+bW9DKCqvsm8Nt3eKjy92Prn1H2JDq416+hLxXqNoSpy67ESxwVDRCQBuZNyZl/uzMfCNbvarCL3FM1nmoU5udJsl7E1M6VHMJQdeNPTGWQywbxJc+Wfp1Cv0bY4pqCyAeV1GijlAgbEBHR1iOQhbP6NiI+PR0JCAvR6PUaNGoX4+HjzR0xMDOTy9qsC2nLgwAH4+/vDy8sL99xzD9atW4eBAweiqKgIABAVFWV1fFRUlPm61jz//PMICgoyf/Ts2bNDcRERdQemRQDFFxmS6gyFlQ3QaPVQyWWIDfaBt1KOWWN6AQBWdXAhgK3tmYDFXLNWNkntOFVuri7S2pn5sqxKYqWZm+DDSETkUM5qz3zkm2xsOlyMjzNOO+X83UGTTo9atSFBZGrPNFWaVTY0OeW5iemNxlEJrlUFfd2QGPQK9UV5nQZf7Gg5/sNUZdYvOuCiS6SIOqrDaeTDhw/j559/xvfff2/1Ya9+/fph3759yMzMxL333os5c+bg8OHD5uubD90TRbHdQXyPP/44qqqqzB/5+R2frUNE5OqigwyVZlLMNDO1ZvYK8zU/eb59XDzkMgE78spxsMD+eWIXFgFc/ImPeYNmK5Vmmbll5s91dvbmsT3T/Ti8O7OTA4H5U0VE3Z2zFwG0N8PK3Vl+7YHGSjNT8kwUgcp6x1eb7TYlzeKln2dmSSGX4Z6Jhmqz97flQmPsSDAxt2bGBXd1aORB7N7JmpubixtvvBEHDhyAIAgQjS9GTE8gdTr7SmlVKhV69zaslB01ahR27tyJ//73v3j00UcBAEVFRYiJiTEfX1JS0qL6zJKXlxe8vLzsioGIqLsytWcWSZA0O1VmSJolhl9Ywx0d5I3rhsTg+/2F+PCPPLx2yzC7znlhptnF39MJ92t7g2ZW3oWkmb4TlWZcBNB9iRaPncjHkYjIoRTO6s80cnRbfXdiSooFeivMb0oq5TIEeitQ3ahFRb3G/MahI5TWqs1vhI7o5VqVZgDwl5FxWP7rcRRVN2Ld3rO4ZXQv83XZxs2Zjl4CQGTJ7kqzBx54AImJiSguLoavry8OHTqEbdu2YdSoUdiyZUunAxJFEWq1GomJiYiOjsamTZvM12k0GmzduhVpaWmdvh8iIncQFWBImlXWN3X5/I/c88YlABZJMwBYOCEJALB+fyEKKxvsOqc97ZnhAaZKM+ukWVWD9YZCe6vFLBMsrDTrvkQnzqbj+Gsi8nTOrjTzZKZ5ZiHGNwdN2quw7wxTlVnfKH/z4gFX4qWQ4+5LDc8t39mSA63O8AarXi/iQIFpCQCTZuQ8difNMjIy8OyzzyIiIgIymQwymQyXXHIJnn/+edx///12nesf//gHtm/fjlOnTuHAgQN44oknsGXLFsyePRuCIGDJkiV47rnnsG7dOhw8eBBz586Fr68vZs2aZW/YRERuKdBHYU4wne/iuWamdyUTmiXNhvQIwrikUGj1IlbbMdtMFEWLRQA2tGcan0w2f/K461R5s2H+rDTzRM585Dr7WpE/VkTU3VnONHNGNa8n/zvZfAmASYgxoVXh4PZMU9JspIu1Zlq6bUwvBPsqcaqsHj8eNMw3P1VWh5pGLbwUMvSN4hIAch67k2Y6nQ7+/v4AgPDwcBQWFgIwLAo4duyYXecqLi7GHXfcgX79+mHKlCnIysrCzz//jCuuuAIA8Mgjj2DJkiVYvHgxRo0ahYKCAmzcuBEBAfylICICDK3xUm3QNCXNEpslzQCY3xH8POsMahptm0vSpBPNT5Jtac80veNa2myTVFZeudX/270IwOJzVpp1X5YJT8fPNHPwCYmIuhm5xT+E9v6dpfaZkmJBvtaVZqGmreEO3qC565ThedOoeNdrzTTx81LgrrREAMDbm09CFC9UmQ2MDXSZjZ/knuyeaTZ48GBkZ2cjKSkJY8eOxbJly6BSqfD+++8jKSnJrnN9+OGH7V4vCAKWLl2KpUuX2hsmEZHHiAr0wpnyehRXd12lmUarx9mKegAt2zMBYFLfSPSO9MfJklqs3ZmPBRMu/vfB1JoJ2Lc9s3l7ZpbFEgDA/sSX3ob2zJd/OYboIG/cPi7ernNT12HFIBGR81hWmun0IpQOXlzoyf9qmxYBhDRrlQw1btCscGDSrLFJh4MFhpEWrrY5s7m5aQl4f1sOjhbV4PejJeYlACk9gqUNjNye3SnZJ598Enq9oX3m3//+N06fPo0JEybgxx9/xOuvv+7wAImIqH2RElSanSmvh14E/FRyRAS0HEYrkwlYcInhHcGVf+ShSadvcUxzaouNSCob3jEMN77jarkIoKaxyfzOY+9IQ1V059ozW15fWNmANzefxD/XHzLP1SDXYzVE2uEzzTq7PdOTXw4SUVdq0ulRXN1o099he1gmzVhp5lhttWc6o9LsQEEVNDo9wv290CvU12HndYYgXyVuTzW8Wfnm5pPmJQBD4jjPjJzL7qTZVVddhRkzZgAAkpKScPjwYZSWlqKkpASTJ092eIBERNS+aFPSrKbrkmbm1swIP/P25OZuGB6HcH8VCqsa8eOBcxc954V5ZrI2z2kpPODCTDPTPJVdpyugF4Feob6IC/YBAHTmdUJrlWZ1ai0AQztpcRfPkSPbiVY5M8e+oGN7JhF1F1qdiJJqNbQ6x/47KLOsNHPwuQHPnmnWVnumaZarIyvNdp0yzDMbFR9i03Mvqc2/JBEqhQx7z1Ril3EW21AuASAnc0jzb2hoaLf4JSMickdRgYZ3HourujJpVgsASAhr2Zpp4q2UY05qAgBgxfbciw4KVjfZvjkTAEKNTx61ehHVDYZEVlauYS7H2MRQ87vg+s5sz2wl5samC1m4ggr7toOSNFztxZerxUNEZC/LmWZNelZdO1JlG+2Zpm2ajqw0233aOM/MxVszTSIDvHHLqJ4ADH9L/VRyJEX4SxwVuTubZprNmDEDq1evRmBgoLnKrC3ffvutQwIjIiLbXFgEcPGqp+/3F+LZ9YeRGO6LEb1CMCI+BCN6hbTaYtkeU6VZa/PMLN0+Lh5vbTmJgwXVyMgtQ1pyeJvHmivNbByM4qWQI8BbgZpGLUrr1AjyVSLTOM9sXFIYfj5k2K7UWuKrPVabN1tJuDU0XZi9VlBZD8B1t015MqtFAExSERE5FJfmOE+VqT2zWdLMXGnmoO2ZoihabM7sHkkzwLBs6rMdZ6DTixgUF2TVKkzkDDYlzYKCgsyVZEFBLH8kInIlkQG2t2euST+F0lo1SmvV2GksyQeA+DBfjDQm0UbGh6BvVEC7T0Is2zPbE+KnwsyRPfFx5mms2JZrW9LMxkozAAj390JNoxZltRpEB2rN88zGJoVi0+FiAB3ZnnmxSjOLpBkrzVyWc9sz+QSdiDybZVW2J8w0a9LpUV6nQaifyumbGk1JsWAf6/ZMU6VZea1jkmY55+tQUd8EL4UMg2K7z2v8nqG+uGFYHL7Zcxaju0mFHHVvNiXNVq1aBcDwj+PSpUsREREBX1/XHhRIROQpTO2ZJRepNKtTa7EvvxIA8OR1A5Bzvg57TlfgeEkNTpfV43RZPb7dWwAA8PdSYHivYAzvZUiiDe8VjEDvC+94mpNm4RcviZ9/SSI+yTqNzcfO40RxDfpEBbR6nL3tmQAQ7q9CXmkdSmvV2HW6Ajq9iLhgH/QI8e1Ee6b156IoWiVJrJJmlUyauSrLR93VKs1cLBwiMqpqaIK/l4KVKzawqjRzxkwzF/uX0jQbLtBb6fBNoc1VXqTSrNxBlWam1syUnsFQ2fHcyxX864ZBGJsUimuHxEgdCnkAm5JmJqIook+fPjh06BD69OnjrJiIiMgOpu2ZtWotatVa+Hu1/k/7rtMV0BqTSvMvSTQngqoamrAvvxJ7Tldgz5kK7D1TiVq1FttPlGL7iVIAhsHnfSMDMCI+BCk9gsytoIntzDQzSQj3w5UDo/DLoWJ8sD0PL940tNXjLlSa2f5sNMy0SapWjYPGKrNxSWEALgwptrdtpPnhehGQW7x+arTY8nmWlWYuy7IKwtFFEHw5TeR+RFHEmbJ6BPko0SuMxQEXZfHvKmeaOVZVgylp1nqlWWOTHvUaLXxVdr2Ub8FyCUB346tS4GbjbDMiZ7PrN00mk6FPnz4oKytj0oyIyEX4eyng76VArVqLkupG+LcxEDUjxzDvKzU5zKpyKshHiYl9IzCxbwQAQ5LpeHENdp+uwJ7TFdh9pgKny+pxrLgGx4pr8PkOw+1C/VQIavYuaFvuvjQJvxwqxrq9BXjoqr7mllJLF2aa2f5uZ5i/4Qlkaa0GWXnGJQBJhhljpkSX3u4yI+vjdXrRquqgUcP2zO5AbOf/OovdmUTuiwkg21iNMnBGe6ZrFZp1GY1Wj1rjlu5gH+vnWH4qOVQKGTRaQ6toZ5JmZyvq8cdJwxuj3WUJAJFU7P5NW7ZsGf7+97/jnXfeweDBg50RExER2Skq0Au157Uorla3uUUoI8fw5CjVWInVFrlMwICYQAyICcTt4+IBAKW1anMCbc/pChwurMYNw+Jsjm9kfChG9ArGnjOVWJN+Gg9f1a/FMWqt/e2ZYf6GSrP8inrsN7aejkvsXKVZ8xxb86Rbo9a6PbN5+ya5BtHida+j2zOFTtaauVq7KBEZZhW6WkugK7PMLWqd0J7pqUxVZoIABDZLmgmCgFBfFYqqG1Fep0GPEPsrIpt0eqz8Iw/Lfz2BhiYdQnyVGJXAhUZE7bE7aXb77bejvr4eKSkpUKlU8PHxsbq+vLzcYcEREZFtogK9kXO+DsXVrS8DqG5sMg/JT01uP2nWmnB/L1w5KBpXDooG0HLOly3uvjQJ93yyBx9nnsbiy5JbvEOqbrK/PTPcWGm2+WgJtHoRsUHe6Blq+LukMCbN7F4E0Ozw5kk3y5lmaq0epbUau7ePkvNZvvjlyzkishWT2rax/DdW64TqPE99GKoaDPPKAr2Vrc7WC/W7kDSz1+7TFXhi3QEcLaoBAIxNDMV/bhxiNbOWiFqyO2m2fPlyJ4RBRESdERdsSBTtOl2OG4a3rADbmVcOvQgkhPkiNtinxfX26khl1RUDoxEf5ovTZfX4evdZ3JmaYHV9R7dnAkCFcWju2KQLracdXQTQvLKs+QbNxibrFwcFlQ1Mmrkg64UObM8kInIky39WPWF7ZlepaGMJgEmoaRmAHUmzqvomvPjLUXy+4wxEEQjxVeIf1w7ATSN7sFKeyAZ2J83mzJnjjDiIiKgTbhweh692n8U3uwvw8JX9WgyPTbeYZyYVuUzA/EsS8fR3h/DB9jzMHhtv9S6quT3TjrVUpk1SJmMTL7QYyIxPBJsnvS6m+dHNk26WlWaAYa7ZsJ7Bdt0HOZ/YxueugC1gRNTdWW3PdELSzNFvdnQXFzZnqlq93p6kmSiK+H5/If71wxGU1hoWOM0c2QOPXzvAfB4iurhO7ZZtaGhAdXW11QcREXW91OQwDIwJREOTDp9mnWlxvWkJwLiLzDNztptG9kCwrxJnyuux8VCR1XWmSjOV3P6ZZiaWX19HK80u1p7Z0DxpVllv1/mpa1i+4HL0ay/LjhlPfWFH5I4Mv878nbaFVaUZZ5p1ml4vYu6qHVi4ZheAlksATGxNmp0qrcOdK3fggS/2obRWjeQIP3xx9zi8NDOFCTMiO9mdNKurq8Nf//pXREZGwt/fHyEhIVYfRETU9QRBwIIJiQCAj9JPQaO90EJYUafB4XOGNzWkrDQDDCvCbx9rWC6wYnuu1XXmmWZ2bM80zTQDDMsQ4sMuDMXtcKWZje2ZpsTJWW7QdEmWj5r9G1Qv5kLWjF1JROSZnDzTzMP+bT1WXIMtx86b/7+j7ZkarR5v/n4CVy7fhu0nSqFSyPDQFX3x4wMTJH/jlKi7sjtp9sgjj+D333/H22+/DS8vL3zwwQf45z//idjYWKxZs8YZMRIRkQ2mDo1FVKAXSmrU+CG70Hx5Vp6hyqx3pD8iA7ylCs/szrR4qOQy7DlTid2nLyyP6cj2zEBvpXng/ziLeWZAJxYBNPv/5q8F1MZKs16hhgRdAZNmLknvxEozSx2qNPOwF4NE5H4408yxMnPLrP4/pI32zJB2kmZZuWW49vXteHnjcWi0ekzoE46NSy7F/03pY9eSJSKyZnfSbP369Xj77bdx0003QaFQYMKECXjyySfx3HPP4dNPP3VGjEREZAOVQoY5aQkAgBXb88wv5k2tmaku8g5jZIA3bjQuK3h/24VqswuLAGx/YieTCQgzVpuNTbT++jrennmRSjNjcq93pD8AwyIAckEWD5uj5+1Yzk1u78z1Gi3OlLF9l6i7EOF5FU4dZTXTzEHtmVZt9Q45Y/fRPGkW1EZ7ZlgrSbPyOg3+/tV+3PJ+Jk6W1CLcX4X/3joMa+aNQUK4n/OCJvIQdifNysvLkZhoaAEKDAxEebmhSuCSSy7Btm3bHBsdERHZZdaYXvBRynHkXLU5WWZaApAmcWumJVMr6cbDxcgrrQPQsUozAJjYNwIhvkpMGRBpdbnMmDTT2dk1crFFAA0aQ5zJEcakGSvNXJJz2zMt7qedU096aQsufWkzDhdaz3z1tBeDROR+LBNcjqo089SCNb1eRFZeudVlbbVnmirQyus1EEURX+8+iymvbMFXu88CAGaN7YXf/jYJ04fFcTMmkYPYnTRLSkrCqVOnAAADBw7El19+CcBQgRYcHOzI2IiIyE7BvirMHNUDAPDBH3k4X6PGiZJaAMBYF6k0A4A+UQG4rF8ERBFY+UcegI7NNAOAF/8yFDufuBxRgdatp3Ljk0V7EybNn7Q3r1IyzTRLijC8e1uj1qKqocmu+yDns3zYHZ0zs3wZ0t4mzJIaw7ayX48UOzYAIiKJWf7L56iZZl3VVu9qjhXXoLK+yWqjuJ+XotVjTdX1xVWNuG1FJh7+aj8q6pvQLyoA39ybiuduHIKgNhJuRNQxdifN7rrrLuzfvx8A8Pjjj5tnmz344IP4+9//7vAAiYjIPvPGJ0IQgN+PluCTzNMAgP7RAS63LWnhpUkAgK9256O8TtOh9kzAsARB0crGzQuVZs5pzwz2VZnbJFhtJo2qhiacrWi9/dEymeXoSjPLd+896YUdkdvjL7TNLL9VjmqBtzxPe29IuBtTa6ZlR0BbG0lNlWZ1Gh0yc8vhrZThsWv644f7L8HI+FDnB0vkgVpPYbdiyZIlWLBgAR588EHzZZdddhmOHj2KXbt2ITk5GSkpKU4JkoiIbJcQ7ocrBkRh4+FivLn5JAAgLTlc4qhaSk0Kw+C4QBwsqMYnmac73J7ZFnlHt2c2+//m7ZmmSjMfpRxxIT4oq9PgbEU9BsYGdjhW6piUf24EAGQ8PhkxQT5W11k+bPb+DFyMVaWZDadu3iDToeUBRNQl+NtpG6tKM4fNNHPIabodU9IsNTkM45LCsOlwMaamxLR6bIivEv5eCtSqtbisXwSenT4YPUN9Wz2WiBzD5lcmP//8M1JSUjBmzBi8//77qK42zOfo1asXZsyYwYQZEZELWTDBUMVletc21YXmmZkIgoCFxjjXZJxCdYMWgOOSZgq5MWlm75P55u2ZzSvNjNszvZVyxAUbEjVcBiCtvWcqW1xmmZhy5pwcW6ohOFaGiNyN9Uwztmd2lOU8s3FJYbjvst74333jEejdeoulQi7Dx/PH4NMFY7Fy7mgmzIi6gM2vTI4ePYpt27ZhyJAhePjhhxEbG4s777yTw/+JiFzQ6IQQpPQIAgDIBGBMomuW7F87JAaxQd4ordUgK8/wTquX0jFr0WUdrDRr3srXcqaZKWkmu5A0Y3umpFprv7SeaebE7Zm2VJoxa9Zt/HakGAvX7EJZrVrqUIi6DcctAvCQTJkF0zwzX5UcQ+KCbLrN8F4hGN87nH9biLqIXW/njx8/Hh9++CGKiorwxhtv4NSpU5g0aRL69OmDF154AYWFhc6Kk4iI7CAIAu6+NBkAMDI+pM3V5VJTymWYd4lhk6bpObfD2jONp2neXnkxzZ+zm95Ar25sglqrs640C2GlmSu42OssR83bafW+O3Ibz3td2G3M/2gXNh0uxvM/HZU6FJKAiO7/+7nzVDn+t7fA6ffjjJlmnT3Nd/sKMP2tP9ucdemKTK2ZoxJCoWxlPisRSa9Dv5m+vr646667sG3bNpw4cQI333wzli1bhoSEBAeHR0REHXXd0Bisvms0/nvrcKlDadcto3siwGJLlKOSZh2tNGvebqcTRVTVN2Ho0o244tVt5plm3gq2Z7qK1h5hy4fd0Tkz60qz1k/epHNMuxJJw7T5lKi7uf/zvViydh8Knfx3ybIqzHEzzTp3nq93n8X+/Er8kH3OIfF0BVPSbFySa3YEEFEHk2YmdXV12Lp1K7Zu3YrKykokJyc7Ki4iInKASf0iERvsc/EDJRTgrcRtY3uZ/9/e7ZltkXdwe2bzw3V6ETtPGeaNnCmvR4Op0kwlQ48QwyyRs2zPlFRrL7SsZ+M4ehHAhaxZWz9eli8i2UHT/WiMi0mIuhNRFM0J3zq11sn3deFzx80069ztqxuaAACHC6sdEI3zNZ9nRkSuqUNJs23btuGuu+5CdHQ0HnjgAfTt2xfbt2/HkSNHHB0fERF5gLlpCVAYk1w+KmmTZi3aM0Wx1RcElu2Z5XUa1Guc+wKF2tZaTszyIofPybFan2nL4dZZs27e/eURMnPLpQ6BJCCKti33cFX1Gp35b56zvwrL75MzZpp15M2O6kbD3+FDhVUOicfZOjLPjIi6nuLihxicPXsWH330EVavXo2cnByMHTsWr732Gm699Vb4+/s7M0YiInJzscE++Of0QcjOr3LYE8eOJs2av9TQ6UU0tdJ64q2QQ6WQIcBLgRq1FoWVDegdGdDRcKkTWnuRa/mCy7kzzbg9k4hcQ3VjU6du36TTo7xOg1A/1UXna1nNNHNQe6Zl0sze0QoAUGP8+nNL61Cv0cJXZfNLXUmYWjNHc54ZkUuz+V+ShIQEhIWF4Y477sD8+fMxYMAAZ8ZFREQeZvbYeMwe67jzyY2ZCnurjFouAhBbzKeSCYBSbjh/XIgPjhbV4GwFk2ZSuVilmVMLzbpvUQoRuZmaxgsVzx35t0mrE1FSrUagtxIXW2RteXpHVZp1dhalqdJMFIGjRTUY0SvEIXE5y4V5ZmzNJHJlNifNvvzyS1x//fVQKFw7Y09ERAQAsg7PNGu5CECjtU6aeSvl5lXvccGGpBmXAUintYfY+sWXtNszmxeaMdFG5Jq6c2smcGGmF9AFX4tTZppdOKm9m68bm3RWf6sPFVa7dNLMep4ZlwAQuTKbM2AzZsxwZhxEREQOJTdvz7Tvds0P1+pF1DYbqOxj8RZ8D+NcswIuA5BM67NvOtfm0x7Bot/Slrk7bM8k6j66c1K7s5Vm9nDOTDPLz+07p+XXDrj+MgDTPDM/lRyDOc+MyKWxeZqIiNySaaaZve9WN3+eXq/WoU5tvUnP2yJpZloGwA2a0mntEbZ68eWYIggzyxyYLT9eLRcBdONX5UTUruLqRpwpq5fkvi1nmjk9aeaMmWYW/6Dq7Px3u6bZPLfDLr4MwNSaOYrzzIhcHn9DiYjILZnaM+1tG2n+1L+iXoNatfWTcS/lhT+fccG+AMD2TAm1Vu1leVFHtrC1e39Wn7PSjIguGPvcb7j0pc2oaujcUP6OqLasNHNyct7ZM83s/XfbVGlm2sR9tKgGWnszb22oU2vxwfZcFDrw7zznmRF1H0yaERGRW1KYK83su53pibrK+M5vZb0Gtc0rzRQtK83Ynimd1hcBWMzGceZrRxaNEbkNUXRchdbZiq6vNqvp0kozy/ZMx88004mGJTzF1Y0tlvG0xpQ0S4rwg7+XAmqtHrmldQ6Ja8OBc3hl03Gs/DPPIefjPDOi7qXDSbOTJ0/il19+QUOD4UWCo9/FJSIi6gyZeaZZx9ozQ/yUAIDK+qYWM828rSrNDEmz4prGFgsDqGu0OtHMsnXI0ZVmFufryJn5lInI/Unxe958rpczWX559i7caYvVIgDxwjZPrQ3tn6bW1EBvJQbEGDZZH3JQi6ZpwcKRc9UOec3LeWZE3YvdSbOysjJcfvnl6Nu3L6699lqcO3cOALBgwQI89NBDDg+QiIioI+Qd3J5pqlAK8VUBACrqm1DXfBGA6kKlWbi/Cl4KGUQRKKpq7EzIbuOnA//f3n3HR1GnfwD/zO4mm14gJAFC712aCEhTAdGz60/PcmJBUSwc9nbqnRXLeXavod7ZT7AXUGmKSO+9hABJSEL6Jtn6/f2x2c3M7GzfzaZ83q8XL7KzU767mUwyzz7f5ynCt9uLmu14R8vrPDIRojk909txvJE4P5Oo3YlF0Ky6GaeEyl9fIEGtQChrUQY7PdP52lMTDBjcOQ1A5JoBWBp/v1Q32HCkPPwMQtYzI2pdgv4p/eMf/wiDwYCCggIkJSW5l19++eX47rvvIjo4IiKiULn+Dg22A5drlokraFZZZ0Gt6tN7+fRMSZKamgFUxqb4c0tiMttwy3ubcMt7mzyCjdEiIFBnUU6hVWcsRPZ4ymP7w5AZUesRTi0wZRZq80fNmrd7ZpPI1TSTX7dDq2mWlhiHIV2c2Vs7QwiaaX3IIs8i33K0Muh9qrGeGVHrEnTQbOnSpXj22WeRl5enWN6vXz8cOXIkYgMjIiIKh2t6ZrCfgLtudDokNwbN6rWmZ+oVj11TNNlBE4rgVXNNVxUCPudJRmrqkJao1ksjolbBVXtLfs2LSaaZvKZZtIN2ippmkTmWfCp9sNdtVxOE1AQDBndpzDQLYjql1e7Ac9/vwZBHv8c7a/I9nnPZUlAZ1LjUWM+MqPUJOmhmMpkUGWYuZWVlMBqNERkUERFRuFzTM4P9tNq1uito5uyeqQyaybtnAkAemwG4yd9vXTNNS3QIzxtE+bc92HPAryCnfnJ2JlHb5qq9ZbXLM82aX6wyzeyRagQg202wcTjX1NTUhDj0y0mBQSehss6KwgDKJuSXmXDpm7/iteUHUWex482VBxXTQyOZacZ6ZkStT9BBs8mTJ+Pdd991P5YkCQ6HA8899xymTZsW0cERERGFKvSaZk6ZSb4aAWhnmh2PYDv61koeoJKaqVSLVsc7eRAtmjePAdU0i97hSYZNqaglCeR8DKY7ZCAU3TMjskfv5C/PGrGaZhGYnpkQB6NBj345jc0AjntvBiCEwCcbjuLcl1dj69FKpCUYkBSvR1FVAzbLgmPy17e/pMbjb4JgsJ4ZUesT9E/qc889h7feeguzZs2CxWLBvffei6FDh2LVqlV49tlnozFGIiKioOlD7p7Z2AgguammmfxGBAASVUGzvExnBjYzzZTZAc2VaaZ1c6ronhnhOZTBTntSNwJgcCc6dhyvRkk1m3FQeML58VTW5PK/fjDdIQNRXS/PNIvudcYRxlRKuZO1Zmw7VgkgvAxheSMAAE3NAIq065pV1Vlx2webcc//tsFksePUXh3w3fzJmDkkFwDw9bamZjZmWaaZQwBbw8g2Yz0zotYn6KDZ4MGDsW3bNpx66qmYPn06TCYTLr74YmzevBl9+vSJxhiJiIiC5p6eGWymmWp6pkN4foqeoJqe6WoEEM1MM7tDYM67G/DHj7a06KCL/P1urgwrITzDWOFkLAR37KjtmkJQ2YzdA4nUhI9HzUH9AU80KbpnhhE0m/f+Jpz/6i/Yf6ImrEBcjaymGQAMaaxrptUM4LdDJzHrb6vw9bYi6HUS7pk5AB/MOQ1dMhJxzrDOAIBvthe5f5+p63NuOlIR1NhcWM+MqHUyhLJRbm4uHn/88UiPhYiIKGJ0jUGzYP+Yd61tNOiRGKdHvdXusY68eybQND2zqKoedodwB+wiaUN+OZbtOgEAePyCIUhLiIv4MSIhmgEqbwQ8syoUHS4jXdIsyGwI1jQjah/kl4PmvhTa7A6YZI1Yoj49U/Z1ODXN9p+odf5fUovO6Qnu5Q4hcLS8DlUBBsJdTRBcvxvdzQBkQTOr3YG//bAfr684AIcAenRMwt+uGIlTumW415nULwupRgOKqxuwqaACY3p2cE+fzUoxoqzWjN3FwXflBFjPjKi1CjrTbNGiRfjkk088ln/yySd45513IjIoIiKicLmmZwabaeYKgkgSkJGkHZhS1zTLSUuAQSfBahcoqYnO9DBXwAxwTitpqeRvd3PdMwrheayoNgKQHyeAddQxMyanEbVMkew42dw/5+o6W1FvBCA7QKg1zax2B8rrLACA0hqz4vfHyVoLfvfqz3hgyfaA9uWuaZbYOD2zMWh2vLIelXUWHDlpwmVv/opXlzsDZpeOzsPXd0xSBMwA5+/36YNzAADfbC8G0JRplpUSrzgWABwtr8OnG48F9LcG65kRtU5B/7Q+88wzyMrK8lienZ2Np556KiKDIiIiCpe7EUCI3TN1koSMpHjNddTTM/U6CbmNn5BHo66ZEAJLZUGzisabjJYoFplmrmOu3l+Kd3/Nb1wqm+YT4TEps0kC6QTAVDOi9kB+PWjuS6E8kNM4gmY7dqg1zcpNFvf7VFZrVrx/B0pqYbE5cLS8DhUm/7/zmmqaOT/sSkuIQ7cOzizwF5buwzl/W40tRyuRmmDAK78fiecvG4EUo/akqzMGZQMA1uc7p1K6Ms1cUz/lAco5727AXZ9sxTvu3z3esZ4ZUesU9PTMI0eOoFevXh7Le/TogYKCgogMioiIKFxN3TOD2871J7uEpg6aaupMMwDIy0zEsYp6HK+sx5jgDunX3hM1KCivcz+uaMmZZhEuuh/QMRu7Z17zr3UAnLVs1NOkhBAeBfkjIaRMsyi9RVa7A+UmCzokxzOLgZpVVb0VpTUN6JudGuuhtBjNXXuyWlXPzHV4IUTEunNq7R8IvaZZaY1Z8bXDyz73nahBl8baodpjER41zQBgSOd0HC2vx3/WHgEAnNqzA168fIS7eY83I7tnAgB2F1WjwWqHpfH9c039NDUGzQ6W1mJPcQ0A4Jlv9yAxTo+ZQ3LdjYTkWM+MqPUK+i+q7OxsbNu2zWP51q1b0bEjo+ZERNQyuD5BPmky440VBwO/gZFNz8yUZZrJ/8jVCpp1zXD+EX4sCplmS3eeUDyubNGZZk1fN9dNo7MRQNOxjlc2eHSui+RQolkvLRyR7sRHFKiiqnrUWyIfmGluQoTZPVP2dXN/fiDvnCkfy03/2Yhr/vUbaj0y0cIjv+aGWtPMM2im/abtPVHjcz/1Vrs7yJYqq/c5LM9ZN0yvk3DX9P744KbT/AbMAKBLegKyUoywOQR2Fla7p2emuoNmztpxSzYdd29jtjlw/+LtGPvkD/jDv9fh4w1HFaUUWM+MqPUKOtPsiiuuwB133IHU1FRMnjwZALBy5UrceeeduOKKKyI+QCIiolB065CEP4zvgXd/PYJnv9uDXUXVWHjJcCTGewa85Fw3OpIE96fLADChTxbWHnJ+SqwZNItiB01XPbM4vbNuWmVLzjSLUSMAdcqXOmBnFwI6Vc5Xg9WO6gYrslMTEDo2AiAiJ0WGazNXNVN3znSN5ecDZQCAVftLccnovIgdT5FpFmKgXhE0qw09aObKMtNJQLLsd/yVp3ZHncWG6YNzPWqX+SJJEk7ploEfdp/AlqOVTZlmjfXSahqscDgElmx2Bs0ePncQzDYHvt5WhF1F1Vi1rxSr9pXiIf12nN43C+cO74LCxr8NWM+MqPUJOmj2xBNP4MiRIzjzzDNhMDg3dzgc+MMf/sCaZkRE1KI8fv4Q9MtOweNf7sKXWwtxsKQWb10zGt06eP+k2XWjI0kS8stM7uWdUo3ur9U1zQAgr7GDZqRrmhVW1mP78SpIEjBtQDaW7jrBoJkGj0YAqsda4zr92eUoqzXjl/vPcHdADVYg2SSSKljX3DfTRNQ8FD/bzZ1p5ieTLNS6Y97I9xbo9EyzzY54vc49Vb60Vplp5u3Xx75if0Gzpnpm8mn4mcnxuGfmwIDGpnZKt3T8sPsEth6tdGeauadnWuxYl1+O45X1SDUacPVpPZAQp8e8aX1xqLQW32wvwlfbirCnuAbL95Zi+d5S935Zz4yo9Qk6zB0fH4+PPvoIe/bswXvvvYfFixfj4MGD+Pe//434eO2CyURERLEgSRKuGd8T7904Dh2T47GrqBrnv/oz1hws01y/ss7iDnoZDTrceVY/AMD1E3vBaGj6lekr0+xYRZ3Hc+FwZZmN7p6JvtkpAFp4I4AYzNBSB8SEEB43X1o3Y2WNN2y/7Nc+H7wJttg3M82IWodww0oidjEzjUwzVbZtpINmsv0Hsu/qBismPP0Tbnhng3uZPNOsrNbsdT/7S2p9HqOq3rOeWbhGNGambT1W6a4Jl9a4f7tD4MN1zlre5wzrrPiboHenFNx2Rj98N38yflgwBQum98eAHGetP50ETB3QKWJjJKLmEfKVpX///ujfv38kx0JERBQV43p3xJe3n46b/7MR249X4Zp/rcPD5w7C7Ak9FZ9K/+Wr3aios6JPp2SM7pEJo0GPYV3TkZeZhO92FLvXS/TSCABw1jSz2R0wRGj6xdJdzuPOGJLjXlZV3zoyzZrrplGrDpE6m8tXBpxOF3pUi1ljRKQl1t0zBeCeEgiEXqzfG/neAmk0cKjUhJMmC1bvL4XDIaDTSYqgmdUuvH4gZLY5cOSkCb07pWg+r+6cGQnD8zIAAEdO1iGjsSmQfP/fbHf+br5oVFev++ibnYI7zuyHO87shwMlNTDbHBjUOS1iYySi5hFQ0GzBggX4y1/+guTkZCxYsMDnui+++GJEBkZERBRJXTIS8cnc8Xhg8XYs2Xwcj3+5CzsLq/HEhUOREKfH8r0l+HTTMUgSsPDSETAanIGxHh2TAUCVaaYxPTMzCalGA2rMNuwprolIod+qOit+a6yjNn1wLtbnO79u0ZlmMalpJjyDV6qHvrIUgo2ZBdsIoLm6ZxJRy9Hc18Jq1Ycpe4trcMXf10ZvPLLdBZJp5mpEYLULlNSYkZueoAiaAUBJtdlju6yUeJTVWrCnuMZH0My577QIZpqlJ8ahd6dkHCo1uUsixBt0SIzTo76xo2bXjESc2jOwTpjsLEvUegV0Zdm8eTOsVufFYtOmTV5btkejlTsREVGkJMTp8eL/jcCQLml46pvd+N/GY9hfUosXLhuOBxdvB+Ccijm6R6bHtkZZoMwVUJPT6ySM7JGJVftKsfFIRUSCZsv3lsDmEOifk4JeWcnY31gMmTXN1Mf0DESp7+F83dPpw8k04/RMhbJaMyQAHVOMftclamtiOz1TmWn25dZCxeNoZpoFsu9ac9PvrWMVdc6gWa0ySHaiukHx2KCTMCIvAz/uKcGeomqcM6yz5r5drz2SmWYAcEq3DBwqbaptGqeXkGw0oN7q7J550ciuYWUqE1HrEFDQbPny5e6vV6xYEa2xEBERRZ0kSbhxUm8MzE3DbR9swtajlZjx11VwCKBHxyTcPWOA5nbyQJlWTTMAGNMYNNtwpALXTugZ9ljdUzMH5wJwFjUGnLXXWir5vVNzxc+E0GoE4FnnzJtgg2byXQUSJFQ3AmjLiiqdN70MmlFrFO41S37d8XXN0XLnh5uRX2bCp7dMCGl6f41Z+WGKXXV8R4xrmsmDescq6jGmZ1NNs87pCSiqasAJVeZZp1QjendKxo97gN0+mgFUN07PjGSmGeAMmi3edNz92KDXIdmoR1mt87GvqZlE1HYEdUW22WwwGAzYsWNHtMZDRETULE7vl4Uv5p2Ogbmp7kDPs5cMR2K8dkBMPiVTa3om4AyaAcDGxmmU4Wiw2rGisePW9MHOemYZic5P0Stbck2zCN+YBULr5lS9yPf0zCgHtVS75+xMorYpnEyzz7cUYuuxKmw5WhnSsavrlZlm6mterGua1ZrlQbM61Fls7mWuOl8lqkyz3PQEnNJYkH+vj6BZU02zyAfN5OJ0OiTHO48xIi8dfbxMFyWitiWoK4vBYECPHj1gt9ujNR4iIqJm071jEhbfOgFvrDiI7h2SfLaCDyTT7JTuGdDrJBRWNaCwsh5dMhJDHtuvB0+izmJHbloChjVO9cxIcmaaVdVbYXeIsKYVRksMYmYQ8AycqYehHpf8hjL4oFlw3TOJqH0QXh/4Jv+wIdQmMurumeos2Eh3z3QEmWlmMiszzVxZZolxevTomATAc3pml/REjO+TBQAoKK9DrdmGFKPn7au7plliZKdnDsxNQ7xBB4vNGRSM00vIasyivXhUXkSPRUQtV9BX5YcffhgPPPAAysvD/xSdiIgo1pLiDbhrxgBcNqabz/VcjQAMOglxXm5qkuINGNz4ifmGIxVhjcs1NXP64Bx3zZT0xhsCITxvkFqKaNU0O1pehwarlw/ttLpnqoNoQp110ZQZEU7sMZDumS0vtElE0RZMZ115FlicPrQrRnWDOtMMqsdNC5buLMaYJ37Az/vLQjoWoLzmBpLFVuMlaNYp1Yjs1AQAwAlVI4Dc9AR0SI5HdqozUOUt26yppllkM83iDToM6dLU7dKg1+GuGf3x8LmDcNW47hE9FhG1XEEHzV5++WWsXr0aXbp0wYABAzBq1CjFPyIioraoc0YCundIwvg+3rPRALibCIQzRdPuEFi26wQAYMaQHPfyeIPO/Sl7RQttBuCIQiXsHcerMGnhcpzzt9Vejum5zF9jAJu9aUE4Nc0CawSg3D+z04jaJnlw3uF/xqKbK5MJUHZqDobrgxTX5cyuGoA8iHbTfzairNaM699ZH9KxAOV1LJjumQBwvFIZNOvUGBSrV30wkpvmDKYNbPwwak9xtea+XZ1DI90IAABG5GW4v47TS+ifk4obJ/UOOSOQiFqfoMPxF1xwAbtkEhFRu2M06PHTXVP8BljG9MzE22vyw8o023K0AmW1FqQmGDCulzJIl54Yh1qzDRV1FvRCcsjHiJZoTM90dYE7VGbSfF4I4RHIUmd5qItiyzMjwul+FsjL5V9NRO2DUH1tMtuQEKf3+3tDXhPMWyazP9WyDpJV9VaP7C91EA1QBuuCJQ+U2R0CQgif94jymmbHK+rdUzE7pTQFzdRy051Bs0G5qVi1rxR7ipo30wxQ1jUz6BgoI2qPgr6yPPbYY1EYBhERUcsXyCfLY3p0AADsLqr2Wn/Fn6WNWWbTBmQjXpV1kJkch+OV9ahqqZlmUYia+cti0HrWI9NMXRTbLp+eGWSmmXy/IaWNMdWMqCUKOwtUEbwXOFRqQmqCAT2zfH/AYQnjegQ4G8e4AmBpiQZU1Vs9rnn2CF92bKognM0hvE4tLThZh8LKevdji92BXUXOrLFOqUZ08tJtt3O6K9MsFYD36ZlN3TMjn2k2snsGAGdpBkOIU2eJqHULOFxeV1eHefPmoWvXrsjOzsaVV16JsrLQ58ETERG1RbnpCeiakQiHALYUVAa9vRACS3d6Ts10yUh0NgOoqLOENc5AWe0OnKhuCKg7GqAMIgVT08cXdZaY1jHlxxIQGjXOlI/lWRha3TcDFdj0zJB3T0StiPpyICBgDiCbS+v6uj6/HN9uLwrouK5MK0kCUozOwJH6uqn1gUY41yarXZ3J5rl/s82Op77ZjSnPL8f6fGX29ebG34+dUo3ISo3XPEZO4/TMATnO6Zm7i6s1r9fRzDTr0TEZ9549APfPGhj9TstE1CIFfGV59NFH8fbbb+Oqq65CQkICPvjgA9xyyy345JNPojk+IiKiVmdMz0wc31KPf/18CAa9hDE9MgOuf3KwtBaHy0yI1+swpX8nj+czkpw3RJXNlGlmswuUVJuRlhAHL01DFSKZaPb2L4dxsNQU0I2dR5BM9byv6ZnBxsyU6wfQCCDC91n+pkERUWRY7Q6UmyzokBwf9LRJ4f7f/zVCa5rkZW/+CgBYfvdU9PKTqeaqZ5YSb4BrmHZ1UEvjQqcP4zpiUwX6rHaHorP0/hM1uP2Dzdijyg6TJOc19EBpLQBn0KxjshE6yfP3hyto1ic7GQadhJoGGwqrGtBV1ZnalWkWjZpmAHDr1L6ot9hxoKQ2KvsnopYt4KDZ4sWL8a9//QtXXHEFAODqq6/GxIkTYbfbodcH8Fc0ERFRO3F63yx8vqUQy/eWYvneUqQnxmHagE44c1AOpgzo5HMKyfeNWWYT+nbUvAFoCpo1T6ZZsCLVPbPOYsNjX+4C0NRcwRshPG9L1eNQP5bf8IWTERdQphki1whg45EK3PTuBjzyu8G4cGTX0HdERH4F+6GBPAtKXWvRF4uPTN6iqnq/QbNqWaaV63rjLdNMPkZ/9RxLa8xYua8UM4fkePw+snrUTGt6XFlnwVX//A0lNWakGg2Kzpk9OybjcJnJ/d50SjFCr5PQIdmIstqm7pn9slPc5QmMBj36dErB3hM12FNUrQiaORzCXS8tLQqZZi4GvYTsNCOnaBK1QwF/ZHL06FFMmjTJ/fjUU0+FwWBAYWFhVAZGRETUWl0yKg9vXj0aF4/siswkZ1Hmz7YU4vYPNmPUn5fhyn+sxb9+Poyj5XUe27q7Zg7O1dx3ZpJzGktlfcuqaVbTYIUQQjEFKJzg0P4TTZ/o+6vT7xCeUyw9p2d6zzQLpssdANVU0OYhhMCdH27GJW+swUmTBfM/2tJMRyZqX8IKosu+dl1iArkOqqc6ygWSDebKNEtLbApsqeNwriCaPIDl7doqhMCnG4/hrBdX4u5PtuLdX494rKPONJNfUx//chdKaszo3SkZX9x+umK9gbmpiseuJgDyZgA9OibhvTnjlNs11jVTZ66ZLDb3eyx//ZEWp9chJy0h5EYNRNR6BRyOt9vtiI9Xzjc3GAyw2WxetiAiImqfdDoJZw/NxdlDc2GzO7CpoBI/7j6BH3afwMFSE9YcPIk1B0/i6W9247N5EzG0azoA4ER1A7YcrQQAnDUoW3PfGUmummbKoFk404jCdaCkFuf8bTUuHZOHiX2yIrLPfSeabowsfipYa2e3qTPNlM/aZPsMJzuuuWqaFVY14PMt/KCSqDm8sHQv1h46iX/+YWxQ28mvBw6NDFhvfHWx9Nd5EwCq65syzVz7Uhfqd32gUWlq+t1hsTk8pnuX1pix8Ps9WL2/qXa1a/qjfF/qa6or02zpzmIs2XwcOgl44bIR6N4hSbHewNw0fLuj2P1YHjTb3VjCrV92KrJTExTbDcjVDpq56pnF6SUYDQxoEVHkBRw0E0Jg9uzZMBqbPgVoaGjA3LlzkZzclDK8ePHigA/+9NNPY/HixdizZw8SExMxYcIEPPvssxgwYIDiuI8//jj+/ve/o6KiAuPGjcNrr72GIUOGBHwcIiKiWDHodTi1Vwec2qsDHjhnEPLLTPhh9wn8Y/UhnKg2Y29xjTto5soyG9k9A9lpCZr7y0jUnp4Z7DSiSNp/ogYWuwMb8yswvnfHiOxTHjSz+imkLaBRgNvLDZ17n7IsiWDrsKlvjIMVSoxOndVBRNHzyk8HAADfbC9yX58DIQ+TOQSAAKdoqhsByDNjAwma1chqerl+N9g8apo5/5c3kXEIwGSxu7s8r88vx7z3N6Heake8Xoe8zEQcKjN5jlcjPddqd6DCZMGDS3YAAG6a3Acju2e6X4PrGuzKGHPpmOL8IEjeQVPrJQ/KdTYD2NPYddNFXs+MtR6JKBoCDsdfe+21yM7ORnp6uvvf1VdfjS5duiiWBWPlypWYN28e1q5di2XLlsFms2HGjBkwmZouzgsXLsSLL76IV199FevXr0dubi6mT5+OmhrtlsNEREQtWc+sZNw4qTdGNd5MmCxNGdtL/UzNBIDM5OZtBBAI17SckyZLxGqa7S1ump7pr3NnndmmuDEVwjOIph6XvVm7Z4Z/Ixeht5WIguCr1pgmxXWosYZYCI0A5NenwIJmTTW9DDqd5j43HinHks3HPDovV8mm+v+w+wTqrXYMyEnFN3eejjMGamc8ywNyruHZHQKPfbkTZbVm9MtOwfyz+rnXkV9jB+Q0Bc0ykuJgNDg/5ZF30NTqUukKth0qM8Fss3u89mh0ziQiAoLINFu0aFHED/7dd995HCM7OxsbN27E5MmTIYTASy+9hIceeggXX3wxAOCdd95BTk4O3n//fdx8880RHxMREVFzSG78ZN9VwLimwYpfDzqnw8wYkuN1u/REV02zltMIwBWQqqizKINRYexTOT3T943rzwdO4vudRZpjco9FPT3TEUammeLrALpnBrd7v8eklumb7UWQAPTNTon1UCgM3gLUrsBPoEHwoKZnqq5x8iL+WgEkNXm2lV5n1tznvhO1+ONHW3HdxJ6K5c4sNWdhfddhpw7ohL7ZyowwOfkHGYlxepgsdny9vQifbymEXifh+ctGKDppyt+HvMxEd+aZPLtMkWmmkdaRm5aA9ERnjdADJbUY0sWZrLGr0Jl51iU90XMjIqIIaFETv6uqqgAAHTp0AAAcPnwYxcXFmDFjhnsdo9GIKVOmYM2aNZr7MJvNqK6uVvwjIiJqaVzTYUyNQbMVe0thtQv06ZSMPp2833RnurpnNtalWfTLYYx76gccKKn1uk04Vuwtwf2Lt2k2LXBxZR3YHcKj1looquqsKK5ucD/2Nz0TAJ77fp/isb/pmZGqaRZKNCuUQuPhZMNR9G0qqMBdn2zFgk+2xnoobUZVndWjllasCCFwzb/W4cLXflE0O/FYT/a1Ky4fyvRM+ezHYDLNUhMMfrs7bjxS4fVYgV5n5I0LjI3BsYXf7QUAzJ3SGyO6ZSjWl+/WoNcht7H8gLz4v/xrrcCkJElNdc2Kmj5U+WlPCQBnoI+IKBpaTNBMCIEFCxbg9NNPx9ChQwEAxcXOIpE5OcpP3HNyctzPqT399NOK6aLdunWL7sCJiIhCkGx03miYzM5pJq6pmdN9TM0EmhoB1JhtsNod+H5nMU5Um/HJxqNRGefnWwuxs7AaSzYf97qOPCBVUtPgdb1A7StRlmDQagTg6+YukOmZiu6ZQQak5McOZMtIlNlhyMy/cLoehitaQev2rKC8DkfKvAfrm5PVLvDzgTJsPVaFI40fIJRUN+C8V37GB+sK3Ot5NAII8JT0mJ4ZZE0z1xTLtMQ4v5lppTVmxeNQPjRwZeoadBIMqvFdN7GX3+27ZjqzwrwFzby9hkHuZgDOpIg6iw2/HjoJAF6nkhIRhavFBM1uu+02bNu2DR988IHHc+pPG9RdXuQeeOABVFVVuf8dPRqdmwgiIqJwyKdnWmwOrGj8tNzX1EwASG9sBAA4b5RcXdNW7C2NTjZS4y63Hqv0uor8Bk9+QxbqeFzZdy4WWf0aF39TKtXHVq8vz+wI520L5IZTisAEzeZINDOZbThUyuAPtW+K6deuumTyIFbjPciLy/Zh+/EqPLB4u2xbea3EwAK5DiEUmVuA8oOIQKZnuoJmmUlxHkEstRJV0EzOdVR/U1BdmboGvaTo1pyVEo8s2TRLb/Iag2bydbNTfTcCAICBnRubATR20PzlwElYbA5065DIadFEFDUtImh2++2344svvsDy5cuRl5fnXp6b6/y0XZ1VVlJS4pF95mI0GpGWlqb4R0RE1NLIp2euPXQSNWYbOqUacUpehs/t9DoJaY0FjyvrLKgxO2+WCsrrcLyyPmrj3X68yuu0JHnWVllt5GutyW8oXTevNo3ube51tJb5agQQRoZSYI0Agt9G40ihbBSUoqp6d+YjUWvQYLVjd1F11Kcvyz8YcP08q7PDAO3Our6G9ummY7jyH2vx2+GTiuUOL40ArHYHTlQ3eEzndHXMTE+M85uZpp6qrm6iAvivw+g6fpxOpziea/qkPxeN7IoBOak4Z1hn97JOKU0do70FCgc27n934/RM19TMMwZks3MmEUVNTINmQgjcdtttWLx4MX766Sf06qVM5+3Vqxdyc3OxbNky9zKLxYKVK1diwoQJzT1cIiKiiEmOb8o0W7rL+eHQ9ME50AUwFSczubEZQF1TphkArM8vj8JInWoabDhUpp2FZJfdwKmn/oRCfY8pv0F03e+pb/zkPt5w1G9NM3kgzkf8zS9vo4jkTbzN7mD3TCINl735K2b9bTW+2a5dtiVS5D9/riBRvMH3bZRD+A+QP/zZDpgsdny1TdnIxKbINJMttwuUVJsVNRkBoLIx0yw9Md5vTTPPcXpON/cXf3KNz6BXTs8ckBNYssKkfp3w/R8nY3SPTPeytEQD4huz1rwdv39j582yWjPKas1Y3hg0m8apmUQURTENms2bNw///e9/8f777yM1NRXFxcUoLi5Gfb3zk3JJkjB//nw89dRTWLJkCXbs2IHZs2cjKSkJV155ZSyHTkREFBbX9MyaBhuWNdYzmzHY99RMl4zGKZrlJktj5zOn9fkV3jaJiM0FlZrL5fdvkQiaqSNR8htIV/DL5iNotu5wuce0SfXq9rBqmsm/9tz2kw1HseDjpoLw6vs/b0fTyiIx2+zYXVSjCI4SRZu3jKbmZncIxTVObftxZxOxjzdEsByLxs+01nRJ+bRE96ayr8NpMCLfNpDp3dWNQbOMJP81zdRCGaXrvDDo1ZlmoU+RlCQJWSnOD4S8vYZkowE9OiYBAJZsOo7i6gYkxulxWu+OIR+XiMifmAbN3njjDVRVVWHq1Kno3Lmz+99HH33kXufee+/F/Pnzceutt2LMmDE4fvw4li5ditTUwNJ/iYiIWiLX9MytxypxotqMtAQDxvcJ7A9/VzOAwsp6RTBoZ2GV++YpGjYfrdRcbpelap00yWqahXgcX9MlXTeTvjrYAZ73verglnx6Z6SbZ97zv234bmdT5kug97BaWSSurxs06rq1dS0lcNMeectoam4lNQ3ID6AZgHyU0Thv5EEsXePdk2ammTxrK4wLi69MWs9DClTWNQXN/NU0U3NojNnfHlznRZxOgkEWPByQq51pNrJ7BoCmWmbeuJoB+HoJrima//z5EABgYt8sJDR28CQiioaYT8/U+jd79mz3OpIk4bHHHkNRUREaGhqwcuVKd3dNIiKi1srVPdN1v3LzlD4wGgL7wz8jyZlpVlDuzMyO00vo0ykZDgGs3Fca9tisdgf+vuog9hYru1h6yzSTZ31FYhqhr33YAsg0AzyDWa6aREIIXP/2etwlywQLOtNMvnfZl66bdU+toxFAS9NSAjcUO4F+7+UBqmicN/KfP1cWlFbQTJlpFtqxzDa7ImhmFwKLfjmMHY1ZdWomi919PXTWNAvu9k5oXM78NgJwNGWayT+o6Z+jnWn2+lWjcMPpvfDejeN87rcpaOb9+AMbA3Mnqp0f0LBrJhFFW4toBEBERNTeuDLNAGfXsOsn9vKxtlJmY6ZZQbkzAyM1IQ4zhzib53y25XjYY/tmexGe+mYPnvxmt2L53uJq1Fk8pwnaw7g5FUJgzrsbcP3b62Wd6ryv77qZ9JeJ4a175v6SWvy0p0QRdAv15ta5refNulokGgGE06yAiELj+qmTNwJwB820pmdqNAII1pGTdYptNx6pwONf7sKDS7Zrru9qAhCv1yExTh9wpllm44cvQitq5oerJmScXlI0oEmKN2iu3zk9EY/8bjB6dEz2uV9X0MxX0G6gqtkAg2ZEFG0MmhEREcVAsixoNv+s/kiMD3x6SXpjTbOjjUGztAQDLjylKwDg10MnURhmF81NR5y10U5UKbOmHALYdswz28Hu5eYwkHvG6npnTbef9pQEVLfLEWKmmUMI2B0CT6sCga7ngqHVbS7a2mOmWSj4PvlWVWfFnuLqWA8j4iL5fdfalbLGmFMgmWahBLsPl5kUHwq4Mrn2n6jVnJZe5WoCkBQHSZKgD6ARgE5q+j0Sylvnnp6pETgMR26ac/pmQpz3/Q7s3DQFdHDnNOSmJ3hdl4goEhg0IyIiioGctASM7ZmJiX074v/G5AW1baZ7emZTplm3DkkY2iUNQgCLNx0La2xbGgNj5Y0ZDIrnNOqaBVN/R62yvukYZruzbpevvbmCZf6y2zwyzRwCX20rxPK9ntNXw6k9FMiW4U/OJIqM8c/8iLNfWq35c9yaCYio1sDT6rCrlWmmGJMQIQXz1JlmrqmQ9VY7SmrMsNkdKJG9zipXPbPGIJg+gCKKGUnx7oy5IyfrsKuwGofLTO4gn79dNDUCaFqxd5bvLLJA/P7Ubrjx9F64dnxPr+t075CExMYaZswyI6LmoJ1DS0RERFGl10n4ZO6EkLZ1NQKotzqDTGmJzl/nZw3KwY7Cavxv4zHMm9bXb10aLWabHbsLnZkolXUWd8QnLzMRxyrqsbnAs0Onv6wvX1wFrAHAYnPeiPkKYrluJm1ad7Ey6l04BFBYqVVvLPjpmf66Z6qpvw+hZJ8EcvNtsTlQXNWAvtmhd7Cjtq3O4rxmLN9TglO6ZcR2MCESQnj+TImm6dFdM3wXmw+FVjaqPNOsaWp56F15XfJPmhTXVKvsA4IjJ01INhpQWmtGdloC4vRApSvTzBU0C2B6ZkZSnDuYf/cnTfUdXZv669jpDprpdBjVPQObCipx0+Tefo/rT3ZaAh7+3WCf6+h1Eib1y8KKfaX43YjOYR+TiMgfZpoRERG1Mq5GAC6pRufjCX2ykBSvR/7JOmw44hncCsTuohpYGm+IrHbhvsl23WBvLqjUzOIKVWW9RtDMx/oB1zRTPXYI4fUmtjVkmgUSaLvsrTWY858NWJ9fHoEjtk0WmwMN1vbXibStKDdZsP14lcfPbESnZ2pMv9a6dsinJrqCXJ7TM4OXf1I5PVOeOZd/0uSxvmt6puv3QiA1zTKT4nHxqDx0SI5Hx+R4pDaWC3Ad1t/nLa7XG6eX8O/ZY/HfG8bh8rHd/B43Uv52xUisvneauykAEVE0MWhGRETUyrgyzVxcmWaJ8Xqc3dgQ4H8bQpuiuVU1bavC5Jw+OaxrOvQ6CSU1ZhSpap15yzQLJNBTKZsCanZnmnlf3x0083OXrH7ad/aan0Gq9x3gfiPJ12Fc09L2nagFAHy5tbBZxtQa7S2uwf7G96mtqmmw+g0MttbSb3UWGyw2hzvL1iXajTK0rhHyTDOLxrUr1Eyz4xX1aLA2Bcpe+mG/++sjJ+s81ndl66YnOn8vBFLTLDMpDvOm9cWmR6Zj4yPT8e/rxgY1RnmmWUZSPE7vlxVSZnOoEuP1yEljLTMiah4MmhEREbUymepMs4SmxxeOdDYE+GpbYUgZNR5Bs8agVmK8HoM6O7uWbS5QrhNOTbNqjUwzX7f0rmPZ/NU0U+3D7vCeERfqzS0QWIaLx71kKN0zfWyj7tpZb7VHpa4T+dCColD5ZXVtNjCYGKdHvdXuzoB1iXbs2t/0TIvd89olRPBTuZ3Hck7D1KKVaeaqC+manhlIppn6g5ehXdIV0zr97cF1/TUEEKAjImrtGDQjIiJqZTwyzWRBszE9MpGdaoTJYscmjfpj/mw5Vql47Mo0A5qmaG45qtxvWI0AZDXNAso0E4FNz1Q/7Zyeqb1uODfcAU3PbOb7ygar3W9QkSiWqhus2HrUc6q3P5IkwWx1oF4dNIvg2OQBd9dXWgF3ecH9L7cWoqSmQZlp5p6yKVBVZ8WO49WwBRjMPlSqHTTTyjRzNwJICrwRgPqDl8R4PQbmprof+5+e6Xwdke6eSUTUEvFKR0RE1MqkJRjcNWgAIDWh6WtJkjCxbxYA4JcDZUHtt6re6r5ZG9LFWSvGJLs5HdktE4BnppnXRgDCmV1RXKVdgB/QrmnmizvTzF+gTl13TQivU7iCzTST3+gHlGkWgapm0Z5+Ri1HNLtAujXTtGJvzv7rKlzw2i/4cXdJ0Ns6HMIziBX1TDPfzz/97R7MeXeD122q6q2wqwL3vgL/h8q0MwWPVtR5bKeuaabX+b+9c3WflJM3hvDfCKAx0yyArDYiotaOQTMiIqJWRpIkdOuQ5H6clqjMGnAFzX4+cDKo/W5rzDLr0TEJvTt5dmA8pXsGAGD78SrFDb3dRyfLRz7fgdOe/hFLNmvXWFNmmjkDdNFqBOAtThBGolxgwSzVfWUoh4tWjOOrbYWY9/4m1Fls0TkABc013bYtZwsWNgbSv9lRFNR2DiHw7Pd7cPf/tiqD11GvaeZ//+rC/65thHBmyNodDkUGl68PCbx90GC1C5TVmhXLmmqaNU7PDHHKpKKbqr9Ms8brf5yBt5JE1PbxSkdERNQK9ejYFDSTZ5oBwMS+HQEA249VurMQAuGqZzYiL8Nj+g4A9OqYjPTEOJhtDuwpqnEvd8XPEuI8/6z479oCAMDz3+/TPGZVfdP0T61i2mpNmWa+s3DUAQeHw3tNoaAzzeRfN1NcI1rHue39zfh6WxH+sepwdA5A5IMuyLnLR8vrcLDUhPX5Fe7p3ED0fj7+8tUubD9WGfA1Ql3DrCnI7/AYo1bQLDnemQFWVmvxeM6lsLJe8diVresKmukDyP7SejUjGz8UcY7X9+t1d89kphkRtQMMmhEREbVC3eWZZglxMOglZKcZYdBL6JyeiN6dkuEQwNpDgWebbTlaBQAY0S0Dmaq6aQCg00nubITNsrpmrkwzX93MjBoBNQCKoJ7F7oDV7lB01FQLNNNM/bxzeqa2cDpgBnIz7dEHIITjRTuTptxk9r8StRktJYct2JCL/Oda/nVEa5qpdvbgZzsCrtso/zk9fLIO1y1aj3/9fBh2h+eUcldmrVxmsvO6q84mkyuuVmahVbunZzZ2zwwgEGnWCNj1zmrKLj5Y6ruRhHt6JmuaEVE7wCsdERFRKySfnpmaYECcXoectAR3YebTg6xrJoTAlsZMs1O6pWtmmjmfywAAbJHVNXPdDGanGpX7lH2dYPCsoQOopmdaHbDZhc/sOFcjAH/1z9SZaEJ4D3AFPT0zyPWl5u4E0Kiq3or9J2r8r4jYjbG5hdMplSIv2EwzefBKnk0aTuA7EIHuXr7el1sLYbE78MXWQtgcDgjVJUsrcNWhMWim7g4qV1ipDJq5PmTICCLTTGs6tk62XW2D7+narun5ceyeSUTtAINmRERErZB8emZ6omeAa0Kf4IJmRVUNKKs1w6CTMKRLujvjQc01hWdzY4ANaApEZKd6zzTTmroJKBsBmO3+p2c++fVuCCE0bzjl1NMz7T5qmoXVPTP4kmahHSeEbYqrGtBgjWIx+Vamqs7Z6MLf1N72oKXEDoON1crHbZV9H6P9csINtjocgB3qTDPP81DrWq5+rqiqaXqmxeZwN2txNQIIpKaZt6Dca1eOQu9OyZh/Vn+f27tqmhkCaDpARNTa8UpHRETUCnVOT3R/nWI0eDw/vndH6CTgYKlJcZPljaue2cDOqUiI07szHtRcmWaHy0yoMDkzHFwBqk6qTDO5BI1ubUIIVCkyzbxnV7hsPFKBbceqQmoE4G2T4GuahVeAPLRGAC0kytGKmRqzawKdakfRF2yGozxQpsw0i9iQNIV7zrgyZB0OgYKTdY2Bf8/rXYaPoJnrg5Li6gZ3Nq48Kzc1IfBMs3ovQbMzBmbjb5ePxIDcVJ/bWx2u6ZnMNCOito9BMyIiolaoZ8ck9OmUjAE5qZrZCelJcRiWlwEA+CWALppbGjtnjmjcRqumGeCsm9M7K1mxjeuGMjtNNT1Tdp+pFTSrt9phkXXhdH3t7/Z0Z2G1/5tY1dMO4T3wFE5AKqBMswjcVzJmRq2FwyGwq7AaDQEEwYP92ZBnZ8k7+Eb7xyOU6ZlyrrqPL/2wH5OfW46P1h/VzDTL8HLdBZo+HDlRbcYN72xAhcniDpqlJRjcwTJDQNMz/X9vfHF3z2RNMyJqB3ilIyIiaoUMeh2+nz8ZX91xuqIWjdzEPs4umv/++TAKTtb53J+rRtmIxkwyb9MzAeAU1xTNxm1cNc2yko1eb4KNBs8/OdS1y5q6Z/q+Q91ZWOVRVFtNnQHmcHjPCQs2iUTZIc//+lIEJmgyZtbCtbKEm2g2ljDbHLA7BMpN3ht6uATbfFFey1BxDYhyVNke4P69va+uzd9YeRAAcP/i7TBrTJ3O8FJLEgCSVRnFW45VursPy4NtgdSJ85ZpFih3IwB2zySidoBBMyIiolbKoNf5/KT/ktF5SI7XY1dRNc7+2yq8/1uBZkDK7hDYftzZOdM1/bKDKuNBPo1qpKsZQOOUTtf0xniDzmM7F61MM3kTAEC7xo+WQDLN1C/TIQQcXrYJp15RQN0zJWU3vFAOF+1Ms3bSB4BaGK2AstXuwInqBs3ac/JrhC3CmWZ2h8CGI+Wa3Xvl181dRdWoqrO6A1bK9bT3rRXkl2fZuhgNeiTFazdNSVYttzuE+xoqD7YFcq2os/ou9O+Pa2opu2cSUXvAKx0REVEb1adTCr69czJO7dkBdRY7HlyyHbMXrUdxlbL72oGSWtRZ7EiO16NPpxQAQGK8XpEdJp8COrJ7JgBgS0EFhBDu2kI6naSohSbPutBqBKAOmrkzzfy8rj3F1X67Z6rvUR3C+36DzjTz8jUAnJQFx+RufW9TcAfxedTIi0Q2HFGwtBKVbHaBkmqzZmDcqpieGVpNsxPVDbj8rV/xzfYixfL31xXghaX7cNcn2zy2kce3rvrnb5i08CfNa5C3Ybg6Z/bulOxe5m36qrep8epMM7ujqdOw/PpsDaDRRSjTM612B77ZXoTf/30tPlh3FAC8BviIiNoSBs2IiIjasO4dk/DhTafh4XMHId6gw8p9pZjx15X4bPNxd/aEqwnAsLx0RRFpeQBMXqC6b7YzsFbdYMNfl+3D2sPOmmkGnYSOKU3byKdnGQ2eN1fqTA1X9oK/G+AGqwMHS2t9r6S6fRU+u2c6n7DaHXj/twLkl5n87NvrYfCXr3d5rCIBWHe4PPB9ah2G8zMpBDUNVhRWejYCaSnnU7CNABSZZorumYG/oMe/3InfDpd7BLJ/2HUCgOe0ccAzo7S6weZ3irhcrdmKHcer0L1DU9djre+LQwhkJmtP0VQHzWyyTDN50EzdOVhLsNMzV+4txenP/oRb39uEXw+dhE4CZg7JwcWjuga1HyKi1siz3RYRERG1KTqdhBsn9cbUAZ2w4OOt2HasCvM/2oLvdhTjyYuGNjUBaJx26ZKZFI+ixqw0efZDQpweiXF61FvtePmnA+7lep2E7NQE9+MDJbWKbdS8ZZoFklXlmhrqjTooYHcIr1MpXcvf/fUI/vKVM+iV/8y57uetdgfKTRZ0SI5HnF6nrGmmGuvBUo2AmyouEEy8osJkwX/WHnE3X4gWTs8MUwsJQqnllzlrGXbJSFQstzXWHPPWJbe5BHveWexNwZ5QM83KarRrrfkai7dp7YGsBwCfbDyGpY1BOReT2XOKZG2DzXumWbzyts1qd6Cy3nN6ZiDBvGAzzfYU1wAAslLiccXY7rhyXHePc4qIqK1i0IyIiKid6JudisW3TMDrKw7i5R/347udxVifXw6D3nm3OFIdNJNlPKgLVHdIjsdxVaaEXpJw46Re+GJrIQBgd1GN+zl1I4B6i919w+cSaE0zIICgmeqxr+6ZrnvMjUe0s8Fc08XSEuKgjv1FuxHAg0u2Y9X+spC3DxRjZsCrP+2P9RCaTWWdBccr6mMfNPNy5tnsDpTXWjzqmnmtaRZE0EyrVppzLN5pJW8F2hwAAIqqPLPKtK53VfVWr9+TZKPy4lNvsaOqsf6aMtPM/3U00EyzYXnpAICxPTNxzfieOHtILuI1mroQEbVlDJoRERG1Iwa9Dnec2Q9nDMzGXR9vxd4TTYEtdaZZYlzTnwm+urq56PUShudlYMH0/nhx2T7sk+1bnml2sLQW5/xttfumMV6vg8XugMXmQHFVAyy28NN2tBoBeK031PiE1hRSzfVlewpkhlY4WVzrj1SEvnE7VG+x466Pt+Lc4bk4Y2BOUNs+v3RflEbV8gRbxy9avDVftDkEKuotHsF2q5fumVX1Vvx68KRierg3Acxe9KBVv0yrsYi3OFp1g2dWmVbQrLLegs7p2hlc6umZdRa7eyppRmLT67YGEDQ7rbGzsj8XnNIVM4fkamYKExG1F/yogIiIqB0a2jUdX9w+EXOn9IFOAvp0SkZuWoJiHXmwJ0V1w6bOMgOcNc0AoGfjVML9sqCZQXZ3/MRXuxQ3jJ1SjQCAjUcqMP2vK/HaiqYpn6FSZ5U5Apie+duhk5rPP/3tbsz970bN6VTB1FLyNjY1V/dAq93hteNnLFjtDhw5GUS9txj4aEMBPt10DNe/vSHWQ6EABBtQNisaATR9fbyyHte/sx4/7D6htZmC3VummY/BuOotymlNg/T206qeig5oB+IqTFav0zPVRffrLDZ3tm56gNMzLxrZ1fmhxv+N8LqOGgNmRNTeMdOMiIionTIa9Lh/1kBcNa47UowGj5tG+SP1c+eN6IIvG6dhuriaCGQ1ZnsUVyu7dLq46uO4ZKcZcbyyHiU12p0n1Qw6yW/dHvWzVofDa3aNK4hVWKU93nd/PQIAWLL5OOZM7q2saabap07yzOKps3gG27QcKKnFv385jAMltbh8TDdcMjpPs25SNAQSvLjqn79h3eFy/Hv2mKCzuJqLt3pVpNJCYrG6IKNmFsX0TM8X8dOeElw3oZfPfci3q6yzIDUhDnqd70nUDVaNTpl+Erokqen6oNVcQCsQV1FnQQcvjQDUWXcms80djJM3alFnDAPA85eNQPcOSRjVPQMGPXMmiIiCwaAZERFRO9dN1tFNztf97GPnDcbZQ3Lx0g/7sL+x4L++cQPXVCGrxk2tEMLdXMAlJzXBYz1f+maneATetI4jZ7d7754ZaFzKV2aJ2WbHv34+rLkvdSDH2+HOe/Vn99fbj1c5g2Ytpc0hmjqAvre2oMUGzaiVCboRgHb3TJd6q/9aXfKM08OlJqQlGtAnO9Xn9U4rwOXQ+EmWZ7/pJQk2Hz+/Zo1A3IhuGcjwkmlm0CmDXe80BvMBZU2zqf07KdZb99CZigYtREQUHH7UQERERJr6Zad6fa5jihHnDu+M7DSje5mroUC6j/pnWtlc8n0EYkiXdL/raDUC8Bau0pq2qTmFsnGZ0Fj22Bc78fzSvZr7LzMFlkGnxV/M7ER1A6rrrQEV//bF19Q0aj5WuwPz3t+EJZuPxXoozSKcTDOtoHwgBe7l2Zs3vLsB0/+6CiXVDfAVwdPMNNNY77Cse66/cLdZ42f2kXMHIy1R+/rpur5qkQfaJElyZ/sCYMCMiChMDJoRERGRplum9sFV47rj/TnjvK7TMbkp4KVvzITI8HLTBwCbNArbT+ybhfTEOFwyKs+jQ5yWIV3S/K6jDjbZHA54KWWkmR0W6E2ya9kH6456HUu0pgxWmCwY99SPGPf0j36nq/oTTOgi1PiavFYbaft6WxF+2lOCf/+SH7F9atXdC6UWXzTIT6UGqx3/XXtE0WlSCKEIhMlrmj20ZLvH/hoCyDSTB83KTRY4BHDqUz8qstgC2a/Q+Jl7+PMdmsfRos40O3dYZ6QnxSHBS3fKOB/TKtWNWrQCikREFBpOzyQiIiJNyUYDnrxomM91OiQ3ZTS4pmeqC1bLbS6o9FjWPycVmx6ZDp0ErM8vh8lc5/OYgQXNlDeNFpvDa6BACOERyCmsqse89zbhglO6upfpXM0MZPsOpFB/Wa0q00xjE3V9uED4m6IaPaFFzWx2gZJqM9IS4sDa4tpMAda/a2lsdoGTJnNAAVH5OvJMs2e+3YO31+SjU6oRr185CgDwwfqjWHe4Al/cNhHD8zJgkU2T1OpIGcj0TG8B5gqT9+C21vTMcAOP6n26ri/eCu8bvLUahXJ6JhBYB00iIgoMM82IiIgoZPJpQK5GAFpT/Vw3mHuKqz2ey0hsLMQtSeiU4n+q5qBAgmaqxxa78Fq7zCGER1HxBxZvx57iGjz73R73Mq171kBumz2CZhru/mRrAHtSH9tbEDDoXYUaByMN5SZLQBlPbYndIVBhsmoW51eTryO/VLg6X5bKGoKsO+zMTH1tubOjrsXP/rUyRNVC6Uirtd9we3Sou2e6Zl8a47Rvz3wV8FcH2kLNPDXoJWSnGX1OBSUiam+YaUZEREQh65jiWdNMy/NL92H1/jKsOXjS4zl5DZ+OKdpFsBXrJ3if/umi7lZnszu83ixrLXYVvZdzBaOExjJfAgmahSJWPQJaS/mzBqvda9ZOMBZvOoaEOD3OGdY5oPWLqupRUWfB0K7+a++p+e7hGDktZfKePMCuDiLJuc51cwSCkd6aawTbCCDcN9Gser3uTDOD9jkb5+X6et/ZAz2WhVrjME6vQ04aa6AREckx04yIiIhC1lE2PdNbUe/O6Qmw2BxYvb9M83m9LIUrK4BMs0B8slFZSN3mEF5vloXwUvhfvZ7GMovN4XcqVFmtunum9y6cwfA25GjXq2qOsE5VvRWV9dGpBReM0hozFny8Fbe+t8lrIMJqd6Cqwar5XKzY7A48uGQ7fjmg/TMHBHbOR5v8XPL1c+QKbGsVzw+Wt1pjvs7raGSaqYNmrunt3qdnet62nT+iC26Z2ifiYyMioiYMmhEREVHIFJlmGvMXk416/LhgCn5YMBldMxID2J/vTLNgs5xcY7LYHF6nLAkh/Bbtdq6n/B8ASmvN2H+i1ud25T5qJYVDq+tnqJorwylQBSfrcKy83v+KUVYtC4Z5e7dtdoGa+sgEzSKVxbds9wm8/1sBnpFNL5ZbvOk45v53Y0DdJqNJHmj3VbzeFeALZPqnP16nLvp487VqpTnCDE5bVNlrrg8PjF4aAcQbPMeXlsBJQ0RE0cagGREREYVMq6YZAJzaqwMAYNaQzpAkCX2zU/HO9aciKyUeN0/u7WN/vjPN7pkxIKjxuW5AzTYH7F5uuB2BBs00bpIdjsC2lWuwep8qGgxve4h2AlFrmZ7ZnCL1lkfqra2s8x/EK6xqwHc7iyJ0RNW+K+u1pzSqyM8ln9MzXf9H4OQOJdNMK7gY7ljUGaj+GgHoNTLNUrxMVZ83zZl9ds1pPcIZIhERgTXNiIiIKAzyTDOLbOrUW1ePxur9pRjZPdNd66xvdgrWP3QWJEnCW6sOae5vTI9MJMbpMbl/Fnp3SsEbKw4CAOZM6oUxPTpgUr+soMaXEKeHyWKHxe6AJGl/VugQwRXOlt8sh3Lj3GC1o6zWjGx57aAQ7r8jmmkWRLSmpWWlkafmmnpptTtQbrIoplb+evAkbv7vRvTLTsGyBVN8bi9PTrX4nJ7pfD2ReFnBBrkB7YDeFo1OwMFQ1110Tc/0lmmm1YjEW6fiu6YPwLnDumBAbmpYYyQiImaaERERURiSZTdtnVKbAmiZyfE4/5Su6NYhCXGyrm9anTXlendKwQdzTsNLl49UFMROjNPjlO4ZiPNyQ+mNa32Lze71ZtkhBNblexb+V9O6YQ+lwpJAhGoOeatpFsK+W2MYzOEQmL1oHR77YmeshxK0pTuL8fKP+z2CW82dxeetDmGgbHaBkmqz4mdryZbjAID9Jb6nLQP+rwcurt1Hol6f10wzH0PZVeTZ9XdjmEEzNVemrk4rOuZFopesNJ1OwuAuaYrsXyIiCg0zzYiIiChkkiRh3YNnosHqCKirpVq83jMIpnWj1zHFGFJXN9f+LTYH4g3aN8tbjlbiq22BT1NTds8UIQU6InHz761weluqAW6zO2CxOZCokVGz4UgFVuwtBVCKx84f0vyDC8NN/9kIADilWwYm9+8U49FEWBBRW28/O+vzyxX1DUUQmWb+suy8Bc18bVZrtvk/cJhCCWBGojssERH5xkwzIiIiCkt2WgK6d0wKadt4H5lj8nvIXlnJIe3fNdXJYvdeR+xYRWAF57Vuxh0i+OwgIYRHplkoQTRvRdF97evXgyfxzXbPAKH6NTRY7QHVpIomhxC44Z0NmPzccs3Old66WcoJIbB8TwlOVDdEY4hhK6kxKx63pKmvDVa7zzpjAHC8os7j5yKYM9nb6134/V68vSa/aZ/uTDP/1F0p1bxNxQ5l2mYwUiNQtP/TWybglG4Z7sfepnISEVHk8EpLREREze6SUXkAgDvP7Od1HXnNrrCDZjYHbI5QJlM20eqeCYSQISIiU3PK6x587Pr3/1iLW9/bhCMnTR7PlZssWHf4JBwOgf0narGv2P/0umgyWx0or7Ogqt7qEVxSs9odKK81e3Q0/GpbEa57ez0mPPNTNIcaMZHqtBro2eXr3D1WUY/9JTVen3/vtyM466+r8Hpj3UH3sYM4tX3NHtxd1HTspppm/ndeF2JH0AaNDpmRlJPqu8lJvsbPpJxOkjC6RybeveFU9zJfHzoQEVFkcHomERERNbtnLhmG6yb2xODOaV7XOS7LAOuakRjScYyN05fqLHakJYQXqNLa2u4IfnqmQGQKmofTCKCoqgE9OioDkX/49zo0WB1ITYzDoFzv35dY8PdKbXaBkyar4n3dVViNH3afABD9LKJQ88PU22l1aYymcEqaPf/9XgDAdzuLcd3EnlE9vreAtZY6iy3gWmly/jLUwpWa6Hv6+k97Snw+75qiLJ/SzqAZEVH08UpLREREzS5Or8PQrukeRa8NegnZaUYY9BKOVtS5lwdTHFvOlWl2rKIeG45UhD5gaE8REyL4TDOtoFko8S9v2wSyK/W2EiQ0WJ1BgxV7fd+8Nxs/b6v8JTgcAjsLqxSZWg4hws4ekr9PLWfiZOREYzpoMFONdZKEwsp63PjOBp/ruQLEgcQ+6yx22OwOlFQ3YNmuYsx5dwNO1vrOVASiH1hNjvedq3Du8M4+n0/SCJqF28iBiIj8Y6YZERERtRhxep274H8kim9HsuaPKxhgl03zFBA+p5j521dY4/ESNQtkOJHq2uhqRhCn0dDB9Xy5yYIOyfGazwfK37S8r7YX4d+/5Id1jBYhQjGQQM+ulhBzue/TbVi9v8znOk0Ba/+vbPPRSozunoHSWjPmvOtsuBBv0OG1K0f53K7OEt1i//5qmo2U1SrT4uomLP8Agd0xiYiij5lmRERE1CLVmcOfqqbXRf5PHXlpNOfNfJCZZkJEZHqmt12s2l+Gr7cXYeORcgghNOtk+Tp8MNlHe4pqsKfIe90rm12gpNrstWmBL/JRuN6ve/+3FTe+swEl1Q246p+/uZ//5YDvoEtL1RxBK1/nWqRiLhUmizuYHFxNMwlFVf6bNDTVNPO/z2e/24M1B08qlu0urPbabdalos7qf+dhSPETNEsx+n5eK9vWS6yaiIgiiJlmRERE1CKFk2l21qBs/LC7BCPy0t11rcKlPT0z+EwzhwivHlnTfrzv4+5PtgIAko16mMx2JMXrsfbBM71uqwhQQeDBJduRFK/HRzeP19z/wdJarNpXihF5GV6zzKLh4w3HAABlAUy3a20q6ywRaRChVmexobrBirQErZpaoUfN5HXDqhus7p+LoLpnSoAhgB+gYPf90o/7YZJljlXWW2GzCzSWOGwW8Qadovuov6BYsp/n5fpmp+BASS3G9OwQ8viIiCgwDJoRERFRi3Tp6Dy8vuIgTusd3I3hs5cMx/RBOdhbXIMumQl4Ydm+iIxHsxFACDXNNh6pgEWV9RJKqCSQ+IqpMVuvzmLHyz/sD2jb/LI67CqqBgDY7A4YNIJiZ76wEgBw1bjuuGJsd/fy/649AkkCnskbrhqrwK6i6ohk2AGAKYSAqjPDL7p1q4LlOnWEEDhaXo/q+shPEfz9P5wZefuemBXxfbvoJAl2rahyANsFMsUwmO6ZLv9Yfdj9dSSmegfrH9eMwYYj5XjlpwMAgHTNoGUTf5loct/eOQkWmyOoQBsREYWGSb1ERETUIs0/qz8WzR6Lf1471u+6ibIUEldHzpQEg6Jodtg0WvgJBN89EwD2Fnuf0hj4cIILAMmzs3xlqclfj9Za8rpSOwur3V9XmCz4aMNRfLj+KGoalFPdvtpWhEvf/BUPLNke8HgjPXNx/kdb8MbKgx7vW4XJosgIiiVHBGrdAdD8xmlN041GSazCynrN5Ta7A9uPVaGoqun5QDPNHMHH4xRc319fxf4T4iJ3reiSkYAOyfGK61JqomeA65s7Jrm/HpCTGvD+4/Q6BsyIiJoJr7ZERETUIsUbdJg2MDugdTOT4lBf5VkDLT6CjQAcwhmoOlhmci8LpXsmAOgjUMwq2ACCvKyYx7ZBjOf7ndrTXeU1oxyqGNTizccBAAdKanGiugE9s5IDPp6WUIIn6/Od3VMr65UBvWMV9TBGMGASDFetN0mSItIcIlhSGOehfEuHEO5srm3HqzTXtza+VnktOinATDMEUdPMF7PNe53EbplJ2F9SG94BGrnqAibIgmZa02MHd0nDDwumoLrBii4Zie7li2+dgH+uPoTCygZsOVoZkTEREVFomGlGRERErV56knZ3xkhmmtmFwLu/HsH/Nh5zLzNb7SEFOwx6ZaAglGmDwW7ikGXZWH1kVskz0oRwPt5+TDsQEmhtNvmrDbgemWyjzarAQaDdP7WaGmit6isDKRChbn2iWlkEP5p9AbTeI63jFVbWo7zOMyvNl7dWHcJfvtrtntbri7zwv8MhEMhb7840CzNqVlrj/dxLjGDBM9d7nRgvyzTzMv2yb3YKRnXPVCwb1T0Tr181Gp1SjREbExERhYZBMyIiImp1Xrr8FMXjjETtekFa9bgA5xTOhZcO13zOG7tD4OlvdyuWfbfzBO7+ZFtQ+wE8M7FCEWwzAZvsoDZVpEIePDlRLQuaQaDSR1dB4St7TSaUaYDygNf9nwb/HkdaOFlZ3sS6xJrWSzpZa0FhhfYUS28OljqzL//za77P9bQCzI4AomaRaJwBAMU+OnUmRKFLgDwQl2L0XdNMyz0zByBer8PNU3pHclhERBQETs8kIiKiVufCkV1xzrDOeGDxdkwZ0Ak1DVb8eugkhuelB7R99w5JSA2yJtCqfaVosHpGu9YdLg9qPwDw4g/7cM7wznj/twJYfEwZ8yXo6ZmyoatjEKHGg7wGM1T78xVwOlpeh44p8UiKV34/fGXwNV+sqRmPFIMAmta3RUBg+d4SrNpXigdmDQxq23qr8lyubrDipWX7cf4pXbzW7Ark3HO9N96C4IEqrm6eoJkkAdlpRhRXNwUfk43B779/Tip2PD4zotPMiYgoOAyaERERUasUb9Dhhf8b4X581qAcJMXpkX+yTrHeL/edgTs/3IwNRyrcyyQp+MyhPREo3u9yoKQWDVY7HgyiML5asFPV5AGuQLf1t1qgsxq9vdUHSmpw1ourkJEUhy1/mhHyONqjqjor7vpkKy4Z1RWzhnX2u77Wt8CVzVdnsSmClq+vOAjAmUk5sV+nkMf4zLd78P5vBfj3L4ex+89nezyvk6SAagK6z90wzgOHQ/jMNEv0U9fu1J4dsC4/sAC5TpKQk5aAVFkdM/X0zP/NHR/QvhgwIyKKLV6FiYiIqE3ISUtwZ6IY9BKy04ww6CV0SI5H9w5JinUlKTqdA4OhniIZLHkgqbzOf50w+fHUR9aq/RWIBllmka/Alrf9L99TCgCorLPCanfgRHWDoqFAoGL8rQyZK3BrtTtw0mSGPYjo4IvL9uKH3Sdwy3ubPJ4LdC/5J00orWnAwRITKjS6azZYg8yCVB1Y3iVWQHjUjjtR3eBRr05zt+6YWeg/M/VWu6KemprRT6bZDZN6BXws1/kon54pD0ouvGQ4xvTsEPD+iIgodhg0IyIiojbDFSxLjNcjJy0BcXodDHoJHVM8GwWE0vUyksKt0ySPPxRWeA8GuNhlNc0iVSPKW8aa+q31WqhfttxmFyipNrs7Sn62udDHgYMZZctnswuU11qDel1ltcEV69eKLOogoabe2fXSFayUf0uDzXJSD199nj325U7F41eXHwhov1pjC1ZxVT2KqrzXakuM0+Oqcd29Pt8lPVHxOJAmI0myRgDy601aIif7EBG1FrxiExERUZsRp9chJy3BY1l2qnKZBAm6GH90GG7c6qc9J9xfP/7VLr/rK7J8Ajx2MGP0lQW0cl9p4Dtq9L9NTV1K1fsO561ryfG2Zh+bn7hxnCowZLU7UG6yoENyvP+NoQzs/vnLXdh6VLsLqz+HykzYf6IG+0tqQ9oeAE7UmH1Oz+yQHI8uGQlen09QTd88f0Rn9O6UgoQ4Pf6s+vkzGvSN2yiz17JS4lFWa8HoHswyIyJqLZhpRkRERG1eYrxq6pWPmmbNVUMo2Jpkaqv3lwW1vrxjp3pmqLxguZy/6XCul1BUVQ+TuWkqX6A5fMcrA+vS2JJqmoX7fZPz9T6FMk01HDaN6bFxekm1jjIb0B/5e/Xh+qNhjW/6X1eFtb3N7kBJjfdpzNlpRhh8zNlWB8A6JBtxxandFUH6py4aipw0o7vWovy64xACP993BjY8fBY6pRpDfRlERNTMmGlGREREbV6SKmgmwXvAwmjQwWLzHbDokp6AQh9ZK4Gos4TWNdMl2CmWNlnUTB0M210UWpMDAaCkugHnvfqzx3N2h0BVg9Xn9ot+yQ/puIEormqIeLGzmgYr8svq0D83xZ1NFC2+AlP7T9Tg+53FimUms82dGaYV2NOqK+eaMrirqBor95bgynE9FIEydaZZsCrrfH//m9NnWwpR6iNo1jk9EdX13sdrVGWaZacakZpgUNR9u2xMN1w5rof7sfy602C1IyFOH9EunUREFH0MmhEREVGbN7FvluKxJEkeRcldEuL0qGmw+dxfJG58S6rDC7oF20dA/nrV23pLsPEXlxNCYPtx7Sl3hZX1KK5qiEjmnnoY6qCQVtbgyz/tR+9OKWEfW84V6LTYHFEPmvmilXV1qNTktXacN67V7/t0GwDAZLHj5sm93c+rM80U22o8pf4+FZTXea4UI9/ucAYZJUn7vE426n0GzdQ/8z2ykhCn12FsY0H/OL3kEWRMkJ0jZj+BeCIiapk4PZOIiIjavJy0BKx94EzFMm/T39S1i7T467QXCF9ZL9Gg6MwoBI6cNLkfepuqCgAVJgvW55drPme2OTQncEqS5O7WGZHJjMLnQ01es5xEYNNCv9tRjLNeDG9KYHDCf6dc32KtTMlCrdes+rYXVdUrtpUHgax2B0pqGhQZi14H0IJlpWhPjZR3t9R8XvUz3y/bGZDt3jEJK++Zig0PT/fYRqeTcO6wzhjVPQP9c1JDHDEREcUSg2ZERETULuSmN9UekgBYvEx/u+iUrtBJwNQBnbzuK5DAmj8nTb67H0aydhagnO7nEECtuSmbzlvITAC44Z31HoXOXUpqzPjbj/sjOErv/E2ZDdRbqw9h4jM/4cN1BT7Xm/vfjQHvM9RvlTxWWVhZj/d+8z2mQJk0pv5e8NovnsfX2FZZ06zpPHfVM/OWodladEz27KQLODPNfDHodbjjzH4AgKtP646ctKZumj06JiM9MU5zu9euGoXFt06E3ke9NCIiarkYNCMiIqJ24/Ix3QAAt0ztA5uXTLOc9ATs+vPZWDR7rNf9eLtBDsb9i7f7fP7RL3aGfQw5ebBDQBno0XnJNBNCoNrPVNXmYLE70P/hb92P1fW+gqnvtiG/AgDwwrJ9ERnbKz/tx6vLD4S9n7d/PRKB0TQK8P1Qf9cdDgFrgEX+tc6Y1hBO8xa8So43+J3eumB6fyybPxmXjenm2VyEiIjaJAbNiIiIqN145pJh2P3nszGoc5rXQusXjeyKhDi9zymLGREImvnzycZjEd2fsqaZwA55LbIIJ8FEO6dGne3ka8ZgtL2+4mBE9iMvKO9i1ehoqbVeyFTn+IFSEyyyY32zvcjj+K2dVsB7Ur8s5GUmaqztKTcjAT07Jkd6WERE1EIxaEZERETthiRJ7gwReXCgS+PUzTi95Le2EQBkJGlP8WrJlDXNlJluvqZntkTFqiYKXuObPl5AsFMqYxE4ck2JNFsdMNucwbKLX18Tsf1LABpsyiCcVTYNduuxKpRUm1FvsaOwqg5mm++sQ0crmLqZluAZNPvPDeMgSRJ0AUyhTE2Ii0imKRERtQ4MmhEREVG7JJ+e+c71p2LagE74ZO6EgLbNSGp9N83yzLpKVZfA3w5rF/pvBXXdvYr02Ic/vhR1lthMVT1cZsK+4loAwK6i6ojtV5I8M9csGsFBq82Ba/+9Hre+t9kdPNQKVLaCmJlHwGt09wz3190yEzE8L939eOaQHGSlxCs6ihIRUfvi/6NUIiIiojZobK8O7q/75aRi0XWnBrxtc0zPjDR53a9nv9sT0DalNQ3+V9LgrzaUloOltSEdy/dAIrcri82BLUcr0beT7y6IwTRw8DUFWM5ss8NoiHwNLUmSPKa6Wm2e4xcAjlU4u28WVtbjlG6ZmvvbcKQi4mOMtH45KYrHAzunub+O1+vw/GUjIAH4ZnsxbpjUC8nxvqdqExFR2xbTTLNVq1bhvPPOQ5cuXSBJEj777DPF80IIPPbYY+jSpQsSExMxdepU7NwZ2aK4RERE1D4N6ZKOL26biHUPnel1ndE9tIMDrXF6pi2ENKDSat8dPkMah0adLgB49PPQ/8YLLatM1hihNafUhcFf90yt9Xw1CpB3ZG2prp3QU/E4Ma4pGGnQ65CTloB+Oam486x+SDEaGDAjImrnYho0M5lMGDFiBF599VXN5xcuXIgXX3wRr776KtavX4/c3FxMnz4dNTU1zTxSIiIiaouG52UgOzXB6/MPnTtIc3m6anrmkC5pmuu1JKHUm6qoDz1o5nA48OQ3u/H4l8pgmN3hrNOlbsRglVXz99bZ1Jv1+drTS1s6u+w1hxOa2Vtcg4Jyk/txoN/pslqzxzKt6ZmK522ttynApH5ZiNPr8PdrRruXJciCZkO7prNeGRERKcR0euasWbMwa9YszeeEEHjppZfw0EMP4eKLLwYAvPPOO8jJycH777+Pm2++WXM7s9kMs7npD4Dq6sjVfSAiIqL2ZVT3TCz742QcLjPhpv9sdC/PlGWavXX1aJw0mfHgkh0e23dOT0BRVWhTHCMtlEyz8trQgmYSJBRVmbHlaGUQ2zQJdqzqxgCtha8Et9KaBnTOCKyjo8XmgEVjWqU/S3edwNieymxK1zTMpjEKbCpomnbpL6jWkg3r6qxXNmNIrntZVkrryxolIqLm02IbARw+fBjFxcWYMWOGe5nRaMSUKVOwZo33rkFPP/000tPT3f+6devWHMMlIiKiNqpfTipmDMnF4lubmgTIs1H6ZCcj3qD9J5WjBU370wfQGTCSgp3y2NzT4JrjW2N3CBQ3Bk2rG6xBZWmV1lo8svH8sdoduPuTrfhxT0lA62udnx+sL1A8Pv+1XzBHFjBuzZlml41pui94/PwhOHNgNib2zYrhiIiIqKVrsUGz4uJiAEBOTo5ieU5Ojvs5LQ888ACqqqrc/44ePRrVcRIREVH7IA8WJMc3TekyGvQ4vW8nzW3URdZjKZSgWTij97VtVb3VY2pga6kcJSACDvDNXrQOpz39I345UIYjZXU4UKJsdhCpOKFofLfXHDyJvScCL2MSSuCwNWea9cpKdn/9h/E9MP+s/kjjdEwiIvKhxQbNXNR/lAjh+w8Vo9GItLQ0xT8iIiKicLnqlmUmxSFBFjSTJCA3PQFvyeokufgqmt4aFKqm6gWqpKYBNof34EppjRknojylUoQV8ouM1fvLAABvr8kH4CeIqvrz1mS24ZWf9qOgvM69zF9dumAzGy12Bw6UBFcr+FiFczxSDMOct03rG/Q2eZnKqa6SJGFg51RkpxojNSwiImqDWmzQLDfXWWtAnVVWUlLikX1GREREFG2pCXHY9tgMrH3wTCTHN5WFzUpx3nR3TvdsKKDVibA1CTXwtLeoBmZrcK9dJ/tQ9J+rD4V03Girt9hD2s4hBKrqrR5TVk1mGw6W1mpu886v+Vi0Jh/z3t/kXnaozKS5LgCYbfagw1g2uwOWIBte/m/j8SCPEnkGjazJjsm+a5MN7uz5QXqcXsfumERE5FOLDZr16tULubm5WLZsmXuZxWLBypUrMWHCBB9bEhEREUVHWkIcjAY99DoJ6x86C789eKa7+16dRkDFV/2nMwdmR22cWpozNHCkvA6HfQR4NDUO0GJz4PtdJyIyjki/5oXf7Q1oPavdgVvfa6oD9uPuElz9r9/w6vIDivWOVtRjQ36FenMAwMESz2Cat6DdD7tO4P7F2/HXH/YFND4XgdACozsLq4LeJpK0phonyrI/5a6f2AvXju+Bpy4eFu1hERFRGxTT7pm1tbU4cKDpj4fDhw9jy5Yt6NChA7p374758+fjqaeeQr9+/dCvXz889dRTSEpKwpVXXhnDURMREREBnVTTunp0TPJYx1cXyH/8YQx6P/hNxMcVSfs1AjeBqK634hVVgMjFW+CnufN9Qsmhq6y3BrTesl0n8M12zxq8S3edQE1D0z6+2laEr7YVYWBuqs/x2ewCtWazogGF3Ms/Od/rYEvoCSFg9zGN1pvCyvqI1WMLhVZ2mDz7EwDOGpQNvU7CQ+cOavYmGERE1HbENGi2YcMGTJs2zf14wYIFAIBrr70Wb7/9Nu69917U19fj1ltvRUVFBcaNG4elS5ciNdXzDwsiIiKiWOqcnojv509GZlIcTn3qx1gPJyIC7cKo5qu01ocbjuLcYV28Pt9cU1rl9b8stsjWP6vyEVzLL6vzWLb9uGfmlnwfNodAhcmKFGNk/3S32AROVJn9r6hSXNUQ086w8hDYH8/qj4ykOCzZrJw2+s9rxzbvoIiIqE2KadBs6tSpPtuRS5KExx57DI899ljzDYqIiIgoRAM0MoZaiuYMcSxac9jrc/tP1KKqnwXdoC7M7vw/2ML0kfDKT/tD2i4/2CmoAN5YedBj2YfrCmAyKzPwzD6m9kZKg9WOwyeDfw1AaJ03I0WeaHbT5N5IjNcjLzMRN7yzIXaDIiKiNqnF1jQjIiIiaq0m9u0IALjh9F4xHkkTezN28jT5KpgvAQ0aTQIi3Y2xrNaCb3d4TpHUEmpG3dTnVwS9zTfbizyW7SisDmjb1ftLgz6eLydNFizbHcJrl4Lv1BlJWtMtzxyUg5/vm4aR3TNw8+TeMRgVERG1RTHNNCMiIiJqi968ejSKqxrQLycV//pZO+tKp5Pw18tHwGS24+HPdkR9TBV1lqgfIyACqG2wwqYK4kmSs5tjuSmwumGRGEdrcdLknEL5tx+168TFQllt855PT188DA8s3g4A6JudorlOXmYSltw6sTmHRUREbRwzzYiIiIgiLDUhDv1y/E/VvGhkHq4+rYdi2aLZylpMD587KCJjaikxoiPldag127G7SJldJcFZu6uyvoUE90JUbrJEPAvr7k+2RnR/4TpcGtqUznCMyMtwf90zK7nZj09ERO0TM82IiIiImtnFo7p6fW7awGz312kJBgzMTWuOITWrHcer8PaafMUyAaC8ztys00ijoaLOEnQXS3+sLew9+WD90WY/pkHfNCVT3ilTxxQAIiKKIgbNiIiIiJrBZaPz8MnGY7jjzH64/Yy+AW2TmhCHxHh9lEfW/DYcqfBYtuN4FRb9nI8pA7I1toi8lhWG8q/C1Loz8MKRmRSnqGOWlRKPR343GAlxOhgNbe/ng4iIWg4GzYiIiIii6B9/GIOC8jrccHovPHnRMMQbPFNjZk/oibfX5OMmVQHzIV3SMKp7RjONNLZMFjvWHi7H2sPlzXpcX53cW5I/f70r1kOIiMFd0rBLo/HB+N4d8euhk5rbfH3HJOSmJWBk9wykGA3Q66QW1WSDiIjaLgbNiIiIiKJo+uAc99daATMAeOR3g3HZmDwMapyK+b+54/H+bwV48NxBkKTIdpWkJieqG3DtonUR36/V7tkdlJwm9O6oGTTrlZXsDpqdN7wzvtzW1GW0S0YiAGDxLRMAgD8TRETUbBg0IyIiIooxvU7CkC7p7sdjenbAmJ4dYjiitk9A4OUf96OiLrhunYfLavHC0r144BztBg33L45+J9S26OYpvdEhOR79c1IxfXCOImjmwmAZERE1NwbNiIiIiKjdqbfYUR5CnbA7P3R2stx+bG2kh9QueIt79eiYjDvP6odykwUGvYQBOanYe6IG2anG5h0gERGRDINmRERERNTuhNuRsjzIDDVy0nrX/3PDqQCAOL0OOWkJAIB/zR6Dt1YewvWsXUZERDHEoBkRERERtUsWG2uPNTd1otlXt5+OoV3TPdbLy0zCXy4c2jyDIiIi8kK7Gi0RERERtTinss5ZRG08UhHrIbQrz10yXPH45sm9NQNmRERELQWDZkREREStxPWn98RpvZoCZxeN7Op3m15Zye7pb6RUWc8pltFy4SldFI9P6ZaB0/tlITUhzr3MWzMFIiKiloJBMyIiIqJWQgigX06q+/GzlwzH2gfO9LnNhzedhtHdM6M9NCKF6yf2wrZHZ2By/064fmIvLL5lAjpnJOKsQTkAgNQEVokhIqKWj7+tiIiIiFqJhDg97ps1EOvzy3HhyK6IN+iQm57gc5uctATUW+zNNEIiYEBOCiRJQpxeh3evV2Y5Du6Shh8WTEFOGrtiEhFRy8egGREREVELd9f0/thZWI3J/TtBr5Pw3fzJmut165CIo+X1zTw6IqXc9ESfz/fNTmmmkRAREYWHQTMiIiKiFu72M/sFtJ7k0ZvQyaDXXk4UDWmJcf5XIiIiagVY04yIiIiolbttWl8AwKPnDcYdGgG2OD3/5KPI6tMp2etzc6f0RnaakcFaIiJq9ZhpRkRERNTK3T1zAG6e0hupCXE4c1AODpbU4uvtRbEeFrVhBl1TIPaHBVNw1osrcfVp3XH/rEFIMfIWg4iI2gb+RiMiIiJqA1ITmqbEPXHhUNjsDlwyJs9jvcGd07CrqLo5h0YtWO+sZBwqMwW9XWZS0/nWo2MS8p85N5LDIiIiahGYq09ERETUxmQmx+O1q0ZhxuBcjedYb4qaJBv1IW33+AVD0bdTCl64bASn/xIRUZvF33BEREREbZBBFch46fJTMKhzGp6+aHiMRkQtkUEXWt2x3p2S8d6ccTj/lC4RHhEREVHLwemZRERERO3AhSO74sKRXWM9DGphEuODvx248JQuiNPrkJOWEIURERERtRzMNCMiIiJqZz666TT8+YIhsR4GtQBJ8drTMx88Z5Di8f4nZ+HOM/vhhwVT8NIVI5tjaERERDHHoBkRERFROzOud0f8YXxPr8/PnuD9OWpbvE3O7NspBXMm9QIA/GF8D8Tpdfjj9P7om53SfIMjIiKKMU7PJCIiImqn/nvDOPxj9SHYHQLpSXH4elsRAOC03h3w9pr82A6OmoVeVdMsMU6Pi0Z1xageGRjXuwMeOndwjEZGREQUewyaEREREbVTp/fLwun9sgAAmwsq3EGztAR22Gwv9LqmiSfZqUb8eNcUpPL7T0REBIDTM4mIiIgIwCndMnDbtL5YeOlwnNa7I2YNzUU/TsVr8yb2zXJ//fdrRjNgRkREJCMJIUSsBxFN1dXVSE9PR1VVFdLS0mI9HCIiIqJWo7rBigaLHac+9WOsh0JR8NLlp+Dc4Z1xoKQWXTMTmWFIRETtRqCxIk7PJCIiIiJNaQlxDKS0YReO7AoAGNSZHywTERFp4fRMIiIiIvLpld+PxMjuGe7HPTomxW4wRERERM2EQTMiIiIi8um8EV2w5NaJ7sd6SfKxNrVUT1w4NNZDICIialU4PZOIiIiIAtK7UzIOlZpw7vDOeOWnA7EeDgVhy5+mIyMpHmcPzcW3O4px/ogusR4SERFRi8egGREREREF5H9zJ2DtoZM4a1AO+man4M4Pt8R6SBSgjKR4AEBWihHXnNYjxqMhIiJqHRg0IyIiIqKAdEiOxznDOgMALjilKw6WmvDyj/tjPCoCgHG9OuCV34/ElqOVuOk/GwEA/7hmNI5X1qNPdkqMR0dERNQ6MWhGRERERCGZf2Y/nDEwG7f8dyMS4/S4+rQe+PNXu2I9rHZpVPdMZKclYPrgHLx25SgUV9Xj9H6dkBivj/XQiIiIWi0GzYiIiIgoJDqdhFO6ZeDn+84AAOh1Ej5cX4B9J2pjPLL2JzfdCACQJAlnDMzGgRJ+D4iIiMLF7plEREREFBa9ToJe5+yo+fpVo2M8mvYpTt+UUWbQS8hOM8KgZ5dTIiKicDBoRkREREQR0zc7BeseOhN/PKt/rIfSrgzPS3d/HafXISctAXF6/qlPREQUDv4mJSIiIqKIyk5NwJ1n9Yv1MNqVQZ3TYj0EIiKiNodBMyIiIiKKikXXjY31ENqcP57VH7dN6wudBAzISXUvd02PJSIioshhIwAiIiIiioppA7Kx7qEzsTG/Ah+sP4pV+0pjPaRWz5XBd/fMARBC4MEl29EpxRjjUREREbVNzDQjIiIioqjJTk3ArGGd8Y4q6+y7+ZMAAGN7ZsZiWK1K14xEAMCV47orlkuShKcvHo4FMwbEYlhERERtHjPNiIiIiCjqJEnCF7dNxA3vbMDCS4djYG4a8p85F6v3l+Kaf60DAORlJuJYRX2MR9ryrLxnKirrrchiRhkREVGzYqYZERERETWL4XkZWP/QWZg2INu9TIim53++74wYjKrlM+h1DJgRERHFAINmRERERBQzwv8q7VpmUlysh0BERNRucXomEREREcVMcrw+1kNosbJSjHjrmlGxHgYREVG7xaAZEREREcXM6B6ZuHJcd/TOSo71UFqc9Q+dCUmSYj0MIiKidovTM4mIiIgoZiRJwlMXDcONk3oDAG6a3Nv93DnDcvH6Ve0v0+ovFwzBugcZMCMiIoo1ZpoRERERUYtx/9kDcenoPPTomASjwTl18+JRXbF40/EYjyx6vrr9dLyx4iD+ML4HzDYHJvfvFOshERERERg0IyIiIqIWRKeT0D8nNdbDaDYGnYShXdPxWjvMqCMiImrpOD2TiIiIiFq0cb06xHoIUZOawM+wiYiIWir+liYiIiKiFu3S0d1gNOjRKdWIhd/twdZjVbEeUsQ8fO7gWA+BiIiIvJCEECLWg4im6upqpKeno6qqCmlpabEeDhERERGFoazWjDFP/BDrYYTt8NPn4KTJgqwUY6yHQkRE1O4EGitiphkRERERtRrJ8a37z9eEOB0eOncwJEliwIyIiKiFa91/dRARERFRu5IYr8e7158KhxCYvWh9rIcTkNQEA+47eyBO6ZaBoV3TYz0cIiIiChCDZkRERETUqkzu3wkAsO2xGRj+2NIYj8a/DQ+fBaNBH+thEBERUZAYNCMiIiKiViktIQ6Hnz4HkiSh5/1fx3o4Hu6ZOQCjumcyYEZERNRKMWhGRERERK2WJEkAgJ/vm4anv9mDr7cXxXhEwPtzxmFCn6xYD4OIiIjCxKAZEREREbV6eZlJeO2qUZj4WwE+Wl+Arceqmn0Mb149CgdLTRjfu2OzH5uIiIgij0EzIiIiImozrhzXHVeO6w6b3YGPNxzDg0u2N8tx9z85C3F6XbMci4iIiJoHf7MTERERUZtj0Otw5bjuOPz0OVE9zodzxmHXn2cyYEZERNQG8bc7EREREbVZkiTh0FPn4P5ZA9EvOyVi+81KiUf+M+fitD5ZSIrn5A0iIqK2qFUEzV5//XX06tULCQkJGD16NFavXh3rIRERERFRK6HTSZg7pQ+WLZiC924cF/b+Ppk7Hhsenh6BkREREVFL1uKDZh999BHmz5+Phx56CJs3b8akSZMwa9YsFBQUxHpoRERERNTKTOybhfxnzsW6h84IetuHzx2A/GfOxdieHaIwMiIiImppJCGEiPUgfBk3bhxGjRqFN954w71s0KBBuPDCC/H000/73b66uhrp6emoqqpCWlpaNIdKRERERK2IwyHQ76FvYffz5/DFIzvj/lmDkJ2W2EwjIyIiomgKNFbUogswWCwWbNy4Effff79i+YwZM7BmzRrNbcxmM8xms/txdXV1VMdIRERERK2TTifhwFOzsHxvCb7eVoRPNx33WOfL2yZiWF5G8w+OiIiIYq5FB83Kyspgt9uRk5OjWJ6Tk4Pi4mLNbZ5++mk8/vjjzTE8IiIiImrlJEnCGQNzcMbAHLzwf6egpLoeaYnx7ucT4vQxHB0RERHFUouvaQY4/5iRE0J4LHN54IEHUFVV5f539OjR5hgiEREREbUB2WmJSIjTu/8RERFR+9WiM82ysrKg1+s9sspKSko8ss9cjEYjjEZjcwyPiIiIiIiIiIjaqBadaRYfH4/Ro0dj2bJliuXLli3DhAkTYjQqIiIiIiIiIiJq61p0phkALFiwANdccw3GjBmD8ePH4+9//zsKCgowd+7cWA+NiIiIiIiIiIjaqBYfNLv88stx8uRJ/PnPf0ZRURGGDh2Kb775Bj169Ij10IiIiIiIiIiIqI2ShBAi1oOIpurqaqSnp6OqqgppaWmxHg4REREREREREcVQoLGiFl3TjIiIiIiIiIiIKBYYNCMiIiIiIiIiIlJh0IyIiIiIiIiIiEiFQTMiIiIiIiIiIiIVBs2IiIiIiIiIiIhUGDQjIiIiIiIiIiJSYdCMiIiIiIiIiIhIhUEzIiIiIiIiIiIiFQbNiIiIiIiIiIiIVBg0IyIiIiIiIiIiUmHQjIiIiIiIiIiISIVBMyIiIiIiIiIiIhUGzYiIiIiIiIiIiFQYNCMiIiIiIiIiIlJh0IyIiIiIiIiIiEiFQTMiIiIiIiIiIiIVQ6wHEG1CCABAdXV1jEdCRERERERERESx5ooRuWJG3rT5oFlNTQ0AoFu3bjEeCRERERERERERtRQ1NTVIT0/3+rwk/IXVWjmHw4HCwkKkpqZCkqRYD4cirLq6Gt26dcPRo0eRlpYW6+FQC8HzgrTwvCA1nhOkhecFqfGcIC08L0gLz4vWQwiBmpoadOnSBTqd98plbT7TTKfTIS8vL9bDoChLS0vjRYk88LwgLTwvSI3nBGnheUFqPCdIC88L0sLzonXwlWHmwkYAREREREREREREKgyaERERERERERERqTBoRq2a0WjEo48+CqPRGOuhUAvC84K08LwgNZ4TpIXnBanxnCAtPC9IC8+LtqfNNwIgIiIiIiIiIiIKFjPNiIiIiIiIiIiIVBg0IyIiIiIiIiIiUmHQjIiIiIiIiIiISIVBMyIiIiIiIiIiIhUGzajVM5vNOOWUUyBJErZs2aJ4rqCgAOeddx6Sk5ORlZWFO+64AxaLJTYDpWZx/vnno3v37khISEDnzp1xzTXXoLCwULEOz4v2Iz8/HzfccAN69eqFxMRE9OnTB48++qjH95vnRPvz5JNPYsKECUhKSkJGRobmOjwv2p/XX38dvXr1QkJCAkaPHo3Vq1fHekjUjFatWoXzzjsPXbp0gSRJ+OyzzxTPCyHw2GOPoUuXLkhMTMTUqVOxc+fO2AyWmsXTTz+NsWPHIjU1FdnZ2bjwwguxd+9exTo8L9qfN954A8OHD0daWhrS0tIwfvx4fPvtt+7neU60LQyaUat37733okuXLh7L7XY7zj33XJhMJvz888/48MMP8emnn+Kuu+6KwSipuUybNg0ff/wx9u7di08//RQHDx7EpZde6n6e50X7smfPHjgcDrz11lvYuXMn/vrXv+LNN9/Egw8+6F6H50T7ZLFYcNlll+GWW27RfJ7nRfvz0UcfYf78+XjooYewefNmTJo0CbNmzUJBQUGsh0bNxGQyYcSIEXj11Vc1n1+4cCFefPFFvPrqq1i/fj1yc3Mxffp01NTUNPNIqbmsXLkS8+bNw9q1a7Fs2TLYbDbMmDEDJpPJvQ7Pi/YnLy8PzzzzDDZs2IANGzbgjDPOwAUXXOAOjPGcaGMEUSv2zTffiIEDB4qdO3cKAGLz5s2K53Q6nTh+/Lh72QcffCCMRqOoqqqKwWgpFj7//HMhSZKwWCxCCJ4XJMTChQtFr1693I95TrRvixYtEunp6R7LeV60P6eeeqqYO3euYtnAgQPF/fffH6MRUSwBEEuWLHE/djgcIjc3VzzzzDPuZQ0NDSI9PV28+eabMRghxUJJSYkAIFauXCmE4HlBTTIzM8U///lPnhNtEDPNqNU6ceIE5syZg//85z9ISkryeP7XX3/F0KFDFVloM2fOhNlsxsaNG5tzqBQj5eXleO+99zBhwgTExcUB4HlBQFVVFTp06OB+zHOCtPC8aF8sFgs2btyIGTNmKJbPmDEDa9asidGoqCU5fPgwiouLFeeI0WjElClTeI60I1VVVQDg/juC5wXZ7XZ8+OGHMJlMGD9+PM+JNohBM2qVhBCYPXs25s6dizFjxmiuU1xcjJycHMWyzMxMxMfHo7i4uDmGSTFy3333ITk5GR07dkRBQQE+//xz93M8L9q3gwcP4pVXXsHcuXPdy3hOkBaeF+1LWVkZ7Ha7x/c8JyeH328CAPd5wHOk/RJCYMGCBTj99NMxdOhQADwv4ZabngAAFHVJREFU2rPt27cjJSUFRqMRc+fOxZIlSzB48GCeE20Qg2bUojz22GOQJMnnvw0bNuCVV15BdXU1HnjgAZ/7kyTJY5kQQnM5tVyBnhcu99xzDzZv3oylS5dCr9fjD3/4A4QQ7ud5XrR+wZ4TAFBYWIizzz4bl112GW688UbFczwn2oZQzgtfeF60P+rvLb/fpMZzpP267bbbsG3bNnzwwQcez/G8aH8GDBiALVu2YO3atbjllltw7bXXYteuXe7neU60HYZYD4BI7rbbbsMVV1zhc52ePXviiSeewNq1a2E0GhXPjRkzBldddRXeeecd5Obm4rffflM8X1FRAavV6hH5p5Yt0PPCJSsrC1lZWejfvz8GDRqEbt26Ye3atRg/fjzPizYi2HOisLAQ06ZNw/jx4/H3v/9dsR7PibYj2PPCF54X7UtWVhb0er1HFkBJSQm/3wTAeU0AnJlFnTt3di/nOdI+3H777fjiiy+watUq5OXluZfzvGi/4uPj0bdvXwDOe9D169fjb3/7G+677z4APCfaEgbNqEVxBTv8efnll/HEE0+4HxcWFmLmzJn46KOPMG7cOADA+PHj8eSTT6KoqMh9wVq6dCmMRiNGjx4dnRdAURHoeaHFlWFmNpsB8LxoK4I5J44fP45p06Zh9OjRWLRoEXQ6ZZI1z4m2I5xrhRrPi/YlPj4eo0ePxrJly3DRRRe5ly9btgwXXHBBDEdGLUWvXr2Qm5uLZcuWYeTIkQCctfBWrlyJZ599Nsajo2gRQuD222/HkiVLsGLFCvTq1UvxPM8LchFCwGw285xogxg0o1ape/fuiscpKSkAgD59+rg//ZkxYwYGDx6Ma665Bs899xzKy8tx9913Y86cOUhLS2v2MVP0rVu3DuvWrcPpp5+OzMxMHDp0CH/605/Qp08fjB8/HgDPi/amsLAQU6dORffu3fH888+jtLTU/Zzr02GeE+1TQUEBysvLUVBQALvdji1btgAA+vbti5SUFJ4X7dCCBQtwzTXXYMyYMe6s1IKCAkUNRGrbamtrceDAAffjw4cPY8uWLejQoQO6d++O+fPn46mnnkK/fv3Qr18/PPXUU0hKSsKVV14Zw1FTNM2bNw/vv/8+Pv/8c6SmprqzUdPT05GYmAhJknhetEMPPvggZs2ahW7duqGmpgYffvghVqxYge+++47nRFsUi5adRJF2+PBhAUBs3rxZsfzIkSPi3HPPFYmJiaJDhw7itttuEw0NDbEZJEXdtm3bxLRp00SHDh2E0WgUPXv2FHPnzhXHjh1TrMfzov1YtGiRAKD5T47nRPtz7bXXap4Xy5cvd6/D86L9ee2110SPHj1EfHy8GDVqlFi5cmWsh0TNaPny5ZrXhWuvvVYIIYTD4RCPPvqoyM3NFUajUUyePFls3749toOmqPL2N8SiRYvc6/C8aH+uv/569++KTp06iTPPPFMsXbrU/TzPibZFEkJWHZuIiIiIiIiIiIjYPZOIiIiIiIiIiEiNQTMiIiIiIiIiIiIVBs2IiIiIiIiIiIhUGDQjIiIiIiIiIiJSYdCMiIiIiIiIiIhIhUEzIiIiIiIiIiIiFQbNiIiIiIiIiIiIVBg0IyIiIiIiIiIiUmHQjIiIiNqEt99+GxkZGbEeBgAgPz8fkiRhy5YtLWI/gbBYLOjbty9++eWXqB+rpZo6dSrmz5/fbMe79NJL8eKLLzbb8YiIiCg4DJoRERFRRJWUlODmm29G9+7dYTQakZubi5kzZ+LXX391ryNJEj777LPYDRLAsGHDcOONN2o+98EHHyAuLg4nTpwIad/dunVDUVERhg4dGvA2s2fPxoUXXhj2fkL197//HT169MDEiRMVy7/66itMnToVqampSEpKwtixY/H2229HfTyRZrFYsHDhQowYMQJJSUnIysrCxIkTsWjRIlit1piM6U9/+hOefPJJVFdXx+T4RERE5BuDZkRERBRRl1xyCbZu3Yp33nkH+/btwxdffIGpU6eivLw81kNTuOGGG/Dxxx+jrq7O47l///vf+N3vfoecnJyg92uxWKDX65GbmwuDwRDWGCO1n0C88sorHkHEV155BRdccAEmTJiA3377Ddu2bcMVV1yBuXPn4u677476mCLFYrFg5syZeOaZZ3DTTTdhzZo1WLduHebNm4dXXnkFO3fujMm4hg8fjp49e+K9996LyfGJiIjID0FEREQUIRUVFQKAWLFihdd1evToIQC4//Xo0cP93Ouvvy569+4t4uLiRP/+/cW7777rsf85c+aI7OxsYTQaxZAhQ8SXX34phBBi0aJFIj093b1uWVmZGDt2rDjvvPNEfX29xzjKyspEfHy8ePvttxXLjxw5InQ6nfjyyy/FgQMHxPnnny+ys7NFcnKyGDNmjFi2bJnH6/nLX/4irr32WpGWlib+8Ic/iMOHDwsAYvPmzUIIIWw2m7j++utFz549RUJCgujfv7946aWX3Pt49NFHFe8JALF8+XKP/QghxIoVK8TYsWNFfHy8yM3NFffdd5+wWq3u56dMmSJuv/12cc8994jMzEyRk5MjHn30Ua/fDyGE2Lhxo9DpdKKqqsq9rKCgQMTFxYkFCxZ4rP/yyy8LAGLt2rVCCCGWL18uAIivvvpKDB8+XBiNRnHqqaeKbdu2Kbb75ZdfxKRJk0RCQoLIy8sTt99+u6itrVW8l08++aS47rrrREpKiujWrZt46623fI49EM8++6zQ6XRi06ZNHs9ZLBb3GKZMmSLuvPNO93Pl5eXimmuuERkZGSIxMVGcffbZYt++fe7n8/Pzxe9+9zuRkZEhkpKSxODBg8XXX3/tfn7nzp1i1qxZIjk5WWRnZ4urr75alJaWKo7/2GOPiUmTJoX9GomIiCjymGlGREREEZOSkoKUlBR89tlnMJvNmuusX78eALBo0SIUFRW5Hy9ZsgR33nkn7rrrLuzYsQM333wzrrvuOixfvhwA4HA4MGvWLKxZswb//e9/sWvXLjzzzDPQ6/Uexzh27BgmTZqEgQMHYvHixUhISPBYp2PHjrjggguwaNEixfJFixYhJycHs2bNQm1tLc455xz88MMP2Lx5M2bOnInzzjsPBQUFim2ee+45DB06FBs3bsQjjzzicSyHw4G8vDx8/PHH2LVrF/70pz/hwQcfxMcffwwAuPvuu/F///d/OPvss1FUVISioiJMmDDBYz/Hjx/HOeecg7Fjx2Lr1q1444038K9//QtPPPGEYr133nkHycnJ+O2337Bw4UL8+c9/xrJlyzS/HwCwatUq9O/fH2lpae5l//vf/2C1WjUzym6++WakpKTggw8+UCy/55578Pzzz2P9+vXIzs7G+eef7576uH37dsycORMXX3wxtm3bho8++gg///wzbrvtNsU+XnjhBYwZMwabN2/GrbfeiltuuQV79uzxOvZAvPfeezjrrLMwcuRIj+fi4uKQnJysud3s2bOxYcMGfPHFF/j1118hhMA555zjfk3z5s2D2WzGqlWrsH37djz77LNISUkBABQVFWHKlCk45ZRTsGHDBnz33Xc4ceIE/u///k9xjFNPPRXr1q3z+vNCREREMRTrqB0RERG1Lf/73/9EZmamSEhIEBMmTBAPPPCA2Lp1q2IdAGLJkiWKZRMmTBBz5sxRLLvsssvEOeecI4QQ4vvvvxc6nU7s3btX87iuTLO9e/eK7t27i9tvv104HA6fY/3222+FJEni4MGDQgghHA6H6Nmzp3jggQe8bjN48GDxyiuvuB/36NFDXHjhhYp1tDLE1G699VZxySWXuB9fe+214oILLvC5nwcffFAMGDBA8bpee+01kZKSIux2uxDCmS11+umnK/YzduxYcd9993kdy5133inOOOMMxbK5c+cqMvfUhg8fLmbNmiWEaMo0+/DDD93Pnzx5UiQmJoqPPvpICCHENddcI2666SbFPlavXi10Op07E7BHjx7i6quvdj/vcDhEdna2eOONN7yOIxCJiYnijjvu8LuePNNs3759AoD45Zdf3M+XlZWJxMRE8fHHHwshhBg2bJh47LHHNPf1yCOPiBkzZiiWHT16VABQnMNbt24VAER+fn6wL4uIiIiijJlmREREFFGXXHIJCgsL8cUXX2DmzJlYsWIFRo0a5bd4/O7duz2K0E+cOBG7d+8GAGzZsgV5eXno37+/133U19fj9NNPx4UXXoiXX34ZkiT5POaMGTOQl5fnzjb76aefkJ+fj+uuuw4AYDKZcO+992Lw4MHIyMhASkoK9uzZ45FpNmbMGJ/HAYA333wTY8aMQadOnZCSkoJ//OMfHvvxZ/fu3Rg/frzidU2cOBG1tbU4duyYe9nw4cMV23Xu3BklJSVe91tfX6+ZjeeLEMLj/R0/frz76w4dOmDAgAHu79/GjRvx9ttvu7MRU1JSMHPmTDgcDhw+fFhz7JIkITc31+vY33vvPcX+Vq9eHfBY/dm9ezcMBgPGjRvnXtaxY0fFa7rjjjvwxBNPYOLEiXj00Uexbds297obN27E8uXLFeMbOHAgAODgwYPu9RITEwFAs7YeERERxRaDZkRERBRxCQkJmD59Ov70pz9hzZo1mD17Nh599FG/26kDG/Jghyu44IvRaMRZZ52Fr7/+WhFE8kan02H27Nl455134HA4sGjRIkyePBn9+vUD4Jxu+Omnn+LJJ5/E6tWrsWXLFgwbNgwWi0WxH2/T+1w+/vhj/PGPf8T111+PpUuXYsuWLbjuuus89uOPVvBHCAFA+d7FxcUp1pEkCQ6Hw+t+s7KyUFFRoVjWv39/VFVVobCw0GN9i8WCQ4cOud8nX1zjcjgcuPnmm7Flyxb3v61bt2L//v3o06dPSGM///zzFfvzFrzs37+/O9AVKNf7qrXc9ZpuvPFGHDp0CNdccw22b9+OMWPG4JVXXnG/3vPOO08xvi1btmD//v2YPHmye3+uBhmdOnUKanxEREQUfQyaERERUdQNHjwYJpPJ/TguLg52u12xzqBBg/Dzzz8rlq1ZswaDBg0C4MxAOnbsGPbt2+f1ODqdDv/5z38wevRonHHGGZoBH7XrrrsOx44dw+LFi7F48WLccMMN7udWr16N2bNn46KLLsKwYcOQm5uL/Pz8QF6ywurVqzFhwgTceuutGDlyJPr27avINgKA+Ph4j/dEbfDgwVizZo0ioLNmzRqkpqaia9euQY/LZeTIkdizZ49iv5dccgkMBgNeeOEFj/XffPNNmEwm/P73v1csX7t2rfvriooK7Nu3z51dNWrUKOzcuRN9+/b1+BcfHx/SuFNTUxX78RZYvfLKK9116dRsNpvi3HQZPHgwbDYbfvvtN/eykydPYt++fe5zEgC6deuGuXPnYvHixbjrrrvwj3/8Q/F6e/bs6fF65UHWHTt2IC8vD1lZWSG9B0RERBQ9DJoRERFRxJw8eRJnnHEG/vvf/2Lbtm04fPgwPvnkEyxcuBAXXHCBe72ePXvixx9/RHFxsTvD6Z577sHbb7+NN998E/v378eLL76IxYsXuwvRT5kyBZMnT8Yll1yCZcuW4fDhw/j222/x3XffKcag1+vx3nvvYcSIETjjjDNQXFzsc8y9evXCGWecgZtuuglxcXG49NJL3c/17dsXixcvdmdFXXnllT4ztrzp27cvNmzYgO+//x779u3DI4884m6AIH9Ptm3bhr1796KsrMxdbF7u1ltvxdGjR3H77bdjz549+Pzzz/Hoo49iwYIF0OlC/7Nu2rRpMJlM2Llzp3tZ9+7dsXDhQrz00kt46KGHsGfPHhw8eBAvvvgi7r33Xtx1112KqYsA8Oc//xk//vgjduzYgdmzZyMrKwsXXnghAOC+++7Dr7/+innz5rkzrr744gvcfvvtIY87UPPnz8fEiRNx5pln4rXXXsPWrVtx6NAhfPzxxxg3bhz279/vsU2/fv1wwQUXYM6cOfj555+xdetWXH311ejatav7XJ4/fz6+//57HD58GJs2bcJPP/3kDqjNmzcP5eXl+P3vf49169bh0KFDWLp0Ka6//npFcHT16tWYMWNG1N8DIiIiCh6DZkRERBQxKSkpGDduHP76179i8uTJGDp0KB555BHMmTMHr776qnu9F154AcuWLUO3bt3cHQ0vvPBC/O1vf8Nzzz2HIUOG4K233sKiRYswdepU93affvopxo4di9///vcYPHgw7r33Xs3sLIPBgA8++ABDhgzBGWec4bOeFwDccMMNqKiowBVXXIGkpCT38r/+9a/IzMzEhAkTcN5552HmzJkYNWpU0O/L3LlzcfHFF+Pyyy/HuHHjcPLkSdx6662KdebMmYMBAwa465798ssvHvvp2rUrvvnmG6xbtw4jRozA3LlzccMNN+Dhhx8OekxyHTt2xMUXX4z33ntPsfyPf/wjlixZgtWrV2PMmDEYOnQo3n//fbzxxht4/vnnPfbzzDPP4M4778To0aNRVFSEL774wp1FNnz4cKxcuRL79+/HpEmTMHLkSDzyyCPo3LlzWGMPhNFoxLJly3DvvffirbfewmmnnYaxY8fi5Zdfxh133IGhQ4dqbrdo0SKMHj0av/vd7zB+/HgIIfDNN9+4p5Da7XbMmzcPgwYNwtlnn40BAwbg9ddfBwB06dIFv/zyC+x2O2bOnImhQ4fizjvvRHp6ujvA2dDQgCVLlmDOnDlRfw+IiIgoeJLwVrCBiIiIiNqN7du346yzzsKBAweQmpoa1LYrVqzAtGnTUFFRgYyMjOgMsA167bXX8Pnnn2Pp0qWxHgoRERFpYKYZEREREWHYsGFYuHBhSDXbKDRxcXHuxgFERETU8jDTjIiIiIjCwkwzIiIiaosYNCMiIiIiIiIiIlLh9EwiIiIiIiIiIiIVBs2IiIiIiIiIiIhUGDQjIiIiIiIiIiJSYdCMiIiIiIiIiIhIhUEzIiIiIiIiIiIiFQbNiIiIiIiIiIiIVBg0IyIiIiIiIiIiUmHQjIiIiIiIiIiISOX/AQpdHqJZHc1XAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Open - Close' and 'High - Low'\n",
+    "create_line_plot(df['Open'] - df['Close'], df['High'] - df['Low'],\n",
+    "                 title='Stock Variation vs Price Variation',\n",
+    "                 x_label='Stock Variation (Open - Close)',\n",
+    "                 y_label='Price Variation (High - Low)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PbUZdfH4Dru0"
+   },
+   "source": [
+    "#### This infers for stable or less price difference volume of stock trading is higher\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the effect of price differences on volume"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "id": "NAT3Sy3hC2a3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JypyaQoECABjXYuMiddYn8gdc/SQ+IVIzZ5/ekvYAgPHptO5EGWxZraG4/fbbtWbNGmVmZqq1tVVPP/203nrrLb388suyWCy6+eabtXbtWuXl5SkvL09r166Vy+XStddee1qvCwAABpc7O1O7NxTJ29N3SS9PdqLScpJClAoAMBTdXV61NrXLZrMqNjH6tP9uA4DJYvbcTDmcdm3bckSVFU2SpPDwMBXMSte8BdmK54IBAJiUhlyiXHXVVX3e7+rq0le/+lVFRkb2Of6Xv/xlyC9eXV2t6667TpWVlXK73Zo7d65efvllnX/++ZKkW265RZ2dnbrpppvU2NiopUuX6tVXX1V0NBtVAgAwmtJykrTmCyv1wWu7VVVar7Awm6bNzdKic2eNyU3lx4LOzh5VVjSps7NH4Y4wpabGKiqKjRIBBI/fH1DR7jJte6dQddUtCguzKbcgTQtW5CklIz7U8QBMAF2dPeru8soRYZfTGR7qOCPOYrFoen6qcnKTVFfXKr8vIFekg/IEACY5i2EYxlAmfvGLXxzSEz722GOnFWiktbS0yO12q7m5WTExMaGOAwDAuOLt8aq5rk22MKtik2K4mtmEYRgqOlil9947qMbG9t7j0VERWnbmNM2cmS5bGMuKARh9+7aWaN2zW/Txv/AioyP0yRtWKsHjDk0wAONec1OHivYe054dR9XZ0aOISIdmz8/S9FnpihkHS70CAPBxw+kNhnwnylgrRwAAwOizh9sH3R8FUlFRtV58cYcCgb5nLVvbOvXaa7sVMAzNm5cVonQAJouO9m598Ob+fgWKJLW3durQ/gpKFACnpLmpQ6/8dasqyxt7j/X0+PTem/t1pLhGF16xgCIFADChcVkkAADAKerq8mrThqJ+BcqJNm0oUktLZxBTAZiMGmtb1HLC3XAfd2jvsSCmATCRHNxb3qdAOVFFWb0O7qsIciIAAIKLEgUAAOAUVVc1q66+ddA57R3dqqgwP/EAACPFah38T7swuy1ISQBMJJ0d3dq9vXTQOXu2lairqydIiQAACD5KFAAAgFPU2eUd0ryuIc4DgFOV6IlRcvrAyy9On5sZxDQTQ2NrhyrrW1Td2Kpury/UcYCQ6O7yqauze9A5nZ096uF3HQDABDbkPVEAAADQl9M5tF+lnA77KCcBMNnZw+1aeu5MvfTUJvm8/j5jnsx45RakhSjZ+HO0ulH7S6u1+0hVb3mSGh+t+dPSlZeepBiXI8QJgeBxOMPkjHDI29Mx4BxnRLjCnfyuM1b5/QEFAgHZ7ZwCBIBTxb+gAACMQR1tXSrZV67SoirZ7WGaUpCm7BlpCuOPnzHF44lVYkL0oEt6RbocShvk6nAAGCk5+am6/AsrdWB7iUoP1chuD1PBGdnKm5Oh6Fg2fR6KnYcq9OIHB+QPBPocr2xoVeUHB7Q3uUqXLp2p+Bg+n5gcIlwOzZ6fpY3rDww4Z878bDmd4UFMhaFob+1S0Y4S7d9yRD3dXuXOylD+GVOUnB4f6mgYA+qrmtRc3yany6GUzHjZwlj2ExiMxTCMgXdCnQBaWlrkdrvV3NysmJiYUMcBAOCkOts6te6Z93VoT1mf40vOn6OlF8496br3CK6DhZV68cUdA24uf+65szRvfnaQUwGY7Lq7emS1WbnyeBgOVdTpmfU75R/g3/Pj8tITdeWK2Qrnc4tJormpXS8/t01Vx/rv8ZaaEacLr1goN0XtmNLd6dW6/9uk4l19/56IjInQZf+8iiJlEuvu7NHW9fu1850D8nb7ZLFalDU9VWdddobiU9yhjgcE1XB6A87CAAAwxhwtrOxXoEjS1jf2qqasIQSJMJi86R5dtGaeYmMj+xyPinTqvPNma/Zs9iEAEHwOZzgFyjAYhqEdhypOWqBIUtGxOpXXNQchFTA2uGMjddEnz9Dy1TMU7XbJHm5TjNul5asLKFDGqGNHavoVKJLU3tKpwu0lwQ+EMePA1iPa8voeebs/XK7SCBg6eqBCbz+/td9yoAA+wm/VAACMMZVHak2P+/0B1VU0yJOdGOREGIzFYtGMGWnKykpQZUWTOrt65Ai3KzU1VlHRzlDHAwAMQVVjqw6Wm//8NVNUXqfc1IRRTASMLe7YSC1eMV2zF2Sru9snp9MuZwRLeI1VdRX97xo67vCecp154TyF2Vm+abLp7vRq14aDpmNlB6tUXVav9NzkIKcCxgdKFAAAxhj7IJuQ28JP/0d3Z2ePyo81qKa2VZKhhPhoZWTEKyqSjXJPh8vl0NRpKaGOAQA4BW0d3UO6C+W4qsaWUUwDjF0RLociXPzOONYNVpCEO8NltVmCmGbsMgxDfn9AYZNkP5Cerh51tHaZjhmGoa6O7iAnAsYPShQAAMaYrOmp2rZ+n4yPncyJiHQodUrSaT33sWONeu31PWpobO9zPDrKqfPPm6Upp/n8AACMRxbr8E4osj8ZgLEsdUqibDar/P5Av7GCRTmT/t+w9tYuHTlUo/27ytTe3qW4hCgVzM5Q9tRkOQa5oG28i4hyKjE1VscO1/QbC7PbFBMXafIoABJ7ogAAMOZk5KXorMsWyn7CXSeu6Aid/9nlik2IPuXnra1r1d9e2NGvQJGk1rYu/f3FnaqoHPjWfwAAJqrYSKfCh3ElcmZS7OiFAYDT5MlK1MrLzpDN1ve0X97cLOXNyw5RqrGhpblTLz+/Xete3KmK8gY1N3aopLhGLz23Te+u26fuLm+oI46aMLtN88+aIavJhQOzl01TYlpcCFIB4wN3ogAAMMZYrVYtWFWgrOke1RxrVFiYTZ7sREWf5pVBxcXV6hjkFu2eHp/2769QWiq/PAMAJpdEd5Tm5KRqa1H5SedarRZNS2M/FABjl8Vi0dzleUrOiFdlSa18Xr8S0+KUnpMsR8TEvdNiKHZvL1H50TrTsT07SuVJj9OseVlBThU8ubMzdNF1K7V9/QHVVzbK6XJozorpmrlkqiwWlnkDBkKJAgBAEHh7fOru7JHDaR90z5MTJaTGKWGECo2uLq/27Tt20nmFhVVavChXMTERI/K6AACMF3NzU7W/tFod3YNfhXxGXrrSEtxBSgUAp8ZisSg1O1Gp2YmhjjJmtDR3aM/20kHn7Nlequkz02UfZF+Z8cxisWjanCxl56epo7VL4c4wRUQ6Qx0LGPMoUQAAGEU93T4V7y7Tzo1Fam1sV5TbpTlLpyl/QabCHeFBy+H1+tTd4zvpvB6vT16vPwiJAAAYW9IT3bpy5Rz9dcMetXX2mM5ZmJeuT8zONV0KBQAwtrW3datrgH/fj2tqbFNXR4/s7ol9UZk9PEzuhKhQxwDGDUoUAABG0fZ3C/X+a3t63+/q6NGbz21Rc2ObVlw0N2i3TDscYYqICFfXSdb4dTrtcjj49QAAMDnleOL1hfMX6UhVg3YeqlBLR5dsVqtyUxNUkJWszKRY2YexdwoAYOwIs9tktVoUCBgDzwmzKWyC3oUC4NRxlgQAgFFSX9WsbesPmI7tfO+gps/NVHJ6fFCyhIfbNXd2hta/UzjovFkz0xUVxe3cAIDJKz7apfhol+blpqnb65PNZpXTzp/OADDexSdEKSs3WSXF1QPOmTEnQxGu4K0YAGB84DdBAABGSX1Vs7wDLKHl9wVUV9kUtBJFknJzk7VrT7kaG9tNx6MiHZqRnxq0PAAAjGVhNqvCbJxIA4DT0dHepfKSelWWNUiSUjPjlDElUa4Q7MNhs1m1YHGOyo/WyWeyhHFktFPTC9KDngvA2EeJAgDAKLHYBl+qy2qzBinJh+LiInXpJfP1zjuFOlpaL8P46Db2tLQ4rfpEvpKSYoKaCQAmE6/Xp5bmTsliUUyMU3bubgAATGCN9a16/bntqvhHgSJJOzZJqRlxOu+TZyg+MTrombJyknTpVYv0/rsHVXmsUZJktVqUnZusJSunKymFv4cA9Mdv7QAAjJKUjHhFRDrU2d7dbyzcaVdKRvDuQjkuKTFaV1x+hiorm1RX3ybJUFxcpFI9sQoPHx+/Fvj8ATW3dykQCMgZble0yxHqSAAwqJ4en4oOVmnXjlLV1DRLkjypsZo7N0vTpntkZ+11AMAEYxiG3n+rsE+BclxleaPef+uALvrUoqDtEXmi7KnJSsuKV01Vs7q7fHJFhivZEyurNfhZAIwP4+NsCQAA41BMXKRWXjJfb/x5s/z+QO9xq9WiFRfNVVyI7vqw2azKyIhXRghKnNPR2e3VoYo67TxUobLaZgUChlxOu+ZNTVN+RpLSEt2hjggA/Xi9fm18r0jbth7pc7ziWKMqjjVqcUOblp05TWFsVg4AmEBqq5pVvL9iwPFD+ytVW9ms5LTY4IU6gd0epvTMhJC8NoDxhxIFAIBRVHDGFEW5I3RoT7lqK5oUnxKjvNkZyszzhDrauNLS0aVXNxfqQFltn+NtnT16b0+JNh8o02VnzlRBdkqIEgKAudKjdf0KlBNtfv+QMjLiNSUnKYipAAAYXW2tXfL7AgOO+/0BtbV2KlmxwQsFAKeIEgUAgFGWOTVFmVM5uX+qAgFD63ce6legnKjH59fzG/bK5QxXdkpcENMBk4thGCFZdmM8KzxQedI5RQerKFEAABOKwxEmWSQZA0ywSOEOTksCGB/41woAAIxpFfXN2n246qTzvP6Adh+upEQBRkFjbYuKdpWqaFeZbDar8hdM0bQ5mYqOdYU62pjm9wVUXdV00nmVlSefAwDAeJKcFqu0jHjTPVEkyZMep5R0fm8HMD5QogAAgDHtcEW9AsZAl7D1tbekSktnZinJHTXKqQBzhmGosrlVVS2t8vr9sttsSnVHyxMTPW7v4GiobtYLv31XjXWtvcdqjjXqyIEKXfiZZYqMiQhhurHNarMMaa+T8HD+LAMATCx2e5iWnVOgF5/5QF2d3j5jDqddy8+ZKbudn38Axgf+tQIAAGNabXP7kOd6/QG1d/YoiT3mEQJH6xu1+Wi59lXWyhf4aA1wu82qmZ5kLZ6Soaz42NAFPEUHd5X2KVCOKy+u1tHCSs1cnBuCVOODxWJRwcx0vfP2gUHn5eenBikRAADBk5mTpE9+frkO7jmm4gMVkiFNK0jT9Nnp3IUCYFyhRAEAAGOaddhX74/Pq/0xvh2srtOftu1Wt8/fb8zrD2jnsSoV1tTpnxbO0bSkhBAkPDV+n19FO0sHHD9yoIIS5SRypiZr65Yj6ujoNh2PjHKyHwoAYMJK+ceyXcvOzldrY7vamjpklTGkfdYMw1DAH5BtCHd1AsBookQBAABjWlpijPYerR7SXJfDrtgo5ygnAvqqbG7Rn3fsMS1QTtTl9enP2/bo+jMXyhMzfpacG+wEh9VKaXkyCQlRuuSyBXrlpZ1qaensMxYXH6kLLpyruPjIEKUDAGD0BQIB7d9UpA1/26rujh7Z7DbNWTlDyy5eIIfL0W++3x/Q4d1l2vN+sVrqW5U+NUUFi3OVnpsSgvQAQIkCAADGuBxPguxhh+U9yQlqSVqQl67YKPZnQHAdqK5TZ49vSHPbe7wqqqkbNyWKLcym/AXZ2vjKbtPxnIL0ICcanzIy4/WZa89UeVm9amtaJYtFyckxysyKl8vk5BEAABPJsaIqvfWnTTICH+5z6Pf6tePNvUpIjdXsFTP6zd+7qUhv/uUD6R/bIjbVtapox1FdduPZyphGkQIg+KyhDgAAADCY5LgoLZ+VfdJ5MS6HZmV7gpAI+EhrV7e2HC0f1mM2l5Srrdt8aaexKG9ulpLT4/sdz52Zruzp/Dc3VFFRTs0oSNdZq2borE/kK39GKgUKAGBSqDxS01ugnKhwy+F+x9qa2vX+q7t7C5Tjerq92rPxoAyj//MAwGjjThQAADDmLZ2RpUDA0Ht7ShQw+cMpPtqly5fPVHLc+Li6HxNHY2enWrt6hvWYps4uNXd2Kcox9k+gN7Z0qKS+WSkLMxWRFqOakjq5oyM064wc5RSkKYLl84Bha2vpkN8fULTbJauV6xoxdhmGIcNg6UacPqvN/HvIFtb/38Dm+jZ1tHaazJaOHa5RV0e3IiL5/QNAcFGiAAAQQhVHanVwZ6nKD9coJi5SBQunKKcgXWF2Nk88Ubg9TGfNydXUtAQVH6tTYVmtfIGA4qNdmpPjUXZKvKK5ohshEDC5qnIo/GP8KspAwNCu4gq9ufmguv6xVJnFYlF0llt1FumYr0dTBjghAgyktaFNZUWVam/uVGxSjDLyPJOqiKurbNKezYdVtLtMfn9A6VOSNHfZNO7owphiGIYqa5p16Gidiktq5PcHlJbiVv5UjzJT4xQezmkkDF9arke2MJv8H1ued8biaf3mhjvtstms8vsD/cZc0U7Z+R4EEAL8ywMAQIgcLazUi79/T95/nKBsqG5WyYEKLTlvlpaeN3vQzZwnI6vVooykWGUkxWrVvKnyBwyF2biCF6HltIfJZrEMqxQJs1oVETa2fw3fc6hSL2/Y32fJDMMw1NLeJUnaeqBM/kBA5y6ZrnD72P5YMDbUVzXqxUffUkNlU++xKbMydN5nVyjS7QpdsCBpqGnRC7/foObGtt5jRw5UqLS4Whdfe6ZyZqSFMB3woUDA0I69ZXpzQ6H8J1wk0NDUoT2FlZo1PVWrz5yuSC5cGbbuzh5VHKlRR0unouMi5clJUni4PdSxgiZtaorOv+4sbXh+i1rq2+SMdGjh+XOUO6//kr2JaXGaNi9LhdtK+o3NWT5dYfzeASAE+JcHAIAQ8PZ49f7re3oLlBNtfWu/cmakKSUzIQTJxgeLxaKwEF8F31DTrNLCSjXVt8kdH6ms6alK8MSGNBOCLzk6SnnJiTpQXTvkx8zwJCkxKnIUU52eto5uvb3t0EnXHN9x8JhmTElRTjr/VuHk9m4s7lOgSFLJ3nId2Vum2cvzQxMqiIr3lPcpUI7z+/za+k6hMnKTuboaIVd0pEbr3isc8N//vQcr5QgP07krZ3CxzzA01rTojT9tUnlR1YcHLFLevGytumqxImMmfol8XP6iqcrMT1NrY5siIp2KSYg2nWexWLTsovny+wI6vKdMgYChcIdd81cVaPqCKcENDQD/wG9pAACEQGNNq6rK6k3H/L6Aao41UqKMYceO1OjF376jzraPNgd3uvboos+vVFZeagiTIdisFovmZXqGVaLMTfeM6ZNPpZUNau3oGtLcwpIaShSclM/r05HdpZI+XP/eYrXK94+LCEoLKyd8iWIYhor2lA84Xnm0Ts31bUpMjQ1eKOBjvF6/tu4uPWmBvuvAMc0pSFdKYkyQko1/29fv+6hAkSRDKtpxVMkZCVp03uzQBQsBV3SEXNERJ50Xmxiti647S7XlDerq6FZMfJTiU9xBSAgA5lgDAwCAULBaZNHAJ1HZwHPs8vb4tPGlXX0KFEnq6vDqvb9vV3enN0TJECp5SQlanJ0+pLnLcjKVmxg3yolOT0OL+WauZo5WNZzyvjCYPGxhNrmTY5Q+LUWOSKcsVotSshMVlxyjqNixe1fWSLFYLLIN8nPd0vs/QOjU1LWovLLxpPN8voDKP3ZX2VD4/QFVHmtUcWGlSo/Uqrtrcvy+1NrYpqLtR03H9m8ultfb/650fMhms8qTnagpBekUKABCjjtRAAAIgfikGKXlJOnY4Zp+Y2F2m5Iz4kOQavRUHq1V8c5SVZXWKdETq7z5U5QxLSXUsU5JQ3WzKkr6f90kqbaiSXWVjUrPTQ5yKoRSeFiYzp0xVTabTZuPlJnuj2KzWrQsJ0tn5U1R+BjfD2W4DBniDDAGY7FYlJQWp9+tfU5+30cbBc9ePl2rZw6tgBzvps/LUk2F+QnqjKkpiks0X9YGCJZukyVmB9I1zAKkurJJm94uVOmR2t7iPSE5RstWTtfU/LF9d+bpMgJSwOi/Qbr0YSElLkQAgHGBO1EAAAiBMLtNS8+fLacrvM9xi8WiZRfMmVBLepQXV+m5X6/Ttrf2qeJwjXZtOKi/PrJOh/eWhTraKQkEDGmQv3dPtgwGJiZXeLgumpmnf16xUKvycpQQ6ZI7wqHEKJdWT8/RjcsX6fyCaXLZx/4msrFDWGbjuIzkWNms/EmBwbXUt2rPpiKlTElSuNMui9WiiCinWhra1DNJrkbPnZlu+rM93BGmhWflyxZmC34o4ARhw/geDB/G/j31NS164c9bVHKops+di/U1LXr5r9t0uKh6WDnHm+j4SOXOyjQdm7EwR3bH2P+9AADAnSgAAIRMRm6yPvml1Tq875iqjtYrOi5SU2elK2v6xLkiLxAIaPvbB/qdJPN5/dr8+h5lTvOMuz8e45PdSkyLVV1FU78xd0IUm8tPYlaLRZlxscqMi9Wq6Tny+QOy26zjrmTISo1TVIRDbZ3dJ51bkDM+7yhDcDVUN6urvUfxKbFyJ0TL7/UrLDxMVptVteX1mjYvO9QRR11sQpTWXHOmDu4q1YHtR+X3+ZWV59HMhVOUNiUp1PEApSRFKzkhWjX1rYPOs1otShvG0kpFByrVOsAykX5/QFs2FCsrJ1F2+8Q8PWWxWLRg9UxVl9apsaal93haTpIKFk8NYTIAwHBMzJ9SAACME8np8UpOn1hLd52ovblTFYfNrzCsLqtXc0P7uLnrpqWtS91en8JsVp25Zr5e+u078nn9veO2MKuWXzxPEZGOEKbEWBFmtSpsnJUnx8VEOnXW/Fy9tHH/oPNm5XqUkRwbnFAY1+wO+4crvhkf7o9y4l0X4c7wgR84wcQlRWvpubM0f8V0BfwBfl5gTHGE27VwbpZeenPvoPPyp6YoNXloJUp3t0/79pQPOqeqolF11S1KnWBL2Z4oOSNeV/7LeSovrlZ7c6diEiKVPs2jyGHc+QkACC1KFAAAMGrs4WEKd4arq6PHdMwePvaXLzlW06QDJTXaU1yhrm6fbDarctMTtOyapequalHF4VoletyaOidTGVO5Kh8Tw+y8NPn8Aa3fVqwen7/PmMVi0eypHq0+I29YS7pg8krKjFdGnkflB6v6HA8LD1N6nidEqULH4Rxfd2Bi8iiY5lFTS6c2bj1sOp6VHq9PLMmT1Tq0O6Z9Xr/8Q9g43fuxnzMTUXRclAoWR4U6BgDgFPFXDwAAGDXOSIdmn5mnDS9s7zc2a8k0uRPG9ka6B45U64V39vY5iRzw+VV4tEaFR2t05twpuvyc1bKzlj0mmDCbVYtmZSk7LV4lFfUqrWqS3+9XUny0pmYkKi3JrTDb+LzTBsEXHm7X6quW6t2/btHRwgoZAUOxidFaccVCebISQx0PwD+Ehdl05sJcpaW4daC4SiXl9TIMKSE2UnMK0pSTmahI19DvoHI67YpLiFZHe/2Ac+zhNkVFOUciPgAAo4YSBQAAjKpZS6eptaFd+z4olt8fkNVq0bR52VqwqiDU0QZ1rKa5X4HycRt3lSg22qX5+elBTAYET1JclJLiorR41sTfswKjKyE1Vpd+6WzVHmuQ3+tXvCdWEZw4BcacMJtVU7OTlJuVqI7OHgUMQxHO8FMqzm1hVs2al6VjpQOXKDNmZyg+cWxfVAMAACUKAAAYVa4op86+eolmLZuq1qYORUZHKDkzQbYxfhX7waPVgxYox23ec1T52cmKYHkWABiULcwmTzabqGPyam7qUMWxRnl7fHJFOpSWHifXGN0bx2KxDOuuk4FMnZ6ieQunaOfWkn5jyamxWrAk97RfAwCA0UaJAgAARp3FYlFKZqJSMkOdZGjaOrq1u7hySHPrmttVUdesqRksSQMAAPrz+wLaveOoNm0oUlent/d4TKxLnzi7QNOmT9y9gcIddi0/u0DpWQnat7tMDXVtckaEa9bcTOVMS1a02xXqiAAAnBQlCgAAwMd09/jU1XPyjVBPnA8AAGBm354yvbVuX7/jLU0devlv23XZVYuUnTNx79IKDw9TXkGaps1Ilc/nl81mG/Lm9GOFYRhqb+6QzW5TRCRLEQLAZEOJAgAA8DE2m1VhNqv8/sCQ5rPBNgAAMNPe1qX3NxwacNznC2jX9lJlZieOu2JhuCwWi+z28Xcaqqa8Xtvf3KeS/eWyh4dp9pnTNXt5vlzRlCkAMFnwFz8AAMDHuKOcmpY5tCtCXU67UhLYEBUAAPRXXdWsttbOQeeUHK5RY0NbkBJhOFob2/TSY+t1YMshdbV3q7WxXRtf3K4PXt0Z6mgAgCCiRAEAAPgYi8WimbkpQ5q7ID9D7qiIUU6E8ai7y6uy4mod2ntM9VVNoY4DAAgBn+/kd7X6/QH5hzAPwVdeVK2mupZ+x/duKlJ9ZWMIEgEAQmH83UcJAAAQBDnpCTprwVS9s33gJThy0uO1oCAziKkwXlSU1Gr989tUW9EkSbKHh2nOsmlatLpATld4aMMBAEad1+tXVUWjWpra1VDfJofDrojIcFkt/ZfsckU55IpyhCAlTqazzfwuIl+PT10dPUFOAwAIFUoUAAAAEzarVcvmTpE7yqnN+0pVXd/aOxblcmhBfobm5acr2sVJj7GirqJBDVVNckVHyDMlSWEhWne9qb5NL/1ho9pbPjrx4u3xadvbB+R02bVo9cyQ5AJGQ82xBpUVVautuUOxSdHKyvMoLikm1LGAkKqva9X61/aqtKRO0TERiogIV/HBKrljXfKkxSo8vO/PpznzshQVxf4aY1Fcstv0eKTbJXdCVJDTAABChRIFAABgAGE2q+bkpWl6dpIq61rU1e2TzWZVSny0YjjZMabsee+A1v9xg7zd3g+XY1s+XWddvUxOV/C/TuWHqvsUKCfa+V6RZizIUZSbJeAw/h3aU65X/7hJ3m5f7zFXjFMXf26F0nKGtq8UMNF0tHfrtRd2quofdyK2tnQqL9+j2poWNTd2yDAMZWQnyvaPTeTTM+I1c05GCBNjMGlTU5R/Ro4Ktx3pPWaxWrTsovmKio0MYTKpu9OrY6V1OlZaL5/Xr+TUWGXmJCkm1hXSXAAwEVGiAAAAnIQj3K4paQmhjoEBNFQ16u1nNsrb7ZUkGYahve8VKnNGhmYsmRb0PK2N7QOOtbd2qaOtixIF415bc4feem5LnwJFkjpaurTh5Z264sbVsofz5yYmn/LS+t4C5bhjpfVaunyamps6VLivQn6fX4meWM2el6npMzyKjuGk91jliAjXqk8tVdaMdJUVVsjhcihnVoayZqSFNFdLc4feenGXjhRV9zke7Y7QRVcuVFoWv7cCwEjit1oAAACMa001zerp6r8ueW1ZXUhKFFf0wHe/OF3h7ImCCaGqtF7tLV2mYxUldaqralJqVmKQUwGhV3a0vt8xw5AqyxsVZrdp4ZIczZiVoZlzM+V02of8vJ3tXaooqVNbc4dc0U6lT0ke9OcNRk5ElFMzl07TzKXB/51iIFvfK+pXoEhSa3OnXnt+u66+fqUi+f4AgBFDiQIAAIBxzRXjki3MKr8v0Od4TGJ0SPJkTE2RI8Ku7k5vv7HZS6YqJi60y38AI8Hv9w88aEh+7yDjwATmG+R73+f1q6aqRbnTvMMqUOoqm/T6n95XzbHG3mNxydE6/5+WypNJWTnZ1Ne0aP+u8gHHmxraVX60TvmzWSYOAEaKNdQBAAAAgNORnJWoeWfP6XMsMSNBU2ZlhiRPQopbF3xmmaJiProC1GK1KH9+tuYtzwtJJmAoGmpbte29Iv358Xf17G/f1c73D6u5sc10bnyKW2F2m+lYdJxL8clsLo/JKSXVfCPyE8UNY0Nyn9evd1/Y0adAkaTGmla9+ew2dXf1L+wxsbW2dMrb4xt0TktjR5DSAMDkwJ0oAAAAk4DX61NlZbM6Onpkt1uVkuJWVNTEWObBarVq2WVnKG2aR3Xl9YqMdSmrIEPuhNDciSJJOTPSlPS1C1R5tFbeHp/cCdHyZMbLFmZ+0hkItcqyBr34zPt9lugqO1SrXR9Eac2nlyjR0/fEcFJqnBZ8Il+b1+3rc9xitWjpebPlimbfH0xOWTlJcjjtA5YbbrdLGZlD36+i5liDyor7L9skSbXHGlRVWqfs6amnlBXjky3s5NdDD1RyAwBODSUKAADABHfkSK02bSpWZWVT77HoaKeWLMnVrFkZCpsAJ/bDHeGaNn+Kps2fEuoovaLcEcqbmxXqGMBJeXt8eu/1vaZ7nDTWtWnz24W66NOLZbFY+owtPLtAsQlR2rXpkNqa2hXvcWvO0mnKnZUerOjAmBOfEKULL5mnl/+2Qz0fu1sgMtKh8y+dN6y9Krq7vDIMY+Dxzv57gmF4GmqaVVveIKvFopQpiYqJG/qdQqGQnBKrxJQY1VW3mI7bbFZ5MuKCnAoAJjZKFAAAgAnsyJFa/e1v2+Xz9V2jvbW1S+vW7VN3l09Llk4NUTrg1DV3delwQ6Oq21rl9QcUGR6uTHeMsmNjFR7GnznDUV3RqIqjdQOOHz5QqdqqZiWnxvY5Hh5uV8GiXOXNy1ZPt1eOiHDZbKwYHUqGYcjn88tu57+BUMqd7tGnP3+mSg7V6uiRWlksUu60FGXnJil+mPt1Rce6FGa3me61YrVZFc0+W6dl3weHtP7ZD9Tzj33MXFFOnffZ5cqZNXb3E3FE/H/2/jM4zi097H3/nXNGo9FAI+dEEMxpb3LnMLMnz2hmbMmy68qqY0nH4Va5rstf7CqXXPYHl4+rXK7je65kS9Z4LEsjTdx5b27mHJBzbIRuoNGNzrnvB5AgmmgkEiRBcv0+bb7r7bdXY3dcz3qeR8HRVxv56Gc3yWTy+8FlMln2H6vBUWrOOx6LxHGPevHOLKFQyimtKqa0ugipVLxnC4IgbIf4ZiUIgiAIgrBDsUgC74yfdDqD2abH5ti6/vmzkEqluXplZF0AZa0rV0eprrFjt4v+BcLzIZZKcWNmhouTU4SSiXXjZUYjZ6qraSkufgazez7Fo0nYeKM7mUyWeGzjvgtyhUyUjnnGUsk0o11TdF8ZJhKMUdHgpPlwDc5K+7Oe2kvL7jBhd5g4fKLusa5TVGKm/Vgdt88Prhtr3F+Bw2V9rOu/zDxTi3zxv6+QWROgiobjfPo/L/HDf/o+xh30rnna6ppLef/7Em5dHmVqfIFQKI5EJqW+3cVsMMq5S8PU1RTjKrUQCkT5/K+uMTU0v3p7iVTCiff20flKowikCIIgbIMIogiCIAiCIOzAaP8sFz/qIuBbabasUMppP1LDwVca0ehUz3h2+ebnl5ldU8KrkHQ6g9u9JIIownMhlkrx0dAQN2ZnNzxnJhjkp11dfKulhQOlpU9xds8vjU6FRAIbVQySyaVoNIqnOylhR26f6+fyb+6s/rt7McTI3Um++Xuv46goenYTE3bFodeakSvldF8eJh5NolQraD1cQ+erYgH8ccxNLOQFUO6LhePMTS7s6SAKQE2DE7vTxPXr43gXgiQzOeb8YbKBHDNzAW7fneSNMy2kFoN5ARSAXDbHpQ+7KCm3oTFqWPQGyWbBatNhLzGtK98oCILwshNBFEEQBEEQhG2anVzk47+8mldSI5VMc+vCEBKphJNvtxe8XdAfwT3qxeddRqtTU1ptp6Tc+sR/oMa2WSc9HFq/m18Q9qLr7plNAyj3ZXI5ft7fj1mtpsYqdmlvxVFqoazajntsoeB4bXPpusbywt6x5F3m5he9647HIgkGbo5vO4gSjSXxLoZIpzNo1UqKi43IRXm2PUGjU3P87XZaD9UQCcfQaFWYd1gWTFgv+1AprO2O7SW3u93c6nUXHMtkc3z6RS/19sLPlVQqzZ0rI0zNBFb798jkUprbyzlyqh6DUfvE5i0IgvC8EUEUQRAEQRCEbRrudhesSQ7QfXWUlgNVWB5a1Jif9vHRT68Q9EdWj8lkUl75WgftR+ueaCBluzXxVWrxlfBJy+VyeGf8LHmWkcmlOCuLMJhFHfudWI7FuDg1te3z09ks3fMeEUTZBrlCxok3Wvhw6TqhQDRvzOYwcviVRrEreQ9bXgyTjBcutzYxMMsr2eym2QrJZIregTlu3J1geTkGgEQiodJl5VBnFVUik2XPMFp1GK3is2O3FFfYkEgl5LL5aXgKlZzi56BM2lIgwt3u6U3PSaczTHuCqLXKldKNayx4ljG5/aTWlH3NpLP03J4kHk/x9gf7RX8lQRCEe8S7oSAIgiAIL6RwMEY2k0Fn1O5Ko+NcLsf0mHfD8WQizbIvnBdESSXTnP/wbl4ABVb6C5z79V2KnBZKK5/c4pTDYcRs0hJYjm54jkQioazU8sTmIEAinuT6533cvTREJr2ys1WjV3HmWwepb694xrN7foz5A4QL9EDZzJ35OY5XlFOs39slWfaCEpeVb//OSSZHvEyOeJBIJVTXO6ioc2A0i93Ie5lKrUAikZArUI/NaNVvGkBJpTJcvDbKzTuTecdzuRwT0z7cc36+/k4HddWix5Dw4nFWFXP0nX1c/ahr9fUjk0l55VuHsDn3/ncjrzdI4l4GyUZkchlz3iCtJWbik77V4/FYgqWFEAaLllw0icmoIRaOI5FIUOvVzEwuMuf2U1Et+ioJgiCACKIIgiAIgvCCWZwP0Ht9gqHuaTLpDM5KG/uO1lHVWPJYO6klEgka7eY9TxTK/K9W3hk/c5OLBc/NZrK4R71PNIii1ao4cqSGTz7t2fCc5iYnJU7zE5uDAEN3prh1biDvWCyc4LO/vIbJon8udrvuBfPh0I5vk8xk8EWjIoiyTWabHrNNT8fRmmc9FWEHHBU2KhudTAw8VOpOAq1Haze97dTM0roAylrpdJYvzg9Q6jCh3eIzUBCeNzKZlENvtFFW48AztYhUJqWkyk7JE/xutlYykWJ6eJ6poXnSqTSuOgcVDaXoDOpt3T6d3l7JMb1Zi0qrzDsWiyWpay9HoVGS9gb5/NMeMvdKmMkVMg6fbmLRGxRBFEEQhHtEEEUQBEEQhBfGkjfIr//iMstLDzI/Joc8uEcXeOcHR6hrcz3W9Rv3VzAzUbhngKPMQnGZOe9YIp6CDRo1A0TD8ceaz3Y0t5SRSKS5fGVktd41gFQqoamplFOnGnYlU0coLJVI0XV5uPBYMs30iEcEUbYpkS5cSm8rmY26pQvCC0Iml/Hqtw4h+cUtJgZmyGVzqLVKjrzZTm1b+aa3HRye33QcIBiMMT3jp7G+ZLemLAh7hkwuw1VfguspP79TyTRXP+nm9ppNFv03ximvL+HNHxzDsI0MQJ1ue4FNR4mZ4680MmjRMd4/h0Ipo+VoLV5fhGgwxs0LQ3nnp1MZLn/WS21T6c4elCAIwgtMBFEEQRAEQXhhjPS48wIo92UyWa592Ud5nQOVWvHI169pcjLeXMpIzwxSqYT7iS0qtYITb7ejUOZf22DWIlfINuyjYi02PvJctksmk3LwUDVV1Xbcbh/hcAKVUo6z1EJJiUkEUJ6weDxFJBjbcDy0Sak1IZ9e+WivXaVMtsszEYS9x1Js4mu/+ype9xLJeAqT3YDZtnnj8WQyhXt2aVvXXxLvVYKwq2bHF/ICKPdND88z1uum42TDltcoLTFhLzKwsLh5pua+1jKcFUU4K4o4/m4KmUzK/Kwfz8ddDNya2PB2Y/2znHp334bfFVPJNOlMFpVKgVQq+mYJgvBiE0EUQRAEQRBeCNlslqG7GzfXXJwPsuQN4qywPdL1Q8EYYyMeUCkob3IyN7GISiWnZX8lDftcFBfoK1JUYqK5s4rua6PrxrQGDeV1jkeay6Ow2fTYbKKk0dOm0aqwFBuZmyhc1s1i33yR875MOsPc5CKLs35kchkllUXYX7JeNuUm845vY1apcRq29zcWhOedTC7DWbWD0jsSCRK2t/AplkcFYXfNb1DuFWDozsS2gigqlYITR2v51Yd3yWQLZ13aiwx5PY3ubyYqKbVQUmrlqr97w2unEmlSyTQyTX4pMK9nmeFhD0P9s6TSGex2A82tZVTXFKNSPfpmJUEQhL1MBFEEQRAEQXghbKtizw6r+izO+fG6/SQTKTyLYUbHFlZLYumL9MhkUkKpFHpT4ZILEomEw683k85kuHFxiEAwRiqdobyyiCPvtaHWi/ryLzq5QkbHiYaV3jgPPf+0RjUV2ygfkoiluPzRXbovD682vpUrZJz51iFajmze7+BFUmU2UWowMBvafm+UIy4XBpV4nQlCIUqFnMqKIrp6N96AcJ/NqnsKMxKEJ8fnCRLwhZDJZRSXWdBusxTWXldf4+Brb+/jq8tDLC8/yHyVSiVUVxbx6okGTAW+p8oVMjoOV3P10x7Gh+byxlQqBa7KIopKTChV+cuGkxOL/OZXt4nHUqvHIuEEE+OLtLWX88qZJtSPkfUtCIKwV4kgiiAIgiAILwSZTEpdu4trX/YXHLc5jFgd29uRns1muXthiMsf3SWdyjDrXiISSXDivQ4CUgmJeIpwaKWfyXIgisNh5sCRwo2Y45ksIaOKyhO1lMXTyBRSohL4zY0R7KPzvPdKM6XF5kd6zMLzobbNxWvfOsT1z3sJB2MgAWdFESff68Bi37qk22jvNF2X1tcr//JvrmNzmnGUP1p21fNGpVBwpqaG/9XVta0+Jza1hlZH8ZbnCcLLrLGumO6+6U03ItisespLRe8m4fkUi8S5cW6InhvjpO5thDFa9Zx8q5X6x+yV9zgcm2RG1+2r2NG1GupLKCuzMDsXIBiKIZVKKbYbKCnevGxrkcPE+z84wi//4hLRSAJyOTRaFTqDGoVCRtuhaqTSB7cPh+J8+nFXXgBlrZ7uaRxOE/s6djZ/QRCE54EIogiCIAiC8MKob3PRf2uC0HJ+DwqpVMLhM82o1MoNbplvemieC7++TS6bIxFP4V+KkM1k+epvb/LGD48zE8//8dh1e5KWdhfqh8odBEMxfnW2l3lf8MHBNb3kF/xh/vbzLr7/Tid2qyg59KKSyaS0H6ujuqUUvzeEXCGjyGlGodzeV/H+G+MFj2fSWWbHF16aIApAi93ON5qb+eXAAOlsdsPzbGoN329vp0gnds8LwmZcpVZOHKnj4tWRguNqtYLXX20WO8uF59aNc0PcvjScdyy4FOaTv76OSqOgovbplVZdq7Tazr4TDes2SZRWF1PXXr7j6+m0Kuof4bE0H6wiHIxx99IwmczK56pcIePQmWZqWsryznVP+wgF44Uus6rr9iSNTU5R1ksQhBeOCKIIgiAIgvDCsJWY+NrfPUH31TFGetxkMllKXFY6TtRR01y67euMD8yQu1dbOpVKk733ozKTybLsDSKTSVd/aAIEl6NEwvF1QZRRty8/gFJAMJKgf8wjgigvAb1Ri95YuPTbRnK5HLHwxgsWqUT6caf1XJFIJBwqK8Oi1nB3fo6u+XlSa4IpJpWaIy4XbY5iEUARhG2QyaQcPlCN2aTlTvc0M3N+ABRyGU0NJbQ3uyh1mp/tJIU9IRqKsbwUQamSY3WYkEj2fqccnydIz42xgmOZdJb+21OU1xQ/k8eiVCk48d4+ymqLmeifIZVMU9HgpKqpdMMysU+CSq3k5Lv7qG8vxzvrRyqRUOyyUlSy/v+xzxfe8no+X5hQMI7KLoIogiC8WEQQRRAEQRCEF0pxqYU3vn2Qw2eayGSyGExa5ArZjq4RWVtT+qEfkLFIArlFlxdEkcmlyOT595FKpbk74N7W/d0ZcLO/qQyjXrOjeQovPolEQnVLGUue5YLjNqfpKc9ob6i1Wam1WTlRUYkvFiWTzaKSySg1GkUPFEHYIblMSnODk7rqYnz+MJlMFrVagc2if9ZTE/aAbDZL//Vxrn3WTcgfQa6QUb+/ksNvtmG27e0NIH5fiFQys+H4zPgCiVgKtXZ7mcq7TalWUr+vgvodlu/abRKJBIfLisO1edm+taW9NruWRLr3A2yCIAg7JYIogiAIgiC8kIyWR9+FXlptZ7RnJQCi1ijRaFXEogkArCUmFh4qZVBbX4LJnL9rMBJL4Q/llxXbSDSeIhJLiiCKUFDD/koGbo4TCeY/n6payiitebl7fpQY9JQYxEKvIOwGhUJGSfHLGZgVNjbaPc0Xf3WV3L3GOelUhv7rY8TCcd7/nVd3vFHlaZLLN5+bTC5DJhcL/ttV7Ni6j1upy4L5KWbSCIIgPC1bh5EFQRAEQRBeEol4itByDFddCSbrysKsVCbF7jAguVfeQPZQyS6FQkZrR8W6kgdSKeykOsTzUBZDeDbspRY++Aen6TjVgMGsxVJs5MT7+3nt24fRaEXWhSAIwssqlUqTjBdu8r0bMpks3ZdHVgMoa00MzDI/tfjE7ns3FJdZNt1U09xZgUK5vbJT/sUQY30zTA7NEYskdmuKz5Uyl5WSLcr77euoQCYXS42CILx4RCaKIAiCIAgvvXAwxvDAHD23J4lGVnqbtJ9uwjftY3JwDp1BTcfJRlArGZt4sGBgtel55fVmyivWN/bWa9WU2U2Mun1b3r/NpMUkslCETRSXWSkus3LsnX1IpZJtL/oIgiAIL57wcpTBm+P0XR8jk85Qt6+C5sM12ErMu3o/yXiSwELhcpLkIBrcXsbts6LVqTjxdhuf/vV1Muls3lhRiYmGbTRwTyZT3L4wxO0LQ6sBK5NNz6l391Hb6noi896r1GoFb7zdxscf3mXRG8obk0olnHylgdq6nTe3FwRBeB6IIIogCIIgCC+1UDDGp7+8w/TEwuqxeCzF+S8HqKwt5pv/8HV0ejVavZpIOE7LjJ9EIoVWq8LpsqJWF17MlkoltDeUbiuIsr/ZhWaD6wjCWir1s6nbLgiCIOwNiViKsz+7zljPg75rt872M97r5oPfe21X+5SoNEqsJWbCy+uDJRKpBJ1x728AaWhzoVYr6L89hXvci1whp3l/BQ37XJhtW5eD7L0+ztXPevOOLfvCfPyXV/nW31dRWmV/UlN/YnK53CNnQBcXG/n2dw4zPe1jfNRLIpmmuNhEVU0RTqcFqeiHIgjCC0oEUQRBEARBeKkN98/mBVDWmhz1UlZu5fDJBgB0ejV1jc7V8XQ6w/TMEsuhGFKpFJtVR7HNsPrDtNplY19DKV1Dsxvef2WphaZqsWtPEITnSzqdYXYuwNSUj2A4jk6rorLCRqnTjFIpfmYKwpMyO+HNC6Dc518IMdE3w/5XmnbtvqRSKe3H6pkenieXzS/pVdPioqSyaNfu60mqqHNQXltMPJZELpduO5szGopx+8JQwbF0KsNwj/u5CqJ4pn0M3ZlgcmAOnUlLy+FqqltcKFU728ijN6hpbimjuaXsCc1UEARh7xHfbgVBEARBeGkl4il6bk9tek737SnaOivX9Z6YdC9x+eYI7lk/90uFy+VSGmtKOHqgGptVj0qp4LXD9Rj0am72ThNPPKhbrlTIaKtzcqyjCoNOveuPTRAE4UkJR+KcvzBE30B+gPjGrXHqah2ceaUJk2nv71AXhOdRwBvccMwzvXX2605Vt5bx1g+Pc+PzHpY8QZQqOU2HajhwuhnZQ43bY5EEkVAclVqBwby3motLJJId9xELBWOEl6Mbjs9usAlnL5qbXORXf/IVsUgcgCXPMtNDcxx+q51jb7eL3nyCIAhbEEEUQRAEQRBeWvF4iui9H5MbiUUSxOOpvB/ek24ff/vhbZKpTN656XSW3qFZ5heCfOvd/VgtOjQaJa8cqKW9zonbEyCRTKOQy3DaTditW5eREARB2Euy2RyXLo+sC6DcNzLqQSqR8N477cgfWmAVBOHxqTcJBOhNux+4kEqlNB2opqqplKA/ikolx1SUXzIsEU/Sf2uSO5dGCC1HUakVNHdW0nG8FqPl8b7reL1BpsYXmJnxI5NKqKq2U15ZhOkpBGmUSjlyhYz0Q9/37tMb91agaDO9V0dWAyhr3T7bR22bi+Iy6zOYlSAIwvNDBFEEQRAEQXhpqdUK1BoliXhq43O0SlRr+pWkUmkuXBtZF0BZy+cP0z04w+ljDavHzEYt5ufox7awuZA/wsyYl2gohqXYSGl1MSqN6FcivPg83mX6BmY2PWd4dJ6OuXIqym1PaVaC8PIoqy1GZ1QTCeYviMvkUqqan1x5JbVWVTCAk8vluH52gFvnH5S9ikeT3L44zMKsn3d/eAyt/tEybgf7Zvnsky5SyQffuUaG5jEatbz7wX5KyyyPdN3tstiNNOyroO/meMHxho6KJ3r/uyGbzRGNxPHN+wuOp1MZljzLIogiCIKwBRFEEQRBEAThpaVSK2jrqODi2f4Nz2ntKEe7ZtFgZj7A7Hxgy2t397nZ31KO6TlouirszPzkIh/9+XmWfeHVY7Xt5Zz57mH0Jt0znJkgPHmzcwEymdym5+Ry4J71iyCKIGwhl8sRCcWRSEBn2N73BaNVzzt/5yTn/uYGi/PLAOhNGk58rZOymuInOV2WFkNMjnpxjy8gkUioqndgNGm5e3mk4Pnu8UXc4ws0tJfv+L7mZvx8+tFd0unsurFgMMonv7nL9390DN0jBmi2q/OVRhbmAizM5gchOk7UU93s3OBWz96cd5nRqQUGxjykUhlCqTRlLaUQSuB7qOybXCGyBgVBELYigiiCIAiCILzU6lvLGB2eZ35m/Q49u8NEU1v+D/9AMLat68biKZbDMRFE2YFEPIVMJt3TP+Yz6QxXPryTF0ABGO2eprS6mAOvtTyjmQnC05FMprd13mYZfoIgwMz4Ar03J5gc8QBQ21JKc2clzm0EH111JXz3D9/G6/aRzWSxOa3on3AfotlpHx/+7xuEww++B40OzlHmshIJxVFvkI05N+F7pCDK6IinYADlvoA/wvSUj6Yn3NzcVmzkg985yfSIl/npJRRKGa6aYkqr7CiVe29JLZfLcXfAzWeXhshkHvz9FsMxununKS+zsr+hhPmheQD0Zi3OyqJnNV1BEITnxt57xxcEQRAEQXiKTGYt737rAIM9brpvTxGLJlBrVLTuL6e5rRyz9dEzC6SIJp3b4XUv0X99lLHeGZRqOa1H6mg8UIXmCe8ufRRLnmWmhucLjg3cHKPzTLNoziq80DTbLFun34OvX0HYK6ZGPfz6J1dIrQlK9lwfZ7R3lq//neM4K7YOpKg0Ssrrn04mRCKW4uxvuvICKA/GknhmA1TU2At+/kllO/9MzGZzjA57tjzPM7/8xIMosNL7pPlAFc0Hqp74fT2u4Qkvn14YJJvLzxg0FxmIRRJMzy4hkUhodlmIL8d58wfH0Ilys4IgCFsSQRRBEARBEF56JrOOI6caaTtQRTyWRK1WotUVbtxqs+iQSCTkcpuXs9Hr1Jie8K7QF4FvPsCv/vQrwoHo6rFzP7/Jwqyf1793BNkeaEydSmUIhWLkcrm8XZ0P2+o5IQgvAleZBYVCRmqTvlAymQSXqK8vCAWlUxmunx3MC6DcF4smuHtllJJy654KyM+6fSx4lguOKdQKkskUsWiy4HensqonmOWwxcduLpcjsBAkk85gthuRK17sJbB0OsON3ul1ARQAuUJOWU0xFruRRDxJzdFa2hrLMFr1z2CmgiAIz58X+xNEEARBEARhB7RaVV7/k0KcDjOVLisTD9WTflhHazkGndiJvZXR7um8AMp9AzfHaTlcQ1mt4xnMakUymWZ0xENX9zTz9+rOV1cVYbQb8M8FkEqleec3HarZU4tegvAkFNkMHD5Yw6Urwxue09lRSYnD+BRnJQhPlndmidGuKTzTPizFJmrby3E94ufT0kKQ2cmFDcfHBmYJ+MJYigyPdH3/QpBYKI7erN21BfJIKL7h2JIvzOHTzQWzNOvbXZRV23d8f1KphOq6YvzXwpue5ygxbTgWWAxy45NuBm+Pk0lnKKt2cOxrnc/0e8WTNr8YZPqh3i3ZbJZELEUul0OpVqA3adGbtCxE4iKAIgiCsAMiiCIIgiAIgrADcpmUU0fqWFgKE4kkCp5TXmqlvXHjEhvZbBaP28/8tI9MOkNRiZnSKhtK1fbK5LxIZse8BY/nsjkCi6FnttiRSmW4cmWEGzfGV49ZTBrioRilTWXMu5dQwWogpbq1jIbO6mcyV0F42g52ViKVSrh+Y4zEmt30CoWMA/srOXSwWgQUhRfG7LiXX/3JWWL3PvMnB2bpuTzEu3/3FLXtFTu+XiaTZbPExWwmSza788zGWDjOrbN9dF0YIplIodapOPh6K/tONaBUKnZ8vbVU6o1vn4inkCtkvPfDo4z3z+H3BlFrVbQdqaahowLVNksAPqy2zkHXrYkN+6KYzFpcG5Q9y2SyXPzFTUbuTq4ec4/O85s/Pcv3//F7mO3bC/KmUxkW5/xkUhksxUa0hr2dYRx9qBdVOBRjYda/EgTLgUIlx+40Y7bp8RXYwCIIgiBsTARRBEEQBEEQdsjpMPPd9zu53T3NwMg8qfRKWRutRsm+FhcdrS4M+sI/tDPpDDfPDXLtiz6ya0pD1baW8eoHnRhMz7YudSqVxrsYIpXKolLJsRcZkMukW9/wERk22QX5qAsvu2FycnE1gCKXS3GatfRdGmJm1INKq+TVr3dSWl6ERqXAZDfgqnGg0r58QbAnLZ1Kk4wlUWlVe6K0m7BCqZRz9HAN9XUOZmf9xBMplAo5ZaUWbDaxs1l4ceRyObouDK4GUO5LpzJc+egurrqSHX9Wma16TDY9y77CWRbFZVaMlp1/F+i+NMSNz3tX/x2PJLj4y1uoNQrqO6uZn1kiGIihVMlxuiwYzdvv+eYst6I3aAiH1vdEATAYNOw7UkPH0TrisSRKlRyl6vECN2UuK2++s4/PPukm/VD5QL1ezTvvd6A35Gf8LswsMd43Q3g5yuXf3EFnUKM1Pvg+Fg3FcA/PbyuI4nX7uPir27hHvORyOQxWHcff7aDxQNWeDRIr1nxfi0biTI14yK4JQqUSaWYnFiEHFaWWZzFFQRCE55YIogiCIAiCIDwCh93Eu6+bONhRSSgcRyKRYLPoMG6xS3FiaJ4rn/Wsq+M92juD1WHi+FttT3DWG8tksgyNerjZNcmcd6V0lUQiodJl5UB7JTWVRU9k0aCuvZz+a6Prdt1a7EZKa4p3/f62I5fL0d8/u/pvh1nL2b+8QiKWBCAWTvDxT69w+HQz/+D/8w0USvGVereEw3Gmp3y4pxZJBWOM3p4gm0hRXldC55lmqltcz3qKwhpWiw6rZfsLsYLwvIkEY0wNzRUcW5wLEFgI4dhGE/i1NDoVB0818MXPb60bk0gl7D9eh2KHvTvCy1Hunh8sOHbl024m3H5GBx40aldrlBw/00jbgcp1pSnXymazZNIZdHo1r7zdyid/e2tdbzClSsHx15tR3Mt20Su2n60RDceZHvUyM75S3sxVY6e81oHmXm+VptYyrDY9E+MLzLiXkMqkVNfYqagqwvxQEGhmzMtv/uwcsUgSh8uCZ9qHVCrBVevAuCa4e/+zfNN5hWJ8+pNL+DzB1WOhpQif/a8raPUqKhpLt/0Yn6ZimwGDXkUonGDZF84LoKy1MBeg9v3Opzw7QRCE55v4xScIgiAIgvAY7DYDdtv265YPd01t2Ai159oo7Udr0RufbrmIbDbHjbuTnLsylHc8l8sxMe1jemaJd8600dq0+4sGFY1OXvvuES5/dJdoKA4SKKmw8eo3D6HVP5ueMolEirm5AAAymZTg/HLBRZe7l4eZGfdStUcXU543iwshPv7wLl5PkFKzhs9+eolcNgcSCZ65ADOj83zj996g8gk8D59HqVSGWU+A6Rk/sXgSnVZFeZkFp8P8RLPHBOFlIpfLUKgURENxJNL8jQQyuRS54tFea00dFWTSWW6cGyRyL7vDYNZx9LVm6lp3/h6XiCWJF/icSiZSzHa50ZZY847HY0nOftSDWqOkobVs3e2i4TijfTP03ZokFonjKLPRfKCCb/74GH13ppka8yKRSqhtctLU5sJZvrNAEkAoEOXzn11navhBcKfn6ijVzaW8/q2D6O59FyouMVG8Se8TWMnyvfFFL7HIyt9AIpFgsOgI+SPMT/nQGbXI7v2/sjnNW85tdmwhL4ByXzaTZfju1J4Noui0Kg60lPPllSFCyxuX61LIpBQZnm3msyAIwvNGBFEEQRAEQRCeosDixk1SE9EkiViyYBAlGowRWAyiVCuxOc27mhUyM+/n/NWNm0Rnsjk+O9+Ho9hI0S43IZVIJLQeq6Oi0YnPs4xCIaPYZUWxSRmQbDbLzOgCk0NzREIxHC4rlU2lj9yEd/2cpEjvLZYplXKCc/6C52VzOeIb9MURdiaVSnP2yz68niAWi5beS4MrARSAXA7v/DIqpZzeqyMiiAL4AxHOXhpkZCK/ObXkhoTGWgenjzdsmRUnCE9bJp3BPeplYnCOaChOSaWNql18795tyWSK6YkFDEVGbpwbRKGUY7HpMJq1SGVS6vZVYC0xP9K15QoZHcdqqW0uZdEbRCIBe4npkTcP6E1aipwmvO78z6vQcowil5VYLLXuNrlcjjtXx6hucORlvsSiCb761R2Ge9yrx4L+KKN9bs58o5O3v3WAWDSJBFYzRh7FwJ3JvADKfeP9swzV2Ok81bjtawV8YdwjD661OL/M4bf38dVfXSWZSBGPJtCZNLQeq6d0G73WouH4hmN+7/rgyl7S3ljKrHeZ0b7ZguNKpZzXjtaj26TPjSAIgrCeCKIIgiAIgiA8RSXlVrwzhRfl9WZtwQWUwRtjXPzlDUJLEWQKGc1Hajn2Xie6XeqfMjK2QG6zLrdAMpVhctq360GU+wwWHYZtlAXK5XLcuTDExQ/vri6yD96exHB+kPd/+xQOl3WLK2xNpZJTV1fMrVuTJBIprBvsgLXa9FiLN98dK2zP7Iyf6UkfACq5tOAilc8XxjPlI5POvNT9UaKxJJ+c62PKvbRuLJfLMTAyTyad5b0321A9ZjNpQdgtmUyWm18N5JWzHO6e5s6FId7/uydwuHaeyfAkJZNprnw5wO0ro9jsehoPVtF7dYRIKIalSM/hMy0ceXvfY29o0Js06E2PH/BUaZQceqOdj/7Hhbx+a8lkmuq2cmbmCi/8z8/6WV6KUOR48Fk2PerNC6Dcl83muPRxD+U1DkzWxyvjl4gn6bs+tuF4740J2o7WbrusmVQiWckUutc6JZ3KEAzEeONHJwksBCmttlO/v5LKxrJt9WoxbdLbqaSyaFtzelZ0GhXvvNKMBgmfftnLwmIIAIVcRmOtg+oSC7JYmiLH1n1hBEEQhAdEnrcgCIIgCMJTVNvmQiYv/BWs81TDul2ds2MePv2LC4SWIgBkUhl6Lg5x+2zfrswnk8kyOuXd1rnTM+sXbZ82z/QSlz/qepClcE8oEOXGF31ks4Xrf+9UfV0JcrmUbDaH2mrAbM/fKS2RSHj1a53Yy0Rj1t2wsCZokkhlKHKu/7tGIwmsTvNLHUABmJpZKhhAWWt4wsvMbODpTEgQtmF+apGrn/euK2cZCkS59dXAloH8p21q1MvtK6MA+BbC6IrNvPmjk5z+7hEOvb2fjtdb91wQva6jgq//g9PU7avAbDfQfLiGN398Es9iZEfXGesv3AMGVsqAzU/7HneqpJJpkon0huPJWJLMQ83kN2O2G6huzi9LloglmZvyYbAZePOHJ2norEalVW7res4qO5XN67MelWoF9R2V255XPJ5kbsbPnHuJWPTpZa7qNCqOH6zljf01fO1UM++fbOKdow3YMrA4usCBVxpXe9gIgiAI2yMyUQRBEARBEJ6i8loHb373MBc+vEskuFIuQqGU03GynuYD63+Yu4fnyaTXLyT0XRlm/5kW9I+ZjZIDtrt2tTvhicczN7m4rqntfRMDs/i9QWyPWF5lrTKXlTffbOOzz3qY94U4/LUD+CYWmOyfQa1T89o3Ojn+RvuullV7mWXXPAn9gRjNx+qYHffm/b9WqhU0Hqx+FtPbU/o3aHL9sOFxDzVV9ic8G0HYnrkJ37rg931j/bP4F0JYi/fOzviR/vxSSOFgnHDwfomnGIpbkzS0up7+xDYhkUiobnFR3eIim80ilUq5e22M3p7CZZ0AHKWWdVklifjmjdfvvy9nMln83iCZbA6TRYtaqyIRSzE37SMaSaDRqXCWW1Fr1gcutHo1xS4rk4OF38+clUWoCtxuIxKJhIOvteB1L7G89KBsqlqr5MR7Hag0OwsYKNUKXvvOYbouDjFwc5x0KkNZbTGdp5txVGydNZVOZRjqneHm1VGW7mWCmK16Oo/U0NTuQql88ktxZpuek2+2MdQ9zcDtSdLpDFX1Tpo6K3BVFz/x+xcEQXjRiCCKIAiCIAjCU9a4v5Kyajset59MJoOt2IRtg5JRqeT6OuawUls+ndp4F+d2yWVSyp0WAps0IL3P6Xj2u24LBZRWxzJZMpnd283c0lKG1apnfNzL+NgC9qZSjr6zj9JyK84CmRLCo7Pa8jN9FqNJ3vw7Jxm9M8mSZxlHhY3GgzVUFdgZ/DLJZnMsBba3q9zn3/o1LQhPSyaz+Xt3doPg+LOyvLT56ywYiO7p0oJS6UrGa2VdMfrL6jUBoAckEug8Wr2uZFZ5jZ3JofnC15VJsBQZmJ1c5MZXA0wNe8hmc1jtRtqO1dBzc5KlNZmF1mIDp9/voLw6P6ArlUppP1rD9PA82YeCazK5lJZD1VtuUsjlcviXwmQzOfSGlaDMt3//daaG51leDKMzaSivL8H2iN9djFY9pz44QOfpJjLpLHqzdvXvupU7N8a4+EV/3rHAUpgvP+oiGolz9JXGp7IJw2zTc+RMM50n6slksgUDWoIgCML2iCCKIAiCIAjCM6A3abeVReKsKrxbsKKlDJNtd5rxNtQ66B6Y2fQcmUxKdfmzr1lvL904eFFcZsG8SR3zR1FSYqKkxMTx4/Xbvk00HGdm1EMsFMNUZMBZVYxSNHDdlMtlwVZkwHdvx24insYdT2NvcVHWUUksnsJVX4Ja/XIvAEmlEpTb7BGgVomfesLeUeQ0bzjmcFkfu8fGbrMWG5jfoH8ZgNVu2LMBlLXMVj3vf+cQX37YxYLnQXBDrVFw/EwTdQUC01UNTm5dHCYaWh94aeyoRCaT8PP/dpFY9EHGikqr5M//06cYLTosRQ++myx5Q3z0v6/znb93CttDPTiqm0p56/tHuPp5H4F77/3WYiNH32ylsqFk08c1PbFI160JJoa9ZDIZTBYd+w5V0dTmou1o3fb+ONukM+4s49e3EOLahaENx29cHqGmoYTiXcia3S6FUo74FiIIgvB4nuk363/7b/8tP/vZzxgYGECj0XDixAn+3b/7dzQ2Nq6ek8vl+Nf/+l/zX//rf8Xv93P06FH+83/+z7S2tj7DmQuCIAiCIDwdrgYnna+1cuds32rNeLPdyKFdLCVV6jBzrMnF6NAscoUcdErm/eG8Ml8nD9fisD/7UivOqiIaOioZujuZd1wmk3LotZZnHqxYnPPzyU8usuB+sPhW0+bizHePYjA/Xum1F5lareTNt9v41S9uEQk/qBsfvLd7urXdRdND9e5fVi0NTuYXlrc8r06UaxH2EFdNMVVNpUwMrJSWSsaTBP1RIsEYDR0V9N+aoLbVhc74+E3Wd0NdUyl9t6c2HG9sLycSjOKZ8pHLZXGUF6E3761A0H3Ocivf/Z2TzE77CAXjKJQynC4rZkv+fLPZLJ7JRYJLYU681szk2AIjvW5yuZVF+JaDVRx6pZG7V0byAihSqYRYNEkoECMRS2Ewa5GvCTDFIgnGh+fXBVEkEgmN+yupqC/B51lGggRbiRG1Nr833MMmRj385q9vklrTMyWwFOHcJ71455c583YrKtWzC7jPuZdIJTfJvEpnmZ32P9UgiiAIgvD4nmkQ5auvvuIP/uAPOHz4MOl0mn/5L/8lb7/9Nn19feh0Kx/o//7f/3v+w3/4D/y3//bfaGho4N/8m3/DW2+9xeDgIAbD7uy+FARBEARB2KuUKgUnPjhAdVs5S/MB1FoVZbUO9JbdWaxJJlLc/LSLW5/3sOALs7AYwmDTcfSDg0z4QpiNGo4eqKGlsXRP9P9QqhS88kEn9jIzPddGScZSFLus7DteR1XTsy31lMvluPFZT14ABWCsx42zys6hN9qe0cyeD6VlFr77g6OMjXro650hncxgK9LT3FpGVbUdlUrsowWoqrChuaEklti4b4FBp6LS9ewzxwThPpVGyWvfPsjALRu3zw8yN7mIxW5g3/E6JgZmGbozyVjvDG/94OieCKRU1Ng5erqJq18NrBvrPFGHUgZ/+R9+TdC30n9DZ9Lyxo9OUN1avnpeLpfbE5+bACq1gur6jbM7EtEklz+8TffFwZXSahKobi7nm799iixgMGuwFZvI5XKM9eX3WNHoVMzPLAGQTKRJxtPI9flZOtOjXg6daih43xqdClfN9oK+iUSKS18O5AVQ1hroclNbX0Jdk3Nb13sSksmtS61uVKpVEARB2LueaRDlo48+yvv3n/7pn1JcXMzNmzd59dVXyeVy/Mf/+B/5l//yX/Kd73wHgP/+3/87DoeDn/zkJ/z+7//+umsmEgkSibW714LrzhEEQRAEQXieyBVyyhuclDfs/qLA1OAcNz7vBSTYbQbMJi3RaBL/4Bzf/e1TlJRY0O6xGto6g5qDp5tpO1pHKplCo1Mjk22vTvmT5F8IMto9XXCs/9oo+19tWsn0ETZks+mx2fQcOFBNOpNFqZQ9k0XITDpDJBhDqVZsuSv6abNZ9HztzTZ++WkXiQKLdVq1kvff3IdpDyxEC8JaBpOWw6+1IFfIMZi1xMMJZscXVsenhueZHJyj5XDNM5zlCplcxuFT9ZSUWRgdmMW3EMJs1VHXXEpRsZGf/acPVwMoAJHlKJ/95CLf/6fv43Uv0XdlhJA/QnVrGY2HajctRbkXjHZPcffcmh4eORjvm8busnD8awdWD0skEhQPNUXPZnMoFLI156y/vnyXGqnPzwbyypIVMjo4t60gSiqRIpVIo9ar1vU6SacyJOMplGoFcsXOyrYZtvHeu51zBEEQhL1lT/2KW15eSUu3Wq0AjI+PMz8/z9tvv716jkql4vTp01y6dKlgEOXf/tt/y7/+1//66UxYEARBEAThOecensv7t0Iuw2TUEPNH0Eikey6AspZKrUC1l3qN5FgtufawbC634ZiwnkwuRSZ/NoGx8f4Z7pwfxOv2oVIraDtWT8uRWrR69TOZTyHVlXZ+65uHGR730jcwSyKVRqWU097ioq7Kjn2X+iUJwm7LZLL0Xh3Jaz6+1nj/7J4IosBKIKWq3kFVvSPv+ESfm+V7PTzWigZj9F4e5sYXvXDv7X7Js8zwnSm++ftvYH3EBudPw8hDJTLv678+yoHX21Ct+S7QuL8Sz5p+MbFIghKXlf4702gN6rxz76vdpcyQRGzjDLz7AoHIpuPxaIKBm+P0XBkhHk1QWlVM+4l6yutLSMRSjPS66bk5TtAfxWjR0nawmrpWFyrN9r5vlFXYMJq0BJejBce1ejVlFUXbupYgCIKwd+yZIEoul+Of/bN/xqlTp2hrWyl1MD8/D4DDkf+lxeFwMDlZ+EP+X/yLf8E/+2f/bPXfwWCQ8vLygucKgiAIgiC87MSy/u4x2w1UNpUy1uNeN9bYWY1CuYcCPkJBEwOz/ObPLpBJr5SKScRSXPrwLv6FEK9/78ieyHi6z2E34rAbObSvkmQ6jVIhRy1KngnPuef5MymVTDE77l33IIJLYcZ6pp95ECWXyxGPJlAo5TvKinx4A0BtSykjPW5mJxdXj2UzWaoaHCCRIJXmp6KUlFupqN2dHk2abWzssGwSRM7lclz9uJu7FwdXj410TzE5NMvX//5phvvm6Lk+tjoWiyTwuP145wKcfKcd5TYyarQ6FW+838Fv/vbmuqCPUinnzfc7MJhEJoogCMLzZs8EUf7wD/+Qrq4uLly4sG7s4RT+zWqLqlQqVKq9lXIvCIIgCIKwV5XXO+k6P7jueEmlnSKn+elP6DkmlUo58lY7vtkAy0sPyryU1hTTdLgaWNmFHYvEUSjlqNR7N8vnSdlLPQIels1m6b48vBpAWWvw1jitR2oprbY/g5ltTq1WoEYET4Tng0wmpWF/BVc+6Sk4XtO8eW+rdCpNMpYqWILpaSkut2EsMhB8KBtFoVKs9BMpwLMm4PAszE8ucPdsH9NDcxjMWva/1kr9gerVv2FdRyXjvevLUTYdrl1X0tBg1vL2Dw4z2jtD/60JUskMrio7p95tZ2psgb5bkyTiKVRqBc2dlew7XI1+l8pXlZSZKSkzMz8T2PCcusaNe7943Uv0XBledzyVSDPc7abnVuHNut3XxqhpKqXyoaykjVTU2PnOj48xPuRheGCWbG5lXjUNJZTs8dJugiAIQmF7IojyR3/0R/ziF7/g3LlzuFyu1eMlJSsffvPz8zidD9I/vV7vuuwUQRAEQRAEYecqGp0ceqOVW1/2kc2u7DY1WHS88o2DKMSu9h1zVBTxnT94i5kRD5FQDJPNQFmdA41OxWjfDD3XxvDOLKFUKWg5VE3T/koMZu2znvYTFQlGGe6aZvDWBOl0hob9ldTvq8Bc9GC3cC6XI7AQJJvJYbYbkMl3VoN+N8QjSTzTvoJj2WyOZV9oTwZRBOF509BRyUiXm8X5QN7xivoSKhsLl33KZrOM3JnkztlelhdDOCrtdJ5pobxxJeiSSqaIRVMoFDI0uie7qVJr0PDmj07y6V+cJ7S0UjpKa9Jy6hsH+epvbxS8jdlufKJz2kxgIciv/7+fEw6slJeKLEf5+L+fQyKV0nBgJcBf216B99Vmui8MrHwXkEB1Szltxws3gzeadXSebGDfsTpy2dxq35DK+hI6jtYQjyVRqZWYLLpdfSwKpYITZ5r51V9dJ5lY3xOq42AVrsqNS2Ut+0JkCgS6ZHIpUyMeMpnshhmH7vGFbQdRAIpLzBSXmDnyysrfcK9uIBAEQRC255kGUXK5HH/0R3/E3/zN33D27Fmqq6vzxqurqykpKeHTTz+ls7MTgGQyyVdffcW/+3f/7llMWRAEQRAE4ZmLx5J4pnwkEinMNj32Ussj/zhXqhQce7+T6rZyfLMBlBoFZTXF6M27u/DxMjFa9RiP6POO9d+a4POfXV8NVMUiCS5/0o171Mvbv3UU3R7qt/GwWCROKp5GZ9buuJxVLBLn87+6zkT/zOqxy3MBhu9O8v5vv4K5yIBvPsD1T7sZ7Z4mm8niqi/h6NvtlNY83U1TCtXK4ms0FC84rtK+fJlDgvAkmIsMvP/bJxnrm2G4awoJEiqbnFQ2OtFtkLEw1jXFR//9K3L33kPHe6aZGZnna7/3BoFggu7bkwQDEZRKOS0d5TS2ubAWPbneQOUNTn7r//11PFM+crksDlcReouOxbkAt77syztXoZJT0/7sSoy7h+ZWAyj35XI5us/3U7e/EqlUikqr5NVvH6bxQDXLvjA6owZHVRHKLcpQymRSeCjmbTTrMD7B7xDlVUV860dH6euaZqR/nkw6g6VIz74DVdQ1l2xacuvhrJr7pDIp2WwO6SbfpZKJ1CPNVwRPBEEQXgzPNIjyB3/wB/zkJz/h5z//OQaDYbUHislkQqPRIJFI+Cf/5J/wx3/8x9TX11NfX88f//Efo9Vq+fGPf/wspy4IgiAIgvBMzI4v8MXfXGfJs9KUVyaX0X6sjsNvtKDZYHFgKzKZlNLqYkqrd6dmuZAvGopx5fPe1QDKWtOjHqaH52nqrHr6E9tEKpkisBhmrHuK3qsjJGIpnNV2Dr7WQnn99hsETw978gIo9y3OBhjvm6H5YDWf/c/LzE89KHUzNTjH4qyf7/7h21iLn14PAYVSQfvxes7+zfqd5FaHCecmu5sFQdgZc5GBzlcasdj0dF0c4tpHXdz+opfmw7UcONOMYU0GQy6Xo+vi4GoA5b5YJMHVz3rwLCdWswuSiTTXLgwz1D/H1793CNsTzADRGbXUtOVnEh56vRWlWkHXxSGSsSSOChuH3mzHWfXsstji0UTB4yF/hEwmu1rSSyqV4qwuxvkcfBdwlllxllk5cqKBTCaLTq/csO9YNBjDM7VIDrA6zJRUFTE/kV9eLZVI09Zezu3LIxvep12UOBUEQXipPdMgyn/5L/8FgDNnzuQd/9M//VN+93d/F4B//s//ObFYjH/0j/4Rfr+fo0eP8sknn2AwPLldJYIgCIIgCHtR0B/mo59ezttRmklnuHNhEJ1BzcEzzc9wdsJGFuYChPyRDccnhz17JoiSy+UYvjtFz5VhluYD3PmqH5vTjLnIyGT/LHPjC3zn/3gTR8X2Agpzkwsbjo31zWC26fMCKPdFQ3Hcw/NPNYgC0NhZTXApTNelYdKpld4odqeZ1753BI1u72YLCcLzaHJgll//9/OrfUSSiTR3LwwSi8R5+8cnVhf30+nMuv4jAOFgjJkxLxqnlUwsv0RTwBem+9YkZ95pf/IPZA2NQcPRdzpoO15PIp7CaNHtqIn7dmWzWeYmF5mfWCCTzlLsslJaW1wwc6SozFrwGjXtFSiewNyepq0atLuH5/j0JxdXnz96i47jXz+IVCZldswLuZVMoQOnW6jbX8nowBzBAp/XJoueitq9H1wSBEEQnpxnXs5rKxKJhH/1r/4V/+pf/asnPyFBEARBEIQ9bGZsYV1JjvvuXhqm+WA1WsPeX+jNZrN4ZwPMTflIJdOYbHpcVUXoDLvTeHav2eob70bfibPZLJ4pHwszfiRSCSWVRdifcEPaka4pPvof5ympKOL22T6S8RSzY15ymRy2UgvJeIrRrultB1Hkm5SCUaoVRIKxDccjGzzXnySVRsHJr3XS0FlFYCGESq2gpLIIlUaU8hKE3dZ3bbRgI/aRu1N0nGpczd5QKORUNJXSfWEw77yAP0L1wVr80cJllvq7puk8WoPpGZSn1Bm16J5QEkw2m+XuhUEu/up2XoZj27E6Tr7fua70YFldCZ2vt3L7y97VD6SiUgutJwv3O3lRxMJxPv/ppbwAXNgf4dIvbvC9f/wu4WCcZDyFqciAzbESsH/3B0c495su5u/3x5JAicvGq+/vw7jL/V0EQRCE58vzve1AEARBEAThJRIJbbzgHA3FiEUSez6Ikk5luHlxiBvnhvKau1pset76zkFKXIV3zD4N4UCUoD+CUi3HVmJGIpGQy+Xwzi/jW1hZhLHa9TjujW1XkcOMzqjZMGBQUbe+90cmneHGF31c/6xndZFRrpRx+luHaD1a9wiPbmvpVJq75wfIZXPksjmS8QcLkwtzfkxFBuRKOb55/7av6aop5uaahbu16tvLVwJnEgqOm59yFsp9EomE4jIrxRvs3hYE4fFlMlkWZwMFx7LZHNFwfm+ituMNjPVM5wVXi8uLkOvU5CKFgyjJRJpkfH3z8eedZ9rHxV/fWVcisufKCGW1DhrvZTamkmmmBmaY6J9FoZJz+nvHyKYz6ExaXPVOdCZtgau/OLzTPgLe4LrjkeUovrkAtfsq142VuKx8+3dPMjnsxTu/jEIlp8hhxGQVARRBEISXnQiiCIIgCIIgPCcMJh1KlZx0KrNu8URv1qI1PFpPlKdpbGCWq18OrDvu94X5/Oe3+M7ff+WRe7s8qlQyTc+VEW6e7SMaiiOTS2noqKT9VAPdd6cZ6JtdDfjIZFLqm5yceKURo3l7C1B6k4Yjr7dy9uc312WdlFTYqKhfH0Rxj3qYGvdiqyxCLpMSXgwR8kc4+7Mb2MssFLtsj//AH5KIJVm6t+Akkaw04L1fSz+dTJNKppEr5djLtn/frtpijr7VzrVPe/Iee9vRWqpbypDKpDR2VjF4ayLvdsUuG+UNJY//oARB2JNkMimOChuBAmW6ZDIp+ocazBdXFPGdP3yXyX43oaUIVqeZUDTF9JgXp8NIIpPF7wuz9i1WrVGgUm/eGP15ND+xWDCDB2C8z01jZxW5XI4bn3Vz7eO7eeOH32ynormMxbkA8WgCm/PJZjfuVRsVRcmks/R2z3DlwhDx2IPgnMmi5dXXW6gt8HktCIIgvBxEEEUQBEEQBOE5EFgMEloKIc1l0elV6M06vDP+1cX9/Scb93zPhkw6Q8+NiQ3HA74Is5M+aptLn96kgKHbE5z/5a3Vf2fSK7XmhyeXiKczSGXSB2OZLAO9M8SiSd77xgHUmu0t0LUeqkKllnP38gi++QAKpZzmA9W0HKpGb8wPxowPe/jFX16n+/o4sLKgWNPgoL6plJmhOWbHF55IEEWlUWIq0hMLx1nyBuk83czlD++szEEuQ66QodGrqGkv3/Y1ZXIZh15roaymmLnJlYU/R7mN0mo7StXK3+6Vbx3C7rLSf22UdCpDw4Eqmg/Xon/Bd0kLwsuu+XAtI13TZNKZvONNh6opLl//Hmd1mLDeK7s00T/DV784z51LwwA4K+3sf62Fmbnl1U0GLR0V2w52P08e3kSxVia98p1gwe3j5uc9eWPpZIZf/clZDk75cI94UCjl7DvVwOE32teVAHsRFJfbMBcb12Wj6ExaSjYoSdnf4+bsp73rji/7o3z4i9t883uHKK/cXjlLQRAE4cUigiiCIAiCIAh7nG8+wG/+9CuWPMvEIgnmJhfJAWe+cxjv3DKdrzTScqjqWU9zS/FocrUs1lpmqxaVQsbyYpChm2NoVHKc1fYdlcx6VIlYitvnB9cd11h0nPtNF9XNpQVLnkyOLzAz7aN2i2wJ77SP2TEPqWSa4nIbX/+7J0nEUyhVcrT69UGviVEvv/7bm7gnfavHMpksw/1zeOaWOX68dnWRbLfJFXI6TjYyP7lIIpZErVVy5ntHGbw5hs6gpf1EA+0nGyjeYck1mVyGq9aBq7bwDl6dQcPB11rpONVILpdDsUkfFUF4GQSXwoz2TDPSPY1MJqNhfwU1beUF3zOeZxUNJbz3O6e4dbYP7/QSSrWC9uN1tJ1o2PT93zcf4KM/v0AymsBmN+JbCDI3uUDklzc58rUDzM8tU1xioq1zfbmmF0FxuXXDMojVzWUA+BeC64JTS54AC+4l4uGVDMNUMs3NL/rQG7XsP938pKe9KxY9QSZHvSx6llFrlJTX2CmrsKJSrw8CafRq3vjhiXWN5d/84Qn0BfqbRCMJrl0e2fC+06kMPXencFXYnsr3E0EQBGFvEUEUQRAEQRCEPW7wxhhLnmUANDoVVU2lxCMJwv4IP/hHb67uzN3r5Eo5SpWcWCSxesxs1eF3L9JzZWXhYraqiNE7k7z+/aO0PKHeH2vFInFC/kjeMZlcij+w0r8klcoUuhkAUxOLmwZRRu5M8slfXCCVfFCTv/O1Fo6/34lCuf5reDqd4caVUdLpLDqjGv9Du2eDgShLwRjHy59cr476/VVk0zlufNlLYDGIzqjle3/0DpUNpRht+ie6cCRXiJ8mAMtLYcb7ZnAPz6MxqKluKaOywYlMLnvWUxOeglAgykc/ucT85OLqMfeoh+kRD69/58gLlzFQ0+qivL6E8HIUpUqO7l5mXiaTJbQcI5vLYTRpkK95/s+MekjEkkilUhylZjQ6JX5fmGgwhjyX49QbLdQ1OTG9oI3AnZV2DpxuZvjuFDK5lHAgSjqVoaq5lMp7mZwPZ6amk2mW5le+R8hV+e+1XRcHaT5Si0qzO8+taChGeDmGWqfEaNHvyjUBRvpn+fTnt0mu+Uy9c22MhrYyXn2nDV2BbFxXvZPf+qdfwzO1svnEUW7bsBeMZ36Z4PLGvecARoe9BPwRLNb1jyuwsMxU/wzxSIIilxVXQ+lqxqUgCILw/BO/VARBEARBEPawbDbLSNdU3jGpVILWoCboC/E8bYZUqRW0Hqji0mcPSmUo5ZLVAIpMJkWnV5HJZDn/y1uU1Tkw2QxPdE4anQqDWbvaCwRAKpOSTK0s0igUGy9cJxOFmxkDRIJRzv3N9bwACsDtL/uobCyl8t5u4bUWvSFmplcyUPRmHQaLlpA/mneOZyGMoejJ/U1kMiktR2upaXMRCa4sgumMe7McTjabRSqVbn3ic8TvDfLhn59ncS6weqz36gjH3+3g0OutYvfzS2C8z50XQLlv+O4UDfsrqW3bfjm954VCKcdiNwKQy+UYH/bQfWcK9+QiuVwOh9NM+4Eq6hudyORSkvEH771SmRSzVY/JoiObyVJdV8yB408+AP8sSSRgKzYyRg7vhJeqVhfNh+uoailDZ1jpJeOssuOqd+IengMgm8mRyWSpanGRiCfzrhePpUin0o8dREmnMvRdH+Xml32E/BHUWiWdp5uR5LIM35lEJpXSfLSWhs6aHQcDfd4gn/4iP4By31DPDDa7kSOvNBS8rdaooXobr5t0euNNE/dl0pnVMqprLbh9/Or//ozlxQffJQ6+tY8T3zyMTPZifU4JgiC8rEQQRRAEQRAEYQ+TSCRoDOuzEgAUKgXyTRb596KGdhcTQ3PMTi2hUMrwzweAlcdZ4rKivFeSIx5JsDjrf+JBFJVGSccrTXz519dWj6WSacwOM3qTFs0m5XNs9o3n5psLEApECo55phYLBlHSqcxqs1u5QkZZTTGhQJSQP4JUKsFg0WGxGzZsiLub1DoVap3qyd/RDmXSGSbHFhjum2V+1o9Wq6J5XznV9Q50hue/1NHg7fG8AAoAObj2aTeVjc4n0gtH2FumhuY3HPNOLz2RIEoykWJqbIGR/lmW/VFsDgN1TaWUVxU99QyovrvTfPFRV17fj9npJWanlwiebuLQ8TqsJeuzLyUSCTKFjKJS81Oc7bPRe3mYL//3ZciBQi5jZnCOdCJN1Zp+YgqVgjd+6zi3vuxl+M4EKrWS4+/vR2fSMfdQkM5VV4z2XvDlYcl4kvGeaSb63MjkMqrbKqhqdRUMDAzemuDsz66v/luukHH517eYGfFQeq+PyNy4lyVPkFe/fXhHQeGp8QWSifUBlPu6bozTsr8c/QaPYzsMBjUymbRgkOQ+o0mDrsBn48C1kbwACsDtz7up7aiktHbzsp+CIAjC80EEUQRBEARBEPYwiURC69F6Zke968ZajtRh2MVSGU+D0azlne8eZnRgjsGuaWL+EGabDrNNj96Yv/jxtBbvGjsrScWT3Piyn3g0gUwqpbLaji8YR7LBDlK5XEpFtX3Da8rk0g1r1ssLlPIC0OqUKJQyUsnM6nmWYiOWYuPqOWarHs0LVs5nuzLpDDcujXDlqwc9bAK+CLPTSzS0lnLqzVYMxkdfQHvWMukMw3emNhjLsuD2iyDKS0Cp2vgnuuwJBM0TsRQXPu+l59bk6rH5GT+9t6Y4drqJQyfrntp7cWApwvkv+jZsnH71/CDlVUWU1Tho6Kxk6PZk3njbsTqcVcVPY6rPTCQY5fqnXes+WzwTC7iH52k8WLN6zGw38voPjnPk7X3kcjn8CyF++f87m3c7pVrBvpNNBQMauVyOqx/e4dYXD7JHe68Mc/q7R9l/uiXv3EQsye1zfXnHDGYtV399mxw5rMVG1PcyXbovDNBypA77DvprLXrWbyRZKxKKEwnFHyuIUlxiprqumJHBjQOZ+/ZXotGuD6LM3Mv4WSubzbG8GBJBFEEQhBeECKIIgiAIgiDscXX7Kwguhbn1ZS+pRBqZTErDgeqn0gg2lUzjv9cM3mI3FOzlsVMGs5b9x2rZd6SaqcE5gt5lcg8tmllLTJRUFD32fW2HUqXgwJkW6vZVEPRHUKoUFJWasVfZ+fzDrnW7UmUyKWfeaqN4k140xS4bzio7c+ML+be9l2FSiLXIQFOri+6HFgbXau+sRPWS1lh3T/ryAigAeoMavVpG15e9DF0a5MjrrTQcqMJWYn42k3wcEgkS2cY7szcbE14c1a0uBm5NrDsukUoo3eC943GMDM7lBVDWuvLVAMWlZqrrHbt+v4W4p3wk4huXScxmc7gnFnGWWTj9nSOUNzgZ7ZpCIpVSt6+CmjbXrnxG7WXRUJzwBlmO9/t7ZdIZZkY9zE8sIJFKcVbbKat1YLDo+dY/fJ2Ru5MszgVwlNuo31+JY4PPWu+Uj7vn+vMP5uDaR3eobqvAZHuwiSMajhN8qL9YMp4ie+/zM51Mw70gSiaVIbwc3VEQRa3Z/HNPJpMikz1esE8qlXD0ZB2L3iCBh0ppAlTX2WlqW59FClBSXcz8RP7nvUQiee42ugiCIAgbe7G/YQiCIAiCILwAlEoFx97toH5/JSF/BI1Ohd1lfex+EKlkmlgkjlKlQF1gZ+XEwBzXvuhl/l6fjpJyG0ffaKWy0flY93ufVCqlotHJq984yOUP7672GLHYjbzx/aNPvZyU0arHuKZZbEubC4NBzVD/LOP3MoEqq+00NJdSUbV5gEehUnDmu0f58q+uri6saA0aXvnWIRyVG2ewHDhczdyMn8UC5dvqG0toaN6dv/3zaGwof6evUiVHnk7x2f+8CqxkLqVjSYZuT/CNf/j6ao+F54VMJqXlUC0XfnVr3ZhCJd9woVN4sVQ2Oul8tZE754fI3avdJ5VJOfHuPkq3eN/ZqXQqQ2+BgM1aw70zWwZRstkcUunjB/ni0Qe9OhKJNKFgjGg4AdwLmBrVBIMrjb+1ejVtx+ppO1b/2Pe7l4QCUWKRBGqtomBTdr1Ji9FmILgYWjdmsunJZrPc/KKXy7+5vZqtIpVKePXbR+h4tYmyWgdltdsLioWWwmTS60tbxcIJosuR1SBKNpslR47yhlLmx73EIiv/z5QaJTK5jGw2m1d6VKGUY7TotjWH+8qr7Ny6PLrheHW9A6v98QMW9mIT3/j+YUYH5unpmiKZzGA0amjrKKem3oFugxKfzUfqGboxRjT0oDF926lGSjbJWBUEQRCeLyKIIgiCIAiC8JywlZh3ZYd9NptlpNvN3YtD+DzLqLVK2o/V0XKwerUHyMyYl9/8xUXSqQeNVuenfPzmLy7xwd9/BVf17uyIlkql7D/dTHmjE99cALlSTkllEdpNepE8TeWVRZRXFhGPpyCXWy1Hsh3F5Ta+84/eYn5qkUwqQ1GpBb1584Uji03PB985yOiwh54708TjSUwmDa0dFdQ2OAqWEXlZLM7nLxrarDrO//WV1X9n0hnS6SyBxRBjPdMcfK31aU/xsdXvr2R6ZJ7JgdnVYzK5lNPfPIRtk8wn4cWhVCk4/u5+qpvL8LiXkMqklFTYcJTbHjtw/rBkIsVygR33ay3MLxc8nslkcU/5GB6cZ2bah1whp6mllOpaO9ZH7GV1P9sgEo4zNb6Y1+h7ORBBpVRw+MSLFTS5LxqO031lhK4ro/eCKErajtTScbwO3ZoyhRq9mqPvdvDZTy7mZXCWN5ViKbUw3DvD1KgHnVFDZHllQT+byzF8d4JIKMaSZxmDRUdNmwtXXcmmfUl0Zi1SmXQ1m+Q+tVaF1rQyp4lhD723J3BPLBJYCKFRyymtL8E7sUBwKcL+M82M90yjWvPZ1fl6K7ZSy47+PqWVVlo6yum7O71uTKmSs/9Y7a69PqxWPdYTdew7WEUqmUalVqDYopSeo8rOd//J+0z0uokEo5RUFVPRXIZcIZbcBEEQXhTiHV0QBEEQBOElM3h7ks/+6trqAkwynuLib+7i94Z47dsHkcll9N+ayAug3JdKphm8NblrQZT7ditA9KSo1Y9WQkuhUlBev7PsEZNFx4EjNbR3VpBKZlYyLp5yc+e9yGDWgPvBvzOpdN7OdalUivReyau58QV47WnP8PEZzFre/tFx3CNeFmf9KNVySmscOCtFFsrLRK6Q4aorwVX3ZHspKJQyNFol0XuZA4UYzNp1xzKZLDevjXH5/CC5NZUYFzzLXL88wtvvtVNeVYRCubP3zdJyKxKJhOlJX14A5b50JkMskiART6F6xPfkvSibzXLl0x56ro2tHotHk9w4208oEOXN7x7K60vTdLgWtU7N8K1xIsEoZfUlKHRqzn3Sy1Cvm4n+OeqanVS1lDE/NIe93MbNL/rouz6GqWglwNV9aYg3f3iCpoPVG87LUVlE64kGus8P5B0/+FY7JpuRwW43n/781mrJS6VGyaI3yPjwPKfeaGFhapH9p1s4/l4HU4NzSKQS6joqqdlXseO/kVKp4NRbrVjtRrpujBEMxJDJpNQ0ltBxpIayit3vF6VSyVFt0qPoYbZSK7bS7ZcoEwRBEJ4vIogiCIIgCILwEolF4lz/om9dDxKA/lvjNB+sothlxT22vpH9fe4xL+lUJq88h7A9uVyO5L2+Nlv9/RQKOQqxi3VVXZOTwZ6Z1X/LFDKkMgnZzMpz2WTVorzXL2ZtWbbnjUanpr6jgvqOnS80CsJOKJQKWvZXcP7T3g3PaSzQA2JyfIFL5/L7E2UyWZY8y/jmAsyOzHPscBWN+6uo3cGCubXIQMfBKrrvFO7RcuREPe7JRWbdS1TXPZ0+LU+Dx+2n7+ZEwbGhrilaD1fjWtMPRyqVUtNWTk1bOdMjHn75p1/Re30UmUJGbVsFtY0lDHS7mRz18to7bSxNL7I456fCVLp6jUwmy+UP71DRUIJ2g2bsUqmUE18/gMNlY/jOBHKFjIYD1dTsqyASinPhs568nmFSmRSb04zJpicYSfH9P3oXW/FKWcWOV1sK3sdOaLQqDp6oo7mjnHAwhkwuw1qk3zSbRhAEQRB2i/hVJgiCIAiC8BJZ9kUIFKilDpDL5ljyBHFWFaHRqQkuFW5eq9GpkMl3t6zMkxANxZkb95JKprA5LdjLnt0O0Uw6w/iIh/67brzzAVQqBc37yqltcmK27qw2/MuqvNpOU7uLge6VdJRwJEnD/ioGbo6jUMqxFa0s1snkUmpaXc9yqoLw3KhvKWV0cJ7ZKd+6sYa2Mioeamafy+UY6Jtdd+7S/DLzk4sAzEwtEWop5cM/O8c3fu8NKnbQR0unV/HW+x0M9s0wNbFILpujtMxC875yyOWQ5XLMj3kw6VVY93D24k74F4LrSmbdl8vm8C8E84Ioq7fzBvlf/9eHDHbdK3GVSHP34iDOKjuOUgueWT+TEz5yy2FUGuW6YElwKYzfG9wwiAIrpbtaTzTQeqIh7/jEiJdwMF7wNnKlnOByjFh04wynx6HVqdA+5Z5pgiAIgiCCKIIgCIIgCC+Rld3762uc36dQyUnEU9S2uZidWEAmWx8saTlUved3fs5PLvLJ/ziP37tSz1+ulHPi/U46Tjdvq256IpZkbmKBeCSBxW7A/hj9CDKZLDcujXDlqwc7t8PEufB5H/1d07z33UPY7I/WQ+BlolIrePXtVhylZrpvThIMRKneX0VxqRnPxCIKhQyLw8jxd/fjqt9+GaTlpTDj3VPMjXsxFRmpaS+npFI0A34SlpfCjPa4Ge6eJpeDhn3l1LS6MNue38yh553BpOXdbx9kqMfN1LiXbDqDQqmkst5BQ2spGm1+H6hEIs2ceynvWCqZZnEukHcsFk+TSWcZvDW+oyAKwJx7iYoqG81tK8HQeDRJOpEkNLdE17kBHE4zd1xWDr/TQeeZlrxSV8+jrbIS5crCyzbTw3NMjeZnjcpkUpRKOa0HK5i2aVGrFZTWVJOIp9fdj1QqQfaIGaXxcAxniYEsEhYXCzegTybXl2QTBEEQhOeVCKIIgiAIgiC8RGwOI9XNpYz2uNeNldXY8fnCXPm/v0SlUaIxaJibWMBk0yO/F0xpOlhFbdve3uWfTqW5+PMbqwEUgHQyzflf3MDusm25wO73LPP5/7rMzKgHWFmU6jjdzNF3O1bLRe3E3PQSVx8qfXOfbyHE3etjvP5+x46v+zLSaFXsP1JD875yIuE4SqUcnUHN0nyAZCKNxW5EvYMdyqGlMB/+6Vk8Ewurx+5+1cfXf+8Nyht2tvArbC7gC/Hh/7jEwprFdq97iYHbk7z/d09gfsRm5MLj02gVaBRSonN+Aosh7C4LurZS1BrlunNlUgkSaX4QPZPOkk6l80+8d8rSfGBHcylxWZBIJASWogSWVpreSyQSLBoZtz7vRSqVoNGpVnp5/eIGVoeJmvbnu/RdSbkNjV5FLLw+c0OlVuDcoN9HeDm6rndZ+9E6Ru9OcPavFwmHE0hlMt7/8XGc1XaWH8pCrWgq23GGZiKa5MZnXVz+zW0GuqYxWg0ceaeDQCxFIpZaPU8ml2Iwqnd0bUEQBEHYy/Z+HQZBEARBEARh10ilUo680YqlKH/BsthlJRrPcPPCMMFAjIW5ZdQGDW3H66lscHLgdBPf+N1XOPONA2i0e7uMhm8uwMyYZ93xXDbH3JrF8kJyuRw3PuteDaDASibJrS96Ge+efqT5TIx48povAyhVcqT3FiIHut0sbVBiTShMpVZgLTKgN2qQSCTYnBacVfYdBVAAJgdm8gIoAMl4ijtf9ZHNFs7WEh7NcNd0XgDlvsW5AMN3H+21JeyO3qujfPrTSyzO+Umn0syNL/Dhn51jpGtq3bkKpZyG5tJ1x1RrAi4ymRTdvYbcZTvsXeIstdDc/iBQr9WrcJSaGb270ifFZjeivt9UPgfTg3M7uv5eZLToOP1B57pMEZlcyqsfdG4YYLQWm1BrHgT2LcVGFqYXmR3zIpPLUCjlyBVSRu5MUF7neHB9CbhqHZz6emfBbNPNDN4c48an3Sv/jw1qgkshvvzLS9jM+SXBGlrLKHKYdnRtQRAEQdjLRCaKIAiCIAjCS8ZeauFbv3eG6REP/sUQWp2anFTChU/ymwsHA1GCgSgSmYRDrzVT+QI18t3I8mKI4buFmxqP3J2k8VDNprcPB6JMDczg8wQwmHVUNJUR8D/oLeNwGEhF4gS8QXRmLYYiEx5PiEQ8tclVhZ1KJVIEl8KotEr0po17zizO+Asen79Xym2zXgHC9mXSGQZvF35dAQzcnqDzlcYtyxoJuy8SjHLj8551x7PZHHfODVDVXIbioXJSdQ0ldN2aJJlcyT6RyaUUu6y4h+fJ5aC53UXIu4zWoKGhs2pH85HJZZx8vRmjScN4rxv30ByyYiMatRxnmQVLkSG/nOTeriy5bQ37KjCYdUwOzLK0GMJs01PZ6KSsKr+0YDqVwT3qZXxgllwuS3VTKbcuDiGXyyirsnPplzexOc2rpbTMFh0arYqQP8IP/+n7+D3LqLQqSiptKJQ7y6zM5XL0XhkCVjZkOF023BMLxGNJlj3LSKVSstkcpRU2jrzauK7sZzqVYW5ygchyFK1Bg7OyCMUjZHcKgiAIwrMggiiCIAiCIAgvIYNZR8uagMCvf3plw3NzmRyzk77nJohic5opqyvBPZy/Q1kileCs3rzXRS6bI5fNFRxLF6j5HgzGWPAEyeVyqBQyLv/tNebGHtSo15m1tLzawihQ4jDS81Uv82saOGv1as781jFUarGQtFvGuqe4+nEXCzM+1FoV+19tpuOVZlTa9aWJjBv04jDbjXk764XHk8utZHRtJJvNkaPw6054ssKBGOHlaMGxxTk/0XAckzX/dVLiNPPeN/bz6Uc9RMMrzcVNRQakMikOu4EymxaDSUvTwWqKXYVLUW1GLpMSmFpk6u4kiXiK2WCU8ppi+t1LSCVrsjIkUN5YuvGFnjPOCtuGpbtgJQhx/Ww/17/sWz1W1eLCVGxi4PY4thITjsoiMllIJtOo1AqKilf+Xpl0BluJGVuJ+ZHnl8vlyKwpH6bRKqmqdxAJxjFZtDTUl+Ist+KqLELzUFbg8lKYcz+/yXj/DPdf6mU1xZz5zmFsImNFEARBeA6IIIogCIIgCIJAcotMiFQyven4XiJXyDn1jYN8+hcX8N2rx69Qyjnx9QOU1hRveluT3UBFUyljBUp31e4rX/3vRCJF150pbl0bIxZNIpFIsKok3P2qn+JiI6p7u2sjgSjeMQ96g5qoP5QXQAGIhuN4Rj2YLBtnSwjbNzfu5cM/O0f63vM1Fo5z+Te3kcllHHyjbd35lS1l3Pqil8iaRWSpVML+083PXbPqWDTBzPgCM+OL5HI5SquKcFXb0eqffV8CuUJG/b4KbqxZ/F2rrt2FQiF+mj4Lap0SlUZJIpZcN6Y361CoFERiCWRSKeo1WQPVtQ5+9NtG3FNLBAIRZHIZJU4TpWUW5I/52pkZ8zJwYwyVWrEaYJZIJFS1lbO8GEStUaHUKDnyzj4qm8se676eJzMTC1w/m/8amhiax1Js5LXvH0euklM2scjU0ByOMgsmk2a1j1ddR+Vj379UKqX5aB0X/vbG6jGFQo65SM+h11qobi3f8LY3v+hjvG8m//GMebn0m9u8/zuv7rismCAIgiA8beKbqiAIgiAIwksssBhiYdaPrchAKpnGtxAilcysO8/uND/9yT0GR0UR3/2jd5mbWCCVSFHktGArtWx5O6lUypG397E0HyCw8KBPSePBamrbVhaIcrkc1y6PcPPq2Oq4RqtkbniGwFKERDxFZZUdxb3SRIvTixz+4DC/+pMv192fUiknHowSXo6u2+0t7Nxk/+xqAGWtu+f7aT5Sh9aQH1CwlVj44B++Ts+lYaYHZzHZjbSfbKR23/PVqDocjPHFz28xsaY/RNfVUVzVdt74zkFMlmf/3GroKGfg9gThQH7Wg96koXEXFniFR2OyGdh3spHrn3XnHU+lM1S0l/O3n9/FvxxFKpVSX1lMfXUx5U4LUqkEg1FDc9vuBzGWCvTO8XuDaAxq3v17pzHZjBhteqwvcAZDKpVhyRcmm8liMKrRGzRMj3golLDl9wbxe4N8+/91mpqat/nkL84TDcZXx2vby6nbpfe0pkM1LM74Gbw5Ri6bQ66Uc/jtdio2yQjye4MM3BovODY5MMfi7BKO8qJdmZ8gCIIgPCkiiCIIgiAIgvASymazdF8Z4con3SRiKeLRJN65AIdea2E5FCcRe5CZYrbqcFU/fwscGr2amraNd8ZuxFFRxHf/8B3cIx7ikTiWYhPOmuLVHb2e+WXu3JjIu006lUFn0gIQiyYJhWJY7wVFdCYdDe0uGtrKWZj2EY+lkEolWGx6TBYdJqsepUp8Ld8NkVCs4PFENEkykVwXRAFwVNhxVNhJpdLI5bJ1dfyfB323JvICKPe5xxfovjrGqXf3PYNZ5SsqMfPB33uF/hvjjPRMQw7q2stpPlhF0XMWpH3RdL7aSC6bpevSEMl4CqlCRsW+cgZ8yyxHEqvn3eqd4nbfNKeP1HGwvfKJZQ9sVEovFoqj0Wuoanlxs09yuRwjQ/PcvjHB7PQSADq9in2dlbDFnzsRS1HX6uL7/+d7zNz//HKYKKtxFCxn+Ch0Jh1v/vgk7acaiQZjmIoM2Lco2ZZMpEin1m/OgJVSfon485PpKgiCILy8xK81QRAEQRCEl9DkwBznfnl7tf+HWqvEajdw+ZNuTn29kzm3HyTgdFk5/f4+DPcCBC8LvVlHU4Em8pFglPFhD6lkGumaBcRkMk1pVTFyxSDpVIbAUnQ1iNLxahMarYpDr7cyNzpPKplBIpWsLkB2vNqERvfsSy69CEqr7fRcGlx/vMaBYYtsjOe1nFQskqD3RuFd3rASYOk4XrcnXsN2pxn7B50ceaOFXI51fROEZ0Oj13Dy6wdoPVpHOBijd9xD3+QCqcT6he9cLsdX14Yx6NU01zmfyHxK6xxo9Gpi4XjecXORYcuSjM+73q5pvvi4h+ya3lyRcILL54ewmDVYHEb8nuC628kVstVsRnORAXORYd05uyUSiiGTyymuLMK4jSw3g0WH0aIj6I+sG1NplBitopylIAiCsPc9n78UBEEQBEEQhMcycHtyXQN1vVGDRqtCKZPw7vcOo9YocVZYUChXMjDSqTTzE4sE/RF0Rg3OKjvKXWiI7veFmBpfxDu/jFwho7yyiLJKG5o91Ng7Go7TfWmI7svDSE06xvpmKCq1YLLpuZ+34AvGeeNHJ+k+P8DywjIGm55Db7TTcKAagKqWMt788UmufXSXZV8YtVZF55lmWo/WP7sH9oKpbHZR3epivNe9ekyjU3PwzbYXtuZ+Ip4q2M9i7XgynoJtVj7KZlcawEulT+7vpdaK4MleZLYbWYonuTs6v+l5uRzc6nVTV1m8WrZwN1mLTbz3O69w/uc3WJi5F9CvsvPKNw+h3wPBwCclGIhy4exAXgBlLY9nmZrKooJBlNZDNU+87GYsEqfr4jB3Lw4SjyZRa1V0nGpg38kGNJu8prV6NYfeaOWLv762rhzZwTPNmG1PLuAjCIIgCLtFBFEEQRAEQRBeQvcbrj9MJpeSiCVpaHflHQ8vRzj/85sM3ZlcXQQpry/h9e8fxWw3PvI8xkc8fPKL28TXlA/rujGBq8rGG+91YN4DO1RzuRxXPrpLz5URAGxFJmKRBO6ReSSSktXdv4lEmplEmrqTTZSXWzl6qgGtQbN6HYlEQsuROqpbXIQCEdQ61bZ28b7MstksizN+4rEk5iIDxi36xmgNat760UmmhuZYnPOj1WuoaHBuqx/O80qrV2Gy6FnY4DVtMGm31VzevxhitHeGwa4pclmobSmlrs313PVDEh7PpNu3rfNm5v14fUHKSp7Ma6u8wcl3//AdfHMBJFIJRaXm1YD+XpZJZ/C6l4iF4xiteop28N7jdi/lfRY+TKlSEk9nqd9Xzki3m1wuh1Ilp/VwLQdfbdyN6W/q5pf93Pqqf/Xf8WiCq590k05lOPn+/k1v23ywGrlCxt3zgwR8IQwmHftO1tPQWbXp7QKBCFPuJXy+MFKJBEeJifIyKzqRxSYIgiA8ZSKIIgiCIAiC8BIqdllZ8q7fzXp/7GF910YZuj2Zd2x6eJ6bX/byxg+OP9IcfIshPv75bRLx9YtG7gkfl88N8s439u9oR3wul9v1nhbeaR/91x80kVdIcmi0SmLRJL75ZQxmHVLpg/tc8kc5fropL4CylkavRrONRe1nJZPJ4p3xEwnFUWuVOMosKJTrfzbEownmJhaIRRIYrXpKKoqQ7+Ku9FAgwuVf32Lo5gSZTBaNXs2x9zpoO9Gw6XNCY9DQeLCGJ7+kuDcoVQr2Havl87+9WXB8//H6LctmLXmD/OZ/XmZpIfTg2FdBem9O8PUfH6ekYvOeB8KLIxzdOKvpYYlk4T4Xu0WlUT6x8l3RcJxUOoNOp961963lpTAXf3GTka4pctkcCqWcztdaOPh662pPrc3EN8kouy+VzvLaNw+x/3g9iUQKo1mHtfjRNzJsl8+zTPeV4YJj3ZeGaDlcg2WTDRUyuYymA9XUtrmIRRKoNaotM1n7B2b54qt+4g99RzAbNbz9VjvlBb6rCIIgCMKTIoIogiAIgiAIL6GmzkqG706RyWTzjsvkUura8rNQErEkfVdHC15n6PYkh95ow/QI5TimxrwFAyj3jQ7M4T1SjU6jZGbUQyKWxFJiwllpX7eoPze1yHDXNFPD82gNapr2V1HdUrppiZHtCgWieX8n38wSJ19p4MvP+khEk6TTGZT35pPL5aipc5COJRm4O4WjzILlCdam321LCyEufdrDxMAc2WyObDZLcZmFMx90YrJoiYYTqDQKEpEEn/3lVRZmVhofSyQS6vdVcOobB3at3M7Nz3vov/YgeBULxzn719cwWPRUt7o2ueXLp35fOcFAlFsXBsmkV56rMpmU9iO1NHdWbnn7/tuTeQGU+6LhOLcuDPHej47tenBS2Jt0O2hArnwCpbyetIX5AH13phnqcZNOZykuMdJ2qJq6Jicy+c4fTy6XY3JglrHuaaYGZxm6PYGpyIBaqyKVTHPt4y5MNgMtR2q3vJZ6G4EWvVGNSi3HWVm05bmp5EpD993ouRUOREklCjeATybShALRTYMo9ymUim1lFI2Ne/n4s55131EAAsEYv/z1Hb73nUMUP0YmrCAIgiDshAiiCIIgCIIgvITK60t44/tHuPRhF+HlKLDS/PXkex24ah1552YyWdKpwjuOM+kM2QKLHNsx6/ZvOp7JZHGPL9D1eQ+h+w1pJdB2rI6TXz+w2ldhanie3/zFJZL3FniWFkKEAjF83mUOn2l+7P4LGp0KiURCLrdSxyyVSBOcXeKtd9twzwSQKuUgAb1eg6PYQMCzzKfdKz05lCo5h19tpONo7a5maTwJiViKL35+k9kJH5l0hsBiiCXPMuN9MwzdnaKls4L56SXspRaW5wNEgtHVYFYul2Po7iRmu4Fj73Y89lwCiyH6r60P3OWyOca6p0QQ5SFKpZxjb7RQ21yKd84PuZUm7sVlli2DH9FIgoE7kxuOjw/OsbQQwvYUdru/jBY9y0wNe/AvhjCYtJTXFlNSbn1mQavKMis3ujZ+PtznLDbheI4CxADeuQC//OkVwsEHDevdkz7ckz5OvN7CoVP1O/67D9+Z4OP/cZHichs3v+wlHkng9wapai5FfS940Xd1ZFtBlNIKKyq1gkQ8hUIpw27RkghGyWZzaE1alkIJWtpcW84xmUgxeGOUrnMDxCIJqltd7Hu1GXvZo2duaHQqZDJpwaCGTC7dlQ0L92UyWe50TRe8r/ti8STDIx4RRBEEQRCeGhFEEQRBEARBeAlJJBKaOqsor3OwOBsACdidFrSG9TtWtXo1NW3ldF8aWjfmqi/B+IhNYeXyzct0ZbNZZke9qwEUrVGDucjA7PgCd77qp/1kA0q1kjuXhrCVmIhHkqi1SgwWHV2Xh+m9OsrU4BxH32qjpqXskRtlOyqKqGh0Mjkwu3osEU0yNzDLez86gb2yiGw2S//tKe5eGSW3pilwMpHm4qe9aLRKWg5UPdL9byaXy7Ew48c7u4RMIsVRacNa/KCDuGdqEfeIh2QsSZHLiquuZMPSTjOTi8xO+IAc3hk/vjk/MoUc0ll6ro1hsxsILUdxllu5fX4AvVmLq6Y4r2F777VR9p1s2LCU2XZlUhnSycK7nmPheMHjLzuJREJxmYXisp31qMikM6vZKwXHM9lNx4VHNz4wx8f/++pqABjg+tl+Xv3aftoOVz+TQIqrxExZiYWZ+c2D3AfaylEonq/lhLvXxvICKGtdPTdAZV0xxTvoARSPJrj6URfZTBapVELyXmZlOpUmsBii5F4QJRaOb6vUpNms48SrjZz/og+7QcXZ/3lx9ZoymZR3/s5JKmu2zkDpuzLEV391bfXfPZeGmBn18J0/eAe95dH6jBWVmqltL1/pi/aQun2VFJWaH+m696Xuvd8rlHIWfSEmpxa3vE1v3wwH9lei0Ww/e0oQBEEQHtXz9a1HEARBEARB2FU6gwZd49YL3u0n6pkammV5Mbx6TKNXceTNtrxF9J2orLYz0D2z8QnZLPHgSpaMuchALBLns59eIpvJcvtsP4O3Jzj+tU6mhz10XxlFIpUglUpYXgpz/O125lNpuq6MsrQQ4rVvHaRtGzuB7wsshJgZmycWTmAuNnL8vQ7kChnjvW6y2RwqjZIDZ5qpaXOhVCmYm16i62p+AGWtW5dHqGkuRb2Liz2ZdIabX/Vz/bOe1UVupUrOq988SMvhWoZujfPpTy/nZRHV7ivn9e8fK9hoPHCvnFMsmmTJswys9CSYnVgAVhqPJ6IJMpksuRyE/FGioTgG84PyXclEimQi9dhBFKNNj7O6mJlRz7qxiqayx7q2kE9nUFNSYWVicL7guNVuwGjZnRJtwgOh5Shf/PxmXgAFVoJW5359h6ISE84KG4lYEveoh+XFEFqDBledY9dK5hWiUip4+5UmfvV5NwtL4YLnnDxUS2NNyWPdTzqVJpvJbdkXY7f4fSGGezf+vMmks8xO+3YURAn5I6vvldFgDFe9k6mBGeQKOclEGs+0j3Q6Q31nFSF/BKNVv+U12/dXoJJL+fM//pvVAIpcLsVi0zN5d4LIW20kwgncIx4SsQSWYhOuOsdqya5YOM6tL3rXP37PMtPDczQfqdv241tLKpVy7N19pNMZxvtmyGVzSKQSalpdHHu7/ZEDfr75ZQbuTDLSNQVA4/5KiiqLyG7wWbpWIpkimUqLIIogCILwVIggiiAIgiAIgrAle5mVb/3+G0wNzuH3LGO06qloKsVWYn7ka5ZX2SkuMeGdXy44vv9ILQPn+gBQapRc+OWaxtm5HCF/hN7r49y5OIxEIkGrVTE7sUA2m+Xcr27z6tcPMDE4Ry6b4+pnPVQ1lqI3bb24PzPq4aM/P094ObZ6rKq5jNe+d5hDr7WQiKcwWvWY15Sy8S+GyGY2XvRZ8oYI+qO7GkSZGJjjysddsOZuk4k0X/7sOgaLjnM/v7GuDNto1zTVLWW0Hq1fdz2ZcqXcWCqeIpddCcpkMtnVxSylWoFvLkAut5KdFA3HiceSeUEUR3kRetOj7XReS6GUc/TdDn79J2dJrGm2XNHopKa9/LGvLzwglUppP1zL9Ih3XfmcRDJN06FqgpE4cqUc+SMGTGGlaXbIH0GhlOe9dl5Ws5OLREKFsyIymSyemSUyqRRDtyeZ6J0hFFjJyLMUG3n3756iuNz2xOZmtxr4zrudjE0tcKd/hlA4hlQqpbrcRlNNCVUu2yMHz+PRBAM3xui9MkwylqKuo4KWY/WP9VmyHelUltQGZSnvS8YLZ79tRKVRotaqiEcTBBZDtBypxTcfIJVIMzexiM6spdhlIxxK8PM/Oce7Pz6OvXTzTDGpVIJKBkVFevR6FbkcKBQylKqVpZuR7mm6Lw/nvy/Wl/DmD09gsOhIxBLEwomC145HCh/fLrPNwHt/5ySe6SUioRg6gwZHufWResnASgDlV39+gWXfg2DdtS/6UBvUlNXYmfEGN729WqVEpRRLWoIgCMLTIT5xBEEQBEEQhG0xFxkxF+2s/nginmLZH0EmlWKx6/NKaukMat7+xn7Ofd7H9NgC91qOoFTK2X+kmn0HKvEMuIksx3APz+Vd12jTY3WYuX1uAJlcSjaTI5PJkL2/+J/OsuwLYXOszDcSjLM4H1gNogQDUeamFknG0xgtWpwVNpQqBalkiku/up0XQAGY6J9h+K6Dg6+1FnycWy0oSqUSpFuUL9up0Z7pvADKfZl0lumhOSIblK0Z750pGERxlluRyaV581y7udho1jKTybLkDXLw9RbO/+JW3u5juUJG5+mmbfV+ydwrf7PZ7uXyBiff/T/fwT00TzQUw+a0UN7gRGd8vCwXYb2qxhLe+u4hrnzeR8AXJhJNkAbqOiq4NjTD2a4JKl1WOlpc1FU7kEq3v+s8k8kydHeSm2f7WfIEUSjlNOyv5MDpJizPQTAll8uxOOsn5I+gN2mwu2y7UmYrHk0WPC6TS3G6rFz85W28Uwt4ppeoaXNR2+piZsSD3xvk+uc9vPc7rzxyicLtMBk0dLZW0NZQSiyRQiqVon/Mvhe5XI5rH93l9lf9q8dufdnH5MAs3/z9NzBYts7UeFQ6vRqDUU1og/dFWHmP2wmjVU/7qQauf9INwNy4l/d/91V6r670c7KX2ZCr5cy7lwC4dW6Qt3/r6JbPH7lCjlQmRftQ6cVMOotnypcXQIGVvmCDt8Y59EYberOOkgob7pGHsvgkYN2FQJVMLqO02v5gTpksM+NevNM+pFIpjgobjvLtvUYG7kzmBVDuiwXjWOLpDXuw3Nfe5kKtFlkogiAIwtMhgiiCIAiCIAjCrkunMgz1THP70gg+bwipVEJFrYPOk3WUr1mAsdmNfPC9w8zP+gksRZHJJDicZqz3Fldf+eAA535+E+/0gx3EZrsRo0WPXCljcd6P3WnB4/Yjk0mRK+QrteeREA0nqKgvIR5d2X17f+F3uMfN2V/eIbZmV25plY3XPugknUgzO7lQ8DENXB/jwJmWgotDxaVmlGrFavmVh5XXFmOxbb5AmEqmQCLZdp+BaCi24dhmWTEbBTnsTjPH3mjh/EddaA0aoqEYEslKVkj7kRpmRj1YHSYymSypTJY3f3iMZDRJKpGipMJG69E6Khqcm87Z615i4MYY431u9EYNrcfqqd1Xsdqgft2cSq3YSx+9GbKwPRKJhIZ9FZTXOhganKV/aI6UBOYCkdVFzIlpH5NuH68ea+BQR9W2AynDXVN89pfXyN2LkqaSaXqvjbLkXebrv3NqtQzRXpSIJbn2SRdd5wdIpzLIZFJajtVx/P1ONAVK4u2EYYOSXI5SM2d/dh1LkZ4lT5BMJsvw3SkWZpY4/Horc+MLjPe6WV4MYVnT/+hJUSjku9b7ZHHWT1eB3lq+uQBTg3O0Hlsf3N1MNBwnl82iM24d/NDqVew7UsPFz/oKjhtMGlyVW/cbeVjnK03kMlm6Lg2RSqRZmA0QjiQoclpYWgrnBbpHetwcfr0Fa/HmmxGKy204Kux4pvI/i2QyKalU4WyZgVvjHHitBblCzuF39uOd+ZLkmmDLvlONOGuKd/z4NhLwhfBMLjLa4+buxUE0OhVSqRSZTMrJrx+g41TDpoGUVCLF8L0SXg+TSGBpZglbdTHexcLZKAadivpax648FkEQBEHYDhFEEQRBEARBEHZd941xzn/cvbqAlM3mmBieZ3ZqkQ9+fJyyqgeLVXK5DFdFEa6K9dcpqyvhvb/3Kne+6iMajqPWKtEaNEhlUpLxFI4yKwarAYVKsdKzI55aXew0WXWEAxHkSjmWIgP2Ugtz0z4+/ev1Za5mJ3yc/dUdTr5VONMEQLLJorGlyMCR041c+Lhn9Vy7wwiZLNlslvrmjYML/oUggzfHGbw9gVQqpelgNY0HqzBusSu7vK6E+clFzHYjEqmEaChOyL9S8sdRWYSl2Ii/QDmUmrbC5bAkEgn7T9Rjtum5fWmY/hvjyJUyXv/mQe5cHCSbY7UMUzgYo+1ILQdeaSCTzqFUybfcebw46+eX/8+XhJdX+twEFkK4R70c90c48lb7prfdq/xLEWZn/CSTabQ6JWUuK/pHWFxPxFOkkik0OvUjl0naDclMlotdk0Sihcv+5HJw7sowxUUGqsq3XnBOxFPc/mpgNYCy1tzEIjPjC9Rt8Hx8HMu+MCNdk4z1uFFrlTQeqKK6tXzDYN1Ghu9M5vWXyGSydF8cwmQzcPCNtseaY2lVEfZSMwuzgdVjCqWcpfllUsk0OqOGpbkHY4HFMIl4CiQ8k4bzuyHkj5DZoKRWsEBGwkb8C0F6rowwdGeSbDZHdXMp7cfrcWxR4qx1fyVLCyH6707nHdfqVbz1zQPbKvf4MI1Bw8kPDtJ6vIFENEn/rQl8C2FSyfWPM5POkE5vXlIMVsqEvf6DY3z1s2vMjnsgB1aHmQOvt/LFX18reBsJD54TFU2lfO8fv8f00CzJWAq7y0Z5YwlK1e70n5kcmOXTn17GZNPz6f+6AoDJpsdZVQTIufDLmxS7rHkZK4XnvDGdVsXJk/V8+lkvoUh+9pC9yMibr7dg22JjgiAIgiDsJhFEEQRBEARBEHZVYCnC9a8GC5aaSibSdN8Yo7Ry+yVxzEUG6jsqCPnCKwvwEine2SUiy1He+N4Rbp4bXG1y7qqxMze5iEKpQGtQ03t9goYOFyff24dGp+LWxeF1AZT7ZiYWicVSlFUXMzPmXTfefKhm0znvO1yDWqPkzpVR1CoZ1z7uIplIU+QwsuT2MT++wPH3O9CsKYkT8kf46M8vsDDrXz12+aO7TI96eO/vnNx0t3tJdREavYbrX/aTSqQoqymmts2FUiWnrKaY1753hI//x0UiwZWMFalUwv5Xm6hq2bgxu0wmpbaljJrmUuI/PIp/MUQsnKBxn4uZiUU8M36sdgP17eVUNTqRyWXIt/mLYuTuFP6FILFIglgsiVQqRadXceOzHuo6KrDucFd9JpMllUyjUMgeuSb/o0qlMty9PcH1y6Mk1jQG1+lVvHKmmcZm57ae37FInIFbE/RcHSUeSWJ3Wdh3vJ7q5tJnslA+Nbu0YQDlvlwux+CoZ1tBlPByFN8mfQ0CC6Edz3EroUCEj/7sHPNTvtVjYz1ujrzdzrF3O7b9d83lcvReGS441ntlmH2nGlE8xqK0WqPkzW8d5POf38I7s/L61+iUeCcWKK8pRq1RYi42Mj+5+OCx+SPIFXKqW0ox2x9kMwSWwsxM+ohFEqi1SlxVRZi30cT8adObtMjkUjLp9SWaDNbt9VIKL0f5+CcX8bofvGf2XR9jcmCOb/7eGYqcG/cc0ehUnHl/H41tLqYnFkkl0hSVGCmvtj/23+t+gDmwFKbrykjecbVWSTQUR6tXkYgmyWazW5ZiKy638a3/400W3X6y2Sw2pxmJVMqd8wMs3guuKdUKZHIZ8WiCpkPVede0l1mxlz1eFl8sEme0e5qRrmky6QzOajsKuYyf/z9fkMuBs/pBZsuyL4xGr6bIaSabzTE34d00iKJQKajvqOTGl4Uzgxr3V1JdZefHPzqO273EUiCCVCLBXmSgrMyCepcCQoIgCIKwXSKIIgiCIAiCIOyqhbkA8Vjhev8AE0Melv1RzNtcNOu7PMTZ/32ZWDhBJBhFa9Tw2g9PUdnoxGDTE4ulGO6dIZeDdDpL04Fq9p+sZ2rIw/5T9Rw600xNqwsA78zSxneUg0goxomvd/LRn50nFIiuDtW2uajvrNp0nnKFjJbOSkrLrfz0P32MtciASq1AIpWQzebouTqCzWmi42Tj6m0mB2fzAij3uYfnmR7x0LC/suB9RcNxLn/YhWfGj0qjIB5NMD3qIR5N8MN//A5avRptvZMf/JN3mRtfIBlPYXOacVQUbSvTQSKRoNFr0Ogf7MxuOlC95e02srwU5vaFQfpuT5LL5tAZV7KJPLMBbPYIS/PL2w6iJOJJxgbn6bs9SWApgs6gprWzktomJ9rHLLG0Xd13prhwdnDd8Ug4wacfdqFQyKit37zUTCad4fLHXfRcGVs9NjU4z8yIl3d+fJy69t3P0NjKlHuT18caoxMLRGNJtJrN+xEoVXKUKvmG/T+UW9z+UUz0z+YFUO679WUfdfsqtr2wnMvlNizPl0qmyWSyPO4yrr3Uwrd/91VmpxYIL8fRGlSM3Jlk8NYEsLIAHw3FCS6tZGloDRoUChmH39y3Ggwa6HZz9sO7K1kq96jUCk6/005zR4H0vmfI7rLSeryervP5rx2z3bBlKcD7pofn8wIo90VCMUZ73JsGUQCUSgWVdQ4q61Zen0ueZebHPCxMLuCsdqAv0Bclm80h2WYGkKumGEe5Fc/0EsVlFiaH5xntcQMSXLXFeGf8HHtnH52nGrZ8L1YoFetKcL36rUOc/9Ut5AoFC55l4rE0Vc0uSne5tFUynuTc395cfS5GQzE+/YuLnPrmQZYXw0ikEjT6IOlkGvm9DC+/dxmrw4hUWjhQ9rDG/RWMdE8TWMwPplodptX3P71ORVPj9p4bgiAIgvAkiSCKIAiCIAiCsKsKle5ZK5vNkctuvcACsDQX4Ku/ukIqkUaukGGyrez2nR2Zo/NMM1KplIqGEk5/cIBUIoVUJkEikeL3RbCUmIhHE/jX7HbXb9CH4D6VWklpdTHf+6N3cI94iEfimO1GymocqLTbW/BdnAuQjCVRFzi/98ooLUdqV3sMeKbXL/auPvb55Q3H3KNeZicWUWuVlFQUUVxmBQlIpVL6b05Q11aOTC7DaNFvWRbsvlwuh9cTZHp8AZ8vjFqtoLyqiLJyK6rH2PXrmfHz4f+6ypIvzPLSSrmxYCCKRqfC5jASWIrgv1eGbCvJZIrLX/Rz9/r46rFwKI5nNsDU+AKvv9+BRvd4za+3ElyOcf1e4+hCMpksd25NUFldhHyTDJn56SV6r42vO57JZLn5VT+VDSWPlenwKNKZrUsNwcpzJZvd/HUOYDDraD5Uze1z6wNOSpU8r6zfbpmfKNzTKJ3K4PcGtx1EkUqlNB6s4vKv76wbq+uoRP2YTdbvU2kUVDeWrv5bLpUyfK9MlUIpx1XnIBo0ggQOv9FKdXMZWuNKcNM9uchnv7y1bsE6EU/x+a/uoDdq8npQPWsSiYRj73RgtOjpubzSQ6SmvZx9Jxsx3ntvf1goFMM94ycUiiNXyFgY9a70vSoQ0HCPeDi6g9KAQzfH+Pynl4iF4mSzWSwOE+///TM4axzkcjnc0z5Ghj1MTiwilUqpb3BQU+vAUbJxwFdn1PD2bx2j5+oooz1uxnpn0OjUFJWYMFp1ZLM5Ln/Uhd1ponKbgaO17GU2TCVWrn/RTyaTQaVWMj7sxbd4lfd/dIzSis1Lmq3l8yzj8ywjk0kpqbChMzwIms+OLawGUACCvhCZTBbfXACpTEo0FMNcZCAZT60GUbKZLLlsDqSsfCZtweYw8fXfPslQ1zTDXdNIJNCwv4L69gos9sLPB0EQBEF4VkQQRRAEQRAEQdhV1iIDCqWsYE14AGeFDeO93b7RcJzJgVmmhuaRySRUNJVS2eBEdW+H+uLcUsHd4O7BWYK+MGa7EZlUylyBnef3qdcsqtc0Oem/NVnwPI1ehUIho/vqKKr/P3v/GRxJeqd5gj/30FoHAgGtkRCZSK0rSxdZVdRkq+menu6d2dmeudsPN2Z3Z7Zf9j6unZ3djd3Zzu7siBYzPd0kmyyyiiwtUmsFJJDQWgRCax3u9yGQkYgEUpQgm+z2nxmNlu4eb7zu4e4g/8/7fx6Dhta+xqeKLrvxuNXrAPlcgUqpUhNRjJbHe/DvJsI8ILQew2wzoNNryGYK5NIPLZgCS2HSiRy2z+EXL8syY3eWOfvxeF1B9vaNBbp6Gnj+lSEs1ifnBewW8ixJEtc+m2RhOoDX50CtUdXs1HJb8x441s3cTJA9B9oxP+F6AKwuhOsElO3MTqzT0dPAwGO6d74qNgNxco/prHjA6nKESDj9xGJrLJioFhx3IbgaIxnL4PLZv8xUPzcel4Xpuc2nHue0m9Drn03g2Xeil1gwxeLkem2bVq/hxe8e/pWcn+kJ9+nn7XzpO9jJ0uQG69uuiafZwdCJ3h3H5jIFCvkSBpMO3TNem91o7fXx4veOcPGXd8hlCqhUIv4uL8998yBdWx11D5idWH/siv9KRWJ6fK0moiQTWQr5EhqdGrv92boAfxUYLAYOvjTE8Mk+KuXKYy0LZVnm/uQG5y5MkclU32+CAA5BYHklSqPPhvaRjJvdukgeRzyU4r2/PMfmSpRYJEWlIqObDyELIn/wf3uTyckA589O1omFkcspbt5Y4Guv76O7x/fYsR1uC/tP9zJ2ZbZqsahVI27rOpFlmcWpQJ2IEo+mScZzaDQi7gbbY/N75u+vMze+jrOh/t2STeW58O4o3/mT00/N/imXKtw+P8WNTycoFat2hGabkTPfOlC7x8Ib9d0+ha13niBU5y+KAtHNOF3DLWxsWc6Z7SZUahUDR7rwdz1biL2zwcaxV2wcfmEPwK/dmlFBQUFBQeFZUUQUBQUFBQUFBQWFrxS3z8bw4U5uXdyZJyCqBPYd6USlVpFJ5vj4R1friqsTNxYYOtrFqTf3o9VrHxuEq9Fr0G4VKn2tLsxWPelkfueBArRuK+a0dDVw4FQvty5M1x0myzJt3V5+8V8u1oqSJqueF79ziI5+P58Hh9eKqBKRKjuLm83dvppABNDa28itTyeoPHKsRqt+bBFKlmWQJMLLIWLBFA2tLpp7GwmuxymXKqi1ajTaaiEqFc+ysRyhXCrjcFvwNjt3tZBZmg/y2Yf3du0umJvZxGTR8+Kru6/wToRTjF2eZvLGArIk07W3lX2n+nA12glvJFiaCZDNFJm+v8Hz3zvK3fP3Ca3F0GjVtA000X+0m6mJDZKx7FNFlIXpABabEUmSyKR2/t737678ykWUUunpXVRVa7knd3Won1Do1GjVqDS//mJie4uLG3cWcJsNSNkiolqkqFYRitd3Cg3vaUL9DLZwAFaHidd+/zjriyFiwSRavQZ/u/tzZ+A8K239fm59NrFDXGhoddHY9uydL9lUnvW5TRo7PHia7AiCiK/NTXOPr04ozCRz3L+9xL3rC+SyBcw2I3uPdtK/rw2d4fOLKYIgMHCki+aeBiIbCQRRwNvkxGipFxsq5QpLjxG83B4zakEgGYyzvhRiZjbExOgKhUIZrU5N/4CfoX2teJ8g8v0qkGWZcrmCWq3aen8//vrMzG7y/odjde8kWQajy0wmW2BtPUZLs/Nht5cA3UPPboG3MrPB9Ngq+VwRQRDQGzQIyNy7PM3otQUu3VgEIJctVrscRaEm+Hzw3ih2hwm3+/HdEqVCGVmuZt8ACKKAw21BZ9BQLlfIbQlDmVSeO1fnGLuxWLVkE8Df7OTo8/11f7ugKkpPbOsOeZTAapTNtRjNT+k+mh1b4fL7o3Xb0oksH/zNFb73L1/E2+zc0QVntBpIxjIsTKxx+OUhzr91k5XpAHtP99N3sJ3gSpTWvkYGjnTR0d/0uUPsFfFEQUFBQeE3HUVEUVBQUFBQUFBQ+Mo5eLoXlVrk7tX5WmeGzWHi2It76OirruBduL9WJ6A84N7VOdr3NNE52Exjpxdfh4fAQr1Fz8jzA7UuDrPVwJk39/PBj6/Vd78IcOT5Pfi3LINkWWZ5JUxRK9JzqJ3QegxBkunqb6SUKzF+fb6u8JpJ5nn/b6/yg//hRVwNz15sbGhxMnysm7sX64UarV7D4NGuOhsaf4eHF753hAvv3KrlRpisBs58+9Bj7VBmR5e58NZ15u6tIssQXo8xO7rM898/yvpShD0HOzBaDEyPLnP27du1Yp0oCvTua+PEa0OYtxWBZVnm/r21J9ozTYytMjzShsdrrdteyBX55EdXWZ7eqG0buzTN+sIm3/rvX6JQKCNJ1VXL2XSBu7eWad3bzuCJPiqyzMpihPSWGPK0fIBMIktqM07g/jIarYbWfj/pQqX2eYB0MkelXPmVFuTM5qfbOGm1aoxPsXvyt3kwmHS132c7vSOt2B9jb/SrxOexMeRz8NZ/+ox0MgdAU7uHoZeHWN7K5WhtctLR8uyWQVDN6Ojo939uQfKL0NTVwCu/f4KL79wmFcuAAM1dDTz37UN1AuaTyKVyfPw3l5i/t1rbplKJ+No8dQJKIVfi7C/vMntvrbYtFkpx9p27xEIpTn997xe+F59mxSeIAmr1zv8739xs5/ZHY2wuR1BrVNw9O8HgmUHKW++2YqHM6O1l5meDfOO7B2lotH+h+W0nk8qzshBibSGELENTu5uWLk9NFM1lCyzMBrk/ukIqkcXqMDEw3ExbVwOGXTruSqUy124s1N5J5WKZSrmCWqchlMrx/HcOcfEXd8hkCthsRtQaFYdfGqR9z7PfX4l4hnyuiEajQiUKhFerXY8Wh4mVxRDJWBpRELAatGDUsrGZZH0liq/Zgc1hYmkx/EQRxWQ14PRaCa3HMJp1WO1Gxi7PENmIY7IZePH7R0knslz8eILJ0Yf3GTKsr0R552+v8a1/coymbcJfpSztvlhg22cLT8gjq45RYWxb8P12SsUyyzMBvM1Omjq9aLTqWqeK1WUmFkySzxRwuM289IMjrC2GMJr1tPT4eOV3j3/pIHsFBQUFBYXfZBQRRUFBQUFBQUFB4SvHYNBy/MUB+ve2EIukUatFPI12DNsKywvjq4/9/PpCiM7BZnQGHa/84XPc/vQe86PLaHQa9p7ew+Ajdjpdg01813aGhfsbBNaiWOxGuvb4aenyVrtesgXe/2CMd98bpVisijpGoxajUYfgNGIslGs2U9sp5kuszG1+LhFFFEWOvboXZ4ON8atz5DJ5mrt9DB7pxN9Rv7L4warzpq4GgmtRRFGgodmJ+TF2O+VSmdufjqM3aPG1uggsR5DlqtVKYCFI+0Azg4c7WZ4N8N7fXAUeCiOSJDN5exG9Uctzb4zUtmczRVa27FgeR7lUIRpO7RBR1ueDdQLKAyIbCVZnAjS0e9Dq1JgteqLhNJWKxML0w9XzFpuBckXC53fg9Dy+YFzIFvnkh5e5+t4o4c1qVsz07QVOfOMgOr2mFqrt9tl+5SuaG/12GhptbG48PrNmz1ATDueTLZOsThMvfu8wH/7wap0FnK/Nxf7TfV/ZfD8P4bUI4x/fw6zXkEvnqUgya4shNBemaDneg7fBzqkj3ZhMu1sw/abQd6CDlh4fkY04aq0aT5MDtebZ/6/v0tRGnYACVXusS+/coqW3sdYVsrYUqhNQtjN2fYGeoWaafkWZJKIo0jfcRPjjh/ehy23h7ifjbG7ZG+qNOtaXIsR+co0T3zvKxubDfKh0Ks+t6/N87Rv7nykw/XHEwik+/OktAqvR2raJ20t4/XZe+e5B9EYtn747xtzUw/dEIp5lZSFE32ATZ14dqvu7ALARSLC5mUCqSEQ3E4TXopRLFXRGLQ0tbiSfjRPfOYxNr6G91YW7yYHH73jm88hlCoTCGfwdXmIbMdbng7V9Iy8McPXyHH6/jfhymEs35pFlmZ6Rdvr7mrg3sY5KpWJlOcLBQx2P/Q6tTsP+07188KOrWB0mPv7byzyIC0sncoxdnEYQBFbW4rt+vlQsM35rCX+rq3Zeao2Ktm4v4UCcUrH6TpYqD9/xKpX4RDs7gGKhRGpbZ5moEmhsdSOqRUJrMRJbYqnb7+Dl3zvGZ393nVymgM6go+9AB3tP9SKXy/g7vbz2R6exuS2f6/7JpHJkkjm0Og32J4hQCgoKCgoKv2koIoqCgoKCgoKCgsKvDIfbguNxhZJnLLw4fXZe+v1THHsji1qjQmfYfYV/Q7OThubqStj1+SAL4ytMXpuhocVNpFTm52/fqgu9z2aLZLNFfvnLOxw53IXTayEeTO0YNxl9ttDz7eiMWoaP9zBwuLNa/HvKCniby/xMGSb5TIHYZpJUPEsuncdiNyJLMmqtGpUocPqNEaZuzjNxa4mpm/PYPRYcDXa0uof/s3/ixjxDRzpxeqqCiCDwzL/FoySfEAifimXYc7iL/n2tZNMzmC060qn6rovhwx3Ewhle+eYIGu3j7V9W5wLM31vBajMQ2Uwib4lDo2fv8/V/9hzRSAZRLTKw79ntfL4oGq2aU8/18c5btygUyjv2O1wm9u5/NkuxrqFm7B4La/NBivkSNpeZpk4vxsfkRPyq2VgIIUkyDR4rdqsRrVmHzWNDkmQOdDWy50D7jhyK3xTSiSyFfBGj2YDBpMNoMTwxc+hJhFZ3z1hKRtPEQ4maiLK++PgsJlmSCa7Hf2UiCkBnXyP3bi6R2CqKa1WwsVjt2lOpRGRZRpJkMskc5ezOjqfZqU0ix1KUCiWWpjaIBpPYnGba+nw0tXueqTh+88J0nYDygOB6nBtnp/C0OOsElO1Mja/hb3Gx92B73fYHz1UinCKw8FDgKGQKrExvoNKoKJUqCH4HA0e6njrHRykVywTWExz/5kFuvHub0FoUjVbNwPEeLB4bRiHF0u0F1heCVLbs+yZvzFPMFfG3NRALp+AZXpk9wy0gy/zsP3yGRqehWCxjNOtpaHaiN2q59sk9+o/2ks3s3j2yMB0gncxjsRmQZZnpmwvcvzjB6MUZ7F4bI88PkMqVa5lYXYN+vH77E+ekM2jxd3gpl8pYnBZyuSLXz02RyxTpHWrC7nm4YKBnXxsNLS42V6pivbfJif0LBr4XCyXGry9w+/wk6USuait3oJ39p/uwOZ89v0tBQUFBQeHvi9/M/wWsoKCgoKCgoKDwD56uwWYWJnZfxd3UuTMPZLuNzpOYG1vmvb84R7lUQZIkbnw6QbJUoae3kelHbMGg2g1y584Sb7w0uKuIYnnGsGJJkliZCbAyvUEhX6Kp00tbn/+xwcmPfjYRqhbm7B7rY4uXOpMOQSWwPLXONj0IUSXS0OZi7NIUgcUwgdUIxUKJ4GqUXLpAa19jLdi4WCiTTeVrIorBqKWt3c398d1/CwCNRoV7l+LZk67Ng26ag6d7SSVySBWZYCBOIpZFlmUGD7bjbLCxZ18zXf2Njx0HqsVUAKNZT2Ork42VKHqDhlQ4ydSNeUavzNHa7aWzw0NLh+dzdR18EVra3Hzr+4cYH11lemqDUrGCwaBlcG8zA4NNOJ9BEHuAq8H2uTqdfpVI0kM7u86hFuanN7ny4TiSJBPeTDN/f51TXxvG0+j4e5xlPal4ltErs0xcnyefK2J1mNh/upf+/R114uHnQW/aXahVqcW6rIjtouxuyE+wyPsqcLotvP6Dw1y/MMPC9AayJCMIAhabAbvLzNLCtg6z3aYiwPJMgCsfjNV14t2+OM3z3zzA0OHOJ35/ZDPJ9GM6cQBi4TRLy48XmgBGby2yZ7ipTkTV6tTIkkxkI77jeFmSSMXSmKwGrNYvJjYaTFrsDiPpdJFsSebYNw9TqUisLIQIhuY5+PIgf/13VzE9IsLNj69yZl87gWAC/5YNWqUisbEQJLgSRqvT4O/y1ULf08kcuXSe4EoYWZLw+Gw4vTY0W/dlKV+mUtopxNbOVQZJrj6Ty5PrfPBfzlMpV/A0OQiuRvnkby/x8j85TS5ToLW7gWMvDSKKT7ZFXJxYI7IeJZPMYQgmEUSRvkE/d64t8Iu/vcqtS7No9Br2Hqn+9lanGetXIHKMXZ7l4nsPc1iKhTKjl2dJRDJ8/Q+O1zLOFBQUFBQUflNRRBQFBQUFBQUFBYW/F9oHmugaambuEducvSd6aXpMqPrTKOSKXHvvLvlsgXg4RSKSpiQKhKIZXhhsxmjSbRU+BfL5ErIkoVarKJcrbETSGMy62qpeqBbzWroaHvt9sixXvfo1akYvTHHuZzdrhdN7l2bo2dfKCz84hmGrKJtO5FhfCpHLFjFbDfjbXKRiGa6/f5eF++sIgkDX3laOvDKM02cHIB5OEQ0mUWtE7C4LvlY3gigibwujtzhNGMxGLrxzF51Bg7fJyfp8iHyuSCqeIZPK1wQPlVqs64wRBIGBvS2srkTJpPO7ZqMM7m3F5bHu2O7v9NLU5WVtLli33eG10tJTzb6x2Iy89v1DDB3uILQep1AoYbUbcbjNeP2OWvDykzDbtsQaoVo4Nhi0te4No1lHR28DRrOei7+4jcVppu9A+1PH/LL4m5z4FIQksgABAABJREFUm5wcPtpFsVzBoNdgeYqVzm86Da1uRFHA1eRk9OYSG1s2bwaTHq1Bw8pciPd/dJ1v/fEpLLZnExefBVmWCa3HSafy6HRqvE0ONM/Q8VIsljn39i3mtgmAiUiaz966RblQ4cCZ/i80n9Y+P9c/GKvlQTyg90A7bv9DAcnX8gQxSQDPU7oCvgq8jXa+/r2DhDeT5FJ5YsshCpkC+Xw1DF2SZPRGLWqTDtL1HQ8et4UL796FR555qSJx/hd38LU4cfsefw65bGFXG8QHiBoV0Uh619yTB6QSWXLZYp2I4vNacbnMTJd3H/vBd3Y/4d38JDRaDUOHOhi7MU+5XGH8kbB2o15dFbJ307JlGZNRR0urqyrSf3CXq+/drb339WYdb/zpC9g8Nt7/rxcpl8po9RpSsSyhtRiFbJGmrgZUahGTWY/6Cfd5S7u79k6ZH1tGqkgIgoC7wYbZaiCbzlNIZvjunzxHQ7O97hqmYmnW5jYp5kq4mxz42j3M3VvlP/7PP6Z9oJkbn06wNh9Eo1XT2OFl/5k9aHQa1pcj/PQvL9LR14DF9mRLwmclGUtz6/zUrvuWpjdYXwrT3vdkIV1BQUFBQeHvG0VEUVBQUFBQUFBQ+HvBaNbz4veP0L23ldWZACq1ipZeHy09PrS6L7YqNRVLE1gOs74YIhnNotGoKKlFSsUy86Mr5HUawuE0Gp0ar89GsViiUpYw24ykcwXsek1NRNEbtbz03UO4dwlerpQrzN5dYuzCJJlEDn93A6lYdsfK85m7y3Tva6N3fztLMwE+/unNWmA3QGefj42pNbLbwtGnbsyTjmV440/PMHlriWsfjVHIVTMzeva1MDexysu/f5yZO0vEg0la+/1YXRZ+9p8+Q6dVk00XGHneSKFYRqvTUiwUKW8rBncNNOP2VVdKJxJZVpYiLM4H0ejUWNVGjEYdoc0Epa1CZW+/n0PHdrfM0Rt1vPS7x7l7bpLp24tIskznYDP7zwxgcTwswGl1Gjp6fXT0+j7Pz1mjqasBT5OT0FoUBBBFgUK2wNHXhpHKUt2K8fvXZn8tIsoD7E/JPnkaawtBVqY2SMezeJudtO3xY/t7CJUHaOzwcvrbh5keW60JKFqdhsYODypVNWsmGkyxthCmf6T1K/nOeCTN1U8mmB1fo1KWQAB/m4uTrw3TuEuAfaVcYWMxRGwzgSTD1N2lXQPWb5y9T/dwC9Yn/D6lYolkNINWp6m7Xxta3Xztj09z+Rd3CK/HUGtU9B3q5Mire+u6xJo7PDR3ulmd35kp1DvcQkPL04O2i4USKpX4pbJ8RFHE22iHRnj9j5/j/f9yAahmDhUKZU5/5wjhWHbH5ywGNZtlGWGX5oVSsUxgOfJEEUVn0KBSi9XfbRdkSXqigAJVe7xHhQSdTsPxY91M3F4gvBYDquKvyaQnny9ishppbXHS0vzFg8x7B5uIBhOUjnZz4Rd3attFUSC+meTQqV7uj67Ufcbtt1MCvvuDI2iA25+O88v/9Bk6gxajRY8giuTTBS6/c5uh03sILIURVSJDR3s4+9YNoGqBaE9lsTrMDB/vRm0xsb4a3zE/lVpk8GB7rbMkm64PlNcbtNX/6LU0d9Zbxm0shnj3P39KasuKUhQFDr46zOJUAKfPwc2z99lYCiMIAuVShVQsw2dv3eDNPznDxmqU1YUQc/c3GDnW/YWv73aS0Qy5zE47uQfEQylQRBQFBQUFhd9wFBFFQUFBQUFBQUHh7w2DSU/f/nb69rd/JeNpdRoqZYlktFow1OjU5IplioUSequeTDiNKAqUCmXWlyM0tbvJpvO4vBZaOr08f6iL6GYCnV6Dv92D1bF7AXbqxjwf/vWFmkXO0v01isUyA8f7qoX+bazPB/E0u/jgR9fJPZJLkI5nuHtpho5+P2rNwyLq2twm928scOGXd+pseNLxHEuTG5RLFfydDXQMtaDSqPnwby8jimLN4mt+bJmhQ50sTgcoFoo1+5iGFidHXhpAEAQCG3Heffs2iW3F1Xy+RLFQ5uRzfZjMOhr9dhqbnU/MwXB4rDz/vSMcenkISZKwOr56f3uTzchrf3Sa0QtTzI0uodVrOPHmAdRaNZFAfcB7OlG1C/syYdm/LqZvL/Lh31yuFaEnrs/jvGjh6398Bpfv12/xJQgC+57bQzxZoLnbhyiKGK36HXk14Ueu+RelWCxx9hd3WJrefLhRrmaN/PK/XeW7f3q6LlOpmC9y6e1bjJ6fRJZlHH4nS+NrNPc0onukWJ/PFomFk48VURbGV7n24RjB1ShavYahY92MPNdfsw3sHGqlqctHPJREo1XjaLDtuKcMJj0vfusgd6/Mcf/2EsV8Cb1Ry/CRDvYe6Xric7M8u8nk7UXW5kMYTDoGDnbQM9y8q/1fPlckny2iN2jQG3e3GntAa38Tv/t/eYPAUohELMvyWpxAMFUTRR+g1qhwus27CigPKO6S+bMdd4ONzr5GZh5jBWi1GWlodXH3xsJjx9gz3IJxl3Pq6/Xxg98/wQ//6jx+rw25UCK2maStzcPQ0S6OnunH8AxdbI9Dp9dw/KVBWju92J0m1uY2AYFsKofJYuDr//Q5pP/8KcGNBNlMAavLzGt/dBrUGhwmLT/6/7yLyWZgbX4TAQFPsxNPsxNRFNlYDNLYXe2SkSoS5VKZ41/fx51zk+QyBUqFMr372zn+9X0gCAgCTD8QEQGbw8iJlwdo73nYadPW72f29uKO8+h6RMyslCtcffd2TUABkCSZ82/dwNfuxWDWVW3Stv9dSWSxe63Mji7ja3KwsRIl+wTR4/Oi0alrXVG7odUrZSkFBQUFhd98lL9WCgoKCgoKCgoK/2CwuizsOdxJJJAgnciBIKAWBDRaNWaPjeRcCJ1Bi1SREEWBRDSD12/HYjMy0OenpdNLy7Y8lkwyx8rcJpsrUdRaNc2dHjyNdm59cq+uCCWIAoHFEAPHenbMSavXsDwX3CGgQNUTv5AvkU3n6wUbWa4W9R6pOcXDKXr3txNcjaDVabBiZm2xWsD0NjspZIvkMgWSkTQLY8u8+P2jSLKMp8WFp9FOU6cHo0lPLlvgg1+O1gkoAHq9Br1ew/3xVb73e8dobNrdrkiWZYqFEhqturZS2vwV2jvthqvRwQs/OMbRr41QLpf52b//ZIeAAtA52PJbIaBkklkuvH1rxyr+aDDF/etznPrGgb+XeQmCgMVuxOF9vIjzRbNGHmVjKcLSzOau+zLJHEszm3UiyuL9Ne6eu1/7t6gSyabzRAJx/I/mKAk8NhtndXaTd/7zZ6TiOYrFMqIokHr3Drl0npd+93jt/tEZtDS0up94DnaXmTNv7GPfsS4K+RIGkw7rU3KU5sZXee9vrlLZsqtKxbME12KsL4d54VsH0W3lQ+SyBabuLDN6dZ5MKofRomf4cCd79rdiMD0+D2R7jkXjUoSrl2ZYXY7WMlz8LU6OHO9GLcjcuzqHwair5jJFM3XddHb3kzuiBEHg8HO9hDcTxMLpun02h5GDp3oRVAJzUxukU/kdn7fZjfQPNT127CNHu7EbtPzof/2QaDiFWqMmF04xdWGaFr8D26EnZ7Y8Da1WjU6rQiNAOZsHQeTQmT3sP7MHrVGL3mJgYctuUmvSodaqGdjfxgd/eY5ioYRVXb3GMjLB1QgmqwGz3YRGp0GrfyjwJCJptHoNJ9/cjyzL9O5rY+S5/tp99sq3D7DvaCfJeBa1WoWvyVGzgHxA53ArK9MbTN98KEi1DzbTM9JRd1winGJ1OrDjXEVBoFQsUy5XENiyKtv6qdVqFVJFJpvKY/UbUalE7M6Hv308kiK4Wl0c4PE7cOxi7fgk3I0OWnp8LE1t7NinM2hpbPfs8ikFBQUFBYXfLBQRRUFBQUFBQUFB4R8Mm8thisUyFrsRq9OE1W1hYznC/tdH+PTqHKJYLVpVKhUEQYUkSTjdZowmLe2PWAdFgwne+YsLLM9solKLGMx6bp4VOPbSIMlIfcHQaNGj02so5uozB0RRoLWvkfmp3QvFerMeQRR2ZC8Iogi7CAG5TAFPkwOr01S1M6pI6A1a3D57tegtyxhMOmKhJJVyBb1Bw4k39mN6JKtjdSVKdCusfTfKZYm52c1dRZSVmQDjV2ZYXwhhd1sYPtFL196WpwYaf1UYLdXi8dFXhvngry9R2ZYNY3OZ6f01Wnl9GcLrW0LfLszcXebwK8O1Yvqvm8ZW12NXjguigL/9ycLCsxINpXcPPN9iYznCyPGHlkJrs/XFYZ1Ohd6oJRlJ4W1x1okmvhYn3seIgGNXZpib3CCzzSJJpRLJpAsMHuv5QkVdu+vZOrAKuSJXPhqvCSjbmb6zTM9QC12DTZRLFS59cI/xbV0ciUiaC++NEt5M8Pw39j+x0+UBzW0ufE12gptJ8tkiOr0Gr8+GSiWwNBdEYzEyObGGRqumo7sBrVokuBKhqdNL0zP8zm6fnW/+4XGWZoLMT22ALNPe20h7T0PtmnzjB0e4fW2e2ckNyuUKao2K3j1+Ro507pq19ABJkpi6uYDFYsCyzbJPlmUuvHObpi7vl+p8C61Feec/fkouUwQEkGXunp9Erkg8//0jjBzvoWeohVy2gKgSsTtNLE+uk45XxWeVRoXDYyMWqoq52XQes93E0IleGjs8CKJQE6WK+RKBpTBqjYqm7x6uE3oFQaDB70AqVVi4v8a9yzM4PBY69vhp6qiKg0aLgZd+/wQDR7tJhNNYHCb8nQ07OrDUGhVqjYpKpYKr0Y5Gp61lqVjcFgqT64gqAbVaVfu7Y3NbKBbLuHx2gpE0fXtb6Oz3Icsy96/Pc/6d2xS2/rZp9RpOvD7C8LHuZxarVSqRE68Ok4pmiIaSte1anZqXvnuoTihVUFBQUFD4TUURURQUFBQUFBQUFP5BsLkU4q//n++wPL1BYCmMyWrA5Xew5+QeUuUKyFX/fYFqxwUySOUKRoOON17ai8v5sBiXSmR5/2+vcPHd0erqbQFMVgONbW4mbi5ishuJBx8Wg9QaNU3dDTgabLXuDq1ew8k39tPU1cDmen3HhFanxtVgR6UWOPraPhbGlqlUJFSqqhBx+JVhEsndC+yhtRiv/+EpTDYDuUyeRDjN+bdv1UQXl9+Bq9GOSiUydKJ3h4ACEA4+XkB5wNzMJief66srlK3OBnj7P35KacvmJxXLsDq7ycu/f5yBw7vnpnwesuk8q3NBIoE4Wr0Gf4cHX4tr12Jd74EODGY9s6PLJCJpmrq8dA234mz49dlglYplNpfD1U4ipxnvlp3PsyCqHl+AVKlFnlWTioeSZFM5zHZTrfvgy+JrcXL4+X6ufnJ/x76Dp3tpfIasj2dBrXnyST7a8aJ5JCsptBLh1Nf3cfmDMcRtvlRGi55Tr4/sGk6fSmYZuz5XJ6AAVCoSq0thlueCv9KV8eHNxK4dVA9YXwzSNdjE+nKE8Zu722BN3l6ib28rbT3PFqyuVqvwbxOUJEnizvUFLnw8QTFfolSWCAUirC6G8frtfO3bBxg60IGgEliaCRBajyMIAp4mB40tzh3X1eYws/eImb1Hdu8M8TbaefWb+zl0optCvoROr8XleXrhPBFOszKzs6sCqoJyaC32pUSU+XurWwJKPePXZhk81o1Ko2ZuYo25iTVUahW9e1ux2h52AAWXIxx7Yz8zdxZZur+KyWrk0CvDjDw/iMGs4/Q3DnDuZzdJxbMIAthcFl78wVG8zTuzfmbGVvjwh1cpb9muLdyHu5dmeOl7R+jf3waAVqelbU/zk09KFOg60EE8nCIdzbAys8L86DI2t4Xf/79+A0mqMHiki7HLs0BVQJFkGVejHavHSlEU+c4/PYneoGV1bpNPfnIdaZtQXcyXOPfWDewuM629z55j4mly8O1/cYbVuSCJSBq9UUtzpxfXEzJ3FBQUFBQUfpNQRBQFBQUFBQUFBYXfOrKpHLlUHrPDhG7LF//e5Rnm7q1QLlVX4AaWI4hqFbN3FgjFchw92I5g0jE+tU4mncdg0HL0SBevvTFCZ0d90XT86hzXPpqo2d8gQyaRY30hhFar5tCZPi789BrhjTjlUgWTxcDhV4Z5/vtHiQYSlEpl3H4Hzi1LpOYuTy2AWW/UYraZOP/+GPlcEX+rk4GTfaQiKZra3PQf6qBtTzOrs5vM3lna0Q3gbLDia3fX7LMKuRLxSJqxKzO1Vf0qjYrT3ziAr3Vnse5J5LIFUvEchXwJqSwRWI3Q0PRQGBi/MlsTUB4gyzI3PrpH50AzetOT8xqeRDyS4qMfXmN9IVTbplKJPPetAwwd7dpVSGnpbaTlcxTyvkrioSRn37rO4v11kKtzHTzWw7Gv79thxbMb3mYnLp9t14L6wOHOHTkkj1IslLj96QS3PxunkCuiN+k4/PIwe0/3P9bG6lkRRZGDp/tweq1M3lkmGkxid5vZs7+Njr7GLxWCvp3GFidqjapWOH6Utl5f/b/7mrj98b3aM1EqlImuR/nDf/M6kiCQSeRwNlhp7fE91nJoaS6E3b37Pq1eQ3AzSTZTwPgl7uUnsnsG+8PdW89waC32xC6dzdXIM4soj7K+GuXiJ9X3m0anxt/uxtlgpVyqoFKrSGWLqLUqPvq7m8xuzzsRYOBAGydfHX6me3w7giA8sevk84xTey9/Sde+8Hps1+2VssTmaoRrZ2fIpB6K2YGVKF6/ne6RNmbvLCHLMquzm3hb3HTvbePIa/to7fdvjVFBrdPQOtBMJpFFEAXMDjOCSrUjsymTynH+nds7noNKWeLe1VnS0RRrs5voDBq69rbRMdi06zO+vhjivf9ykUggztTNBeKhJL42NwdeHiayHuPKu3f57r9+ha69VTux+zfmKRYrNLS6aen34/LZ6RlswuWt/k4L99fqBJQHSJLM3Njq5xJRAMxWI/1fUf6ZgoKCgoLCrxtFRFFQUFBQUFBQUPitoVQsM355mhsf3SOXzOLw2Tjy2gg9+9uZG12uWY4IQFOnt1psK1do72tkfnQVuSLR2eVF1+AAWcJv1GF/JNQ4splgbTFUG+sBgijQ3OmluaeB+ekAjf0tuFs9lAolHA12ChKk4jk6BneuFG5ocnLmjRHOvnMHh8fKxz+7hSTJiCoRtVZDOJpDbzRSVKlp7vWj0app3+Pnld87zrWP7hELJhFVIu17/Bx9Zbguf0Rn0HDqzf10DbcQWo2iUqtobHfhbd69gwPA7d1ZzEzGs6zMBWsF6v49jbz15xc4+sIAIye6qZQrbCyGdnwOqlktmWTuS4ko967M1QkoUO0OOP/2bRpanHibnEhS1ZbmNyHz5MbH4yxOrNf+XalIjF6cwuG1MvJc/1M/rzNoee7bh3j/v14kuy0von3AT9/Bjid8ssr0rQWuvHu79u98psD5n93AZDPi724kmciiUou4PBY0TxBVCvkigeUI5WIFh9dSE/7UGhU9Q830DDVTLlUtmL5q3D47x18e4MJ7Y8iPCAb9+1tp6awXN5t7fbz0Bye59PYtMoksGp2aoRO9DB7rfmJGyHbmpjZwNjkxmvVkH+lG2Xeqn43NBJFgEmPHr6Ybxdlgxe62EH+MnZ6/rWqh9bgQ7gfIT9n/JBZngzvG1xu0sNW0Nj8VwGkz1gsoADJM3FzC6bFy4FTvF/7+Z8XmNtPe72d2bAVvkwOpUiGXymMw6zFaDTQ0fT6R+FFcPjuzo8s7tqtUApFgqk5AeUBwPU7jsW5c/gSR9ThQ7c7oP9pNS99DUWF6bJVPfnazTggLbyZZngmgN2ox2I1UKjImo5boRmxXaz+n18rMrQXuX5qqdZlN3Vrk6Nf2cexr++qOLeZLnP/5TdKJLKVCCUEUsLnMZFN5spkCvnY3mUSOzaUII2f2MHCoi9f+4CSlYgmT1VjrgtxOKpbZse0BiVj6sfsUFBQUFBT+IaKIKAoKCgoKCgoKCr81TN+a5+yPr9b+HVmP88FfnUNv1OJufGhXUy5XKKdyCIJA195W2va1srEYJpevsDy1QVt3A33DzWhUYH9EUMil82RTeXytLlbngkDVKWvkZC+3L06jNWi5fnYSg1GHRqemtcdHKVgtKJ179y7P/d5hClRQiSJukxGzriosDB7qwN1o54Of3MDmMqPTazBbDei3RJx8rsjqQpj15QidfY0IgkDf/nba+hqJh1Oo1SqcPtuudlEabXUerT2+Hft2o7nViafBSmizaklWLJZZWwzXCqs6vQa7Vc9GOMWlD8ZoaHLgb3fjaLCSiOwsnpksevQm7Y7tz0o2lWfy1uKu+8qlCpvLEZanA0zdrtob9e3voGdfK7bPaV8VDSYIr8cRVAINzc4vbAUUDSaYur37fMevzjB0vPuZukFaenx8///8KmtzmxSyReweK00d3h05B49SqUjcuzi1Y3u5VOH8L+5gbFqp/bY+v50Dx7ro3rqntrM6H+TsWzeIBKrHanVq9p/pZ/9zfWi3dcJ8VQKKLMtIFRmV+uE9vPdYN1aHiYnby4Q34pgsegYOttM14Eenr78OgiAwcLSHtv4mEpEUepP+c9u3lSsSyUyBr//hKTbmAuQzBbQmPUa7kaIgEo9lkR5VdL5CDCYdR18a4MMfXdshZLT3N9LcVc3A8PjtTxzHs0veiyTJrEcShBMZZAlMRi0tHjuGR23QNpM7PrudXLbIymIInUGD022hXCyhUqsolSWiwSSjV+eeGm7/VSCKIgee30OpWObGB3cJLIWB6n2w71QfhWwBs934lFEeT+dQM3fOT9YE83KpQqVSYeBIN+vLkR3H641a7F4rmWyBl//wFLl4FkmScfns2Ld12RQLJe5cnN7RSWQwabE2Ofnpz29SUVWfKb1OQ1uTA1+Xl8B8sO4zapXA/L2V2j3xgJsf36NruAVP00NbveBalM2Vavj7g9tX3OoYmx1b4cXvHiaTyJHPFNhYDGEw6bB7rE/sKPI2u5gdXdl1n6/lywlYCgoKCgoKv20oIoqCgoKCgoKCgsJvBeVSmbvnJndsr5Ql5saW2Xemn89+eo1cplDbJ6pE/J0NGDQi3/9nJ9lYjSIKAplYhkw4wek/PrNDlDBa9KQTWfYcbGdtIYQsyTS2eZi9t0K5VCYaStUCnUuFMouTG7QNNJKplJi4HaTYbWZOqq7gdRj0HG5tps/rxmsx43BbKJckmp6QuZCM1q/+1Rt1+Fq/Wmshg0HLy1/by3vv3CEWSZNN52tWMgajljMv9BPaKiJKkszKfIimDg9Dx3tZntzYUfzdf2YAk/WLFzPL5fKuQdtQLSZf/MUdioVSbduljTssjK/ytT88heUZiqjVLpFprn4wSnHLjsxg0nHmW4fo3cob+DxI5QrlUnnXfYVciUpFQv2MmfB2lwW76/MFK0uSRP6RTimpLLGxGiOdK9Hutde2B9bjvPfWLV5+Y4Q9ww+7pOLhFO/914tkUw+fl2KhzNUP7mG2Ghg88uUzbh4QCadYmA8xMbFGpVzB53fQ19dIa5sLtVpF10ATnXv8FAtl1BrVrqvit2OyGTHZvtj91tHt5fYnIX7yo8uo1CrUahU6g4bj3zhEOJRCb9Bgc5i+0NilUplSvozBrHtit1TvvlY0OjVjV+cJrkbRGjQMHuqgf38bhi1R1d/uonugidmJtR2fb+9rpKmt/h2yvBnjysQSc2uROhHIaTVybKCNoQ4fmq2iutn85PdJpVzBoNei04ice+t6TWRoaHGx/8weAmtxspniFxJRkrEMqwsh8pkCJpuRpnY35l1ymx7ga3VjtRsQALvbglanxmwzUS6Vmbg2y+lvH/7cc3iAt8XFm396hmsfjHHvxjzZbJHeA+3ECxXS2SKN7W42lsIIQGOnh2KpQiaTRy6X+d/+px+iEgT2nejhyKt768ZNJ3NEg/U2fTqDBr3XxgcfjSOqRXqGmlGpVeQLJUYn1khsJjg83ML6dDUDRhQFktE0oijUhPYHlEsVYsFknYiy3QpMb9Si1qhr76hyqYIsy0QCcQIrYW6encDX7sHuseJssONutNPY5t4hlnbs8XPn3OSOji29UUfn0FOyWRQUFBQUFP6BoYgoCgoKCgoKCgoKvxVUyhL5R4o5D0gnsnTtbeMP/+/f5rO/u0JwNYrTZ6dnfzuJaIbDrwzj7/CwsRAktpnAYDbQ1O3FZDXWBboDOL02Bg51Mn1niZe+e4i7l2dx++2M35inc6CpWuTdlgmhNWuZCUYoClXveKkswdZwsVyeD6ZmubK0wg/2DdFstaLTq0nvshC8XK5QKpQplStUypWvLHficTT4bHzvd4+yuhzh7s0FbCYdTrcFo1ZNaDlSV5TLpqtWM52DzbzyBye5+ck4sc0ERouekTN7GDrW/aXmYrYZ8Xd4WNhmjwWAUBVxIpuJHWLJxlKYpal1ho4+/bsX769x/p1bdau8c5kCH/3oChanicYtC6XHkU5kKRZKmO1GtFoNNrcFX6u7tjJ+O11Dzej0WioViWwqj96g2RGI/mXRaNR0723j5if3Hs4xnScRzzB4qo9kLEs+U0AQqqvS1Vo1l89N0trhwbRVQF+dC9YJKNu5e3F6q9D/5ee9shLhl2/fIZt9+F3xeJbJiTUOH+nkyLFutFo1giCg03+112k3TFoV1z8YRZarxeVyqUI+V+Ta+3fZ9+pe2rp92D+niFIulZm+vcjouUlSiSxNnQ3se66fpq7dM0sEQaBzTxNtvY3kswXUGvWOc9dqNTz35ggOj4XxW4tYbQZAwO420dbdQCqeRqOrdqYtbET5u7OjFHYR9qLJLL+8cp9EJs+p4Q7UKpGOHh8Tj+kwAPC3OKkUS1z65Z267ZsrEa68P8qJ10fQGz7/b7U0vcEHP75RJ3RbHSZe/cHhmo3Zo8iyzOpMAFejfce+ubFljr+x/0tlAFk9VtQuK70n+pBkiEUzxINJFu5vYDLrOHikE51eQzgQZ/TCFMVUlmKuyMhze1hfCvH+315ldSbA7/2bN2tdUVqtGq1OQ6788DwdTU4+/Ow+kiSjUYsI28R7nV6DqFWzuB7HYdWTSeaRZBmdUYu70V61WnuER7c5G6pdJblMAY1Og7/Dw+rsJpVyhYYWJ7HNBO0DTcQiSbytbs7/cpRMMoe32UlDq4vuoWZOv7kfyzZx0uWz8/ofn+bmJxMsT28gyzLNPT4OvTCId5uAo6CgoKCg8I8BRURRUFBQUFBQUFD4rUBn0NI+2MzYhZ02Rg/CxQ+/MoSvzc3S9DrxYBKLw8yJ1/fT1Fm1Q2nrb6Ktv4lyqczsnSXGLk2TTefo3ttG/6GuWqFu+EQPkkbF5lqMU98+gFCqsDK7ibe5Gha/PLNZnZNFR7CUp7I1P4NBCwYVPFKbTuYL/PDOPf7o0D4G9rdx/v2HxW9JkohH0oQDSURRYHE6wOp8iIOneml9xMYFqmJLsVhGrRbrLJceEN5MkknnyCVyhAMx8ukCbr+D9j4/dk99x4PZoqd/sAmjTs17q1GSgTiJXbIWHuRkCIJA/8EOOgaaqhkoRi1Gy+NXkT8roiiy72Qvq7NBSsWHhWCDUUcuk8Nk2X3F+9pc8JlElKlbSyBXi93ZZB5ZljGYq2OuzAQeK6JkkjlGL01z79I0hUKJhmYXB18coHOwhaOv7eWXf3GOUuHhfK1OE4PHulm8v8adc5NsrkQwWfWMnO6n90AH2q9QTBk41sPS5Drh9aqFTzKRo6WnEYPLipTMUcxALJTC5bHS1l613llfCtMz2LR1btm68fLZAqlomnyuSCaRIbKZwNe687pENmJsrkQRBGhodT/RTiudzvP+u6N1Asp2rl+bx+kyM7BLjtCvinQ0Q3Orm9XlMJXyw9DsaCBOo8/GyOEOcuk8yzMBAkth1Fo1zV0NNHd5Hyts3r82xyc/vFL798ydRZam1vjOv3p112v4AJVKxPSE58dsNXD8lSFcXisf/OgaNoeR5VCCD/7qAiqVyJGXhzj48iDvTizuKqBs5+LYAn6Xld4WDy3tbnr2+Jm5v77jOLVaxXOvDvP2//HJruPEQ0lsDuMT570bsXCK9394fUcHVTKW4aO/u8H3/+ULGHexlhIEAbVGRXg9RqVUwWDSYbabENUiRrMB8SldS09jdnKDhdnNum0qlYi70cbqfIh4Iotbbebdv7qA2awjuRlHkmQ+/fFVnv/eYWLhJCsLIVam12vPgtlmpH+kjdsXp6vjqVUksgXKW/ebw21FFOs7lVwNNtKJLEN7W1mZWMPqMLHnQBupcIrKI4HzvnYPDY+8s6wOM0dfHeazt26ADEarAU+zk0wyy7Gv7aNSKiOqVRTyJT77+W2K+WpnXzQQx+G1Mju2itVh4tTrI3Xj+ts9+P7ZaWKhJMjVbqBftcCvoKCgoKDwm4gioigoKCgoKCgoKPzWMHyyn+XJdRLbQpmb+xrpGq5aMgmCQGtfI63bAn53Y+zCFOfeul77942Pxli4t8Ib//wFVjaSXL8xTzyeJZ8tUFmNcmBfK85GOxqNGrPNgOnBamGdSCqWx6Krrt4dOd7DRmX3bplUocCd9Q1ODbQwN7nO+lK1+B0NpgisRhFFgede30skkEClFrn+8T0u/SxHuVCmfaCJ9uEWIpkCY+NrpFN51BoVe/ob6e700uC1EY+muXFhhvmpDaxmHWd/dgOdVo3Xb8dsMXDHOcXr//TUriuIG5qdONwWNnbJATAYdTvEHJ1Bi26X1dFfhtYeH2/+s9OMX5tjdS6IVq9h7/FugqtRJm8u7PoZ7TN2LsTCSbKpHCvTAYr5ahFXrVbR1N1AOp7d9TOyLHP5vTuMX5mtbVtfCLL5lxG++d+9QPueJr73r15hYXyNeDiFp9lBx2AzmXiWX/zns7VOnnymwMc/vEqpWGb/mYHPc0meiLPBxrf+h5dZndkgFcsQDKVIZsuUSxU++flt8rkSgwdaiQXiXPz5TSwOI3N3FnnxW4cYPtZV19mTz+ZZHF+tCVgms55rv7zNme8fw+auCm+FXJG5sRVufDRGLLiVoWLQ8NIPjtF7oGPXOa6uREgldwZmb2dsdIXuHl/NIu9XjUarxmI30KVvJJ3KUSiUUIlVMaNnjx8kiQ9/dI3FyYcCw83PJjj6yjCHXhzcYTWWy+S5+fG9R7+GYq7E/OjSE0WUZyEaTPLpz25itukJrUa4f32+tu/c27eYm1nH/1wvsWcY6978Bj3NbnR6DWdeG8LtsXL31iLZdB5BEGhpd7P/aBc+vw2tRsRo1u+wcrLYjI8VNZ/E2kJoh4Ci02sQBIF4JM36YojuXcS0tblNNBp1NS9kC4fPRmOHl+GTvbtmRD0rpWKJ8Ts7g+UBbE4TsgzZfImle8vY7UbMZh3p0EObrrFLM3QMNbM8HSC+7W8SwNCRLtaWwgRXoxhMOta3nhmL3Yjd9TCLSa0WcduMUCghOo10DDZx+tUhtHoNGo0ai83IxbdvEQnEEUSB5q4Geg918tlPbyCqBNr6/LT1NaIzaOnZ30a5JDF+fY6F8RUcbjP7TvayNLHKzN1lnI12jry2D1GtwmhRk98SdiplCXQwfn2e4WPdO7KmRFHE1WD/wtdZQUFBQUHhHwKKiKKgoKCgoKCgoPBbg6fZybf/9ausTm+QTmSxe2y09DZieoKn/qMkY2mufzi6Y3tkI86ti9Pcna8KCQICBuNWt0Iggd3vQCiWEIDXfucod67McndxHY1WjdVuYt/xbsqtRvKFh0VjjUqkWW1AjhaqnvQbCfL+Rk5+bZjVuRD3biyyuRqlf18rLZ0eYuEU5XIFh03P+391HqvdSEunl/WlMB+8dYM9Lw2xGUmTzRTIJLJM3FnC5jDxO98/wr3rC6wvR2j027n49q2aVdHyXIi27qoIcvvcJK/+3vEdeQ06vYYXvnWAs2/fYW0pVLO9sjpNvPStgzi9Vn4dtHRXV/znMgU0GhUanYa5eyuPFVHaniKWPcDX5uba+6M1AQWqHT1rs5tYHLtnawRXI0xuK1g/oFKqMHljntbeRhpa3TQ8UiS//sFYnRXaA25+Mk7PSDvmL5jlsRtmm5H+Q9XsknMfjZNZjfLhT2+Sz5XwNTvIxzLcv7kIQGEjQTSQ5OIv7yIIAt3DLZjtRtLxLPFQqq4DaM/BNhbGV2gbaKL3UBeTNxa4/tE9Jm/O42t10znYTHAlTDFX4uMfXsHd5Ny1IyUYTO3Y9iibgQTpdB7nI4XbrwpZllmeWmfyxgLRjTjtQ81UShW0ejVO/cPOrLZ+P43tHu5dmasTUKqDwLWP7tHU1UBzZ72gmE8XSCd2F+KigcSu2z8PqwshCvkSTo+FS488B4VckdXVCPbNBKJFsyOr6FFm1sJEk1lcNhMmk54jp3sZGGkhlcyjVos4XWZUalXVtqnbRyaVJ53Mk03lEcSquGa0GnDvYq31NPKZAg1NdgSqmR3FXImNhU2kikRDq5t8Zme3kizL3PlsnHgowfPfP8rNT8dJRdKUC2UOvDhE90g7UA1yX1oIMT2xTjScwu4w0TfYRGuHZ4ftVaUiEQkmKZbKZBM5VuaCZNJ5jGY9RrMeUVV9NwqCQEuHG5fNwCcX7hPZiFO0GdDo1EhliXKpQjKawuYwo9OpsT2Sa+TwWHjjn5xgZWaT4Hqc7EKQpooMVO3JZFmmoclJZ6ONyz+6TDqRQ6USSc5t8u1/8WLtvdIx2Exjh4fYZoJKRWLsyhx/+2/fIx5KgQB2j5XjX9vH/jN7uPDeKBvLETr7fCyMr1TPM19CFAQqEmwsholuJsjniqhUKowWA5VSGY1OvXU/lcjnijy+t0xBQUFBQeEfL4qIoqCgoKCgoKCg8FuF3W3F7v7iRf1MIlfnyf+AXK7I/FQAQatFluuLkbIMvmYn9y/PMHp+Cr1Jxyv/4jSmZCN6nRaVRcNGJV8noOjUKlrSKi797CrZbAGrRodZpca4kEFl1FOUoG9/K7IkIVUkNpYjVMoVHC4zNz8aQ5Zk0okcxUKZwGacRDJHeyhJKpVjeXqzVjCNBpOc+2ic4HIEq81IuVCqWz1ekSRikTQms56F8VUS0fSuQebuBhvf/Kcn2FiOkk7k0Bu0+Npcu1rs/CoRBAGj+eFK95YeHwdfGODWZ/drv4sgCBx8YQ9Or5X7N+cpZIvY3Bb87Z5dO2RaOr1UKjuFDbPNgPUxwfSZZI5KRdp1X2A5jCzLO8QoSZLYXNnZzVMdL082mftKRZTtdPQ0sDIXJJuu3tttHR4u/PxWbb9aoyIRy9DQZOf2+Ul697fy9X9yggvv3GZxvLoa32wzcuBMP+lwCmRIxTJce3+MuxeniATipGIZUrEMS5PrvPC9w6zNblLMlwgshXcVUR4frf55D/rizI4u8/5fnq/9ltFggs69bQSWQhSyBQSxupr/1DcPoVKrmL67uOs4siSzuRTaIaIYrQbsHiuRjfiOz3hbXF96/qmtTqlivoT8iEgiigKFUoVEMIXO4SVXLD1xrHJFolSufw7MFgPmR6y5BEFg8Gg3i5PrqDXqus6J5q4Gmjp22gw+iVwmTy6Z4crPbwACnYN+pm7MIyFQKla4d2kGs1nHngPtdVZRuXSejflNMokchUyBIy8Po9KoqFQkHC4zGq2aQr7EhU8muHf7YUdJNJRmfnqTngE/z786iNFUfZ+sLoa5eXmWWDhFMZkjsBQiU5YJBhKoVAJunx1vixOVSqy+h3RqLn84jstnZ20hRCadx+21EFgMYTTrsbmtLE2tozfqWV+O4pjeoK33obBrsRkZONTBAGC6ucDf/MX5OmHN2CTz9r//mHJFQqrI6A0aNpfDfPrjq3z3X71a67QTEEgnskzdXGBpch2P30E4EKdcrBBYCjN1e5GVpQiFLYuuRDjFypblZGwzid1lJhZKkoplSEbShNaiyHLVqsvb5EC9dc0NJh0m8+fvMlJQUFBQUPjHgCKiKCgoKCgoKCgo/KPCZDWgN+l2rHxOZwo0W/Sk8juL7S6Dhl/814sIQEu7G5UoEtiMcyOxTnNnA2JhpyjThJ7zP7lMoVDCodFRDGW5vxRm9sYCp795iNBmEv1re7lybpqOLg/FXInwRpy+oSbmJ9bQ6jQYLXoKhRLJLUuk0FqUZKFSt+LcaNIRWI0SC6Ywm/V14ekPSMazlBqrAcMLYyskwilMNgNt/X68LQ87KTRaDa3dD8Ow46Ek83eXKOaKuJoc+Dsb0PyabJceoNVpOPbqMG29jTWBoqHFhSRL/PjffUw29VC4ault5MXvHd5hR+P0WXnxO4cYv77AykwAUSXSPdRMxx5/LaekkCuxthwmHEwhCGAx6RAEYYegVv1+9w4BBaq2N94WZ83uajtGix7jF7BBelb8zU7MFgNanZpSoYRUkWpzF0UBo0mHVJEpl6UtQSdPY5ubb/+L52lqd7M8tY4gy0TWorWuFKPNxOX37u74rmKhxMrMJmabgWwyhyztLjZ5G54udjY2OrB+jk6yz0MxX+LGh2N1Yli5VGFleoNDLw3S0utHrVHhaXbWivdS+fHdHLvcCugMWg6/Msz7/+VCnchhthvp2tv6pc/BvHVttDo1glA/B0mS0WpU2NwWouUnZ6IAqEQRtfrZ7K/a+/187Q9OcvOzCYKrUTQ6NX372xk53ffMVn7B1Sj3b85z98IU4bUYncOt2JxG3vuL8xRyRZq6GygVK+j0aqZvL7L/+QGauh6+f7R6DVanhUwiR7lUIbgaQaNVUy6W0b84BFQzTbYLKFC9371eC7GVMFfeH2X4aDclSebtH16jWCjj85q5+OkEUqXCwPEeZibW0GpV5LNFtAYNKrUal9vMzc/uoxIF2gdbmby1QLFQJhbJ0NjuBVnm2Nf3kUrmUGk1zI6tsDS1wbf+9Az+Ds+Oa+Ew6chte1cVi2WKqSypeBaLw4QkSNjtRqIbceZVIsHVCM3dPkKrEc7+3VWmbsyzsRJhYyGEwazn1LePcPPsfQA0ei3Td5drnXlavbZ2r5SKZWKhJIVcEbVGhdGqx2DSk03liQTideLnyMmeX5nIq6CgoKCg8NuOIqIoKCgoKCgoKCj8o8LqNHPwpWEu/vxG3XaLw4xoMkA+Xbfd5TAxfnWmJlxIMlgtBjRqNclolqw3v2vhqRzKVvMWBAEhU2ZlbhNZlpHLItlkjumxZfR6DQ6XmcW5EDqVQCFXJJcrYnWYSUbTmK0GCqVyTRcx2Yyk50J13yMIUKnIxBNZbOE0JoMGqVL1uddo1CACMniaHATmgwRmA9UPATc+vMfrf/o8bf3+HfPfWNjkF//pMzIPMkMEGHl+gONvHPhKA9KfBZVaRXN3A81bAk8mmeWH/98P6wQUgJXpDe5emOa5bx6o2+7yOTCadLR2uNlzoA1ZkklGUmQTGXztHiKhFJ/84g7ry9HaZ0xmHVavjfBKuE44UqlF+g/tngECMHC4i9nRlR1h0AdeGMBsN1EqlQmsxkjEMqjUKvzNjh2izxdBpRJpaXfT1e9nYyWCRqdBVItotWrUKhGtXoNKI6JSiegMGvTGaoeRRquhe28r45cmySYeXk9Hgx2tTlMLXzeY9IiCgLRVxd9YCrHvZC/FfOmxHRfNLS7sdhPxeOax8x7e21JbCf9VkUnliIZSFHNFgqvRHfslSWb6zhJHvjayI+OkZ18rm6u7dBMJVfFuN3oPdKDRqrl/bZZoMEX7Hj99hzpxNTq+9Lk0d3nR6jVkskX6DrQzuWXPBlUbvka/E6PfRij1+Gv8gJ5mN06L6Zm/u2u4hdY+H8lopmpb+Dnu0+BalJ//p7Nkk1nmxla2Mok2OPOdQw9eP6SiaWweGw3NDlQqgUQoWSeiqDVq9r8wSCKSxN7kJpUtkk0XcHfa0FqNFAolxm5Xr4dKJSJJMrIs42+wcP7HlwlvJFCJAs0dHo6+cYBCtojRoiewFEEqV0glsmwuhOjq87E8HyKTzLE2H8JsN2G36okE4thsJtYWwrz4O8dZHF8luBbF4rYw8lw/S4shNldiNLe60GrVlIplpu4s7SqilJI5nj/Vy6dnp8jli9V8GFlAZ9CSTeUw6LVkI2kykoTepKNSqjBxeZqJa7O89xfnUGtVSLKAJMnk0nlmb1et9QLLYUrFcu05Bchuu1d0eg2RQBwAd5ODWDjNmW8eYPTKLMHVKNmtfK3BI50MHe1+5t9XQUFBQUHhHxuKiKKgoKCgoKCgoPCPjr2n+jCa9YxdmiaXytG1txWNw8ylm0s7jtVrVGyuPoxtFsVqBVCK5fHZLRTzJXYzkX9QRLdpdUQWQ8iyjE6nwWI2oNNr0OrU3LuxwHf+xQv89b8/S98eP/lMgaWZIPvO9HPhZzcxmnQ1ixaDSYfZY6N4f6Pue7KZAl1tHuYmyqTiGe4FE4yc6uP8z2+hNWirn7PoUasEssksxm3WPcVCiSvv3sHf6UGjfSiMVCoS198ffSigAMhw59MJ2vqbaB/YGQD962RjKULqMYX5mbuL7H+uD4v9YbFYEASOvXGAa+/eYfrWApIk0dzt48S3DqHWqTn701t1AgpAJl2gsclOl9vM6tQGlVIZX5uHwy8N0ta3U3R6QGufn9f/+DlufzZBaC2GyaJn33N76D/UQSSU5Nx7YyzPPxTCdHoNx57vZ/hgvZXRF6G53Y3La8Fi02M26Rg62MHqfBC1RoWoEnF5rCBD/8EOrE4ThVyRdCKLyWbg2//6NeZuLxEJxGhoddO1t410Mle125KrnTQNbW4CS1UrM7PdiFSReO7bh/A0OXedj8mk47Wv7+Wdt2+TeSSgHODY8R66exp2+eQXo1yqcP/2EtfPTZFOZPH4bAQ34tXMim0CQCFfRKVRszS1jqvBVpdn0b23hdl7KwSWwnVjj5zqo7F9Z3EcqvdX53ArncNfvvPkUVxeK6/+4Cgf/+Q6TT2NWBxmJm/MI6pEjrw8xKFXhnl3Ymd2z24Mdfhq768HlIpl0skcarUKyy7WdhqtBpfP/rnnPXVrkWwqhyxDZctCTK1Vk4plcTbYiAeT6HQaWrq9qDVbdlLbOrXy2QKhtRhqg5bBF/fy8/92hXQyi9VpIStB+EfXOXamH0mS8DoNJEIptAYtjW0erv7iJmvzIaSKjCAKFPNFfvrvPmTka/spFysUckWKhTJSRWZ+fI2uoWZOvrCH6Yl1SqUKDX4bXX0+NmcChDcTLE4HqFQk7H4HzmY34bUIgbUYxUypJqA8YHkmQKVc2fEs601aQpPrvHCyh1Aiy83rCzgbbagEAZfHSjaeIRxMIIpC1a5SEPj0R1cwbnUilYsVzA4TlXIFUatmZSbA8TebCCyH0erUDBzuwOwyU8iXEFQiQ6f34PLZmbmzRD5XoLXPj6vRwdi1uWreS08D/SNtdA40cfTlQdyN9l276xQUFBQUFBSqKCKKgoKCgoKCgoLCPzq0Og0DR7vpP9xJpVxBo9UwPx+EXUSUiiyhN2rJpPJo1CKGLSub+FqckX0+7pV2D4/Wu6tFfBGBcqmCz+8gn8yTCCbQalTsP9TJ0mKYVCrHydeGiAeSqNQqioUSskrNH/yb17n50TgagxpXq5vOAx3MrMYQRaHOzkuWIVco0uCzIeVL5DIFFudDnPnuYaKBOAJw+vURgovBOgHlAZvLYRKRNO5tq+ZTsTSrs4Fdzyu0Gv17F1FKhZ3ZDzaXBb1RQzKS5sYn47T2NqI3aZFlAZfXit1j5ZU/Os3BV4eRKjIOrxW1Rs3yfJCVhfruHrVahbfFwWY0zfJqlL3DzVjMelo6vDianp5z0TnYTGuvj2wqj86gRWfQks8V+fjtO2ys1Is1hXyJs++NoTdq6R9u+VLXxdNo49TLg5z74B65fJn9L+xBb9KyPLOJw2lCKpcxmrSEl4O8/R8+RVAJLNxbxWDWM/JcP/tfGERnfGjVZHYYaerwsjYfBMDtd2C0GMil87zw3cP0jLQ/tjvjAf4mBz/43aMsLYSYmtqgWCjjb3bQ3eOjucmJ6hntpbYTj2dIxLOAgMttrtrYAffvLPPp27drx8UiaXoPdnLx7ZsIooDFZiSymSCyEad9qJV3/uICOoOGE1/fx9CRLmRZJpcrMXSyF1+7h9BqFIvNQMdgM219jbVi/5dFkiQ2lyMEVyKIKhF/hxfXE8LaO/f4cf3ZS6wvhikWyrzwvcPYnSacPgcqlcjXrDp+cm6UYmmnFeEDTgy10+l/+FtJksTsvTXuXJohHEig1qjo29fK0JEOXN4vFy0uSRKL99cBEEQBm9tCcMuKL7AcpmeknbELk7ga7eSzRQq5Ii29jVi2hK6lyXXOv32LYrGM1++gkCvy4kv9oNczPR2gUpGRJJngWpT5GwssTa7Vvnvf8R6CC6Ga7ZkoCiBDMpaBSoWl2QCDw83cPDdZ+8zcvVWCqxG8jTZGnutHa9Kzvhiic6iJ8Gb1/Z6KZ7E5TJTLFUrFCk3tXjaX64U2AJPFgPhId1OxWEIURVr7/eTSecRomlazFotWzcmXBjn/1k0KhSIAOqOO/sNdBFcjlItlNNqHNm4qtYjdZSGdzGK2GcjnS2h1arpH2nj/nbuktnWSqTUqjp/p48z3jhJai3Dz/DRry3NA9Z0zenkOWZLoG2nh7vlJDr08tGtWloKCgoKCgkIVRURRUFBQUFBQUFD4R4soiojaasGrqclBc7OT1Ufsf6KJPHsOdnDjs/u43GZkSUaSJEDELatpbnCQruzMI4jqJYaPdLNwdQGPz8bivVUqpQp9+9uZurXA/PgqL/7OMTL5CnfurPA7f3AMtSRVi4rZImtLEfYc72HoaDeT6xGW1+JoDWocXiuRQL1wc/fWIn/2r17h07dukUnmSMaz3Lk2j6vByhu/d4yTrwzx6Q+vsD63uWOeGp16R86JVqtBq9dQKuw8L53+12vltRsOrxWVSqxlXdg9FmKbCcavzmEw6wltxPj0J9c49OIQyzObpFM5ho50c/BMH41t9d0EsUi9fZtKJeJtc/LJ2UnyW11A0yYtNoeJ2ZUodvsy33h9BK9nZ96HLMuE1mPkMwXMNmNd3sDaUniHgLKdO1fm6erz1XUEfV4EQWDvkQ7sLjNTYytsrMY48MIgr//BCQLzm2STORLhJBNX51ie3qC1r5GOoRYiGwkuvnMbSZI58spwbTyNVsPz3z3M+Z/fYmUmgCzL2D0WXvrBEfad7n9mWzeHw4TDYWLkQPsXPjeAVCrHndtL3Lu7UvttzGYdIwfb6e32cfP8VN3x5VIFzDpOvnmAmVuLCGQplyXOfO8IiWi1k6mQK3H2rZvojToW50NMja3W7iubw0Rri5u2vsavzMJOkiRuf3afy7+4XfsejU7Niz84RnNv9fff7RmzOc2PtX3r8rv4nef3cWl8iYWNaF2Oj81s4MRgG0MdPjTbuiMmbi7y6c9u1cSGcqnC3cuzrMwF+cYfnfhSFnOiKGIw64htaZN2j5VULEMunSe4GuX41/dic5tZmQkQDsTp3teOvcHOT//9Z5z+xgif/fQGeqOWhiYHk9dmCa1E0Oo1tPQ2Mny8h+W1JPlciXggTmAhiCzXHArJZfIkImksVgPJLeGjWChjMOoQVSpikQyiVk1ji4vVLXEQQKtVk03mWV8I4Wlyks+WaGhy0LO3hem7K+QyVZFDb9DyrT95jonrc7ue+8ChjrqOjvBGjLM/vcHafJB8pkAqmqZrbyvqUJKVyQ0iwQTH3hyhUqwgqkUqkszknWo3nbfdg8Nnx9HoJJfOI4giTV1e8tki+54fIJsp8P3/8TVu3lzC7raQTuVruTzlUoULH0/wB//9C/Qf6uDSB/cAKBUrZBJZZFnm5e8dJh5MEFgMk0nmeP2Pn/u1Z14pKCgoKCj8tqD8hVRQUFBQUFBQUFAAdDoNL700wMcfj7O6zb6rWCzj9Ns5cLyLmWtzhOeDGEw62vY0cehAN4cbjPzo9hjxfH24/OJGGJdLwys/OEo5lKWj10+pWGJjMcTcvVUAlifX6fDY0Rk0RENJtJJMLPQwlDwaSuFqsHHIZyWweZdyWaKh2YlWryG6maBcqqDVaXjta8OsTa3TP9DIwEgrxUIZlVpAqxLRigKiKNK1t5Xxq7N14dcAg0e66+yMKhUJWYChE31cffdO3bF6k46mXl/dtmw6z/p8kGwqh8Vpxt/ueebg6e2k4hmkiozVaXqqrYy32cng0W5GL00D1c6i8atVmxqn10osmKS5u5H/8v96F3+nl2wqz8L4GvdvLfA7/+pl2noba2OJYv2qcbffzvlLM7UiPVA3n3g8y7kL03znmwfq8jSSsTRX3xtl+u4SlbKEVqdm76k+Dp4ZQGfUEt4lbH47wY0YiXgOt/fLFetFUaS9p4H2ngbKpQoqtcjqbIALP7kKMiDLRAMJkGF5coPe/Q/zXW6fvU//oQ6sjocFdJfPzpt/cobgapRivojNZcHhfXJgfCSSZmUpTDSaRqNR429y0NTsRP8lBLhspsBHH9xjcb6+ayidLnDh7BTZZI54JL3DrioRy6LWqNj7wiDN7S4WJzcIbSTqurkqFYmrH4+TLlQwmnVYbAYEBDKZApc+nkClEjlw/KvJiwgshrj4zq265zASSPBX/8vPOfTaPpKxLMNHu+g/0I7RrH/CSPW0+Zw0eexshBOEE1lkZIx6LS1eGya9ru7YTCrHtY8n6kLqHxANJlmY3GDkRA8AyUiKSCCOVqfB0+pC+4wi38DhTta3Orx0Bi3te5pIJ7Ko1Co6h1tp7vGjNRvpFgSSsQwby1EEQeDmp/dJxzP0jbRy7kdXuPLLO0D1GRy7OIVap0HjNFNO5xEseixOI8XNFLIkIcsy85Mb9B3qZHlyrRqkbtKSTeXZ/1w/oUgGWZZZWYow8vwe/J0eFiaqx518dRiVSuDOpWn6D3ehNuQIBZI4Gx18baAZAZmWLi9tvT5sLjOiKHDr3OTD31GA4WPddG3rJivkCtw5NwkI2FxmkKsizNp8kKNf28fSVIDrn04wP/6wk0ajVdPc6cXeYCNzc5nY/Q2Of/MgUzfmuHthmva+Rr75L1+ie6QdvVHD5EQAjU6DTVe1iEwlchS2OlTMVgML80F+75+d5s/+H9/j2ifjTN5apKXLy8jJHqRyhbWtjsPFyTUCS2Faeurf7woKCgoKCgpVFBFFQUFBQUFBQUFBYQuXy8w3vrGftbUYS0th8vkydocBq07DZCiO+5Uh8tkiarWITq9Gp9fgc9j5J4f2M7q+wc2VdbKlEsV8CVdWIH8/wDv3b2PIy2gNOnpG2hhdi+HwWjFY9KyuxuioSBw/3YvVoiO4rUAsCALHXhmisd2NSiXyzTf2c/3mAqtrUdwNNhxuC1aLnqOHu6jEMlwaW931nA4+PwBAa7+fF3/nGFfevUsmkUWlUdF/qJMDLw0B1ULy/P117t1YIBxI4PJa6T7YyeLYCrIk0dju5sjX9+PyPbT9Cq9H+eBvLhPa1r3T1u/nhe8ffeaV7JHNBKMXpohH02SSeRLRDJ4mB0NHu2jvbcRo2VlIFkWRY68NY/dYmL6zRHA1itluxNVgo1gs0drr59L7Y0iSXO1GoGqHE1yJcuWDe/jb3Gi2Ogsa/HZUarEWzFxGJpV6mN+h1qgwGOuL0MsrETY24jQ3O7fGlrn63ij3by7UjikWytz4eBydQcvB5wcQHyMMqVQiGq2aSkVCFCESiBNejyGqRBrb3Jjtzx4E/igP7KeyqVxVQKEaql7IF2vHFLaJRflMgXy6UCeiPBjn0bDsZCzD4lyQ5bnqav7WLi9t3V7W1mJ8+tEExWIZQRDQ6zXcubVIo9/BS68M4XB+sfNZXAjtEFC2s7mRIJMpYNnlfimXKgQDCUxmHeGN+I79uUyBzZUoPYc6uX9nmetnp5EkCX+biz0jrUzcWaJ3oAmzbacd3udlYzFcJ6DEI2nW5oPIskw+Ve2iuPDLu4TW47zwnYOfqwNGrRJpaXDQ0vDkQPtwIEE6tTOj5gHz99fZd7ybiauzXPj5DfKZAgjQ3N3I898/+kwZKV1DrYTWY4xdmkGSZNRaNb42Ny/94Cj+Di9/82/fR5bBYjfg8FiIR9KUCmWSsQyZZJ5cKseNj8aqgwlVW7B8usDFt27w/X/zBrfOz5BLZNh3rBuNepNIMEksnKYSz6E3aDnzvaNMXpsll85z4IVBmgdbWFmOEgok0GhUxBI5ciWJnkNddHR5OP/WDYJrUV7/p6dZW4ry4Vs360Sm/Se7MdmM9I5o0em1HHtliPa+RjZXo8iSjLfFSWOru/bMrS+GOPvWTc797CaSJNHa08ieQx0EFkMYTHruXZ7lwAuDLN9fY3ZLVLc6jOgNOg69PMjGYoR7FycJrkRIxTK09TXyg//xayDArXNT3Dk/xfCJXqYXI7VOHJ1eQ7lUQaNRoVaL6A0akvEssWiaoSNdDBzq4OI7t1lfDLE6vV4vqMtb7wkFBQUFBQWFXVFEFAUFBQUFBQUFBYVtGAxaursb6O5+GHj93p9/xvo265cHLPeu42v34LOa8Vl7ONzazHoiSTSUYHT0NhP3NsmtJlBZDQRXogSWQwyeHODWpWkSgSSuBiuNzU7UGjXHn+sjOhhndT6IJMk4vRZMZj2peAa7y0JHu4eWZiebwSS5XBG1WkWD14rBoCW6meD+jXliW90OWl3V9svX6sbhrhbERVFk6Hgv7QNNJMJpdAYtrm1hwqNX5zj/3mit2L66EEKjVdF7vJfBQ+14m6rzfIAkSVz9YKxOQIFqnsG9SzOcfHP/U691OpHl/f96Ea1By42zk0S35q9Sq1ic2mDgUAcvfufQrivy9UYdI6f6GDjUyYc/vEKlVC3ary+GkCSZUrFqRbZdushnCgRWIoQDCRrb3AB4fDb2H+3ixsUZtDo1oUfsvbw+GxptfRaGLMuEI+maiBJaizF1Z5FcpkClXMHiMOFrdVEqllmdDdA70kaDv76wrdWpcXssJMIp0vE0HX2NTN9Y4PbZ++RzBbLpAnqjjhd+cJTevW1fqoBvdVhqWTqiSsRg0tWs2vTbM1BsBkzP8D2b6zHe/fENErFMbdvc5AYdA37uT22g1Wnwuc0Uk1kiy0HMdhNyvsC5z+7z+psjn9sySJZlxu/tLhI+IFcsV+cu7dJeAbT3+jAadhck8vkS/Ue6+OAnN9EbNfQNNiKKIpsbCT762W2ef30vsWjqS4soyViGYqFUCx7PZQrEg0kMRm21M2ZbU9TU3SX6D7TVdU09K5IkEQ+nkWUJm9OyI8tF4MmdXgIC63ObfPLDy0hblmPIsDqzweV3bvH6nz6/o4PrUXQGDafePED33laigTgarQZfmxu720IymsbhNjNza4Hrv1hCFEX6jnTR0OGlmC+zsRiimCsiVWQQqkJjuVRBEARCaxHUGhVrq1FsFj0XPxhj8EgXc9MBbC4zJqMWp9dGOi9h9ntodpkQNWoWpjcx2wz84Z+9iAzkcyWQZdZnA9z6ZBydXsOeQx2ENlNc+mgcu8tMKp5DlmW8jXYigSS3Ls3S2Opk79FuVGoVTZ1emjq9O849Eojzi7+8wOZKpGbZtjS9QWAlwplvHmB9PoggCizPBRl5YZD+Q50sTqwiqEQ6BpsZPtHLf/if/45MMockyWh0GgIrUT76m8u0DTQxcX2BxmYHbn+YwGIYtUGHwaxjfSFEOpatzcNg1tHU1VB7x4uiiEolEljcKUaKooDF8cUFWwUFBQUFhX/oKCKKgoKCgoKCgoKCwlPIJLK7bs9n6i28HEYDDqOBe8tJxt6/R7FQolQoI0kyckVCKlSw2w04tyyrXvzBMfQGLSde2IO30Y4gy9y+OEUqmmFuTEIQBPxtbvoPtGGyGfH47TT5d64ydzbYePOfPcfcvVU2l0Nkk3nmRpdYm1pn+sY8R14dZuhELyq1CrPNhNlWXyyLBJNc/WSiJqA8oFSscP/uCr5WN/72+mJhbDPJwvjuhe37N+fY//yep9oRrc5skknmCIdSNQEFoFKukIimmZ9Yp2PPOoOHOh87hlavoWuohXuXZ5BluU4UEEQBlUYFW90WepMOQRDqMiMEQeDQyR50eg3jd5bJ56or9LU6NR6fFftjOmq2X6pYKMnSVIBkLENDixONTsMv/vwcKrWIWqMmny7w4u8eo2+oial7a2i1ahx2A5/85AblUgVRFPB4rLz355/R0OwiHs2Q2VoVvrYY4sx3j7LnYAe9w81PvJ6Pw9vqYvB4D2MXq/ZnzgYbqXiWvgMdZNMPOxIOvTSEyWp84ljlUoULH03UCSgARrOOmekAa8sRjh3vZvzTewSWI7X9Wr2GF373BGtrMdof6Wp5GqVSmWTiyavko9EMew60M3dnqdZVVPtunZqR492UiyXGr88/FAa2aGh2MjsVoKu3gcR6hCs/u06lItE50MzIwTZuXpzlxIt7PtectxPeTDB2c4mpe6u4XSZWFkNoRJHIRpx4JAWAr82Fp9FBKJDcsl2D0Hr8c4soG0thbp6dZGl6A1mSaWxzc/D5PbT3PRzH3WjDYjeSiu/+Xusa9LM6GyAVzZCOVy2wTFYjZruRhfEVIhtxPE3Ox5/veoz1uU3KpTKeFhcDR7rrrO9ElcjktRnm7i5vbakwdn6SUr5E79FuXA02bB4bGq2aUqH0sCNEgK7hVhYWwrVOGqtRh06vIZctkssWaWhy8I1vHGDyzgrLCyFm769jd1tweC10DTQxcqQTjV7N5nKYv/3/fcS9q3OoVCIqtQpno52psRVEUUCn11AxVzCa9WTTOYqRMiCzMh9m6HDnE0Wkhfvr5NIFtDoNokqs3W+FXJFwII5Wr6H/UAeuJifXPp6gUCjTMtiCzWFi+GgXgaUQqa3MHqkiIYgCVruRzeUwbXv8FPMlSqUKqViG1g4v9+4uo0tq6wQUgFy6gFwsYd/W/dW9r5WxyzPks/V/u3oPdNDQ6n7sOSkoKCgoKPxjRxFRFBQUFBQUFBQUFJ5Cx3ArqzOBHdsbO+qFhWKxRGg5AhWJSllCFEW0OjXxcApng41SoYzRrGPocBddI22o9VqOvzyE3WmimC9x7p3bxIPVoqooCvjb3dw+e5/PfnKNzoEmbC4zp75xkN79bTvm4vBYOfTCAHfP3eezv7uGwahDkiSyqRyf/eQaBrMeX4eXRCyDWq3C3WhDs9VZsrkapbhLiPwDpkZXGD5SL2RUJImKJO16vFSWdhSydyMWSmLzWBj95eiOfdmtIunUnaUniijh9RjLU2tYrAZun59Eq9fQ1NWAWqvC43dQyFWtqwQB7G4LrgYbrm2B71C1wTl0soe+oWbujC6RypfQ6TU7VvADFPMlUrEMocUgn85s0NLXyPpimEKuiMNrYe+xbhburTByshdRLbI0tcH6QpBr749y5rtHMFkMhDbiNQHFYNLS2OIkl85RKlQIbsTp3ddKLlsgFkzha3WhUQuMXpnBZNHT1P5shc58tmpRVSlXcPpsHH9jP+4mJ5PX5qhUnJz69mGKhRKL42t4W5zsO9VH7/72p44bWIuytstKdovDxJ3z05RKZTLBRJ2A8uC6TVyaomuo+XOLKGq1GoNBQzL5ZCFFY9Tyxu8f5+7VOdYWQoiiQFuvj71HOmlq91TDtL9/hEvvj5LeEhDsbgt7T/by0/96iWIkyfTtxdp4s2PLxMJJ+k/uIZctPuZbn0xoM8E7f3uN5Nb3hSMZnvvWIX70b99DKldQqUQMZj17T/Zx4Z1b9B7sJLyRAEClfnK3x47v2ojzi7+6QDb9sEC+thBiczXKN/74NC1b3XVGs55jLw/y8U9u1GfDlCsYjFo2FoOsTq6zPL2OTq8ll8wSXo3S0O7B0+xEKlceO4eF8RXe+/OzFLeES0EUOPb6fg69MlwTHsJrUUr5IohCXefQzK0F9r84yHf/7GXWFjb5zv/pVX727z6kVCwjSQI2t5lX/ug5/tP//hkGgxZRFMgWKzT3NvKCQYsgimRLZULRDAazlr2HO8nniljsRuwuM64GK5IkcfnntygDNz65TyadQ61SYbTq0Rm0bK5EUWvVVEoVtBo1sc1qfpDRaiCdqHam7JYls53gVnee3qjD2+RAa9RidpmRKzL5bBF/q4s9BztpaHXR0ddINJRCpRJxN9rR6TVsLoVrY2l0airZaudhIVdCqsjVrBeznnKpgteqR2/QEA0mdsxDlmVa21wkwikMrVVLQm+zizf/9Ax3zk2yOlvNUxk61s3gI0KXgoKCgoKCQj2KiKKgoKCgoKCgoKDwFHoPdLI2E2B+rLpyWhAF9p7up22Pv3ZMLJjgsx9fZXlyDY/fyf4z/SyMr6FSi5SC1U4LX7uHtuE2lpciLC1EeP7Nkdoq4eBGDFmScPlsxMMpPI0OLr5zm9TWiv9cOo9KreLDv7mMRqdGUImoNapqkU6nIZ3IkknluPnpPZLRNNFgkmK+hN6oxea2cPZnNzF57YQDCRDA1+zi2It7aO1ueKrgsT034wEOjwVfm5vAYnjHvo6BZiz2J3c0ABgsOgRRoFzcKeBot0LIi/nHizuFXJGPf3iFwGIIi9PMK793nHQii8Gs4wd/9jIf/fgalbKERqvG2+zE7jZz9JXBxwbfW2wGBgdbuHtvlcLWnCplCWQZlUZFIVdkeWqDRp+dwOQG8WCSy++N0rWvlVPfPEBsPcY7/8enxMNJyuUKBpOe1//kDPFIivnxVQ6/PMzpV4e4cW4Sf7MDQRQwGLVodRqKuSJDxzoJrUQZPTuByWJg6GQvmysRfvnn51DrNejUIp4/OPHUnIyV2QBnf3qj1t2jN2o5+uoww8d72Huyr3ZcpSKRfz2PVq9B84yB4dl0YdcisrA1nsVmZGMX6zuA9fkg5V3upachigIDw81sbk488bi2djftPT5auryk4hkQBGwOU83OSBAE+g+009rTQGgjjiAIePx2ouEMbW1uPjp/H4fLjCgI5PMlsuk8kY04Dqt+R2D9s3L/7kpNQHlAaCPO0a+PIEsSer2GTCrP3UszlIpl9hzuqp6zSsT3OTsD5sdX6wSUB5RLFcavzdPc5a1di/6RVrQ6NaNX5wiuxlCpRRxOE5VSmfErcxiNGkJrcbQ6NQ3NTjKJLKGVCL3723E+JhOlkCty8ec3agIKgCzJXH33Di19fhrbq+JZqVDGYjfR0t1AZCNOLlNAVIk4PBZaehqIJnNMzIbQeOz8d//LH7AxFUCtU+FudvF3f3OF/Fb3VKlQDWyPJnNcuDhDIVdErVGxNBfEUizRNtDC/dtLdA74yWYKrC9HECWZZCrH3Pg6epOWTCpHuVwhk8iRTRfQ6tRUKtUbfG1+s3YeiUgam8uM2VKfj7QbD959rmYH1jYXY/dWGR2tdrgcPNZD33P9uJuqHYVmmxGzrf5d2dbXWOsU0hm0lPJlJFlCrVGh1WlweixYtrKSNheCvPLGCO///Bax0AMrQhlBENh7oJ17n01gs+ho7W2k/2AnGq2aps4GGts9ZBI51FoVBtOTOwYVFBQUFBQUFBFFQUFBQUFBQUFB4amY7UZe++MzrM9vkklksbktNHZ4Uakfdirc/GiM5ftrqLVqNDo1epMOvUFDS083eouexfEVDn99P8FAAqvdyPOv76O914csyyxOB7j6yX1uXZ6jsdVFd38jcrlM5x4/m+sxAothKhUJqSIRWo9x7u1bpBJVv/zWXh9uj4XJmwtYnSZmR1dZmdlAp9dSKlVIRDMkYlmSiRyt+9opFStotGoCKxF++TdX+OYfncLhtSAIPHaFdXuvb8c2jVbD8ddG+MVfnK0rmlodJvae6ttx/G40d/m4f3ORxnY3q3MPC+8CYNvy5+8daaFUKte6ZrazsRSq+funomlS0TQIkIqkee47h/nT/+lbTN5aoJAv09rdsFVA33ku23E6Tbz84gDv/PIu4fUYsWACSZKxuyzIgF6nZqjHx9qWlVkhV+TKu6O8+SenufXB3VpRFrnaDXLx5zcZOtVPqVCu5bSIgoDZ+jBfo1yq0DPYxFv/7gPW5oJYbEZEQeDtf/8JI8/twd3kYG0hxPv/7RKtPQ3sO9X/2PnHIyne/+tLtU4egHy2yLmf3cJsM9E19NASTKUSn2rf9SgPxK1HKeRKeLwWYrEsFu/u2QpGix7LF8wV6ej0MuZdIbzVqbXb/uZmF1A9L7vL8tixjBYDbZaH87BJMo3NdvRaFaGVMJWyjMmqx9NgI5cvo9aosLt2t3V7EulUjsnRlbptarWKaDjNzGhVkLVY9RQzBURRxGDUVXOHRDj68iANzU8OiH+U5emd3XIPWFsIkssUahZ7giDQNdBEe6+PTDpPPJTinT8/S7lYoVQss7gU5tjX93H1vVFy2QIqtYjRYuDAi4NoHiPixYIJIuvxHdulikR4LUpju4dSoYQoChTyRXR6DW39fsqlMqIo4vE7SGbLnD87WftsJJ7D2+VjeXyVC+eukC+Ukbc64CoSDB/pZGZyvfZsyUCxUCafK7I6E6Cly4vB+FD4WFsMsj4bYGZ0hf797Vx89251jpLE1K0Feoebmb639vCdJlf3yTKo1QJSpcLlX9ziyGv7EISqIKTRa1CpxFqnTdseP2vLEWY3EszNVYWY6nMmky6U+fjcJLlimYHeRsw2A6IokoxmiIeTaHQaPE1OvvdnL/PWf/iMlu4Gqm5gMh17/Ny/No+/w4PeVD2nSlnC47Py2pv7CAVS5AslpHKFZDDB7NUZbE4T0UCC2TvLyBW59m4WRVHJQFFQUFBQUPgcKCKKgoKCgoKCgoKCwjOg1WtoH9g9kyIZSTFzZwmAhhYXn/ztJUr5EkargUKuiEqtYuTFQS6/fQuVWuTkNw9RKRYRBIGJW4t88rNb5DJFXA1WYqth/vKn1/D4rEQDCbr2ttKztwVZhlg4xeZKFH9nA81dXjRaNeVCib/+f79LW68PT5Mdo9VANpWnUpYoFsrYPVZCG3G6RtqYv78BCLR2e1FrVBQLZaZGlzn9tb107mlibmJt53nr1HQPNu163q19jXzvX73K8vQ6qVgWu9tC2x4/Tq9t1+Mfxd1o59grQxgteja2hCKVWkVDixNPkwOX18LUlVlGP5mgtb+RwWM9db79hezOVfcPwkpy6Rwn3jjAoef3UClLu1pzPY6+3kYCC0FupvPYdGqMWhUatRqPz4ZagNVt10lUqcgkcyTDKaLBFAaznkwyB8jIEgSWwgwcl1Brqivtgdp/byeXKZDPFBEQMNmMRANxAGZHlzj13SNVeyqVyN2L0/Qf7HxsN83aXLBOQKldFllm+s5inYjyRfA1O3C6LUTD9WJGLJyir9/PpYvTNHb7mLw+V9fh1NTh4eBze5ALJWLBBI5nvEceYLMZ+frrI1y6MM3CfLBmQ6VWi/QPNHHseDf6xwTHPw2dVk0xU0SSZFQqFSpRppQvEV6L0dLbSGuPD2+j/XOPWy5WKJXqra9y2QK+dk9NRJEBp89GYDlaFUplOPLiIMNHu54a3v4oepOOXCaPVJHR6tR1YodGp0GzyzOgUquw2k3Mj61QLlbnqtaoSERTSLLE8z84iqgSaGhyojNo0JoMjF2fRxRFvE12PNu6UjQ6NWqNinKpQrlUIRFJkQinEEWBdDxDeD3K2R9fZXMpTHt/M+d/dg1Hgx2Xz47epOPg1/Zx9sLMjjmuLUcoAz372oiHk6RTOdw+G/0HO9iMpNlcj2E068ik8iDLeNwWAncixIJJXv2j00TC6dpY5VKFtfkQzgYr8UiKvce72VyJ0j3cDJJM52ATrd0NvP+j6wiCQKVSFVCMZh2HTvWwPL6KKFQ7hWKbCaKbCTbmg7ga7Rx6ZZjeAx20dDVgblln4fJs7XtFUcDT5MRk0RNej/MX/9vHvHJmD6piGX+Xl9HLs2SSeQRBoKnLw4nX9/HK75/g3Nu3yGaKaLRqCsUKe452EQvESUUzaHRq9p7spW9fOw63lZlb5yjkiizdXycVz6DVadh7so/1rc6wW59N0D3S9tSsKgUFBQUFBYWdKCKKgoKCgoKCgoKCwleE0WpgZWq9topZpVGxdH+dcqmMr7OBWKhqr/TZ311FkmXsHjsXP7iHJMkYzTpUkpobv7wNQEWSq4XvWws8//1jSJLE0nQAo0VH15CfiSsz5NMFDBY9fftbiQbiZFJ57F4bNpeFSCCB1qBBqlQtwgrlqhVTPJrBbDPg9dsBWJwKcOKVIU5/fS8qjcjE7SUqFQmNWo3XZ+Pka8M0ND1+RXwhWyCXzFEpljCYtJjMBnLpPCvTG0Q2YuhNelp7G3H5dx+jY6AZb4uLpq4Gbp2fIhnNYDTraWpzMXr2PoatFddjF1PMj63w7T97BffWWA6PDZVa3NWOzNVYPUYQhM8loACEViPc+2CMnn4/49dmmV+KoFIJ3I1n2f/CACaroZapYbLqUWlEtHotsWASX7uLcqlMLJhEEAQEUaBSqTB0ohfjVveDv91DS08DKzMP7YLSsQwmqwGby4woCsSDCUw2A5WKRGUrg8LhMhMPJVmcXEeWqyKU+5Hifj6zU0B5QHQz+bmuw27o9VpOvzrEL398vbb6/wGCJPHia8MEg0le/N0T3PlsnNBajIOn+kgE49z9bJyFOwtoDVpOfeMAQyf6avZSz4LbY+GNb+5nM5AgFssgIOD2mvF4rJ9rnEfZWAyzPLPBc989xAd/dYHSlpggyzIHXxxg5FTvFxrfaNZhd5oIb7vukiSjMevxtbrYWAqDBIGVKDJw8hv7iUXTbHwao1yWOPHq0DMLKZFAHLVGxfy9NWS5mpvh9jtweq2IKpGBQx2P7SAB6rrqNFo13hYX6/NBYptJGlpdtPb4iMTzvP/Tm7WONbVGxcGTPew/2YNWq8bZYGfPkW7unp9kYzFIfMtOzuV3sDixSmAxxNLkOqHVKOuLQQ69shdRpaLvYAc9+ztYXI7uEJ0AKhWZxdkg5WKZN373CK52N5lsien5EE6PhT2DzciyzPJimEQ8i9dpYk2ScHqtqLTbnn0ZHN5qLkpXfyOf/uQmew51sO94F3fPTyIgsD4XoKW7gR/88+e4+NEEpXyRpnYPdruBu+cnOf3mCMgyV94bBUli6f4a2VSeubEVgqtRooEEB7+2j9XNBF2DTeQyBZCrApfeqGVzJUJoKzNlPZggPbPJxz+6wnPfPkwmmUeWZVbngtz4eIKb56co5IqIyBhNWnR6Dctzm5z5xn4cTgtmuxHnVr5TS4+P7/zLl5i6vUgxX6TH3oq70UFoNYq89YOlYhly6bwioigoKCgoKHwBFBFFQUFBQUFBQUFB4UtidVnoGWljYyHE4r2H9j2yBMUt+5p8OldbpZ0Ip5AlidnRJUwGDXaHERkYP38fk9VALlMglylitpsoF8uszWzQe6CTSrnCia8N8/b//jHriyHsbgvhtSjORjsHX96LIIqMXZ1l4HgvlXKZSlnC39nAzOQGNy9M09jhRZIkAqsxrA4jeoMWUSWSyOSZW4+waQT/0VbK+TIOm4muXh8NTc7Hnvf0rXk++KvzNRFj/NI0/Ue6QBCYvD5XO66aDfI8zbtYaUmSRDycRpDhyPN7MJj0WFxGPvzL8zUB5QGxzQRX36taZhlMevoOdnDklWEub1nyPKC1r5GW3sYv8EtWyabyGK0GJq/PMX93ubZdb9Zx8e1bfOOfv1gTUbQ6DSMne/G2uTjx5n42F0O499tx+mzM3F3G5bPT1OmrhmhvodNreOFbB7l5dpKpO0uUSxXMNiM2lxmNVk0ylsVgMZBN59GbdMiSjMlczeXIpgtcfn+MZDSDRqvm8MsD7D/VVyuCW59gY9Xc7f3C1+T/z95/R0lyH+a58FPV1Tnn6enJOW7O2F0ssIgkCIJgUqYkWpLtax/Llo7lIMnyta6Pr3nMT+dacpBpiRIliqSYCYLIYXOe3Z2c80x3T+ccq74/ejCL4QIgFSjSYj3nALvd1RW6Qm/37633fd9OW7efZ376OFN3V1ncjpBq72mgb08zLq+N+fkQY3dX2fvkflxWA+vjKyRlBZvdRLlYpVSo8NoXr+BqcBLs9P+V1q3RiDQGnTS+h7D3VyWfKaDUFCqFCk//8lky8Sy1mozdbcXhs+5Ey32/VCtVIqsxSvkyfUNBLnyXeBUJpRk81U/bQIZUOImiKDR1B8jlyhSzBQBuX5qlcyBIoMW9a95MMs/i1AazY2vINZmO/iBNnR7e+NpNirkSe092c/v8DNVKjdByFFEU2H+6l979re+5zYE2L1qdtCOMuRocaHUSqWiGI48OUVJEEok8ouaeKFGt1Lj6xhQmq4HhQ+0IgsDhx/dSKlZYmlhFZ9DSPthEx3ALC+OrLI6t0b2vja21GAChxXqXUc/+Nlx+O5NTG++4bVq9hN6kA0FgK5LBbDNx4dxtTpzoIrGRYG5sFUFR6NnfRsdje7j90l10ei39RzpJxuqdUigQjyTRCs04vTZe+sJljj2+B51Ww/OfPYfeoMPhteALOilki4ydm8DjMBBZyzP65jiFbJG23gD+oItzX7/OobNDTFyZxdVgp3tfK1vrCVLRDOOXZ/D3BcnlSuiNul2OsVKhTGwzufN4dS2OHYVCrkQslESrl6iUqngDdsZvLLIwtoJuu6soFc3garDT2OFncSrE8Ce67hPY/C1uvE1OSoUSc7dXdhwob2F1mjFZVQFFRUVFRUXlr4MqoqioqKioqKioqKj8LXDwkWEuPXeLQLuP8EoUjaRBb9TtFFKbbCYEBExWIxqNADJce/Euo1fnsTpNPPTsEQx6Lebtu9eLxQpGkw6rzYjOoMXmsfD4T59gfXqTje0ekEq5it6kZ2s1TiGTR5Ht2JxmRq/MUanWsLitVBBYmgljthmRZRm704zeIFHI1ePE/K0uXrg4jpivkt/KUNKImL0WFpIp7ry5xUB7A48e6cFs3C1oFPMlrn7n9n0ukMvPjTBwrGvXc4Vckasv3qGhzVPvfNimVpO59eYkV1+8uxPPBHDwTB+p74qLKpcqrMxsIukkzHYTm7ktFsZXeeADB3jy508zu9190jHUTOeelr/R3dZWpxm7x8rVF3aLM3JVxtvkIrqRQGfQUi5WsLnMPP5TJxi9NM3i6Aob82EKuRIajcj7fvEMBruJN741wj/8vz9MPJwishYHoR77dvbZQ+w72U0pX0Zn0GIwSMzcWkTSSWg0Aqszmxx9Yi/pVAFvwMH6QoSHP3qUjaXYzvG/9J272F1Wuvc0A9DU4SPQ5mVz+xx5C51eomffew+k/1VobHHT2OKmsB2p9vbeicGhZnp6GykWymTjWabfGEOpKawubpFJ5VEUMJp0TN1aJNDu/SvHVv1tY9kuny8Vq5Q2kiCAgEAslKJt4J2j7N6NVCzDua9eY3FsFUVW8LV4aG11s7wc2/W62FaWPQfbWJnepJgvE95I7Zou1xQiG4ldIkoqnuXFL10ltHJvWZvLUSRJxBdwkorlsDrNPPLRI6RiWeSaQkOrizMfPIDV/t7dN56AgzMfOszrX71GtVJDEASsLis9hzpoHmhiZnSdQIubaDh93zV/+/Ic3YNBDEYdVqeZ9uFmjr//wHaMV5612RCKrJDaSiPpJAaPdyMIAnJNRmvQUinVnXvmdyk412hEvAEniViGfKZEIZXnZ372AZ7/s/NEw2kMRh1Gg5a1yXW0tSpHHx2iXK1hcVqZ3xZmyqUKew62Mn5tnkCrm8PaQUJLWxgNOoLtXkRJQ2ObB6vDTDaVJ7qRoGu/i4mrcyg1GbPVQMdQExqdhkCbl5f+7DyhxXvX2N4H+zHZjNRkmWqlQi5d76zS6iUM20JKtVJDrt3bd4rCjmgVCyUJtPuplKo4vTbe+PpN5NrukqpEOIXDayMRSVMp19Ab7r9uRFGka7iF6RuLuycIcOjskFoir6KioqKi8tdEFVFUVFRUVFRUVFRU/hZw+uw89jOnWLizTKlQolZTUGoyBpMeX6ubUqlCtVojtpTE7rZy4/VxdHot+0/3ce2lUf7sP3+Lp3/xDDffmAABfEEXRrMeT6OT4+/fz8GHB7l7aZZXP39xZ525dAFPg4NCpkAinMZkM3P47CCvfvk6JklDNJIhly5w7LE9LM1GiKzHyaYL2JwmBKCxw0usWGTq8jyLM5sYTDq0kgZFUdh/shd7wMLEYgiTQcujR3ZHL+VSeRLbcT3FXIl8rrhTpl6tVFFkKBVKKErdebG5GCETz+H03+vC2FiIcOWFuztxM2+xPB0CQaRSLNXjuHQSmUSOYq6E3Wsln96OrFLg5ivjfPxfPEnPvjYqpQqxcIpirojJakCj+esNzrsDTpp6Gu7bLp1BS7DDR7DTR/fBDrRaDY3tXqIbSW68Oo7RYsDmseH0iwiCwLVXxth3ZpATT+6hWq7wpf/vBcql6s6yTj99kIEjnTvLP/ORY7T0BlgYW0OjFfEEXEQ2EkTWYgiiSO/BdqKh74rkUmBqZGlHRDFaDDzysaPcuTjNzMgKtWqNxnYvBx7sJ9Dq/Wvtj/fiLfEkHk6xMReiXKrgDbpo7PRjtRkpZQtk0gWW5yO7BpDz2RKrC1uM3lhi75GOv/Xt+qvQ0OKhe28LM9u9RiigoKA36ugabn7HefKZIgjcJ9bdfmOchbv33EuRlSjWfImz7z9APJ4nnyvh9lpp6fCysbhF7D0i1hR59/k3O7q2S0B5i62NJIIooDdqySRyZBI5NBoRQRRYm428Y9zdOxFodXPoTD/xUBKTzYiglViai/CtP7/C2sIWVqeJo2f6ScSylLYjCyvlKguTG9y+MIMoCgQ7vJisBjYWIxS2u3mMZj2iKOBpchFo9fDNP3yFbKLuENFoJbr3tbGyHkfRCqQyBURBwLztknsLm8tMJJLGbNazPh+mkMjSP9xMprlAOpFFQMBqM7A8F8Hf3cjGRhpTPE/fcBNKpYJGFNBoRLbW4tw6N40gwNCRTrLxLPFQEkG8t79z6QKVco3+g+3Y3Rby6QK1Wo35sTXa+4PM3V7a9dlithupigKH3rcPg1lPaDlGIpwmmcwjigJOrxVvwIGk1SBqxJ3roLXZzdLFuhDj9tspFsoA1LadacV8mXprTv1zV1GgXKzg9FrR6t49orBtIMj7fv40I+cmiW0ksbrM7D3VR+/+tu/rPFBRUVFRUVG5H1VEUVFRUVFRUVFRUflbQquT6D3ciSvgYHFslVQ0w+HH9zJ+fZ7pGwvEQ0l8zS56DnYyenkGUdQgKwoGs45sKk8imuHQo0Pcem2CyHoCb6OTnv1tDBztRCNpaOz0EWjf7TJIJ3K09DXS3N2AIgCyzD/63Y8yfmOR2bF1bC4zHf2NiKJApVxGEOougJYODwce6Wd9IUx4agOP2YAsK+SjOTRakckLM5z6+BFSlLg9s87e7kb8LtvOeo1mA2abkeXpDULLsZ0ByEK+xLH3HyCdXGNtNoTJZmTf6X68Tc77uklW58MoioIgClgdJgRRJLqZZPz6AkOH2rn2nRFESVN3fRQqWF1mLA4zmUR+ZxnFfIl0IkcmnuPic7cIr8QQRYGWviAPPLUPT+O7x5G9F3se6OPoE3sZvTiNvB3tZPdYkbQS/Yc76TnQDkAsnOLWuUkWJzcxWfUYzXpKxTKlQgWdQUtrb4DmLj+Xnr+9y21TLlZ4/SvXcPpsBNrq4obJaqB7fztNfUG0Og1mS70TpVKqcOO1cW6dm3nHbf1u147Ta+PMM4c5eGaAWkXG6jL/tQWl74e12RDP//FrFDJ1VwoCHH50D0ef3IfVZcHisiLXQrvmEUQBs8PMldcnae3y43D91SKz/jaRtBpOPn0AT6ODhdFVyqUK/mY3vjYvc5MbrC1FaWr34mtyEd1MMje2Rng7lqmh0UnXUBOegINsMn+/AwDIRDOUUjlOPza86/lSvlwfH1fumwVBFPBu9xZBXayYvLW08zifK5NJF8jl6qJloVjhzFP7mRlZAqXu8qIGFocRg1FPuVwhtLBFLpXH4bXia/XuOidCy1t8+zOvkU3mUVBYW9iiqbcRV4tvx02XSeR5/VsjnH3mIBsrMYr5EqtzdXFsdT7M5lI9Puz0+/diMhuYuFwviTeYdDR2NfDEz53i1qvj5Laj8LQ6CU+Tm/Ebc1SNWmLRDMO9fqYWo8wtRwn4bFi3RapSscL+wx1YTVr27WvmxnduEQ9naBtsonuwibmJdZbmttAbtVSLVawOIxZJ4Ev/v29jthpw2I1kknmCvUHWtz931ufDHH10GEGESrlGpVwlFcuiKAqde5opFcvodBq0LjM1WcHf7MZiN2G2mYiHkjS0emg+3kVSo3BzKcTG5Dx2SctQ0MfJY21kMmXujm8SC6dRFGhsdePy24msxpC0GrwOE1sWI8VcGXejc8dhloxl6BoMsrG4RSFfQm/QoTNIiKKIVi8xcKjjPd1bgiDQMdRMa18jhVwJvVGLVvfufTgqKioqKioq3xtVRFFRUVFRUVFRUVH5W8bb5MbbdC+Gp/tQB+e/dqMe8aLI3HhlHFHUoMgKk9cXOHh2iOhmiuWZTbwtXh76iRMY9Fo6h5o48b69OxFY/qCLBz90hMWxVYr5ehyXJGmQdBKHHxume387JrMerV6LxqCnisDiTIjLL4+yMh3isY8fRdJJbK7GCba4uP3qGPMTG8RW42xVqrgbnBSrNUrFCkIkTWRiE+eQn0SmwHIouUtEMdmMDBzr5upLY7vumO8/3MmF5+/QMRiktT+IRq9l5M1J5CuwFcpw7LEh+rYFiEqxgq/FQ6Uqs7IQoVyq4nAYcXitTN1epv9oD9M359FIGnoONmF3WwkvR3fta40kolQVvvNn5ynm6oP4sqywNLFGIVvgQ//o0V29BN8LRVFIxrLUqjUe+5mTCIJAZvuueQTYc7KX1v56xFOtWuPCd+5SLtfvLM9nShjNOkw6Cb2phs1pomu4iXQiv0tAeYtaTWZ9PkygzUu5XGH69grj1xdRUJC0Ei2dfgYOtmF1mLA4LO+6zcH2d+46sTq+f2EilS6wFc+iyDIWix6/x74zeP5eVCtVrj5/656AAqDAjZdHaelrRDIZCPYFCa/EiKzFkWUFUSNw7Ml9RGM5yuUa4fX4X0tEyaXz9W4YvRZ3g/1dy9+jm4l6z43VgMtvJ7yRJBZJIwgCXr8db8BOIpzi1qujZOI5ioUSN14dZc+pAWSNSD5bQiOJPPj0AaYmN5iYXCeXK+HzWollikQSGY6f6sP4HufZd7tKAAItbvr2tTA1snLftKFD7TQ03RMAZVmmWqm7mOLxLBuriR2nlCwrpJN5llaiNPUEWJve3Jlvz4luFLnGy5+7xNztJVBAFAX2PTTI0ffvR6fToigKI69P7PT8FLIlUokcqcuznG33YTTpd/qcatW6YBJocZNN5DANNuIJOtFa9Pi7/ZjMei4+d5NAu5f3/eIZUrEsRosBu9eKp9HJ+nyYxg4/siwjywquZhepaI4XPvM6eVlBkkRa23ycPt7NlWsLiAK0d/jp6vEzM7ZOZjPBjVdGkUsV8pkCE1dmiaxE6TrcxeZqHL1Ri6SXcBm0vPH5C9RqMulkfsfBks+WaO0NsDS1gSwrJOM5Wnobmb+7wtp8GK1Owmw1sudED+e+cnXn88bqNPPAM4dx+m1YnGb0Zj3OA828cG2KQr6E1qClJ+Bh88oCX/3GbTSSiM1i4uipATYtOhLJPPFImkqlhsVp5sBgkI2pNfae7CHY1cDdK/UOqUK+SCICwXY3hx7u5+brkxSyRaoVieaeAGc/fJiO/sb3uix20EgaLN8jxk1FRUVFRUXl+0MVUVRUVFRUVFRUVFR+wDi9NnLZIoV8mWqlWndWlKooikKtKqM3aLE5TPTsbSUWzVLIl6mUa2QzxV0dIgAHHxlCEAWuvXiX6Eacxk4/+88MMnC0m3ymyOjoKqlYFleDDY1O4tjD/Vx+/jannz7AS1+7ST5dxOYyszm9Trlao5QroxEESqUqoZUoTT0NlIoVFAXW5yMcPtxGIlMgkyve/8YkibM/dYL5u8vkkgVa+htRBA3pXAV/i4dsKs/MzSUaWjwUCxVe/uIVxq/O8cnf+hB9B9rwNLl5+RsjRDbrcUQCAhtLUYwWPY9+8ABf+H+/id1jxeG34w44GL00g8vv2LUJvQfbSUbTOwLK2wmvxNhc2qKt/3v3WpSLFebHVgmvxSlXZRKxLFaniZMfOkK1VKZSquJucOzqdQmtxVmZj+BwmWkbCLI0sU4hV3cXaESB7n3t9B5o5/w3b73ret+K8Ll5bprVmRDpcJKF0VUknYZiMksqnuHU+/fR2tuA3WPBaNIjyzKCWI8FSkUz9Oxr+Z7v793IZAqMTKxyd3yNwnZEk0YU6GjzcnBvG82B9y5wT8ey9/WvQF2Miq7F8XY0sLaWZPDMID25EqlYBgSBuyOrlMtVGlrcVCq1d5w/GctSrcpYbIZdnSuyLDN+dZ7rr4ySTRbQSCLde1s58vgwDrd153W5TJEbr44xfm2eaqWGotQ7StDpiGxH0WkkkYPHO1m8NkchU0TUCITXEhRyJa48P8KjP3OKfLaE1WXm8uU5Lpyf4tD+ViqRFCPX59FqNfQcbEdv1PPQ43voPtDG3XNTu96LKAoE3kHo0uokTj6xB7fPxt0r8+RzJcxWI3uPd9K3vxWNdM+5pTfoaO7yc/2NqV0CylvL79nTTCiUYnM9SXebm9RWhqEjnQwe7mDq2jxzI0tv238Kt14dI9Dho2tvG9lknpWp9Z3ppVJ1xx0T30wgy+AN2IlHMpRKFVKxLBaTxIVvjbDvsWFWYhnWp9ZIJ/NY7Ub2tfu49MId0qEkFqeZQqHC8Kl+kqkiroCD1ZkQoihgsJnwBN289BeXaNnunjFIGhYn1jFbjQx0N2CxGzl8uB2X387qXIQr56eobXeUxCNpUBSSW2mQZSRJg8NlRjLoKGcLlLe7VoC6kGIzEttMcGComZWZEAaTnq31BAajjoc/eoxsKl/vn3JbeOlPzxELpzCa9VSKFRLRDDdfGcXiteMMOClbdbx8Zw6dWY/RbsTts8FWjrmxVQBqVZlMLs/I1Ske+uAxvvntuxQyRYb3NtPX3UA2ksLc7ifY28jhhwboOdBGMpphfS7C3NgKmVSBlk4fHp+NcrmK3Wtj6GgXA4d+uNF3KioqKioqP66oIoqKioqKioqKiorKDxizzUhTp5/ZOytIWgl/i4f1+TAg4g06yWeKtPQGsLotFIrVnfkCbZ77lmU06XngqQMMneihkCliMOtxeKyszoX4zucubOfo12npa8Rg1NHcHeDcC6OUChUEUaC1w8PouQl0FgOtvY0kQkkUBQRBQX5bf4K70UF2OyrqneKgElsZbl+eR1YUtEYDt68ukk3nOfnEXs5/4ybRzSTZZB5ZUfA2Otlzopu5u6vceG2c3v2tLC9skcuV0UgatDqJ9cUIxXwZURC4YNbz+M+d4vk/foPIagy3387BhwYpFSqElrfQ6rUMHO1i/5kBJq/Nveu+L+bvF1e+m8hqlO/88ZuMnJ+ikCtjsBroO9HHq8/dobHFzVM/cZRDp/rum6+QLaHICololu6DHbga7MyOLNcHtQ+20zLUAghYnWZymWL9Lnlpd6SZN+giGkqyPB1i5uoMq7PhnWnhlRgHzw7SNdREW2+A3v2tPP+nFwivxlGApg4vH/iFBwl2vLMT5XuRyRX5zutjLK/Fdz1vNOoo5MqcvzDNYG8j3V1+TG8TMd6OVieh1WupVe/fzzqjDpvDhNGkY3kuwtLkxn2OnPXFLb77zNpYiXHn2gLzU5vUqjJ2p4l9xzrp39uC3qBlfnSVN75yfUdIqFVlpm4uUiqWefJnT+3Exo28OcGdi/UINEVRWF+OMXZrmcMPD2IwaikWKtSqMmvzEUavzdHQ6CSfLVHYFuQURSGXrrszLD47L/7lNQb7G7nz6hjxt3WZXH95jEKmSEdfgP0PDZKOZliaXAelvg9OfvAQZqeZyVtLFIsV7E4zja1uDCY9JouBg6f76D/YTqlQrpekm995X/fvb+P8S2P3dfVIkobe/W0srcbJpvJ07Gsj2OQkuC34zd9ZfsflbcxH6Nrbhs6gRW/S7QiRoiggy0o9lq5UY34qXL/GdRJun41sMk8EmWPPHubNq7MUsiUkvYSv2U1zo5M3vnyFxFaGri4fW+tJsqk8+ewIj/zMaZr6m5mf2MBkMWAPOtEbdJx55jCSWUcuU2T88hylUoXRq3N0HOnh6rlplsbWefYTJ2kI2Heu52K53vWztR5HkRWq5Sot3X4Onh1ifT2Fz2Xc9V5rNRlJJ+Hy2/E0OHAHHNSqMiaLjnQii9FqoFyqIGk1CAgktjLUqjIWu4myoYIgCKQTOVLRDJ52H2OZJDVZQanW8Da56OtvYv6rI/dWKNT/t7oao5TMMdTqweowsnZzmdDtVQ6e7iGyniQWztDWG8AXdGG1mzj3jZukolkAUqKA3WXBaJHYWouT7y+843FUUVFRUVFR+cGjiigqKioqKioqKioqP2AEQWDPAz0sT29SLlZweG1o9RK5VJ5jT+4jmy6gCALh1Ti5dJFcpoDVbkJvqA8smq2G+5Zpd1mwu+oRT5VShcvfubNLQAFYmdpAqxVp7vIjSRMoei2VcoVqRUajEckmc7j9dvQGXd19AjsRTg6PBVGrQdmOSGpwWflu9EbdfcXYrT0BRt6cRKMR0ZvqA8KJrTRbGwmaUnksDhMTNxZYX46yNBMi2OklHk4ze3eFakXGaNITbPcgCSDoJA48PMj1F0epVWUK2SLP/pPH2FiIsLWeILwU4c6bE1jfJQpKI4k4vbZ3nPYWlXKVc1+5xsztZdLJPKl4DsIpalWZlsEWlue3+PP/9houn42O3sCueY1mPYJQL3wOrSdx+52c+Vgj+VyRZCxHtVrj+T89Ty5doFqusr4Qxt/kxu6uH7em7gZcPivrc2EcDsM7lobfvTDNvtP9yDWZ669O4A44sDjMgILOoOXKS6MEWj00tLjvm/d7MTW7uUtAEQSB5gYHywtb3Lg0j9NuIrEWZ/L6PJ09AQYP1OPidu0Dq5HOfW289qUrVKs19EYtFqsRd8BBsNtfdyYc6eTrf3rhHSPNOnsD5JLZncebqzGe+8IVCvl7LoJUIs+b3xklnchz/Gw/Y5fn7hMSAJYmNwivRgl2+ElGM4xduSeuFXIlkvH6ekavzHLqmcNsFpJvvXMyqQIOl6XeJfI2JEmDzWVmZjaE3W7CbtKR3NrdQQMwM7LMwsQGHU/t532ffJjISpRivoSrwcHGSpw//H++SSaRr8dF2YwEO7ycfeYg3oADAJNZf9++/W4a2zwcfWSQW+enmRtfp1Ss0NTuZc/xLuYXIqyvJshlCty9ucRX/9cb9O1t4fCp7vu6iN5Cb9Ru/6lj76l+zn31GlA/r6vVGrIMWpN+57hVSlVCq3Ge+MghquUyL1+ZI58t7UyTqzJ6QSCVyCEIkM2WKBbrn0n5dIFapcbqWoKTzxxmfT5E/942brw2yupcmHKlRtdgM/tO9nD11XH0Bh2Vat2hVCpVuP76JI9+5DCtPQ0sz4So1WRKsoK32Y1WEtnzQC/xbIm1tfoxrYkigVYvm8t1l5TFZiRTqTL8QC81WWHoWBdt3Q0IgkI+XeDiN64T377uh451ceSxYVamNzHbjdQqNVbmwogaEYNZh9ZmwJSAB4/2sbCVxOqxIOm1mN8WnaXRiJQKZRRZoaTIrM2EsDvN5NIFalWZXKYeEScIEAml2YxkSCbzaF1WGpwWCvEMqWiW5Nv6jiSdOnyjoqKioqLyw0L9V1hFRUVFRUVFRUXl74Bgp58P/tJDzIwssTYfoXtfC30H22nta2R+fJ0bb0wS3UwSC6foHm6mpbuBV750maZOP4987Cg257t3YkRDKUKr9w/AAyyMrbPnwX6cXiuJrTTFfIn1lRgDR7uZuDbHhe+M8MRHjzF5a5nVuRA6o47Bw358zS4WF0O4DjThVCRaGnbHOlXKFcqlKk6fjcR2NFKlXMXmMDF6fhp/s4tSoUIymiHQ4iEWTrI0uc7Q8W7y2RJyVaZaqaHTacmlC+h0EnaHmb69TSyOrjE5tkoplWPoSBcnntpHPp1n7+kTpKJZXvnzCzv9DQC+FjeBNg+bS7v7UvafGcDX/N7iQmwzQXQzgcFqoFiooJE09YHNtTi9J3oByKYL3L4yT3tPw67eDX+Ti6Z2H5lkDq0ksDK5RrVSo6W3EZNZRzlfYmlqA4Du/a009zSwOLGOwaxn36k+tJLAV/6/FwivxigWygwe7mB+fI1M4t57q5Sq1Ko1xq8toCgKgiBgMN3r3qiUqyxNb/yVRZRcvsTIdvTQWzT5HVw8N0MqXaCzxU0llOS1V8cwm/TodRoeeHwPz/6Dh7Bti3flcoVrb8ywEc3RdaiTsQtTZCMZbD47J54+hN1dF7CGD7cxNbJEbDO5S0hp6/bT3uUlvHpPyBm9sbRLQHk7d64t0NUf2DWw/HYUWSGfqcfOFXIlyqV7rq7idkQdsC023tuORDJP76FOEmtRdDppRxgzWgzoLQZMRi0lFKp2HbV0HptZhyBpSCTzO4txeK1U8yVe+YtLVIplmnsCdOxpIbwa5zP/zzfJZQro9BK9w80IskxuK8XN18c586FDGIzvLZ68hSAIbMXziCYDB870IwgC0a0MN64vElpPYjTrKeVL5DMFUvEsV1+fIBpKcPBIO7WqjEa65/mRtBpa+uoRWvlMAV+rh4OPDjN5ZZbiVoa9x7tpG25ldjqEIAiUyxUUWcEfdKIAGpuJUrm6a/tq1RqKrGAw6pBrMnJNoSbX3UpGqwFFUdjaSBILpTj15B6uv3iHYlWmXKmh0YjMj66AAIFWDz0HOxifqbuyBCARzVKryXzkHz7CV/7by0S3BQ+TRc+J9+2jobOBuVcndrYlupVl7yPDuMZXWRxbJdDs5uEPHyGTKRJei9M1GOT5z7xG22ATr3/pCkaLgZa+RnLZIkuTG7QPNqHRSdvdVgorc2EGj3YRXYsxcnmG5UgCo0nHoUf3kBNFEtkCfce7mRldplysIggClXINrU6DbNHh8duYG13F2+RCEASWpkP0729GMOh47pu3MJjrQnk8kmJjcYvegUaaW9xEtoVVSauhsd37fZ0nKioqKioqKn/7qCKKioqKioqKioqKyt8RgTYvgbb7B8K6h5sxmnTotBoUWa5n48/XBxDX5sLM3F7h0EMD771w5a0iYSPVSo1curD9tILFbqSp04fTZyUVy1IqlHE2ujn4kI6RC9Ncf32c4eNdnPnoEVaWIiwtREjPbXD86YOs5Ys8fWoIk2F3aXapUGF1YYvDZwdZnw8zd3eVYqGEL+jC3+xiazOJw2VBkRXCa3F8TU4UWUFRFI6cHcTmNKMzaCnmSuQzRbLJPIdOdvPGV69TKdUHIWPhFBeeu8Xppw8wcLiDrr2tXH/57i4BxWw3oZE06A06OoebiYWSOL02+g530DbY9K5l41Dv1thaT1DMV8hnigwe6aRUrHDrjUk0kohWJ6HRiNRqMsuzYbLpIlb7vZggSavh5PuG+fZnz/PaV+/1nixPb3D44UEK2nvrjqzG0WhEuoZbaB9oxGLT8/LnLgD1iLZ4OM0bf3mFMx89zu0L0zvzGUx6PEEnM3d3Cx5vJx55Z1EhuZUhk6gXe7sDjl37Ip0pkkrfiwcyGrSE1hOk0gW0Wg2mWo2L206OmiyjKBouvjhKc7uXsx89BsDCZIibl2aB+kD5kacPIwoC2XyZ869N0djhx+Gu97gM7W/BZJTI58rIsoLJrKeUK7K5EOHoY8MApBI55qfulaLff7wUtiJpfEEXmUTuvumiRsTqrLuSzDYDBpP+HePczDYjb/exFPJlfO0+Ak1ONudD2F0WDFYD+84MEQ4lae308cYXL1Oryhw9009kOVaP0fPbSSbzGC0G9h5q59W/uEgg6EKn0zA/usLcnWVMLiu5TH0/Dx1q48JXrlHIFtHptVidJgw6iTMfOfqu7/m78fusnH9xdOexpNVQqdSolKuIGpFKuYr2bZFx85Ob9A81ceyp/czeWiSbyOEOODny5D4C7T6mbsxz+blbpGNZTFYDBx8ZZn0hwsiFac5/7Somu4lDJzqZuL1CS6cPUavh8otjDD0xiM5Qd7IUciVkuR73VRQFuve2srEUwaCVSMdFLFYj/hY31W2XjywrJMMp8vkSyVyJxg4fiVCKUqXG8sQ6H//1p5iYjVCryggC6PVaqpUqhVyJrr2tfPLffZjV6U2KhTKeRid6s45UsoDDbmR+un7+aHUS1UoVZ4uPX/mpB2jt9GFxmEknsmwuRfnfv/kFKsUyuVQeURTIpfKszWzS2OlneWqT1FaaPaf6uPriHVwBB4/97EkkSeL5Pz2Pzm7YOW/Of+MGH/hHjxAGUlaJ93/iDNdfukM0kqK5w0f/6T5WUznSkTS1ao3QcpSmTj+CCBa3leefu01r772ieJvLQiFbYnpiA43UjMNpppQvceaZw/iCru/7PFFRUVFRUVH520UVUbap1WpUKu98x5PK31+0Wi0azTvb21VUVFRUVFRU/i7ZXI7uCCcAgihgdZgQBIGpW4vsPdmDVvvOX9/dfjvde1vZXIywMrWJ0aqnpTtANl3A5jLT3htg5PI8GkmDzWEmny2yFUlhthh43y88RNtggPVKiUK+grXFxb4OD4JVT0Yj8PTRYXpa7gk/iqIQ30xSKpUxmHRsLMcw2U0ceXSYaChJPJbF7DCTSebR6qXtu98VRFGkY08Qh8fK4bODONwWBva1cvvKHFqthkCrm/k7K9QqNTSSiM1pRqnJ2ANOFiY3eOijR5G0EpuL90rM9UYdWoOWVz5/Ca1eovtAG1aHBZPNSGtvEJ1O+577fOzSLK9/+Rpzd1cIrcTIZYs0tHrYd7qPWCjJ/HQIvVFXd8wYtO8YIVUtVUmEEjR3+igX6wPZZque5cl19pzs3fXaWk0msZXGvG4kvHDvWOuNOuxuC5lEllQ0jd6ko7QdzfbQR4/Q1tvA1M2ldxQOAFy+3VFrlXKVO+enuPHKGKVCGUmrYeBoF0ce30MhVyYeSVNUZMqlKjp9/Zxy2c28caN+J3/Qb2f25sJ96xFEuHV+ihNP7kOr1zJ6Y3HXOsOh3dFua0tRHG9Fl3X5uf7qGKVC/TdXGsilCxTzZWKbSV743AXaBoJIWpHye9TYlAoVBo93sTS5fl/0Vt/BdnxN9YFmm9PC/tO9XH7hLgAGow5REJAVhX0ne4lFd+/LfKHCh3/uAarFCtl0npW5CKNX57C7Ldx8ZRSTUU9VkSlVarT2N7I2G0IS6tdo93ATyxNriJK4S8CYu71M63ALAIEWNzPX5ihk606ZcrlCtVLj4jduMHi8G+/2AHkyliGdzKPVSngDjl1RXLIsQ6WG2WIgly0iigJ6g45YtO5WqFaq9Aw0srkQ2fXewqEUzZ1ePv5rT1HIlrA4TGj1WlZnN3nlzy9Q2+5B0pv0fPuP3iCTyJFMFVEUhWQsSzKS5tAT+7j2+iRagw5BUZBrMoIg1K9BvYRWp6Wh1YPGrGf40WF056cILYSxO800d/gZOtXH0mwYSdJQrdavcaNZz1YyR6wmY29w0GjUkcvWhZX1bXeSxaynmC5QEeDai3e48ZLIwYcHOPz4HkIrUS48d5u5u/UeoqbuRjraPZRrCplUnkDAzrGHB+joDaAoCoVcEZPFSClbIBFKYbIaUGRlx6GjNWiJrMWButir0UqYHBYqZRmzzcQbX70OgEYQEAQBRVGQtBo2pzYJmzUsVip4/A4O/vRxlGKVydlNXhtd5PhgG9FSBahbnEr5MgMH2pidC+MOONAb7n1OSZKGQJsXu8dKMlPkwWcP0truxRPY7QRUUVFRUVFR+bvlx15EURSFUChEMpn8YW+Kyg8Jh8NBQ0PDe96lqKKioqKioqLyg+bthe6+ZheVSj1CBkWhYyBIciuLt9Gxa55aTSYRy5JN5lgYX2Xs0iy17R6BmVtLnHz6AMef2IPLa+PhD+zj6396kUyqgKTV4PI7kCSRzqEmegYaccUyxLIFaq0iWq0Gn9NCs89JtVAiFkpidZmpFKtce+E2E1dmqVVlAr2NLG2lARuhlRjh1TiCCHuPdqLTakhupXH57ZRLVToGgzz2kw/Qs68Fq6PuFuje00QqV0Rv1WPUirz2xcvUajIGvQ6dViS6kcQdcLKxHGPi5hINLR48jU5WtyOyXA0Ozn2tXjCuN+sRBJFsKs/45VnaBpro2tPyrvs7nchy5cW7yIqM0W7C4a2QzxUJL0fZf2YAZ7OHK2/O4PBYae7y0d7TsMuF8hapWAZREHb6ad6ikC1RyN6vBhhMOoL9AW69PEoomkYUBExGHQ6PFY0k4m92Y3KYEEWRAw8NsPeBHvQGPYOHO1iZCd1fKq7V0PpdXS3zd1e4+K17zphqpcbIG5OkUwUi0SyFXBlvk5PYRhKtUYvLZ6sP7m7HXwmCQKVUoVqpPzboTJi2XUNb60nmxtfxBp07HSOCAP4GO7VimWqlitFmIpEskIzfEyp8QRfv+7lTXH1pjI2lCNlkHgHY+0A3i2Nr1GoyMyNLtO9vYykfe8f+FACn20JbXyNP/OxJbr0xwdZ6Ap1By/DxLoaO9yCK9yKr9pzoQSNpuH1+ChSFlm4fzb1BZI2GSr7uZjJZDDjsRmrVGq9/cwRvwEF7b4ADp3po6fKTzxRYubNMc5uHtbU4CxPr9O9vpbWvkdhmApPHxt4Hennhc+fxNzgQ7q0eWVEo5upih8NtYfri5L2JSt0lJssyyUgKi8PEyKU5Rq/OUypWEAQItns5/sgQge2otlpVJrIa57H3DRNajREPpdDpJdpaHExMhWgMOjFpRObmd4sotZqMyWrEYDbsxEYBLE+u7wgoAAaznpWpdURJg8FiRJZBI0ApX0anEenf38bs2Cr9B9sJ+O1oJA2g4HTacAccOzFzq7EswWPd9JzooS3oJhPNMHl7Ga1Wwh90UpUVuve2sTi6jM9uqothokBNEvC1eXZ6TXQ6DSZJw8r0Jg88NsSlb9/G5jSzNhfimX94lu987jx3L80iV2XSiRzXXxnn2BN7yWaLHHywn5WZMKvTG4iKwvi1ecKrcUwWPS1dfkSNSC5doGOvlUq5iiKDTq+lmC8jaSXah1pYnNqgkCuBAgvj65itBhKRNNV8BbvFSLZYolKqUi2UGehvJ5upH+tYLEs0XyBZLiOIAnZF3BbFjZSLVbxBBx2DQZbemKpfe9+FKApYbEawGRF0kiqgqKioqKio/AjwYy+ivCWg+Hw+TCaTOpD+Y4SiKOTzeSKR+o+MQCDwPeZQUVFRUVFRUfnB4d2+g76hxc34rRU231Yyns2WqNZknvzJ43ga7AAszUe4c2OR9ZUYellmYmQZZ8CJJNbjdQxGHbH1BDqDjq3NJCPnpmhuc1MsVMmk8jjcFvaf6CKbyPGl//oS1UoNSauhfSCIs82LKBa5dvM2UzcWqBQr+Fs8BLt8TFyZpVqpCzX5eJamVjfzk5uszIXJJHLIsoK/0cGTP3uSjaUtivkSJouR40/uobWn/n2rXK5y9/Yy168tkM0UiW+lMekknAEXdqeF+EaCTDxHS3cD8a00Wr1EOpkntJage18b45dmKBcrVKs1SoX6QKXb79i1P+OhJLyHiJLaylDIFqmWq6QTOaweCx02I4VciWgkzWY0v1P87HBbGDzQ9o6/FYxvG5R+O2abEZPVQOpt7hFHg52CScfLtxdpbHAQujyzM81q1tPot/PAB/bT0t+EVqdB+zYnTftAkNNP7+fqy2PbnR5gdZo5/YH9BFo8u9Y9fnV21+NqtUZkPcHSXIijTx2su1FCKQa6A5y/MotGo6El4EQUBWRZYWU1RlN3A6HVGA67iVq+zMpaHI2koXtfK1denQBRoLHDy9zEJo1BB1e/eYNYOImigFYrcepDh7E7jGwubaE36nD57TR3NdDQ4iEWSjJ2ZY71+TBrs/ccOQgCS6OruNv9bIV3u1oAGpqcWKwGJm8uoigKDz57GK1OQqeXMNtM971eb9Rx4MF+eve3ks+WEDUCk6Pr3Lled9CYLHp0GoE7l2dxe21oJJHFyQ1e/OIV9h5uJ70Rp+dwB4E2D1q9FpPNwMzEBpMjy+gNWk4/c5CKyUge8Acc2CyG+9bv8tdj1Ir5Eja3hUQ4jSDUo8dMZgNmqxGjxcC1N6a4c3luZ15FgbWFLb79F5f50C+cwu2zo9VJtHb7mb+7zJ1zU4gaEVmRqZRrnHpyH0vLMRZnQ8iyvEtMagg6aer037d/3nI7vcVbQq4A2JwmNhajRMMp5JrM6lyYuzeWOPXUPgJBO729jQxFUhSLFbTvUHieSBdIiQInHw7gf6CHvgNtZFJ5DAYdCAKT1+do7WkkdX6KjoFGNKJIQ7uPtsPtTI5toJM0mHQS+USWo2f6GLs4TblQIVpIkk3mmBlZZuzK/M75rWyLbjdeG+PkBw6SiGUJtLioVWq88fUb5FJ5wqtxcpki+WwRb7OLlckN5m4vceTxfdx8dQydQUupWMHb7MLus7EyH6ZariIr4PBYMRi0FLPFevybRUdpq4rTZKSprYHvPHebSqW+/3x+Oyce6uNWNMNTj+5Fs5jCYjMi6TT0DDVz4sk9NHY2cOXGEtWqfN++ezu12juLiSoqKioqKip/t/xYiyi1Wm1HQHG7/2pFjCp/PzAa63cTRiIRfD6fGu2loqKioqKi8kMj2O5l4HAHy7PhXQKKVifh9FhJRLOMXVvgzNP7mZve5IWv36JWlbE7TazeXqJcqrEVTuNwm/AFnWST+frA58UZFmfD5DJFBAEQ6oPc8VCCqRuLLI6vIWk1WJ0m/M0eJq7Pc/H52zS1erh7cYaGVg92t4XV2U0uPXeTE0/tZ2OuPvCdjKTY8+AAuUweb8COqBEo58vM3F3hf1yc4fGffoDYVob+w53odBKbS1u4/HZGRpa5fLE+0C9JGjSiSDqVp30wyPlv3aKxwU5qS6BarVGryuw52YnebmQrkuLAsS6e/pVHGL04TalYxuax4PI5sGx3YbzF28vX3wm9UYeoEXf+y6YKmCwGBI2IwWxAn6mg2E00tXl44iOHaO3yveNyAm1emrobWJsNAfVy7Xy2hMGip2O4BZvXzuLEKjqDjqxJy2o8jcNjRbCZGDzaxcS1ORQF8sUKnt5G3G1eTJb7hRmNRmTviR7a+4JEQ0lEUcAXdGGy7n6tLMvkUve6TtKJLImtDBvLMUSNSGwjSTpdwOayYBFFmptchMNJkq1uOtq9zExvkoxladvbQqDNi1Susr5Qj1Dzt3ho7PQTi9QdKBsLWzS2uJi8OMnaQoRypQoKuAMOkuEkL3/uPJJWQtJp6D/UwaGHB7E6zRhMOmZvL1Mp7S4mB6hVqnT1NZCI53bEOoDmdg+BgJ2vf+YNKtuF5pJWw6Ez/Rw43feex9psM+2ILCf9DvqGm4ltpcmlC7z69Zv4Ao6d14a3HR6JcJqH3reHyEqMfDrP0uQGrgYHDz42RC5bRKPRYLeb0bkstHb6aLIZuPHK6K712lwWeg+2kStUuHlumv0PD/HSn55HlmVsTjO+Rgd9RzrRmgyMXb8/Pg0gnymyOLWJ21cXTz1+G187P4UgCkiSSGyzHv/2zc+8zrEPHACxfv4WskU0mrrT7MjDg1gc5vuW3dDq4dzXrpGNZ+tRfx4rRosRBMhnSxTyJSSthiqg0WrQSCJzd5Zp7zxIc5uHx3VDvPjqGDVZQavV4LEaEGsyskZkK13g2KFOGhsciKJA89tEnK21OAu3FtEatDz+0ye4+PWbFPIlVsbXqBTLHH9yPw3PHiaynuDzn3qO8aUIbf2N6PQ6FEVmaz3O2lyIWrWGIAoosoIgCgiigFyr/93hMnPn/BQT1xco5st4AnaGj3VTyJcYOTfN0bMDiJKG2HqCTDLLEz9/Gnejk2KuzNLUBtM3l8in69FrTq+F8FqcaqVKpVgFWcbnsiIiYPDZGF/e2hFQACLhFK985y4f+uAh5FCKvKJw5EwvSk3B7rHQ3h9Eq5Nweyz3ReB9Nw7H/eKgioqKioqKyt89P9YiylsdKCaT+sXkx5m3jn+lUlFFFBUVFRUVFZUfGnqjjsNnB7l7fQmT1UCtKmNzmbG7LRhMegBm7q4yfLyTS29M7cTwVKsyxrcNuse3siiVGqlIfXCumCty7eVRAu1e3A0OUOr9FQ6PhdFLs4gi9Tv1UwUufP06tZrC/jN9XP72bSqVKusLYbR6CZ1OopArkYrWB1xr1Rpmu5H5sVVuvTaBqBEpFctYnWbyqTyVchVZljnwQA8Ld1e4/sooKGBxmHC0eNBuF2JDfeC/Uq4Sy5c49PAgyxNr6M0GFEmktbsBwWbiG1+7RUunl1iywIHD7TzxiQcp5ApUK/KOgPEWRouBpq6G99zfnqCTrj3NzIws42lwEFmPk88WMZq0HDjdS1s8j0YjMHS4g57Bpnddjs6g5aGPHuXai3e5fX6KzeUY7UNN9B3uZGE2TCFTZN+pfswBG998cxyHtx7fE8kUsA8EeWSgiUqhjNaiJ1GpMb0Sxeu1v+v6bC4zNtf9A+JvIYoiHcPN3Hx1nHy2yNpcGL1JT7VSo7M/SHgjQTSUor1fIrISZ7jVh89jJZXK090bYHZmE0WB6fkIZ546gJLM0zaQRKvXojXp2QqnEbczq9KJPF39AcauzlEt3xM8Bg62c+FbIzR3N9DU6adahtFLs1TLVc5+/BhanYRWJ72jiKLVSbT3BWjva2RzLUG1UsPuMiNXKrzw+Su74sxKhTKvf+0GeqOO4WOdu9wX74XHb8Pjt3Hu+Tvo9ffcPsV8meT2dVMqlDGYdTz/mVcBAbPDxPSNeSw2I+GVKJlEjuWxlXpsXIubg2eH0Jv0jF6YolQs09wV4MDDgwTafZjtJmxOE+GNJB//tfezMbtJtVShsTtA/7FuktHMrlit72Z1PsKh032Uy1VCazFkUSSfK5JO5KlVa3jMeoKtHgRFYWujHm/W1OnH3+Ti7Pv2EFuPkYlnCHb4sW6LjbIsk4ik8AQcrM9uojPouPn6BAceHmTuzjLz4+vIVRmDUcepnzzEViiNx2NBkjQYbGZMViP9PQZ0OomxiXXSS2GufOEiuXQBr9/O+37mNAf2tiCK97u3tjbiyDUZt9/Bd/74HKV8iVKhjCzLjJ+bQpAVfG0+7F47g8e7KWRLXPrObTKJHFqdxPCxbhpaPRjNeoqFMgh1B1WtVEUUBRrbvFx54Q5b63HymSIoCqHlGKlolqOPDaM361lf2iLY00hzbxABBbPNxHc+e47W/iZESURnkKhWqwwe7cJsN3HhuREQBB54cg/+oBOjxcAen41rt5bJh1LodVoq1RqiKGA16Qn6nURXkzz2yAArkxuUS2Vaextp7Q/uiKRDe1sIh8be9bg7nCaamtUyeRUVFRUVlR8FfqxFlLdQI7x+vFGPv4qKioqKisqPCqJGg8VhomOwqZ7n813fU6rVGluhFPGtDJlUnlQsS7lYYd+BFqrX55EkiUq5SjqZR5I0eAIOarKCokBkJY7VYUa3XWIsaSUyqRxde5qRazIGiwGbx4LJYsTtd2BzmfE1uXAGXRisRkxGLfFIimwyj9GkQ2fU0tQbZOLmIqKkqReUCwKhlTgWmxGn10ZDk4sX/vwiFocJ57Z4sDwf4ebVec58+AhrkQxQ76UQZYVSqcbsbIiDRzoZ6A+QjGYoZApotBoeeqiXjViOsdFVNjeTPP3MARxOMw995ChXX7jD/N0VarJMY7uX4+8/gKvB8d77WhTp3t9KeDnKwtgKOq0Gd6OTgWPdLM+FKRUqdA+30NH7vSNfZQUw6Bk6NUDbgSI3zs1y4dMvYbYb6RtuwuI04dYKeJwWrJJEMV1E1Ajo9DqiuSIFWYZ03T1yZ2KNvX1BrO8SE/b90H+og/m7q0TW4siygiAImKwG2oaaufbG1PYAehqDSUd4OYbJrOfpjx+hVK3S4LbwwtdvkdxKsTYfYWspRngjgdNloaPbj+VtsVmiCJJeornDT3g1RrUm09YTILS0hSzLlMpVctkiVnt9npmRZfae7MXX7GbwaCfXXxm/b9s7h5vxNNQjsLxvc4i8+IW3CSiKQiqeJbKeoJQv8+JfXCa6EWfP8R68we+/PyK77TR4C7kq73SxmG1GVmc2KRcraPVa8ukCwyf7mLw2t/2cRHip7l569QuX+MAvn8XhsXLymUNY7WZ8Le4dUadcrDI/tg4CFEQRZ5MHUSOSTOS4fWmOvSd73nM79XottarM7esLRDcSRFej1LYFDr3dyFY4hc1uoq3Lj6TTIisyQ0e6iK3F+cPf/BInn9xDpVghHc8yfLyH4VO9VEpVRl4Zxey08NhPnwJJQ3g9Qb5Y48zHTuC/No+sKOgMOjbWEjjcFmwuC6JG3BHMBEGgq92HksrzxS9dwGXQ4bMaMVuN3HnpDu3dDbT0Be97P2/Ff2WTOVKxTF2wMukwmA2IWg0bCxG2YnmS8Rxt3X5uvDaOzWUhly5QKVcZOT9FY6eX3n1t3HhzgnymQLVSrfc0tXmIR1IUc2VqFRmNJFIqVBCFesyh3qhjYz7M5PUFGlrdlPJlNubD2FwWBo50sjy9ST5XZO/JXh788BFuvjHFxefv4PTbsbvMXH15jPb+IN37W5ndzLA8FUbUCDSY6i42FBBqMm67mUq5Rk0jcfZjx97xuHb3BghtJBkfXbtvmsGo5ezjQxi3BXQVFRUVFRWVHy6qiKKioqKioqKioqLyI4LZZiDQ7GJxOnSfgALgDTiQFYVoKEl4NQ7bY8rTEyH2nuxl4toCtUqNigBNXT4Gj3bV78SmLsCUi5UdEaWYL+FvdtMx0MytN8Y59/UbWKwGJI3IzVfu8tgnHmR0apMbb0xhMOnxNTrpP9xFR6ubpbvLGO1mvv25C/QeaCcaSlIt19AbdTi9VrbWEwhiPY4rHc+i0WpweqFcrJDPFklE0qzNhCgbdLh9FhpsemZDCShVOHSglc6eBl783HmiqzGSW2kURaF9oIknf+lhVuIFYtEM01ObHD3ehavBwROfOE08lKJWq+H02XZ1ibwbs3eW+eJ/eY5CtkjncAsmsx6tQUtzdwNtWglfk5NAsxu9cXcsWKVcZXM9TiZdRNSIeH02rrwyjsmi5/blOUYuz+8culyqwMzoOhvLcZ74icMUVpNcu7HIW1qApNVw5MFetBY96Xy9hD5bKJPOFv9GIoo74OSDv/ww575xk8WJNTyNTio1hdefu0NuW6yJ61PUqjUaWj20dTfQ3OxGoxHp7vAj5UssL24hShq8dhNaAUxGHWabcdd6yuUasXienoPtFHJFBEHA4bUye2cFnV7CoNfWXRsWPZJOS0VWmJ/aRKPXMnyih1Qsy+ydlXqfhQBNnX6OPjb8jjc5JSL3Yo/SyTxrc5EdUSUZzbC5Emd94SLP/NJD2Jzv7tR5Ox6/jbm36Tg6kxb9di+GxWYkFa2vU2fQko5l0ekkwitRDCY9epMdRamSzxZZnw3x5leu7TiiDCY9D33sKD372wFIRTNYHSYi63UHRiFXRGeon1frCxGOPjKI2Wogl9kt6rxFx0CQzY0kF755C0GR0Uga8pkiuXQBjaTB3ehA0WioCQJjF6awOs109gR49YuXGTjUztUX7pDY7jYZvTjN8KVpHvmpB5BlhUwsgyjAejjL0nQIjUbE5rOzsZ4ktBanlK87RJo7fZitRhweC+6Ge04pWZYZOz/J2uQGtUoNSSvh9NvxBJ1szIffUUQJtPuwOs0UCuX6NdTkIhnNks8W2ViKYrKb6BsKAgJXX7xLKpolk8jhDbooF8sYLQbCazEe+egxIusxlqY3yWeKNHd7OPuxYyyOr5FJZqlVq9hd9SguWQFRI1CryWwsbqHRahAEYacXJr0daZZJZLE4Ldw+P02g04/ZbmT/QwNEQyn0FiOHzg4h12RSiTzxXAW7x0ImnqOYqV+/Or1EsNO/IxSVS5V3Pf8MBi2nHuqnqcXNxNgasWgGSaOhd6CR7p4GfA3v7khTUVFRUVFR+btFFVH+ntPW1sav/uqv8qu/+qs/7E1RUVFRUVFRUVH5HoiiyNCRTlbmItRqu+N9BFHgwMkeisUSFpOOhFaivN0NEY9mKJUq7Ht4AK0o4nSZMRkk8pkCDreFzsEgCxPriNK9uKNsqsDeU71EN5Nc/NYtRI2A0aRjay2Ov8PHi1+6QvO+diqlCiarkUK+zPR0iMYOL/sfHWbqxiIDR7owWQ3YXRYq5Rq9B9uwOs3UKjV0eg2ZRB6DSY9GEinmS6zOhkGroVSsEFqJYvDbMVUqXDk3iaJApVIlvBLDKCgUkjmSW/VBbEmrYX0hwuz1eYJ721grVBgbXWF4bzMmk55CtkgqXo9E0hm0ODzvLaIUc0XOfeUaK1MbdZfOahyr04zVZcHuslIolpm7vUj7QBPBTh+lfJm528vEt9KY7CYqlRo1RcHptTFyPovLY6VWlbn08jiyrCCKAkaLAVmWKZerFAtlpu+skS6VeFsaFdVKjUuvTPDwB/cTr9S2i+y/t0talmWKhQoajYje8M7v1eG10djpY3MlRjKew+a2Ui7eG9DVGbSk41nMFgMHTnaj0Yg75+CBU30Y9RITV+Zoa3WztRbH7rYisHvbuoeaiETSmBtcDJ/sZfTSLIloho6BIKGVGCszmyiKQq1aQ6uTaOkPsroc5fbNJR794AEe/YnjDB/vJpPK1wUaWWF2ZIlqpUpDm5em7gC67bgtf7OLyEYCRVGIh1O7Yr08AQfFXIl8psjaXIiBw53fcx8CtPU2cPPCNJVtZ4UkaWho95KL5+geCCDKNXRaCWeDfVffidFioFapX3tyTWZ9Pkz3gfad6cV8ide/dBWNpGFpaoObr0+STuQJdvpYnguTimVx+e24/Xb0Bi16o54HHh/m1a/dvO+67+hrpLXbz60rs0zenEcQRQ4/voeR18aJhZKUSxW8jU4C/U1MT29idZh44H37WBhbQ9JpqBQrbCxGdorXi4Uyc+Nr+K/O4Wl2E12NoTXoSMVyABgselLxHL37WpmfWN/ZjkqpSigRRZJEmjrv9QOtTG0QWY0hSRIodaE0EU4h6aR3PZctdhMPfOgwc7eXWOoJsDi+Vne41KCQKeJpciNXZVLxLEuTG1RKFRQgHk7ibnDgaXRSLFTJ5ko88PQhHpBlalUZQRSYvL6AVi8hajSUixVsLgMWu4lcuoCiKNhcFiqlKt6Ag0qp7ih6i1wqj1ytYbboaWzzEmjzcvnlcbbWE8g1mWK+hKgROfboEE6rgawosrmaoLHTh0D92jFa9DsCCoDR+N7dTAaDlv7BID29AUqlCqJGxPAu17SKioqKiorKDw9VRPkR5gMf+ACFQoFXXnnlvmmXL1/mxIkT3Lx5kwMHDvwQtk5FRUVFRUVFReUHQVtvA49+5DDXXpsgvlWPu7K7Lew93M6d18eYujFPaCNF97424tkSm2tJAHKZIiNXF2nu8dHS6eXyt26xvhBBkkQGj/dw+un9KAgYzXoUWaFcqtLc3UA5V2LPiR62NmPEVuO4GhyEQykQ4ESHl3SpSiFXxuG10tPm5pXPXUArCvWB5r0tCBqRD//jRygVKkzdWWH0xhJGs55DD/ZiMuvoHAjganAQDaUoFSsYtRokrQaH14rVZebCX17BYNIjaTXodBK+RiebcyHS2+9doxF3eusSW2kcsQwajUQ+X6FUrBJdjXPlxTt194cgUC1XGTrWzdDx7vsGcaObSSZvLBAPp7j52jhavRZPoxNfi4d4OEUmkSMWTqKIIq9+9TpancTDzxxi5NwkngY76/NhtjaSDB3vpqHNy9f/4GW8TS42VuOcffYIw4fauXNtAVlWyGcKmG1GatUatZrM4kyIjj1NQHRne+SaTLlUZeT8DPqgjXimSFuHF9u7RPgoisLSbJiJkWU2V2JotRK9e5vpGWrCZNZRLVUx2Y07MVJtfUFG3pyinC9ze2yC008MsbYSZ3M5hs1hpqHJQXOHF7Nl9/pMViMHzw6z78EBarUaLQNNXHppjPJ2h4kgQMdAIz37Wnn+azcp5MsYHDYe/MkHEAUBu0XHZ377y4BCqVhBkkQq5Sqdg02UKjXK5Sovf+MWH/mFUwS3C8enby7w8p9doFa9161y8JFhjr9/PxpJQ9eeZiZuLlIqlCnmygiigN6gQ6MV6RpqYmlqs36ORDPf97Xma3Ty6IcP89o3blHcdiQ0NLuRmuzM3ZhDkWVCS1F0Bi2HHh0mshoj0OFDo9GQS+URRAGtTsIVcFCt1HYt293o5Mu//zJavURkNcbSdIipm4uc+uAB8pki4dU4tUqN7j3NjF6dw2Qz8sRPHmVxYpO1xQhGk57+A210DgYxmvWktjIoCpSyJc6/cIeuwSb6jnahkUT0Jj23R1cJtvp45GPHSSbz1GoyZquJyGoUQRBQuCc6SZKGlcl1znzsOHK1RqVUxu42k0rk8Da5KRUrRNYTnHxyLyMXpslnioiiSFOHj+FjXZTeJsYtjq3S2NXAy39xkcK2k8bf6qFSqWL3WN9xv8uyzOLYKqOXZuk/1k0qlmVlagN30Enf4U6m766yuRKne18LDo+VxLYbTWfQgUZkZT6CpNWwuRLnzefv0r+3mT1HO4iuJXB6behNOgLtHg49PEg6kaNWrrAwvobRYqSlpwGLzUAhW3cDOX02EpE05UIZq8uM2W7CaDVy4OEBpkaWEUSBQrb+vgRRxGg2cPfqPM/+gzNYG7WsLkbJpgo0v01Yegur3Ujw++w00UgiJkmN7lJRUVFRUflRRRVRfoT55Cc/ybPPPsvy8jKtra27pv3RH/0R+/btUwUUFRUVFRUVFZW/ZwiCQM+eZlq6fMTCaRQFtFqRb/z3lyhkipTyZURkLj93k+NPHSSmu+dIATh4rJvZm/MktzKYrEYESURr0OJv95FJF9iYjZBN5uk70MrMtTmuvzzKysQa3fvb6NvbxujVearxLN6gi0Qiz/z4OqJGpLfDyzf/12tIOgmbw4zVoGXqxiKnnznE5nyY89+8BYJA194W9p7q45v/61WQFZRqDY1GxNvipr2/keXpTZqa3Vg9NiqFEuVyFUmrobHVQ0Ozi3K+jMdnxeaycO2VUXKpAgr1gV+b00IxX0awatHrJKqlCiNvTrI+G2JmZIlaVaapuwG9UYfDa6W5+16fSTyc4vw3blAqVqhVa7gDDnLpPDavjTe/dh2AQr5ENJTE7rXR3hfAbDXynT85h91rIxZOE1mLI+kkVqc2sDrMVCs1ttbimC0G7lyaob0/iMVuJJsqoCgg1xS0eg2KrKDTa6m+TSCQazK5TJFqpUZkI0lfh5vV9QQenY6p2yscfKD7vqL08VvLvPHc7Z3eDigxdXuZ1YlV4usxqCk0tPsYPNGNr9mDJ+DgfT93ikvfuc3c6Aq3z02y53g3Z96/h3goRSqaZnVqnVpl3zueixpJg0bSsOdoJ03tXsLr9aJ3p8dCQ7ObarWGy20hHstSLJTZLJTRaERSoSpnP3aUyesLhJajWJ1mBo50kkpksATrg8rlcpWV+Qhev518usDFb93cJaAA3HptjPahJoKdDTR3+nn0o0e59to4Op2GarnG5nKUzoFGFkeXaO4JsrEc2ynt/n7pGgjiCzhYX4pSzJfRiAqvfu4cnkC9W8ViN5GKZlmZ2uTAwwMcPDvEua9eo5gr0dDupaHVg6/Fy/pcGEmrwdPoRKuTSEQzFAslSqUKhXwZT8BOdDPFrdcn2XOql7mxdRLxLLIo8ue//woC0LWnmff/1HFOv38PGknacQcBmLd7ZWRZBkVgbmwNQRSRazKHHxlCkDQ0NDvZ3EiSyxRp7PAxeWORti4vq9MbyNu71t/sJhXN4A44+PLvv0TXQJCh4110yHDl9Snauv1kYxm0Sg2zVc/DHzqI1qDD12AnspFgcyVKIpyiUqyAUo8mmx9d5cQHDjF/d4m2/ia2VqNoRJHb5ybxt3pw+h279nlkJcaNl0bRGnW88PlLeBudnHjmMNVyldWFKGsLEVxeK517muk71M6l5+/UC9tdZkKrccw2I/2H2kmmCrh8NmbH1sgkcph0dSeIZNRicdu5fn6GXLqAxW7iwQ8fxd/oZH1pi+79bZjtJvLpAuVShZ4D7WzMh3H5HTT3BDj9zCHKpQqjl2ew2M0MHO1kcylKPlukWpNpbPKQ2MoSaPMQbHKRTOXvv3Y0Iicf7MNkVoURFRUVFRWVvw+oIsqPME899RQ+n4/Pfvaz/Lt/9+92ns/n83zxi1/kP/7H/8hXvvIVfvu3f5u5uTkCgQD/9J/+U37t137tHZe3tLREe3s7IyMj7Nu3D4BkMonT6eT111/nzJkzvPHGGzz00EO88MIL/Kt/9a+Ympri+PHjfOELX+DmzZv8i3/xL1hfX+f9738///t//29MpvqXeUVR+NSnPsX/+B//g83NTXp6evit3/otPvKRj/zA95OKioqKioqKyt9HDCY9wXYvAGMXp3fu8jaa9YRWYjQ0Otic3aB3Tzvzs2F8ATt7DrVj1Wso5csY7Eb0ZgOudi+rmSLf+r0XsDrNtHX46Oz0EluL8cqfn8flt6GRNNx6dYxAu4/eg23Ekzn2PdjPlcvzCIKAxWIgth6jXKoiK5BNF7AYJLxBF7O35llfjJKKZwGYvD5POVfE2+hiYXyNzr4A+UyBudvLPPjhBnzNbg4+1E9NoyERyyBXZdwNdsxmHee+cg1JFHEHHCQiSQ4/sofL3xmhVCjj8tsRtBKOBgebySL79reSiWWYu7vM/OgqiqzQ0OahWqly8Vs3CXb4dokoS9MbLI6vMXVjAY2kYeBIB+mYjpuvjiNKIpViFUkrgSgye3eVMx86RLUqk8sUtgWXArWagsNmIhFJkU3m0Bm0FLNFLE4ztWqNmTvL7DnUwaVXJ7fXqqA36gi0uLG7zChvEwnK5eqOe8Fk0VMq12gKOLEIGq69PkVLhw//20rSk/EsNy/O4G+wUUjnEUQRs9NMMZXnpT+/iMtrQauTuH1xhvPP3eLEM0fwBBzsO9HNh375YbwBJ6GVKOViha2VGLKiUClX6d3fzuZylPmxNWxOM83dDVgc93eKSBoBoVpFKVfQCPV+CYNWxwMP9fOdb9zaeS+1moykl7j9+jhGi4GDDw1QKpaZurVIsN1H5W2Ojeh2z0kqliETy963znKxwtzIMkuTG1gcZtwNdnw+K3PX54iHUrQPBinnSoxdnGVxbI3THz5K07az5d3IZYosTm8yc3eVUqFCS7efrsEg/ftbd641cdv5BGC0GDFa6l0wB88OI9dkOoZbqNVkjCY9JruJL//XF5B0GjwNdi596wZOv4PIegLJoMPsMNe7iPQSwXYP2VQBb6B+LlfKNSZHlrfPFFic3OClr1zHZDlFe0/Dru1u620g2O7lzoWZeiybUBdcnV4bFreV4sQmwSYXyxMbAFRl6DvQhsVmwOI0IwAmi4FcKk+5UsPhdxDeWCayGudqZpSf/PWnkDQaPv9fnmN9LkylVEEjiZz+0GF6D3Rw4evXaRsM0tbdwPOfu0AmliERSfH0PzjD1PV5optJ3vfzD3L5uRtUy1WKuTKrM5tUKxU+/E/fh/Ft4lY8nKKYL6Iz6TCa9SxOrDE/ugoCBNr9aDQi5VIFo0nPyLlJHnj/PubvLpNJF9HpJYaOdSGZDcyPrRFeiYGiYDTpCQw2koiluXN9mWqlhq/JhaARqZSqfONPLnL6qb3Mj61x6FQvF749QiaeI5PIYXWZOf2BA2hEeOynTvD5//ebdO5vZ2M+gqKAJ+jE1+Qik8zjcFvIZQqkE1kMRi1D/Q2IFiNb8RylYgVBEGhu9bDvUCsd3+NcVFFRUVFRUfk/B1VE+RFGkiR+7ud+js9+9rP89m//9k4cwV/+5V9SLpc5fvw4R44c4Xd+53f4+Mc/zqVLl/jH//gf43a7+fmf//m/0bp/53d+h9///d/HZDLxsY99jI997GPo9Xo+//nPk81m+dCHPsR//a//ld/4jd8A4Dd/8zf56le/yn//7/+d7u5uzp07x8/8zM/g9Xp58MEH/6a7QkVFRUVFRUXlx5piobzzd4NZjztgZ2s9AQp09/jxea1USmX2HGjj4vO3Ca3FMTvM4DJz7fYKhVyJalWmWCizshSlVqxQXg6jNehQZKhWqji8NlLRDI2tHh796VPMLkbJZotoNCJavbRTUK/ICtVKFZPFgDfo4Mq3RzBvF3mLooDHbye2EefUhw4TaPUACrVSGYNZj0YjsOdkDyuzETLxLHa3hRNnB3F5rbz6xSvUSlVkUSCbzNHSH2RpaoPTHzpEciuL3W/H5LSQk8HtsWAVYeTNCW68NErvoXZ8TS7m7ixTq9ZoG2giv12g/hYb8xGWpzbQmfQUciXmx9c59vgexq8toNPrsDktoBGJbiYBhcRWGk/QiSAIiBoRoSbvOCUq5Sp6o5ZqpVYvqBZFcvkSBoMOg1FHY6sbWZZxeu24G+wYzXr6hoOMX1/E77YSTWR3RSL17G1BtOgIWk2EFmMArC/H8Aed1Goy0Y0Ea8tR5HyRV758G3m7N6NnXyvZeAYE2FyNY3eaURQFl8+GVlQIL0d5M5GjocVDKlcitpWmkMqzNr2B3qjj4MODJCNppm8t7myLy2/jyZ89hafxXgzR2lyIF/70HLntO+5FUeDok/s5eHaIjp4Gnv7oEcZuL7M4G6ZWk/E0u2np9DE/vk48lKzPoxHpPdLF5tY9scRoqvdF6IxaJJ1E9W2OqlKhzMrMJk09jYTX4ji9VkLLW1SKFUKLETKJHBvzIYJdDbT1B1mdDeH22fA23hOevptcusArX7/J8kx457mtzSSj1xZ4308cpaXLj8H8zk4WrV7CaDXi8t9f9v3wR48xenGaVz9/gVK+jMVuwt3oJLqZpFyskE7kUGoyepOO4eNdNHb4iETmqVTq55HL68fhtlAtV3F5rMyOrtLc4an3jLxFuUbPgXZqFZnRK7OUi1VaexvoOdzBwuIWe490sO9wO8FGJ5l0AZvTzPCxDiJrCQLtXiavzlFI57G5rTR2NbA2Fya1lSafyCJqRG68PEpsI45WAH/QSblUQVAU7rw2BtUanoCD269N0D7cRKDVjUGnwe23YbKYCHYHMNmNrE6tk0vk658PdhNWp4WNuTAb82E6995LVtAbteSSeYr5MsMnurn07dvbXUIiJqsBfVJH74E2YqEkK9MhirkyH/jkg2yuJilXaqyvJ0lOhaiVq3T1NVDOFlibXcdhEmnsCjA7vlHvgylXCa3GgbqTbfLWMsfPDvCNz55DFAQa27y0DzWRTxUYvTLHh375DC/92Xkq5SqprTSBVi+R9TjJSBp/sweDSUc6Ue+OCbR6yMRzlAtlnv3lA+itBvK5EhpJxO22opF2u8hUVFRUVFRU/s9GFVF+xPnFX/xFPvWpT+04RKAe5fXss8/y6U9/mrNnz/Jbv/VbAPT09DAxMcGnPvWpv7GI8ru/+7s88MADQD1W7F//63/N/Pw8HR0dAHzkIx/h9ddf5zd+4zfI5XJ8+tOf5rXXXuP48eMAdHR0cOHCBf7n//yfqoiioqKioqKiovI3xNvkAgFQ6nefewNODEYd7qCbXLqIy2ul70AbLd0NjJyfIp3I0by/jVffmMTltxELp4D6vOVyFaNBy/xSFIfVQHIrTSaRpVquYbIYMFgMjG9usbGeQKeXqFZqJGNZ+g63MzOyhCAIWG31O/MNJh1Gix5FAZPZgM1lIpfMkQinWJ0OceO1MbxBF2eeOUghW0IjaYhuJKmWq2wub7E4uc6DH9iPUqttd5qAq8GOw28nlshjMulpHW5Du5HA4rRQFgRsDhNdjQ7OfeUadq8Vh9+GXK3x3Gdew2Q1ItdkQotbOL1WCtki4dUYcq1GKpYhGk5T2hakkrEsgdkQiiAQ7PQjagRmR9d29rnepGd1LsLeUz2EVxNoDdq6mJQt4vTbURSolCr4m92UZCgXqxw61YvRrKOYK+JpcBBodWN1mDjxcD9CTebCt26R3Ezh9tsxeKzIikJHXyMD/UHWZ8OEorGd9VfKFeKRFJe+c4eliXXaB5t44XPn0WolDCY9glAXJpbG17C4rSRjWTx+O4NHOrj96hhrU+uUKzJNfY0c+8BBcpkSxXSBhbsrlAplJK3Epedu0TbQhM1rJZOoCyTxcJpbb0zy6E+eQBAEKqUKF795c0dAAZBlhSvPj9DYXi9/b27z0NTqJpWod3FYrAYOHunk1S9f4c6lGWwuKz2HOklmS7vO65aOeo+Eu8HJ4LFu7pyb3JmWiKRx+GzI2wXyBrOe2ZFlTFYDGkmD0WJAZ9CSCCc5cKYfnUGDTv/eP2/nJjZ2CShvUS5WuPjiKP4mF42dPrxNLrbW4rteM3Si9x0FFIDufW1kYhm8jU4UBYw2Ax3DrZz75giZeBalJmO2G3ngAweYHlnm6390juWZMA6PhSd/8jhTd1d47as3qFVrGC0GWrr8WExaeve34W5wUCqUGbs0zfzVWYLdAVzNLor5CmurMa5emOaxZ49w/MFeGls8tHbdc7BsrMa58fokV18ew2wzsv94J2985RpzY+uYrPVzSBBFFAWyyTxXnr+DRiNQK5UpZ4sko/UelnKxgkaSqNVkdDot1164zeZivdtndWaTUx88hFyt8twfvkIqVhf19GYDtUqVTDxLcit1b1+XKxTzJVwBB6szm7j8doaOdnHn4jQGq4F0PMPBh/rpP9hBtVpj+Hg3lVKFhdE1NrdyJOI5REEgl8rTO9zE/M15Qsv1bTGZtNx6Y5ITTx1kdSWBVq9FZ9CibMffJbYy5DNFalUZg9UACuTSRSqVGmaHmZnbq2g0IqIgEFmOsPfMEK98+TqgUMyXCLR52dqoL9do1mG26DnyyBC+7d4Tu8P0nuefioqKioqKyv+5/FBFlHPnzvGpT32Kmzdvsrm5yde+9jWeeeaZnemKovDv//2/5w//8A9JJBIcPXqUP/iDP2BwcPCHt9F/x/T19XHixAn+6I/+iIceeoj5+XnOnz/PSy+9xL/8l/+SD37wg7te/8ADD/B7v/d71Gq1nQLOvw579uzZ+bvf78dkMu0IKG89d+3aNQAmJiYoFos8+uiju5ZRLpfZv3//X3sbVFRUVFRUVFRU6gQ7/Rx4eIhbr42BUh88b+zw8/5PnsHX7EEj3fve19DmpbHTz+ziVv0Jpe4cAAFBFJBlmc1IiqauBlanN3BZjWTiWVCUekxNLEtT0EksnN5xwNRqMtlylaauBrbW41gtBoq5MrWqTPfeVlYXtqiUq2hEgWgsi9lmRLMdoxMPpRi7Oo/VbmDPA73Mjq/X+wssRvaf6kWjEanJCp5GJwaLgVA4zdxchEqpglYnkSmUKQgiRq2GIwc76Bts5Ov//RUUpR6V1TXczOtfuoIi1+OpFFmhVpNZGF3ljS9fZWZkEYfXSqUqkMsU0Ugi23oNqa0MFrsJBfA1ualUZdbmwgiigNlmJJctsffMINO3FlmfD+FpcuN0WTj6xB6+8ZnXcTc4kIw65EqN5k4f+0/1UiqUOXLmWXRGPQaTnoYmF/H1ON/6ny8ztLcZw8keivkyiiigNekpFCrMjqzcd8wtNiOvf+U66wsRAAqZArVyjVq5hiAKGIw64uEU/hYP2XQBg0FHS2+Aq8/dIh3PEuwOkEplGL0wTXNnA85mD1O3lhBEAZ3ZQLUmk07kmLw+z9mfOLEjogDMj61yLJ7D5rYQD6cIrWzdt32KohBeidG0HZkmCAIO170YsIY2L09+4gzmBjfJeJbNyO7C96H9bTS2uHceH358LwaznrFLM5RLFVr6gniaXGxsn8eV7VL7fLaIw22hXCgjSRokqe4E0um0u9wz302lUmVjKUpjm3v76NfdLvFIvXdoazNFZD1Bc6ePx3/2NCNvjLMwuoqk0zB0vJfhB3reddlQ7yxx+u6JLHqjlr6Dbdy9OMPDHzmEIsP8rQV8AScGm4mN5Rh6g45v/PGbHH10mEq5ik4vIcsyawsRzn1rhGw8y5mPHiUeTpHLFNmY3mRrOUrbnlZcbgs2UwNyuwenScvLX77BROsSD35gP56G+nYYDVp0GoGhA22IWhFRo6GQK6IoCul4jmCHb8dhVr8OFHRGPcV8GVlW0Ook5JqMpNVgshnoP9zBjVfHMNtNuBsdFDJFCtkS0yNL2GwGgl0NhJejNLb7iW0mSEXT5DIFVqZDmB0WnH4b5755iwvfuFEvqX+gj5uv3MXusfHh/+tRSvkykdU4NoeRiZsL3Lk8xzOffJB0IofVacHdAX/5mTfx+W2US1XMJh2R1brwqDNoqZSqCILA3QtT7HloiEpNwemxkopnUWQFq8NELlPAvB0ttrWZwGCqd5YU82VMVj0WpwWIoMiwfHeZJ37iGGPXFzGY9TjcFvYe76b/QCtWhxmn14pWp96XqqKioqKi8uPAD/Vf/Fwux969e/mFX/gFPvzhD983/T//5//Mpz/9aT772c/S09PD7/7u7/Loo48yPT2N1Wr9IWzxD4dPfvKT/JN/8k/4gz/4A/74j/+Y1tZWzp49i6IoOxFfb6EoyrsshZ1iyre/plKpvONrtVrtzt8FQdj1+K3nZLkeI/DWn9/+9rcJBoO7XqfXq0V6KioqKioqKip/UyStxPH376e1r5HoRgKdQUdTdwMOr+2+1+rNeo49uY8/+dPzQL0DwuW3U8yXqVZqaHUS4c0Up453sT4fwmDRUylVUYCTTx9g/Mocw6f6OX66lxe/cYtstYYiK4zdXePkQ/085LOS3EhQqci0DDYhDTYR37pMMV8mFc1QrdTYf3aIyHq8XoYty4RXYpz59fczMrWMI+Dg0IFm1m8tE91IMr+eINjhI5vKky3XyGbrd4oLooi/1cPtkWXyZZnstQUSsSxNTQ7SsfqA/MZciEC7r/59VBCQazI6vRab20IhWySxfQe8yWEhHs1hNOvQ6iQMZj1yTSEWSnL2Y8dYmtkkk8yh1Uo0djZw8qn96CwGHJsp5ua3kBwWDj22l0Crh+nJdTa2snz8197P6tQGkZUYDW1eho51MXd3DavLzN1LcxQLFcx2A03tXpRKFX+nH0kUuPXiCJGVGCanGVfQQ/eR7vuOodVhwmjU7QgoUI9RM9uM5NIFSvkyeoOWyFqcAw/2MX9nuR4rBqTjWVwBJ9WqjKLUy+Fnbi3wQJtnZzlVuYZOJ1Grymg08k6fyVuYbUbi0Qyba3Hkag25qiBqdv/uABC/R2SRy2vl5KNDTNxZZuruKuVyFbvDzJ5D7fQON6F72wC02Wbk2Pv2s+dUH+VShZf//NKOgAL1QXJJq0GWZWxuK6V8iVpNplqqYrGbaOlrRG/SkUvld0rYFUWhkC+xOLnJ1O1l8vky89MhkvF6nJrHb2foQBuZRI58prjT1+JudPLIT50km8yj0YoY3yXi6+00dvqxuswIokhDmxezw8z86Aof+MUHufTtWxSyZTZXoszeWcHb5OLkE8NM31khnymxOLFOW18DkfUkpXwZq9/I7QvTeP02lsbXsLmtZFN5eg60M3l9nuWxFYqF+u84s8NE74EOtNo060tRXvnqDZ75+ZNodRJTN+ZIbibIZ0vM3F6ipSfA4z9xgm995g1qNRlBALPVQFNPAI1O4sRT+9mYj9DS20g6keX26+NY7GYsDhM6g45cJkNiK0OlKpOMZXF6rZjsJnKpHLHVKMFOP0MP9DB9c5FyoYJWL3HsffvZ2oiz9vkLtO9pYXV6k9RWhuh6nP0P9tPY1YDDY2Ps/BSyrFAuVQktbfHEzz9I+0CQbLpAaivDxsIWbUPNDO5vJbn9OaPRCGgkDaKi4G2wE17ewtvsIbGVxuW3sRVKY3UYiW0mKZcq2HsDaDQiJouBZCyL2Waoi4p6CRSwOsw0+K3MjSwh12RS0Qy3Xx1l8EQPj/7sKXxBF06vded3tYqKioqKisqPDz9UEeXJJ5/kySeffMdpiqLwe7/3e/zbf/tvefbZZwH4kz/5E/x+P5///Of5lV/5lb/LTf2h8rGPfYx/9s/+GZ///Of5kz/5E37pl34JQRAYGBjgwoULu1576dIlenp63tGF4vXWi0k3Nzd3HCK3b9/+G2/fwMAAer2elZUVNbpLRUVFRUVFReUHhKSVaOkL0tIXfM/XeXx25mZDeIIuHNXq9g0xEutLWwiCgKQV0eqMXL21xPGPHser0+BvcmO0GIisJ1me2iDY3UCjWc8nPnmafLGKXCwDCtWyTCaRJZUps7kWw9Psplgo8sTPP0gmmmF9IYJcU4isx5ErMr4mFzqjFgwSNzY2ee3GDGabEbvLQrvXib/VSWZ8jYmbC7zvkw/xzc9dQGfUodUrBNu97HlokHSmSCKaoeY0IxcqrK7E8AZdLE9t1O9Qd+XpGGwmncii1UmYbUYqlRr5bBGDUbez7zbXYnzwVx7m9S9fJ7KWAAFaewOkc2V6D3dh1GnIpnJ4WzzcuTjL1moMV8BBV3eAuZkQ5XKNciZPa6sHvUnP5tQaxVSetl4/8Y0kf/npb/PM//U4X//jcyAIhFZi6Axa9AYtnQON+AN2Fm4tsLkQQVEUcoksCAKbM0YsAQ+5bUeAyaLnoffvpZQvggDeRieiICDpJE4/fYCJGwssT20iywoajUBoNcbH/sVTLI6voREUgt2B7dixEpJGwOG2YbIZsHps7HlokNhqjI35MOVyFYvDVBeeDFrcDQ40kojepCebq/DtP79MTZax2I1o9FrS0TTWt8UVSTqJ4HYcF0AxXyK8EqNSruDyO3airzx+G6cfG+bA0S7KlSpmiwG9YfcNWm/HZDVishrp2NPMxtI9ESUZzbD/oQGmbyxQKlWpKgI1WeDUh4/h8NkYvTDFnTcmMNtNHDw7DDqJ1bkw1UqNdDxHQ6ubl745gkYjImk1GEw6tjZTvPqtEU4/MQyKgt1Zf3/lYgVFUbA4TBQLJRYm1sgm8phsRhrbvZgs94sqVqeF0x85zitfuMgX/svzyIrCnlN9JLbSJMJpqlV5p/MlHc0gKjImixGtXiKbytPU1UBkLYFWp0FEoVqukUnmmby+wMMfPYrTa8NsNSJIAhuLW2yuJXD6bBx5ch+rCxE8fismsx5Jq2FlLkw5W+Db/+s1xi7NoNFKDJ/sRZEVQitRnvjEKZYn1rE4TLga7PQdaOe5z7xOMVdAFEUiK1G8QScPfuQYTp8Ng0XP61+9Se+BNjwBB6G1ONWKTGQtgdVuItjuwbO3haWxNQ49to/hUwOsTG6gN+tIxrIYzQYyiRyprQyFXJHm3gClYoVKqcrY5TlcPhtmuxFBFDHbjbj9Dl787JukElna+4PorUZCy1tM313l2GPDHHuoj4svj6HT63A32NFpNWQSWRQFDAYtg0e7kPRaEpE02ViWpg4vWp3EnqMdGE06VufDVCpVzFYjlVKFZCSNVifROdiETqfh9IePMH1zgWq5ijvg5MFnj9C3v+2+GxhVVFRUVFRUfnz4kfWeLi4uEgqFeOyxx3ae0+v1PPjgg1y6dOldRZRSqUSpdC9rN51O/8C39QeNxWLh4x//OP/m3/wbUqnUTt/Jr/3ar3H48GH+w3/4D3z84x/n8uXL/P7v/z7/7b/9t3dcjtFo5NixY/yn//SfaGtrIxqN8pu/+Zt/4+2zWq38+q//Ov/8n/9zZFnm5MmTpNNpLl26hMVi4ROf+MTfeB0qKioqKioqKirfH/5mF163FZfTxPLydseGUKapw0chWySfKaDVa2ls81IGFibWWZnapFwsk0nkaO0L4m/1Mnp5Dn+zi2I6T3g1TqlUwWDU0Xu4k3Qyx+kn9xJaiXL5xVFqNZnWngYGD7QxenGKtdkQAlBFIZMvcuDBfiRBxBNwUsgVKeRLhEpFbi+s8tDjg8x8e5T1UIqBR4axmfUgQLFUY3p8g+mJDRRFQVEUNBoNia0sT39oP5dfvItSk7l9fpL2oSZuvjKG02cnFc8gIHDmI0fJZ4o0dviJrsdxOk0sz0fpP9HLAPVItEQ8x8VXJtHpNPz0P34YgDe/dIV0Ik8+VyIVzxJZjvLYTx4nly4wO7aOp8FOeGqdV/78AuViBUEU8Da66DrQzsTVOfRGHdlUAavDVI8XUxQmbizS+rEjzI+v4XRbKOZKVMs17E4z5WyBg8c7SaYKlIoVctkir3xzBLfXSv/hTs597TrhlRiCKGCyGvA3uQi2eYiGUjQ0u3n8Z0/RvbcFjVZibnwFh9/O9K16d43FaSYUShMcauGbXx8hn8qh14gceGyYO69P4Ak46T/ShVytMXl5mmKuRPNAEw2dAWTq7vVsqkCgO0CpUKaQK2M067DYTZz60GF8zXV3y+bKFrdemyCbKlDIFYhtJtl7qo9Ahx9f0InTY2VzMcLMyBK5TIG2/iBN3XXxKbwaw2I30tZfP+/eou9QB1vrcWZuLaMoCvlMkZaeBpp6Gpm8sYDDZyfY7cdo0vG1//EKzZ1+9AYt8UiKz/7Hr/Hwx08giAIvfPYNSvkyZruRow8PsRVOsTIbJtDdgLW3gbvXl7hxYZaf+KUHkbQarr08yuTNBWo1hYFD7czdXSUeufeb0hNw8OjHj+MN7i6wz2UKvPrFy9x4ZRyNTkISqDswpjewOEwUciVEjQiKgk4vkY1laGx1s7G0RXtfI/4mFzpRweaykIplSYbTmKx6tDqJ5elNDp0d5PaFKfY9OEjLQIbhB4tUqwoj52eIrMYRBYVCtojFbuKn/tkTXPzGdULLW5gdJtKxLDdevsve0/2Mja6w73Q/wZ4GQosRxi9H0Uoiya0UZpuJlu4GAu1eysUye0/14fDauPziHXr2t9Ex3Mrs3VUkrUStWkYQBNyNTkILEZbH11gcX2VxfI1SvszgiR7GL89hdZrRm3QYzAYMJj2+ZjcLY/XuofahZiauzIEgbL/vDN5GF3fPTaI367HZjExcncPmsrDv0WEEQSS8FmPu9hJDx7tobPGy0eljYy6M2WakpTuA3WPlyV84Q65YYebWEnaXmfb+Rnx+O4lIklpey9GzQyxNbXDnwgz5bJGGZhdDRzt59YuXae8P8hP//Eke/uhxyqUyTr8dm9PyA/msVlFRUVFRUfk/hx9ZESUUCgH17o234/f7WV5e/v+z999Bltz3fff77nByzpNz3J3Z2ZyxwCIDBEgCpACKMilawY9M6dHDq7r2dXjKpVCWHlvP9ZWvfWVblmQFSiZIkBRBZGCBBTbn2dmZ2Z2cZ07O+XS4f5wVJBYl288jQbTEftVW7Z6emdM9c3p+tae//f1+/tKv+7Vf+zV+6Zd+6WM9th+En/zJn+R3fud3ePzxx+nq6gJg//79fP3rX+df/It/wa/8yq/Q2trKL//yL/83Q+V/93d/l5/4iZ/g4MGDDA8P86//9b/+nkLV/12/8iu/Qjgc5td+7ddYXl7G6/Wyf/9+/tk/+2d/5ec2GAwGg8FgMPyPM5tlDjwwREMS2di8hKbq2G1m4utJRFHEajej6xrx9QSnTxyn4ndgliXq1QZ94504/Q4uvHkHs0Vm6twcoqCj6TqBiJdCrszdK4s8/eOnWFuI8eF3b9GcFKtTyJRYmduhY6CF9bvb6JJAsVqnc6gVi9dOaTOHySxjcXmbgeUNBYvLyvVEkie/eBylonH5jSlEUaClw0/I52BmaoN69f74WUFAFBViO1nef/cup184yvKddbYXotjdDj7z808yfXEeT9DJwN4e+sY7Wbu7zTsvXUIURfY8tJt3v3MVd8BFsN1HpVT/06fl2MOjvP/NK/j8TqbOz2G2mgl3+HF7bbh9Tt5/+Srtw23cvrjAsUdGOfcn16lWmiO1KqUaO2sJBg/0El1NoKk6a/e2mheGfQ48fn8znLum0KgriJKIIAj427w4vXYkWcJiNTH7wRyN+p+N1TKZJS6/Pke92OxI0bVmIWFnLcnjnzvGZ44OEOkMYL0/amrv8QG8QQeirlMqVCnlK6TSRfacGqWEyNZylO6hViqFCpN3tjj9wlGGd7ejNhRe+U/vgq7j9NiZvjDP5kKUw88cILqVBWBnM0O4v5WhsXbau4P4W70474/MysTzvP/N60xemKecr+LxO+gcjPD6H5zn2JMTXM5WaO3yc/Xt25QLVWSzTLlQ4b2XLiKKIpb73UI33pvh6S89SM+uDgAcLhuPvniMsaOD5NNFbE4ry/MxZq6v4GkLIAigCiLX3r5DtVwnkyzQ0uEnlyo2w8PrDd57+QrpaI5wh59Stsw3//1bnPrMYVLxHIGwG5fdxMOP7yKeKuH2OXjna5c+GiFmd1m58vY087fX6BpqQVU01IZKKV/m3Ks3+dRPPoQkSxTzZbaWE2RiWa68OQkISPfHnCk1FZe/OVpOliV0VUM2y4BAoM2PKop09Ydo6/Lz/tcvU8iU0DSNUJuPx188zMB4J2dfvsKlV2+y58FRqsUaF16dZGMpgTvg5PhTEzTqDfadHKKYKyPJAnaHhZ3VONOX5qlV6rj9Ttr6IsQ3U2wtRnny7z1AS2+Q62emyaWK+Fs8rMxs4g97cAcciLKIzWTF5rASX09SzJfQkNhYjbK2lGTPyWGy8TyiKOAJOPEFnBQTOSSzRFtfGJPFTDFTQpJEWvpCJDbSVIpVOkeclIoV3vjD8wiCwMTJIRIbKZ760gNMnZujVKgyfKCP5dtr+MJuNhaa1wMsVjMWqwl/wMW3fvssLp+DndUk0fUUJouJz/zMw7T2homuJIh0BQi0elmZ3STQ6qUl7CC6VmH6g2nCHQG8QTdlUcDuddC/ux3L/XFyuVSByXP30FSdldlNyvkKvbv+2x1/BoPBYDAYfrj8T1tE+VN/UebHf6uN9p/+03/KL/zCL3z0OJ/P09nZ+bEd39+UY8eO/YV5J5/5zGf+wjyZP7W6uvo9j0dHR7l06dL3bPvzz/vQQw99336+9KUvfV9h5hd/8Rf5xV/8xY8eC4LAz//8z/PzP//z/53vxGAwGAwGg8HwcXO57Tz48C40AS5fXaKUq1DKlprdIfXmxfzh/jBv/+cz7Dk+zK7jQ1x5Z5oz375OpCvI+lyUgw8OszK1ji/oJLGVQVc1zFYz5UKFxFaa6atLyCaJRl1F15rh85oO2XiOBz9ziIqiUGkoJBI5Jm8sEWrx0uhwk0kUKRUqON02rDYT+WyZaWuMNtFCrVJHlETa2/1MXl76ngKKIArN3ghdZ+bWOhMHewn3RBja38fW4g7TV5ao1xW6hlqxO6y4PA4mz881g+Y1FU3VsDmtzWyY+8WKerXB/hMDnHnpEv6QG6XSQJIl6tU628txnB472VSR2Gaa0SMDhDr9lHJlzBZT86K6qiOKAoqisb0YZeTECNO31hDE5vuVYq6CKGXxhT1YHBbCHQFcfgctPSFMFhP5VJFwV5D1+R0CASfRaI77DSDYLCaS21mgWQSrleuIooDL6yC2nuKRF45+FIoNzewTq83C9maWBz93gng8TypRYObOJul4AR1IxXK4vA5kqxl72MPuY4N8/f98Ff/9QPRcuoQgCuSSBRrlP+vuB8hlSmytpzn2xJ6PttWqDd782iXefukymtrMYIlvpVmY3uD0pw8S20jR2hvipX/7Bt6gi3K+giiJ5Np93L22ROdgK6H2Zhh8o6Zw5c3btPdHMFma475kk0zHQAsAyWiWu7euAM1weWiOPhMEsDutVMs16nXlftaFjc2FKKrSzG40myXWtjPous7K1DpPfPYQb/7uWW6Xa4wdHWToYB/RxW10VcXltVPIlvEEnNz84C61cp2dlSSFbAlREECAxGaa0f29mGwm3vvWdcrFGn3DEXLpEqIo4HDbMJllCpkio4f6uP3BLJVChbbeEMmdLIqi4GvxkMuUOfHUBDfOTGOyyNicVjRVRdfB43Pwxn/5gEwsx66jA2wvxZi7uYrZasbjsyNLAu+/fIVHXzzGy7/5DtVyndaeED0DIURAVTSq5TrVShpvyE1rT4jkVoZMPMvi5Apjx4dR6ioWqxmH08P0hTky8TxOvx1PwEW5VMPf4cekNn/O+VSRXKqApmuMnRymsz9MqVAms5UhHcshCALhTj+aqtE30UV0PUmoM0h8PYVsljn8+B7eeukyB06P0j3QwsbCDsVciaU7a4weGqCtL4zL52Dj7hZrc9tIcrPYWMiXsTotZJMFTGYJTVGRTRIIAoVClUtv30HQNEr5Kg1NJ7qaYP7mCk//5MMEWn28/42raJpGNl6gf7wLb9DF6KFezr0yydTF+e85xwUg0PJnmUsGg8FgMBgMf+p/2iJKS0vzP8vRaJTW1taPtsfj8e/rTvnzLBaLEWZuMBgMBoPBYPihZzbLnD69i67uIO++eZtCojmSqLXNR4vPzvS7d9hajNE72sG9myv0jzcvfDbqKo1ag3CHH6fbSnIrg8kkUcyWqZazdI+2US3VUBUNb9BF50AESZZwuK1IskQpmWNhao357SQ6zUDwWl3B7nUQqzbQNA1NbQbBJ2J5Iq0eZjZitPV00tsfZub2OnaHmWQsR7jFw9GjfThsJhAForECK4sxSg2VjZUEN96+TfV+ceHAqRHGDnSzPLnC1vw2NpeF/vEuMvE8tUoNl8+Bx+dAUVR0VSXY4qFcqKI3FPKpEk6XFVPQiaKoSJKIpmpoukYuUWoWixSV5E6e9lYPiAIms0y9WsdsNaMqNdxBF1anDaWhommgqSqiqJNLFfEGXSCInH7hGBtzW9TLddbubeP02PG3+Hj5N9/FH/Fw6MkJtjazAKiq2gxu1yHU7keWRcwWE2arCUXRaNSV7ymiADQaCgJQqSl88N49SoUKACaLjKbpoOkU0kX6drezuZ4mnylRLVU/+npJ+rPAbKWuYndasDss1OsK+UwZh8v2Pftbmd1k8tw9LFYTmk7zddV1FEVl9voKRx/dhdkiUy5Um3kegoDL1ywClQs18unSR0UUgMXb61x5a4rEdppQR4Du0XY6+5vv/VRFQ1P/7GYvt8+BySTja/Uxd2sNk1nGbDXhcFkxWc0ggM1uQRRFlHozG0jXdboGW7j66k1KhQoDE11Y7SZe/9338YfdCJLEyMF+/B3+ZrdUTaFcrH7ULZTczjSLharG+sIOV87OsbEYw2SWaO3y09IdYmclTilfxe1rdhndu77M6ReOcufCPMntLIcfH2dwby/+Vg/94928+Qcf4PM7MO9uJ5MooNYVdE1n6vw9HG4btXKN1t4wV9+aolKsUsgU7/8sNCJdQTbmt+9/3zb2HB3gzoV7WF02RLOExWamXm1gs5vZWY3T2hshlypy58I8akPl8JN7WZrZpH2oFVESWJ3ZJBXLUcpVcAecSBYLb37zBiefnqC1L0DPUAs2pwV/xE06msMZdGFx2VERsFpMbCzGqJZroMORJyfo2tVJ+0ALHf1hEtsZxo8OMPXhXa6/cZtquYYoChx8bA8bCzvc/OAuX/ynn8LmsqHUVeoNFZvdjICA1W6hVmug6Do2k4QoCZgsMiaLzM5ygrEjfazMbOH2OagUK/jCHuJrcUaODHD0E3u59f4slWKVaqVG38RuBsa6iK2nWZnZIJcuoWs6NqeFYIsHt9eBxW5cTzAYDAaDwfC9/qctovT29tLS0sI777zzUQh6vV7ngw8+4F/9q3/1Az46g8FgMBgMBoPhf35ms8zoSBtKpkRIlppZI7kyV169RXyzmZcimSSyiQJTF+aYODbI8P5eouv9dA9GmPLYqZfrVEs1rA4LvpCbbCxPa0+Ie1Mb9Iz0cOHNKRr3L1JbHWYe+/R+Vhe2qdzvZJBMEroOLSNt3Lq1gNVhweZs5p54/A5KpRpWmwlF12nz2sn3hlAUjV3jHYQtIu//3vvkkwWcXgennjvEvokuzrw9Q6OuUMxVqJbrmEwS01dXSGylGR1rJ9jqR6krFBI5ghEP3rAHXRKxe2yUC1XsTivFbJlGrYHaUBElAV3T8YbdaJqG2WJCNsno9wsDo4f6SERz1KoNKnUNQZbwt/tRaw10TcMXdjNxejeaKJHYzmJzmLE7rYTafJSLVdw+B7VqneEj/Zz+7BFuvT9N12g7mq6zNNPMh0jtZFmf3STcE0YUweuz0zPayuZCHEmWcHr+rIDR3hfG4bZ93+vtDTixWJtdMpVSDe1+J4YgNkdM/WnHRrVUQ/FpiJJI71gXd87fA8DmsGC1mRHNEv3jHcxPrnH7vXncfheD+3sZHO/4nv2tzG6RiuaJb6XR9WaHuz/swSyLrN7d4oFnJli/u02gxYOmqgiiQKPWwBtwoqkaovhnEwZSOxk2F2NYHBbmbqwgSSLHn93P0acmCLX52FqKEfA7kC0Sokkmny7z9svX2LWvi0hXkMRWmq2VJFabmf2nd+Fv9bI0vYnNaUZTdcxWmUqpTqDNy40zdxBFgY7+CBe/ewOr3UIpV8Yb9nLnwj1OffYIjVqdQIuHfLqIySyxOb+DpulY7RaqlTozVxZxeazkks2w9MXpTUaPDpCJ5+5fsK9js1vYc3KYK2/fQdV1Ap0B1hdjzFxb4bmfeQR/xI2qaGRTRRq1Bj272nF57dRKzaJfJlHA7rRic1pRVY1GTaFSqmJ32rA6LCxNrdGzq53xY4NcPzNDMVtEVXWmLi9y6tOHufTqDSRZpJgtE2zzsev4EO984yq7Dw8gW01893fO4vI7WJhcw+W1M3iwn8KZabxhN92jHVx/f5bBsXbOvznFj/zEKV7+d28R30jR0umnd28vN9+e4eCJASx2M1tLMVRFw2SWMMkSgiiytZLg3OuTHH1sjIc/fYCv/8Yb1Ct1Bvf34Gn1o2sagqax+9gQy7NbNBoqfXu6ScdybK/EAfC3enjg+cNcfHu6OZrQZaPVIqPpIAkC9UodTdEYOzbIxPEhauUqd87PsbkQJdwZxO60cuCRMSRZZPhAPxvLCXLpIsP7erh7dYlKqYau6ZjMMqIk4vTaaesLYTAYDAaDwfDn/UCLKMVikcXFxY8er6ysMDk5id/vp6uri6985Sv86q/+KoODgwwODvKrv/qr2O12Pv/5z/8Aj9pgMBgMBoPBYPjbpa03xOU3JikXml0HNkfzTuuhvT0kNtNAc5zS3WtLHH9qD3cu3KOczhNs9XLv+jJmi4l6tUG1VOPIkxO4Aw6OPj7Ou9++zv5Hd4NFRmmo2M0yd66v8PgXHqD80nmyySItPUG69/VwfSuGbJKQZQlBEKgUqwTcdsZ624lE3OwbaGfy/XscGGvH5bUhtrs599Il8qkikixRK9V4548u8MBnDzO+txOX19bMGakpKA2VsE2m0VDp2t3Be390nuR2Btkks70YA0Hg5GcOc+zhUS68M8PuQ30U8xWq1TrhDj+P/Mhhlu+sM3ttmUdeOMbslUXiGyl0TefA6VECHUFKuQoHTvSzs5lm4tFxctsZkpspIp1+2gbbMLvtzF9f4enPH0NAZ/H2BtG1BG29IU4+uQen387Y0UFUVWVtPkohXWp2SzgsoOuUi1UsZompM7eJb6Zxh110DrQwfLiHtZU4iggumxm7o3lhXhTF73udvQEXe44NsLGSYO++TkqlOovzUSqlOk6v/aMxY426ittra17kPzXC9lKM1E4GSRY5+OhubC473/r3byFKIj27OthZT3Lr3Sl27e/+aF+lfJnttRS5dBGbw0q5WEUQBRLbaYKtXoItXjRFZXMxisVqRjZL5NPNvBJf2IMki7j9zcDuerXByswmYyeG2ZjfAZrj4S589ybt/RHOfP0yjZpKKpZjZz3F8N5uJFvzHJ69tU7f7i4OPzlBtVSjVKhh9rmYub2FqjdzaRxuG9lUHo/ficvvoF5TCLX7iK+nsFjNWO3mZhfO/ZrO5twOrUNtjB8dJLWTJZvIo6k6JquJWrXBocfHmbuxTNdox/3uG7h5bp5Hnz/I3tO7EQCrw0yjoWF326hX6pTLdXLJItDMWzHZLFx5dxp/i5dsqojFamJ1dou12U1URWVgoou+8S5wWfEEnGSTBVI7GQDK+Spmq0ywzY/La8dkVahX6oBAsNXL+kKUC29MMnZsmFC7j2K2RL2h8sYfX0RVNNr6Irz39Uu4Ay5K+QoWq5m1+R22VhOcfPYA185Mk8/XiW42x7GFWj1cfH0Sh8NC3+52ukY7uHB2rjlCraGy5+QIZouJ6Eocb9jD3gdHuXnuHpIk0berndkbqxx5bBxV0Rg7Pc7F92aZmpnBE3Lji3ixLCVJp8u89vJ1unqDPPsPHyO1lUYQBfKZEvFolsG9XVjdNsqFCrl0iXK+gt1p5fHPHaNWqXPn8iLr89uc+a8XcXrt+IIurrx5G1XVGD3UT3wzQ6AjT7lYJZ8uMTDeySMvHOXCqzcp5ZvrYrDFw6nnDxlB8gaDwWAwGL7PD7SIcv36dU6fPv3R4z/NMvnxH/9xfu/3fo9//I//MZVKhS9/+ctkMhmOHDnC22+/jcvl+kEdssFgMBgMBoPB8LdOoMXLJ/7+g9x4b4b1+SgtPUFaekKIssjczdWPPq9zqIVsIo8/6OT8KzfQdHjw+UNkE3lkk0yo3c/ARCfjxwapX1qkZaKLi7fXURoqqqYhChAJusFupf/pCdI7WVSLzI3NOB6Pk5qi0VBU6rUG+wc7sCSLbL47y2ZDZcXj4MQnDzB3Z5N6oUY5kaWULSGJAr6wm91HB9HvB9yHOvxIVjOHHhohkyiSSRVw+53YLCbmr69w+Y3bSLKEJDfvLA+0ekmsJ3EFXfzozz3GG9+4xvpiDEkSiW5kqOTLjB8aYObcPT585QZjh/s5+tQEfbvbsTpsfPDNq0xdmGvmTvSGQA2yHSvwiS89SDpRIBXLMdzi4666iFKpc+vsLIqiIug6uUSeN796nsNPTXD00Wb3hckkEe683y2TLrLnSB9Or4Nz376KI+giL2jsrCfYyRQ5cmKE/olONleSBDt9nHh0nJbuAJVSFavd8n15kS6XhdjdDZYvzlMo1tjzwAhlJFKpZhaNKAhYbGbG93ZhtZmx2vw893NPsL0Uo1atc/mN29w6e5PtlThKQ2VtdovTP3IEQRC4dXaWwb09WGxmoutp6pU6siyhqhqtPUF6hloQgHSiQOdQhMkP7rGzEmf/6V3Uaw3yqWYRYWMxyt/7p58iuhJDVXW8IRdPfPEB1IbGxnz0o++ltSfElbenUBoqsizhcFqIdPhZn9+hY7gNs9WESZZo7woQ20gSXU0QaPMRtgZYW4jxwBNjyIJGOVPi4Old2D12Fu9sMrS3m2wihyfoYmdZolau0z7Ygs1po1apI1vkZoh8rsSLP/8E7798hR1LinCXn2Cbj9vn57CYZfKpAg6XlVqlTqVYZWMlQf/udsrFKm3dIWq1BroEvogHbSdLudwMet//8G6++3sfIonNTor23iCyJLA63fxdCnUEqJbrXPzuTR753HFunJlm/0OjvPXV8x9l5jRqCsE2H06vg9R8lI7BFgLtPoJtPu7eWEEUBS68MUlrT5De4XZW57ZRFQ2r3UIuVcBsMWGxmaiUaljt5mZ3Uq5CLlmglKvg9DqwOa2szGxy9KkJ3vgvH9DZEyTU5sPusdNoKEiyhKqonHvlBm29IUaOD5NLF3n32zfw+R0UCiX6xrvYWUsR38pw9JMH+dYfnKdSqGJ32ZCtFtYWY8R3cowd6GZ4MMK9K4u8+wfnCEZclPNlDjw6jibCiU8fIva7H7I6t4PFIhMIe+gebqWYLWN3W/GHXUxfnEepKxSzZWxOK+jNLBSHx07PeBebKwmgOWIQYORALx0DEZI7GSRJJNwRwGIzf3wLscFgMBgMhr+1fqBFlL8oxPzPEwTh+wLMDQaDwWAwGAwGw/91bb1hWv5+kFyqiCAILEyt8d43rmC2mJBMEsN7uzn+9F5unrmDJ+hGUzXyqRIXv3sTd8CBx+8kvZOhtSeEIEtcvrnK8loSm9PSzKoQoFFtkK/U+OOvXuTZzx3m9u1Vjgx30sjFqW3HOLCrnZpd4ta9TSzZCju3N4iuJNi1t4eW7iCXXr1FW3+YSKuHjK5hscgE2/30jnVx/rXb6LpGx0ALdreN0YN9pDZSIAh09YVJRLP4e4JsL8cQRAFN09AbOpVijUqxhsksceyZA1z+4B6Duztwu61sriRQGwpOr4PbV1eYODmMcGkBT8DVzJ/IV5m7tszm/DblYpV6pdkxUSvXePhzJ5ojhhoqP/Kzj2JzWjj82Dh3ryyi683ClSBAYjuLkilz48w0x58Yp3eknZFD/bz8/32TrcVmwUBtqBz7xD7cASeryRzlSh0ARde5M73KJ3/+ScwjEbLFKn/8/iQnVuLEp7do74uw+0g/HfdzQ8r5Cm/+7lk25ncIt3hobKa5c+4eh5+cYGWugMVhweq2sedAD8OjbR+dGw6PncH9vcxeXWRpeoNsIod2PxtG13UuvHKDJ77wAPlUkXKhgsVmplat4/TYef5nHqacr7Bxb4ub796mnK/SN9bJyJ5DzF9ZJNTuJ7aW4MhTewl3BZFEEW/QxcFHx7DaLdx6f4bLb9xi9soSkixx8pP7ESSJfLJAsN1HKV/lzsV5FEXD6rDgDjjx+J1EV+PsPTqE22vl4neukU0UqVbq3LuxQmYnw5OfPcCFV26yencTi8XE8N5OBB1Ov3iMXfu7WZ3ZRGkoNOoKke4giqJRqzboHmnj5DMHcPodeAIuNhZ2MJklWjp9gM7Zb10DIBDx0Lerg4XZ63QNtzC8r5fVhRhXP7hH73AbhXKdN75+Gcls4tnPHMDpspJJFQm3+Xj19z+kmK/idFowmSTMZpmb78/S1hchny6S2Erj9DYD3i02M5lYDgR4+LNHSO5kKeUrRLoChDr85LMlzBaZbDxPLlFgZyPN6RePUStVqVcbuAIunF47awvb+EMuPAEnNocFs92ML+KhspKg2YLTLMYJAgRbvdRqzcwdl9eO3WnD6XMQ6vATavfjDHkIRjwkdnIUchV8IRdbywlW56IoDQV0ECUn4fYAse0MkknCZJaYurnWPCafA4fXweZqEkFo5ptksmUKiTxLU+vY3VbMNguyLLI+u8kTX3qIRrFGz3Abuw/2oisaZruZ7aUYl96aItTmZffhflbvrDezXGSJarlOS1cAp8eBJInEtzOgQ6TDT0tn4KNz3+mx4/TYP/4F2GAwGAwGw99q/9NmohgMBoPBYDAYDIa/XqIo4gu5Adj3wAgtHX521lIIAoQ7AwRavNx8bxpVUQm2+xFEsVlMSJdR6iqtfWF0AS5fmCeeLIAgIJtkMN1/fgFks8zORprt9TRHO1p4+6sXkE0SlXKd1cUonX1hHn94F3e+O0l6O0tnfwSbzcKZly5jtpiIbaawXFth76lhesc6CXUG+eA7NxFFAUmSEUQBpaHynd85y+nPHGbu+jIWu5mOvhBrs5v0DrcCIJskxPtB6dVyDZvLzlf/w3tc+3Ae2SzRO9TC0EQXU+fnETyQ2M5Qq3fij7jYWIxx6JHdzFyc5+Zbt2nUFVq6QzQUtXk3v9VEKOzi2S+epG+0DbfXwbd/5wMmz90lEHDSP95JJp5rdh08NEohU8LtdXDv6iJ3Ly9SrdQpZktUyjV0DZSGgiA0g+FFWcTmsCCJAjaHBUESqdYUYoUS26txsvECYlVhyGRh7tYqy7ObPPPjp+gcbGHt3ibzk6ukdnIIIvgjXkx2kbXpDcYP9CCaZR7+xARje7txOK3fd37E1lJk4jmsdiuFbBlda2aqVCt1SoUKbr8Du8tGpVRjezXJ9XP3UBWVzi4/K9MbtHYGKRdrVItV3v3j8zz5xZPEVpPUqg0kWSLY6qNcrtE33kWkM8DMpQUSmynaeiP3w71t2BxWzn/nGvHNNME2H/lMiUc+d4LbFxdI7mSJbqToGGhBlCRaOv0s3VgitZkhly5gtVtoafdSSJeo5svENpKIgoCiqJRyFVLbGW6emSYdzSKKAvseGecL//vzvPtH59ic3yHQ5uehzxzm4GNjlLIV3vz9s9RrDfaeHOHlf/cWVqcFb8CJDgRavYR6Qjz+o0eRZIlMNEvAZ6W7N0C+UOONr13m0IMjXDu/wJvfmeQzXzpJuqSgrKeoVRV0XW928dQbyGYT5UIVXddJbmUw20wodZVqqUa1XKNzuI18Ms/S7TUqhSqSWWL6Yoonv/AAawtROgZasDnNKIpKo9bg5rk5NEBTderVBpEOH5/40oOszmyS2EzT3h9hYzFKJlmkYzCCrur3CwnNzBFdFGlU6pQLVUYP9TI3vcGjLx4jX6iytpOFdJVQf4SRQ31kE3l2Hx3iyttTaJqOUm/+7qHD4N4uzn53khNPTWCymslmyrj9TrLxPA6/C4vdTCDiQTbL9A9GuHvhXjOYHgF06OoPUSvXWLq5gsVlZfLcHP5WH4FWL0pdYXMpTiFTAl1HUTWcXjuJjTRmazPTSG2olEs1LHYLlWyz2PPgp/Z/1IliMBgMBoPB8D/KKKIYDAaDwWAwGAw/hGSTTOdQG51Dbd+zfXh/H7fP3WX/6d2899IlEAQkk4Q35KZabtBQ4f13pu9nm4Cu6eg072B3uO3ksmVcXgdasUZidgub1US9oWJzmKlVFTZX4pyU9mCWRXRNY2hXJ++9dAkd4aMLy2azzO1zczz79x9k6tICogAWh4VIVxBBFNhaSSAIArlUkWyqhGxOcejR3UQXYxQyZfrGu+4HXasIgo5sNWPx2Nn4cJFgq4fWNi/VaoMP353loSfHmb+5gt1lZfnuNl6HmY7BCG999RwdgxGq5TrlUpXyzAatvWGi62kkUzOsW9M0yvkK187MMHnuHt6gi3wyz60P7gJw4PQurrx2k0alztq9bWxOK49+/iT5bAld1+ke7aBeraNpGioC9YaK22ZG0gFVxyyKjEz0YnZZMOdLlDMVxvtaMNdVfO1uLGaZxE6WqYvztPeHiW2mqZaaXSwWqxk0DRlw2U08/en99O5qwxtw/6XnhMkioyoaDpcVWZZQFLV5rpib2ydOjWKxmTn32iR3ri4BAv27Orj2xk0qpRr1mg2n24baUGntDnHzzAxzN1YIdQa49eE9/C0eDj0+wXvfuYHL72BzMcr1d6fJxHLUqvVm4UsUGdzbTTFXweawUCnVuHHmDp6ID4fbRiDsRpQEhg/0YZIEJs/OUMqX0RSNQqaEUldoH2xh/d4WTreNWqmG2SLTqCsI6Eydv0e4M0AuUWB1ZoN7lxfY/+g4feM91Cp13vv6ZYYP9hNfixNbS+ALexDQ+eI//xQLt9ZoNFRsHjuyzcJbX7/KY88f5MJ3rpOO5/AG3WzO73Dw9C5Cj+2iVKzx7AsHefuVSYqlOqsLMYZ6A+i6jsNlw+m2kk0qzN5YoX+ii+2lGKqqUS3WkUQRQWgGyA/u6+H6u3foGGplezlOvdog2OajVldo7w1RKZYZOzyAw2Pnylt38Ld40U0SiqpRKVUZHu/gra+ep1FV0DSNfPo6bQMtrNzdZnFqg8E9XYwd66dzqI2L70xTrytUizUGxzvp6ItgtpuYX0wwe2OFel0h0OoltpNDlCQOHu2jmMhz5PFxNhZixLfS9Ay10t4b4s61FY4+NobHa8fjd9DdG6St08/mSgKz3YpslbFYzfd/x2o0VI1wh5/4ZoZysUqjrpLczuBv8dIdcaM0VOLrze4VHR1d1zFbTTjcNuZvrTK4t5eNuSiSSUKUJPwRD317Otlzchi7y0ZbXwiHy/YxrKYGg8FgMBj+rjOKKAaDwWAwGAwGg+EjY8cHia4nyCULPPDcIW5/eA9BEhmY6EZFYGstwU48j91jwxd0sbkcp1FXkE0ysknCZJHRVA2trpCO5bBXNdxOK1U0LGYZQRQpZkrsPzpEYmabaqmGroMoNS+MipKIKAvEV9KsLkQJdgQYPtRPraZQyJapVxsEWrzEN9PomobT27xob7Wa6RyMsDS9Sbg3TKQnRDZRoK0vjGCW2d7Jsf9oH6u311m6Mo/VZmbvkQFWF6J0DbeSS5fwBV2UM0U2F6JoqkZqO8vu44NMn58DQaBWrmG2yshmE4qmsTKzRaVYY/LiEtVSDTniJrbRDMS22s3IkkAmmiUVzWF1WOje3UEuU8LpsX0UjC1JIoIgsLq0w8OfP8G1NydZmFrHZJLpGWpFFkWK01EayRzPTPTzyu+/TzpZYD7kxWu38tjfe4BcqsD1d++g1FUGJrpZndlAVVS2l2Lo6IwcHmBteh2Hw4wsSdjdtr8wmL6lO0hbb5hMLEfXSBvZRJ5SvsLhJ/YwdKCPtv4Q87fXuPnhXSRJor03hN1hplFXEUWRcrFG5P7IJ5vTwsZiM6tDUVQsVhPpaI7EehKTzcLGYoyNue3mqCqaRR+z1cTmQpThA70MH+hlazFKuCPA4tQ6p54/wtmvX2T6g5n7HQdJTj13mECbj1yqiMVmQhBEVEWlkC4xfKCP2euryGYZr9+JySRRylfoGm6lmClTzFVQago7qwkWJ9dQNZ1qqYbJIjN/YwmlodI62Mbc5DrJS0tY7RYOPDjM+NEBShWFt75+lUDEQ3o7RUdfGLvdRKVY4cADw5z/znVEWSKbLRPp8HHo+AjZTIlKsYpglrFaTYi6zvp8lEK2jNNto+OBYaxWE/H1NIIIokni0GN7KBYqDO7toaU7xOU3b6EDrb1hwp0B6g2Va2/fJrqSINIZwOG24g+5qBYruPxO2rqD2MY6SW+lsdnMxNaSiKJAPl2kXKjy2I+dJBnL4fTYmTgxSLDVT1tfmO21FNVqHVkUSG2nSBUbXD47i9vnbJ47gsDA7g42VhJcu7zEyVND3Hh3Go/PwUOfOczoRBeJrQy+sJtauYbNInP+m1dZvLuFbJIZOzGM2W0nuZFk9c46kiThn2hH3NXOh69O0tbpp1qqEd9IYnVY8ASdVItV2vvDbC3FyacKCKJIo9rAF3IzfKCXC69Nko7lePhHj5GLF1DqCqc+fZCxE8ME23x/w6uowWAwGAyGv2uMIorBYDAYDAaDwWD4iDfk5hM/8RDr97aZurjAgcf3UMhVmLu9TqVYo3tXG3a7mWKuQj5TJtjqRZIFggE3ZrNEOltmbmoDySShqRqZnRxmqxmX34HTZsbptCJkqlTRGdrbgyiLiKJwP4vBh6aq2JxWvEEXoXY/yVietaU4pXwVq81ErVInny3R1h2ktStIbC1Jz0grssXE+Ilh5m9vcOmtO7T1hdh1oA/JJNHaHyZXqHHr7SlW7m0DkE+XSHz7Gsc/sR+TScbhttHWHWA5V0JRVTx+J9vLMVo6A4Q6A6zNbmJ3WQl3+jn85F7u3lzDbpGJS2l0NKwOK7lEnmQ0S1tvGE/AxdZCtHnXvKYzcrCfZLzA9Deu8OQXH6BSrLEys4k36KJcqhHoC2L1OQgMtNC9pxtZh+V7O5z51nVae4LsOb2Lb/7GG9gcFjRVo1FT8PV5ee9rFykXa7T3hVib2+HgI7tx+Rx88PIlTGaJh148jqaovPwbr9PSGcDhdTDx4Chmq5ldRwfoHetCEATW7m1z5qULtPeFkCSRjfltukbaGNzfi9lqRpJEvv7/fh1vq5+715YJdQYItvow2y3sPjrAyp0NRLGZb9HaHSS6lqRarLK9FEOURPwRLy63lc3FKLtPjpKO50lFs99z7skmCdkskdrJsOvIIIVMiWKuwq5jA2R20jSqDdr7I1SrDZbvbpGLv8NjP/4Q63M7zeKbAEod7E4zIwd7WbqzTilfweW20agpiJLI6JFBZi4tUKs2KBcqH40ns9ibgeJOj51KoYqn1ceZP7mFLgiIJhOqBudem0ITRMwWmXDISe+DI2zObXLtzVtkEnmOPb2PV37rDACt/RHUhoYgiNw6O8MzP/kIK1473UMtVFIFLr85CYCqaKTjeWpVhf2PTjB8aIBsPEet1qBUqtHV4efCa5McfWIPg/v7aCg6m4sx7lxZwmRqdlss3lzD7rAyf2OZ409PUC7WEEWBWqVOa1+EncVtsvEcNqe1WbTUNHKpAkt31lmei1IuVMklctTKdU5++hCD452IksDL//ZNDj6xhzf+4/vNwHVBoJgrU8qV6bSZ6OwLkY7l2VzPcPSxPQRCLvRajWwsSzGZ48Jrtxia6Obst66iazrtQ23EtjLcvbrI4Fg7l1+7iabqqKpGOprhxf/1CWZvrJJOFujoCrC1EOXIExOU8mW2l+IceHQcs7WZhWJxmlEVlYMP76JaaWCxmigXalx84w4PPX+IkX3d7Ht4N56A629s7TQYDAaDwfB3l1FEMRgMBoPBYDAYDN/D5rAS6Q6hnl/g3OtT3/Ox2GqS8Sf28Np3buCPuBjsC1OvNFhfTKDrOj1DEUIPjNBQFNq6g8TXkoiSQCaex2IzE2lxY7Gbuf7eDPsfHKV3pJVCukQ+VaRRa6ADtVKN1p4gFruZ7dU4bb0hFu9sIptlJJOMpmlEuoN0DEZQGwot3UEK6QKzt9bp3dPJQ88fYntxm7tXFrDYLbz3rau88LOPI6gqHT1BsukSpfsXmm+fu8dzP/sYxx4bo5zKEYy4CLZ0kk8V8IVdXH17iqF9PTz8oydo7Q1jtlvIpEsoNYVkqoAki7T3hrmbXsFqt6JrEN/K4vDY8bd6ycZyCAI4g26uf3gPgLnJNQ4+MUE5V2JtdovDj++he183y4tRbl9fwu+yk1xPo6kaNoeZXLKA224lk8hTzpvwhpy4PXYqhQpzN5bZdXQQi82MxW7mytt3+MSXTiHKIrJJRhThrd+/hNVmZmsphtVhZe3eFp/8mce4/PokhUyZ/r3dnP3GJaqFGrVqHVVVOfJ0M+g+fD8bZ+nmCoIg4PI5MFlNJLfSWKwmBEFgz8kRiukiuWSx2VUQ8bC5FGN7OY7SaI4Ey6eLtPdHaO0Nows6vpCLcFeQ5HaGRrX5umuaTqDFizfkRqkr+CMequU6uw8P8M4fnadarlOKFxCaGegoHo3YWoKTzx1i8dYK5fsh50c/sY9X/8uHTBwfolFXuPDdm7T2BHnkcyc4+61r1Eo1+sbaae0NsTS5hqJomHUQJRFv0Elrf4Szr0+BJKDWVXRdJ5NoFgyvv3+XsUN9XHtvhkwih8tuppApAiBJErVKc5yaAKiaRrlUw2ozY5bg6Kkh1u9tE2zz8fzPPEIxU0bVNDQENEVjYymGIIKv1Qe6TnwjyeZiHIBcIk/fRCe3z8+zvRxHU1XsLjt9Yx1EuoMoDQW70wa6zs7iDtVynXqtQXIzyfjxYZanN1HqzSwWSZIQJQGH10G9rtLSE2L0cD+1YpV3/+gcia0M3SNtPPjpg8SSBYLtfvKZIrlkAYvdTLDV13x9PHY8rW68IRc+j4WXf/27tPSGkE0SsfUUhx8ba3ZDqTpKQyW9naa9J0T3aBsXv3sTp8tKIV/F67FjNpv443/7Js/+L4+RT+Xxuqwce3IvW8vbbC5EqVXqzN9YpmMgwuFHduNv9TF3c5Vr79xBMkuceHoCTdOxOy2090dYu7vF8acmPva10mAwGAwGww8Ho4jyt9hv/uZv8uu//uvs7Oywe/dufuM3foMHHnjgB31YBoPBYDAYDIa/A/uQTVEAAQAASURBVFxeO8E2L3anlXKx+tH2arlOwGnB7bFx+PAAV87OUchVPvr41kYKj9/Jcy8cJi42u1Hmb2/g9zroG23j8ONjxDZS9I62M315kY2FKCc/fYDFyXWuvDFJtVzjwKNjjBzo451vXycVzTN+dIDe3R0s3NlA0zS6h9vwhFysLe4wd3uNybMzuHwODj21l3wD1lcTvP+t6+i6TrjVS2dviK2FHcq5MjtrSbwhFy1tXuLRHJqq0tbhJ9zu48a7d1i4scKOx87OagKzWeKRF47y7n+9QDlfplqqEukOsXRvh/0PjPD+1y8zdmwQs8OCKImEuwKYrTLVUo3l2S0ef+EIiY0UXSNtxNaTuP0uPCEXtXKdu9eW6B5pJ9IfweK0kIsXkSWBQraMXRfoHWmjqztAIVVqXrgOOGnv8JPNlAgHXAyMtnPn3D3MZhlNVamWqgQiHsqFKusLUZZnNvEEnGSjWURBAKGZgyOZJKw2E1tLMa68McmVt25z9Km9mG3NsG2330nP7i4mz99jZzXJ0L4e2vvDdOzqYHk5zvJqEpPDRq1Rolio4vY72VpPc/jp/aj1Bt0jrVTzVVq6g839/ikdsvE8uw73I9ltLM5uEWz1kdxKo9wfBxfpahbGjjy5l+WpdVp6Qhx/dj/JaAGn10612sBsab6FVZVmx1Kw3cfafJSDj46j1hUANheiZHfSbC7GKBWq9O/tYWMpTrjaoFFrUC3XSMcL+JJFHnjuEJfemiLQ4qGl08/uo4OIZpm56yskttIoDQWbw0r7QAuJrQzekAtBbAafZ5NFXF1+0EEQBESp+f0KAmiahtfvpJCr4HBZsVpMmCSBrCjw3ksXySYLOFw2zDYTT/zYSd5+6TJ7jw+ST+SZu7JIx1Aru48NsL2UwOW1IwhQzlWwOy3U6wqCKFAp1zj3yi2OPbWHroEWHB4rU+fuUa82KBer9/+u0TGQZ3BvD1fenkKSRBRFYWRfP8t3d8iliuw9PkhyM8Orv/0e9WqdYJufOxfmmL26xE/+yxeZvLZKKppFaSgoeQVVUTl87Ajnv3mZVDSHbJYZGG7h0GNjXHjtFr27O8nEc1TLdarlGoqiIpkkSrkyVouMzWZG0HUCQRduj516QyWXLoIO6ViWM69O8uAjuxgZ78TlcyKbJATBTHI7gyxLHH/2IP17mh1U87dW2VyKkU0UCLR4OPToGEtT6xx+dAy7kX9iMBgMBoPhr4lRRPlb6qWXXuIrX/kKv/mbv8mJEyf4T//pP/HUU08xOztLV1fXD/rwDAaDwWAwGAx/y0myxOB4Jw88s5ez37n50R32ALVcmRdfOMKZt2fIpZsh6YIAgtQczeV2mJk8P8dDT4zTu6uNvadGyadLRDfTfP0/nmVwvAMRsFoktua3+a3z99h9dIDHfuw4vrCX6WvLvP2NKzQ0yCQLTN9YoaLp2OxmLFYT01Pr7BppY/ryHKlkkXDEg9lq4tXfP8eTP3Ga+dlNVFVDUzXK5TqVbIme4RYqhQoms0w6mkNtqDgcZiKdfixWmVKuzLV37qDrkEsVsTrMmEwyO2tJXvh/fIKZywvcODPNwUfHefhTB1id3eDY0xPUGxqZ9RQPP3eQudvrPPGFB5h8f5ZqpU46keehF49RSBXJ3x9/ltzKfFQAuHNpnq7RdkI9Ya6+eZuDz+xFNkn07Wonv57ktf98G13X8Uc85DNFeodaiW6kqWYrbC9GKRdrWBwW1ud2sLusdAy04A26cHnt9O/pIrWdweG2YbbKFNLFZneRorI0FaV7rAurw0K1WGNpah1/iweL3Uygzc+Zl6+iKiqIAtVKg9uXFpm/uwNmE/GdLPuPD3LhjdtsrKawOq34Ay5iW1mOPznOvpNDvPkHH7Jwa5VHPn+Cm+/N0NITJNzhxxt0Ua81WJ5co2eknanz99j38Bi3z86iKmrz4vwTe2npi9DSE8bf4sHhsnH13WlGDg/ywbeuUi3XmwWKoJNAZ5BqXWX93jYbs5toms7+07uwWM1YbRa8ITeX35qifaCFbLLIpbfucPDxiWYOjSRw5PE9lIpVOkbasFhkIt0h6nWVWx/cJZ8p0TncCqJEuVSlWlfwt3opZUu4/A5sDgvVUh1nwEXfni5ia0lyqQJt/WFyySImq5lyo4qm6VisJq68M83uI/3MXFmgUqzROdBKz+52+nZ3sD6/w2OfPcQ3//1baIpGrdpgdWaTQrLAxAMjvP6H57DYzKSiOcaODnDwkd1cfP02NRq4fXZauoKk43mC7V4qpRqZZIHkdhYEMJllzn7rKl/6F59h9Oggy9ObVMp1tteSrNzdZnh/N3aXlXf++ML9fWSpVRv07+mmUVdYvLHCwGCY8QM97GymuXhmhn2Hejn3zcsfdZdZrSaWJteoFKu09oaQJAFJlpj88C7Hnpzg3vVl0HUEQaBeVahW6ticVrKZEuVilWqpht1lw+13kM1WCLR6Gd7XzdrsJntOjmCyWcnFc7j8TtwBF+996xqiSWLfqREiXX6mzs+TSxVQVY3oaoKRA72MHRv6ga2dBoPBYDAY/u4xiih/DRr1BttLMXKJPJ6Qm7b+CCaz6WPd57/5N/+Gn/zJn+SnfuqnAPiN3/gN3nrrLf7Df/gP/Nqv/drHum+DwWAwGAwGww+H7pE2JJNEuMPH9moKXdPwhd3IJpHl+RilZAG/10a50hzHZLeZCUXceL12YmtJivkKF9+cYmctRalQxRNwkksVuXpmlgef3Us+U0ZRVILtPrKJAv0T3SwvxPF3BTFnSnj9LjaX4miCgNUkIulQL9XZNd5NKZokGPYQDLkp5srUGypenx2bWSQUcVFo81Iu13HYzSSSeZZmNhk7PsSVd+4AkE8V6Rnv4rEXT3DzyhIOs4yi6qiahmyW8Hpc7KzE2VlNEGrzsXhrFYvVTMdABEkWee7LT6LqKm9+9SJKQ2Vhco29x4coFsvI5j0UsyVqlQZ1FZYW4jz9Y8e5c2kB2WLCZDdTris0VJXN9SR1p4lMq4OSovKlrzxNcjHO9T+5DoA/7EYySVx5c5Iv/vPncXodrM9ts7MS59RzB/ng29cpFyo4vQ5mriwCAkMHeolvZhje142AzuLkKu39LaSiWVxeO6EOP4VMGbvLhtlqwu6ysTEf5dgn9nH3xhq1aoNKuYZskqnWFdLpEpl7UR5+4QjRrQyTl5c4eHoUq81CpVhlZF83IxPddPSHEEWRSGcQVdGYvjDHg585zMzFe9x4Z4papY4oy+w5NUomlmH28gLt/RFGjw7g8jkQgTsX5lic3qJSrGKxmekeCrM0tc6ekyP4Ih5uvHuHhqLSPtRKz+4uvvrrrzbHyPWFiK4myMQu8cTfewBBBLNFRjY13/I2ag0Abp2fp1KscfSJMW5/OMvOcgK7y4LNYeX8d2/SP9HN6vQGQwd6mL65hqbpSHJzTJfLayfS7sPldfDAJ/eTjucwO6xIdiv+Nj+1ap2jT+1laynO1nqKQqZMW2+Ig4/v4dv/8QybyzHGj/QTbPWRy1dRVY25KwukohkWri4QCLspF2s0ag00XWfm8jx9451kojn8LV40TePG+7Mcf2Yfj754lHKxiqqoRDcy6EoDt89JNlloZqJIzeyWWqWOoqjcu77M/ofH6NrVweZilE5zC52DEWJrKRJbaRLbaVxeBzaHlXBXgHy6SGIrjd1jI52uUC5WOfXcQU5/cj/UGhRzFTRVA8BqlsjVFdbvbvO5f/QsmXgOb8CJ1WGhpS9EqL0ZFO8Lu6nXFfKpAk/+/Yf42m+8SVtfiHCbD1XV6Bzt4OrFRXoHwrhcFrpH2lif26aUr7B0d4eOgQjlUrOY++Gf3KClM0hbT5hIR4DYeopysYLT4yDc6UcUxb/JpdJgMBgMBsPfcUYR5a+oUW9w4dtXmXx/GnRAgL2nxzjx3OGPrZBSr9e5ceMG/+Sf/JPv2f74449z8eLFj2WfBoPBYDAYDIYfTh39ETr6I6iKypV3prn+3gw2h4Vgd4jYcgJBFHC4bcgmCa1YxdLuw+lxYN9tpVhukE6VcQddFPNVGnUFs9VMKV/m7q01dh8dZGN+h+3lGHtPjzF9N8q5t6fRVA1dh/1H+zn8+Dg3LsxTLTeoVOqYLTKnHtvNtbtrxFcTKIpKoMXHznoSr9dBYiWOy+0gvpLA3+LBbjeDrrN+b5tTnzqE1WUjE8/hcFk5/sx+XvnGNQ6f3kVuI8nGQhRd07HazBQzZZw+J9VSjUZdweq0cPSpvdx8b5qBfT3kMkVESeT0Zw6jaiq1Qh2z3cRv/vOXiW6mcHvs6JqGbDFx8OFRqvUGw4f6uXNlEVQViyjgtNl44Nl9SLJERlF4e3aVE/v6aG/z0t4foVysks+WKKeK2Oxmbr03Q7g3wpGn9nHtzDRr81H6xjuJriTYXo7jDjg5/sx+zn7nBvVynZ21JM/9L4/wpV/6Ed76Lx9gspgYOTyAJ+zhyhuTlAoVHG4bJpNMuDtIsN1P/JWbVMt1XB4H3oiHTKKAoEOkw4euaWiahqoJ7GznaOkJYg+6ifQE6RqMfHTO9I510ru7k2q5SilbIrGRRjbL+Fu87KwkmLu2yOiBx3C4rKiKyvytVewOC4VUkd3Hh7DfnwLm9Nr44JtXcXkdJHay3JjawtwRwmUxkamrRN+8jaZpSJJEqVzDZJZp1Brcu75EpDtENpGna6SVarmBJIlomo7JJGFv8TCwu4P3vnYRVdXQNQ2TxYQoCpitZrr2dFGqagiT6zSqDcz3s180TWdgootMsUYumScVzaHrcPIzR4mtxlEbKprFjK+/FVdnCF3TKZZqVApV2ntDlItVrA4LSqKEzWHh9tkZZFGge6SNuWtLNOoK3aMdZJN5dE2jXtUo58uoqkagzUukO4DVbaNcquG2mjn36iQWq4nTnznMvod342vx4PQ4ECURh8tKLlVE1zXcfhfVaoOr79zB3+ZleXqTVDSLN+hiZXaT/rFOQu1+auU6Lr8DgO3lOCaLCV/Iw/JCHE3VeP/lqzz9Ew9SzJSQZQnBbsbvtVPKltBUnba+ILHVOO99/Qo2pwW334nZauKFrzzF1mKMXKZIqM1Po95A0XS+8E8+yeKNZVbvbmJzWsmlCowd6GH/sQHOfO0ShUypOVbPauaJHztBJpFH13QAirkysY0kTk8nkizR1hf+G18bDQaDwWAw/PAwiih/RdtLsT8roADoMPn+NH17uuka7fhY9plMJlFVlUgk8j3bI5EI0Wj0Y9mnwWAwGAwGg+GHWzZZYPJcMxi9UVdA02jtCdKoN8cwubx2ZJNEIVUg1O5HUTSK+QqaDg1Fo70vTCaewxd2UylWyWfKOH0OUtEsu44PkawoJFfXycRzCAhIJonFu9scOjXC0UfHuHtzFUXROPnoLs6/co1Im5/FqQ2UhkK1WKNrVzsbczuUSzUKuTJHntrL9TN3sDstRDoDDO7v5fqZaRZvLBPq8DOwt4erFxaItPuJr6WoZIqoioYgQKlYpVxq5lqMnRgi0hNCUTUmz83hDzqplhv863/4u3iCbkLtPsLtPsJtXlp6glTLNdxuG8mtNJIs4fLZEWSZxbkY1Xqdhz51gGKqgKpoqKrKzTN3eLw7yP7RLt65cI+XVq/yD588QC5VRG2omGUJwW7BZJHxBJyUsiWS0RyzVxaxOW1YnRae/uIDbMzvYLGbufzuDOlYHlEU0HWd+ck14tsZDj17gPR2lrGjA/zH/9cfUcyWkc0ymqJSyJTYfXyI8eND3PhwDl0QiW1lSNzZwOqwUirVEAR45IWjFOsaoixic1oREKiU62TSpe85V+xOC0///VO8943LTJ2dYXBfL6IsotQVRg8PMn9zmXQsS2wtQSlTItIXpl5V0DSd9oEW4ts5oJkzojQ0PCEXmiRRzlfYypYpF2u0dviRaw10XUepK6TiBYZ3tbG1GL3fSVOnUW/wqZ96mJf+3VuYLCZkk4TVYWH34T6y8RyqqtGoNyjlKmiajtkiY/fYcXWH+fDMNEceH6dRaVDMN3NNAm1eUtkyO1eWOXGsl4uvT6EoKgcfGWNxKc7Oepq2nhCpaBZV09h/uBePWeD8Ny/jcNs48MAIwTYfl87cY3Sig3yygD/kwmSV2f/IGOv3tsgm8niDbsqFCjaHBUESef5nH0Myy8hmmVQ0iyRL2HwOXviFp9i4t03/RCeZbIX8tRV2HR+mkCmRTeSxu6zYHFb2PTLG5IUFOgYjDE90M31xEVEQURWVlu4QsY00Bx4YYer8PRCgUmp2srj9Dhqqjstjo7W7+btezVfo393B1sw6qc00tVIV5/0iqjfkYnMxSqPewKzIuP0OLr56E5vTiiYIqIrOG394gf0PjeKqNZia2SS+nUFFIJspUyvX2ffQLuavL1Er13F67JhtFmSTyN2rS+w6OkBqJ/vReaZpOgaDwWAwGAx/E4wiyl9RLpH/swLKn9Ihl8x/7PsW/nxQI9yfRS38JZ9tMBgMBoPBYDD831cp1VAaKgCqqiFoKhKQzTY7MiqSgCSLeMMeAERR+ChYXGloqIqGJ+xGEAS6d7VRKVRR6goun4O+A3188O4M+VQRi80MCMhmicHd7Zx7dwa320aoww8IxNYSrM5F8YU89O/tZvXOBrVqHa2ucOr5w9y+NE+lUCPSH+HwU/uw2EzsOTnMhdduUciUEASBSHeYzt1dvPXqJK2dQZxWifkbK5x4dh9X35qidn88mdVh4fDjE/z2v3gJARGnz87Bp/Yxc2URX8hNfDtLKV9hc2GHob09yCaJSIeP2StLHH50jFqlGaydyVdxhdz4ymXe+6/nKeXKAAjAwy8cY+7iPB1jnTxxcJCVeIaZeIbhQ31cfm0Sq92M1lCbgeayTKjFSzbZLFRlkgXqaYWNxRhTF+cJdwTQFJVIh59KuYZaVxElEQGwO22spDbIJAscemKCK29MoirNcUz9ezqpVurcODtLqN3PB69M4nBZGN7bjarpLM1u0dEf4dq5BQYmOqnWVURRIBxxo9YVSqkid64s4Qu72FrYIb6eQlNUdpYTHHxiL6/+9vtkE83Cjtlion0ggsVh5aEfOcbMhTmK6SJdox2MnxpFvT8iCiCfLrHv9C5W56Ioywmiy3G6RttZLyfY2Uhx7Hg/K3e3AJAlEZPVRHt/hJOfPEC13MDmsuLwWPix/+czLM9sUM5XCXcFcLqsRFfjaIpKpVijUqphtZvJpYoc6AqwuZlGlCQuvn8Xq9VE33gHmVKNjRur5FJFLHYLrk/vY+/p3dSqDURRZM++bg4/MMytC/O0dPjZe6iHa6/eYGthp5mzkygQXUnwD37tcyDoKA0Fs0Vm/MQwuVSBYraEzWFjz8lRZq8tUc5X2HV4gP6xTs68fIVasUatWqd/optbFxaw2EyEe8M88OlDvP/qLRrlOqqqcfzhUfae3oXFIqPcL3BuriSpVWt0D7Vy9e3bmM0SqxtJWIdQhx9JEimWqjz02cNsLcVJbmXon+jG4rAiySIet435q0u4fHb2HOlncXab3SdGuPXWJOt3N7E5rIyfHKZ9oIXpi3N0j7RTLlRIbWexOaxoqsb87Q1A58iTe0htp+kZbuH1//weFoeFtv4whbRCfDXOhT8pceqzxxBFgVCbj1KhQilXIbGdwWyWPjo3LDYTwVbvx7fgGQwGg8FgMPw5RhHlr8gTcjff/fz5QooAnqD7Y9tnMBhEkqTv6zqJx+Pf151iMBgMBoPBYDD8dWgGlJuoVxvomo7SUDl4rI8zr03RqCsIgoAginhDbkRR4NjpUdbmdj76el2HakWhmCuj1BVOPbOX4X1d6JrOrSvLVEv1Zi7Kni7W5rYp55sjmKxmmY2lOLGNNADDvX5kWeLejRXcPjsPfvYIxXyFnqEWLp2ZJrGVaYbHx3IszW5hNst4eyM0zGZe+MrTaKrK+ddvk0jk2b2/lytn73HgSC+J7QyqpnH8kweoV+qIoki9plAu1zj41H5sDgvx7RyLszusTK4yeqSfjaUY1VKV9r4wc7dWmTg5zL6Tw/QOt5LYSmMyS+QyDeamNzn4iXHadrVzon6Q5TtrhDr8tHQFufzmFJVCBdFmYTVdpFZXECYcjD69l97xbhZvreIPu/GG3VicNi6/N0t0LcWJJ/eSS+aJriWa4eRz26zNR6mWm5kR3pALp8+OIApE11PENlJ0DrWRTxWYvrTA0af3YbKYkGSR1FaGpal1ZIsZ3Szxws89yu0PZlm+s4HL5+TJFw6Dxczbf3KL1p4AZpeN1hYPF9+cIp8p0drhY2c5zubcNsmtNEqtTqg9wPjRARZvr1IpVBCE5k1g9VqD5HaGYq7M5mKUsQdHaeuLUC3XuPbuDD27Oxg91Mfd68vUqw08kVaqk+t0eG088swElaqCWmtQrasogsjB07u5d20Jq8VEPlEg1Blg4c4GLT1h9FKVarHOt37zHRweG1a7mfROCqWh0T/eidVhIR1v3vymqTpmq4nhg/1c/+ol1IaKySwjWUxkkkXiG2kinQG6BiK0hZzc+2CWYiyP3etgZnKNwd3tLF5doJIpc+ixMaqZAtuLUTqH2th9dIBKsYokS80CxVgnTo+Vo0/v5frbdyjlyzi9dpweO3fO3+PUZ4+gazrdI+380a9/l1q1QTlbppivsHxng8e/8ADX3p+llCqQ3UnjNomsbSZo7Qvjdpi5dmmBG+/PIorQPdzK0L4eDj0yRj5VYPKDu7QPtHLi6b3ENlOYLCaOPbGHtbktXvmt93jmp08zfLifb//2WYYnurj25m3KhQo6UMqXufDKDXY/MMorr0/yk//b4wzt7yWxkSLQ4WPy7AyVYg1d1+keaUUXBKYvLpDYzhLbSFHMllic2uDwI7tRVQ2by0q91qCYKZOO5nB67FjtFvLpIjaXFeF+IaVSrH004g+a59GRx/fgC31877kNBoPBYDAY/jyjiPJX1NYfYe/pse/LRGnt//iKGWazmQMHDvDOO+/w3HPPfbT9nXfe4VOf+tTHtl+DwWAwGAwGww8vX8jNxIkhrp2ZASC6nqa9L8RzXzjO1maGVLKIL+xmeKKLXXu7sVpllu5uM3aol+lrK3/2RLpOz0grjYZKo66QSxcoFauIooAsi5RyZYJtPmqVBm2dfj54bQrE5l1L5WINd8hNtVzH7LWxvZpE0CEdzdIz0srG3A6CIOINOMmVaoiySOdwC6vrKfYdG2D+5hKBjiCZZJFARwCLVUapK1RrCt6gi2KmzI2zdykXqmiaTvdwK52FKjubzfFMuqozdqSfzFYKu9PCscf30GgoON02WnvDJLcznH/lBul4ntROFpNF5sf+8bNkCnViM1v4fHYkUWBwopvtxSiL6RK7DvWxsRRDkkWqVYXodhqPz0ki4iOxmWXw2DAetxWvz0FD1fD4nZTyVZaW4lSqdfafHmNhdpvW/hZiWxn8YTdmiwwCHHlsnPhWGlEUcXnsTF2Y48Qz+9DuB42bzDJWhwXZLDN6qA/JLBLqCPCtf/cWSqM5ok3TNM595wanPnuY46dHaAm78LZ6OPudW2SSBdxuG3abianz91i8vYbJbCLc5mVzKYov5GJrKU64O0i92qBeqWN32UAQ2Li3g6IDgshrv/M+7qCbVCzPvevL9I138MgLx7j6zjSqoiKL8ObvncUbdpNJlhg7OczycpJiokD/7jbGDvdjtUgodZXl6XVEBO5eXeLRzx3lw29do1quUS3XAJAkkeFDfVw/M83xTx7k3rUlNuZ38IbcTDwwgmySae8NkclW0EwSGjrFukqwL4QkQm+7h9d/9yztHT40VaNcqHHqR45SrSukYjkq+QoLN1c48MhuXvjKU2wsRHnnj85jspjwBFx0jrTSu7ePD16/zcEjvZQKFZSGSjZRQLaYaOkOUciUCLR4WbqzQSaex+VzUMw3C1GapjN3fRl/yM3QeBcffO0i8XgBEbBaZH7/l7/Jkaf3IckimqqxenebfKbEj/2jZ3nz9z8kGy+QieUBgUCrF0GAa+ic/uwRZEFAV1VURae9P4LWUKgUq837BXWdSFcQySRx79oiQ2Md7GykcHjsvPXVc/SOdrD/4TGWp9bxt3rJxvPUK3VOffogVrcdSRYRZZF6pcHVMzMceWw3oiThcJtw+5rZKQgCoc4AuWwJt7eZzeL02OkZbUOWJVw+B56Ai8GJLnpG2/4GVz+DwWAwGAw/7Iwiyl+RyWzixHOH6dvTTS6ZxxN009of+dhC5f/UL/zCL/CFL3yBgwcPcuzYMX7rt36L9fV1fuZnfuZj3a/BYDAYDAaD4YfXvlMjmMwyk+fnKBeqZBJ5Dj08xtNfOIGmNe8QtzstiGIzb8HttaM2VB777EGyySKapmG1m9lYTiAIArHtDP1jXVTvbbO6nMQdcJGJ52nUFcLtXgrpMl6/g1y2jCiICKLA+laWvQ+OMnNpnkDYQ61co2dXO5vrGU48c4CLr91EtpiQaio2p5Vdx0eQ7RZ8HjvvnZvn0JM2Bvd0YhKhYyjC0mCE5bkYDzx/mLtXF9lZjoMAo4f66NrVQbWikE0VyCWLgM7m/A6+sBtJltD1OqVsGVEQSG2lufj6JP6IB7ffgdvvQJQkZq+vkEgWKdYb7D05SHQtwZ1z9xBFgUZdQZREvvDPnyOWrxDdXkRTNWZvr/H4UxPMLtzk+ndv0DvUgi/oYuRAL4+9eJR0ssB2LI/TaUGvNrh1aYFIp5/PfeUpZi7OUynVCEQ8FNNFIp0BBElicy1N355udtZSHHtmP3evLFFrqMR2sphtFjprGsV0BpfHwc5aAl0DTdWQLTKHHx1j7dYq0a0086ZmNkvX7k7QIbWdpb3Tx9ZSDHRo1BqoqoZsllFVlZFDfdSrzW3rc9uYTDKJzRR2j426opLezuD0OKjXVewuKyazzOZ8lEatwfDBXkqZIncuzFHOVyikiwxMdHPvygKf/KmHeeur50kubHLw9C5uf3AXURJYvdfsfpp4cJRatUE6lv2ec7iZg6Lg9DnZWIiiITB0oI96rYE37Ea0SOw7Ncw3XrpCI6dQrzYQAEEUOHywl8mzs7i9NsyySK2hotTqVNIFhg70ohUrzW6QjgCCrrFweZF3X7qEJImI5Tr5dBGTRaZjqI1wu5/F6U18LV4EUUAUm+d3o9ZgZXqLjqE2SvkK9bqCruuIsoh+f9RZLlWkc7SdRq1BqVABwGI1oasauVSRtdkt3EEXpWzpfmGyQi5VZGBfDyuzm5TyFdB1ktsZBAF6d3eQz5aIDLSSz5exOMw8/WMnWJleZ+76MoIo4HTbsbmsxHay+IIu9j0wwtpynGqxyhM//RgmQSfS7sfpc/D1//NVRKmZm5PYStMx1NYMrq80UOoqDpeV9YUYJz99gNsf3AUB6jUFh9vGIz96nNuXFj8aBQjgCTh54vMn6Nv98WSOGgwGg8FgMPz3GEWUvwYms+ljC5H/y7z44oukUil++Zd/mZ2dHcbGxnj99dfp7u7+Gz0Og8FgMBgMBsMPD6vdwsGHdzNyoI9qqYrdZcPusv6FnyvJEice2cVb37rJzlYWSRJBgGS8gGyS6OgJsLOSwO6ycvyRMba2zuOPeNheidNIFZEliXu3Vtl7tJ+L782i6xomk8zmSpKWDh8PvniM4V1tlApVVu7uMHV1hbHDvXzxf3+ebLKA1WHG2+pndSnO7M1V2tu8PPjZwwTCHr72G6+T3MoQaPFw6Im9pFo9vPqtm3T0Bjn0if1YrSZmbq1TU1RMms7YwV62V5O0dgaY+nCGUq7CF//5p7nw+m2e+sJJzn37Oq29QVp7Q4Q7/Ny9ttQck/T0BN/+7Q9o6Q5hMclU8mWiO1la+yMUUwUURSPQ6uXerRUUvwtV0ZBMEk6HhfhijJW72zhcVnxhNzaHhcvvz1LTwdcVJK02WNsqcmqsk0OnhvGH3bz6u2fRlWZeyfyNZbxBF/17urH53Fw/exelofLgJ/cyerAfq89FOV+hXlMolmpcv7BAsMXHsrhJIOIhuZPDZJHxRdzUilWmLszRPdKGzWmlXq5x/a3bHP/kQRKbKWRZQjbJzcISOgggyxJ2p5W7VxdZv7uFbJY5+vQ+GnUVpa4Q6gjQUDSUap1CvoIoS5gtMhabCZNFJpMoYPfYWbq5TL3WAEFAVXUSW2n8YTfz15bQ6w1kk8TKnQ3iG0mCbT5ESUDXYeHmCqc+fYj2gRYWbq1issgM7e1BVVTUhsqDnzpAsCPAxe/eoJivEmrzceP9WVxtXlr2dNHXE2JmegNRFNBUHTQdi0kikynS0x1kez5KLlVg/yNjTH04y92rCzg8du5OreFp8XL4kTG2luPomo6qayAJyCaZfLrI6tQaD3zqAKt3d1icXEUUBRyuZnbI1kqCT/7UaVKbKawOK06PnWy6hC/oIh3LIQjQ3h9G01R0TcXtsaPWC1jNIiaThGyWycSzBLtCZOM5ygUFURTJJgvMTG1y4rkjXHrlOoVMEXQdQRIZ3NtDulAnsZ1BajQ484fncbmt9Iy08+Bzh5i7uUI2U2ZjZotIux+zw8Z7b0wR284SCrtJp4q43DZ8EQ9v/+E5fC1e7C4ryWiOUqbM1Ll7HP/UQbaW4wTbvPjCHuxOK1a7iad+4jTby3FMZglJlrl3Y5UX/9fHSWxnSe1kcXntdA23Eu4I/A2vdgaDwWAwGAx/xiii/C325S9/mS9/+cs/6MMwGAwGg8FgMPyQcXpsOD22/+7nBSMePvljR9lcSbC5mkQQoKMnhC/gZOXuFqVsCUXRMMsSz3/xAc6+NYUv7EGpK6gauH0Opi4t8Pgn97F0L8racoxGXcNmt+Bv9bO8lmLtXgyby0pkIML0nU0+eP8eAFabiU/++Ek2NzM4vHaS2xlMqs4f/tq3MVvMaJpOKprj7rUlRo8OEoq4yWVK3Lq2isUq89CT41gEnfe/cRm9odI1GGForA2LSUCSJTYXY4wfH0QUBYJtHsKdAa6/c4epD+9hsppAgPlbazhcNkQRfC4bsg7b22msVjM9/S3UqnWy2TKJW2vsfnQcydQMzt6zp5uFG80slFDEjdZQSccLCILArfNzDJ8ScWvQYjOxMb2OVq2zcHWRPUcHmL2+TD5VRNV0LE4r18/d49hTe1EVFUGEm+fmGTs2SCxWwOO1kYkXiG6msdisKA2FekPD7Xcim2USW1k6B1qYuTDXfM29dtKxPC6fnUKqSDFTpF6uI5skatUGJouJcrGCJIr0j3fy9lfPMbSvl0h3iHQ0x/nv3OCpLz1IsMOPxe8kIIjImsbc5Dq6Xkc2OT8qWphtZnQddE0j0uFnZzWJomrUqw0KmRLt/RHsbhttfSEWb65SLdXIp4oEIl4i3QGC7QHW5rbpGmzB6bZjssi89fsfUMqVMZll4msJDj05QT5dZHs5TrXSwB12s3R1mXGThMUhcer0KBtrKTKZElarTNdwC7nlNnKxHIqi4PDacbpsFLMlfC1eKnWVfKGKSpZ8PIcgi3gCTtBBVVQ0VSMTy1HrD7N8ex3ZYqJ3tJXVmS3yqQIgEOkO0tYb4g//5Xd47MdPMbCnm6mLcwiyhCfoQlM0WnrCCLJAIOzhndkL6DrkRYFcIk9bTxBvxEd0K43S0NC0ZqHO4rBgc9qYubnKsWf38/YfnsPmtPDQZw7hbvVx47XbhL1Wzn77Gla7GUkSSO6k2VzYpmdPD7O3b+B0WalVG3Tu6uDa5WVURSOTLuFwWamU60Q3MwiiQGw9hSRLOL0O6tU6NocVm8NCpDOAdH9kXzFXYu3uFiefPUB0NYmqqPTu7mBgoouW7hCtPeGPcwkzGAwGg8Fg+L/EKKIYDAaDwWAwGAyGj43LbWN0oovRia7v2R5p97Hv5BCqomNzmFEUDbfLzO3rq6w4LeSSeYb3dLC9sMPVNyaZODlCZ6cPQRQpFioEIm7uTq4TjWWx5y3EdrLoOpjMMoIoYLGYyKdKbC3HcHsdePZ2kVqNYbGakU0yPUMtqKpOtVrnzuUFnnrxOKlkkXKximSSEDWVV37vA1RFpbXDz62zs9z64C5HP7Gfq+9O86l/8DDLd7e5+NYdJEkg3BlgeyWBqqiY9GZXhmSWKRcquANOlm6v0Td8BKvVjNJQKBQqlAtVCsUK7X0RypU6f5qcPTzaxq2NFFZZJLoSR5IkKtU6dreNzuFWtGyJm+/cZmsx2ixMdYfY//Buvvvb7/PQjxzl6ltT+CMe4tEcsiyh61Cr1LE6LXhDbkq5ElqxzO0bCwgI7DkxTCxeIJepUK2r2L0OUtsZwm1efEEXdocFf6uXdKwZ/p2OZpHNEqqqkY7nmb2xytCeLuZvrxGIeAi0ecknC/gjXnRNx+62YbFbqJZrJLfTdO/qJLuTY+72Oq0dXp7+0oOgaWwsRlmf22HkUB9Ov4u1u1sM7u9l9vIikQ5/M4zcacVkkjBbTazcWaecLxPpChBbS6CqKi29IVRF48NvXWPPqRFyyQKlfIW+3R2YzSZsbT58YQ+lYo3v/tYZTnzqEKtz0WbAvN1MIVdm7sYKgw/tIp0pcGBPO6IOFqeFRKbE0PFhbnz3BuHO4P2Cj8qRp/aRSeaZndnCYjfTqDYwW030jXVx9+oysgiVUjOTxeG20TPawfTFOTpHOgi0+Rk7NkQ2UUDVdLpH21i9t41slohvpTG77Tzz04+QS+QJtHho7Q0Ti+YQNI30Vpr2gRa2l2Jomk692kDXdPY+OMprf3AOX8iFzWmlc6gFWRJpbXHhsMsEOgJ8+f/4UXKpIvVGg1uXF7GaRO5eXcTusuL02NBUnbWFOKGIm/7dHQiyiDvgIluocfn8AvWagsVmQlFUsukSXo+NWKxAoM3P9nIcTdUxmSXMFhOaptOoqyS3M0S6Ajz8/CFWptZwe+wk1hOMHe4FQSCfKrKzGEXXdYQ/N87LYDAYDAaD4QfNKKIYDAaDwWAwGAyGHwiL1fzRv0VRYHt2E6emcvxoLxaLidhakseeP8TFN6eaWQ7FGuhw/MlxBAna+kNsrCZQG0pzbJKigiBgd1jo7AmxthzDZJJxWmVe/+2zDE10kksWMZllFEVpjs9y2hCkCuvLCVZWkyS20rS2+1jPl6hVGwCkE3mUukq9rlCv1NF1OPut6wzt66aQLpJJ5Onsi9A51MLGfJRysUaw1Usg7KZarlMr16mW6ixNbbBrops715aoVRt4Ii50QWfocD/vf3AXn8/BqQdGsZtNdA+0sHl3i3q1gWzWKZdqlIs1nvjccd759hVquTIAAgLpeJ7MTpbe0XZqhQq7DvczdW2Jek1h1/5e0vECDreNSHeQ/tFWzr18ldW5bWwuG6V8hQvfuc7pF4+haGk211IcOjVMW1eATDyHzW3j5HOHmLm8ADRzUvLpEu0DEVw+J5Issjq3w97jgzz64lF2VhL4wm7UcgVv2E02WaBarmFzWPCF3QiCwNLMJtlkkXwix8rUGja7mT3HB/H4HTz/c09QUTRCrR52VuJoiDz0I0d5+4/OoSkaFquJB188xsVXbzT301Dp3t3B1lIU2SQTbPdz+Y1JukbbKdYU4gsxTKJI9PVJTjx/kOnzc2wsxvCF3eh6M4ujWqrij3gYmuhmQFUREBjsDTOXLfHG739ArdLMdOnoC/PsTz/CvscmqGeKRNr9pDZSXHlzklCHn8c/c5jF26tszO3gctvZmI9y6lMHuPTaTQQERFnkgU8dJLGTQRRFWnsC1MoNzn/nOt6Qm4OP7aGtJ4SmaAwf6MVht2CymqlVG4hmGavTxtpClMROjo4uH3euLjF+dJDhfb3EN1N4Q25sTiueoAtfxIuqquw+2Ec6muEb/5/XqFXq9I514HLbcZgESoUyoc4QN66u4XJZECUBt89BMpqjXms0s41UjYW5KDs7RQ6NdvDKt8+CpiNb5PvZRwqKomKymkglChx/ZJy7VxZRFY3EZppQu5/jz+6nocIDn9xP10CYzYUoFruZnpE2Nue2v2dN2PvQLkRR/JtZgAwGg8FgMBj+BxlFFIPBYDAYDAaDwfADpWkaUxfmkEwyV9+9zvKdDVp6Qjz03CEquSKHHxyirmiYzCYsNjPf+g/vgCDwU//yRXYWY6wtx2nv9JNKFJoh4DoMjndy5eI8oYADvVhhazXO0L4uJFkkHcvR1hcmm8gjSSLdYx2sryapFKs4XDaqxSp6rowoCoiS2Ay5bv5B1zQ8QTeZeB5F0VAUlXK+QqlQoZAt07OrnWq5hj/splyocOpTB1hfjBFs97O+FOf4k+OMHRkgnypQQadjVwelap0Xu4L4PQ7e+9olch0BRHQiXUG2lmLUGyroMLSvG0VRkQWRqta8W99mNVHOV7h7fZldRwfJxLIoqk6oxUfXcCsjB/s4883rhDsDCALoqkqjVkdpqKiKiiSJlPIVbrx7h2B/K/4Wb/Pn4HOwsZFh++oKQ3s6sXscbK+mcLis+CNeDj22h4ai8fmfe4zoVhpJEukb78QZdOOwy1hkieU7GwjomMwyO2tJsskCL3zlad762kUsVjONcg2Hy0q1XOfG2Xs8/w8foV6tY5ElLr96HZvLjiQ7kWwWfuKXXmD93hblQpVqrcGJTx7k3T8+T6VYY2Vmg89+5RMUsmV0XadjpJ2qolEtVCkVqjid1ub4sUId0STjb/Fgd1gQgFK+Qmonx6lPH2D5+hJbqwk8QRersxsM7ulGFEQaDRVJFIhvpFi9scKNqU0O7OnkO//pDGaTRDFbIhPPszG3zaEn95JN5JmfWgdBxBt28+l/8CjVcpVAm5/lO+tcfvUWslmic7iNi6/d5LHPn6ShqOysJrj+3ixuvwOXx8bCzWXa+iJMX11k4uQI3/2P76CqGq29YTwOE7KqMn1hjmK+QtdQK7GNJG6fE4vDQqgryMhEJ3/y/3uLXLKAzWVDqassT20wemiAreUYuqbT3hfE6jCzvhTnwPFhrr091cygAXRdx+awoGg6awsxRvZ2YTbLVCt1QEAQBWqVOiaTxMbCDrJJJtwb5rP/25Ms39nEZJIY2NfD8t0tJEni2JMT5FMF9hwfonesk+2lKDtLUVRFA6BjqJXRI4M/qGXIYDAYDAaD4S9lFFEMBoPBYDAYDAbDD9TOWpKl6Q0uvXaL1E6WvvFOfH4nL/36d3H7HQTafHQOtZLcyaBq4I942FlNMn9xjsHeIC0RN8uzW7SFnbT3R+gaagFJ5LEnx7nw7WtsLcYQBIG5W6sceXIvZ1++0sz/qDYYG+vA1hZk7uICka4A+VSRZCLP8eMDJN6aJBzwYreasMgi5UKVtv4Ic3e2QIByrtIMUHfZUBUVXQdRFPEE3fTv72Py6gqhNh+94x2sz0UZP7iXYrrAjTOz6JpG90gb5nKDxUuLWF0W7mxl8fiddA9EiK7GGT3Sx8iBPnKFCpqms7meolapYzGJmIMutIaCWlew2cz4Ix6SsSwjh/px+51U81WmL81z671p9p8cZGcjS7lUwxtwgqZhtpioVeqoitrMNKnUmxfI6yoDuzt4++tXKGRLyCaZudsbtHb5efCFo9idVroGI1x57Rbzk2vYXTbGjg8S3UghW8ykYllaWtwkVhP4Ix7unJ/D5rTS2hOkrS9MbDON2WImlyyQTxVx+hy094XYfbCP+Eqcexfn2HVsiJbOIJtLMVA1WvrCVEs1cqkiiqKR3M6gqTqPfP4BBAF6dncSaPWwMLVOsVAlna9iMstUSjUEUaBeVwh1+FFUlXSmREvYTSlXZuRwP7lMiUhPkPhagsR6CpNZxua2Ieo6xUyBZ144ytZGku2NFOVMidvn7tE22oHVLFHOl2lYTLgCLmx2M9VSFV3T2f/YBOe/ewurVWb28jx/7598kjd//0NyiVvkYnlkWSTU7qdWqWN3WqmUa6zc22Z7OYGqaiAIWCwyn/nyY/z+L32Dg4+Ms7m4Q6NSo1FTKCYL1Mo1vGEXTq+D2StLJDZSRNeSfP4fPcuHL19hJ1ZA1lVyqeJH56fNbUVRNBJbKQI9EWQBdhaj7D3Sz/Zygo7RDjYXo+TSZXRdwxt0cfjpfSzMxwHYWI7RPdjCwswmZquJbLKAJInYbSaKmRJmk8zKnXV6http6W1h5soS73/7Bu6Ak0/91GmOPrEH+X7uD0BLd5D2gRbS0Sw2h4XWvghWu+UHtQwZDAaDwWAw/KWMIorBYDAYDAaDwWD4gUrtZIitpyjmKnQMttDWFeC9r11CEJvjlirFKh+8fIWnf+I0Z799jUOPT5DayeJv9fJff/01fC1u2vsjWGxmtqfXufD1izz/s48T3cqwMruFw2VF0zRyySLb6ykOPr6HzsEWCrkSssXEyk4eUWwGxkuyhCAK+Nt9HD29m9vvz5BpKOi6zlNfeoiaolIpVmnUGrgDDmauFpBkkdnryxx/ai+ioOPwubh8boFGXWV1McbkxQV+5KcfIhvLkornCXb4cXlseAJOpi/Ok43l6PS0cvzR3Zz5rxfZnF6nXKjQOdRG394epm+skkkXOfX4GI1KlWwshySJ2Kwm8pXmqLDukXYadQVP0MU7f3AOEXAFXCzdXufetRWe+elHEM0mWrr9qA2V9t4AO+tpVEVDlAT693Rj99g5MNZOvdqgUq5jspg+eo121tOk4gX2nxzk3T86z8KtVTRVJxvLsbUY5dmffhhVEukdauHqqzfYWY7hC7k59sx+NFWjfTDC1kqCa2em6R/rpHi/a0RpKIwfHuC9ly7gC3tQGwq5VIFwZ4DhgwOszm7g9juoV+qktzO09IU5952bzewZs4wv4uH5n32MC6/dItDuJ1dXkWWJQrpEIORErTUoZssMPzBM50g76Xgen9fOocf3kIoXqCsaj714hA+/foVwu4+GLFLTdfYe7OPM1y6yMrVObDtL7+4O9hwZZHl2m77BViy6SrDdT6PaIJ8p0TfW0ewCsVuIdAY5/tQE1968jdkkEV1JMHKgj3qpSiaew+62Y3HauPzKdUaODPDhK7do6wujNFSk+3kvSl3hzsUF+vd0E2j3ceuP71IpVQGBUIcPURIxW8w0qgonntmPIAqUi1Vmri7hCTp4/NQoic0ULq8dVdVQNZ1auU64zUujrjI/s83Gwg6f/7n/P3v3HSbXYRb6/3vOmd7rzvZetE29rbpkFXfHdmLHzjV2CE5CQkLqj5sAeW4CCTcQgi8EcIBAgCS202zHTZYsyerSSqu22l1t73V67+f8/lhb95oA9yY4GMj5PI+e1c5557SZM8/svPO+735aOkuYGKknEkmREbVsf89mADLZAv1XZ9HqREwWA/0XJ3nwI3tYnAuTTuVAAafbglwoIooCqzfWceSpM2y5cw0j16bZ//BW6joqkWWZzq7GtyRQAARBoLTGS2mN99/x1UalUqlUKpXqZ6cmUVQqlUqlUqlUKtU7ShREIv4YiVgavUnP0nQQo8VAOpEmGU1hsRvR6jWEl6IU8wXMNgMCkEnnqWktY3p4geBchEKhiN6ow+62kkpmSafzlNd5WZoJvVExYkApyvR3j+Lw2ug+1EtoIcqOd2+k+o7VXLs8idNno7rWjUkSGJkNsn5fJ3JRxuayMN43RW1HDVaHCZPVwNJ0CI1WQi7KZNN5CoUiW+9YzWDvHBqdhmg4SSaZZe3OFkJzIY4/10OxKGO26HGV2HGVWElGEmh0GiKBBD2vHULSSET9ceo6KhntncLmtnDL3WvJ5/LEg3FG++dYuaeD5GKExakADStraFpfTzSQYOjSGFVNpYTnI3grXaQiKUoqXSAK3Lg0itbrYGh0kfV3rKX3eB9Wix53iQ2Xz07XgZVE/FHigRglVR4a2isYH5h9c9Y9Wr2WyvoSlEKRdDL7xlyZIgigM2gZuTzBqgOrCM2FQJbRajVMDc4zNTiPIArYXRbqV1XjLnOQy+axe6xo9RrKaj0MXBxFb9IjCGB1mkklMsyOLrJiYyOCKJLPFjBYDFS3lnP8RxfJprI3EzCb1q5k6NIEdo+VykYf8b4Zbn1oC7lUlhPPdhMLxGnb1MTU2CLHfnKRWx7ZgaQROfPKVdbv7WCkb5ryOi86s45EOIVG1LBqcxMnnu0mk8xi99pQFIXR3ilKK90ceN9WrlwYp7GhBLkogyiw/e41LE0HuXDoGk6fg1Qqw+77N9F1YBVHf3CGqcE5ul+7TlNnFflsnpYyF1M3ZomHk2h0WjKpLHqTnmJRRm/W46vxkkplmZsK0LSqerkyxagjk8pS1VxGsVDk+A/PY7QY8JS7WJoJ0XXHGiS9lunBeVwlVnoOX2XLHWtpWl3N4nSITCqHRiORiKVwV3mYvjhBPleg+9QQ0cUIdr2E22UGQeBy9zhanQar1UAqlkIpyngqXVBiIRFNsXFbM+FggnAwQT6dxeWyUF5hZ6h7BKvTTDEvk4ynOfjdU2y/ax317ZXY3dZ39DVGpVKpVCqV6t9CTaKoVCqVSqVSqVSqd5TerMdkM5FN50nG0thsDkRJpJAvotFp0Og06Aw65KJCaV0JYX8UvUFLaCFCSbUHs93EQPcouexy0mTVznamxxYZG1xg894OtALEoym0Gonx/mn+23+/mxs9E9R1VOGr8mA06li9qoJynxWDxYhULPLq3x1lbmQRl9eGAsyOLlBa48XutvDQp25jon+OsD+Gu9SB1WlEo9dR0VTKjavTTIwFCC/FMFkMrNpYR2m5k6M/OEcqnkFnWJ7rEg3Gmbwxy/a71jB2dZKKRh/X4xlcpXYqGn0YzDoaV1Yxdm0Ci8vCyLUpAvNhtt61DoNFj82ix1FiZ3pwjvG+Ga6dvEEuk6empRyDSY/dbSUWTbGwGCWbzqM3aOms8NDXO0MuV+SW9+0gF0mQTmQBgXQsxdjVSUavTmL3WDE7rey8czXD1+cQJQGzdTmRlYqlkfNFHB4roiSi0UhEQgkmBmbZePcGCvkiADa3hdBidHkOi6yg1Wsoygr1rRUYrAZefeocSlGmot5HcC6Ew23F5jKTTecQRRFZlsll8pQ3lNC0tpZCvogM5PMFNDoNxXyR2rYK9AYdr33vNHse7OL7f3yNeDyDIomUlTu49X3bKMgKx1/oYez6NCAw3T9DeVMZKAqJSIrq+hJe+fZJVm1t4uSlC/iq3egFAUEQcJc5MVmNmK0GLDYjhWweq9UAyQwnX+yhqrmM6Ruz5LJ5ek8PYXWY0GhEsoksh79zivf993vYsH8VF18fQKORWJwOQlFmZmQBk81ASZUbSRJxlzlIxlLsvm8DmWSWkD9GTYMPnUmHqMgMX5umfWsLl4/1UdNWwaUjfctJl4KMyWqgkC9y5fV+3v2J23G6zTz7jUMgCIxdm+K+3ziAfXSJ8evTGMx6Nt65lqs9EySjKXzVHoLzEWKlNrpfuEhJrZftd6zn+uVJktEU+UyesloviXCS6Rtz3Pf4LirqvCzMhFi9roqeYwNE/TJT18a5/FIQg1lPdXMZLp8NURRRigpVjT5WblHnnKhUKpVKpfrPTU2iqFQqlUqlUqlUqndUNJSkvrOKvu4RIktxOjc1cKN7DLPdhN1lIbQQQavXUFLjxuq2MDU0T0mlE7koMzO6RD5bYPMdaynk8izOhDn09Fne+8lbaV5dy/zoAsPDCxiMWryVbrbcu4mfPH0eWQGH28rK/StpaKtg+PIEDqeJvmO9yEUZAYH1+1Zy5sVLpOJpBAS0eg0737OZNrcVvV5icWwRh8+O1mJgcXqJnjOjiJIAWg2CRqK6wUt4MUZ0MYrOoEXSiBQLRQr5Itn08nD38FIck9VANpUll8lRLMjojFqigQTFvEwuU0AQBXRGHVaHmWPfP8vm21Zz8tkLVDSU0LSmlvI6LyIgSSKVjaWMX5++uS2DJGJxmaluq2BpPkIunSedyTM0FWb1ykpGXrhIXUs5p5+7QN+5YQQB4qE4rjIXOr2GTbeuIjAXQavTMjmySEWVk1wmRywQp1iU0Rm0JGJp2ruamRpdRKvXgyih0yt4yh2EFmMoRZmWDQ0gCHjK7UwMLrJ6azMAde0VmC06pofmmJsIIEoCRqMeg1mPw2ul++BVjn//HHUdVWw4sJJd92+k52g/ZruRhs4qLh3po7q1gr5zI/hnQ4haDTXNpcwMztN/dpgd967HatSxfnc7Pa/3szQVpHFl9XKiqcSGXJS59bHt5FJZbnnvFgZ7xhAUcLqsmOwmIoE4VbUlFApFZobmOfrMGaZuzNO5fQXTo0vc9aG9nH72AuUNPor5ItFgHK1u+bFOxTP4F8MohSJyoUgylqak3IUkCWi1GhCgvqOSWCJLaYWTi4evEY9llmelmPWs2tZCe1cj108N4i13sGFfJ1anGQC9UUdZrZdIMMHiVJBsJs/FY30ko2l2vHsT/WeHyWfzLEwGGB3107CxmWw6x5WeSRRFoHpFBYVcgemRBWQFuu5Yw/TwIhpBpqzCwcBSjFg4ydJsmKrmUu7/0G4kReZvv/B9BEHg3b+xnxvnh1mYDCDLyvLsmXSe8voSKupLaOysRKORKK/1otWpHzuoVCqVSqX6z019N6NSqVQqlUqlUqneMYV8kb7Lk8gFmfd87FYmB2YJLUS49zcO0Hf6BuHFGK0bG1mzp4N8QUYRonTdvhqDWU8um8fiNHPsR91cOXmDYkGmmC+y4971KIrA0KUxLr9+A4fXSlWDl0wRvvunh6jrqCKdzrEwH2XyBxexOwdYu6WByEQQQacl44+xelc7PUd6yaayiKIIKEhaiakbc5TW+bjwWi/5TJ7a1XWcOdyHzWUmmciSjKXYcEsH02N+3F4bUzfm8JbYKBYUdAYtmWSWbDqHRqdBq9OgN+tYnFjCYNLjrnBhMOmIBRPIRQX/TJB1ezvxzwQRUYj6Y+TSefwzIcprvcwML1BS4aaQyXHx0DVESUSSRHzVbkZ7p5mbCiK/kehYs7sdu1bLzFQIk9VALpPn0vlRtt6xlrFrEwxeGEFn0KLRSMhFBY0kYLLqOfg3R0klc0g6DRv3d1Ja42bnfRuZH1sElmfWzE8F6dzRxk++exZJEtlxWyfBySVSsTQ1K8ro6GrBXekkGojjLndz4oWr5LN5TFYj6Wye1V2NXDzSSyaRxWI3kQgnWXdLBxF/HFEQWL2rjZ4jvVw/M0h7VzNrdrYwNxHAPxuivqMSb5Wbl/7uOFqdBpvTzMi1KSSNRC6bJ53MMnhpnLaNjVTU+2hZW0upz47nXesQBInZqSC93eN4yhys2liHxqClcXUtV08OMNo7hdFsQBQFlqaDbHvXevrOj1HIF7h+6gbr9q1ifGCOdCpDYC5883wIokA2m0cBsok8xYKM2WpEFAUK+QIt6+qpXV1H++4YMrDjnvW8/K2jFN8473qHCZ1ew9TgHGaHCXelB41eS217BSXlLiYHZshmCmQzeaaHF1AU0Ok0oMDFY/3Ut1UgaiQWhuYx2Uy4q7xcONpHIpJCo9WgM2pJxtJ4y51o9Vp81W6e+crz3PexA0xPhlhaiNDUWYnJYsBiM5BPZvB4zJz88QUsNhMmi56B86PUtJTT0FnNeP8MGo2GurYKZkcWSG9qRKfXUlLpwlftfsdeW1QqlUqlUqneLmoSRaVSqVQqlUqlUr1jFBRQFOLRFIPRFBabkYa1Lsw2A3Vr6/AEEkRDCV77wXmQFVZtX8G2d63n5HM9RIMJzrx0ie13riWdyr7R3shIZYMXq9NMIZ2lqbMSrU5DZUsFr714FZPViKSRiMUyZFI5dAYt81NBws0l6EQRo8VIeZULo8WwvIOCQDFfwGDSU9lYin8uhCLLKPnlOSnXL4yRz+TIZZdnegAosoyv0kU8kiS8FGPFqmoGLo7jKrFhc8kkIkncpXYEUaCywcfI5QnCwSS73tPF/PgS104PUVrloqKpFEmnYbB7FKfXhkYjodFKFHPLH9SX1nhZmglS1VxKIV+gsb0WjV5DeWMpDatqGeubJpstYDAbCMxHqG4q5dY7VyEZdUzPRYmFEmxYV0vEH0eQlme7CDoN6Xia8sZSTv74Ip4KJ5FAHF+Nh/OvXMXltdJ7sp94OEUymqKysZS7P3gL4+N+CvkCiiLx8lPnqFtRzp0fOYDdYSIWShIIJJiL5ih7Y76JIApEQgmqWsp46e9Psv7AauRcAblYxOIwEw3EmZvw465wcuyZMxSLMgDTg3O4Ey7ufP8uTj5/geFLE3gqXNS1VpBKZpFEEXfp8tyVbDqHXJSRiwrDl8fZ89AW6tqqOPH8RYKLMSKhBOXVHjbtXMHxg9cIB2I0t5ezNBdi3d6VdB+8svxcCcXp2NJMPlvAYjMwO7JAXFagWMRk1rFuTwcv//0JUBQEAfLZAmabEWepg2Q8TUVDCal4Bk+ZA61Rh9Zt4+ArvVjNeqprXGhFEbNVj1KwEoskMRh0pBNp5EKRfCbPwMVRtHotQ5cnuOdDexEkiUwyQSKWQavXgqLQdeca/EsxKhpLSaVy7Lh7PRWNpcRDcXyldoavLbco0xl1pOJp0sksVocZi81IdCHMpjtWMzK8SN+FMfLZAr1nRxFFAa1Ow5571hBairFiQwOKLDM/4UeWoefIdWrbKqmsKyEWTtL96lWMViP5bB53qYPd929Cb9S9My8sKpVKpVKpVG8j8Z3egf8KMpk8kxMBBm/MMTkRIJPJ/0K3VygU+J3f+R3q6uowGo3U19fzpS99CVmWb8YoisL/+B//g/LycoxGI7t27aKvr+8t68lms3zsYx/D4/FgNpu5++67mZmZeUtMOBzmkUcewW63Y7fbeeSRR4hEIm+JmZqa4q677sJsNuPxePj4xz9OLpd7S0xvby87d+7EaDRSUVHBl770JZQ3p1S+4fjx46xbtw6DwUB9fT1PPvnkTx37j370I9ra2tDr9bS1tfHss8/+PKdQpVKpVCqVSvUfhFarobGj8ubviVia+ekQI/1zOMtcuMqc2JwWmlfXct9H9/Hu39jHiR9fXJ4vgcJdv7aH3jNDnHvlKolIEo1W5NAz57n0+gD+mTB6g4Z0MkNRENFqJZKxFPF4mog/Tj6bRxQFdHoNFq1E3/Hr/OSvj3Di2YsMXhzFU+HEV+2hsrmU8gYfS7NhkpEUJquRbC6Ps8xJyB8nncySiqWwOc0IgsDU8AINjV6cHhuSJDE/FaShs4pELEUhX6C8wYev2s2ue9ZhthjYed8GyppK8YdSBOJ5tr57Mx27O3FVeQguxQABvWl5joosy1S1lJHPFnD6bCSiKRxeGy0bGqhfWcO5ly7zzB+/yGtPnWbw8gSechc6k46Z/hkO/t3r9LxyhdM/OEe504jDaSKXySHni5TVl6DRaijki5TWlTDRP4tGryEZz5CKZ4gFEmg0Igf/7jjZbJGlmRAmm5H5qQCnf3KR6hoP++9dx6pN9ey4fRUtnZVcPTHAX/7W9xA0IiaXhaZGH8GJRUqr3ARmQmQTGbQ6iUwyy/mD17h84gaRUIrhK5NMjyyiFIuM906TyxQwmPUYTHoSsTRhf5zzr1whtBCleW0dtSvKyWZyZJIZlmaCLE4GUIoKzhI7ngonq3esYMuda1m/p4Of/M0x/PMR5IKMBhi7NsnV169z5/3rMFNEKBQREJgcmqd5czM7H+hi0x1rSSWyXDlxg1QsTTadRynKLIwvceP8CDJwy4Nd+KrcmO0mqleUcdv7d/HCd8/QsLae9q0tuMtdVK6ooGJVLQPX5/A6DDTUuvBPBJgemiObzLE45ScRThKYDRGYDbM4FaC02kMhX0RRFMJLMSL+OIVCka471uIqddDR1cTOd29iajzAaO8MkWCSho5KCrkC2WyBtq4W3B4zTe0VGMx60okMGr0Gk9VIIpbiPY/vYqJ3Br3NRCySppAtoCjKzX+yLHPy5WsIksSlo730nRumrqOKmhXlmO0mktEUM6NLRPxxXD4HHZub6Nzawrs/up/SGs8786KiUqlUKpVK9TZTK1H+DRRFYXR4kbNnhgn44zdv93itdG1poqHJh/Dm19HeRl/96ld58skn+fu//3va29u5ePEi73//+7Hb7fzmb/4mAH/4h3/I17/+db797W/T3NzM7//+77Nv3z4GBwexWq0AfOITn+CFF17g6aefxu128+lPf5o777yTnp4eJEkC4OGHH2ZmZoaDBw8C8MEPfpBHHnmEF154AYBiscgdd9yB1+vl1KlTBINBHn30URRF4c/+7M8AiMVi7Nu3j927d3PhwgWGhoZ47LHHMJvNfPrTnwZgfHyc22+/nccff5zvfOc7nD59mo985CN4vV7uv/9+AM6ePcuDDz7I7/3e73Hvvffy7LPP8sADD3Dq1Ck2bdr0tp9nlUqlUqlUKtW/j+bOKgYuT5KKZ/73jQrMz4S59YFNlFU6EUQBs83ExMAsS7MhADKpHLlsgfKmMna9ezN950c48ZPLy7NAzHpsbguzo0vUtlciCJBJZrE6LcQiaVAUFFkhly3QuaaGE89dwGDQoNFqSKVyLE0H6dzSwvy4n0wyS7Eok8vkKKvzkU1nKWTzmCwG9G+06FIU0GglNu/vJB5KcvbVa+y4cw3NnRWEFqKUVDi45b4N6A1alEKewEyI8GyQYsaCfz6MrdTDmddv4PBYuXB+nGQiw7pN9RQRcJQ7yeeKgEDrhgZmJ/34A1FSmSxltSXE4xm8VR5e/ccTCKKAViehFGUWp4IMXx6nprkMvUmHXbCQTmVBEjn7Qg8PffYuYksxWjY2YrAauHj4GktTQXR6DYgSnnIni1NB9EYd+VweSSMSDWQw24yAwsJkgPK6EkLzEVAUdBIYBZmFoeUvZlU2lbL1nrU4vTaO/dUROjc2MnFtGqvLvFx9FEqSS2YpZAtkkllKqlyMXZtEkRXK6ktw+hyMXpmkUCggCAL5fBGL00AxV6BQlKlsKmVxws/EwBzr961kanAeq9NM1B/j0rF+3v2JA2TTBeYmAoz0TpFKZOjY2MD8TICrJwYp5Iv4Kl2MXp3EW+ZgaWKJmeEFtt61lrA/TjiQZH4yiEEnMTOySD6bR+u1odVJy1UpTjOXj/fTddda+i5MsPOBLnQGLalEjsWZEG3r6pkaXcJV5qRoNLIQyrBldS1XDl1jLpXl2FQAm8vCrY/tpGltLUOXxjHZDCiigCCC3WVBa9BgshlBgfbNjYxenaD/7AiechdWj40CcOTHPdg8luWYdbUc+/55KhtKiIUSjFybYt97u1i9roa6ljKCgTixUBKH08yqrgYiCxFsHit9PZM4vDYUWP4bVlAQBIFiQSaRTaPV68hnCsSCSeZHF/HVelm3u53QUhRJIyFJIjaXhdt+ZTsNHVX/zq8gKpVKpVKpVL9YahLl32B0eJGXX7xys7T8TQF/nJdfvMJtd66iqbnsbd/u2bNnueeee7jjjjsAqK2t5amnnuLixYvAcnLniSee4Ld/+7e57777APj7v/97fD4f3/ve9/jQhz5ENBrlW9/6Fv/4j//I3r17AfjOd75DVVUVr732GgcOHGBgYICDBw9y7ty5m0mKv/7rv6arq4vBwUFaWlo4dOgQ/f39TE9PU15eDsAf//Ef89hjj/HlL38Zm83Gd7/7XTKZDN/+9rfR6/V0dHQwNDTE17/+dT71qU8hCAJPPvkk1dXVPPHEEwC0trZy8eJFvva1r91MojzxxBPs27ePz33ucwB87nOf4/jx4zzxxBM89dRTb/t5VqlUKpVKpVL9+/CWObjrfVu4em6E0f45isUivgoXq7c00dBW/sZMkmXZ9FsrnnPZPAsTAURBoP/C2Bsf/BaZ6J9lz70biIUTyIUiBr0GQRBwlNiYm40gSAKiJIICOp2AAKTfSJZIWon58QALk0HuevwWul+9SiKc5Lb37yIRSRELJbE6LfQcvsaKLSu4em6Uzk31OBwm+s4PI4kSW/a0YTBoWbOlidefvcDl4zeQbpGwmTScf+kS8XCSQn452bP7vVvRaZY/NA+GkmQzBUwWPaePDLB2SyMr11azOLnE9vs2cOKFi0wNLwAQT2V577s38Q9/+SrbtrYiSiKZVA6b00LxjZZi6/d0MHhxlEKuQNPqWvR2I5dODJLP5MmEEixNB6htr8BT4WLbuzYgIKA1aElE0zz7l4coFmQkjYTJZiQZz9Cyto4bF0aQNBKCICBKAmW1bmbHFrFaDPT0zZCKp0EQyKSyHHh0F9fPDJFKZNHpNVw7OUCxqLD59jUgimQzeVZtbyEwHyayFMdkNRBeiiFJIpUNpcTXxYmFEoiiiMVhQNJrEBFo6KjiH7/yHA/9f/fwgz97FY1OYusdaxAkkarmUm55bxfJSJLesyOYbQYkjcTs6BJXTwxyy3u7uHSkH1EUkIsyhVwBRZYJL8XwVLrJpvOYzDoW52Pkc3matrWQCMdJRtMkoimMFgPr9zURjyTxVrkJLcaYGZnHU2pj4MIYc1MhioUioiiy76EupsYCjA3Os357C4e/c5JsIoOoyMhFmYg/xtkXL7Jp3yp2P7iZ8WtTpNM5Orc2ozfomB5eIJfJ09BRRcemRg5/5yR60/IcnaYNTcTCSfY9vIVMJk91vZdjPziH3qglGV+eeRKcj/D8Xx3hjkd3YDTqSM8FWRpeYCyaQsxkGb8xy6bb1tJ3bZaELkVptYf5CT8gIGpECtkCdo+VYrGIqFm+Dsf7ZvBWuVi3u42SGg/hxRg6o5bKBh++arX6RKVSqVQq1X89ahLl55TJ5Dl7ZvinEihvKhZlzp8Zoarag8GgfVu3vW3bNp588kmGhoZobm7m6tWrnDp16mYCYnx8nIWFBfbv33/zPnq9np07d3LmzBk+9KEP0dPTQz6ff0tMeXk5HR0dnDlzhgMHDnD27Fnsdvtbqjw2b96M3W7nzJkztLS0cPbsWTo6Om4mUAAOHDhANpulp6eH3bt3c/bsWXbu3Iler39LzOc+9zkmJiaoq6vj7Nmzb9mXN2O+9a1vkc/n0Wq1nD17lk9+8pM/FfPmcatUKpVKpVKp/vPyVbrYd/8GNu5OUizK2BwmtLqf/nPFWWJH0ogUC8vvw40mHXqzjmw6iySJy5UWb3SNvXJ6kPV7V7JiTQ2KAi1ra/EvRNFIAjmEmzMsREHE5jCxOBNCb9QhAslYGr1By/DVScKBBAaznuvnRrE5TTi8VlyldmpbK2jZ1IjDZ2dpfJFTJwYQxOXZE0szAXbft4HJ/mna1tWydlcrHp+Do989QTQQBwEEUSCfKzB4cZS1ezvYureNqcFZUMDstnKpe5yrF8bZemsH3//2UXQWPZ0raymr96HRa8gXZZayWe79wB6y/jhmhwlftQeD2YBGK1HTWs7RZ06TjmfI54tcOHIdo83A6h1tDJ4fYfjKBFdPDeIodXD1xACtGxqZGF6gosaDfzaEs8ROYDaM2WpAo9ciSBK+Gg8T16eIhxJkUznMthQbmsppWlfHt7/8HKGlGNnM8qyZhtW1XD8zSDSYoKzcgbPEhkavIRdNc/LZblrW1rB6Vwc6k46mVVUMXZogk8yyZucKmtbUE/FHaVxbRzQYJ5vOkcrkSSRz7LpzNQtTQZrW1HLpeD+CJLBx30pef/YCGo1E122rePqPXiCdXG5Ftjwjpprx3ikyqSyDPWPUrCjDPxdBEgUMZj0Ojw2NdnnmzED3CO/6yD5OPN/DtdNDRPxx7v/1W5ALBUKLMURBYOjKBAsTfoxmPQiwcmsLZ16+gs1lQVFkJElEZ9By6fV+Ora3UsgVqW/2cebH59AZtFTXumlcXYuiKIiSREVjKQf//nUqG0tpWV/P/FQAvVFH84ZGylsq8FW6+P7XXiAVS1PfXkmxUKRxZTUvP9NN+PochXwROV9gaS5CTVMp6XiK2bElsukcgiCQjGd49R9P0baxAQWFxlXVTA7NMzu8SGprioaOSq6dG6G6uYyKhhLyuQKKrKDVajBa9ESXYpRUuin4litwttyxjrL6EnT6t/dvXZVKpVKpVKr/iNQkys9pcT7ylhZe/xy/P87ifISaOu/buu3f+q3fIhqNsmLFCiRJolgs8uUvf5mHHnoIgIWF5W+m+Xy+t9zP5/MxOTl5M0an0+F0On8q5s37LywsUFJS8lPbLykpeUvMP92O0+lEp9O9Jaa2tvantvPmsrq6un92PT6fj0KhQCAQoKys7F+MeXM7KpVKpVKpVKr/3ARBwOG2/Ksx3gona3e2ceHIdQBymTylVW6MZj1j12eQZS2FQhGtVoPTa2P0yiTbbl/NmZeuUFfrIhZKIDnNpFM5CvkitdUuVu9cQTIQuTmcHkVBq9fQtK6O4EKUpdkQckHGXWrn7sd388yfvEhwNkIhV6BQKFLXXsnU1TR2jxVFUbDZTRTzBU4/38MtD22h58h1mtfWYtRryeUKbyRQRJSCjCIrRP1RSmtK+OFfHGZhNgyAwajlvo8eIBnLEAvEWLmqlisXRzlzvI9ivoAsQ9vqWjw5hReePc+2nSuwV3rwTwaQ/QnWbGtmvG8GUZLwVLpZmPCjlQQKqRz5VBa9SUshX0Rn0LE0F6VxczOj40FykgaN00qty0JtexXFYpFivkhRAXuJnXw6y873bKb74BUK+SI77tvI9MgSks1MJJgAwGwzsnZHG90Hr6LIRRxeO7FQguB8hH3v28aLf3UEOVegcW09izNBSms8DHSP4vLZ0eu1jF2bJpfO076lmevnRui6ex2JSApZVnD47MxPB9FoJdzlLroPX2fLHWs4/+pVsukc625fzcnnLmC2Gskks4iSyGDPOGarEb1Rh8GsJxFJUdbgI/1G+7i6tkp6Tw1gNOnQaEVy2QL5fIFMpsDWu9eRyxQ49MNuVm1qpP/sIBqtxOJUEJ1Ow6pd7Vw4foPmlTUUCzJWp5lCQUar15BKZFmYDtGSylLfVk5elkEUcJbYqG2r5NiPuinkigCEl6I8+Nm7ufZ6H3Oji1jcNnQ2C91H+qlvLef8oeuU1XnpPzdCMV/EV+Oh+8QgiWiSfGa51ZrdZUGn0zA1vEBVfQlyIYIiKwgiN2ec9HeP0nXbKvq7R7C6rWRSWS68eo0Nd6xhbsJPNpXD6jIhCAJyoUgqkcFo0pEKxzHbTQjAtnetp6a14hf0KqBSqVQqlUr1H4+aRPk5ZbL/b8Pj/1/jfhbPPPMM3/nOd/je975He3s7V65c4ROf+ATl5eU8+uijN+P+6TwWRVH+rzNa/mnMPxf/dsS8OVT+7Yj5RcydUalUKpVKpVL9xyQIAuv3duAqtTPYM04qkWX/ezcTiyQZvjJJYC6Mq8SG1WEiHoyx5Y419F8Yo+f1PlZubaGjoxyz24Zk1GNwmBif8PM33zzK+nV11Egi2UgSOZOnurkUjV5Hf/cYJZUuBKBlXT3+uTA6vZ7yeh+uUhurdrZSLMgkoyn0WpF0PM38aAyzw4SnwonLZ6djawurdrQQmo+SSWXx1XgJzoWRC8stnVZub2X02gS5bB4UECUBm83EiWdO0ba5he+91M2K1XV0rqphatJPNJFmxbp62je18Pd/dQwBOHr4Ojt3rMBkMdB3ehhHqQOHx0IkmGT4ygROn53ATAitXgOKwprdHZx84RKb7lhDX/88wniAWChBJpVjaT5KNJzkgffvYOzyOOM35mlfX0f38RukYhl8VU4q2mupay0jFojSsauN868PoDdq8c9FsNhNxIJxUok0ZpsRq9OMIsukYilymQJr93aSz+QwWY0MdI9y5fV+Iv4YKFBS5aZz+wpGrk6xZlc7i5NLjPZOMTO2hFavpaGtEneVh/IaN3ZFQaOVlhNCBXl5Lo1GIpvKoTdoMZh1pFM5nF4rA90jrNu7ksBchPrOKhxeK6G5MB1dTUQXI7x+dgiz3QiyzJqdbUwPLzJ0ZZLR69NIGg2iJHD2UC+b93dS3VSKfyaIKImce32A8FIcnV6Do8TGzEQAq8MEgoCsKDhcZspqPAQSWS6cHaV1fT01TSVk41m6DqxCkWW0Jh3D16Y5/qML7Ht4C93HBrhxfZbQYhRREhm6MsFdj+3A6W7AW+HCV+WhvLmMF797hkwySzQYRxQF5qeCGK1G8qEEuVyBwpvVWlYDclFGEAVSsQxWj4XOLc0YbQYGzo8iSgKXDl+ldU0Dpw9fJ5vJUd1Uuty+bXUNtTVuZm7MYDDpWbmzlfau5nf0NUClUqlUKpXq35uaRPk5GQy6tzXuZ/HZz36W//7f/zvvfe97Aejs7GRycpI/+IM/4NFHH6W0tBRYrvIoK/vfM1mWlpZuVnKUlpaSy+UIh8NvqUZZWlpiy5YtN2MWFxd/avt+v/8t6zl//vxblofDYfL5/Fti/mm1yNLSEsD/NUaj0eB2u//VmH9anaJSqVQqlUql+q9Np9eyYl09K9bV37xtZmie2x/ZujxM/eoUKDKdt63GU+bEPx9GUeDy8QHcpXbaN5sx2w0cevUa2VwBg0HH0PAiHq+FhvYqHE4LU5dGuXZyCEeJDRQwWA20bqjn2A/Oo9Vr8JQ7ad3YSF/PODVNpegMWuZGFsikchRyBfL5IulkFkVRaNlQTzycQlZg+7s28txfHsJd4UKr1aAz6WjbsoKX/uYIqUQOURKQJBERheBsFKPNQCKZ5fyJAdwlVu791d0MTC2hc5k5+foAhfxyJYMoCpw4NcSa1dW8/wv3Mnp5nPHeSVLxLKu2r2BxJkQ+L+NwmzHZTbz0DyfxVbsZHfUzPx2irNZDYD6KIstYHGaMFj3PP3WONetqaFlZTfdrfUiSiN6oIxpKEQsmmZ3ws/2OVfR2jzF4fY7N+1Zx6B9O4PRYWZgMIIoipTVe5seXyKZypJMZBOCOX9uDr8bDwLkRdHotclHGbDWSzeQJL8VQijIWp5ngXIhCXmZ23M8b369ifipAbWsFR5/r4faHuqhoKiUVz1AoFLG5LGh0GkSNSD5XxF3mIDMdIpvKIUoizhIbVqeJtbvaiCxFKavx0HtqkOoVZbzrYwfIpnIIAtStrubqiSFEUaRYlFGUAqIsIkkiF17rIxbPsuVAJ8N906zfvgJFUdDpJFrX15HNFuk+2ofNaSZfkBEFgaKi4Ct3EI9nKCuvwioqPPPtE8sVT7rlwfE779/E4JVJRq7P0HtuBIfHRlldCWabEa1WolAosrQQpX51HYlYhpOvXicWzdC6rh6NVuTc4ev0XRxnxx2ruHFhlGJued6O3qBlzc5Wrp8bxlPhIufKEQ/EySSzWB0mPvpHD/MP//N5EuEUyWiG2+5fj8VlQWvQYXOZaWiroLrRRzycQG/SYXfb3oErXqVSqVQqleqdpSZRfk6+Ujser/Vfbenl9VopLbO/7dtOpVJvGa4JIEkSsrz8TaO6ujpKS0s5fPgwa9asASCXy3H8+HG++tWvArBu3Tq0Wi2HDx/mgQceAGB+fp7r16/zh3/4hwB0dXURjUbp7u5m48aNAJw/f55oNHoz0dLV1cWXv/xl5ufnbyZsDh06hF6vZ926dTdjPv/5z5PL5dDpdDdjysvLb7b56urq4oUXXnjLMR06dIj169ej1Wpvxhw+fPgtc1EOHTp0c19UKpVKpVKpVL+8Smo95I70komlaFtXiyIrRPwxSqrcmO1mJGn5/fPSTAjT8CILV2eZHprD4rSQjKYAhfnRBSLBBPl8gX23rSYRTjEzvICr1M7aXW1o9Bq23LWW+XE/o30zjP3DSbRaiXgwTtOqGqZvzGF1WdDbjSQyeZo2NjDgj1KhlVjoncEgCTSvq+NXv/QAo1cnMZgNFGWFdCaHu8yB0WEmGUtjMunIpbLYHD5sLgsIAiATi6S43jPG9VCUjrZq3C4ze3a0LLdz0muZmAlR4rHx4reOEZgN07q6mnQsw8VD19h2zwaC82E0Oi2pZBYUhfpVNQyNBXD57OSzBaoaS5CLMg1tFaRTeXpOD6IgkElm0Rm0ZNM5wtNBNBoJnUFLJpmlrNZLWaOP1NF+entn2PGeLgw6iUI6h6TTEFyMkkxkMZn1CKKAu9zJ0mQAvVHD5WN9BBfCFAsy2Ux+eZ3pHIszIcx2E7lsnmJRZsOeDgwmHflcHr1Bj91tQRRFfvyt49z3+G6igTh2j5V4OEkum8do1iNIIuFAgoaOKgLzYSobS0EUuOP9u+g+1ItOp2F6cA6H10o2mWVg3E8mlSM4HyGRyLFiXR0DlyZAUdDoJLQ6LaXVLqobS8lm8mQTGcwOM9de72d2dJF0KkdpuYNsMkvXvk6Grk5S1Gu49aEtXDw5yMJ0CIvTTHt7BcefOoXFYSKfLyLLCnJB5vLR63hqSxA1y+3X4vEMcraAqJGYXohSKMhs2t/BwR9exD8bpry+hLmJAJNDC9hdZnbetYYzB69x4fUbbN7XQW2Tj/mxBWZHFrly4gbZTA6TucDGAytJhBJcPTHAFQXqOqt49PP3cuXkAMgQmPKTDMVYu7udzbevxWQ1AGC0GN6pS1ulUqlUKpXqHacmUX5OBoOWri1NvPzilX92uLwkiWze0oT+FzBo76677uLLX/4y1dXVtLe3c/nyZb7+9a/zq7/6q8Byi4NPfOITfOUrX6GpqYmmpia+8pWvYDKZePjhhwGw2+184AMf4NOf/jRutxuXy8VnPvMZOjs72bt3LwCtra3ceuutPP7443zzm98E4IMf/CB33nknLS0tAOzfv5+2tjYeeeQR/uiP/ohQKMRnPvMZHn/8cWy25W8pPfzww3zxi1/kscce4/Of/zzDw8N85Stf4Qtf+MLNVlwf/vCH+cY3vsGnPvUpHn/8cc6ePcu3vvUtnnrqqZvH/Zu/+Zvs2LGDr371q9xzzz08//zzvPbaa5w6deptP8cqlUqlUqlUqv9cdDotux7YQv/ZQQYvjqEzaNlx/0ZkRaD76HW23b2O0y9eIpvKYXRZiMzOUtFYyuJ0EL1RSyaVw1ftIZ3KYbIauHRxjO0HVrJyazN2txVJLzHRN4dGEgguRpgcmieXzlFe62Vu1M+qLc3c9/EDXL04TiZXoKOjimAqy8JijKGRJQrZPKvrfPzgiZfZfu8mmjY0Ln/YL4r0XZrEVeVl/vQNRFFg5NoCOo3IxtvWcP7ly9x970a6j15HY9BithnR57JUljuZOTXC6VODKIAAdHQ1YTfqSESS1LaUkoqn0Rq06Ew6Fqf93PrIDhw+G3/+/z2NRivRtLYOWZQQgVyhiIyC1ajjxrkhjBYDW7c24fRauT44hygIJKJpNBqJXDaPRiMCCiO903RuaaJYkEnEMlw4P44gwJYdzShAMpZmxYZ6SiqcKEWZFRsaWJwO8uo/nMJT7mTs+jRltV7mxxZRZA2CIOCrdqPRSpisRlZubeHYD84RCyURBFBk2Hr3Wm59uIuX/vE0h3/YTefGeu7/6H5OPX+RcCDObe/fxZEfnMNs0rM0E6S2tYINB1ZSzBdwlNhoWFVDNp7i/MErLM0EKaly461wI4oCnnW1TA/P43Bb2HRLGydeuEJwIcqOu9cwdX2GhD9GMprk4iuX2HbfJpLxNMlEFkkS8S/GsDvN6HQS2961Hm+pg6e+cZhkLE3b2lpKSu0UEhmMRh1RUUSvE1BkBVmWCS5EWLW7nTwioWACSRIxmXT45yPkc0UMVgPhUIq5iQCSRiQVz2C2mYgG48RCSabH/DhLbPhnw8xOBqhu9NLYWc3SVAij1UBZrZf69grCC2EuHOolk1re54HzI+SzBbR6DddODrL5ttU0rqyhfXPTzQSKSqVSqVQq1S87QXlz8MR/UbFYDLvdTjQavfmh/psymQzj4+PU1dVhMPzsbxAVRWFkeIHzZ0bw/x8VKV6vlc1bmmho8v1C5nXE43F+93d/l2effZalpSXKy8t56KGH+MIXvnCz0kNRFL74xS/yzW9+k3A4zKZNm/jzP/9zOjo6bq4nk8nw2c9+lu9973uk02luueUW/uIv/oKqqqqbMaFQiI9//OP85Cc/AeDuu+/mG9/4Bg6H42bM1NQUH/nIRzh69ChGo5GHH36Yr33ta+j1+psxvb29fPSjH6W7uxun08mHP/zhtyRRAI4fP84nP/lJ+vr6KC8v57d+67f48Ic//JZj/+EPf8jv/M7vMDY2RkNDA1/+8pe57777/k3n89/6PFCpVCqVSqVS/ceSz+YRRAGNdrkS4gffOIQgCLhKbAQXI4gOC6eO38DqNJPPFZAkEUEQ0Oo1FPNFAnNh8rkC2zfVk49naF1Xy8nnL5KKp1mYDCCIAptuX8PZV66hN2pxeqxsv3c9w6EE02NL5PMFlvxxTFYjzhIbs6OL6PQavGVOao06Lh28RjFfZMPeTrRagZXbW5EVhWImx9UT/eRzRTwVLnrPDDE3vEDz+nr8gRi5dI47PrKPK8EwpYrE9VevMzvhv3ncoijwvo8c4OzLlwnNh0nF0svV6gq4y53c8+t7KaRz5GUFh8fG0nSQ5/76KMWigsGoZdvtqxkdmGW8bxa9SU82k+eeD+winZc59eJVCvkiLp8VFIhHU8SCCeraK9i4u42engkWZ0LIMqTiaYwmHfc9soVsOMGlo9eJLEYxWQ1E/HF23LeR+bElfHUlnHuph2w6h8Nro5AtYHGYuOWhrQz0jLNhXzvnXr7K+VevoShgsRsxO0zojHpaNzUhGXUYLQb8C1FmJgOUlTuxWvVozHoaGn3Mji5g1Gm4cX6E3hMDiBqRd/3GAbQ2C4szIYqpDMOXx8mlcsTDCTq6mimt9TJ+Yw5HmYvRvlmaV1Zh91iILUbo7x4lm87hKbWjyAqJaJLWrhUMXplElhVAQaORUGSF7e/ZzPDlSQr5AuU1bsauTDByZZJtd61lsHsYjVbD7OgixcLyrBJXiZ3feOJX+Os/epnAXARREnF5rMy98fi+92P7efn73RQyecpq3OiNOkRJYrR/lny2gCiJdO1tZeLGPLc/1EXPK5cIzIVYs6edhckg9e1VLE0HOPOTHkRJIB5KImlEtHotFY2lbLlrLZlEllU7VlDe4KO0xvsOXb0qlUqlUqlU/z7+tbzBP6UmUd6GD88zmTyL8xEy2TwGvZbScscvpAJF9YuhJlFUKpVKpVKp/msbH5jl2I+7SYRTjN+Yp3pdPTeGF1icCaHRahAFMFn0iJLI3Ojy7D6jSc/Dj25htm+aaydvEF6MUtdeSdO6ehLRNFq9lqqmMo4/fwFJEtn/63v5/rMXiYcTRP0xDGYD6WQGg0mPzWEiMB9B0og8+NBWzn73NIVsgU37O0EpUtFYTmgxgsNl4eyLPZhtRiweG6lIkuFLE8iyzM73bmFuIUzP2SHu/PAtXDs2QGDCTyaaIRpOAmA26nnw1/dy5OkzhBejFAtFFAXkQpGObS3k8zIru5qYGJihfXMTixN+goE4vedGMRo0LE342fPQFo7++AJGi4FUIktdk4/bPrCbV54+T11jCTOD8xQKRapaylicjeCrchONJOjY2MiZk0NMDi3PMDTZjJRWuWhv8ND9ymXi4SQ6gxaNVoPNaWbDgZWcevEyrevrWZwIEJgLsWJDPVUt5Tz7l69hshnZcvtqpobmCcyGqWuvJBlZHnxv99qxem2UNpZy8PvnCfoTpJNZKhtKaFhbx8kTg2xdXYmUz3PhJxcp5PLkMnmKBRmH18beX9lJPJUjnSmyalMdN84MojPo8Fa60Oi1pBJZjr1wmcXpIFqdhn0PbubMcxcI++NotCK+ShfJWBqjUcv6fSt57flLy4PtZRlRFKlu8nHfr+/lB395FEFR8JXauHDoGpIk4qty09RZxaUjvdg91uWZNgJsvmMtoVCC1bva+f6TR/HPhnF5rYQWY2za14HNbeHahXFa28qZ6Jsmk8ywYkMDokHHUO8shUKRW9+9AbNZx6VDV+g52oecl9GZdNS2V9LQWYXZZuLpP34RlOW5Qtl0Dp1BS2VzGRv2r1yuQllV845dpyqVSqVSqVT/nn6WJIrazuttYDBoqalTv6mjUqlUKpVKpVL9R1TXWkHJxw6wOB1icnAOfzDJpcsTuErsiJKAXq9hrHcab6ULRVFQFIXm1jKmrk0h54ssTQXZdPsqFERe/vZJtHoNqXiGhtXV3Pb+XUzMR7jcO8P8YhSNRqKsuZxCKks+W6CQLyJqJGxWA40NPpKJDOv3dnD+pat4K930nBzk5GuD6E16NmxvxFvlIR5OcuPcMHqTntW72wjOhckksvRfGiefzZMIxDEYtcRSGfQGDeU1HuRsASUvY7abWb2zleM/7CZfzAPgq/XiKXdx5uWrtG1soGVtHT2HrnL99BCuMgerNjeQjKdZHF8iNB/B5rKQiKapqvMSXowSDyZYv6WRH37jEIqiYLEbsTlNrN3SiKjVUCq7aF1djbvUTi5bwGg14F+MohUELr96BUEQsLksZFNZIktRsqksCxMBNh9Yyckfd+OtclPZXEp1ezXh+TDb716H3qRDZ9BRUV/CinV1vPjXR0lE0xTyBTRaDZvvXIOuys1tj2xjpH+OfL7Ioj/O6ZNDSJKE0aQjH0gjF4vIBRmtToPeJKLRafDPBJAMBq6dHyUcjLNpexM9R/o4/uJl8pk87/rofny1JfhqvIQWImQzeRAERElAEEWymTwarYTDayOXK2K2GAgH4iiAVifSsr6BoeuzGE06ZFlm7NokxUIRQYDF6SCbDqykc1sLCxMBBE2R2o4qLp0aYmZsCaPdwt2PbmN+JozRoGN6dIG+yxNU1nppay/n/MuXMVv0KAr0nxvG6rbiKnXhX4ySzuZRikXmx/0kwik0Ool8rsBQzxiXj/bxyW+8H5fPwcKEH0VWsHsslNZ4sTgtbNjXqSZQVCqVSqVSqf4FahJFpVKpVCqVSqVS/Zdntpmobzdhtpv43v86SFWVm6sXx5E0EiXlDhQFBHG51axWK9HaXsHL3zjI3oe3suuBTVidVl77/nlc5Q40muUPp3UWE9/9s0Nsum010VSWXCaPolOYnw1jsRpwltoRUOja2Mj8jTl6Tw4ydmGMNWvqeO+nb+eF75xmangRQRCIBRNY7KvofvkywdkwcqGIKIkMX5nkvo/sY7BnDF0yR4nNjJKXKanzMt43g91qRC+IiEaFyFKERDRJcDHB/l/ZQWghAkAsmKD78HXyuQL1beW89DfHCM6FKRRkCrkik71T7Lh/Ew6nFYNNz/xMCIfTjCAXqW4qxeG1crZ7hO13rcFd5sTuNnPmhUs8++evUt7gY/XONo48e5HzRwbIZnJo9Vr23LuOuiYf+UyO8FIUh9t6s+1UPJQAFAbPD9O2uZHRa9O0b2oiE09x8tluNtzSwY3uETKpLBVNpcwMFtAbdWTSOQRRoCjLTA7M0bm7k0Ayx7FjNxAkEeGNx06rFSmt8zIbipFOZFDk5eYLgihQ1VRGsSAT88cwmvSkE1mSiTx950fQaCU237qKYqHI0JVJfJUu7F4r0XCa2x/dwezIApFgnMhSjGJ+OVlU3VLK6LUp7HYDMiKd21oYmwii02uxuS1E/TF0Bh0arUQhLyNpYGkmRO+5ETq3NjM9GeTs4esU80UkrUQ8nubwDy7QuLqaseEFCsksoaUY9S1lLIzMQ7HI/NgSoiSiM+gQFIXqtkpcPjunjw1gc1to29HG6LVJ5IKMzqAlnylgthkZ75vijl/dxZFnzmIw6fCUO7E4zKze1UbDyup35sJUqVQqlUql+k9ATaKoVCqVSqVSqVSqXxq+Shf7H9hM97F+AEaHF8ll8pjtRgq5Ir5KF51t5cz1z2D3WMkksoxdn8FgNRGPJIlHktjdFhrW1LK4ECWyGCOfzGC1GFBkhVw2j96gI5st4C5x4tZq6Dl4lfmpIAajDqPZwOzIAsH5CCXlThZnwm+03JLfaB+lw2QzIssykiiiN+roOdZPZXM540OL2LUaGqtLmBya4+53baL/3DCDPWOU1fm47bFdzE4F8FQ6ud49SiqWxj8dfGPmiJX1t3QQD6fo75nAW2rH5bORiqUIzoU492IPklZD4+oaVm5soL97lHQ6x8Y71yMXFepbK7h66gYoCi988zVESaSk0oXJauDFb5+gfXMjNreZpekcmWSWV757hvf8+i10bGkhGUsTnAsjF2XymTwarQYUGLo0wfr9q8gXZMb6ppkZnKNtYwNnXrxELJzAYDbQ3tXMlWPXERDQShJGgw5PpYtENElg2s/kUpLaei/+pThyQSabyqK1GpganKeuoxpftYfgfASdQYvDayMRT9Ne4yEcSjHYP4/D5yDkj6IzaOnc0sxAzzgOn4Odt61kYdKP3WWioa2S6+eGiMxHMJh17L53PQDJVI7vPvEqK7ua0GgkmtfWcL1/gbHBBbQGDe96bxenftJD8/p6ZscWEUQBQRSwOk3k8wUWpkJM9M8BIGlEGjqrGB9cRAAEBeLRNE1tFZhsRsrKnZzvGSGfySNpl+euZNJZFib97Cmzc/bcKIuzYRBFRnN5VmxsYuD8CJJGolgoUlLppqTKi8Gk467H9yxX5hh1VDT4qG6tWH5MVCqVSqVSqVT/LPWdkkqlUqlUKpVKpfql0r6hHleJldLTw6xaU4MiCMyOL2EyainxWBk4P8xo7wx77tvAy393nMrmckTtcqUDQD5bQNJpiYUSKCjkcgWsRh2SJFIsyoiSCAIszUdp7qzi2lQQuSiTSmSorHARnvQj6jNYnGZESSCbzuOucJKMZ5idCaHTa6ioKUEUheVB9zNBmtbWUd9eSUVrJd/7m+Ns29PGy3/3Orsf6EJv0hNcjPL6j7rZ+/BWtAYt82NLjF2dwu6xUlLlpnpFOWt2tbIw7kcUBTLpHN5SO9FAHEEQEEWR8kYfs6OLrN7dSSYnY7QbGbmxgNFmovtIH7FAjMrGUgDkokxwIYKrzIFclBm5OsnqvSuJh1NkUlmKBZmjz/Xw4S/cQ1GWOfb0WYJzIeweG+tv6eD8wcsAjPVOUVbj5eSPzpOKpdGb9CSiqeV9EiAZSeLyOZgemqes1ks0lCAeimO2mahs8HHizBm23NLBwecukYqlEQQBq83I0nSI8wev8p737+b6qRvMDM2hNWrp3N6G1qijoslO5WwUg2m5mkMQBSx2M55yBwtjC8yOLbEwHaKixs2ZZy9w669s58a5EeRikdnhBTbeuppwMIHLZ6f37DD5bJ6hq5OsvKUTt89OJpOn79o0a3a0YLbouffX93PtRB+ZdJ6ha1O877fu4diPLwAgCNC4shp7mYupC+PYnWY0OolULM3A1Um0GpHt+zu4/IqGtChgMOqQZQVBXH4cBFFgbjpMNpVldnSB2rZK1u5bydzIAolIkrbNTex6zya0Oi1Gi4HGVTX4qj3vxKWnUqlUKpVK9Z+SmkRRqVQqlUqlUqlUv3TKarwUijJHvt+N3qBFCcW5cGGMRCRFXXsFW+9YRU1bJSabkZGrE+y4byMj1ik0Og2ZdI5MOgeKgl6vo1BUuHpikC1bmjjXPYpGpyERSWK2GpALMoqiIEoiZouB+FIMX7WbUDBFIpbB7bMT8ifIFmR0puVZF9lMgWy2gKQVifpjWD1WPFUeZqfDxEJJ3D4Hh39ymU17OvHPhrhy4gaKrGA067lxYYT2rmaMRi277tuAp8KFxqAluBDlyLM9VNWX0LKmhny+yMLoIggCWqOWms5qzh/pw+mxEAnFuXphnLK6EiZvzOJ0msikcqSTyy3LBBFQQJJEUvEMsqyQSmTIZ/NodRoEoCjLFAsyS/MxBIOOuz90C0MXx4iFElw4eAW724rdY0UQQKfXkIqnsbutGEw67G4L+WwBRVFYmPBT01bBRP8MmWSWRCiBpNXQuXUFwekgVqOWs0f6WLWmilxBZmY6DApUN/mIzoU4f7gXX6WL1q2tKChYHSamhxaYGffTsKaO9s1NnHr+AmW1JWQzOcprvBz/cTdWh4n61nJigTganYazL12mvauJS0evU8gVCM5HGL42xfoDq3j9uR50Bi2BuTAWq5GF2TCKotC2uhq7y8zI1SkiwTir967C5bVy7dIEaRmqOqopqfMttzhLZpgd9+N0WzAatFTUeGhoT2Iw63E6jCj5Is1r6jj1/EUKynLlCoJAdVMp0TdmxSiKQjadZ2Z0kb23reQDX3oPFqeZ2rZKfNUeBEH4v10WKpVKpVKpVKp/hppEUalUKpVKpVKpVL+Uqup93HL/Bq6dHUZsraS6tYJ4MIHZbkKjlcikshSKMnVtlcRCcdq7mug52odclAEwWo2s393GxfOjRMNJbC4T61bXMjSxhKSREERh+ScCRqMOp0m/nBSxGREF0Bt1xBNZHD47gUCCggI2h4lIII7FZmB+MkBoIUrzunpef+Ey8+NLmG1G9jywiYnBOUxuK3PXJiir9ZJNZUEAURQBgWw6h7vcybULYwxcGENRFBxeG+lklv3v3sTxH55Ha9AiZXOs3tXOYO80hVyBmTE/u99jx1vlJhKIk0lmyWXzxKMpSqo9WF0WdHotIKAoUHwjSdSxqZnZyQD53PLcE5NJR31rBRSLxEMpkoUC2UyObCrLyh2t+KcDODxWKpvLCC5GaVxTy+zIIrFQklQig8GoR5ZlkrEUp567yF2P7yE4F6aiqRRXqQNvtZtkPM2mzQ30XJriyrkRUBQ6NzVSLMoYbEYcbgtzQ/MMX56gdWMj1c0+Tj3fw/z0cms1OZfD5TSy5daVzI37yecKRAMJjCYdsWACpSgjigJarUg8nEDSiGTTeYxmPblMDq1eQi4UkYvL58DuthD0x9AZtAhAba2bH//VUdxeG9FggomBORwuC3c/vouXXrxGU2MJZ48OsH5rI1Iuh5BMUd1STsPKaiQROtfWMDXhp/v4IP0WPT6HgTt+bTej12dIJzKUN5YSDScJ+JeriWRZQaPVoMgKFpuBqioX1Ssq3ni8VCqVSqVSqVQ/LzWJolKpVCqVSqVSqX5pVTWVUtFQQjKWRpJEzr5ylf4LYyiygkavoWVdPeO904z0TlPfUcmB920ll81T215FJJXj4tlhEvE0oigweGWKTV4bezY3M78UxWDW4bIZaWwsITIfJR5MkM8WyOfy6LQSpdVuhq7P4K0tIZvOc+X8OBv3raSQzJAMJ9Abdex690Ymx/zkMnkqa70kIwkmrk7y0OO7CQTjpOJppgfn0Og05DJ5DjyynWKuwKYDK1mci9LfPXrzWHPZPP7ZCNNTIbQuO3ffvZ7B7hGunhkiEUlishgoqfYgSyKaNwbAi5JILlsABPzzUZZmgmy+fQ2nX7xENpXDbDNQVuvFXupgcOAGuXSOXfesJRGIkfSHOffCJbbcs5be4/2s2tXK4pifC69eJZvJEZgLEwnE2XT7GprX1XPs6bMMXRpn5/2b6T11A7PNQMQfp6yuhMBsiJ6jfbhK7fSeGWbrPes5+oPz7H7PJvbd2o6gX8O1SxMIgoS31E42mqJjcyPVDT7Gr0+zensL10/dQC4WcXutACiFIq/8zVG23rue/nMjtG1qpqzWSzqRRRAFUoksnjI74cUMkkZCFEU0GhFRFPCUu7h+bgS704zdY6VYKLJ+XyeDIwF8ZXa23dLOhcNXiQaSJONZKus8REJJYrE0h54+y657N9J9eoiHfn0PR//xBPFQAp1ew9iVcWYG59j/+F5eePoc3koX0VCCoizT3F7BleP9eMvspPNFertHWbOngwvnRhBEAY1WgygJ1DSVsmpTIy6v7R26qlQqlUqlUqn+a1GTKCqVSqVSqVQqleqXmiiKWB1mAFZta7lZATIzvMjaXW14ypycev4iMyOLSBqJ2vYKek/dQLGa0FuM1HdWAwJ6k454LEu0d4ZMLEN9pYPXf3SB1dtXMBjJkE3nMJr1iJJEY2clOq1IY0cVOWDfnStJheKYtVC7vRmTxciP/voo544PosgKXpeJ6aE5tFoNgdkwl472UtdWSSaZI5vOIRdl7vrQLZjtJubH/UhakeBiFINJT6EgY7EZMFgNSBqJRDTDwJUpIuEUdY0ltG1qJB5KUtNWTh6JUDCJJInojDryuQJ9PRNsunUVQz1jTA7OozPq2Xr3OrwVLjQ6DSaHmb/96kvoDTrWbGui93g/iqJQ1VhCOJTk8HdPs/X21YxcmeL6yQHMViOKABa7iUJB5viPumnZ2EjdyhrW7l1uUfauj+5non+GyqZSul+5wqWjfeTSOcKLUawuM1a3hfd/8X5C/jjzEwGSoQSbd7ezMBUiEUnhn/LTe3YEk8XA5ts6cfjsDF2eIJ3IAlBW7WZmaB5JEhEFEZvTzED3MA9+5k4qGkqYHl4ARUGQRFylDqqaShm/PoVclGnd1EgsmqRpdQ2FfBGPz8q6PR0IBh1GpxWbw0R4KczSdIjKei+gkMvksTtMAMyO+pkamMOk1xBbDGM0aYkHZaKBOMVCkeoV5Yz3TmKyGfFPBwGYG1vihckA67e3sGp9HcGlKMlMnuuXp0gmcugMWhQU9AY973pfl5pAUalUKpVKpXobqUkUlUqlUqlUKpVKpXqDp9zJuz64h9nRReLhJDaXmeoVZaAopFNZ5ib89J0fw2jUYlFkWtprOPLSFRLRNJ4KJ4Ki0LamhtV723n6j14ktBjj9R9foOv2NZTWeDAYNIz3T7M0GWRh6gabbl9NQ0cV3/+Tl8ll8sTCSQTxLA9/6nY8XivJSBqNJLI4sQSyggKU1XmZGV2gmMlxy4ObGbk2RfvmZgYvjXHk6bPoDVrW7V+JoihYnGaMViOJRIZUqoDDY8DhtiBKIvlsntNHB6hvLUc0mzhxdAhFgT13r6G80YfTZ+fcwaskY2l6L47xvo/tZ+jyOOlEFq1Bx3DvNNdODlK9opyHProPRQBRUUgshjFbjSzOhJBEkakJPzq9hm13reXSa73ksjHKakvQ6CQGL02gKFDaWMbk6UEEBPa8ZzNavY7r50a4eKSPtTvbSKdyRJZibL59NZ5KD7JGw4tPdxOYC+MosfHg4zsZ7B5lemQRu9eGpJHYc/8GLr7eTy6VQ5Ak1uzuYOTqJALLg+QBvDUe0okMoYUIocUoJ5+9wJ73bGS8f47JgRkkSaK+s5qGVTW0bGgguBBhcnAem8XAbb+6m/HBeTwVHs6/1ofda2Okfw63z876Pa2UVrkZvj5DMV9AAbQ6DWargco6L/UtPuKRJNHZIPPjftKpLDq9lqpGH+FIivBCBLPNwOjVSewuC6W1XlAUFhaiBF7t5cA9a+k+cQNnqQOrx0okEKe20cf+e9ayeVfbO3oNqVQqlUqlUv1XoyZR/o2SmRyT/jATS2HS2TxGvZbaEie1JU5Met07vXsqlUqlUqlUKpXqZ2Sxm2hZW/eW2/QGHUe/f450IksqkcFb4aS5s4pCvsCv/vpuMnmZdCqLVoDuFy+xaNGy74GNiBqJbKZATWs5/pkQ33/iFYqFIpWNPuxuK9Ut5fzkr44wO+5HlmUcbgtajcQz/+sVHvrsXVw4doOKajcaSaSgkbA4jDi8VqZuzJEIxMll86zcsYKRK1M4S+zc9Wu7ARi8PMGKzc1cOT/O3HRweai4IJCZyVG+EKV5ZRVLc2Eqaz1Mjy2xan0tvk21GM16vCVmDv5okFWbGujoaiK0GKWlvZxkLEkqmeXa6WHyuQKVTT40Og2j16aYHpxn533roVAkn84xNhVEq9eQjKTIZfP0nx+h67bVGGwmitk8iALB+Sh6gw5fjYfFqQAr1tYzP7HIa989hSwoNK6sRpQkzh68grfKzaY71+GucJGIJpmbCLCio5wbKNxyzxp+/I1XKa3xYnMYmbw+hd6kY2Z4ga23rcLksvL6oT4qqjwUr04R9sepqnHhKnOwdk8Hw5fGWZoOUl5fgkYrMdA9wvzYEjVtlThL7PSfHcJRYqPvyiQ2pwWbx0rrxgYOPXOOmZFFalZUYLYZkTQi3nInwcUonjIHngoXQ71Ty63ANCJajUQikqKirgS5UCS0EMPpsS8n34B4ME4+myedzFJS7eXq1SlEQSAZT5OIpSjmi0gaifJGH9FgAlIZ9j3YRTiVp6rSRUdnBXaH5R24YlQqlUqlUqn+axPf6R34z2x4LsDfHbnAD8/0cnFkhr7pRS6OzPDDM7387WsXGJ4L/MK2feLECe666y7Ky8sRBIHnnnvup2IGBga4++67sdvtWK1WNm/ezNTU1M3l2WyWj33sY3g8HsxmM3fffTczMzNvWUc4HOaRRx7Bbrdjt9t55JFHiEQib4mZmprirrvuwmw24/F4+PjHP04ul3tLTG9vLzt37sRoNFJRUcGXvvQlFEV5S8zx48dZt24dBoOB+vp6nnzyyZ86ph/96Ee0tbWh1+tpa2vj2Wef/amYv/iLv6Curg6DwcC6des4efLk/+10qlQqlUqlUqlU/6qGjioe/swd3PGrO7n9sZ3UtFcxObhANJjg+T87yPe+8H1+/D+fZ7Zvmr3v3UJgNszcyCKB6QALY/NcO9FHSYWT5jU11K4oJ58tsGJjPRM35lmYWMJk1qM3aNFqJfzzEZLRNDNjfu794G5qWysor/PSddsq1u9uZ/DCKEouTyFXQKPTMnJlkpnheV78m6M89bUXuXi4l037OpElkY7NDRQLCnJRppgvIssKcxNLbNnfQUNbBfFois3bmxk40c/Vo9cZOD3I839+iNvuW4PTZWTzzmYe+vAutt2+itnRJZo6q+nsaqSjq4ma1gp2v3cLt7xvK3c9vod0JIXTZ0fSakgls4iSRDqZRRAESirdDF6eYNOBlUSDCRLRFMloCgTo3NEKKATnQ/SdHiKTzJCMZ7hxaQK5KHP7B/awdv8qDGY9P/zTg5x49iLnX7nK0afPsGZjHdlYktqWMm50D3HyxxfIpbOUVLqpbS5jbjJEz9lRxm7M0987w+rb1rDjgS667t3Emv2rmRmeJ5vKYnOa8U+HKKly07F1BZtuW0NwNsRQzyi3PbaL0loPtz+8hep6L94SG/MjiyxMBrG7LYxcnWRudJHQXJhiJotWKxJZihONpmlaWU2xUESr01DIFyirdrNuTxsXzoxz/JVehgbmMVhNjPbNIel1CILAqq0tFLQaYuEUxaJMIV8ABRTAZDVgsRrIptK4KpwYbUY2bm5kw6YGNYGiUqlUKpVK9QuiVqL8nIbnAvzgzDUKRfmfXR5KpPnBmWu8Z8tKmso9b/v2k8kkq1at4v3vfz/333//Ty0fHR1l27ZtfOADH+CLX/widrudgYEBDAbDzZhPfOITvPDCCzz99NO43W4+/elPc+edd9LT04MkSQA8/PDDzMzMcPDgQQA++MEP8sgjj/DCCy8AUCwWueOOO/B6vZw6dYpgMMijjz6Koij82Z/9GQCxWIx9+/axe/duLly4wNDQEI899hhms5lPf/rTAIyPj3P77bfz+OOP853vfIfTp0/zkY98BK/Xe/P4zp49y4MPPsjv/d7vce+99/Lss8/ywAMPcOrUKTZt2gTAM888wyc+8Qn+4i/+gq1bt/LNb36T2267jf7+fqqrq9/2x0GlUqlUKpVK9cvDbDPR0F7Fy71nyaZytGys5/j3zyNIEuWNpVgcJqwuK69+5yRRfxy5KFNa6+X2X93N0kyQ5755mMaVteiNOmZGF3H6HAz3z+MpczE7toivys3STAhRFDFa9aRTeRZmZzGZ9Gy9e3k4+9LYAvHFKMWizC0f2M3ItQkyiRwD3aMIgoAoioxcm6JudS3TiwkcLgsHHtqMSa9FUIoYzXqKisKpQ73c9sBGfJUuIhML2BwmUvE0c5MBGjoqOf/iJSxOM54yB4VMntmJJdbf0smL3zxMRaMPX52PVE5GRERntZDIyzR0tWC36CibDDI6MEs+V8Bo1pPN5Onc1sLF13rxVLjZeu9Gyms9ZFJZFqeCHH7qLHves4nj3z+LIAqY7EZS6TyJaJpzr/ay3Wommchw4+wgRVmmUJDRGbRkMzmWpvxU1Ho49fzFmy26pgfnCcyGuf83b+PUkQFsPjuuUjvBhQgXQkn0Bi1DQ4ts3dVCWbWbpUk/NpcFp8/BzNgil4/20ba5ib2/shO9XsuxH5xjYdxP66YGjjx1GovdxC3v247DY2Hs+jQajYTdY2V+3E8mlaWs3ofVYcS/EMVX4eBdH9xDLBBDZ9STzxc4daiPcHB5kHwgkKCk1MbeR7YjSSJ1rWVYfQ6e+dZJTFYDxXyBbCpHsVDEYNRR2VxOS3sF225ppaqlHIvd9A5fFSqVSqVSqVT/9alJlJ9DMpPj1cuD/2IC5U2Fosyhy4NUuG1ve2uv2267jdtuu+1fXP7bv/3b3H777fzhH/7hzdvq6+tv/j8ajfKtb32Lf/zHf2Tv3r0AfOc736GqqorXXnuNAwcOMDAwwMGDBzl37tzNJMVf//Vf09XVxeDgIC0tLRw6dIj+/n6mp6cpLy8H4I//+I957LHH+PKXv4zNZuO73/0umUyGb3/72+j1ejo6OhgaGuLrX/86n/rUpxAEgSeffJLq6mqeeOIJAFpbW7l48SJf+9rXbiZRnnjiCfbt28fnPvc5AD73uc9x/PhxnnjiCZ566ikAvv71r/OBD3yAX/u1X7t5n1dffZW//Mu/5A/+4A/ejlOvUqlUKpVKpfolVtdWwfbbV3P+tT4y8SzZTA5RErF7rNR1VNF98AqRQIKm1TUkIklmRxYYuz5Fz7F+8pkC4aUY7lI7u+/fyNJchEQkyZpbOihkc+j1GkRRRJIUNu5fxdiNeSgUGAvECc2HuPXhLSyMzpOMpPFUupBlGZvTyqUj5wEoFmQURUEUBGxOC/UWI/lMnvIyG2PXJrly4gayrCAXCmy5ZyNhf5xkKMHI1SmW5sLwRqV4bWs5hWyBubElJq5Pozdq6djSwtzIAoqiMHh5koFLE2y5cy0Tk1EmhxcxmvUAbL+1k7V7O0ArsTQVoLaljFymwNVTg8hFCC3FCC7FEAx6HA4jF472UcgWEUURrU6DxW7C6jSzND+PoigUCkUQQKuRiAbimGxGtDqJgl6DTq9Fo9cydm16+cFRQJQEECCdyCAXZBAEFEHEajeh12uZn/STSWZpXVWDfyrAzMAM2ViKQr6IzqDDU+kmtBDBZDWSSWQZvjSOfybI7OgiNW3lGN5ICi2MLVDTXMbEwCyyrKAzaBEE0Ook7A4jmUSamD9KeC5IPJxiZniefDbPgf+2jYXZCK4SK27PcuXI3GSI6TE/iqwQCCVpba9g/dZGXn/5GmX1PjLJLFqthEanweWxsP++9dS3lP37P/lVKpVKpVKpfkmp7bx+DpP+MKFE+v8pNphIM7kU/gXv0VvJssxLL71Ec3MzBw4coKSkhE2bNr2l5VdPTw/5fJ79+/ffvK28vJyOjg7OnDkDLFd+2O32mwkUgM2bN2O3298S09HRcTOBAnDgwAGy2Sw9PT03Y3bu3Iler39LzNzcHBMTEzdj/s99eTPm4sWL5PP5fzXmzX3J5XL09PT8VMz+/ftvxqhUKpVKpVKpVP8WkiSyZmszD3zkFsrrPFQ2lFDfVoG33InJrEcuyhiMOkILEeZGF1m1s41iQaGq0cdtv7KdVDzDS3/7Oq/8/XFsDiPrd7YyMrhEw4YmOne0sXZvB7f96i4WlhJotSKKLKM36hi5NsXCZAC92cDidJCX//Z18m+058qkshjNeiSNiMNro3NrM4lQnFM/Ps/FQ1d59k9fYfL6NHWtFWRTWQxmA9dODGAyarC6zFg9VuxOExV1Xho6KimpdHP2xUvMji6QTWWJ+uMc/LvX0Rt11LZXEQunsLksvP5cD+VlNnR6DfFIkmQshd1uZH4mzOTQAuFYhht9swhaCW+Fi+Y1NTSsrmHl7nZmJwMMXp1i//u2sX5vO7lcjs5tKyir9aAoCiaLfnnuSmMpi3MRZEVGo5MQAEWWyaZzaPUaCtkCZocRg0mPVq9B0kiggMVpJpnKsvPutSQiCeLhJKGlKAaTDrPViMdt4uRzF0kncwQXoiQiaRYnA9jcFiSdhomBGdxldhweKzUrKtj/yDasDjOrd7SRz+YJzIXxldtZs2MFVa3lFBUBo9PKmj0dbLl9NaG5MPlMjkwqh86gxeGxotVriYUS5LM5irk8w5cniIeTLEz4CS9E8c+GiIZTpBIZipEEGzc3UFbhpLK+BJfHwobNDey/cxV1zaXv9GWgUqlUKpVK9UtFrUT5OUz8jEmRiaUwrVW+X9De/LSlpSUSiQT/83/+T37/93+fr371qxw8eJD77ruPY8eOsXPnThYWFtDpdDidzrfc1+fzsbCwAMDCwgIlJSU/tf6SkpK3xPh8bz02p9OJTqd7S0xtbe1PbefNZXV1df/senw+H4VCgUAgQFlZ2b8Y8+Z2AoEAxWLxX41RqVQqlUqlUqneDi6vjZY1tfSdGbo56+/Nn1qdRDqRZstd6zj7ylVMVgPNa+t46usvU8wXcJU6SCeyhBZjXDo3RtAfp//SBGXVLlxeG+d/cJGaJh/JUIKlqQA2p4XSGi9Gk46rx/oZvjIBAgTnwlgcFsrqSyip9uCqcBNYitPUVs7lI724vLblllmzQRLhBHXtldgcJnLpHGazHqmoUFHjxu5Yw5HvnGJuNoyiQPOaWoqyTDFTQEHBbNYjywpj16cpqyvB5jKTSmZRFAWNTkMmlUMuyuy5ay3XTg8y0jfHwx/bx8Vj/UwPzzMxssiaPR1Y7Eae+9vjJGNTtHVWkktmOPfqNeweK7e/fyeB6SDFfIGlmSCVDSVYnRYa1tVz8mAvJRVO1u7uYHF8kUQ8gwIgKzSuriaXyhMPp4j64ySiKYwWA5JOg7fSw5HDfbSua+D8kV4yiQyCKJBN50jHUssVJDoNoigAMlqdxPzoIt4KF3a3FUVRGO2d4vLRPrQ6La5SO2tv6WDn/ZvIpLLoDVqa1tRinQ4jF2UsTjN954f5/qUJtu3v4JZ713Px1BD+2RDeChfFooxWq6GkwkVsKUJlUymhxQiiJN5c5nBZCS1ESIaTHHh0BxSLTPTN4PTZqWoqo2ltHYIgvFNPe5VKpVKpVKpfSmoS5eeQzuZ/pvjUzxj/byXLy23G7rnnHj75yU8CsHr1as6cOcOTTz7Jzp07/8X7Koryljfl/9wb9Lcj5s0/MN+OmH962/9LjEqlUqlUKpVK9W9VVu9j8+1rOP/KZWRZQaOVsDrNSBoJSSPSe3aYVDyNw2tD0ogkoykKuQJmmwlniY2xwXnmx5fwVLiw2I3kcwVqV5RRzBeZuDGL2axDFEUKuQJbb+2kmM0TXopgc1vIpnPkswV6Tw/y4Kfv5CffOcPVF6/i8FjRawQWZyOUVDoRBAGzzUg6nmHo0gQmq4HAXBjljQHzla2VLPoTrDywmumBmeW2UVoJSaclm84higL5vIzRsjxbMZ3MUN9eic1rw2gxUJRECvkCGq2GyhoPJ567RKFQ5Jm/PMItD2zE3VDC6I0FXnn5Kl07V+Ard5J1ZJHzRQKzIXw1Xho7q5kb9/Psnx7EV+OmrN7HljvXMjsVZGp4idVdjVidJiprPXh8NvwzIQSNyKrtrYQDMSKBKKl4hng4ga/WS76osOHAKqamguRzRS6eGWbFyhrOvXYdp8/Gqk0NVJY7KK92Y7YaCADxUBJJI1FWX4KrzMXg5QkuHe+neU0dH/36r9DzWi+nnr9ASbWbhYkAG25dicZo4ORLV/DPhknH09g9NixOMzq9lrNH+nnvh3fjq9jE8PUZtEYdNfVu2tbXkcnkmBcUwv4YCAKSRkTSSpTVePFV2MmGYrRubKDr1lUIgsCWO9e9s090lUqlUqlUql9yahLl52DUa3+meNPPGP9v5fF40Gg0tLW1veX21tZWTp06BUBpaSm5XI5wOPyWapSlpSW2bNlyM2ZxcfGn1u/3+29We5SWlnL+/Pm3LA+Hw+Tz+bfE/NNKkKWlJYD/a4xGo8Htdv+rMW+uw+PxIEnSvxqjUqlUKpVKpVK9XSRJZP3eDqqaSvHPhNAZtazY0MD3/+RlrG4L18+NIkkiFrsRRVYQRQFBFEjF0wiiQDyaJhqIg6IgaSWcPjv93aM43Fa69nVgd5kIzoRIx1Icf+o07/n0ncyMLJDL5PFWuEmEEzhLHZx69TpIEvWdVctJGrsJBJifDFBe48Fc62WwZwyjRU86lcNk1tO6oZ6hK9MUEBEkDYdevIqrxIooFLGli9i8drLJDLlUFqPFQOWqaqqaSnGVOTj7kx6GLo7iq/Xiqi5h1cZ6RK3E0kwYQQRRFMhlCrz6vXM0tJXT2lqORqfBU+bAbtahRWBmcA6tvoKKxlKWFmKcff0G5R01uF0mTvzwHKlYmr2PbGd6ZB6TScP80DwjPWOYrAYe+PitGC06Zkb9HHn6LLlsnvL6Ejp3tiHLMis2NvLqK9dpaSsnNBfG7rNjdZnxVjjoXFPL2cO96DY1UMwXmZ8IYHVbyaRzdGxtoWltHVOD8zjcVqZuzHMp0o/WoGPzPRvYfPc6rr3ex8ptrRx/tpvyFcuzVMpqvciygqgRmR9bQpRETFYTYzfm6T8xQGgxzG2P7qTvzCBXX+5h/+P78M9G0Gg1oAAKuEoduErtuOxGlqJJVu9oVb8IplKpVCqVSvUfhJpE+TnUlji5ODLzM8X/e9LpdGzYsIHBwcG33D40NERNTQ0A69atQ6vVcvjwYR544AEA5ufnuX79+s1h9F1dXUSjUbq7u9m4cSMA58+fJxqN3ky0dHV18eUvf5n5+XnKypaHGx46dAi9Xs+6detuxnz+858nl8uh0+luxpSXl99s89XV1cULL7zwlv09dOgQ69evR6vV3ow5fPjwzeqaN2Pe3BedTse6des4fPgw9957782Yw4cPc8899/xbTqlKpVKpVCqVSvXPEkWRsroSyur+dxtcSSMxO7LA6LVp5KLMSO8U9Z1VWBwmcpk8JpsRURSxOI2YrEY0Og1mm4mZkUUcHisT/bPoDFr2vmstZ5+/gM1pprS+BFmWUWQFjVaDIMCV4ze49+O3cuq1AcLBBMV8EQWFzvV1FApFCrkigYUobo+F2tZKVu1opefIdVbvbMNd7WGkd4ahq1M0bmpEFAVC/jjZdB6bxYDFaycUTlJa7aGYyRELJSir83LmJxfRm3RUrqhgejzA3EyErXevpXJFFYlInG0HOjn+4lW8ZXYKRZnh67P090xgcZi499d2Y3ZaKHEaWZwOEVuMkRlaJDAbwmjWEw6nyBdl/tvv3Es8keW1l6+Sy8vk80Vq1tRSVukin8hw6WgvGq1EMpVDEJYrz0euTTFydQpZVsgXFQRZIRJMotdpEOUibaurqavzcOj753G6LEzemGP9vpXMjS4wP7bIuz95B4sTAX74pwdJRFLoTTru+MBupqbDHDvUx/B4kGwyS0NrORavlfZNjYyO+MlnC0QDcRRZwVXmQKvXkklmSMXSJGMlSBoRT5mTpcklFif8mB0mRi4MsXZzHUsLMWKxDGarkZJSKxaznkQwzq77N9K4qvodfFarVCqVSqVSqf5PahLl51DjdeKyGP+fhsu7LUZqfgFJlEQiwcjIyM3fx8fHuXLlCi6Xi+rqaj772c/y4IMPsmPHDnbv3s3Bgwd54YUXeP311wGw2+184AMf4NOf/jRutxuXy8VnPvMZOjs72bt3L7BcuXLrrbfy+OOP881vfhOAD37wg9x55520tLQAy0Pb29raeOSRR/ijP/ojQqEQn/nMZ3j88cex2WwAPPzww3zxi1/kscce4/Of/zzDw8N85Stf4Qtf+MLNb1d9+MMf5hvf+Aaf+tSnePzxxzl79izf+ta3eOqpp24e42/+5m+yY8cOvvrVr3LPPffw/PPP89prr92srgH41Kc+xSOPPML69evp6urir/7qr5iamuLDH/7w2/4YqFQqlUqlUqlU/5yWdXUIokBHVxODPeOU1/nI54tUt1YwM7SAt8JJPJKkoaGU2o5KUGB6cA4RECURuVDE5rATmAlS21pBJBinc2sLmWSGzm0rGLo0DgIIosDcmB+jzYhGr0WWFZLxNOeO9bP9XRu50T2M3qClpMxBaYOP6fEgnrpScgj0nR/FbDNhcprRaDWYbUZikSSiJDI4ME9Leznb7q3EZdWxNBXEZDHgnwkycnkCURRoWt+ARiNQ2lCKpCj0HesltBilvL6ERz+xj6WFGANXp6hbUY5ep8HptVLI5HC5LSSDCQwGiZWbm5iZCiIIEA8nyWcLFAsyWY2O5793HFepHaPNhCgKTEyGiCZz1JbbsNqMmOwmXIqAvyFARaMPq9PMjcsTmC1GVu1sZWRoEaPdhN1pJBnPcuH4DRKBOKGlGHaXBbPdyPTYEhF/nPatLQQXosyMzCMXZfK5AmaHiYPfO03H9jasbyRdJI3IzMgCI30zNNW76ehq5OBT50iEU1Q0lDA7vEBFo49CrkA6nsHqMOEqseHw2hi5PE6xICMgULOikuM/Pk+xINO8ro61m2vp3NqMKEqY7UYsdvM7/RRWqVQqlUqlUv0f1CTKz8Fs0HFgTQs/OHONQlH+F+O0ksj+NS2Y9Lq3fR8uXrzI7t27b/7+qU99CoBHH32Ub3/729x77708+eST/MEf/AEf//jHaWlp4Uc/+hHbtm27eZ8/+ZM/QaPR8MADD5BOp7nlllv49re/jSRJN2O++93v8vGPf5z9+/cDcPfdd/ONb3zj5nJJknjppZf4yEc+wtatWzEajTz88MN87Wtfuxljt9s5fPgwH/3oR1m/fj1Op5NPfepTN/cZoK6ujpdffplPfvKT/Pmf/znl5eX86Z/+Kffff//NmC1btvD000/zO7/zO/zu7/4uDQ0NPPPMM2zatOlmzIMPPkgwGORLX/oS8/PzdHR08PLLL9+swFGpVCqVSqVSqX7RNFoNbRsbsTnNHPreaWYnAvRfnaZ1ZTUdm5sY7Z0mFk5gsehx+2yMXpshFkqgKAq5bJ6qljI61tVy9vlufFUedt2/GbPDhN6op6zOS01rOcG5MHaPDXuJndGxXkKLMcx2E6GFCGabiZ5zozz8oT0EZkPEA3GGL01S0165nEyIpHC4zKQTIbasqyMRTb1R5QKSRkQURcZGlkgmc5hzaW5cHKe+pZTW1TXk0nkMZj2JUIK6xhKqW6t4/YfnqGgqwz8bYn58ieBsiK3vWk/5HSuZmQhwrXuMwjWZjrU1HPyrw1TUeRnsnUZv0LHt3o0MXZvGbNET8cdYt6edY69co7K5jFQiy8JEgHyugCgKWBxmOlZuJR9JcOrZC0haDdUtZQyeHyYWjLNmdzuldSUc+vYxcpkCe967hbQocv3SFFv2tDIzukgslCCdzFJa5UZCJjAXobGziotHrmN3mlAU0GgkzA4z89Mh9DoNEX8MARAQQBBYmA7SurISh91ELr1cDZOKpymt+//bu/PwKus7///Psy9ZTvblZE+AkBDWRCCggAsIiohtlY4tauvYUq2Uoh2r7XQ6nVat2jqt1qUz87MzTitOS3FFBBVBZA+EfScQsu97zn5+f/DltBGKoChLX4/rynVxPp/3fZ/3fed95Up4n8/9SaG7rReDAYaW5VM2ZSg7jSHe+I/3MJqMxKe4iI6PIi03mWv+YSKhUAhvr5e96w8ybOwg0gfrEcQiIiIiFyJD+MTu3Zeorq4uXC4XnZ2dkZURJ3g8HqqqqsjLy8Nut5/1uQ/UtbB86z5aT7EiJTHawbTRhQx2J33i3OXz8WnrQERERETkb+nv9VB3uInqIy34PH4OVB6hubYDb08/h3bWcMeDN1J3pJm1b2/H5w2QkZtESrqLzoYO0nOSyBuexfuLN7J740GcMQ6+ePc03l+8AavdTP6wLAxmE/0BWPnaFrKGpNPR0kN/j4cv3jmJDa9vpraqBZvdgtNppa/Xw6QvjWfZog2YLUam3DCawWNy2bq5mvaufvZsOwYGju/TYYCyCYNp2XWE7tZuHDYz+cMyqdp2hLqqJtz5qaTnp9De2InfF6Sn10cgECQ21kHtoQYmf3E8Hy7dSmJaHCVThlF3rJ363cfoau3GlRCF0WyivaWH6DgnmSNy2be9hqGjsgkYoK2jH4fVRNWuGkyW4x/wMhoN5A1Jw9jvYVCRm2UvvM9VcyawYekW+ns8x/chibZjNBkYOn4I696sJD45luJJRSTkpmIKh2mtb+fDZTvw+wJYrWYGlWQS9PrJGZLKtpW7iHbZ6e/10dfdT9hkoqO1h2lfvYIPVuzEYrUQCATx9HiwOa3kDXWTl+nCaLOy5u2deD1+Mgen4/P4MJuNjCkvYN/avRSNH0zTsVb6uj0MKc0nOSOe/ZsPE/AHB9TJF+6dTlah+/MvUBEREZG/U6frG3yUVqJ8CoPdSWQkxnK0qZ0jTe30ef047RZyk+PJSYn/TFagiIiIiIjIxcMRZadgeDb5JVnUHm2htbaNXRuraGtoJ6MglT0Vh0lJiSExzkYoZKPpYB2739+JxWbG2+/FPSSN9LwUqvfVMWhkLmvf3ELBqGzSspOJT4nFlRRD2GAkbDKxd+sRXInR5Bel01bbRs3hZgD6e72EgkFi46OwmIxc9YXLMNvMpKS76Onox2kzkz4sg8IiN811HRiM4HQ5OXqokVFTitj4+haiYhxsW7Wb0VcOw2g2EQ6H6W7vxWq3Egx68Xn8RLsc1FU1EQ6FMRoNePt8NNW0Ur3zGIPK8tm7ehfBQIiezj5SMhMIBoO0NXYyLM6B3xfAZjPTUNOOPdpOf68HgHDo+Gf+AsEQWblJrH9lE6lpLuJTXHS3ddPb2Yc9ykY4FKa9uYvE9HgCXj9xSTGYLEbiE6Ko3nKIfZXVFF+Wx5SZI9m8eh/dnf0EAkG6Wrvoao+i6LICmo61YMCIwWCgz+PHbDHjiHUQFeukp7MPo8lAMPD/nkQQhrySLNrq2/ny3VeDyciBbUfx9fnwtPfw2tNvkZqZSDAQZO/mKlIy40nLTuTQ9uqTGigJaS6SMxM+v6IUERERkbOiJsqn5LRZKcpKpShLS69FREREROTUDAYDmbnJXHHDGKoPNGK3W/D0efng9S3cdt91DBmVx/L//QCfx4fVbiGnKIMJM0sJhcLEp7kYPCafrMFp7Fy3D19/gD88+ir2KDt2p5WCkdlcf9fV5OQlcXBXLcPG5rPzgz2YzSY8fV4AersD9HZ7qDncRFZJFsv+sJYv3zOVdSt20lrXwYTrRlK9v4G+Ph9eb4BwKMSYKwqx2kwMGzeIuoN1AOxet5+pX51EV3sv3e3dWKxmdq0/SHdrN/GJUYSDIQwGA2abmVAoTCgU5tiBOkZfVYLNYaW7vRejyUY4DITB7rTg94cIB0N0dfUTG+/E7w0QDkNMQjSePi9miwmTyURMjJ24OAcmk5FQKISv34fRaCAUCGEwGrBYTISDIfzeAHanlaJxg1jx+zXEJMXS1+1h5/pDJKbGMv6qYla/vZO+Ph8hg5G4tDjcmfH0dfexa88BjCYjiVlJlE4dwZa1B7DaLdh8Vrpau7E5rQT9QdKz4vH7gvh8AQJeHy01baz+39UEA0HC4TBGo5FEdwIGk4lQIEjp1JE013cw9rpRbH9/N20NnQDEJsVw5ZwJ2KO0Il5ERETkQqUmioiIiIiIyOckqyCVrz80i/Vv7+CD17fgiLLz/utbuPHOK/nWyGzamzoxYKCtuYv/efgVfB4/837+D2xcvoO+Xi8jJhSy5DfLAQM+j4+E1Fi2vLcbR7SDUChM7tB0gj4/KVmJGIzH3zP0/1ZzGI0G4tPiaGvsxNvjw+6w0tHSzfAJg3h70Xp8/X4S0104ou3093hZ+9Y2brpzMh3VzZReMwJ3QRp+b4BdGw/SVN1CfVUTZVOHUzR2EHVVzXj7fdidVsbPHMPRPXVYrCasNgvOaDtHDzQwYtJQ1r2+FbPZiCPKSlxiNIWXFbBnRw0AZouJ0vIC1r2/F6vVRG84jNliwuawYbUaMRugr8eD0Xh8RUhsUix+XxCTxYwBSMlOovFIM/GpLmoPN1J7sAGL1YzfH8JgMoDRQGdHH/3d/cz46gRiYx1U7TzGlnd3825LFzd94yqyC920NXSQMyyT91fsoru9D5vTSnpuMiaTkb6efuISYxk1fhD+3n783iApmQm8/6cNTL65nJaaVjx9XvKGZ+P3BzAA0782BWesg5aGDqwOO1/67vU0VbcQDkNKViLOGMf5KEUREREROUPaE0V7YfzdUx2IiIiIyOctHA6zddUedqzdz9ZVe/D2e0nNSKDhSDMGoxFvvw9PnxeTycjISUPJH5HDK8+/y6QbxvDqcysIh8NExTjo7/GQNSQdW5SVonGDaa5tJ6MwgyN76wj2e6lcvZdgMIQBGDWlmK5ePwUlmbTWt1M0IoOqQ814+vxs/WA/BsPxvMwWM+FgCKPZyKjywfQ0tZKWk8z2D/biiLbjjLFjMBg4tO0oMQnRfGH+tdQebCYYDBIbF8XaNyro7erH5rDS2dbLZdNHsXHVPgpKMskdlEJbTSvtLT0MnziEkNlMU207BoOBnPwkTISpr22nr8+HLcrO3r0NtDV3M3laCRvfquDyqcOpWLGD0VcOo7erj+7WHo7uriEmPopgKERqVhLtzV34vH6sdht9fT6aGjoxmoz093gxGA2k5SSRVphJZnYC9YcaqK1qpqulm6g4J2OvKuK9//0Ab5+Pa26fzL69DXR39RObEE1Pey+p2UlcMXUYva2drH5lM9V76vnivdM4WHmUgD+APcpOdKyD6v31hMNhyq8bRWdLF0lZyRitFqJinVz31Ynnu/xERERE/u5pTxQREREREZELmMFgIKcwnbVvVOD3+IiKdtDT0Ut9VTODx+RRc6gRA2AwGji6t56+Xh8jJw7BEW1j0Mgc+rr78fX7iE+JpaOlm7y0LFyJMaRmJxIMQVN9B+68ZG6Ydw1tDR2EDUaOHWmhobqVYaW5RNuMxCfGYHHa+ODNHZGcLDYLBoOBkNFAqjue1oZ23BkJ7N14kMumjWDDW5V4ejw4o+1MmTOB9LxUjuyuIz03me4uDwEMDBqVR3dLF66kaIaUDaK6qoXsQakkp8aSV5yJ0WKiqbWPZX/aTGp2IqFAiLyh6ZgN8Prz7+CIceILhggEofyGMRyoasXhMOO0mqnaVcNl146g6WgLcSkusosyKBo7CJPZiNFswmK34Pf4CBsMNB5toaW+k8IxefR192O2mmmobSc5M5GEjATeXrSeotE5JLnjaKltw9/UidlqJTkjAU+fl2O7aygqziC1IA2r3Yozxo7FaKBmXx1RLge1BxqJiXdS8d4u0nOTObK3Dk9VC67EKFrrOzAYoK/bQ15JLseOtuDt7yKvWB/aEhEREbnYqIkiIiIiIiJyHiSmx3Pz/Bm8+8f1bF+zj9wiNz5vgGMH6nHnp9BS14EzxkZ0XBQ1hxoxm00YzSaOHWzAYjERCoaJS44Fg4Fx00fSXNfOh69vITrOwVWzy3hvySYOVh4lPi0evy+A2WTkC1+7nHVLNuJKjOL9xRvIGupmRHkBjbXt9Pd66evuxx5lJ9bloK2xgxETBmOzGji6p46ya4Yz447J9HZ58AXD7Ks8SsWa/dicNox2G4OK3VTvq2XY+MEEAwF2rN7L7x9ewtgZo7jh9okc3d/EO3/ehNVho7A0j2FjDdQcbKK4LJeYaCtv/sd7BH1Bejt6ccbYMZsM7P1gN1/+/mz2bjxIV3MXuUPSeeXZFdjsVsLhMBabmbTsZPy+AK6U43ufFIzMob6mnWGleTRUNbFh9W4AgsEQxeMHUzw2n//55duEQmH2VB7l6hvHcHRvHYnp8Xj6vFx27QisditGswm/x8+BtfvIHOrmSGsPRoORnCI3JiNc+9WJ+P1BNi7fQXJmAsPLh9Ba307AHyQxPY6ya0YQDMORQ02RzeTzhrrPZ8mJiIiIyCegJoqIiIiIiMh5kpqdxPSvTcFdnIGno48ju2uJT44lFAqTkZ+M0WRk9JQilv7PB3hCPnZvPszkL41n74YDHN1diz3KRsHoXELhMKv+vJFgIERukZsdq3aRlZ1AX7+fzrZesnKTGTlxMB0N7RjCYULhMEf31LF302H+4cEbGVSSQeOxNgDCoTB93f14+33YbGai4px89cEb6evx4vcG8AVCrFyyGaPZiNFoxBlrYtWSzezbmkRigpMt7+wiLimGYeMHk5DuwpUcx8ZV+9m2Zj9hwvR3e9i1/iCJ7jjGTxlK/b5aOqJsxKe48PX7aGvspK2+HZvTRsAXoPFwA5Uf7GXCrFK8PV5MZhN9PR4MBgOu5FjqatpITI4hLSeZd19ex6TZZRSW5dNwtIXag404Yh14+33EJsXQ3d7D0d11BIMhwqEQFoedcBiS3PEA5BdnUL2nlmNVLTRWtzCyfAiF5YUs/+MGWho6SXIn8O6rWxgxYTDRdhNb3tnBtNsmsXPdASZcP4bisQX093rZVXGE6kONhIJ/eXq2OzeJnKHp56PMRERERORTUBPlUwiHw9T3dnOgrYX97a30BXw4zVaGxCcyOCGJ9KgYDAbD+U5TREREREQuYAkJ0YwqL+Tw4Sam/MNEtq3cxdE9tcQmxZKam0Rnex+tDR0kpsXR0+Xhw7d3MKQkgylfHEd9VRNRMXa2vr+H1vpOolwODAYDR3Yco6W+jYS0OBxRdnYebcTX1cPls8ooGJ2HI8pOTHwsR/fWsv6NrZTNGE1LbRu1h5oIBkIkpru4+guXcXRfLUNGjOG1Z5dTd6iRa2+fzMrXthKfGofP68NgMNJc04bBaKCppp3iMbm01bWzf0sVKVmJ7F63j8HlRRw93Ex/rwerzXJ87xUDNFW3smvLUUaPz8OVEM3R3bU0HG0mFAwRCoaJspowWYwQNuDp9dHd0U/xZflsff/4yhKb04rPH8LvCxDEQO7wLCb1+1j1p3UUTS6hv72X3h4vfn8AwtBU30lHWy+F4wYTnxxLX48Hv9dPV0sX1XvrGD1pKPFJMezs6CPW5aDTbCQhzcX69/fQcKyNxPR4mmtaCQZCbF29j8uuLiYhI4FVSzYx4brRpGYlkjn4eJMkIS2O/duqObD9GCazkaLSPAaVZBLjcp7PUhMRERGRT0BNlE+ow9PPymOH2dpYhz8UGjB3uLONd6sPMSbVzZXZ+bhsjvOUpYiIiIiIXAzi4pyMGZNLbbyTAxVVJGcl09PZR+XqfcQkxTDj9kkc21fPwd112B1WUnJScCXHULWjms6WLmw2C5n5ydij7FTtOobFbsFiNdNS04bZasZoNHBkTw0Fo/L44NXNBAMhJt10Gd0dfTii7axfto3Bw7PIL8og4A/Q3tTJurcqGTwym/VvVWIymXBE2QkEwxhMRhqONpNVmI7fG6CtwUOUy4nRaMDrCeDr9xPwB/D0eUnPT6XmSAs2h/X45u/+43879fd4CIeh+kADU79YRl9nH0Muy2fPxoP4QwEs1uMfRiu9ejhtje24EqNxxDhY8cpW0galsW/zYULtfTijbRiB0qnD2bruEIauborLh7Jj3UHyh6TR09FDlCuKrrZewuEw3n4fzhgH0TE2bA4L6VkJhMMhJl0/Cle8g2AgRGZeEv/3izcwW82kDXFzqPIomYPTCIfCBANBjGYjoUCI3VuOMHpsPtve38moKUUUjS2IfD8T0+IoT4tj7NXDMBgNGI3G81FWIiIiInIOqInyCXR4+vnjvp0c7mz7mzH+UIgN9TW09Pdxc2GJGikiIiIiIvKxMvJSuP72Sbz9+w/ZX9mD2WLG4bBSVFbAhFmldLf1YrNbiYq20lrdgisphr5uD/HpcbQ0dNDf7SE+OYZQIEQ4DEaTEb/XT5TLSXxKHD0dPQT8QTy9XjYt387460fT1tBBTHIsxw400N7UhclsxGA0YLKYsDtt1B+ox+awEAiECAVDmEwmYhOjjz/KK9pOdJyTno4+rHYrDqeVxmMtpGYnY3faSEhz0bqlhsZjDSRnJdHW0IHP68fmtGI0GknLTabpWCs7Vu9h1JRivvLQTezbdBAwkJ6fTFtDB3aHFXd2AlGxDprq2skuSOGKm8bSVt+O1W7BPSidzav2EpsYTcnwTIJhA0azmZDRQNaQdJpr2rBHWfH2+UjJSiJsNNHb2cuwsYPIzk2gbn8drqRososz2bpmD4c3H2LQiGwsVjPdnR5sThtdLd1ExUURDIQwGA1YbGYsVguxqS6GXV5IwfBsrHbrSd9Pk9n0+ReRiIiIiJxT+jjMWQqHw6w8dvi0DZS/dqijjZXVhwmHwx8ffIYeeeQRLrvsMmJiYkhJSWH27Nns27fvb8Z/85vfxGAw8O///u8Dxr1eL/feey9JSUlERUUxa9YsampqBsS0t7czd+5cXC4XLpeLuXPn0tHRMSCmurqaG264gaioKJKSkpg/fz4+n29AzI4dO5g8eTIOh4OMjAx+8pOfnHRPVq1aRWlpKXa7nfz8fJ577rmTrmXx4sUUFxdjs9koLi5myZIlJ8U888wz5OXlYbfbKS0t5YMPPvib90ZERERE5EKTPyyT2x+6ke/88it891dzue/Xt3PlFy5j1PjBDB2ZzfaVO3jnxQ+oXL2X4ZOHkj8ql5ScZJLcCfh9AfJKsiObm7vzU0nPTyElK5GSCUMAmDhzDJO/MJZol5PVf95EX4+XZHc8Cakuejp6aa5ppe5Q4/FVLGYjDqcFR4yDzMFpdDS2k1+SSX+Ph2AgSGN1C86Y4x8Ys9jMGAkTDITo7eknPi2OGJeT5PRYDEYjrXVtpGYnkZgWR1JGAlEuJ72dvRzdXcPh7dV0NncR5XJQMDIHkwkaj7SQNcRNX3c/Pl8QW7SD8VOHM3hULuYoO44kF2GbncoNh4lJjKG7tZeGI634PH5a69o5uLOWoROGMnb6KIaMyuWar1zOiKtK8Hh8jCzLJTs7jo76Do7uqWP/lir2VlRht1nx+wI017bh6fVid1gIBoOEQmHCoTAp2UmYrWZsUTY8Hj9hs5ExU4aRnJl4PktGRERERD5DaqKcpfrebrY21p3VMVsa62jo7T5nOaxatYp77rmH9evXs2LFCgKBANOmTaO3t/ek2FdeeYUNGzbgdrtPmluwYAFLlixh0aJFrFmzhp6eHmbOnEkwGIzE3HrrrVRWVrJs2TKWLVtGZWUlc+fOjcwHg0Guv/56ent7WbNmDYsWLWLx4sXcd999kZiuri6mTp2K2+1m06ZNPPXUUzzxxBP88pe/jMRUVVVx3XXXccUVV7B161Yeeugh5s+fz+LFiyMx69atY86cOcydO5dt27Yxd+5cbrnlFjZs2BCJefnll1mwYAE/+MEP2Lp1K1dccQUzZsygurr6k99wEREREZHPmTPaTu7QDIaOySMpPS4yfrDyCL0dfcDxD3h1t/dTV9PGH59aTkdLN6OmFBMIBJg17xqiXE4MJgM2p42yqcMJ+vwcqjjCxrcqWf96BY4oG5fPGkPNwQba6tsZPjaPKTddRnpuCu7cFC6/YTQ2m5nicYPpaukm4A9Q8e4ucvKTSc1Kor/HS3+PF2+/j8zB6Uy7ZSx71u9n8hfHUnb1cLa8t4v3/7SRtIwEQqEgAV8Ab7+XpuoW2hs6aG/soHhkNoe2HaH0mhJSc5JpqW1j17r9NNa0sX9LFW+98D7RqQlUrD/E8kXrWPPWdl5/cQ37d9QQGx9FY00bjmg7TcfaKChyU72vhu6WLqx2M12tPRzZ10hzWz+dvT7WrdjJyj9toK+li91r9lCxYgeV7+/CGeugta6dqm1HMVqM5A3LJC45ln6Pn2R3HH5vAKfLiT3GTnJWIvkjcrA5baRmJjD8sgIm3zRWe2GKiIiIXMIM4XO5ROIC1NXVhcvlorOzk9jY2AFzHo+HqqqqyKqFM7Gq+jDLjhw46zym5w1mclb+WR93Jpqbm0lJSWHVqlVMmjQpMl5bW8u4ceN4++23uf7661mwYAELFiwAoLOzk+TkZF588UXmzJkDQF1dHVlZWSxdupRrr72WPXv2UFxczPr16xk3bhwA69evp7y8nL1791JYWMhbb73FzJkzOXbsWKRRs2jRIu644w6ampqIjY3l2Wef5cEHH6SxsRGbzQbAo48+ylNPPUVNTQ0Gg4EHHniA1157jT179kTynzdvHtu2bWPdunUAzJkzh66uLt56662/3Nfp04mPj+ell14CYNy4cYwZM4Znn302ElNUVMTs2bN55JFHTnn/PkkdiIiIiIicD3/+zdsc23v8Q10ms4nBEwr50/PvUjImj/6uPmx2C1aribqD9bjzUoiOi6JixQ5MZiMx8VFsX70HR7QDZ6yDYCDImKtLiE2KpbW+g9iEKDIGpXJ0Ty1NNW00HWshIz8Vs9WM2WqmvamLnWv3Uzx+EFfdUs7md3fh9wcxGAwkp7lwxlghDLs3HiR3WDahUJjmo8201bczZHwhG1buoau9j5jEaMwmI6WTCzmwbh8xCVHkDctiz4YDVO08RlxyLCMmDeXdP6yl/KaxbFi1j4IROfR19WO2WamvacNkNpFf5MYRZaO3q4/+rj6yMlysfWUj8alxjL2+lHf+XEF0fBQtdW1kF7rx9HooKcvDagzT3tTFlnd30tvZx5Sbx3N4Vw1Gs4nr/vFKOuo7ePcPa3BG27HGOhl02SD27ajDbDdz5GATFquZ7Lwkrr6plMk3jiHKYTvPVSEiIiIiZ+t0fYOP0kqUs7S/vfWTHdf2yY47E52dnQAkJCRExkKhEHPnzuV73/sew4YNO+mYiooK/H4/06ZNi4y53W5KSkpYu3YtcHzlh8vlijRQAMaPH4/L5RoQU1JSMmCly7XXXovX66WioiISM3ny5EgD5URMXV0dR44cicT8dS4nYjZv3ozf7z9tzIlcfD4fFRUVJ8VMmzYtEiMiIiIicjFLzvjL7/yJ7ng2fbCf8VNHsPatbaxbsYs1y3YQCEFzbTstte0EfH6ajrWQPTSD/VuqSM5MxBljx2Izk5gez7o3t5BRkELFiu2sXryBre/tYs2STTgcFlwJ0Wx9fzcV7+ygqbqFkVcWM/Url+Pp9bH+za1k5SfjtJmwmuBwZRV71x+kpbaNoztrsNrMVKzcRcHIHDqau1n6/AqS4myMGZ9PWXkBX7prMu6MOKZ+5XJGXF7E1vd2YTQaMRgM1B1qZP0bW5k4q4zmhi78/X48fT7aGjtpqm4mISkaZ5SVmsON5A1Np+yKIQwZksI7L36AzxPAGW1nxe9WMqY8n/JpJRSV5uLOTmTGl8eDz8f+iiq2rdpDKBgiOi6KpMwEouKc5BS52bZ6D0tfXE1yXir5Y/KYeEMpSRkJ5BW5SctOJLsghbGThzJl5igKh2epgSIiIiLyd0BNlLPUF/B9fNApj/Of40yOC4fDLFy4kMsvv5ySkpLI+M9//nPMZjPz588/5XENDQ1YrVbi4+MHjKemptLQ0BCJSUlJOenYlJSUATGpqakD5uPj47FaraeNOfH642ICgQAtLS2njTlxjpaWFoLB4GljREREREQuZoNH52JzHt/A3OK00VDTRsgAl88cRWpWImaLiZ0VVXzh3ukYLSYsDhtJ7gSiXA6C/iDd7b14+nzYHFZCoRBWm4UDW6oIB0NEuZzYo2wEA0FWL95I/aEmgv4gvZ19lM8cg6/HizPaTlK6i8bqFtLykomOi8Lb5yU2KZbujl4q399DxuB0erv6GTIqF1eqizFXlxAV52TPuv1U76om6PHyXw++RGdLN9X7G9jwViX1Vc3UHmzEEW3HaDLS1dZDfGosHe29pBek0t7QQWdLN53N3XQ0duDt7gd/gOgoM/7efrIL07FHH9/PJOAP0tXWQ8jnZ8MrG6jbcZQ9q3ey+v/W0nSslZoDDSSmx2GPtpExKI2D245ht1mIS4mlp62XzpZedqw7wJb397D6tQq6O/sxWc0cOdRMbGI0TbXtpOcmkV+ccZ6rQUREREQ+D+bzncDFxmm2fsLjLOc4k+O+/e1vs337dtasWRMZq6io4Fe/+hVbtmw562fzhsPhAcec6vhzEXPiKXLnIuajY2cSIyIiIiJyMUrLSebGedewe8NBPN4A8Ukx+AMh9m07RtbgVLIL0yAMOzceJoSRlvpOxs0cw94NB4hPjaPxSBOZhW5a6tqJT3Ex/rox7Fy7j77ufvz+AIRCXHlLOTs/3Ie334cjysb0Oyaz48N9uPNS2bPhAAnpcVw2bQRVO2torW9nw9JKQqEQcPx37tScJKJcDta+uZVQMEh/Vz95w7Ow2a10t/Xw7ktriY5z0tXWS2K6i5oD9fR29QFhkjITMFtMtDd24vcFGFSSRWNdJ03HWgmHwkS5HISCIYKGIJ4+L1arhf17asgpymDBb+6kZl894VCY/OHZVO+rpf5wEwF/AMKQlptM5uA0dq7ZR3Sck4KROWQMSaf2cBNDRuXw/p824OnzYTKbMAFRsQ6iou3Ep7nYsWI30S4nXl+QkRMHM7ysQH9jiIiIiPydUBPlLA2JT+RwZ9vZH5eQeM5zuffee3nttddYvXo1mZmZkfEPPviApqYmsrOzI2PBYJD77ruPf//3f+fIkSOkpaXh8/lob28fsBqlqamJCRMmAJCWlkZjY+NJ79vc3BxZ7ZGWljZgY3eA9vZ2/H7/gJiPrgRpamoC+NgYs9lMYmLiaWNOnCMpKQmTyXTaGBERERGRi116Xirpecd/v7XGx7K78iijJxex9s0t9Pf4CAaCxCVGUzwmh+Hlg6l8dyfp+SmMnlzMwe1Haa1rJ+APkFOUQYI7jsSMeDqauwj4AgRDsK/iMO6CVCbeeBmHdlSzevFGIIzNaSMu1UVLfQdWu5X6Q42EQyHKpo5gy3s7CfgCpOYmM2rKMJb9fjVtde1sXrGT4RMG09fZT83eehzRxzdnD4VCmK1mNq/YSWFZARuXVRIKhqg90IArKYbi8iEMGpWL1x+i/e2dxMRHYXPaIBymt7MPZ4yDzPxkuls6iE2I4b3/20B/Vz9DSvMpu6aE915eS097L1aHhVAwBMCgkTls/2Av078+hZyiDLa+v4ejB5poqG4hJTuJvm4PBqOBMGFcCdEYjEYKywrYsvEIgTD093goHp7FtC+UYnN8Nh+SExEREZELjx7ndZYGJyRhMZ7dbbMYjQyJTzpnOYTDYb797W/z5z//mffee4+8vLwB83PnzmX79u1UVlZGvtxuN9/73vd4++23ASgtLcVisbBixYrIcfX19ezcuTPSRCkvL6ezs5ONGzdGYjZs2EBnZ+eAmJ07d1JfXx+JWb58OTabjdLS0kjM6tWr8fl8A2Lcbje5ubmRmL/O5URMWVkZFovltDEncrFarZSWlp4Us2LFikiMiIiIiMilZMRluUTHOAiaTFz5pfEMGZWNOzeJuOQYRkweSldHLzUH61nxv2s4vKeGzrZeJt08npl3XY09xsbOD/dROCafgD+II8ZB07FW9m46jCPaTlNNCxmDUpnxtcmUTRtBjCuKcdNHceWXxpNTnEl6fiqHd1Szf2sVY64u4fIvjGXQqByaa1ppb+giFA7T1dqNz+vHGePE6XLiiLbR1+1h9JUlxCREYzAZcETbKB4/GKPRgM/jx2qzMGLSUN75/RqWPr+c+FgbNqcVT3c/wUCQcCiMKymGsZML6WztYeX/raehqonO1m6O7avlvT+t55aFM8kqTCch1UVKTiJX3zqR3u5+At4APe29bHh7BxuWbSMhJYbh5YPp7Ohl+MRCnDF2BpVkkZKVQP6ILLJHZTN0dDaXTSpk6uxSvvT1y0lJj//4b4yIiIiIXDIM4RPPTLpEdXV14XK56OzsJDY2dsCcx+OhqqqKvLw87Hb7GZ0vHA7zysHdbKyvOeMcxqVncuOg4nO23Pvuu+/mD3/4A6+++iqFhYWRcZfLhcPhOOUxubm5LFiwgAULFkTGvvWtb/HGG2/wu9/9joSEBO6//35aW1upqKjAZDIBMGPGDOrq6nj++ecB+MY3vkFOTg6vv/46cHyFy6hRo0hNTeXxxx+nra2NO+64g9mzZ/PUU08Bxze+Lyws5KqrruKhhx7iwIED3HHHHfzoRz/ivvvuA6CqqoqSkhK++c1vctddd7Fu3TrmzZvHSy+9xBe/+EUA1q5dy6RJk/jZz37GjTfeyKuvvsoPf/hD1qxZw7hx4wB4+eWXmTt3Ls899xzl5eX89re/5T/+4z/YtWsXOTk5p7w3n6QOREREREQuFE11HezcepT9u2qIjrHjinOSOSiFupYOPEfbWbt4Pb1d/ZgsJhLT4zlUeRS700pUnJOGIy2k56Uw6Ytj2bR8O36vn4T0OPKHZ9PR3EV/t4dNK3ZgtZnxeQMYjAYmzjq+EiM61skrv3mbzpZu4PjjrzIGpVIwKo+Vf1pPX7cHwmCxWxg2fhAz//EqGqtbOLq3lujYKLav3k18WhwV7+xkWPlgCkbm0NvRh8liJOgP0d/bj8Npo6u1m+LLi8BspuFoK3a7mfwiN3s27GfX+oO0N3QSDoXp7erHlRSDzWmjaFwBIy8fSnd7D1abmcZjrbTUtmM2m4hPi+OV594lFArxrce+QmNdG8FAiJyhbgKBAP2+ABannbDFwtG6DgDMZiNXXzOMYdoHRUREROSScLq+wUfpcV5nyWAwcGVWPi19fWf0WK9BcQlcmZ1/Tp+X++yzzwIwZcqUAeMvvPACd9xxxxmf58knn8RsNnPLLbfQ39/P1Vdfze9+97tIAwXg97//PfPnz2fatGkAzJo1i6effjoybzKZePPNN7n77ruZOHEiDoeDW2+9lSeeeCIS43K5WLFiBffccw9lZWXEx8ezcOFCFi5cGInJy8tj6dKlfPe73+U3v/kNbrebX//615EGCsCECRNYtGgRP/zhD/nnf/5nCgoKePnllyMNFIA5c+bQ2trKT37yE+rr6ykpKWHp0qV/s4EiIiIiInKxS3HHcZU7jrKJg/D7gjijbTT19rH10DFCHb2UXDGUmn31bFq+nbScZFrq2rA5rBhNRqLjnDTXtnKw8gg9nX1YHVZsThsHth4hu9DNmlcq6GrtwWIz4+3zYrVb2b3+IGOnjcAfCPIPD8xi26o91B5oYNiEIQwdO4jfLHwRZ6wDV2IMAV+Q2IQoMvJTWft6BW317ZRNHcn//NufCYXCZBdlkpwRz4EtR+ho6qKns4/LbyzjwJYDNB5twRljJz4llpceXkJKZgIllxfiiHXR1dxJf7eXjqYu/L7A8f1SYh3EpcRiMBgIB0O89/KHlEwYQl8X1B9qpOlYK7kl2VSs3I3NYWHw6FyaGtrp7ejFEWXnw2XbGD61hEM+P57OfsqGZpKUFE1BQQr5+cmkawWKiIiIyN8lrUT5hCsQOr39rKw+zJbGOvyh0EnzFqORMalurszOx2U79eoQuTBoJYqIiIiIXGo8fj9vrd7BhlcquKwoF29PPytfXkdKdiK71+4nFIZwKERqdhJWp5X03GSccVHkD8vC6bLT1+UhFAiy6Bdv4u3z4en1YDAYMJlNFJblU3pNCQ1VTaRkJVIwMpeqPTU4HFa6O3rpaOpi9ZJN2O1WHDF2nLEOvjR/Bi//8k1aatuYMHMMu9btx+8NYLGaGVKaRxgwW8zEJkaTnpfMU9/5bxwxdowGAxabGWeUnbHXjSJ/RA6vP7scm9NGYWk+h3fVsHXlLkwWM44oGzEJ0XS1dnPtbVdQ8XYlDVXNTL55PM64KNLzUnln0VqajrVRMDKHIWPy6O3up2rHUVxJsWSPzCWhOJ3dR5qx2yzccfN4YqLs2kBeRERE5BKklSifA5fNwY2DihmXnsX+9hb2t7XSF/DjNFsYkpDIkPgk0qJi9Au3iIiIiIh87uwWC7kFqdQXZ9Lm8WDo85E/Ohez0YjX48dgNBAV66Sppo2kzATyhmfz4RtbaalrJyE5lj0bDjD9jsmEAkGCgSAmk5FwGOJTXTRWt9DV0sPu9Qc5uPUIezdXcflNl9HR0oXVZsFoMjLjjsm0N3WRmB6HuyCVYCiMwQhZhenYnVY6mjrp6/bgSozh0LajHNlTS25RJjFJ0Rzb38DN372epf/1Hn5fgL7ufuJTXCS643nn9x/Q3+053ii5fTIJ6fF0tHQTCoYIh8LUHmwge6gbR5SNuoONYIAt7+6kdNpIFv3iDW781jRqDzeRnJmIM9rG7g376Wrr5co5E2g3mTjS2AlAbLQdh82iv+dERERERE2UT8NgMJAeHUt6dCyTs/LPdzoiIiIiIiIRg9OSOFaaRf+xTvrruxlbkkl7XQcttYUc2VWL0WIkIzMNi92KKzUOZ4ydrEGpvPL0cuxOK4e2HaFs2kg2vb0Nb78Pm8NKbEI0rfXthEJBGqqacCXF0NLYRUxiNOn5aRzecZSc4gzi4qNJzkxk96aDvPt/G5j/y6+QNSiNjW9vx5UQjdFoJBwOY3NY6OvpJ7MglbHTR9Lf58WVFENyZgJfXDCD/m4PsQnRtDd18sHiDRzdXUN/t4e4VBdt9e0s++9VXDZtFF0dvfS09ZBb5MY9KJXVf1qP1WGNbETfUtdGYqqLQCDAoNE5vPwf79He2ElOfirTvjKRfU1dEGOlxxvAZjUzsjgDi0V/LouIiIiImigiIiIiIiKXpGi7jcklg9hkP8aHLV34A15yMhO49s4raavtoOZQI9FxUeQWZ7Dy1QpGTCmm8UA9US4Hfq+f6j11DL9iKOOmj6LmYD193f3YomxMv20S696oIBwO01rXTlJmIuFgCEwGvP0BXn3uXcwWM/3d/RgMBm64cwp9vV6ajrVSfv1o2ps7mfmNq6neW8vejYeIinUy5qphRMU5CAUDfLh4A6FwmEGj8vD7/eQNy+TY3lr6uvrx9HqJT3MxdvooVr68jituGsvONftore/AaDIy5upitq3cid/jJxQI4u33M/qqfHZvOozFZsFkMbP1UAvDrxmBzWomMyeRvnCIjq4O/F0BYqLspCRGk5+dfL6/fSIiIiJygVATRURERERE5BLlctqZMiyfQ3saaOrswdjXg6fdA+EwQZOZNct3snPrUcZcXsi6ZdsZUZpFalYiJosJe5SNfZsPE+1yMv22SYQBq9XMa8+tIBwKY3da8Xn89LT3YLWY6W7sYPY3r2TM5EI6W7tpre8gKtaBI9pG89EWGqqaObTtKPZoO0f31FFYmseVcyaQkBpL47E2mo600NrYzqxvTcXT7+PIrhpyh2VS+d4uPP1err51In5vgMM7jnGw8gh+X4CtK3cxZEwuo64aRvXeWsKhMEkZiYTDYUxmE4NG59F4rBW/1487PxWv2cSRY610dvUTE2snMc1F0GmhzxDEEDSQm5HINVcMxRWjfS1FRERE5Dg1UURERERERC5hZpOJ8SPy+K+l64mx2IlJdOC0WvH0+Bh7TQmePi/OGDtjJg8lJsZKb1c/XW3dZBW66e/2ULWrhuS8VPzeAIZQkK62bgwGI/YoGyaLCbPJhMEIrvgonrv/RQAc0XZGTSnG0+dj3+ZDxCe7GHf9KNa9vpW+nn4S0+LYu+EgI64o4oV//j8Gjcqlt6ObjuZuNr+9nX94YDYmi4n//vEfSclOorG6haodx7DaLQwak8f+zYdoa+zE7rRyxezL2LBsK8217SS74wn4AzhiHEy7bQqrFm+gqbaNvOHZTPjSeDbsrqe5pYuc7GQKB6eSMTiFigP1DC5I5fLRBRTkJGGzWs7zd0xERERELiRqooiIiIiIiFzicjISccfHUt3bS3p/kOojzeSkJTLqiiF8uGw7+3bVEp8YDWYLX/zudaz479UE/EEyB6cz+prhvLd4M6WTCtmwdAtXfbmcjqZuAoEgmYPSSEiLA8KsfHkdjUdbsDmtdLX28OZ/vsfEGy/DZreSXeSms6Wbr/5gNg1HWmipa8Xv8VOzv55gIIh7UArvvXQQv9eP2WKmrb6dYDBIMBiiv7sfR7SdxupW3PkpxMZH4+n1kJgex2XTRrJv8yEGjcylek89Df4WDAYDmdEOag43kZKXSsGYfBypcWzaXUfIYGDMmFyGlmRiiLVQUd1AZnY811xWSEpCzPn+NomIiIjIBUhNlE8hFA7R5Gmjuq+BRk8rnqAXu8lGqj2RbGcaKfYEjAbj+U5TRERERET+zsXFOpgzvZQn//g+u8K9lI5JItmayI6tR8gZnMaqNyo5sP0Yw8pySc1I4AsLr8diMbNu2TbsMU5Kxg/C6rASDsP21Xtx5yWTmJHAykUf4oi2kVuSTWtdG+n5KTQfa8UafXw1h91hpWzqcKr31lG9r57l/7sGR5QNo8mI2WykaNxgJn1xLB++uhmz2UTAF8BqN2OPttHR1EVKViIGo4H0tDiO7q3H6/HT3tRJ4WWDMBgNHNx2hP5eL4FgiK/92y1s/2AvWUPdxKUl4IyxU3u0BXu0nbhUF6kFqdhdDjq9Xlp7+3A5bcyaNJzstHicdut5/g6JiIiIyIVKTZRPqL6/ma3teznSV08oHBowd7Svns3tu8l1pjM6fijpDm1KKCIiIiIi51eeO5Gv3TSOjfuq2X+oGmuik44uL709fq760lhaGzoI+YOYrWYsDhtdrT2k5yTz7qK12KJsDJlVSnpuElEuJy31HcSluoh2ObFYzbiSYjBbzHQ2dZKWm4zVbsHb7yMYDNLe2IXPF2D76j04Yuy0NXRgtpqYedfVdLd303ikCbPZBKEw0XFRJGclYI92YGjtpa6qGQhjtVtIcsfhjLbjiLbh6fUcf5SY1Yyx34fJZKT6cDNGp4P0wRlsXLUXPwaa6zsoHJGFJdpOUn4ipaPysFn0Z7CIiIiInDn99vgJHOmtY0XDOrwh/9+MCYVDHO6tpba/ialp5eRGuT/HDEVERERERE42MiuDXkOQnpgAfkMfJdn5dO7vpafHx56qZtLc8eRlJVBb3Yqnu4+EpGjcBSnExEXR1tjB8ElFrH9jM0nuRKp21jL961PYtHw7VpuV/h4PBgMYjQaajrURHe9g2MShmEwG6o80MfWrV9DV1k3AH2TQiBw2r9hG3aFGxs0YTd7wbI7tqyfa5SQ6MYat7+/CEe1g3IyRHNhyhIAvgKfPR9m0kfR39dLf68XT56WpupX0/BTSCtLYt6OGsVNLePfVCq64bhT+YIi4pGh6e/qwJkQxZKhbDRQREREROWuGcDgcPt9JfJa6urpwuVx0dnYSGxs7YM7j8VBVVUVeXh52u/2Mzlff38wbdatP20D5KJvRwkz3JK1IuUB9kjoQEREREblYhcNh9ra2UFFfQ0N3I5YeI5mWRKL6jNTVtPPh2v2MKMli3OgcDlQeobOxgz3rDxIIBDGZTQwty6PosgKOHWwkJSsJu9XEujcqyB2WSd2hRppq2nBE2ykeP5i+Hg9J7njWvbmFYeVDsFhNJGcksv6NLdQdaqCno5ec4iyKJwxh78ZDJKbHY7KYqNpVg98XYNSUImLinNQfaqK7s4+45Fhi4qPobu3ig1c2kZGfxtgZo4hKcmF1WPH6AiSmxdHZ78VkM2MwhHBkxDIsO524KMf5vvUiIiIicoE4Xd/go7Rhx1kIhUNsbd97Vg0UAG/Iz9b2vSc99uuTevbZZxkxYgSxsbHExsZSXl7OW2+9BYDf7+eBBx5g+PDhREVF4Xa7ue2226irqxuYk9fLvffeS1JSElFRUcyaNYuampoBMe3t7cydOxeXy4XL5WLu3Ll0dHQMiKmuruaGG24gKiqKpKQk5s+fj8/nGxCzY8cOJk+ejMPhICMjg5/85Cd8tHe3atUqSktLsdvt5Ofn89xzz5103YsXL6a4uBibzUZxcTFLliw5KeaZZ56JNENKS0v54IMPzvi+ioiIiIj8PTAYDBQlJXNryShuGTaeScOLcWe78EQb6CbA+Mvyae/oZU3FIWLS48gfk8/U2ydz3demcP2dV1I0bgiBMGAwsPatrRzdX8+WVXtYvmgtsSmxmG0W2po6+cNjr7Nv82E2rdhBel4KbfUd7PxgH0/c9Tz1VU1kDXFz7R1T8PsDNBxupmrnMfZuPMi293dx1S3jue7rkzFbzLTUtWMwGfH2eVm1eAOePi+J2cnc9qObueFb00jKTcHutFLX2E1tr4c/rdrJyopDtMUE6MgwMb4wVw0UEREREfnE1EQ5C02eNo701n+iY4/01dPsaT8neWRmZvLoo4+yefNmNm/ezFVXXcWNN97Irl276OvrY8uWLfzzP/8zW7Zs4c9//jP79+9n1qxZA86xYMEClixZwqJFi1izZg09PT3MnDmTYDAYibn11luprKxk2bJlLFu2jMrKSubOnRuZDwaDXH/99fT29rJmzRoWLVrE4sWLue+++yIxXV1dTJ06FbfbzaZNm3jqqad44okn+OUvfxmJqaqq4rrrruOKK65g69atPPTQQ8yfP5/FixdHYtatW8ecOXOYO3cu27ZtY+7cudxyyy1s2LAhEvPyyy+zYMECfvCDH7B161auuOIKZsyYQXV19Tm57yIiIiIilxKjwUC2K47R6ZmUFwxievkwLhtXgC/WyuXThhGXGceOhna8YVj+5g6Wvradxb9fT3tHH54+H0nueC6fOYbkzATMFjO5RZl88OdNbFu9h8M7jmFzWOjv8bB5+XYsdjNJ7niaa9pwxjioPdBAxbs7WfLrZaRmJ+Hp99Lb1U9Hcyctte3s3nCAQ5VH2fBWJdFx0exYu4/2pi6m3DyerMIMXInRJGcl4g1BTJyTzn4/oVgr6w/V0BP0k1IUT58jxDV5gzAZ9WeviIiIiHxyepzXWTzGaWPrTja27fzEuYxNKGFsYsknPv50EhISePzxx7nzzjtPmtu0aRNjx47l6NGjZGdn09nZSXJyMi+++CJz5swBoK6ujqysLJYuXcq1117Lnj17KC4uZv369YwbNw6A9evXU15ezt69eyksLOStt95i5syZHDt2DLf7+J4vixYt4o477qCpqYnY2FieffZZHnzwQRobG7HZbAA8+uijPPXUU9TU1GAwGHjggQd47bXX2LNnTyTnefPmsW3bNtatWwfAnDlz6Orqiqy4AZg+fTrx8fG89NJLAIwbN44xY8bw7LPPRmKKioqYPXs2jzzyyN+8d3qcl4iIiIjIX+zYW8MHmw7i7feRnZZAssuJv9vHljX7aTjWdrwp4fNy5awxEApACJa+8D7xyS52rt1Lf7cXk8WIOz8Vvz9A3cFGpn71clpr26jeU4vBYKC7vYeAL4gj2sYt99/Ahrcqaa5tIyk9nsFj8ti1fj/X3Ho5Xo+fre/vxp2XTGdbL4TDtLb0MfGG0cSlxhGfEsX+qhZ2HmqiI+DFbwxRNiaXoSMzGJ2RQez/+xtEREREROSv6XFen5FGT+t5Pf5UgsEgixYtore3l/Ly8lPGdHZ2YjAYiIuLA6CiogK/38+0adMiMW63m5KSEtauXQscX/nhcrkiDRSA8ePH43K5BsSUlJREGigA1157LV6vl4qKikjM5MmTIw2UEzF1dXUcOXIkEvPXuZyI2bx5M36//7QxJ3Lx+XxUVFScFDNt2rRIjIiIiIiIfLzhQzOZ95XJlE8uJDYlmsaefsKxFjIHJzNr7kTKrizk2n8o5+j+emLiY7A6rMz+1jSyCtMYcflQYhKiyCnKxNvvx2gwYLFZMFvMOGMdtNZ3EAqFKSzNJz0/heSsRNoaO7E6bBSW5dPT1c87f/iQgDdI07E29mw+wpQvjiNraAbp+akkuBOZcfvlDBmdi6/XQ8BgwpLoZNTYXGbOGMm37riSOdPLmJyfrwaKiIiIiJwT5vOdwMXEE/R+quO9Id/HB52hHTt2UF5ejsfjITo6miVLllBcXHxSnMfj4fvf/z633nprpKPW0NCA1WolPj5+QGxqaioNDQ2RmJSUlJPOl5KSMiAmNTV1wHx8fDxWq3VATG5u7knvc2IuLy/vlOdJTU0lEAjQ0tJCenr634w58T4tLS0Eg8HTxoiIiIiIyJkxGg0Mz0vnbd8BWvwBEm0WskdmYvQEyRiSSsgTIDU3ie3rD5E/zE13Wy/uwekMKx+CyWKm4WgzPZ19hMNhZt89jbqqRuxOG+78FFzJMWAwEJsYQ0xCFGm5KRzYUkVjZy8Wq5nU7CR6u/rwevy4kmPo6Oijs7mbossKaKxpJSM/hYO76/AbYNXOfZQV53BdWSHRTq0qFxEREZFzT02Us2A3fbpPMtmM1nOUCRQWFlJZWUlHRweLFy/m9ttvZ9WqVQMaKX6/ny9/+cuEQiGeeeaZjz1nOBzGYDBEXv/1v89lzIknyJ2LmI+OnUmMiIiIiIh8vCirlRlDh7C9voEttfUU2lz4/CGSohw09XuJG5RKXGIs9UeaSUxzYTGZqD3aytiZYzAbDfR29NFwtIUD249ic9hwxDqYfudVtNS04unxEJMYQ2xSLIFAkMLLCti2ei82h5WoOCcz77oKV2oczU1dNFY1kZgex4GdxxgxYTCH9jXQZg7RZAoyY2Qxl5fkYTGbzvftEhEREZFLlJooZyHVnsjRvk+2sfyJ488Vq9XKoEGDACgrK2PTpk386le/4vnnnweON1BuueUWqqqqeO+99wY81y0tLQ2fz0d7e/uA1ShNTU1MmDAhEtPY2HjS+zY3N0dWe6SlpQ3Y2B2gvb0dv98/IOajK0GampoAPjbGbDaTmJh42pgT50hKSsJkMp02RkREREREzo7DYmFcdhYj0tOobe+gtbuPlsYeuoMB0lwufDYL2XYLe7cexQhUvLcbo9HAkBGZHNtTy+QvlNHe3EXDkWaiYhz0dvWTnJPCurcqCR1pxWwxM2pSIbnDc0jLS8XmsBCX4sIXCNLd7cFhNTFi/CCiE6Lp6/PjTI4mKtqENRjiCncig7NOXj0vIiIiInIuXRR7ojzzzDORTb9LS0v54IMPzkse2c40jIZPdsuMBiM5zvRznNFfhMNhvN7jjxs70UA5cOAA77zzTqQRcUJpaSkWi4UVK1ZExurr69m5c2ekiVJeXk5nZycbN26MxGzYsIHOzs4BMTt37qS+/i+NpeXLl2Oz2SgtLY3ErF69Gp/PNyDG7XZHHvNVXl4+IJcTMWVlZVgsltPGnMjFarVSWlp6UsyKFSsiMSIiIiIi8sk4LBYGpSQzriCHYUPdDMpPpr6rl53HmnAmOhk5voAJ1w1n+q3jsViMdLX2YrZa+P9++io5JZlcNaecmIQorrhpDJn5idx8z9V8+7E5lEwYTHNtB54+L80N7cS74/EHg3TUtRP2+klyu4hOjOLwvkZ6zbCjphmPJ8CwvFQ1UERERETkc3HBr0R5+eWXWbBgAc888wwTJ07k+eefZ8aMGezevZvs7OzPNZcUewK5znQO99ae9bG5znSS7fEfH3gGHnroIWbMmEFWVhbd3d0sWrSI999/n2XLlhEIBPjSl77Eli1beOONNwgGg5HVGQkJCVitVlwuF3feeSf33XcfiYmJJCQkcP/99zN8+HCuueYaAIqKipg+fTp33XVXZHXLN77xDWbOnElhYSFwfNP24uJi5s6dy+OPP05bWxv3338/d911V2Tly6233sq//uu/cscdd/DQQw9x4MABHn74YX70ox9FHrM1b948nn76aRYuXMhdd93FunXr+K//+i9eeumlyDV/5zvfYdKkSfz85z/nxhtv5NVXX+Wdd95hzZo1kZiFCxcyd+5cysrKKC8v57e//S3V1dXMmzfvnNx3ERERERGBrIR4shLiac/sp7Grm/52LwaDgUN76kjLiuOK60ex4Z2d5BWlU3u4gfde3kDB8Eymf3k8f/jJn1nz5+Or2SfeNJYb753Bod31bHh7GwXDswn7AvS29pKWl4zT5SDgC+Hxh4kZnkxVewfZ7njK8rOIj3Kc57sgIiIiIn8vDOETm09coMaNG8eYMWN49tlnI2NFRUXMnj2bRx555GOP7+rqwuVy0dnZOeCRVnB80/WqqqrIKpczUd/fzBt1q/GG/Gd8DTajhZnuSaQ7ks/4mNO58847effdd6mvr8flcjFixAgeeOABpk6dypEjR8jLyzvlcStXrmTKlCnA8Wv/3ve+xx/+8Af6+/u5+uqreeaZZ8jKyorEt7W1MX/+fF577TUAZs2axdNPP01cXFwkprq6mrvvvpv33nsPh8PBrbfeyhNPPIHN9pf9Y3bs2ME999zDxo0biY+PZ968eQOaKACrVq3iu9/9Lrt27cLtdvPAAw+c1Pz405/+xA9/+EMOHz5MQUEBP/vZz/jCF74wIOaZZ57hscceo76+npKSEp588kkmTZp02vv5SepARERERESOa+3q4eDRFvz9fqr21JMSayc1KYa+nn4IGzi6u5ZBY3KoWLqFxb98Y8CxX1hwPdfNm0bFu7uIjrGRmpuMM8aOPwTd3R66nGEOdHWRlxzPMHcq2Ylx5+ciRUREROSScrq+wUdd0E0Un8+H0+nkj3/8IzfddFNk/Dvf+Q6VlZWsWrXqpGO8Xm/ksVZw/GZkZWWdsyYKwJHeOlY0rDujRorNaGFaWjk5Ue4zPr98vtREERERERH59Po8PmqbOmhr7aa/rZ/EKAst1a04nTbSC9L46Zd/SdWO6gHH5AzL5AcvzaeuqgVntB1Xiovmll5cqbEkpMdis5iJttswGS+KJ1GLiIiIyEXibJooF/Rvoi0tLQSDwZM2Bk9NTT1pA/ETHnnkEVwuV+Trr1dWnCu5UW5muieRH5XxN/dIMRqM5EdlMNM9SQ0UERERERG55DntVgZnp1A2Mo/EnAQ6zQbiMpOIcsdjj7WQmHHy442TsxKxRkWRkBaH32Cis9dH1uAUhg7NIMUVg8vpUANFRERERM6rC35PFGDAY5/g+CbqHx074cEHH2ThwoWR1ydWopxr6Y5kUu2JNHvaOdpXT6OnFW/Ih81oJdWeSM7/2wPlk25ELyIiIiIicjEyGY0MH+Smpb2H2qZOenw+TF1ept42hZ2r9+Dp8wFgd1qZ+tXJ1DV24XDZIdpCbmYK8THO83wFIiIiIiJ/cUE3UZKSkjCZTCetOmlqajppdcoJNpttwH4cnyWjwUiqI5FUR+Ln8n4iIiIiIiIXi6T4aJLio4HjH4RzJcXw/f/9Dvs2HwJg8Jg8ci/LpDNkJjcljmiHHq0rIiIiIheeC7qJYrVaKS0tZcWKFQP2RFmxYgU33njjecxMREREREREzpTBYMCdHId79lgmzh47YO7cPzdAREREROTcuaCbKAALFy5k7ty5lJWVUV5ezm9/+1uqq6uZN2/e+U5NREREREREREREREQuYRd8E2XOnDm0trbyk5/8hPr6ekpKSli6dCk5OTnnOzUAPIEG+vxH8AU7CIW9GA02rKY4nJZc7Oa0852eiIiIiIiIiIiIiIh8Qhd8EwXg7rvv5u677z7faQzQ4ztEl3cnnd6dhPGdNG/AistWQqythGhrwXnIUEREREREREREREREPo2LoolyIQmHg7R7KmjsXUaY4N+Ow0eHdwud3m2kRk0n3l6KwWD6HDMVEREREREREREREZFPw3i+E7jYtHsqaOhdetoGyl8LE6ShdyntnorPODMRERERERERERERETmX1EQ5Cz2+QzT2LgPCZ3lkmMbeZfT4Dn0WaYmIiIiIiIiIiIiIyGdATZSz0OXdecYrUD4qTJAu765znNFxjzzyCAaDgQULFvzl/cJhfvzjH+N2u3E4HEyZMoVduwa+v9fr5d577yUpKYmoqChmzZpFTU3NgJj29nbmzp2Ly+XC5XIxd+5cOjo6BsRUV1dzww03EBUVRVJSEvPnz8fnG7hPzI4dO5g8eTIOh4OMjAx+8pOfEA4PbEatWrWK0tJS7HY7+fn5PPfccydd6+LFiykuLsZms1FcXMySJUtOinnmmWfIy8vDbrdTWlrKBx98cCa3UURERERERERERERkADVRzpAn0ECnd+enOkendweeQOM5yui4TZs28dvf/pYRI0YMGH/sscf45S9/ydNPP82mTZtIS0tj6tSpdHd3R2IWLFjAkiVLWLRoEWvWrKGnp4eZM2cSDP6lUXTrrbdSWVnJsmXLWLZsGZWVlcydOzcyHwwGuf766+nt7WXNmjUsWrSIxYsXc99990Viurq6mDp1Km63m02bNvHUU0/xxBNP8Mtf/jISU1VVxXXXXccVV1zB1q1beeihh5g/fz6LFy+OxKxbt445c+Ywd+5ctm3bxty5c7nlllvYsGFDJObll19mwYIF/OAHP2Dr1q1cccUVzJgxg+rq6nNzw0VERERERERERETk74Yh/NHlAJeYrq4uXC4XnZ2dxMbGDpjzeDxUVVVFVi2cTlv/Bhp6l37qfNKiriPBMe5Tnwegp6eHMWPG8Mwzz/DTn/6UUaNG8e///u+Ew2HcbjcLFizggQceAI6vOklNTeXnP/853/zmN+ns7CQ5OZkXX3yROXPmAFBXV0dWVhZLly7l2muvZc+ePRQXF7N+/XrGjTue8/r16ykvL2fv3r0UFhby1ltvMXPmTI4dO4bb7QZg0aJF3HHHHTQ1NREbG8uzzz7Lgw8+SGNjIzabDYBHH32Up556ipqaGgwGAw888ACvvfYae/bsiVzfvHnz2LZtG+vWrQNgzpw5dHV18dZbb0Vipk+fTnx8PC+99BIA48aNY8yYMTz77LORmKKiImbPns0jjzxyyvt4NnUgIiIiIiIiIiIiIhe30/UNPkorUc6QL9h+js7TcU7OA3DPPfdw/fXXc8011wwYr6qqoqGhgWnTpkXGbDYbkydPZu3atQBUVFTg9/sHxLjdbkpKSiIx69atw+VyRRooAOPHj8flcg2IKSkpiTRQAK699lq8Xi8VFRWRmMmTJ0caKCdi6urqOHLkSCTmr3M5EbN582b8fv9pY07k4vP5qKioOClm2rRpkRgRERERERERERERkTOlJsoZCoW95+g8nnNynkWLFrFly5ZTrq5oaGgAIDU1dcB4ampqZK6hoQGr1Up8fPxpY1JSUk46f0pKyoCYj75PfHw8Vqv1tDEnXn9cTCAQoKWl5bQxJ87R0tJCMBg8bYyIiIiIiIiIiIiIyJkyn+8ELhZGg+3jg87oPJ/+cVHHjh3jO9/5DsuXLz/t46cMBsOA1+Fw+KSxj/pozKniz0XMiafInYuYj459kusWEREREREREREREfkorUQ5Q1ZT/McHndF54j71OSoqKmhqaqK0tBSz2YzZbGbVqlX8+te/xmw2n7TK44SmpqbIXFpaGj6fj/b29tPGNDY2nvT+zc3NA2I++j7t7e34/f7TxjQ1NQF8bIzZbCYxMfG0MSfOkZSUhMlkOm2MiIiIiIiIiIiIiMiZUhPlDDktORiwfqpzGLDitOR+6lyuvvpqduzYQWVlZeSrrKyMr3zlK1RWVpKfn09aWhorVqyIHOPz+Vi1ahUTJkwAoLS0FIvFMiCmvr6enTt3RmLKy8vp7Oxk48aNkZgNGzbQ2dk5IGbnzp3U19dHYpYvX47NZqO0tDQSs3r1anw+34AYt9tNbm5uJOavczkRU1ZWhsViOW3MiVysViulpaUnxaxYsSISIyIiIiIiIiIiIiJypvQ4rzNkN6fhspXQ4d3yic/hsg3Hbv70KyJiYmIoKSkZMBYVFUViYmJkfMGCBTz88MMMHjyYwYMH8/DDD+N0Orn11luP5+Jyceedd3LfffeRmJhIQkIC999/P8OHD49sVF9UVMT06dO56667eP755wH4xje+wcyZMyksLASOb9peXFzM3Llzefzxx2lra+P+++/nrrvuIjY2FoBbb72Vf/3Xf+WOO+7goYce4sCBAzz88MP86Ec/ijxma968eTz99NMsXLiQu+66i3Xr1vFf//VfvPTSS5Fr/M53vsOkSZP4+c9/zo033sirr77KO++8w5o1ayIxCxcuZO7cuZSVlVFeXs5vf/tbqqurmTdv3qe+7yIiIiIiIiIiIiLy90VNlLMQayuh07uNMMGzPtaAiVjbsM8gq1P7p3/6J/r7+7n77rtpb29n3LhxLF++nJiYmEjMk08+idls5pZbbqG/v5+rr76a3/3ud5hMpkjM73//e+bPn8+0adMAmDVrFk8//XRk3mQy8eabb3L33XczceJEHA4Ht956K0888UQkxuVysWLFCu655x7KysqIj49n4cKFLFy4MBKTl5fH0qVL+e53v8tvfvMb3G43v/71r/niF78YiZkwYQKLFi3ihz/8If/8z/9MQUEBL7/8MuPGjYvEzJkzh9bWVn7yk59QX19PSUkJS5cuJScn59zeYBERERERERERERG55BnCJ3bvvkR1dXXhcrno7OyMrIw4wePxUFVVRV5e3mk3aP9rbf0baehdCpzNbTOQFnU9CY7LzuIY+bx8kjoQERERERERERERkYvT6foGH6WVKGcp3n58n4/G3mVntCLFgInUqOnE28d81qmJiIiIiIiIiIiIiMg5pCbKWTIYTCQ4xmI1JdLl3UWndwdhfCfHYcVlG06sbRjR1oLzkKmIiIiIiIiIiIiIiHwaaqJ8QtHWAqKtBSQ4xtHnP4Iv2EEo7MFosGM1xeG05J6TTeRFREREREREREREROT8UBPlU7KbU9UsERERERERERERERG5BBnPdwIXgnD4bDaJl0uNvv8iIiIiIiIiIiIicip/100Ui8UCQF9f33nORM6nE9//E/UgIiIiIiIiIiIiIgJ/54/zMplMxMXF0dTUBIDT6cRgMJznrOTzEg6H6evro6mpibi4OEwm0/lOSUREREREREREREQuIH/XTRSAtLQ0gEgjRf7+xMXFRepAREREREREREREROSEv/smisFgID09nZSUFPx+//lORz5nFotFK1BERERERERERERE5JT+7psoJ5hMJv1nuoiIiIiIiIiIiIiIRPxdbywvIiIiIiIiIiIiIiLyt6iJIiIiIiIiIiIiIiIicgpqooiIiIiIiIiIiIiIiJzCJb8nSjgcBqCrq+s8ZyIiIiIiIiIiIiIiIufbiX7Bif7B6VzyTZTu7m4AsrKyznMmIiIiIiIiIiIiIiJyoeju7sblcp02xhA+k1bLRSwUClFXV0dMTAwGg+F8pyN/Z7q6usjKyuLYsWPExsae73REPjHVslwKVMdyKVAdy6VAdSyXAtWxXApUx3IpUB3LJxUOh+nu7sbtdmM0nn7Xk0t+JYrRaCQzM/N8pyF/52JjY/WDXC4JqmW5FKiO5VKgOpZLgepYLgWqY7kUqI7lUqA6lk/i41agnKCN5UVERERERERERERERE5BTRQREREREREREREREZFTUBNF5DNks9n4l3/5F2w22/lOReRTUS3LpUB1LJcC1bFcClTHcilQHculQHUslwLVsXweLvmN5UVERERERERERERERD4JrUQRERERERERERERERE5BTVRRERERERERERERERETkFNFBERERERERERERERkVNQE0VEREREREREREREROQU1EQR+Yx5vV5GjRqFwWCgsrJywFx1dTU33HADUVFRJCUlMX/+fHw+3/lJVOQUZs2aRXZ2Nna7nfT0dObOnUtdXd2AGNWxXOiOHDnCnXfeSV5eHg6Hg4KCAv7lX/7lpDpVLcuF7mc/+xkTJkzA6XQSFxd3yhjVsVzonnnmGfLy8rDb7ZSWlvLBBx+c75RETmv16tXccMMNuN1uDAYDr7zyyoD5cDjMj3/8Y9xuNw6HgylTprBr167zk6zIKTzyyCNcdtllxMTEkJKSwuzZs9m3b9+AGNWxXAyeffZZRowYQWxsLLGxsZSXl/PWW29F5lXH8llSE0XkM/ZP//RPuN3uk8aDwSDXX389vb29rFmzhkWLFrF48WLuu+++85ClyKldeeWV/N///R/79u1j8eLFHDp0iC996UuRedWxXAz27t1LKBTi+eefZ9euXTz55JM899xzPPTQQ5EY1bJcDHw+HzfffDPf+ta3TjmvOpYL3csvv8yCBQv4wQ9+wNatW7niiiuYMWMG1dXV5zs1kb+pt7eXkSNH8vTTT59y/rHHHuOXv/wlTz/9NJs2bSItLY2pU6fS3d39OWcqcmqrVq3innvuYf369axYsYJAIMC0adPo7e2NxKiO5WKQmZnJo48+yubNm9m8eTNXXXUVN954Y6RRojqWz1RYRD4zS5cuDQ8dOjS8a9euMBDeunXrgDmj0Riura2NjL300kthm80W7uzsPA/Ziny8V199NWwwGMI+ny8cDquO5eL12GOPhfPy8iKvVctyMXnhhRfCLpfrpHHVsVzoxo4dG543b96AsaFDh4a///3vn6eMRM4OEF6yZEnkdSgUCqelpYUfffTRyJjH4wm7XK7wc889dx4yFPl4TU1NYSC8atWqcDisOpaLW3x8fPg///M/VcfymdNKFJHPSGNjI3fddRcvvvgiTqfzpPl169ZRUlIyYJXKtddei9frpaKi4vNMVeSMtLW18fvf/54JEyZgsVgA1bFcvDo7O0lISIi8Vi3LpUB1LBcyn89HRUUF06ZNGzA+bdo01q5de56yEvl0qqqqaGhoGFDXNpuNyZMnq67lgtXZ2QkQ+V1YdSwXo2AwyKJFi+jt7aW8vFx1LJ85NVFEPgPhcJg77riDefPmUVZWdsqYhoYGUlNTB4zFx8djtVppaGj4PNIUOSMPPPAAUVFRJCYmUl1dzauvvhqZUx3LxejQoUM89dRTzJs3LzKmWpZLgepYLmQtLS0Eg8GTajQ1NVX1KRetE7WrupaLRTgcZuHChVx++eWUlJQAqmO5uOzYsYPo6GhsNhvz5s1jyZIlFBcXq47lM6cmishZ+PGPf4zBYDjt1+bNm3nqqafo6uriwQcfPO35DAbDSWPhcPiU4yLnypnW8Qnf+9732Lp1K8uXL8dkMnHbbbcRDocj86pjOV/OtpYB6urqmD59OjfffDP/+I//OGBOtSznwyep49NRHcuF7qO1qPqUS4HqWi4W3/72t9m+fTsvvfTSSXOqY7kYFBYWUllZyfr16/nWt77F7bffzu7duyPzqmP5rJjPdwIiF5Nvf/vbfPnLXz5tTG5uLj/96U9Zv349NpttwFxZWRlf+cpX+O///m/S0tLYsGHDgPn29nb8fv9JnXORc+lM6/iEpKQkkpKSGDJkCEVFRWRlZbF+/XrKy8tVx3JenW0t19XVceWVV1JeXs5vf/vbAXGqZTlfzraOT0d1LBeypKQkTCbTSZ8GbWpqUn3KRSstLQ04/kn+9PT0yLjqWi5E9957L6+99hqrV68mMzMzMq46louJ1Wpl0KBBwPH/Y9u0aRO/+tWveOCBBwDVsXx21EQROQsn/jP54/z617/mpz/9aeR1XV0d1157LS+//DLjxo0DoLy8nJ/97GfU19dHfsAvX74cm81GaWnpZ3MBIpx5HZ/KiRUoXq8XUB3L+XU2tVxbW8uVV15JaWkpL7zwAkbjwMW4qmU5Xz7Nz+SPUh3LhcxqtVJaWsqKFSu46aabIuMrVqzgxhtvPI+ZiXxyeXl5pKWlsWLFCkaPHg0c3/9n1apV/PznPz/P2YkcFw6Huffee1myZAnvv/8+eXl5A+ZVx3IxC4fDeL1e1bF85tREEfkMZGdnD3gdHR0NQEFBQeQTH9OmTaO4uJi5c+fy+OOP09bWxv33389dd91FbGzs556zyEdt3LiRjRs3cvnllxMfH8/hw4f50Y9+REFBAeXl5YDqWC4OdXV1TJkyhezsbJ544gmam5sjcyc+eadalotBdXU1bW1tVFdXEwwGqaysBGDQoEFER0erjuWCt3DhQubOnUtZWVlkVWB1dfWAPapELjQ9PT0cPHgw8rqqqorKykoSEhLIzs5mwYIFPPzwwwwePJjBgwfz8MMP43Q6ufXWW89j1iJ/cc899/CHP/yBV199lZiYmMiKQJfLhcPhwGAwqI7lovDQQw8xY8YMsrKy6O7uZtGiRbz//vssW7ZMdSyfvbCIfOaqqqrCQHjr1q0Dxo8ePRq+/vrrww6HI5yQkBD+9re/HfZ4POcnSZGP2L59e/jKK68MJyQkhG02Wzg3Nzc8b968cE1NzYA41bFc6F544YUwcMqvv6Zalgvd7bfffso6XrlyZSRGdSwXut/85jfhnJycsNVqDY8ZMya8atWq852SyGmtXLnylD97b7/99nA4HA6HQqHwv/zLv4TT0tLCNpstPGnSpPCOHTvOb9Iif+Vv/R78wgsvRGJUx3Ix+PrXvx75HSI5OTl89dVXh5cvXx6ZVx3LZ8kQDv/V7sAiIiIiIiIiIiIiIiICgPHjQ0RERERERERERERERP7+qIkiIiIiIiIiIiIiIiJyCmqiiIiIiIiIiIiIiIiInIKaKCIiIiIiIiIiIiIiIqegJoqIiIiIiIiIiIiIiMgpqIkiIiIiIiIiIiIiIiJyCmqiiIiIiIiIiIiIiIiInIKaKCIiIiIiIiIiIiIiIqegJoqIiIiIiMhZ+N3vfkdcXNz5TkNERERERD4HaqKIiIiIiMinduzYMe68807cbjdWq5WcnBy+853v0Nraer5TO2srV67kuuuuIzExEafTSXFxMffddx+1tbXnOzUREREREfmcqYkiIiIiIiKfyuHDhykrK2P//v289NJLHDx4kOeee453332X8vJy2trazneKZ+z555/nmmuuIS0tjcWLF7N7926ee+45Ojs7+cUvfnG+0xMRERERkc+ZmigiIiIiIvKp3HPPPVitVpYvX87kyZPJzs5mxowZvPPOO9TW1vKDH/wgEpubm8u//du/ceuttxIdHY3b7eapp54acL7Ozk6+8Y1vkJKSQmxsLFdddRXbtm2LzP/4xz9m1KhRvPjii+Tm5uJyufjyl79Md3f3p7qOmpoa5s+fz/z58/n//r//jylTppCbm8ukSZP4z//8T370ox/9zWOfffZZCgoKsFqtFBYW8uKLLw6Y//GPf0x2djY2mw232838+fMjcz6fj3/6p38iIyODqKgoxo0bx/vvv/+prkVERERERM4NNVFEREREROQTa2tr4+233+buu+/G4XAMmEtLS+MrX/kKL7/8MuFwODL++OOPM2LECLZs2cKDDz7Id7/7XVasWAFAOBzm+uuvp6GhgaVLl1JRUcGYMWO4+uqrB6xoOXToEK+88gpvvPEGb7zxBqtWreLRRx/9VNfyxz/+MdLQOJW/tQ/KkiVL+M53vsN9993Hzp07+eY3v8nXvvY1Vq5cCcCf/vQnnnzySZ5//nkOHDjAK6+8wvDhwyPHf+1rX+PDDz9k0aJFbN++nZtvvpnp06dz4MCBT3U9IiIiIiLy6ZnPdwIiIiIiInLxOnDgAOFwmKKiolPOFxUV0d7eTnNzMykpKQBMnDiR73//+wAMGTKEDz/8kCeffJKpU6eycuVKduzYQVNTEzabDYAnnniCV155hT/96U984xvfACAUCvG73/2OmJgYAObOncu7777Lz372s091LbGxsaSnp5/VcU888QR33HEHd999NwALFy5k/fr1PPHEE1x55ZVUV1eTlpbGNddcg8ViITs7m7FjxwLHm0EvvfQSNTU1uN1uAO6//36WLVvGCy+8wMMPP/yJr0dERERERD49rUQREREREZHPzIkVKAaDITJWXl4+IKa8vJw9e/YAUFFRQU9PD4mJiURHR0e+qqqqOHToUOSY3NzcSAMFID09naampr+Zx1+fa968eX8z17/O80zt2bOHiRMnDhibOHFi5Jpuvvlm+vv7yc/P56677mLJkiUEAgEAtmzZQjgcZsiQIQNyXLVq1YDrFRERERGR80M9TtWUAAAD4klEQVQrUURERERE5BMbNGgQBoOB3bt3M3v27JPm9+7dS3x8PElJSac9z4nmRSgUIj09/ZR7gvz147QsFstJx4dCob95/srKysi/Y2NjTxkzZMgQOjs7qa+vP+vVKB9tvvx1QyYrK4t9+/axYsUK3nnnHe6++24ef/xxVq1aRSgUwmQyUVFRgclkGnCO6Ojos8pBRERERETOPa1EERERERGRTywxMZGpU6fyzDPP0N/fP2CuoaGB3//+98yZM2dAk2H9+vUD4tavX8/QoUMBGDNmDA0NDZjNZgYNGjTg6+MaMafz1+c58Vixj/rSl76E1WrlscceO+V8R0fHKceLiopYs2bNgLG1a9cOeMSZw+Fg1qxZ/PrXv+b9999n3bp17Nixg9GjRxMMBmlqajrpetPS0j7ZxYqIiIiIyDmjlSgiIiIiIvKpPP3000yYMIFrr72Wn/70p+Tl5bFr1y6+973vkZGRcdI+JR9++CGPPfYYs2fPZsWKFfzxj3/kzTffBOCaa66hvLyc2bNn8/Of/5zCwkLq6upYunQps2fPpqys7DO7jqysLJ588km+/e1v09XVxW233UZubi41NTX8z//8D9HR0fziF7846bjvfe973HLLLYwZM4arr76a119/nT//+c+88847APzud78jGAwybtw4nE4nL774Ig6Hg5ycHBITE/nKV77Cbbfdxi9+8QtGjx5NS0sL7733HsOHD+e66677zK5XREREREQ+nlaiiIiIiIjIpzJ48GA2b95MQUEBc+bMoaCggG984xtceeWVrFu3joSEhAHx9913HxUVFYwePZp/+7d/4xe/+AXXXnstcPyxWEuXLmXSpEl8/etfZ8iQIXz5y1/myJEjpKamfubXcvfdd7N8+XJqa2u56aabGDp0KP/4j/9IbGws999//ymPmT17Nr/61a94/PHHGTZsGM8//zwvvPACU6ZMAY4/huw//uM/mDhxIiNGjODdd9/l9ddfJzExEYAXXniB2267jfvuu4/CwkJmzZrFhg0byMrK+syvV0RERERETs8QPrHTo4iIiIiIyGcsNzeXBQsWsGDBgvOdioiIiIiIyMfSShQREREREREREREREZFTUBNFRERERERERERERETkFPQ4LxERERERERERERERkVPQShQREREREREREREREZFTUBNFRERERERERERERETkFNREEREREREREREREREROQU1UURERERERERERERERE5BTRQREREREREREREREZFTUBNFRERERERERERERETkFNREEREREREREREREREROQU1UURERERERERERERERE7h/wdvHZX1AH8NhgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the effect of price differences on volume\n",
+    "plt.figure(figsize = (20,10))\n",
+    "sns.scatterplot(x=df['Open'] - df['Close'], y=df['High'] - df['Low'], hue=df['Volume'], palette='viridis', size=df['Volume'], sizes=(20, 200), alpha=0.6)\n",
+    "plt.title('Effect of Price Differences on Volume')\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.ylabel('High - Low')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the effect of price differences on adjusted close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "id": "B8zeVZt7C2fT"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the effect of price differences on adjusted close\n",
+    "plt.figure(figsize = (20,10))\n",
+    "sns.lineplot(x = df['Open']-df['Close'], y = df['High'] - df['Low'], hue = df['Adj Close'])\n",
+    "plt.title('Effect of Price Differences on Adjusted Close')\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.ylabel('High - Low')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the relationship between 'Open - Close' and 'Adj Close'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "id": "EpjnJ3bJC2jq"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Open - Close' and 'Adj Close'\n",
+    "plt.figure(figsize = (20,10))\n",
+    "sns.lineplot(x = df['Open']-df['Close'], y = df['Adj Close'])\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a pair plot of stock features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "id": "omOa3x81C2uS"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1250x1250 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a pair plot of stock features\n",
+    "subset = df[['Open', 'Close', 'High', 'Low', 'Volume']]\n",
+    "sns.pairplot(subset)\n",
+    "plt.suptitle('Pair Plot of Stock Features', y=1.02)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a 3D scatter plot of 'Open', 'Close', and 'Volume'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "id": "gNjDWptzC2zP"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a 3D scatter plot of 'Open', 'Close', and 'Volume'\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "fig = plt.figure(figsize=(12, 8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.scatter(df['Open']- df['Close'], df['High'] - df['Low'],df['Volume'], c='r', marker='o')\n",
+    "ax.set_xlabel('Open Price')\n",
+    "ax.set_ylabel('Close Price')\n",
+    "ax.set_zlabel('Volume')\n",
+    "plt.title('3D Scatter Plot of Open, Close, and Volume')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the correlation matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "id": "Soovqm1IC24P"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the correlation matrix\n",
+    "correlation_matrix = df.corr(numeric_only = True)\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', square=True, cbar_kws={\"shrink\": .8})\n",
+    "\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d2g_ypkeDvdO"
+   },
+   "source": [
+    "The high correlation between 'Open,' 'High,' 'Low,' 'Close,' and 'Adj Close' shows these features are highly interdependent and tend to move together in the same direction.\n",
+    "The negative correlation of 'Volume' with price-related features suggests that increased trading volume does not necessarily coincide with an increase in stock prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### More Charts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotly.offline import plot\n",
+    "import plotly.graph_objs as go"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A line chart is a simple and effective way to visualize the closing price of the stock over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the closing price over time\n",
+    "plt.plot(df['Date'], df['Close'])\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Closing Price')\n",
+    "plt.title('SBIN Stock Price Over Time')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A candlestick chart is a more detailed way to visualize the stock price, showing the high, low, open, and close prices for each day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'candlestick_chart.html'"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Plot the candlestick chart\n",
+    "fig = go.Figure(data=[go.Candlestick(x=df['Date'],\n",
+    "                open=df['Open'],\n",
+    "                high=df['High'],\n",
+    "                low=df['Low'],\n",
+    "                close=df['Close'])])\n",
+    "plot(fig, filename='candlestick_chart.html')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python312\\site-packages\\plotly\\offline\\offline.py:557: UserWarning:\n",
+    "\n",
+    "Your filename `candlestick_chart` didn't end with .html. Adding .html to the end of your file.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "'candlestick_chart.html'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the daily volume\n",
+    "plt.bar(df['Date'], df['Volume'])\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Volume')\n",
+    "plt.title('SBIN Daily Volume')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the scatter plot of closing price vs volume\n",
+    "sns.scatterplot(x=df['Close'], y=df['Volume'])\n",
+    "plt.xlabel('Closing Price')\n",
+    "plt.ylabel('Volume')\n",
+    "plt.title('Relationship between SBIN Closing Price and Volume')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gAEZGRli2bFmVNf369UPLli1x4sQJ9OrVq9LF1NS0wnbOzs6YOnUqBg0aVO+nsenh44MZqclKS0uTrg3Izc3Fnj17sGrVKhgZGSE+Pr7CHSx3W758OXbs2IHAwEA4Ozvj9u3b+PrrrwFAeqCjpaUlXFxc8NNPP8HPzw82Njaws7Or9hbx6mg0GgQHByMyMhKOjo5Yu3YtEhMTERMTI92N1Lt3b3To0AGzZs3CnTt3YG1tjfj4eOzdu7fC/jw8PLBx40YsW7YMnp6eaNasGXr16lXpsefNm4dffvkFvr6+ePvtt2FjY4N169Zhy5YtWLBggXRb+oOKjo7GoEGD4Ovri1mzZsHU1BRLly5FWloavv322zo9cfiDDz7A77//jtGjR6N79+4wMzNDeno6lixZgqtXr+LDDz+ssE1KSoo0pn/++Qdff/01Tp06hddee63CaaKaiIyMxLp167Bz505YWFjct/6ZZ57BW2+9hffeew+nTp3C+PHjpQczHjx4ECtWrMDo0aOrvPX+tddew5o1axAYGIh3330XLi4u2LJlC5YuXYpXX31VCpavvPIKzMzM0K9fPzg6OiInJwfR0dFQqVTo3bt3rcd5r9DQUCxatAjPP/883n//fbRr1w6//vortm3bBqBmjzzQtzZt2uCNN97Ae++9h8LCQjz33HNQqVQ4ceIE/v33X7zzzjto0aIFPvvsM4SGhuLatWsYOXIk7O3tceXKFfz111+4cuUKli1bBq1WC19fX4wZMwYdO3aEpaUlDh8+jISEBIwYMeKhj43qWUNe0U1UH8rvxCpfTE1Nhb29vRgwYICIiooSubm5Fba5906V/fv3i6eeekq4uLgIpVIpbG1txYABAyo8iC8pKUn06NFDKJVKAUC6E6iqBwBWdiwh/t+DGX/44QfRpUsXYWpqKtq0aSMWLlxYYfszZ86IgIAAYWVlJVq1aiWmTZsmtmzZUuFOoWvXromRI0eKli1bCoVCoXNMVHJ33LFjx8SwYcOESqUSpqamolu3bhXuEqrqQXrld2Td764iIYTYs2ePePzxx4WFhYUwMzMTffv2FT///HOl+6vJXWYHDhwQU6ZMEd26dRM2NjbCyMhItGrVSgwZMkRs3bpVp7ayu8xsbGyEl5eX+Prrr3Ue4liTu8zu9sYbbwgANXowY7nk5GQxcuRI4ejoKExMTISVlZXw9vYWH374ocjPz5fqKnswY0ZGhhgzZoywtbUVJiYmokOHDuLDDz/UGcPq1auFr6+vcHBwEKampkKj0YhRo0aJo0ePVhjTvXeZVTaOyn53MzMzxYgRI0SLFi2EpaWlePrpp8XWrVsFAPHTTz9VO/7q3s/KjlvTvychhFizZo3o3bu3aN68uWjRooXo0aNHhd/P5ORkERgYKGxsbISJiYl45JFHRGBgoNSf27dvi0mTJomuXbsKKysrYWZmJjp06CDmzZvHB0M2QQoh7nO7DRERUS1ERUXhf//7HzIzMw36K2KI7sZTZkREVGdLliwBAOl7A3fs2IFPP/0Uzz//PMMQNSoMREREVGfm5uZYtGgRLly4gKKiIjg7O2POnDn43//+19BdI6oVnjIjIiIi2eNt90RERCR7DEREREQkewxEREREJHu8qLqGysrKcPnyZVhaWtbp4XFERET08AkhcOPGDWg0mmofFspAVEOXL1+u9jt2iIiIyHBdvHix2kdBMBDVkKWlJYD/3tDaftcRERERNYz8/Hw4OTlJn+NVYSCqofLTZFZWVgxEREREjcz9LnfhRdVEREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7xg3dAQLazN1y35oL8wMfQk+IiIjkiTNEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DRqIdu/ejWHDhkGj0UChUGDTpk0Vak6ePIng4GCoVCpYWlqib9++yMzMlNYXFRVh2rRpsLOzg4WFBYKDg5GVlaWzj7y8PISEhEClUkGlUiEkJATXr1+v59ERERFRY9GggejmzZvo1q0blixZUun6v//+G/3790fHjh2xa9cu/PXXX3jrrbfQvHlzqSYsLAzx8fGIi4vD3r17UVBQgKCgIJSWlko1Y8aMQWpqKhISEpCQkIDU1FSEhITU+/iIiIiocVAIIURDdwIAFAoF4uPjMXz4cKnt2WefhYmJCb755ptKt9FqtWjVqhW++eYbjB49GgBw+fJlODk5YevWrRg8eDBOnjyJzp0748CBA/Dy8gIAHDhwAN7e3jh16hQ6dOhQo/7l5+dDpVJBq9XCysrqwQZ7jzZzt9y35sL8QL0ek4iISA5q+vltsNcQlZWVYcuWLWjfvj0GDx4Me3t7eHl56ZxWS0lJQUlJCQICAqQ2jUYDd3d37Nu3DwCwf/9+qFQqKQwBQN++faFSqaSayhQVFSE/P19nISIioqbJYANRbm4uCgoKMH/+fAwZMgTbt2/HU089hREjRiA5ORkAkJOTA1NTU1hbW+ts6+DggJycHKnG3t6+wv7t7e2lmspER0dL1xypVCo4OTnpcXRERERkSAw2EJWVlQEAnnzySbz22mvo3r075s6di6CgICxfvrzabYUQUCgU0uu7/7uqmntFRERAq9VKy8WLF+s4EiIiIjJ0BhuI7OzsYGxsjM6dO+u0d+rUSbrLTK1Wo7i4GHl5eTo1ubm5cHBwkGr++eefCvu/cuWKVFMZpVIJKysrnYWIiIiaJoMNRKampujduzdOnz6t037mzBm4uLgAADw9PWFiYoLExERpfXZ2NtLS0uDj4wMA8Pb2hlarxaFDh6SagwcPQqvVSjVEREQkb8YNefCCggKcO3dOep2eno7U1FTY2NjA2dkZr7/+OkaPHo3HHnsMvr6+SEhIwM8//4xdu3YBAFQqFcaPH4/w8HDY2trCxsYGs2bNgoeHB/z9/QH8N6M0ZMgQvPLKK1ixYgUAYMKECQgKCqrxHWZERETUtDVoIPrjjz/g6+srvZ45cyYAIDQ0FLGxsXjqqaewfPlyREdHY/r06ejQoQN+/PFH9O/fX9pm0aJFMDY2xqhRo1BYWAg/Pz/ExsbCyMhIqlm3bh2mT58u3Y0WHBxc5bOPiIiISH4M5jlEho7PISIiImp8Gv1ziIiIiIgeFgYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpK9Bg1Eu3fvxrBhw6DRaKBQKLBp06YqaydOnAiFQoHFixfrtBcVFWHatGmws7ODhYUFgoODkZWVpVOTl5eHkJAQqFQqqFQqhISE4Pr16/ofEBERETVKDRqIbt68iW7dumHJkiXV1m3atAkHDx6ERqOpsC4sLAzx8fGIi4vD3r17UVBQgKCgIJSWlko1Y8aMQWpqKhISEpCQkIDU1FSEhITofTxERETUOBk35MGfeOIJPPHEE9XWXLp0CVOnTsW2bdsQGBios06r1WLlypX45ptv4O/vDwBYu3YtnJyckJSUhMGDB+PkyZNISEjAgQMH4OXlBQD48ssv4e3tjdOnT6NDhw71MzgiIiJqNAz6GqKysjKEhITg9ddfR5cuXSqsT0lJQUlJCQICAqQ2jUYDd3d37Nu3DwCwf/9+qFQqKQwBQN++faFSqaSayhQVFSE/P19nISIioqbJoANRTEwMjI2NMX369ErX5+TkwNTUFNbW1jrtDg4OyMnJkWrs7e0rbGtvby/VVCY6Olq65kilUsHJyekBRkJERESGzGADUUpKCj755BPExsZCoVDUalshhM42lW1/b829IiIioNVqpeXixYu16gMRERE1HgYbiPbs2YPc3Fw4OzvD2NgYxsbGyMjIQHh4ONq0aQMAUKvVKC4uRl5ens62ubm5cHBwkGr++eefCvu/cuWKVFMZpVIJKysrnYWIiIiaJoMNRCEhITh69ChSU1OlRaPR4PXXX8e2bdsAAJ6enjAxMUFiYqK0XXZ2NtLS0uDj4wMA8Pb2hlarxaFDh6SagwcPQqvVSjVEREQkbw16l1lBQQHOnTsnvU5PT0dqaipsbGzg7OwMW1tbnXoTExOo1WrpzjCVSoXx48cjPDwctra2sLGxwaxZs+Dh4SHdddapUycMGTIEr7zyClasWAEAmDBhAoKCgniHGREREQFo4ED0xx9/wNfXV3o9c+ZMAEBoaChiY2NrtI9FixbB2NgYo0aNQmFhIfz8/BAbGwsjIyOpZt26dZg+fbp0N1pwcPB9n31ERERE8qEQQoiG7kRjkJ+fD5VKBa1Wq/fridrM3XLfmgvzA+9bQ0RERLpq+vltsNcQERERET0sDEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsNGoh2796NYcOGQaPRQKFQYNOmTdK6kpISzJkzBx4eHrCwsIBGo8ELL7yAy5cv6+yjqKgI06ZNg52dHSwsLBAcHIysrCydmry8PISEhEClUkGlUiEkJATXr19/CCMkIiKixqBBA9HNmzfRrVs3LFmypMK6W7du4ciRI3jrrbdw5MgRbNy4EWfOnEFwcLBOXVhYGOLj4xEXF4e9e/eioKAAQUFBKC0tlWrGjBmD1NRUJCQkICEhAampqQgJCan38REREVHjoBBCiIbuBAAoFArEx8dj+PDhVdYcPnwYffr0QUZGBpydnaHVatGqVSt88803GD16NADg8uXLcHJywtatWzF48GCcPHkSnTt3xoEDB+Dl5QUAOHDgALy9vXHq1Cl06NChRv3Lz8+HSqWCVquFlZXVA4/3bm3mbrlvzYX5gXo9JhERkRzU9PO7UV1DpNVqoVAo0LJlSwBASkoKSkpKEBAQINVoNBq4u7tj3759AID9+/dDpVJJYQgA+vbtC5VKJdVUpqioCPn5+ToLERERNU2NJhDdvn0bc+fOxZgxY6SEl5OTA1NTU1hbW+vUOjg4ICcnR6qxt7evsD97e3uppjLR0dHSNUcqlQpOTk56HA0REREZkkYRiEpKSvDss8+irKwMS5cuvW+9EAIKhUJ6ffd/V1Vzr4iICGi1Wmm5ePFi3TpPREREBs/gA1FJSQlGjRqF9PR0JCYm6pz/U6vVKC4uRl5ens42ubm5cHBwkGr++eefCvu9cuWKVFMZpVIJKysrnYWIiIiaJoMOROVh6OzZs0hKSoKtra3Oek9PT5iYmCAxMVFqy87ORlpaGnx8fAAA3t7e0Gq1OHTokFRz8OBBaLVaqYaIiIjkzbghD15QUIBz585Jr9PT05GamgobGxtoNBqMHDkSR44cwS+//ILS0lLpmh8bGxuYmppCpVJh/PjxCA8Ph62tLWxsbDBr1ix4eHjA398fANCpUycMGTIEr7zyClasWAEAmDBhAoKCgmp8hxkRERE1bQ0aiP744w/4+vpKr2fOnAkACA0NRWRkJDZv3gwA6N69u852O3fuxMCBAwEAixYtgrGxMUaNGoXCwkL4+fkhNjYWRkZGUv26deswffp06W604ODgSp99RERERPJkMM8hMnR8DhEREVHj0ySfQ0RERERUHxiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPbqFIjS09P13Q8iIiKiBlOnQNSuXTv4+vpi7dq1uH37tr77RERERPRQ1SkQ/fXXX+jRowfCw8OhVqsxceJEHDp0SN99IyIiInoo6hSI3N3dsXDhQly6dAmrVq1CTk4O+vfvjy5dumDhwoW4cuWKvvtJREREVG8e6KJqY2NjPPXUU/juu+8QExODv//+G7NmzULr1q3xwgsvIDs7W1/9JCIiIqo3DxSI/vjjD0yePBmOjo5YuHAhZs2ahb///hs7duzApUuX8OSTT+qrn0RERET1xrguGy1cuBCrVq3C6dOnMXToUKxZswZDhw5Fs2b/5StXV1esWLECHTt21GtniYiIiOpDnWaIli1bhjFjxiAzMxObNm1CUFCQFIbKOTs7Y+XKldXuZ/fu3Rg2bBg0Gg0UCgU2bdqks14IgcjISGg0GpiZmWHgwIE4fvy4Tk1RURGmTZsGOzs7WFhYIDg4GFlZWTo1eXl5CAkJgUqlgkqlQkhICK5fv16XoRMREVETVKdAdPbsWURERECtVldZY2pqitDQ0Gr3c/PmTXTr1g1LliypdP2CBQuwcOFCLFmyBIcPH4ZarcagQYNw48YNqSYsLAzx8fGIi4vD3r17UVBQgKCgIJSWlko1Y8aMQWpqKhISEpCQkIDU1FSEhITUctRERETUVCmEEKK2G61atQotWrTAM888o9P+/fff49atW/cNQpV2RKFAfHw8hg8fDuC/2SGNRoOwsDDMmTMHwH+zQQ4ODoiJicHEiROh1WrRqlUrfPPNNxg9ejQA4PLly3BycsLWrVsxePBgnDx5Ep07d8aBAwfg5eUFADhw4AC8vb1x6tQpdOjQoUb9y8/Ph0qlglarhZWVVa3HV502c7fct+bC/EC9HpOIiEgOavr5XacZovnz58POzq5Cu729PaKiouqyywrS09ORk5ODgIAAqU2pVGLAgAHYt28fACAlJQUlJSU6NRqNBu7u7lLN/v37oVKppDAEAH379oVKpZJqiIiISN7qdFF1RkYGXF1dK7S7uLggMzPzgTsFADk5OQAABwcHnXYHBwdkZGRINaamprC2tq5QU759Tk4O7O3tK+zf3t5eqqlMUVERioqKpNf5+fl1GwgREREZvDrNENnb2+Po0aMV2v/66y/Y2to+cKfuplAodF4LISq03evemsrq77ef6Oho6SJslUoFJyenWvaciIiIGos6BaJnn30W06dPx86dO1FaWorS0lLs2LEDM2bMwLPPPquXjpVfsH3vLE5ubq40a6RWq1FcXIy8vLxqa/75558K+79y5UqF2ae7RUREQKvVSsvFixcfaDxERERkuOoUiN5//314eXnBz88PZmZmMDMzQ0BAAB5//HG9XUPk6uoKtVqNxMREqa24uBjJycnw8fEBAHh6esLExESnJjs7G2lpaVKNt7c3tFqtznetHTx4EFqtVqqpjFKphJWVlc5CRERETVOdriEyNTXFhg0b8N577+Gvv/6CmZkZPDw84OLiUqv9FBQU4Ny5c9Lr9PR0pKamwsbGBs7OzggLC0NUVBTc3Nzg5uaGqKgomJubY8yYMQAAlUqF8ePHIzw8HLa2trCxscGsWbPg4eEBf39/AECnTp0wZMgQvPLKK1ixYgUAYMKECQgKCqrxHWZERETUtNUpEJVr37492rdvX+ft//jjD/j6+kqvZ86cCQAIDQ1FbGwsZs+ejcLCQkyePBl5eXnw8vLC9u3bYWlpKW2zaNEiGBsbY9SoUSgsLISfnx9iY2NhZGQk1axbtw7Tp0+X7kYLDg6u8tlHREREJD91eg5RaWkpYmNj8dtvvyE3NxdlZWU663fs2KG3DhoKPoeIiIio8anp53edZohmzJiB2NhYBAYGwt3d/b53fREREREZsjoFori4OHz33XcYOnSovvtDRERE9NDV6S4zU1NTtGvXTt99ISIiImoQdQpE4eHh+OSTT1CHy4+IiIiIDE6dTpnt3bsXO3fuxK+//oouXbrAxMREZ/3GjRv10jkiIiKih6FOgahly5Z46qmn9N0XIiIiogZRp0C0atUqffeDiIiIqMHU6RoiALhz5w6SkpKwYsUK3LhxAwBw+fJlFBQU6K1zRERERA9DnWaIMjIyMGTIEGRmZqKoqAiDBg2CpaUlFixYgNu3b2P58uX67icRERFRvanTDNGMGTPQq1cv5OXlwczMTGp/6qmn8Ntvv+mtc0REREQPQ53vMvv9999hamqq0+7i4oJLly7ppWNERERED0udZojKyspQWlpaoT0rK0vni1eJiIiIGoM6BaJBgwZh8eLF0muFQoGCggLMmzePX+dBREREjU6dTpktWrQIvr6+6Ny5M27fvo0xY8bg7NmzsLOzw7fffqvvPhIRERHVqzoFIo1Gg9TUVHz77bc4cuQIysrKMH78eIwdO1bnImsiIiKixqBOgQgAzMzM8NJLL+Gll17SZ3+IiIiIHro6BaI1a9ZUu/6FF16oU2eIiIiIGkKdAtGMGTN0XpeUlODWrVswNTWFubk5AxERERE1KnW6yywvL09nKSgowOnTp9G/f39eVE1ERESNTp2/y+xebm5umD9/foXZIyIiIiJDp7dABABGRka4fPmyPndJREREVO/qdA3R5s2bdV4LIZCdnY0lS5agX79+eukYERER0cNSp0A0fPhwndcKhQKtWrXC448/jo8//lgf/SIiIiJ6aOoUiMrKyvTdDyIiIqIGo9driIiIiIgaozrNEM2cObPGtQsXLqzLIYiIiIgemjoFoj///BNHjhzBnTt30KFDBwDAmTNnYGRkhJ49e0p1CoVCP70kIiIiqkd1CkTDhg2DpaUlVq9eDWtrawD/PazxxRdfxP/93/8hPDxcr50kIiIiqk91uobo448/RnR0tBSGAMDa2hrvv/8+7zIjIiKiRqdOgSg/Px///PNPhfbc3FzcuHHjgTtFRERE9DDVKRA99dRTePHFF/HDDz8gKysLWVlZ+OGHHzB+/HiMGDFC330kIiIiqld1CkTLly9HYGAgnn/+ebi4uMDFxQVjx47FE088gaVLl+qtc3fu3MH//vc/uLq6wszMDI8++ijeffddnecgCSEQGRkJjUYDMzMzDBw4EMePH9fZT1FREaZNmwY7OztYWFggODgYWVlZeusnERERNW51CkTm5uZYunQprl69Kt1xdu3aNSxduhQWFhZ661xMTAyWL1+OJUuW4OTJk1iwYAE+/PBDfPbZZ1LNggULsHDhQixZsgSHDx+GWq3GoEGDdE7dhYWFIT4+HnFxcdi7dy8KCgoQFBSE0tJSvfWViIiIGq8HejBjdnY2srOz0b59e1hYWEAIoa9+AQD279+PJ598EoGBgWjTpg1GjhyJgIAA/PHHHwD+mx1avHgx3nzzTYwYMQLu7u5YvXo1bt26hfXr1wMAtFotVq5ciY8//hj+/v7o0aMH1q5di2PHjiEpKUmv/SUiIqLGqU6B6OrVq/Dz80P79u0xdOhQZGdnAwBefvllvd5y379/f/z22284c+YMAOCvv/7C3r17MXToUABAeno6cnJyEBAQIG2jVCoxYMAA7Nu3DwCQkpKCkpISnRqNRgN3d3eppjJFRUXIz8/XWYiIiKhpqlMgeu2112BiYoLMzEyYm5tL7aNHj0ZCQoLeOjdnzhw899xz6NixI0xMTNCjRw+EhYXhueeeAwDk5OQAABwcHHS2c3BwkNbl5OTA1NRU5xEB99ZUJjo6GiqVSlqcnJz0Ni4iIiIyLHUKRNu3b0dMTAxat26t0+7m5oaMjAy9dAwANmzYgLVr12L9+vU4cuQIVq9ejY8++girV6/Wqbv3idhCiPs+Jft+NREREdBqtdJy8eLFug+EiIiIDFqdnlR98+ZNnZmhcv/++y+USuUDd6rc66+/jrlz5+LZZ58FAHh4eCAjIwPR0dEIDQ2FWq0G8N8skKOjo7Rdbm6uNGukVqtRXFyMvLw8nVmi3Nxc+Pj4VHlspVKp17EQERGR4arTDNFjjz2GNWvWSK8VCgXKysrw4YcfwtfXV2+du3XrFpo10+2ikZGRdNu9q6sr1Go1EhMTpfXFxcVITk6Wwo6npydMTEx0arKzs5GWllZtICIiIiL5qNMM0YcffoiBAwfijz/+QHFxMWbPno3jx4/j2rVr+P333/XWuWHDhuGDDz6As7MzunTpgj///BMLFy7ESy+9BOC/IBYWFoaoqCi4ubnBzc0NUVFRMDc3x5gxYwAAKpUK48ePR3h4OGxtbWFjY4NZs2bBw8MD/v7+eusrERERNV51CkSdO3fG0aNHsWzZMhgZGeHmzZsYMWIEpkyZonPq6kF99tlneOuttzB58mTk5uZCo9Fg4sSJePvtt6Wa2bNno7CwEJMnT0ZeXh68vLywfft2WFpaSjWLFi2CsbExRo0ahcLCQvj5+SE2NhZGRkZ66ysRERE1XgpRy4cHld/CvmLFCrRv376++mVw8vPzoVKpoNVqYWVlpdd9t5m75b41F+YH6vWYREREclDTz+9aX0NkYmKCtLS0+97FRURERNRY1Omi6hdeeAErV67Ud1+IiIiIGkSdriEqLi7GV199hcTERPTq1avC95ctXLhQL50jIiIiehhqFYjOnz+PNm3aIC0tDT179gQA6Ws1yvFUGhERETU2tQpEbm5uyM7Oxs6dOwH891Udn376aYWvziAiIiJqTGp1DdG9N6T9+uuvuHnzpl47RERERPSw1emi6nK1vGOfiIiIyCDVKhApFIoK1wjxmiEiIiJq7Gp1DZEQAuPGjZO+9PT27duYNGlShbvMNm7cqL8eEhEREdWzWgWi0NBQndfPP/+8XjtDRERE1BBqFYhWrVpVX/0gIiIiajAPdFE1ERERUVPAQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLJn8IHo0qVLeP7552Frawtzc3N0794dKSkp0nohBCIjI6HRaGBmZoaBAwfi+PHjOvsoKirCtGnTYGdnBwsLCwQHByMrK+thD4WIiIgMlEEHory8PPTr1w8mJib49ddfceLECXz88cdo2bKlVLNgwQIsXLgQS5YsweHDh6FWqzFo0CDcuHFDqgkLC0N8fDzi4uKwd+9eFBQUICgoCKWlpQ0wKiIiIjI0CiGEaOhOVGXu3Ln4/fffsWfPnkrXCyGg0WgQFhaGOXPmAPhvNsjBwQExMTGYOHEitFotWrVqhW+++QajR48GAFy+fBlOTk7YunUrBg8eXKO+5OfnQ6VSQavVwsrKSj8D/P+1mbvlvjUX5gfq9ZhERERyUNPPb4OeIdq8eTN69eqFZ555Bvb29ujRowe+/PJLaX16ejpycnIQEBAgtSmVSgwYMAD79u0DAKSkpKCkpESnRqPRwN3dXaqpTFFREfLz83UWIiIiapoMOhCdP38ey5Ytg5ubG7Zt24ZJkyZh+vTpWLNmDQAgJycHAODg4KCznYODg7QuJycHpqamsLa2rrKmMtHR0VCpVNLi5OSkz6ERERGRATHoQFRWVoaePXsiKioKPXr0wMSJE/HKK69g2bJlOnUKhULntRCiQtu97lcTEREBrVYrLRcvXqz7QIiIiMigGXQgcnR0ROfOnXXaOnXqhMzMTACAWq0GgAozPbm5udKskVqtRnFxMfLy8qqsqYxSqYSVlZXOQkRERE2TQQeifv364fTp0zptZ86cgYuLCwDA1dUVarUaiYmJ0vri4mIkJyfDx8cHAODp6QkTExOdmuzsbKSlpUk1REREJG/GDd2B6rz22mvw8fFBVFQURo0ahUOHDuGLL77AF198AeC/U2VhYWGIioqCm5sb3NzcEBUVBXNzc4wZMwYAoFKpMH78eISHh8PW1hY2NjaYNWsWPDw84O/v35DDIyIiIgNh0IGod+/eiI+PR0REBN599124urpi8eLFGDt2rFQze/ZsFBYWYvLkycjLy4OXlxe2b98OS0tLqWbRokUwNjbGqFGjUFhYCD8/P8TGxsLIyKghhkVEREQGxqCfQ2RI+BwiIiKixqdJPIeIiIiI6GFgICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WtUgSg6OhoKhQJhYWFSmxACkZGR0Gg0MDMzw8CBA3H8+HGd7YqKijBt2jTY2dnBwsICwcHByMrKesi9JyIiIkPVaALR4cOH8cUXX6Br16467QsWLMDChQuxZMkSHD58GGq1GoMGDcKNGzekmrCwMMTHxyMuLg579+5FQUEBgoKCUFpa+rCHQURERAaoUQSigoICjB07Fl9++SWsra2ldiEEFi9ejDfffBMjRoyAu7s7Vq9ejVu3bmH9+vUAAK1Wi5UrV+Ljjz+Gv78/evTogbVr1+LYsWNISkpqqCERERGRAWkUgWjKlCkIDAyEv7+/Tnt6ejpycnIQEBAgtSmVSgwYMAD79u0DAKSkpKCkpESnRqPRwN3dXaqpTFFREfLz83UWIiIiapqMG7oD9xMXF4cjR47g8OHDFdbl5OQAABwcHHTaHRwckJGRIdWYmprqzCyV15RvX5no6Gi88847D9p9IiIiagQMeobo4sWLmDFjBtauXYvmzZtXWadQKHReCyEqtN3rfjURERHQarXScvHixdp1noiIiBoNgw5EKSkpyM3NhaenJ4yNjWFsbIzk5GR8+umnMDY2lmaG7p3pyc3Nldap1WoUFxcjLy+vyprKKJVKWFlZ6SxERETUNBl0IPLz88OxY8eQmpoqLb169cLYsWORmpqKRx99FGq1GomJidI2xcXFSE5Oho+PDwDA09MTJiYmOjXZ2dlIS0uTaoiIiEjeDPoaIktLS7i7u+u0WVhYwNbWVmoPCwtDVFQU3Nzc4ObmhqioKJibm2PMmDEAAJVKhfHjxyM8PBy2trawsbHBrFmz4OHhUeEibSIiIpIngw5ENTF79mwUFhZi8uTJyMvLg5eXF7Zv3w5LS0upZtGiRTA2NsaoUaNQWFgIPz8/xMbGwsjIqAF7TkRERIZCIYQQDd2JxiA/Px8qlQparVbv1xO1mbvlvjUX5gfq9ZhERERyUNPPb4O+hoiIiIjoYWAgIiIiItljICIiIiLZa/QXVcsFrzMiIiKqP5whIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItnjc4iIGhCfL0VEZBg4Q0RERESyxxkiIpngbBQRUdUYiIhIwtBERHLFQETUBNQkyBARUdV4DRERERHJHgMRERERyR4DEREREckeAxERERHJHi+qJqoD3o1FRNS0cIaIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI8XVRMZOD6Fmoio/jEQEd1DXwGEQYaIqPFgIJIZ3i5ORERUEa8hIiIiItljICIiIiLZM+hAFB0djd69e8PS0hL29vYYPnw4Tp8+rVMjhEBkZCQ0Gg3MzMwwcOBAHD9+XKemqKgI06ZNg52dHSwsLBAcHIysrKyHORQiIiIyYAZ9DVFycjKmTJmC3r17486dO3jzzTcREBCAEydOwMLCAgCwYMECLFy4ELGxsWjfvj3ef/99DBo0CKdPn4alpSUAICwsDD///DPi4uJga2uL8PBwBAUFISUlBUZGRg05RKJGh9ehEVFTpBBCiIbuRE1duXIF9vb2SE5OxmOPPQYhBDQaDcLCwjBnzhwA/80GOTg4ICYmBhMnToRWq0WrVq3wzTffYPTo0QCAy5cvw8nJCVu3bsXgwYNrdOz8/HyoVCpotVpYWVnpdVyGdjeS3D/MDO3n0RjJ/XeIiAxHTT+/DfqU2b20Wi0AwMbGBgCQnp6OnJwcBAQESDVKpRIDBgzAvn37AAApKSkoKSnRqdFoNHB3d5dqiIiISN4M+pTZ3YQQmDlzJvr37w93d3cAQE5ODgDAwcFBp9bBwQEZGRlSjampKaytrSvUlG9fmaKiIhQVFUmv8/Pz9TIOIiIiMjyNZoZo6tSpOHr0KL799tsK6xQKhc5rIUSFtnvdryY6OhoqlUpanJyc6tZxIiIiMniNYoZo2rRp2Lx5M3bv3o3WrVtL7Wq1GsB/s0COjo5Se25urjRrpFarUVxcjLy8PJ1ZotzcXPj4+FR5zIiICMycOVN6nZ+fz1B0l8Z6YS2vDyIiosoY9AyREAJTp07Fxo0bsWPHDri6uuqsd3V1hVqtRmJiotRWXFyM5ORkKex4enrCxMREpyY7OxtpaWnVBiKlUgkrKyudhYiIiJomg54hmjJlCtavX4+ffvoJlpaW0jU/KpUKZmZmUCgUCAsLQ1RUFNzc3ODm5oaoqCiYm5tjzJgxUu348eMRHh4OW1tb2NjYYNasWfDw8IC/v39DDo+IiIgMhEEHomXLlgEABg4cqNO+atUqjBs3DgAwe/ZsFBYWYvLkycjLy4OXlxe2b98uPYMIABYtWgRjY2OMGjUKhYWF8PPzQ2xsLJ9BRERERAAMPBDV5BFJCoUCkZGRiIyMrLKmefPm+Oyzz/DZZ5/psXdERETUVBj0NUREREREDwMDEREREcmeQZ8yI6LGqbE+loGI5IszRERERCR7DEREREQkewxEREREJHu8hogq4Ndb0MPA64yIyJAwEFGTwBBHREQPgqfMiIiISPY4Q0QNiqdNiIjIEHCGiIiIiGSPgYiIiIhkj4GIiIiIZI/XEFG94Z1fRETUWHCGiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPF1WTwePF2UREVN84Q0RERESyxxkiIjJY/GoXqm/8HaNynCEiIiIi2WMgIiIiItnjKTMiInpo9HWTxMM8jcXTavLAGSIiIiKSPc4QEREZIM5KNC78eTV+DERE1OTJ+cNKX2NvjO8hn2FGtcFARESNGj/0DAd/FtSYMRARET1kDA7y1Bhn2eSEgYiIqJHSV7BqjAGtMfaZDJus7jJbunQpXF1d0bx5c3h6emLPnj0N3SUiIiIyALKZIdqwYQPCwsKwdOlS9OvXDytWrMATTzyBEydOwNnZuaG7R0SNAE95kCHg72H9UAghREN34mHw8vJCz549sWzZMqmtU6dOGD58OKKjo++7fX5+PlQqFbRaLaysrPTaN079EjU8fd1pRWQIGIj+n5p+fstihqi4uBgpKSmYO3euTntAQAD27dvXQL0iIkPCsEMkb7IIRP/++y9KS0vh4OCg0+7g4ICcnJxKtykqKkJRUZH0WqvVAvgvaepbWdEtve+TiIjkqz4+qxqr8vfififEZBGIyikUCp3XQogKbeWio6PxzjvvVGh3cnKql74RERHpi2pxQ/fA8Ny4cQMqlarK9bIIRHZ2djAyMqowG5Sbm1th1qhcREQEZs6cKb0uKyvDtWvXYGtrW2WIqkx+fj6cnJxw8eJFvV97ZOjkPHZA3uPn2OU5dkDe4+fYDXPsQgjcuHEDGo2m2jpZBCJTU1N4enoiMTERTz31lNSemJiIJ598stJtlEollEqlTlvLli3r3AcrKyuD+yV5WOQ8dkDe4+fY5Tl2QN7j59gNb+zVzQyVk0UgAoCZM2ciJCQEvXr1gre3N7744gtkZmZi0qRJDd01IiIiamCyCUSjR4/G1atX8e677yI7Oxvu7u7YunUrXFxcGrprRERE1MBkE4gAYPLkyZg8efJDPaZSqcS8efMqnH6TAzmPHZD3+Dl2eY4dkPf4OfbGPXbZPJiRiIiIqCqy+i4zIiIiosowEBEREZHsMRARERGR7DEQERERkewxENWjpUuXwtXVFc2bN4enpyf27NnT0F16YLt378awYcOg0WigUCiwadMmnfVCCERGRkKj0cDMzAwDBw7E8ePHdWqKioowbdo02NnZwcLCAsHBwcjKynqIo6ib6Oho9O7dG5aWlrC3t8fw4cNx+vRpnZqmPP5ly5aha9eu0oPXvL298euvv0rrm/LY7xUdHQ2FQoGwsDCpramOPzIyEgqFQmdRq9XS+qY67rtdunQJzz//PGxtbWFubo7u3bsjJSVFWt9U34M2bdpU+NkrFApMmTIFQBMct6B6ERcXJ0xMTMSXX34pTpw4IWbMmCEsLCxERkZGQ3ftgWzdulW8+eab4scffxQARHx8vM76+fPnC0tLS/Hjjz+KY8eOidGjRwtHR0eRn58v1UyaNEk88sgjIjExURw5ckT4+vqKbt26iTt37jzk0dTO4MGDxapVq0RaWppITU0VgYGBwtnZWRQUFEg1TXn8mzdvFlu2bBGnT58Wp0+fFm+88YYwMTERaWlpQoimPfa7HTp0SLRp00Z07dpVzJgxQ2pvquOfN2+e6NKli8jOzpaW3NxcaX1THXe5a9euCRcXFzFu3Dhx8OBBkZ6eLpKSksS5c+ekmqb6HuTm5ur83BMTEwUAsXPnTiFE0xs3A1E96dOnj5g0aZJOW8eOHcXcuXMbqEf6d28gKisrE2q1WsyfP19qu337tlCpVGL58uVCCCGuX78uTExMRFxcnFRz6dIl0axZM5GQkPDQ+q4Pubm5AoBITk4WQshv/EIIYW1tLb766ivZjP3GjRvCzc1NJCYmigEDBkiBqCmPf968eaJbt26VrmvK4y43Z84c0b9//yrXy+E9KDdjxgzRtm1bUVZW1iTHzVNm9aC4uBgpKSkICAjQaQ8ICMC+ffsaqFf1Lz09HTk5OTrjViqVGDBggDTulJQUlJSU6NRoNBq4u7s3uvdGq9UCAGxsbADIa/ylpaWIi4vDzZs34e3tLZuxT5kyBYGBgfD399dpb+rjP3v2LDQaDVxdXfHss8/i/PnzAJr+uAFg8+bN6NWrF5555hnY29ujR48e+PLLL6X1cngPgP8+19auXYuXXnoJCoWiSY6bgage/PvvvygtLYWDg4NOu4ODA3JychqoV/WvfGzVjTsnJwempqawtrausqYxEEJg5syZ6N+/P9zd3QHIY/zHjh1DixYtoFQqMWnSJMTHx6Nz586yGHtcXByOHDmC6OjoCuua8vi9vLywZs0abNu2DV9++SVycnLg4+ODq1evNulxlzt//jyWLVsGNzc3bNu2DZMmTcL06dOxZs0aAE37Z3+3TZs24fr16xg3bhyApjluWX11x8OmUCh0XgshKrQ1RXUZd2N7b6ZOnYqjR49i7969FdY15fF36NABqampuH79On788UeEhoYiOTlZWt9Ux37x4kXMmDED27dvR/Pmzausa4rjf+KJJ6T/9vDwgLe3N9q2bYvVq1ejb9++AJrmuMuVlZWhV69eiIqKAgD06NEDx48fx7Jly/DCCy9IdU35PQCAlStX4oknnoBGo9Fpb0rj5gxRPbCzs4ORkVGFBJybm1shTTcl5XeeVDdutVqN4uJi5OXlVVlj6KZNm4bNmzdj586daN26tdQuh/GbmpqiXbt26NWrF6Kjo9GtWzd88sknTX7sKSkpyM3NhaenJ4yNjWFsbIzk5GR8+umnMDY2lvrfVMd/NwsLC3h4eODs2bNN/ucOAI6OjujcubNOW6dOnZCZmQlAHn/3GRkZSEpKwssvvyy1NcVxMxDVA1NTU3h6eiIxMVGnPTExET4+Pg3Uq/rn6uoKtVqtM+7i4mIkJydL4/b09ISJiYlOTXZ2NtLS0gz+vRFCYOrUqdi4cSN27NgBV1dXnfVNffyVEUKgqKioyY/dz88Px44dQ2pqqrT06tULY8eORWpqKh599NEmPf67FRUV4eTJk3B0dGzyP3cA6NevX4XHa5w5cwYuLi4A5PF3v2rVKtjb2yMwMFBqa5LjfthXcctF+W33K1euFCdOnBBhYWHCwsJCXLhwoaG79kBu3Lgh/vzzT/Hnn38KAGLhwoXizz//lB4nMH/+fKFSqcTGjRvFsWPHxHPPPVfpbZitW7cWSUlJ4siRI+Lxxx832Nsw7/bqq68KlUoldu3apXMr6q1bt6Sapjz+iIgIsXv3bpGeni6OHj0q3njjDdGsWTOxfft2IUTTHntl7r7LTIimO/7w8HCxa9cucf78eXHgwAERFBQkLC0tpf+XNdVxlzt06JAwNjYWH3zwgTh79qxYt26dMDc3F2vXrpVqmvJ7UFpaKpydncWcOXMqrGtq42Ygqkeff/65cHFxEaampqJnz57S7dmN2c6dOwWACktoaKgQ4r9bUOfNmyfUarVQKpXiscceE8eOHdPZR2FhoZg6daqwsbERZmZmIigoSGRmZjbAaGqnsnEDEKtWrZJqmvL4X3rpJen3uVWrVsLPz08KQ0I07bFX5t5A1FTHX/5sGRMTE6HRaMSIESPE8ePHpfVNddx3+/nnn4W7u7tQKpWiY8eO4osvvtBZ35Tfg23btgkA4vTp0xXWNbVxK4QQokGmpoiIiIgMBK8hIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICKiBqVQKLBp0ya97CsyMhLdu3fXy77qw7hx4zB8+PCG7gYRVYKBiIjqTU5ODqZNm4ZHH30USqUSTk5OGDZsGH777bd6Od6sWbPqbd93i4yMhEKhgEKhgJGREZycnPDyyy/jypUr1W73ySefIDY2tt77R0S1Z9zQHSCipunChQvo168fWrZsiQULFqBr164oKSnBtm3bMGXKFJw6dUrvx2zRogVatGih9/1WpkuXLkhKSkJpaSn+/PNPjB8/HpcuXcKvv/5aoba0tBQKhQIqleqh9I2Iao8zRERULyZPngyFQoFDhw5h5MiRaN++Pbp06YKZM2fiwIEDVW537NgxPP744zAzM4OtrS0mTJiAgoICaf2uXbvQp08fWFhYoGXLlujXrx8yMjIAVDxlVn6K6qOPPoKjoyNsbW0xZcoUlJSUSDXZ2dkIDAyEmZkZXF1dsX79erRp0waLFy+udnzGxsZQq9V45JFHEBQUhOnTp2P79u0oLCxEbGwsWrZsiV9++QWdO3eGUqlERkZGhVNmZWVliImJQbt27aBUKuHs7IwPPvhAWn/p0iWMHj0a1tbWsLW1xZNPPokLFy7U7AdARLXCQEREenft2jUkJCRgypQpsLCwqLC+ZcuWlW5369YtDBkyBNbW1jh8+DC+//57JCUlYerUqQCAO3fuYPjw4RgwYACOHj2K/fv3Y8KECVAoFFX2ZefOnfj777+xc+dOrF69GrGxsTqnrV544QVcvnwZu3btwo8//ogvvvgCubm5tR6zmZkZysrKcOfOHWks0dHR+Oqrr3D8+HHY29tX2CYiIgIxMTF46623cOLECaxfvx4ODg7S9r6+vmjRogV2796NvXv3okWLFhgyZAiKi4tr3T8iqh5PmRGR3p07dw5CCHTs2LFW261btw6FhYVYs2aNFKSWLFmCYcOGISYmBiYmJtBqtQgKCkLbtm0BAJ06dap2n9bW1liyZAmMjIzQsWNHBAYG4rfffsMrr7yCU6dOISkpCYcPH0avXr0AAF999RXc3Nxq1e9Tp05h2bJl6NOnDywtLQEAJSUlWLp0Kbp161bpNjdu3MAnn3yCJUuWIDQ0FADQtm1b9O/fHwAQFxeHZs2a4auvvpIC36pVq9CyZUvs2rULAQEBteojEVWPM0REpHdCCACoduamMidPnkS3bt10ZpX69euHsrIynD59GjY2Nhg3bhwGDx6MYcOG4ZNPPkF2dna1++zSpQuMjIyk146OjtIM0OnTp2FsbIyePXtK69u1awdra+v79vXYsWNo0aIFzMzM0LlzZzg5OWHdunXSelNTU3Tt2rXasRYVFcHPz6/S9SkpKTh37hwsLS2la6NsbGxw+/Zt/P333/ftHxHVDgMREemdm5sbFAoFTp48WavthBBVhqi7Z0n2798PHx8fbNiwAe3bt6/2miQTE5MK+ykrK5OOV1U/7qdDhw5ITU3FiRMnUFhYiB07dqBdu3bSejMzs2oDoZmZWbX7Lysrg6enJ1JTU3WWM2fOYMyYMfftHxHVDgMREemdjY0NBg8ejM8//xw3b96ssP769euVbte5c2ekpqbqbPP777+jWbNmaN++vdTWo0cPREREYN++fXB3d8f69evr1M+OHTvizp07+PPPP6W2c+fOVdm/u5mamqJdu3ZwdXWFUqms9bHd3NxgZmZW5WMCevbsibNnz8Le3h7t2rXTWXi3GpH+MRARUb1YunQpSktL0adPH/z44484e/YsTp48iU8//RTe3t6VbjN27Fg0b94coaGhSEtLw86dOzFt2jSEhITAwcEB6enpiIiIwP79+5GRkYHt27fjzJkz972OqCodO3aEv78/JkyYgEOHDuHPP//EhAkT7ju7ow/NmzfHnDlzMHv2bKxZswZ///03Dhw4gJUrVwL4772ws7PDk08+iT179iA9PR3JycmYMWMGsrKy6rVvRHLEi6qJqF64urriyJEj+OCDDxAeHo7s7Gy0atUKnp6eWLZsWaXbmJubY9u2bZgxYwZ69+4Nc3NzPP3001i4cKG0/tSpU1i9ejWuXr0KR0dHTJ06FRMnTqxzP9esWYPx48fjscceg1qtRnR0NI4fP47mzZvXeZ819dZbb8HY2Bhvv/02Ll++DEdHR0yaNAnAf2PdvXs35syZgxEjRuDGjRt45JFH4OfnBysrq3rvG5HcKERNTpYTEclEVlYWnJyckJSUVOUFz0TU9DAQEZGs7dixAwUFBfDw8EB2djZmz56NS5cu4cyZMxUuyCaipounzIhI1kpKSvDGG2/g/PnzsLS0hI+PD9atW8cwRCQznCEiIiIi2eNdZkRERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHv/HzXkvD25ZwxLAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the histogram of closing prices\n",
+    "plt.hist(df['Close'], bins=50)\n",
+    "plt.xlabel('Closing Price')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Distribution of SBIN Closing Prices')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A moving average chart is a type of chart that shows the average value of a stock's price over a certain period of time. It is used to smooth out the fluctuations in the price and to identify trends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the moving average chart\n",
+    "df['MA_50'] = df['Close'].rolling(window=50).mean()\n",
+    "df['MA_200'] = df['Close'].rolling(window=200).mean()\n",
+    "\n",
+    "plt.plot(df['Date'], df['Close'], label='Close')\n",
+    "plt.plot(df['Date'], df['MA_50'], label='MA 50')\n",
+    "plt.plot(df['Date'], df['MA_200'], label='MA 200')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.title('SBIN Stock Price with Moving Averages')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This chart shows the closing price of the stock (blue line) along with its 20-day moving average (red line) and two standard deviations plotted above (green line) and below (orange line) it. The Bollinger Bands are used to identify volatility in the stock price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the Bollinger Bands chart\n",
+    "df['MA_20'] = df['Close'].rolling(window=20).mean()\n",
+    "df['Upper_BB'] = df['MA_20'] + 2*df['Close'].rolling(window=20).std()\n",
+    "df['Lower_BB'] = df['MA_20'] - 2*df['Close'].rolling(window=20).std()\n",
+    "\n",
+    "plt.plot(df['Date'], df['Close'], label='Close')\n",
+    "plt.plot(df['Date'], df['Upper_BB'], label='Upper BB')\n",
+    "plt.plot(df['Date'], df['Lower_BB'], label='Lower BB')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.title('SBIN Stock Price with Bollinger Bands')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " This chart shows the RSI of the stock (blue line) along with two horizontal lines at 30 (red line) and 70 (green line). The RSI is used to identify overbought (above 70) and oversold (below 30) conditions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the RSI chart\n",
+    "delta = df['Close'].diff(1)\n",
+    "up, down = delta.copy(), delta.copy()\n",
+    "up[up < 0] = 0\n",
+    "down[down > 0] = 0\n",
+    "roll_up = up.rolling(window=14).mean()\n",
+    "roll_down = down.rolling(window=14).mean().abs()\n",
+    "RS = roll_up / roll_down\n",
+    "RSI = 100.0 - (100.0 / (1.0 + RS))\n",
+    "\n",
+    "plt.plot(df['Date'], RSI, label='RSI')\n",
+    "plt.axhline(y=30, color='red', linestyle='--')\n",
+    "plt.axhline(y=70, color='green', linestyle='--')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('RSI')\n",
+    "plt.title('SBIN Stock Price with RSI')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This chart shows the correlation between the open, high, low, close, and volume of the stock. The correlation is measured on a scale of -1 to 1, where 1 means perfect positive correlation and -1 means perfect negative correlation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the heatmap\n",
+    "corr_matrix = df[['Open', 'High', 'Low', 'Close', 'Volume']].corr()\n",
+    "plt.imshow(corr_matrix, cmap='hot', interpolation='nearest')\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D6ssCbaNXem6"
+   },
+   "source": [
+    "## Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WWb3qGu1Xa30"
+   },
+   "source": [
+    "### Handle missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "id": "yi9UnG0BXtGG"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop unnecessary columns('Date' and 'Adj Close')\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "id": "_QwN3imwXZGZ"
+   },
+   "outputs": [],
+   "source": [
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "WYCTE93pXhyC"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cYIyc2QpXvoM"
+   },
+   "source": [
+    "### Adding Indicators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IUH2Nt70Xy-X"
+   },
+   "source": [
+    "#### SMA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PzQGu8JnX8Bk"
+   },
+   "source": [
+    "Its the avg of stock price over a specific time period\n",
+    "\n",
+    "SMA = (sum of closing price os past n days) / n\n",
+    "\n",
+    "It helps identify trends by filtering out shortterm fluctuations\n",
+    "\n",
+    "Price above SMA indicate Uptrend and price below SMA indicate lowertrend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "id": "CFGQWjNTX4Mf"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the Simple Moving Average (SMA)\n",
+    "df[\"SMA_10\"] = df[\"Close\"].rolling(window=10).mean()\n",
+    "df[\"SMA_50\"] = df[\"Close\"].rolling(window=50).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "id": "LoX_CWbbX-tN"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop rows with missing values\n",
+    "df.dropna(subset=['SMA_10', 'SMA_50'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jaU2IGH_YcIe"
+   },
+   "source": [
+    "##### SMA Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "of how many past days you want to see graph:  3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the SMA graph\n",
+    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
+    "days = int(input(\"of how many past days you want to see graph: \"))\n",
+    "ax1.plot(df[\"SMA_10\"][-days:], label = \"SMA_10\", color='Red', linewidth=1, alpha=0.8)\n",
+    "ax1.plot(df[\"SMA_50\"][-days:], label = \"SMA_50\", color='Green', linewidth=1, alpha=0.8)\n",
+    "ax1.plot(df[\"Close\"][-days:], label = \"Close\", color='Blue', linewidth=2)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qCxKwtnTX1Ck"
+   },
+   "source": [
+    "#### RSI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bbNsRzqmYiJi"
+   },
+   "source": [
+    "It is a momentum indicator used to measure the speed and change of price movements. It ranges from 0 to 100 and helps identify whether a stock is overbought or oversold.  \n",
+    "\n",
+    "RSI > 70: Overbought  \n",
+    "RSI < 30: Oversold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "id": "lTkIdld1YaxN"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the Relative Strength Index (RSI)\n",
+    "delta = df['Close'].diff(1)\n",
+    "\n",
+    "gain = delta.where(delta > 0, 0)\n",
+    "loss = -delta.where(delta < 0, 0)\n",
+    "\n",
+    "avg_gain = gain.rolling(window=14).mean()\n",
+    "avg_loss = loss.rolling(window=14).mean()\n",
+    "\n",
+    "rs = avg_gain / avg_loss  # Relative Strength\n",
+    "df['RSI'] = 100 - (100 / (1 + rs))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop rows with missing values\n",
+    "df.dropna(subset=['RSI'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bW10tgm0YlSM"
+   },
+   "source": [
+    "##### RSI Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 807
     },
+    "id": "dqR2l5r-YngD",
+    "outputId": "07d7a94a-4fd6-4f87-cf37-54b6980fa5cb"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### MAPE"
-      ]
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "of how many past days you want to see graph:  3\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9woaoeNVcGx5"
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of MAPE values from mape1 to mape10\n",
-        "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n",
-        "\n",
-        "# List of corresponding labels for each MAPE value\n",
-        "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, mape_values, color='purple')\n",
-        "plt.xlabel('MAPE Variables')\n",
-        "plt.ylabel('MAPE Values')\n",
-        "plt.title('Bar Graph of MAPE')\n",
-        "plt.show()\n"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the RSI graph\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), gridspec_kw={'height_ratios': [3, 1]})\n",
+    "\n",
+    "days = int(input(\"of how many past days you want to see graph: \"))\n",
+    "# price graph\n",
+    "ax1.plot(df['Close'][-days:], label='Close Price', color='blue')\n",
+    "ax1.set_title('Stock Price')\n",
+    "ax1.set_ylabel('Price')\n",
+    "ax1.legend()\n",
+    "\n",
+    "# rsi graph\n",
+    "ax2.plot(df['RSI'][-days:], label='RSI', color='orange')\n",
+    "ax2.axhline(70, color='red', linestyle='--')  # Overbought line\n",
+    "ax2.axhline(30, color='green', linestyle='--')  # Oversold line\n",
+    "ax2.set_title('Relative Strength Index (RSI)')\n",
+    "ax2.set_ylabel('RSI')\n",
+    "ax2.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LaU9_eV9X2Zc"
+   },
+   "source": [
+    "#### MACD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {
+    "id": "4j9soKAXXvZX"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the Moving Average Convergence Divergence (MACD)\n",
+    "# Calculate the short-term and long-term EMAs\n",
+    "df['EMA_12'] = df['Close'].ewm(span=12, adjust=False).mean()\n",
+    "df['EMA_26'] = df['Close'].ewm(span=26, adjust=False).mean()\n",
+    "\n",
+    "# Calculate MACD and Signal line\n",
+    "df['MACD'] = df['EMA_12'] - df['EMA_26']  # MACD line\n",
+    "df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()  # Signal line"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vJU2F-TiYrSB"
+   },
+   "source": [
+    "##### MACD Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 807
     },
+    "id": "BV8z1ge1Ysl3",
+    "outputId": "9ddf6a13-ee48-45ea-f6f4-e361d1decc11"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Precision"
-      ]
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "of how many past days you want to see graph:  5\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bbRSmd3tcIIX"
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of precision values from precision1 to precision10\n",
-        "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
-        "\n",
-        "# List of corresponding labels for each precision value\n",
-        "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, precision_values, color='red')\n",
-        "plt.xlabel('Precision Variables')\n",
-        "plt.ylabel('Precision Values')\n",
-        "plt.title('Bar Graph of Precision')\n",
-        "plt.show()\n"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the MACD\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(10, 8))\n",
+    "\n",
+    "\n",
+    "days = int(input(\"of how many past days you want to see graph: \"))\n",
+    "# Plot stock price on first subplot (bold)\n",
+    "ax[0].plot( df['Close'][-days:], label='Close Price', color='blue', linewidth=3)\n",
+    "ax[0].set_title('Stock Price')\n",
+    "ax[0].set_ylabel('Price')\n",
+    "\n",
+    "# Plot MACD and Signal line on second subplot\n",
+    "ax[1].plot( df['MACD'][-days:], label='MACD', color='green', linewidth=2)\n",
+    "ax[1].plot( df['Signal_Line'][-days:], label='Signal Line', color='red', linestyle='--', alpha=0.7)\n",
+    "ax[1].set_title('MACD')\n",
+    "ax[1].set_ylabel('MACD Value')\n",
+    "\n",
+    "# Show legends\n",
+    "ax[0].legend()\n",
+    "ax[1].legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VC-x8kPuY_FU"
+   },
+   "source": [
+    "#### Corrrelations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 425
+    },
+    "id": "I7pi-rfLZBGf",
+    "outputId": "549af121-3ef6-476f-e359-2e3ce2cbb166"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>MA_50</th>\n",
+       "      <th>MA_200</th>\n",
+       "      <th>MA_20</th>\n",
+       "      <th>Upper_BB</th>\n",
+       "      <th>Lower_BB</th>\n",
+       "      <th>SMA_10</th>\n",
+       "      <th>SMA_50</th>\n",
+       "      <th>RSI</th>\n",
+       "      <th>EMA_12</th>\n",
+       "      <th>EMA_26</th>\n",
+       "      <th>MACD</th>\n",
+       "      <th>Signal_Line</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Open</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999812</td>\n",
+       "      <td>0.999793</td>\n",
+       "      <td>0.999613</td>\n",
+       "      <td>-0.246129</td>\n",
+       "      <td>0.985126</td>\n",
+       "      <td>0.936083</td>\n",
+       "      <td>0.994502</td>\n",
+       "      <td>0.993450</td>\n",
+       "      <td>0.992257</td>\n",
+       "      <td>0.998788</td>\n",
+       "      <td>0.992463</td>\n",
+       "      <td>0.060667</td>\n",
+       "      <td>0.998913</td>\n",
+       "      <td>0.997290</td>\n",
+       "      <td>0.201107</td>\n",
+       "      <td>0.211920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>High</th>\n",
+       "      <td>0.999812</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999717</td>\n",
+       "      <td>0.999829</td>\n",
+       "      <td>-0.243362</td>\n",
+       "      <td>0.985031</td>\n",
+       "      <td>0.935987</td>\n",
+       "      <td>0.994398</td>\n",
+       "      <td>0.993568</td>\n",
+       "      <td>0.991903</td>\n",
+       "      <td>0.998675</td>\n",
+       "      <td>0.992366</td>\n",
+       "      <td>0.063160</td>\n",
+       "      <td>0.998821</td>\n",
+       "      <td>0.997184</td>\n",
+       "      <td>0.201486</td>\n",
+       "      <td>0.211589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Low</th>\n",
+       "      <td>0.999793</td>\n",
+       "      <td>0.999717</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999819</td>\n",
+       "      <td>-0.247681</td>\n",
+       "      <td>0.984818</td>\n",
+       "      <td>0.936005</td>\n",
+       "      <td>0.994254</td>\n",
+       "      <td>0.992972</td>\n",
+       "      <td>0.992266</td>\n",
+       "      <td>0.998567</td>\n",
+       "      <td>0.992135</td>\n",
+       "      <td>0.064062</td>\n",
+       "      <td>0.998719</td>\n",
+       "      <td>0.997029</td>\n",
+       "      <td>0.202962</td>\n",
+       "      <td>0.212715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Close</th>\n",
+       "      <td>0.999613</td>\n",
+       "      <td>0.999829</td>\n",
+       "      <td>0.999819</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.244964</td>\n",
+       "      <td>0.984837</td>\n",
+       "      <td>0.935956</td>\n",
+       "      <td>0.994232</td>\n",
+       "      <td>0.993180</td>\n",
+       "      <td>0.991986</td>\n",
+       "      <td>0.998513</td>\n",
+       "      <td>0.992144</td>\n",
+       "      <td>0.065853</td>\n",
+       "      <td>0.998676</td>\n",
+       "      <td>0.996998</td>\n",
+       "      <td>0.202614</td>\n",
+       "      <td>0.212016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Volume</th>\n",
+       "      <td>-0.246129</td>\n",
+       "      <td>-0.243362</td>\n",
+       "      <td>-0.247681</td>\n",
+       "      <td>-0.244964</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.241808</td>\n",
+       "      <td>-0.156613</td>\n",
+       "      <td>-0.247522</td>\n",
+       "      <td>-0.241989</td>\n",
+       "      <td>-0.252852</td>\n",
+       "      <td>-0.247841</td>\n",
+       "      <td>-0.247594</td>\n",
+       "      <td>0.051904</td>\n",
+       "      <td>-0.247778</td>\n",
+       "      <td>-0.248238</td>\n",
+       "      <td>-0.025686</td>\n",
+       "      <td>-0.041019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MA_50</th>\n",
+       "      <td>0.985126</td>\n",
+       "      <td>0.985031</td>\n",
+       "      <td>0.984818</td>\n",
+       "      <td>0.984837</td>\n",
+       "      <td>-0.241808</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.954341</td>\n",
+       "      <td>0.992381</td>\n",
+       "      <td>0.991173</td>\n",
+       "      <td>0.990316</td>\n",
+       "      <td>0.987473</td>\n",
+       "      <td>0.992357</td>\n",
+       "      <td>-0.008551</td>\n",
+       "      <td>0.988224</td>\n",
+       "      <td>0.990678</td>\n",
+       "      <td>0.085060</td>\n",
+       "      <td>0.103564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MA_200</th>\n",
+       "      <td>0.936083</td>\n",
+       "      <td>0.935987</td>\n",
+       "      <td>0.936005</td>\n",
+       "      <td>0.935956</td>\n",
+       "      <td>-0.156613</td>\n",
+       "      <td>0.954341</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.943263</td>\n",
+       "      <td>0.942357</td>\n",
+       "      <td>0.941031</td>\n",
+       "      <td>0.938292</td>\n",
+       "      <td>0.947045</td>\n",
+       "      <td>-0.005434</td>\n",
+       "      <td>0.939204</td>\n",
+       "      <td>0.942774</td>\n",
+       "      <td>0.046120</td>\n",
+       "      <td>0.050529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MA_20</th>\n",
+       "      <td>0.994502</td>\n",
+       "      <td>0.994398</td>\n",
+       "      <td>0.994254</td>\n",
+       "      <td>0.994232</td>\n",
+       "      <td>-0.247522</td>\n",
+       "      <td>0.992381</td>\n",
+       "      <td>0.943263</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.998546</td>\n",
+       "      <td>0.998185</td>\n",
+       "      <td>0.996501</td>\n",
+       "      <td>0.993994</td>\n",
+       "      <td>0.010021</td>\n",
+       "      <td>0.996732</td>\n",
+       "      <td>0.996960</td>\n",
+       "      <td>0.148834</td>\n",
+       "      <td>0.176405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Upper_BB</th>\n",
+       "      <td>0.993450</td>\n",
+       "      <td>0.993568</td>\n",
+       "      <td>0.992972</td>\n",
+       "      <td>0.993180</td>\n",
+       "      <td>-0.241989</td>\n",
+       "      <td>0.991173</td>\n",
+       "      <td>0.942357</td>\n",
+       "      <td>0.998546</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.993487</td>\n",
+       "      <td>0.995246</td>\n",
+       "      <td>0.992805</td>\n",
+       "      <td>0.013123</td>\n",
+       "      <td>0.995469</td>\n",
+       "      <td>0.995654</td>\n",
+       "      <td>0.149849</td>\n",
+       "      <td>0.175100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lower_BB</th>\n",
+       "      <td>0.992257</td>\n",
+       "      <td>0.991903</td>\n",
+       "      <td>0.992266</td>\n",
+       "      <td>0.991986</td>\n",
+       "      <td>-0.252852</td>\n",
+       "      <td>0.990316</td>\n",
+       "      <td>0.941031</td>\n",
+       "      <td>0.998185</td>\n",
+       "      <td>0.993487</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.994474</td>\n",
+       "      <td>0.991904</td>\n",
+       "      <td>0.006521</td>\n",
+       "      <td>0.994714</td>\n",
+       "      <td>0.994990</td>\n",
+       "      <td>0.147187</td>\n",
+       "      <td>0.177256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SMA_10</th>\n",
+       "      <td>0.998788</td>\n",
+       "      <td>0.998675</td>\n",
+       "      <td>0.998567</td>\n",
+       "      <td>0.998513</td>\n",
+       "      <td>-0.247841</td>\n",
+       "      <td>0.987473</td>\n",
+       "      <td>0.938292</td>\n",
+       "      <td>0.996501</td>\n",
+       "      <td>0.995246</td>\n",
+       "      <td>0.994474</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.994842</td>\n",
+       "      <td>0.036431</td>\n",
+       "      <td>0.999915</td>\n",
+       "      <td>0.999055</td>\n",
+       "      <td>0.179858</td>\n",
+       "      <td>0.201548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SMA_50</th>\n",
+       "      <td>0.992463</td>\n",
+       "      <td>0.992366</td>\n",
+       "      <td>0.992135</td>\n",
+       "      <td>0.992144</td>\n",
+       "      <td>-0.247594</td>\n",
+       "      <td>0.992357</td>\n",
+       "      <td>0.947045</td>\n",
+       "      <td>0.993994</td>\n",
+       "      <td>0.992805</td>\n",
+       "      <td>0.991904</td>\n",
+       "      <td>0.994842</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.009370</td>\n",
+       "      <td>0.995598</td>\n",
+       "      <td>0.998145</td>\n",
+       "      <td>0.083592</td>\n",
+       "      <td>0.101745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RSI</th>\n",
+       "      <td>0.060667</td>\n",
+       "      <td>0.063160</td>\n",
+       "      <td>0.064062</td>\n",
+       "      <td>0.065853</td>\n",
+       "      <td>0.051904</td>\n",
+       "      <td>-0.008551</td>\n",
+       "      <td>-0.005434</td>\n",
+       "      <td>0.010021</td>\n",
+       "      <td>0.013123</td>\n",
+       "      <td>0.006521</td>\n",
+       "      <td>0.036431</td>\n",
+       "      <td>-0.009370</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.033634</td>\n",
+       "      <td>0.013165</td>\n",
+       "      <td>0.579522</td>\n",
+       "      <td>0.425595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EMA_12</th>\n",
+       "      <td>0.998913</td>\n",
+       "      <td>0.998821</td>\n",
+       "      <td>0.998719</td>\n",
+       "      <td>0.998676</td>\n",
+       "      <td>-0.247778</td>\n",
+       "      <td>0.988224</td>\n",
+       "      <td>0.939204</td>\n",
+       "      <td>0.996732</td>\n",
+       "      <td>0.995469</td>\n",
+       "      <td>0.994714</td>\n",
+       "      <td>0.999915</td>\n",
+       "      <td>0.995598</td>\n",
+       "      <td>0.033634</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999384</td>\n",
+       "      <td>0.173035</td>\n",
+       "      <td>0.193561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EMA_26</th>\n",
+       "      <td>0.997290</td>\n",
+       "      <td>0.997184</td>\n",
+       "      <td>0.997029</td>\n",
+       "      <td>0.996998</td>\n",
+       "      <td>-0.248238</td>\n",
+       "      <td>0.990678</td>\n",
+       "      <td>0.942774</td>\n",
+       "      <td>0.996960</td>\n",
+       "      <td>0.995654</td>\n",
+       "      <td>0.994990</td>\n",
+       "      <td>0.999055</td>\n",
+       "      <td>0.998145</td>\n",
+       "      <td>0.013165</td>\n",
+       "      <td>0.999384</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.138353</td>\n",
+       "      <td>0.160681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MACD</th>\n",
+       "      <td>0.201107</td>\n",
+       "      <td>0.201486</td>\n",
+       "      <td>0.202962</td>\n",
+       "      <td>0.202614</td>\n",
+       "      <td>-0.025686</td>\n",
+       "      <td>0.085060</td>\n",
+       "      <td>0.046120</td>\n",
+       "      <td>0.148834</td>\n",
+       "      <td>0.149849</td>\n",
+       "      <td>0.147187</td>\n",
+       "      <td>0.179858</td>\n",
+       "      <td>0.083592</td>\n",
+       "      <td>0.579522</td>\n",
+       "      <td>0.173035</td>\n",
+       "      <td>0.138353</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.952623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Signal_Line</th>\n",
+       "      <td>0.211920</td>\n",
+       "      <td>0.211589</td>\n",
+       "      <td>0.212715</td>\n",
+       "      <td>0.212016</td>\n",
+       "      <td>-0.041019</td>\n",
+       "      <td>0.103564</td>\n",
+       "      <td>0.050529</td>\n",
+       "      <td>0.176405</td>\n",
+       "      <td>0.175100</td>\n",
+       "      <td>0.177256</td>\n",
+       "      <td>0.201548</td>\n",
+       "      <td>0.101745</td>\n",
+       "      <td>0.425595</td>\n",
+       "      <td>0.193561</td>\n",
+       "      <td>0.160681</td>\n",
+       "      <td>0.952623</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Open      High       Low     Close    Volume     MA_50  \\\n",
+       "Open         1.000000  0.999812  0.999793  0.999613 -0.246129  0.985126   \n",
+       "High         0.999812  1.000000  0.999717  0.999829 -0.243362  0.985031   \n",
+       "Low          0.999793  0.999717  1.000000  0.999819 -0.247681  0.984818   \n",
+       "Close        0.999613  0.999829  0.999819  1.000000 -0.244964  0.984837   \n",
+       "Volume      -0.246129 -0.243362 -0.247681 -0.244964  1.000000 -0.241808   \n",
+       "MA_50        0.985126  0.985031  0.984818  0.984837 -0.241808  1.000000   \n",
+       "MA_200       0.936083  0.935987  0.936005  0.935956 -0.156613  0.954341   \n",
+       "MA_20        0.994502  0.994398  0.994254  0.994232 -0.247522  0.992381   \n",
+       "Upper_BB     0.993450  0.993568  0.992972  0.993180 -0.241989  0.991173   \n",
+       "Lower_BB     0.992257  0.991903  0.992266  0.991986 -0.252852  0.990316   \n",
+       "SMA_10       0.998788  0.998675  0.998567  0.998513 -0.247841  0.987473   \n",
+       "SMA_50       0.992463  0.992366  0.992135  0.992144 -0.247594  0.992357   \n",
+       "RSI          0.060667  0.063160  0.064062  0.065853  0.051904 -0.008551   \n",
+       "EMA_12       0.998913  0.998821  0.998719  0.998676 -0.247778  0.988224   \n",
+       "EMA_26       0.997290  0.997184  0.997029  0.996998 -0.248238  0.990678   \n",
+       "MACD         0.201107  0.201486  0.202962  0.202614 -0.025686  0.085060   \n",
+       "Signal_Line  0.211920  0.211589  0.212715  0.212016 -0.041019  0.103564   \n",
+       "\n",
+       "               MA_200     MA_20  Upper_BB  Lower_BB    SMA_10    SMA_50  \\\n",
+       "Open         0.936083  0.994502  0.993450  0.992257  0.998788  0.992463   \n",
+       "High         0.935987  0.994398  0.993568  0.991903  0.998675  0.992366   \n",
+       "Low          0.936005  0.994254  0.992972  0.992266  0.998567  0.992135   \n",
+       "Close        0.935956  0.994232  0.993180  0.991986  0.998513  0.992144   \n",
+       "Volume      -0.156613 -0.247522 -0.241989 -0.252852 -0.247841 -0.247594   \n",
+       "MA_50        0.954341  0.992381  0.991173  0.990316  0.987473  0.992357   \n",
+       "MA_200       1.000000  0.943263  0.942357  0.941031  0.938292  0.947045   \n",
+       "MA_20        0.943263  1.000000  0.998546  0.998185  0.996501  0.993994   \n",
+       "Upper_BB     0.942357  0.998546  1.000000  0.993487  0.995246  0.992805   \n",
+       "Lower_BB     0.941031  0.998185  0.993487  1.000000  0.994474  0.991904   \n",
+       "SMA_10       0.938292  0.996501  0.995246  0.994474  1.000000  0.994842   \n",
+       "SMA_50       0.947045  0.993994  0.992805  0.991904  0.994842  1.000000   \n",
+       "RSI         -0.005434  0.010021  0.013123  0.006521  0.036431 -0.009370   \n",
+       "EMA_12       0.939204  0.996732  0.995469  0.994714  0.999915  0.995598   \n",
+       "EMA_26       0.942774  0.996960  0.995654  0.994990  0.999055  0.998145   \n",
+       "MACD         0.046120  0.148834  0.149849  0.147187  0.179858  0.083592   \n",
+       "Signal_Line  0.050529  0.176405  0.175100  0.177256  0.201548  0.101745   \n",
+       "\n",
+       "                  RSI    EMA_12    EMA_26      MACD  Signal_Line  \n",
+       "Open         0.060667  0.998913  0.997290  0.201107     0.211920  \n",
+       "High         0.063160  0.998821  0.997184  0.201486     0.211589  \n",
+       "Low          0.064062  0.998719  0.997029  0.202962     0.212715  \n",
+       "Close        0.065853  0.998676  0.996998  0.202614     0.212016  \n",
+       "Volume       0.051904 -0.247778 -0.248238 -0.025686    -0.041019  \n",
+       "MA_50       -0.008551  0.988224  0.990678  0.085060     0.103564  \n",
+       "MA_200      -0.005434  0.939204  0.942774  0.046120     0.050529  \n",
+       "MA_20        0.010021  0.996732  0.996960  0.148834     0.176405  \n",
+       "Upper_BB     0.013123  0.995469  0.995654  0.149849     0.175100  \n",
+       "Lower_BB     0.006521  0.994714  0.994990  0.147187     0.177256  \n",
+       "SMA_10       0.036431  0.999915  0.999055  0.179858     0.201548  \n",
+       "SMA_50      -0.009370  0.995598  0.998145  0.083592     0.101745  \n",
+       "RSI          1.000000  0.033634  0.013165  0.579522     0.425595  \n",
+       "EMA_12       0.033634  1.000000  0.999384  0.173035     0.193561  \n",
+       "EMA_26       0.013165  0.999384  1.000000  0.138353     0.160681  \n",
+       "MACD         0.579522  0.173035  0.138353  1.000000     0.952623  \n",
+       "Signal_Line  0.425595  0.193561  0.160681  0.952623     1.000000  "
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr = df.corr()\n",
+    "corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 746
+    },
+    "id": "1OGAC3uxZCxG",
+    "outputId": "9d7ad372-d66d-4c2a-b72a-717179294401"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,8))\n",
+    "sns.heatmap(corr, annot=True, linewidths=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "GvIj_HpsZJ26"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "v8aLHujGZQu_"
+   },
+   "source": [
+    "## Model Training Preperation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "id": "FV2-jJCSZQaA"
+   },
+   "outputs": [],
+   "source": [
+    "# Select features and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {
+    "id": "MStjwvxqZV1e"
+   },
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {
+    "id": "adMP-Zn1ZW7s"
+   },
+   "outputs": [],
+   "source": [
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "7nfzuX5hZZtj",
+    "outputId": "6e0f44fa-598b-482f-db74-449179691b30"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Recall"
+     "data": {
+      "text/plain": [
+       "((5609, 4), (1403, 4))"
       ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "avggYFQCZiF8"
+   },
+   "source": [
+    "## Model Training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i63wO9AVZkjf"
+   },
+   "source": [
+    "#### 1. LINEAR REGRESSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {
+    "id": "9AJm7PhjZZ9p"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a linear regression model\n",
+    "model1 = LinearRegression()\n",
+    "\n",
+    "# Train the model\n",
+    "model1.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "pred1 = model1.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "id": "lYpraDj7Zwqr"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n",
+    "mae1 = mean_absolute_error(y_test, pred1)\n",
+    "mape1 = mean_absolute_percentage_error(y_test, pred1)\n",
+    "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n",
+    "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n",
+    "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n",
+    "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n",
+    "f11 = f1_score(y_test > pred1, y_test > pred1.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xOAnEaKoZxuY",
+    "outputId": "202a1add-cae6-4637-8106-735d3f958007"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 1.5855428625582284\n",
+      "MAE: 0.9162461522010917\n",
+      "MAPE: 0.006286719404596185\n",
+      "Accuracy: 0.827512473271561\n",
+      "Precision: 0.8384279475982532\n",
+      "Confusion Matrix:\n",
+      " [[585 111]\n",
+      " [131 576]]\n",
+      "Recall: 0.8147100424328148\n",
+      "F1 Score: 0.8263988522238164\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse1)\n",
+    "print(\"MAE:\", mae1)\n",
+    "print(\"MAPE:\", mape1)\n",
+    "print(\"Accuracy:\", accuracy1)\n",
+    "print(\"Precision:\", precision1)\n",
+    "print(\"Confusion Matrix:\\n\", confusion1)\n",
+    "print(\"Recall:\", recall1)\n",
+    "print(\"F1 Score:\", f11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HxCNbk0JafKE"
+   },
+   "source": [
+    "#### 2. SVR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### With Grid Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "id": "XxOnG95gZ0NR"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an SVR model\n",
+    "model2 = SVR()\n",
+    "param_grid = {'C':[0.1, 1], 'epsilon':[0.01, 0.1, 0.5], 'kernel':['sigmoid']}\n",
+    "GV_SVR = GridSearchCV(model2, param_grid = param_grid, scoring = 'accuracy', n_jobs = -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "heZ6MwXaalFT",
+    "outputId": "732d3ac8-014f-47fb-f1af-e3f30afbb037"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning:\n",
+      "\n",
+      "One or more of the test scores are non-finite: [nan nan nan nan nan nan]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1], &#x27;epsilon&#x27;: [0.01, 0.1, 0.5],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;sigmoid&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1], &#x27;epsilon&#x27;: [0.01, 0.1, 0.5],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;sigmoid&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: SVR</label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=0.1, epsilon=0.01, kernel=&#x27;sigmoid&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=0.1, epsilon=0.01, kernel=&#x27;sigmoid&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
+       "             param_grid={'C': [0.1, 1], 'epsilon': [0.01, 0.1, 0.5],\n",
+       "                         'kernel': ['sigmoid']},\n",
+       "             scoring='accuracy')"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "GV_SVR.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred2 = GV_SVR.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {
+    "id": "984H1ej2apPP"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C': 0.1, 'epsilon': 0.01, 'kernel': 'sigmoid'}"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GV_SVR.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Without Grid Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVR()"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fitting without grid search\n",
+    "model2.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred2_1 = model2.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {
+    "id": "ErnLBNW6apM4"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n",
+    "mae2 = mean_absolute_error(y_test, pred2)\n",
+    "mape2 = mean_absolute_percentage_error(y_test, pred2)\n",
+    "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n",
+    "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n",
+    "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n",
+    "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n",
+    "f12 = f1_score(y_test > pred2, y_test > pred2.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "82Gd1kSwapKR",
+    "outputId": "1dc4a6a9-f0ad-4b96-eeaa-9682f4912767"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 156.84695243787496\n",
+      "MAE: 126.89278487685152\n",
+      "MAPE: 2.6555431984901294\n",
+      "Accuracy: 0.9992872416250891\n",
+      "Precision: 1.0\n",
+      "Confusion Matrix:\n",
+      " [[714   0]\n",
+      " [  1 688]]\n",
+      "Recall: 0.9985486211901307\n",
+      "F1 Score: 0.9992737835875091\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse2)\n",
+    "print(\"MAE:\", mae2)\n",
+    "print(\"MAPE:\", mape2)\n",
+    "print(\"Accuracy:\", accuracy2)\n",
+    "print(\"Precision:\", precision2)\n",
+    "print(\"Confusion Matrix:\\n\", confusion2)\n",
+    "print(\"Recall:\", recall2)\n",
+    "print(\"F1 Score:\", f12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GsVk_xFZauR6"
+   },
+   "source": [
+    "#### 3. Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {
+    "id": "YwerJPNxapGt"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "# Create a Random Forest model\n",
+    "model3 = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "fspXTQOXapBl",
+    "outputId": "16e51cc3-390d-4dad-8a04-68b48104ac0b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 249,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model3.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {
+    "id": "Hu0iAgE9ao-_"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred3 = model3.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {
+    "id": "WTu2YAoQao6Q"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n",
+    "mae3 = mean_absolute_error(y_test, pred3)\n",
+    "mape3 = mean_absolute_percentage_error(y_test, pred3)\n",
+    "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n",
+    "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n",
+    "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n",
+    "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n",
+    "f13 = f1_score(y_test > pred3, y_test > pred3.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9_7z9JcRao4C",
+    "outputId": "9b6dbe5a-de58-4519-d12b-afd1783b5024"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.029204610750147\n",
+      "MAE: 1.1867529135400767\n",
+      "MAPE: 0.007926835753080005\n",
+      "Accuracy: 0.8717034925160371\n",
+      "Precision: 0.8688760806916427\n",
+      "Confusion Matrix:\n",
+      " [[620  91]\n",
+      " [ 89 603]]\n",
+      "Recall: 0.8713872832369942\n",
+      "F1 Score: 0.8701298701298701\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse3)\n",
+    "print(\"MAE:\", mae3)\n",
+    "print(\"MAPE:\", mape3)\n",
+    "print(\"Accuracy:\", accuracy3)\n",
+    "print(\"Precision:\", precision3)\n",
+    "print(\"Confusion Matrix:\\n\", confusion3)\n",
+    "print(\"Recall:\", recall3)\n",
+    "print(\"F1 Score:\", f13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kg80FkpDa4Am"
+   },
+   "source": [
+    "#### 4. Gradient Boosting Models (GBM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {
+    "id": "D75bIlqqao1t"
+   },
+   "outputs": [],
+   "source": [
+    "model4 = GradientBoostingRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "uUHqBGsTa9TV",
+    "outputId": "4694a027-32b2-4c7b-c9cc-ee84e6c4b425"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GradientBoostingRegressor()"
+      ]
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model4.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {
+    "id": "T7XyO3qIa9QU"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred4 = model4.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {
+    "id": "j2DCsz23a9M3"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n",
+    "mae4 = mean_absolute_error(y_test, pred4)\n",
+    "mape4 = mean_absolute_percentage_error(y_test, pred4)\n",
+    "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n",
+    "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n",
+    "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n",
+    "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n",
+    "f14 = f1_score(y_test > pred4, y_test > pred4.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tl4jTkC9a9Je",
+    "outputId": "4d2f1ee3-2886-4176-e9dc-2062b3890377"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.6474866528787193\n",
+      "MAE: 1.6833712037473636\n",
+      "MAPE: 0.011965660284805309\n",
+      "Accuracy: 0.9059158945117605\n",
+      "Precision: 0.9058823529411765\n",
+      "Confusion Matrix:\n",
+      " [[655  64]\n",
+      " [ 68 616]]\n",
+      "Recall: 0.9005847953216374\n",
+      "F1 Score: 0.9032258064516129\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse4)\n",
+    "print(\"MAE:\", mae4)\n",
+    "print(\"MAPE:\", mape4)\n",
+    "print(\"Accuracy:\", accuracy4)\n",
+    "print(\"Precision:\", precision4)\n",
+    "print(\"Confusion Matrix:\\n\", confusion4)\n",
+    "print(\"Recall:\", recall4)\n",
+    "print(\"F1 Score:\", f14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aDoDcLUdbCq6"
+   },
+   "source": [
+    "#### 5. Extreme Gradient Boosting (XGBoost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {
+    "id": "rmgm3VADa9Gb"
+   },
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "# Create an XGBoost model\n",
+    "model5 = xgb.XGBRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 253
+    },
+    "id": "txadd-U3a9AP",
+    "outputId": "0a5d9230-a87f-486b-cda4-197face55486"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model5.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {
+    "id": "xYWVMZJ5a89t"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred5 = model5.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {
+    "id": "qeDxXt9pa87W"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n",
+    "mae5 = mean_absolute_error(y_test, pred5)\n",
+    "mape5 = mean_absolute_percentage_error(y_test, pred5)\n",
+    "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n",
+    "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n",
+    "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n",
+    "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n",
+    "f15 = f1_score(y_test > pred5, y_test > pred5.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TdY1rRy_a83-",
+    "outputId": "d43b90b2-d098-402f-fbf7-46130f5310e3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.2456035075665794\n",
+      "MAE: 1.410556742694623\n",
+      "MAPE: 0.010246789191938107\n",
+      "Accuracy: 0.8959372772630079\n",
+      "Precision: 0.9013698630136986\n",
+      "Confusion Matrix:\n",
+      " [[599  72]\n",
+      " [ 74 658]]\n",
+      "Recall: 0.8989071038251366\n",
+      "F1 Score: 0.9001367989056087\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse5)\n",
+    "print(\"MAE:\", mae5)\n",
+    "print(\"MAPE:\", mape5)\n",
+    "print(\"Accuracy:\", accuracy5)\n",
+    "print(\"Precision:\", precision5)\n",
+    "print(\"Confusion Matrix:\\n\", confusion5)\n",
+    "print(\"Recall:\", recall5)\n",
+    "print(\"F1 Score:\", f15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EbxPon-VbMGE"
+   },
+   "source": [
+    "#### 6. AdaBoostRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {
+    "id": "Xze9G-tUa802"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import AdaBoostRegressor\n",
+    "# Create an AdaBoost model\n",
+    "model6 = AdaBoostRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "DEmXpAV8a8wC",
+    "outputId": "5cadcb29-13ce-45f0-cfe1-f9e21a24866b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "AdaBoostRegressor()"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model6.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {
+    "id": "KvI77shea8tk"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred6 = model6.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {
+    "id": "catCNPwla8rL"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n",
+    "mae6 = mean_absolute_error(y_test, pred6)\n",
+    "mape6 = mean_absolute_percentage_error(y_test, pred6)\n",
+    "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n",
+    "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n",
+    "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n",
+    "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n",
+    "f16 = f1_score(y_test > pred6, y_test > pred6.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wpUqeumZa8oZ",
+    "outputId": "db2f5904-a96b-46d9-fbc3-ec519d5b7c7a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 8.722169035954618\n",
+      "MAE: 7.095684834597975\n",
+      "MAPE: 0.17762022210893855\n",
+      "Accuracy: 0.9786172487526729\n",
+      "Precision: 0.9716981132075472\n",
+      "Confusion Matrix:\n",
+      " [[858  15]\n",
+      " [ 15 515]]\n",
+      "Recall: 0.9716981132075472\n",
+      "F1 Score: 0.9716981132075472\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse6)\n",
+    "print(\"MAE:\", mae6)\n",
+    "print(\"MAPE:\", mape6)\n",
+    "print(\"Accuracy:\", accuracy6)\n",
+    "print(\"Precision:\", precision6)\n",
+    "print(\"Confusion Matrix:\\n\", confusion6)\n",
+    "print(\"Recall:\", recall6)\n",
+    "print(\"F1 Score:\", f16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ubml65zDbUZu"
+   },
+   "source": [
+    "#### 7. Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {
+    "id": "qCvEszOKbVGV"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "# Create a Decision Tree model\n",
+    "model7 = DecisionTreeRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "5lYl0STGbWw8",
+    "outputId": "9aabd48a-73aa-4737-e243-ae4eb5899765"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeRegressor()"
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model7.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {
+    "id": "Wu34WtF-bXy0"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred7 = model7.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {
+    "id": "H0NdEFHXbYuI"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n",
+    "mae7 = mean_absolute_error(y_test, pred7)\n",
+    "mape7 = mean_absolute_percentage_error(y_test, pred7)\n",
+    "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n",
+    "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n",
+    "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n",
+    "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n",
+    "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wq6aws7ZbZft",
+    "outputId": "3f0b27ba-3257-458b-b5d3-abba03fa5b47"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.607949173764777\n",
+      "MAE: 1.482377351389879\n",
+      "MAPE: 0.009969005736653144\n",
+      "Accuracy: 0.8752672843905915\n",
+      "Precision: 0.8674521354933726\n",
+      "Confusion Matrix:\n",
+      " [[639  90]\n",
+      " [ 85 589]]\n",
+      "Recall: 0.8738872403560831\n",
+      "F1 Score: 0.8706577974870657\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse7)\n",
+    "print(\"MAE:\", mae7)\n",
+    "print(\"MAPE:\", mape7)\n",
+    "print(\"Accuracy:\", accuracy7)\n",
+    "print(\"Precision:\", precision7)\n",
+    "print(\"Confusion Matrix:\\n\", confusion7)\n",
+    "print(\"Recall:\", recall7)\n",
+    "print(\"F1 Score:\", f17)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5OX62RD6bb-V"
+   },
+   "source": [
+    "#### 8. KNeighborsRegressor(KNN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {
+    "id": "5kz2DGKMbaem"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a KNN model\n",
+    "model8 = KNeighborsRegressor()\n",
+    "param_grid = {'n_neighbors':[3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]}\n",
+    "GV_KNN = GridSearchCV(model8, param_grid, cv=5, scoring='neg_mean_squared_error')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 80
+    },
+    "id": "PFdqC4oXbeg9",
+    "outputId": "b06c9cd8-c45c-4a8b-bec3-17fe23c6dc6d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsRegressor()"
+      ]
+     },
+     "execution_count": 304,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model8.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {
+    "id": "_Rv2V1W8bfhp"
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred8 = model8.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {
+    "id": "K_QgGMDybgj8"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n",
+    "mae8 = mean_absolute_error(y_test, pred8)\n",
+    "mape8 = mean_absolute_percentage_error(y_test, pred8)\n",
+    "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n",
+    "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n",
+    "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n",
+    "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n",
+    "f18 = f1_score(y_test > pred8, y_test > pred8.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "PKv6I2oCbha3",
+    "outputId": "ef250676-44d3-47ff-f7bd-ade1697c4789"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 145.2817848019368\n",
+      "MAE: 108.28649615205543\n",
+      "MAPE: 1.7063415745133514\n",
+      "Accuracy: 0.9935851746258019\n",
+      "Precision: 0.9904458598726115\n",
+      "Confusion Matrix:\n",
+      " [[772   6]\n",
+      " [  3 622]]\n",
+      "Recall: 0.9952\n",
+      "F1 Score: 0.9928172386272945\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse8)\n",
+    "print(\"MAE:\", mae8)\n",
+    "print(\"MAPE:\", mape8)\n",
+    "print(\"Accuracy:\", accuracy8)\n",
+    "print(\"Precision:\", precision8)\n",
+    "print(\"Confusion Matrix:\\n\", confusion8)\n",
+    "print(\"Recall:\", recall8)\n",
+    "print(\"F1 Score:\", f18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-9 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=150)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_neighbors=150)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsRegressor(n_neighbors=150)"
+      ]
+     },
+     "execution_count": 312,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Grid Search parameters\n",
+    "GV_KNN.fit(X_train, y_train)\n",
+    "pred8_1 = GV_KNN.predict(X_test)\n",
+    "GV_KNN.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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r12rjxo0aNWpU4PkPP/xQdXV1uvDCCxUZGanIyEh9+eWXWrJkiS666CJJUlpamlpbW1VfXx/0nnV1dYGVpbS0NB06dKjTzz18+HBQzZkrTvX19fL5fJ1WqDrExMQoMTEx6GFndOcDAAAA7CekEGWapubPn6/Vq1drw4YNysrKCrpeVFSknTt3qrKyMvDIyMjQgw8+qN/+9reSpNzcXEVFRam0tDTwupqaGlVVVWnixImSpLy8PHk8Hm3bti1Qs3XrVnk8nqCaqqoq1dTUBGrWr1+vmJgY5ebmhjgN9nRqJYoQBQAAANhFSN355s2bp1dffVVvvfWWEhISAitBhmEoNjZWw4cP1/Dhw4NeExUVpbS0NI0ZMyZQe++992rJkiUaPny4kpOTtXTpUo0bNy7QrW/s2LGaOnWq5s6dq+eff16SdN9996mgoCDwPvn5+crOzlZRUZGeeuopHT16VEuXLtXcuXNtv8LUUzSWAAAAAOwnpJWoFStWyOPx6Oabb1Z6enrg8frrr4f0Q5999lkVFhZq5syZmjRpkuLi4rRu3To5nc5AzSuvvKJx48YpPz9f+fn5uuqqq7Ry5crAdafTqXfeeUcul0uTJk3SzJkzVVhYqKeffjqksdhZAi3OAQAAANtxmKZpWj0IqzQ0NMgwDHk8HluuXu07clw3PbVRrqgI7f6ft1s9HAAAAGDQCiUbnNc5UehfHStRLb52+fztFo8GAAAAgESIsrVhrlO3rLGlDwAAALAHQpSNRTkjFBt18j4xmksAAAAA9kCIsjmaSwAAAAD2QoiyuVNnRbESBQAAANgBIcrmTp0VxUoUAAAAYAeEKJtjOx8AAABgL4Qom0sMrESxnQ8AAACwA0KUzbESBQAAANgLIcrmToUoVqIAAAAAOyBE2RyNJQAAAAB7IUTZHNv5AAAAAHshRNlcx0oU50QBAAAA9kCIsjlWogAAAAB7IUTZHI0lAAAAAHshRNlcIo0lAAAAAFshRNkc2/kAAAAAeyFE2VxHY4kTPr98/naLRwMAAACAEGVzHStRktTEahQAAABgOUKUzUU5I+SKOvlrYksfAAAAYD1CVBjgrCgAAADAPghRYSCR5hIAAACAbRCiwkBCoM05K1EAAACA1QhRYYA25wAAAIB9EKLCQCIrUQAAAIBtEKLCACtRAAAAgH0QosJAIER5CVEAAACA1QhRYYDGEgAAAIB9EKLCQMdKVAPb+QAAAADLEaLCQOCw3ROsRAEAAABWI0SFARpLAAAAAPZBiAoDp0IUK1EAAACA1QhRYeDUOVGsRAEAAABWI0SFAbbzAQAAAPZBiAoDHY0lTvj88vnbLR4NAAAAMLQRosJAx0qUJDWxGgUAAABYihAVBqKcEXJFnfxVsaUPAAAAsBYhKkwEzoqiQx8AAABgKUJUmKC5BAAAAGAPhKgwkRBoc85KFAAAAGAlQlSYSGQlCgAAALAFQlSYOLWdj5UoAAAAwEqEqDCRENOxnY+VKAAAAMBKhKgwEViJ8hKiAAAAACsRosIEjSUAAAAAeyBEhYmOlagGtvMBAAAAliJEhQnOiQIAAADsgRAVJtjOBwAAANgDISpMcE4UAAAAYA+EqDDBShQAAABgD4SoMME9UQAAAIA9EKLCREeIOt7qV5u/3eLRAAAAAEMXISpMdGznk6QmDtwFAAAALEOIChPRkRGKiTz562JLHwAAAGAdQlQYSYw9uRrVQHMJAAAAwDKEqDBCcwkAAADAeoSoMHKqzTkhCgAAALBKSCFq+fLluuGGG5SQkCC3263CwkLt2bMnqObRRx/VFVdcofj4eCUlJem2227T1q1bg2q8Xq8WLFiglJQUxcfHa8aMGdq/f39QTX19vYqKimQYhgzDUFFRkY4dOxZUs2/fPk2fPl3x8fFKSUnRwoUL1draGspHCiunDtxlOx8AAABglZBC1KZNmzRv3jyVl5ertLRUbW1tys/PV3Nzc6Dm8ssv13PPPaePP/5YZWVluuiii5Sfn6/Dhw8HahYtWqQ1a9aouLhYZWVlampqUkFBgfx+f6Bm1qxZqqysVElJiUpKSlRZWamioqLAdb/fr2nTpqm5uVllZWUqLi7WG2+8oSVLlpzPfNga2/kAAAAA6zlM0zR7++LDhw/L7XZr06ZNuummm7qsaWhokGEYev/993XrrbfK4/FoxIgRWrlype644w5J0sGDB5WZmal3331XU6ZM0aeffqrs7GyVl5frxhtvlCSVl5crLy9Pu3fv1pgxY/Tee++poKBA1dXVysjIkCQVFxdrzpw5qqurU2Ji4jnH3zE2j8fTo3qrPfTfO/X6H6q1NP9yzb/lMquHAwAAAAwaoWSD87onyuPxSJKSk5O7vN7a2qrf/OY3MgxDV199tSSpoqJCPp9P+fn5gbqMjAzl5ORo8+bNkqQtW7bIMIxAgJKkCRMmyDCMoJqcnJxAgJKkKVOmyOv1qqKiosvxeL1eNTQ0BD3CCStRAAAAgPV6HaJM09TixYs1efJk5eTkBF17++23NWzYMLlcLj377LMqLS1VSkqKJKm2tlbR0dFKSkoKek1qaqpqa2sDNW63u9PPdLvdQTWpqalB15OSkhQdHR2oOdPy5csD91gZhqHMzMzefXiLdDSWaCBEAQAAAJbpdYiaP3++du7cqddee63TtW9+85uqrKzU5s2bNXXqVM2cOVN1dXVnfT/TNOVwOALfn/7n86k53SOPPCKPxxN4VFdXn3VMdpNAYwkAAADAcr0KUQsWLNDatWu1ceNGjRo1qtP1+Ph4XXrppZowYYJefPFFRUZG6sUXX5QkpaWlqbW1VfX19UGvqaurC6wspaWl6dChQ53e9/Dhw0E1Z6441dfXy+fzdVqh6hATE6PExMSgRziJj3FKkv5U16Qtfz4if3uvb2cDAAAA0EshhSjTNDV//nytXr1aGzZsUFZWVo9f5/V6JUm5ubmKiopSaWlp4HpNTY2qqqo0ceJESVJeXp48Ho+2bdsWqNm6das8Hk9QTVVVlWpqagI169evV0xMjHJzc0P5WGGhpKpGy9/bLUnaXduou14o1+QnNqikquYcrwQAAADQl0Lqzve9731Pr776qt566y2NGTMm8LxhGIqNjVVzc7Mef/xxzZgxQ+np6Tpy5Ih+9atfadWqVaqoqNCVV14pSXrggQf09ttv6+WXX1ZycrKWLl2qI0eOqKKiQk7nydWW22+/XQcPHtTzzz8vSbrvvvs0evRorVu3TtLJFufXXHONUlNT9dRTT+no0aOaM2eOCgsL9ctf/rJHnydcuvOVVNXogVXbdeYvqmPT4orZ12lqTvpADwsAAAAYNPqtO9+KFSvk8Xh08803Kz09PfB4/fXXJUlOp1O7d+/Wd77zHV1++eUqKCjQ4cOH9eGHHwYClCQ9++yzKiws1MyZMzVp0iTFxcVp3bp1gQAlSa+88orGjRun/Px85efn66qrrtLKlSsD151Op9555x25XC5NmjRJM2fOVGFhoZ5++ulQPpLt+dtNPbZuV6cAJSnw3GPrdrG1DwAAABgg53VOVLgLh5WoLX8+orteKD9n3WtzJyjvkuEDMCIAAABg8Bmwc6LQ/+oaW/q0DgAAAMD5IUTZnDvB1ad1AAAAAM4PIcrmxmclK91wqeuTr042l0g3XBqflTyQwwIAAACGLEKUzTkjHFo2PVuSOgWpju+XTc+WM6K7mAUAAACgLxGiwsDUnHStmH2d0ozgLXtphov25gAAAMAAI0SFiak56Sp76BYVTRgtSZpwcbLKHrqFAAUAAAAMMEJUGHFGODTp0pNtzFt87WzhAwAAACxAiAozGRfESpIOHjth8UgAAACAoYkQFWY6QlRdo1feNr/FowEAAACGHkJUmBkeH62YyJO/tkMer8WjAQAAAIYeQlSYcTgcGvn1atT+Y8ctHg0AAAAw9BCiwtCp+6JaLB4JAAAAMPQQosJQxgUnz4uiuQQAAAAw8AhRYWjkBXGSCFEAAACAFQhRYahjJeoAIQoAAAAYcISoMNTRWIIQBQAAAAw8QlQYOv3AXdM0LR4NAAAAMLQQosJQmnFyO1+Lr131x30WjwYAAAAYWghRYcgV5dSIhBhJNJcAAAAABhohKkx1bOnbX0+IAgAAAAYSISpMjeSsKAAAAMAShKgwlWGcai4BAAAAYOAQosLUyKSvQ5SHEAUAAAAMJEJUmOq4J+oA90QBAAAAA4oQFaZOHbjbYvFIAAAAgKGFEBWmOlaivmryqsXnt3g0AAAAwNBBiApTSXFRio1ySpJqPaxGAQAAAAOFEBWmHA6HMr5uc36ADn0AAADAgCFEhbFAcwlCFAAAADBgCFFhrKO5BGdFAQAAAAOHEBXGCFEAAADAwCNEhTG28wEAAAADjxAVxjICK1F05wMAAAAGCiEqjI08bSXKNE2LRwMAAAAMDYSoMJZmuORwSK1t7TrS3Gr1cAAAAIAhgRAVxqIjI+ROiJEkHajnvigAAABgIBCiwlwGHfoAAACAAUWICnN06AMAAAAGFiEqzI2iQx8AAAAwoAhRYY7tfAAAAMDAIkSFObbzAQAAAAOLEBXmMi5wSWIlCgAAABgohKgwN+qCOEnSkeZWtfj8Fo8GAAAAGPwIUWEuMTZS8dFOSaxGAQAAAAOBEBXmHA4H90UBAAAAA4gQNQjQoQ8AAAAYOISoQWBkUsdKFGdFAQAAAP2NEDUIjGQlCgAAABgwhKhBoKPN+YF6QhQAAADQ3whRg0CG8fVKlIcQBQAAAPQ3QtQg0HFPVM2xFrW3mxaPBgAAABjcCFGDQGqiSxEOqdXfrq+avVYPBwAAABjUCFGDQJQzQqmJ3BcFAAAADARC1CBx6qwo2pwDAAAA/YkQNUhw4C4AAAAwMAhRg0THWVEHCFEAAABAvyJEDRIjO86KIkQBAAAA/SqkELV8+XLdcMMNSkhIkNvtVmFhofbs2RO47vP59NBDD2ncuHGKj49XRkaG7r77bh08eDDofbxerxYsWKCUlBTFx8drxowZ2r9/f1BNfX29ioqKZBiGDMNQUVGRjh07FlSzb98+TZ8+XfHx8UpJSdHChQvV2toa4hQMDmznAwAAAAZGSCFq06ZNmjdvnsrLy1VaWqq2tjbl5+erublZknT8+HFt375dP/7xj7V9+3atXr1an332mWbMmBH0PosWLdKaNWtUXFyssrIyNTU1qaCgQH6/P1Aza9YsVVZWqqSkRCUlJaqsrFRRUVHgut/v17Rp09Tc3KyysjIVFxfrjTfe0JIlS85nPsIWIQoAAAAYGA7TNHt9Ouvhw4fldru1adMm3XTTTV3WfPTRRxo/fry+/PJLXXjhhfJ4PBoxYoRWrlypO+64Q5J08OBBZWZm6t1339WUKVP06aefKjs7W+Xl5brxxhslSeXl5crLy9Pu3bs1ZswYvffeeyooKFB1dbUyMjIkScXFxZozZ47q6uqUmJh4zvE3NDTIMAx5PJ4e1dtZQ4tPVz26XpK069+mKC460uIRAQAAAOEjlGxwXvdEeTweSVJycvJZaxwOhy644AJJUkVFhXw+n/Lz8wM1GRkZysnJ0ebNmyVJW7ZskWEYgQAlSRMmTJBhGEE1OTk5gQAlSVOmTJHX61VFRUWXY/F6vWpoaAh6DBaJriglxJwMTrQ5BwAAAPpPr0OUaZpavHixJk+erJycnC5rWlpa9PDDD2vWrFmBNFdbW6vo6GglJSUF1aampqq2tjZQ43a7O72f2+0OqklNTQ26npSUpOjo6EDNmZYvXx64x8owDGVmZob2oW0ugw59AAAAQL/rdYiaP3++du7cqddee63L6z6fT3feeafa29v1q1/96pzvZ5qmHA5H4PvT/3w+Nad75JFH5PF4Ao/q6upzjiucZHzdoY/7ogAAAID+06sQtWDBAq1du1YbN27UqFGjOl33+XyaOXOm9u7dq9LS0qA9hWlpaWptbVV9fX3Qa+rq6gIrS2lpaTp06FCn9z18+HBQzZkrTvX19fL5fJ1WqDrExMQoMTEx6DGYjEyiuQQAAADQ30IKUaZpav78+Vq9erU2bNigrKysTjUdAerzzz/X+++/r+HDhwddz83NVVRUlEpLSwPP1dTUqKqqShMnTpQk5eXlyePxaNu2bYGarVu3yuPxBNVUVVWppqYmULN+/XrFxMQoNzc3lI81aLCdDwAAAOh/IbVwmzdvnl599VW99dZbSkhICKwEGYah2NhYtbW16e/+7u+0fft2vf322/L7/YGa5ORkRUdHyzAM3XvvvVqyZImGDx+u5ORkLV26VOPGjdNtt90mSRo7dqymTp2quXPn6vnnn5ck3XfffSooKNCYMWMkSfn5+crOzlZRUZGeeuopHT16VEuXLtXcuXMH3QpTT43sCFH1hCgAAACgv4S0ErVixQp5PB7dfPPNSk9PDzxef/11SdL+/fu1du1a7d+/X9dcc01QTUdXPUl69tlnVVhYqJkzZ2rSpEmKi4vTunXr5HQ6AzWvvPKKxo0bp/z8fOXn5+uqq67SypUrA9edTqfeeecduVwuTZo0STNnzlRhYaGefvrp852TsBU4K8pDiAIAAAD6y3mdExXuBtM5UdLJe6Em/nSDopwO7f6ft8sZ0XWDDQAAAADBBuycKNiLOyFGzgiHfH5TXzV5rR4OAAAAMCgRogaRSGeE0hJPtjnfz31RAAAAQL8gRA0ynBUFAAAA9C9C1CDT0aGPEAUAAAD0D0LUIJNBiAIAAAD6FSFqkOHAXQAAAKB/EaIGmcCBu8daLB4JAAAAMDgRogaZkUls5wMAAAD6EyFqkHEnxEiSPCd82rC7Tv72IXuWMgAAANAvCFGDSElVjW7/xYeB7//p5Y80+YkNKqmqsXBUAAAAwOBCiBokSqpq9MCq7arxBN8LVetp0QOrthOkAAAAgD5CiBoE/O2mHlu3S11t3Ot47rF1u9jaBwAAAPQBQtQgsG3v0U4rUKczJdV4WrRt79GBGxQAAAAwSBGiBoG6xp61M+9pHQAAAIDuEaIGAXeCq0/rAAAAAHSPEDUIjM9KVrrhkqOb6w5J6YZL47OSB3JYAAAAwKBEiBoEnBEOLZueLUndBqll07PljOjuKgAAAICeIkQNElNz0rVi9nVKM4K37MVERmjF7Os0NSfdopEBAAAAg0uk1QNA35mak65vZadp296jqqyu1xMlexThkG4dm2r10AAAAIBBg5WoQcYZ4VDeJcN1/02XyIiN0glfuz452GD1sAAAAIBBgxA1SEVEOHTDRScbSXzE+VAAAABAnyFEDWI3ft2NbyshCgAAAOgzhKhB7IavQ9RHfzmq9nbT4tEAAAAAgwMhahC7MiNRcdFOeU749Fldo9XDAQAAAAYFQtQgFuWMUO7oJEnSNrb0AQAAAH2CEDXIjf+6uQQhCgAAAOgbhKhBbnzWqRBlmtwXBQAAAJwvQtQgd3XmBYp2Rqiu0asvjxy3ejgAAABA2CNEDXKuKKeuzjQksaUPAAAA6AuEqCFgPOdFAQAAAH2GEDUEjM8aLunkeVEAAAAAzg8hagjIHZ2kCIe07+hx1XhOWD0cAAAAIKwRooaAYTGRujKD+6IAAACAvkCIGiJOb3UOAAAAoPcIUUMEIQoAAADoG4SoIeKGi06GqM/rmnS0udXi0QAAAADhixA1RCTHR+vy1GGS6NIHAAAAnA9C1BDSsRrFlj4AAACg9whRQwj3RQEAAADnjxA1hHSEqE8OetTY4rN4NAAAAEB4IkQNIelGrC5MjlO7KVV8WW/1cAAAAICwRIgaYjpWo2guAQAAAPQOIWqIGU9zCQAAAOC8EKKGmI6VqD9We9Ti81s8GgAAACD8EKKGmNHD4+ROiFGrv12V1cesHg4AAAAQdghRQ4zD4aDVOQAAAHAeCFFD0I00lwAAAAB6jRA1BN1w2krU6u37teXPR+RvNy0eFQAAABAeIq0eAAbeF3XNckjytrVr8f/9oyQp3XBp2fRsTc1Jt3ZwAAAAgM2xEjXElFTVaN6r23XmulOtp0UPrNqukqoaS8YFAAAAhAtC1BDibzf12LpdnQKUpMBzj63bxdY+AAAA4CwIUUPItr1HVeNp6fa6KanG00LXPgAAAOAsCFFDSF1j9wGqN3UAAADAUESIGkLcCa4+rQMAAACGIkLUEDI+K1nphkuObq47dLJLX8dhvAAAAAA6I0QNIc4Ih5ZNz5akboPUsunZckZ0dxUAAAAAIWqImZqTrhWzr1OaEbxlzxUVoRWzr+OcKAAAAOAcQgpRy5cv1w033KCEhAS53W4VFhZqz549QTWrV6/WlClTlJKSIofDocrKyk7v4/V6tWDBAqWkpCg+Pl4zZszQ/v37g2rq6+tVVFQkwzBkGIaKiop07NixoJp9+/Zp+vTpio+PV0pKihYuXKjW1tZQPtKQNDUnXWUP3aLX5k7Qg1PGSDrZ/nzipSkWjwwAAACwv5BC1KZNmzRv3jyVl5ertLRUbW1tys/PV3Nzc6CmublZkyZN0k9/+tNu32fRokVas2aNiouLVVZWpqamJhUUFMjv9wdqZs2apcrKSpWUlKikpESVlZUqKioKXPf7/Zo2bZqam5tVVlam4uJivfHGG1qyZEkoH2nIckY4lHfJcH3v5kt0mXuYfH5TJR/XWj0sAAAAwPYcpmn2+mTVw4cPy+12a9OmTbrpppuCrv3lL39RVlaWduzYoWuuuSbwvMfj0YgRI7Ry5UrdcccdkqSDBw8qMzNT7777rqZMmaJPP/1U2dnZKi8v14033ihJKi8vV15ennbv3q0xY8bovffeU0FBgaqrq5WRkSFJKi4u1pw5c1RXV6fExMRzjr+hoUGGYcjj8fSofrD6z41/0lO/3aO8i4frtfsmWD0cAAAAYMCFkg3O654oj8cjSUpO7nk3t4qKCvl8PuXn5weey8jIUE5OjjZv3ixJ2rJliwzDCAQoSZowYYIMwwiqycnJCQQoSZoyZYq8Xq8qKiq6/Nler1cNDQ1BD0jfvubkHJbvPaKDx05YPBoAAADA3nodokzT1OLFizV58mTl5OT0+HW1tbWKjo5WUlJS0POpqamqra0N1Ljd7k6vdbvdQTWpqalB15OSkhQdHR2oOdPy5csD91gZhqHMzMwej3swG5UUp/FZyTJN6a3Kg1YPBwAAALC1Xoeo+fPna+fOnXrttdf6ZCCmacrhONVa+/Q/n0/N6R555BF5PJ7Ao7q6ug9GPjj8zbUjJUlrduzXeezwBAAAAAa9XoWoBQsWaO3atdq4caNGjRoV0mvT0tLU2tqq+vr6oOfr6uoCK0tpaWk6dOhQp9cePnw4qObMFaf6+nr5fL5OK1QdYmJilJiYGPTASX89Ll3Rzgh9dqhJn9Y0Wj0cAAAAwLZCClGmaWr+/PlavXq1NmzYoKysrJB/YG5urqKiolRaWhp4rqamRlVVVZo4caIkKS8vTx6PR9u2bQvUbN26VR6PJ6imqqpKNTU1gZr169crJiZGubm5IY9rqDNio3Tr2JNbKN+sPGDxaAAAAAD7CilEzZs3T6tWrdKrr76qhIQE1dbWqra2VidOnGpGcPToUVVWVmrXrl2SpD179qiysjKwamQYhu69914tWbJEH3zwgXbs2KHZs2dr3Lhxuu222yRJY8eO1dSpUzV37lyVl5ervLxcc+fOVUFBgcaMOXmuUX5+vrKzs1VUVKQdO3bogw8+0NKlSzV37lxWmHqp8OstfW9VHpC/nS19AAAAQFdCClErVqyQx+PRzTffrPT09MDj9ddfD9SsXbtW1157raZNmyZJuvPOO3Xttdfq17/+daDm2WefVWFhoWbOnKlJkyYpLi5O69atk9PpDNS88sorGjdunPLz85Wfn6+rrrpKK1euDFx3Op1655135HK5NGnSJM2cOVOFhYV6+umnez0ZQ93NY0bIiI3SoQavyr84YvVwAAAAAFs6r3Oiwh3nRHX2gzUf69Wt+/R3uaP09N9fbfVwAAAAgAExYOdEYfDp6NL33sc1OtHqt3g0AAAAgP0QohAk98IkjUqKVXOrX6Wfdu6QCAAAAAx1hCgEiYhwBFaj3txBlz4AAADgTIQodPLta06GqE2fHdaRJq/FowEAAADshRCFTi51D9NVowz52029vbPm3C8AAAAAhhBCFLpU+PVq1Bq29AEAAABBCFHo0vSrM+SMcKiy+pjWbN+vtyoPaMufj3AILwAAAIa8SKsHAHsakRCjMakJ2lXToH/5v38MPJ9uuLRseram5qRbODoAAADAOqxEoUslVTXaVdPQ6flaT4seWLVdJVXcKwUAAIChiRCFTvztph5bt6vLax2b+R5bt4utfQAAABiSCFHoZNveo6rxtHR73ZRU42nRtr1HB25QAAAAgE0QotBJXWP3Aao3dQAAAMBgQohCJ+4EV5/WAQAAAIMJIQqdjM9KVrrhkqOb6w6d7NI3Pit5IIcFAAAA2AIhCp04IxxaNj1bkroNUsumZ8sZ0d1VAAAAYPAiRKFLU3PStWL2dUozOm/Zm3jJcM6JAgAAwJDFYbvo1tScdH0rO03b9h5VXWOL6ptb9ei6Xfp/fz6iii+PKnc02/kAAAAw9BCicFbOCIfyLhke+H5XTYP+7x/264drqvT2gsmKdLKYCQAAgKGF/wJGSB6+fawuiIvS7tpG/Z8tX1o9HAAAAGDAEaIQkuT4aD009QpJ0s/W71HtWQ7lBQAAAAYjQhRCdsf1mbom8wI1t/r17+/ssno4AAAAwIAiRCFkEREO/XthjiIc0ts7a/Th54etHhIAAAAwYAhR6JWckYbumXiRJOl/vPWJvG1+awcEAAAADBBCFHpt8bculzshRnu/atZvNn1h9XAAAACAAUGIQq8luKL0o4JsSdJzG/+kfUeOWzwiAAAAoP8RonBepl+VrkmXDpe3rV3L1lbJNE2rhwQAAAD0K0IUzovD4dC/fTtHUU6HNu45rPW7Dlk9JAAAAKBfEaJw3i4ZMUz333SJJOmxtZ/oeGubxSMCAAAA+g8hCn1i3jcv1aikWB30tOg/PviT1cMBAAAA+g0hCn0iNtqpx2ZcKUn6Xx9+oc8PNVo8IgAAAKB/EKLQZ24dm6pvZaeqrd3Uj96kyQQAAAAGJ0IU+tSy6dlyRUVo696jerPygNXDAQAAAPocIQp9alRSnBbeepkk6fF3PpXnhM/iEQEAAAB9ixCFPvfPky/WJSPi9VVTq55Zv8fq4QAAAAB9ihCFPhcdGaH/WZgjSVpZ/qV27j9m7YAAAACAPkSIQr+YeEmKCq/JkGlKP3qzSv52mkwAAABgcCBEod/8YNpYJcREaud+j17dts/q4QAAAAB9ghCFfuNOcGnplDGSpCdLdqvW06Itfz6ityoPaMufj7A6BQAAgLAUafUAMLjNnjBa/1VRraoDDfrm0xt1wtceuJZuuLRseram5qRbOEIAAAAgNKxEoV85IxyaNi5DkoIClCTVelr0wKrtKqmqsWJoAAAAQK8QotCv/O2m/r8tf+nyWsdmvsfW7WJrHwAAAMIGIQr9atveo6rxtHR73ZRU42nRtr1HB25QAAAAwHkgRKFf1TV2H6B6UwcAAABYjRCFfuVOcPVpHQAAAGA1QhT61fisZKUbLjnOUpNuuDQ+K3nAxgQAAACcD0IU+pUzwqFl07MlqdsglZroks/f3s1VAAAAwF4IUeh3U3PStWL2dUozgrfsJcVFKTLCocrqY5rz0jY1tPgsGiEAAADQcw7TNIdsb+mGhgYZhiGPx6PExESrhzPo+dtNbdt7VHWNLXInnNzCt/WLI7pvZYWavG3KTk/Uy/90A/dHAQAAYMCFkg0IUYQoy1Ud8GjOS9v0VVOrLkyO08p7x2v08HirhwUAAIAhJJRswHY+WC5npKH//u5EXZgcp31Hj+s7Kzar6oDH6mEBAAAAXSJEwRYuSonXfz+Qp7HpifqqqVV3/qZcm//8ldXDAgAAADohRME23AkuvX7/BE24OFlN3jbN+d8f6d2Pa6weFgAAABCEEAVbSXRF6eV/HK+pV6ap1d+uea9u16ryL60eFgAAABBAiILtuKKc+s9/uE53jb9Qpin96M0q/fz9zzSEe6AAAADARghRsCVnhEM/+ZscLbzlUknSz9//XP/jrU/kbydIAQAAwFqEKNiWw+HQ4vwx+rdvXymHQ1pZ/qUWvLZd3ja/1UMDAADAEEaIgu3dnXeRfnnXtYpyOvTux7X6x5c+UmOLz+phAQAAYIgKKUQtX75cN9xwgxISEuR2u1VYWKg9e/YE1ZimqUcffVQZGRmKjY3VzTffrE8++SSoxuv1asGCBUpJSVF8fLxmzJih/fv3B9XU19erqKhIhmHIMAwVFRXp2LFjQTX79u3T9OnTFR8fr5SUFC1cuFCtra2hfCSEiYKrMvTSnPGKj3Zq85+P6K4XynW40Wv1sAAAADAEhRSiNm3apHnz5qm8vFylpaVqa2tTfn6+mpubAzVPPvmkfvazn+m5557TRx99pLS0NH3rW99SY2NjoGbRokVas2aNiouLVVZWpqamJhUUFMjvP7VNa9asWaqsrFRJSYlKSkpUWVmpoqKiwHW/369p06apublZZWVlKi4u1htvvKElS5acz3zAxiZflqLi+/I0PD5aVQca9He/3qx9R45bPSwAAAAMMQ7zPFqeHT58WG63W5s2bdJNN90k0zSVkZGhRYsW6aGHHpJ0ctUpNTVVTzzxhO6//355PB6NGDFCK1eu1B133CFJOnjwoDIzM/Xuu+9qypQp+vTTT5Wdna3y8nLdeOONkqTy8nLl5eVp9+7dGjNmjN577z0VFBSourpaGRkZkqTi4mLNmTNHdXV1SkxMPOf4GxoaZBiGPB5Pj+phD3u/albRi1u1v/6ERiTE6P/843hlZyTK325q296jqmtskTvBpfFZyXJGOKweLgAAAMJAKNngvO6J8ng8kqTk5GRJ0t69e1VbW6v8/PxATUxMjL7xjW9o8+bNkqSKigr5fL6gmoyMDOXk5ARqtmzZIsMwAgFKkiZMmCDDMIJqcnJyAgFKkqZMmSKv16uKiooux+v1etXQ0BD0QPjJSonXGw9M1BVpCTrc6NUdz2/Rf3zwuSY/sUF3vVCu7xdX6q4XyjX5iQ0qqeKwXgAAAPStXoco0zS1ePFiTZ48WTk5OZKk2tpaSVJqampQbWpqauBabW2toqOjlZSUdNYat9vd6We63e6gmjN/TlJSkqKjowM1Z1q+fHngHivDMJSZmRnqx4ZNpCa69Pr9eRqflaxGb5t+VvqZajwtQTW1nhY9sGo7QQoAAAB9qtchav78+dq5c6dee+21TtccjuAtVKZpdnruTGfWdFXfm5rTPfLII/J4PIFHdXX1WccEezNio/TSnBsUE9n1X+OOfaqPrdvF+VIAAADoM70KUQsWLNDatWu1ceNGjRo1KvB8WlqaJHVaCaqrqwusGqWlpam1tVX19fVnrTl06FCnn3v48OGgmjN/Tn19vXw+X6cVqg4xMTFKTEwMeiC87dzvkbetvdvrpqQaT4u27T06cIMCAADAoBZSiDJNU/Pnz9fq1au1YcMGZWVlBV3PyspSWlqaSktLA8+1trZq06ZNmjhxoiQpNzdXUVFRQTU1NTWqqqoK1OTl5cnj8Wjbtm2Bmq1bt8rj8QTVVFVVqabm1Fat9evXKyYmRrm5uaF8LISxusaWcxeFUAcAAACcS2QoxfPmzdOrr76qt956SwkJCYGVIMMwFBsbK4fDoUWLFuknP/mJLrvsMl122WX6yU9+ori4OM2aNStQe++992rJkiUaPny4kpOTtXTpUo0bN0633XabJGns2LGaOnWq5s6dq+eff16SdN9996mgoEBjxoyRJOXn5ys7O1tFRUV66qmndPToUS1dulRz585lhWkIcSe4+rQOAAAAOJeQQtSKFSskSTfffHPQ8y+99JLmzJkjSfrXf/1XnThxQt/73vdUX1+vG2+8UevXr1dCQkKg/tlnn1VkZKRmzpypEydO6NZbb9XLL78sp9MZqHnllVe0cOHCQBe/GTNm6Lnnngtcdzqdeuedd/S9731PkyZNUmxsrGbNmqWnn346pAlAeBuflax0w6VaT4u6u+spPsapay+8YCCHBQAAgEHsvM6JCnecEzU4lFTV6IFV2yWp2yB1TeYF+uVd1yozOW7gBgYAAICwMWDnRAF2MDUnXStmX6c0I3jLXrrh0v03ZSnRFanK6mP66//4UO99TLtzAAAAnB9WoliJGjT87aa27T2qusYWuRNcGp+VLGeEQ9VHj2th8Q7t2HdMkjR7woX60bRsuaKcZ39DAAAADBmhZANCFCFqSPD52/XM+s/0601/liRdkZag52Zdp0vdw7oNXwAAABg6CFE9RIgaejZ9dliLX6/UkeZWxUY5NfP6UVq/65BqPKdaoKcbLi2bnq2pOekWjhQAAAADiRDVQ4SooamuoUWLXq/U5j8f6fJ6xxrUitnXEaQAAACGCBpLAGfhTnTp5X8cr2ExXXf47/h/FR5bt0v+9iH7/zEAAACgG4QoDEkVX9arydvW7XVTUo2nRdv2Hh24QQEAACAsEKIwJNU1tpy7KIQ6AAAADB1d72cCBjl3guvcRTrZ1a8DXfwAAAAgEaIwRI3PSla64VKtp0Vnu+vpof/eqc8ONWlseqKeLNlNFz8AAACwnQ9DkzPCoWXTsyWd6sbXoeP7q0Ya8pvSb37/hf7l9cqgACVJtZ4WPbBqu0qqavp/wAAAALANQhSGrKk56Vox+zqlGcFb+9IMl349+zqtXTBZL959fbdb9ujiBwAAMDSxnQ9D2tScdH0rO63be53iYiLPGpBO7+KXd8nwARo1AAAArESIwpDnjHB0G4B62p3vf334heJjnBo30pDDQbMJAACAwYwQBZxFT7v4fbC7Th/srtOFyXGadlW6Cq5KV3Z6IoEKAABgECJEAWdxri5+DkkXxEUp75Lh2rj7sPYdPa4Vv/uzVvzuz7o4Jf7rQJWhy1OHEagAAAAGCYdpmkP2jviGhgYZhiGPx6PExESrhwObKqmq0QOrtktSUJDqiEQrZl+nqTnpOt7apg276/T2H2u0cU+dvG2nzpi61D1MBV8Hqkvdw7r8OZxDBQAAYJ1QsgEhihCFHiipqtFj63b1+JyoJm+bPvj0kN7eWaNNew6r9bRDe69IS1DBVemadlWGslLie/X+AAAA6FuEqB4iRCEUvV0pamjx6f1dJwPVh58fls9/6n9yV2Yk6jJ3gt6sPNDpdWeudAEAAKD/EKJ6iBCFgeY57tNvd9XqnZ01+n9/+kpt5zhfyqGT51aVPXQLW/sAAAD6ESGqhwhRsFJ9c6t+9bs/6YUP956z9pYr3ModnaRRSbEalRSnzORYjRgWQ7MKAACAPhJKNqA7H2CRpPho5Yw0elS7YXedNuyuC3ouJjJCI78OVaOSYpX59deOoJUyLJqQBQAA0A8IUYCFenoO1XeuGynJof31x7W//oRqPCfkbWvXF4eb9cXh5i5f44qKCASsUV2EreR4QhYAAEBvEKIAC/XkHKo0w6Un/+7qoHuifP521XpaVP11qNpff0L7j3b8+bhqGlrU4mvXn+qa9Ke6pi5/dmyU82SoSu46aF0QF0XIAgAA6AIhCrCQM8KhZdOz9cCq7XKo63Oolk3P7tRUIsoZoczkOGUmx3X5vq1tp4es04JW/XFVHz2hQ40tOuHz6/O6Jn3eTciKj3YGrWSdClsnvxqxhCwAADA00ViCxhKwgYE+J8rb5lfNsZZTweqMoHWowXvO9xgWExkUqk5vejEqKU5GbFSfjxsAAKC/0J2vhwhRsJPenkPVH1p8fh08diJ4Bevrr/vrT+hw47lDVoIrstumF5nJsUpwEbIAAIB9EKJ6iBAF9E6Lz68Dx06o+mjwClbH16+aWs/5HkZsVHCw6ljV+nola1gMu40BAMDAocU5gH7linLqkhHDdMmIYV1eP9Hq14FjHatXwU0v9tef0JHmVnlO+OQ54dMnBxu6fI8L4qI6rWB13Js18oJYxROyAACARfivEAB9LjbaqUvdCbrUndDl9eOtbWesXp1qerG//rjqj/t07LhPx4579PEBT5fvkRwffarpRVJw04uRSbGKi+YfbwAAoH/wXxkABlxcdKQuT03Q5aldh6wmb5sO1HdsFzwtaB07GbQ8J3w62tyqo82t2rm/65CVMixaI8+8F+u0oOWKcvbnRwQAAIMYIQqA7QyLidSYtASNSes6ZDW0+HSgixWsju8bWtr0VVOrvmpq1R+rj3X5HinDYgKdBM8MWhkXELIAAED3CFEAwk6iK0qJ6VEam971TZ+eE75OWwX3159qhNHkbdNXTV591eTVjn3HunwPd0JMp7btHd9nXOBSTCQhCwCAoYoQBWDQMWKjZMQaujLD6HTNNE01nGg77Wys452CVnOrX3WNXtU1erW9i5DlcEipCa4uz8calRSrdCNW0ZERA/BJAQCAFQhRAIYUh8MhIy5KRpyhnJFdh6xjx33Bweq0oFV99IRO+PyqbWhRbUOL/vBlfRc/Q0pLdHXbXTDNcCnK2bchy07njAEAMNgRogDgNA6HQ0nx0UqKj9a4UV2HrPrjvtPOyDotYH39tcXXrhpPi2o8Ldr2l84/I8IhpRsnuwh21V0w3XApMoSQVVJVo8fW7VKNpyXwXLrh0rLp2Zqak96baRhQBEAAQLjhsF0O2wXQh0zT1JHm1qB7sM7cMuhtaz/rezgjHEo3XEHBKtBdMDlOaYmuQMgoqarRA6u268x/kHdEkBWzr7N1kAr3AAgAGDxCyQaEKEIUgAFkmqYON3m7PB/rQP0J7T92Qq3nCFmREQ6lX+DSSCNWf9zv0Qmfv9va1MQYbVx6s2KjnHI47LW6E+4BEAAwuBCieogQBcBu2ttNfdXkPe0+rOCmFweOnZDPH/o/th0OKSYyQjGRTrmiOn91RTlPXv/6q+ssX7t6/elfY077PtoZ0WV487ebmvzEhqAVqKDxSkozXCp76Ba29gEABkQo2YB7ogDARiIiHHInuuROdCl3dOfr7e2m6hq92l9/XG9VHtTK8i979L6mKbX42tXia5fnRB8P+iw6wtuZYazNb3YboCTJlFTjadEP1nysy9zDAuGuIwieDGpf/7mb56MjIwhgAIB+QYgCgDASEeFQmuFSmuGSz2/2KET973uu17hRF8jb5leLr/3UV59f3rZ2tZzla1D9aV+9p33f8vX3p3/t2ONwenjrjdc/qu7V6zpEOR3BQeu0MBZ9egiLiui2riOcRTvPrOsizJ3xPqE0CAEAhA9CFACEqfFZyUo3XKr1tHS6r0g6tSXuG2PcA7oiY5qmfH7zVKjy+TsFsMrqej3528/O+V43X54iIy46ENq8be1qbWuXt+3U96dfa/H51X7aZPj8pnz+NjV5+/EDn0WEQz0KW+cT1KK7qvv6z91tpwQAnB9CFACEKWeEQ8umZ+uBVdvlkIKCVMd/Ni+bnj3gW9ocDoeiIx0nDxx2dV1z48XDtbJ83zkD4Itzxoc8/jZ/R8g6Fdpa/cFh69RqWtdhrPWM13dXd/L74Ne0+k+turWb0gmf/6zNP/rbmatrQStw3ay6nRnaToa7s9e5Tgt3p/+MCLZUAhiECFEAEMam5qRrxezrOrUJT7N5m/D+DICRzpPb6OJj+mKkoWtvN7sMbS1nhLHWM0JYz8Pd2cPcmS30A8+1tFkyH2fbUhm0ahZiuOtyBY4tlQAGCN356M4HYBAI1wNrOSeq75nm1yGuu6B2jtDW2oMwd7ZVvjO3VFrNGeHo2X1w5xHUurzXji2VQNihxXkPEaIAwHrhGgDRva62VPYqqPm62F55jlU4b5u/V8cA9KdzNyw595bKc94Hd8aWytPfny2VQM/Q4hwAEDacEQ7lXTLc6mGgD4XLlsq+uQ9u8GypDPU+uJhu7oNjSyWGAkIUAAAYVCIiHHJFOOWKckqKGvCff/qWyi6DWl/dB9fNKp/dulSevqXyrPfBnUdQCwp8Z2yvZEulfYXzTgRCFAAAQB9yODpWfpzddqjsb+faUtmbhiXdd7s8+5ZKf7up461+HW/1S/JZMh993bCkuzPn2FLZc+F+Tyz3RHFPFAAAQJ/yt5uBcHb6Vsmzbak8n/vgToW7rrdUWi3aGREIY315H1x358qducpnty2VJVU1emDV9k5HXHREzRWzr7MkSHFPFAAAACzjjHAoNtqp2GinJT+/uy2VvbsP7tT1TvfBneW1p2+pbPWfDHmNNttS2ek+uBBW4c56LMEZITHK6QhsqfS3m3ps3a4uzwg0dTJIPbZul76VnWbrrX2EKAAAAAwqVm+pNE1TbYHVuPMMaj06lsDeWyodjpNbKjvuT/Oc6H4MpqQaT4u27T1q66ZDhCgAAACgDzkcDkU5HYqysEvl6Vsqu21M0of3wXUV7jqYptTia1eLr+fbLOsaW85dZCFCFAAAADDI2HVL5Ud7j+rh1R+f8/XuBIu6svQQIQoAAABAn+puS+VFw+P1iw8+V62npcv7ohyS0oyT7c7tzF6tOgAAAAAMWs4Ih5ZNz5Z0qhtfh47vl03PtnVTCYkQBQAAAGAATc1J14rZ1ynNCN6yl2a4LGtvHqqQQ9Tvf/97TZ8+XRkZGXI4HHrzzTeDrh86dEhz5sxRRkaG4uLiNHXqVH3++edBNV6vVwsWLFBKSori4+M1Y8YM7d+/P6imvr5eRUVFMgxDhmGoqKhIx44dC6rZt2+fpk+frvj4eKWkpGjhwoVqbW0N9SMBAAAAGEBTc9JV9tAtem3uBP3izmv02twJKnvolrAIUFIvQlRzc7OuvvpqPffcc52umaapwsJCffHFF3rrrbe0Y8cOjR49Wrfddpuam5sDdYsWLdKaNWtUXFyssrIyNTU1qaCgQH6/P1Aza9YsVVZWqqSkRCUlJaqsrFRRUVHgut/v17Rp09Tc3KyysjIVFxfrjTfe0JIlS0L9SAAAAAAGmDPCobxLhuvb14xU3iXDbb+F73QO0zS7uqerZy92OLRmzRoVFhZKkj777DONGTNGVVVVuvLKKyWdDDtut1tPPPGE/vmf/1kej0cjRozQypUrdccdd0iSDh48qMzMTL377ruaMmWKPv30U2VnZ6u8vFw33nijJKm8vFx5eXnavXu3xowZo/fee08FBQWqrq5WRkaGJKm4uFhz5sxRXV3dOU8ZlkI7lRgAAADA4BVKNujTe6K83pPHMLtcp/Y3Op1ORUdHq6ysTJJUUVEhn8+n/Pz8QE1GRoZycnK0efNmSdKWLVtkGEYgQEnShAkTZBhGUE1OTk4gQEnSlClT5PV6VVFR0e34Ghoagh4AAAAAEIo+DVFXXHGFRo8erUceeUT19fVqbW3VT3/6U9XW1qqmpkaSVFtbq+joaCUlJQW9NjU1VbW1tYEat9vd6f3dbndQTWpqatD1pKQkRUdHB2rOtHz58sA9VoZhKDMz87w/MwAAAIChpU9DVFRUlN544w199tlnSk5OVlxcnH73u9/p9ttvl9N59oO+TNOUw3FqH+Tpfz6fmtM98sgj8ng8gUd1dXVPPxoAAAAASOqHFue5ubmqrKzUsWPHVFNTo5KSEh05ckRZWVmSpLS0NLW2tqq+vj7odXV1dYGVpbS0NB06dKjTex8+fDio5swVp/r6evl8vk4rVB1iYmKUmJgY9AAAAACAUPTbOVGGYWjEiBH6/PPP9Yc//EHf/va3JZ0MWVFRUSotLQ3U1tTUqKqqShMnTpQk5eXlyePxaNu2bYGarVu3yuPxBNVUVVUFtglK0vr16xUTE6Pc3Nz++lgAAAAAhrjIUF/Q1NSkP/3pT4Hv9+7dq8rKSiUnJ+vCCy/Uf/3Xf2nEiBG68MIL9fHHH+v73/++CgsLA40kDMPQvffeqyVLlmj48OFKTk7W0qVLNW7cON12222SpLFjx2rq1KmaO3eunn/+eUnSfffdp4KCAo0ZM0aSlJ+fr+zsbBUVFempp57S0aNHtXTpUs2dO5cVJgAAAAD9JuQQ9Yc//EHf/OY3A98vXrxYknTPPffo5ZdfVk1NjRYvXqxDhw4pPT1dd999t3784x8Hvcezzz6ryMhIzZw5UydOnNCtt96ql19+Oei+qVdeeUULFy4MhK8ZM2YEnU3ldDr1zjvv6Hvf+54mTZqk2NhYzZo1S08//XSoHwkAAAAAeuy8zokKd5wTBQAAAECy8JwoAAAAABjsCFEAAAAAEAJCFAAAAACEgBAFAAAAACEIuTvfYNLRU6OhocHikQAAAACwUkcm6EnfvSEdohobGyVJmZmZFo8EAAAAgB00NjbKMIyz1gzpFuft7e06ePCgEhIS5HA4+uQ9GxoalJmZqerqatqmW4D5txbzby3m3zrMvbWYf2sx/9Zi/vuOaZpqbGxURkaGIiLOftfTkF6JioiI0KhRo/rlvRMTE/mLbCHm31rMv7WYf+sw99Zi/q3F/FuL+e8b51qB6kBjCQAAAAAIASEKAAAAAEJAiOpjMTExWrZsmWJiYqweypDE/FuL+bcW828d5t5azL+1mH9rMf/WGNKNJQAAAAAgVKxEAQAAAEAICFEAAAAAEAJCFAAAAACEgBAFAAAAACEgRAEAAABACAhRfehXv/qVsrKy5HK5lJubqw8//NDqIQ1Ky5cv1w033KCEhAS53W4VFhZqz549QTWmaerRRx9VRkaGYmNjdfPNN+uTTz6xaMSD1/Lly+VwOLRo0aLAc8x9/ztw4IBmz56t4cOHKy4uTtdcc40qKioC1/kd9I+2tjb96Ec/UlZWlmJjY3XxxRfr3/7t39Te3h6oYe77zu9//3tNnz5dGRkZcjgcevPNN4Ou92SuvV6vFixYoJSUFMXHx2vGjBnav3//AH6K8HW2+ff5fHrooYc0btw4xcfHKyMjQ3fffbcOHjwY9B7Mf++d6+//6e6//345HA79/Oc/D3qe+e9fhKg+8vrrr2vRokX64Q9/qB07duiv/uqvdPvtt2vfvn1WD23Q2bRpk+bNm6fy8nKVlpaqra1N+fn5am5uDtQ8+eST+tnPfqbnnntOH330kdLS0vStb31LjY2NFo58cPnoo4/0m9/8RldddVXQ88x9/6qvr9ekSZMUFRWl9957T7t27dIzzzyjCy64IFDD76B/PPHEE/r1r3+t5557Tp9++qmefPJJPfXUU/rlL38ZqGHu+05zc7OuvvpqPffcc11e78lcL1q0SGvWrFFxcbHKysrU1NSkgoIC+f3+gfoYYets83/8+HFt375dP/7xj7V9+3atXr1an332mWbMmBFUx/z33rn+/nd48803tXXrVmVkZHS6xvz3MxN9Yvz48eZ3v/vdoOeuuOIK8+GHH7ZoRENHXV2dKcnctGmTaZqm2d7ebqalpZk//elPAzUtLS2mYRjmr3/9a6uGOag0Njaal112mVlaWmp+4xvfML///e+bpsncD4SHHnrInDx5crfX+R30n2nTppn/9E//FPTc3/7t35qzZ882TZO570+SzDVr1gS+78lcHzt2zIyKijKLi4sDNQcOHDAjIiLMkpKSARv7YHDm/Hdl27ZtpiTzyy+/NE2T+e9L3c3//v37zZEjR5pVVVXm6NGjzWeffTZwjfnvf6xE9YHW1lZVVFQoPz8/6Pn8/Hxt3rzZolENHR6PR5KUnJwsSdq7d69qa2uDfh8xMTH6xje+we+jj8ybN0/Tpk3TbbfdFvQ8c9//1q5dq+uvv15///d/L7fbrWuvvVYvvPBC4Dq/g/4zefJkffDBB/rss88kSX/84x9VVlamv/7rv5bE3A+knsx1RUWFfD5fUE1GRoZycnL4ffQDj8cjh8MRWBVn/vtXe3u7ioqK9OCDD+rKK6/sdJ3573+RVg9gMPjqq6/k9/uVmpoa9Hxqaqpqa2stGtXQYJqmFi9erMmTJysnJ0eSAnPe1e/jyy+/HPAxDjbFxcXavn27Pvroo07XmPv+98UXX2jFihVavHixfvCDH2jbtm1auHChYmJidPfdd/M76EcPPfSQPB6PrrjiCjmdTvn9fj3++OO66667JPH3fyD1ZK5ra2sVHR2tpKSkTjX8u7lvtbS06OGHH9asWbOUmJgoifnvb0888YQiIyO1cOHCLq8z//2PENWHHA5H0PemaXZ6Dn1r/vz52rlzp8rKyjpd4/fR96qrq/X9739f69evl8vl6raOue8/7e3tuv766/WTn/xEknTttdfqk08+0YoVK3T33XcH6vgd9L3XX39dq1at0quvvqorr7xSlZWVWrRokTIyMnTPPfcE6pj7gdObueb30bd8Pp/uvPNOtbe361e/+tU565n/81dRUaFf/OIX2r59e8hzyfz3Hbbz9YGUlBQ5nc5Oyb6urq7T/0uGvrNgwQKtXbtWGzdu1KhRowLPp6WlSRK/j35QUVGhuro65ebmKjIyUpGRkdq0aZP+4z/+Q5GRkYH5Ze77T3p6urKzs4OeGzt2bKCJDX//+8+DDz6ohx9+WHfeeafGjRunoqIi/cu//IuWL18uibkfSD2Z67S0NLW2tqq+vr7bGpwfn8+nmTNnau/evSotLQ2sQknMf3/68MMPVVdXpwsvvDDw7+Ivv/xSS5Ys0UUXXSSJ+R8IhKg+EB0drdzcXJWWlgY9X1paqokTJ1o0qsHLNE3Nnz9fq1ev1oYNG5SVlRV0PSsrS2lpaUG/j9bWVm3atInfx3m69dZb9fHHH6uysjLwuP766/UP//APqqys1MUXX8zc97NJkyZ1aun/2WefafTo0ZL4+9+fjh8/roiI4H9tOp3OQItz5n7g9GSuc3NzFRUVFVRTU1Ojqqoqfh99oCNAff7553r//fc1fPjwoOvMf/8pKirSzp07g/5dnJGRoQcffFC//e1vJTH/A8KihhaDTnFxsRkVFWW++OKL5q5du8xFixaZ8fHx5l/+8herhzboPPDAA6ZhGObvfvc7s6amJvA4fvx4oOanP/2paRiGuXr1avPjjz8277rrLjM9Pd1saGiwcOSD0+nd+UyTue9v27ZtMyMjI83HH3/c/Pzzz81XXnnFjIuLM1etWhWo4XfQP+655x5z5MiR5ttvv23u3bvXXL16tZmSkmL+67/+a6CGue87jY2N5o4dO8wdO3aYksyf/exn5o4dOwLd33oy19/97nfNUaNGme+//765fft285ZbbjGvvvpqs62tzaqPFTbONv8+n8+cMWOGOWrUKLOysjLo38VerzfwHsx/753r7/+ZzuzOZ5rMf38jRPWh//zP/zRHjx5tRkdHm9ddd12g5Tb6lqQuHy+99FKgpr293Vy2bJmZlpZmxsTEmDfddJP58ccfWzfoQezMEMXc979169aZOTk5ZkxMjHnFFVeYv/nNb4Ku8zvoHw0NDeb3v/9988ILLzRdLpd58cUXmz/84Q+D/qORue87Gzdu7PKf9ffcc49pmj2b6xMnTpjz5883k5OTzdjYWLOgoMDct2+fBZ8m/Jxt/vfu3dvtv4s3btwYeA/mv/fO9ff/TF2FKOa/fzlM0zQHYsULAAAAAAYD7okCAAAAgBAQogAAAAAgBIQoAAAAAAgBIQoAAAAAQkCIAgAAAIAQEKIAAAAAIASEKAAAAAAIASEKAAAAAEJAiAIAAACAEBCiAAAAACAEhCgAAAAACMH/D/7oxqkzvxjSAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "results = GV_KNN.cv_results_\n",
+    "mse = -results['mean_test_score']\n",
+    "k_values = results['param_n_neighbors'].data\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_values, mse, marker='o', linestyle='-')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### KNN Hyperparameter Tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# KNN Hyperparameter Tuning\n",
+    "def knn_hyperparameter_tuning(X_train, y_train):\n",
+    "    k_values = range(1, 31)  # Example range for k\n",
+    "    mse_values = []\n",
+    "    \n",
+    "    for k in k_values:\n",
+    "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
+    "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
+    "        mse_values.append(mse)\n",
+    "    \n",
+    "    return k_values, mse_values\n",
+    "\n",
+    "# Perform KNN Hyperparameter Tuning\n",
+    "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
+    "\n",
+    "# Plotting the results\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
+    "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
+    "plt.xlabel('Number of Neighbors (k)')\n",
+    "plt.ylabel('Mean Squared Error (MSE)')\n",
+    "plt.xticks(k_values)  # Show all k values on x-axis\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-yoFias0bkhT"
+   },
+   "source": [
+    "#### 9. Artificial Neural Networks (ANN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "oj1tv3-Sbm4M",
+    "outputId": "0d547cbc-d317-400a-a304-1bc283287926"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning:\n",
+      "\n",
+      "Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create an ANN model\n",
+    "model9 = Sequential()\n",
+    "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+    "model9.add(Dense(16, activation='relu'))\n",
+    "model9.add(Dense(1, activation='linear'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {
+    "id": "Llqm4lw3bnxu"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model9.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "8gzrlwUTbpG7",
+    "outputId": "c3b2372d-3881-4568-efd3-59ffe8bce6f3"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "tDNk7iuWcJpS"
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "# Assuming you have a list of recall values from recall1 to recall10\n",
-        "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
-        "\n",
-        "# List of corresponding labels for each recall value\n",
-        "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n",
-        "\n",
-        "# Plotting the bar graph\n",
-        "plt.bar(labels, recall_values, color='cyan')\n",
-        "plt.xlabel('Recall Variables')\n",
-        "plt.ylabel('Recall Values')\n",
-        "plt.title('Bar Graph of Recall')\n",
-        "plt.show()\n"
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1e3b1fdbe60>"
       ]
+     },
+     "execution_count": 324,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
+   ],
+   "source": [
+    "# Train the model\n",
+    "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {
     "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.12.4"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cI6QW1utbp-J",
+    "outputId": "1c8b27b2-3380-40a1-ac68-17a68092f650"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred9 = model9.predict(X_test_scaled).flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {
+    "id": "-qDeEJUgbrUq"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n",
+    "mae9 = mean_absolute_error(y_test, pred9)\n",
+    "mape9 = mean_absolute_percentage_error(y_test, pred9)\n",
+    "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n",
+    "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n",
+    "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n",
+    "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n",
+    "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 330,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dEhvx1BTbvXO",
+    "outputId": "af73599a-ae1e-4d56-9288-8193e4aaad2b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.7093653508543825\n",
+      "MAE: 1.6660259540370086\n",
+      "MAPE: 0.010817771201279731\n",
+      "Accuracy: 0.8880969351389879\n",
+      "Precision: 0.9202453987730062\n",
+      "Confusion Matrix:\n",
+      " [[496  65]\n",
+      " [ 92 750]]\n",
+      "Recall: 0.8907363420427553\n",
+      "F1 Score: 0.9052504526252263\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the evaluation metrics\n",
+    "print(\"RMSE:\", rmse9)\n",
+    "print(\"MAE:\", mae9)\n",
+    "print(\"MAPE:\", mape9)\n",
+    "print(\"Accuracy:\", accuracy9)\n",
+    "print(\"Precision:\", precision9)\n",
+    "print(\"Confusion Matrix:\\n\", confusion9)\n",
+    "print(\"Recall:\", recall9)\n",
+    "print(\"F1 Score:\", f19)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qosFFsxrbyX7"
+   },
+   "source": [
+    "#### 10. LSTM(Long Short term Memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {
+    "id": "3QajxGzXbxM4"
+   },
+   "outputs": [],
+   "source": [
+    "# Reshape the input data for LSTM\n",
+    "n_features = X_train_scaled.shape[1]\n",
+    "n_steps = 10\n",
+    "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+    "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+    "\n",
+    "# Reshape the input data\n",
+    "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+    "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "gNRiL_B0bxJ3",
+    "outputId": "045b5c85-f8ef-4fc4-bf38-1c253d886c82"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning:\n",
+      "\n",
+      "Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create an LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+    "model.add(Dense(1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "metadata": {
+    "id": "DsndIHIQbxHV"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "metadata": {
+    "id": "SzhA1fh9bxEz"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1e3b1f32f60>"
+      ]
+     },
+     "execution_count": 339,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 341,
+   "metadata": {
+    "id": "AQXh8a46bxCx"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make predictions on the test set\n",
+    "y_pred = model.predict(X_test_reshaped).flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {
+    "id": "iIXfZ2eubxAD"
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate evaluation metrics\n",
+    "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n",
+    "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n",
+    "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n",
+    "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+    "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+    "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+    "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n",
+    "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "metadata": {
+    "id": "9Gjh33nNbw-O"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 14.360781467875562\n",
+      "MAE: 10.393914758397726\n",
+      "MAPE: 0.18937449614271654\n",
+      "Accuracy: 0.9899569583931134\n",
+      "Precision: 0.993581514762516\n",
+      "Recall: 0.9885057471264368\n",
+      "F1 Score: 0.9910371318822023\n",
+      "Confusion Matrix:\n",
+      " [[606   5]\n",
+      " [  9 774]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print evaluation metrics\n",
+    "print(\"RMSE:\", rmse10)\n",
+    "print(\"MAE:\", mae10)\n",
+    "print(\"MAPE:\", mape10)\n",
+    "print(\"Accuracy:\", accuracy10)\n",
+    "print(\"Precision:\", precision10)\n",
+    "print(\"Recall:\", recall10)\n",
+    "print(\"F1 Score:\", f110)\n",
+    "print(\"Confusion Matrix:\\n\", confusion10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "l6QVMqW-bw66"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Performance Graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6r0qumqybw4g"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {
+    "id": "-hFs2mnDbwGQ"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n",
+    "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n",
+    "\n",
+    "# List of corresponding labels for each accuracy\n",
+    "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, accuracies, color='blue')\n",
+    "plt.xlabel('Accuracy Variables')\n",
+    "plt.ylabel('Accuracy Values')\n",
+    "plt.title('Bar Graph of Accuracies')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### RMSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {
+    "id": "1tBYk4qmcEMk"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of RMSE values from rmse1 to rmse10\n",
+    "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n",
+    "\n",
+    "# List of corresponding labels for each RMSE value\n",
+    "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, rmse_values, color='green')\n",
+    "plt.xlabel('RMSE Variables')\n",
+    "plt.ylabel('RMSE Values')\n",
+    "plt.title('Bar Graph of RMSE')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {
+    "id": "BS0oljCZcFhM"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of MAE values from mae1 to mae10\n",
+    "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n",
+    "\n",
+    "# List of corresponding labels for each MAE value\n",
+    "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, mae_values, color='orange')\n",
+    "plt.xlabel('MAE Variables')\n",
+    "plt.ylabel('MAE Values')\n",
+    "plt.title('Bar Graph of MAE')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MAPE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "metadata": {
+    "id": "9woaoeNVcGx5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of MAPE values from mape1 to mape10\n",
+    "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n",
+    "\n",
+    "# List of corresponding labels for each MAPE value\n",
+    "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, mape_values, color='purple')\n",
+    "plt.xlabel('MAPE Variables')\n",
+    "plt.ylabel('MAPE Values')\n",
+    "plt.title('Bar Graph of MAPE')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {
+    "id": "bbRSmd3tcIIX"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of precision values from precision1 to precision10\n",
+    "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
+    "\n",
+    "# List of corresponding labels for each precision value\n",
+    "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, precision_values, color='red')\n",
+    "plt.xlabel('Precision Variables')\n",
+    "plt.ylabel('Precision Values')\n",
+    "plt.title('Bar Graph of Precision')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "metadata": {
+    "id": "tDNk7iuWcJpS"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v37+/jhw5otjYWO3du1fz5s3Te++9p+eff74o4gMAUOSK+hQCVzytoEgPS23fvl2tWrWyP4+NjZUk9e7dW/Pnz1dKSoq96EhSSEiI1qxZoyFDhujtt99WYGCgpk+fztfAAQCAnc1cOyP3NpGeni5/f3+dOXNGfn5+RR3HZbhC0/6z2+oXE7AQ/j4pXLfL++3M53exOucGAADgr1BuAACApVBuAACApRSr69wAQH7cLuckALiKPTcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSKDcAAMBSShZ1AOB2ZCvqALkwRR0AAAoI5QZAnlHKABQHHJYCAACWQrkBAACWwmEpAADEYVcrYc8NAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFL4tBQAuim/vAPlDuUGxxl/+AIA/47AUAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwFMoNAACwlCIvNzNnzlRISIg8PT0VGhqqDRs23HD+woULde+998rb21uVK1fWk08+qZMnTxZS2r9mc8EHAAC3kyItN0uWLNHgwYM1atQoJSYmqkWLFoqKilJSUlKu87/77jv16tVLMTEx+umnn/TJJ59o27Zt6tu3byEnBwAArqpIy82UKVMUExOjvn37qk6dOpo2bZqCgoI0a9asXOdv2bJF1atX16BBgxQSEqLmzZvrX//6l7Zv317IyQEAgKsqsnJz+fJl7dixQ5GRkQ7jkZGR2rRpU67LhIeH69ixY1qzZo2MMTpx4oQ+/fRTdejQoTAiAwCAYqDIyk1aWpqysrIUEBDgMB4QEKDU1NRclwkPD9fChQvVtWtXubu7q1KlSrrjjjv01ltvXXc7GRkZSk9Pd3gAAADrKvITim02x1NejTE5xq7Zs2ePBg0apDFjxmjHjh36/PPPdejQIfXv3/+664+Li5O/v7/9ERQUVKD5AQCAa7EZY0xRbPjy5cvy9vbWJ598oocfftg+/txzz2nXrl365ptvcizTs2dPXbp0SZ988ol97LvvvlOLFi2UnJysypUr51gmIyNDGRkZ9ufp6ekKCgrSmTNn5OfnV8A/lWt+Oykvf8DkLjjkLlzkLlzkLlxWzu2s9PR0+fv75+nzu8j23Li7uys0NFQJCQkO4wkJCQoPD891mQsXLqhECcfIbm5ukq7u8cmNh4eH/Pz8HB4AAMC6ivSwVGxsrObOnat58+Zp7969GjJkiJKSkuyHmUaMGKFevXrZ50dHR2vZsmWaNWuWDh48qI0bN2rQoEG67777FBgYWFQ/BgAAcCEli3LjXbt21cmTJzV+/HilpKSoXr16WrNmjYKDgyVJKSkpDte86dOnj86ePasZM2Zo6NChuuOOO/T3v/9dEydOLKofAQAAuJgiO+emqDhzzC4/iuuxT3IXHHIXLnIXLnIXLivndlaxOOcGAADgVqDcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS3G63Bw9elTHjh2zP9+6dasGDx6sd999t0CDAQAA5IfT5aZHjx766quvJEmpqalq06aNtm7dqpEjR2r8+PEFHhAAAMAZTpeb3bt367777pMkffzxx6pXr542bdqkjz76SPPnzy/ofAAAAE5xutxcuXJFHh4ekqQvv/xSHTt2lCTdfffdSklJKdh0AAAATnK63Nxzzz165513tGHDBiUkJKhdu3aSpOTkZJUrV67AAwIAADjD6XIzceJEzZ49Wy1btlT37t117733SpJWrFhhP1wFAABQVGzGGOPsQllZWUpPT1eZMmXsY4cPH5a3t7cqVqxYoAELWnp6uvz9/XXmzBn5+fkV+PptBb7Gm5eXP2ByFxxyFy5yFy5yFy4r53aWM5/f+brOjTFGO3bs0OzZs3X27FlJkru7u7y9vfOzOgAAgAJT0tkFjhw5onbt2ikpKUkZGRlq06aNfH19NWnSJF26dEnvvPPOrcgJAACQJ07vuXnuuefUpEkTnT59Wl5eXvbxhx9+WP/9738LNBwAAICznN5z891332njxo1yd3d3GA8ODtbx48cLLBgAAEB+OL3nJjs7W1lZWTnGjx07Jl9f3wIJBQAAkF9Ol5s2bdpo2rRp9uc2m03nzp3T2LFj1b59+4LMBgAA4DSny83UqVP1zTffqG7durp06ZJ69Oih6tWr6/jx45o4caLTAWbOnKmQkBB5enoqNDRUGzZsuOH8jIwMjRo1SsHBwfLw8NCdd96pefPmOb1dAABgTU6fcxMYGKhdu3Zp0aJF2rlzp7KzsxUTE6PHH3/c4QTjvFiyZIkGDx6smTNn6v7779fs2bMVFRWlPXv2qFq1arku06VLF504cULvvfeeatasqV9//VWZmZnO/hgAAMCi8nURv4LStGlTNW7cWLNmzbKP1alTR507d1ZcXFyO+Z9//rm6deumgwcPqmzZsvnaJhfxyx25Cw65Cxe5Cxe5C5eVczvLmc9vp/fcLFiw4Iav9+rVK0/ruXz5snbs2KHhw4c7jEdGRmrTpk25LrNixQo1adJEkyZN0gcffCAfHx917NhREyZMuO5eo4yMDGVkZNifp6en5ykfAAAonpwuN88995zD8ytXrujChQv2KxTntdykpaUpKytLAQEBDuMBAQFKTU3NdZmDBw/qu+++k6enpz777DOlpaXp6aef1qlTp6573k1cXJzGjRuXp0wAAKD4c/qE4tOnTzs8zp07p3379ql58+ZatGiR0wFsNscdasaYHGPXZGdny2azaeHChbrvvvvUvn17TZkyRfPnz9fFixdzXWbEiBE6c+aM/XH06FGnMwIAgOIjX/eW+rNatWrptddey7FX50bKly8vNze3HHtpfv311xx7c66pXLmyqlSpIn9/f/tYnTp1ZIzRsWPHcl3Gw8NDfn5+Dg8AAGBdBVJuJMnNzU3Jycl5nu/u7q7Q0FAlJCQ4jCckJCg8PDzXZe6//34lJyfr3Llz9rH9+/erRIkSqlq1av6CAwAAS3H6nJsVK1Y4PDfGKCUlRTNmzND999/v1LpiY2PVs2dPNWnSRGFhYXr33XeVlJSk/v37S7p6SOn48eP2k5h79OihCRMm6Mknn9S4ceOUlpamYcOG6amnnnL6a+gAAMCanC43nTt3dnhus9lUoUIF/f3vf9cbb7zh1Lq6du2qkydPavz48UpJSVG9evW0Zs0aBQcHS5JSUlKUlJRkn1+6dGklJCTo2WefVZMmTVSuXDl16dJFL7/8srM/BgAAsKgivc5NUeA6N7kjd8Ehd+Eid+Eid+Gycm5nOfP5XWDn3AAAALiCPB2Wio2NzfMKp0yZku8wAAAANytP5SYxMTFPK7ve9WkAAAAKS57KzVdffXWrcwAAABQIzrkBAACW4vRXwSVp27Zt+uSTT5SUlKTLly87vLZs2bICCQYAAJAfTu+5Wbx4se6//37t2bNHn332ma5cuaI9e/Zo/fr1DrdFAAAAKApOl5tXX31VU6dO1apVq+Tu7q4333xTe/fuVZcuXVStWrVbkREAACDPnC43Bw4cUIcOHSRdvSnl+fPnZbPZNGTIEL377rsFHhAAAMAZTpebsmXL6uzZs5KkKlWqaPfu3ZKk33//XRcuXCjYdAAAAE5y+oTiFi1aKCEhQfXr11eXLl303HPPaf369UpISNCDDz54KzICAADkWZ7Lza5du9SwYUPNmDFDly5dknT1rt2lSpXSd999p3/84x8aPXr0LQsKAACQF3m+cWaJEiXUqFEj9e3bVz169Ci234zixpm5I3fBIXfhInfhInfhsnJuZ92SG2du3LhRjRs31vDhw1W5cmU98cQTXLkYAAC4nDyXm7CwMM2ZM0epqamaNWuWjh07ptatW+vOO+/UK6+8omPHjt3KnAAAAHni9LelvLy81Lt3b3399dfav3+/unfvrtmzZyskJETt27e/FRkBAADy7KbuLXXnnXdq+PDhGjVqlPz8/PTFF18UVC4AAIB8yde9pSTpm2++0bx587R06VK5ubmpS5cuiomJKchsAAAATnOq3Bw9elTz58/X/PnzdejQIYWHh+utt95Sly5d5OPjc6syAgAA5Fmey02bNm301VdfqUKFCurVq5eeeuop3XXXXbcyGwAAgNPyXG68vLy0dOlSPfTQQ3Jzc7uVmQAAAPItz+VmxYoVtzIHAABAgbipb0sBAAC4GsoNAACwFMoNAACwFMoNAACwlDydUOzMycQdO3bMdxgAAICblady07lz5zytzGazKSsr62byAAAA3JQ8lZvs7OxbnQMAAKBAcM4NAACwlDztuZk+fXqeVzho0KB8hwEAALhZNmOM+atJISEheVuZzaaDBw/edKhbKT09Xf7+/jpz5oz8/PwKfP22Al/jzfvLP2CRuyCRu3CRu3CRu3BZObeznPn8ztOem0OHDhVIMAAAgFuNc24AAICl5PnGmX907NgxrVixQklJSbp8+bLDa1OmTCmQYAAAAPnhdLn573//q44dOyokJET79u1TvXr1dPjwYRlj1Lhx41uREQAAIM+cPiw1YsQIDR06VLt375anp6eWLl2qo0ePKiIiQo899tityAgAAJBnTpebvXv3qnfv3pKkkiVL6uLFiypdurTGjx+viRMnFnhAAAAAZzhdbnx8fJSRkSFJCgwM1IEDB+yvpaWlFVwyAACAfHD6nJtmzZpp48aNqlu3rjp06KChQ4fqxx9/1LJly9SsWbNbkREAACDPnC43U6ZM0blz5yRJL730ks6dO6clS5aoZs2amjp1aoEHBAAAcEaerlBsJVyhOHfkLjjkLlzkLlzkLlxWzu0sZz6/nT7nZtu2bfr+++9zjH///ffavn27s6sDAAAoUE6Xm4EDB+ro0aM5xo8fP66BAwcWSCgAAID8crrc7NmzJ9eL9TVq1Eh79uwpkFAAAAD55XS58fDw0IkTJ3KMp6SkqGTJfN3NAQAAoMA4XW7atGmjESNG6MyZM/ax33//XSNHjlSbNm0KNBwAAICznN7V8sYbb+iBBx5QcHCwGjVqJEnatWuXAgIC9MEHHxR4QAAAAGc4XW6qVKmi//u//9PChQv1ww8/yMvLS08++aS6d++uUqVK3YqMAAAAeZavk2R8fHz0z3/+s6CzAAAA3DSnz7mRpA8++EDNmzdXYGCgjhw5IkmaOnWq/vOf/xRoOAAAAGc5XW5mzZql2NhYRUVF6fTp08rKypIklSlTRtOmTSvofAAAAE5xuty89dZbmjNnjkaNGuXw1e8mTZroxx9/LNBwAAAAznK63Bw6dMj+Lak/8vDw0Pnz5wskFAAAQH45XW5CQkK0a9euHONr165V3bp1CyITAABAvjn9balhw4Zp4MCBunTpkowx2rp1qxYtWqS4uDjNnTv3VmQEAADIM6fLzZNPPqnMzEy98MILunDhgnr06KEqVarozTffVLdu3W5FRgAAgDyzGWNMfhdOS0tTdna2KlasKOnqncGrVKlSYOFuhfT0dPn7++vMmTPy8/Mr8PXbCnyNNy8vf8DkLjjkLlzkLlzkLlxWzu0sZz6/83Wdm2vKly+vihUrKjU1Vc8++6xq1qx5M6sDAAC4aXkuN7///rsef/xxVahQQYGBgZo+fbqys7M1ZswY1ahRQ1u2bNG8efOcDjBz5kyFhITI09NToaGh2rBhQ56W27hxo0qWLKmGDRs6vU0AAGBdeS43I0eO1LfffqvevXurbNmyGjJkiB566CF99913Wrt2rbZt26bu3bs7tfElS5Zo8ODBGjVqlBITE9WiRQtFRUUpKSnphsudOXNGvXr10oMPPujU9gAAgPXl+Zyb4OBgvffee2rdurUOHjyomjVratCgQTd1VeKmTZuqcePGmjVrln2sTp066ty5s+Li4q67XLdu3VSrVi25ublp+fLluX41/Xo45yZ35C445C5c5C5c5C5cVs7trFtyzk1ycrL9OjY1atSQp6en+vbtm++Qly9f1o4dOxQZGekwHhkZqU2bNl13ufj4eB04cEBjx47N97YBAIB15fmr4NnZ2SpVqpT9uZubm3x8fPK94bS0NGVlZSkgIMBhPCAgQKmpqbku87///U/Dhw/Xhg0bHG79cCMZGRnKyMiwP09PT893ZgAA4PryXG6MMerTp488PDwkSZcuXVL//v1zFJxly5Y5FcBmc9yhZozJMSZJWVlZ6tGjh8aNG6fatWvnef1xcXEaN26cU5kAAEDxledy07t3b4fnTzzxxE1tuHz58nJzc8uxl+bXX3/NsTdHks6ePavt27crMTFRzzzzjKSre5OMMSpZsqTWrVunv//97zmWGzFihGJjY+3P09PTFRQUdFPZAQCA68pzuYmPjy/QDbu7uys0NFQJCQl6+OGH7eMJCQnq1KlTjvl+fn457jo+c+ZMrV+/Xp9++qlCQkJy3Y6Hh4d9bxMAALA+p2+/UJBiY2PVs2dPNWnSRGFhYXr33XeVlJSk/v37S7q61+X48eNasGCBSpQooXr16jksX7FiRXl6euYYBwAAt68iLTddu3bVyZMnNX78eKWkpKhevXpas2aNgoODJUkpKSl/ec0bAACAP7qpe0sVR1znJnfkLjjkLlzkLlzkLlxWzu2sQru3FAAAgKuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEuh3AAAAEsp8nIzc+ZMhYSEyNPTU6GhodqwYcN15y5btkxt2rRRhQoV5Ofnp7CwMH3xxReFmBYAALi6Ii03S5Ys0eDBgzVq1CglJiaqRYsWioqKUlJSUq7zv/32W7Vp00Zr1qzRjh071KpVK0VHRysxMbGQkwMAAFdlM8aYotp406ZN1bhxY82aNcs+VqdOHXXu3FlxcXF5Wsc999yjrl27asyYMXman56eLn9/f505c0Z+fn75yn0jtgJf483Lyx8wuQsOuQsXuQsXuQuXlXM7y5nP7yLbc3P58mXt2LFDkZGRDuORkZHatGlTntaRnZ2ts2fPqmzZstedk5GRofT0dIcHAACwriIrN2lpacrKylJAQIDDeEBAgFJTU/O0jjfeeEPnz59Xly5drjsnLi5O/v7+9kdQUNBN5QYAAK6tyE8ottkcd6gZY3KM5WbRokV66aWXtGTJElWsWPG680aMGKEzZ87YH0ePHr3pzAAAwHWVLKoNly9fXm5ubjn20vz666859ub82ZIlSxQTE6NPPvlErVu3vuFcDw8PeXh43HReAABQPBTZnht3d3eFhoYqISHBYTwhIUHh4eHXXW7RokXq06ePPvroI3Xo0OFWxwQAAMVMke25kaTY2Fj17NlTTZo0UVhYmN59910lJSWpf//+kq4eUjp+/LgWLFgg6Wqx6dWrl9588001a9bMvtfHy8tL/v7+RfZzAAAA11Gk5aZr1646efKkxo8fr5SUFNWrV09r1qxRcHCwJCklJcXhmjezZ89WZmamBg4cqIEDB9rHe/furfnz5xd2fAAA4IKK9Do3RYHr3OSO3AWH3IWL3IWL3IXLyrmdVSyucwMAAHArUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClUG4AAIClFHm5mTlzpkJCQuTp6anQ0FBt2LDhhvO/+eYbhYaGytPTUzVq1NA777xTSEkBAEBxUKTlZsmSJRo8eLBGjRqlxMREtWjRQlFRUUpKSsp1/qFDh9S+fXu1aNFCiYmJGjlypAYNGqSlS5cWcnIAAOCqbMYYU1Qbb9q0qRo3bqxZs2bZx+rUqaPOnTsrLi4ux/wXX3xRK1as0N69e+1j/fv31w8//KDNmzfnaZvp6eny9/fXmTNn5Ofnd/M/xJ/YCnyNNy8vf8DkLjjkLlzkLlzkLlxWzu0sZz6/i2zPzeXLl7Vjxw5FRkY6jEdGRmrTpk25LrN58+Yc89u2bavt27frypUrtywrAAAoPkoW1YbT0tKUlZWlgIAAh/GAgAClpqbmukxqamqu8zMzM5WWlqbKlSvnWCYjI0MZGRn252fOnJF0tQHeLorrT0ruwkXuwkXuwkXuwnUrcl/73M7LAaciKzfX2GyOO9SMMTnG/mp+buPXxMXFady4cTnGg4KCnI1abPkXdYB8InfhInfhInfhInfhupW5z549K3//G2+hyMpN+fLl5ebmlmMvza+//ppj78w1lSpVynV+yZIlVa5cuVyXGTFihGJjY+3Ps7OzderUKZUrV+6GJaoopaenKygoSEePHr0l5wXdKuQuXOQuXOQuXOQuXMUhtzFGZ8+eVWBg4F/OLbJy4+7urtDQUCUkJOjhhx+2jyckJKhTp065LhMWFqaVK1c6jK1bt05NmjRRqVKlcl3Gw8NDHh4eDmN33HHHzYUvJH5+fi77S3Yj5C5c5C5c5C5c5C5crp77r/bYXFOkXwWPjY3V3LlzNW/ePO3du1dDhgxRUlKS+vfvL+nqXpdevXrZ5/fv319HjhxRbGys9u7dq3nz5um9997T888/X1Q/AgAAcDFFes5N165ddfLkSY0fP14pKSmqV6+e1qxZo+DgYElSSkqKwzVvQkJCtGbNGg0ZMkRvv/22AgMDNX36dD3yyCNF9SMAAAAXU+QnFD/99NN6+umnc31t/vz5OcYiIiK0c+fOW5yqaHl4eGjs2LE5Dqe5OnIXLnIXLnIXLnIXruKa+3qK9CJ+AAAABa3I7y0FAABQkCg3AADAUig3AADAUig3xVj16tU1bdo0+3Obzably5cXWZ68InfhK67ZyV24yF24ikPu4pAxN5SbfOrTp49sNptsNptKliypatWqacCAATp9+nRRR7N75ZVXFB4eLm9vb/uFC1099+HDhxUTE6OQkBB5eXnpzjvv1NixY9WrVy+Xzi1JHTt2VLVq1eTp6anKlSurZ8+eSk5Odvn3/I8yMjLUsGFD2Ww2dezY0eVzV69e3Z7x2qN+/foun1uSVq9eraZNm8rLy0vly5dXcHCwS+f++uuvc7zXf364Ym5J2r9/vzp16qTy5cvLz89P999/v9q1a+fyuaOjox3e39KlS6tv374ulTG3z5k/S0pKUnR0tHx8fFS+fHkNGjRIly9fvqW5KDc3oV27dkpJSdHhw4c1d+5crVy58rpfay8Kly9f1mOPPaYBAwY4jLty7p9//lnZ2dmaPXu2fvrpJ02dOlXvvPOOdu7c6dK5JalVq1b6+OOPtW/fPi1dulQHDhzQo48+Ksm13/M/euGFFxwubV4ccl+7Tta1R4MGDVw+99KlS9WzZ089+eST+uGHH7Rx40aFhIS4dO7w8HCH9zklJUV9+/aVj4+PS+eWpA4dOigzM1Pr16/Xjh071LBhQ3355Zdq1aqVy+ZOTk7WunXrFBQUpA0bNmjlypUKDg7WRx995DIZpet/zlyTlZWlDh066Pz58/ruu++0ePFiLV26VEOHDr2luSg3N8HDw0OVKlVS1apVFRkZqa5du2rdunX21+Pj41WnTh15enrq7rvv1syZMx2WP3bsmLp166ayZcvKx8dHTZo00ffffy9JOnDggDp16qSAgACVLl1af/vb3/Tll186lW/cuHEaMmSI6tevX2xyt2vXTvHx8YqMjFSNGjXUsWNHPf/88zpy5IhL55akIUOGqFmzZgoODlZ4eLiGDx+uLVu2KDs72+WzS9LatWu1bt06TZ482T5WHHL7+vqqUqVK9kepUqVcOndmZqaee+45vf766+rfv79q166tu+66S9WrV3fp3O7u7g7vc7ly5bRixQrVqlXLpXOnpaXpl19+0fDhw9WgQQPVqlVLr732mrKyspSRkeGyuVetWqUSJUqoUaNGat68uR566CEtXLhQFy9e1Oeff+4SGaXrf85cs27dOu3Zs0cffvihGjVqpNatW+uNN97QnDlz7Hf5vhWK/CJ+VnHw4EF9/vnn9ntczZkzR2PHjtWMGTPUqFEjJSYmql+/fvLx8VHv3r117tw5RUREqEqVKlqxYoUqVaqknTt3Kjs7W5J07tw5tW/fXi+//LI8PT31/vvvKzo6Wvv27VO1atVuq9xnzpzJcWEpV8996tQpLVy4UOHh4SpRwvHfEK6Y/cSJE+rXr5+WL18ub2/vXOe4Ym5JmjhxoiZMmKCgoCA99thj9vW7au6dO3fq+PHj9g+u1NRUNWzYsNj9jq9YsUJpaWl64IEHdOXKFZfNXa5cOdWpU0cLFixQ48aN5eHhodmzZ8vT09PhMIqr5c7IyFCJEiUcbvCclpYmSfZtFHXGvNi8ebPq1avnsEe4bdu2ysjI0I4dO9SqVasC2U4OBvnSu3dv4+bmZnx8fIynp6eRZCSZKVOmGGOMCQoKMh999JHDMhMmTDBhYWHGGGNmz55tfH19zcmTJ/O8zbp165q33nrL/jw4ONhMnTrV/lyS+eyzz3IsFx8fb/z9/YtdbmOM+eWXX4yfn58JCwsrFrlfeOEF4+3tbSSZZs2ambS0NJd/z7Ozs027du3MhAkTjDHGHDp0yEgy0dHRLp3bGGOmTJlivv76a/PDDz+YOXPmmPLly5tatWq5dO5FixYZSaZatWrm008/Ndu3bzfdu3c3Hh4eLp37z6KiokxUVJTL/34bY8yxY8dMaGiosdlsxs3NzQQGBrr87/fu3buNzWYzJUqUyJGxffv2LpHxj/74OfNH/fr1M23atMkx7u7uniN7QWLPzU1o1aqVZs2apQsXLmju3Lnav3+/nn32Wf322286evSoYmJi1K9fP/v8zMxM+x1Nd+3apUaNGqls2bK5rvv8+fMaN26cVq1apeTkZGVmZurixYsO99qyeu7k5GS1a9dOjz32mDIzM+Xj4+PyuYcNG6aYmBgdOXJE48aNU69evVShQgWXfs/feustpaena8SIETlec+Xc0tVDgdc0aNBAZcqU0aOPPqqIiAjNnTvXJXNf+1fzqFGj7PfFi4+P17Jly1SrVi2tXLnSJXP/0bFjx/TFF1/o448/1sqVK13698QYo6effloVK1bUhg0b5OXlpblz5+r9999XeHi45s2b55K577nnHjVv3lzff/+9Ll68aN/T99NPP6l58+YukTGv/rj36RpjTK7jBYVycxN8fHxUs2ZNSdL06dPVqlUrjRs3Ts8884ykq7sMmzZt6rCMm5ubJMnLy+uG6x42bJi++OILTZ48WTVr1pSXl5ceffTRAjnDvDjkTk5OVqtWrRQWFqZ3331XTz31VLHIXb58eZUvX161a9dWnTp1FBQUpPbt27t09vXr12vLli05DousWrVKVatWddncuWnWrJmkq39xumruypUrS5Lq1q1rH/Pw8JCvr6+ys7NdNvcfxcfHq1y5curYsaNWrlzp8r/fq1at0unTp+Xn5ydJmjlzpj788EOdPHnSZXNLUo0aNVS2bFnNnj1bPj4+9m9Mbd++XU899ZRLZPwrlSpVsp/jc83p06d15coVBQQEFNh2/oxyU4DGjh2rqKgoDRgwQFWqVNHBgwf1+OOP5zq3QYMGmjt3rk6dOpVrq96wYYP69Omjhx9+WNLVY6OHDx++LXIfP35crVq1UmhoqOLj43Ocs+Kquf/M/L/btmVlZdmP47ti9unTp+vll1+2P09OTlbbtm0VEREhd3d3l82dm8TEREmSp6eny+YODQ2Vh4eH9u3bp+bNm0uSrly5onPnzqlKlSoum/saY4zi4+PVq1evHL/Xrpj7woULkpTj7xGbzWb/f9QVc//RtRIwb948eXh4aM2aNcrKynKpjNcTFhamV155RSkpKfZiv27dOnl4eCg0NLRAt/VHlJsC1LJlS91zzz169dVX9dJLL2nQoEHy8/NTVFSUMjIytH37dp0+fVqxsbHq3r27Xn31VXXu3FlxcXGqXLmyEhMTFRgYqLCwMNWsWVPLli2zX+dg9OjROU6U/CtJSUk6deqUkpKSlJWVpV27dunUqVM51uNKuZOTk9WyZUtVq1ZNkydP1m+//SZJunjxoku/31u3btXWrVvVvHlzlSlTRgcPHtSYMWN05513qkKFCjp79qzLZv/ziYOlS5eWdPVbSH/+QHCl3Js3b9aWLVvUqlUr+fv7a9u2bRoyZIiCgoJy/IvVlXL7+fmpf//+Gjt2rIKCghQcHKzXX39dknKUG1fKfc369et16NAhxcTEXHeOK+UOCwtTmTJl1Lt3b40ZM0ZeXl6aM2eOzp49m2PPgSvllqS9e/fKy8tL+/fvV0JCgoYNG6ZJkyZpwYIFLpMxt88ZSapZs6ZKly6tyMhI1a1bVz179tTrr7+uU6dO6fnnn1e/fv3se9JuiVt2No/F9e7d23Tq1CnH+MKFC427u7tJSkoyCxcuNA0bNjTu7u6mTJky5oEHHjDLli2zzz18+LB55JFHjJ+fn/H29jZNmjQx33//vTHm6kmdrVq1Ml5eXiYoKMjMmDHDREREmOeee86+/F+d6NW7d2/7CWh/fNx///0umzs+Pj7XzJJc+v3+v//7P9OqVStTtmxZ4+HhYapXr2769+9vjh07Vix+V/7ojycUu3LuHTt2mKZNmxp/f3/j6elp7rrrLjN27Fjz+OOPu3RuY4y5fPmyGTp0qKlYsaLx9fU1rVu3Np06dXL53MYY0717dxMeHm5/Xhx+v7dt22YiIyNN2bJlja+vr2nWrJn9PXfl3DVq1DClSpUy7u7upkGDBmbBggUul/F6nzNfffWVfc6RI0dMhw4djJeXlylbtqx55plnzKVLl3K89wXJ9v/CAgAAWAIX8QMAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQFQ5KpXr65p06bZn9tsNi1fvrxIsji77ZdeekkNGza84Zw+ffqoc+fON5ULQN5RboDbWJ8+fWSz2WSz2VSyZElVq1ZNAwYM0OnTp4s62nVFR0erdevWub62efNm2Ww27dy5M9/rT0lJUVRUVL6XB1D0KDfAba5du3ZKSUnR4cOHNXfuXK1cuVJPP/10Uce6rpiYGK1fv15HjhzJ8dq8efPUsGFDNW7c2On1XrsTcqVKlXLcIR1A8UK5AW5zHh4eqlSpkqpWrarIyEh17dpV69atc5gTHx+vOnXqyNPTU3fffbdmzpzp8PqxY8fUrVs3lS1bVj4+PmrSpIm+//57SdKBAwfUqVMnBQQEqHTp0vrb3/6mL7/8Mt95H3roIVWsWFHz5893GL9w4YKWLFmimJgYnTx5Ut27d1fVqlXl7e2t+vXra9GiRQ7zW7ZsqWeeeUaxsbEqX7682rRpIynnYakXX3xRtWvXlre3t2rUqKHRo0frypUrOXLNnj1bQUFB8vb21mOPPabff//9uj+DMUaTJk1SjRo15OXlpXvvvVeffvqp/fXTp0/r8ccfV4UKFeTl5aVatWopPj7e+TcLuE1xV3AAdgcPHtTnn3+uUqVK2cfmzJmjsWPHasaMGWrUqJESExPVr18/+fj4qHfv3jp37pwiIiJUpUoVrVixQpUqVdLOnTvtdxc+d+6c2rdvr5dfflmenp56//33FR0drX379uW4I3lelCxZUr169dL8+fM1ZswY2Ww2SdInn3yiy5cv6/HHH9eFCxcUGhqqF198UX5+flq9erV69uypGjVqqGnTpvZ1vf/++xowYIA2btyo691mz9fXV/Pnz1dgYKB+/PFH9evXT76+vnrhhRfsc3755Rd9/PHHWrlypdLT0xUTE6OBAwdq4cKFua7z3//+t5YtW6ZZs2apVq1a+vbbb/XEE0+oQoUKioiI0OjRo7Vnzx6tXbtW5cuX1y+//KKLFy86/V4Bt61beltOAC6td+/exs3Nzfj4+BhPT0/7HX2nTJlinxMUFGQ++ugjh+UmTJhgwsLCjDHGzJ492/j6+pqTJ0/mebt169Y1b731lv25M3ctN8aYvXv3Gklm/fr19rEHHnjAdO/e/brLtG/f3gwdOtT+PCIiwjRs2DDHvL/a9qRJk0xoaKj9+dixY42bm5s5evSofWzt2rWmRIkSJiUlxRjjeOfsc+fOGU9PT7Np0yaH9cbExNjzR0dHmyeffPK6GQDcGHtugNtcq1atNGvWLF24cEFz587V/v379eyzz0qSfvvtNx09elQxMTHq16+ffZnMzEz5+/tLknbt2qVGjRqpbNmyua7//PnzGjdunFatWqXk5GRlZmbq4sWLSkpKynfmu+++W+Hh4Zo3b55atWqlAwcOaMOGDfbDaVlZWXrttde0ZMkSHT9+XBkZGcrIyJCPj4/Depo0afKX2/r00081bdo0/fLLLzp37pwyMzPl5+fnMKdatWqqWrWq/XlYWJiys7O1b98+VapUyWHunj17dOnSJfthsGsuX76sRo0aSZIGDBigRx55RDt37lRkZKQ6d+6s8PDwvL9BwG2OcgPc5nx8fFSzZk1J0vTp09WqVSuNGzdOEyZMsB9amjNnjsPhHElyc3OTJHl5ed1w/cOGDdMXX3yhyZMnq2bNmvLy8tKjjz5qP4E3v2JiYvTMM8/o7bffVnx8vIKDg/Xggw9Kkt544w1NnTpV06ZNU/369eXj46PBgwfn2Oafy86fbdmyRd26ddO4cePUtm1b+fv7a/HixXrjjTduuNy1Q2XX/vtH197T1atXq0qVKg6vXTuROSoqSkeOHNHq1av15Zdf6sEHH9TAgQM1efLkG24XwFWUGwAOxo4dq6ioKA0YMECBgYGqUqWKDh48qMcffzzX+Q0aNNDcuXN16tSpXPfebNiwQX369NHDDz8s6eo5OIcPH77pnF26dNFzzz2njz76SO+//7769etnLxMbNmxQp06d9MQTT0i6Wij+97//qU6dOk5tY+PGjQoODtaoUaPsY7l9SyspKUnJyckKDAyUdPUr6SVKlFDt2rVzzK1bt648PDyUlJSkiIiI6267QoUK6tOnj/r06aMWLVpo2LBhlBsgjyg3ABy0bNlS99xzj1599VXNmDFDL730kgYNGiQ/Pz9FRUUpIyND27dv1+nTpxUbG6vu3bvr1VdfVefOnRUXF6fKlSsrMTFRgYGBCgsLU82aNbVs2TJFR0fLZrNp9OjR9r0XN6N06dLq2rWrRo4cqTNnzqhPnz7212rWrKmlS5dq06ZNKlOmjKZMmaLU1FSny03NmjWVlJSkxYsX629/+5tWr16tzz77LMc8T09P9e7dW5MnT1Z6eroGDRqkLl265DgkJV09Qfn555/XkCFDlJ2drebNmys9PV2bNm1S6dKl1bt3b40ZM0ahoaG65557lJGRoVWrVjmdHbid8VVwADnExsZqzpw5Onr0qPr27au5c+dq/vz5ql+/viIiIjR//nyFhIRIktzd3bVu3TpVrFhR7du3V/369fXaa6/ZD1tNnTpVZcqUUXh4uKKjo9W2bdt8XYcmNzExMTp9+rRat27t8M2r0aNHq3Hjxmrbtq1atmypSpUq5esKwZ06ddKQIUP0zDPPqGHDhtq0aZNGjx6dY17NmjX1j3/8Q+3bt1dkZKTq1auX4+vyfzRhwgSNGTNGcXFxqlOnjtq2bauVK1c6vKcjRoxQgwYN9MADD8jNzU2LFy92Oj9wu7IZc53vPwIAABRD7LkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACWQrkBAACW8v8B9NG9WBNqo0sAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assuming you have a list of recall values from recall1 to recall10\n",
+    "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
+    "\n",
+    "# List of corresponding labels for each recall value\n",
+    "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n",
+    "\n",
+    "# Plotting the bar graph\n",
+    "plt.bar(labels, recall_values, color='cyan')\n",
+    "plt.xlabel('Recall Variables')\n",
+    "plt.ylabel('Recall Values')\n",
+    "plt.title('Bar Graph of Recall')\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }

From 38dd51f79d2eb855618a5cbb33f41523dc81fc75 Mon Sep 17 00:00:00 2001
From: Maryam0330 <maryampanjri63@gmail.com>
Date: Tue, 15 Oct 2024 17:48:08 +0530
Subject: [PATCH 28/76] Updated

---
 Stock_prediction_Data_Analysis.ipynb | 264 ++++++++++++++++++++++++++-
 1 file changed, 262 insertions(+), 2 deletions(-)

diff --git a/Stock_prediction_Data_Analysis.ipynb b/Stock_prediction_Data_Analysis.ipynb
index 2da9745..c683bd8 100644
--- a/Stock_prediction_Data_Analysis.ipynb
+++ b/Stock_prediction_Data_Analysis.ipynb
@@ -197,6 +197,228 @@
     "df.head(10)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eec26960",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7064</th>\n",
+       "      <td>29-01-2024</td>\n",
+       "      <td>619.000000</td>\n",
+       "      <td>629.500000</td>\n",
+       "      <td>615.599976</td>\n",
+       "      <td>622.950012</td>\n",
+       "      <td>622.950012</td>\n",
+       "      <td>19572140.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7065</th>\n",
+       "      <td>30-01-2024</td>\n",
+       "      <td>625.450012</td>\n",
+       "      <td>633.500000</td>\n",
+       "      <td>624.200012</td>\n",
+       "      <td>626.750000</td>\n",
+       "      <td>626.750000</td>\n",
+       "      <td>16966577.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7066</th>\n",
+       "      <td>31-01-2024</td>\n",
+       "      <td>626.400024</td>\n",
+       "      <td>643.200012</td>\n",
+       "      <td>622.000000</td>\n",
+       "      <td>640.500000</td>\n",
+       "      <td>640.500000</td>\n",
+       "      <td>23270745.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7067</th>\n",
+       "      <td>01-02-2024</td>\n",
+       "      <td>642.750000</td>\n",
+       "      <td>652.950012</td>\n",
+       "      <td>633.250000</td>\n",
+       "      <td>647.650024</td>\n",
+       "      <td>647.650024</td>\n",
+       "      <td>26587707.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7068</th>\n",
+       "      <td>02-02-2024</td>\n",
+       "      <td>652.000000</td>\n",
+       "      <td>660.549988</td>\n",
+       "      <td>646.700012</td>\n",
+       "      <td>649.650024</td>\n",
+       "      <td>649.650024</td>\n",
+       "      <td>27471811.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7069</th>\n",
+       "      <td>05-02-2024</td>\n",
+       "      <td>647.099976</td>\n",
+       "      <td>654.799988</td>\n",
+       "      <td>638.750000</td>\n",
+       "      <td>642.950012</td>\n",
+       "      <td>642.950012</td>\n",
+       "      <td>28339525.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7070</th>\n",
+       "      <td>06-02-2024</td>\n",
+       "      <td>644.450012</td>\n",
+       "      <td>655.000000</td>\n",
+       "      <td>637.700012</td>\n",
+       "      <td>650.250000</td>\n",
+       "      <td>650.250000</td>\n",
+       "      <td>14079390.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7071</th>\n",
+       "      <td>07-02-2024</td>\n",
+       "      <td>655.049988</td>\n",
+       "      <td>677.950012</td>\n",
+       "      <td>655.000000</td>\n",
+       "      <td>675.250000</td>\n",
+       "      <td>675.250000</td>\n",
+       "      <td>41696232.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7072</th>\n",
+       "      <td>08-02-2024</td>\n",
+       "      <td>680.400024</td>\n",
+       "      <td>718.900024</td>\n",
+       "      <td>678.500000</td>\n",
+       "      <td>699.549988</td>\n",
+       "      <td>699.549988</td>\n",
+       "      <td>74222434.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7073</th>\n",
+       "      <td>09-02-2024</td>\n",
+       "      <td>703.650024</td>\n",
+       "      <td>728.349976</td>\n",
+       "      <td>694.200012</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>43235061.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date        Open        High         Low       Close   Adj Close  \\\n",
+       "7064  29-01-2024  619.000000  629.500000  615.599976  622.950012  622.950012   \n",
+       "7065  30-01-2024  625.450012  633.500000  624.200012  626.750000  626.750000   \n",
+       "7066  31-01-2024  626.400024  643.200012  622.000000  640.500000  640.500000   \n",
+       "7067  01-02-2024  642.750000  652.950012  633.250000  647.650024  647.650024   \n",
+       "7068  02-02-2024  652.000000  660.549988  646.700012  649.650024  649.650024   \n",
+       "7069  05-02-2024  647.099976  654.799988  638.750000  642.950012  642.950012   \n",
+       "7070  06-02-2024  644.450012  655.000000  637.700012  650.250000  650.250000   \n",
+       "7071  07-02-2024  655.049988  677.950012  655.000000  675.250000  675.250000   \n",
+       "7072  08-02-2024  680.400024  718.900024  678.500000  699.549988  699.549988   \n",
+       "7073  09-02-2024  703.650024  728.349976  694.200012  725.250000  725.250000   \n",
+       "\n",
+       "          Volume  \n",
+       "7064  19572140.0  \n",
+       "7065  16966577.0  \n",
+       "7066  23270745.0  \n",
+       "7067  26587707.0  \n",
+       "7068  27471811.0  \n",
+       "7069  28339525.0  \n",
+       "7070  14079390.0  \n",
+       "7071  41696232.0  \n",
+       "7072  74222434.0  \n",
+       "7073  43235061.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display the last 10 rows of the DataFrame\n",
+    "df.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23974fec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns: Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the columns in the DataFrame\n",
+    "print(\"Columns:\", df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "538be913",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data Types: Date          object\n",
+      "Open         float64\n",
+      "High         float64\n",
+      "Low          float64\n",
+      "Close        float64\n",
+      "Adj Close    float64\n",
+      "Volume       float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the data types of each column\n",
+    "print(\"Data Types:\", df.dtypes)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -218,6 +440,44 @@
     "df.shape"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ce71bbd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size of DataFrame: 49518\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the size of the DataFrame\n",
+    "print(\"Size of DataFrame:\", df.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5ffbb9a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numeric Columns: Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display numeric columns in the DataFrame\n",
+    "print(\"Numeric Columns:\", df.select_dtypes(include=[np.number]).columns)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -1057,7 +1317,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1071,7 +1331,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

From 5bd3b331db5801bdbc41138fd05b9d2a16a95fa5 Mon Sep 17 00:00:00 2001
From: Shristi Rawat <shristirawat68@gmail.com>
Date: Tue, 15 Oct 2024 18:45:40 +0530
Subject: [PATCH 29/76] Resolve Warning by Using Input Layer for Input Shape in
 Keras Sequential Model

---
 .../Stock_Price_Prediction-checkpoint.ipynb   | 1679 +++++------------
 Stock_Price_Prediction.ipynb                  |  869 +++++----
 2 files changed, 901 insertions(+), 1647 deletions(-)

diff --git a/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb b/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb
index 6018533..40b633e 100644
--- a/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb
@@ -2,8 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
-
+   "execution_count": 2,
    "metadata": {
     "id": "qCDSjVhXLr_Z"
    },
@@ -17,9 +16,8 @@
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from sklearn.linear_model import LinearRegression\n",
     "from sklearn.svm import SVR\n",
-
-    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor\n",
-
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
     "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
     "from sklearn.neighbors import KNeighborsRegressor\n",
     "from tensorflow.keras.models import Sequential\n",
@@ -28,22 +26,38 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running in local system\n"
+     ]
+    }
+   ],
    "source": [
-    "from google.colab import drive\n",
-    "drive.mount('/content/drive')\n",
-    "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')"
-
+    "try:\n",
+    "    import google.colab\n",
+    "    In_colab=True\n",
+    "except:\n",
+    "    In_colab=False\n",
+    "\n",
+    "if(In_colab):\n",
+    "    print(\"Running in google colab\")\n",
+    "    from google.colab import drive\n",
+    "    drive.mount('/content/drive')\n",
+    "    df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')\n",
+    "else:\n",
+    "    print(\"Running in local system\")\n",
+    "    path=r'C:\\Users\\SHRISTI\\OneDrive\\Desktop\\GitHub\\Stock-Price-Prediction\\Data\\SBIN.csv'\n",
+    "    df=pd.read_csv(path)\n"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 26,
-
+   "execution_count": 6,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -154,9 +168,7 @@
        "4  76613039.0  "
       ]
      },
-
-     "execution_count": 26,
-
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -169,9 +181,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 28,
-
+   "execution_count": 8,
    "metadata": {
     "id": "7LaYGXsfN-8y"
    },
@@ -183,9 +193,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 30,
-
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -277,9 +285,7 @@
        "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
       ]
      },
-
-     "execution_count": 30,
-
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -290,9 +296,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 32,
-
+   "execution_count": 12,
    "metadata": {
     "id": "dydEPoNeM6eN"
    },
@@ -305,9 +309,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 34,
-
+   "execution_count": 14,
    "metadata": {
     "id": "OQ3cGqgTMBwt"
    },
@@ -320,9 +322,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 36,
-
+   "execution_count": 16,
    "metadata": {
     "id": "9Oz-bwJOMEWD"
    },
@@ -334,9 +334,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 38,
-
+   "execution_count": 18,
    "metadata": {
     "id": "ugapDyXODtn3"
    },
@@ -350,9 +348,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 40,
-
+   "execution_count": 20,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -367,9 +363,7 @@
        "(5659, 4)"
       ]
      },
-
-     "execution_count": 40,
-
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -380,9 +374,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 42,
-
+   "execution_count": 22,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -397,9 +389,7 @@
        "(1415, 4)"
       ]
      },
-
-     "execution_count": 42,
-
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -410,9 +400,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 44,
-
+   "execution_count": 24,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -427,9 +415,7 @@
        "(5659,)"
       ]
      },
-
-     "execution_count": 44,
-
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -440,9 +426,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 46,
-
+   "execution_count": 26,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -457,9 +441,7 @@
        "(1415,)"
       ]
      },
-
-     "execution_count": 46,
-
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,9 +452,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 48,
-
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -492,9 +472,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 50,
-
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -517,9 +495,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 53,
-
+   "execution_count": 33,
    "metadata": {
     "id": "RdZ1SpzdMHAJ"
    },
@@ -531,9 +507,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 55,
-
+   "execution_count": 35,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -553,9 +527,7 @@
        "Name: Close, dtype: float64"
       ]
      },
-
-     "execution_count": 55,
-
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -566,9 +538,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 142,
-
+   "execution_count": 37,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -581,9 +551,7 @@
     {
      "data": {
       "text/html": [
-
-       "<style>#sk-container-id-12 {\n",
-
+       "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -613,17 +581,15 @@
        "  }\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-12 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-12 input.sk-hidden--visually {\n",
-
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -635,9 +601,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-dashed-wrapped {\n",
-
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -645,9 +609,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-container {\n",
-
+       "#sk-container-id-1 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -657,9 +619,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-text-repr-fallback {\n",
-
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -675,17 +635,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-12 div.sk-parallel-item::after {\n",
-
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-parallel {\n",
-
+       "#sk-container-id-1 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -693,38 +650,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-parallel-item {\n",
-
+       "#sk-container-id-1 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
-
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
-
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
-
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-serial {\n",
-
+       "#sk-container-id-1 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -742,18 +689,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-toggleable {\n",
-
+       "#sk-container-id-1 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-
-       "#sk-container-id-12 label.sk-toggleable__label {\n",
-
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -763,9 +706,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -773,17 +714,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
-
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-toggleable__content {\n",
-
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -792,16 +729,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
-
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-toggleable__content pre {\n",
-
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -809,105 +742,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
-
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-
-       "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-12 div.sk-label label {\n",
-
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-
-       "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
-
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-
-       "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-label label {\n",
-
+       "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-label-container {\n",
-
+       "#sk-container-id-1 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-
-       "#sk-container-id-12 div.sk-estimator {\n",
-
+       "#sk-container-id-1 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -917,24 +824,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-estimator.fitted {\n",
-
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-
-       "#sk-container-id-12 div.sk-estimator:hover {\n",
-
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
-
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1021,9 +922,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-
-       "#sk-container-id-12 a.estimator_doc_link {\n",
-
+       "#sk-container-id-1 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1038,52 +937,43 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
-
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-
-       "#sk-container-id-12 a.estimator_doc_link:hover {\n",
-
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
-
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LinearRegression()"
       ]
      },
-
-     "execution_count": 142,
-
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Train the model\n",
-    "model1.fit(X_train, y_train)"
+    "model1.fit(X_train_scaled, y_train)"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 63,
+   "execution_count": 39,
    "metadata": {
     "id": "X269co2kMS4z"
    },
@@ -1099,7 +989,6 @@
      ]
     }
    ],
-
    "source": [
     "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
     "metrics[\"Model\"].append(\"Linear Regressor\")\n",
@@ -1119,9 +1008,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 65,
-
+   "execution_count": 42,
    "metadata": {
     "id": "0xQewd7QWTtq"
    },
@@ -1133,9 +1020,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 144,
-
+   "execution_count": 44,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1148,9 +1033,7 @@
     {
      "data": {
       "text/html": [
-
-       "<style>#sk-container-id-13 {\n",
-
+       "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -1180,17 +1063,15 @@
        "  }\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 {\n",
+       "#sk-container-id-2 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-13 pre {\n",
+       "#sk-container-id-2 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-13 input.sk-hidden--visually {\n",
-
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1202,9 +1083,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-dashed-wrapped {\n",
-
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1212,9 +1091,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-container {\n",
-
+       "#sk-container-id-2 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1224,9 +1101,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-text-repr-fallback {\n",
-
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1242,17 +1117,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-parallel-item::after {\n",
-
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-13 div.sk-parallel {\n",
-
+       "#sk-container-id-2 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1260,38 +1132,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-parallel-item {\n",
-
+       "#sk-container-id-2 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-parallel-item:first-child::after {\n",
-
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-parallel-item:last-child::after {\n",
-
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-parallel-item:only-child::after {\n",
-
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-serial {\n",
-
+       "#sk-container-id-2 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1309,18 +1171,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-toggleable {\n",
-
+       "#sk-container-id-2 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-
-       "#sk-container-id-13 label.sk-toggleable__label {\n",
-
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -1330,9 +1188,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1340,17 +1196,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {\n",
-
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-toggleable__content {\n",
-
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1359,17 +1211,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-toggleable__content.fitted {\n",
-
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-toggleable__content pre {\n",
-
-        
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1377,113 +1224,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-toggleable__content.fitted pre {\n",
-
-        
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-
-        
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-
-        
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
-        
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
-        
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-
-       "#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
-        
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
-        
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-13 div.sk-label label {\n",
-
-        
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-
-       "#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {\n",
-
-        
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-
-       "#sk-container-id-13 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-label label {\n",
-
+       "#sk-container-id-2 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-label-container {\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-
-       "#sk-container-id-13 div.sk-estimator {\n",
-
+       "#sk-container-id-2 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1493,24 +1306,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-estimator.fitted {\n",
-
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-
-       "#sk-container-id-13 div.sk-estimator:hover {\n",
-
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 div.sk-estimator.fitted:hover {\n",
-
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1597,9 +1404,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-
-       "#sk-container-id-13 a.estimator_doc_link {\n",
-
+       "#sk-container-id-2 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1614,52 +1419,43 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 a.estimator_doc_link.fitted {\n",
-
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-
-       "#sk-container-id-13 a.estimator_doc_link:hover {\n",
-
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-13 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
-
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "SVR()"
       ]
      },
-
-     "execution_count": 144,
-
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Train the model\n",
-    "model2.fit(X_train, y_train)"
+    "model2.fit(X_train_scaled, y_train)"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 68,
+   "execution_count": 46,
    "metadata": {
     "id": "OQ1nL4oYfkAC"
    },
@@ -1675,7 +1471,6 @@
      ]
     }
    ],
-
    "source": [
     "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
     "metrics[\"Model\"].append(\"SVR\")\n",
@@ -1695,9 +1490,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 72,
-
+   "execution_count": 49,
    "metadata": {
     "id": "f7raXT_hf2ij"
    },
@@ -1708,9 +1501,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 146,
-
+   "execution_count": 51,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1723,9 +1514,7 @@
     {
      "data": {
       "text/html": [
-
-       "<style>#sk-container-id-14 {\n",
-
+       "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -1755,17 +1544,15 @@
        "  }\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 {\n",
+       "#sk-container-id-3 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-14 pre {\n",
+       "#sk-container-id-3 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-14 input.sk-hidden--visually {\n",
-
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1777,9 +1564,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-dashed-wrapped {\n",
-
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1787,9 +1572,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-container {\n",
-
+       "#sk-container-id-3 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1799,9 +1582,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-text-repr-fallback {\n",
-
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1817,18 +1598,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-parallel-item::after {\n",
-
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-parallel {\n",
-
+       "#sk-container-id-3 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1836,38 +1613,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-parallel-item {\n",
-
+       "#sk-container-id-3 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
-
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
-
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
-
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-serial {\n",
-
+       "#sk-container-id-3 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1885,18 +1652,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-toggleable {\n",
-
+       "#sk-container-id-3 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-
-       "#sk-container-id-14 label.sk-toggleable__label {\n",
-
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -1906,9 +1669,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1916,17 +1677,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n",
-
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-toggleable__content {\n",
-
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1935,15 +1692,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-toggleable__content.fitted {\n",
-
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-14 div.sk-toggleable__content pre {\n",
-
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1951,104 +1705,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-toggleable__content.fitted pre {\n",
-
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-
-       "#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-14 div.sk-label label {\n",
-
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-
-       "#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {\n",
-
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-
-       "#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-label label {\n",
-
+       "#sk-container-id-3 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
-       "
-       "#sk-container-id-14 div.sk-label-container {\n",
-
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-
-       "#sk-container-id-14 div.sk-estimator {\n",
-
+       "#sk-container-id-3 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2058,24 +1787,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-estimator.fitted {\n",
-
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-
-       "#sk-container-id-14 div.sk-estimator:hover {\n",
-
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 div.sk-estimator.fitted:hover {\n",
-
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2162,9 +1885,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-
-       "#sk-container-id-14 a.estimator_doc_link {\n",
-
+       "#sk-container-id-3 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2179,52 +1900,43 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 a.estimator_doc_link.fitted {\n",
-
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-
-       "#sk-container-id-14 a.estimator_doc_link:hover {\n",
-
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-14 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
-
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "RandomForestRegressor()"
       ]
      },
-
-     "execution_count": 146,
-
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Train the model\n",
-    "model3.fit(X_train, y_train)"
+    "model3.fit(X_train_scaled, y_train)"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 80,
+   "execution_count": 53,
    "metadata": {
     "id": "8nRU_pzEgnCt"
    },
@@ -2233,14 +1945,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 2.2053909891328036\n",
-      "MAE: 1.2608162799481166\n",
-      "MAPE: 0.008015308194076972\n",
+      "RMSE: 2.2416715812863233\n",
+      "MAE: 1.2631590379125215\n",
+      "MAPE: 0.007993049070058203\n",
       "\n"
      ]
     }
    ],
-
    "source": [
     "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
     "metrics[\"Model\"].append(\"Random Forest\")\n",
@@ -2260,9 +1971,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 83,
-
+   "execution_count": 56,
    "metadata": {
     "id": "TI8idoxOg6jF"
    },
@@ -2273,9 +1982,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 148,
-
+   "execution_count": 58,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2288,9 +1995,7 @@
     {
      "data": {
       "text/html": [
-
-       "<style>#sk-container-id-15 {\n",
-
+       "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -2320,17 +2025,15 @@
        "  }\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 {\n",
+       "#sk-container-id-4 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-15 pre {\n",
+       "#sk-container-id-4 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-15 input.sk-hidden--visually {\n",
-
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2342,9 +2045,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-dashed-wrapped {\n",
-
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2352,9 +2053,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-container {\n",
-
+       "#sk-container-id-4 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2364,9 +2063,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-text-repr-fallback {\n",
-
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2382,18 +2079,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-parallel-item::after {\n",
-
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-parallel {\n",
-
+       "#sk-container-id-4 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2401,38 +2094,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-parallel-item {\n",
-
+       "#sk-container-id-4 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-parallel-item:first-child::after {\n",
-
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-parallel-item:last-child::after {\n",
-
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-parallel-item:only-child::after {\n",
-
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-serial {\n",
-
+       "#sk-container-id-4 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2450,18 +2133,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-toggleable {\n",
-
+       "#sk-container-id-4 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-
-       "#sk-container-id-15 label.sk-toggleable__label {\n",
-
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -2471,9 +2150,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2481,17 +2158,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n",
-
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-toggleable__content {\n",
-
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -2500,16 +2173,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-toggleable__content.fitted {\n",
-
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-toggleable__content pre {\n",
-
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2517,105 +2186,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-toggleable__content.fitted pre {\n",
-
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-
-       "#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-15 div.sk-label label {\n",
-
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-
-       "#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {\n",
-
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-
-       "#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-label label {\n",
-
+       "#sk-container-id-4 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-label-container {\n",
-
+       "#sk-container-id-4 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-
-       "#sk-container-id-15 div.sk-estimator {\n",
-
+       "#sk-container-id-4 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2625,24 +2268,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-estimator.fitted {\n",
-
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-
-       "#sk-container-id-15 div.sk-estimator:hover {\n",
-
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 div.sk-estimator.fitted:hover {\n",
-
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2729,9 +2366,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-
-       "#sk-container-id-15 a.estimator_doc_link {\n",
-
+       "#sk-container-id-4 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2746,52 +2381,43 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 a.estimator_doc_link.fitted {\n",
-
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-
-       "#sk-container-id-15 a.estimator_doc_link:hover {\n",
-
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-15 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
-
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "GradientBoostingRegressor()"
       ]
      },
-
-     "execution_count": 148,
-
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Train the model\n",
-    "model4.fit(X_train, y_train)"
+    "model4.fit(X_train_scaled, y_train)"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 87,
+   "execution_count": 60,
    "metadata": {
     "id": "Jj9DXdUPhh9V"
    },
@@ -2800,14 +2426,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 2.6985863368468084\n",
-      "MAE: 1.692542658558929\n",
-      "MAPE: 0.011883244132236716\n",
+      "RMSE: 2.698775447278503\n",
+      "MAE: 1.6926225037051141\n",
+      "MAPE: 0.011883357895258613\n",
       "\n"
      ]
     }
    ],
-
    "source": [
     "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
     "metrics[\"Model\"].append(\"GBM\")\n",
@@ -2827,9 +2452,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 90,
-
+   "execution_count": 63,
    "metadata": {
     "id": "DyhhdlZAhx94"
    },
@@ -2842,9 +2465,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 150,
-
+   "execution_count": 65,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2857,9 +2478,7 @@
     {
      "data": {
       "text/html": [
-
-       "<style>#sk-container-id-16 {\n",
-
+       "<style>#sk-container-id-5 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -2889,17 +2508,15 @@
        "  }\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 {\n",
+       "#sk-container-id-5 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-16 pre {\n",
+       "#sk-container-id-5 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-16 input.sk-hidden--visually {\n",
-
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2911,9 +2528,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-dashed-wrapped {\n",
-
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2921,9 +2536,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-container {\n",
-
+       "#sk-container-id-5 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2933,9 +2546,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-text-repr-fallback {\n",
-
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2951,18 +2562,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-parallel-item::after {\n",
-
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-parallel {\n",
-
+       "#sk-container-id-5 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2970,38 +2577,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-parallel-item {\n",
-
+       "#sk-container-id-5 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-parallel-item:first-child::after {\n",
-
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-parallel-item:last-child::after {\n",
-
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-parallel-item:only-child::after {\n",
-
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-serial {\n",
-
+       "#sk-container-id-5 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3019,18 +2616,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-toggleable {\n",
-
+       "#sk-container-id-5 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-
-       "#sk-container-id-16 label.sk-toggleable__label {\n",
-
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -3040,9 +2633,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3050,17 +2641,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {\n",
-
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-toggleable__content {\n",
-
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -3069,16 +2656,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-toggleable__content.fitted {\n",
-
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-toggleable__content pre {\n",
-
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3086,105 +2669,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-toggleable__content.fitted pre {\n",
-
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-
-       "#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-16 div.sk-label label {\n",
-
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-
-       "#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {\n",
-
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-
-       "#sk-container-id-16 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-label label {\n",
-
+       "#sk-container-id-5 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-label-container {\n",
-
+       "#sk-container-id-5 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-
-       "#sk-container-id-16 div.sk-estimator {\n",
-
+       "#sk-container-id-5 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3194,24 +2751,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-estimator.fitted {\n",
-
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-
-       "#sk-container-id-16 div.sk-estimator:hover {\n",
-
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 div.sk-estimator.fitted:hover {\n",
-
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3298,9 +2849,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-
-       "#sk-container-id-16 a.estimator_doc_link {\n",
-
+       "#sk-container-id-5 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -3315,31 +2864,25 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 a.estimator_doc_link.fitted {\n",
-
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-
-       "#sk-container-id-16 a.estimator_doc_link:hover {\n",
-
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-16 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
        "             colsample_bylevel=None, colsample_bynode=None,\n",
        "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
        "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
@@ -3349,9 +2892,7 @@
        "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
        "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
        "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-
-       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
-
+       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
        "             colsample_bylevel=None, colsample_bynode=None,\n",
        "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
        "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
@@ -3377,34 +2918,28 @@
        "             num_parallel_tree=None, random_state=None, ...)"
       ]
      },
-
-     "execution_count": 150,
-
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Train the model\n",
-    "model5.fit(X_train, y_train)"
+    "model5.fit(X_train_scaled, y_train)"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 94,
-
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-
       "RMSE: 2.733930065274145\n",
       "MAE: 1.502457380471909\n",
       "MAPE: 0.010026410639661481\n",
-
       "\n"
      ]
     }
@@ -3428,9 +2963,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 97,
-
+   "execution_count": 70,
    "metadata": {
     "id": "HNq66cXRiYPJ"
    },
@@ -3441,9 +2974,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 152,
-
+   "execution_count": 72,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3456,9 +2987,7 @@
     {
      "data": {
       "text/html": [
-
-       "<style>#sk-container-id-17 {\n",
-
+       "<style>#sk-container-id-6 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -3488,17 +3017,15 @@
        "  }\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 {\n",
+       "#sk-container-id-6 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-17 pre {\n",
+       "#sk-container-id-6 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-17 input.sk-hidden--visually {\n",
-
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3510,9 +3037,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-dashed-wrapped {\n",
-
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3520,9 +3045,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-container {\n",
-
+       "#sk-container-id-6 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -3532,9 +3055,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-text-repr-fallback {\n",
-
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3550,18 +3071,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-parallel-item::after {\n",
-
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-parallel {\n",
-
+       "#sk-container-id-6 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3569,37 +3086,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-parallel-item {\n",
-
+       "#sk-container-id-6 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-parallel-item:first-child::after {\n",
-
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-parallel-item:last-child::after {\n",
-
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-parallel-item:only-child::after {\n",
-
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-17 div.sk-serial {\n",
-
+       "#sk-container-id-6 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3617,18 +3125,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-toggleable {\n",
-
+       "#sk-container-id-6 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-
-       "#sk-container-id-17 label.sk-toggleable__label {\n",
-
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -3638,9 +3142,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3648,17 +3150,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {\n",
-
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-toggleable__content {\n",
-
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -3667,16 +3165,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-toggleable__content.fitted {\n",
-
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-toggleable__content pre {\n",
-
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3684,105 +3178,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-toggleable__content.fitted pre {\n",
-
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-
-       "#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-17 div.sk-label label {\n",
-
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-
-       "#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {\n",
-
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-
-       "#sk-container-id-17 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-label label {\n",
-
+       "#sk-container-id-6 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-label-container {\n",
-
+       "#sk-container-id-6 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-
-       "#sk-container-id-17 div.sk-estimator {\n",
-
+       "#sk-container-id-6 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3792,24 +3260,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-estimator.fitted {\n",
-
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-
-       "#sk-container-id-17 div.sk-estimator:hover {\n",
-
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 div.sk-estimator.fitted:hover {\n",
-
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3896,9 +3358,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-
-       "#sk-container-id-17 a.estimator_doc_link {\n",
-
+       "#sk-container-id-6 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -3913,52 +3373,43 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 a.estimator_doc_link.fitted {\n",
-
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-
-       "#sk-container-id-17 a.estimator_doc_link:hover {\n",
-
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-17 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
-
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "AdaBoostRegressor()"
       ]
      },
-
-     "execution_count": 152,
-
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Train the model\n",
-    "model6.fit(X_train, y_train)"
+    "model6.fit(X_train_scaled, y_train)"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 105,
+   "execution_count": 74,
    "metadata": {
     "id": "Bf1m5ukOi2VM"
    },
@@ -3967,14 +3418,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 9.175482477551942\n",
-      "MAE: 7.527617905792734\n",
-      "MAPE: 0.1858930099598583\n",
+      "RMSE: 9.335685430996156\n",
+      "MAE: 7.7362997117558505\n",
+      "MAPE: 0.18753095198352016\n",
       "\n"
      ]
     }
    ],
-
    "source": [
     "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
     "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
@@ -3994,9 +3444,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 108,
-
+   "execution_count": 77,
    "metadata": {
     "id": "23DZ2biSjF9a"
    },
@@ -4007,9 +3455,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 154,
-
+   "execution_count": 79,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -4022,9 +3468,7 @@
     {
      "data": {
       "text/html": [
-
-       "<style>#sk-container-id-18 {\n",
-
+       "<style>#sk-container-id-7 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -4054,17 +3498,15 @@
        "  }\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 {\n",
+       "#sk-container-id-7 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-18 pre {\n",
+       "#sk-container-id-7 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-18 input.sk-hidden--visually {\n",
-
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -4076,9 +3518,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-dashed-wrapped {\n",
-
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -4086,9 +3526,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-container {\n",
-
+       "#sk-container-id-7 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4098,9 +3536,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-text-repr-fallback {\n",
-
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4116,18 +3552,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-parallel-item::after {\n",
-
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-parallel {\n",
-
+       "#sk-container-id-7 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4135,37 +3567,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-18 div.sk-parallel-item {\n",
-
+       "#sk-container-id-7 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-parallel-item:first-child::after {\n",
-
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-parallel-item:last-child::after {\n",
-
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-parallel-item:only-child::after {\n",
-
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-serial {\n",
-
+       "#sk-container-id-7 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4183,18 +3606,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-toggleable {\n",
-
+       "#sk-container-id-7 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-
-       "#sk-container-id-18 label.sk-toggleable__label {\n",
-
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -4204,9 +3623,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4214,17 +3631,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {\n",
-
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-toggleable__content {\n",
-
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -4233,16 +3646,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-toggleable__content.fitted {\n",
-
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-toggleable__content pre {\n",
-
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4250,105 +3659,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-toggleable__content.fitted pre {\n",
-
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-
-       "#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-18 div.sk-label label {\n",
-
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-
-       "#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {\n",
-
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-
-       "#sk-container-id-18 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-label label {\n",
-
+       "#sk-container-id-7 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-label-container {\n",
-
+       "#sk-container-id-7 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-
-       "#sk-container-id-18 div.sk-estimator {\n",
-
+       "#sk-container-id-7 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4358,24 +3741,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-estimator.fitted {\n",
-
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-
-       "#sk-container-id-18 div.sk-estimator:hover {\n",
-
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 div.sk-estimator.fitted:hover {\n",
-
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4462,9 +3839,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-
-       "#sk-container-id-18 a.estimator_doc_link {\n",
-
+       "#sk-container-id-7 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -4479,52 +3854,43 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 a.estimator_doc_link.fitted {\n",
-
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-
-       "#sk-container-id-18 a.estimator_doc_link:hover {\n",
-
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-18 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
-
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "DecisionTreeRegressor()"
       ]
      },
-
-     "execution_count": 154,
-
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Train the model\n",
-    "model7.fit(X_train, y_train)"
+    "model7.fit(X_train_scaled, y_train)"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 112,
+   "execution_count": 81,
    "metadata": {
     "id": "BFJ9q_tvkgRC"
    },
@@ -4533,14 +3899,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 3.12966540689625\n",
-      "MAE: 1.6497286032971983\n",
-      "MAPE: 0.010286427942970355\n",
+      "RMSE: 3.1226439476949475\n",
+      "MAE: 1.6374545820957844\n",
+      "MAPE: 0.010237146690906872\n",
       "\n"
      ]
     }
    ],
-
    "source": [
     "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
     "metrics[\"Model\"].append(\"Decision Tree\")\n",
@@ -4560,9 +3925,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 115,
-
+   "execution_count": 84,
    "metadata": {
     "id": "JVDSed7yktFY"
    },
@@ -4574,9 +3937,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 156,
-
+   "execution_count": 86,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -4589,9 +3950,7 @@
     {
      "data": {
       "text/html": [
-
-       "<style>#sk-container-id-19 {\n",
-
+       "<style>#sk-container-id-8 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -4621,17 +3980,15 @@
        "  }\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 {\n",
+       "#sk-container-id-8 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-19 pre {\n",
+       "#sk-container-id-8 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-19 input.sk-hidden--visually {\n",
-
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -4643,9 +4000,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-dashed-wrapped {\n",
-
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -4653,9 +4008,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-container {\n",
-
+       "#sk-container-id-8 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4665,9 +4018,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-text-repr-fallback {\n",
-
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4683,18 +4034,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-parallel-item::after {\n",
-
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-parallel {\n",
-
+       "#sk-container-id-8 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4702,37 +4049,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-parallel-item {\n",
-
+       "#sk-container-id-8 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
-
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
-
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-serial {\n",
-
+       "#sk-container-id-8 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4750,18 +4088,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-toggleable {\n",
-
+       "#sk-container-id-8 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-
-       "#sk-container-id-19 label.sk-toggleable__label {\n",
-
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -4771,9 +4105,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4781,17 +4113,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {\n",
-
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-toggleable__content {\n",
-
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -4800,16 +4128,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
-
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-toggleable__content pre {\n",
-
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4817,105 +4141,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-toggleable__content.fitted pre {\n",
-
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-
-       "#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-19 div.sk-label label {\n",
-
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-
-       "#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {\n",
-
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-
-       "#sk-container-id-19 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-label label {\n",
-
+       "#sk-container-id-8 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-label-container {\n",
-
+       "#sk-container-id-8 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-
-       "#sk-container-id-19 div.sk-estimator {\n",
-
+       "#sk-container-id-8 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4925,24 +4223,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-estimator.fitted {\n",
-
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-
-       "#sk-container-id-19 div.sk-estimator:hover {\n",
-
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 div.sk-estimator.fitted:hover {\n",
-
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -5029,9 +4321,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-
-       "#sk-container-id-19 a.estimator_doc_link {\n",
-
+       "#sk-container-id-8 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -5046,52 +4336,43 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 a.estimator_doc_link.fitted {\n",
-
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-
-       "#sk-container-id-19 a.estimator_doc_link:hover {\n",
-
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-
-       "#sk-container-id-19 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
-
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "KNeighborsRegressor()"
       ]
      },
-
-     "execution_count": 156,
-
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Train the model\n",
-    "model8.fit(X_train, y_train)"
+    "model8.fit(X_train_scaled, y_train)"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 119,
+   "execution_count": 88,
    "metadata": {
     "id": "hbfbbjcSlDn7"
    },
@@ -5107,7 +4388,6 @@
      ]
     }
    ],
-
    "source": [
     "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
     "metrics[\"Model\"].append(\"KNN\")\n",
@@ -5127,22 +4407,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create an ANN model\n",
     "model9 = Sequential()\n",
-    "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+    "from keras.layers import Input\n",
+    "model9.add(Input(shape=(X_train.shape[1],)))\n",
+    "model9.add(Dense(32, activation='relu'))\n",
     "model9.add(Dense(16, activation='relu'))\n",
     "model9.add(Dense(1, activation='linear'))"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 124,
-
+   "execution_count": 126,
    "metadata": {
     "id": "ZIf94WLMlv04"
    },
@@ -5154,9 +4434,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 126,
-
+   "execution_count": 128,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -5168,12 +4446,10 @@
     {
      "data": {
       "text/plain": [
-
-       "<keras.src.callbacks.history.History at 0x1a508bc2c90>"
+       "<keras.src.callbacks.history.History at 0x24f75a139e0>"
       ]
      },
-     "execution_count": 126,
-
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5185,9 +4461,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 128,
-
+   "execution_count": 130,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -5201,11 +4475,9 @@
      "output_type": "stream",
      "text": [
       "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
-
-      "RMSE: 2.801001091255311\n",
-      "MAE: 1.7605365826618848\n",
-      "MAPE: 0.0126215060590655\n",
-
+      "RMSE: 2.789749884117495\n",
+      "MAE: 1.7292806558281988\n",
+      "MAPE: 0.011206408479601398\n",
       "\n"
      ]
     }
@@ -5229,9 +4501,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 131,
-
+   "execution_count": 149,
    "metadata": {
     "id": "uACvajfImrbB"
    },
@@ -5250,21 +4520,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create an LSTM model\n",
     "model = Sequential()\n",
-    "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+    "model.add(Input(shape=(n_steps, n_features)))\n",
+    "model.add(LSTM(64, activation='relu'))\n",
     "model.add(Dense(1))\n"
    ]
   },
   {
    "cell_type": "code",
-
-   "execution_count": 135,
-
+   "execution_count": 139,
    "metadata": {
     "id": "YpSfHu6gov35"
    },
@@ -5276,9 +4545,7 @@
   },
   {
    "cell_type": "code",
-
-   "execution_count": 137,
-
+   "execution_count": 141,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -5290,12 +4557,10 @@
     {
      "data": {
       "text/plain": [
-
-       "<keras.src.callbacks.history.History at 0x1a508dda390>"
+       "<keras.src.callbacks.history.History at 0x24f761db920>"
       ]
      },
-     "execution_count": 137,
-
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5311,7 +4576,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
+    "rmse, mae, mape = evaluate_model(model10, X_test_reshaped, y_test)\n",
     "metrics[\"Model\"].append(\"LSTM\")\n",
     "metrics[\"RMSE\"].append(rmse)\n",
     "metrics[\"MAE\"].append(mae)\n",
@@ -5320,11 +4585,9 @@
   },
   {
    "cell_type": "code",
-
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-
    "source": [
     "# Create a DataFrame for metrics\n",
     "metrics_df = pd.DataFrame(metrics)\n",
@@ -5343,11 +4606,9 @@
   },
   {
    "cell_type": "code",
-
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-
    "source": [
     "# MAE Plot\n",
     "plt.subplot(1, 3, 2)\n",
@@ -5360,11 +4621,9 @@
   },
   {
    "cell_type": "code",
-
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-
    "source": [
     "# MAPE Plot\n",
     "plt.subplot(1, 3, 3)\n",
@@ -5384,11 +4643,9 @@
   },
   {
    "cell_type": "code",
-
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-
    "source": [
     "# Import necessary libraries\n",
     "import lightgbm as lgb\n",
diff --git a/Stock_Price_Prediction.ipynb b/Stock_Price_Prediction.ipynb
index 6113649..40b633e 100644
--- a/Stock_Price_Prediction.ipynb
+++ b/Stock_Price_Prediction.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 2,
    "metadata": {
     "id": "qCDSjVhXLr_Z"
    },
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 6,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -168,7 +168,7 @@
        "4  76613039.0  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 8,
    "metadata": {
     "id": "7LaYGXsfN-8y"
    },
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -276,7 +276,6 @@
        "</table>\n",
        "</div>"
       ],
-
       "text/plain": [
        "        Open       High        Low      Close      Volume\n",
        "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
@@ -284,10 +283,9 @@
        "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
        "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
        "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
-
       ]
      },
-     "execution_count": 30,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -298,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 12,
    "metadata": {
     "id": "dydEPoNeM6eN"
    },
@@ -311,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 14,
    "metadata": {
     "id": "OQ3cGqgTMBwt"
    },
@@ -324,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 16,
    "metadata": {
     "id": "9Oz-bwJOMEWD"
    },
@@ -336,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 18,
    "metadata": {
     "id": "ugapDyXODtn3"
    },
@@ -350,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 20,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -365,7 +363,7 @@
        "(5659, 4)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 22,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -391,7 +389,7 @@
        "(1415, 4)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -402,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 24,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -417,7 +415,7 @@
        "(5659,)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -428,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 26,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -438,14 +436,12 @@
    },
    "outputs": [
     {
-
      "data": {
       "text/plain": [
        "(1415,)"
-
       ]
      },
-     "execution_count": 46,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -456,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -499,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 33,
    "metadata": {
     "id": "RdZ1SpzdMHAJ"
    },
@@ -511,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 35,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -531,7 +527,7 @@
        "Name: Close, dtype: float64"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -542,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 37,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -555,7 +551,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-20 {\n",
+       "<style>#sk-container-id-1 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -585,15 +581,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 {\n",
+       "#sk-container-id-1 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 pre {\n",
+       "#sk-container-id-1 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 input.sk-hidden--visually {\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -605,7 +601,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -613,7 +609,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-container {\n",
+       "#sk-container-id-1 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -623,7 +619,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -639,14 +635,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-20 div.sk-parallel-item::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-parallel {\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -654,28 +650,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-parallel-item {\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-20 div.sk-serial {\n",
+       "#sk-container-id-1 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -693,14 +689,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-20 div.sk-toggleable {\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-20 label.sk-toggleable__label {\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -710,7 +706,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -718,13 +714,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-20 div.sk-toggleable__content {\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -733,12 +729,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -746,79 +742,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-20 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-20 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-20 div.sk-label label {\n",
+       "#sk-container-id-1 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-label-container {\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-20 div.sk-estimator {\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -828,18 +824,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-estimator.fitted {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-20 div.sk-estimator:hover {\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -926,7 +922,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-20 a.estimator_doc_link {\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -941,31 +937,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-20 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-20 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "LinearRegression()"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -977,7 +973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 39,
    "metadata": {
     "id": "X269co2kMS4z"
    },
@@ -1012,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 42,
    "metadata": {
     "id": "0xQewd7QWTtq"
    },
@@ -1024,7 +1020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 44,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1037,7 +1033,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-21 {\n",
+       "<style>#sk-container-id-2 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -1067,15 +1063,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 {\n",
+       "#sk-container-id-2 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 pre {\n",
+       "#sk-container-id-2 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 input.sk-hidden--visually {\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1087,7 +1083,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1095,7 +1091,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-container {\n",
+       "#sk-container-id-2 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1105,7 +1101,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1121,14 +1117,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-21 div.sk-parallel-item::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-parallel {\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1136,28 +1132,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-parallel-item {\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-21 div.sk-serial {\n",
+       "#sk-container-id-2 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1175,14 +1171,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-21 div.sk-toggleable {\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-21 label.sk-toggleable__label {\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -1192,7 +1188,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1200,13 +1196,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-21 div.sk-toggleable__content {\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1215,12 +1211,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1228,79 +1224,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-21 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-21 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-21 div.sk-label label {\n",
+       "#sk-container-id-2 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-label-container {\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-21 div.sk-estimator {\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1310,18 +1306,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-estimator.fitted {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-21 div.sk-estimator:hover {\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1408,7 +1404,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-21 a.estimator_doc_link {\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1423,31 +1419,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-21 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-21 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "SVR()"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1459,7 +1455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 46,
    "metadata": {
     "id": "OQ1nL4oYfkAC"
    },
@@ -1494,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 49,
    "metadata": {
     "id": "f7raXT_hf2ij"
    },
@@ -1505,7 +1501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 51,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1518,7 +1514,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-22 {\n",
+       "<style>#sk-container-id-3 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -1548,15 +1544,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 {\n",
+       "#sk-container-id-3 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 pre {\n",
+       "#sk-container-id-3 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 input.sk-hidden--visually {\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1568,7 +1564,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1576,7 +1572,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-container {\n",
+       "#sk-container-id-3 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1586,7 +1582,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1602,14 +1598,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-22 div.sk-parallel-item::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-parallel {\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1617,28 +1613,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-parallel-item {\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-22 div.sk-serial {\n",
+       "#sk-container-id-3 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1656,14 +1652,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-22 div.sk-toggleable {\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-22 label.sk-toggleable__label {\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -1673,7 +1669,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1681,13 +1677,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-22 div.sk-toggleable__content {\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1696,12 +1692,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1709,79 +1705,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-22 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-22 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-22 div.sk-label label {\n",
+       "#sk-container-id-3 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-label-container {\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-22 div.sk-estimator {\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1791,18 +1787,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-estimator.fitted {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-22 div.sk-estimator:hover {\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1889,7 +1885,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-22 a.estimator_doc_link {\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1904,31 +1900,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-22 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-22 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "RandomForestRegressor()"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1940,7 +1936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 53,
    "metadata": {
     "id": "8nRU_pzEgnCt"
    },
@@ -1949,9 +1945,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 2.2053909891328036\n",
-      "MAE: 1.2608162799481166\n",
-      "MAPE: 0.008015308194076972\n",
+      "RMSE: 2.2416715812863233\n",
+      "MAE: 1.2631590379125215\n",
+      "MAPE: 0.007993049070058203\n",
       "\n"
      ]
     }
@@ -1975,7 +1971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 56,
    "metadata": {
     "id": "TI8idoxOg6jF"
    },
@@ -1986,7 +1982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 58,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1999,7 +1995,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-23 {\n",
+       "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -2029,15 +2025,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 {\n",
+       "#sk-container-id-4 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 pre {\n",
+       "#sk-container-id-4 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 input.sk-hidden--visually {\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2049,7 +2045,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2057,7 +2053,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-container {\n",
+       "#sk-container-id-4 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2067,7 +2063,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2083,14 +2079,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-23 div.sk-parallel-item::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-parallel {\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2098,28 +2094,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-parallel-item {\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-23 div.sk-serial {\n",
+       "#sk-container-id-4 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2137,14 +2133,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-23 div.sk-toggleable {\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-23 label.sk-toggleable__label {\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -2154,7 +2150,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2162,13 +2158,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-23 div.sk-toggleable__content {\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -2177,12 +2173,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2190,79 +2186,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-23 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-23 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-23 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-label-container {\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-23 div.sk-estimator {\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2272,18 +2268,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-estimator.fitted {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-23 div.sk-estimator:hover {\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2370,7 +2366,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-23 a.estimator_doc_link {\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2385,31 +2381,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-23 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-23 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" checked><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "GradientBoostingRegressor()"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2421,7 +2417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 60,
    "metadata": {
     "id": "Jj9DXdUPhh9V"
    },
@@ -2430,9 +2426,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 2.6985863368468084\n",
-      "MAE: 1.692542658558929\n",
-      "MAPE: 0.011883244132236716\n",
+      "RMSE: 2.698775447278503\n",
+      "MAE: 1.6926225037051141\n",
+      "MAPE: 0.011883357895258613\n",
       "\n"
      ]
     }
@@ -2456,7 +2452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 63,
    "metadata": {
     "id": "DyhhdlZAhx94"
    },
@@ -2469,7 +2465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 65,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2482,7 +2478,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-24 {\n",
+       "<style>#sk-container-id-5 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -2512,15 +2508,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 {\n",
+       "#sk-container-id-5 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 pre {\n",
+       "#sk-container-id-5 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 input.sk-hidden--visually {\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -2532,7 +2528,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -2540,7 +2536,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-container {\n",
+       "#sk-container-id-5 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -2550,7 +2546,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -2566,14 +2562,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-24 div.sk-parallel-item::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-parallel {\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -2581,28 +2577,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-parallel-item {\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-24 div.sk-serial {\n",
+       "#sk-container-id-5 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -2620,14 +2616,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-24 div.sk-toggleable {\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-24 label.sk-toggleable__label {\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -2637,7 +2633,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -2645,13 +2641,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-24 div.sk-toggleable__content {\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -2660,12 +2656,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -2673,79 +2669,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-24 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-24 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-24 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-label-container {\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-24 div.sk-estimator {\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -2755,18 +2751,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-estimator.fitted {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-24 div.sk-estimator:hover {\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2853,7 +2849,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-24 a.estimator_doc_link {\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2868,25 +2864,25 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-24 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-24 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
        "             colsample_bylevel=None, colsample_bynode=None,\n",
        "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
        "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
@@ -2896,7 +2892,7 @@
        "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
        "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
        "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
-       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" checked><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
        "             colsample_bylevel=None, colsample_bynode=None,\n",
        "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
        "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
@@ -2922,7 +2918,7 @@
        "             num_parallel_tree=None, random_state=None, ...)"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2934,7 +2930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -2967,7 +2963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 70,
    "metadata": {
     "id": "HNq66cXRiYPJ"
    },
@@ -2978,7 +2974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 72,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2991,7 +2987,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-25 {\n",
+       "<style>#sk-container-id-6 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -3021,15 +3017,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 {\n",
+       "#sk-container-id-6 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 pre {\n",
+       "#sk-container-id-6 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 input.sk-hidden--visually {\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3041,7 +3037,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3049,7 +3045,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-container {\n",
+       "#sk-container-id-6 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -3059,7 +3055,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3075,14 +3071,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-25 div.sk-parallel-item::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-parallel {\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3090,28 +3086,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-parallel-item {\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-25 div.sk-serial {\n",
+       "#sk-container-id-6 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3129,14 +3125,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-25 div.sk-toggleable {\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-25 label.sk-toggleable__label {\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -3146,7 +3142,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3154,13 +3150,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-25 div.sk-toggleable__content {\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -3169,12 +3165,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3182,79 +3178,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-25 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-25 div.sk-label label {\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-25 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-25 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-25 div.sk-label label {\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-label-container {\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-25 div.sk-estimator {\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3264,18 +3260,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-estimator.fitted {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-25 div.sk-estimator:hover {\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3362,7 +3358,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-25 a.estimator_doc_link {\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -3377,31 +3373,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-25 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-25 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-25\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" checked><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "AdaBoostRegressor()"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3413,7 +3409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 74,
    "metadata": {
     "id": "Bf1m5ukOi2VM"
    },
@@ -3422,9 +3418,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 9.175482477551942\n",
-      "MAE: 7.527617905792734\n",
-      "MAPE: 0.1858930099598583\n",
+      "RMSE: 9.335685430996156\n",
+      "MAE: 7.7362997117558505\n",
+      "MAPE: 0.18753095198352016\n",
       "\n"
      ]
     }
@@ -3448,7 +3444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 77,
    "metadata": {
     "id": "23DZ2biSjF9a"
    },
@@ -3459,7 +3455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 79,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3472,7 +3468,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-26 {\n",
+       "<style>#sk-container-id-7 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -3502,15 +3498,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 {\n",
+       "#sk-container-id-7 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 pre {\n",
+       "#sk-container-id-7 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 input.sk-hidden--visually {\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -3522,7 +3518,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -3530,7 +3526,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-container {\n",
+       "#sk-container-id-7 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -3540,7 +3536,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -3556,14 +3552,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-26 div.sk-parallel-item::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-parallel {\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -3571,28 +3567,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-parallel-item {\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-26 div.sk-serial {\n",
+       "#sk-container-id-7 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -3610,14 +3606,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-26 div.sk-toggleable {\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-26 label.sk-toggleable__label {\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -3627,7 +3623,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -3635,13 +3631,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-26 div.sk-toggleable__content {\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -3650,12 +3646,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -3663,79 +3659,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-26 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-26 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-26 div.sk-label label {\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-26 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-26 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-26 div.sk-label label {\n",
+       "#sk-container-id-7 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-label-container {\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-26 div.sk-estimator {\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -3745,18 +3741,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-estimator.fitted {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-26 div.sk-estimator:hover {\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -3843,7 +3839,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-26 a.estimator_doc_link {\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -3858,31 +3854,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-26 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-26 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-26\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "DecisionTreeRegressor()"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3894,7 +3890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 81,
    "metadata": {
     "id": "BFJ9q_tvkgRC"
    },
@@ -3903,9 +3899,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE: 3.12966540689625\n",
-      "MAE: 1.6497286032971983\n",
-      "MAPE: 0.010286427942970355\n",
+      "RMSE: 3.1226439476949475\n",
+      "MAE: 1.6374545820957844\n",
+      "MAPE: 0.010237146690906872\n",
       "\n"
      ]
     }
@@ -3929,7 +3925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 84,
    "metadata": {
     "id": "JVDSed7yktFY"
    },
@@ -3941,7 +3937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 86,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -3954,7 +3950,7 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-27 {\n",
+       "<style>#sk-container-id-8 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -3984,15 +3980,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 {\n",
+       "#sk-container-id-8 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 pre {\n",
+       "#sk-container-id-8 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 input.sk-hidden--visually {\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -4004,7 +4000,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -4012,7 +4008,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-container {\n",
+       "#sk-container-id-8 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -4022,7 +4018,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -4038,14 +4034,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-27 div.sk-parallel-item::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-parallel {\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -4053,28 +4049,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-parallel-item {\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-27 div.sk-serial {\n",
+       "#sk-container-id-8 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -4092,14 +4088,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-27 div.sk-toggleable {\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-27 label.sk-toggleable__label {\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -4109,7 +4105,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -4117,13 +4113,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-27 div.sk-toggleable__content {\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -4132,12 +4128,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -4145,79 +4141,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-27 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-27 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-27 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-27 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-27 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-27 div.sk-label label {\n",
+       "#sk-container-id-8 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-label-container {\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-27 div.sk-estimator {\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -4227,18 +4223,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-estimator.fitted {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-27 div.sk-estimator:hover {\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -4325,7 +4321,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-27 a.estimator_doc_link {\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -4340,31 +4336,31 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-27 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-27 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-27\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" checked><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
        "KNeighborsRegressor()"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4376,13 +4372,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 88,
    "metadata": {
     "id": "hbfbbjcSlDn7"
    },
    "outputs": [
     {
-
      "name": "stdout",
      "output_type": "stream",
      "text": [
@@ -4412,20 +4407,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create an ANN model\n",
     "model9 = Sequential()\n",
-    "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
+    "from keras.layers import Input\n",
+    "model9.add(Input(shape=(X_train.shape[1],)))\n",
+    "model9.add(Dense(32, activation='relu'))\n",
     "model9.add(Dense(16, activation='relu'))\n",
     "model9.add(Dense(1, activation='linear'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 126,
    "metadata": {
     "id": "ZIf94WLMlv04"
    },
@@ -4437,7 +4434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 128,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4449,10 +4446,10 @@
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x1a508bc2c90>"
+       "<keras.src.callbacks.history.History at 0x24f75a139e0>"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4464,7 +4461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 130,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4478,12 +4475,11 @@
      "output_type": "stream",
      "text": [
       "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
-      "RMSE: 2.801001091255311\n",
-      "MAE: 1.7605365826618848\n",
-      "MAPE: 0.0126215060590655\n",
+      "RMSE: 2.789749884117495\n",
+      "MAE: 1.7292806558281988\n",
+      "MAPE: 0.011206408479601398\n",
       "\n"
      ]
-
     }
    ],
    "source": [
@@ -4505,7 +4501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 149,
    "metadata": {
     "id": "uACvajfImrbB"
    },
@@ -4524,19 +4520,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create an LSTM model\n",
     "model = Sequential()\n",
-    "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n",
+    "model.add(Input(shape=(n_steps, n_features)))\n",
+    "model.add(LSTM(64, activation='relu'))\n",
     "model.add(Dense(1))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 139,
    "metadata": {
     "id": "YpSfHu6gov35"
    },
@@ -4548,7 +4545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 141,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -4560,10 +4557,10 @@
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x1a508dda390>"
+       "<keras.src.callbacks.history.History at 0x24f761db920>"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4579,7 +4576,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "rmse, mae, mape = evaluate_model(model10, X_test_scaled, y_test)\n",
+    "rmse, mae, mape = evaluate_model(model10, X_test_reshaped, y_test)\n",
     "metrics[\"Model\"].append(\"LSTM\")\n",
     "metrics[\"RMSE\"].append(rmse)\n",
     "metrics[\"MAE\"].append(mae)\n",

From bb10983825c7af39b6bfcea42193ec23d4297047 Mon Sep 17 00:00:00 2001
From: spandana2004 <110383668+spandana2004@users.noreply.github.com>
Date: Wed, 16 Oct 2024 20:05:59 +0530
Subject: [PATCH 30/76] Add files via upload

---
 VWAP_included_stock_price_prediction.ipynb | 204 +++++++++++++++++++++
 1 file changed, 204 insertions(+)
 create mode 100644 VWAP_included_stock_price_prediction.ipynb

diff --git a/VWAP_included_stock_price_prediction.ipynb b/VWAP_included_stock_price_prediction.ipynb
new file mode 100644
index 0000000..ad573bf
--- /dev/null
+++ b/VWAP_included_stock_price_prediction.ipynb
@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fa438f6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Linear Regression Model:\n",
+      "RMSE: 1.6880694911329877, MAE: 0.9432271009469612, MAPE: 0.006084051219316342, Accuracy: 0.8282685512367491, Precision: 0.8607594936708861, Confusion Matrix:\n",
+      "[[560  99]\n",
+      " [144 612]], Recall: 0.8095238095238095, F1 Score: 0.834355828220859\n",
+      "\n",
+      "SVR Model:\n",
+      "RMSE: 139.76497837705972, MAE: 80.8494314698498, MAPE: 0.5135323618594273, Accuracy: 0.9992932862190813, Precision: 1.0, Confusion Matrix:\n",
+      "[[738   0]\n",
+      " [  1 676]], Recall: 0.9985228951255539, F1 Score: 0.999260901699926\n",
+      "\n",
+      "Random Forest Model:\n",
+      "RMSE: 2.2696019969365957, MAE: 1.2959525326831525, MAPE: 0.008225147954228209, Accuracy: 0.8600706713780919, Precision: 0.8626292466765141, Confusion Matrix:\n",
+      "[[633  93]\n",
+      " [105 584]], Recall: 0.8476052249637155, F1 Score: 0.8550512445095169\n",
+      "\n",
+      "Gradient Boosting Model:\n",
+      "RMSE: 2.685918356233534, MAE: 1.69115479259762, MAPE: 0.011694869673820272, Accuracy: 0.8989399293286219, Precision: 0.890961262553802, Confusion Matrix:\n",
+      "[[651  76]\n",
+      " [ 67 621]], Recall: 0.9026162790697675, F1 Score: 0.8967509025270758\n",
+      "\n",
+      "XGBoost Model:\n",
+      "RMSE: 2.804661454159519, MAE: 1.5301211794416443, MAPE: 0.010211801526489423, Accuracy: 0.9003533568904594, Precision: 0.9134751773049645, Confusion Matrix:\n",
+      "[[630  61]\n",
+      " [ 80 644]], Recall: 0.8895027624309392, F1 Score: 0.9013296011196641\n",
+      "\n",
+      "AdaBoost Model:\n",
+      "RMSE: 9.866286038032165, MAE: 8.043651625035126, MAPE: 0.19044334186374637, Accuracy: 0.995053003533569, Precision: 0.9920948616600791, Confusion Matrix:\n",
+      "[[906   4]\n",
+      " [  3 502]], Recall: 0.994059405940594, F1 Score: 0.9930761622156281\n",
+      "\n",
+      "Decision Tree Model:\n",
+      "RMSE: 3.090476936764667, MAE: 1.6747161107176924, MAPE: 0.010524837872635435, Accuracy: 0.8678445229681979, Precision: 0.874826147426982, Confusion Matrix:\n",
+      "[[599  90]\n",
+      " [ 97 629]], Recall: 0.8663911845730028, F1 Score: 0.8705882352941178\n",
+      "\n",
+      "KNeighbors Model:\n",
+      "RMSE: 3.567538180054403, MAE: 2.177264935594977, MAPE: 0.017010749895821846, Accuracy: 0.9215547703180212, Precision: 0.9369369369369369, Confusion Matrix:\n",
+      "[[680  42]\n",
+      " [ 69 624]], Recall: 0.9004329004329005, F1 Score: 0.9183222958057395\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import dependencies\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "import xgboost as xgb\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "\n",
+    "# Load the dataset\n",
+    "df = pd.read_csv('SBIN.csv')\n",
+    "\n",
+    "# Data preprocessing\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)  # Drop irrelevant columns\n",
+    "\n",
+    "# Handle missing values\n",
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)\n",
+    "\n",
+    "# Calculate VWAP\n",
+    "df['VWAP'] = (df['Close'] * df['Volume']).cumsum() / df['Volume'].cumsum()\n",
+    "\n",
+    "# Select features including VWAP and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume', 'VWAP']]\n",
+    "y = df['Close']\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "# Function to train and evaluate models\n",
+    "def train_and_evaluate_model(model, X_train, y_train, X_test, y_test, params=None):\n",
+    "    if params:\n",
+    "        model = GridSearchCV(model, params, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n",
+    "    model.fit(X_train, y_train)\n",
+    "    predictions = model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate evaluation metrics\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+    "    mae = mean_absolute_error(y_test, predictions)\n",
+    "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+    "    accuracy = accuracy_score(y_test > predictions, y_test > predictions.round())\n",
+    "    precision = precision_score(y_test > predictions, y_test > predictions.round())\n",
+    "    confusion = confusion_matrix(y_test > predictions, y_test > predictions.round())\n",
+    "    recall = recall_score(y_test > predictions, y_test > predictions.round())\n",
+    "    f1 = f1_score(y_test > predictions, y_test > predictions.round())\n",
+    "\n",
+    "    # Print the evaluation metrics\n",
+    "    print(f\"RMSE: {rmse}, MAE: {mae}, MAPE: {mape}, Accuracy: {accuracy}, Precision: {precision}, Confusion Matrix:\\n{confusion}, Recall: {recall}, F1 Score: {f1}\")\n",
+    "\n",
+    "    return model\n",
+    "\n",
+    "# Define models and their hyperparameters\n",
+    "models_params = {\n",
+    "    'Linear Regression': (LinearRegression(), None),\n",
+    "    'SVR': (SVR(), {'C': [0.1, 1], 'epsilon': [0.01, 0.1, 0.5], 'kernel': ['sigmoid']}),\n",
+    "    'Random Forest': (RandomForestRegressor(), None),\n",
+    "    'Gradient Boosting': (GradientBoostingRegressor(), None),\n",
+    "    'XGBoost': (xgb.XGBRegressor(), None),\n",
+    "    'AdaBoost': (AdaBoostRegressor(), None),\n",
+    "    'Decision Tree': (DecisionTreeRegressor(), None),\n",
+    "    'KNeighbors': (KNeighborsRegressor(), {'n_neighbors': [3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]})\n",
+    "}\n",
+    "\n",
+    "# Train and evaluate all models\n",
+    "for name, (model, params) in models_params.items():\n",
+    "    print(f\"\\n{name} Model:\")\n",
+    "    train_and_evaluate_model(model, X_train_scaled, y_train, X_test_scaled, y_test, params)\n",
+    "\n",
+    "# KNN Hyperparameter Tuning\n",
+    "def knn_hyperparameter_tuning(X_train, y_train):\n",
+    "    k_values = range(1, 31)  # Example range for k\n",
+    "    mse_values = []\n",
+    "    \n",
+    "    for k in k_values:\n",
+    "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
+    "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
+    "        mse_values.append(mse)\n",
+    "    \n",
+    "    return k_values, mse_values\n",
+    "\n",
+    "# Perform KNN Hyperparameter Tuning\n",
+    "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
+    "\n",
+    "# Plotting the results of KNN Hyperparameter Tuning\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
+    "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
+    "plt.xlabel('Number of Neighbors (k)')\n",
+    "plt.ylabel('Mean Squared Error (MSE)')\n",
+    "plt.xticks(k_values)  # Show all k values on x-axis\n",
+    "plt.grid()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1adb6df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0732c9951e7dd3cfd3373712ac1195fb756e0ec3 Mon Sep 17 00:00:00 2001
From: spandana2004 <110383668+spandana2004@users.noreply.github.com>
Date: Wed, 16 Oct 2024 22:20:54 +0530
Subject: [PATCH 31/76] streamlit web app

---
 stock_price_prediction_app.py | 117 ++++++++++++++++++++++++++++++++++
 1 file changed, 117 insertions(+)
 create mode 100644 stock_price_prediction_app.py

diff --git a/stock_price_prediction_app.py b/stock_price_prediction_app.py
new file mode 100644
index 0000000..86c6e1e
--- /dev/null
+++ b/stock_price_prediction_app.py
@@ -0,0 +1,117 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
+from sklearn.preprocessing import MinMaxScaler
+import xgboost as xgb
+import matplotlib.pyplot as plt
+import streamlit as st
+from sklearn.impute import SimpleImputer
+
+# Load and preprocess the dataset
+@st.cache_data
+def load_data():
+    df = pd.read_csv('SBIN.csv')
+    df.drop(['Date', 'Adj Close'], axis=1, inplace=True)  # Drop irrelevant columns
+
+    # Handle missing values
+    imputer = SimpleImputer(strategy='mean')
+    df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
+
+    # Calculate VWAP
+    df['VWAP'] = (df['Close'] * df['Volume']).cumsum() / df['Volume'].cumsum()
+
+    return df
+
+# Train the models
+def train_models(df):
+    # Select features and target variable
+    X = df[['Open', 'High', 'Low', 'Volume', 'VWAP']]
+    y = df['Close']
+
+    # Split the data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Scale the features
+    scaler = MinMaxScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+
+    models = {
+        'Linear Regression': LinearRegression(),
+        'SVR': SVR(),
+        'Random Forest': RandomForestRegressor(),
+        'Gradient Boosting': GradientBoostingRegressor(),
+        'XGBoost': xgb.XGBRegressor(),
+        'AdaBoost': AdaBoostRegressor(),
+        'Decision Tree': DecisionTreeRegressor(),
+        'KNeighbors': KNeighborsRegressor()
+    }
+
+    trained_models = {}
+    
+    for name, model in models.items():
+        model.fit(X_train_scaled, y_train)
+        trained_models[name] = model
+    
+    return trained_models, scaler, X_test_scaled, y_test
+
+# Load data and train models
+df = load_data()
+trained_models, scaler, X_test_scaled, y_test = train_models(df)
+
+# Streamlit app layout
+st.title("Stock Price Prediction App")
+
+# User input for stock features
+open_price = st.number_input("Open Price:", min_value=0.0, format="%.2f")
+high_price = st.number_input("High Price:", min_value=0.0, format="%.2f")
+low_price = st.number_input("Low Price:", min_value=0.0, format="%.2f")
+volume = st.number_input("Volume:", min_value=0, format="%d")
+
+# Calculate VWAP for user input
+vwap = (open_price + high_price + low_price) / 3  # Simplified VWAP calculation
+
+# Dropdown for model selection
+model_selection = st.selectbox("Select Model for Prediction:", options=list(trained_models.keys()))
+
+# Button to predict
+if st.button("Predict Closing Price"):
+    input_data = np.array([[open_price, high_price, low_price, volume, vwap]])
+    input_scaled = scaler.transform(input_data)
+    
+    # Make prediction using the selected model
+    model = trained_models[model_selection]
+    prediction = model.predict(input_scaled)
+    
+    # Display the prediction
+    st.success(f"The predicted closing price is: ${prediction[0]:.2f}")
+
+# Optional: Visualize KNN Hyperparameter Tuning
+if st.checkbox("Show KNN Hyperparameter Tuning Plot"):
+    k_values = range(1, 31)  # Example range for k
+    mse_values = []
+
+    X = df[['Open', 'High', 'Low', 'Volume', 'VWAP']]
+    y = df['Close']
+
+    for k in k_values:
+        knn_model = KNeighborsRegressor(n_neighbors=k)
+        mse = -cross_val_score(knn_model, scaler.transform(X), y, cv=5, scoring='neg_mean_squared_error').mean()
+        mse_values.append(mse)
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(k_values, mse_values, marker='o', linestyle='-')
+    plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')
+    plt.xlabel('Number of Neighbors (k)')
+    plt.ylabel('Mean Squared Error (MSE)')
+    plt.xticks(k_values)  # Show all k values on x-axis
+    plt.grid()
+    
+    # Show the plot in Streamlit
+    st.pyplot(plt)

From 19b08b80b26bfa03852847533f0fce88166373e0 Mon Sep 17 00:00:00 2001
From: spandana2004 <110383668+spandana2004@users.noreply.github.com>
Date: Wed, 16 Oct 2024 22:24:51 +0530
Subject: [PATCH 32/76] Delete stock_price_prediction_app.py

---
 stock_price_prediction_app.py | 117 ----------------------------------
 1 file changed, 117 deletions(-)
 delete mode 100644 stock_price_prediction_app.py

diff --git a/stock_price_prediction_app.py b/stock_price_prediction_app.py
deleted file mode 100644
index 86c6e1e..0000000
--- a/stock_price_prediction_app.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
-from sklearn.linear_model import LinearRegression
-from sklearn.svm import SVR
-from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.neighbors import KNeighborsRegressor
-from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
-from sklearn.preprocessing import MinMaxScaler
-import xgboost as xgb
-import matplotlib.pyplot as plt
-import streamlit as st
-from sklearn.impute import SimpleImputer
-
-# Load and preprocess the dataset
-@st.cache_data
-def load_data():
-    df = pd.read_csv('SBIN.csv')
-    df.drop(['Date', 'Adj Close'], axis=1, inplace=True)  # Drop irrelevant columns
-
-    # Handle missing values
-    imputer = SimpleImputer(strategy='mean')
-    df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
-
-    # Calculate VWAP
-    df['VWAP'] = (df['Close'] * df['Volume']).cumsum() / df['Volume'].cumsum()
-
-    return df
-
-# Train the models
-def train_models(df):
-    # Select features and target variable
-    X = df[['Open', 'High', 'Low', 'Volume', 'VWAP']]
-    y = df['Close']
-
-    # Split the data into training and testing sets
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    # Scale the features
-    scaler = MinMaxScaler()
-    X_train_scaled = scaler.fit_transform(X_train)
-    X_test_scaled = scaler.transform(X_test)
-
-    models = {
-        'Linear Regression': LinearRegression(),
-        'SVR': SVR(),
-        'Random Forest': RandomForestRegressor(),
-        'Gradient Boosting': GradientBoostingRegressor(),
-        'XGBoost': xgb.XGBRegressor(),
-        'AdaBoost': AdaBoostRegressor(),
-        'Decision Tree': DecisionTreeRegressor(),
-        'KNeighbors': KNeighborsRegressor()
-    }
-
-    trained_models = {}
-    
-    for name, model in models.items():
-        model.fit(X_train_scaled, y_train)
-        trained_models[name] = model
-    
-    return trained_models, scaler, X_test_scaled, y_test
-
-# Load data and train models
-df = load_data()
-trained_models, scaler, X_test_scaled, y_test = train_models(df)
-
-# Streamlit app layout
-st.title("Stock Price Prediction App")
-
-# User input for stock features
-open_price = st.number_input("Open Price:", min_value=0.0, format="%.2f")
-high_price = st.number_input("High Price:", min_value=0.0, format="%.2f")
-low_price = st.number_input("Low Price:", min_value=0.0, format="%.2f")
-volume = st.number_input("Volume:", min_value=0, format="%d")
-
-# Calculate VWAP for user input
-vwap = (open_price + high_price + low_price) / 3  # Simplified VWAP calculation
-
-# Dropdown for model selection
-model_selection = st.selectbox("Select Model for Prediction:", options=list(trained_models.keys()))
-
-# Button to predict
-if st.button("Predict Closing Price"):
-    input_data = np.array([[open_price, high_price, low_price, volume, vwap]])
-    input_scaled = scaler.transform(input_data)
-    
-    # Make prediction using the selected model
-    model = trained_models[model_selection]
-    prediction = model.predict(input_scaled)
-    
-    # Display the prediction
-    st.success(f"The predicted closing price is: ${prediction[0]:.2f}")
-
-# Optional: Visualize KNN Hyperparameter Tuning
-if st.checkbox("Show KNN Hyperparameter Tuning Plot"):
-    k_values = range(1, 31)  # Example range for k
-    mse_values = []
-
-    X = df[['Open', 'High', 'Low', 'Volume', 'VWAP']]
-    y = df['Close']
-
-    for k in k_values:
-        knn_model = KNeighborsRegressor(n_neighbors=k)
-        mse = -cross_val_score(knn_model, scaler.transform(X), y, cv=5, scoring='neg_mean_squared_error').mean()
-        mse_values.append(mse)
-
-    plt.figure(figsize=(10, 6))
-    plt.plot(k_values, mse_values, marker='o', linestyle='-')
-    plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')
-    plt.xlabel('Number of Neighbors (k)')
-    plt.ylabel('Mean Squared Error (MSE)')
-    plt.xticks(k_values)  # Show all k values on x-axis
-    plt.grid()
-    
-    # Show the plot in Streamlit
-    st.pyplot(plt)

From e0d9ce1d3859c7ddeefa238441fbb1a0a3bb97bd Mon Sep 17 00:00:00 2001
From: Priyansh985 <jainpriyansh2007@gmail.com>
Date: Sat, 19 Oct 2024 18:26:51 +0530
Subject: [PATCH 33/76] optimized the code using intel ai analytics toolkit

---
 Intel_Optimized/ARIMA_V2.ipynb                |  339 +++++
 .../Stock_Price_Prediction_1.ipynb            |  962 ++++++++++++
 .../Stock_prediction_Data_Analysis.ipynb      | 1339 +++++++++++++++++
 .../buy_sell_recommendation_system.ipynb      |  200 +++
 Intel_Optimized/hybrid.ipynb                  | 1030 +++++++++++++
 Intel_Optimized/readme.md                     |  102 ++
 ...ed_redundancy_stock_price_prediction.ipynb |  303 ++++
 Intel_Optimized/requirements.txt              |    7 +
 8 files changed, 4282 insertions(+)
 create mode 100644 Intel_Optimized/ARIMA_V2.ipynb
 create mode 100644 Intel_Optimized/Stock_Price_Prediction_1.ipynb
 create mode 100644 Intel_Optimized/Stock_prediction_Data_Analysis.ipynb
 create mode 100644 Intel_Optimized/buy_sell_recommendation_system.ipynb
 create mode 100644 Intel_Optimized/hybrid.ipynb
 create mode 100644 Intel_Optimized/readme.md
 create mode 100644 Intel_Optimized/reduced_redundancy_stock_price_prediction.ipynb
 create mode 100644 Intel_Optimized/requirements.txt

diff --git a/Intel_Optimized/ARIMA_V2.ipynb b/Intel_Optimized/ARIMA_V2.ipynb
new file mode 100644
index 0000000..67a9b29
--- /dev/null
+++ b/Intel_Optimized/ARIMA_V2.ipynb
@@ -0,0 +1,339 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Intial code\n",
+    "for reference purposes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pandas as pd\n",
+    "# import numpy as np\n",
+    "# from sklearn.metrics import mean_squared_error\n",
+    "# from sklearn.preprocessing import StandardScaler\n",
+    "# from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "# from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
+    "# import warnings\n",
+    "\n",
+    "# # Ignore convergence warnings\n",
+    "# warnings.simplefilter(\"ignore\")\n",
+    "\n",
+    "# # Load dataset with parsed dates\n",
+    "# data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# # Set the index to the Date column\n",
+    "# data.set_index('Date', inplace=True)\n",
+    "# # data = data.asfreq('D')\n",
+    "# # Feature Engineering: Add day of week and month\n",
+    "# data['day_of_week'] = data.index.dayofweek\n",
+    "# data['month'] = data.index.month\n",
+    "\n",
+    "# # Add lagged value of the Close price and moving averages\n",
+    "# data['lagged_close'] = data['Close'].shift(1)  \n",
+    "# data['moving_avg_3'] = data['Close'].rolling(window=3).mean()\n",
+    "# data['moving_avg_7'] = data['Close'].rolling(window=7).mean()  # New: 7-day moving average for long-term trend\n",
+    "\n",
+    "# # Add Volume as a feature (scaling might help)\n",
+    "# data['volume'] = data['Volume']\n",
+    "\n",
+    "# # Drop rows with NaN values\n",
+    "# data.dropna(inplace=True)\n",
+    "\n",
+    "# # Standardize the features (important for scaling)\n",
+    "# scaler = StandardScaler()\n",
+    "# exog_features = ['day_of_week', 'month', 'lagged_close', 'moving_avg_3', 'moving_avg_7', 'volume']\n",
+    "# data[exog_features] = scaler.fit_transform(data[exog_features])\n",
+    "\n",
+    "# # Split the data into training and testing sets\n",
+    "# train_size = int(len(data) * 0.8)\n",
+    "# train, test = data.iloc[:train_size], data.iloc[train_size:]\n",
+    "\n",
+    "# # Tune SARIMAX hyperparameters (ARIMA order (p, d, q))\n",
+    "# order = (2, 1, 2)  # Consider using AIC/BIC for finding optimal order\n",
+    "# seasonal_order = (1, 1, 1, 12)  # Adding seasonality with monthly frequency\n",
+    "\n",
+    "# # Fit the SARIMAX model\n",
+    "# try:\n",
+    "#     model = SARIMAX(train['Close'], \n",
+    "#                     exog=train[exog_features],\n",
+    "#                     order=order,\n",
+    "#                     seasonal_order=seasonal_order)\n",
+    "#     model_fit = model.fit(disp=False)\n",
+    "# except ConvergenceWarning as e:\n",
+    "#     print(f\"Convergence warning: {e}\")\n",
+    "# except Exception as e:\n",
+    "#     print(f\"Error: {e}\")\n",
+    "\n",
+    "# # Forecasting\n",
+    "# forecast = model_fit.forecast(steps=len(test), exog=test[exog_features])\n",
+    "\n",
+    "# # Calculate RMSE for forecast\n",
+    "# rmse_arimax = np.sqrt(mean_squared_error(test['Close'], forecast))\n",
+    "# print(f\"Improved ARIMAX Model RMSE: {rmse_arimax}\")\n",
+    "\n",
+    "# test_prices = [i for i in test['Close']]\n",
+    "# # Check residuals diagnostics (optional)\n",
+    "# residuals = test_prices - forecast\n",
+    "# print(\"Mean of residuals:\", residuals.mean())\n",
+    "# print(\"Standard deviation of residuals:\", residuals.std())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### immporting necessary libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import modin.pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from sklearnex import patch_sklearn\n",
+    "patch_sklearn()\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Udating features to dataset for proper time-series analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Ignore convergence warnings\n",
+    "warnings.simplefilter(\"ignore\", ConvergenceWarning)\n",
+    "\n",
+    "# Load training dataset with parsed dates\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# Set the index to the Date column\n",
+    "train_data.set_index('Date', inplace=True)\n",
+    "\n",
+    "# Feature Engineering: Add day of week and month\n",
+    "train_data['day_of_week'] = train_data.index.dayofweek\n",
+    "train_data['month'] = train_data.index.month\n",
+    "\n",
+    "# Add lagged value of the Close price and moving averages\n",
+    "train_data['lagged_close'] = train_data['Close'].shift(1)\n",
+    "train_data['moving_avg_3'] = train_data['Close'].rolling(window=3).mean()\n",
+    "train_data['moving_avg_7'] = train_data['Close'].rolling(window=7).mean()\n",
+    "\n",
+    "# Add Volume as a feature (scaling might help)\n",
+    "train_data['volume'] = train_data['Volume']\n",
+    "\n",
+    "# Drop rows with NaN values after applying the rolling window and lagging\n",
+    "train_data.dropna(inplace=True)\n",
+    "\n",
+    "# Standardize the features\n",
+    "scaler = StandardScaler()\n",
+    "exog_features = ['day_of_week', 'month', 'lagged_close', 'moving_avg_3', 'moving_avg_7', 'volume']\n",
+    "train_data[exog_features] = scaler.fit_transform(train_data[exog_features])\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "train_size = int(len(train_data) * 0.8)\n",
+    "train, validation = train_data.iloc[:train_size], train_data.iloc[train_size:]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training and savinng model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model and scaler saved successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the SARIMAX model\n",
+    "order = (2, 1, 2)\n",
+    "seasonal_order = (1, 1, 1, 12)\n",
+    "\n",
+    "model = SARIMAX(train['Close'], exog=train[exog_features], order=order, seasonal_order=seasonal_order)\n",
+    "model_fit = model.fit(disp=False)\n",
+    "\n",
+    "# Save the model to a file using pickle\n",
+    "with open('sarimax_model.pkl', 'wb') as f:\n",
+    "    pickle.dump(model_fit, f)\n",
+    "\n",
+    "# Optionally save the scaler as well\n",
+    "with open('scaler.pkl', 'wb') as f:\n",
+    "    pickle.dump(scaler, f)\n",
+    "\n",
+    "print(\"Model and scaler saved successfully.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading saved model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the model and scaler from the files\n",
+    "with open('sarimax_model.pkl', 'rb') as f:\n",
+    "    loaded_model = pickle.load(f)\n",
+    "\n",
+    "with open('scaler.pkl', 'rb') as f:\n",
+    "    loaded_scaler = pickle.load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading and processing Test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the test dataset\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# Set the index to the Date column\n",
+    "test_data.set_index('Date', inplace=True)\n",
+    "\n",
+    "# Apply the same feature engineering on the test data\n",
+    "test_data['day_of_week'] = test_data.index.dayofweek\n",
+    "test_data['month'] = test_data.index.month\n",
+    "test_data['lagged_close'] = test_data['Close'].shift(1)\n",
+    "test_data['moving_avg_3'] = test_data['Close'].rolling(window=3).mean()\n",
+    "test_data['moving_avg_7'] = test_data['Close'].rolling(window=7).mean()\n",
+    "\n",
+    "# Add Volume as a feature\n",
+    "test_data['volume'] = test_data['Volume']\n",
+    "\n",
+    "# Drop rows with NaN values\n",
+    "test_data.dropna(inplace=True)\n",
+    "\n",
+    "# Standardize the features in the test dataset using the loaded scaler\n",
+    "test_data[exog_features] = loaded_scaler.transform(test_data[exog_features])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predicting share prices using model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
+      "  return get_prediction_index(\n",
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
+      "  return get_prediction_index(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Data RMSE: 4.673693537736142\n",
+      "Mean of residuals: 0.311316834051805\n",
+      "Standard deviation of residuals: 4.664969245987366\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Forecasting on the test data using the loaded model\n",
+    "forecast_test = loaded_model.forecast(steps=len(test_data), exog=test_data[exog_features])\n",
+    "\n",
+    "# Calculate RMSE for forecast\n",
+    "rmse_test = np.sqrt(mean_squared_error(test_data['Close'], forecast_test))\n",
+    "print(f\"Test Data RMSE: {rmse_test}\")\n",
+    "\n",
+    "# Check residuals diagnostics (optional)\n",
+    "test_prices = test_data['Close'].values\n",
+    "residuals_test = test_prices - forecast_test\n",
+    "print(\"Mean of residuals:\", residuals_test.mean())\n",
+    "print(\"Standard deviation of residuals:\", residuals_test.std())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Intel_Optimized/Stock_Price_Prediction_1.ipynb b/Intel_Optimized/Stock_Price_Prediction_1.ipynb
new file mode 100644
index 0000000..4fa33c9
--- /dev/null
+++ b/Intel_Optimized/Stock_Price_Prediction_1.ipynb
@@ -0,0 +1,962 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "qCDSjVhXLr_Z"
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearnex'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearnex\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m patch_sklearn\n\u001b[0;32m      5\u001b[0m patch_sklearn()\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearnex'"
+     ]
+    }
+   ],
+   "source": [
+    "import modin.pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearnex import patch_sklearn\n",
+    "patch_sklearn()\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense,LSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import google.colab\n",
+    "    In_colab=True\n",
+    "except:\n",
+    "    In_colab=False\n",
+    "\n",
+    "if(In_colab):\n",
+    "    print(\"Running in google colab\")\n",
+    "    from google.colab import drive\n",
+    "    drive.mount('/content/drive')\n",
+    "    df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')\n",
+    "else:\n",
+    "    print(\"Running in local system\")\n",
+    "    path=r'C:\\Users\\SHRISTI\\OneDrive\\Desktop\\GitHub\\Stock-Price-Prediction\\Data\\SBIN.csv'\n",
+    "    df=pd.read_csv(path)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "Sc4id6VxL8BS",
+    "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b"
+   },
+   "outputs": [],
+   "source": [
+    "# Load the dataset\n",
+    "#df = pd.read_csv('/content/SBIN.NS.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "7LaYGXsfN-8y"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop the 'Date' and 'Adj Close' columns\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "pqbTBdnBOKJc",
+    "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55"
+   },
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "dydEPoNeM6eN"
+   },
+   "outputs": [],
+   "source": [
+    "# Handle missing values\n",
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "OQ3cGqgTMBwt"
+   },
+   "outputs": [],
+   "source": [
+    "# Select features and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "9Oz-bwJOMEWD"
+   },
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "ugapDyXODtn3"
+   },
+   "outputs": [],
+   "source": [
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "997ZEgibCZIO",
+    "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76"
+   },
+   "outputs": [],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bmtt76RuCeyG",
+    "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1"
+   },
+   "outputs": [],
+   "source": [
+    "X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "CeJkUJ92Ciqd",
+    "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917"
+   },
+   "outputs": [],
+   "source": [
+    "y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7HGC7VuTCjWc",
+    "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75"
+   },
+   "outputs": [],
+   "source": [
+    "y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to evaluate and print RMSE, MAE, and MAPE\n",
+    "def evaluate_model(model, X_test, y_test):\n",
+    "    predictions = model.predict(X_test)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+    "    mae = mean_absolute_error(y_test, predictions)\n",
+    "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+    "\n",
+    "    print(f\"RMSE: {rmse}\")\n",
+    "    print(f\"MAE: {mae}\")\n",
+    "    print(f\"MAPE: {mape}\\n\")\n",
+    "    \n",
+    "    return rmse, mae, mape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics = {\n",
+    "    \"Model\": [],\n",
+    "    \"RMSE\": [],\n",
+    "    \"MAE\": [],\n",
+    "    \"MAPE\": []\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c6Ek8jRlO2_I"
+   },
+   "source": [
+    "## 1. LINEAR REGRESSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "id": "RdZ1SpzdMHAJ"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a linear regression model\n",
+    "model1 = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mPM035IzMY04",
+    "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac"
+   },
+   "outputs": [],
+   "source": [
+    "y_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "qBhQ9HbYMI3d",
+    "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model1.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "X269co2kMS4z"
+   },
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Linear Regressor\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GxtMzlg-gR2P"
+   },
+   "source": [
+    "## 2. SVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "id": "0xQewd7QWTtq"
+   },
+   "outputs": [],
+   "source": [
+    "# Create an SVR model\n",
+    "model2 = SVR()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "22SaCsQmfhgP",
+    "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model2.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OQ1nL4oYfkAC"
+   },
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"SVR\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hcIfVMWdgcKt"
+   },
+   "source": [
+    "## 3. Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "id": "f7raXT_hf2ij"
+   },
+   "outputs": [],
+   "source": [
+    "model3 = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "fF002Yepgk55",
+    "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model3.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8nRU_pzEgnCt"
+   },
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Random Forest\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mZsLwLivhLGH"
+   },
+   "source": [
+    "## 4. Gradient Boosting Models (GBM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "id": "TI8idoxOg6jF"
+   },
+   "outputs": [],
+   "source": [
+    "model4 = GradientBoostingRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "2gpbDxshhexj",
+    "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model4.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Jj9DXdUPhh9V"
+   },
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"GBM\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d8nSGoyuh9dx"
+   },
+   "source": [
+    "## 5. Extreme Gradient Boosting (XGBoost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "id": "DyhhdlZAhx94"
+   },
+   "outputs": [],
+   "source": [
+    "import xgboost as xgb\n",
+    "# Create an XGBoost model\n",
+    "model5 = xgb.XGBRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "RAIwxIp5iH9Z",
+    "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model5.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"XGBoost\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "A_J776rtiovq"
+   },
+   "source": [
+    "## 6. AdaBoostRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "id": "HNq66cXRiYPJ"
+   },
+   "outputs": [],
+   "source": [
+    "model6 = AdaBoostRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "P0oB5wjQivBr",
+    "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model6.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Bf1m5ukOi2VM"
+   },
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Q9DzOt3CkWFX"
+   },
+   "source": [
+    "## 7. Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "id": "23DZ2biSjF9a"
+   },
+   "outputs": [],
+   "source": [
+    "model7 = DecisionTreeRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "6mQEQf-ykc9F",
+    "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model7.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BFJ9q_tvkgRC"
+   },
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"Decision Tree\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LH-B-Xd6k5UD"
+   },
+   "source": [
+    "## 8. KNeighborsRegressor(KNN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "id": "JVDSed7yktFY"
+   },
+   "outputs": [],
+   "source": [
+    "# Create a KNN model\n",
+    "model8 = KNeighborsRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "9fn64o-ZlBka",
+    "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model8.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hbfbbjcSlDn7"
+   },
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"KNN\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "X5XtlzMXljps"
+   },
+   "source": [
+    "## 9. Artificial Neural Networks (ANN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an ANN model\n",
+    "model9 = Sequential()\n",
+    "from keras.layers import Input\n",
+    "model9.add(Input(shape=(X_train.shape[1],)))\n",
+    "model9.add(Dense(32, activation='relu'))\n",
+    "model9.add(Dense(16, activation='relu'))\n",
+    "model9.add(Dense(1, activation='linear'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "id": "ZIf94WLMlv04"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model9.compile(loss='mean_squared_error', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "FX5DTKqslxWf",
+    "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OVW2qpNsmGVq",
+    "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a"
+   },
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n",
+    "metrics[\"Model\"].append(\"ANN\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vjSMQNcOnFPJ"
+   },
+   "source": [
+    "## 10. LSTM(Long Short term Memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "id": "uACvajfImrbB"
+   },
+   "outputs": [],
+   "source": [
+    "# Reshape the input data for LSTM\n",
+    "n_features = X_train_scaled.shape[1]\n",
+    "n_steps = 10\n",
+    "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n",
+    "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n",
+    "\n",
+    "# Reshape the input data\n",
+    "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n",
+    "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(Input(shape=(n_steps, n_features)))\n",
+    "model.add(LSTM(64, activation='relu'))\n",
+    "model.add(Dense(1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "id": "YpSfHu6gov35"
+   },
+   "outputs": [],
+   "source": [
+    "# Compile the model\n",
+    "model.compile(loss='mean_squared_error', optimizer='adam')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0vHjcluaoxzP",
+    "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933"
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rmse, mae, mape = evaluate_model(model10, X_test_reshaped, y_test)\n",
+    "metrics[\"Model\"].append(\"LSTM\")\n",
+    "metrics[\"RMSE\"].append(rmse)\n",
+    "metrics[\"MAE\"].append(mae)\n",
+    "metrics[\"MAPE\"].append(mape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a DataFrame for metrics\n",
+    "metrics_df = pd.DataFrame(metrics)\n",
+    "\n",
+    "# Plot RMSE, MAE, and MAPE for each model\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# RMSE Plot\n",
+    "plt.subplot(1, 3, 1)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='lightblue')\n",
+    "plt.xlabel('RMSE')\n",
+    "plt.title('RMSE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MAE Plot\n",
+    "plt.subplot(1, 3, 2)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n",
+    "plt.xlabel('MAE')\n",
+    "plt.title('MAE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MAPE Plot\n",
+    "plt.subplot(1, 3, 3)\n",
+    "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n",
+    "plt.xlabel('MAPE')\n",
+    "plt.title('MAPE for Different Models')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using of LightGBM and CatBoost For Optimizing the model accuracy and time complexity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import lightgbm as lgb\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "\n",
+    "# Function to train and evaluate a model\n",
+    "def train_and_evaluate_model(model, X_train, X_test, y_train, y_test):\n",
+    "    model.fit(X_train, y_train)\n",
+    "    pred = model.predict(X_test)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, pred))\n",
+    "    mae = mean_absolute_error(y_test, pred)\n",
+    "    mape = mean_absolute_percentage_error(y_test, pred)\n",
+    "    accuracy = accuracy_score(y_test > pred, y_test > pred.round())\n",
+    "    precision = precision_score(y_test > pred, y_test > pred.round())\n",
+    "    confusion = confusion_matrix(y_test > pred, y_test > pred.round())\n",
+    "    recall = recall_score(y_test > pred, y_test > pred.round())\n",
+    "    f1 = f1_score(y_test > pred, y_test > pred.round())\n",
+    "    return rmse, mae, mape, accuracy, precision, confusion, recall, f1\n",
+    "\n",
+    "# Train and evaluate LightGBM model for from this directly print accuracy \n",
+    "model_lightgbm = lgb.LGBMRegressor()\n",
+    "metrics_lightgbm = train_and_evaluate_model(model_lightgbm, X_train, X_test, y_train, y_test)\n",
+    "print(\"LightGBM Metrics:\", metrics_lightgbm)\n",
+    "\n",
+    "# Train and evaluate CatBoost model\n",
+    "model_catboost = CatBoostRegressor(verbose=0)\n",
+    "metrics_catboost = train_and_evaluate_model(model_catboost, X_train, X_test, y_train, y_test)\n",
+    "print(\"CatBoost Metrics:\", metrics_catboost)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Intel_Optimized/Stock_prediction_Data_Analysis.ipynb b/Intel_Optimized/Stock_prediction_Data_Analysis.ipynb
new file mode 100644
index 0000000..8d47a6d
--- /dev/null
+++ b/Intel_Optimized/Stock_prediction_Data_Analysis.ipynb
@@ -0,0 +1,1339 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "86e09793-d225-40df-b93d-d2fed5d72cb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import modin.pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "51377ca9-8d46-4049-bdc8-f247e84977e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"Data/SBIN.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bd0d2b4c-20f3-4259-afee-2f45fe342261",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01-01-1996</td>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>02-01-1996</td>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>03-01-1996</td>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>04-01-1996</td>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>05-01-1996</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>08-01-1996</td>\n",
+       "      <td>17.478724</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>16.922047</td>\n",
+       "      <td>17.063574</td>\n",
+       "      <td>11.249807</td>\n",
+       "      <td>55395172.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>09-01-1996</td>\n",
+       "      <td>16.889023</td>\n",
+       "      <td>18.681711</td>\n",
+       "      <td>16.705036</td>\n",
+       "      <td>17.997660</td>\n",
+       "      <td>11.865635</td>\n",
+       "      <td>82057540.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10-01-1996</td>\n",
+       "      <td>17.407959</td>\n",
+       "      <td>17.714603</td>\n",
+       "      <td>17.054138</td>\n",
+       "      <td>17.172079</td>\n",
+       "      <td>11.321342</td>\n",
+       "      <td>54360749.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11-01-1996</td>\n",
+       "      <td>16.983376</td>\n",
+       "      <td>17.926895</td>\n",
+       "      <td>16.983376</td>\n",
+       "      <td>17.827826</td>\n",
+       "      <td>11.753670</td>\n",
+       "      <td>65973105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>12-01-1996</td>\n",
+       "      <td>17.879719</td>\n",
+       "      <td>18.233540</td>\n",
+       "      <td>17.573074</td>\n",
+       "      <td>17.837261</td>\n",
+       "      <td>11.759888</td>\n",
+       "      <td>102152486.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date       Open       High        Low      Close  Adj Close  \\\n",
+       "0  01-01-1996  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+       "1  02-01-1996  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+       "2  03-01-1996  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+       "3  04-01-1996  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+       "4  05-01-1996  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+       "5  08-01-1996  17.478724  17.643839  16.922047  17.063574  11.249807   \n",
+       "6  09-01-1996  16.889023  18.681711  16.705036  17.997660  11.865635   \n",
+       "7  10-01-1996  17.407959  17.714603  17.054138  17.172079  11.321342   \n",
+       "8  11-01-1996  16.983376  17.926895  16.983376  17.827826  11.753670   \n",
+       "9  12-01-1996  17.879719  18.233540  17.573074  17.837261  11.759888   \n",
+       "\n",
+       "        Volume  \n",
+       "0   43733533.0  \n",
+       "1   56167280.0  \n",
+       "2   68296318.0  \n",
+       "3   86073880.0  \n",
+       "4   76613039.0  \n",
+       "5   55395172.0  \n",
+       "6   82057540.0  \n",
+       "7   54360749.0  \n",
+       "8   65973105.0  \n",
+       "9  102152486.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eec26960",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7064</th>\n",
+       "      <td>29-01-2024</td>\n",
+       "      <td>619.000000</td>\n",
+       "      <td>629.500000</td>\n",
+       "      <td>615.599976</td>\n",
+       "      <td>622.950012</td>\n",
+       "      <td>622.950012</td>\n",
+       "      <td>19572140.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7065</th>\n",
+       "      <td>30-01-2024</td>\n",
+       "      <td>625.450012</td>\n",
+       "      <td>633.500000</td>\n",
+       "      <td>624.200012</td>\n",
+       "      <td>626.750000</td>\n",
+       "      <td>626.750000</td>\n",
+       "      <td>16966577.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7066</th>\n",
+       "      <td>31-01-2024</td>\n",
+       "      <td>626.400024</td>\n",
+       "      <td>643.200012</td>\n",
+       "      <td>622.000000</td>\n",
+       "      <td>640.500000</td>\n",
+       "      <td>640.500000</td>\n",
+       "      <td>23270745.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7067</th>\n",
+       "      <td>01-02-2024</td>\n",
+       "      <td>642.750000</td>\n",
+       "      <td>652.950012</td>\n",
+       "      <td>633.250000</td>\n",
+       "      <td>647.650024</td>\n",
+       "      <td>647.650024</td>\n",
+       "      <td>26587707.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7068</th>\n",
+       "      <td>02-02-2024</td>\n",
+       "      <td>652.000000</td>\n",
+       "      <td>660.549988</td>\n",
+       "      <td>646.700012</td>\n",
+       "      <td>649.650024</td>\n",
+       "      <td>649.650024</td>\n",
+       "      <td>27471811.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7069</th>\n",
+       "      <td>05-02-2024</td>\n",
+       "      <td>647.099976</td>\n",
+       "      <td>654.799988</td>\n",
+       "      <td>638.750000</td>\n",
+       "      <td>642.950012</td>\n",
+       "      <td>642.950012</td>\n",
+       "      <td>28339525.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7070</th>\n",
+       "      <td>06-02-2024</td>\n",
+       "      <td>644.450012</td>\n",
+       "      <td>655.000000</td>\n",
+       "      <td>637.700012</td>\n",
+       "      <td>650.250000</td>\n",
+       "      <td>650.250000</td>\n",
+       "      <td>14079390.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7071</th>\n",
+       "      <td>07-02-2024</td>\n",
+       "      <td>655.049988</td>\n",
+       "      <td>677.950012</td>\n",
+       "      <td>655.000000</td>\n",
+       "      <td>675.250000</td>\n",
+       "      <td>675.250000</td>\n",
+       "      <td>41696232.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7072</th>\n",
+       "      <td>08-02-2024</td>\n",
+       "      <td>680.400024</td>\n",
+       "      <td>718.900024</td>\n",
+       "      <td>678.500000</td>\n",
+       "      <td>699.549988</td>\n",
+       "      <td>699.549988</td>\n",
+       "      <td>74222434.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7073</th>\n",
+       "      <td>09-02-2024</td>\n",
+       "      <td>703.650024</td>\n",
+       "      <td>728.349976</td>\n",
+       "      <td>694.200012</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>43235061.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date        Open        High         Low       Close   Adj Close  \\\n",
+       "7064  29-01-2024  619.000000  629.500000  615.599976  622.950012  622.950012   \n",
+       "7065  30-01-2024  625.450012  633.500000  624.200012  626.750000  626.750000   \n",
+       "7066  31-01-2024  626.400024  643.200012  622.000000  640.500000  640.500000   \n",
+       "7067  01-02-2024  642.750000  652.950012  633.250000  647.650024  647.650024   \n",
+       "7068  02-02-2024  652.000000  660.549988  646.700012  649.650024  649.650024   \n",
+       "7069  05-02-2024  647.099976  654.799988  638.750000  642.950012  642.950012   \n",
+       "7070  06-02-2024  644.450012  655.000000  637.700012  650.250000  650.250000   \n",
+       "7071  07-02-2024  655.049988  677.950012  655.000000  675.250000  675.250000   \n",
+       "7072  08-02-2024  680.400024  718.900024  678.500000  699.549988  699.549988   \n",
+       "7073  09-02-2024  703.650024  728.349976  694.200012  725.250000  725.250000   \n",
+       "\n",
+       "          Volume  \n",
+       "7064  19572140.0  \n",
+       "7065  16966577.0  \n",
+       "7066  23270745.0  \n",
+       "7067  26587707.0  \n",
+       "7068  27471811.0  \n",
+       "7069  28339525.0  \n",
+       "7070  14079390.0  \n",
+       "7071  41696232.0  \n",
+       "7072  74222434.0  \n",
+       "7073  43235061.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display the last 10 rows of the DataFrame\n",
+    "df.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23974fec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns: Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the columns in the DataFrame\n",
+    "print(\"Columns:\", df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "538be913",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data Types: Date          object\n",
+      "Open         float64\n",
+      "High         float64\n",
+      "Low          float64\n",
+      "Close        float64\n",
+      "Adj Close    float64\n",
+      "Volume       float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the data types of each column\n",
+    "print(\"Data Types:\", df.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9fb3e6dd-607e-48b8-8be8-ac6c04bcb5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7074, 7)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ce71bbd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size of DataFrame: 49518\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the size of the DataFrame\n",
+    "print(\"Size of DataFrame:\", df.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5ffbb9a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numeric Columns: Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display numeric columns in the DataFrame\n",
+    "print(\"Numeric Columns:\", df.select_dtypes(include=[np.number]).columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5c6a7e88-f015-4570-acaa-1bd858e81a97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7074 entries, 0 to 7073\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Date       7074 non-null   object \n",
+      " 1   Open       7065 non-null   float64\n",
+      " 2   High       7065 non-null   float64\n",
+      " 3   Low        7065 non-null   float64\n",
+      " 4   Close      7065 non-null   float64\n",
+      " 5   Adj Close  7065 non-null   float64\n",
+      " 6   Volume     7065 non-null   float64\n",
+      "dtypes: float64(6), object(1)\n",
+      "memory usage: 387.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a0f79bde-74d8-4ae8-a179-38eedc9ac003",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7065.000000</td>\n",
+       "      <td>7.065000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>180.682841</td>\n",
+       "      <td>183.085167</td>\n",
+       "      <td>177.998209</td>\n",
+       "      <td>180.448294</td>\n",
+       "      <td>166.021712</td>\n",
+       "      <td>3.130217e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>154.773229</td>\n",
+       "      <td>156.345078</td>\n",
+       "      <td>152.980516</td>\n",
+       "      <td>154.630549</td>\n",
+       "      <td>152.903249</td>\n",
+       "      <td>3.462744e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>13.478195</td>\n",
+       "      <td>13.935802</td>\n",
+       "      <td>13.214009</td>\n",
+       "      <td>13.346102</td>\n",
+       "      <td>9.531410</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>28.423565</td>\n",
+       "      <td>28.824560</td>\n",
+       "      <td>28.022570</td>\n",
+       "      <td>28.456589</td>\n",
+       "      <td>19.854374</td>\n",
+       "      <td>1.299123e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>173.100006</td>\n",
+       "      <td>176.500000</td>\n",
+       "      <td>170.250000</td>\n",
+       "      <td>172.925003</td>\n",
+       "      <td>152.411270</td>\n",
+       "      <td>2.064292e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>265.500000</td>\n",
+       "      <td>268.899994</td>\n",
+       "      <td>261.299988</td>\n",
+       "      <td>265.174988</td>\n",
+       "      <td>245.764954</td>\n",
+       "      <td>3.651478e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>703.650024</td>\n",
+       "      <td>728.349976</td>\n",
+       "      <td>694.200012</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>725.250000</td>\n",
+       "      <td>4.469483e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Open         High          Low        Close    Adj Close  \\\n",
+       "count  7065.000000  7065.000000  7065.000000  7065.000000  7065.000000   \n",
+       "mean    180.682841   183.085167   177.998209   180.448294   166.021712   \n",
+       "std     154.773229   156.345078   152.980516   154.630549   152.903249   \n",
+       "min      13.478195    13.935802    13.214009    13.346102     9.531410   \n",
+       "25%      28.423565    28.824560    28.022570    28.456589    19.854374   \n",
+       "50%     173.100006   176.500000   170.250000   172.925003   152.411270   \n",
+       "75%     265.500000   268.899994   261.299988   265.174988   245.764954   \n",
+       "max     703.650024   728.349976   694.200012   725.250000   725.250000   \n",
+       "\n",
+       "             Volume  \n",
+       "count  7.065000e+03  \n",
+       "mean   3.130217e+07  \n",
+       "std    3.462744e+07  \n",
+       "min    0.000000e+00  \n",
+       "25%    1.299123e+07  \n",
+       "50%    2.064292e+07  \n",
+       "75%    3.651478e+07  \n",
+       "max    4.469483e+08  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ea5c3266-fbae-4977-98bc-9754505daac8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "be22ff6a-e78a-4b82-87b5-30f28bd79d47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1996-01-01</td>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1996-01-02</td>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1996-01-03</td>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1996-01-04</td>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1996-01-05</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date       Open       High        Low      Close  Adj Close  \\\n",
+       "0 1996-01-01  18.691147  18.978922  18.540184  18.823240  12.409931   \n",
+       "1 1996-01-02  18.894005  18.964767  17.738192  18.224106  12.014931   \n",
+       "2 1996-01-03  18.327892  18.568489  17.643839  17.738192  11.694577   \n",
+       "3 1996-01-04  17.502312  17.832542  17.223972  17.676863  11.654142   \n",
+       "4 1996-01-05  17.738192  17.785366  17.459852  17.577793  11.588827   \n",
+       "\n",
+       "       Volume  \n",
+       "0  43733533.0  \n",
+       "1  56167280.0  \n",
+       "2  68296318.0  \n",
+       "3  86073880.0  \n",
+       "4  76613039.0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "79e195f0-a0b5-44bf-ac5d-58cd608374e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7074 entries, 0 to 7073\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column     Non-Null Count  Dtype         \n",
+      "---  ------     --------------  -----         \n",
+      " 0   Date       7074 non-null   datetime64[ns]\n",
+      " 1   Open       7065 non-null   float64       \n",
+      " 2   High       7065 non-null   float64       \n",
+      " 3   Low        7065 non-null   float64       \n",
+      " 4   Close      7065 non-null   float64       \n",
+      " 5   Adj Close  7065 non-null   float64       \n",
+      " 6   Volume     7065 non-null   float64       \n",
+      "dtypes: datetime64[ns](1), float64(6)\n",
+      "memory usage: 387.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa2ae458-9d1a-4202-b0f4-16784fffaba5",
+   "metadata": {},
+   "source": [
+    "### EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8c98bfa4-bcf1-4059-b378-b349010dab37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96a4fe8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to create scatter plots\n",
+    "def create_scatter_plot(x_data, y_data, size_data=None, title=None, x_label=None, y_label=None, figsize=(20,10)):\n",
+    "    plt.figure(figsize=figsize)\n",
+    "    sns.scatterplot(x=x_data, y=y_data, size=size_data)\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    if x_label:\n",
+    "        plt.xlabel(x_label)\n",
+    "    if y_label:\n",
+    "        plt.ylabel(y_label)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25e91892",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to create line plots\n",
+    "def create_line_plot(x_data, y_data, title=None, x_label=None, y_label=None, figsize=(20,10)):\n",
+    "    plt.figure(figsize=figsize)\n",
+    "    sns.lineplot(x=x_data, y=y_data)\n",
+    "    if title:\n",
+    "        plt.title(title)\n",
+    "    if x_label:\n",
+    "        plt.xlabel(x_label)\n",
+    "    if y_label:\n",
+    "        plt.ylabel(y_label)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "194da9e9-9bf8-497a-81f2-9d6f54c897f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(x = df['Date'], y = df['Volume'])\n",
+    "sns.kdeplot(x = df['Date'], y = df['Volume'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69d2c9c2",
+   "metadata": {},
+   "source": [
+    "Early 1990s spike: There is a high trading volume around the mid-1990s, peaking at over 4 × 10^8 (400 million trades).\n",
+    "Post-1996 to 2020: The volume significantly decreases, showing lower levels of activity with some fluctuations and minor peaks, particularly after 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "cbf3fc8a-59c9-40ee-bd5c-4c7e762b016d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3v/3tWL58eezcubPlu5tvvjmXwAAAoM48fgEAQHpVeO1Ijo8kM7L+4LOf/Wy89a1vjcMOOyxuueWW+O3f/u34+Z//+Vi8eHG8/OUvLyJGAAConSo8BALpqDABAKCdzEmUz3/+83HmmWfG6aefHo973OPiAx/4QFxyySXxnve8JzZs2FBEjAAAAAAAXUmM11gVBkWBBJmTKMuXL4/jjjsuIiIOPPDA2LRpU0REvOlNb4rzzz8/3+gAAKCmPAMCAEB6Vbh9loQjSeYkyqxZs+KRRx6JiIinPe1pcf3110dExJIlS6LhSREAAKYZ9dvkb9ywLF77+Wtj3Zad3SeGEqgvARgcldRA3WROovze7/1e/OhHP4qIiL/+67+O973vfXHCCSfEn/3Zn8VrXvOa3AMEAADq7cQL7oibl6+Pz16+qOxQAKDFqL/oALQaG5JXKxat2hTfmre87DCGxv5Zf3DmmWfGxMRERES84x3viJ/7uZ+La665Jl796lfHO97xjtwDBAAAhsO2neNlhwAAQEVVIqnZlEOpRDw9OuHTV5UdwlDJnESZMWNGzJixrwHL61//+nj961+fa1AAAAAAwPAZjvf8gVGSKomyYMGCOProo2PGjBmxYMGCjtMec8wxuQQGAAD1VuNX1wqiD3SqStkEgPJVYbztYb0l+LVZB5cdQq2lSqIce+yxsXLlyjjssMPi2GOPjbGxscRCPTY2FuPjmugDAECz8h8HAQCqYUzmmjbcMxfnz5/31LJDqLVUSZQlS5bEk570pL1/AwAAZKfShGoalkFkAQDIX6okytOe9rSIiNi1a1fMnj07PvShD8XTn/70QgMDAAAAAIaLtDXtVKA3r6FtKTWs6zUoM7pPss8BBxwQF1xwQVGxAAAAAABDrAL15NBWc6pB3oFJmZIoERGvec1r4vvf/34BoQAAwPCowpt0QDoqSQCACPcEJEvVnVezZzzjGfGxj30s5s6dG895znPiCU94Qsv373nPe3ILDgAAhkFDRiUiPJQCALrzoj13zFRV5iTKWWedFU984hNj/vz5MX/+/JbvxsbGJFEAAAAAAMjEi0dUVeYkypIlS4qIAwAAGHLePAWgahrefR84LVOpsmEtno67/mQeEwUAAACGiXoFAGAqDWOYlLklSkTEAw88ED/84Q9j+fLlsXPnzpbvTj311FwCAwCAOvPMBQAA9TKmyQYJMidRLrvssvijP/qjOPLII+Oee+6Jo48+OpYuXRqNRiN+67d+q4gYAQCg1iRUAAAmFVtJrRKcfig9JMncndcHP/jB+Id/+Ie444474vGPf3x897vfjRUrVsTxxx8fr3vd64qIEQAAakfzf6gRFW4AUDr3z1RV5iTKXXfdFW9+85sjImL//fePbdu2xc/8zM/ERz/60fjUpz6Ve4AAAMBwUE8NQNWotB089wO006hC++0hLZ9DuloDkzmJ8oQnPCF27NgRERFHHHFE3H///Xu/W7t2bX6RAQAAwACoWAAAoJ3MY6K84AUviGuvvTae+cxnxitf+cr4h3/4h7j99tvje9/7XrzgBS8oIkYAAKidhldbpxlTVQ0AQBtVuH12v0qSzEmUU089NTZv3hwREbNnz47NmzfHt771rXjGM54Rn/70p3MPEAAA6q4KD4QAwHS6lgKaOSeQJHMS5elPf/revw866KD4/Oc/n2tAAAAwDORNpvNQCgBAO+6fqarMY6K89a1vjcsuu0z3BAAAAAwFCT4YXaq3Bs8pl7pwf8CkzEmURx55JF75ylfGU57ylPiHf/iHuPXWWwsICwAAAACAUVGFpObQ5k1khPqSOYnywx/+MFauXBkf/vCHY/78+fGc5zwnnvnMZ8ZJJ50US5cuLSBEAAConyo8BAIAVI26XNppVKxDL/fzTMqcRImIeOITnxh/8zd/E1deeWUsW7Ys3vrWt8a5554bz3jGM/KODwAAaq9qD4RlUWdCVY0pnQBASPKRrKckyqRdu3bFTTfdFDfccEMsXbo0Dj/88LziAgCAWpM4AQAYPHXg9VWFlh9erCBJT0mUK664It72trfF4YcfHm9+85vj4IMPjh/96EexYsWKvOMDAACAQnnrFGBwVFIDdbN/1h885SlPiUceeST+4A/+IL74xS/Gq1/96nj84x9fRGwAAMAQGVNTDUDFVODFd6BChvV2dUhXa2AyJ1H+7d/+LV73utfFz/7szxYRDwAADAe1MgAAALWXOYnyN3/zN0XEAQAAw0tCBQAAOmpUYVAUSNDXwPIAAEAyj4AAANMV3V3SsHbHxGA0l5+GO3oeI4kCAADASFPfBqPL8T98NGaor2rsu31nhYmJEsOgUiRRAAAAABhJlaizBSpjWFsyDet6DYokCgAAFKAab9JVi4c3qkrZBBgcp9zp7lu9KS5euLLsMJhiwg09j+kpiXLuuefGi170ojjiiCNi2bJlERFx2mmnxQ9+8INcgwMAgGHg8QsAgHZeeupV8Tfnzo8bFj9Sdiilqto9sxwKkzInUc4444x4//vfH694xSti/fr1MT4+HhERT3ziE+O0007LOz4AAKglA1FON+bdUypqTFMUgIEp+pxb51P6woc2lh1CqcpIWvzg1gfjL868PtZu3hERrS2ltERhUuYkyuc+97n40pe+FCeeeGLst99+ez9/7nOfG7fffnuuwQEAAAAAw6OhYpoK+ftv3hrXLX4k/v2ie6Z9p6QyKXMSZcmSJfHsZz972uczZ86MLVu25BIUAAAAABRNfT5UR5ktuTds2xURrS2Zhun8oEV4fzInUY488si49dZbp31+4YUXxjOf+cw8YgIAgNobpocuAIC86EKRKposls3JBq2mmLR/1h/80z/9U7zrXe+K7du3R6PRiBtvvDHOP//8OPnkk+Oss84qIkYAAKg1z18AQF0sWbslfnTbQ/GWF/1yHPL4A8oOBwYiKbfnFp5JmZMob33rW2P37t3xgQ98ILZu3RpveMMb4hd/8RfjM5/5TPz5n/95ETECAEDteOiazounAFB9L/v0nNg13oila7fEqX92bNnhMELKfPFosgVKa3de7ujZI3MSJSLibW97W7ztbW+LtWvXxsTERBx22GF5xwUAAFBZm3fsjkvuXBm/92uHx6EHeksXgOGxa3xPxfFNy9aVHElv6vzSRp1jz0OpKYuEbT8hh8JjMidRlixZErt3746jjjoqfuEXfmHv54sWLYoDDjggfvmXfznP+AAAgCExTPUC//jt2+KihSvjxUf9Qpz7188vOxz6NOqVVgBJihrk2zmXtirQ8qO5eGqJwqTMA8u/5S1viblz5077/IYbboi3vOUtmeZ1xhlnxDHHHBOHHHJIHHLIIfHCF74wLrzwwr3fNxqNmD17dhxxxBFx4IEHxkte8pJYuHBh1pABAGDgPHQNt4sWroyIiKsXrS05EgCgmVsw+jE21jywfImB5Ezysj+Zkyi33HJLvOhFL5r2+Qte8IK49dZbM83rKU95Snzyk5+Mm266KW666ab4vd/7vfjjP/7jvYmSU045JU499dQ4/fTTY968eTFr1qw44YQTYtOmTVnDBgCA0hT1JieQj7GhaicFkI9hqkCGbpLuBOpwCExMNOID37ktvnzNkrJDGWqZkyhjY2OJSYwNGzbE+Ph4pnm9+tWvjle84hXxK7/yK/Erv/Ir8YlPfCJ+5md+Jq6//vpoNBpx2mmnxYknnhivfe1r4+ijj45zzjkntm7dGuedd17WsAEAYKDq8NAFANBOUUkUb8TTTpn3z2MJBXOiBpnEufc/Et++6YH42I/vLDuUoZY5ifLiF784Tj755JaEyfj4eJx88snxv//3/+45kPHx8fjmN78ZW7ZsiRe+8IWxZMmSWLlyZbzsZS/bO83MmTPj+OOPT+xObNKOHTti48aNLf8BAADlU2kCADQ76+rFuc/T/Qa9mCw2zV3y1iGJsnnH7rJDGAmZB5Y/5ZRT4nd+53fiV3/1V+PFL35xRERcffXVsXHjxrj88sszB3D77bfHC1/4wti+fXv8zM/8TFxwwQXxzGc+c2+i5PDDD2+Z/vDDD49ly5a1nd/JJ58cH/nIRzLHAQAAAAAMzsf/56546a8fHr/8C08oOxQqoMycxWTyrTmEiYlSQslE0nAwMrdEeeYznxkLFiyI17/+9bF69erYtGlT/NVf/VXcfffdcfTRR2cO4Fd/9Vfj1ltvjeuvvz7+9m//Nt785jfHnXfua340tSlVo9FIbF416YMf/GBs2LBh738rVqzIHBMAAPSt+i+uDVyn+3gAKINxy8q3abs36YfNHQ9uiG/NW97SqiONqh2PdWiJkpa78P5kbokSEXHEEUfESSedlEsAj3vc4+IZz3hGREQ897nPjXnz5sVnPvOZ+Od//ueIiFi5cmU8+clP3jv96tWrp7VOaTZz5syYOXNmLrEBAEAehuj5C4aS/B4AVTAsl6NXfe6aiIj4+SfMjJc+s309bpXs685r32d1SKIMS5mpulRJlAULFsTRRx8dM2bMiAULFnSc9phjjukroEajETt27IgjjzwyZs2aFZdcckk8+9nPjoiInTt3xpw5c+JTn/pUX8sAAICiVe1NOgCAKhibUu3rnml43bNqU6YkShVyFs3lcaIC8VANqZIoxx57bKxcuTIOO+ywOPbYY2NsbCyxOdbY2FjLgPPd/Ou//mu8/OUvj6c+9amxadOm+OY3vxlXXnllXHTRRTE2Nhbvfe9746STToqjjjoqjjrqqDjppJPioIMOije84Q3p1xAAAAA68BYnwHRZu2KCOpvsdrZuxV53uYORKomyZMmSeNKTnrT377ysWrUq3vSmN8XDDz8chx56aBxzzDFx0UUXxQknnBARER/4wAdi27Zt8c53vjPWrVsXz3/+8+Piiy+Ogw8+OLcYAAAAAIDBKLrOd2pLF0hjb3depUZBVaVKojztaU+LiIhdu3bF7Nmz40Mf+lA8/elP73vhX/7ylzt+PzY2FrNnz47Zs2f3vSwAABikur3FNgiqNACgPtzKMGillrnHblSH9R5eg5X+zMgy8QEHHBAXXHBBUbEAAMBQGtJnMRgaKhYABscpl3bKTGDUtQVTPaOun0xJlIiI17zmNfH973+/gFAAAGB4DOtbbH3xlAcAQKW5iWe6VN15NXvGM54RH/vYx2Lu3LnxnOc8J57whCe0fP+e97wnt+AAAAAAoCgrHt0aX712Sfz5b/9SPP6A/coOp1IG9UJIoxFxx4MbYsEDG+IvfvupBsoeYY0KJDDq9iKUw2UwMidRzjrrrHjiE58Y8+fPj/nz57d8NzY2JokCAAAAQC2cf+OKiIhYuXFH/MvLf63kaEbXqz53TUREHHrgAfHKY55ccjSMorEhHxOF/mROoixZsqSIOAAAYKh4/oL6qGs/6EB+bljySNkhVE4ZrQLuXrlREoVSTN4JVKE1TBZaogxG5jFRmjUajWhIzwEAQEfumfdQUQ1AVblUD06n7rpGfT+MfFdmZQ4sX9OWKGnvr92H96enJMqXv/zlOProo+Pxj398PP7xj4+jjz46zjrrrLxjAwCA2pI4AYD6cNWezq0Mg1ZmkZtMMij2JMncndeHPvSh+PSnPx3vfve744UvfGFERFx33XXxvve9L5YuXRof//jHcw8SAACov1F/uZLqUjYBoLNVG7fHYQfPbNtaZmKiEWs374jDDnn8gCOD4mVOopxxxhnxpS99Kf7iL/5i72d/9Ed/FMccc0y8+93vlkQBAAAAoF40uxiYqVXwjZa/+98PW3fu7nsetPryNUviYz++M979e8+If3jZryZO867zbo4L71gZ5/71b/e8nDJbcte1Oy8GI3N3XuPj4/Hc5z532ufPec5zYvduJykAAIjQFUASL/sDAEX6yjVL4v41W8oOY+h87Md3RkTE5y6/r+00F96xMiIivjhn8UBiKkrdBpZ3gz0YmZMof/mXfxlnnHHGtM/PPPPMeOMb35hLUAAAwPCp2SMpAIy0wq7bYx3/2ZePPlbZT3l27B7v+bdltgKpa9eeqcOu6fpVRebuvCL2DCx/8cUXxwte8IKIiLj++utjxYoV8Vd/9Vfx/ve/f+90p556aj5RAgBAzTQ/BOoWAKqtXf/uwOhwqS6PbT9cdu6e2Pt3md1zZedegPYyJ1HuuOOO+K3f+q2IiLj//vsjIuJJT3pSPOlJT4o77rhj73RuQgEAgGaeEACgPmpV/01l7GhKomRVZpGra1W2OvjByJxEueKKK4qIAwAAAKixFY9ujf+e/0C8+YVPi5//mZllhwOZSBgkGfxGGfX9MAz14c0tUbJW8I/6/qe6eurOCwAA6MZT4FTDUDEAtPenZ8yN1Zt2xK0r1sfX/r/fLjscoCbq1eUT3eyoaXdee29T6xMyA5R5YHkAAACAqVZv2hEREdcvfqTkSIAq807FcOunOy+K47jrjyQKAAAUoEYv3g3MmMc3KkrJzFed3jyGSQ2vn0Muduwe7/m3ZR6HdW0xXdOwa0cSBQAAAMiNHAoMh6KOZQNhD7e6tkSp68s+DqfBkEQBAABgpKmAyJccCnUk+VcNdkP99dMasczj0L0AnUiiAABAAVQCAKNqQm00NaTYTjeoTWLTUwWTOZS6lce6tqCpG0kUAABgILzhR1UpmgCD0+mc208yy3hM1Mmu8Ym45M5VsX7rzrJDIQVJFAAAKEDzc7xneqg2h2i+nPNgOAwqKSGRvc+ob4tRSoR9cc798bav3RT/5wvX9TWftC8pGYuoP/uXHQAAADAaPLoBAFM1V5v3U887QvXvA/Pg+m1x0AH7lR3GQAw6yfCj2x6OiIj7Vm8e6HLpjSQKAAAUoOHddqgNCT7AVbsa+urOK78wiIh1W3bGiz55eV/zkNgarEajocVJQXTnBQAADIaHOgAqapS6EUqrqC1S1O2AfZivRT22kOhnoPMy9+BkuRxYN3Y5HQfurgdDEgUAABgMlRtUlLc2Aaph1FvynnrJvWWH0Ld+9mGZt4r9JH+qwq12cSRRAACgAC0Dy494hQAAUD91q5CtWbiJ1m3dVXYI1E3ageWLjWLoSaIAAACD4W1/KkrRBBicot74r1vSZ1j1151XeTuxrvcCzdvbIVAcSRQAACjA1IeYnbsnSokDAKAXgxobQvKjmowxUz/2WXEkUQAAoGCLVm2OX/l/F8ZHfrSw7FBKVdMX/BgByiag7nFwChtY3nv49GGyWNa5FG330lZhJFEAAKBgn7lsUUREnH3t0nIDARLVucIEYKj0cUIe1UTYV65ZEu/95i0xPpHvBhgroX+rUgeWH/DqFrF93/WNm3OfJ3vsX3YAAAAwjJqb03szco+69jUNwPBzraauPvrjOyMi4hW/+eR42W/MKjma/pR5FJaRNMpDc9hz7l2Tajqy0xIFAAAKNqpvRkJdqFcAmM7tS71s2bk71/n1PL6GiypDSBIFAIBSNBqNuHvlxti+a7zsUAoniQLAsGk0GrF7XP/7g7ZrkNu8Zvcv7rcqoqZdstU191PXuOtGEgUAgFJcdMfK+MPTro7XfeG6skMBRp0+LiCzN589L573iUtja85vv5elDhXw1963No468cI4Z+7SskPJWQ02fg2kKcM9ty4ZIYPaRHndedS1G7K6kUQBAKAU375pRURE3P7ghpIj6W7Dtl3x+/95ZZx68T1lh1JrY96VAxgaV927JtZt3RXXLFpbdihD6Se3Pxz3rNzU8tnff/OWiIj48A8XDiSGMqrb+1nmqI9r063y/4e3PRTP+8SlMW/po8UG0tftnqYoVJMkCgAAdHHO3KVx/5ot8dnL74tXfObq+OiP7uz6m+YH2dF+pIfqU28CVOlaPff+tfHOb9wcf3DaVWWHUoii3pzXyKKz95x/S6zdvDP+v7PnDWyZddolXvahE0kUAABKUUbT80ajEYtWbYqJiWyPdLubpr/z4Y3xlWuXZF4uekwCGEaucPm786GNZYfAENud8T54kEodE2XA96nDvrxhI4kCAEApyriP/4+L74kTPn1VfOIndxW+rFHvUgIAIKt+XjwZ9TuvtOs/McBMRdb7/X5C27h9Vyx7ZEvPvx/0s0leSQ3JkcGQRAEAoBRl3PD/1xX3R0TEl6/J1pIEGG4qIKB3VWpsOTHRiH/7wR3x7XkrMv+2Sq1G27fWHezJqqhtUtRaVGkfVtkgN9Mg98hzPnZJHP/vV8biNZv7mk/dSpFbmMHYv+wAAAAAAKBfl9+9Or523bKIiHj9855acjS9G7VKUbmPfKRNIlW5tXQ/se0a3/Pb6xY/Ek9/0s9k/r0XKuhESxQAAEoy3E8qLQPLV/dZFQCGxvptu3r+rUv1dEVtk6Iqq0d9H6Zdf/elw0XyZzAkUQAAoGBVfuNvkDzjAQCdKvH7qeAf+eRAyvUf5JgodTI24DvVYV/esJFEAQCAbnp42NQSBepDxQL0ozoXub6O5OqsRmXeLK/d/Uvd4i1JlTdTmWWuKsdddrUNvFYkUQAAKEV9H1TolX0OAPVRVEvaTvcD19y3tpBlsk+Vk2N5hNbr+k0Wy7RjyzBaJFEAAChFrerT1f4DQCL1jfkb5buOu1duivnL1vX021HvPjXv9S8zoTEKPF7UiyQKAAClqNWDQy/debX5G6ieWp2PgEK4Vg/O1HPu1G1/24r1Pc1XhX395bEPe76m1/RmoKZh144kCgAAFEy3AFAfjleor2GvTKzK+p197ZI46Sd3lR0GU1Tl8tVcTKtSZrEv+iWJAgBAKWo1kHMPTx3NFbEVeaYt3ZinNypKyQSqlECtyvWy3Sb5yI/ujDOvWhx3PLihp/lOvQfMa22rswfLkff697pfWlpjl7BT+h0Tpf18G/GfF98TP17wUG8LyLi8Qc+HzvYvOwAAAKi8ClWsAMVqNLytCaOoSlf6upyDtu4cz2U+eW37KiXCsqhq3GVEVea4NpPHXbsIrr3vkfjc5fdFRMSrjjliMEFRGVqiAAAAANCTalb/koey9m1dkkgk62v3lXhC6dZKfs3m7QOKJJuqtFwbdlqiAABQimG/32+0/QdQZQ5XqK9hv7eomqJaUPQ627qev/PajHnujrn3r43rFz/a93wckx3YOLUiiQIAQClG6blhoqLdNAB7jNL5CPI2LJe4YVmPUWYf5mPbzvF4w5duyGVeWfdJHruw12u6ewE60Z0XAAClqNXA8r3wIA9QS1fcvTr+5L+ujftWbyo7FDIalnuLuqxFr90IqaxulduYMDnNaevO3bnMpyzFDSzf23yL5nAaDEkUAAAAeExVB/hlcN761Xlx64r18a5v3FJ2KAxQmQNaT1OVLENJm6TX1a/UPiyBy1dObEcSSKIAAFCOitQPMDhVqRPKwzCtC8Pz9jr5Wrd1Z9khQItBn6m6JSXySjrnVvlf08rvYUve93OPVOa26BZ33qHldTy7Jx0MSRQAAEox7Pf7zRUPw/VoDMPN8QrZjPrb/0UY9nukunabRDrbd030/Nsyd32v3dOVzYsggyGJAgAABTOw/B4e8qgsRZMEztz1098b8PnFMSzqtk1qFu5eI94Qp0UeZa6muRAqThIFAAAKULeKB7LxfD68HLtMUhbSsZ3IrEstd89joiiLRHHlIO/Z5pXsSTufura0qQpJFAAASuFGfvTY5TDcHOPUWZUq4B1LoyW/MWEqVIhraPK4a9dF4bCNXUM2kigAAJRC/QDAcHFer7frFz8Si1ZtKjsMYvi7vyxq7UZ9fJ5hWPtBr0OaxMj/99V5hSRQhvsoHz6SKAAAUIDmRy0vrkF9jHolXD9mVOj1+RMvuD3+/pu39FnxNTplYdkjW+LPz7w+Tvj0VWWHQgeDPsSKOgKmzjevCuq63m9V7brTLZodu8czzKta6zZVc5lpl7y8/O7V8cC6bQOKiKqSRAEAoBQVqmsrXF0f6mFUjNDpqFBVOa/vHp+Ib9ywPH5w60Ox4lEVX2ncv2Zz2SFA39xu9Wb7rvG46+GNqZNZs3+4sLBYBt1lVvPSul3DlK/RJokCAEApKlLXVhiJk+mGaZ8b02d4OXZ7V5UuiJp34e6Jid7noyykYjPlryqXmKIqtCuyepWR12budT5/edYN8fLPXB0/uPWhiOi+f86/cUVvC0qhzO68Bl0uB30v6bjrjyQKAAAApduwdVe85vPXxjlzlw582ZJi+ajKZswrjFFKDlQlAVYmg0bX36jvw17X/6Zl6yIi4r3fujVOvfiekTr3Ja3riBcj2pBEAQAAoHRnzLk/blm+Pj5cYDchFKsqSZRm6sJS6mPfVaniOq+E6CObd8QpF90dS9duyWV+WVXlUOq2Z3vd80WdKypUFGvrs5ffFzctfbS8AErch13LpfI10iRRAAAoxbC/+d08kGaVKpjKNOS7nD5t27m7tGUrmvmocmuGDVt3xRfn3B8PbzBGSpLq7rls+lmP5iv1e791a3z+yvvjj//r2n5DqrWybl/yLI/LHiknEVaGvHbXo1t25TSn6kszsHzVecwYDEkUAABKUc/HlN5MeLiB2lAZ0bsqJ0r/6Tu3xckX3h2v+8J1qX8jAT665j32Jv6GbaNTmdyLOnSd98WrFhc493yUPSbKsMmyGRopp67y9Y3BkEQBAIACeJDdQyUkjI4q1jFNnoLm3LsmIiIeWJe+JYqzF2WpS4Vtz915FTTfpFsO9yH1kjapkdvymluidCiYY2NjucdWk8Ocx0iiAABQjho9OXj8ZqoaFV8YmGHvpnGY2XetlalVrXeva3dDZarqvmw26MRBv/abUVw5zGN/9RvdoMrMoE+7TvP92b/sAAAAGE0qAkbDMPQ1PWwmJhqxY/dEHPi4/coOpZLqVplVJVWsoHlw/bY45EBVH91UcNf1pIplsBfDfr3slrTrde2Tzt8Tdcii5GRQa7pfhgOt6ps/S3xVX5duhv28UjR3EgAA0EUvjxyNlr9r/tTFNHWuqHvTV26Ia+97JK7/4O/HrEMfX3Y4lVDn/VklVdmMzWfcN3/lxoiImLl/9o446l5hNijDsp2ar9VDskojJ6ks1mFcurodQzMK7Ncoj23RczdzHW4G7l21Kf7le7enms8jm3fEzAP2i5+Zqdp9mOjOCwAAuuj3ea4OD/BFaV51FdXVcO19j0RExI9ue6jkSKqpbpVZVaJLqPqy6xz7ZSpy249US5QBrWuWlihpJi1z3Jrm5GmnUN969rxU89uwbVc85+OXxtEf/mmfkeXPeb4/kigAAJRi6G/kR+ihHepu6M9HAzJs29GA1JRmyI6lQUk8YmtwGNcgxBYzMoyJUvXTaN7x3bNyU+ppda9VL5IoAACUok6PDXWKtWpUQlI3Smzvqnyu7GW/Kgv1k1ulZMk7v91aDFuiMm9J9xyj1BJlUPYvcmD5AR98SS2m69YNb9p4nT76I4kCAABd9Pso5fkdqs3boPmoSndezrnZOQZa1a0StSy9HmuDLG116FK1bi+c7FdgEiUPWaJr3vZ5rFVFLoOJqhxbHUiiAABQimG/kW90+NcoGd01rz6VhOSt0vVqintH/VyTq3Qu6W89qqMqCcm6SdqHo9QSJa9V7Vb8spTPrEV50APLt7ZEGfBx5zCvFUkUAABKMUpvvY7Q8/vIGKXyO2rq9kZwtQzZcZGiKOzcPRHX3rc2tu8aLz6eAg3ZnutJ86HvNJBOr3XORdVVJ+23OuzLGoTYIkt3XnXY/lW0ZtOOmL/s0Zzn6kzfD0kUAAAogIfGPUZ5O1x739q44u7VZYfRlkQQeavyy/NFtZb4yI8WxhvPuiH+6TsLCpk/jIIiWzONVEuUnLZjt002I+eTfZm7qHnZVbqG/fZJl8afnnFdzL1/bdmh8BhJFAAAYCBGqXuS3eMT8cazboi3fnVerNuys+xwyGB0qtvyV+UjvJdKujQ/+cYNyyMi4ke3PZR9AUPi4Q3bY+fuibLDGCpVPpaaVS8/MT2g6sU4XV4xDqo7ryLHRBn47mpOokx+NKAgOm3FyRiuvS+/JMoI3YYXQhIFAIBSjNKNfA2e38nZeNMT+Mbtu0qMhDRG6XxUpKpsx6S3sXs5D49U12597LtTLron/vi/rs0vlj70VwQbCX9RhKktIfNqGZl0yI5SS5S8dNtkVR9YPosqjelEtUmiAABQiqpUtvWqW+Va8/cjVRE3RfPDac13+dBRcZBshA/XvlW5i7hRPg+n0e++u+vhjTlFUg3KSzp53cvl1g1Vys8qp2JBdtsfReZQBn3otSwvhwJdxlUw7Tar7hW6HiRRAAAoSb1v5bM85FXs2Zg81Lv4QiGG6OVkRlCV8ibt6nKrdohVaZu1M2wJsanrM+feNfu+G1AMw9USZZ9Br1WanE2exXeUutUtgiQKAAAUYLge2Xs3ZHUXPanqNqhyqwHqadgqaCp66BaiedcNW6VzL8reAkN2KGXW67mkrkU3bUucRzbviBecfFl84n/u3PvZ+791a0FRtZdlYPnsu6SmO5GhJ4kCAEAp6l5BUOdHvKvuXRP3r9lcdhhQTXU+uImIduMiDD4OBq+fewtFZICm7qe8BlYf8r34lWuXxKqNO+JLVy9J/H5QA9Tn3RKlzL3WnDSePH9UqRTl+bxU80ev0kmiAABAD7K8qVulNyMXPrQh/uorN8bv/+ecgS+77omzZllWZZjWu0hltmIYthYUZRm2zVilc/cgjep6Ux1Vbw21eM3muHtlfuMApV3dpGRwo+XvRjQajfj7b94SH/jObbnEliRLEqXql4W8S1re18GKHwojRRIFAAAK0PzQU6XKgPtWa4HCHlV7Y7cqx0nVtkudDFsSZZQ07zpHgIrLgctrgPoB7LdGoxG/959z4g9Puzo2bNtV/AIzaDQi1m7eGT+49aH49k0PFBZfliRK1l1S6sDyAzboblVdo/sjiQIAQCnqfh+f5ZmrSnUxBz1u/71/D6LSunkRdd/nvVIZx6jI0k8+1aI1VnUSuRHDP2bVtLWbsumrPCZK8zLWbNqezzx7WPa+z1o/fNx++6p6t+zYXUg8+42NxfhEI1Y8urWn+Zfl3OuXxf/7/u0t22yUXpxwmu+PJAoAAKUY9hv5qj6SPeFx++39e+f4RImRDLdhrwAbZhWqR62dYSv1w1C51mg0Yv3WnV2nG56B5YsthRu27YqJAQyw0+4eSbKraga7P1Kdk5pC6jWJ0s2MGWPxnm/eEi8+5Yq44JYHcp13kUfXh75/R3z9+uVxzX1rC1xgvmXCIV8dkigAAJSi7pXMda1jOrApibJ1x3jhyxuGSshe1GG9q3YMljomSmlLHi7DVsFb1/N8s3/87wVx7EcvibnNlYZdDMFq96Tbei9eszme9ZGL4y++dP1A4klrYqIRn7l0UVyzKP0+zkuna919qzfHJy+8Ox7dMj2JV9SpYhDX3iKWkDpx2WVMlKkfbO41idIlnv3GxuJ/FjwcERGfv+L+3pbRdtHF78NN25O3y20r1j8WQz7L6bYuaY6DNLGkDbdq9311I4kCAAA9yPSgXqEaqf1nNHXzsLOYNxSphzokespgq/RuyHIoQ+G7N+95S/z0K+5L/Zs6J4/6KYPd1ntyW96w5NHeF1KAHy14KD596b3xl1++IdPviq6sfsVnro4vzLk//vm7CwpdTrNBjYkyKa9zXuruvJI+6/DjLQW9LNM8JkoVz/tL1myJL8y5P7a2uc9tGbew6fNv35Rvq5puqrjtaE8SBQCAUgz7g0Nrf8vVtG3nAFqiNI+JMmWnj0804l++uyC+eePywuPIW7fy622/7MrsQmjYz0eDMmybsarn7l50O7yGbd8Nk27nxuWPZB+XYvOO3fHiU66ID34ve4KjJYnQoeRMdhl662Nv9w+LMs8LSWWh5X5zyvc9t0TpovmamcdYWHlf/79y7ZL45IV3x7//9J7k5UXzNst10a3LqdpFxIm+L5IoAADQgywPRlXtX37LAJIonVxy58r45rwV8S/fu73UOID+zbl3Tdy/ZkvZYZCDYWmlVtVrbxX84NYH44F12+L8G1eUHUrttLwcUsA885xPUQPL18X8ZesSP29tiZLv2nopY3hJogAAMDI82LQ+LG4t6A3FtNZv3VXq8vvRraXJsFRCjiIVr71581duLDsEOuh2TmodWL7gYGpqkC0Mm1tuVnl/5HWty2sVB9KdV1O0gx4HKmn9pn7Ucp9XULetnVoZJ0389nNvig99/4508+4jrqke3rA9/vi/ro3vzm/fTVehLVGKm3VPPAb1RxIFAIBSuJEv39ZBdOfV9PfU5+yqPVxmMQxJkqp1OVbuwPLV2hb0J7dKsfof5hkMxzHQvBZZy0GVEqgt61HI/Mvd32Uvv2rS3lNMZCwMO3ZP9BBNNt325N0rN8VPF66Kc69flmp+eR6GazbtiNtWrI9/+O/bWpfR5u9un+Zt0MdBmfdZw6DUJMrJJ58cz3ve8+Lggw+Oww47LP7kT/4k7rmntb+6RqMRs2fPjiOOOCIOPPDAeMlLXhILFy4sKWIAAPJS9xv5TN15FRdGXyZKrjCqUH3VSBqGRFARbBUmDdMx4nzb3ShtorxuwYqqBO41vkEcs5U7ltoMkj4o3fbVrvHiEzlZDSphmsdyqlbcRlmpSZQ5c+bEu971rrj++uvjkksuid27d8fLXvay2LJlXz+qp5xySpx66qlx+umnx7x582LWrFlxwgknxKZNm0qMHACAOsrzUb/bg3pLf8sj/ARUpTd78+Qt2vyVWVbKriyfv2xd/OsFt8f6rTtLjYPhlaWED8tpO+/VKOvdj27nxkHvrqzjSSSFn6IHqJ4MuuzmViRSxr1x+/RuUItY5W7bsbVLs5yXne/sui8v50JT5bvDKsdWB/uXufCLLrqo5d9nn312HHbYYTF//vz4nd/5nWg0GnHaaafFiSeeGK997WsjIuKcc86Jww8/PM4777x4+9vfXkbYAACQSdkVtM0aJb+x2KxK2wUmlVGB/KdnzI2IiF27J+LfX/eswQfAyGsZE8W5uXSt+6Pe1m7eUXYIuWodD2Rwy31o/bb4TsLYHs1JgEajjERS543QOr5PI7ElepllvMwxUdKUnzRFbFhfWKqaSo2JsmHDhoiI+Lmf+7mIiFiyZEmsXLkyXvayl+2dZubMmXH88cfH3LlzE+exY8eO2LhxY8t/AABUT51680ocyDPDW3pVNZgBWPep0S7vW5WSVdTH4rVbuk9EV7kNdj1CB++wnJ+nVtiSbFj291SD2OMtrTBy2pJT4/7CnPtj8ZrNLZ9deMfKFPPJ69yXfj7dWxW1JnnKkiWOYTh1JO3DOj17VVFlkiiNRiPe//73x//+3/87jj766IiIWLlyzwni8MMPb5n28MMP3/vdVCeffHIceuihe/976lOfWmzgAAD0pO7dIQ3B81Xp6vyQ6kE0f3UfJwnqrDX5UGIgZarQejffIxWxP8o+3U5d/tR17HlMlCEpvJ+88O74vf+c0/JZu3UrY41bW+N03llX3LNm3++KCiiFv/jS9U1xtE+ofHHO/XHZ3atzWWYexbGXWfy/798ex33y8v4XTovKJFH+7u/+LhYsWBDnn3/+tO+mHpDtmn9FRHzwgx+MDRs27P1vxYoVhcQLAED99FpJ229lQ3Wf6Qc8AGvZtTYlGc21rq86tCJjMIaqJGRYmWFZ76zrMSzrnUbdX2RpZyAtUQpYSJ7zHHQ5zlKSJlKsaJpE2Nadu+PHCx5KHCOmnesXP9q0jKblTdliJ194d+p5JmlJSJd0Vvn69cvj4Q3bp30+rMf9oFQiifLud787fvjDH8YVV1wRT3nKU/Z+PmvWrIiIaa1OVq9ePa11yqSZM2fGIYcc0vIfAAD0I7k7rwwDy+ccTz+aYxl0cmfJmi1x18P7utut0nahfMPyBjHDY5jKpORg/8qqfuy27wZdTEe9JFXt3ZCW+82cdk6W2WTZBnnFd+IFd8TfnXdLvOsbN/f0+zqd2vMsYlUor3VWahKl0WjE3/3d38X3vve9uPzyy+PII49s+f7II4+MWbNmxSWXXLL3s507d8acOXPiuOOOG3S4AADkqIwb+TwXmen5q0YPa7lrWvevXLskXv6Zq2P91p2PfTcaG2Y01nKI2GGMoObrY52TR63rUVoYfWsZWL7G65GH/7rivvi7826OiYk0rRiKj6eI4yNNknOQ5SDL/XKWafNK5l5wy4MREXH1orV9z6vQgeVL6s6LYuxf5sLf9a53xXnnnRc/+MEP4uCDD97b4uTQQw+NAw88MMbGxuK9731vnHTSSXHUUUfFUUcdFSeddFIcdNBB8YY3vKHM0AEA6NOwvwxVh4eesmJcuXF7PPGgx9ViGwH1M+qVzv2y+UZAXjdhAygs//7TeyIi4v885ynxkl89LHNAeZ8PymqJ0i4B0TK+R27L6vJ9hjFR2v0uzedFaV7cQxu25TrvTAmoAb9RNuzPXkUrNYlyxhlnRETES17ykpbPzz777HjLW94SEREf+MAHYtu2bfHOd74z1q1bF89//vPj4osvjoMPPnjA0QIAwD51rKSb+vZkHdehKjyI5q8qA8s7LOpl287xiIg48HH75T7vYSoL3c73w9jyIetb711bGAzwHNVuSXmFUPbZtpexGSaP9bKVNSZKu4Y4Lb9tNAbSkqz52MrUEqUi55bmsVn+6b8XlBhJPiqyWYdeqUmUNAf22NhYzJ49O2bPnl18QAAADExF6kt71+VWttOglWXYPT4Rf/L5a+PRzTsHutxO616Vh2moSlmsczdKZdg1PhG//m8XxX4zxuLej7889puR74VllHaHAYerWxFZtXLYcp7Kqdik6s4qzXwGvK2qkPwvYpWzbMcZGbZBuoHl0y87Dw+uz7clSqWVX1xrrdQkCgAAo6uMB8+ynnWrUAGy4MENcceDG1s+Kyu5o7KOKqvC8Uo6j27ZkxQen2jE5h2749ADDyg5ouoalXG8hrFFTTvzlz0aCx7Y0NO1PLd7sB63cS+LT7M/kya5/J7V2ReWdSEDmGVly3OWgeVTTTOIgW2KX0RE9faZ++/+SKIAAEAPsjzkVeEZKumxaTADsBa/jDJU4e1Tho9ylc14U/82zY1QhvS0MzBVaD1Zhipdr1qSQW32x5+ecV1ERDzzyYcMIqRCTa3cTbxn6bFc5j8mSm9dWeW53ClftPyZx+pmGhMly3yrdJBVgCt+vcwoOwAAAKi6pHrNbs+BVauE2n9G9W796/ww3S32Gq8a1EZz1zBZupQZRd3OWcOy+YZlPZqrV7tdTxav3dzH3Kth6j1T0iqnaonSdtyQ/C7KxYyJkk83V1OnGcS9SJZjrt24Ls0G8oLPgO7Rq/YsMDznx3JU70kKAICRUMZ9fK/N2PN8oPuvK+6L131hbmzfNdgBUpNyKIN4tOu0jGo9WsIeZVZ61DmxWIbmzSWJ0lmWkqUYJqtCCevlpY5CDXCjpOvyKnmqNJX3vcRRhdNO2RX1me6t24R6+4Prc4klrTzLw1RVKBMUQxIFAIBy1PwhI0tXB80P9f/+03ti3tJ18e2bVhQTWBtJAy6XVWE7+YBZ54o63S5B+dIMUkx2w7JVsxaPsiujm40V3D1dP5ewRtt/FKufe5bdExM5RrJPXmNMpGtlkjxRy/3mwFpY7JOlLCXF9+D6bXu7phs23fbroG8l3bn2RxIFAAB6kOVhPmnKQbdE2a+kSn9v1lM3ZRZZyblsinybeNS0DshuwyYp6/Achf0xdRV73dTtttS2nfndc5W1P9p2VTZlmkGHl7U7r/GJRix7ZMvez+5b3dodnfH6qCpJFAAASpHX23t1kPSwNujKvxkJLVHKVudn2OptTRg9zS1Rmt9wHoVKZ5I131tkfSu/6GIzMdGIj//4zvjxgodynW8vYdcxX9vPmCjfuGF5fnHkNqfil9try5Qs59As9/ONRiPeff7Ncfy/Xxnfnf9AL6G1ne+jW3bmNr+8tNuKu8cnYsO2XbndS6bdXV7U6M/+ZQcAAMBoKuU+PsdltnteWb91Z/x4wcOxYduujr8fH3AWJaklymAG7+zw3RBXdKrQra+89tadD22MWYc+Pn7uCY9Lv2xlJRPbK72u3cq0JB9GU9Hrfcldq+Ksa5ZERMSrjjmi47TNV+yucfUQeNkvskytzJ16i5I4sHwfeyjPCvaqnXZKOQ82LTLL/fyu8Ub85PaVERFxxpz740+f85Rp8feyn//f9+/IlCgrstuzNMfWKz57ddy7anP8+pMP6TptnrtXDqU/kigAAJCjd3x9fly/+NGu0w36obdKD04VCmWkVa0iaJjc8eCGeNXnromxsYglJ7+y7HCGlu680hvFgeWrth5rN+9IPW1zkqFq61GEflqZtEzTpqQnjQvXqyIq4PNb/2p78SmX7/178j74luXr+55v1pZGgzqm2t3r37tqTxdmdz28cTCBkAvdeQEAMDLyrLxv9wCWJoESUY3Kv0EMQDoKlT/d6D6h+vIuptfet3bPfJX/QhlYvhhVGmB9mJTd+qNZXwPLD6B4JIWXarltppmR53W4hIHcOy1r6qeDPn6z3OPsGm/ugnGPz1y2qGWasltJV205eRbd6pyB6klLFAAASlH3G/l+H1IH3Z1X0kOp+sfRJreTrMwuouqccJsxNvjk8MTEvr8bLRWbTNOlXA9N4qTpEKraGvV8eHdZkUHuu4c3bIsbl6R7WaSTXjZFP2s565CZffy6eGn2YduB5Rvtp+n1cpbld7W8ahV4nc/7Mu5evTokUQAAKEWN6wpzsXrTjtixezxm7r/fQJZXxWewYX4wbK3UGOIVJVd1LiszxsYG3jJES5SCjOpmzTBuTC+y/Lp1TJRG0+fl3jy9+FNXxO6mbOkgi0qa473dFL/8C0/ILY4qDyzfa0JtbCzbPVlLmSwoOVikgbVEqdi5dNSfvfqlOy8AAOhFnw9G59+4PH7vP+bkE0uPBtNlQvuF1PrNZw+iQ6tqlR51UUbljH1VjGHZrFmTkrW+Jg3I7pyamxV1vmjbWmMAyyh8nmkX3GayiQ77Lmt3Z82h9NpVWrtoBnEUju61w81rPyRRAAAoRRlvUxbUJXbPHly/LYe5dLbskS0xPtFIrEwq6xnSm3DVMLqVCBShjHN685vpinNntk//+r12Zfl987TdztW9nMvz6jowy1w279gdqzZu731hfQwsn+cB0LyMQV5He1lU82++cu2SttPN6KM49NwQZRRuQnJYxXSH6ghsywqQRAEAoBQq0ot3wS0PxPH/fmW88xvzywuiw3PdKDw/QxZ1HhOljNB151WMOm/W1m6wqqXXRGO39ehlPfM6XLMs+9iPXBzPP+myWJ2QSEkzn36O9zxbGfV7fNzx4Ib45+8saEko5RZdhxl9de7Stt/12pokooBz/yBaSdfoJJdnqDW+xagESRQAAOhBHZ6/vjhncURE/HThqsRn0rIfImuwCdsahudQD9PVU/Yx2Y9SuvNq93nGzTh/2br45IV3x/Zd433HVFW9jndAjkb8nDvZFdgtK9YXtow0g6/3vYw+f/+qz10T37ppRbz/27dmW26Bh+XUJEq3c0Drt8UkB4s0sDFRnEuHioHlAQAYGXl2N1O3B6Okh++B9Dvd6bt6bcJMhnjVIFE/bzKX7U/PmBsRETP3nxHvO+FXSo6mHM3n4zqfm5tbc2Vdjyqtd2t3XhUKLAd5j6GRZpqchnLJ1b2rNu/9O80+TtMSpxHtt0GnzT61O68sRa7XU3+ZxXrIDqnU6nuVrgYtUQAAKIUb+VFm71Ndo1q50q+ykyh5VDTft2Zz94kYWkUf+nkcIUmHWS9lP6/DtZfZjCX87ge3PDhloulz7qs7rxxP7M3zGuTlIt3Y840p/0437366kux5TJQ2a9TtJaXv3fxAj0tsXsZguJ8YLpIoAACUo0ZvLSc90NXvwaicpij1206jw77Zpypvetd6TJSyA6CjLK0nq3E05CDnFem3jGc5vptbzhaxP/JqmdvTeCwJi/7e1CRK0rLSDCzfZqI8t2Fel4tBj9+Ta2vspo2Qd0uUeUvXxfu/dWts2r4rXnrqnDjlortbvn//t2/rbYEDUuXLeJ3vMapAEgUAgFLU/TZ+GCqZyu6SrOzlD9o1i9bG679wXdy32tvuVVZmuaxKMqcndT+ps1ety2Efil7vLIdI83lo2HbHjLGx7pW5CSvdT3dew7ANq7oOeSZnJn3vlgfjvBuWx32rN8fnr7w/9/kP6hxXtV3mMt0fSRQAAEZGry9gFfGAOGhlPXx3qpCuaoVAGr28zfeXX74hblz6aLzzG/MLiAjKVXp3Xm3/QUS9z7dZtL7dPxorXbe17PlU0VchzrM7r9xmlWmeacpzo5FPfEvWbkk97YzHapZ3j09kWka3OHc3DWSTdd5QBEkUAABKUacW5cndedWr2iIp2rJWoU77vledysejW3a2/fwf//u2uHHJo0WF1WIU9kMvanZoV4byNDwcA9PtHp+I/7zk3r7m0XO3R10qz3vZX2Uer6kSrgnTpFrNNhMVVaYHei/Yw6Kay06Wff7NeStShzL5otG//XBhltAy+diP7yxs3kUaWIuX1GPfFBvHsJNEAQBgZOT57DAMlUyDWIVh2E75Sy6Jn7rw7vjO/Afi9V+8bsDxQP/KbomSiyE+X43iuTjrOnea/Pu3PtRXLBH9tMDoe9HTlHm0jo31tvx+WmvkOiZKISeKFK1McplLsr7Kw2M/Pu+G5Zl+liXBcM51yzLNu5uJUTwhxnC0rC+TJAoAAIyAKj4v1q01T17aVaQ9tGHbQOMY0c3flc3Sm7KrZpTnzrJsHttyuvVbk1sQZlGlCswyc569bod+7hnyLNOldY+aYsHfvfmBtkmeonZ5dUp19VTtVLrfDHurH5IoAACUokqVCaOq7IqyspdflnbPsAc9br/BBgI5qlJDlFEZC4Ppmsth1lLQ6Zo06OtV8/KqXJp72S691uOmaonRZqI8Wx7kNaeWspqmlU2KadZv3dVadgZQeHoZIy6i+3Ys8pqS53bZtnM8Pn/lfbFo1aaIaH2+yWM5eV7P9t+vQhfqGpJEAQCgFK0Pj1WuIkhO+FQ85GkSx3UZQNVM0hJG7RFu6jZol0B8wuP2Lz4YKEivFWlUjyRUMXoeE6VpdxRxlA3iHqx5GT1XuveRaFi/bVdPy0xeRqPp79xmm+jc65bG6o3b9ywr5W96CinjLsmjTNbtPrqdz1y2KE656J444dNXlR1KV1qi9EcSBQCAUjTfxg/qQar3t+Xq/6Q3LA+rVdGtKHXa3O1+e9DMwbZEUeedrOpJ3aoqozgN866648ENMfe+taUse1i2q2O5k3zflu9mornSfay3608/YX7o+3f08ev84sg6zw/9YGH8+ZnXZ5tXmx2aZ6K7eQm9tywq7/jMc8m3LF83Zd6NxL97lWfL/f0lUfoiiQIAAD2QWEm7jKQWMK3/X2XrtuzMvSKu3QDcBw24JYr6RfJU+sDyQ1aeX/W5a+INZ90QD63PZ6ykbucx54POcqkMrdCLHP10e9ZuPp00d6fV67kizbV4GIvx4rVbIiL9MTroY7mOrRBz7d6tRoWu9Ot0zUmiAABQirwe4AdhKLrzSoi37FWo+jacc++aePbHLol//u6Cad9leQxNO23zmCjeoC6PLd+bMl5wHYX6oAdzSqJkUedjoGU8ghLjyFPRl4NBDNje0v1ToWNdDKJrsnzmk2XsjE9eeHfqiv+BJ1F6/N1I3OYMaEyUtIsxJkp/JFEAACjdoCqMe37QSxxPhDQSkzc12XinXXpvRER8+6YH+prPtDFR2hTE5jFRduye6GuZZbpv9eY448r7Y9vO8bJDYYDKeBu53bmkLueYNMpYF0ncYmQ5QnrdBdt3VfO829oSJaLb1kj6tjrFcvCBfGHO/amnbVfp3mmLZz575zAoSpm7M8+yNHV7V6ecTmdMlP5IogAAULoKP2+09bv/cWX88LaHYsuO3WWHkkriQ3VpT3qNx/639+Vv2Lordo3XM9HQrjuFxze1RKlLuUry0lPnxKcuujtOveSewpaxfdd4bNqe30DBzapcAUJ7w9DFYpK8EhpZZjMsWzLXitIc5tXzwPIZpn3lZ69OF0uP8+9V6/brviGSYkrTEmPw69K/K+5ZHSdecHtu8xt8S5Reu2fLOZABmzw3d1qPqq3i/jOkAfph6wEAUIo69aHc7gHxPeffEm/96rwBR9ObYerOa9XG7fGsj14cLz11Tr4B5ajTurUr+s0vCO6saYKo2fxl67pP1KNjPnJx/Obsi7V2qZAy6mbyvoxUMRFTvYiqrYr7cFLvlc3p1+n+NVvSxTKWviupPExtiVLUwPIDGest5/m99ex5cVOK62XactA22VTQbXdRt/N5Dqg+VR7J6SvvWbNnXn3PaXC0ROmPJAoAAKWr+ttonZ45blzy6OACGRKT+7vX3X7lPasjImLZI1vzCWjAkorTLcvXxYkX3LH331U/JtIochV2Ptbd2bJH01UYUrwiK7zaKfs4qdP4C12X03TElr1dk0xMZA+qygmVKhnEdspnYPlUU/U07zpIu2bN0xV1jmqea+9n/nrvq7Wbd0RE522c3KVtvuu9ZtOO+Nzl96Wadn9JlL5IogAAUIrWgeUH9CDV47NDnVrNNLt75aaO3w/kjc0CllFGZW2ekiqQprZoetOXb4hHHntAp1VzBUQxZaHeFTtlqVLdzGC69GnE//nCdcUvJ6e1yTafah0DD6zbGs/+2CVxykV3lxZDHluk5+68WgZlz/9AG3TrjTSrkDgmSkXKZVlJxrTLHXh3XjmU60HLY9mNKf+fPE3CmIo5r/fbz70prrp3TapptUTpjyQKAMCIu23F+r1vU5Wlim+9NqtpDqWrsgYP7nupA9ofHQdh7adQJPx06mDA96/ZEqdcVNyYInVW9fPFqOr17fK8DLpcbN6xu9Au6/ZS3uOzly2KDdt2xeevTD+4dkRUbttlGli+sCj2GPTR2mjqobLIMTQGkxDKp9VW1lNm+iRSDwPL93H+7rllUc9L7F8ey041JsoAutK9efn61NNKovRHEgUAYITdumJ9/PF/XRvP/filA192Ga0Jel1i2ZWDeahkxXMlgypeUmlKKmPrtu4sPpgCFbV7iy41ZQ4gXWsjts6DqozKq7xnGli+YqfmLENEFXbeGbLzwqBjae7Oq8hl121g+S07due+3ObpBnE8VKlcp5XHdpnsYbBzS5SkZWdb+BfnLI53n39Lpt+0s18dd1aFSKIAAIywa+9bW3YItTAMjxyJXQqUtdw+x0SpRVKr48Dy0+OvxTplVFT5KqsFVRY1CDF3gyjBO3dPxJ/817Ux+4cLB7C0zobxmJ1UteLbdqDsKbbtHI/bVqzf+++OlZt9HqSDPA8V0i1mc5eqAx9YvrfXaKpy7s8zjLefOz/9cvucLs+u4Jr358RExM3Ls7fKazQacU+XbmeLkkfXcHs3wZQC0ZrEyufe+0e3PZRLF68ztETpiyQKAACF27B1V5xy0d1x3+rkh6WKPBe3NawPHVXf7u1UYW/0E0NScRri+tjcNY8vndd2a6n0yGeWI2cQSYXL714Vt65YH1+du3Tad8O638o4T1ft2jCeclD5v/jS9fGfl9xbcDR79LaNqnmiH8zA8v3Po5/uvH7hZx7XfwCTy8hpe41FxDUZXmZKW+bSHi8tsWQsms3L+NZNK+K1n5+beZnrtu6KPzjtqr5j2pyhNU+eJhNJnZO16T6jHiRRAAAo3Id+cEd8/sr746Wn7ntYqlOlcZ1ibadKA6FOVkCM6oNk0ju4w/xWe96aK7BsteoYRBHeNZ7upFGVN9bzkN/A8l2+r/AmG08Z3K1NrVAi8l2nqfuhl1m3tv5IP4cqn/PSls+p0/VyvkizpHbx/MFvzMq+wD6WV4S0LRH+5Xu3FxzJYKQ9RJ7zsUsKm3fnefR2L5u2Zd1U7hPLJ4kCAEDhplZsRLRWBAzqIbTXrgzKGL9lEMquM+t1v9f9OTIp/qFsnVJQrWyVK3v7VedVG0QFT523T69KaYlSsS09kUczhpz1kqhrue/p8vPm+RfSnVdTNAMZjH1ALVHa/rb/xe+bV0nF8eI7V6Warl2XdnmeoXtNBBRhx+4MgyY9Jo/oJ1vjdDpf5rmZJFHKJ4kCAEApBt0fdz+GoTevPAa3zG+5rf+f1aCeI/vpP7z5oXrqeibNN2mQ6p8uXBU/Xbiy5xjKVvHDuq2qn49G2fRzVnk7a1DlJK/FZGr5ULFjoNcK2yKTQb21RGlKXOQXSm+a78EGsLip+7CXl1PS7M92RaVqZbruBpHXLPReL4cC0WZIlNb7vw7jAmY1pga/dHYBAAClq+qzbaPRiDsf2hjbdo2XHUrfhql7m+bKlw//4I5Yv3VnidFkl9zqJLm2IMugs6NiiIryNHXO1+Y5aHEvhukc12xY1yuL8ewvmucuj93QfIRkSQwVUQJaW8X0voS0yZDmSvdGNGJGD7WRqcZE6eGbIuKom6xn7yq1ROlFHtGneSGoU5e2WdX5/mBYSKIAAFCKMrrIylrHd+W9a+IVn706Trt0UTEBjYCkypl+n72b9+M51y2Lj/7ozv5m2GcMefx2EK2dGo1GbBngAKxF1bG0jA9QwHarWldGdTGYMjz1k+SFlrUHf3jbQ7E75xr/Ruw5di+64+FYunZLX/NJPW3FDoHxid62aZHr0WhE/PtP747f/Y8rY8PWXal+0+v5quhEWj9zT3u+nNolWy9dE/UVZ0Hj41TtWJmqOb48r5dVX+9BSDWwfOLvCgmHAZBEAQCgFL0OsDpIP7r1obJDyE2nbrXKktfi71m1Kac5FWNqxUVZA8u/+/xb4jc+/NPCl1O01gqI4Xo3s9djotFoxA9ufTDuenhjrvFkMZgxUSrUnVfCst9z/i3x9euX5b2guPSu1fGOr98cL/mPK/uaT9qvq5ZIHK9WOBGxZxv91xX3x5K1W+Kc65Zm/33bbqcace19a2P1pnSDiNdVT+eLFDct7e4n82w5Ufa9UxXUviXKY+Fv2p4uAdppHlPLXPM/k7ZTVZ956G7/sgMAAACPE8MrMXnz2Kej8hw5dT2T3tgfRAX0jxc8XPgyBqHwt7Lz6LZnwLmda+5bG3//zVsjImLpJ1852IU/ZhDr3LHblOIXn8qV966Jt7zoyFznefPydXv/3j0+EfvvV+z7sKm6TRrgCbzXgeWLjLBbRWnX37eJ7sp71sRbvzpvyrRNcjrOWsZnGcCunLqNksYB66YqLVGyWrRqUxx60AFx2MGPb/l80F0g5tkCfBBJlEKHRHmsNH3tut6T3r2O0dPzyxI9/o78aIkCAAA1NzHRiNUbt3ecJs9+mctW9tgLaXSsX0iIv9Mqreqyb6uqqPLVbq6NRiPOvnZJ3LT00UKWm0XPA8f2uLwyW6BMGsRRWYfEa95dtUw9jj510d35LqAGqvDWe6ekUdrK6TSXrqsXrU1YdqrZZ9ISykCSKE2La3TfFknf97Md8lzFLPNa8ejWOOHTV8Vvf+KyHCMoX4897FXGZFna0ceYh5NlunO5zK9L20Yj4v41m2Ncf2ClkUQBAKB0g6ofqX7Ve2/e8fX58dsnXRZX3bumw1T5j02SRmLyZvLBc0QH10yKv1NLlH/93u2FxFH07i9sTJQ2873kzlXxkR/dGf/nC9dln2eN+rhPUsYYU9ODGER3XlP+XeK+arfsXltNtDO1svJLVy/Jdf510GulYZGtZZpn3ct4QO1Cy/swWrt5R9cymWfC+7YV6+Mf//u2aS92TE2E9TYmSm9v/nf6vBfN5arbbBc+tCG/Bfcg7b7N+nJK3bukmoy+n5dy9o2Jsm9b/NsP7ojxRuf7iV633TdvXB6//59z4j3n39LT7+mf7rwAAOiq0WjExm2749CDDshtni0PLhV9FqtoWNNcfOeqiIj40tWL43d+5UklR5NBr2/rD6i+uKjFJHfn1X76hzfUtCVKxv17/eJH9v69/JGt8dSfOzC5gqPNQLmL+xh0uyrqcs5JMpiB5dNtoTLr93ZX9BXtLJukavWjRbRE6XeWzRWnM1IW/jTJzuQpegv2xiWPxuu/eF289NcPi7Pe/Ly20+W5ef/4v66NiD3Jm9ZlTOnOq8uFvNMLGB1/12Zb5ZkoyjanCiS4H5PnvdOwNIbIY5s0l8uvXbesJUGY2KVtj9vujDn3R0TE/9z+cPxXb7OgT1qiAADQ1dvPnR/P+ujFcduK9YXMf1DdStWhG6ii5Nkvc156Xf7UiqhyKvx6L0tJ5bDTW7lFrV6VjoaVG7bH3Ss37f337/z7FfHx/7krcdo6dEM36FNNFU5tU8twUsKj37eXO46JUpFikXcOJa/VyrLtq3aM9d4SJb8Yps5rkOWt12V95Zo9rZYuvWv1tO+KPmcsmZLUnroLZ/RQG9lPxX2RZaGTKpybi1CFLvb6MRl+Hq04p26JNU0JxFzHRKn3Jh8KkigAAHQ12dLhq3OX9vT7pAqZOjxX1q27gqzhDmb18l9I3SslksLvtE51K4e9eHD9tmmfffma5G6LmivSmjdbXpspjwrkQY+JUoUEcbcI/vqr8+L1X7wu1+6u8j4ysuy3dpPmvSsajcaAxpup7nmm6mMA9NQ11YC682qnufK4iK07dTWmr++gu/OqdhkqQ9J1N61BbM0ilzFZlvppQbm3a9ppGdbpy2m2ZcfunpZX98TVMJBEAQCgFC29eXkuyEWnCoYqbeKvXbc0IvKr1Chj3bpVdHWKKem3HVuiVGnnZVBU2CrDpis/hdK5DO8en4jL7l4d85aui6WP9N7tWtEtJHaNT8SrPnd1X/PYL4d+zbKMuVCEVN0mDTCwKuRQpo3H0/R36sRHSy+mWVoG9SbtMgZxTs1aAZy4Tfsol/muYfOYF53n3EuCLU+dwvv8FfcVMt9amGyJkkd3XlP+3VzWk7bTST9JbmXb7M6HNvYZFUWQRAEAoHDdmstX9VmsqnG1k7Wrm0F02ZK03G/f9EBs2Lqr53lWocK4H1m78ypK0Xs/S8VcltUvPu6CF9Bp2T3+rgINUaYdmEVUZjZXpv/haVfFfas3N823/x137X2PxB0P9ld5lUcSpVkZ5bFq174qtkRpPr/lucuTrg/NZWDxmnzGfip6WLqp65HHW/T9zKG07rzyW+w0S9duifnLHs1lXr20C6qzXKOfMrNuSZQ5967pOstXfDYhmV/vTT4UJFEAABgZVahnrJIyK4t3jI/33uXRoLo7KWg5mbvz8uTcornc2DL1k9db73ev3BQf/N7tycvosWSMZxk3pM20eSdR8irldT5Weq2AL/Ia9/ovXr/37zy788pT2vEe+omlbbdkOS5j3zxSdOfV5vM8u0LKMqci71de8h9Xxp+ecV2GWPILpvYtUR5TRFeYtyxfv/fvLNeiEy9Ivp7tmxdlk0QBAKBw3cZE0T1PPjq3RClnGxez1MFkUYraZEnP7J0qXh0erZrPJ1XdNgMfWH6wi0s0rdK0zXRFVNjmJof559GqrCVRmNdYP1nGe6nYgVWFlihTN8ldD2dvsdRy35Nimn3T9phE6tTFZ9GbdMqKdHs7P4103cwlT5Tn6uY1sPwgrhOFdatZ0HwHZbKcFNGd1+pNXQaWb7PxvnHD8nikaVD66b+r+1avP0kUAABK0fz2l8eC0TNjbKzeXRf1IamSteOYKAXFUaXNmCWW1nqE5rEjqnMmGcmB5Tu2ptqnn/rwjm+TV2T3596dV65zS7ecimzKvbK0EGo2qHNC2uMv1XQJkxRdd1rEdureEiX7MtP8ou00uXbn1Xzd6Sxta6AyNBfHrJeQug9yPhl+HknvrNui0/S7O1wg673Fh4MkCgAAhes6JsqAngyyPivV7Rkx68Dyg3irrVNXH3lVNBe1Hv08W7fG1Bpf8sDyaedFEZujKpu41zBy70GqB1PP8+3fCO99YxffEGX6En5w64OZYtkv54RWbi1RsgxkXpHjYdJEBVqidNLL8Vep83oBoRQyJko/rdhSrOT2XeP5t3qqwLl5Up6hrO9jXLsqmNzLRe+e5PEI2+t0nFTplDGqJFEAACBHZb4QnnVg+bqqxFv3ff12+q/LWKfCK6RzWMDc+9ZOn2+b7rzK6PaoMqpwTKQMoa/tm/bHOe7Dv//mrZmm32+/HLrz6nsOw6Xnlih5tj7osFd6GhOlzedJ14fiW6Lkb+pa9JqbaG31sefv+1Zvjs9cuig2bU+oyG+znImJzt3Cbds5Hr85+6dxwqfndI+p6xT7VLnr2oqFM02R8U3OO5fuvDo2kMzaSiW/eZE/SRQAAArX7ca/qg8GvUTV6/PYhm39v9WXNd5BPEC327djY2OV3e9Fy9wSpbhQCpVl/7ZLIr3hrBumz7c5cZI5qsGoQE6jUpr3WT9vpNch8Zd7S5RSSnn3ZQ4yqomJAS6sjU5lI21LlF5LRhFlYNBjSyUlQ7LPY8//v/TUOfHpS++Nk35y1/Rp2sz7ooUr4wUnXxZbduxO/P62B9bHrvFGLF6zJXUcaTQn2Kb+biDXiaaF1u26NIh4++nOa7JM59l6pFOirepJr1EgiQIAQCkadagJHZCTL7wrnvWRi+OiO1YWtoykioUyN3s/3XlVoeuibFoDTkoYdHyQH/HjY6qWjtKK6Norhw0+8DFRevxdnqYW4XaboK/BoTu+pdu/POaR+5goJbSwqlplXa9dLA1sNXppidKuq8sqHMw5mLoeRfTINn/ZukzTr9m0Iy65c1Xidwsf2ph6PlkSUM3boWrjiLTuo+oVvEJbogzo7JC1K90qJIxpTxIFAIDCVWdgzarE0eqLcxZHRMTHfnxnfzOq1vN5RBTzEFy/SqYpY6IkTDHqA8tn0VwBceOSR2L7rvESo8lXr/u6CsdEp/N8lkrHW5avi2M/ekl8a97y6fMpuBIyy/zbTZrHQMVZBq4eBb1259VJnnNMu8dbika7JErCZ1UeWP6uhzfGnQkJiKnng14TCK1dNqZoIdXjqmS6/8qwjObt4FiujjyOqcl5dO5Kt/XLDdt2xa7xDkmUTi1RMkU33ZG/8IQ+54AkCgAApRumB8syx+voWBGS8FWZL0XWYZ/3k/zrtH5JL6p3bIhSsbdX0yoq7Ob5fugHC+M959/y2OftWi80MiVa6ri586i479e0lihN2/HSO1fv+7zL0f+ub9wcG7btin/+7u3TvqvDrtkv51qWvI7/brOpcuPQXgeWz/Pc2WlO967aFK/7wty4YfEjseLRrbF0bYouoSq0lfvZTCdfeHe84rNXTzvHTj0f3LT00UzLO//G5fHGs66PzTv3db819WeJ48d0me+gT5VVaolSgctE5eSd9J723ZR//9kXr+s8r56/7O7/vvjI/mZA7F92AAAADL+uY6IM6Lky67NSLxUwVX1GTexSoMRKnH4qtwbVsqnT9unnuTsp0dapC6Ci9lLh40tkmDbL5pxadC5u0z3LpL/6yo1x9aK1Me/El8aTDp6ZYUm9G3hF3WAXl9nVi9bs/btrZX6n7zq+8ZtuHr0uO639ZtT/XdWqJRJ7Hlg+5zja+dp1yyIi4s/OvH7vZ3d99A/jwMft1zJdmmtXGRXdeWynbTvH4/EH7Nf2+/+4+N5M87vjwT2tW866avHez6pSLnu9tpURf/Mip5a/6rQSL0/Rx9vUfX73yk0dp++UaCs7CYeWKAAAVECV3sjsV5lv+lXx+SpLNwep1fy5P3N3XhXcr2XKer64etHaiIj4ye0Pp5x/xP1rNseFtz9c21ZAVdBuP/WzRSu1N9oEk0dLlKnj/gz6ulK1ct/rmChlWr9tZ8fvyx6jpuhd3K1V7nfnP5hqPhu3N7dEaQ163dade1vArN64PRqNxkCul63L6DYoyr4/q1YJXvV77yLjmzzH5dISpY9vp+rU6q7frVGx4ldLWqIAAEAbg37eKLKiLOnhqezuvLIu/r7Vm+PNX7kxfvMXD22dV0HrkddbmlPjS6pc6jyufD2efBuNRmxL6DZrfKIR7/vWrXH0Lx4Sf/M7/6un+TZvs3b7u1s5yFKB9fv/OSciIs5+6/Pid3/1sNS/SxtL3qrQTUvargz7qUgsfkyU9stNu3775bwz8jr+8952g0y09LqoXEPMOLOk60dz0Wg3tzxbB6QNuYh92W0tdo5nH0F7apirN+2IF33y8vjYnxwd7/zGzRER8SfHHpF5vpnjyHBMNu/PqfXjg2oJsnXn7jjwgP06XieqcA0ZpMld0c96T84jbQvJNHbXMGE8SrREAQCgcN0eFL0dtUe/D7FZN+MgNnvbt9F7WPi/XnB7PLh+W1y0cGXP8WzbOR6fvWxR3L1y+kC4WfRTppP2cxktUfKuM/nH/14Qz/y3n+7992TF3JX3rI4f3vZQnPSTu9vH0iGYqXUKvW6OXuombluxvselDVYVumWZGkGnhESvyrpWZCk7M6Z0zVfHVhRTI165YXucdum9sXrT9lLiqeM27PV6nvS7ohPpeRxXWdY3z3P4I1t2xqcv2ddV2PdvfajHuaeXZXu1JM5KOIHd9fCmeOa//TTe961bB77sKstzV3Q6PrMuptO5rmotBEeRJAoAAIVLesBotPm7SJnrNHoIrMzKzPnL1rX9rmqtGRqRvduNnbuzv7k61WmX3hunXnJv/OFpV/c9r14ld+fVfvrCBmhPMc2u8Yk497qlcd/qzV2n/e7NDyTOf/OO3dMnfszChzbEo1t2dnz7cmrLhV4rEjoO/tpInq42dRbl51BSV6J226a9rkrzOS7v/ZbUeqbdObW5JcpHfrQwjpn903hg3dZMy2stj5l+2of22+/NX7kxTrt0UfzN1+YPKpgWvbdeKu8A7laOs5zHeu75ssTzwjC3bMiyO5o3Qxm5wEvv2jNm2PdvfWhamaxC8r0sk+fvPLZAni1ROp3r+u7Oq8/fozsvAGDENBqN2LRjdxzy+APKDoVhVfIz6fqtO+OJBz1u2ueJz2UDqJ3r+HCZVzc1GeZzSx8tCy664+FYuWF7vOVFR/Y8j4h9rU52j0/E7B8tjBc8/edz6Ze72cREI358+8Px7Kc+MZ76cwf1PJ9zr1sWH/3xnRERsfSTr8wrvIiIuHHJo/H6L17XdbqplQq9VkQNsj/6URxYvlMMeQz6vue35VQDZSk7++23b0ucfe3SiIj4wpz74+N/8ps9Lrunn03Tz2zuWbVnMORba9IyqwiZt1/CAdEyyHj6n/Ws8/U33XRpTRu0PKcVaT32kgNdlCLJPyltt3x5ae2KsjHlu4GGMtyZrV71sU0md2enwyfrSx+dXijp+zitzVsh1aUlCgAwUv7pOwvimNkXx7ylj5YdCk00Ud8jj7cCd40nb8u/+sqN0z6bnPKsqxfHKz97dazf2joQ7r2rNsUVd6/uO6ZEjXKe53b30A/7pHd8/eaY/aM7477VmzL9bupqTj6zf//Wh+Lr1y+Pvzvvlo4VO70cH9+9+YF4z/m3xItPuSLzb5vdvLx966auJisY2oR/2d2r0s1m2u/bdBHXZT5pd30dz0aDrhjsJ4Z+jvuyenXK1H1Pwnk8z3PdIHZ11Vou9qrMW4vEctDjvOq4N4po5TDRf2PUXGRprdh8vJbdK135V4ne5X3eW7tpZ7z8M1fHudct7Xkek+fJtMnKNCYmGnHeDct7joliSaIAACPlO/P3dDdz+uX3lRzJaKnKmCiDqHzqZxF5xNfLPD7+P3fFwoc2xhlz7m/5/GWfvire+tV5cfsDG/oPbIqy6hIy9a3ftC2bf7dlx/TB07OY3EdrN+/Y+1mn7rx6cf3i4UkUT76JvGrj9rjw9odTJ0PazWcQBj6w/GAX15ei9kOR2zxp3u2W94U598cNix/pb3ktXWvl1GIvy2zqWGtfsKy7odu1uO38En5Y9Ism3ZJm1yxam+/yMqxP8/liUMm9bvH13p1XuQfW1KLV/O86XUPycNHClXHXwxvj3lXpWzG1l1/rkfGJRvzrBbf3GQ9FkUQBAKBwdX2rtZe4K/BCeGzYuiv+5ms3dZ1u6sNduzFH+h2EPc2y+3Hvqs2px0tp11Knm607943rcdDj9us6faeyM/nGfnNR6TiwfPfw4n8WPBxz78u3omuqiR5fo+33+J9c7PH/fkX87Tdujm/csCx5OXl1e1SD09XmHbvj335wx97K+iqcdzoNLJ9lvJLOrbJ6CCwHWSs//+zM61v+3V8XZoOXZpmDiGv3+EQsWpWt5V+zMg/lrmOitImujEO5W/G+YUn2pGBeLwY0X3by6Xasu67L6TGOsq8tnc7RVVT1+Cbl2W3teIErXZPNWWmSKAAAUEPtKrTHIuLTl94bF9/ZvaukqQ93RXS/MefeNR2Wn59P/M+dqabL1BKlydad+1qfPG7/GZkqrac+E0/+tHkeMzo8mXV7pl7x6NZ413k3xxvOuiF9UCk1L7rTvuz0237rBCYrsLfv2pMou+Ke3rqY62Xf9xp60UmNz1x6b3ztumV7K+srkURJPbB87wUibTIj78R9nm+QNxqN+NiP74xvz1uR8ge5Lbp23v/t2+KET18VO9okyVdv2t5zcredzTt2d59owIooAi1dUnWZNtWhPW3U8nxOSnmNp5RpmV2/Tx9J69gzI3ww96kCl7hpDj1wz/iaHfdqDy1RqC5JFAAASlfV58qqdskz+4cL44WfvCzWbdk57bvv3vxASzdRmZbfJoB+NsO///SeNvNsZN6+nSY/57pl8eiWnfGRHy2MOx9q33Jmd4+dqm/JsWJtstVJc+uTjm/fd9kDqzf1tr9TaVr0xu27sv200b2/8FTzmbLL2s2v23YquyuVPC1Zu7Xl380J0PIq6lrLcLv9Ucc6oqSQM/WO1TTxVYvWxpevWRIf+O6CVNPnlRDqNp+WZVZkH/3wtofafnf1ojXx25+4LP72G/PbTtPxDfE2Xx794Z8mjp2VdT80n9Mnrx8pxkhPvA4XvT+qXLmfZfyRQcmS2GmetuxzX6f7jCok4jvp1Fq3LKdefG/MuXdNx+Mn6z6XRKk2SRQAAFLL80G7iEqibopoaZHnMtIOzPzVuUtj1cYd8WdnXjftu5N+cnfcszJd9yeNRmuLlrZLL2D3FFEZ8qHv3xFnX7s0XvHZq9tOszuHligR+Y99s19fXRhNn6CXYyqp4jBbHFNj2KPf5MXU7i16nV363/VfOIuu7OvUt31ZdTBTY7q+zbgg/ZzvO12DilztqYm8Huaw969VG7Zn+2UJ+7MOXXB+cc7iiIj46cLurS6z2rR9etI88/nvsR+cdum98Rsf/mn8dOHK1u97jq4eOl0js6x7UWOi7B6faHs+KSqpVLVEfgXzEm3tl/fAcTnYtGN3vPkrN3YslVnLbOrWlj2UpYoVv1qSRAEAgBwN8qH03lWb45GEVicrU1bSNWJPy5VJecS+eM3meP0Xr4urF3Xu+qnR9L95ufPh7mO37O55TJR9SZRGo7/I93XntW+DF10/0O2B+2vXLY1f/7eL2lZ8R/ReAdTvXp663F4ruHqKv6K1Dr28YT9o/99Xb4pz5i6NHbvHc2vl0Om3ExONOPf6ZXFvH2NotJ13n9u0+efbdo23nzDpt1P+3eupoiLFIjdp9kkVkkGnXbooIiJOvOCOVNMnv4hR7Hp0nXsPNwd53Qulab2TxdjYnlaVz/vEpfHu829JXmaWmLpOu2/iqWV20GmB6qUh0tu/gkmUSZ2vx9muyWnvUefcuyauundNXHTHyu4T742l/PNh3UmiAACQWtqWElkN6r6+Tm/dpTW1hUQWjUa0jJ2Sx/599/m3xI1LHo03ffnGjtP9+LaHct/vaaLvtTJ0y86M3Xl1aGm1rzuv6Z91mVXPuq32v/1gYewab8R7v3nrlGXv+2FSNxPjE424f83mWLWxQ+KuzxWYlkRpt5guy+m071vq6XKqqBuk5mO3tJYoCZ99+IcL44wr72/5rJ/wOv32W/NWxIe+f0e87NNX5X5uSSo7WSqk+kqilNESpQZ1bVV7q3+qfCvi848h73PeVHmdAptbIuYR5liMxY9ueyjWbd0VP17wcOI03bZHljiK3s5Z1O0euLk72Cq2RJnU6Vow3mjEa8+YG+84d37iS09TpT2vLX90a/zVV26Md3y9fXeG5G//sgMAAICqVoUM4oG3+eGrY/cXjUbqJEeWsNO83Zflbd41KcfnOPnCu+PZv/TE1PNNI83m2ZWhJUrz7LbuaGqJEn1WEI1Nn3/HMVF6akHR+s88BuROmsX7vnVr23ELJqfv923wqcvt9bgcpq7Gp3Xn1fR3Fd6+bzZv6aNxxKEH7v13P5XfnX562wPre55vN72UnXbnwl4S33l0RdlrxW/baUouZmmGt8ozxhw6+Ws5Ntsdp+VUdE+PZWxs3/ZLFVJB5aG1FdtgCl338YOa9mO3hEtOrfDyMPU8ctldq+P9J+yMJx70uIF0d9uP/ferbhuATrv1zoc2xi3L10dExOK1m7vPK2UZ6bVVNf2pbikEAKBwWR/Wi3qAHaYm5nlUrk/1jnPnxx+ednXs6jJmxaS027MRjZa3+9rFnumN2fSTxgPrtmWYurs0A4+O9ziw/PaMb493MllZMcjuvNJWAneaburYJBGdB36erIjq9/CeWuneayV8+kTSPp+9/L6486Hu3cSVrbnol3U6bXf4TT3k+jnfl5Ug6iXm3/2PK/f9vinurOeSqevca4vBLG/X1+GaXPmWKD0mf7t15lVEkqWQlih9jfPVPG2+LVEiuicl82yJ0mxad14Db7LY+s8H12+L131h+th6VbTfjLGYmGjE/GWPlh3KdB2789r35b2ruidR0t+rZS+F1T5j1oMkCgDACKt4HUTpeqmwy/pQnGYfXLRwZdyzalPMX7YuczydFz6li4QcnufLLFNpNn2vA8v3W8HY/JvJTd4cb8fuvDK86dpO+sFK2/87qTuvQZi62F6j6LVsvuKzVw9sWWlNLS3NlYKlJVHanECmj2nT+zLKWrekxXYLZfOOfV0ANse9NUXXgFV6e72qUo2JUqFtNzWUrN15bd81HkvWbsk1pk5aW0p2n37q/VJeibgqdYfVi2WP7NtnVUz8LVrdvWI/DwcesF9fv99vbCzOu3F5/OkZ1Uv6bNrR/pyedZenfe7o9V6W/kiiAACQWmFjoqSY5rvzH4hLmsbv6MUg3vkrchlFzLu1JUryErI9qqWfOv8xUbpvoV67QJhaIZTlWGg0WhMBkz9tnsOMDk9mzUuee//auPKe1amX3RxDuuk6deeVsSTs7c6rPxMT6Srnui1n6nzqbOqatLREqdj7po3IryK0rDXr91zV/PNtO7O1hpte+V78VqhWCUpWZG82j2xJ1y1lJ1PHiZq639qF3+7S8orPXh2/+x9XFpJIKGJT3vxYF0b9aj5t5xFnHrexaffBHQ9uiH/53u17/132JajMDrve9MKn9fX7/WaMxX/PfyCnaAYn6y7/5+8sSDVdWS+1jDpJFAAASpGlUu3B9dviH/77tnjb124qNKapBjImStPf3R5w805ibd6xO9WYKMXJ+ope5+nTbJ48HjzTzGHqNM1voCZ155VmTJSJiUa84Us3xFvOnhf3rd7U8/Kz/K51HqlmMX2efW7yvFoy7J5oxLYU41HUcmD5pr9LG1i+zTrfuKS1+5W+wsuhHPe02BzH9cncNeCUdR7IAMs1qJ9Lk0zqdb+99NSr9v49Wa6znhde9blr4ge3PtgUS+/nlkajEYvX5NwKJefWTkXdLzWf//NIIKY5/rovpimmDmVs+os/5R5YZQ4s3+++23+/qo/akizram9JOWZWL/eyFWwIVTuSKAAAI6wqY6J0s27LzlKWO2jdkiRp91favfSNG5a3vNnXbv5Zux0pS5ok0+4pAzQ0Go22D6N5Vjg0b5fJVifN89+vUxLlsf9vHpPkwfXbp32/Zzm9tdJo9/vmf/baFUm/FdCpu/PqEt9X5y6NF59yeapEStVN686rZUyUcg7CtMdLP13alJUgynOTpurOq6WStlWasZ+SVLEroX7UYX3+8+J7237X7jjdL6FZYtFr2u0cnX/1dZabiqY/c9gQ7//2bbGlQ/dLexbZeUFp45h5QOu+rGrjgd3jE6UmWNLYb2ys8jEmKaplaE9JlALiGDWSKAAAI6w6dRCDCWQgg3gWmJgayzh9uuW3zj9xmixddPW47Dyk2fRTnzvf+tV58cKTL+tasd5vd0StFX6PtURpirjTy+V7u8VKWO6/fHdBy8Cw+6ZtnThthWPHgeUzPrQnxX3udUszzWNPTFMTO70XnLWbd3YdVyCPY2zw59amMVEGvei9EaQb16c61530kkLOlFxumkPWvuynLqfXliiPbtmZuku7NOf8sruNm0jRK1ourcr6n0VETI/lxwseTpzugP0GX1OctJ1aW0qmmEeO8TQrotuiL1+zpOP3eQ0sP3P/1nFAyk78tTtHb9zePbFbtoG0wKuRssvSqJJEAQBGUh3fZqqCwsZE6fbAWqNnhULHRBlL9yZjr9tr0MdF1jC7Td9pXJF2rrxnTazetCPm3r82+49TajSmJKsSBpZP1Z1Xwo795rwV6WJIOQzD9H779/2795Yo+/xHhzez08bU7hhIG91Bj+tvgNsqammJkm3IjRyDSDdZP0mqsiru+06s9fHzqcvu1Gqtm5VTxuloXU7y31U1qErEyXNz/126NeLyu/eNZ/XJC++O5Y9snTbd4/ZPaIlSwv4Ya/P3oLV055XT8d/pONiznM7S7o+p+zJN4q9I7U4dU1voFqHfMiyJ0srA8uWQRAEARlIdKghGSdcH1rIqzga8jO6PiGOF9gPf7i3Fb9+UfjDPLJWNebeq2bW7j8rZ7oWw3T/Szb/pN5N1AS2VVJ1aoiR9lrHbrjzGRDnpJ3enmsfeeT22zOZYe6n4nFpX0G/labff51Eqe63n7jkB2jyPks6XaVe5W91Pp22XZ73RRXc8HC89dU7c9fDGrtMO+p6hJaEx5bsZfVQmpu4Ssgb3SAPPa/WwvObtvXH77vjq3KUt36/eNL0y/4D9BlNN19JlXA77O9u1P/18L24aV2RQSYi81mXqmHNTrz2DTgu0O/4nJoqPpd8iNipjoqSVtlVhs7K6+hwmkigAACOsLmOi1Om+v5/WOkk/bd7mY2PpHkTzboly24r1qedR5q66Z1XnwdY7ybsbsqnTND/vTlYFZB3boFPF6r5pkr9JShwkTVrEsdYyzx7mPy32AipP61650HzeqfoLqoNI8qTZn+/4+s1x3+rN8c5v3Jxifgmf9djNYS/XiOafzBiLuP2BDfHI5h3Z51PLashk4zU/ZiOSr7lJSZRej5m0myhp/lVssV3EueNPz5ibMAB8PnFM3YRlF9l2x38duobaMyZKBQtlF0VtWS1RyiGJAgAwwqry3JRX/9O9Lbv93AexfbJ0ZXb2tUtjw7ZdhcVSv8fT/HSrdO2n8qYRjZZKir31ACk3+Kbtu2Pn7olUFR3tW6KkW1angeV71XdLlClvH7dPIGWPp5/5FCFtHdHU6VpaopQ2sHyHLulyeuu9iFXblGI8gDy6cur9t63/Xrd1V7z69GvieZ+4dO9nW3bsjmWPdB7rJyJDS5QsAZYk1fkwhxUZ9HUxcUyUnHZIo9GIe1ZumjbOSLftVOaYKC3LKGAh85eti7d97abW5RQUR1WTFUWMO5O32nbnVdA+r8M+G0aSKAAApFbYmCglVdms2bQjfvuky+Lkn9yV2zzz3kTND90/uu2heN+3bm35ft7SR6f9psxHq2yDLVdHlufRRvTSimvf35MtUJpbonTbbt+7+YFsrWWmLT/dm8Z57pOkefXy3J/nwPJ7ft/XzyuzjLbLLmm5aQ+JfuIrJkFUfGV81p83T9+u4rX5WHrRpy6P4//9yrh7ZeeuyVLvo4pW9jYbeBdrPfwmTRedRZp6jj/98vviD067Kv7f928vdLlFGdQuz+vFnqnbf1rrqQHnBdp259Uo/i683+N1/xkzavmiT1HbVRKlHJIoAACUormS5rYV6+Pt597U9k3avCp0pj5AfnHO/bFm04744lWL20WZy3I7aX50TepqYWoEVy9qHQD9/d++LbdY8ugepah+0YuXLZissScmMTL8fv22Xa3zaLP8dnGlfd7uOl5IDzuttTevHlqipB5YPt28q1z30Msx8dazb4zv3rxv3KKy3nZOP95G5/iyjg/Ui+b+5NOUh8RJsiRemw/dAvbP+q17WihecfeazhN22rYZ40qa/KyrF8e35i3f++/NO7q38ulVmkrEPKqGq9CDUK9rMXUfffrSeyMi4vwbV2Q616R5gSbL/G5Zvj79xD0uoy9dWwj31rKul3EsBqEOFfIz1F636KU7r2rdc9eTYggAMMKqMibKP3/39vjpwlXx9nPnDzSGItYma31L8+ok7Y+tO8b7iieLQfexXKW3nZNCaU5qtVaCppjflNI10WU/dzM+ka46sN1UqQeWnzLZ9BYtqWbTMm3zb3priTL13322ROmyJetW8XrFPWviwjtW7vugpMOq4ypnPH7azqbDj8cytOz6q6/cuPfvdN1CVedc1Y+59z2Sarpe1/bj/3NX/PN397Ry+PZNK+LoD/80zr52SY9z62xQycIqjCOT16q2S4Z0u/7l7RM9t/4dzD7/zGWL4o4HN/Q9n6nXrm/csDwuvP3hvuebt0Hc+vV7Xd1/xoxKJDSroqoJuWEniQIAMMKqVi+04tGtiZ8XFWae699oNKLRaOTe5dkffuaqHoLpbVm7x3NoidL3HMqR9Xk0y25uNFor/PYOLN/0NNbtTdBGoxGNprFB2lVItG+JMv2LNAPL5zGme6PtP9KZ1p1XDzG0zG+i+zRlyeP0UcljsGm9+qn8zuucfc19+1r0pamM6nexzb9P91Z/b2+6d/PeKd1Btl9+/8v6wHcWRETER350Z/8zS5AqmZ3zNT6rsgfCnrr4duuQR+J4EN2yDuqe9SvXLolXfe6avuaxe3wiPvi91m7TLrjlwfjbb9zc13z70a48TjQahW/bfue/34yxSiQ0sypqu+bRcpzs9i87AAAA6qPoCoF2jwTpKkvyT2Ck1Wg04i1nz4utO3fn/sbywxu2Z/7NzvHeaolz6dIhS0uFrLMu8Jmxe+uE9NMm/r7Lm77dKpYnGq3L7dpne5dkSNvfddsOjUbcsnx9HLDfjDj6Fw9NNa9+B5afdkwlzGLxms3p59e1r5bUs2o/i4LrNzpVJpXXnVe6fri6DmLdYd2KWLNeK+MH17NQGfuz+hV0deiCqJvEcakSy1pv65r6vJ80XQXrq6uyx9Ns16VtuqctU7tdWodjaf+aDixf1Pm7l31W1viTw0RLFAAAaqBz5fEVd6+O533isphzb+f+4DvV8X3wewvie03jCrRbVpIduydizr1rYt7SdbHusb7p62h3Dq/otyQb8hqddQCy1jvn0RXeWMvb+Z1/Pz7RiDPm3J9toS3Lz2e6jdt3x2s+Pzde9blrenyIz27qYpIqAhav2dLTOu4en4h3fmN+fPmaYrocyiqP/EfVX1DtJ7wi1i1d0qm/BWceb6Tlt30tuhSD6P4sVTdseSyoj7rbvKp9i+jOq3syM6NBdAlVkYOh5YWGKt3IdNFpYPmqu3rR2lizeUfZYWRW1KYddPe77CGJAgAwwqoyJkr35Xb+/q1fnRdrN++INzf1c59qvk0Pv+ffuKLnQdr76+N/399ld/2RS3deLa0O+p7dwGSpROhlfzdvi8ly11qh1Xmmi1Zvii/OWZw6tqlzS1q/pOI2fbrWf69sahm1q0uLp+QxUbJvvKndLSWVq6zdq026+M5V8ZPbV8bdKzft+z5rgAnKPJSrXiHWLb7ODVra/7bX61Oa81S/57Ii98hD67flPs+KF6GIGPz1pZdt0u0nVXm3vmucKQJ95zdujpMv7HWsk3Sy7oJf/vmD4k0veFr+ceSV1MpnNqndtmJ94ucTE9VPBu0cn4gla6vXuqeborZqL2Oi1OG8XnWSKADASDI44R51vKEeZMhpl9XPw2eV+jU+6HG99/a7e3wi7lm5qWVLVLkyN03PEM3niayrMr0HqoQkRtPf3bbVui2tLZzaTd73wPJdvn/FZ69OPc/G3v/v3JKsmx/e9lDrfFOO79JOc9xbd45nD6jiyjjsNm7fFRfc8mDb7xtt/zFdp0OzrJYoacYP6jyDDNMm/LTTNjnuk5fv/bufe5tGm797mldFWiXk8eLHoG8Xk87hRW/OPLbTTcvWpUr09yNrmH/6W0+JVz/riIHGsWXH7spW9rdrKT2I+9CqtCIaFnXogm0YSaIAACPJvXxvShtzJKf5TO1rP69y0M98/vbr8/MJIgfP/qUn9vzb93zzlviD065qqZSucm9e+83oXhbaJir6bIkyqflw6jaMzdSK3nYVv+0Hlk83bbZkRL7TtfONG5anml/6pOc+Vetmva4J/n/M0IqvqORqr9endJ159dmdV8bf99t6q191uEfqpeJ3fKIRqzZmG2dsslj1skm6lcg1m3akqgzNqwK6XTw12N0R0eM+GPA59Xf/48r43f+4Mm5bsWGwC+7DICrk61LG6qKX7rzsg/5JogAAUBlpB5Cvmn4iunrR2r1/V6X+tJdt/JPbV077rGtLhRL35YwpNSvdB5bvL9bmrhcmV7s5qbd4beeB0acnUZKnaxdlr9u608+q9CZkloqy5m05tRxEdD8Pbd813rUrs16l3U2d1reMSveL71yVetp+ik2nctzr+TPNsbFlx/C1WCrD5h272363aXu28cTSdGczdYq3fe2meP5Jl8XVi9ZUIlH0N+fOjzeedX3LZ2XElbTM5nPM1BdQypL1OjY2Vsz2bGm1NWX+qzftGbfjkhTnxLK7cN2nUYnjYRgVtV3HcxjDkOxKTaJcddVV8epXvzqOOOKIGBsbi+9///st3zcajZg9e3YcccQRceCBB8ZLXvKSWLhwYTnBAgC5eGj9tvjbr8+PG5c8WnYoxPCMiVK23N4SrcDz9LfnrYhjP3pJ3Lx8XeHLaq2IGOxO3j9FS5R2+yOvvsOb53/L8vUdp51eX5gthiLyHd32WdKYKEVKP7D8vgmzHnPbdo7Hr33oojj+lCuy/XCAKn++7FJ2mysW123ZGd+4YVls2Langr1TOe51tdMcG396xtxYO2VQ4yzngXb75JqmJHrW3xap/5Y3rSYmGnHVvWvi6A//ND7yo+l1OtcsWhu/OfvimP3D9PU9vWyXy+9eHRERX712afYfF+T6xd3vh4soAt3muX2XStp20tyvVOF+Lq2qXzPqrKixZnIYwpAelJpE2bJlSzzrWc+K008/PfH7U045JU499dQ4/fTTY968eTFr1qw44YQTYtOmTYnTAwDV9w/fvi0uvGNlvP6L1w182aubunCo08NNkery4FRe8ibdcmuyGbtqRMQHvrsgNmzbFe8+75a+51fpMVGmJFGyJhmyvJnbaCRviyxdSaVuifLYdFPLbqeB5XttUdL9d4/FMoAjpNeB5ZPeBO4U750Pb4yIiIc2ZOsSaJCqeNw1l8csxe1vzr0pTrzgjvj7b2Y7H/U6Rk4nkxXwvWi3iL/88g1tftDzonKRdxH6g9Ouir/6yo0REXF2QgLjUxfdHRERX507/bt20nTnlcd6TJ7re5pXXveaPa9H6w/bnye7JTZ7XX6+etkHRdw/Vu8M259BrE8FL0sDUaWWKKO6D/LU+8iROXj5y18eL3/5yxO/azQacdppp8WJJ54Yr33tayMi4pxzzonDDz88zjvvvHj7298+yFABgJwsf3Rracv+1wtu3/u3G8neVGFMlH523dTw83q4Vp6SdR0TJeN2y7MyfvqYKOlaVUz9O8mqjdunJRiSK47TH09Tf992TJS2v28f9MULp3fFNj7RiM9etiiuvb/9m/JpxyQYWEuUlOWjeVsO45gozVth9cbt8ZnLFsWbXvi0+LVZh/Q/8xx0O9aaN8G8pXtaxF15z5o9vy2gum8gg6D3EXej0SikJdn05eQ5r9aZLVrdubvCXo7DfpKFvfxyEMngiOp051VFvezzQlatJtsrrYmJQZXu0ZOm28Fe1OWYHTaVHRNlyZIlsXLlynjZy16297OZM2fG8ccfH3Pnzm37ux07dsTGjRtb/gMAqqPMMRDKTOBUVVXeLuymrGKTerE5xVf2/sj7+Ow6JkqJj+3TuvPKab7XLFobzz/psvjrc/7/9u46PIqzawP4vRv3QCAJhOAOwd3d614KpaVClbpQWqjT9q28dfta2r5tqVNDSnF3d3cIniDx+f4Iu5mdHd+ZlXD/rqtXye7Yzs7MzjzPc85Z7vG6eF8UFpfgnX+3GUqZ5h1ZojTdxf8rvC73Wm6ed52C39ccxH9nbldN6RJMKbmlkUGu9E9yxPvSTE0Uo06eK8Dvaw4iv0i7roYV6xYvY8SXy/Ht0n24/L2Fvi9YhaGaNFqNSqr1XvSvx25GvitfvldB0F9EOK+wGNM3HsE5ldojutapc7ssY+LHT98u8X0jffldti4Qxd4BH0F0WlnOnpoo5WuP+SUSpZztM72C6jfrEv0OrBS0nShHjpSOhkpLS/N4PS0tzf2enFdffRVJSUnu/zIzM23dTiIiIjJGfPv2yI9rMGHqloBsR6Abq0NVMBR192UTxF/72fwifLV4r8/bA1iXPidYirdaxcjD48HTF3Q1MlvFq7C8ZoePJ6VryBcLdwMAth0tG3ktSIq2frt0H975dzs+nbdL9/ZKI1uMnotGj1E9nc5akShlNVH8f934cM4Oxff8HYny4ewdGD1pDR7+YY1ly1T7DRPvb1f6sYJie3u8wjR+VMVHgA99KJfM6FtxY9eFwmIsVogIk55b7/y7HXd/s9Kd/mzdgdNe82Tn5mH9gTPq6xcELN11Ald9uBAbDqpPawUz56Ev15VguJdRIrdltnQEiBaqtfxguTMxlc4rQA3HofSc4Y8UkEF8ytkqGNNrknlB24niIk0ZIQiCahqJp59+GmfOnHH/t3//frs3kYiIiAwQ30v+uuogPp67M+DbcSkL5H6QHRmv8LBrx0PwD8u17xN1F6r2cVuCkSUNAAbSeXV5bTYue28BgNLi3XYQj4D3TuflPb2VjSC+Ntp5d6IAU9cfllmR/Px21Hx58ud1qu+7VhmI68wnc5U7qMTXE/maKNb6fEFpx9qU9cqDAa0UiOuRtMaQlPjdPSfO+bCm0Lza+rLVH87ZibUKnR5K5/W/m7Ox/sAZXP6+dwRSu5dn4rL3F2h2jtzw6RKs3ndauW6LCqOfVy4iTIuea5oVkReuLSsP942h1LAvZ8exszhfoD/Kyq4UtHqOhVAaFPPKlM22H9/l4PQJeeXhGhZoQduJkp6eDgBeUSfZ2dle0SliUVFRSExM9PiPiIiIgodcY7hd+WK91s2bR58FqiaKmFUdKoUWjswO5lGtZlnxVRsdgbft6Fks230SjZ6bZkuUmjhywisSRWtmnaN2lY6FdRojvw2sHkDpvr3n21Xe08l8kk/m7sS1HymnRJaj5/tfsEO5XorLoh3H8eaMbYbWbZreTk+PSJTAX9OsFoiRr1qRBOIt+lojAlDtd0bvR/soQAM0lNj1lah914t3qZ+fS3ad0LWO0+eVU+MpOZqTZ2h6M5Eo0o5lOZsPl4/07kaPnw9mK0fiyS+/bAWLdh7HI5KouWC5TB7LzUfft+YFbP2CIODQ6Qv60t3pmCpIdis2HMxBjkoKTCuUw9tkXRiJUr4EbSdKrVq1kJ6ejhkzZrhfKygowNy5c9GpU6cAbhkRERH5Qu5eUm9x4lBxNCcPA96Zh/8tsSZVlJ389WDs81dswyFiZRofq/oBA91QYfXDntbS5N5/+e9NACAbpebr5hWrRKJY9dmVlvLoT2t9Wq50+5QaEN2Tid5+deoW2ZoKruPNrnQngiDg5s+Nj2K3m3hXTtvgn+gQsU2HcjBz81Hblh+In1StdF5GmE3nJZ7v+2X7rNoc5W2xc9k6F6523dJahlqKN1+PoS6vzTY0vV2j9p/9faMty7WTFYMy3pi+FTl5+hvFxWu8+bOl+HX1QY/3Xd/P8bP5Pm+brw6evmBsBgtP1Of/3IROE2bh26XBf39vlP3PYuXrWU8vuzpRytmjc8gIaCfK2bNnsWbNGqxZswZAaTH5NWvWYN++fXA4HHjooYfwyiuv4LfffsOGDRswYsQIxMbG4uabbw7kZhMREZEP5O759IwmtHrdau09RT5GKLw+bSu2HMnF2MkbfFpOMLI74sLuoqfiEc5WjkC3qhE60KMSP5xd1nFhRaOW5sOjzNuFxfYdY2qdKIaKRKt832oF3H0h3ZdK+9bIqvQ2SIcEI4XNRR/8l1UHvN63+zo36N35GPnVCtOj5IuKS7DxkPK8gWhc0UrnZZVAF8Y9da4Ab/6zFXuOG01JZs92q90/aa2xSHKtFe9btf381j9bcfp8ga7t0yvQAwjU+BKBa1X0rpmjJ7+wxOtaIP5dF7+l95qxYs9JE1sSWFaeeRMX7QFQGrnhXr7CCkIpnZc/lNhblitoBVNhefJdQDtRVqxYgZYtW6Jly5YAgEceeQQtW7bEc889BwB44okn8NBDD+Hee+9FmzZtcPDgQfzzzz9ISEgI5GYTERGRD2QjUQJwh6n00PPy35vQeNx07DbcQFImr9B/xbFDhbW1JaxZjp7GDb2rKi8jwrZnn9WeyAAzI/A22Zh+pUitE0VjXun7SkeP3HKsODykl8lz+dZdZ+yuB+Mvus9XW7cC+HXVASzZpd3YaLwhvtQzv23A3hPnFd8PRPoQ6fkkpXeT9p04r3odCvS19slf1uG9WTvc9Zv0smu75SLM9K7TbErLd2ftwJjf1vvUoTXud89BJlrHj9WMfB/+3LJT5wrw+fzdXq+b6djt/NoszNyS7f67sLjEI/roR1FdOK3v0vV7UB7TH9pFz67i7iz/7BqUwWMnMALaidKjRw8IguD138SJEwGUPtiOHz8ehw8fRl5eHubOnYumTZsGcpOJiIjIZ943k2qNAP722fzdKCgqwbszt5tfSDm+sQ1UTRQ7nkH0fBK9Dz+Bbtizg18Ky/t5VLlHJIr0Axr4Eo1+31Y0akuPRaWaMcFUnyd4tsST1vfhy3ZvPpyDR35ci1mixkslZhqOBUHADyv2a0/oZ1Y1rvZ5e67q+4E+ppZfHImfm19k6Fwzut16py9WidzTur6qRf1pfbRVe0+rT6DhK0ldHDYIlnrg+9XYejTXkmUVFHl2kklTzM7UcY2SCsVOlED9JAZDDUEj7O58D3QUYaAEUwROMN0fhqqgrYlCRERE5VMgI1HEN48h9mwT8ny9b7cjXZaeY8CKvPSGlLMDU+vU1rPbiksELNp5HLkG8rsrKVJ5mtWMRNH5Fcs9pFrx4CrNV65Uz8DMmuTmsaIByJ/P6w4Y2M8ak63bf8b0dhw4pT9ff0SY8cfx6Ru1a7gEprC8RiSKzuVIG369lqOyIH98arPrUDs2zUaEAPZFomjNa/VPlb8b5wUYuK9w1Y4ycV4Z/VQLdhyXfd2KY3vdAZXrms4V+DtiyAp2N94XCwJu+nQJnvplned6Q6zB2u7NDaLxcn5l1+/xop0nDM9TIyXOhi25tLAThYiIiGx1+nwB+r89Dx/O2QFA/jlNrWEzFNnZGDBv2zHc8dVyHM3Js20daqx8KDTyYGvHM4ie70lvB59Vm+dAacPWkl0nykVaOK3vWM9++3Lhbtz82VLc/JnvBcrVvs8SA0/4Rh+KrWg80HuZdG1aMIz6DNZGJK3v7+1/t9m2bvE+CQ8z/luxev9pHeswvFifabWtWnUsqB3X/mjetWPfztt2zPS8qh3DGhur2omisV6r97W403bFnpN+uXYE6eVJlt3bqmfxoyetxvN/brR3Q2xg975bufcUFu86gUnLgy9CMJiE0vlmpWD52Fe1zMDgrCqB3oyQx04UIiIistXHc3dh69FcvD5tKwD5xqtgqoliBTsbcoZ/sQz/bs7GM7+FVtH64KyJoj2NdPS/EiMN8Fr+M30rbvx0CUZPWm3ZMgPFit3y88rSwt/rD57x+bsXF1KWNsbKRmMonM1XfbgIe1RqUkhZcXzoTi0XNI/sylbtO2X5Mg+dzjMQLWTNOs009Bar1OXRQ0+x4kAcAeFaNVEsWk+gG+LE37mRbVGbVO4eSO+xJS0O77kM9Xm90nkZ+DxWpyoSHz7XfrwYf68/bOnyrRD8V1Z1aseU1rGy98R5/L7mkKHfvWBQOT7Kp+/tti+XYf529U7O/KLQH/DiD6Fwb2KHQESGyrmrW204QzCSLNiwE4WIiIhsJU3NIXcvqdYIQPLsiEQ5mpOHKz9YiF9XHVCcxp85notLBJw8VwDAWOPFzZ8twQezd2hOp+ez+JJmxaz/W7AbADB941G/r9tqWg2B/o5UEDdWSlettSm+1MexojNJb4ee2nYYmdbOU33y6oOWL3PMb+t1T+tLo4Z4v8gtRus4EadfCncafxzX870EJJ2XwcaZYI1S0hJsW602CEVrW9XTeanPbfX1Qbq4qRu009b5iy8f1br9FNgjL1gago26ulWGT/PP3noMw/5vmeo0h07rT+FYHnx8SytzM2ocQo/3b2BuuUEuRE8dUsBOFCIiIvIruQdzv9VEEf3bzgbCUC1rMWHqFqzZfxqP/LjW8mWbeYi46dMlaPXiDGw+nIPvl+7TPd+inSfwxvSt8m+KvxsdG5VfqK8TxaoGBodDPcd9qAm2h0e1fWvVdyg32tKKBmPdh4VVkVoWxNRZ+fXr2YdyU8jWqLFge8wS17Ixk85Lj8Ck89KIRJFsVM6FIlPrCXjni8nVCwIU6zo5HA4IgmCq7pN6Oi/1eX1K52VzTZQ5W7Lx8t+brF2JiCAIus+TnLwi/BjgVE32p/NSX4FWpFkwGpxVBeFhTtuvGf9bov/+tDxIiI4wNZ/Wt9C5biV8NryNqWUbcWvHGoam97VzJ1Q7IEkeO1GIiIjIr+RuJf3WaOyn1fglL7sNH+Z8QVmj1ubDOfLrtflhQLz0ZXtOAgB+WnEA00TFlK367EpLEX9GpbokvRqmSuaxZJOwet9paxZkASs+U7A9PHpEohic15dPYsUlTn86ryCisDFmrpFWpvexrj6HceLIy8KiEvxvyV78tlo5+k9Kz74LREeDVmoyqzYpVPuY5247hqzx/+CtGfL1du7/bjWyxv+DjYdKi3/r/ZiqheU1luJLFLDT4bC0YV8aGXquoBifzd8tO22RRRGiRu4lnvhlnanPa0VntD9ofbZQTgMUqEuGnuNF7vgI1YFYWrTuBx0AalWyv/B5hbhIQ9P3aFDZp/WF6m8WyWMnChEREfmXzM1kIGqi2MmfKa+sFBFWdmv4tkJDj1nBskv0BKKs2FtWr0GpE0WaviHYOguChWaKLFvX7b108aht6fvW1cnwfs2K40PvdTKYDsXcfPloAwGl59aXC/boXtaqvadNbYN8yi1Ti9K3Po33xQ3Ar0zdgrGTN+DhH/RH/+m5lgbiENDarmA6Ln3h68d4d+Z22dddnYRfGDgnAN9qohSodUZozGv1T7qRNvqbP19q8dr1MTOAw6pBH6N/WGPJcpRobeX0IEqvZlTNFPsb5l2yLUi1GxlWPptpg+U3wGjHppnaZWLB8nwQLM9hoa58np1EREQUtOQjUfxfd8JOfolEseGeXNyJUlFhpFYwdBDZXRT6QkFpx0leYTEOnZF/IN517JznsqzZpKBixVet9fCo57s0+33L9TmodUTIbevu4+dkplRnVyeK3r5mV6OdsaLX3hPbfap/NGeneiOuRITJ1Fdyu8GqfnszER8fztnp/vfa/aet2RCJQLTZhGml81K5Sq7cewrP/LYep88XaK4n0NfaYGkQc7GtJorG3NYXlte/vGW7T1qyziD7KlVJ6wtaTetapnQvFApqVYrDxNva+mVdXyzc4/73zmNnTS3jm5HtkZYYZdEWWc/seaMnRWAQPGJ40fpt0xLwFJRkKXaiEBERkV/J3UyWsz4U//Si2OCUqAHLaEj9mv2nVUfg6R4N7sdnDaVVuUadfb14j+K8keGet9Hl8RnJis8UyN3y1C/rcMMniz0aGYtU0nnJbetBUcSR3v0hXxNF37xq9DbeWtXYZvdlbMVeYw2hVtYPsbMhPDdPvdbHxEV7fFq+nlG0Sp/v8/m7UGJT5Kcv6byu+WgRvl26DxOmbtFcj9p3d/xsvub8VrLjMDLaCao6CEVjIdIoFvFfWuu3+voQiMZTf/w+BVM6r8lrDgV6EwKmR4NUpCbY3zEhvgZtOZKrOb30uHc4gHa1KmLpmD5Wb5pp0x/qZsly9HQm+ONsMXqt8TUSpTw+H1zK2IlCREREfhXISBR/3cf68tD866oD+GNtYB50G1VJdP9b2kngIvcQtOHgGVz5wUK0e2WmbdvmsQ1WLUdhQa4HpmO5yg1y0gZdjjST5+t+OXXOc2T6JoVaPXJ+WnkAS3efxPI9ZY31Hg3Ikk0L9nReehdxViGFVjARBOP7O9yp/egq31kr06llbNXK65P8/f2yfXjsJ/2puXStQxA8PoOudF4KH/Clvzfj19UHLdoyT5qF5SV/j/tjA84XFOHFv8qKh5uJ/BLbeEj/9cEs8b49fUE7csb4Ckr/p5RKUko1nZfGvEYiwbw4rI2OMBKJYhXxeSWtcyY/vZ1bE1jl+KO5+eMznvBzR64/SE9NsynqguUYM3ql8bUTZdHOEz7NT8ElPNAbQERERJcWuYfQ8lYTxaxT5wrwyI+lDXD9m6QhKjxMcVo7HuZrpsSaWr6e1BrBEqIvTkGiFYmitg+kDbrfLdvn66YFnQsyjXiCIBhK4+LrcbrnhG+NqoBnB4YvRZi/XbrXh20wPaub3uvkufxiXCgoNtTQIfc92X3OGu1Y0pPOSzYKSG46qwrLSxbz9K/rLVmuS0mJgKs/WoT4qHB8M7Kd7nNPgICSEgFnLhR6vbc9W3t0tBlafVzSfTV5zSEcOpPn8fsRoacWQIBvF8TH2PN/blKZUtkuHWl+vl2q7zdFNZ2Xxr5ypfNavuck3pu1A/0ap5XNq7FeB4DJa6zrkPP3PYJ039zZtTZmbcn270YEE96GqyopEXRFlhw/a0PHaoBZdmpqRrc5/JIy2GmwU8TXDl6532EKXexEISIiIr+Sa+RSa9gMFF9umc3eb58rKBtBXlQsIMrPd2riRgWlb0TuASdYOkis4npg+nqxcqN5pKRB90tRHuzyQhqJ8/Hcnfi/Bbvx86iOqKGzUKuvp7bVD9TFaum8NLZ15zF9HTp2Ndrr7XRYsP0Yrv9ksc/rC6Y0NAAQZjISxZfpAqWkRMB3y/ahYlwk1lysm5JfVILoiDBd38rsLdm4+TPrim8XFpdgxJfLkJWRjKcGNpSdRrMmisxOXyOpCaOnoyzQNUnEq1+595SpZRw4dcHjb/Gndi3+QoG+iDJfOoZd18PrPi69XszbdqxsXo3d7HQ4fG4czM7Jw0t/b8bwjjUCE4nih3WEyv2R2eiCUOLLpWPMb+sxafl+zemMRqJIt0l8uHx1ezvc+sUyQ8vzB7P3BqF6jFmZSpRCH9N5ERERka2kN82BjEQx0pDpyxaZvd02NMLf5Dr0LtPq9FS+LM4rlYBlo8jll+OKRFFLdRKhkO4sVFSIjcDo3vUMzTNh6hYcy83HK1M2657H1wZPQRAsffD2qIkiSK9NVuXz8n7JSNqcKIVjS+++fHfWDt3rcvF304YAwfA1wWwDu13p1QD7GoX+XHcIYydvwL3frvJ+U8fvxGfzdyu+Z6YBbP72Y1i44wQ+nrtTebkmWoul9VnC9USiBJjd54rR65BaOlStWytfmgYFAKfP+9aJ8vSv6/HH2kO49uPF8DFjDn5eecDQ9NJrkJ5zOTSbgPUJ9o5la5j/kHo6UADjKfK8a6KUvdAgLcHQsuwivS6b/d3Tk7k5GLsrfC0sT+VL8N+lEBERUbkiXxOlfD29WXG/HYg9ouchWq6Bx+7HCysf7vVsa7iO1hxdaWeCVJ9GqVj8dG/Emwx1MtJGEGwNM8UqT/HW9aF4L+j+71brnr97/cqyr5ezy6SJmija5+WC7ce91yOX4suXTl2F1/OL9NWw0GOTH2p7WE3r+5Hb5cWSL0JXyrZAnwc2rP/dWdtNL161JooP26rVULoj+ywmLtpjfgUAZorSZ/kaiWK0FtHyPafw3syy/R7oHpIiX+rTUNAwes6ppQjTe0qsHdcPL1zRxNiKDRCn+jUjOTYCANBHlC5Qjr/6Koyux2j6LyrfQvfpj4iIiEKC16hX2UiU8vXw6I80OHYUMrczTYqVD0dWbaXSx9XTmKOnQTdYtapRAdERyvV2tOn/Bj6cYzwqQszqdF7iBkfpp3hzxjYstqAAqK+nkVLqCFvTGMl1jtp4iDvgMDyaVU+Uwi6dhckti0QRLeazebssWSagfoYF4sojrs+l9Nuj1dCkJ0pIWmtKjtpxkxIXqTm/r+yIPlp34EzZ8i8uXu+1T7Umig/b6vfOKj8f2AVFJfh8gXLElr91eHUmLhRY1xFrVMA7J/3AH5/xxDnfaqI4FP6tpF5qPJJiItCqegWf1qvkts41fb4P+/eR7vjq9na4umWGRVvlX6F8v0/WYycKERER+ZVcQ4raSEorXQLPiD7xTOclP418TRT/jh7ema1dlFcPpQYmHe14iAzhdF6uTj6zz8VGIiL+WnfY3Eps4lETReZz3PTZEj9ujTylhuTy1MhlVzov2XXJrMfsrjxyJg//bDoq+96a/WdkXzdDrqMikN+/OMVcfpH8oAetdqY5W4+pTwB9uefV9oM0ssUOwXYeqkby+hSJ4l+/rrKuSL0Zej6vnd/98bMFWLrb9058s4LssCbou7d2TWJXTSErBoVVio9C9/qVgyaiw+h5bHa766bGm5qPglvoPv0RERFRSJJN6aE2klIQ8NhPa/H8nxvt2ygZvtzq62mEt3qdlhA9WfijAKTcGvSs95dVxnKf28Ff39UZH3PO28GOKCgl74vS3FjBH6kDfV3DpVDEtEQwvp/0RCnoZfYY7vr6LHw0R74uSKFF6XiUtsw1ACEQ6dnFkSh5hfKj5cMsaCCL1BFtpPbNSWus2MH2migGp1ePRLm0+PLbpG9W48s3Mop/xJfLDS/fKv78Xfcb7UD4oKbn0HF1ntj1uyC33PJ4qKgJczjwcJ/6hudrVT3Z+o2hgGMnChEREfmV3IOaWsPm3hPn8fPKA/hy4R6fG0j8d+Pv+9NMIB5o9USiyNZE8XOjni9tleJtVfqMk5ZpFxA1cije3rmW/oklOk2YaXpeJb5+X/6szfHv5mxLz1uPSBSbmlR8PXf9mTpCKa3Vkl0n8P2yfbatVxAEw/vJys4ls19RoSRqUrycudu0Iy30kts+dyeKj78vZs5/cQdJXqFSJIrv34+e77hAIRIGKB91gwSDnWVq9096zrEd2Qo1GUKwpdTOGjAAMH2jfBRaeRB633b5JI560HMJcNjdiXLx/x8NbWXPCkJAmNOB0X3qGZ7PH6mdjQi27QlV7EQhIiIiv5J7UFPLT2/V6N5Q4NHArzGtHe0bJR4NzNaytCaKVfUMFF7XUyzXSAP8FS2q6p5W6pwNOdKN5tz3mt/CbfE3cYPj/pMXcNLH/OVyfI9E8d8j2hvTt8p2Tt/46RLsPKavvogZgolIFNPHq2yHhKlF+YXSpwyWTc4vkr8mWdKJohFt9MTPa1U7q9SiMqxi9wAH45EoyvdImw7naM6/9Yh8esxgOd6MsHub7fi9CBZ3f7MSH8z2rYZZsAuFaBvxVVTPNdXV52JbOq8gb3fv3TDV8DxGjwOzUZbBvu/IHHaiEBERkV/J3buq1UQRFP4dzMzeOAd6lJDpmih6lq3zy5ObTrpKX4pCi/exT6NWDcwbbA9SrkY3sw0KodAQIUcQBK8Gx1YvzgjQ1ijzdxHT4+fy/X5tFQTj51+ehR2KVkUh2RHNdCxX/vsQLh66dl1P1M5r8efMKyyRndaKdF5adW9+XKGeytHumiinzxf4rQNO7/2ANDpKbOGOwNXYCIRQ/W0KFm9M3xroTbBVKBwd4s4QPdd61zR23TWYHbzw0MXIjZvaVTc0n+E0n35Ifxpmch+Y3XcU3NiJQkRERAGnd/Sorw/I/qjzAVjzMBOItgDxOv21r/SQ7gurGrF8+YxGtiHQnWNSrka3v9ebK/oeiu1URcUluOKDhXjyl/W2r8vX/RPhx0gUwL4RrGoEE2ffK1M3m16XlJlriL8aaA+eviB7DFl1TZb7th/8fjUGv7tAV+Tnyr2n0O6VmfhxuWfaQ2vSefl27NtdE+WVKVtsXT4Aw62IdkXfhOJ1XjuCVy31mbXbQmSGuC9aqRH+7u61RdPb+/stt3Q9p8p9Pevi30e64+Urm1q9SR7MXP8iw439zpgtLM8+lPKJnShEREQUcOo5vUX/9sO2WMF0JEqAb7jN1kSxesO1GiutaijzLRJF/8yB/l6lii5GY2w9opALX4MvkUCBsvbAaaw7cMYv6/J17xh9wPdVmMPh9wZEQYDhE/D0+ULz69L1ojq5y44d+83pUO/4Ufu9NOuPtYew6XAOlu0+Kfu++HOO+W09juXm44lf1nlMY0UAla8diKF4bZJyffdW1ETxaTtCcF8qbfLOY6Upy/JV6+nY83mD7Of/khYKh7RaJMrN7avj51Ed8Xi/Bl7T2xX1YLbmSpjDgbqp8aY7IPQyc/1rUjXJ5/VOvq+z5jQ898sndqIQERFRwOkdYRsKD0CARZEHGp/VjkgROxtNjDQItXhhBmZvyVac16rGDl8+77CONSzZBitkJMcYmt6VPs9sY73fG9wtWIbdKXisHA3u70iUQHTyCQhsXRIz6/ZXA3240ykfiSIIWLLrBL5fts+n5U9ctAezt2ZrT2iQJem8fFxGMNe6MUrv/iyyqW5cOdqVmHTxnMkvVN5XgYjIC0abddTRCRmheBA7ZP8JAHisXwO0qVnRI2LPnc7LrsLyMssVX5q+GNFG93x2MHPv5eu2vXdTS1333aF4+JE2dqIQERERAPsa0PU09uu9Bw6mFFN2C8Rn1XMIWFkTRWl9Zy4U4raJy91/z9/uWUhYJQW8ZPneE4o335cGt5vbVccDvepqTpccG2H7w2REmAODm1XRPb1r5F6kycb6UBztbfcmT1590LKVRfohx7dYaX0S/36npYXl/bNO+UAUM5Eo3vPY8QmUaquXCMCNny7Bsdx8n5Z/vqAYt325XHtCg6xohJ65xfrOnVDjOsyaVE3UNb1dkSihSOuacqFQvq6SwwF0rJNixyaFnPJcFyUUoqvEV1Hx/fbCp3qhYlyk1/TuSBS7N0ykQ+0UZGUk4ZpW1dCzgXxhd7ORMUa/I7WamgBwfZtqqF05DnGRYe7XfP2tcjj0dcQU631YoZDCThQiIiLC8j0n0fqlf/HH2kMBWb/egra+Pv/46/nJikZzrW2147OI9/XERXvQ8z9zcPD0BetXZNC2o2c9/tbbiK81mU8F6h0O1KoUpzndo33r214TJSLMiZiIMO0JL3KNXDYb8eDvx0J/NnyYPXcf/WktVu87BQBY62PaMH9HogTiMV8QBL9dj+WOH1e7s5HUgHIjXl3LXrrLugLeSteLYB9EYEUkypr9p33fkBBn9LywqybKPxuP2rJcO2ntuzyFThSg9Lr7YO96Fm9RWfrMUFGeO+VC4ZOJ01/puaK6Jrcrkqpsc8qWHxHmxJ8PdMGb1zcPePF0revfM4MbY9ajPZAcW9YBZcW+0rOEY2d9G/BAwYmdKERERITbJy7HyXMFePD71QFZv/8a0/yzHrO354FOJiHeP8dy87H7+Dm8OmWzZBr16A6/0Pk9lggCVu49hfu/W4XDZ7w7g75ZvFd5FToOlrP5RZrTOBwO2/eP0bRcrkaSqAiz6byUG5Pt4M/Bo758VeLoKV9EG+gQs0IgIotMlESxfP0AUGiggVMpzc2BU+dxw6dLLNgqdf7YX8q1sJTnOV9QhG8W7/E5QoZKCRCw6VAORk9ao2t6nxq9HQ5sOSJ/XC+2sGMwWOQVKUSiXPy/zeUbQkKhSt2YkBcCvShGD0GzNUt0L//iFnWqmwKHA8jK8L2eiNjtnWv5NL/eTspwUYSvr+e5Aw7ERoZrTjcrCCIrf7u3U6A3odzR/uaJiIio3NMKh7abWiOeR2H5EHgAAqwp8Kj1Ue3YFXJtMWqFWI2w8gFP78jbEgG45qNFAIBT5wvw7R0dPN4/ca5AcV49x9q5fOVRrWXLsX/EvdHIBXdNFLORKDKf57Vp9qUAsaKRX28njy/n7nkdx4MeUeFODO9YA1+rdPJZqTS1ln8Jgv/iKtTSeRUa+O275qPFssved+K8yS2TVyIICh2Vlq7GELVv6+W/N+Pbpb7VaaEyggDc+fUK3dP7UhMlOycP783aYXr+QJKLItPqBMxTqIniuu7bMZo/VO5bXQptqrFD+igdg0r3MDVTYu3cHFzZsioAIDE6AptfGGD6vtEuWs8Drt0pjpS04hktJjIMlROign7wgBURouQpuM4AIiIiCnkfzdmJb5YYa/xjTZSLRPe6gcjdLLd/pZshXxNF+ybdyo+jO52X6PPsNdjQqWcN53REogiw/7iNDHPimlbVdE/vGn1vNm3Uir2nvF77eO5OU8vSo9if6bx8mNeqiA6nw4ERnWpasiw9AnGtmbzmkN8KGKvVY7KiKPfO4+d8XoZYcYn8FSNYaxHN3XZMeyIy5LiBNDC+pPM6fCbP9LyBtmind6TMSpnfJjGldF52RqJszz6rPVEQOV+gfzBAhIH6XQHO+gQgJAJRPO6x1fbZD3d1wPVtqmHMoEYArO8AfPHKplj1bF/UTU1wvxYdEeaRbswKvt4f643EE3f+WPUR5j/RU/X90TakBzRK/HwWDOdgecBOFCIiIrKskffQ6Qt4bdoWPDt5g6GGOdWaKCEZieL7MjQjUWzYGfKNjcG3062qieLrOvSk8xIEY6PdzYgIdxgqiuuKRAmVByp/ppT3pSHCsk4Up8OvecaD7wy3X4mJSBQ5ggA8O3mDFZvkVlQiH71m9fd0NCcPS3adwBcLdmtOq3Ron7lQiPAgH+k65cGuyKwYE+jN0C0nr9DQd12ea1ioOV/g/ft7y/8tVZ1HrSYKYM0I9VC3yUDntpFabH/c18XM5lgqGO9npcSHoLjhX1pUvn3tFLx+bXOPWh9W6li7omwhezuZiYrV2kbX7vRM5+V7YXlAO/Vq/bQE1fftVjMlNmTu80MJ03kRERGR7kgQLeKHWkEovdHUE6WgP7IgNFhRSDwQz3pyjRLSzQiGmij603mZ34l65q1WQbthThAEzYYbXxlNr+Daf3YVJLaaFQ0fupfgw7Fs1e50Ovybmz9UjgPTZD6e6yMHa9FnuYENcumLfDF60mos2XVSc71q3vpna9CnC6mTGoewEGpJkn4nWqyIpirvXEe1cjqv0v/bVZy7vDJytciqZm0tjfJKfDkND3NiwZM9UVwiaNbgEB+6XetVwvztx01vw2fD23hEoNhJfM4ZjTqeeFtb1Kkcj66vz9ac9soWGdhwMAf1UuN13burMRvF7W/B/tscqkLj2yciIqKQYLatU20+caNOKIwi84k46kbj8dSOPfHBbO+UTFaNrpdrmzAbAaU7/ZvMdHpHmur52MM61tBeDqyrK6PE6AOdK+d51WTzD5JXf7gQ0zceMT2/EVaMtNZ7GAfDI2eY02FJR6xev60+iJwL2lFV5Ynr2lNY5OOxFcI/SRsP6h9xrvQxD57OC/oGJafDUa4bxy/VSBQz8hULy7tqovhza8hu4ZJ0Y6FwpkivVdUqxKJGSpzmfOJ727eub+HTNjRM918Ehbih38iAji9GtEGPBqnIrKivJsxtnWth4m1t8fOoTkiJj1Kc7t4edfDlbW1l37urW210rJ2Cng0q61pnoH92QuF4D0XBfcdDRERE/mHDnZaRReqviRIaLHkQD5IPq68mivHl+EJvx44dkSjPX94EU0d3BQBEhWunshAEoEpStOnt0MNwYfmLJ9zLVzY1vc5V+07j7m9Wmp7fCH/Wggj0Qy9Q2ojiz+2YszUbb/+7zX8r9LP/ztzu9Zq7JkqQRqLI1Xuw+jywqvE92Ee7OhAc57Vdyn0kmYIX/95keB6tqNDy3Nnmi+ZBEEUyOKuK4Xmk16ZQ+HbNbqN4vugIp8/RFv4iPueM/MYZTb0X5nSgR4NUJMVGqE73cN/66NkgVfa9MYMa4fu7OiA8QAMHDKdXE8r3b1+gsBOFiIiIbIk2cEWN6Ik2UFu/lTVRDEWy+HDjafamVVD4t+bENgrG5hm9X6O4benE2QIA+r/WZydvlH391k410ahKos6llO6/+mkJePemlhjczHgjgB6R4eYiUVITo/HXA4HPU67l+MXvzhd6o578GQGixN+dKOU5wO/3NQfxxULvmh+u3wJf25+tqicmteuYd7H6vm/Ns3QdFwykGVT67VywI/iLyjMSpXzaf/KC7mldh69iVOjFw6McHyY+saqjdPJ9nU3P27ZmBcPzRDg9743io4O/moHZujwez1/w/F0fO7iRbxtlI/GhVVQsXw9Mjt69ZHR/WhlZafXlpG5qvKHpBQTHPW15w04UIiIisoWRx3rdnRsWtxXsP3ke3y3dJ5/iIcDtEoJQul8+nrsTC3eYz23s+3YIqn8DwVsTZc7WbPe/LxQW443pW3SPPP9l1QFT2yZVP630oefy5lXRurrxRgA9IsKMfQHi/dc0I/AjTIOJ+Fi2ugaFXmFO/xY49mekj7+NnrRG9vX9Jy/gg9k7cOZCoU/L9+euKwjC2hd5hSXYeEh/WrBAcDjKd4QBa6Lop3RNdx0d5fk48YXifjF4/WuRmWx6G8xcasMk90YJ0epRCMHA7CEobix3PT+4XN8209fNso34ezVyL2K0FqC/9GucZmj6+U/0xOP9G+iaNiHKeCcgL2nWC/6uWCIiIrKdVe1AdtRE8ZjO4p6Nnv+Zg6ISAdm5eXioT33Llmu2AVS6H35eeQATpm4BAOyZMNhzWlNrCBwrb+T1PmhJG1Dlar7YrUvdSravw2hqgaLiUDt6/EfcWDRp+f6AbIPD4d+xg5fiQPYfVpR+tw3S/Jf7PZSF8iHi8HNkl78FeydWMNG61gV5ZrqAcQbBjjHzfBEu2e7EEIhEqWcw2sDF46MKntfsOI2i9MHCSGpCcQT25Ps6Y+r6wygRBHw23zvy1J8GNE3HP5uOAtD33JNZMRb39ayLN6Zv1Zw21mAniiAI5fq3L1CCs/uOiIiI/MqOkchGFulLoXAjxLM7UJYGY9EO7/zzPqXzMj+rW3ZuHh7/eZ0FS/KN9NiwqoPIF6Eycj4mIsxjf9m11UZH5AVrHQhbmUgRMX3jEVs2RUuYw4HoCO16O1YxlOawnNl6NNen+cvznhMEASO+XIaRE5eHfMo3f0Z2+dv27LOB3oSg5/r6lQ5j1/vB0FkQjMKC4Pwxc98nTUMWCpEor13bzNyM4ihawTMtltF0bIFKJ2qkE0WcdqtFZjKeHtQIiUHw/dq5DUajzpnOyx7sRCEiIiLLeObk1X8zrPfhyMp2HM/6Ixa3EJmuiVK2HQs0Unj5q+FTz2r8fZMufc7KyfMtJU+gWJWnWjraUkt5y6GfkWxhAVVxfu4AdTY5nSYKiPqgfB0NZJXjZwswZ+sxzNySjRwf054FWuWEqEBvAgWQ6z5G6b7JdQ9TnjvbfOG0sdUwLjIMMx/trjmdmVvefo3TPf5OjAl8I7uW1IRoU/N5pPOCffW6rCbeTrORKC6B7gT9dFhr9GqYiutaV8MLVzRBjMURQEbTDQosLG8LdqIQERGRZaNMzafz0ltYPjQeCqzoVAiWjyrt4LLyOzC7KOk2fb90nwVbY05mRf0N+NJ9d0fX2pZsQ4ThwvKBP7iCdcCveLNCIe2ZFQ/IoRLZFYzKcz0KceNWqB8jr16dFehNID/r0yjV6zWlw9gdiRKkv0uBptR4a9VVoU5l7RRWRjoFejdMxV8PdEHHOikeryeEQDovs8SDaRwInTSd3hEz+jZcrgC80QFFVqqaFI1+TdLhdDrwxnXNMbxjTcvT+Rr9eAIExqHYgJ0oREREZAur0nmJH5zMPBPoafQPlvYhI9vhr03WtU1+vkuXHi/FAfwC7+5WR/E9f40AizAaiRIEDb/+HvGr9wgRb5eRUZFWcgXA6GkQiLBgiPD5gmKfl3Gp6vr67EBvgqWUDvlg+Y00KyM5BlWTzI3wptB0betqXq9pNcSzsLw8o+mgjNB7aTHycxwfHY6mGUlerwdDuie7VIiLxBUtquLy5lVRIS7Sp2u2v+/P7upWG53qpKBrPeVOB2naWrk0tnLHqb8+SUykdwpWI+fNI32163IaHaAnjUTh1c0a7EQhIiKigFN7sPWMRDG23KLiEgz873zc/c0Kr/klNRgtZcXzZrBE3SzdfdKj0T0Y0l2UBNEQOzsbF/SSG5Gn5mHJw9rDfbQf3qzm7+Nb7+rEX2dhgI4zV6fgt3e0R5WkaLx1fXPFaa04/nYdO+fzMi5V+UWB75C00q1fLMOZ86Wpu1z/B0I/EgUAejT0jkyg8kyc3qj0+FW6pLumDIKf86AUajVRlCYtz5EoAPDfG1vi3ZtaXvxL//4KxD2giyAIGDOoEb67swPC1e5lJYegXDqvQEaiyHWiGPFg73r49d5OqtOI05V9MaKN5jK7168Mdp1Yj50oREREFHBqbZXiByejOX5X7z+NLUdyMX3jUa/57WwSMvu86VGnJYjarD6cs1P1faMfN6/Qt5Hv0giBQBZOVHtm89d3aCSd17IxvTEoq4rHa6P71DO97iHvzcd9360yPJ8/D+/V+07j9elbdE3rGYkSmAZyVwdT+9opWPx0b1zWvKritOEGC40Saflu2T5M23AEfd+e535NfL7G+thYFChjBzfCi1c0CfRmkN94/8oop/MyXhOlXc2KqCKJbvrhrg5IiA7Hs0Ma69/MEODL4JkXr2iC31Qah/XeJ9WuFKd7nUodLre0r6F7GaHOyP1nIGtGSbdTqW6NNOJarsh6mMEBRVbqUCtFeyINWmeZkT6iF69ogmcGN2JNFBuwE4WIiIhsYeQG/sCpC8hVKA4uKP5hnWCJ+hDT2iKjmywIAn5csR/bjuYa3pafVu73WI4ZB05dcP+74bPT8NWiPfhh+X6VOZRtOpwDANiRnYsnfl6LA6fOm1qOFdQaF6Rv2XWYGRl9l5pobUqbDQdz8Pe6w4bnqxRv7UO7WkTGG9O3Yt2BM4aXGaiaKNJOZbVIo0COvKTyqbikBK9N8+x0FF/39aR/G9AkXXMaf8hILqtZFRsZjmEdawZuY8iv5AbnaEUzGIm4iI4Mw8Ine+GOLrXcr7Wonoy1z/XDSNFr5YEvbdNXt6qGltUrKL6vNTjq3ZtaYuzgRuhv4JqitMSk2Aj8b2R73csJZXrvXtISo7wGY/jzrkK6nakJ0Xj7hub4+JbWHum9pJEnvkaipCVacw864+FueLx/Azzar4HPy5I+T9zQJtPjb3G6wbxC9UE+wzrWRGxkOONQbMBOFCIiIrKFkaiRP9ceQqsXZ8gvxyMSxRi1m0c703kFY2H5P9YewhM/r0M/0ehivbQ+j9YoxdX7Tnm9Nu6Pjdh30rfOj2s/XowfVxzAtwEsLG82zUXTjETLtkHuYTKYVYqPxFe3tbN0mdMf6mrJcsTP4EWBqokic/KLG4PF4qLKd3oS8r+iEsHrGDRaRqlCXKSFW2Te93d2MDWf3ChnCi3iQ/jLhXuwYPtxxWld33bX+voLQZeUCHA6HSgUnRyRYU6PlDtW+HRYa1PzNa5i3T2GL2kjteZ1fU/10+SLy7erWRF3dK1tKBpGbbCPBWXEbKOVzskIPQOekmMj8NPdnQI6GCNdZmDPVS2rYUDTdLx1fQvc2bUWZj7a3ev7lxtcYuR+PC5S/d5J6XiUqpeWgPt61vU5nRfgHWnyQO+6Hr9h4vfP5Rf5vD4yJ4gvIURERBTKjHYCFCqM+valJore+2mrOyxMp/OyMSJmvYmR+C7iG3czaR1mbs42vW41p8/LRy/5k5EHcnHHYpe6lS3bhtK8x9oSgqTB/ce7O6JxVesaeGIiw1A3NcGipZUd30VGW44BfDB7h89bINd3o3TafTTUWAPbiE41jW8QXVKKigXsPeHZwV1s8LfJ39GdbWvKj3SvnhKrOl+qQhobowMhrm/jXcScAkvaEXjL/y3FwdMXZKfNvdggmZqgP1LTlVZUXDvLjppx/UxGdV3ZUjkNpFFOHz6X3gb6T4fJ13gws2q1TJyBTP+qpZVKxI5Req7AYwY2QvWU2IDU9susGIOh7atjeCflFGuVE6LwzODGqFPZu0NDb2H5QEtSSE8mR+48i4oo+5ye6Wa9v+HqFWNxc/vq+HJEW9l5yBrsRCEiIqKgJr5PNFoTRRqL4tEhY36TDK7VHOOfVZ0v99FaN+EeUT0yjWfloSixEiONC+Ld0KeRNUWOFz7VCzVS9OUKD5bvQe7hzxfvuQup+s7XSJQ3pm/1eRtKZNardJxlVUvCNyP1RfWMGdQQ4y9nTQhS9/d67/R8RusD+fNaM7R9dfz3Rv3XgAZpZR2uPRtYcx3OrKDeWUP+J3cMfmdh1KqrY9FMZ7s/hDudhmooqImOMD/KXm/DttJvnJmPoHb/XE9nhEGok7uPkHLtJ2lUhz/a3fs2SsfLV2UhKlzfsSXdJLmIL7kacYqfReMzBiIQWbqtDofD47lBfI7IbV5MRBheuSoLPRuW/a6xC8V67EQhIiIiW1h1/+mRzsuHSBS1kbGW3yubfAIxEnVjtJPFl5GEevdj6fsyr5les9p6gqNDQG40nIu06KxYm5oVfV53x9opimme5BgdTW4XpagzM6IjnGhkYdoS8bEeuJoo3uu1olEjQNnJTKkUHxzpoC5Fu4+f83rNaDuxP4+1zIrGRlKL0x+KIwmrqlyvSd2YQQ0DvQl+52qkDtTvhJaIMIdlI/NrVJTvJIyO0G5O1BqE49p7FZWu+aLZh7avrrk+QP36o6ce25zHeuhaTzDTc1S6bjUCEcERE2l9U3S4TGi4Uk05rU8ciEFH0iip0mtM2XY4PZ7FZOaX+VAMRLEeO1GIiIjIFlY1cgsK/9bDY9SOZGaP+0qLb5atiUSxmC+RKJK/1+4/jf9M34q8wtICwx6dLDLz2/EsEiwNwmpFv1++Ksvjb6s3+aE+9QxNHyz7rDBIR+5KBaomSmWZFENqp6/e8ytI+tB0WTqmT6A3gUT0jGoW8+exVlBUYklD0RvXNfd9IZeo/k3S8fYNwbX/7G4EdQ1KKPDD79mSp3sbnifM6bQslU+tyvLRrnd2rY3mmcmyv1mAzk6Pi19TfFQ4pjzYFTGSqBfxffzzOiMpff3qjaRg0uuKFtalV9NFxz5wTSKtJeJryrNOdVI0p4nVqEkipedQFt+OTx3dFTMe7qbciaK1wADcL0n7gEoEwTMSRdSLInd9kxssF8zp60IVO1GIiIjItBNn87HlSI6t6yjxiEQxdlcrvnUsEQSfOmQMrdeSXhTjER/+4HA4cMUHC/H+7B34UKb+g9x3ZHVqMgAoMphexi4RKkXdlfLtW8VoG3+wRO8Ey3cnx+mRc9r/29mmRgV0qetd3FgtkkytI08sWNK56RGMuc0vZcFcE6WwuER2BLISXb/PBg+/0Dmz7OGUpJ0JBma355+Hu+maztdIFCPFvNOTor06F7SEOY0V2laTJlP8GwBaVq+A3+/rjDu61JJ9XzqQRI74/rBx1UTUqlTWYdOzQWWkxJVFqISHObHlxQHay/TxYLRj9H6sBYXHjdCzB1y1RqS/t77cs//3xhb4eJh2rbasjCSDS9b+UsJEvwONqiSiXpqxWnntRBHigbhfkt7nFZd4fhNa6bwYieIf7EQhIiIi01q/9C8GvDMfO7Jzvd6z7PbTosLyag3Odt4qm32Yu3AxysMqvoxG2nnMO70LAGw+Uvq9a0Wi2LGDra6rYZZaOi8pq5/JjB5b/tplVqbX8jcHgLzCYszddszyc1CP0X3qyY+QVDl9a1bSV48hWDrRKPQYvd762gDVvJr+BraC4hJEyOTC14ctTFaQq0VgpaYZxn9TzP7e1U9LwK0dlYtdu7giFc0OCrisuTWRCTe1k4/2cDgcltVEUeoAaFer4sV1WbMewPPa8eVt7bx+D6MjwpCu0Knj4usvXbiB+zq9mlVLtnyZajpejAaRG8zzx/2d8c4NLdzfn7S+iC/311e0yEBitHIkz18PdMFb1zdHt/qVTa9DiZGOSemUL17RxKPzx46U1FqSJRFQ0kgUj1NBZySKGDtUrMFOFCIiIvLZst2nvF6zqr3OqoZf6cgqjXtR5BUW46FJqzFtg3eRXS3iDgsj2y/ejs/m79Y9ra5tsujmWfxAsHb/aczbdszj/b0nznvNY8eIrkClWpKKDFfesdKH/6tbZQAAejW0ppixdBf8em8ntK1ZQXH62pX0FaD31dTRXWVfH9mlFno3TEXLTOVtDDSHw4Gnf12PW79YhrxC/0einC+Q77hRO32rJMXgvze2wKtXq4/6DZJThkKQ0Wu4r8eakULWBUUlstFYNVLkOxelY661p9GWWVF/baryKMzmSBS90XZivnQaPzFAu8aLa7BAgYlIlDeva474KGPpjOQsf6YPOtSWr68W7nTIFt82o0Kseo0qX2ruSb+mxlW1O8y0Vmc01amUFd+N2JhBDXF9m0zVaf68v4ul63zj2mZ4vH8D/HpvJ6/3mlVLxpUtM9x/SyOWrKxbJ9U0IwlXt6pmeL4Wmcnuf3dX6IAxG8Ea5nRgWMeaqCiKegpEJEpqYjRev7aZ+++iEsHjOubUGLAm9/HZcWI9dqIQERGRPUzcf8rlXRd3fhi9p5XWRDEy/5T1RzB5zSGM+t8qYyuFsULs/mLHfXR2bj6Gf7EMB09dcL/W5625XtPZsQtmb8m2fqEmGGncSUuMxpYXB+D/bm1jybqlD3mtqlfAV7e3k512SLMq+Gy4vvU+M6iRz9sm59khjfF/I9pa1rBjl99WHwzYus8XFMm+rtVIdUWLDAxsmq46TZBcikhk5djQqP1iNGWRrw1QRiIbbmibKXsd/ub29qbXL04hpOX9m1vi8uYZeLBXXVRSKo7tgyiVlJHBwul02BrRa64Txfz64qLCNY+BZwc3BgAUmaiJEua0JkokzOlQrEcS5nRY9lueFBOBV6/OUuyoH6Dx26PmhSuaevw9bkgT3N2ttuJgDED9fnbLiwM0oz7EDfJ2u7l9ddzVrY5qA3/rGhWQZSD6To/k2Ejc17MuqlXQjlSVZkMsKAq+lKuvXZOFu7rVxl8PdMHE29rKTmMoEkU06fSHvI+1QGWdvVbUwSSNCLrm4nstqycrFJaXqYnCXhTLBf8vMhEREV0y5PKui+8hfcnTK23U8ayPYl9h+SW7TuJcvnzDqJQdtUOsJndDvv/kBZkpy9jxqf5eZzxCyA5qjTtyDYnREWEe+9B8Ghr5B8bYyHBUivduVHn/5laoqTMSZZiOVCblVaCfN8/mK0Si6NgurZR9oVQT5VKRInOuBiOjx46vh5qRke0N0xNlGygVF6HwhjgNYfcGqbizq3ydB6lwpxNhTgce6ddAtp6Rr8RROXoKNgdCmMOBxGhrR++LKdXkUOPr9U6t8fHx/g2QFFuaekepg1Gt0dzhMN64qRRZ1bF2CgY08e7EcDgcGGJRyjCHozRtmFLqMD0N9VI1U2Kxcmwf3CwpPp8UG4GnBzUynRZUTxSbnvuuV3TUc9FDz7f8490dLVmXWdLrbTDWJEuJj8KYQY3QNCNJ8dxJUEkjJjWqex0AwIAm6aibaqx2ip08iseXeD4/1a4cjzXP9cXPozrJDtCTjUSxYRsvdexEISIiIluY6RCQy8MrvlH0vSaK/AJcy7Vq9JV4vbf831IM/XypJcv1hZ2Nw1rftR2Nt/9sOmr5Ms1Q60TRE4U0+b7O6Ns4DS2rJxt6aO/TKBXta8s3qA1tL9/Q4U8N04PnodSIA6fUOwTt1lih4UhPTSOHxpNdoLtQMpKVUx7d3b22rmVEhsCo/PJo6e6Thqb3dUDA+oNnfJofUP7NE7/sdDiwbnw/LBvT2yOVi9MBPHMx0sDIeuw4x6Ijyo55pUjDQHM6HOjTKM225Y/oZLxj39eUcnrbkQsUIlHWjuuHF65oojif0XuyT4a1Rv8maV4daQ6HA08N9E4/FubwpRKefVy1MIZ1rGm6E9nX0fV6Ogmk0Sr9m5g7vvV0CAe600K6/rqp8QHaEt80zUjEiE41MWaQdjq+q1tVw5zHeuCDoa1k37fqucWXpWRWjPHajuTYSIQ5HbLXN/lIFB82gGTxLpSIiIh8ll/kPXpa7v7zpk+XqC5HvhNF9G+D2+VZm0S9Jspvqw+g/tipBtegz5r9py1fptE0YXIPcoIgYJ9MDRMr11s6j+FZQoZaYXk9WT6aVE3CZ8Pb4Ld7O3uNyFTz+a1tFR+87+ymr0FaiRUPXd/e0d6j0VxthLZVKXA+vqW19kRB6o4utfDV7e3QuoZ8vRh9kSjqAp1aMClGeZSo3jQc17TK0J6ILDfDYKe1r6lQTp8v9G0B0Nd4Ge50IDE6AqkmIh1c7G6jEo+sN5PWyh+SYiPgdDrwYG/falEoiQrXXyPHxdeOPLXjR/yWUmH5+KhwVEmS7zh2OhyGC2vXSInDJ8PaICujLO2TK+2d3KaGOa1rQLWyO+bTYa3x+32dcVunmpYt06hwaf4qGeJJxgxqiE+GmUvDGoRBHV58qWkTTBwOB8Zf3gR3dauja/qaleIU76P9EbmrlGJ36ZjemPNYDyTHRio++OquiRKUXamhLTh/hYmIiCikPP/nJqw/cEbzkXXxrhOq78ul8/KsiWLsptazNon6tA//sNbQstXXa+6m1c57drkt+mrRHkxavt/nZZfnThJAvfE3QqWwvFynoD8YyQstx4oH6pT4KI8G7/GXK4/qVotQkBqUpZx73Ze87IGWUSFGsVgqoO+aojVNoNN5jeqh3LAhLWyrxEjBcQqcQB9rgL4G5DAdjala7G6AjDbRgeBPMaJz0q4GYzP72Nef35hI5f0ubphUqxeklDbK4QB61K+M7+/sgK71jKWAE0e+uOrlyDWUOh2OoLw3i44IQ/PMZJ9qo/l62uqpuSQ+5nw5x0OhJkWgI2GCkT+iXvs2lo9uSkuMdqffVTqF5Z6HGYniH+xEISIiIktc9v4CrD9QloLDzLNbsczDqHiQn+FIFEmBd6WoFj3LzSss1t0o7o+bViuejf/zzzbD8+h9ICzd34L736HsoT7Ko2uN1kTxB18fiC07fPU2Qhg4YT4c2hrDA1iz5T/XNbdluVrXFukeapiegBGSkbxaezFAfXoASo/Jy1Xy8+ttUBN3aF7buprKlMHvQ4U0IuWBFcea3hRvSuKjtGt0GClgr8Tu3/uoCPnfmM51jddHSUu0vgaP+PPb1aFkptHcyH1HjEzn7Hs3tVRetugOzBVlmRzrPdiis0IEpgMOOBwOdKyTgl4NU3VvJ+DZaaMWCRvmdFgWuWTka/XH/a+400i8Pr0F4/V02jt1HtdjBzdy/3tIsype74tnvalddTTN8EzZeZePkcNWYB+Ktw9vbo2M5BjV60AgyV3elhlMu0nmsBOFiIiILLNi7yn3v800nIsjUc7lF+HDOTuw58Q50TKNLc8znZfydFrbei6/CFnjp2Pwu/ONbYBBRj5ecYmAlXtPyaZSk6WQzsuo8wVF3suR2bbL31+IO75aASCwjbdWUHu+VButFqhOlIgwJz4dZj61lVUjJ8VLUe3YsbBo9Y1tM9G1XiXLisJKxUfZMyrc6LEy7aFuGH+5Z759ra9Nug49jcxWqZKkni5Jb/RUiehi0qSqucLDwWJQlneDW3kh/W1pWT3Z8DKeGtAQy57pbWr99dPiFYsMi88TpetSMDUaRyn8xpjpsLAjHZh4O+wa0W7msxq5pH50i3eHZtOMJMVO88On89z/frRfA7x0ZVNMebCr13RK+9tIlLRUoSgSRe232ul0IDLcqRq9aQd/p4YSr+3/btWXckut0/6JAQ1Kl+sxCER5WSO71CrbFrlIANEWvnp1Fv56oOw4iYkIw5hBjbzm8TfxdybXoXgpyqqWhIVP9cJlKoM/dPHxMcDXxwj2j1mPnShERERkGXEBVFORKKIGslembMbr07bipb83i6YwfzepVhNFy/I9J1FYLGDLkVxd0/sjB212bj6u+WgRHv9pna7ppVu0et8pnCvQ2QEjMmfrMa/XpDf5mw/nYP3BM5i5Jbv0/YCXtPZN5QTlBmD1miiB+9z9mqR7FaDVy46jVz2/vHVrnHBNM3wzsj1ubl/dlhop4m1VStVihlb9nE2HczSXoXndkRyO/iweWyMlVvV9vZEo5wqK8dcDXfD0wIa4pUPgIpKslFlRfzq7UCG98vVs4DnaftkY7c4Rh8OBVJVrr5rbOtfSngjKI9KN/Ibb/XuvlMLOaGN1w/QEWxq4xUu0q/3cTN+M3o7pEZ1qokcD+WgQpYEmJ88VuP8dExmGWzrUQFUDaSl96WuSK2RfNTkaleKjkC6q7eM6trvUVa+98li/+qa2Q6njW29qRl+IVyH+TdZbqF5tC+/tUReAJJ2XyhcmXr/cVGr3CcGQ9hDw7PyMs2mgyKXqrRta+DS/0vOT7ucq9qJYjp0oREREZBlfG43F8y/f4x2W7Mvzhj+fVcw+Q74+bYvhef5Ye0jXdNJtuurDRYbXBej7jqX7OkieE00b2DQdI7vUwgc3e49WlY40FReLDXQEjqtQfRuFYuVKLCtGK06HodIIYWVnhJjSCG5fiBtW4nRGcrx6dRZGaxRctqIxxWgkilIReyt9dXs7XN68qntEtzQFmYveSJSrW2WgaUYS7u5eBxFhTvx+X2fVNGH+5nAAcSq1FFzEKWA+Gtoajask4tG+5hozg1GJIOCbke3QtV4l/Hl/F1SrUNbAXLtynE+F3PWQS6vjIj7SlBq+qyQb2D7RAl3Hd2+DKZrUdKojnxLKaNTH5Ps6W3Jt//Hujh5/e0T2mFxBTY1OVjMtgXp/f68W1e7S68S5fMPziIl/R4xe+Qtl0t6Ghzmx+OlemPN4D9l1qLm/Vz1seXGA1+tax7BSlI0FZYY0iY99M0ecnn0jPr30DvQ4db7A67VgTPkqJd6fsZHGI1QTo0vn8efAjFChVPdEL6VDRO/1zbOTnz0qVmAnChEREZlyNt87rZP4gcDMs4G4gV5udKfRRUqL0isX6NNajjFmblPzi4oxdcMRE3P6V7HMzvpl1QGPv71q0di9UTZzOh14dkhjDJZpmJM2ZIlHrlr9gJwSF2lo+sFZVTD9oW743x3tDc1nXTovUZoXlWUaTTFzZUt9jV5W1DuQcnoc2/rmualddTys0UBuRdSS1tcm3d5ohVoLUuJGxh/u6mBom9rXqoh3b2qJKkmljdVKaZ3UIrrEmlRN8vi7eWYy3rU4Z/k/D3czPW+406ErqkbcqdA0IwlTRndF21oVTa832AgC0LVeZXwzsj2yqiV51HH4SdIIL0dvB2gFmToUABCu0pIrvr5d18azrs6Xt7XFLR2q43adkSyAZ4Nsy+oVsPrZvvhsuL60QnoofUajnShhToclkSgN0hM8/hanFexjssFwz4nzqu9nGIjycNGbrlRtMqXfwhNnvRvLjYjyIWVSYZF82GJEmNOjM9p1Cui5A4uOCPMazPC0KM2U3G5QGvzQLCNZc32+GtW9DgDg+jbVTB3Tejp6PNLU6VxHkWwHl/e8t16s6/Z4/wa6lms38e/RgxoDPuT8em8n3NAmE1+OaGvlZpUbV7YwP9BDKTJI7rr1whVNvF5jYXnr+S8RLhEREZUr/5m+1eu1Yo8i8CZqoog7UWRu/Hxpk95x7KxHCgZDjNZiUbhp/XfTUTz8wxrk5hdhWIcaePHKpu73SjTS+fjKY+SjDztSz6ziaf5adxhr9582vb5A++r2doamF3/2WB0j0vV6uE99DO1QHe/P2oEb22XqmsfhcHg1eNmtfxP5RjS19r5wg50oeiMo1BpSzRI3kPtyHklZ0omi0X1rehWi+drXTsHYwY0kaRaVSRvDlTrM2tUyl3rODrUrxZmet6hE0NWoly/TEGpnW8e1ravh55UHtCe0iLQDuVD0A6cn5Y6ejrERnWriyQENZd/T23AkPR57Nkj1SD2WEheJExr3DdJVVTDY2a1F6bPojd5yLwfWHGNJMZ6dOle3KuuIqlPZ3Ej0inGRivdnK8f2QYyJ31K9NZ/ULotKu3joxShPs8TXRbU6W3JRbUU6bxZdDf9xOiMLHu5bH69PK7uv1zqHpPXg1o/vhwsFxVi17zSW7TmJejZGJVzXuhpaZiajZqU4TF6jLxpbTM81WjyJ3gEHYU4HVo7tg+V7TmHU/1YCkP/NG3dZE4zoXEtHBJZ/xEaGY9kzvXHkTB6aVUs2PH/d1AS8dm0z6zesnHjz+hb4c91hU/d5rapXwLAONVBTcl8i94wtd82TDPMyvH7yxkgUsl1eYTFmb8nGBRN514mIKDgJgoAZm456vV7iazovjUZJox0z4sWNnbxBcbqtR/XVOvHVHV+vQO7FCJ5vluz1eE/vg7FZ4htpu2t1bBPtzwe+X42Nh7TrOQSr7vXV84lLlQgCXr6qKe7tUcdr1LwvRveph0rxURh/eRM0TA/egtqje5dFXOhN5xWpM2LE6KjNyHA7IlHEnSjq02Ykx2DJ02W1H9ISyxqPH+/fAK+LGh2MRC09NVC+4ViLtCFIEHQ2xEomMRKpJJ1WLoLxmlbVbEvpZobRTj0xQdAXIVAg04nStmZF1TRUvuhc17+dVNLDWW6Etpr+TbSLYQ9smq7YuK72HRg50mY/3gNTR3fF1NHeRcNd9I6ENxpJ6KJ0vumtIyRejp5NHZyl/xjsZzLyZPJ9nT3+VitmrbfOhdRVrTI0azEB6tdepe92eMeaprbJRdwBcWXLDPRplOpVw6xj7RT8IBO1VaDzXHKdA4ObVUG/xmkYM0j9d2NUtzoef4s/u1wHvXSQQkJ0BFITo9G/SRr+eqCL13dsJYfDgXppCYgIc9qYzqtsGmkDtpLiEgEp8VEe54Xcb5vT6UCtSnGW1oPzVWpCtKkOFNIW5nSYHpzhcDjw4pVNMbKLZ3Rke5nIVdlOFAP3rKQPO1HIds/9vgG3TVyOR35cE+hNIbLM72sO4q0Z2ywdhUoUSm76bAkOnr7g9bq4E+TImTwMeGcevl26T/dyPSNRZNJ5GTzlxNNLG62MLMpo542ZQrNGG5l8odVZ5atHf1pr6/KDjTjFUYkgYGj7GnhCYYS0EWZSmASauPFS3Dil1mjRpqZyGiPxPuhcV742gBJbIlFEH0Or46NWpTikJ8nXVrivZ11c36YsoqjIQMem0mhvpYbjcZc1RvPMZNwtaSQDgMVPaxf4VqrJYMae4+dkXzfaICy1VEehcn/R81HkikM7nQ68L1N3SUlsZBjukDSsKMkvLMF1ratpT2gR6W9moczn9cXzlzdB+9rKHUNWFVBPjI5AoyqJqlGFehrqAeDfR7p7vXZfT+9zUkrpkxitP+KA+n55dkhjfHV7O7x5fXPdyzQ7UKBFZrKp+YyICg/DD3dpp45TT+cl/7qv1ytx+qSo8DB8fmtbr6jX7+/qgKYZ3vu3SOVcEt83u7YxIsyJT4e3wV0y138x6WfS+oQRCoMUHA4HmmYk6a4Z5iszp7r4oyp1LonvyetU0hdV43qGEe/LFpn21x6j4Gd1f1nrGhXx06iOHr8rWpEobLWyBjtRyHY/rigNHQ+FPO9Eeo2etAbvztyOFXtPBXpTiAJiyS7vou+AZyTKq1O2YMuRXNkRt0o8a6J4M9yJIrpl9CVKxuh69d4s7zp21v3vW79cZmwlBomvV3anDrvUiBvzrOyfsjtiyA6enQxl/5Zr8JvzWA98NrwNuqh0johHpBtNX2O01ooeTocD9/esC4cD7mLpSqQf2ZVr/GqZmi6+RvEBpZ0ob1zbDAlR4R4RVLd1roXf7+uMJJnaCpUTtEd5S/e60reQrqNYuNx36HAANSrGuhujFz/dS3M5UmkWFCq/rHlVfH2xIVMpTZQeehrwjfwuKlk3rh/GDmmsa9q8wmK8fFWWz+vUS/obY6STUEtGcgxuvVjAXYnapcJMY5Y0hRUA3NAmE9/e0R6ZFbU7UeqlxqNCXKRHeqbPh7fBLR1qqM739e3tLCvW7HCof/aRXWqhe/3KiJZEhdRPU15/sUU3E0oRRT72VSAhWk9DvvKxaWWkQJ9Gafjz/i6YdFcHpCZ4X6/0/l7p7ZA02skm5VHbTmYf2fH7aoapmiiiee7qVge9G6Z6TZNRIQZtalTA4GZVZH875YgHKM16tDu+GNEG7YKk1lVzP3Rckn+1rVnRY6BOvMz1zrNGpT+2qvwLjisfEVGIMl1fgaicEj9AyKVt0Zxf1NAiN4jclxLl0ugLI49ddt14bjyUg2+X7sWO7FysO3DG9HK2HlFPR3b8bD7mbjvm/vvrxXtMr4vUNbSwBondEUN2cCg8scmdzzUrxaGvQjqYSXd1wOP9G+AKUUFOo402dqSIKhEEPNa/Aba9NBADs6qgaYb+1GpD29fAnMd6yHa+GOkwU/tU17XJxPrn++Pdm1qiSdVE06m/xJIlDUhKbVYdams3FlVW6OwID3Ni1qM9sOuVQe4i9J8Pb4OE6HBUUYjmMULpOBN776aW6Hax8+meHtoRAkr0NOpZEZlhJO1YXlEJIsOdHink7CSN0lIbPW+Unihwtcbvl67MQnJsBMYObqQ4jVRybCS+HNEW397R3v1aoyoJuqPjvruzNFpRfJr3aZyGKkkxuKmdcn2NOqnxaFm9At69qSV+l6RH0nvFaFI1EV3rVYLDYa6wvDiyVhodadVvlFI6L7nXjVzX9dQlU/sIvnbieK5HQFa1JHRQiaD6v1vbAAAiVc5to+m8jBB3cou/d7l9pLaN/uQahGHkd0J6fZDbV2FOB36+pxM+MBAdKP4dr105Hr0amkt3Z4ceBlPTkrXaqkRc+0J85MpHoojOY8aiWCI4rnxEREQmHDx9AYIgYPbWbNz/3SqcPs9OrUATP0CYGXnqGYlibTovq9pwFu04jpUWRaF9t3QfnvltA/q8Nc+n5WilzMzOyff4+9WpW3xaH3n78/4uuLtbbTxmsG6HmlBMGSluiyjWmc5L7PH+DfDXA13QoXYK7utZ16NxI9xgp4gvtS2UuCIIXB064pRcUnKfuWalONlUMFqNkeL0L3oKVyfFRODvB7tiVHflzgC9R1fPBqkY0akm3tSIvNHTyXV9G++UUq69EeZ0eOybPo3TsPa5fpg6uisGNk3H58PbqC778uZVFd/7+JbWmtum5rVr9Edx6ErnpRKJUkHnqGepiDCHYo0KVySF2vFqJenhfF2bTDgdwACFWidGCiwr3VqIzxE1DdITsGpsX9zRtbbudQJAz4apHp0mVQ2kW3RFfMmlANRTV+Ty5lW9RpLr/X3464Eu7ggrM8SXsX5NPLdV7T6vZ4PKmPKgci0ZsfGXy0dUxYiKon8+vA3evqE5aqbory0g15km7RRWLyzvey/K7Z1LU+493Le+xpRA70Zp2PnKIGx7eaDiNIUq1w7x1prZdPHxWTU5GpUTolC9Yiyiwr2v7cESifL6tc3weP8G+GmUduo2F+k1Wm+Hk1qHJ2Cstpm/3dOjDp4a2BD/PNwt0JtySXp6UCM8MaCBbFSjL8TXqIpy94aMRLFccFz5qNwoKRHw9K/rMWmZ/vz3RERmfLt0LzpPmIXn/9yE275cjr/WHcZr07YGerMuCWoP7uIHCDPpaewcee/Lw414zps/X4prPlpkSQO3VZ0xh8/k4cz5Qvyz8Yhs45wdBbbLk1s7qqdU0SOrWhKeHtQICdHWPSAFWzovPY274kYrj3ReOhsp7upW2yMPvPgBMUImnEWt/pBa+q9Vz/bVtT1S0vNLbTSukUYsrevlYFHB8VbVk/UvWAetxmen04HxlzfBNRdrash9rOgIp65R+VHhYbKpU9TWnRwbiY9uaY0+Go3N/72xBdY+10/2PTOjssUqxOovCq6nXsJlKh0+eurUyGlVvQI+Hd7G65hsWT0Z17TyXz0UwPv3Ni0xGpteGICPbpEf1T3n8Z7uf2vtvqxq8nU4ntOZ2gzwrabFxNva4pG+9XVFN0nJ3TaYHR0sTSMmHonvEVHgcLivy2Y6BeqI0om5tt8VdaZWZ2dE51q6roE1U2LRuob8KO0sUaRfn8ZpuKplNURFmG/CSoqJQCVJoXq1a68VkSjPXdYYW14cIFvfRI7Wtap2ZX2dSGauea9eXdpZ/FCfeggPc2LRU70w69Husp1RRgc12CU5NhL39ayLahX0d8RKzwO9+0pa/0jaIefP2oZGRUeEYVT3OqifZl20NOkXHxWOe3vUNTRgQI+YyDBMuDoLE67OQrLMfYrVtViInShksQ9m78D3y/bhqV/XB3pTqBzYc/wclu46EejNUMUe/cCZMKV0JP3ERXvcrx05413oPBicuVCI/y3ZW27Sv6m19YkbfQtN5MoWP8zKPdP4cs751IkiM68V579ccWEzTp4rQJfXZ+Gub1big9k7vLbXjgLbgdLNhpQE4gbNtETtGhH+EmydKGE6jiOPIpaizdfbeCedSrxKoyn+1BpKzY72l56zaqNxjTRYanUgizuQrMrTn3ixw2/SXR0Up0mQSw+hsP7Lm1fFx7e00mzki5SMapbL422Gw+HQnbveKCOjR9W+99G962Htc/1QQ2U0vbQmhV6u3zhpTZmXr8zy2ud2e1amQyM6IkzXsVunsnwNjtSEKNzRpRYmXC0fFWTVcaSlR4NUPNi7nqnzUOs+5JuR2hEj393RHkPbV8f9PesqTpOh0DGqdAkfnFVF/g0AL1zexOu1b+/ogDXP9UXdVOUGWQf03SfJRQz+cX9n3Ng2E69f6x39ZiQSBQA+FnXc1UyJ9foO1DbRqmut2XNazvOXN8FN7TIxWZLeDfD8LEZriAHADW2rY9WzffFQn9KomYgwp2JEZw0dtYCCVZok9ZfeqDJxFFBGcgx+HtVJshzfU09S+WbHbf2N7arjRo0oKYDtVlYpP0/UFBBjJ6/HPf9bCUEQsHjnCbw5Y5vXNFbmEqVLS4//zMENny7BtqO5OHE2H0M/X4I/1h4K9GZJ8NcoUMKCZASUHo/9tBZjJ2/AHV8tD/Sm6JZXWIx3Z27HpkM5Xu8VqXSOHBGljTITieKRGkLm4dXoiE3PdF7Wnq+BCNv/Z+MRxfdy80pr0PywfD+6vDYbT/2yzv1eebpSRdpw7repWRGvXJWF7+5srxrZ4G+BfOAZO7iRV3okpf6CWpXkG7XEnXlm7wfF34fRkbVKhXXHX9bYUONYbdHny5dEokSoNE4b2Vqt/lQrR/2+fk0z9GlUmqILUI+m+ecR79Qfcg3ybWpUhNPpwICmVZAmUzRZTPw9tqtZEQ/2qqdzy/X564EuqiPkzZAb4alE7TgtKC6xpKNnaPuyBpNhF4uTu9IFpUhG2kvrr4zube3+lvrytramihj/cX9nDGiSjs8U0rbVS4vH2CGNvT6fSyiMsJa7bxBf46SRjHJHUqe6lfDyVVmIk+ng/Hx4GwxpVsVdW0NtXS4pcZF496aWitss3t+ua3rYxQgxLXL3bH890MXjb7l7xWbVkjHhmmbuNGhiz0s6dVzXMSUDmlbBb/d2wsCm6XjvplZe61P7nZXurb6N0/DyVU1V12e3lPgovHp1M7SQOcfEl570JP3p5sRkUwKJfH17OwzOqoLnLvPuXAsVo7rXxmXNq+KjoaUdbA/0qoshzarg02HqaR/F19KFT/VC3YtRWpPu6oBBWel4+Sr9aR/p0uTvZ8fgeaIpP9iJQqbsPn4OP688gP8t2YepG45g29GzmLbhsOy0vobP06Vp57Gz7n9vPHQGb0zfioU7TuDB71cHcKsomJgZYRUoMzYdBQCs2nc6sBtiwAezd+CtGdsw6N357tfyCosxadk+HDqdpzjf2v2n3f/ec+K84fU+/vNa97/lvmHDNVFED/BmOnVcLhQWyyzb/+76ZqXmNEdy8nDw9AVMWr7f/VqwRTT4wmwe7of6qDcc3ty+OjrV0Vck2B+SYiICml/7jq618e5NLbHphf7u15Q6JVrXqCD7unj79d4PSjs3xLMprV+J3KjrZtWSMOJijvq7u+uriTDtIeUc4mqdekZG/2dppHqxMv/89W0z8fmtbRFzseiyeKSxuAFt5dg+7iLvYle2yEAzSUqld25s4f631vVG/Pv946iOumq8GNE0IwldLY5YMxKJ8tb1zWULvAJAfqE10YdPDmzo/vcLVzTB+vH9FK9f0k4Uu1N7GT1PXcXKm1VLxsfDWqOmQqeslkrxUZj9WA8se8ZcOjR/GN6xJgB4pAIT36dIo0iN7EpBKE159f7NrVA3NQGzHu2OFWP7eEwjdxmunhKr+/ps9BdJ7ifMldaq5cW0hNcY7PBMiY9CemJZR+3YwY3w+32dVWvLtKxeAR/d0hrVU2K9ov5UoxlFX8C/j3TDZ8PbYGh739N/2sXhcGDJ070x/4meitcgX3WrXxkfDG2l2dkSzGIjw/HeTS0x8GIEVkJ0BN6/uRX6KdRscilQSNfVoXYKPhzaGmmJjEQhdf6+rRffU7OwvDXYiUKm9PzPHDz2U1lDl/TmXExPKoP/LdmLT+fttGTbqHy493+r3P8uKhZwSmfB8F9XHcBVHy7E0RzlRl4qH6wo9nipEQQBE6ZuwZT18p3eLrO2HMWGg2e8Xn97xjY89et69PzPHJu2ENh/8gKe/nU9CotLZBsPfHmAN1Po3mX0pDVer6k1cNtxk3y+oMj0vEYa412jmoOV2WLhqRqj410CfWn59o72aFwlEd+MbGdrjSC9YkWFfeUiAB/oVVdxpJtnYJm5dF7i2YzWMZBrzBU38D89sJGu5USGO3F3t9poUjURlzXzjM7pUDtFcb67uml30vzzcDe8eGVT3NBWveC3nQMHxFEuPUSdD0oj/mMiw/DH/V0QczFFzeje9TzqDIy7vDHiIsPw5ICGsvPrSQsXbMLDHF7f5zs3tJCdtmX1Clg3rh861/U+NvKKvDvk5XwiMyK6SdWy+hCC5NxSq8VU6Oc8/XrX9vt9ndG7YSq+ur2tZeuuVSlO97Xebl0u1gi6qmWG+7Uxgxrh69vb4d0b5SM/rBzvULtyvFf9D7kfOLn76WiFuiPtasnXLjHj69vb4ZuR7XC3juuklLgWRXiYE80zk3U/F3gFU6vsc3HktVKauWCTnhTtVS/HpV1N676/S5FamxeRHv6+q1dKsUvmhd4dLIUcrZEthcUlGDt5A16ZsqXcNnzn5BXKFvolZYdOl9W2KBEE3elVHvlxLVbvO41Xpmy2a9MoSMg1KPHeQN2MTUfx8dyduPfbVarT3T5xBQ6e9q4vM2tLtl2b5uH7Zfvw2+qDCpEo+r7l8wVFmLM126N+gdU3j+LlZefkYdqGw7ZGfDz/xybT8y7bfVL3tIkx/skrb5betuSfR3X0+Lt+mr4GkEB3z3auWwlTRndFs2rJsoXUA0muU8LpcCg2XpmJpJEuSrwIoyPc5e5Bpeeo3uPp6UGN8PeDXd3RGy7JsZFYP16+mHnL6vIROmL10xIwrEMNzftls52Heoh/T2/vUgt1Ksfh4Ys58dX8+2h3vH5NM9zbs47H602qJmHtuH64p0cd2fn8EUnavZ53JEpzhYLkADBmkHyHj0tRsYAxgxp51PpoX1u5QdLpdOC/N7bE1aIGdADIK9DXidKnUZrHv7e+NAB/3t8FdVPj0SAtQbZWjRJpw5+Vo1Ef6+d9nOj9nW6emYz/G9FWta6G53INbVrAfXhLK7x3U0uP9E+R4U50q1/Z6zpSRhKJovGLpKejVkzu1JN77X8j26NahRh8fjG12oIne+KTYa1Va6fIkX5nYweXdVwnREega73K7mvbt3e0R0ZyDP43sr3mct+5sQUyK8bgzevKaqaIj2ul6EjAOyWt2m2bOH2jVfVRAumzW9vgPZXUbaSOnSjkK72/j1YpB5etoBNcT2YUsny5Foh/jHzpaDh9viAo05WcOV+IZuP/QdfXZwV6U0KKuCB1cYnxHwBXbQC7hdoDXXkSSjVRgsURSUf1vG3HsHjnCdlp9530TsXlzxuxU+cKZB9YZ27Olo2SkXrw+9UY8eVyjw5Vq0f1ixfX9+15GPW/VagzZgrmbM22JWT6hxX7tSeSselQDsZO3qB7+qjwssadqyQNgMEgOlxfgdY2ohGXXetVQhWdhUODiVzO+0CofnFUa/+m3qkunA6Hx7VB/G8zp5z0vBcvwmgkitw1RLpNcgV/YwwWAVaLArBK13qlo9rV6peYJT6naleOw8xHe2C0Rvo7oDQN0/VtMz2uGS5qnT5W1ndRkhQb4dXA/PGw1hjRqaZs8W65oqyT7urg/rer0buDqOMkJa5slL9cx1Cl+Ci8dUMLbHqhP7pdjPAZqjPST7y4B3vXRVR4GJxOB6Y/1A1TR3c1dC7EKjbY+6ZV9WTcL1PPhvfGpRKjI3BZ86oe0XxyOtethNqV4jA4q4rXvlPubCk1ZlBZp4Se+w65Dm+562SbmhWx4Mle6HMxRVa1CrHo3yTdUEeCw+G9TXd0Ve706Vy3EhY+1Qtd6mmn1WyYnoj5T/TySAX29MBGCHc6UDUpWrGuDuBdf0ptv+XJpHINZUkxpcckmdMgPfjrLlFw839NFFE6L/42W4KdKGQJtYuB1q2WOMTc7EPVzmNn0eKFGbj5s9Ii5MHUmbJq3ykAwFFRsWXSViQ6LooFISC96Cv3nsKe4+c8XtuRnYupGqmQQk0wnS9GhAfZCG1/OHO+EM//uRHrD2h3IsgRn1c7ss9i+BfLcNNnS3DglHeHib/Tf0iFhzllfz/en70DQ95boDn/v5tLo2ZWi+rQSH+rfP2E4uWduVDo/veIL5f7uGTrnC8owur9pwzNI67j0FElVVEoaVwlUXd+8GAabfrW9aWjbJ8ZpC/tlF1+vqcj3r6hOR7p6z3q3OlQfjCz4mHRIxJFpuFYbaS23PTSbZKLuPhJEsUUDOqnJWDGw91sqffgdDqw+OlemP9ET80GXyuYKTpuhrSOSZWkGIy/vAnqyUQ+yEU5daidgl/u6Yjv7mjvXlaTqkmYfF9nLB3TG5HhTmx4vj82Pt8fUSr1b2Ijw/HliLZY/kwf1RHyYuLrkLjhO8zp0NWB0vBiY9/VLTO81mlVrQSlOj3Mu25MVHgY/n2kOz4Y2sorKsJILR495I4cu37xKsZF+rXRrmalOGx7aSAWPd1btV6HuCB7fFS46jnZv0k6YiPD0KOBtTWWAu3tG0rvLd64tlmAtyS03NAmE88MaoS/HugS6E2hEOX/miiidfO32RLBMbyNQp7a6ajVGCGORDlfUIxfVh5A3yZpSDQwqu+nFQcAAEt3n0S/t+fhhjaZeC1IbgqCqC0mpIhrF5SU6E/n5aI19Yo9J1EjJQ6VE8pGERaXCO4Gl/0nz+OajxYBrImItAAARotJREFUAP59pDvqppamgenz1jxD2xHsnp28AX+sPYQZj3QLmhzSeoVQXXnLvPT3Jvy08gC+XLgHeyYMNjy/OIXBugOn3f9eude7kT3QnWvhTofl10/pjeucrcd8Wp7RBmK5/Wy3X1YdNBzlKR7pXiIIqJsajx3ZZ63eNA/XtKqGX1Yd0DWtkf3+/Z0d8Pf6Q3igdz3D0QXBoFPdStjy4gDZaAl/Sk2IxlUtS0f89mmUhn83H3W/55Q06oq31ZJOFNFdplxDt9pDoXj6iDAHCosFXNXKM7rqjq610KNBZew6fg53f7MSAFAvLR43tMk0Hf0FwJZGlnpp9o2ClSsgb5drW1VDQVEJ2tTU16FgllLUjtwYDKV0aq1reKfskjbCqs0vXr74ntMIM5Ekfz/YFRcKi2U7TJRq3RildOZ51ZywSHkeReu6htqd6mV0n3oY9n/LcF3ravhpZelvrt6i8np9NLQVDp6+gCZVk7Bm/2n367Urx1m6Hjl6OhhfuyYLn86Pw3Wtq6F6xTiPgSNSybGRWP1cX1siAAPpqpbVMCirimwUISkLD3PiThM1fIhcAvkzVp5/Q/2pfP0aUMDoveH7fc1Br9fEnSiP/7QWj/60FqO/X21o/dIQel8eeq1m9Y3ppcjqxtxFO47j2o8Xo9ebc9yv7T1xDs2f/wevT9sCoCyCCAB2HVNuPAz136JvluzFmQuF+Gbx3kBvimFykSjl/eZg69Fcn+b36JwU7av8wuDL8fv3+sOGO0/97aFJa7DtaC52ylwj5I5FV8esP504m48X/zJWS0XcoFAsCKiZIl+g1EpvXt9ce6KLjPwkdKyTgpeuzEJ8VHjI/h4HugNF6tNhrbHmub7uv50OByJEkcRpiWUd8nq/q5qVlBvXxOeS0QBE8fST7uqIibe1xV2SdDIOhwP10hLQr3EavhjRBvMe74mo8DCfRuxlVoxB0wzl+huXOqfTgVs61EDD9ETtiX3Qsnqy/PplOuN8vT7YUbNmzKCGGNW9DmqbKGgd5nRYFnGiSOEU0RttQ97MXHWuu5jSanRv7TpGXetVxupn++J10WDD9CRrB1ENzKriTtslbiP4ckRbS9djVkp8FJ4e2Ah1UxNUO1BcosLDgipC1SrsQCHyP7+n8/KIRCErsBOF3AqLS/DA96vxzRLjjamqkSiif4+etMZ7vUVlc6+6mHZltsHRwUZzZPuTUrFV0q+4RLA01nz6xiMAPOumvDVjG87mF+HDOTsBeB6rBZdAETk7UzeV2BTREKoNor7w9RMXi75n8YPtE7+s83HJ1lu2+2TQR/LN3JKNfm/PQ+835wZ6UxTN337c8DziRoUSAXjlqiwrN8ln17Sytk7Lf29s4fF3sB93geZ0OpAcW5YqJS4qTLEDW+8gm/iocKwc2wfrZAq0ix84DReWF02fEB2OHg1SFRu7HQ4HejVMQ/WLnYaj+9RHpfhIPNirrq51/Xl/WeSJ0e0ke7SpWRGfD2+Dfx/p5vG6bCeKj9+ZXE0UX93VrQ6eGqhe8N4sV12Efo3TNKZUJtfRuHZcP1RQSaVE6sy0r712TTPMe7wnbm7vXddHToW4SI9OgVop9kWI1BJ1kNewcT1ERKHA7+m8PGqisBvFCuxEuYQVFJVg1pajyM0rzeM+Z+sx/Ln2EJ41UHzWxdUoLUvjmcKKBupgflgVb5pdjclyCotL8OD3qzFp2T6/rVOvP9Yewj3/W4lz+fqKv+/IPutxGP2x9pDmPGp7OldmvWq/KWqj9E+cLat1U1Rcgg9m7/AIXQ9m4h/SYptyL/y76SiaPf8Ppm1QuUaYpFVD6Wx+0SV/s5BXWIw/1h7CqXMFADwjUUwVfPZzZMjS3Sf9uj4rBcuRZyaFmLiBsaREQGpicKX661S3Ej4Z1hptTaQCWvBkT/x6byf3361rVMAVLTw7ZYL4liKoPDmgITrUrojr22QqTmPkpyUlPkozlatsUWSdNVGMtnFnJMdg+TN98Ei/Brqmz6pWFnlyKXbyB6s+jdNQV1IDRe7r8XVA1ke3tEJCVDhevTq4Op2VvHNDC8x6tDuGGCwy/ZYoalB6HxEZ7rS8hofYpZDP3UzKK6fT4e78NaOPDx1pWpJjIzHv8Z5YNsb6Wk5ERKHG320TjESxHjtRLmFv/rMVt09cgTu+WgHAs0HyrM7GbZdP5u7SPe3yPZ6NYoUWdKJoNabqbay3g/iBv8iPnSi/rTqIP9YewlO/rvfbOvV68PvVmLrhCD6dp++4+WHFfo8RUw+K0r2ZOX6MHg9qHX3P/r7R/e+vF+/FG9O34soPFhrepkC499tV7n/vOXEes7dm45O5Oy1Nn3bH1ytwNr8Io/630pLlFRSV4Mz50o5ftSivdQdOo+m46Xj85+CLsFAze0s2Ln9/Abb5mLbL5c1/tuLB71fjlv9bCsAzNd6FwmJL1hFMCopKMPTzJXh35vZAb0q54c/fLSP6N0nHj3cbL/5drUIsWlUv63xJlalRwBQX+tzTow4m3dUR0RFhuPZiOplmoo4EwJq0BeJC53IN3WoNqw6HA0PbV8fApumoYyIlktkULkoFtyk4JMVEoEvdSrpS+ejVukZFrB3XDze10xcNEGhhTgdqV45HXYXzYvxljb3Sge1+dRCublXN/XdyrKTDJDh/LkJKmh8HLSx4sid+u7cTGlWxN61e9ZTYoBuMQUQUCP7+meSQHuvxDv8S5qob4hrp62qYBEqLalvh+Nl8j5RJAHD7xOUef1vRiaI24u/VKZvRZNx0LDCR0kSPHdln0XnCLPxPIQ2auKHXn4WaT18o8Nu6zPp8vnwnilwP/aZDZzz+nrvtGFbvO4X6Y6fiPZkGU7UfDKOdhGcuFKq+P3/7MazcewovGKw7EGhTRdEhMzYdxW1fLserU7foLu4cCP3enovmL/yDY7n5sqkzTp8vPe4/mL0DAPDzyuD4LOJ2uAsFxfh73WHkXIwC3HjoDJ6dvAEnzubjtonLse7AGdyj1OlksEHvr3WHL64jBwBQKBoWfvq8+nEdiqasP4yFO07grRnbAr0p2H3snCXL6VK3kiXLMUJ8lAVzNJcvOco/HdYaXetVwvjLm3i9984NLXzYqktT88xkLHm6N365p5PH61YcPbUqxWFU9zoYM8hcWqOXr8rCR7e09mtOe6ZyDW4OhwPfjGyHdeP6ISrcaVndp2BOL6ykcdVE3N+zLGWd69Ad2qEGYiQF7V3n0Me3tEa7WhXxwhVN/badl5K/H+yCzIoxXqkmrVatQixaigYVEBGRvfxdE4WsZ3O1OQpm4vN33rZjeOiHNe6/j+TkWTIq5ZbPl3q9Jk2NpNaJsuHgGcRGhmkWVIxWGEkmCAI+uRjt8MqUzZgyuqv7vaLiEkuKQL7w1yYcPH0BYydvwC0dani9L36eKvbjRTNIBw97OFdQjOd+34BxlzXx6AgTF3V3yc7J9/j71i+Wuf/95oxteKB3PY/31T7+2XxjI/AnTN2Cy5pXRUZyjOz7w/5vmezrRhw4dR5Hc/LQukZFn5flq53Z3kWyg8WeE6UdvIt3ncDhM3le72fn5nu9FgwcKDsmx/+xET+s2I+OtVPw/V0dMPjdBQBKr7suxxQ+h9HmGY8C4SUCikQ1Ud7+11hHw+bDOTh+Njj3r8tLf28O9Ca4uQYq+CoQbbHidfqz8//fR7qjz1ul9WX6NErFv5uzbVtXvybp6NckXfa9phlJ2DNhMOZszUa1CtY0rl4K5IoTW5XGVK0uhL/TDKp5vH8DvDF9K164wrtzjoKLw+FAdEQY1o7rZ0s9k1DSs2FlvH9x8MnG5/tDEEqjqZQeWwY0TceApt7XT7vTbV0qbU9NqiZh/hO9Ar0ZRERkMb/XRBE91F0qv6F2YyQKAQCGf+HZCFxQZE19hC1HvFPSSHtfC4rkz+ZjufkY8t4C9NJRsDcuyrs/8MyFQkxZXzbSftPhHBRd7LD5ccV+NH5uOuZuM1bA/tS5Aq9RuVqjdMUXrmIbi3eHqq8X78Xf6w97vJZzwTtSxIraOQt3HMeh0xdMpXf7ecUB5Bf5nv5o/8nz7jpEYl1em41rPlqMDQfPyMzlX5/M2+URmRaMHAAOnr7g9bor9VCw3SSIRyW7GtcX7zrhkbZr08VoEQAoVLhWSBvUz+UX4coPFiJr3HRslbneRok6UeqMmYL/W7Db1PbP23YMA/87HyfOBXeEW7B38gSzSvHeaa0A/6Tz+t/I9gCAuqniARPejZrd6lfGx7e0tn17XHo0SJVsExnljxF3wVQn4b6edbHtpYFoUzPwAyJIn+iIMEsGVYUy8T1KTESY6LkqeM4tIiKiUOf/wvIea/fvysupS/uOkRSZSbGl95SUNsgorWufgZRicjVR/vvvdvy93rMAuasg+RM/r0NBcQlu/WIZFu3Ql+brn41H0PLFGXj+T8+UTVEa+ZTFjZ5FJSXIKyzGp/N2Yv0BY43lS3adwFO/rNPduC2+QI/5bT1mby0d0Zudk4cZm47qHl1cUiJoprOS+mXlAdzwyWLdDZrSUfeT1xz0msbXhrwflu/D0M+X4pEf1yBH5vNoFV+eteWobGSVEXuOn0PX12ej/SszFadZoPN4NONoTh5mbj6qKz3P839txK+rDuChSavxwp+bcOOni011rkZKGiYKikqQnesdQSJn/8nzHueo+JhVSpVSZEFnmx2Uogn6vT3P/e8iUaotvdfgz+fvxpr9p5GbX4T+78zDJ3N3Ytex0kiik+cKsO2oNVFF0o72YLRaJoKtPPBHCqKNz/fHT6PK6ouIV6kUgeeycmwfn9ffpZ53yrLKCZHuf29+YQDWPtcPX9/eTnb0M1Da8HdFi6qY/0RPJMVE4L6edXzeLvLdpfi4ZmWdDSJ/EN9TKY1a/fP+LprLCbYBLERERMEkoIXl+RttCabzIll5hSVYtOM4WlRPBgCcOFuAzIqx+Hz+LhzNkW8AlY6czpEZbS8/n1Jjof6zXG4RXyzcjWGS9FoTpm7BwKZVPF67+fOl2DNhsOY6JkzdAgCYuGiPO4d6bl6hZrqRV6eUpZd56tf16NkgFa9M2YIaKbGY+3hPzfW63PjpEgClHUYvXZnl9f6Gg2cgCKUP7+mJ0R6jP79bug/fLd2HPRMGo/dbc5GbV4SPhrbCwKwqXssREwQBtcdMAQBMe6grGqbrS/H26E9rAQAfzt6J5y5r7PHezM1HvaYXp/vYf/I8fl9zyGsaoyllZm3JxunzBUiOLW2Ic9VIWLLrpMd0giAgOzdfNqpBbK3BTi+xkhIBXy/eg60Xow7OFxSj5lN/o1OdFHx3ZwePaaXp7owQBAE7j51DrUpx7vRoO4+dxVv/bMN9Peviyg8XoqCoBP+9sQWuaJGhuqxVe0/h11WenVlfL96D69pkIikmQmEub+FhDhSIAniu+WgR1l+Mthk7uBHu6Fpbcd5Hf1qLZbtPYu62Y9gzYTDyRIXQlTJv5F/s6JEeLcUlAtYfPIMmVRNlC/6eOV+IJGmBVBMEQYAglOVFFwQB78/aoRhZ4rmNZf/W22l47Kzn9fjVqVswYdoW7H51MFq9OEP/hgfY2MGNDKfiEgTB3diz5UgOrvpwkR2bFnB2d6Hc1C4TcVHhqB4Rht4NU7HnxDl0qJ2Cr25vh2W7T+Cy5lVl5xvRqSba1aqIFIUIFj1ev7YZWmQme7z24dBW2HXsLKokxeD7ZaVRWzGRYV65+V0GNEnHoTMX8Nu9nd3XvVXP9lWtlUb+4490cMGUzosoFCldL8Vnb1a1JP9sjAq2/xARUSjzd8p9j4ER/l11ucVOFJL15j9bcfhMHrrXr4xNh3NwLDcft3asga8WyxdPB7xHgM/dqi9VllInyqIdJ3RvryvKQqqCpFE0OzcfjZ6bpnu5YnIXnbGTN2jOt3xP2ejoGZuOIvZiQ9DeE/ojbcT2n/Ru7C8oKsGQ9xa4/46PCsc9PbxH4RYUlSA3rzSV1bqDZzQ7UeaIvsNvl+zDi1caKyBZUOyd/urJX9Z5vfbylM0Y0bkmIsKclqbj+e/M7Rh3WWmHl1LPe2Gx4BUZsvv4ObSvVRFLd5+Un0mHouISfDJvFzrUTsGBU+cx/k/vovOLdp7Ajuxc1E1NML0esW+W7MVzv28EAMx8tDvqVI7H7ROXY++J85izNdsdSfLjiv2Yscm7M0tMribHS39vxkt/b9bV6ehS2mFRehzsOnbW3YHiWp5aJ8rhM57H+llRGjalwfkp8aWdZtLv+51/t+G9WTtwfZtqeKx/A3wydxdubl8ddSrH47fVB/DwD2vxeP8GuE9UXNWM2ycux96T5zFtdDdEhjux/uAZvKmzyLmetDfSj10k0zkjCPDocAoFd3Stjb0nzuObJcq/MVJrD5xxN8APeGe+TVsWeHYHorx6dTMApY1o/zeirfv1Kkkx6F6/suw8Lasn45nBjWQ7JLW4akYAwPVtMr3eH3Txd+m31Qd0Le/jYa09OtQA5QZB8j+OeiMKfkrRvf4eMUtERFSeBTIFLX/SrcF480tUYXGJaoomV8HmuduOuRtT1TpQAO+R03obfgoURmiLGx6lN/FLdp3AflG6r7/XedbUcJmy4Yjs61aZvlF5+YIgoOZTf3u9biZVmliETOoyaa2Oswo1P34XpcmqlRInm57p5LkCnLxY9+CYqEPjp5XGiyQnRnuP7K+ZEic77b8ajfpmTF59EDd9ugSTlu1TnEbu++j5nzk+daAAQN1npuKN6VtxzUeLsPmwd60Kl/UHz3iM1PXlh/U/FxsmAWDsb6UdfK7OunOicJCFO07gL4VzxkU8vZwd2bm6os3Ex6tcfaPC4hLM3HxUc1nLdp/EHV+tcP+tdN3oUteVFsjz/fdmlRZM/XHFATzyw1r834LduOL9hQBK0/sBcDfs+mL21mPYdewcVuwtPX6MnO96RmxLUzspRbi4PlMwGdVdPb3SC1c0MZQG58oPFhquaxVqxl/WGI2q6IsAlGpS1dx8Wh7sXQ+/3dvZcAdKtQoxuLZ1NdzVrTZevqopZj3aXXX6yvHeRcqV+CPlGZlzqdVEIQpFtSrJ35u7ormJiIjId4HsyODACGswEuUS9em8XZYv87fVnql/1NIrFJcI7pGihTrqLOQXlSAizIl5248hMsyJoRdrU2iNiN+Rbb4egCAI2HQ4B3VT4xEVHobdx895TaP2GZVGVIuL3Z/LLxIVb1TfFpfFO/VF6Kw7cNrrtcdFDatP/boOL/61Cf880g1Vkkpz3ucXFbtTAO14eaBHvZc8nWmmxI3GiTJpn6omxwAy9UdOni/APxuPWPrAdup8IRbvOoHFu5T32XmNzgIrqDXw5BWWeKTI8+W3TdyReb5AviPNCu/P2o7//LMNidHhWDe+v+q04U71htb3Zu3AuzO3o1X1ZPx6b2eP98Tn1/WfLPZ4T6k+S76O68mqi7UzzuYXYd+J87pSbRlVXCIYrs+iNv1Xi/Zg3B8bPV6buHA3flklP1rfVf8pmFSroF5bw+FwoFqFGOw65n2tVXLrF8uw65VBvm5a0BrRuRYuFBRj9pZsbDlS2hn73JDG+Hv9YcU6Tje2zUS9tAQMaJqOzhNmWbYt/zzcDTM3Z+O2zjV1Tb/wqV6IDHPisZ/W4tT5Ao90W0Pb19CYG+hcNwUP9qqLBjrTSFJw8kcnChH5JiYyDGuf6+dVY/KjW1rh8Z/W4ZF+9XUth2c7ERGRMn+n8xLjb7Q1GIlyifpdpnC31dQGhtYZMwXTLkaJ6Bmp3fi5aWgwdipu+3K5uwPFZdvRXDTN8K2RRa5X9qcVBzD43QW4+5uVWL7HMyrhnX/l0/OIP4srrZKah35Y4/Xat0v34tEf13qMzBd3YJwrKHanL/tj7SH8ufYQSmR24fSN6pEdJQKQm1+ELxfuQX5RMbYdzUV2TlnkyYXCYlOje39eWdaoK9dBpNSg8sxvG3DXNyu9Gsv1ytVZg0dqyHv2pwFSa0PKLyzGSFGEhdEft+zcPHeHifj486VOgZb//FN6/OfkFaGkRMCe4+cwZ2u2bCdARLj6MfTj8tIIp1X7Tnu9p3b4SaOv3K9fPFf0tts98uMaxfeW7zmJ537f4D62zuYXYcH24x41fMTEr+cXlqDuM1NxzUf6j+c8hQ6gkhLBqwMFgGyKuGDw1MCGuEEmTZNSuhCj00i56jaVJ93qV8bLV5WmT4yJDMMTAxq436tVOQ6/3NPJY/oPbm7l/nd8VDhGdqmFjOQYVE3SH82hpX5aAu7pUQfREd61SW7t6N0pkpEcg8oJUZh4W1v8fl9nwym2HA4HHunXAIObqaedpODmjz4U1kQh8l1SbITXfXvD9ET8+UAX9GyQGqCtIiIiKj8CGQ3CcU3WYCTKJcpIyhSztNpLRv1vJRY82VNxNK1YiSDf+D5v2zEM/2KZ2U10KywWEClp7P1k3k4ApXVB5kjqu7zz73asP3AGFyR1B75bug+3dqqpe70zNh3FzmNnUadyPADgg9k73CmF+jRKddcskabnmrz6IFrXqIAHv1+te11KPp23C8fP5uPXVQdxdauyYuMHTl3wusiXlAi4UFiMiYv2YEDTdPd2i209Upa66rxMWjG7RqVmjf8HneumGJ7vaI51NViUXFCJdjl+tgCbD+e4/1aKsMjNK8SKPafQrX5ld2Nkdm4e2r08EwlR4Vj/fH+PiIqUuEis3qd9bvlK3IA9ZlBD9GqYitenbcWDveuhaUYSIjQiUcQdP3O2ZqNheiIe+XENqiTFyNb7cHnmN/l6RH+vP4wPAMzcIl8nSerAKe8aQy7XfVzaARIfFY4nBjTEx3N24v3ZO3BDm0y8dm0zr+mLRcf2ruPGo+CU0nkVyvWSBrGRXWph65Fc/LDCMwVgXJR8YXCx8ADVsmhWLQnrDpyRfa9yQpQ7reVd3WojOtyJdy+mh/NFQnQ4vr69Ha76cJHH6/f1rIPH+zf0eC1MdB5FyaTRGtysCu77rvTfBaJzqm5aAg6dyfOa3mrjL2+CJwY0RJNx073eY6qtSxvTeRFdOmxvHOKpTkREISyQP2O8X7YGI1EuUWaKwRr1p0bNBQDo8tps/LRSX/FYOVZ0oAClI85z8wo9ijFrpXmSa6TderSsA6F3Q32jtnq/ORe5eYVYu/+0R00Gcc2a75Z61vQoEYDVMiP3zfp11UGP/wPAwP/Ox+hJazym6zhhJpqMm443pm/FQIVCzq1rVHD/+9WpW7zed6Uzs6Pw78Id+lKd+Ztasez3Z3s2xn48dyeWydRjefrX9bht4nL8VxQFtXx3aSdJrkxn1d6T570aZ+322+pD6PPWPPyz6SiGvLcAmw7laF5rxA2+I75cjg6vzsSinSfwy6oDOHhauYNDjaumjxLxuX1OoX6QuCHAtR2fzi9NgyjtHHARd4JYOTJaqWPNLt/f2QFvyHQSKZFO64B8FFFPHdfEQBUEn3B1M9SpLJ8T/oe7OgAo/UxjBjVCkijlYIO0BEPriY4oOx8aV0lEy+oVsP3lgR7TyNWOCRPt0AjJIIjqFWM9/hYfLwnRnmNl+jRKM7S9ejkcDl2pKenSE8i0BUREREREwYJ1SUIfO1EuUZF+6ERRKvYejIZ+vhRZ4/9Bu5f/RV5hMb5duheHTYzerRAbge1Hcw1fHLPG/4MrPljo8dpTv67Hy39vwtGcPLwtSR/259pDuNWiDiQjxFEbBQpp2JRG0wuCgDPnCzWnkxqcFdypXFpVT7Z8mdKUZluO5LgLwbtGwEv3n7RWjlxHjN3O5numVBv07nzkKaTdcsnNs752i6uuj8s8leLjch1QgGf0V+1KpRFXtVLkG9ldxN+J0vlhhq81W8QN93J+vLujx98p8ZGG0mo1z0z2+NvhcHjNHx8VjvhIz0b2T4e19lrWwKbpAEpTQfnLngmD0bhqIn6/v4vXe5FhTtSuHI+Zj3bH6mf7er3/wdCWmsuPiwzDh0Nb4Y1rm2H1s/283o8Ic+Kfh7vh8+FtsOmF/kiI9q4lJe5ccv1+T76vM3o1TMUXI9p6TCs+9hIlnSif39oG85/oqbnNZmkda3Tp8cfDItN5EQUHNg0REREp4+Ci0Mdhg5copXReVZKiTXUe6PHTqI7u9DjBKievCA2fnWZ6/g9m78QHs3datj2fzd+N+duPW7Y8qxWXCF4jx4skvwzZOXnYcewsHvhuNa5tXc39elJMhEe0jZIb22Xi7/XB2yEn/rSxkWGWFaoX79sBkqifbUdzcdUHC5EhKtR902dLLFmvL/af9I4c2XvifAC2xJORiLV9J86jekosTos6/AqKS79Tp+RYLy4R4HSUpSsSH/tqKdz0+nvdYWw9motNh3K0J1bRrlaKYkdSzZRY1KzkGckQExGGDnX0pcZTirgTd6LMfqwHqiZHe+2/Po3S8J/rmqND7Yru1+7uXgeZFWPRsXYKrv14Mfad9N/xEx8VjvduaolP5u3EhoOl+7xCXGmHhjh1oTjyo25qAtaP74es8f+4X+vbuPRzjZ28AX+uPYR3bmyJvo3VI0DqpyWgvkpUi7jYr+v3u0VmslcHCuAZiRIvig4ZM6g0RVhmxVjMe7wnur0xG7UrqXcMGtWjfiqmbTxi6TIptPFhkYiswlQkREQUygIaicKfUEuwE+USFaXQiZIcG2lbJ0qg0rSEui2iGiPB5sTZfKQmlhUuLiwuwZjf1ntM0+6Vme5/fzJvl/vftSvH6UpJFswjTHs3TMXJ82Wpo/o2TsPvaw5Zsuys8dPx8S2tZSMC+r09DwCw7ajxuhukbtHO4zhfmIw/15Z9j/O3H8dj/QSPOk8jJy7HzC3Z6Fa/Mr6+vR0AIEfUKSitl2TGfd+t8nkZDgfw1vXN0ealf2Xfz6qWjHBRvY2G6QmoViEGDocDC57sibzCEvR5a67i8j8d3ga7JfVfpOm84qLCEBXuXQ/F6XR4dKwCpVEZV7Qorc30w90d8O/mbFzbqhpenboZq/adcnduXNGiqiXnmvT0uqx5VVzWvCqmrD+MCVO3eBRsd+nTKBVPDWyIrIwkAPCIHOlarxI+G94GAPDeTS3xxrXNZAuxA8buY8XXAaVBEAlR4cjNL0K3+pXdr93cvgY+m78b3etXxl3dytKEVU+JxYqxfbzSfflqwjVZSIgOxw1tMy1dLoUuf9REIaLgwNOdiIhIGftQQh/zLlyilBphutarZNs6w1hcNiDu7FoLcZHaBZ3NaPfKTNR86m+8/PcmnMsvQs//zNFdv0FPbv43r2vu0XD9zKBGiFFokNRj/GWNTc8LlEaafDOyHepUjsOjfevjvZs9U/m8eGVTn5Yvdr6gGMO/WIZb/m+pZcvUIk3LdCl66tf1GPDOfI+IsnUHzuDtGds8GrJdNZFcER6nzxeg6+uz3e//34Ldftpi4P9ubYOrW2UgKcY7DdQXI9qiUnwU+jfxPN9+HtUR17Wuhhcub+JxbX792mbuyJpqFWJRNzUeFePKaoB8IknBVdo57nltl17qI5zmbjWqJMVgWIcaiIkMwwtXNMVfD3R1vze8Yw1Ty3QZ0KQ0bdgrV2XJvj8oqwrmPdETWdWSvN5zOBwY1b0OOtct+738740t0KhKIl68wvMaoNSBYlS4TDovqZmPdsfnw9vg2lZlHVO1KsVh7bh+shErleKjZDu3fJEcG4k3rmuONjUrak9MlwQ2qhIRERERBbiwPO/JLcFOlEuUUrHnXjqLoQOlxYTfv7kltr00UHPaibe19UrzJCUtjkvWeGZwY/wgqXlgtc/m70aTcdNx4JT+QuARYQ7VhtC0xChc07oaWtWogMyKMeharxLu7FYbG5/vj4m3tUX9tHh8cHMr3Nqxhq7G/8ubV0VKfJTmdOIGY8AzXVGJIKBrvcqY+WgPPNC7HmIlNR4SZWoZBDPpKPQXr2gSoC3xTSUd36uv3p21A0rBdCUlAt75d7vt26Ckd6M0vHV9C6wd1w9rn+uHD4eWRU+4QobfuK65xzxtalbEG9c1R4W4SGj1cYg/d/8m6V5RhdJOE4fD4VEfJizMug70pWN646dRHdG6hnIj/TWtquGbke3cf395m3cHwtODGmLVs31xU7vqlmzXFS0yMHV0V9S0OD2Wi0dNFIVBEKmJ0ejTOM0rbVpSTAQjQSlgGIlCRFbh5YSIiEJZIO+LmRLTGiHRifLhhx+iVq1aiI6ORuvWrTF//nztmUiV0kjW1AR9jZFLx/TGdW0yMaRZVcUGHbEeDVLRIjMZV7fKUJzmOklaF1/8PMreTgNf9WhQGY/3b+CRdsWlaUaiZetxjT5vmpGEtETP77ZqUrTcLH7jdDhUo0pcReyjI8Iw+9Ee7pRJTqcDPRqk4p+Hu2Nwsyp4/oqmePO6ZprRNl3qVdL82RjRqSZWPdsX39/Zwf1ap7r2RGfVT4v3iPxKiA7H2MGNbFkXADw3xDMK586utfDPw90w69Hu7te0Rs1LoxnMSk/07dj7740tPP7+dHhrtMhM9mrMt9raA2dkX689ZgomLtpjerlDmlUxPa/0MyfFRmBQVhV3x1LzaskASjv4lK774Rq9KA7JSqQN8nIp58SdKGYjUeSkJUaj7cUoh1/v7YSxgxvhvZta4ub21S9uC/Dm9c3RLCPZPU81mSL1sZHhXh2moULpeyQKRuxEISIiIiIKcDov3pJbIuifxH/44Qc89NBDeOaZZ7B69Wp07doVAwcOxL59+wK9aSFt3OVlI86dHrnr9eVHTzPQCOpKmxLmdOCt61soNpzriRJQkiDZ7jY1K2L7ywMx/4me6FhbX4FkLeIG5KVjeuPhPvXdf39+MQe+nG/vaO/1WmxkGO7rWRdf394OA5ume7w38bZ2XtOb8Xj/Bh7ppUb3Ltveu7vXxqKne+Omdvry1lfW2blmRJjTodpoXz+trJBzeJjTqyFXrG5qAtaM64c9Ewa7/3tyQEP3++/d1BLXtqqmWchr/MXzQtxLL67ZIDe73h+jOpXjUCUpGuFOBx7sXQ9f3d4OX4xoiw9uboUNz/fH+vH9cUfX2voWpsOrV5elKbqqZQaOn833eP/y5hmokhSDKkllDcxOh3dni9g9PeoCKC1o3bJ6svv1vx/sYmjbnhzYwND0Yilxkbi8eVX898YWyKwYg/dvbolW1Stg8n2d8b+R3ucaAFSR6TDsLurAvLdHHa/3rZQYHY46leUjFAZlpeO9mzzTwmXINPor+fN++X0/5/EeWDamt8d1taBYPtWeVh+H9MyTpmaUOzPFDad2RUG0ql4Bd3StjcuaV8UrV2XhuzvbY/HTvUvXKYp+kbt0xOv8rQsW4utMhI6BC0TBgoXliYiIiIgCGw3CW3JrBH0rwltvvYWRI0fijjvuAAC88847mD59Oj766CO8+uqrAd660BUfFY6lY3qjsLgER87k4dqPFwMAYkzWzhjdux5+XnkA34xsh8mrD6JdrRS0q1UR87YdQ7vanmlXalaKw+bDpcWBZz/WAxsPncGC7cdxbetqXkXJpf43sj3qp8Vj+BfLPAquX9kyA4XFJZi0fL/7tYgwJzIrxuoeBfl4/wa4okVVjPltA27tWAOP/LgW/ZukoWX1CigRBDRMT8T0jUcBlHY2FZWUNUj2bpSK3+7thKs+XOS1XLnUaeLOg671KmPqhiMAgC9GtEGKwujotMQonDpXqNgQKnZb55q4r2ddj9eua1MN6w6cRqe6lXB586oASusBPDO4MWIjwlB7zBT3tH0apeLfzdnuv5NiInAs17MRXs0zgxrh5SmbVacJczpUj7d3bmip+J4c6X6uJUqrM6RZFTgcDt3HQmFx2XTiCBezPzzzHu+JKsnREITSxmXx9z9YEoVwWfOqHkXNlbSuUQEr956SfW/bSwMRGe7E07+Wnk9R4U7c2LY6PpxTVufDVetB2oB+e5daeOGvTbLLbZGZjPlP9ERqYmktBUEQ3J1bIzrVxA/L9+P/bm2D5//chK1Hc2WXAQBhCq32EWEOj30vp1GVRDgcDlzRIsNdgNylc91KmPZQVwx4pyxasXJCFBY/3RsjvlyGOVtL65d8fEsrDGhaBe/P2o5P5+3C5S2qIjs3Hz+vPOCeb/MLA7Dr+Fk8/MMabDvqWTjdqHXj+wMAaj71t/u1xlUS0b52RYwd3Nijg7BhegKmPdTNY1ola5/rh6RY+RRy8VHhujsKtCJRpJEmWum8AEkkioXpvNR0qlMW2RWu0XETHRFaHRHFomsXI1EolGgNXiAiIiIiuhQEcnAR78mtEdRP4gUFBVi5ciX69evn8Xq/fv2waJF3YzUZk5YYjWoVYj1qlcSaLIL7cN/6WPhUL9SuHI9H+jVAl3qVEBnuRJ/GaV51IsRNW7UqxWFIs6qYcE0zj7RgSkEHXepVQmpiNKY91M0j/VeY06EYEaCn0wEobSisViEWX9/eDr0bpWHtuH54/drmuKlddQxtXwP5hcXuaWMjwnAuv+xvh8OBltUrYO7jPTD38R7oVKcs+iVcpgFRHI0jbg/r1TDNo0F1cFZZA/uLVzT1aDBd9kxvxc/SvpZ3vYCIMCcmXNPM3YHi2u74qHCPHPqNqiTi81vbYsnTZcuXjjwH4JGK6q8HuuCpgWWRH9KOgRaZye4IERenw4FYlU6UxlV9S2sW5XE8lW6/qN9LNf1XYVHZhOHiL8jk7071lFhEhDkRGe7UTJn11vXNZV/fM2Gwxz7/5Z5O+PXeTlgxtg+2vjQAeyYMxte3t8PfD3bxSrFXUFyC6imxsjWPPBvItRu7MyvGuotRi4/V8Zc3weYXB6BT3Uq4Ryay49aONbDlxQHY+tIAjx/wtc+VXd8f7acdoSKOsJHTMD0R14gKa7euXgFAaWely4Cmpcfn/b3qYe24fmiYnoiXrmyKl65siuTYCDzQqy5iIsPQpGoSfrjLMzWguHG+mUzR8Rva6IvumjK6K8Zd1sSrQ0LuerF2XD+v1wAgMcaacRBagSLS9/Wk8xJ3WKpFkdlFK/olENvki2LRxctfnVJEVmAkChFZhZcTIiIKZYHsyOBvqDWCuhPl+PHjKC4uRlqaZx7+tLQ0HDlyRHae/Px85OTkePxH6sQN2eFhTiTFqBfHlhajNkpPAfmmVb0bJ6WqVShbTmxkmFfNDxe9qXFS4tXz40eJGr+dTofs9DVS4lAjJc6jAT863LvRPFO0DyrEKq+3eWbZfoiNDEcN0XypCcop1ZJizOf6b3AxjVZ6UrS7IbJhlQSv6a4URQE0zUjy6PBJiY9EZsWy/Z4p852nxEUaSgtnVAWZiB7xsd0kQ/kYU+rcqS2TkqmSxnFjlFzkkmu7W2Qme7zeqnoFVIqPcndqdKtfGU1kzp3KF1M6VU323t/iDjKrRudf2TLDo9YKAFRJjkF0RBiiwsM8riHijkGtc7VrvUqyx5JUtQply6l6cZkV4+Sva67G9OiIMNzSoQZWP9vXozNHfBz1bFAZMx4p+1z/vdE7WqqW5BgRt+W7osyUrlVA6TVEKikmAq1E6dOk265X4sX9Lt3P4uXIHfvSz1Q3Nd7j7yiZ4yYmIrCBruLjOkrmGhwMjKRtE+/PUOsAotDhSn1oZbo7I8e5WTUryadLJCL/cA0wqWXzueiP6wkREZFdaleK157IJqGWzjpYhcRelDYYiFPISL366qt4/vnn/bFZ5UZWRhJu61zT3bnxw90d8N7MHciqloTNh3MwsGkV/L3+MEZ0qoEvFuzB/b3qaixR3YtXNkV4mAND29fweu+Dm1th5uajePmqLIz/YyPWHjiNTnUq4UJhMZpLRn3f3b023v53GwDghraZqBQfhX0nz2NglmcUxHOXldZ4uKVDDXy1aA+mbjiCIc2q4K91hwGUpvHac/wcrmqpXPQeAFpVT8atHWugduXSC9+ITjWx/Wiu1/qA0pRIFwqLUTMlDg3TEzDxtrb4ddVBdKidghV7TmJEp5ruafs0SsNN7ap7fL6Pb2mF6RuPYliHmoiLCsfGQznoVCcF1Su2wGvTt+DubqW1M165KgtjfluPJwc0RP20eIz8agWubpmBDrW9I1G0fHdne3y3dB/GimpifDa8NSYt24/H+jVA/bQEnMsvQrEgIDkmEle2zMDyPSfR6uJI/8yKsbi7e20kxUQgKjwME29rh+s+XoyKcZEYd1nZMl+7Jgtr9p9G/4u1chqmJ2DLkVyM6FQT4U4HzuYXoVWNCoa3X6p5tSTc3rkWMkQN6r0apuLm9tWRlZGEHg0q46W/N+PKFhn4Y+0h3Ni2LIKgY50U3NKhOhqml0bD/Hl/F3w8dyeeGOAdKfFQn/rILyrBoIvHwRcj2uDeb1ehfloCUhOicFc34/U2/nNdcyzbfQK3d6mF92buwIO96wEA7utZF/tPnkffxukaS4B7W35fcwj3XTxnH+vXADkXinCNKIrL6XTgiQENkJtX5O6YfP2aZpgwbQu61K2EZ4c0xkt/b8JlzarKrkNJ7crx+Gx4G9z37Sr0aFDZ45jvUb/sewBKj4nV+05jUFYV7Dt5HmfziyAIpZEWDdMT8OzkjaiTGo/XrlGPQnG5u3ttTFy0B/lFxXiob+m+u6xZVSzeeQLta6nXSJL7bfnhrg74eslejBvSGKmJ0bizay1Uio9CrUpxGNW9DiLCHNh/8jwGNK2CHg0qY+uRXNRPS8CGg2c8onK+u7MD3p25HQ9f3CYx1/k37rLSujxf394Ow79Yhkf7ltYyevemlujy2mx3GjBxJ7Jek+7qiA9m78Cj/ep7vTdmUEMcP1uAuqneHab/ua45Xv57s/s7fPemlnhlymaM7FILQGmH7gO96uK9WTswZlBpRFqjKgkY2aWWVz2aF69simcnb8Ab1zYzvP1GiI/rzIqx+PaO9vhu2T4UFpWgdyPviCx/+vaO9pi0fD+eVak/JOXan1XZgEQ2+mZke7w9Yxse6O3bfZ7Yc5c1hgDgprb6ovSM+PP+Lvh43k482b+h9sREZJvf7++MD2fvxGP9zde8U/PdHe3x/fL97mc6IiKiUPTp8NZ4ffpW3NPd3pqsYi9c0QS7jp1DGwva2AhwCEGcGK2goACxsbH46aefcNVVV7lfHz16NNasWYO5c+d6zZOfn4/8/LLaDTk5OcjMzMSZM2eQmOhbeiAiIiIiIiIiIiIiIgptOTk5SEpK0tVvENTpvCIjI9G6dWvMmDHD4/UZM2agU6dOsvNERUUhMTHR4z8iIiIiIiIiIiIiIiKjgj6d1yOPPIJhw4ahTZs26NixIz799FPs27cPo0aNCvSmERERERERERERERFRORb0nSg33HADTpw4gRdeeAGHDx9G06ZNMWXKFNSo4V1Pg4iIiIiIiIiIiIiIyCpBXRPFCkZymxERERERERERERERUflWbmqiEBERERERERERERERBQo7UYiIiIiIiIiIiIiIiGSwE4WIiIiIiIiIiIiIiEgGO1GIiIiIiIiIiIiIiIhksBOFiIiIiIiIiIiIiIhIBjtRiIiIiIiIiIiIiIiIZLAThYiIiIiIiIiIiIiISAY7UYiIiIiIiIiIiIiIiGSwE4WIiIiIiIiIiIiIiEgGO1GIiIiIiIiIiIiIiIhksBOFiIiIiIiIiIiIiIhIBjtRiIiIiIiIiIiIiIiIZLAThYiIiIiIiIiIiIiISAY7UYiIiIiIiIiIiIiIiGSwE4WIiIiIiIiIiIiIiEgGO1GIiIiIiIiIiIiIiIhksBOFiIiIiIiIiIiIiIhIBjtRiIiIiIiIiIiIiIiIZLAThYiIiIiIiIiIiIiISAY7UYiIiIiIiIiIiIiIiGSwE4WIiIiIiIiIiIiIiEgGO1GIiIiIiIiIiIiIiIhksBOFiIiIiIiIiIiIiIhIBjtRiIiIiIiIiIiIiIiIZLAThYiIiIiIiIiIiIiISAY7UYiIiIiIiIiIiIiIiGSwE4WIiIiIiIiIiIiIiEgGO1GIiIiIiIiIiIiIiIhkhAd6A+wmCAIAICcnJ8BbQkREREREREREREREgebqL3D1H6gp950oubm5AIDMzMwAbwkREREREREREREREQWL3NxcJCUlqU7jEPR0tYSwkpISHDp0CAkJCXA4HIHenKCRk5ODzMxM7N+/H4mJiYHeHKKgw3OESB3PESJlPD+I1PEcIVLG84NIHc8RImU8P4wRBAG5ubmoWrUqnE71qiflPhLF6XSiWrVqgd6MoJWYmMiTikgFzxEidTxHiJTx/CBSx3OESBnPDyJ1PEeIlPH80E8rAsWFheWJiIiIiIiIiIiIiIhksBOFiIiIiIiIiIiIiIhIBjtRLlFRUVEYN24coqKiAr0pREGJ5wiROp4jRMp4fhCp4zlCpIznB5E6niNEynh+2KfcF5YnIiIiIiIiIiIiIiIyg5EoREREREREREREREREMtiJQkREREREREREREREJIOdKERERERERERERERERDLYiUJERERERERERERERCSDnSghbN68ebjssstQtWpVOBwOTJ482eP9o0ePYsSIEahatSpiY2MxYMAAbN++3WOanTt34qqrrkLlypWRmJiI66+/HkePHvVa199//4327dsjJiYGlSpVwtVXX23nRyPy2auvvoq2bdsiISEBqampuPLKK7F161aPaQRBwPjx41G1alXExMSgR48e2Lhxo8c0+fn5eOCBB1CpUiXExcXh8ssvx4EDBzymOXXqFIYNG4akpCQkJSVh2LBhOH36tN0fkcgn/jxHxNO2aNECDocDa9asseujEfnMn+fHtm3bcMUVV6BSpUpITExE586dMXv2bNs/I5EvrDpHPv30U/To0QOJiYlwOBxe90979uzByJEjUatWLcTExKBOnToYN24cCgoK7P6IRKb56/xw4bM6hRorzpGTJ0/igQceQIMGDRAbG4vq1avjwQcfxJkzZzyWw2d1CjX+PD9c+JyuDztRQti5c+fQvHlzvP/++17vCYKAK6+8Ert27cLvv/+O1atXo0aNGujTpw/OnTvnnr9fv35wOByYNWsWFi5ciIKCAlx22WUoKSlxL+uXX37BsGHDcNttt2Ht2rVYuHAhbr75Zr99TiIz5s6di/vuuw9LlizBjBkzUFRUhH79+rmPfwB4/fXX8dZbb+H999/H8uXLkZ6ejr59+yI3N9c9zUMPPYTffvsNkyZNwoIFC3D27FkMGTIExcXF7mluvvlmrFmzBtOmTcO0adOwZs0aDBs2zK+fl8gof54jLk888QSqVq3ql89H5At/nh+DBw9GUVERZs2ahZUrV6JFixYYMmQIjhw54tfPTGSEVefI+fPnMWDAAIwZM0Z2PVu2bEFJSQk++eQTbNy4EW+//TY+/vhjxemJgoG/zg+Az+oUmqw4Rw4dOoRDhw7hP//5D9avX4+JEydi2rRpGDlypMe6+KxOocaf54cLn9N1EqhcACD89ttv7r+3bt0qABA2bNjgfq2oqEioWLGi8NlnnwmCIAjTp08XnE6ncObMGfc0J0+eFAAIM2bMEARBEAoLC4WMjAzh888/988HIbJJdna2AECYO3euIAiCUFJSIqSnpwsTJkxwT5OXlyckJSUJH3/8sSAIgnD69GkhIiJCmDRpknuagwcPCk6nU5g2bZogCIKwadMmAYCwZMkS9zSLFy8WAAhbtmzxx0cjsoRd54jLlClThIYNGwobN24UAAirV6+2/0MRWcSu8+PYsWMCAGHevHnuaXJycgQAwr///uuPj0ZkCTPniNjs2bMFAMKpU6c01/X6668LtWrVsmzbiexm1/nBZ3UqL3w9R1x+/PFHITIyUigsLBQEgc/qVD7YdX648DldP0ailFP5+fkAgOjoaPdrYWFhiIyMxIIFC9zTOBwOREVFuaeJjo6G0+l0T7Nq1SocPHgQTqcTLVu2RJUqVTBw4ECvUGOiYOcKW6xYsSIAYPfu3Thy5Aj69evnniYqKgrdu3fHokWLAAArV65EYWGhxzRVq1ZF06ZN3dMsXrwYSUlJaN++vXuaDh06ICkpyT0NUSiw6xwBStNL3nnnnfjmm28QGxvrj49DZCm7zo+UlBQ0atQIX3/9Nc6dO4eioiJ88sknSEtLQ+vWrf318Yh8ZuYc8WVdrvUQhQK7zg8+q1N5YdU5cubMGSQmJiI8PBwAn9WpfLDr/AD4nG4UO1HKqYYNG6JGjRp4+umncerUKRQUFGDChAk4cuQIDh8+DKD0xyMuLg5PPvkkzp8/j3PnzuHxxx9HSUmJe5pdu3YBAMaPH4+xY8fir7/+QoUKFdC9e3ecPHkyYJ+PyAhBEPDII4+gS5cuaNq0KQC406SkpaV5TJuWluZ+78iRI4iMjESFChVUp0lNTfVaZ2pqKlOxUMiw8xwRBAEjRozAqFGj0KZNG7s/CpHl7Dw/HA4HZsyYgdWrVyMhIQHR0dF4++23MW3aNCQnJ9v8yYisYfYcMWPnzp147733MGrUKPMbTORHdp4ffFan8sCqc+TEiRN48cUXcffdd7tf47M6hTo7zw8+pxvHTpRyKiIiAr/88gu2bduGihUrIjY2FnPmzMHAgQMRFhYGAKhcuTJ++ukn/Pnnn4iPj0dSUhLOnDmDVq1auadx1UZ55plncM0116B169b48ssv4XA48NNPPwXs8xEZcf/992PdunX4/vvvvd5zOBwefwuC4PWalHQauen1LIcoWNh5jrz33nvIycnB008/bd0GE/mRneeHIAi49957kZqaivnz52PZsmW44oorMGTIEPeAFqJgZ/U5ouTQoUMYMGAArrvuOtxxxx2mlkHkb3aeH3xWp/LAinMkJycHgwcPRuPGjTFu3DjVZagthyjY2Hl+8DndOHailGOtW7fGmjVrcPr0aRw+fBjTpk3DiRMnUKtWLfc0/fr1w86dO5GdnY3jx4/jm2++wcGDB93TVKlSBQDQuHFj9zxRUVGoXbs29u3b598PRGTCAw88gD/++AOzZ89GtWrV3K+np6cDgFdPfXZ2trtHPz09HQUFBTh16pTqNEePHvVa77Fjx7xGBhAFI7vPkVmzZmHJkiWIiopCeHg46tatCwBo06YNbr31Vts+F5EV/HF+/PXXX5g0aRI6d+6MVq1a4cMPP0RMTAy++uorOz8akSV8OUeMOHToEHr27ImOHTvi008/9W2jifzE7vODz+oU6qw4R3JzczFgwADEx8fjt99+Q0REhMdy+KxOocru84PP6caxE+USkJSUhMqVK2P79u1YsWIFrrjiCq9pKlWqhOTkZMyaNQvZ2dm4/PLLAZR2xERFRWHr1q3uaQsLC7Fnzx7UqFHDb5+ByChBEHD//ffj119/xaxZszw6DwGgVq1aSE9Px4wZM9yvFRQUYO7cuejUqROA0uM/IiLCY5rDhw9jw4YN7mk6duyIM2fOYNmyZe5pli5dijNnzrinIQpG/jpH3n33XaxduxZr1qzBmjVrMGXKFADADz/8gJdfftnuj0lkir/Oj/PnzwMAnE7PW3Kn0+keYUwUjKw4R/Q6ePAgevTogVatWuHLL7/0Ol+Igo2/zg8+q1OosuocycnJQb9+/RAZGYk//vjDoyYwwGd1Ck3+Oj/4nG6CX8rXky1yc3OF1atXC6tXrxYACG+99ZawevVqYe/evYIgCMKPP/4ozJ49W9i5c6cwefJkoUaNGsLVV1/tsYwvvvhCWLx4sbBjxw7hm2++ESpWrCg88sgjHtOMHj1ayMjIEKZPny5s2bJFGDlypJCamiqcPHnSb5+VyKh77rlHSEpKEubMmSMcPnzY/d/58+fd00yYMEFISkoSfv31V2H9+vXCTTfdJFSpUkXIyclxTzNq1CihWrVqwr///iusWrVK6NWrl9C8eXOhqKjIPc2AAQOEZs2aCYsXLxYWL14sZGVlCUOGDPHr5yUyyp/niNju3bsFAMLq1avt/ohEpvnr/Dh27JiQkpIiXH311cKaNWuErVu3Co899pgQEREhrFmzxu+fm0gvq86Rw4cPC6tXrxY+++wzAYAwb948YfXq1cKJEycEQRCEgwcPCnXr1hV69eolHDhwwGNdRMHKX+eHIPBZnUKTFedITk6O0L59eyErK0vYsWOHx3L4rE6hzJ/nhxif07WxEyWEzZ49WwDg9d+tt94qCIIg/Pe//xWqVasmRERECNWrVxfGjh0r5OfneyzjySefFNLS0oSIiAihXr16wptvvimUlJR4TFNQUCA8+uijQmpqqpCQkCD06dNH2LBhg78+JpEpcucGAOHLL790T1NSUiKMGzdOSE9PF6KiooRu3boJ69ev91jOhQsXhPvvv1+oWLGiEBMTIwwZMkTYt2+fxzQnTpwQhg4dKiQkJAgJCQnC0KFDhVOnTvnhUxKZ589zRIw3ZxQK/Hl+LF++XOjXr59QsWJFISEhQejQoYMwZcoUf3xMItOsOkfGjRunupwvv/xScV1Ewcpf54cg8FmdQpMV54hSexgAYffu3e7p+KxOocaf54cYn9O1OQRBEIxGrxAREREREREREREREZV3TChLREREREREREREREQkg50oREREREREREREREREMtiJQkREREREREREREREJIOdKERERERERERERERERDLYiUJERERERERERERERCSDnShEREREREREREREREQy2IlCREREREREREREREQkg50oREREREREREREREREMtiJQkREREREIWXEiBFwOBxwOByIiIhAWloa+vbtiy+++AIlJSW6lzNx4kQkJyfbt6FERERERBTy2IlCREREREQhZ8CAATh8+DD27NmDqVOnomfPnhg9ejSGDBmCoqKiQG8eERERERGVE+xEISIiIiKikBMVFYX09HRkZGSgVatWGDNmDH7//XdMnToVEydOBAC89dZbyMrKQlxcHDIzM3Hvvffi7NmzAIA5c+bgtttuw5kzZ9xRLePHjwcAFBQU4IknnkBGRgbi4uLQvn17zJkzJzAflIiIiIiIAoqdKEREREREVC706tULzZs3x6+//goAcDqdePfdd7FhwwZ89dVXmDVrFp544gkAQKdOnfDOO+8gMTERhw8fxuHDh/HYY48BAG677TYsXLgQkyZNwrp163DddddhwIAB2L59e8A+GxERERERBYZDEAQh0BtBRERERESk14gRI3D69GlMnjzZ670bb7wR69atw6ZNm7ze++mnn3DPPffg+PHjAEprojz00EM4ffq0e5qdO3eiXr16OHDgAKpWrep+vU+fPmjXrh1eeeUVyz8PEREREREFr/BAbwAREREREZFVBEGAw+EAAMyePRuvvPIKNm3ahJycHBQVFSEvLw/nzp1DXFyc7PyrVq2CIAioX7++x+v5+flISUmxffuJiIiIiCi4sBOFiIiIiIjKjc2bN6NWrVrYu3cvBg0ahFGjRuHFF19ExYoVsWDBAowcORKFhYWK85eUlCAsLAwrV65EWFiYx3vx8fF2bz4REREREQUZdqIQEREREVG5MGvWLKxfvx4PP/wwVqxYgaKiIrz55ptwOktLQf74448e00dGRqK4uNjjtZYtW6K4uBjZ2dno2rWr37adiIiIiIiCEztRiIiIiIgo5OTn5+PIkSMoLi7G0aNHMW3aNLz66qsYMmQIhg8fjvXr16OoqAjvvfceLrvsMixcuBAff/yxxzJq1qyJs2fPYubMmWjevDliY2NRv359DB06FMOHD8ebb76Jli1b4vjx45g1axaysrIwaNCgAH1iIiIiIiIKBGegN4CIiIiIiMioadOmoUqVKqhZsyYGDBiA2bNn491338Xvv/+OsLAwtGjRAm+99RZee+01NG3aFN9++y1effVVj2V06tQJo0aNwg033IDKlSvj9ddfBwB8+eWXGD58OB599FE0aNAAl19+OZYuXYrMzMxAfFQiIiIiIgoghyAIQqA3goiIiIiIiIiIiIiIKNgwEoWIiIiIiIiIiIiIiEgGO1GIiIiIiIiIiIiIiIhksBOFiIiIiIiIiIiIiIhIBjtRiIiIiIiIiIiIiIiIZLAThYiIiIiIiIiIiIiISAY7UYiIiIiIiIiIiIiIiGSwE4WIiIiIiIiIiIiIiEgGO1GIiIiIiIiIiIiIiIhksBOFiIiIiIiIiIiIiIhIBjtRiIiIiIiIiIiIiIiIZLAThYiIiIiIiIiIiIiISAY7UYiIiIiIiIiIiIiIiGT8P5nWVugJXvuaAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "create_line_plot(df['Date'], df['High'] - df['Low'], \n",
+    "                 title='Price Variation Over Time', x_label='Date', y_label='Price Variation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b64048ab",
+   "metadata": {},
+   "source": [
+    "#### Stable period until 2004: \n",
+    "The price variation was relatively low and stable until around 2003-2004, generally staying under 10 units.\n",
+    "#### 2004-2008 increase: \n",
+    "Starting from 2004, price variations gradually began to increase, peaking sharply just before the 2008 financial crisis. Some spikes went beyond 40 units.\n",
+    "#### 2008-2020 fluctuations:\n",
+    "After the 2008 peak, price variation showed continuous fluctuations with noticeable peaks, though they were more frequent post-2016.\n",
+    "#### 2020 onward:\n",
+    "The recent period (2020-2024) shows significant and frequent fluctuations, with peaks reaching 60+ units, possibly influenced by events like the COVID-19 pandemic and other global factors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "00a2e645-259a-4873-9a12-5572f5bcea9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "create_line_plot(df['Adj Close'], df['Volume'], \n",
+    "                 title='Adjusted Close vs Volume', \n",
+    "                 x_label='Adjusted Close', \n",
+    "                 y_label='Volume')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22e7a95e",
+   "metadata": {},
+   "source": [
+    "The overall trend seems to be somewhat volatile, with periods of upward and downward movement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "bd630c6a-adbb-4b34-ac1e-be3e6ac5bfe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "create_scatter_plot(df['Open'], df['Adj Close'], size_data=df['Volume'], \n",
+    "                    title='Open vs Adjusted Close', x_label='Open', y_label='Adj Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc30e23a",
+   "metadata": {},
+   "source": [
+    "The scatter points generally show an upward trend, indicating a positive correlation between the opening and closing prices. This means that, in general, when the stock opens higher, it tends to close higher as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e1d8a700-e88b-4171-96e5-41039fb624e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "create_scatter_plot(df['Close'], df['Adj Close'], size_data=df['Volume'], \n",
+    "                    title='Close vs Adjusted Close', x_label='Close', y_label='Adj Close')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01218d24",
+   "metadata": {},
+   "source": [
+    "The scatter points show a very strong upward trend, indicating a strong positive correlation between the closing price and the adjusted closing price. This means that, in general, when the stock closes higher, the adjusted closing price is also higher."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "19880056-db20-414e-97ec-be1bb240a179",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "create_scatter_plot(df['Open'], df['Close'], size_data=df['Volume'], \n",
+    "                    title='Open vs Close', x_label='Open', y_label='Close')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "21fe1ab7-1c01-4434-a21d-3a1569b0de37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "create_line_plot(df['Open'] - df['Close'], df['High'] - df['Low'], \n",
+    "                 title='Stock Variation vs Price Variation', \n",
+    "                 x_label='Stock Variation (Open - Close)', \n",
+    "                 y_label='Price Variation (High - Low)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "b155bf2f-9c0b-4c63-8b9c-b70b377b4c9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "sns.scatterplot(x=df['Open'] - df['Close'], y=df['High'] - df['Low'], hue=df['Volume'], palette='viridis', size=df['Volume'], sizes=(20, 200), alpha=0.6)\n",
+    "plt.title('Effect of Price Differences on Volume')\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.ylabel('High - Low')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69bc5038-8a14-488c-9b7e-3ac4fa7c4a3d",
+   "metadata": {},
+   "source": [
+    "### This infers for stable or less price difference volume of stock trading is higher\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "6de0b1f4-eccf-4190-b331-52118a20e608",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "sns.lineplot(x = df['Open']-df['Close'], y = df['High'] - df['Low'], hue = df['Adj Close'])\n",
+    "plt.title('Effect of Price Differences on Adjusted Close')\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.ylabel('High - Low')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "bb1f08d4-e5dc-489b-ab50-767366c358d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "sns.lineplot(x = df['Open']-df['Close'], y = df['Adj Close'])\n",
+    "plt.xlabel('Open - Close')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "60e01b84-7b4c-493f-a583-b11ceb6dc444",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nn67MmTNHcbvdMesvWbJE6dOnj6LT6RRAuf/++6OPvf3228rQoUMVo9GoxMXFKaNGjVKKiopa/c6NGzcqV1xxhWK1WhW9Xq9kZmYqU6ZMiR5DW+8Fn8+nXHbZZYrRaFSWLVvW7vFEum3++9//3u9xr1ixQhk3bpySkpKi6HQ6pWvXrsq4ceNinldbW6tce+21SmpqqmI2m5X8/HylsLCwzf/v/b0uixYtUvr27asYjUalX79+yj//+c92u2229/784YcflF//+tdKamqqotPplM6dOysjR45UXnjhheg6M2fOVIYMGaIkJydH4+MPf/iDUl1dvd/XQgghhBAnPpWitBj/L4QQQgghhBBCCCGEiJKaZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ0IIIYQQQgghhBBCtEOSZ4CiKNTV1aEoyrHeFSFEGyRGhTj+SZwKcXyTGBXi+CdxKsTxS5JnQH19PUlJSdTX1x/rXRFCtEFiVIjjn8SpEMc3iVEhjn8Sp0IcvyR5JoQQQgghhBBCCCFEOyR5JoQQQgghhBBCCCFEOyR5JoQQQgghhBBCCCFEOyR5JoQQQgghhBBCCCFEOyR5JoQQQgghhBBCCCFEO7THegeEEEIIcXS43H6qG/zUeQMkmnTY4vQkmfXHereEEM0kRoU4/p1McXoyHYsQHU2SZ0IIIcQpoMLpYcabayncWh1dNizbxqOXDSTdYjqGeyaEAIlRIU4EJ1OcnkzHIsTRINM2hRBCiJOcy+1vdYEM8OXWama+uRaX23+M9kwIARKjQpwITqY4PZmORYijRZJnQgghxEmuusHf6gI54sut1VQ3yEWyEMeSxKgQx7+TKU5PpmMR4miRaZuH6DfXXEvF3to2H0vvlMw/X335KO+REEIIsX913sB+H68/wONCiI4lMSrE8e9kitOT6ViEOFokeXaIKvbW0vXSP7f52K63njjKeyOEEEIcWKJRt9/HEw7wuBCiY0mMCnH8O5ni9GQ6FiGOFpm2KYQQQpxEXG4/26oaKC6rZdveBlxuP7Z4PcOybW2uPyzbhi1eOmsJcSzFG7UUSIwKcdxpeU7ValQnzbn0l14XtHWtIcTJTkaeCSGEECeJ9jpnPXbZQB69bCAz31zLl208Jm3phTh2KpweZr2znsnn9iCsKBSVOKKPSYwKcezse0416zUsmHImCrR5nj2R4jTJrD/s6wLp0ilOVZI8E0IIIU4C++ucNePNtcyblMu8SblUN/ip9wZIMOqwxetPqIt9IU42LeN21TYH0/KzmJaXhS8YxmLS0Ss1nrRE47HeTSFOOW2dU93+ENNe+Zb7xvdj1vh+NPqCJ/S5NN1iOuTrggN16Zw3KfeEfC2EOBiSPBNCCCFOAgfTOatXarxc1ApxHGkZt25/iPnLS2Ie/+yPw0lLPBZ7JsSprb1zqtsf4q7/ruOzPw7n9MzkY7BnR1aS+dASfwdzrSHXGeJkJTXPhBBCiJOAdM4S4sQjcSvE8Ulis23yuohTmSTPhBBCiJOAdM4S4sQjcSvE8Ulis23yuohTmSTPhBBCiOPAL+1cJR01hTg2fknsStwKcfQcSqyeDLHZER0xT4bXRYjDJTXPhBBCiGPsUDtXudx+qhv81HkDJJp02OL0v6hzlhDi8LQVu+f3TeWBS/rjDYRbxei+JG6FODr2143arNecdOfUg7muaO9aYn9O9NdFiF9CkmdCCCHEMbRv5yqzXsMNw3qSb7dRXuPG6fYTZ9BiMelIMusPeEEsHTWFODr21HkpczTyx/NP486L+lDb6AcFEs06Zry5lpUljui6+0uGS9wK0bHa6hBpi9czLS8LTyDEnf/5gcJ24vVEi02X209VvY+yGjdT87IY0iMZRYEBXZPwBcPscDSiVasIhpVDumnX0on4ughxJEjyTAghhDiGWnaussXrWTjlTOq9QarqfRh1GgpLqtlY4eKGYb3oZjFx11vrDtgiXi5ghehYlU4PK7fupU96IlV1PlQqFd+X1aJVq/j2pxqKWnwRh9Yxui+JWyE6TuQ8a9ZrmJafxZDuycTptcQZNMx6Z31MohtO3HNqhdPDjP+spbDk55txC6cMYfvexug6nkCY7dWNPLe8JLpexIE+p1o6kV4XIY4USZ4JIYQQx1CdN4BZr+H28+1c0LcL97y9juIyJ9Pys8jNsJCbYeGi/p3ZWOHCpNNIi3ghjjGX20+t289HG3azo9ZDboYFbyDEub2sWEx6nvl0a5vPkxgV4tio8wawxetZNPVM1u50EQor7G3wYdSbWyXOIk60eHW5/eysdXPX2L7c6gsSZ9Bg1muoqvPy3rrKmIT+69cNbZU4izjRjluIo0mSZ0IIIcQx4nL7Mek1LJp6JmmJRu55qylx9uykXBYWlTJ/eUl03QK7lSxb/H63Jy3iheh49Z4AT328hd8O7d4qTl+ePGT/z5UYFeKoipxnF18/lJoGf0wi6fmrBu/3uSdCvLrcfhyNflTAvM+2xkw/Lci2cfN5vSguc8Y+x7P/4zoRjluIY0G6bQohhBDHQIXTw/Qlxfzq+VW4vEHKaz0UljiYlp/FwqLSVtO+CkscBELh/W5TWsQL0bF2OBopd3rok57UZpweiMSoEEdP5Dz7f3//Gp1azbzPS2Ji1qDd/1fh4z1eI8dX+ONeZr2zPiZxBlC4tZr5n5cwLT8rZvmJftxCHCuSPBNCCCGOMpfbz6x31jMow8Kr085CBWjUKgByMyztfiFftd1BgbSIF+Koc7n9bNldR0lVAxq1ut04LS53kme3trkNiVEhjp6W59nXrxtKgz/YKmZP5Hht2QRhaC9rq8RZRFGJg9wMS8yyE/m4hTiWJHkmhBBCHGWORj9XDs3kf2W1XP7CV1y76DsafUEAfMH2R5ctWFnKrPH9WiXQpEW8EB2nwulh+uJiLnymkGsXfYdJr243ThesLGVqXhYFdolRIY6llufZnbUeGr2hVutE4nXfRFLBCRCvLZsgNHiD+11338+r6OeUXEsIcUik5pkQQgjRwVxuP9UNfuq8AeINWgxaNe/9bxdT87KYMaYPDd4QaQkGCrJt+51O4faHqGn08eQVg2jwBqVFvBAdbFetmzpPgLvG9uH3/hDJZj2BUJgkU9vTmtz+ELctKWbp9HzCikKjLygxKkQHa3mOTTTpiDdo0atVOOp9/OH80wiGlOjo7pYi8TotP4u7x/Zlt8tLt2QTnRONx3W8utx+fMEQz181mLREIxpV62Nrad/rCrc/xBtfl/HQhP6Ewsi1hBAHSZJnQgghRAeqcHqiUysiRvdJ5Z7xfbnv7fWsLHFg1mt47srBzBrfj29/qiHPbm1zSlhBto1uyWbSEo2kJR7NoxDi1OJy+3F5AlS6vMxfvpU1zY08nv70R67P70lKvJ58u7XNTn1ndE/GpFOTnmw+BnsuxKmlzXNs31TuHdeXd3+ooLDEwcuTh7Bul4sCu61Vl0m3P8QP5U4uG9yVnK5JpCUaj/YhHJK2jnfupTltHhtAgd1GVZ13n2VWrjw7k90uL5nWOHql7r8ZkRCiiSTPhBBCiA7SsiaJWa9hWn4WuRkWbPEGZjUnzgCm5Wfx95XbOSsrhS6JRu4f35+H3tsQU8Mk327l4Yk5dJUv5EJ0qN1OD4FQmN11TYmzwhIH00faow0CcjOT2VZVzx0X9kHFlpgvrPl2Kw9fOkASZ0IcBW2dY4dkJpOebOK+t38uoF9c7mRjhYup+T0AJbYjpd3KnIk5GDTq4z5x1vJ4W5qzbBP/uekclPc3xST0C+xWpo+0s7q0KYHoC4YxaNUUlzt5bfUO+qUnsfaLbcyblCsjzoQ4CJI8E0IIITpIy5okz07KZWFRKQtWlvLPG8+OuXjPzbAwf3kJxc2jWx79cBODMpOZkpeFLxjGYtLRLdlEOKwcw6MR4uQXmQ4VViBOr43GaSRGoale0LOTcnn2sx8ZlGlhSl6PmDjNTJHEmRBHw77n2MVf7+D0DAt7630x59hIzL62ekerc2u6xYQO6GwxHbsDOUiR492X2x+iqs7H6ZnJTM3LIs6gpdEXZN0uF25/iG9Ka3j6k63R9QvsNibn9eC2JcW4/SGqG/ySPBPiIEjyTAghhOggTrcfaBpZFhm1Mn2knao6X8x6kWK+Leuv5GZYoneJLWYd762r4NyebXfaFEIcGS5PgGAYvil1kNpiFErLgtttxWl3q5ltextITTQci90W4pRUu885NjczmYVFpVw1tHvMevvGrFmvpVOCBq1ahVYF6da4Y7H7h6zOG2j3MXcgFE3wP3/VYG5+/XuA6Ii8ac0JQ4NWTeckI1e88BVuf1MTBZcnwLaqhmjNOFuc1D4Toi2SPBNCCCE6QFWdlzhD02m25aiVfVvGQ2wxX7f/5wvgiKW35jF/+TYu7Nel43ZYiFOYy+2nzhOgqKSaAd0sMYkzaLvgdss4XXz9UMx6LeoDFO4WQhwZLc+xgzOTmb+8hGl5WdG/99UyZl+ePAS3P0hGspluJ0jiDCDR2HajEoj9jDqYa4pI4gzAGwjxq7+uiv48LNvGo5cNJP0EGI0nxNHUfksvIYQQQhyWiho31Q0+istqKbBbY0at+IJhisud5Nmt0WX7/txSgd3KZ5uqGNI9GVu83AkW4kjbVePG5QlQ5w3SJcmExx9qFaf7j1EbHn+If35ThjVOYlSIjlbp9FBa3cj/ymoZ3acTZp0G+HmEaHG5k4J24jXPbmVPnZeqeh9pJ9hIUVu8nmHZbY9Ar6rzUtD82P4+r/LsVoKhn0tAFGTbWLU9tvHJl1urmfnmWlzNI/uEEE0keSaEEEIcIS63ny2769hV50WlUjG0p5XZE3KwmH6+W2zQqlmwspSpeVnRi9vIz/n7XOwW2K1Mzstiwy4Xj102UKZRCHGElTsaqfMFCYQUnJ4ASWYd/lC4VZzuG7MRkdpBb3xTxoMTciRGhehgFU4PTk8AlUpFbvdk7r+4PyGlKRkUGXG1YGUpU/OzKLDHJpry7Famj8imV6d4htltJ1y8Jpn1PHrZwFYJtHy7lbN7Wpl9Sf8Dfl5NzcvC5Q5En3ff+H688U1Zq9/15dZqqhskeSZESypFUY5p9eFdu3YxY8YMPvjgAzweD6eddhovv/wyZ5xxBgCKojB79mxeeuklamtrGTp0KM899xz9+/ePbsPn83HHHXewZMkSPB4Po0aN4vnnn6dbt24HtQ91dXUkJSXhcrlITEzc77oFY35F10v/3PaxvPUEhR/89yCPXAhxsA4lRoU4ViqcHma9vZ4+6YnROkgWsw6DVo0t3sC9b62nsKSa6SPtFJfVUlzmjKmZZNZpMOo1JJl0VLo8pFtMaFQqNGoV1hOg/ojEqTjRlDsaKav14PIE6J5iJkzTJfFHG/aQnmRk2brKmDgNhhW6JBkJK+APhkgy6dCqVahUEqNCHA1ljkZmL91A3/Sk6LkzyxbXHKu15GYmU1xWS1GJA7Neww3DepJvtxEMK8QZNMTptPhCIZKMuuO2I+7BxKnL7afC5cXlCRBv0JJg1PL0J1v4/ajT+Gq7g7REY/Tzyh8Ms7fBR6JRhzcQYsnXZdwy0o7HHyLBqOX5L0q47IyMaPOAlt6++VxOz0w+GoctxAnhmNY8q62tJS8vjxEjRvDBBx+QmprKtm3bsFgs0XUef/xxnnrqKV555RVOO+005syZw/nnn8+WLVtISEgA4Pbbb2fp0qW88cYbWK1W/vSnPzF+/HjWrFmDRqM5RkcnhBDiZOdy+3G6A4TCCnPf38hvh3ZnYVFpbC2k64YSDivcPKIX8HPXL4hdL89u5cFLcggT5ozM5OP+i7gQJ7IdjkbueWsdK5s78r1xw9kYtGrc/hALVpby3JWDmT7CzvzPS2LitCDbxoMT+hOnN7aqiyaEOLIi59hGfxCTTsNDyzZyZfN5dsHKUqblZ2GN00fPq4u/3sHU5npnRSUOnvl0K898upWCbBsPXNyfoBImPcl0cpxfFYVwWOHDDbsZlm1laJaVvQ0+UhONLGhuUBSRZ7cyNS+LN74pY+bYvrh9QTonGimpbuCKIZnsdnm4cXhPXlyxPeamnlGvweWWTpxCRBzTkWczZ86kqKiIwsLCNh9XFIX09HRuv/12ZsyYATSNMktLS+Oxxx7jxhtvxOVy0alTJ/7xj3/wm9/8BoCKigoyMjJ4//33ufDCC1tt1+fz4fP93Omsrq6OjIwMGXkmxHHil8SoEEdLpdNDWY2becu3MjUvi/UVLjrFG0hLNDZddOo0fF9Wi0mnZlSfVNRqNaFQmAqXt9UdYb1GTXG5k4mnp2NPTTjWh3ZQJE7FiWqno5HCkmpSm2M1Tq8lLdHAbpcXW4KB8fNWYtZruHF4T0b0TgWaim5r1Sqs8XqybPHH+AgOjsSoOJG1PMeuLHGw9NZ8lm/eQ6d4A+lJJrpYjMx5byN3j+vHmL8URrtKDslMJsmsQ6tRUesOYDXriTNo0ao4LpsDHE6cbqtqoLrBQ5LZwK+eX8ULV51BerKRPXU+rn/1O6blZzE4MxmtRkWyWUcwpOByB8hIMeMJBHnkg82tkmsPTcghGA7z0NKNFLZ4TJoHCPGzYzry7N133+XCCy/kiiuuYMWKFXTt2pWbb76Z66+/HoDS0lJ2797NBRdcEH2OwWBg+PDhrFq1ihtvvJE1a9YQCARi1klPTycnJ4dVq1a1mTx75JFHmD17dscfoBDisEiMiuOdy+1nZUk17/5vFytLHFx9Tg/GDujCt6U1AGjUKjrFGxjdJw2jXo1erebJjzdz03l2Xln1E4Vbq1ttsyDbxvX5rTuEHa8kTsWJaE+tmzDQwxaHUadBq1GjBtbtdBIMQ1W9lwK7lcISB09/spWnP9kafW6B3cqTvz79WO36IZMYFSeqfc+xAP5AkEsGprOn3ost3sDsdzdQWOIgGAqTb7eyssTRqqtkQbaNuRNz0ADpx2HiDA4vTuu8ATonmlm1rZrcTAs3vb6G6SN7MX5AOmd0T271OkBTfbNHLh3A3Pd/jEmcQdMovfvf3cCYnM4xiTP4uXnAvEm5MgJNnPKOacOA7du389e//pXs7Gw++ugjbrrpJm677TZeffVVAHbv3g1AWlpazPPS0tKij+3evRu9Xk9ycnK76+zrrrvuwuVyRf+Ul5cf6UMTQvwCEqPieFfd4CfLFseaMifTR9rpnmIGBVSARqWih9XM4x9uYuLzReyq9TB54Tf86cI+/OXTH5l8bo/WRXyzbTx+gjUEkDgVJ5rdTg+eUJivtzuwxhl46uMtXDxvJbvrvNy/dCPpFiM9rGZuHmFvM0bnXDqAtBNoqqbEqDhRRc6xhc21y24fnU2iWQcqeG55CTsc7miS5+bXv+ehiTmtGu7k263MmZiDmuM3cQaHF6eJRh3uQIiFRT8x99IBnNE9mcc//JFf/XUV947r1+Zr8eCEHMocbj7bvLfNbRZurW73802aBwjR5JiOPAuHwwwZMoS5c+cCkJuby4YNG/jrX//KNddcE11PpVLFPE9RlFbL9rW/dQwGAwbDidWaWIhTicSoON7VeQOEFSVaY2VojxR0GhXvratsKli8sjZ6Z9cXDFNe6+E3L37FY5cNpHOSgXvH9kNRQZ0nQKJRi7V5uueJROJUnEh2ORrxhxX21HkJhBVmv7chJkbd/hC/e/17bhzek1F9Unngkv74A2Ea/UESjDrqvYGYrrknAolRcaKq8wYIhhTMeg3PXTmYvfVetGo1s95eT2GJg0lDu0fXLa/1cM3L3/DcVYO5W6Om3hMgsTlW9UCX4zhxBocXp7Z4PVv31DP/ysE8tHQDgzIsTDm3B75gGKfbx5yJObj9Ieq8QeIMGqrqfPz6xa94cELOfrfrC4bbfazeGzikfRTiZHRMk2ddunShX79+Mcv69u3Lm2++CUDnzp2BptFlXbp0ia5TVVUVHY3WuXNn/H4/tbW1MaPPqqqqOPfcczv6EIQQQpyCkk06NGoVL63YwORzepBuMTHr3fUUlTiYlpcVM2XCoG0a5F3d4OfaRd+12tZHtxeccIkzIU4kVbVuAoqCJxBi3uclTMvLipm2FIlRtz/UaqomwJLrh9LDGndCjQwV4kRmMekIGMO8f2s+VQ0+FqysIKerJTraLBKzEeW1Hi6ZXxT9+fXrhtIt2XTcJ84OV5JZT0q8gfvfaUomfrrPaLI8u5U/jD6N3760Omb5vq/bvvb3eILxxLp5IERHOKbTNvPy8tiyZUvMsh9//JHu3ZvuJmRlZdG5c2c++eST6ON+v58VK1ZEE2NnnHEGOp0uZp3KykrWr18vyTMhhBBHnMvtR61WUecNcs25WVjMehQUisucQOs7t8XlzlZTwCKGZdvoLIkzITrMXkcjvrBChdNLgy/Etfk90WliL3/3F6MF2TZ6doqnixTLFuKo0atVmDUavKEw9d4gU/N70ugPRh8/UMxmJpvofpImziICoXCr+mQRRSUOgqHWPQH3+7rZreyp87b52LBsG7Z4uXkgxDEdefaHP/yBc889l7lz5/LrX/+ab775hpdeeomXXnoJaJquefvttzN37lyys7PJzs5m7ty5mM1mrrzySgCSkpK49tpr+dOf/oTVaiUlJYU77riDAQMGMHr06GN5eEIIIU5gLref6gY/dd6mKSC2OD0efwh3IMTX2x2kJZnQqlVo1Crc/hD/uekcPt64B6NOE7OdBStLeXZSLkDMaJeCbBuPnWB1zoQ4kex2NOIJKzT4g6ibY3XtTidnZaXErLe/GH38soEyMlSII6yt82vkXLizxk0wrLC73kucQYtZryXBqKVlMZ79xezDE3PIOMkTZwAN3qZkYqTLaG6GJdo9OBgOR6euttTe65Zvt3Lfxf3Z7fSSZ7fGPDZMrlWEiDqmybMzzzyTt956i7vuuosHH3yQrKwsnnnmGa666qroOnfeeScej4ebb76Z2tpahg4dyscff0xCQkJ0naeffhqtVsuvf/1rPB4Po0aN4pVXXkGj0bT1a4UQQoj9qnB6mPHm2piumE9cPpBBGYkEQ/D+ukrWlDmjF6wAaYlGuiYZ6RRviLn4dPtD3LakmGn5Wdxynh2dRk0wHKZnp3j5Ui5EByl3NLLL6eGlwu30S0+KfrEcmpVCpwRDtDsfxMbozefZMWjVBEMKmVazjDgT4ghr6/w6LNvG3EsHoCgK3lAQrUrDS1/+HLu1bj89rGYKsm0Ubq2OidlpeU1dqrtaTCToNXQ9BRJnLrcfs0EbrQlX6fIAoFKBNV7P2p1O1u1ytUqERV63e8f15Q+jT6Oq3odBq6a43MmjH2xiYDcLuZnJTMvLIsGoxRpnwBavl8SZEM2OafIMYPz48YwfP77dx1UqFQ888AAPPPBAu+sYjUbmzZvHvHnzOmAPhRBCnCpcbj9V9T7KatxMzctiUIaFBStLATijezI6tYq7l65jTZmTZyflsrCoNKa+WYHdSqbVzJ0X9eHxDzfHJNB+KHcyYVA6T32yhRkX9ZXEmRAdZJejkZUl1XyyaQ9XDu3eKk5H9enE7Ak5PPDOBgpLmr7Au/0hfiir5fLB3Xjm0y2MH9QVbyB0rA5BiBPS/kaURR7fN3EG8N2OWr4pdTAwMwmTRsuspRtaxa5Zr+HlyUMAogm0+ctLyLNbuXVkNvGnSOKswulhxn/WcseFp/HatWfiDyosW1cZkyTLs1u5Lr8nA7slAbGjzHIzLaQmGrlmwTe4/bGfcVcN7R6tzfrZH4fTKzX+KByRECeOY548E0IIIY4Hbd0NH9mnE/+68WwMWg0bK1zY0xIoLHEwfaSdhUWlMRekAIUlDsLAXWP6kJuZzC3n2dFr1cQbtWjVKiprvfRKTaDO4wdO/ot8IY62ckcjdd4gqYlG+qUntRmnn23ei4pN3HdxP8pr3AB0TTYRp9NQVuOmW0octy0pZvF1Q4/FIQhxQmpvRNmjlw0kvXkEZ3WDn8Kt1TFTDYNhhZ62OF4u3MaQ7inU+0Ntxq7bH+LaRd9x3/h+/H5UdnTUVFWdj65JRrqdAokzl9vPjP+sZU1ZLfFGLWpU3PvO+lafcZGfz8pKiY4k8wXDZNniWLauktuWFLdKnMHPNVsLpMaZEG06pg0DhBBCiOPBvnfDzXoNt4/O5g/nn0ZYgceapzM4GvwA5GZYWl2sRhSVOFCh4pyeVpLNeqxxOnyBEKOf+pKrF37D/OUlxBmka5UQR9quWjcrS6rZXefFFwzvN04/3VxFMKzQOdGIPTUOo0bNBxsquerlphh1+0PSXU6Ig9TeiLIvt1Yz8821uNxN5846bwBbvJ43bjibc3pa8QXDpCeZeOyDTdx0np2KOg8ud6Dd2HX7Q9z133WYdBqscXrSLSby7dZTosYZNCcfS6qZlp/F3nofDf7gfq9FctKTmL+8hGsXfcfNr39PgzcY/Xxri0GrpsBuZfYl/WWqphBtkOSZEEKIU17kbjg0Jc6enZTLdz/VMHXhtxi0am46z06jP0SiqWnA9r4dNffV6A9R5mikwReguNzF51t+biMvXauEOPJcbj8NvqYRZ9D0JfBAcVrvCeAJhFDC4A6EeObTn6d2SpwKcfBankP39eXWaqqbbzwlmXT8ffKZPPbhZq76+9fMemc9eq2Km0fY8QTCxBm0xBk1B4xdlydAWoKBJL3mlEmcQVPyEZpu4MUZtDR69z+1fN/X0WzQUNBut00b3a1mZlzU98jsrBAnIUmeCSGEOOVFLkjNeg3zJuVi1mv43Xm9+PeN5zDnvY1c/sJX1HkCBEMKeXYrBu3+T5/xhqYkW0q8ntPS4nnpy+2AdK0SoiO43H6cngC7aj34gmGKy53sqfOS1Ea3uZaMOg2KoqBRq2Lq/0icCnFoIufQ9tQ3Px5v0LKpwsW0vCz+ds0Q/n3jOTzx4WZq3AHqPAEavCGq6nwHjF2zQYtGrSL9FEqcASQ2j4b1BcM0eENoNar9rt/yWqUg20acQcPU/Czy9kmg5dmtTM7rwdz3NxFGwRonn31CtEVqngkhhDjlJRp10RFnrxSVsqbMyXNX5jLrnfUUNk+JSDTp2OPyMjUviz11rdu5RxRk21CrIM9uQwkrGHRqFl83lASjTrpWCXGE7axxEwiFmfXOen4/+jQUT4AFK0t57srBpJh1MV01WyrIthEIhUhLNPLDLhevXzcUT/NUTYlTIQ5N4gGmOMcZtJRVNxJSFN5rLm5/15g+vLqqlEGZySwsKuWecf0IhcPcuuR7/n3jOfuN3U5xerqmmDvqcI5btng9w7JtGLRq4o0aPttURYHdFm180lKe3UpxuRNoes2mj7Dj9oaYvrip2+bdY/pS4/bjb77hEKmD9ucL+8jnnxDtkJFnQgghTnm2eD33je/HwmjibDCpCcZo4gzArNNQ6fKw+Osd7G3wcf/F/Smw22K2U5Bt46EJ/YnXa1ApCpY4PdmpiZyemUyv1Hi5IBXiCKpwNOINhql0eSkscaBvLh6em2nhlsXf8/mPVTw4IYeC7LbjNC3BSDisUGC30buzxKkQhyuS1GlLQbaNYFhh5bZqZjUXtz8tNZ7hvTtRWOKI1jcLhsI0+oL06ZzANQu+Ydb4/m3G7sMTc07JxBlAklnPo5cNpKreR6MvyLqdTqbm92g1FbPAbuOhCTkM6JrE4uuGcm1+FlNf+RZUMG9SLiqgqt7Hjf9Yw7WLvoupg+Zppx6aEEJGngkhhBAkmfUMzrRw13/XMX2knUqXh7CixKyzvbqRc3pZ6WGLY/7nJby4YjvT8rOYktcDaOrWpwFUQDCs0KOTtHgXoqPsqXUTBma/u55JQ7sDUOny0i3ZxK0js5m3fCuPf/gj85dvY1p+Fr8b3iva+TZOp2GHw02iScvAjORjeyBCnAQiSZ2Zb67ly326bd41ri/ryp0M6mbh7rfWY9ZrePHqM6hweYGf63Ld8vr3vHbtWTxwSX8eeHcDE58vajrHntsDaDrHJug0dD3FpmruK91iYmxOZ5xuP3eN7ccj729kUGYyU5o7aiaZdKRbjFzz8jf0sMVxfUFPbn79e9z+EPXeIF0tJtbtcvHQsk1tNg440JRZIU5lkjwTQghxyquq89LoCzJ9pJ0L+6dRVedrtU5YUbj65W944f8G89CEHDyBEG5fUxMBnUaFChVatYoPNlYyuk/nY3AUQpwadjoaaQiEQIHCEgdT8rIA0KpV3PjaGm4fbefBS3LwBptiNMHUlDBTKRAGyhxubnhtDUun5x/bAxHiJJJuMTFvUi7VDX7qvQESjDp0GhUhRWHFlip6pcYzfaSdC/qlUenyRhvwROpyldd6+L+Xv+Glqwfz8KUDmpM9TduJ12tINOlkVGizyOuwp97DveP64Q2GqfcGSDfqMOs07G3w8vRvTseo1fDrl76KJsk0ahUfbdzNmh21bSbO8ptrum7f24DLEyDRpMMWJ9PYhYiQ5JkQQohTWrmjkQZfkESTns0VLs7qkYIvGGZjZV1MXbPicieZVjPj5hW12kZBto25E3Moc7hZ+aOD35yRebQPQ4hTwg5HIw8t3UCf9CSGZXcCmmKzoLm+T26mhbnvb2Hu+1tinleQbePusX0waTVc/9oahnRPlm6aQhxhSebYRMsORwPFO2r584V9UKlUdE0yolKB0xPApNdE63JFzrXltR7GPPvzObYg28YjE3PodoqPNmuLPxjGqNVy91vr2qwNl2e3kpuZHE2S5dmtfPHjXhasLOXd6fnMfndDTK20PLuVKXlZ3P/uevqmJzF/eVP34WHZNh69bCDpFtPROTAhjmNS80wIIcQpq6zGzeylG6iq9/Ho+xv57dDuaNQqDFo1C1aWMjXv565UkZ/z26gtMvuS/vxU7abM6ZEufUJ0kHJHIw8u3cBvh3anuKyWOL0GaIrN+8b3Z2OFKyZmIyIx+vHGPex0eRjSPVniVIgO5nL7QYH+XRPRqFXsqfOgAI3eEAatGpc7wNS8rPbjNtvGHEmctcvtC+INhbjzoj6tasPl261MzctiwcpSoCkxFvnZ7Q/hCwa5Z1xf3r+tgL/+32CW3ppHbmYyty0p5rPNe8nNsES39eXWama+ubbp/1OIU5yMPBNCCHFK2lPn5cF31zPl3CxsiQb6pCexsKiU3Mxk0pOMnNF8ITktP4s/jD4NlyeARqVial4W0/J74g00fQHYU+dl6doKLuzfmYEZSfKFXIgOsMvRSHmth77NcVpU4kCrUUVHrHywoZLRfdPQqlTccUFvZo5RUesOEAwp7HZ5WLq2gqc/2cqHtxcwb1KuxKkQHcjl9lPvCeAJhtBrNPhCYUIKLFtXybS8LIrLnaQnGfl00x76pSe1iludWkVXi4lMSZy1T6WivNrD+goXVwzpxr3j+rKz1kO6xRS9NnnyikEYtOqYbpr5diu7XU2lKV5Z9ROTz+1BhdMbHWkGP9ehi/hyazXVDf52Pzddbj/VDX7qvDLVU5zcJHkmhBDilOFy+3E0+lEB/nCYq8/pQddkE1X1PnIzLMxfXkJxc7fNu8b2Ye77m5i/vIQFK0t5dlIuf1+5PWZ6RORu7uebq5hyTg+5WBSiA5Q7GnEHQjg9gWicAuxxebl1RDYAL67YzrOTcvnbyu3Rqdbwc4zOWbaJYdk2uiQaJU6F6AAut5+aRj9qtaqpXpZBi0GjYXedl9QEI/M/L6GoxEFuZjIbK1yckZnM9QU9mf95SUzipiDbxu9HZUvh+gNwB0IkmXQM6mqhh83MLqeXaxd9x4tXn4FZp6FbsonnPi+JuWbJt1uZdXF/nv5kC71SEyjcWk1YUZh9SQ7TR9qjI9MidehaqvcG2tyPCqeHGW+upXCfRhEy1VOcjCR5JoQQ4pRQ6fTwxY976dclnmSzASXY1BWz0R8izqDF0dg0JcHtD3HL4u/5z01n89DEHCqdXpyeANrmUWe3jLCjUqmIN2j5cMNu/vlNGQ9OyJEv5EJ0gJ2ORhoDIeo9QQxadcyIiLACvmCI8QO6MC0vi2BYYcZFfQgrEAiGMek1fLhhN7ctKZapmkJ0oAqnh4eXbeT20afx3XYH6ckm/MEwJp2GQEjBGwxFk9qRm1H/WP0TgzIszLioDwAef4gks46te+rpZjFJrB5AvSeAUa8hJV5Poy+Ey9OU3Io0TrnjgmweuXQAdd4gdd4g8UYNVXU+nv5kC5edkcFtS4oBKCpx4HT7KS6r5dlJuSz+egfF5c5Wvy/B2DqZ6XL7WyXO4OepnjLKV5xspOaZEEKIk57L7ae81o1GBYlGPUUl1exyevAFw7j9IYw6NZYWd7nd/hA/OTyMe3Ylq7Y7MGjVuANNRXeLtjmY9sq3uP0hLj29K09eMYgucndViCOu3NFIWa0Hjz+EWa+huNwZG6eBEL97/Xt2ubwAhMIKe+t9fLppD5MXfkOjL8h5p3Vi6fR85k3KlTgVogO43H5mvbOem8+zU9voQwGCIYWqeh+N/hDJcTrcvp87O7r9IW5bUky/9CRy0pPYWetBo1aRZNKhVsGw7E50llg9oASTjkqXF51GTZ03GB0tFmmc8uB7m9m2t5EPNuwmzqChvMYDQK/UhOgUzoi9DT6KShy8UlTK7aNPi9ZKixiWbWuzwUp1g79V4iwiMtVTiJOJjDwTQghx0qvzBEgwaBnQLYk9dV7eW1dJUYkDs17Dv286h0ZfkM6JRvLt1ugUB4NWjdsfiplO0pI1Tk+v1PijeRhCnDLKHI3c/dY6vi9z8uq0szAY1WyucDFhUHo0Tg8Uoxazjt6dE4/yngtxaqlp9HP7qNMw6zU0+IJ8tmkPfdOTyM2wEG/Q4guGMBs0Mc/ZN24/ur0Ak04jNc4OQZJJR7xBS4M3SKJJS9G2avLs1ujIPjXwXVktxWW15GZYuPn179vdll7TlHhbWeLgFl8wJrE2LNvW7qjdunamcka0N9VTiBOVjDwTQghx0nK5/Wzf20BIgTnvbyIQUpjXXHfFrNfw7KRcQmEFjz9EhdPDQxNyKLA3da0qLne26v4V0d5dWCHEL1dZ6+ae5sTZs5NycXkC7KzxMGNMX577fCsPNsfp/mK0INtG50TjUd5zIU4NLrefbVUN/FBeCyp47MNNhBWFv325jUnN3XCvXfQdvmAIXyAECq06QkYUZNtIMGglcXaI0i0mMpNNxBu16LVqNjV3Lc3NtHDbkmLO6JHCmJw07h/fn6o6X/uflc2fpREGrYbP/jict28+l8/+OHy/o3YT25jK2VJbUz2FOJFJ8kwIIcRJqcLp4U///oGfHI3c9/a6aL2VyN/T8rNYWFRKTYOfOIOWhatK+WBDJbMu6cf7txUwPNvWlEzb54K/YD93YYUQv0yl04PLF6SwxBGNUYA7/vMDOrWKoVlWdtV4uO/ifowb2IXZl7Qdo49cOkBiVIgOUOH0MH1JMaOeWsHKkmpmvb2ewhIH/pAS7VodOc+63AHiDFqe+3wrd17Yu81YfXhiDunJ5mNxKCc0l9uPo8FDvF7D7lov943vz5Kvd5Cbmcy8SbkM7p5MnSdIhdPDwG5JMTcHI/LtVu64sHfMNM0kk45eqfGcnplMr9T4/X6O2uL1DGsnKSo3GcXJSKZtCiGEOOlEarBMPqcHSSYdhc0X8i3rrkS69uVmJrOtqp7bRp3Gkx9v4fEPf4yuM7pPJx6a0D9aVyTJpCM1wSBfyoXoADsdjdT5gtR7g0BsjPbunMDVC77h5cln8tCyjdE6O2a9hvvG9eXusX1p9AWJM2hJMmrly7gQHaBlgfiMZBOj+qbxxEc/Nk3Z9AZjuuFC07TBbVX13DTczl8+28qgDAtTzu2BLxjGYtKRkWwiQ0acHZbqBj8VdQHmfrCFBycOwBcMcf2wnsQZtDR4QySaNOjUGma/tyE62n5afhY3ndcLg1aNPxQm3qCN1nCFpmTmoSS8ksx6Hr1sIDPfXMuX+3TblJuM4mQkyTMhhBAnneoGP7mZFgxaFYFwmOkj7eRmWEg0/Xzai3Tti9QH+ctnP7a6sO+WbMIXDpFhMdFNLvCF6DAVtW4aAiEqXV66Nk8R2jdGFxaVMvH5IqblZzHl3B4AdE4ykmDQogbitGqS4vTyhU2IDhIpEG+L1/OP686i0Rfi+asGk5FsRq1qKjzfUsvza7/mOmiSODsy6rwBDFo13/zk5OttDoZkJdPgCzFveUl05J9Zr+HecX2ZOaYPO2s96DVqvtruYETvTuyodvPQso3RxFme3cqDE/of8udnusXEvEm5VDf4qfcGSDDqsMXL57A4OUnyTAghxEknpCiM6pOGy+MnxaxndJ80Kus8hJvrrhRurY52pop0/pqWnxW9sDdo1XRJMhIOK+hUapweP92Qi3whOkK5o5GdTg+BkEJaohG9Rk1Btq3NGJ2WlxVNqvWwxqHTqFADk/7+NfMm5ZLZSZp4CNFRGnwB7h7bm4v6d6HC6cGg05CRYkYFhJWmKX8t7Xt+TTTqSDRpSTDI6NBfKtGo47PNVeTZrTz20RaWTs9n8dc/kpuZzMwxfSiv8WDQqikudzJn2aaYJgAX9e/Mnnov/7zx7Jj1HA1+stqehblfSWZJlolTgyTPhBBCnDRcbj9Od4A1P9XQxWLCrNfi9odQUIjXa6mu9/HQhBzufXtdtNh4UYmjVeevAruVSwZ1JSVeTzCsYJcv5EJ0iDJHI6u3O7CnxmMxaVGAmkYf943rx5odNe3HaLaNqXk9WLy6jFtHZVNe65Hi1EJ0EJfbj6PRT6JRx+g+ndnp9KBWqdCqVRh1amobA3y1vZoL+qbFdK2GnztrFmTbmH1Jf4xqlSTOjgBbvJ7NlXVMzcsCSvlwQyU3FPRk3udNn5PFZbXREWgt5dutxBs0DOpmocLpjenCOX5Al6O1+0KckKRhgBBCiJNChdPDxxv3UFXvBSAUVqh1+wkroFGp6GIx8fLK7eyqcfPQhBzyelmZNb5fqwK6eXYrN4/IxpZg4NYlxSQYtFL0VogOsNPRSKM/SL/0RCxmPXPf38jYZwtxeYNcu+hbzulp5YGL+1OwT5e4gmwb1+ZnsXh1GVeenUlto1+KUwvRQSINApaurcAbDPF1qQN/IIxJpybOoOXBdzdQ7w3w4ortVDf4uXts31bn1YJsG3eN7YterSJdpmoeMfeN68cb35SRm5lM/y5JpMTrGT8gnSGZyW1e3xRk25iSl8Wc9zZS6fLQJennjsT5divJcfIZKsT+yMgzIYQQJzyX28/OWjdndk9md52X99ZVUlzm5NlJuTz64SZyM5Ojd2EHZSaz7Zt6Lj8jg0RTmLvH9gUVNPiCmHQa9Fo1H6yv5MUV2xmcaaG71SzTEYQ4wspq3Dy4dAO/Hdqdqjovy9ZVRkdJFJc7ybSaufzFr3jy8kHcf0l/fIEwbn+IBKMWtQoa/UFyuiXx2uodnN+vsxSnFqIDuNx+Hl62kWl5PehhjWNPnZdPNu3hyqHd2VBRF43bQZnJ5GZauPG1Ndwyohf3ju9LMKzg9oWIN2ox6tQYNWq6yIizI6bOE8ATDHL3mL5U1nmp8wYIhMIMzUqh0R/E5Qlw7/i+NPiCVNX7SDTq6JJkZPy8lbj9ISYN7Y5K1VQX7YxMC3MuHUBaovHAv1iIU5gkz4QQQpzwnO4AL63Yxj3j+jHv86ZiudNH2llYVEpRiYNpeVnRKV+RAsYLmh+LyLdbeWjiAMY9W4jbH6Ig28Yjlw6gq1zsC3FEudx+7vnvWgZlJrOwqJRpeVkxsdiyQcCUV76NLs+zW7l1RDYmvZpLn/+KgmwbD03IIdmsk8SZEB2gttHPny/sTYXTS1iBeZ83db/dN24jMQulPPHRjzzxUVPX6ny7lbvH9iVOryVVEjNHVDCsULzDSaY1jqv+/jXQlAiLfHa2/EwtyLYx+dweeAOhaO0zXzBMVZ2PZbfmU17jJhxWjslxCHEikeSZEEKIE9reOi+ORi/3jOtHgz8YvWDMzbBEE2aRAuPQdvHxjBQT8QYtvmCAJ68YRE9bHF2SjPKFXIgjzOX2U1nnpbDEwZTmpPZVQ7vHrLNvjMYbtE2jJ+p8+IIh4o0aXp48BHuneLrbZAqYEB3B5fbjC4cJBBSsCXq8wVDMzaiWcdveeVWrVpOg10jirAO4AyFSE424PIGfl7Xx/9DdaibBoOGRDzbzpwt6R9eNNGS595315GYmk2iSmpFCHIgkz4QQQpwwXG4/1Q1+6rwBEoxajBo1AUVhW1Uj7/ywldtGZUfXbZkwi1wkRuxbfHzprXk8/uFmeqUmsLbcybxJuZI4E+II21vrxhtW2FXrAX6O0X3jE2Jj9IPfF6DVqFi3y8Unm3bTLz0pGqdCiF+u5bk13qDFqFXz7U81DMiwsG5XLUOyUmjwNiVp2ovbfc+rH/y+AItJRxeL6egdyCmk0ReKdgdvad//h2W35VPp8nHHhX1w+5r+D/PsVqrqfQzomkRuZjJDMpMx6jW43P5W1z4t3xuJJh22OOmsKU5d0jBACCHECSFStHjUUyu46u9f85PDzZcl1Tz54Wb6d7WwssRBvOHnO6ctLygjnTXbkme3sm6ni8vOyGBLZZ3UThKiA+yqcdMQClPnDUaXRWJ0f/FZkG1j+eY9XPbXr/j2pxquHNpd4lSII6jlufXS51cx4bkiKlweuqWYUAGfbdqDPxhGo1YBBx+3iUatJM46UKJJi0GrPuD/g0mn4W9fbuf7HbWoUJNntzJ9RDY9bXF8tnkPxWW1pFtMfLZpD7cuKabC6Yk+f9/3xqj/t6LVOkKcSiR5JoQQ4rjncvuZ8eZaCrdWA3Dj8J5U1XnJzbTwhwv6UN98RzwQCke7S7W8oFywspSpeVmtLjALsm3MmZjDWVkp9LTF8eQVg+RiX4gjbKejkXp/AG8gRKM/GI3NyN8His8ze6Sw7NZ8Zo3vJ3EqxBG077kVYPrIXiQYdeys8bC23Mnd4/pR7wmysqSaArvtoOL2YakX2uESjTqq6nxsrHC1+f+Qb7dy15g+BEJhfjs0k16d4tBqVMy4qA/f/ORg6ivfkpOeRFGJg4eWbaRfl0S+3FrNzDfX4nL7m94b/4l9bwAx6whxqpFpm0IIIY571Q3+6AWcWa9hTP8uPPrhJs7taaXG7SPJ3DTirLbRz9T8HoDSooAxFJU4onVAbjnPjl6rJs6glSklQnSwCkcjYRQMGg2rtzsY2M0Sjc3FX+9gal4WUBpTpwega7KJOJ0GDWCLN2CVqUJCHHEtz63QdH4dm9OFOe9tZNa4fqBWcddb65g5pg8vfbmd+Vfmsnj1/uM20aAlXRJnHS7dYiLPbiXLZualwu3kZiZH65xZTDqs8Xr2uHykJhlY/PUO7rywD6WORn732vfRbUSm4BZurebOi5rqoX25tZrqBj/BcJjCkuo2f3dkHflMFqcaSZ4JIYQ47rk8fsx6DTcM68movqm4PH7uG9cPlUqFSafl+x21FNhtfFdWy8YKF4Myk7m2oCdmvYaHJuTgC4Zp8AZJMGrZXeclJV6PxaijsyTOhOgwFTVuAgp8U1rDuz9UMCUvi911XgZnWqJfug1aNfeN64cCNHiDxBu1xOk1+MMhVEC6VRoCCNFRWp5b8+02woqCJxDi/nH9UNQqyms9FJU4qKrzMTjTwvTFzXGrUXPvuH5AbNwmmaTz7dGyp85Loy9AlyQj947rh8ff1EkzwaQlHFL4cMNu1pY7uX5YL/7v7O6gAr0mdtJZy/IWDd5Q9N8uT4DaA4wsi4z4F+JUItM2hRBCHPfijFrmX5nLmp9q+M2Lq3F5g+yp97JqWzU17gBzlm3i3vF92Vjh4sqh3Skuq2XKwm/59YurGfn/VjBn2UYsZh2/+usq/l64nURJnAnRocpr3NT5Atz79jqyOsVTWOLAFwwz48213De+P4Mzm7rhXv3yN1z0l0IeXraRTgkG9FoVj36wCaNWSzdJnAnRoeIMP59bL3/hK6Ys/JZdTi8BoLzWE+3k2CpuF3zDmOa4TY7TYzFqybTGSeLsKKpt9FPvC3PZC18RCCk88sEmrnjxKy56ppCx81bybWkN943vz/TF37NwZSkqmspZRESmzkfEGzXRf5v1P/+7PQlG6c4pTj0y8kwIIcRxz6zVsHBlKWvKnDx3ZS4LVpZy55g+0Tbtbn+ISqeXfulJaFUq7rigNzPHqKh1BwiGFL4vq6XC2TTi5eFLB5AuiTMhOkxlrZtKl4cEo47CEge3jFCAplEO1Q1+rvr7ah67bCAzxvShwRsi3qihqs6HLxTiL5/8yIwxfekm076E6HAmXdO5tbDEgS1ez4IpZ/LEh5u5e1w/XJ5AdGRSe3GbaNChVkEXidejrs4bJM6g2e9nqtsfaJqaW+IgrDTVfwUosNuYnNeD25YUA0310arqfAAMy7ahVquite2KShytfndBtg1bfMcnSqXTpzjeSPJMCCHEca2i1k2dL9icOBtMaoKBwhIHN3tj27R/V1ZLcVltTIv2iIJsG7/K7cpjlw2UIsZCdCCX2483FOalFdu4YbgdALOhaRRDyy9j1y76LuZ5+XYrcy8dwN3j+kniTIijoMLpYZfTE02cvX7d2VQ3+CgscVDXnDhrGbPVDf6YuC3ItvHIxBwZIXqMJBq16DTqdj9T8+xWZlzUJ/pzgzfIy5PPJNGoJaQoTHvlW9z+EPl2Kw9NHMA1L39NQbaNuZcOIBAKt6ob23K7D03I6fAkVoXT06qZxbBsG49eNlBugIpjRpJnQgghjlsutx+XN4AShrduPpfvd9QSVppGscQbNbj96pjOX21d6BVk23h4Yo7UYhGig7ncfuo8AWa9vZ5BmcmY9D9XBzlQjM6ZmINOkREsQhwNLrefWW+vZ/ooO4umnkUPq5m7317HVUO7A5Bo0lFYUh3t5AixMZtvtzJHEmfHVHKcnkZfkFtHZAOtE1zTR2Tz+Zaq6LJ4o5YxfykEmj5z37jhHOo8fr7cWs3DyzayYOqZvL+uktlLN3D/xf3J62WNaQgRuVlZVe8j2dyxUzbb6gILP3f6nDcpV67nxDEhyTMhhBDHrT31XgxaDfcvW8+UvCxSE43Rx6rqfFTVeVtc3Lfd+StBr6VrinwhF6Ij7XZ6aPSH2FPnpbDEwZS8LBSlaXrQ51uqmD7CzvzPS1rFaOdEI0lGLa9/XcZN5/U6xkchxKnB5Q5w50V9ePzDTfx2aHcafEGKShzRuNRqVGxqriG6+OsdMZ0ck0w6MpNNZEji7JhKSzRSureBJLOW8QO6xCS49tR58QZCvLhiO9CULAuGwtHnFm6t5v531zNuQBfO653K6d0s1DT4GdUnDUWB2Us38MAl/bnrv+tiRvMPy7bx2GUDOzxxtW8X2Jak06c4liR5JoQQ4ri0q9ZNWIH731lPYYmDSc13xDdW1jVNR3hzLUtuOJssWzwvFW6Lubg36TR0tZjQqJDEmRAdbFetm11OD/M+2xqNU18wzOdbqrh1lJ2XvtzOwK4WxrX4ggdQVe8jOzWeV1b9xLUFPeXLkBBHQVmNm6Jt1by3rpLczGQWFpUyvXn0UmQk9y2vfc9L1wzhofc20C89idwMiyTOjkMqoNEbJDczmVq3nziDllBYocLlZc6yTbj9oejI3v/7+9cxz40kS5/8aDOnZyZHk2QFdiuTmz+n503KpbrBT703QIJRhy3+6NQcqztAJ0/p9CmOFem2KYQQ4rhT7mjE0eBBUaCweSqCQavGoFWzYGUpU/Oy6N05gUkvrcYfDHHv2H6MyelMolFHT1sc3ZJNaFVQ79t/q3UhxC9T4Wik3hck3tDUHCBSg9CgVfPiiu00+kKMyelMaqKewZnJpFtMpCYY6JZsoqCXFU8gwNXn9KCL1LARosOVOxq5579rSU00UlTiIDfDQlGJI9ppMXJ+7ZRo4IZXv2Pm2L6MG9CFBIOWLFsc3VPMkjg7joRQsMUbWLvTSYJBR5JJizVOz7gBXXh12ln893fn8sDF/fAEQpTXelo93xcMs7L5fRBRWOJgYVEpobBCkllPr9R4Ts9Mxhavp7rBT3FZLdv2NuByd9z1VeIBOnlKp09xrMjIMyGEEMcNl9uP1xNAUSDFbGRHi4u94nInXZOM5GZaYqZ+NfhDlDoaqarzMrh7MnqNmg83VtI9JR57p/hjeDRCnNwqHY0EgV21nuiXmeJyJwV2K8XlTnIzLdyy+Hum5WdhMetjauZ0STTSKUGPyxOkf7qMDhWio1XUumnwh2JGckdHgdb5yLdbWVniiJ5fczMsbN3TQJJJR0qcHmucPqZ0gjj2TFoNnmCIUFhhT703+hlbXO5kwcpSBmdamJqXRbyh7a/8kZsdkfdBRFGJg1BYiXa7rHX7CYTCFG1zsGBlaXRE2yOXDqBbB4zut8XrGZZt48s2pm4OO0qdPoVoiyTPhBBCHBd21brxBv3o1TpCikJjIBxzQbdgZSnv3JJHD1sc8z8vianDUWC3MTW/B063n6+21/DMp1tZfN1QucASooOUOxqb/m5OcMfpfx658vbNeTz64aZoLcKWsZpnt3L/xf159INN3HFBb7pbzTJdU4gOEEl8NPgCJJl0PPHhZibn9QSIGSEKMOPNtbx+3dk89N4GVpY4ojGbb7fywCU5JOg1kjg7DjX4gwCc3cvKfW+vZ+U+TR1mXdyfK/+2moVTz2r13Lzmmxzw8/ugpUZfkOlLimNqj+XZrTw7KZfbmpfP/O/aDulinmTW8+hlA5n55tqYBNrRqrkmRHskeSaEEOKY21nj5oUVJdw4rBc7nR4sZj0N3iDfl9VSkG2jcGs1bn+I99dX0iXRGG2/7vaH0KpVrCyp5vXVO5g5pi8vfbmdfLtVvpQL0UEqa9wEwwreYBiXJ8DGyjomDEqnwG6lsMTBBxsqGd03Da1KxR0X9GbmGBW17gDBkMJul4cfymq5vqAXSUbdEf/SJYSACqcn2q3w9tHZlFU38KcL++ANNN2QitQ2i/xdVOLgqr+v5rHLBjJjTB8avCESjdqmP9Kp+rjlcjddJ3VKMDB9pD36fxdv1FBV5+PKv62mb+cEGn3BmOfl2a1MzcvitiXFMUm0lox6DWt21MYsi3T0nJafxfzlJawscbDD4SbeoD3i75F0i+mY1VwToj2SPBNCCHHMuNx+XO4A3+2o4YZhvSiv9RBn0FLnCRBv0LJgZSn/vPFsUKCwpJoXV2zn2Um5PP7h5pg7rAV2G7Mn9Gfygm84o3syj1w6QL6UC9EBKmrd+MJhZr2znukjs6N1CK8Y3JXpI7MBVTRO/7Zye/TLFvwcp/8rd5KZbJJmHkJ0gD11Xmb85wfWlDmZPtLORTlpxOm6cvdb67h7bD8K7DYWrCzl2Um5LP56R/MI0abEyLWLvgOaRi09fOkAqW92nDPrNfzls6387ZozABWPfrC51WfuQxNzQFF4/7YCKl1NI4WLy53ctqSYwZkWpjQn0VoqsNv4odwZTZK11LIrK4BKpaKyzgtwxBNbSea2k2WRUZV13gCJJh22OEmqiaNDkmdCCCGOiQqnh1lvr+eucX0YlGlhZUk16UkmkuPU6DRqjDo1uZkWpi78ln/ecDb13hDeYIh4g4aHJuTgDYap9waIN+gw69XUeQK8MvUsuTMpRAcpczTy9XYHA7pZKCxxcPe4fhSWVJObaWHZ+kpG9E7lktPT+f3obMKKwoOX5BAIhZsbCmgxNk8NGtUnVWJUiA5Q4fTwU3Ujm3bX88YNZ+PxBzHrtDQ21zoLKWEenNCfWe9siNY2M2jU3DeuHwrQ4A0Sb9QSr9eQaJKi7Mc7s15DbqaF619dw+2j7cy+pD++YBi3L0SCSYtRp+bjjZU880lTAmxafhbn9LQyPLsT4wZ0wazT8NB7G3D7Q9Ft5tut3HFhbyb9bTXzJuW2+XtbltRw+4NM+ttqCrJtPHBJf1SAtQOTWS1HVUYMy7bx6GUDSZfGM6KDSbdNIYQQR53L7WfGf9aS291CIBhmb52PZesq+a6slgqnB51WhT8UYvoIO707J/Cbl1ajVkMorFBW42F7dSPL1lXywoptuDwBVKgYmJFMr9R4+VIuRAfYWeNml9NDIKywy9k0eiFer2FThYupeVl891Mtu10+AqGmqZzVDX5+cjRSXFZLOKxg1mnI6hRPVieJUSE6gsvtZ8aba6n3Bfn75DN55tMfMeq0rCypjsZsWAFfKMzE3HTe/N255GZY8IfCvLeuksv+uoq/fPYjZr0GvUYtcXoCiDNomT7CTm6mhbnvb2H0U18y7tmV/HVFCVq1mvve2sDcZVtw+0O4/SGKy2pRq1Ss2l5Noy/I059uoU96Ei9PHsLzVw3m9euGcudFfbh20be4/aFWjQQiIjXSWk75LNxazax31vPf4l3cuqSYCmfr7p6/VOQ9XrhPI4Evt1Yz8821HdoBVAiQkWdCCCGOgap6H4Ul1dw7vi+hsMK8z0tipgJ8snE3Y/p3Jj3JxPgBXZiWl0Wly0uXJCNxei3BcJhxAzpzQb80tuyup1+XhGN8REKcvFzupukx8z8viZmu8/mPe5g1vj8PvreBfulJqIC+XRLRa9R4AiESjFrSEoxs2l1Hn84So0J0pOoGP4Vbq/nzhb154sPNnNEjhSc/2syUljG7pYrh2VbO6J7C4x9u4rIzMkhNNJDXy8aY/p2lxtkJJjXRiDcQil4nRTsa13nRqVVMzE1nSl6PmOXeQIgXV2znzO4pXDyo68/bSjDwxY97o900oe1GAi1r5U3dZ8pn5Dpu/vISZr65lnmTco/oeynyHm/Ll1urqW7wy3tXdKjjZuTZI488gkql4vbbb48uUxSFBx54gPT0dEwmE+eddx4bNmyIeZ7P5+PWW2/FZrMRFxfHJZdcws6dO4/y3gshhDhYe+q8BMMKS64/m2BYIRhWojU6fMEwxeVOvvuplvIaL7VuL/l2G+kWE8lmHTqNGmucjmAwxGV//YonP9pCvt0mF0tCdJA9dV4qXN5onEZiNM9u5dEPfqTS6eb+i/s3TQEyaDDqNMQbtaQl6Llu0Xc8/tFmiVEhOpjL7UcFvDs9D51GTWGJg3y7jcJ9YvbFFdupdYfYWOli5pi+pFtMePwhLGYdSZI4OyFlWuPIs9tISzRii9eTbjFxdk8r3nCIgRkWtBoVBq2a1EQDe+p93LL4e3IzLaza3nTd9UpRKRkpZm56bQ3zl5dEE2cFdht7mmuZRRTYbcy+JIchmcnkZiZz25LimCmfAP5Q02i1SDLrSKrzBvb7eP0BHhfilzqskWeNjY08+uijfPbZZ1RVVREOxw7p3L59+yFt79tvv+Wll15i4MCBMcsff/xxnnrqKV555RVOO+005syZw/nnn8+WLVtISGi6g3n77bezdOlS3njjDaxWK3/6058YP348a9asQaPRHM7hCSGE6CBljkbuemsdxWVNhWiHZXciHFaij0eKjz87KZdXviqlX3oSuRkWfMEwSSYdcXpN0xQwg5al0/OlvpkQHajM0cgDSzcwNS8LnbrpfmvLGIVSpr26hmn5WeRmWABINOkIhxVqPX6pQSjEUVDp9LCjxs285VvZsrue5686A4BgqOncum/M3rL4+6ZaZ9qm70ldk02oQKZqnsAcjX4ufX4VAK9MOZPvymoZnJmMWa9Bq1bz7U+O6IiyAruNyXk9uG1JMYumnsUZPVJ47INN/PaszGhzgDy7lan5PVi3y8XLk4fgC4bJSDHx0YY9+IJBprzybbv70ineEP33kU5mJRr3X4cv4QCPC/FLHVby7LrrrmPFihVcffXVdOnSBZVKddg70NDQwFVXXcXf/vY35syZE12uKArPPPMM99xzD7/61a8AWLRoEWlpaSxevJgbb7wRl8vFyy+/zD/+8Q9Gjx4NwGuvvUZGRgaffvopF1544WHvlxBCiCOr0umhwRdk5kV98AbC6LVq1GrwBX6+AVNc7iQ30xItZBxJnBm0aspq3HRPNpEQp6d7p/hjeCRCnPx21bpp8IW4aXgvEow6Gn1BoHWMRqYKQdN0bHunONbucjEsu5N8EReig1XVeWnwBYkzaLhtZDYWs47mgT+YDU3JsYOJWUs7XQ3FiaFlUum7slqKy2qZv7wEs14TvZZ68opBWEw6PIEQtzaPGKv3BXnm060A/OnCPgzOtKAoTe+Z6Yt/HlWWZ7eSm5lMcVktgzMtFNitFLbo6hmRZ7eibzHV80gns2zxeoZl2/iyjambw7Jt2OLlPSw61mElzz744AOWLVtGXl7eL96BW265hXHjxjF69OiY5FlpaSm7d+/mggsuiC4zGAwMHz6cVatWceONN7JmzRoCgUDMOunp6eTk5LBq1ap2k2c+nw+fzxf9ua6u7hcfhxDiyJEYPflU1LrxBMIoCngCYeKbL+o1ahWbK+spsNsoLKmOuTvesj16nt3KnAk56OTO+HFD4vTktcvRSJ0/CCoVKpo6uq3f5aIg29ZujBbYbcye0J8yh4ch3VMkTo8DEqMntz1OD+5AiGBYweMPk2DUolOrUZQweXYr0HTubDdms23MvXQAGSnmY3QEAo5MnLZMKv38/91Ug6zlaLL7L+7PxOeKcPtD5NmtfF9WG91GmcONVqNiYVFptJRG5HlT87JY/PUOrs3PQq9Wc9fYvvD+ZgpLfk5iFdhtTM3vQaWraapnRySzksx6Hr1sIDPfXBuTQBuWbeOxywb+4vOOy+2nuqGpxmeiSYetA7uGihPTYSXPkpOTSUlJ+cW//I033uD777/n229bD/3cvXs3AGlpaTHL09LS2LFjR3QdvV5PcnJyq3Uiz2/LI488wuzZs3/p7gshOojE6MmlosaNyxfgf2VOUhON+IJhPH4tnRL0PPHhZq4+pwfTR9oBKCypjt4dv+U8O2q1Crc/xG6XB7UK0qQN+XFD4vTkVFHrpj4QosLpRatWk2TWMXvpBq4+uwd3jenDI+9vihnBApCaaGBTRR1L11YwNqcLXSROjwsSoycvl9uPOxhi1jvrY0YAFdht3DO+L1PzsthcWcf0EXbmf17SKmbTLSZS4vSkJRqP1SGIZkciTvdNKrW8jtJq1Lj9QSqcHj5YXxlNnO1b7D8jxURto59peVlcX9ATs15LglELSlPty37pSWzeXUduRjJPfryFQZmWaDOCJJOOFLOOz3+sondaInl2Kw9OyOmQxFO6xcS8SblUN/ip9wZIMOqOSHmACqenVSfPYdk2Hr1sIOn7nNMkyXbqUimKohx4tVivvfYa77zzDosWLcJsPry7FeXl5QwZMoSPP/6YQYMGAXDeeedx+umn88wzz7Bq1Sry8vKoqKigS5cu0eddf/31lJeX8+GHH7J48WKmTp0ak60HOP/88+nVqxcvvPBCm7+7rQx/RkYGLpeLxMTE/e53wZhf0fXSP7f52K63nqDwg/8e1PELIdr3S2JUHF9cbj+VLg8uT5B5y7eysvkif/pIO8VltRSVODDrNdw4vCdj+ncmrMCu5vbmxeVOFqwsJTfTwq0js+nbOUEuTo4jEqcnnzJHI/e8tS76ZXzfOJ0+shfjctLZ5fTg9AQwaNUUlzvZWOHiyqHd+ec3ZTx5xSCJ0+OExOjJa2dz/dC2ps49cmkOn22uYkC3JMbmdOG7n2qiN64MWjVV9T5GnNaJzpLkPi4cyTh1uf3srvOyszb2OuqMTAsPXJJDeY2bkKJEl+87LXP+8hIK7Dbuu7gfHn+QBl8QbyDMtYu+A5oaUjz24eaYkWkReXYr94ztSzCs8PHGPVzQN5WBGcmt1jseudx+pi8pbrOT57BsW0zX0ENJsomTz2F12/x//+//8dFHH5GWlsaAAQMYPHhwzJ+DsWbNGqqqqjjjjDPQarVotVpWrFjBs88+i1arjY4423cEWVVVVfSxzp074/f7qa2tbXedthgMBhITE2P+CCGOHxKjJweX209lnRedRs38FokzgNwMS/Tiy+0P8fQnW5n4/CoqXd5od6d+XRKZNymX8QPT6ZFili/kxxmJ05NLhdMTkziD1nH6+Ic/ctkLq4g3akky6fAFw+RmWOiXnsQ/vynrsJEG4vBIjJ6cdtW6qfOF2kycATy0bBMzL+rDdz/VMuG5InY1T6MzaNV0SzYxNqezJM6OI0cyTpPMenp3TsTeXBc2ch01KDOZ37z0FenJJl4p+immq2ZkFNqClaVA0wyA2Us3sLGini5JJtZXuMhvngYMtJk4iyxXFPjtS6uZv7yEOMOJU7y/usHfZuIMYruGutz+VomzyDoz31yLy31ku4uK489hTducOHHiL/7Fo0aNYt26dTHLpk6dSp8+fZgxYwY9e/akc+fOfPLJJ+TmNs3b9vv9rFixgsceewyAM844A51OxyeffMKvf/1rACorK1m/fj2PP/74L95HIYQQh6flnbn3b8tvdZEfKVbcktsfinYBG9AtiXpPEFu8gSHdk+ULuRAdqMLp4afqxoOK0+oGP799aTXT8rMY078zLk+ACYPSSc3PkjgVooPtrHEz479ruWpo93bXcftDbKtuZPbF/fEEQuyocWPUaehuNdMtWeqbnQpq3P7oaLEIs14DKIwd0JkpeT2IM2hp9AUpLndy25KfmwNAUyJsWl4W97+7gYsHduG+8f158L0NuH0h9qfeF8TtD51wxfvrDtAVNNI19GCSbHIePLkdVvLs/vvv/8W/OCEhgZycnJhlcXFxWK3W6PLbb7+duXPnkp2dTXZ2NnPnzsVsNnPllVcCkJSUxLXXXsuf/vQnrFYrKSkp3HHHHQwYMCDafVMIIcTR5XL7Katxc+dFvbn5PDuNbVxsGbRtD3x2+0PMX17CmP6dyUg2kWGN6+jdFeKUVuH0UFXnQ6Nu3Tn9gHGa05kuSUaypPutEB2uqs5LnTfAjIv6oFG1jteWtGoV1Y1+jDo1i7/ewcOXDpDE2SmkZffNSMfN807rRHmNh7QkE2t3Ojmje3KrBFtLvmCYwq3V/H5UNlf9fTV/PP80LHH7H02mVauOWPH+oynxAF1BI11DDzbJJk5ehzVtE8DpdPL3v/+du+66i5qaGgC+//57du3adcR27s477+T222/n5ptvZsiQIezatYuPP/6YhISE6DpPP/00EydO5Ne//jV5eXmYzWaWLl2KRqM5YvshhBDiwPbUeflxTx2VLi9JJh3eQBiLWYfZ0PrzuLjcGe0Gtq+CbBvJcXpJnAnRwXY5GvEGQui0KuKNre+nHihOaxp8HHLhXCHEQXO5/WzdU8/3O2po8AVRq1T4AmFMOg0F2bY2n5Nnt1Jc7iROryHeoOWRSwfQXc6npxSDVk2+3YpZr+HZSbkUl9VyzYJvKC53oigKA7omYYs3HHAbAArw9BWno1KpMOnU7b7vCrJtdEowMG9S7gnXNCbSrbQtLUfRHWySTZy8Dit5tnbtWk477TQee+wxnnzySZxOJwBvvfUWd91112HvzBdffMEzzzwT/VmlUvHAAw9QWVmJ1+tlxYoVrUarGY1G5s2bh8PhwO12s3TpUjIyMg57H4QQQhy6MkcjD767Hrc/zMPLNjLmL4Vc8cJXjPlLISpo9QV8wcpSpuZlxdTRgKaLr8el6KoQHa7c0YjLF+T+d9Yz7tmVfLJxT6svRe3Gqd3G1LwevPrVDqxxJ87oAiFOJBVOD3/61w+8v76SeIOOucs28t66Suq8AbbubeChCTmtYjNSv2pjhQujToNerZIbUacIl9vPT3sbKHc08sC765mWn8V94/qysKiU4jJnNIl27aLvuHbRdyxbV9nq/RMRScAC+INhMqxmTs9IorrBz4MT+rc6Vwxrvnbrbo07qBFnLrefbVUNFJfVsm1vwzGvFRbpVrpvAm3fUXQHm2QTJ6/Dmrb5xz/+kSlTpvD444/HjAIbM2ZMdEqlEEKIU8OeOi+PfbCJ28/vzeylG2IaAwB8vqWKW0dkA8QUH1/89Q7uGdcPRVFw+0MkGLV0TjSeUEP9hTgRVTgaUYBH3t9EYXMnzUHdLFzQLw1F2RSN4Uic3nlRH27xBdFp1KjVKr78cS9LvpYGAUJ0lD11Xma9vY4rz+5Odb2Pxz7cxKSh3VlYVMr85SUA3HnRadwzti817gCuFt1vF3+9gxsKemHQqOgmibNTQoXTw5MfbubW0dmU1XpYtb2Gq87uQd8uSdz11nqmj7RHk2jTR9rJzbCgAL/K7cr9726IqeMVScDetqSYPLuVr7Y7+KGsljN6pPDMp1sx6zXcN74f947ri8cfIsGowxavP+hzwfHarTLdYmLepFyqG/zUewNtHlckyTbzzbV8uc/+n2hTVcXhOazk2bfffsuLL77YannXrl1bdccUQghxcnN6/Nwy0o4/FG6VOAMIhhVUKoVxA7owLS8LXzCMQatmT52Xem+A9EQjXS0muegQ4ijY5WgkqIDTE4g2CJiWn8XfV25ny+56Fkw5k995gzFfxp/9bCuzLu5PKBTG5Qlw8cB0pp7bQ2JWiA5Q4fRQ5mjk6nN60NlioKvFRFmth4VFpTGdDucv30bfzknsdnlITTRGu992tZjommyiW4rUODsVuNx+Zr29nhlj+3Df2+u5cmj36Gd6pLFEboaFBStLeXZSLou/3oFZr2ZM/y5891MNMy7sw91jVVQ4PQDRBgK5mZZoEs3tD3HnmD689OV23P4Qd/13HcOybcyblHtI54EDdas81O0daUnmAycBDybJJk5ehzVt02g0UldX12r5li1b6NSp0y/eKSGEEMc/l9vPT9WNaNUQp9e124UpJz2Jqa98R4XLG7O8wuVlysJv8YUUuegQ4igoczRS7w9S4fLQ4AtGl+dmWCgqcUQ7aX613YFBq45+Gb9vXD8yU8xkdYrn9MxkeqXGS8wK0QEiiZCUeD1xejV6tYbqBn80RluKdKje5fLSJcmELV5PD2scY3M6kyGJs1NGVb2PPumJ+ANNNzDNOg0X9EtjWl4W3ZKbRnL5gmGm5Wex+OsdXH12D0b2TmV3nZe3f6hg/PyVXPbXVaQ0T8Hv1yWReZNyyc1MjunCWVXnY96k3OaunU0Jr0qX95CmXh5Mt8oTQZJZT69UOR+eig5r5NmECRN48MEH+de//gU01SYrKytj5syZXHbZZUd0B4UQQhx/Kp0edtS40WugU7yRwpJqBnWztLmuLxiOdudri3QnEqLj7ax1s7KkmtMzLMz/vISZY/pEH/MFw9F/txWrb998Lt2R6V9CdLQ99V4mDc1kzU+1ZHWKY9Y767lzTB9cnrbPk5F4zbfbSDLp6JUqnW9PNU5PgNwMC43+EGa9Bkucnic+3ExhiYPpI+3k2a0YtGpyMywAVLo8pCUamP95SUwpjZpG/367bwIYdU0NCCJJte3Vjdz8+vfAwU29lG6V4kR3WCPPnnzySfbu3Utqaioej4fhw4djt9tJSEjg4YcfPtL7KIQQ4jjicvtxevyU7m3AGm+kzhskLdGIPxSmwN66kGqkY1N7pDuREB1rh6MRlztAWqKRYFihqMRBMKREG3lIjApx7LncftQqNUadhtREI3EGLYXNsZpk2n8MGrWaA64jTk5xeg2+YJh4o5Zp+Vk88dHmaC1Lk07NQxNyqKrzAU2jjFueB1oqLndScIAGAi5PgIVFpUzLzwJizx2RqZf7G4Em3SrFie6wRp4lJiaycuVKli9fzvfff084HGbw4MGMHj36SO+fEEKI44TL7cfpDtDoD+INhDi7pxVPIESdN4gvGMblDnDv+L7MeW8ThSU/D8vfU+elwG6LWRYh3YmE6Fg7HY3c+9Y6bh6R3TQKtHl6tcsdYGpe0xeg4nIneXZrqy9TIDEqREdyuf1U1ftwegLY4vXMfnc9k5rrVDV4f47VlHg9+XZrm3VFC7JtWON00qX6FBWn12Ix6VDCCnm9mpJfNxX0oluKiUqXl61VDQzKSEKtVrF9byNAm2U2Fqws5e2b85j93oaYc0HLBgLzJuVSVOJgWl5WNNH28uQh+IJhjDoN35fV4mj0tzuNMdKt8ss2pm7KuUacCA4reRYxcuRIRo4ceaT2RQghxHEqMk1z3vKtFJc5mX9lLs9/XsINw3sRb9Tg9qv5rqyWPJ2VKXk9uOm8XtGC4+srXEzN70GY2Dud0p1IiI61w9FIhdNDYYmDO8f0we1Xo9WoAPiurJaNFS5yM5MZkpnMxQO7tEp8S4wK0XEqnB5m/GcthSXV2OL1/OPaoRSWOJjSnNSONzbVlvqurJZtVfXccWEfVGyJidF8u5U5E3PIkK6apyytWkXnRCOORi+dEoxsqnAxum8a976znqISB7Z4PX+ffCZbd7nommzGEwhFzwMtuf0hPthQ2aq5U8sGAsXlzuj6913cn0c/2MTyzXujy/LsVi7N7druvkq3SnGiO+zk2WeffcbTTz/Npk2bUKlU9OnTh9tvv11GnwkhxElmT50Xp9vP37/cRm5mMn8YfRoN3iB3jumLLxiiqs5HVZ2XjRUuLuiXxt8LtzNpaHde/3pHNFlm1mu4b1xfZo3vd1itzYUQB29vnRdfIMQ32x30sDXVQIrEqaPBR4HdFu28trColPnLSzDrNUzLz+J35/XCoFNjMeklRoXoIHvqvMz4zw+sKXMyfaSdC/unReuaFZc76ZpkIs6gId9ujcbqs5/9yKBMC1PyeuALhrGYdGQkmyRxdgpzuf3c/+56/njBaXRONHH3W+sYlJnM/M9+5MweKfzx/NMIh0GnVjGkRwoatYqikuroeWDfGQEvrtjOgiln8tznJTGF/VuOPgNISzS2SpwBFJU4eODdDczfT9dM6VYpTmSHlTybP38+f/jDH7j88sv5/e9/D8Dq1asZO3YsTz31FNOnTz+iOymEEOLoi0zT/KbUQU6Ghd8O7c7ColIWrCxl3qRcHlq6gT9f1If/rCnnD+f3poctjr9+UcKto05j/mdbyc1Mjt69lIt8IY6OCkcj/rACQIbVTCDU1Axgxptr+deN57C33sv0kXbmL9/KbUuKmZafxbTmkS5dk00km/WkJRqP2f4LcbLbVeNmb4OfNWVOnp2Uy+KvdwAwNqcL0DR97o0bzsYXCPLAJf154N0N0ViNFH3v2SmOeJ2GbnJOPaVVN/j5dPNe/u/sHmgsataUOZkxpg8TBqXz2AebCIYVcjMs7G3wkWzW0SXRyDm9rOypazoPgEJhixkBZ3RPxhav57r8LH43vBfO5hkEkdFnbn+IgmwbRp26VeIsorC5a+b+kmFJ5l+eLHO5/VQ3+KnzBkg06bDFSQJOdLzDSp498sgjPP300zFJsttuu428vDwefvhhSZ4JIcQJbmeNm29KHWSkmMjqFIcKWFhUSnHzxb5Rp6GwxMGQLVVMPqcHT3+yhd8MyeRPF/QmEAxzx4W9UYBGX5A4g5ZEo5auyeZjfVhCnNR2OxoJAahgt8tLSIGvtjsosFspLHEwecE3LJx2JmpUPHBJDt5giEZfCINWTbJZh16lksSZEB3E5fZT7wlQ4fIQDMO0/CwWf72DK5tvTF3QLy0aq9Ne+ZZXpp5JMBxmzsQcPIEw9d4A8QYdCQYNOqCzJM5OebXNxfndgRAN3iDPTsplU0UdH23YzW+Hdo8mZnMzLNR5gySZQnj8ITz+MN1STNHzgNsXIt6gZXedh9++tJpB3ZK4ddRpPPdFSUy5jXy7lcnn9mBbc+209hxs18zDTYBVOD3MeHNtzOi4g+n2KcQvdVjdNuvq6rjoootaLb/ggguoq6v7xTslhBDi2NlV6+b+d9dzeqaFlHgjoTCEQk31yqblZ7GwqDQ6veTFFduxxhsoyO5ESFH4cU8DFS4vxWVOgqEwN722BrUKSZwJ0cH21rrxAytLqgkrMP/zElyeAAtWlnLf+P7k2a2U13q4ZF4R/y3exZ46L25/CLNeg8WkQwN0TpE4FaIjVDo97HJ6qPcHUanVqFUwsncnrhranUVFpRSVOKh0eaOxWt3g59cvrua9tbupcHpp8AUx67XES+JMNHO5/fiDTSOLDVo1FrOehUWlZHWKp096UjQxW1xWy61LitGoVTz8/iZc3iAhReGS+UUAzH1/E1e8+BVjni1k6ivf0btzAreNOo16d4B7x/Vj6a15/PvGc1h2az6nZyZz25JitOrWNdNaOpiumRVOD9OXFDPqqRVc+vwqRv2/Fdy6pJgKp+eAx71v4gwOrtunEL/UYSXPLrnkEt56661Wy9955x0uvvjiX7xTQgghjg2X288Oh5uBGRb0ajW1jT7mL99KTfPFyODMZIpKHNH25G5/iPfXV7bazi6Xh9+8tJq+nRNiWpkLIY68PU4PvrBChdPDJ5v24AuGWdkcpy2LQL88eQhPXjGI3AwLq7Y3jUR77IPNlOxtwNM8vVMIcWS53H6++HEvT328BbNOw4tflGA2aIk36KKjuKGp8Pt+Y/XDzWjUKkmcCaBpyuaq7Q4Ksm0UlzsJhMIUlTgIhpqmag7pkUy8QcPMMX1ZNPVMetriuG98P7JsTTdJ3P4QnkAw+n57/qrBvDx5CLmZyUz622peKNzGsnWVXDyviGA4TCCssGBlKW5/KNqhuS0H0zXzlyTAqhv8rZ7X8vnVDZI8Ex3nsKZt9u3bl4cffpgvvviCc845B2iqeVZUVMSf/vQnnn322ei6t91225HZUyGEEB1ub4OPeKOWiQPTCYYV4gw6Cksc3H7+aQDRu42RC6eiEgcvrtjOs5NyWdB89zwiz25lcl4WdZ6DG74vhDh05Y5GGnxBtBo18z4vITczOXrnPhKn7cVogd3K5Lwe3LqkmMXXDT1WhyDESa3W7WdQtyS6JBmpcHnpk57E+l0uPlhXyaSh3aPrFZc72Vjh4sqh3VvFaqSrpoziFhG1bj/vFO/ilaln8cj7mxiW3QkAs0FDgz/I2JwufFNaQ//0JKzxBu5/Zz1rypy8f2s+nZun5+9yeklNNPJKUWlM7bOWDQLy7Vbc/hDzPy9hWn4W85eXRJtYAIfVRb26wc+aHbVMH2knN8OCLxjGqNPwfVktC1aW7rdmWt0BpoQe7JRRIQ7HYSXPXn75ZZKTk9m4cSMbN26MLrdYLLz88svRn1UqlSTPhBDiBOBy+6lq8KJTqwkEQ9SGFbbuqSez+Q63TqMmz24lOa5pKP6+F06RYsa3nGdHp1VT7w3yfVktty0pZun0/GN2XEKczHY6Glm93UF6shmLSdc0tbq5+D+0jNPSVs0BUhMNfLapKloE+mCm2QghDp7L7afG7WfW2+uZOaYvr331EzcM68V5p3XC5QlQWOJgShvxuvjrHdJwRxxQvFHL3yYPRqtWMapvKnF6TfSxXrY49tR5yUgx88NOJ8vWVVJU4mD6SDsVLi/f/FRDQbYNrVrFrUuKuWFYT+4c04eqOh9AtEHAGZnJTM7rgUalijm/uP2hmHNKkklHkkmHXqOmqt6LJxAizqClwRtss55Zgy8Q0+0Zmrqy3zuuL/+88WwcjX7Y29BmDbTEA5yr5FwmOtJhJc9KS0sBqK6uRqVSYbW2PWxTCCHE8c3l9rO7zsuuWg/pFhMGjYo4g456X5Ccbha0GhVmvYZKl5epeVkoCtERZy0vnHzBMEkmHZ0SDby/rpKnP9kKHNzwfSHEodtT6yagKAzo1lQI2u0PAeALhtlYWddunJp0GhKMWn7z4urocyROhTgyIgXQa91+dBo126rq+f3obDRqFTcM60WnBAPbqxvxNdeqajmKu2VCIqarpkFLNxlxJvZh0qjRqDXc9dY6fMEQ+XYbBdk2Pt9SxcUD09m2t5Fl6yqZlpcVHR2Wm2HB5Q3w0pfbeevmc1mzo5bcTAvPfLqVl77cHn3v9euSyN+uGYI3EOLWJcU8ecUggOj7FpoSaPOXlzAs28YjvxrAzP+uo3BrNWa9hmcn5fJKUSkrW450zrbxyKUD6JZixmLS8/hHW6L7FXnOwqJS7n5rffQ5bTUBsMXrGZZt48s2pm7KuUx0tEMuRON0Ornllluw2WykpaWRmpqKzWZj+vTpOJ3ODthFIYQQHaHC6WH64mIufKaQaYu+46K/FDJ76QYCIQW1ClzuAL5AmKXT8+kUb+C2JcUEQ2Gm5mWRFxnGv7yEaxd9x+tf78ATCOEPhnhxxXbg4IfvCyEOzd4aN4Gwgscfwh8KE1YUzIamUQcGrZoFK0vbjdMuSUamvvJtTOJM4lSIX65lAfTLX/iKCc8V8f763cQbtDT6gyQYdcx6pykxEKkF2jJW4eeExCtFP2FPjSdBp5HEmWiTolLREAjxk8PN01ecTjCsMH2EnXU7XXj8IdISjRSVOGISXr5gmLQEA9PyswiFm2qjzZmQQ0G2rc1rulubRyZH3q9JpthRXfl2Kw9NzGHu+5uidcgijaVaJs4ACrdWM/O/a9lV6yYQDsdM94w8p2if57RVAy3JrOfRywYyLNsWs66cy8TRcEgjz2pqajjnnHPYtWsXV111FX379kVRFDZt2sQrr7zCZ599xqpVq0hOTu6o/RVCCHEERIu1llRji9fz2GUDSU004vYFUavBWR9gxptrqW7wk2e3cu/YvpzRPZnlW/ayscIVM6XEoFVTXO7kjW/KuGtMH/5x7VlYTHps8QfXclwIcfD2OhrxAg3+EPXeIGa9hji9lqo6L/l2K8XlTnIzLa1GnBm0aqrqfGjVKv52zRBqGv10SzbROdEocSrEL7RvAXRbvJ7HLx9I5yQTTneAeKOWoBKmsMTBoMxk0pOM7Y4OjUzTNKhVpEriTLRhT52Xe95exx9Gn8aCKWfyZUk1762r5KysFP54/mk0+kLRpJlBq8as1zAtP4vMFDOeQIhze1l5f/1uFqxsmk22YMoQfje8F05PIHpNF5nSn9d8Xsm3W/EGQrw8eUjMtd9D722kT5fE6L7lZliiUzH3tbLEwQ6HG1uCIWb5/p4TaQLQ8jyVbjExb1Iu1Q1+6r0BEow6ueYUR8UhJc8efPBB9Ho927ZtIy0trdVjF1xwAQ8++CBPP/30Ed1JIYQQR1akW5EtXs/r153Ng+9taFWc+PXrzuaqv6+mqMTBEx9vYdb4fjzywSauHNo9pk4FNE3lfPCSHGzxenqlJhyLQxLipLe3xo0buPetda2KO9820s6si/vz+AebmZqXBbSO0al5WWzaU09mspkh3ZPli4YQR0jLDoDtnVdfnjwEaBpt9tyVg5k+wg401Q2NxGqB3cacS3PQSeJM7Edto5/iMifJZj0PvLueKc1TM6flZfGTw01Giik6Wmx9hYuXJw9h/uclrc4Jz07KbUrevvIdL08ewpKvd7TZOGDJ1zuYdXF/rvzb6ja7WU46KzP675Yj3dri9ARIiYs99xzoOW01AUgyS7JMHH2HlDx7++23efHFF1slzgA6d+7M448/zk033STJMyGEOM7Ve/1MH2nnwv5p7Kx1c21+T3Izk6NtyFeWOJizbCMLp5zJb15azfLNe/nTBafRLz0JrUrFHRf0ZuYYFbXuAMGQwvdltQTDYbmQEaKD7K3z4lMUdtV6mDS0O1Pze0Y7k0W+oE8YlM6fL+pNpdPbZozetqSYeZNy8QZCEqtCHEF13kB0dE9759UItz/ELYu/58bhPZlxUZ/oMotZhwrQAZ0lcSb2o84b5MbhPQmEm0Yz/t85PZg+0k6cXgsE+fLHvaTEGcizW1EUeP7zklZTIotKHKhVKpbemk+ZoxF/UOGecf0IhRXqfUHiDFqCoTAud4C+6Uk8+sEmfntWZkwCLvKe75xo5NVpZxEKK1jj9Zj1mpj3fEsGrRq3PxRTtyyS6GuPNAEQx4tDSp5VVlbSv3//dh/Pyclh9+7dv3inhBBCdJxKp4d4g47istp270K6/SEKt1bzu+G9mH9lLtMXF+MPKvywz3Mi8u1Wrj67+9E8DCFOGXtq3HgVpc0RZ5GYLSpxcG1+T3zBMC+vLKWwpHUx5cj0m0tP73o0d1+Ik16cQdOqeyDExui+zQGe/mRrtLlOnt3K+IHp5PWyki5dNcUBJBq1jOqTSkPz1P0saxyvfvUTuRkWisudbKxwMS0vi1tHZhMKKzzz6dY2t7NmRy3hsMKClaVtjjiLXA9G3D76NAZnJvN9WS1vfFPGo5cNbPWeL7BbWTD5TKYt+rZVAq3lOejRywYy8821fLm1OiY29lWQbUOrUeFy++WmjzjmDqlhgM1m46effmr38dLSUum8KYQQxzGX288XW/bywNINbd6FXFhUyrT8rOgypyfAwpVNy1zuALObC8u2lG+3MvfSAaQlGo/KMQhxKqlwNOJvI3EGrWNWq1FR6fLywIT+reI08mVoS2WddCMT4ghxuf1s2V1HOAyL2ih43jJGI80BCuyxsVmQbWPOxBxG9LKSKYkzcRCscXoSjDrijVqm5WdFpwgXlzvZVlXPPeP6sbColDU7atBr2v+6Py0/iweXbjjguSViZ62Haa98y8YKF0tuOLvN93xhiYPnvijhvnF9Y5bvew6K1C377I/DuaBvKg9PHNCqCUCe3crkc3sw5i+F3LqkmAqnp9UxuNx+tlU1UFxWy7b/z96dx0dVX40f/9y5syeZLDNsAQKBCUsI0CiCmgSXuqCoaO0i+GtlsdpHUfvYulWt+9a9Qm21FbWt0D6tD65V6/YooKBiKvsSiCQQIGRIJslsd+bO/f0xmSFDFkAJSzjv18tXJbPdm3r43jn3fM/Z05o2XECIw+2QKs+mTJnCnXfeyVtvvYXVut9e5UiEu+++mylTphzWAxRCCHF4+IMau5rDDMp18LWCXGa1NSe2W9S07V+zywpTpfiD8xxMnzSEIXlOWiIxduwNcfKQXP7rjOFYzSYy7WZynVZJnAnRA+p9AXRA0w1mlhUyfdKQVLz+7eMarphYQOngHDKsZk5qa0Le0BIBDC77Wj53Th1NjS+IalKorG3i7x/XcP+0Erl7L8RhsH1vkDv+dxVLqnw8N2siEwrzuKXdNkyLamLJ5j089cFWZpcVEtR0blxUyd+uOZWfqKNpDGhkOyy47GbsikKfPNmqKQ5OJBbnzsWreWBaCacPc7NgaTVzz/ZyUkEuZxT1QYvp3HvxGFo1nbhhdPk+EwoSQ/5mdnM92F5y+MCMSUOob450SLolLdncwF1TR7Pw6kmpIQRr6vw0BjR+fP5ItjYEcDk0PBlWhvfNTL1u3vRS6lsi1OwNAqQNLkhO3pw3vTS1htU1hdIGdUBi6uajl48jP8fx5X65QnTjkJJn9913HxMmTKCoqIjrr7+eUaMSC8S6det44okniEQi/OUvf+mRAxVCCPHl7W4KEdHjxI04+TkO/rPf9ssKrzu1PTMWNzrdflJR5OHei8fQL8eG06xiU030kYsTIXrEdl8AA2iJxHjkX+vTvqScPapPqiH5/jH64KUlRHSdU4bmYQaG98mkJRzlsq8NxFNeKIkzIQ6D9omzEX0zGZLnwGZWeOyNDWmVOBVeD/NnlBLVEwmM0oIc/r1uN5O9HvJz7FgUBStI4kwcNH9Q447/XcXSKh+anph62dk1W7nXzcyyQswmhYoiD0s2N6RujJYOziEWNyhwO/nT0q1dbjVu38g/ueVydnkhzyyr5spJ3bfqCGk6Y/JdNLRqBCJRpo0fyN0vruGWf65KPWf/RFe200pDq8ac5z7t9D3bT97cf8Jt++fsn2QT4nA5pG2bgwYN4qOPPqK4uJg77riDSy+9lEsvvZQ777yT4uJili1bxuDBg3vqWIUQQnwJ/qBGa1TnJ4tX0xiMcu/La1jaSZn9M0u/4JrJwxiQbeeZzkrxNzfw05fXYBgQicexWtUjeRpCnDDqfAEUIKobHRJnAMX52R0m+UHb3f7Fa3j5PzvZ3RLB6bAwvG8mXyvIZXjfTPkiIcRhsKMxiC+gpRJnT181gY+2+pjXSVP2JVUNPLO0mgHZ9tS2tQVLq8lyWDArCmagj2zVFIegoVVLrQlxA/IyrJ1esy2t8vHcsmqG5Dm5/szhnDOqL49PL6WyppEbFlViNinc81LH68H2WzaTjfzb/7dbOjiHZVW+g2ryn+1MVJYNcWdw90trOvTiTCa62m+1bO5ksmZ7ycmb7Sfc7i+ZZDveyBbUY98hVZ4BFBYW8vrrr9PY2MjmzYnmg16vl7y8vMN+cEIIIb48f1Bjb0BDNSnsaQ5z54WjUEymrsvsqxr40XkjMZtMnTZthcRFlabHOf83y6Q0XogeUOsLoMUNTArsbA53Gq+lg3M6HdwBiTieWTYUfyjKDYsqJUaFOAz8QY36lghNwSgZNpWoHmdwroM/XjWBmsYQfV32LtfNJVU+7jCZKC3I5cZFlZxUkEOmVcVmUekjLQ/EIfKH9iVUWsIxFLu5y//21u9qIRjVmf3cp/ztmlP5+RsbqKxp4vHppdgtapfXg8uqfFx/ppe8TCtPXzUhbftkshqtuyb/5V431nZN/g8m0ZW8ueM6wGTN5OTNg02yHS9kC+rx4ZAqz9rLzc1l4sSJTJw4URJnQghxjNnZFOL/NtUDcP8ra9HjBg6rhR2NHZutthfV42zbG+j2OY3BxAVJZ3cMhRBf3nZfgIAW4+OtPh56dR16vPNeNe230nSlsrZJYlSIw2D73iBzF37Gub/+gG89+REXPr6UeDzOwqsn8mFVA6qiHDAmt+0NMP/dKk4qyOGhy8Yy2J0hiTPxpTit+2pfIlG9yySS06ryzMxTaA3HCGp6qmItue3SH+o+uWRRTei6wfMrtrGgbXDU01dNIM9pZcHMU7CoCnPKCynzpg8LrCjyMLu8kC17Aqkm/4eS6PJkWjsMDkiaXORJDbw52CTb8eBAW1BlDT92HHLlmRBCiGObP6ixvTFIP5edOn+YmWWF5Oc4qGsKoShKt6912lSG2rvfQhLT932h3/+OoRDiy9nVVnEWjhoMyHZwxaQhqKbO4/VA22X6umwsWFoNSIwK8VXsaAxy2/+uYuOuFp6+agJ9XTYCYZ3cDAsxA15dvZPZZYUHjMmCXCev31RBplVlsGzTFF+ByaSkKr40PY4ny9bp866ZPIyWcAxr23+b4agO7Ktc3n8YwP6aw1Eef3cz910yBi2m87M3N3bojTZuYA6nDM1jdtvAgWyHBdUEIS1ORI+nkj93X1Tc7We1T3RlO608evk4bn9hFR/sV4X12OXjUmtZMsn2QScVbe2TbMeDQ6nME0eXJM+EEKKX8QejxOIGT7xXxZIqH06ryl/mTExNPCr3ujv0uIC2MnuziZf+U9dlKX6Z181nNY1pPzveSuOFONbU+wJoRqLBcjiqk+208It/b6S0ILfTWKysbeoyjiuKPLyzvp6gpqd+JjEqxKHzBzW2+YJs3NWSGtCRjMVbzh/B8i0+llX5KG2bdNvVulnh9ZBlN2MGMhzHTzWMODaZTQqzygoxKQqqopBpVVMDAdqrKPKwuznCpt0tVHg9qQTvwWy7TA4HWLK5gbtfWsNFYwcwY9IQlm/dm1pbllX5MAG3TBnF9sYQOU4LmTYzs5/9hJH9s7itbfLsB5sbsKqmLhNd5V43dkt68jk/x8G86aU0tGq0hKNk2S14Mq1pCaSDTbIdD3rbFtTe7Etv2xRCCHFs8Ac1tu5pZdPuFjbubkY3DH7XLnH2+PRSwtHERCZVUZhZ1rHMvqxtKlNLKMqCpdXMKiukYr+y+fYNY9s7nkrjhTjW7PEFiAJ1/hABTcdpUzGrCsuqfKlY3D9eN+xs5v5pJZ3G8V1TR/PUB1vTfi4xKsTBS66pO/1hmkJRHrt8HA+0S5w5rSpnjeyX6he1YGk1A7IdzD3L2+kWtnsvGYOdxDa44+kLvTg2uTOs/O3jGh66tASnzUxrJMY9FxdTsd9/e/F4okr5qQ+2Mqt8KPXNYcq87lQSrav1Zf9rvWVVPvq67KkhAu0tqfKxpyXCdc9/xow/ruCxNzbw6OXjqKxpQmu3lbklrPHgZWOp8Ha8rpxZVsg9L6/tsDUxOWygu4E3ySTbOzefwYvXnc47N5/BvOmlDDjOeoT1pi2ovZ1UngkhxHGsrinET19awxUTC1i4YhtXlxeSl2lLVaRcM3kYzy6r5rYLRvHRVh+lg3O4cVEls8sLU2X2NrMp1Qz2l98eT1DT+duKGmaVDWXm6UOJxOIMyXPy+tpdqYaxScdbabwQx5I9vgBhIA787r0q7riwmFg8TnMoBkBQ0zuN14E5Dq5a8DH/deZw/vucEdS3RFJxvLMpLDEqxJfUfk1VSCQf+rnsLG27GTW7vJDzivulVYIENZ3rF37GtWcMS1XbhKNxXHYzAS2GVVWwOSySOBOHRbbTyt1Ti4nFDTKsKtG4we69Ie64cDS3xA38oSh5GVZMisL7m/dQWpDD3IWVXHvGMO65aAwrt+1NVZwl15fb2qrH2l8Ptl9HIrE4y6p8nW71bN/vL1mNds3kYexpjQCJZHOm3cr9L69hfEEOM8uGprZ4ZtkTlWoNrYmBHF8mRrKd1uM+tnrTFtTeTpJnQghxnEo2GB0/OIeFK7bxg8mF9M92stMfxmlVufaMYZxf3J/fvL2ZH+kGG+r8XFgygKCmdzmpz6qaqPC6mXFqAXMXJi6eJhd5eOQbY1lV29ThS/nxVhovxLFily+AAehG4gv7kiofP9bjbNrdQvEAV+p5ncXrs7NOocDtpK/LzvcWfJyKy/L9KggkRoU4eO3X1GeWVXPdGcOJ6HEiUZ3/PreIC8YM4IFX11I6OIc++/WZCmo6v35rM79+azMAr99UgcNiIstqY6D0OBOHWSSm88Cr67j5vJGpCq9Y3KAlHOPav6zkD1eeTF+XjQ11zcwqKwSq+fVbm3ny/a1ce8Yw7rt4DPe9uo4lmxuY/24VpYNzuO75z7r8PLtFbfvcjoMx9u/3t6TKx60XjEIBnr5qAnaLyk5/iFH52SxYWp12HVnmdXPFxALmv1t1wAEGvVlv2oLa20nyTAghjlPJBqPXlA/jkvH9sJkt3Ll4NXPKh/H49FLqm8PsaEpM1/QHo8w4dQgbdvo77Y0Bib4sQ90ZjC/ITUucPXb5OAYcRP8JIcTB2ekLECdRcXbni6u5ctIQABoDGv1ddlSTQoXXndoW1l6F18PgXCcXjctPqw6YXOTh4cvGoulxzhnVV2JUiEOUXFPnlBWS77IyKM/JA6+s5Y4Li+mTaeO+tq2b09vitcseZ0UeMq0qKpAviTPRA3QjkaSaUx6lf44dX0Djx89+wqLvn8pJBTl8VtvI2SP7csuUkTz2xgZKC3LTmvqHYzqXfS2fOy4YTUs4UanW1bVhudeNJ9OK06p2SJQle6Ptr745Qn1zmDsWr0l77uPTS9PWrfbVbE6rehh/Q8efg+nzJo4+SZ4JIcRxyB/UiMR0nvzuyQxxOzArCs3RGA9cWoJJUbhj8eq08vpPaxpZV+fn5CG53HvxGO59ZW3aRVJFkYdZZUPRjTiXfW1gp1++e0NpvBBH2w5fgEBMx2IysdMfTvvykIzTU4flcf+lJfz0pf3i1OvhqrY4vbCkPxOH5slFthCHQfs1dajbycg+GXxc08g9F4/hJ4tXM7OsMJUos5lNvLexnrlneQHSEmgVRR4emFaCGRggiTPRA+qaQqltw5/WNHKm3UOOw0JDq8b0Py5n0fdPZU9LhHc31nNqYR7F+dmcVJCLWVXon21Hjxs0tGhs9QW566W1AG1bOou575V1LKnat+Yke5L95u1N3D11dFqiLNkb7cZFlZ0e54j+WTitalqiDGB2eWFaNXUkFqfM6ybDKmkJuc4+9sl/pUIIcZypawpx2z9XsbKmkSf/38mYgOZoDLNJ5a4X1/Df545gWZWPKycNYd3OZsq8bhYsrebx6aU8s6ya3723hdnlhcw8fSgAA3MdvLN+NwtX1PDLb42XhVuIHlLjC/DY6+v5/hleMOI0tW1TSU49ax+nv3m7Kj1Ocxy8s2E3f/t4X5xKrArx1dU1hbjthVWs3NbIEzNOwgrohsH4wbn4AlGWtKs2g0S8rqvzM25gDlPHDkjrR1jfHMakSOJM9Izk1uKrKxI3XBYsrWbikDyGepypCczT/7icJ648mac+2Mo5o/vxeU1TKlmVHCL1zLLqtKTvx9V7mTYun5OH5qZ6ku3f/+yW80dxkgLlwz3kOC1U1nTsjQb7qtFOG9Z9pVlStsPCDWcXkeOUpvji2CfJMyGEOI74g1oqcTZ/Rik5TjMtmk5LJMbj76xnaZWPuWcZQOLuePLLOFSnNR6HRNLMqpq4asHHDHU7efCysfJlXIgeUucLcP8ra7nxnBE89sYGbjirKG3qWXdxurc1wv97egWj+2dJnApxGCWTESu3NfL49FL8IQ1MGRjAXYtXM/esIiC9r1MyXv+y/AuK87Pp57ID4LColHs9DJbEmeghDa0aK7c1csv5I1PJsh88v5Ifn1/EQ5eN5a4X17BkcwOtkRhBTWfmMx/z92tO46cvr2FZlS81hOauqaO5/YLEkIAheRmAgS+o8Zu3N3f52V/4Aixcvo3xBbksWFrNqzeUU1qQk5aEa1+NduHYAWyraeTaM4alegE6rSp5GVaevmoCkVicXKeFHKeVPBmoIY4TkjwTQojjSGNQY2bZUG4+bwSLV9Yyq3wYdf4wOU5LasKm05boG1FZ20RpQcfpmpDoRzGibya7WyI8dvk4suzmxJhwuegX4rCr9QUIRnVG5WfTEo6xrMrHbVNGsXRLQ4epZ8k4dVhUBuc6aAxpKIqJx68oZUCOnRyH3J0X4svyBzUaWjWaw1GyHRYsJoVZZYXcfO4I/vJhNTedO5LtzWEyrGaWVvm4tW16ZrI6tH0CYnZ5IaWDcwAY3icDl0WVijPRo5rDUWaXJ7ZRziwrRFEUlmxu4P5XNvDM0m38efZEInocDFI9zF5dXcdF+1VIVtY28eBr6yktyKG0IJf571bx/NWTuv1sq2piSZWPmWWFBDWdlnC0Q+VlslKttCCHSFTntdU7ueeiMTz5/lYAHp9eyq/+vTGtn+fkIg+PXj6uR39vQhwukjwTQojjxPbGIIGIjtNqxmlVuWbycPyRGP5QFLNJSXtu+y1gUJ3WX6LC6+aeS0qobgjSN9vGG2t28dQHW3llbvkRPiMher+6tuEAzaEYpYNz0iaKravzt01CS2xnScZpudfNQ5eNRYvHueKpFbzwX6fz5tpdXDi2v9ydF+JLar898/qzhnP+mP40hWM4rSpOq8rN547kjsWruePC4rQ4TV9PSSXQ5r9bRUWRhwcvLcEJ9JHEmehhLruF0sE5zH+3iuVb9/LaDWWAQqsWIxDWaQxGybKbeWvDLu6eWswDr67lyfe3Mn9GKc8s/aJDP7P2Pcs+2urrcmhA+8EAyZuwjYEofV12Fuy3BTT5vv5glGVVPh54dS3XTB7G2IHZPLususMgnA82N3D7C6uY1xZfyeS2y2HBkyHtCcSxRZJnQghxHNjmC/CTxatZVuXDaVX53YyTsFtMmBQFm9lEln1fNUqykfH896o6bgHLceC0qmxrCPLnj6opGZTDb97ezOQiD55MuUAR4nCq9QWI6AYP/2sdPzpvJHtaI6ntX+9trOf7FcP445KtaZPQchwW+rvsNAfDvLvJx8lDcnl3w24+29bIrLb+Z0KIQ9N+e2Zy/bzn5bWpNfX3V56EJ9PGkiofN0RiZNoTFdzdraf5OQ5cVhUzkjgTR4Yn08oXvkDqz7qhcN/La9ISUhVFHq4/czgz/rScKyYWMKt8GFazwr2XFBOOxdnmC3boZwaJ7cgvzy3nvpfXdptkS65hyQE37dev5PsuXLGN4vxsIDEV9NYLRtEcinU6QRoSCbRdzWF+/uZG3l5fn/p5siotP8dxmH6DQnw1kjwTQohjXF1TiDvbEmeQmFS00x/itdU7+e9zRlBZ20ShO4MKr4clVQ08+f7WDo2MIbFVs8DtZMueAM8v38YVk4Zw46JKJhd5eOzycXJ3T4jDaLsvwM7mMPPe2cz4glzsZpVsh4WPtvoo87pTcXrO6H70c9lTXzy27Q1iURVaIzqra5uYVTaURStqJEaF+AoaWjWWbG5g7tne1PqZTJzNm15KnywbjcFEtVmGVWVXc5gKr6eb9TTM8D4ZqEA/SZyJIyTbaWVQbiKRNLu8sEPiDEhUjhlw87kj+MniNamfl3nd3H1RMdc9/1mn7x3UdJpDGtefPZwfnDkcfyjaIclW0a4CLVmN+dyyLzrsbrhqvymc9c2RVOx0ZXtjiFEDXGnJs/ZVabL+iWOBJM+EEOIYtrMpRFMwuq+fmVXlzBF9aG7rm3TK0AY27GxGLR3I/dPGcPdLa1ha5eP6hZ8xu7yQIe4MMmxmDMOgr8uGHjcwqwq3TBlJJKrzytxyPJlSFi/E4bTTFyAWN3A5LKkeMSYF8pwW1qe2alan4rSfy47NbGJgroNhfTIIRmK4HBZuu2A0qknhFzIFV4gvzR/U8Ic05p7t5fwx/TEMg58sXoMn08qfrjqFX765gZllhfR12QAwqwr/XFnLfdPG8NOX1nSI074uG3sDGnZFob8kzsQR1t9lp6LIk9q+2ZklVQ3cceEoXrmhjMZglJhusMsfojkUTd1o3V+Z180HmxsYPygHu8XE8yu2pW3HrPB6mFU+lLkLE0mxoKazcMU2ZpYNZWbZUAD6umy8s76+wxRORVHom2U74Lklewi298HmBnwBDZAtneLok+SZEEIco/xBjR1NIeJGIml2/VnDmTJmAF/4AljURNn8Ux9s5dUbyllZ08iYQS7uv6SEXc1hmtruGH601ceGumZuPKeIp5dWc8rQPB55fQPv3HwGIwe7jvIZCtH77PIFiBiJ/jF926bwRWJxavcGcVpVfnTeSH71701pW10cFpVBuQ5+9dZGJhW66ZdtZ6g7g2F9Mo/y2QhxfKtrCrGsqoGvDc6hsqaR+e9W8cSVJzE418Gf50zk7hcTN5ymTxpCTDco87p5e/1urjqtkMff2cQ9F49hT0uEprYeaJW1TWzY2cxPLyqWxJk4ah68tISq+tZun/OFL5iqMqso8nD3RcW8vX4Xs8qHEsdIS4yVe93MbFctdu0Zw7htyigUBUKaTqbNwpodTcxduC8pVuF1c9fUYjbXt+LJtLJpVws37Jc0S352QZ6jrXKt68RdZW0TxQM6Xpc6rSoGMHdRZVo/NtnSKY4GSZ4JIcQxqimk8du3N3HXRcXMn1FKU0Dj3pfXMLOskNyMRI+zoKazY2+IEf2zUBUTK7ftZeQAF5l2M0FN55xR/Zgypj+/f6+KGZMKmLuwUvqbCdFDan0BAprOA6+tS/VFgkSPmJv/8TmLrjmV/2xr5MZzioBE/LozVHKcZn7z1iZuOHsEr66u490N9fziW+OP1mkI0Ssk+5zdcv5I7ntlbSpZkGU18+c5E9nZFE5VddvMJvzBKLPKClm4Yhsl+dlMKnSzozFEttNClsNMSyjGxeMGcOXJg2SqpjhqGoNRQlqM7ANMXi70ZPDPH5yGRTWhKgqX/m4ZQU3Hk2llwcxTaAknBk7ZLSqeTCu/fXtTKvH167c283H1Xm49fxTBSGIQwUlDcvnz7InsaY1gVRPbOWt8Qa79y0qcVpXHp5dSWpCTXq1W5OH6M71cMn8ZAC9eV8Z9r67tdMDAjYsqU0MD2ptdXsi9L3XcnipbOsXRIMkzIYQ4RoWjcSIxHadFxWlRyR+Yzc3/WMX4glzOK+5HmdfNsiofK7btZV2dn/GDczhrZN+21+q47BbicQOLqlDUP4u5CyuZMCRXeicJ0QPq2nqcxfTEHf0rJw1h3c7m1B31kf2zmP7Uch67fByKoqS2ZlpUhdZwjP8628t3nvqI4gEuiVEhDoNkn7Nbzh+Z1vqgf66dcDSeqiaDREVZfradt9fvbmt0rjBucA6KkohPq2picK4DOzIcQBw9/qDG3S+u5rqzili2pSF1Hbi/iiIPZlXhs5q9lA3vg92icnJBLkuqGrhiYgGPvbEh7XVOq5oYhlE+jLhhkOO0sGZHM9P/uDyVUHviypM69Eube7Y3dQztB2okh9+YTDD72U9S7/H62p1p/QPb91Q7qSAn1U+tvdOHubvcnpocNCDrpThSJHkmhBDHoLq9Qawq/PJbX8MXiJLtsOJva2a8YGk1F40b0NY3aV/T1meWVfPrtzan3qPc6+aBS8cSi+ucOaIPF4/Ll/5mQvSAPb4AOuCyW9jakJiEZjObUrG5cMU2ZpUV8syyauY892nqdRVFHuae5aVvlo0tDQH+OmcSA7LtEqNCHAbN4cSaGWr74u7JtPLsrIkEwjpRPZ6aGgiJdfR3M07i+xXDmP9eVXoD9CIPD15agl010Ue2iImjqKFVY2VNE9kOS2p9MSlK2nbGcq+bq04fyrT5y9p2IOzlR+eN4P5pY3joX+uZUtyfM0f04YazDDJsKqDw7sbdPPn+VhYsrebF68p49I313DZlVFolWft4SUoeA8CyKl8qbsq9bu6fVsJF85ambeN88v2tPD69lAXLqtOSd5PbYuyBV9elvf/kIk+nn9ve9sYQ/V2yboojQzEMwzjaB3G0NTc3k52djd/vx+XqvgdQxQXfYOBlt3T62I7FP2fJ6//bE4coxAntUGL0eOcParSEosQwiETjtEZi5DqtRGJxono8Vfr+6txyfvP2RkblZ1M6OIdY3GBAth0tFqc5HCU/28GOphCLP9vOQ5eNlYsK0eNOpDhtr84XoDWmE49DSzhGczjKnOc+Ze7ZXj6vaWJlTSOzywuZUJBLttOCWVVoCSWqzhwWE6+u3smT729lwpBc2X4ietSJFqPbGgLsagqS67KxtyVCP5eDYFQnGNFx2lReX7OLz2uaUj2YnFaVa88Yxlkj+6IoENbiZDnMZFpUbIpCnzznUT4jcSLoLk4/r21kT6vGnuYwr67eSWVNE2/cVEFdUxhVVYi3XQsGIjFaIjqZ9sTAqIYWjY31fs4Z1Z9d/kRfXLtF5bOaRrbUt3Dj2UXoBjQGNcwmE0EtxoBsO/9as4vSwTlEYnEG5Tr4+RsbOmyfdFpV7po6mhH9sqhviVCQ5+SNtbuYUJDLzGc/6XB+ySq3C8b0pyUSI8dpYUBbf9D6tv6CmVYVp9VMjtNCQ6vG13/1fpe/r6evmsBQdwbD+0qPUNHzjmrl2SOPPML//u//smHDBhwOB6effjqPPfYYI0eOTD3HMAzuu+8+nnrqKRobG5k0aRK/+93vGDNmTOo5kUiEH//4xyxatIhQKMTXv/51nnjiCQYNGnQ0TksIIb6UusYgRjyOjsLdi9ewsqaJx6eX8qu3NnHlpCEMynWkyuPf2bibOZ3cIS/zurnhrCL0uEF9S4S7phbLl3EhekiNL8D9r6zliklDeGZZNbdNGUXl5ibKvG4WLK1m4fdP5RdvbugQo7PKClmwtJrrz/YydmAOd19UzFkj+kisCnGY7GoK0dAaoU+2DbMpUV1z10trUtUub908mXV1fn50/kgMDJZW+QhqeqrX06yyQhat2Ma9F4/BZlLokyuJM3H05Tis/OzNjVS2XR+aqCagRcnNsPL4O5u4/kwvYV3HblF5+PUNqYo0p1Xlnz84jZ+2TWRPOmd0X+6aWszdL65OS4qVe93cOmVUashG8j0en15KHNKqxkoLcujrsvO9BR8T1HReuaGM+e9W8fRVEzo9h6CmM//dKkoH5zDnuU+7PIbkQABPppWKIk9adV1Ssi2COyOxdvqDmkzkFD3qqCbP3n//fa6//npOOeUUYrEYd955J+eddx7r1q0jIyPRT+BnP/sZv/rVr3j22WcZMWIEDz74IOeeey4bN24kKysLgB/+8Ie88sor/O1vf8PtdvOjH/2Iiy66iJUrV6Kq6tE8RSGEOCh1e4MEYzFsqsp9r6xlfEEut0wZxc/b+lLMLivkvY31zD3LCyRK38cNzOnQO6K+OUwoqjP7uU+YMCSXM0b0OcpnJkTvVOcLsKyqgR+eM5LH3lif+jKxrs7ftqW6mquf+4QFM0/hv9oaMyf7u/xtxTaumDSEK/+0gqCmM7nII7EqxGHiD2pEojqqKY7VpLLdH+J371WlfeGPRHW+XzGMJ9+v4tYpozrG6Mc1/PTiMbgcFvnyLY4Zmh5P/Xd846JK/nbNqbSE41z3/Cf873+dTiASA0PhodfXM35wDjNPH0okFmeo28kj/1qf1vtvdnkhZ47ow5Y9rcwqH8b4glwWLK0mqOksrfJhfXsTt5w/CkhchwY1nRsXVXL3RcX85ILR1DQGU4MDbmybslnmdRPTE5vaKmubDjhdE2DUABd3vri6Q++29gMBHphW0uE57QcNTBufz8ZdzexsCuPOtKHHDTbtbqHeYaEgz8lASX6Lw+SoJs/eeOONtD8/88wz9O3bl5UrVzJ58mQMw+A3v/kNd955J9/4xjcAeO655+jXrx8LFy7k2muvxe/38/TTT/OXv/yFc845B4C//vWvDB48mLfffpvzzz//iJ+XEEIcCn9QI6bHcagqzZEYPzpvBHocLKopdReusraJdXX+tISZpscpGZhNPA6xeBy7RWX1Dj8PvLaeoKbLJCIhesiuth5nX2trKJ6M0/c21vP9imH8cclWSgtymV1WyE5/mMG5DvIyrLRGYlwwpj9mk5L6sgEyNUyIw6k1FEUB+mU4iALZDmuHrWbvbKjn1MI8Kor6sLdVI9tpweUw0xyKcUFJf644eRCDZDCAOMa0RmKpfw9qOtsbQ9gtKkFNpzUSIxLTqapr5savt5vonKliMu1bp5IVZM8sq06rKrtr6mj+8YNTieoGqslEY0DDqirMLitkTvkwIjGdQncGq7Y3Ud8S4a/Lt6VP1vS6+fH5o2gMaECiH9qrN5Rzz0tr0xJo7ZNeAKWDc7odCNDQquHJtHLRuPxOBw2cPCSXT79o5IHX1vH49FIebXczCxJVdI9+YxyDZNu1OAyOqYEBfr8fgLy8PACqq6vZtWsX5513Xuo5NpuNM844gw8//JBrr72WlStXEo1G056Tn59PSUkJH374YafJs0gkQiQSSf25ubm5p05JCPElnGgxGg5FMSkKMcOgJRLFk2nnnpfW8P3Jw1PPSTZl/cvyLyjOz6afy44eN2gNx8jLsBI34Jt/+CitMSvsu/CQL+TicDvR4jSpzhcgCty1eDXjC3IpHZyTeixZEXrO6H70c9lTF/mf1/rx9s2gT6aNC/droJwksSoOtxMxRrc3BrEAZpNCmESctl9Lk5KxCqAbBjv94VT1dkGuRxJn4og5lDh12S1pf7aZTXxW08hdU0ezeXcLJYOyGeJ2YhgQielk2czYLSp72xJaALPLE8NrKmuamHu2lwlDcnFazWTZVKwWlUf/tSYt2ZxMdm3c1cLC5dtYUuXbN52zLZmV7bDQ12WjoSXCpzWNQGI75yur6hhfkMMPzhyOxaTQEomlVaoBRGLxbn8/LeEow/tmcsaIPtz+wio+aLd9s6LIwz0XF/Pa6p1cM3kYz+w3iABgaZWPOxavZr7cnBKHQffjK44gwzC4+eabKS8vp6SkBIBdu3YB0K9fv7Tn9uvXL/XYrl27sFqt5Obmdvmc/T3yyCNkZ2en/hk8ePDhPh0hxFdwIsVo3d4gMUBRIBo3qKoPcPdLiQuX3Ix9F0nJcvnitgEBkVicQk8G23wBHBaV7y34pNMv5JC48BDicDuR4jSpzhcgBvxkcaI3S/vEGSTi9PqFn1HnD6d+lp/jwKwq5GZYCcX0LuMUJFbF4XWixWitL8Auf5AoEGFfnLZfS5Pax+qAHAd9Mm14Mm2Uez0MlsSZOIIOJU49mVYmF3lSf07uSigtSFwbxg2DXKcFl93MvHeqmDpvKRfPX4rDuq+NUengnFTPtMqaRmY+8wnffvIjXluzi5++tKZDleayKh/PLKum3OtJPZbsWzbnuU+57vnPuPJPKwhpOoqisGBpdSrh9uT7W5n/bhVX/mkFsbjBnOc+Zf67VWnr4IGmaWa1JQzzcxzMm17Kmz+sYMHMCfzzB6dxy/kj2bInwClD8zivuH+HxFnSkrabU0J8VcdM8mzu3LmsWrWKRYsWdXhMUZS0PxuG0eFn++vuOXfccQd+vz/1T21t7Zc/cCHEYXeixOh2X4CIHscAYsA9L6+ln8ueWvxjukGZ1516fvuLledXbENRoNzrIRLXu70oyLJ3/OIgxFd1osRp0o62xFltYygVo5FYnMrapi7jdNHHNdjNCiX5Liq3NWI3d9+HVWJVHE4nUozWNQaJxQ2ynTY03UiL0/3X0qSgplNZ00hMj+POtOJxWiRxJo64Q4nTbKeVRy8fl0qgLVhazYxJQ1Jri4KJ+haNl1fVccHY/jx91QR+8a3xOCwqFW2vicTiqeqztMb/g3O6TD4tq/Khx41uzyOo6WTZzcyfcRKlBblp1WUAH271pY6hvcraJso7iU9IDA3wZKZXi/38zY3EDfj125u4ZP4yrv3LSmb8cQU7/aFuj09uTonD4ZhInt1www28/PLLvPfee2kTMvv37w/QoYKsvr4+VY3Wv39/NE2jsbGxy+fsz2az4XK50v4RQhw7enuM+oMa23wB1u1qRlESZexBTWdZlS+tfN0fjDKrrLDDRX/yjp5JSfwlbjGZ0u5EttfZhYcQh0Nvj9P2anwBgrE4AU3HH9p3AW4zm1iwtLrLOL1/2hh+/uZGUBROH+4hL8MqsSqOmBMlRmt8AXY1t31xNiAUTY/TA62lFpOC3aQwUBJn4ig41DhNVmC9flMFf/zeBAryHJhNJuqaw9zz8ho+2upj9XY/fV12Fiyr5rrnP6N2b5D7p42h3OvGZjZ1mig70PbJTFv33Z7sZhU9bvBZ24TO/ausFyyt5u6pxVTsF4fr6vzcfdGYDvE5ucjDY5ePS9tq2dCqMWqAq9PtmQciN6fE4XBUe54ZhsENN9zA4sWL+b//+z8KCwvTHi8sLKR///689dZblJaWAqBpGu+//z6PPfYYACeffDIWi4W33nqLb3/72wDs3LmTNWvW8LOf/ezInpAQQnTDH9RoDEbRYjp/+L8q/vvckdzzylp+dO5IAm13xNqXr39a09hWjp/boUnq3z6u4d6Livnte5uZe2YRj14+rkMviM4uPIQQh2a7L8COphAuu4XWSCwtRitrmygtyOHGRZVp/V9sZhP1LRGaghFunTIaiwID25oVS6wK8dX4gxoNrRqtkShup5VdzWG2NQQYnGeQYTfTGtYPfi1dsY17Lh5DviTOxHEk22llc30ra3b4OWNkH1o1nQybmWVVPiprmnjx+jIee3196r95m0UlpMV48NISgprOznatBZIOtH0ybiQSzp0lrSqKPOQ4zXzREOzQziApqOnUNYW4ZcooborFUwnuytomrvzTcq6YWMDsskQuYFCug/4ue4c1sTkc7XLAQLIKvLPj+7I3p5J/1zSHo7gcFjwZVlmnT3BHNXl2/fXXs3DhQl566SWysrJSFWbZ2dk4HA4UReGHP/whDz/8MEVFRRQVFfHwww/jdDqZMWNG6rlz5szhRz/6EW63m7y8PH784x8zduzY1PRNIYQ42nY2hdi2NwhAboaFm84ZSaumU5yfTWskhsuRuCPWfvFPDgloPxEJEhcpD11awuPvbeYbJw0m22kh22ll3vRSGlo1WsJRsuwWPJmyyAvxVWz3BdDiBvPfq+Kmr4/A5bCwpKqhQ4zCfjHqdfPgZWMJaTG++YcPKR7gSk3STFYNSKwKcejqmkL89KU1jBrg4qyRfdDiBk+9v4Vbpozi/lfXccNZRWTaVZZt6Rinna2lD15aIls1xXHJnWFl6eY9PPL6Bv5+zanEjURPtF98czxWk8I9F4+htjGEPxSlr0vlrfX1nF/cj7U7/IwdlNPh/bpLPpV53by7cTezygpRSDThT6oo8vCTC0fz2bZGtvq6Tp6Ved18WtNI8QAXP/7H5yy+7nTuf3Vd6vPmv1uVuL69bCwFXUzGdNktnSb+YN9gLROk9W37sjen6ppC3PbCKpbsd6Pr0cvHkZ/jOKT3Er3HUU2e/f73vwfgzDPPTPv5M888w8yZMwG49dZbCYVCXHfddTQ2NjJp0iT+/e9/k5WVlXr+r3/9a8xmM9/+9rcJhUJ8/etf59lnn0VVu+8tIoQQR4I/qFGzN8jv3t3M7uYIf/reBIIxneZQ4g6aalKwqibKve52X8YTPSaSFS3Xn+nFajaRZTfjsqg0x6KUFuQxNM+ZuiDIdsoXcCEOl+2+AK1RHV03WFbl484LVZwWlfV1fma13R1vH6PXnenFZjbhsKq4bGYicZ1v/D4xAXf/SZoSq0IcOn9Q46HX1jH37CJ++eYGFiytZvH1p3PlqUOJtcXpf58zgvrmyAHjNMtuJsuiSsWZOC7tbg6nNfd3OSwoSpx/Xnsa0bjB3S+tYf2uFh67fBxD3E5MisLq7X5KB+UyZmA2q7f7qfC605JMyevPzpJj910yhhpf4gbwrLJCZpcPwzAMBmQ7WL/TT3MoSm6GjQUvreWF/zq9QxKuwuvhx+ePZM5zn/DY5eMIajq7/BFumzKKlnAMfyiaqgZ94JW13DetpNMElSfTyu7mzrdfJgdrvXZDObG4QSAS+9I3p/xBrUPiDBJTsW9/YVXqZpg48Rz1bZsHoigK9957L/fee2+Xz7Hb7cybN4958+YdxqMTQojDwx+K8ucPq5ldXkh+joPdLWGy7BZcDgu+gMbSqgYK8pzcd0kJ97y8Jm0LGMDAXAdOi4oZCMV1rnruUx7+xlguLOkvi7cQPaDWF6AlEsOkKKm+LfE4/Ke2ibsvGsMDr65N2wKW7bBQkOtAi+vYTCrb9gaJxUnr+SLNioX4anwBjevO9PLrtzZy8tA8br9gNEYcdvpDONumCS6tamDz7hbunFrMQ6+t6zROw3Edp0kSZ+L41RjQWFLlw2lV+eE5XrKsKjFD5cMtDby2emcqcTXnuU95+qoJ3LCoksenl/LsR9WMH5zD10f15eShJdzz8tpUgiio6SxcsY1bp4zi+kgMs2oiEImR7bDwyzc3cuuUUYSiOq2RGE6rGUUBPR6nqF8W722s58n3t1JakIOuG5QW5HLdmV50wyDaNljnt+9sYt70Uj7+Yi/lXjexeJzH3tjQaaVbJNZ5girbaWWI20m5152W4EuaMCSXvMOwtbKhVeuQOEva/2aYOLEc1eSZOHZ953tzqNvT2Olj+X1y+fufnz7CRyTE8Sus69x07khWfrG3razehgE4LCayHRae+mArf7/2VBoDYW46p4gMm4XWtu1cTosKhoEZePD19XxzwmA21beSK5UrQvSIOl+AWNzgkX+t5ydTi8lyJC6V3tm4m9OHudnbGuKW80dhkEiI5dstOCwm4nEDs2IiGjd4elk1xfnZae8rzYqF+GriGJjNCjNOHcLC5YmJ0+cV96efy45qUgB46oOtLLrmVOa/s4k7LixGi8VpjSTi1GY24Q9FyXZYZDiAOK41h6M4rSpP/r+Tyc9xEDUMfvriGmaWFXY6CCBZlTVveikZNjPhaJxAJMy9FxfTGtGJxOJk2hIJsWgsTp8sG1c8tZyGVo3nr57ETeeO4PI/fMjoAS7uu2QMe1oiPP7u5rTPKvO6mXtWEe9s3M3nNU18bXBOh4mb0ZjBJV/L59FvjCOgxbps+t9dgmpgrpNHvzGOOxav7rCl8nD1Dm0+wM0uuRl24pLkmehU3Z5GBl52S6eP7Vj88yN8NEIcn/xBjaaghk0xEYjpvLN+N6Pys7mwZACBSIxd/hjD+2RwUkEOs575hGdnnYJJUYjpceIGBCIx1uzwM3FoHv5IlB+eO5IZf1wuU/mE6CE7fAGihkFQ01lS5aM1EsNuVqko8vDk+1sZNzCHPS1hRg1IXD7FjcQXkwyrChjsbolQ3RBgxqQh3LioMvW+ErNCfHnJpt0WRSGOQVNA486pxYkqmHCMSCzO0qoGKoo8LNncwJxnP2HBzFPY0xLGblFxWFX2tEawmBQG5Tikx5k47rkcFmaXF2KzmPjpy2u4fcoollT5mD5pSIfnJgcBJJNYVzy1nMG5jrYYiWCzqDisJhoCEcwmhUybOZU4qyjyUJDrYHtTkMenlxLSdL795Ef8z7WncdHYAelDcpojuDMtTBqaR0l+NjfslzgDWFLVwN0XFTMoz0llTedFGkndJagG5TmZ34O9Q10HuNklN8NOXN2P1RBCCPGl1DWF+NH/fI6qgMmk8Kt/b+SKSUOorGmkORQlw2Ymbhhc/dyn3D+thNEDXHz7yeX8a80u9rRECERiOCwqpwzN5bfvbCJuKMz443KKB7hkKp8QPaDWFyASN9jlD9MaiQGgKgq+1gg/OGMYJw/J5fqFn1HTGGJPS4Q9LRGybGbcTgtPvFdFXXOEPlk2FEVJu9sukzSF+PLqmkLMXVTJd576CJNJwaGaOGlIHne9tIad/jB2iwmb2cRTH2zl+xXDqCjy0NCqccVTy1m2xUdI0wlFdPpl2RgqiTPRS1hVE6cPc5NhM1NZ00QomlhvOpuYmRwEAImbPQC1jSGm/W4Zy6v3YhgQicYxKwofbvGlEmdlXjdXnT6U83+7hPnvbSGo6dywqJKGVo0de0OMGZiN3bKvv/gOf4hHX99AX5e908RZUrKq66smqLKdVob3zeRrBbkM75t5WNdYT6aVyUWeTh+Tm2EnNqk8E4eVbPcUInGX/KcvruGaiqHEUdjeGGJUfjbPLKtmWZWP26aMYpc/RH1LhD4uGxfNW8r1Zw3n1ikjCWmJKha7RcVsgp+9uZFbzx+FP6Tx92tOk6l8QvSAbb4Ady1eza1TRjPvvSpumzIKgA8276Gfy85b63Zx6rC8tBi1WUzsDURQVbj2zOHktW2lznVaOakgVyZpCvEVJZt2r9zWyD+uPQ1/KEy2w87di1ezrMrHlZOGYAD1LRFKC3L4wV9XdrqW2lQFm6LQp4sJfkIcb/a2RrCYTYS1GIuvPxWMRNKss4mZ7QcBtE+uBTWd37y9mac+2MrvZpzETn+I0sE5/PLb4xmSlwEY1DaGmDe9lMraptRNoYoiD9ubQlz7/EpmlxdSOjiHSCxO6eAc8rPtBLVYl4kzINWfMJmg+qCT3mJHO0GV7bTy6OXjuP2FVWnHJzfDhCTPxGEl2z2FSDQ1vuq0oeTnOLlj8WpmnDqE0sE5zH+3KvWc//m0llvPH8VQt5P571Xx8zc38fM3NwGJqURzz/bSL8vGw5eNbVuk5W65ED1he1vibGVNEyaF1JeOMq+bpz7Yyv9ce2panCYl49SmqmAgkzSFOMwaWjVWbmvkD1eehKoqZFrs+MOx1IRAm9nEexvrOa+4X7draaEngz4u+9E8FSEOqwy7BUWBTLNKXIHlW31UFHk6TGyHRJLsbx/X8MClJcTjibhYUrUvITS7vJA/Ld2alnB7duYpxAyDP3/0RdrPy71uZpUNRUGhtCD9urbC6+Hui4uJG0aHz0gq87rJsCbSD8d6gio/x8G8HtwaKo5PkjwTQojDzDBgYJ6Dlra+STedM4L6lkjq8fc21vO904by23c2MffsIu68cDSxeKLPktmkUN0QID/bLttLhOhhO32BVJzOPdub2q753sZ65p7lZf57Vcx85hOem3VKp3Hqspv55b838tBlY4/ymQjR+7RGosyfUcqAHAd63KA5EmNXczj1eGVtE+vq/JQOyqVftq3TGB2Y46CvJM5EL5NlNxPT40QNg7qmMPe+so7Xb6rgrsWdTGzPcfD+pj1MfXwpQCq5lkxu7X9zFyAY1fnxPz5PvU8kFsduUfFkWvnN25tYvnVv2mf0ddl4Z309l/5uGdeeMYzrz/ISx+gwUOCGs4vIce7bjnmsJ6jkZpjYnyTPhBDiMPEHNfYGNILRGBaTidZw4ou41ZyYqpmUbDw+sdDN9r0hsp0WzKpCPG6Q6bRQXpjHAEmcCdGjduwNosUNWkKJ/iulg3NSE/uSMTq1rSHy9qYwA7LtxA1QgEybmWAkxq/e2sT900rk4lqIw8wf1HDZLZhMCrG4AZD636Rklc2zH1UzfnAOZ43sCyRiNMtuTqylslVT9DJ1TSHueGEVs8qHMSDbjj8UJajphKJR7p82hq0NAbLsFqJ6nI+2+jr0H1u4Yhszy4Yys2wokVi8095iNrOJoKZ3SKo5rSqzywv50Xkj2eVPJLIra5tYsLQ69Rmf1zZx1si+qfUzNVCgJcLQPGeH9VISVOJ4Iskzccg2bVhPxQXf6PSxzVVVDDzCxyPEsWBHY5CmUJRAJEaW3UJzONY2gQ92+hNfvMu9bpZW+QhqOtcv/IzZ5YX0c9nZG9DIz3FgVhVyLCr9JXEmRI+q8wWIkbi7ntWW2E5N7GvbbtI+RvW4QWs4xgCXnYfe3cwdF4zk1OFuLi0dKBf9Qhxm+6+nyRtRetxI6+kU1PRUlU1JfjZ7WiIMynXgdliwWlTZqil6nWQfwORkzeZQNNXHrCmg8/l2H0urGpgwNJeJQ/OorGlMS5yVed3MmDQkLaH2yg1lHT6ns95pkNgCWlnTiNmkcGHJAO57dW2H6rIZk4Yw+9lPuGJiAf3axWDp4Bz65zgO6+9DiCNNkmeHUXdJpd7ULD9qmLrsa7b24auP8NEIcfTV7A1y5/+uSvVhAXjtxnIcFpUyrxuzSWH2s5/wp6tOQWEjS6oaUnf0yrxuHry0hEhMZ4DLLokzIXrYnsYgmgF3vriam74+gn4uGxVFntTEvvkzSgGDJVW+1F33Cq+b+y8tYd47m7lz6mjihsHI/llH90SE6IVqfAHuXLy6w3pqUhQCkViHnk7JtbTC6+GBS0uwKpAv66jopRpaNZa09QezmU24HBaWVDUkrjVVhd++s5nHp5eycMW2VPX0f58zAn9bhXX7xv+QSHYFIjEqvO60mGs/ZGDpfsmxWWWFLFyxDUUh7f3zcxy8tnpn6v33r1p746aKHv7tCNHzJHl2GHWXVJJm+UL0Ttv3Brn/5TWML8hlZru+EHazCbOicMPZRWzd08rI/lnM+ONyZpcXpkrlk2XsFkXBZrWQL9tLhOhRdb4AOrCjKcSVk4bgcpgJRKI8MK2Ej7Y0UFqQw9yFlW1xmr7dxKTA8L6ZbNrdirdP5tE+FSF6jWTLA9WkcN8razuspw6LitmksGaHn5MLctN6OrWPUZuCtDwQvVpzOJr698raJrx9Mllf52dWWSFb97SmxYdJURjf1o4g12nl3lfWphJv0NaD7KwiBmTZuH9aCT99aW2qD1pQ0/nbihoevmwsO5pCNAYTFW6VtU0sXLGNGZOG8LePa7jjwtEoQFHfTMKxOP/Zr9ItqdzrJjdDqrTF8U+SZ0II8SUltpZoXHnqUHb6Q6iKwoBsO2bVRFSPY1YUBrhs1O4Ncs9FY3jg1bXpk4mKPDx4aQm6YTBILviF6FG1vgDBqI4eN8iym1FNChkWlV+/tYX/PqeIcq+HU4bmcd8rHeP09imjuPz3H9HQqrHw6kl4MuVLgBCHQ11TiNteWMXssqG47GZ+eM4IWiMx7BYVs2qiMaChACpw8pBcCvKczH93c4cYfejSEkmciV7P1a4/2YKl1Vw0tj93XFjMI/9ax7jBOdx9cTEPvLpuvymYbu68qJiHLy3BF4gSjuk429qKbNzVwoBsO794cwPjC3JSN3ezHRay7Gau/NMKfnflSYSicexmE1PHDmDa+HxUk8IvvzU+rW1BNvDwZWP5yeLVadVq5V43D182Nm0LpxDHK0menaC+87051O1p7PJx6V0mRPf8QY1oLI7VbMJuMfH2+t3MmDSEX/x7I8uqfDitKguuOpl8l4Nxg7IxFIM7pxZjQFsfFzN2i8Jv397EbReMPtqnI0SvVucL0KrFaApGmf9eVapHy4i+mfzxexP49dsbuXryMDJtFu6bVkIoqhOM6LgcZpZv8fGtJz8iqOmUe90McXdseCyEOHT+oMZDr63jmopC+mbbsaomHnh1HTMmDUmL0/8+t4ivj3STbbdjVeH+aSUE22I0y2Em06LKDShxQsi0m9P65/6/pz/mL7NP4fuTh5NhU2kMaNxzcTGRaJzWSAzdMMh1WgGDbb4gO5vDjB2Yzba9QcwmhZhu0NAa5qrTh+KwqmxvDGEzm/hoqy81BCCk6XxW08iCpdVMGJLLvOmlXa6BBe4Mfvntr9EY0GgOx3DZzeRmWCVxJnoNSZ71Yt0lyDZXVXHmLU92+VrpXSZE5/xBjdZQlDigG2A2mZj/XhWlBbk8s6w6dbEf1HRmP7eSH59XxNdH9ScY1VNjuPtm2Xh7wy4+rNrLTy8qlosKIXrQDl8ALW7wn5omXl29M6258ab6Vr7/50958rsnE47F2dUcJstuwWUzE43FuPJPK2ho1YBEdcsjl41lYK5srxbicPAFItxy3kg+397EEHcGdy5ezfj91lLYN/22ttFPSX42rZqeGiYgiTNxIglEYswsK8Qg0fOvoVXj8j8s566poxk3KAeTohDVDVRVweUwY1VVTApoehyXw8KnNY3c8/LatK2VFV43t0wZxaOvb6C0ILfjhE2byro6P49PL+XGRZU0tGrd3kDq57LLda3otSR51ovV7WmUxv5CHEa7m0IEojpRPc7P39jAD8704rCqLKvyMbussMMFR1DTuf/VDdz/6gZevr6MAS47TeEYX+wNMrp/NlPG5JMvk4eE6DG1vgCtkRhxoK/L3mFyGCQSaGf98n1eu6Gcx9/ZzCOXjWXLngBrd/p5/IpSrGYTuU4rnkyrVJwJcRj4gxq7W8JkmFW0uIEeNwhGdZZU+ZjZxVqanH5bkJeBPxQlL9NKlkVloCTOxAnEH4p22vOvsraJB1/7iD/PnkimVSUO3PXiGpZsbuCfPzgNfyjKnOc+7fQ9l1T5uAVS17LtVRR5CERijB+cwzPLqpldXkhLu75rQpxoTEf7AIQQ4njgD2qEY3GaQ1F+/sYGrpg0BH8oSjCSuHsXicW7fb2mx3l97U4unreUP32wlaHuDEmcCdGD6nwBdAN+/u+NBCL6AWM0oOk8dNlYPtqyh5nPfsLyLT7CMZ28DCvD+2ZK4kyIw6CuKcSP/udz6pvDROMGe1rCjB7gojkUA7peS5PT+1ojMQblOiRxJk5ILrslFQtznvuU657/jDnPfcr8d6sIajpZdjPbm0I0h6Lce8kYyr1u9rRGDrj+JSvR2j+vwuvh3ovH8KcPqrl4XD4bd7VwUkEuWe36rglxopHkmRBCHISWUBTdiBOK6pQMyuHZZdWphqqQGBnenSy7mZOH5PHOzWcwb3opAyRxJkSPqfUFaNZi3P3iaorzs3E5zAeMUZfDjGoYDO/rYsHMUxhfkMuiFTW4ZUKYEIeFP6jx05fWcMWkAp5d9gVaPM6WPQH2BqNktDUwP1Cc5jgtWEESZ+KE5Mm0UlHk6fSxiiIPjUGNPy7ZSjgap25viFunjKIg13nAuDKbFACG5Dn585yJPH/1JK4/ezihaIy3N9Tz05fW8I9rT2OAy0amXTauiROXJM+EEOIgxAyDh19bj2pS+Proviyt8mE1mwCDiiIPlbVNlHndnb62osgDhoG3T6ZUsAjRw2p8Ae57ZS1xQ2FJlY/SwTk4LCbqWyLdxmimReWRNzawpKqB2c9+wqraJu6fViLxKsRh0tCqMWqAi/9dWcuN54wgHk9sp/aHophVhQrvgddSl9VMf0mciRPY9WcO7xAjFV43910yhrAWpzg/m1g8zopte/nN25vY1Rymvrn79W9pVQPlXje6YfDssmpMikJIi7OjKQzA0iofO/1h6vxh7lq8mrqmUI+fpxDHIkkdCyHEAdQ1hQhH45QMyiHTZmanP3ExsdMfZlCOnR9+vYjfv7+FWW29Itr3Vaoo8nD/tDHYVJN8CReih+3wBVJNx3f6Exf3kVicd9bv5syR/Sj0JL507x+jD0wrIRCLccuU0TSHNC4ely89zoQ4zFrCGueN7gfF/Vizw08/lz3Vs+nt9bu54etFPPVB12vpw5eWMCBPBnaIE1dDq8bs5z7t0POsT5aNhpYIwajOhIJcMm1m1tf5mTFpCM+v2MZ3Tx3K3LO8QMe4mlNeyF+Xb+OBS0sIaTF+eM4I3ttYz5Pvb2Xe9NLUc5tCUbIdFr4zsYB7XlrDL741XtZIccKR5JkQQnSjrinEL95Yz4xTh1Lu9aRNKDKbFK565hMWXXMqF47tz4BsG3dPLcYgMREp025m485mrCYT+TKhT4geVesLpDUdT7KZTfzi35s5bZgbswkeuKSEUEwnGEn0h7GqJh5/ZxO3XTC6bUKYVLUI0RPynFaC0Ti+gJaaxpdsdr6uLjFJ88KSjmtplt2My26RdgfihNccjqZ6nrX3xJUnMSjXgS1iIttp4Tdvb+KGr49g3jubKM7PRgGcVpX7Lykh3G79A2gKJSpCpz6+NO0at8zrprK2KfVnm9mEPxTl+RXbKC3IPeDUTSF6I9m2KYQQXdjdHOYXb6znhnNGkGEzE9MN9rRGUttKKmubGNk/i+lPLcfbN4umYIytDQHqmkL836Y9/OLNjZw0JI+BcqdciB61zRfgnlfWpjUdbx+npQU5fHfBJ1jNKjubw2zzBWkOR3l19U4efn09N587MvVlXghx+O1uDBI1wCAxMTAZo7ubw6xrq5B57qMv2OoLsssfYWtDgL0BjX5ZNrKtZkmcCUFiYEBnkj3NdjeHsaom3tmwh5ZglB+eM4LTh7kJRnUaWjUCWoy31+/m8Xc2M+W3S/jG7z+kNaJTWdPYIXE2q6yQBUurU3+urG3CZjaxrK0dgkzdFCciSZ4JIUQndu4N0hjUmFk+jLteXIMCZNhVrKqJBUurmVVWyLo6P7PKChnZP4srnlrOR1t9qQuYi8YN4P5LxjBYEmdC9KhaX4CfLE4MBnC2azp+UHE6NhGngyROhegR9c1h6htaicQNVmz1sTegYTObUjE6INvB9yuGsXDFNorzsykdnEMkFifHYaEg14Ed6C/xKQSQGBgwuZOBAZW1TWzc2czwPhmpJFimw8xjb2xgxp9WpKZy/nvdbj6vbeK+aWMo87oJajo3LqqktCCX56+exD9+cBoLvz+J0oJcblxUSVDTU4m0dXX+VCVaJBaXqZvihCTbNsURs2nDeiou+Eanj+X3yeXvf376CB+REJ2rawzSosXY0RhiQI6DZVU+DAUcFpXdzWFKC3K4cVEls8sLMSsKPz5vJGZVoSUUw51pxaRAjsNKH6lkEaJH1foCtGo6y6p8zC4rxGFV06rNuovTDTubycvIpq/EqRA9osYXYGdTkPwcJwFNT/U4q6xtIj/bTmlBDtcv/IxrzxjGD88ZAUBQ03FnqHgyLNgUhT6SOBMiJdtp5dHLx3HbC6tYsrkh9fN1dX6+ddIgdreE24ZZgVlVWNquvxnAgqXVPD69lIbWCBeNHZDWN+2jrT7+9nENPzp3JOeM7kfxAFdqW/XCFduYMWkINy6qBCDHYcFsUqisacTlsODJkB6h4sQgyTNxxEQNEwMvu6XTx3Ys/vkRPhohOucParRqMeJG4s8toURZuqoorKptwtsng7lneZn/XlVaz4mKIg8PXlpCMBIm2+mUxJkQPWxHW+KsuS1GI7E4QS3G3LO8/HHJ1ram49Ud4vShS0v484dfMKdimCTOhOghe5rDhKIhBuQ4U3EaicVZt7OZDXXNnFyQm1pLf/3WZn791mZgX4xmOSzyZVyITuTnOLjzwtGgwI7GEH2ybPx73W7+39MreOjSEvq57FQUeWgMdNxWmaw0+/s1kzh1uJu7X1yTlmAr97o5dXgehmEQdFhoCkUpHZwDkKpEK/e6yc2wcsHjS1JVbpOLPDx6+TjyZXu16OUkeSaEEO1ooSiGAc2hKJW1TUwdOwCA3U1hSgbl0BiIULs3yG1TRgGJCxG7RcXttKAYBvk5WXLBL0QPq22XOHM5EltHbGYT//XXz/jLnImcO7pfqtrs9gv2VZu5LCqBuM4NXy+SOBWiJ0V1MiwOWtrFaWskxoKl1Txx5Un8dfk2xg7KTq2lIU0n22Eh06oyyC1DO4TojgG0BKMM9WSwyx9O3ST63jOf4LSqPHHlSVhUpdPXBjWd+haNWc9+ys++OY6fTC2mORQlw6ZS3xzhW3/4iHGDsrltymie+L+qtORaRZGHOy4YxXefXpHWI+2DzQ3c/sIq5k0vlbVV9GrS80wIIdrU+gKEgbgBLoeFBUurybSqlHvdrNi2l1+/tZGQFuekIbmoikI4GifDprJ1T0uiVN5qlosGIXrYdl8AXyBKS9sXcofFRHnbVs0Ct5PvPv0xYwZm0zfbjhaLo5pM5GZYsasKS6v30ifDLnEqRA/a7QugAS2anopTq2qivq3twXXPf0ZpQQ5nj+pLOBpHVRQ8mTayJHEmxAHVNYXItKpk2MzU7Q0xMMdOhXdfH7SgpnPd858RiupUdNIfDRKDBUb3z2L2s5/yzd9/yNKqBva0aFjMJp6bNTGR1FYMZpcXsmDmKTxx5Uk8fdUEbjl/JN/8w0c0tGod3vODzQ2d/lyI3kQqz46Q7vp9gfT8EuJo2+ULsNMfIsthpTUco5/LxkkFOby1YRcPXjqWB15dy/RJQ/jj0q0s2+8u3NyzvPTLsssWMCF62E5fgBZNJxzVyXZayLCqVNY0pmJ0Vlkhzyyr5pL5y1KvqSjyMKe8kL8u38YD00okcSZED9rTGEQDonEDfzBKtjNRTfbJF3uZOMzNUE8G89+r4udvbuLnb24C9rU9GCiJMyG65Q9q3PbCKh68pBiX3cyq7U0srqzlvmljuOfltak+aEFNZ+GKGh6cVsJdL65hSdW+/mgVXg/D+2Qyq7yQOLCsypeqXCv3unnw0rEEtShBLQ4ofFbTyIKl1QQ1nSeuPCmt4mx/MoFT9HaSPDtCuuv3BdLzS4ijaY8vQChuULUnwPA+Ci6HhVpfMPWFfEQfF7ecPwoUuHtqMQYQiMTIspuxmk1kWM2SOBOih9X6AsRJbKl22lScVpVV25v42uBcGlpDXcaoSUlscfnlt8ZL4kyIHrSnrXrbF4hiURUy7Ik4Xb+rmQlD8tjdEsJqVnhgWgmhqE4wopPlMJNpkYozIQ5GQ6vGks0NVPtCeN0OTh/uYVifTH755kZmlw3ljgtG0xqJkWFNxJ5FgYcuK2FvMEo4qqd+bgCumJm7pxYTNyCoxbC3DcX65h8+ZGT/LGaVFbZN4szh8eml3LioMjWpuisygVP0dpI8E0KcsPxBjZZQFAMIRhOTwFxtPVfmf7yNm88fyT0XjyEY1cEAw0h8Ic+0m+mfZZOLfSGOkFpfgB3+ENkOKy6HhagexwyMznexNxDCk2lHN0j7Qj4gy4Y5rhMxqRRIrArRY9qvpa1tlaFRXaE5HMVqNjG8byb+UIgBLgda3EjFqastcSYVZ0IcHH8osS3yx//8nMU/OB2LAvnZdn58/kjCsTit4RgZNhW7WWVPaxhPpp2ormMxKdgdFlrCMUwmBYdFxWFRCetx/lPTyIOvrU+rKGto22Exu7wwVZU2u7yQ3c1hKoo8aZM+kyqKPOiGwZY9rTJ9U/Ra0vNMCHFC2tMUoiUUpa45TKumE4joRGJxMq0qwWiUW6eM5ldvbmLrngBmk4IeN2gNJypZsiwqWQ65uybEkbDdF6AlopNhM9MSipJpVdm4s5lQPIZFMRGNJ7aIhdri2GlVybCoxOM6j727hWyJVSEOO39QY/PuFqr3NBNst5YmK0NdDjO3vbAKs0nBopgIxSCsx9Pi1CmJMyEOidOaqHtpaNUIx3VicQMMiMbj2FQTGTYzreEY4ZgOKOzyh7jsiY949I0NaHqc/3p+JVMfX8o9L69FN6CuKcxPFq/pdCvmsipfatLmsiofpw1z4+2byQPTSjr0Uiv3urnq9KFc+rtlfP2X7zN3USVb97SydU8r/qD0QRO9hyTPhBAnFH9QY8+eFiJ6nFYt8YW8ORTFZTdjM5uIxGM4LRaeXrKVWy4YSf8ce2LctwKeTCtb9rSimhS5oybEEbDdF6A1qtMSjtIa1slyWAhoMSYWuonF4M8fVtPf5SCqG6ltmll2M899+AWf1Pq55fxREqtCHGZ1TSF+9D//ob4lTKZiojm6by1N3lgyKTB6gIvvPLmc7Y1BBiTjVEvEabbdzGBJnAlxSEwmhTKvG4BINE4wFueRN9ZT1xjGAAzDwKQoqCaFPlk27BaVZ2aewvjBOVzx1HIaWjXKvW4emFbC3kCEDKva7edFYvHUvyuAO9PKd59ewfjBOTx91QSevmoCr91QzoShedy4qDKVhFuyuYF7Xl7LF74AP/7H5+xsCuEPamypb6WyppEtklQTxynZtimEOGHUNYV44dMavlk6KDEFLBzDMAyyHBbsVhP1LRFe+XwHt50zgjmThyWarLYrTa8o8vDIZWPpn+s8imchxIkhmThrDsXIslvQjThOi4nfvL2J/z5nBGZV4f+dXsid+8VpudfNA5eWkOeUbSNCHG7+oMZt/1xFeZGHlkCISK6T5lCMuGGktlRv3NnMsD5OHpg2hrtfWsuMpz9Ovb6iyMNDl5ZI2wMhvgSTArPKCgHY0RSmqG8mc88u4pHXN3S4Xn3w0hI27PJjNqmcP6YfY/JduOwWBuc6CGhRMu0WFKX7z2vf4yzXaeWh19YzrXRgaisnQJnXTWlBbofqtSWbG/ivM4bzkwtHE4rq3PrPz1nSbuDW5CIPj14+jvwcx1f5lQhxREnyTAhxQvAHNf62YhvTJwymOZrYWpJpN6OgENPjfF7TzOnD8ij0ZPDY25u4umIY910yhpCmE9QSvVn6u+zyZVyIHpbsn5RMcGfYVCxmhebWGP/zcQ0/PGckv357I7PKh2Exw50Xjk4NCIjqcb7wBSVxJkQPqW+JsKSqgbsvGY1NVblj8Wpuv2A0iqJgVhWeen8rN50zgvqWCJ/XNvHAtDGEonFa2qrSMq2qVJwJ8SWpisLCFdsoLcglP8dOKBpjT7PGTy4YTfyCRHsRp01FMRQ27m6heEAumXaVrQ0Bsu0WBuU6eOqDLcyYNASHxUQsbnTZw6zM66aytgmACq+bcEzn3Q17uHLSkLTnLavyMbstobc/s5r4jP/UNLKypintsQ82N3D7C6uYN71U1mtx3JDkmTgmbNqwnooLvtHl4/l9cvn7n58+gkckehstoPHtCYMJxw2aQzFcDgt2swnDgHnvVnHTOSNoaA2T67Rw09dHEIzq+Fo1suxmBrhkOIAQR8LuphBa25bqZILbaVGprGnkawU5XFVWyJ+WbOGmc0YkvpAH930hf/i1tSiKiccuHycX4kL0kOZQFACLYuKuxatZUuXjPqsKBjz5QRU3nTOS3769ke9XDGfcoBwCWttwAKcFl0UlX9ZSIb60pqDG7ReM5v5X1gJQ7vXwl+VfMH3SEF5YWcs3Tx6M06bSGolRkOvEZjbha40wKNdJTI/zq7c2csUpQ3DZzLREolgtKjec7SVuGCxrVxVW5nWnpm1WeD3MKh+KP5iI/fZbOZM6+xlASzjKb9/ZxNyzvPxuxklcv/CztAq1DzY30NCqyZotjhuSPDtGdJc8OhESR1HDxMDLbuny8R2Lf34Ej0b0RhGTQosWI26QaChuVVGAYEzn+rOL+O3bm5hTMQyLqhJsV23msqgMkIt9IXqcP6ixN6QBSqIPYVuC22yCQblOfK0RFAXmVAxLJM7CicRZllUlENW495KxeDKl4kyInrKnKYTTlvjqEI7FU1uwLCYFBfj+5OH8/v82c93ZRcR0gz0tEbIcFvq7bNiBPrKWCvGVZDusPPyvdXytIJcJBbm4M63cNbWYB19bx+j8bABq94bIcVqwWUx8Ur0Xd6aN+lYNm9nEpGEe8nPs1O4Nsqx6L1vqW/jx+aO4aGw+c8qHYVEV3Bk2rGaFpmCUv8yZyJLNDfx1+TaK296//VbOpM5+lqxcSyblpo4dkDa9M6klHD3cvyYheowkz44R3SWPukscfed7c6jb09jpY5urqhh4WI5OiOPbdl+AFk3HQCEQieHJtKLpOg5VxaGaCMZ0vjNxCAawuzlCpl1FNwwyJXEmxBGzqzmMgUJLW+Isw6piAupbw/R32VhRvZeRA1w0h2MENR27WSXTqrZVhUqcCtGT/EGNmB5HIfGluCW07wtvMBrDqqpYTSYuO2kw4ahOa1gn067iD2lkWh2SOBPiMIjG47y9YQ9vb9gDJG4Gv35DGfdePCaxNkZ1orE4H27x8bePa7hiYgGDcp1kOyxk2s2M6JtJWNcxW1QuGNOfeHF/3ly7Ey1m0C/bjlU10RTU+HCrjwVLqwlqOhVeN1e1VaG138qZVOH1sLs5nPaz9pVrkNjaedfUYk5RFf72cQ0NrfuGBWTZZSK2OH5I8uw40F1V2uaqKs685clOH1v78NU9eVhCHBeSibPkVpNspwWrSSEQg5rGIINynIBC3ywbIU3HbFIwm0wMyrYwUC72hTgian2BtKbjmVaV1XV+RvTPxJ1ho3K7n1MK81K9k7LbnqPH9QO/uRDiK2sJRRPT/DCYVVaYmqoJcPVzK3l+zsRUojsUjaOadMwmEwOzHdLjTIjDpDkUS/tzUNOpagiycMU2Jha6OXNkHzTdYOrYAZw+zI3NYsKimtjpD6PF4jhyHVgUE30zrTzw2nreXl+feq8Kr4dbp4zkg817KB2cw7zppfR12XhnfT03Lqrk5CG5XH+ml9nPfZJ6TZnXzazyoaze4efpqyYQicUZnOfgzbW706ZvAlQ3BFi4YhvPX30qV/4pMflzcpEHT6ZUi4vjhyTPjgPdVaVJgkyIriUTZ63hRI8zA8i0qCyu3M6lpYP4+bIvOGlIDucW9yccjdMaiZFpN9MSjpKfbT/ahy9Er7enMUgkbtCq6TitKopJIdOqUrs3SMmAbCK6zm//bzMzywoJtms6HtXj/PrtLdx2weijfQpC9HrJtRTAqqr87eMa7rmomAqvmyVVPmobQ1z59Mf87sqTUnHqattSLf1ChTh8nDa1w89ue2EVC79/Kve/spbfvrOZJ648CbvZhMOqYlYVGoNRLKqJD7f62FDXzB0Xjiamx7lr6mhmlRXiD0WxW1QMw6A1EmPCkDxynBasqgktHmfi0DzOGd2PjbuaCUd1/jx7Iv62G9KVtU3MXbgvSZacvLn/1kxIbO1cWuXjgVfX8tjl43j2wy+475IxVNW3kmk3o5oUTIqCOyPRfsEf1Gho1fCHNJw2M6qi4DCbMIBo3CAUbeun6Ei81qKaCLdN6M6wqTgsKnaLyoC2aZ7+oIYvoBGLG8QNg2AkRrbTiiej63YP/qBGfUuEplCUDKtKps2MzaLSGo7RHE78Pdfd60XvI8kzIUSvVN+u4izTZibTqtKqxdjZFOSSrw3itVU7uPuiYu56cQ0/e2NT6nUVRR5+Jg3Hhehxe3wBwpAaDtAny0YkFmdNnZ/iAdmEYxEyLDZu/PoI7n5pTdo0sIoiDw9dWkI/lyS5hehJdb4ArdF91dv9smzcNbWYN9fs4MFLx3LXi6tTCbRL5i+josjD/dPG4FAU+kviTIjDym5WKfO605r7N7RqzPjjcp648iSy7BYCkSjuDBs/fXlth3XzgWklhLQYu5sjOK0mPJlW8jKsBCIxHNZEwikajxPXDZq0KP5glE9rGllX52fGpCFct/AzTh/m5pYpo7j/1bVpx1HhdXPdWUXMaVeZltR+u+fSKh93Ti3mwpL+TJ23NJV4qyjyMKe8kF/+eyO3nD+Ku19czcqaJq49YxhnjeyLokBTMDFIaPX2Ju59ZR3BthtvC646hd+9tznVhzH5mXPP8qLF4lgUaNZi2MwqD7yyNu15yb+zDCOOxaTSqum0RmK47Gb0uEFtYyiRfNzSwKmFbua/u7nD6x+7fBz5OY5Uwq99Yi35/1Fnybbk81sjUXKcVrRYop+r02bGpCiYTfuSid3p7HPbv+ZAjx9IV6/fP7mYYTOT47B85e9QX/V4D9d7dEaSZ0KIXmeHL0AcUk3HbWYTFiDTYsaZrRKNh7ho3EACMZ07LxyNAQQiMbLsZvq77JI4E6KH1fsCRNiXOHM5EtupVbOJ4gHZNAVD5GU40OIGET3eIU5znFZJnAnRw3akEmeJ6m1IDAdoDEU4v2Qg8XiMRy4bS4umJwZ42BO9Cm2KQr8851E+eiF6H1WBuWd5AdISVyP7Z6HHQY/H+cn/ruHpq07mwUtLCLaLzUyrSiim06rp9HHZsJtVTAq0RmJkWFX+U+vngdfWATC7vJDThrmxW1WmlgzgvOJ+aLE486aXMsTt5IqnlnPFxAJmlxUSicWxmU2s3uEnHNU5uSCXJVXpSburTh+a6n8G4A9FuWPxmrRzSyb67r9kDCYF7rhwNC3hxJrfHI4S1uIEozotkRh6HP743ZOJ6HH6uey0hGLcdsFoZjaHue2FVQQ1ndKCXOIGbG8K4cm0YlFNfPLFXmaWFTJ90hDsFpXPahpZsLSau15cw0Xj8il0O5n93KdplXSzygqZu/Az7po6ukPiLHnct72wivsvGcOn2xppDEY4Y0RfwlGdTbtbyWqrqvtwSwO/e28LE4bk8ujl41CAW19YxcptjTw+vZSfvbmx04mnj/xrPfdNKyG/rYJuf3VNIW5re59kojGoxQjvbiXHacFpVbnn5bVpW3QnF3l4tC3hdyDJ92+fiD13dF/uvqiYOxevSfv/uszr5oazixiS50xV/B2qzj7vUI73cL1HVyR5JoToVbb7AgSiOnGDVO+kQFRHwcCqmIkbYFcTX8pjupEq+R6YZZPhAEIcATt9AWKkJ84yrSpVe1oZ7snEpBi42xJn7bdluCwqngwrfSRpJkSP296WOGtp2wKVaU1sF3v58+1MHTeI9buaKe7vQmvbAqWgYFLAopro9xW/nAghOmc2KaiKkphc2S5xVd8cIT/bzmNvrGdTfSuX/f4jfv3t8fRx2VEVBatqIqDp+ENRDAMybWZaQlFa26bLN7SEKS3I4X+uPY36ljAuu4X/27SHdXV+ivOz+U9NI7PKClmwrJrZZYWM7J/V6dbMs0f1YWbZUGaWDSUSi5PtsJBlN3PFU8vT+p9ldLL9FBKJqK0NAZ5d9gU/On8kP/7H50wrHcjpw9xk2M30ybIltmK6rNitZn764pr0KjCvm/+59jTihsF9L69NO8aKIg/XnTmcOfslxx6fXsqNiyqZUz6Mee9VpU0ETSazZpcX0s9l75A4a3/cLZEYJQNdGAbc/+q6/aryPNxzyRjOHNGHq575hNtfWMUFYwewclsj86aX8uyy6rTnJz/bpChtwyCihOt1GkMamW1VaapJIcNm5qcvrWHltkZ+N+Mk7BYTj72xIfVeTqvK3VOLuemcIi4/eRA2876E4e0vrGLe9NJuCwb8Qa1DEgpg5AAXdy5e3eH3kfzci8blc2FJ/y9VLdbZ532wueGgjvdwvUd3JHkmhOg12vc4ixsG+dl2rIBiVokDUSOGajIn7pK39U7q77JhB6wOmfYjRE/b4wsQjhuEY/G0xFmDP8gwTyatkRAuuwMdiMTibZVmFjItKhZFkcSZEEdA+7XUaVVxWlSsQNyAC8YN4rVV27lo/KBO19I+kjgTokf1c9nYsqc1/YcKxA2Dm84ZwfdOK0Q1KWTazShArtOKFk/09XU5LCgkqridNjOhqI4n04on08qb63bxeW0TMyYN4XsLPqa0IIdZZYUsWrGNn148hl+/tZFZZYXc1paAMClKWoKi3OtmxqQh3NA2KKDc6+b+aSV8+8mP0hJn5V439c2RLs8vEouzpKoBA4M/z57IXS+tSUuClXnd3HnhaB56dV3HKrAqH/e8tIYLxg7otEIsbhhdJsfMqsKyKh+zywrTXpf8WSQW7/b/l+2NIdbtbKayprFDImxJVQP3vLyGi8YO4E9XncKMPy5ndlkhj08vxW5Ru03K7WoOE4rqLFyxjRmThvDdpz/m5IJcZpUPZeGKxETVsYOy2ekP8drqnWmJs8enl/LMsmruWLw67feXTBg2tGrdJpIaWrUOSSiA0sE5nSZP2/++DvTeh/J5kEh+Hcx7Ho736I4kz4QQvcL2/XqcmUwKFsDU9o9Fgahi3neXXEncJTcDqCbZqilED0v2OAtG9w3xyLSq2ImT53Liaw7SN9tJpF3FWbbDgsuiYjYp9MmVbWBC9LT911KHVcVmUojEY5gVMxkKXDR+EK3J7WBtgwFsFlWS20L0sJZIDF+rxtcKcjEglbwemGNHNYEFE/2ybUT1OPF4IuG9N6iRaTfjzrRiMiAOhGNxmoIa7gwbWjxOSzjGecX9uXhcPk1BjRf+63QUAAXumlqMFo9zy/mj2BvQ+MucSTS0RLi6vJDbp4xKJeJsFhNaLM6CmaekJmL/6q2NNLRqqeMv97p58NKxfPMPH3Z5jjazCUj0RqvzhzutyHrkXxsYX5DD2xv2dHj9kiofM/dLgLV/bWfJsTnlw8h1Jm6id5YkS1b4dcdmNh1UUumXb25kdnkh2U4Lv/j3Rq6cNKTb9/WHojy/YhulBbk8s6y6XfLPYHzbz26bMoo9LZG039Xs8kKe6aKiLfl4Szja7Wc3d/H4gRKJkbbebYeqq89LOpj3PBzv0Z3u/ysQQojjQPuLfZfDgsOqkmVVeW/TLqJGW+IMaNF0GlojmBSF/i4beRYTTodF7pQL0cP2NAZTPc4CET2VOKtrDBI2TDgx6JPtpHm/GM21qIkYlcSZED2us7XUZlIIxcOgmAnGQ6khH8k+SsmJmpI4E6LntUZ0rvnrSiyqws6mEHEDdjaFeHX1LrbuCXLRvKV80RCkMRBl3jubiepxND1OczCCWVEIxuLsaU1UffXJtGFTFUJaosLUbjZhNinY26ZJGoCzbYBAXVOYxqBGrtOKSQG7RcWTZcNpVRmQbcesKjQF9g0VsZlN+AJhfnjuSF6/qYL/ufZUXr+pgtnlhfxrTR0j+2d1en7tBwsAqame+1tS1UDp4Jwuf0/dJXc6e8xuNhHTDYBOk2Q2s4nK2ibKvO5O37Oi7bgPJqmUPPZkpdvBJOWWVfkoHZyT+l9IJAmTP+vsvNo/tr/k+2TZu9914+ri8YM55gO996F8XtLBvOfheI/uSPJMCHFc8we1tIv9DKuK1aRgxEOcXtSf9zfvIgKJirP4vr4sLoeFPu5MqTgT4ggIx412cZqYftscCDIgx0lYDxNVFIz9XqMAfd0ZEqNCHAHbG4Npa2mmNVHxGY+HsCh29jQHsamOThNnQogjI8thJqjpbPMFeWZZNUEtxpznPmX+u1V8WtPIyQU5XL/wM5ZX+/ivs7xYzSbynFayHDZ2t0RQFPBkJpJbrZpOXXMEu0VN9UTb3hjCZFJSCbCWcIyWUIyheU4yrGZ8gQgt4RjVDa2s2+Hn0y8aeaFyB1E9TnM4yi5/mBcqd3DRvKX8/N+b2dkUBgz+8P4WXlu9k6eXVjP/3S3MKivskIiqaGuQv2Bpdepn3SVpuktUdfe6zh7LzbDiD0Y7JO9gX0JvwdJq5p7lpaLI0+Hxuy8ew4Kl1QeVVEpqDsUAuk3KtT+e5Pm2P+/kvwc1vcNnHyiRB+DJ7P76ypNpZfJ+55s85opujrm+JXLA9z6Uz4NEw/+Dec/D8R7dkW2bQhwHvvO9OdTtaez0sfw+ufz9z08f4SM6djS0aukX+0A0bmAoDkLhIOUj+nfoyzJYLvaFOKLShgNYVJ5v69NhGCGcZgeRuEEwNRwgsVUzX+JUiCOmJRxLW0stgG5ArG0t7ZvtTCTO2tbSTEmcCXHEZVpUKoo83PbCKv7xg9NYvsVHmdfNsiofC5ZWM39GKSz9gl+/tZlfv7UZT6aV56+eiMWkglVFIZFUaQ3HyHKY6e+yEYjqNAY1suwWBuY6qG8OETCZsKgmdvrDZNrMhKNxGlojXPvXlZQW5DD3rCLMJrBZVF78fAdPfbCVayYPo9zrocLr4YIx/bFbVD6vbSTTnsX3K4bxxyVbmVVWCFRz46JKZpcXprZQ9nXZeGd9PTe29UuDRJP9/RNZ7WV30au4wutmd3O408e6So7FdIOdzWHmnlXEnOc+SXtsVlkhNy6q5OSCXDJtZk4dlsfM04emtnLubg7z+pqdBDU9lQjrrOKr/WcPyt2342XB0moen14K0GHIwFVl+6aUJpNj7ZNkyX+3W1S27mlN++wDJfIG5ToOeHMy22nl0cvHcfsLq/igXR+xjTubeeiysdz54pq0/mLJaZtD85xf6sZnV583ucjDY5ePO6j3PBzv0Z1ekzx74okn+PnPf87OnTsZM2YMv/nNb6ioqDjahyVEmi+bBKvb08jAy27p9LEdi39+2I7veNQcTk+cmQ2DmKLQHA6T7XCm9WXJtKrYLZ1P+BFC9Jz2X8pt8SBXTCzgbyu2ceWpQxI9ztomfiWnasrkWyGOrPYxagVMBsSAqKGTsd9aKhVnQhwdKvDgtBLuemkN1/55JU9fNYFJw9zc89JallQ1MHdhJddMHsaPzh9JNBbHaVUxDLCrCgoqoVjiJlWGTcViMrGnNUJTKEp+joMMi4oW1+mTZScUTfRBG5TrwNl23ey0qvzPtadhGLBhZzMFbiezn/2EKyYWpJJgTquZf6/bxVMfbOVPV03A5bQy85lP+O5pQ/jxeSNRgLunFqMbBnsDGnrcIC/Dyq/f2sS77fqXlXnd/Pj8kTz+zqZOfw9lXjd5TgsVXndas/0yr5tZ5YUoKB2SWBVeD9ed5e00OWa1KAQjMXKdZv70vQn4w1H6ZNqwmk3sbU30gNPjBr96a2PacZZ73cwsK+TB19YD+xJhJuh4XG1JuMlFHvq3bXOfXOThg80NacnE5JTScFRPDV9IJt7aJ+CSW0UnF3kYkG0n12FhWJ9MIJGE6y6R1/4YDiQ/x8G86aU0tGqpqmNPppVsp5X500upb4ngD0VxWlUyrGZynJavlKDq7vOO5Ht0pVckz/7+97/zwx/+kCeeeIKysjKefPJJLrjgAtatW0dBQcHRPjwhUnoiCbZpw3oqLvhGl4/39so0l92CRVWwAxgQQcFuxMBh77C9xAbSl0WIoyA1HCAeJKQ4cRohrjx1SKIqtC1G+7tsWIF+8qVciCNu3wAPwEj0CbUpYCiqbNUU4hhhBgzg4UtLaNV0GgIRPBk27ps2JjVoJ8thxmZONO83gAyLih43UBSIxhLTNSN6HF8g0pZUsGEyDB56fT2vr9md+qyKIg9zygv5y0dfcOfUYqLxOLGIgcthAQWWV/t47PJxRGJx7BaVvlk2pv9xOQ2tGhVeDyFNT1WSffpFIxePyycaj2NWTDz0ytpUxZLTqnL3RcX86NyR1DQGsaqJ/mJPfbCFuy4qRtPXdqhumntWEXtaNC4Zn8+dU4vxh6JkOy1Ubmtk7sJEpdbs8kKuO9OLalIIajqrtjdRuzfA3689le2NodTn/O3jGm49fyRlRR5W1zZR3xrhrJH92OkPAbB6hx9lB0wcmsct54/iR+eOJBTVcVpVMm1m7n91XapaLqjp/P3jGh6+bCwBTWdHU+I9KmubuHFRJROG5KZVP7WvkEoOGkgm5G5slzibVVbIwhXbUgm4Cq+HWeVDWbSihscuH0e/tu82OU4LD186loAWIxzVufykgdzz0tqvXIGV7ew88dTVz7+qw/G+PXVsvSJ59qtf/Yo5c+Zw9dVXA/Cb3/yGN998k9///vc88sgjR/noxOHQXYKotyeHDiRqmLpMyEHvr0zzZFoJh6JE4wYWRcGmQFgxd0ycybQ+IY6aLLsZWzxKUHHiNIJoJue+ybfsm3wriTMhjo4suxlr3CBu7JtQ3dVwACHE0WGYFCxtje3tFhNgZldzBKdNxWlRybSpGHGwKgqKxUQ4GmdXS6RtIIBKjsOKalKI6PveUzcMDODW80cx9+wigppOlt2MQiJRd/sFo9mxN8SKbXvZsLOZayYPY0dTiLEDs1NVUll2cypxVu518+BlJYSiOs/NmkiWw0xlTRMXzVtKUEskne6aOprbLxhFSNPJsCU+y2Y2UejJwB+McmHJAFQTbN0T4M4LR6Mo0BSMkmEzY7eoRHUdjMR2z93NET7Ztpe/fVzDzeeO4B8/OC31vnaziYgex2xSmFLSn8qaJr7z5PJ9W0OLPDxy2VgG5SW+H+Q6rTQGNIJajKK+mWxvDDGiX1aiaf8WHxt3NnP/tBIGtBs09stvje+ywmlAtp2GVg13hpXLvjawQ/VTZxVSmXYzgUiMv86ZhNOqYlIUDMXgrguLaQxp/O91p6MqCqpJ4RffGp/2fp0ljHqqAutEddwnzzRNY+XKldx+++1pPz/vvPP48MPOx+BGIhEikUjqz83NzT16jOKr6y5B9GWTQ192C2V3rwOoqd5CQeHwTh/bXFXFwC5e112CsLvX9UaHEqPZTiuaphM14uyNhXFaOlacZTm+WgmxEKKjQ4nTQblOanwBnMSJmJy0RnVaQjGcNhVPphWHSaG/fCkX4rA61Bjd6QsQJ7FVU5WKMyGOiEOJ0765Tnb4AoTicSymRE8rAwOToqAoYBgQ1uPsaY2RZTfjtKoEI1FMioJZVYjEdPSYgd2s0jfTxt6gBphRlMT7u2xmVAVaQjFcDjMOs4kv9gYJaDqnDXNzfnF/tvlauaCkPy3hGBk2MxlWlWBU54krT0okf6wqDa1hVFXFYTUR1eNgGMybXprqE1bfEiHDqqLrcZwWFU2P42/bOj7Uk0FrOIY/FGVgjiNxXhjkZ9sJRnUaA1oiWWhV2dMSQTcMige4eOzycdS3RHA7rfTPd3T43fmDGo5hbv75g9MIajrZDgt9s2xp3w/6ueypKi5I9FVLJp4u+9pAPOWFHb5PdFfhdDDVTz1VIXWk3v9Ec9wnzxoaGtB1nX79+qX9vF+/fuzatavT1zzyyCPcd999R+LwxDHsy26h7O51AGsfvrrLx9c+fHWXr+suQdjd6w7keKzaO9QY7ZPjwB/UMEL73SW3mxkk1WZC9IhDjdMCdwY7GoNosTh6PHHn3GxSsJlN9Jc4FeKwO9QYHeDOoL4xSDyuyloqxBFyqHE6sG0tbQ7HUjGaYVUx4gZ7gxpOm5l8lw2VRN9Cp81CSyhG3DBwWlQcZoVQTMcfTNzAsplNWE0KwZhOq6YTiCQqzxwWlXBMJ8Nqpm+WHQVAgeL8bEyAI8tGMKqzqzmcmGCfSsYZeDLthGPxtgSbymnD3YRjcYKRRNJqwpDcbhM6/VwH97vIc1rTqqq6e98vk0SSxJPY33GfPEtSkinzNoZhdPhZ0h133MHNN9+c+nNzczODBw/u0eMTx5feVAXWE1V7Pe3LxKgscEIcWV8mTgfKF3AhjpgvE6N9JUaFOKK+7Fra2XeRIYf52A5V4RH+PLn2F0facZ8883g8qKraocqsvr6+QzVaks1mw2azHYnDE0dAT1RW9VQVmDg4EqNCHPskToU4tkmMCnHskzgV4vhx3CfPrFYrJ598Mm+99RaXXXZZ6udvvfUW06ZNO4pHJo6U7hJd7z0yu9dUkAkhhBBCCCGEEOLIO+6TZwA333wz3/3ud5kwYQKnnXYaTz31FDU1NfzgBz842ocmjjKpIOve8dgPTQghhBBCCCGEOJJ6RfLsO9/5Dj6fj/vvv5+dO3dSUlLCv/71L4YMObid34aRaFx8MFM347qOHtM7fczA+FKPfZXX9sRjcjy951gP9LhmKPS/+OZOH9v+4s+PyCTarKysLvsTJh1KjAohDq+DiVGQOBXiaJEYFeLYJ3EqxLHtYGJUMZIRegLbvn27DAwQ4ijx+/24XN2P1ZEYFeLoOZgYBYlTIY4WiVEhjn0Sp0Ic2w4mRiV5BsTjcerq6jpkG5PTTmpraw/qL7vj1YlynnDinOvxdJ4Hk+XvKkbh+DrXL6O3nx/0/nM83s/vYO+Wn6hraW8/P+j953i8n5/E6ME7kc4VTqzzPdbP9avG6f6O9fPtjBzzkSHH/OUcTIz2im2bX5XJZGLQoEFdPu5yuY6b//C+ihPlPOHEOdfecp4HilHoPefald5+ftD7z7G3n9+Jvpb29vOD3n+Ovf38TvQYbe9EOlc4sc73eD/Xg7nmbe94PF855iNDjvnwMx3tAxBCCCGEEEIIIYQQ4lglyTMhhBBCCCGEEEIIIbogybNu2Gw27rnnHmw229E+lB51opwnnDjneqKcJ/T+c+3t5we9/xx7+/kdSG8//95+ftD7z7G3n9+BnEjnfyKdK5xY53sinSscn+crx3xkyDH3HBkYIIQQQgghhBBCCCFEF6TyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyTAghhBBCCCGEEEKILkjyDDAMg+bmZgzDONqHIoTohMSoEMc+iVMhjm0So0Ic+yROhTh2SfIMaGlpITs7m5aWlqN9KEKITkiMCnHskzgV4tgmMSrEsU/iVIhjlyTPhBBCCCGEEEIIIYTogiTPhBBCCCGEEEIIIYTogiTPhBBCCCGEEEIIIYTogiTPhBBCCCGEEEIIIYTogvloH4AQonfwBzUaWjWaw1FcDgueDCvZTuvRPiwhhBDiuCFrqRDHvt4Up73pXIToaZI8E0J8ZXVNIW57YRVLNjekfja5yMOjl48jP8dxFI9MCCGEOD7IWirEsa83xWlvOhchjgTZtimE+Er8Qa3DwgvwweYGbn9hFf6gdpSOTAghhDg+yFoqxLGvN8VpbzoXIY4USZ4JIb6Shlatw8Kb9MHmBhpaZfEVQgghuiNrqRDHvt4Up73pXIQ4UiR5JoT4SprD0W4fbznA40IIIcSJTtZSIY59vSlOe9O5CHGkSM8zIcRX4rJbun086wCPH24zZl1D3Z7GTh/L75PLwmeeOqLHI4QQQhzIsbaWCiE66k1x2pvORYgjRZJnQoivxJNpZXKRhw86Kf2eXOTBk3lkJ/bU7Wmk/yU3d/7Yy786oscihBBCHIxjbS0VQnTUm+K0N52LEEeKbNsUQhwSf1BjS30rlTWNbNnTCsCjl49jcpEn7XmTizw8dvk4GXcthBBCHIT7p5VQIWupEMeU9te9voDGw5eN7RXXvNlO61e6ft//+4AMGBAnAqk8E0IctK5GWj92+TjmTS+loVWjJRwly27Bk2k9ri4ihBBCiKMhubau3NbI7PJCZp4+FIBBuQ76u+yylgpxlHR23Xvu6L488o2xhKPx4/6aNz/H8aWu37v6PvDo5ePIz3H09GELcdRI8kwIcVC6G2l92wurmDe9lOF9M4/S0QkhhBDHn/3X1vnvVqUem1zkYd700qN1aEKc0Lq67n1rfT2RWLzXXPdmOw8t8dfd94Hb274PHI+JRCEOhmzbFEIcFBlpLYQQQhxesrYKcWyS2Oyc/F7EiUySZ0KIgyIjrYUQQojDS9ZWIY5NEpudk9+LOJFJ8kwIcVBkpLUQQghxeMnaKsSxSWKzc/J7EScySZ4JIQ5KcqR1Z2SktRBCCHHoZG0V4tgksdk5+b2IE5kkz4QQaboaPf1VR1oLIYQQJ4qu1tL9ydoqxNHXWbyeCLF5sH9PtXci/F6E6IpM2xRCpBxo9PSXHWkthBBCnCgOtJbuT9ZWIY6eA8Vrb43NQ/17qr3e/HsRojtSeSaEAA48erp9Bdrwvpl8rSCX4X0zZaEUQggh2hzsWro/WVuFOPIOJl57Y2x+2b+n2uuNvxchDkSSZ0IIQEZPCyGEEF+VrKVCHD9O1Hg9Uc9biK9KkmdCCEBGTwshhBBflaylQhw/TtR4PVHPW4ivSnqeCSEAyLR1/9eBjJ4WQgghOvIHNRpaNZrDURxWtdvnyloqxNGXjFmbufs6kuM5Xtv/veRyWPBk7OtJ5jrAeR3P5y1ET5LKMyEEdU0h/lPbRIXX3enjMnpaCCGESOcPamzc1cyn2xrZtjfIOxvqeWPNLsplLRXimFXXFGLuokounr+Unf4wFV5Pp887XuO1s7+XLp63lBsWVVLXFALAk2ntMC0z6Xg9byGOBKk8E+IE0/5OVLbDgtOiEtZ0xg7KprQgl/teWZvWB6FCRk8LIYQQaWr3Blm+pYHRA7IZkG2nNaJzwZj+OKwqZ4/qC29sYGmVL/X8ybKWCnHE7X/N67CoBCIxbp8yiqCmk2lXuW/aGO55aQ1LekG8bt8bpGZvkKZQFLtF5bOaRrbvDfDS3DLC0Tg1viDNoSiZVpWHLxvLTxav5oP9pm0ej+ctxJEiyTMhTiDtx1I7rSp/uPJk8nPt3PvyWpZW+XBaVWaXF/KDM4ajmhTihkGu04rzANtQhBBCiBPFdl+AOn+IAncGj72xPu1Ld5nXzU1ne7ltymjuMiuENZ0suwVPplW+kApxBO1/zfv7K0/CZjYx770qlrWL2bNH9eGWKaOY1aphGAb9XXY8WTb6uexH8egP3TZfgJ8sXp12bmVeNw9eWsIDr67j3Q17Uj8v97q5b1oJj35jLKFonJZwVP6eEuIgSPJMiBOEP6jx05fWMH5wDrPKCnFaVPq6bNz90prUQhvUdOa/W8X8d6uo8Lo5eWgen3yxl4cvHSuLqRBCiBPezqYQ0bhBjS/Iy5/XpSXOgNR6OnXsAE4ZmsfXCnKPxmEKcULb/5q3X5aNVdubeHX1zrTkEsC7G/YQicUpLchl/rtVlHndPHzp2KN05IfOH9RoDGrc/eKaDue2rMrH3S+uYVZZIVdOGkIkFk9VpD382jq+P3k4o/tnMbxv5lE6eiGOL9LzTIgThC+gccXEAiprGpn97Cdc8cflBLRYh4U2aUmVj6+P7ktlTRMBLXaEj1YIIYQ4ttQ1hbj1n5+zzRdkVL6rQ+IsaVmVj34uO3rcOMJHKISAjte8u5rD9Mt2dHnNu6zKx0ltie5lVb7j5ro32b/tC1+wy7+Pllb5sFtU5jz3Kdc9/xmzn/2EyppGpk8aQpZdpaFVO8JHLcTxS5JnQpwg9LjBs8uq0y4cWsN6t6+pb44wu7yQgNb984QQQojebHdzmNv++TlLqnxkOy3UN0e6fX4kFicoa6cQR0UsbvBMu2veSCyO2aR0+xqzuu/x4yF2/UEttS3Vbum+vYo/FE3787IqH88sq8aEiZZwtItXCSH2J8kzIU4QumGkNS8GyLQfuJdZ6eAcLKopNaFHCCGEOJHUNYXYUt+aquxo/yW7KzaziWyHpacPTQjRibhhpN0stplN5GZ0H4+5zn2PHw+x29CqpQZ85RzgeG3mjl/5l1X5MBTIsh/75yrEsUKSZ0KcAPxBjbrGjsmv+uZIlyO6y7xuKmubAPi/jfXc/sIq/EEp7RZCCHHi2NkUIhCJYVb3XTI3BqJU1jZR5nV3+poKr5v65jCZdmktLMTREIikb7usrG3CMOgyZsu8bmJ6Ypt1RZGHvlm2Hj/Gr8If1IjEdJ648iQWzDwFh0WloptzS17P7681HMWTKT2NhThYkjwT4gRQ3xpmQI6jw89ve2EV900b0+FioszrZlZZIQuWVtPXZeOpD7byweYG6YsghBDihLHdF6AxqHH/K2vTtjbF4gYLllYzq6yw0/XznkvG0D/b0eELvBDiyMi0pSeuFyytRjXRZczOKivEH4wyucjDzy4fd0wPyUr2Obvw8aWpHmYPvLaOey4Z0+GGeIXXk7qe70y2w3JMn6sQxxq5JSZEL+YPatS3RFAUE/5QmHKvO23rZkOrxutrdnLR2AHMLiskEotjM5uorG3ixkWVnDwkl3fW16d6P0hfBCGEECeCusYgMcPgoX+tZ1mVj/EFuZR53Syr8vFZTSOlBTncuKiS2eWFaetnfXOEcFTn+oWfsfDqSUf7NIQ4YfiDGr6AhmHAZ9saqfC6U1utg5rOW+vqWbO9idKC3A7XvH/7uIY7LxzNvOmlx3QyqX2fs/beXl/PecX9uHBsf2aWDcViNqEqCuGozvMrtnXaw63M6+azmib6u+zH9DkLcSyR5JkQvVRdU4jb/rmK8QU5VNY08v3yYcwsK8SAtD4QK7c1qHlrSQABAABJREFUctuU0Tz42rq0xbiiyMOtU0bynSeXp34mfRGEEEL0dtt8AT6saqDAnZFaLxcsrebx6aX7/Xs189+tSr2uosjDA9NK+OYfPiSo6bJmCnGE1DWFuO2FVYwfnLjmraxp4sXryrjv1bWpGH7qg608fdUEnnhvS1rclnvdPHTZWIa4M47W4R+09n3O9ufOtDHnuU8BcFpVHp9eysIV25gxaQiRWDzt2j9ZbXfjokomDs2T5JkQB0mSZ0L0Mv6gRlMoyl2LV7OkysfMsqHMf7eKKycN4cf/+LzDXfLK2iau/NNy/jJ7EnWnh8iwmclxWFAUuPJPK1J3qyYXeaQvghBCiF5tuy/AXYtXM6t8WNqEuqCmp1WaxeIG914yhtj/Z+/e45uq78ePv3K/tE1SEgottKWQSqEFVgFB2+IN5Sro/G0T3OQ2dVNgbjqZ9wvO23Q6QbfpQNkm+N3mvKFTQRy2RUCgk7u0UmmhhdK0SdrcTnKS3x9pYkPLRaRy6ef5eOwxzDlJE+gnn895n/fn/ZYj1Lt8ZJgNqJRw01830dgqiTlTEL4jsU64m2uc/OLy3HhgrNrh6ZBltvGrJob3S2VmUT+MWjXJehUWvQbTWdAgAMB9jB0ggVA4/uf231dqhYI7rhzIPRNVeKQQnkCITfuamb+iAq8k0+IP4vJKNLZKuP1BTAYNtiStCKgJQidE8EwQziEHmr3UNHkxGTTxVHVJjk6mOrUSryQn3G1rLxQJM2fZJt7/RQk/XrKBJ//fsHiNszG5Np44w2tACIIgCMK3Uevw0CrJbK5xcqtGRSQSSTh+5By6ZMYIMiwGIhH48ZINPPWDYexpaBVzpiB8Rw40e3H7Q2yucfLctMKEgLdaqTjqmhei4zc1SYNepTxrxqrpGNmsR3bU7Oz7CiBCNHs2dnNcr1Hx4c5DPPD2joQb5o9fO5SMTuolC0J3JhoGCMI5Yn+TlwWvb2XaSxto8X1dpLhncrRj0LE6gxXbrTS4AxTZrUhymMHpJgbYknjzlov46FcXs2haYacNBwRBEAThXFDr8HDA5cPlDTK7OAc5Ejl2R81cG02tAdQqBXOWbWJQuok+FoOYMwXhO7K/yRvtBO+LjtmXyxOL4h9r/BbZrTS0BEg1aul5Fo1VW7KWMbm2To8dcvspOcqx9h03l5V/xas/HcXSmSN59JoCPth5kLf/V8fi6YUYtSoAPqlsjP7dekWjMEFo77QHzw4cOMCPf/xjrFYrRqOR733ve2zevDl+PBKJ8OCDD5KRkYHBYOCSSy5hx44dCa8RCASYN28eNpuNpKQkpkyZwv79+7/rjyIIp4XLK7Gv0cNd/94abwZgMn59Z0qrVlJktx61M1hJro2FVw/h3c/rmHtpLl8cbOGJa4eSbUvie1mpDEhLPmvuyAmCIAjCN1Xr8ODwBEnSqUnSqyjMtPDpXgc761ydzpvFdit3TcjjghwrH39xKN6hL7dXipgzBaGLubwSNY7oundzjROLUUNhpoXyKkdCwOxYHXHnXZbLGLuNXib96fgIJ81s1PL4tUM7BNBK7Dbsack8NCWf4qN0E91Z56Ki1klpVSNNHonZr3zGf7YdZEgfM5trmnm5rJrZxTnx531S2RjfgSIIQtRp3bbZ3NxMUVERl156Kf/5z39IS0vjyy+/xGKxxM958skn+f3vf88rr7zCeeedxyOPPMIVV1zBF198QUpKCgC33XYb77zzDq+99hpWq5Xbb7+dyZMns3nzZlQq1Wn6dILQdeIdhYDH3t3JrZflxrdpAigixLsM1bv8zCrKAaoT6rUApJl0hMPw25U7uX3cQIwaFYPTU8TCXxAEQTjnxWqEHmj2oVQoiATBK0Uzt2NNAZZv2JdQN8ls0JBh1vODP3/KoHQTj1xdwI+GZ4l5UxC6SKweV2sgiMWgZfO+Zgb3MVFa5WDuZXYq9jWT1hYEa9/Yo7zKEV/33nqJHa1aiUGrwqhRoVcrSU81ns6PddIyLAYWTSvkoNtPszeISa9mR50bvyQTIcI9EwfT3PbdFqttHGscMH9FBfB1fbTSqkbCRJhdnMPiNVXMLMpJ+Fktx6ixJgjd0WnNPHviiSfIzMzk5Zdf5oILLqBfv35cfvnlDBgwAIhmnT377LPcc889fP/736egoIBly5bh9XpZvnw5AC6XiyVLlvD0008zduxYCgsL+fvf/862bdtYvXr16fx4gtAl6pw+5q6o4N8VB/jtuzuZeVEOcjixLkuYMLOKcyjJtaFWKpi/ooLCrFQWTStkcLoJiKaz/+jP62lsDbB6dwMqhYJsW5K4ABAEQRDOeQebvLh9QZo9QVRKBUadimS9igWvb6WPxRAvuD04w0xhpiVecPzTvQ72ObzxrnchOSLmTUHoIrE17+W/X8t/9xzmoNtHptWAyxsN6hRmWlj47i7STNESJbFxW5iVypIZI3jqB8MozLSQrFfzx/9WkaRT0yNJe9YGztrbWuvEbNDQ4A4wLNPCXz/dR43Dh9svYTFq0KmVBEJhCjMtDM4wxxsEQGJ9tPIqB4WZFiCx6QAgOgYLwhFOa+bZ22+/zbhx4/jBD37A2rVr6dOnD7fccgs33ngjANXV1Rw8eJArr7wy/hydTsfFF1/MunXruPnmm9m8eTPBYDDhnIyMDAoKCli3bh3jxo3r8HMDgQCBQCD+3263uws/pSCcOi6vxILXt1Ja2cjMi/ph0qvo08NAvdOfcN5BV4DlG/axYFweOo2S4VmpnRZNbV8DIXa3/UwgxqggnPnEOBXOVoeavAQikXhX6iUzRmDQqvAEQgzqncJHuw9RYrdRWtXYYe48cgvYmZyZIcaocDZrv+YFGJvXC7c/yOLVlfEMqUAojFeS+WhXQ3zHxZGF8kvsNh6cks8d4/JINWjOuGD3yYzTxlaJDLOBJK2KepePhhY/V+b3wmxQY9SpMahVvLLuq/jfXXvt1/7x9xD6urlYjOgYLAgdndbMs7179/LHP/6R3NxcPvjgA372s58xf/58/vrXvwJw8OBBAHr16pXwvF69esWPHTx4EK1WS2pq6lHPOdJjjz2G2WyO/y8zM/NUfzRB6BINLQE272tm7mV2zAY14/LTue/N7azb60hY0C94fSt3TxrMC/+tJALccukASuyJ9RFiNRCWlkULrJ5Jd5fEGBWEM58Yp8LZqKHZS7Bd4Ayimdi7692oFPDAlHwq9jmZUdSv01pJ7edNOLPmziOJMSqczWLZnUatirmX2TFoVbzwcRWl7WqbxYI9L36ylxmd1fW127jlUjsOT+CMDJzByY1Ttz9Inx4GHv/PLkbk9CDbaiSzh5GnV+1h/LOlTHiulBkX9Ttq/bP232EQDZqVtAuqiY7BgtA5ReTIPtzfIa1Wy4gRI1i3bl38sfnz5/PZZ5/x6aefsm7dOoqKiqirqyM9PT1+zo033khtbS3vv/8+y5cvZ9asWQkRe4ArrriCAQMG8Kc//anDz+0swp+ZmYnL5cJkMnXBJxWEb6/O6WPPwRZCkQgvl1ezcGoBrf4QU54vx6hV8dy0Ql4ur6a87WIgM9XAslkXIMkyOrUKpVLBgWZfQg2EWKvqMbk2Fk0rPGMmyW8zRi+Z/AN6T/lVp8cOvv17/rvyn6f0vQpCdyXmUuFsU+vw4Jdl5DCMf7Y0/rhRq+L56edj1Co50OxjWKaFUDhCbIV8wOkDSJg3gTNu7jySGKPC2WzTV03csHQjf7z+fBrcfgr6Wpjwh+i4ja17G9x+3t1WT3mVA6NWxezinPg2a4tBQx+LgUgkQmqS9pwap182tCLJYdbsPkS6Sc/3sqJJJJEI+EMyXkkmRa9GpVAQCIVpDYSQwxE+3etI+A6DaEBt8tAMLuxvxe2TSNJpsCWfuX9fgnA6ndZtm+np6QwePDjhsUGDBvH6668D0Lt3byCaXdY+eNbQ0BDPRuvduzeSJNHc3JyQfdbQ0MBFF13U6c/V6XTodLpT+lkEoSu5vBJPfbCb28aex0GXj4en5qNWKAjI0TTrWI2HWDOAWG2WgCyzfb+L3F4mWqUgGWYDf/xvVUJzgTPx7pIYo4Jw5hPjVDib1Dg8PPGfXcwfO5ADzd6EY15J5tblW7j54v5cOjANfyiMNxC9+AxHIvTvmcT9b+6gtOrrLVBn4tx5JDFGhbOVyytBJMzKecVEIhH+UrqXFMPXYy227r354v48MDmfhSt3UFrliG/XLLHbuGtiHhoFZNiST9fHOCEnM05tyVr2HGphfH46D769nV/9c2v8WJHdyk+L+6NXKznQEiAQCmPUqLAkaamoaU4InJXk2lg4tYBUYywrL+lUfSxBOCed1uBZUVERX3zxRcJje/bsITs7G4CcnBx69+7NqlWrKCyMdk6RJIm1a9fyxBNPADB8+HA0Gg2rVq3ihz/8IQD19fVs376dJ5988jv8NILQdZo9EvMuz+WJ/+zitisHEgxFOOzzIber63lkjQeAd+YV8evXt7FkxggAahxeJgxJ5+5Jg/FKISwGrbi7JAiCIJzTDjg83PPGNmYW5fDwyh3xjtPteSWZZ1ZV8syqSt6bX0KyXs0HOw9yQb8e6DUqJg5N597Jg/BJMil6kZkhCF2p2SPRM8XA1gNO/vFZLaVVDu6ckJdwTmzM/nntXmYX53D3pMG0+EOk6NX8r9ZJsk5NhvXcDAaZjVpsyTruf2t7wg1xIL4D5Zdjz2POsk3xx2OZebGb7P1sSagUkGrU0NASYE9DK0laFUk6NZYzdIurIJxupzV49stf/pKLLrqIRx99lB/+8Ids3LiRF198kRdffBEAhULBbbfdxqOPPkpubi65ubk8+uijGI1Gpk+fDoDZbGbOnDncfvvtWK1WevTowR133MGQIUMYO3bs6fx4gvCttG/NnWrU8sR/dvGrK/No8YcIhGTMBi11Th9Fdmt8omyvJNdGSI7uOYllotW7/fTvmYw1ScugdLFlQxAEQTi31Tk8SOEIM4tySNFrmFPcn0gkcvS5025FqYj++c9r93JRfxtapYKJBb3FxaQgdKH2616rUcvijyu5+WI7xiI100Zlk6RVx5sCtOeVZCpqmvn+9/rgjkRI1qoYM8BKn3M0cBYjyWFKO9muqteo2FLTjF6tSviea3+Tvchu5dfj8vAGQmzZ5+SuN7YB0QDbvZMGMayvhaqGVixJWmxn8JZXQfiundbg2ciRI3njjTe46667ePjhh8nJyeHZZ5/l+uuvj59z55134vP5uOWWW2hubmbUqFF8+OGHpKSkxM955plnUKvV/PCHP8Tn83H55ZfzyiuvoFKpTsfHEoRvrc7pY8HrW9m8r5k/XT+cJL2KO8bldbjDNDavJw9clc9D7+xIuAgoslt54Kp89jdFt6bo1Er6pBrwSSH69TCSZtJ/559JEARBEL5LdQ4PgXCkw9x5WV5P7p+cz8MrE+fOEruN+64ajByO0NgSwCvJmAxq+p7jF+GCcLodue5N0av4+aW5CY09jFpV204KRcIW6lgRfIcngMWoQQ/0PMfHbJ3Th9sXwqhVsXh6IUvLqo/oMGplyrAM5hTnoDzK35dBq6TFGyHdHL0maF8/+e43tsfPH5Nr4/Frh5JhMXx3H1AQzlCntWHAmcLtdmM2m0UBVeGM4PJKzF1RQWllI/dflcfYgb1RKODudguI9sYOSuMXl+dyuFXCH5TjzQAmFfTm3e0HqahpZvKQdIrtNkxnaRr2NxmjomGAIJweYi4VziT7HB427nXw1ud1lHUyd16W15PrR0XLhMSys/tZk9hS04xSAdUOL5/XOll8BjcE+KbEGBXORLF17+Z9zbx0w3ByUo3IdL7ujWVGndcrhYaWQEIDrH/97CJSDWrSU42n54OcIscbpy6vxHvbDzK0j5n/7mlg/ZeOTq8PSnJt/HrcQJJ1apo8EiE5Qopezapdh+hjMZCbltK2k0XD+zsOolDAxuqmTjNyz/TmKILwXVGe7jcgCMLXXF6Jerefm8f05+M7LqbY3hNFJII3KHc6MQKs3tVAOALhSIQ7/vk5c5Zt4n81zajVSnbWuZh7aS5FdhuZ1iQx6QmCIAjnvFqHh4fe2UFehqnTwBnAmt2HUSgUzFm2iVte3cLL5dUogH5WIz2SdOyud/PkGd4QQBDOBU5fkNuvyOX9+SVkmPXIgNsf6nTd65Vk7n5jOy5fkFte3cKcZZtYvKaK87MsmHSqsz5wdiKavUFWbq1DqYDL83od9fqgtLKRwy0BLnt6Lc+s3kOPJC0KRYTR/a28/b8DXP1COT96cT3j/1DK5q+amJCfTkWNs9PX+qSykcZWqQs/lSCcHUTwTBDOEHVOH3NXVPD9F9YhyWHuf3M7q3cdIqJU4PaFjvncQCjMy+XVzC7Oodhu5bfXDCEcCXP3xMEEgjJNnsAxny8IgiAI54Jah4eaZh+DM8w0uI8996lV0eJmsXlTqQCtWokCWDA+j3SxTUkQulSd08fCd3agUCjZUO0AFLQEQhx0+4/5vEDo645ZsfHbHbZXu7wS9725jYoaJwatCpcveMzzY39P5VUOFq7cQYpOw/NrKjsE3Erbjs8u7thMJabFf+yfJQjdwWmteSYIQpTLK8VrPSyaVsjLZdWUVjl4cEoBtc0+Uo9z5ztZp6a8ysG9kwbzo+F9WbX7IA+/szt+/L35xV39EQRBEAThtKp1eKht9uHyBSnMtBz3/J7JOv7zixKStSqm/2UDtc2++LGPfnVxF75TQRAOuf3c/+Y2po3K5qkPdrNg/CBcvhDKE0jtyLEl8Y+bRpNi0JCiU9O3x7mfcQbQ2CpRWuVg7mV2nN4QBu2x63vr1F//ZZZWOXAHOs/oix2f2Ukn4pgUvebk3rQgnENE5pkgnAEaWyU272vmuWmF6DWq+MQmyWFcviAKogU+O1Nkt6Jo6wzW4g/SKoV46oPKhONJWhEnFwRBEM5dh9x+WgIyTl8QnVpJIBSmotZ51Lkz1lXT1EngrCTXhi1ZbNcUhK5S5/RRfbiV60ZlY9BG171KJfiDMt6AfOyxm2vDqFGRlqLD3I0CZwDutuyvwkwL/qAMHPv6oKLWmfBYi//YO1mOZoz4ThQEQATPBOGM4PRJ3DSmP6+UVyekYLvbLgJ8wRCzinI6TJCxjjk+KToZmg0afrJkI17p6wl13mW5WIzibpEgCIJwbqp3+nD6JFr8wXgBcbNBw9Ky6qPOnfddlY9CAc0+f0LgrNhu5bFrhohaZ4JwCrm8El82tFJR00zloRbW73WQlqLntQ37iG6UhtaAjFGnQq1SHHPsPjw1H5UCktRKMrpR4AzA1Jb9FQiFMepUfPxFA3MvtR/1+mBpWXXC42bDsa8H+qQaKMm1JTw2JtfGE6L+oyAAYtumIJxWh9x+mr0S4XCE8fm9eXZ1JbOL+8ePmwwaSqsayUo18OGOQxRmpTK7KCfeGayi1slrG2so6GOmJNeGTq3kTz8ezuHWAFqVkoaWAP16GMWEJwiCIJyTahweNux1UJjdg2SdmtKqRnbWubhycC/Oz7Iwf0UFs4tzEubOhpYAn9c6GZ3Tg0POAEtnjsQflLEYNGRbjfTpBkXHBeG7Uuf0xUuT3DSmPxefZ2NYXwuHW/1cNyqbcCQCRDtphiMR9h5uZXhW6lHHrk6hQKNS0rMb1iS0JWsZ07beB/i81snQPhYmDUlndlEOGrUSlULBp3sdzF9REb+ZDtGMvcMtfors1k47ahbZrby//SCPTC0gFI7Q4g+SotdgS9aK6whBaCOCZ4JwmtQ4PNzT1oZ77mV2zm+rzxKJRCi2WymrcqBRKdlV52J4Vio3junP4jWVLF5TFX+Nklwbd44fyB9WV/LYNUNI0auRw6BWKkjRaxiRnSomPEEQBOGctN/hoc7pIxiO8NDb25lZlMPuejfTR2Xzx/9Wcce4PJ7+4IsO8+ZdEweRrFWh16jISDW0XSQaxUWiIJxi7Wv6Lp4erekbCkfYVefijivzeG7NTgqzUimyW4lEYHe9m2yrkbmX2Ttd8/726gLU0C0DZwBmo5bHrx3K2j2HcbQGuLGkPy+V7mVwhpleJj0+Saa/LYnPa50dAme3XGKnoraJWW11zdoH0GKZaq9trGHWRf3E96AgHIUIngnCaVDn9MUDZxCtXWBqS6VWKRTMLMohAny4s567Jg7isfd2MTTTwq/H5/FrwCfJmI0akjUqWoMhHp6ST0bbnXIx4QmCIAjnugMOD8FwhEUfVzG7KIfSKge7Drbwj5sv5NF3d5GXYcLpkfjVlefxmwl5eKXoljCTTo0SopkrJj09Taf7kwjCuauxVaK0spHbxubGm2HFitKHIxHKqxxU1Dh5blohu+pdZFuja9l9Dk98zeuVZPRqFdYkDXqNip4m/Wn8RKdfhsXAxILeNHslGtwBxg7qRS+TPp6d978aJwvG5zGrKBDPqPWHZOYs+4xF0wqZt6KC+yYN4r5Jg/nK4UWjUlBR62T5hn3cWDIAly9Ik0fC6QtiMmiwJYmbCoIQI2qeCcJ37ECTF7cvmNDtJhAKo1UrKbJb8QZl5q+ooDArlcG9zUSIcOOYAVyWl4YnIKNQQARAAZEI7KxrIUkn4uCCIAhC93DA4SEUAW9QprzKQSAUBqIX6jOWbmTBxDzG5vVCr1WhUiqQIxG+cnhQKhX4ZRk5EiGtm2auCMJ3ye0PYkvWcuXg3vF1byAUpjDTgsMjAdHg2PwVFdS7/fRI0lHj8JKXboof02uigbNMa1K3D5zFmI1akrRqMsx6RvbrQW+znp7JOqzJOg64fPzwz58y+5XPWLFhH/6QzNzl0S2cyTo1/7z5Qi4aYCUoy2hUCgKhMBML0hmcYWbOss+4+41t7G30cM0L67j86bXMW1FBndN3/DclCN2ACJ4JQheLFUndtr+ZWoeHzbXN1Lv8Cefo1ErqXX5mFeVgNmjwSjKL11Qx85XPmLp4HWVVjTS4AzR7JZQKBTUODwaViulLNvCrf3xOY6t0mj6dIAiCIHx36h0eQsC9b27DG4huS4rV/wGobfYxdXE5q3cfwhMIsb/Zh0oZLUjuaAkwdfE6AnLkdLx1QTjnubwSXzW2sqPOxWfVTRi0KpbMGElDy9fr3lg33FD463HolWSeWVXJ1OfL2dfs43BLgEAwjKUt8ynTmnQ6Ps4ZrTUQYnNNM+FIGKNGxVeNHpJ1Kob0MfPUD4axZMYIhmWlxgNnAKFwhHq3n92HWmlokZizbBO3vLoFTyBEYaYFo1ZFWZWDNJMu/nM+qWzkN69vxeU9+rVG+4YQXx5uPea5gnA2E8EzQehC9U4f720/SG2Tl2SdhoNuH9/LMJNuSbxzVlHrJEWnZv6KCvxBmRL7151uYoG0Ocs2sWJDDXq1isLsVDxSMH7XrsUfRBAEQRDOZTUODz45wt1tZQ+S9dGs64paZ0K3uYR5c2MNBrWKggwTwbagmScQOi3vXxDOZQeavOyqd3PPm9uZ9FwZM17eSDAU5vVNNaSbv173VtQ6sRg0bKlp7tAlMjZ2X1n3Fb1MOsx6NX27WUfNExWQw/RPS+adrQfZut/JiH6pKBUKXin/ilte3cKcZZtYvKYqHjgrsdvwt+1uSdGpqah1xl9LoYCXy6t59aejsSVrafXLCT/rk8rGo96or3P6mLuigst/v1ZkqwnnPBE8E4Qu4vJK7GvysmrnQeRIBH9QpmeKHhmo2Nec0Ap6aVk16RY952dZmLeighlF/TosKEpybTx8dT4/evFT/EGZpz7cw+ziaN2IFP2xW08LgiAIwtlsv8NDeVUjB93+eKFrlSJ6Qbi0rJpZRTmdzpuzivpR2+yj3h3gL2V7mV2cI+ZMQTjFDjR7WVt5mEUfV1Fe5cCoVbF4eiHBUIgbxwxAo1LGx2dszburztXpuC22W1k4tQCzQUO66Hx7dBFocAdYWlbN0L5m/EGZR97dyYNT8ik54u+0yG5lRlE/5q2o4PwsCyl6NUvLqoHod2hZVSNlVQ4WrtzBE9cOJVmv6vDjOrtRH2sIUVrZmPD4iWSrCcLZSBRKEoRTzOWVaGyVUAAvfvIl00dl8+/Ntfzqyjx8UphwJMLCd3fx9txiHnh7O+VVDrySzH+213P3xEE89t7uhPbcAL1Nehpa/NQ5fQzqncJHuxpYs/sw14/KZkyuDVuyKOQpCIIgnJtqHR5aJZk0kx6n7+sLuFW7DsW78nU2bzZ5Atz89y0smlZIRa2T8ioHt15iF3OmIJxCh9x+9jm89DLpEwJnRo2a1CQdbn8IX1BO6PL4n+313H7lQH7/4R4Ks1KZXZRDIBTGbNDQw6glSasSReqPIwKYDbEbAQqavEHW7D7Mr64IcdWwDO6ckEeDOwBEs/3mr6hgeFYq9101mOkvrccryZTk2phTnMMtr24BoKzKwV0TBnHQ7e/w8zq76RBrCNGZWLaa+HcUziUieCYIp1Cd0xe/A/P23CIGZ5h5fXMtt40dyINvb2dmUQ5GrRqvJBOUwwkLBqNGRSgcYcKQ3sws6hcvgHzI7UepUHDr8gr+MmMEM4pymL+iIv4zn7h2qJiYBEEQhHOOyxvt+Hag2YdapYx3k4t5/uMv+eP1ZiYOSWdm21wK0OD2o1Yq+PmrW/BKMmkmXTzLQqdRijlTEE6ROqePrxo98aC2UaviuWmF8c6a78wrotUvY9KruWHpxniAOxSOoFEpuXxQGmntOkXWODxkpRpEY4AT4AmE0KqVPHRVPq1SCFfbv8EBp58eyTqeWbWHwRlmCjMtDE438dINI0hL0XHQ6efhqQXo1ErSzXpW7ToU39oJ4JFCLHh9a8LPOtqNevdxysaIsjLCuUYEzwThFHF5Je5/azvDMi3MvKgfwZDM5CHp1LssPLJyB6VVDqaNyibNFE2FVqsUfF7jZPGaqvhrGLUqZhfnkG1NQqNWkmrUUFHr5JF3dzE8KxVfW0ei2CSX1cNIuugYJgiCIJxjDjt9BOQw976xjWmjssnuYaRFrYzXN4tlbf/81S3MLs4hzaRHp1aSZtJRUetk4bu7opkVdisf7WqIz5sWgwicCcKpENuy9+PR2fQy6XB6g9x66QCWlVfHO2u2+mWS9SrUKgWFWZZO17xZ1iRMBg0mvZpB6SYyxLr2hKTo1Xzl8DIk04zTE4zfWFArFcxry8S95LyeuHzRY5/udbC0rPrrGmi5NoZlWijMtCS8bpJWnVDfbEyu7ag36k3H2QIvtsgL5xpR80wQThGHR+K6C7KoqGlmwetbSTFoOejyk242xBcROrWSBneAEruNVTsP8vDUfIqPKHJcUdOMLyjz13Vf8cGOQyxeU0VhliVeqyA26Y3JtZGWouv0vQiCIAjC2eqw249XDlPT7KO0yoFOrUStUtDg9rPziDpJsQLjL5dXY09LZtbLn8WLZEfr/OTw4id7gaNnTwiC8M01tkps3tdMf1sSSVoVLm+Qy/N6xde8AMl6FQ3uADsOuJh3aW6Hxh4VNc2olArSU3Sk6jUicPYNKBUKUnRqWnwh1CpF/MZCRa0zHqi8YelGIsDS8uoOzQN+PW4gS8uq4xm7EK03l5qk5aNfXcybt1zER7+6mEXTCo96o96WrGVMuxrO7YnvW+FcJDLPBOEUCYUjvFxeTXmVg6UzR/DQOztYMD4v3hETojUHvmxo4aGp+fz23Z0MzbDw8JQCDrr9ONvuDFXUOnltYw13TRiE0ydx1dB0ttQ4EzLOjnUXSBAEQRDOVoecPqR2GWcQnTtdXgl7WjI5tmReLP0yoeyBxaAhM9WAyxdg2ewL8EkyUijMur2O+Nwp5k1BOLVcPonZxTls+qoJlVLByH492NvoSTinwR2Ili+5YiAur8TkIenxcatTK2loCdDHrCfFoBFj8xtSKRX0STXQ6g/x3z0N7K53M6soh+Ub9iXUl4vVg7z1EjsatRKtSonFqOHaP67DK8nxjLViu5XfXjPkGwUwzUYtj187lN+8vpVP2tU+E9+3wrlKBM8E4RSJRCLxxXysaKoUCmMyfD3MlpZV89y0Qp77aA+/mTAIR2uAlkCQNJMOk0GDJxBi0pB0Rman0tjqJ6+3CbNRS2+Tngv69aDFHyRFr8GWrBUTkiAIgnBOqXP6cPuDRCJQWuVgZtsF4NKyap6ffn7bOV4WjM8jJEfwSDIZejXJWhVzlm3i2eu+h0mvIT/DjMsr0cukZ2xempg3BeEUq3f6MGhUTCjoRSSiQKEAb1DucN6C17fy6k9H8+yqL/jhiCxG5vQgEAzjlWRMBjWD0pLJsCadhk9w9rMmaal3+zBqVaQl67hn4iB++94uBmeYUSsU3HHlQO6eoMQXlEnSqQmEZJyeIGtrmhmf34vGVomSXBsZFgP/+UUJerUSi+Gbb7PMsBhYNK2QxlZJXKcI5zyxbVMQThG1UklFTTNzlm1in8MLwOHWACE5krC9ZP6KCvr2SOJAsw+jTo1WraLJE6ShJYBJr+Ggy89fP/2KvqnG+MRjNmoZkJbM97JSGZCWLCYkQRAE4Zyyz+Fhwb8+Z/yzpTi90SLTsW1IXknm1uVbWLfXgb1XCoFQmNZACINWiV6jZM6yTfQy6zHrNfTtYQTEvCkIXaXB7ccblHn6wy/QqFQ88Z9d7HN4afWH4mM2prFV4vq/rOfa4Zn0TNHhaJUIE8GapCVJoxKBs2/BbNSSpFETDMsMzjDT0BKgoI+ZS87riVEXrTMnRyKUf9nItX9cx9XPr2PmK59RUdNMOAJFdiu3XmJHq1ZwyOVn0qKyhFpn3/S9iO9boTsQwTNBOAUOuf08+PZ2ytvVNgPQqpS4vMFO67O8VLaXhpYAh1x+GlsDvFxWTZ3Lx6sb9vHQlAL6pBpP2+cRBEEQhO/KgWYv976xLV4rKVkfbayztKw6Pn96JZlnVlUyZXE5v3v/C3qm6PjDqkre21ZPL7Oex64ZQp8eYt4UhK5U5/Tx0a5DPPXBbn4x9jwWvrMjXpcwRa9JGLMxja0SS8urMWpVzHrlM558/wu0KgV9ReDsW1MC4Uj02sKoU/Hs6kr+358+ZebLn3HIHeCJ93fzzKrKeNmXIruVeZflkqxTc+/EQfiDMlctKkeORPBKsuiOKQjHIbZtCsK34PJKHGrxEw6TUCC1fdHODLOe1bsOJdRnidc227CPvAwzF/W38uCUfLxSiN9eM4ReokW3IAiC0A3UNHnZ3+RNmEMb3AFKcm2UVjbG6/XE5k+zQYNKCYGQzF0TByFHwvx4VLbIdBCELnbI7WfBvz7nppIB/PKKgTS2BOLjtqLWSY41ifOzLB3GbLRZlh+ZCMOzU/nt1QUicHaKaDUqDBEIqCM0uAMU262UtXUibv/vAJBm0rH9gJtAUObRd3dRmG1hULoZryTHmwaI7piCcGwi80wQTtKBJi/vbatHCkY44PQlHIvdedtZ5yLdbOCmkv7xLZ23vLqFOcs28XmNk+mjs9ld7yYz1cCiNXtI0WtE4EwQBEHoFuqavNzz7604fYnZDgte38pDU/Ipsdvi2dpzlm3i1Q37UCoU0S5zWjXBcAi1UikCZ4LQxeqcPqoPtzIsK5U+PQwsfGdHwrhdWlaNPxTiwSn5nN/W6TG25n25vJqROVY0KHj8miFkicDZKeMPytQ4vCgU8O8t+7lvcj7FnXQiHtAzGY1SQWaqgZ+/uoXVuxu4LK8XJr0Ko1aFTq2kRHTHFITjEplngnAS6pq91DZ7WbXrEAV9zR2Ot7/jowB6JGt5ZGoBrVIIT0BGrVRQVtXI8vU1zCnpTzAcYVR/G6lGccdHEARBOPfVNXtpkUIJjQFiGlslZizdyNJZI/H4ZfwhGaM2upVzd72b4f1SkSMRFn/0Jb+9ZsjpePuC0G24vBL3v7mdOycMpL/ViCSHO4xbryRzw9LPeGXWSOZelsuCCXm0+mWS9So8ARmdSoESxNbqU+iQ289+pw8pHOa6F9fz3LRCnl39BbOKcuJ//ya9GpNeTasU5CdLPuNPPx4e38JZ5/SRY03i3kmDaHD7eeyaIeJGhCAch8g8E4RvyOWV8AXDLPv0K345diDegNyhQCp8fcdnSVk1aqUSbzBEOAyeQIiGlgAj+/Xgp2Ny6Jmi5fcffsEl5/UUk5YgCIJwzqt1eKLbNZujWdudzaG1zT6mLi5n10E3GpWC/c0+DrcEkMMRNEolf/ioknsmDRbzpiB0sWavxC2XDkClVDA008KBo4zbxlaJH/55PWVVjbT4QkQiEdRKJX3MevQaFX1Extkp5fRJKBQKKmqdFLZtlx2QlgJAbZMPrxRi634XLYEQj7y7m+suyCIUjiS8hi8kc35WKoXZqaToRU6NIByPCJ4Jwjfk8QVRqxT8elweT76/ixS9utMCqRAtzPnQ1HyCcpiDrgAKBcxZtokVG2vIsOjpnaJHpVDw22uGkG4xnKZPJAiCIAjfjf0OD2VVjSz6uCr+2NHm0OHZqYzsl0pZVSO3vLqFV9Z9xUV2G+FwhAeuyhfzpiB0sQNNXj6rbsJk0HK4JcADb++IH+ts3Holmc9rnKSZ9PSxGDBpVWRak+gpSpKccpEwyOFI/N+h8IjtskvLqxndv0d8a2dhpiWewRury9zqD+HwBPj+C+tOutOmIHQnIsQsCCfI5ZU47PGjUai4981tzCruT2mVgwfUyvgdnyMLpB5y+/FKIVTKaJxao1Ly/m0ltPqDvLetnvH56QxISz7Nn0wQBEEQul59k5fWoEyaSU95lYPCrFSK7FbKqxydNgZIM+kIhGTGDurFhTlWUpO1hMMRcnqKeVMQulKsIZZGqaQwO5XDLX6S9eoTGrf+oEwoHEavEtlmXSkCfLrXcdRrkAZ3gDARdGoVS2eOjGcMFtmtzCrKYf6KCiYNSUevVmLUqkSnTUE4ASLzTBCOweWVqD7cyleNrTz09nY0ShV3v7mN0ioHaqUCgIMu/1Hv+KSZ9Bxw+mkNhKioddLiD9LqC+IJyPyvxikKcwqCIAjdwj6Hh/9WHsbRKsU7u7XPXGnfGGDFhn34gjIHmnxIoQgNLQFSk7T8YdUeeiSJeVMQuoLLK7H3cCt7D7dy+z/+h8MdQKVU8MDbOwjKEVr90VpZRx+3NfiCMis21JCsUYmOml3MEwgdM+vs/GwLDW3/hk+8v5tsqxGVQkFhVirzV1RwfpaFOqeP+97azl9mjESnUfLl4VZcXpGBJghHIzLPBOEoDjR7qW3y0suk53fv7+aO8Xnsb/ZR3taWOzUpWtxfkiOd3vGpqHUyf0UFi6YVxrd2Th6Szke7D/G/GicPTy0QtVoEQRCEc16tw8O9b2xjZlEOJoMafzB6Ed6+uU77+bNnio7rXlzPv35+EQ0uP73Nep5dvYd7RY0zQegSB5q97HN4SdKpWfTRHm64MIc+PYzUtq17fzMhL37u0cZtttXIY+/t4v6r8kXg7Dtg0muO+m9RUeuk3unHlqIjEokwfVQWwXCYH/z5U7ySTLHdyv1X5TP9pfU0tkoo+IJfjx/ItX8sY0R2Ko9fO5QMsS1eEDoQwTNB6MT+Ji8L/r2VC3J6cOl5aVw7IpM6px9Xu7bcITlCkd3Klppmhrfd8TlSkd1KQ0sAtVLB8OxUVEoF4/PT+fGobHEBIAiCIJzzDjg8uP3RrprTRmUTkiMccvvj275imSsxRXYrhVmpDM9OZVedC0mO0M9m5FHRCU4QukRszVte5eDfP7+IvAwzBp2S2mZffN0bkiMEQtGgS1kn47bYbuXhKQU8eFU+mSJw9p0wGzWU5NoorWzscA1SZLfSx6ynocVPbq8UXi6r5p5Jg3l55kiMOhU6tSoeOAMorWrk54EBPDetkPkrKvjN61tZNK3wtH/nurwSja0Sbn8Qk0GDLUl72t+T0L2JbZuCcASXV+Kuf2/li4MtTB2WQUAO09ukx+ULolMr250XZFZRDjvrXMwqzqHEbkt4nSK7lXmX5jKiXyr/2FTLk9cOJbdXCgPSksUXvyAIgnDOO9DkRYpEaA2EANCplbi8QdLNBuZeau+0yc6sohx217uZe6mdzB5GLj2vJ/1sYt4UhK7g8ko8+PZ2CrNSWTpzJKFwmBFZqSTrNAnrXpc3iBSM8OCUfIqPGLfFdiuPXDMEnUohAmffoYAk8/CUfEpyO15/zL00l/49k+mZosflDVJa5SACBEJhDrdI+IMyXklOeJ7TG2RZeTU3jenPJ5WNp72BQJ3Tx9wVFVz++7Vc88I6Ln96LfNWVFDn9J3W9yV0byLzTBCO4PBIjB5g5Z5Jg9nX6CXbauRwqxRPg47dLd9U08zOOheDM8woUXDHuIHcNXEQrYEgBq2KZK0aBdAqhbj/qnx6iU5DgiAIQjdx2O0nEolw/5vb+fX46JavilonGWY9q3cdYlimhQXj81AAvmCYJJ2KcDiCTqPknomDUACpIstAELqUwyPxkwv7Uef0oQRMBg09k3U0HrHuja15R/RL5YEp+QRDEVr8QZL1ajyBEHqlgt6pxtP9cbqVVimETqNi3mV27rhyIIGQTJJWjVajJBCU+Wh3A9v2u8hLNwHRGmk3LN0IQIndGs8yiwXRdGolpVUO7pyQx4uf7D2tDQRcXokFr2+ltLIx4fFPKhvPmKw4oXsSwTNBOEIEWP+lg+c//pLF0wv5dK+D72WlsrbycDTLrCgHiBZMfW5aIS+XVyekS5fYrTx8dQG/XbmLmy7uT2aqUQTOBEEQhG7jcJOXQCRCa1CmtMrBr4lmQywtq+b56edzY0l/Fn9cxTOrKuPPKcm18fDUfJ5bvYdfXTFQZLAIwncgHImgUytZtesQPx6dTcW+ZlZuq+feSYMT1r3LN+xj+qhsXi6v5sn398SfX5Jr47FrhojA2WngkWQUCiACXinE4o+r4nWZAUrsNn49fiDXvbgegGT915f9pVUOwsDs4hwWr6miyG6lotYJQIM7wOziHFL00drOp2PrZGOr1CFwFhPLihPBM+F0EMEzQSA6MTR5JNRKBQ++tZ3SKge3jc3l5bJqZhbloAB21rmYPiqb5Rv2UZiVyuyiHELhCPdMHIRKqcDlDZKs17CjzoUUkrnvqsFYDBrx5S4IgiB0G4eavPgjEcqrGhnQMxmAj79oYO6ldhZ/XMWty7dw88X9o1lnCvBLYVIMar6od7Or3s1tInAmCF0utu5VKRQs+riKkf16xNe85VWODuvewRlm1AoFd1w5kLsnKPEFZVL0alJ0ajJE4Oy0sBg0hMJhLEka/lfjZEFbhq9XktGrVaiUCma9shGvJFOSa0OrVCQ8v7zKweyiHIrtVn49Lo9pL62PH7uovxVbspb9TV7u+vdWStsF5cbk2rq8oYD7OFlvpzMrTujeRM0zodurc/q4/Z+f4/KHqHX64hNEsd1GaZWDQCiMLxhKWEAUZloIhMIk69SEwhFCcpgeyTqeX1PJ8OxUTHot2dYkETgTBEEQugWXV6LO4UGKRLj3jW30MukxaFUA/HntXvzBMJOGpLNoWiG5aSkcbgmw/YCbnslaFn1UyXm9TQzrayFbBM4EoUvVO318uPMQ/lAYX1CmvMqRsOYFOl33etvqZMmRCGajmt4mvQicnUZpKToUKHC0StQ0+2hslaL/PuEIq3cf4kcvfkpjq0RJro2HpuTjDcodXiNJp+Z7WamoVQqe+sEwlt84CgC9RklrIMSCIwJn8PXWSZe362qimdqy3o4m5TjHBaGriOCZ0K3F9tTnpZt4+oPdyOFI/FhIjv5Zp1aydk8jr22oSQic6dRKPt3r4LmPKknRawjKMkW5NpRAumjvLAiCIHQTh5w+3L4gwUgEhyeYcBFeZLfilWRuXb6FOpc//hyDRkXxACsBWea2sefxkyUb8AfDp+sjCEK34PJK7Gvy8tb/DnDtH9fF6121X/MCx133piXrxQ3i08xs1IIiGgD7X00zs1/5jNmvfManex0UZlqiwbCfjuLhKfk4vRIf7W7o8BqeQIjFa6rY5/Byy6tbmP7SBpaWV5OapKWmyZuwDbS9rm4oYEvWMuaIRggxY3Jt2JLF755weojgmdCtxfbUj+yXSmmVA2uyLn7MqIveMa+odbJ1v5Ppo7P4vKaZOcs2ccurW5izbBOf1zRz76TBPPafXVTUOEkz6XH6RCqxIAiC0D24vBJBOUy924ckR3B4AkD0Ijy2XTMWQFu8poo5yzaxYkMNFqOGLTVOlEol015aT2OrJLbiCEIXa/ZKLFpTyZYaJ89NK0RqC3K3X/MW2a28+Mnezte9tU4WTi0QgbMzhE6l5M9rv+SOcXmU2G0J37PLN+wjSafGI4UwaNX8ee3ehOe2r3MWC5pCdDvn/W/tIChHOJau/L42G7U8fu3QDgG0Mbk2nrh2qPj9E04bUfNM6Nbc/iDnpSWTpI0OBSXRgv+xFOVYgePnphXy9/X7GJaVysyiHAKhMBaDhmS9Gkern19eMZD/bK/nkXd38c7c4tP4iQRBEAThu+HySri8QVoCIXok6Xjo7R3MbGuqU1HrZGedi6F9LEwaks7strlTp1bS4PZj0Kjo3zOJaS+uj2cwiK04gtB1Gpw+/MEw5VUO5l5m5+XyagqzUimyW4HENS9UM3d5BbOLc+Jjuk+qgXSTyDg7kzR7Je4YN5BHVu5kWJaFmUX9CITCmA0aUvRq/vjfKuZdlosC4lmGEP23nlWUw/wVFQlBtJjSykbuHD/wmD+7q7+vMywGFk0rjN9YSdFrsCWLDszC6SWCZ0K3dcjtx6BV8ZcZI2jxhwCIRCLcNzmfh1buSChwPH9FdAFRmGkBILuHkYYWPz99cRP/d/Norn6+HK8ki1RiQRAEoVvY3+QlEolQ9mUjvUx6epv0lFY5GNZ2MR67CP/b+q8YnGGOd502aFQU9DHj9ktMe2lj/IJOzJ+C0HUOu/345DBNnmigujDTwuI1VVS0ZaDtrnd3WPPObgua9TbpMevV9BX1CM84eo0KnxTijisH0uQN4vIF49trd9a5uLFkAEatCkkO8/4vSjjg9AHRmxvzV1RQmGWJB9GOFJIjFNmtnW7dLMm1kaxX82VDa5d24TQbRbBMOLOIbZtCt+PySlQ3tnLHP/7Hyq31tPhD6DRKSuw2NGoldU4fhVmpDOtjwZqsZfKQDBZNK2RwugmABrefhpYAty6vYHhWKh/taogHzkQqsSAIgnCuO9DsZcG/t9IqyfGgmKctCLa0rJpZRTkUZlmYv6IiXjMJoL8tiYraZp5dvYdVuw4nBM7E/CkIp5bLK/FlQytb9jXhD8rc/cY2LG1jLFaT0CvJzF9RQb3bjy1Zx+S2ph7xNW9LgBQRODtj6TUqGtwSB90B9jk86NRKAqEwhZkWrhjUm2SdilqHD51aiUJJPMNscLqJ/7t5NIVZqcxfUZGQlRbjD8rMKsqJZybGFNutLJxawCd7DnPV4jKueWEdlz+9lnkrKqhrC84JwrnqW2ee+f1+9Hr9qXgvgtDl6pw+yisbeevzA5RVOZhZlIPbH0IZgHsnD6LJE6Bnio7Fa6oAMGpVzC7OIc30dS20/D5mrntxPcOzU3lwSj61Di8f3FYSvTMnFv6CIAjCOczlldjniBaSdvuCBEJhdta7mZDfG/j6YjyWuRK7SM+2GrnuxfXk9U5h4dUFBOUwY/PSxFYcQegCdU4fC17fyuZ9zbxw/fnx7ZoatYISuy2hxpVXknlmVSV/Xru3bc2rx5qkxWzUMKh3iuioeQYLymF6puj4wZ8/ZXZxTvxmBsABl4+F7+7k37dcxOEWP+GIgs9rmuPXOHMvs1NR09xp4Kwk10aSTs1LpXspzEqNf5fHtoNWHW7lzf8d4LlphfHgW6wL56JpheL7XDhnnVTwLBwO89vf/pY//elPHDp0iD179tC/f3/uu+8++vXrx5w5c071+xSEb83llbj/ze3cdsV5/Pr1rUD0zluaKVoktd7pZ+dBF5OHZMTrnsUKb8aU2G38evxA/nnzhaTo1cjhCIVZFjFJCIIgCN3CQbc/3hjHZNDQGgixtKyaCfm9Kcm1UVrZ2HHuzLVxx5UDee66QjJTDZgMGjFvCkIXiXWS37yvmeenn0+qURvfrtfcKvHw1HzW723qsCUvNm5Lcm3cM3EQJq1aBM7OcBEgHIHhWZaE79yYErsVIhAOK+iRouWO8XmE399NeZUjvrVeAZS1+z0oybWxcGoBP1mygamFfTp0W11aVs2iaYXx353ZxTnxnx3rwtnV3+8ur0Rjq9SlW0YFoTMnFTx75JFHWLZsGU8++SQ33nhj/PEhQ4bwzDPPiOCZcEY66PYzJNOMJxCKPxYtXBwgSaem3uWjvMrBig21LJk5koUrd1Ja2Rg/tyTXxsNT8lEAqeJLWhAEQehm9jk87G/2xbNWkrQqGtx+CrMszHh5Iy/PHAnQYe588Kp8QpEwvc3JCZkRgiCceo2tUjTjbPr56DVKGlsD8TGrVCrwSCEGpBl5aEoBD729g9KqI9a6U/PRKpVk9BCBszNdsk7No6t3cvu4POCLDv+WC6cW4JWCfFrtiHfbvHfSIH459jxC4QgpehWzinKYXdyfSCRCmknHR7saeGTlDp78f0OZs2xTh8y09g0Gyqsc8dp4MV3dNTmWVdl+nhmTa+Pxa4eSYTF06c8WhJMKnv31r3/lxRdf5PLLL+dnP/tZ/PGhQ4eye/fuU/bmBOFUOdDsxeULcdnAXrjbfalX1Dr5sqGFBePy6JmipZ8ticUfV3H18+XRDkMX9QOiHYZ0KiU6tVLchRMEQRC6nVqHh3vf2MbMohwqap0U2a2U7jnMaLstPnf+6MX1x5g7k0/vBxCEbsLlk3huWiFpJh2/fW8Xs9uN2bKqRmoaPcwfm8sht58JQ3rHOzTq1EoaWgKoFQr6iMDZWaFvqpE7xw9i4codHbptZqYaWPxxJf/YdCB+fpHdSppJzx//W8UdVw5EjkQA6JmiJRyGH/15fTxYFghFuHfSIO5+Y3vC849sMBDbmh+TpOu6foSxrMr2gTNAbBkVvjMn9dt94MAB7HZ7h8fD4TDBYNdGmwXhm3B5Jerdfg40+0gz6VEAn+51xFPVYynLz360h/mX59LbpGfhlAJ8QRmvJGPUqdCqFBjUKlLENhNBEAShG6p1eHB4gvFumjvrXMwqymH5hn30T0tCo1Ly8JQC/G1zZ4pejV6jRK0ArUZNmsg2E4QuceT2Na1Sgdmg5elVe1gwPo/yKgeFR4zZH4/O5rnVe5hd0p/R/a34pOi4NRnUDE5LJl00BzirKCIRHrwqn1ZJxu0Lkm7REJLD/HTZJmYV5zC7qD+HWwME5Qhbapp5bcM+po3K5ulVe7hn0iB2HzzMvBVfsmhaYUKWWWlVI78Ym8s784qobYpmHMe6dLY/r339vCK7FbVSgcvbNVs3G1ulDoGzmFOxZVRsBxWO56S6bebn51NaWtrh8X/+858UFhae1Bt57LHHUCgU3HbbbfHHIpEIDz74IBkZGRgMBi655BJ27NiR8LxAIMC8efOw2WwkJSUxZcoU9u/ff1LvQTi31Dl9zF1ewfhnS5mzbBMf7DiIV5LjncCK7NZ4YeO+PZI45A4QkMME5DBeSUarVkY7cYrAmSAIgtBN1TR5qWn24Q9+3U1z+qhslm/Yx+AMM4FgBJVSiT8o45FkjFoVEaLZCAqFUgTOBKGL1Dl9zF1RweW/XxvveHjXG9uQ5GhzAG+g8zGrRMFPLspBgYImjwSAHIlg1KhE4OwsZEnSUnm4lWv/uI7SqkYOuvx4JZkHpuRzyO3ng50HkUJhIpEIk4ekU9A32gl59a4GPAGZ332wh8IsS3wrZnsNLQE+2HGIVzfsY86yTSxeU5UQOGu/hTOWlbanobXLOm+6j7Ml9NtsGe1sPIkOosKRTip49sADDzB37lyeeOIJwuEw//73v7nxxht59NFHuf/++7/x63322We8+OKLDB06NOHxJ598kt///vcsXryYzz77jN69e3PFFVfQ0tISP+e2227jjTfe4LXXXqOsrIzW1lYmT56MLHfsHCJ0Hy6vxIJ/bU3Y+7+0rJoUvToeMCvMSmXJjBE89YNhFGZaKP/SwUGnnzqnj2SdGrNBTV+zHoNKKQJngiAIQrdT5/Rxz7+34vIFMeqizXVic+jgDDOFmRa8QZkmj4RWo+TFT74kRa/GqFGSrFGJrV+C0EWOtn2trK0LLoBapQCOPmblSIRWf4gUvZpMs55METg7K5mNWvpYDPGGD7Nf+YzZr3zGp3sdDOljJjcthTSTjnSzng92HuTZ1ZXxANj+Zl886LW0rLrDa+vUynjSQYndlnCsxG5l4dQCRman8q+fXUhhVirzV1SgViri2yhdXumUflaTXnPM4ynHOX40x9sOeuTncHklvmxopaKmmS8Pt57yzymcuU5q2+ZVV13F//3f//Hoo4+iUCi4//77Of/883nnnXe44oorvtFrtba2cv311/PSSy/xyCOPxB+PRCI8++yz3HPPPXz/+98HYNmyZfTq1Yvly5dz880343K5WLJkCX/7298YO3YsAH//+9/JzMxk9erVjBs37mQ+nnAOOOT2JwTOILp4kMOReDewI7vSFNmtAEwekk6yVoXD6yfDnERPcddcEARB6GZqHR5aJZnSKgcz2wpCx0oeHNlNs8hu5Z6Jg7hr4iB0gEIrtmoKQlc61vY1lTIaNCurajxqB9wiu5XCrFQmD01Hp1LSSxRaP6v1Nunj/9ZAwr937N96fH7veNOAmGyrkclDMjpsxYw9r6LWiVeSWb5hHzOL+vHzSwagVikxtDWLeeqD3fzsEjvXvRitldY+E60rOm/akrWMybXxSSe/+2NybdiST+5nfZPtoKJhQfd2UplnAOPGjWPt2rW0trbi9XopKyvjyiuv/Mavc+uttzJp0qR48CumurqagwcPJrymTqfj4osvZt26dQBs3ryZYDCYcE5GRgYFBQXxczoTCARwu90J/xPOHS6vhMsf6vTYR7sPcdeEPIrbAmUxsbsuu+vcKBXw0MqdpKUYReDsNBFjVBDOfGKcnrv2N3k54PLR0jaXVtQ62V3vZu6l9viNppgiu5V5l+VyuCWAHA7Ty5okAmdnCDFGz13H2r4WC5q9+Mle7rhy4NHXvPVu0k16ETg7zU7FODUbtTx2zZBjXN+4WL3rUEKArMRu44MdB+lp0lGYZen0eUvLqimyW+Pbfr1BmZkvb+SqRWUsLa/m+8MzafYE44GzIzPYTnXnTbNRy+PXDmVMbmIW3JhcG09cO/SkA3Unuh30m2aoCeeek8o8q62tRaFQ0LdvXwA2btzI8uXLGTx4MDfddNMJv85rr73Gli1b+OyzzzocO3jwIAC9evVKeLxXr17s27cvfo5WqyU1NbXDObHnd+axxx7joYceOuH3KZy5Oivs2OINYtSqOj3/z2v3cnleGvdMHESTN4jLF4wXwHxtQw03jumPRqXkqR8ME1s1TyMxRgXhzCfG6bnH5ZVo9kr4gmFUCiXJ+ugycWlZNc9PPx+VAiYNSWd2Uc7X3fncAQJBmb+t38fTPxh2mj+B0J4Yo+ee2Lq3fZH2I734yV7enVfMfW9tZ86yz1g6cyQ/94cS17wba3jwqnyx1j0DnKpx2reHkSeuHco+hxfnEdc3c0oGMGfZ19fbxXYrD03N52d/28z3+lqiDV9CbQ1fdGo0KiUun8S/fnYRCgU4vRKDMszMX1GBUatiUVs3V49fxmzUsOpXY1i182CHDLajbaP8NoX5MywGFk0rpLFVosUfJEWvwZb87Qr7n+h20K5uWCCc+U4qeDZ9+nRuuukmfvKTn3Dw4EHGjh1LQUEBf//73zl48OAJ1T2rra3lF7/4BR9++CF6/dHvUCoUioT/jkQiHR470vHOueuuu/jVr34V/2+3201mZuZx37NwZuksbfaKQWncO2kwSqITQ1mVI+E5Xknmj//9kjvH5RGBeA20S87rSXaqEX9QJgLii+80E2NUEM58YpyeW+qdPmqavCxeU0lp29w59zJ7fC69dfkWbr10AOPye+MLyngCMmqlgm0HXHy06xD3Th4s5s4zjBij55b26965l9nj26iPdH6WBY1SwX2TB+Nolah3+Uk369Gplbj9QSbk92b6yEwyUkVNwjPBqRynfVKNJOvUNLQEqG32Mi6/F1mpBjZ+5WDRtEICoTBmg4Y+Fj2SLPPnnwzn/re2x7/z4euss9c21nDPpEHUOHzMeHkjEN02+epPR/Pwyh0Jv3vFdiv3Tc5nadlX8eBZyVG2UZ6KbY9m46ntgnmi20G7smGBcHY4qW2b27dv54ILLgDgH//4B0OGDGHdunUsX76cV1555YReY/PmzTQ0NDB8+HDUajVqtZq1a9fy3HPPoVar4xlnR2aQNTQ0xI/17t0bSZJobm4+6jmd0el0mEymhP8JZ5ejpc0OTDdR/mUjv/vwC2a2ddRsr9hu5dZL7XilECqlAoUC2kpDcMDl4+/r92FNEov/002MUUE484lxeu5weSX++8VhFrULnEE042xmUQ7Fbd2pf/fBHqYsLmfbfhdJOhWRSPQCaVxBb1KNJ1eoWeg6YoyeO45c9y4tq2bh1IJOt+nNLMrhgXd2EJTD9DTpUCsV7G/2kaRTk2NNom+qQQTOziCnepyajVqUCgWzX9nEj/68nppmHwUZZgKhMHqNCpNeQ4Pbz+avnNx7ROAMoLzKwcvl1eSlm7j/zR2YDF/n2jxx7dAOgTOINqpYuHIHT1wbbf5XbLfy2DVDOgS4ztRtjye6HbSrGhYIZ4+TyjwLBoPodDoAVq9ezZQpUwDIy8ujvr7+hF7j8ssvZ9u2bQmPzZo1i7y8PBYsWED//v3p3bs3q1atorCwEABJkli7di1PPPEEAMOHD0ej0bBq1Sp++MMfAlBfX8/27dt58sknT+ajCWeJo6XNFmZaAFiz+zDr9zYxuzgnYXtJRa2TBneANJMOpzdIaVUj4/J7cdWi8m+9X14QBEEQzjYHmr24/SHSTLpOs7Xnr6hgdnEOCybk4QnImA0a3t1Wz5rdDeSlm/i81smTYu4UhC7V0BJIWPd6JZmAHOZ7WanMOmKdG9s6d/u4PBytEnOWbaIk18ZTPxhGL1GP8Jzn8ko0tQWh2jcOiG23lMMRWgIyQ/pauOuN7Z2+RnmVg9lFOSxeU8VdEwexZMYIAqEwGRZDp9mOEA2g3T1xMMt/Oopsq5E+nQRoz+RtjyeyHbSrGhYIZ4+TCp7l5+fzpz/9iUmTJrFq1SoWLlwIQF1dHVar9TjPjkpJSaGgoCDhsaSkJKxWa/zx2267jUcffZTc3Fxyc3N59NFHMRqNTJ8+HQCz2cycOXO4/fbbsVqt9OjRgzvuuIMhQ4Z0aEAgnFuOljYbCIXjfz6ys1DMC9efT2tAJlmnZledi/93fh8++tXF33q/vCAIgiCcTWocHsqrGsmxJSfMn+3F5tLB6dFsCJVCwec1Th65ugBJlrmxOEfMnYLQheqcPvY3+zo87vYGO13nxrT4gqiVCkpybTx57VAROOsGYlsiZ17UL/6YUavi5ov7MyE/nYUrd8QzzV64/vxjvlZsTmjxB6modbK0rJqnjlPXsjUQJD/DdNQ54Uzf9ni87aCxDLXfvL41IYAmEjC6j5MKnj3xxBNcc801/O53v2PGjBkMGxYdSG+//XZ8O+epcOedd+Lz+bjllltobm5m1KhRfPjhh6SkpMTPeeaZZ1Cr1fzwhz/E5/Nx+eWX88orr6BSdV4wXjg3HK0hwLEKqLY/J0mrQqmAB67KJ8uadKrfniAIgiCcsVxeiVZfkFA4wnvb6plZlHPc+TN23KhT8eg1BZgMGsxGMX8KQldyeSU++8rBwN4dt/KlGI69Rcxk0KDXKFk8rVBc1HcDh9x+Fvzrc0qrHAzPTqXEbmVzjZPnphXS4Pbz0BHbLfWaY18rx77zWwMhPq9xsvzG0TiPs63SpNcc83ftXNj22BUNC4Szx0kFzy655BIaGxtxu90JnS5vuukmjMaT30P/3//+N+G/FQoFDz74IA8++OBRn6PX61m0aBGLFi066Z8rnF32OTw0e6VOGwJU1DrpY9YftYhqid1KgztAP2sSRq2KdNGeWxAEQehGDjZ5CYQjrPuykVW7DjEsK5WeKTp2HHBRYrd2qH8D0TpKh9x+APrbksgUN50E4TvR7A3iDchsrXV2GJ8hOdzpWhiiNaeStSoxVruJeqePJo9EaZUDo1bFsL4WRuX0oLrRw8vl1cwuykm4LjJqVfRM1h7196fIbqWi1kmJPVoDbHNNM099sJsHpuQf9Rqr2G4l9Th1o8+VbY+numGBcPY4qYYBACqVilAoRFlZGeXl5Rw+fJh+/fqRlpZ2Kt+fICTY3+zlnje24ZfCnTYE2FnnYni/Hsy91N7hWIndxtzLcvlelhmjRikCZ4IgCEK3cqDZSyAS5t43t5FuNjB9VDYVNc1c9+J6epsNzL0sN36xFFNktzL30lzsPZOxpyXTQzTVEYTvxCG3n/vaxmrPFD23HLG2vfXVLTxy9ZAOTQOK7VZ+e80QETjrJmINX1y+6JbH2cU5/KVsL/NWVDCkr5nyKkeHbfm3XjqAP6ze0+m1VKzb5s46FzOK+vHqhn08N62QLTVOpJDMw1MKKOnkd+7Ra4Ycd2vwiRbmF4Qz1Ullnnk8HubNm8df//pXwuHoYFSpVNxwww0sWrToW2WfCUJ7Lq+EwyMRCkcIhyNEiBaknD4qmzv++TmLphVyyyV2XL5gvFDq9JfW85MLs7l34iDCgDcgk6xXQySCQqnApFWTLroMCYIgCN2IyyvhCwaRwwpKqxz86sqBPPXhF/EMgluXb+Hmi/uzYHwedysVtEohjFoVerUKFBE+r3VxUX+ruLgRhC7g8ko0tkq4/UFSDRrUSgWtkpwwVitqnB0aYX1e28yj1wzBI8nxLWQi46x7aWiJNkIzt3U8Lsy0sLSsum27ZgBot+1eq2LuZQO4LK8Xv/tgD+vamqvNKe6PUasiRadGksO4vEEGZ5jjzScCoTCzi3PY5/Bxxz8/595Jg7hn8mDcvhAmvZrUJO0J19Q7Vdse248Zk0GDLUlkgwld76SCZ7/61a9Yu3Yt77zzDkVFRQCUlZUxf/58br/9dv74xz+e0jcpdE91Th/3v7Wd6y7I4uXyar442MKffjwciE4CXkkG4Pq/bOjw3GdWVfLMqkrenV+M2x8kWa/GqFVjMR57L74gCIIgnItafEG0KjX1rdHtl2qVImHrjVeS43MnwHvzSwiFwwTlMFq1ksvz0sT8KQhdIFbkvbSykcxUA6/+dBSBcJgWfwhIHKudNQh4d14xW/e7GNW/B0k6tWgM0M04fUECoTBKFBTZrYTCERZNK0SvUZGij17qV9Q6uSyvJz8Z3Y/eJh0ubzRLrX1ztSUzRnDdi+s7/Rmx7puWtgDd3W9sZ0yujUUnWU/v2257bD9mYsbk2nj82qFkiJ1FQhc6qW2br7/+OkuWLGHChAmYTCZMJhMTJ07kpZde4l//+tepfo9CN+TySix4fSt56SZeLq+mosbJX2aMxKj9ehIosluP2h0spsUfoqElQIZZT7YtSSz8BUEQhG7F5ZXY5/Cw3+WjttmHoa3hTrPn2F3NvFKILw62kG7W079nspg/BaELxNa7pZWNGLUqlswYSVlVIxv2NsWbYx1/rMp8L8tMsgicdUtJWhU6tRI5Euanxf3pb0vilfJqrv/LBj7ceYgiu5WlZdXcNvY86l0+mrzB+DzQ3vGuqQD2HGzhuWmFGLUqPqlspLH12A0EukL7MdPeJ5WN/Ob1rbiO09RAEL6Nkwqeeb1eevXq1eHxtLQ0vF7vt35TgtDQEqC0spHzs1KjdzuKc3j6g90oFdHaZUvLqplVlIP5OJ2GzAYNY+w2segXBEEQup2DTV5afEHufWMbSTp1vCZOLDvhWIxaFSVi/hSELtXYKsWDADeN6c/ClTvoZdLTy6RHksOU2G3HHaspejXJWjVpInDWLSVp1TS4/SgVCtw+CYcnwJ0TBvHPm0dzbWEfHrm6gOHZqTS2SvQy6RPmgfaO13E5zaRj4bu7og0IinMAaPZKVNQ08+Xh1u8saNV+zBzpdAX0hO7jpIJnF154IQ888AB+vz/+mM/n46GHHuLCCy88ZW9O6J4OOn24fUF+eUVuPN24MNNCaZWDSCTC7eMGcn6WhfkrKvAH5Q7FjWNKcm2k6NX06SHqmwmCIAjdy+FmL+FIhNZgtG5Sq19Gp1by8RcNzL3UzkGXr8PFU0xJrg1rso7eYvuLIHQpp1fCqFXxyytyGZ/fm9K24u6BULTu1O3jBnLoOGNV1Djr3jQqBfa0ZJI0KoZlprJoTRW3/H0zliQtXkmmqVXi7gl5JOtU8Vp5sXmg/e9VRa2zQ/OJmBK7lY92NeCVZMqrHBRmWgBw+YJc88I6Ln96LfNWVFDn9HX553X7j52J2XKc44LwbZxU8OwPf/gD69ato2/fvlx++eWMHTuWzMxM1q1bxx/+8IdT/R6FbiRa0Dha4H/ikHSCbSnEsVRitVrJS2u/5M7xebx0wwhC4Qj3TR7cadeXhVML6CsaAwiCIAjdjMsrEQhHCAIHmqMXM8l6FRW1Tj6vdeIPhlErFTwwOf+o86fY/iUIXcvllUhN0vLPn43mikG9aAlEa5zp1Ep0aiWbapr589oqhvQ18+BVHcdqSa6NR68ZQl8ROOu2XF6J+9/aES3qH47w8ModXDTAyt9mX8Cm6iYOuv0caglQ7w6gVavizdVi88CkIeksmTGCF64/nxFZqdw9cRAluR07Ls8oyuHFT/bGHwuEwvHkhdj24u9q26RJf+xdRynHOS4I38ZJNQwoKCigsrKSv//97+zevZtIJMJ1113H9ddfj8Eg7lIK39whtx+nV0KlVPL4ezu5blQ2Bo2KT/c6KLJb46nEgaDMrZfZefrDLxiUYaYw00K1w8NdEwdxFwoOuf2kGrXoNEqSOtnPLwiCIAjnsvpmLwE5zEGXH7ndbq8Gd4BddS6mj8rmb+u/YnCGmbQUPb+6ciC/maDEF5RJ0qrRqcX8KQhdxeWVcHqD+EIhdGoVj74bXfO+XF7NbybkAdEMoD5mAzvbxuvvV+1hWKaFX4/P49eAT5KxGDWY9BrSRXZot9bYKrF6dwNDMs30MuuZPiqb5tYA9W4/q3YdYnDbtZI/KKNVKWhoCbD7iHkgdqNEo1aiVSu5+nsZ3DVhEF85PPFgW6zrZozFoGFGUT9e3bCP56YVxo9/UtnIAaePRo90Ut0vT6SDpi1Zy5hcG590snVzTK4NW7IoNSB0nZMKngEYDAZuvPHGU/lehG6qxuHh7je28b2sVCpqminMSuXl8mquH5Udb7V8yO2nyG7lo90NXNS/B3dcOZAmbxCXL4hOreS97QfZXefm3smD2bKviZH9etBT3DUXBEEQupEah4fyqkYiwLvb6rl+VDY7690U2a0seH0r/3fzhTz67s74BZU3KKORlFgsGpaU7WXeZblolAoxfwpCF6h3+tjX5OXFT77ktrHnsXDl9viat7zKQUiOxIu7v3bTaPrZjLxUupfBGWYKMszsb/ZhMWjokaQlRaMSgTMhvoWxIMMMEXi5vJoF4/N4dvUepo/KZvmGfUC0/E1ts48LclIZmZ3Ko//ZFZ8HAqEwZoOGFL2apz/8gmuHZ7LjgJO3P6+jtF1H5piSXBv+kMxvXt/KdRdkYdCo+NOPhxMKR9hS00xts4+b/7b5G3e/PNEOmmajlsevHcpvXt+aEEAbk2vjiWuHijqdQpc64eDZ22+/fcIvOmXKlJN6M8K553h3EA65/dz9xjbKqhzMKsph8ZoqZrf7f68kM39FBTdf3J8HJufz5Pu7GNrHQnWji7x0Eyl6NV5JZuygXvxoeF8CYZmi3J5iu4kgCILQrdQ6PNzzxjZmFkULOZdXOZhdlBO/CfVyeTUzl25k6ayRSMEwbn+INJOKBneApz74gjvH5aFWQB+xBUwQTsqx1rwur8R/9xxm5dY6CrNSafGH4mN08ZqqtnOCzCrKAaqZ/cpnvDJrJHdcOZBQOIJXkjEbFFQ3eshMNZAhxqnA11smA6EwEaLf+wCDM8y8vrmW28aeR2sgRDgcoZ/ZQFAO4/GHGNrXwthBvfAHZQwaFZIcpqlVYkBaCq9tqGHBxDy+l5XKw+/sSAigldhtzCrqx53/2srj1w7l5fLq+O8vRLd4ThmaEe/G+ZvXt7JoWuFxA1rH66B55GtkWAwsmlZIY6tEiz9Iil6DLfmbZ7qdaU4k8044vU44eHb11Vef0HkKhQJZlo9/onDOO5E7CE6vRFnbl3LgiPpmFbVOiuxWyqscPLOqkj+v3cvs4hwUQF66CZVSgU+SSdGpUSoVaFRKUTBVEARB6HYOODw4PEFKqxxMG5Udf7yi1klhW4Od2cU5zC7KYe9hD+lmPQaNikAwTB+LgaF9LTzy7k6e+sGw0/gpBOHsdbQ17xPXDsWoVVHv8jO0j5m7/r2N2UU58Y6HsTUvwKaaZnbWuSjMSmV2UQ4HnH7SzXqkUBivFCLDYuCiAVb6iHq+AtFAi16joiTXhk6tpLWtZp43IDMiK5Wxg3rx5Pu72VLj5Llphdz/9nbKqxz862cXEgpHeOw/u7jhwn5sO+DikvN6Ikci8UYAUxeXAzC7OIefXWJHo1LS4g/SM0XHdS+uZ3ZxTjxjsr3yKgcLV+5gdnH0xs3QTAv1Lj97Gz2dBoNiwaJASD5uB80jg0hm47kVWDrRzDvh9DrhhgHhcPiE/icCZwIc/w6CyytR7/DQ4g/Fj8XqmsX+f2lZNbOKcuKdYLySzOI1VSwpq0avUfHMh3uwJkXrmxk0StEVTBAEQeh2GhweWoIy/lB0/RUrNg5fz6OFWRYWr6lizrJN3Py3zfzugy9ITdJgNqr5/h/XUV7VyMNTC86pCxFB+K4cbc27aV8z+5q8zF1ewZ3/+hxfMDpGYx0P4es1L0TH6/RR2VTUNMfH6pTF5Ty7eg8DeiZj0qhE4EyIc/mC/K+mmYen5tPQEkCriv4uqVUKzEYNL62t4qfF/XlvfjHL2gW6dGoVF/aPJieolQoWr6nihqUbiQBL2zLJvJKMV5L5vMaJLyjjDYSYs2wT+5t9eCWZwkxLh8BZTGmVgxHZqbx202gu7G9lb6OHZm+QNyoOcMc/P4935Kxz+pi7ooLLf7+WrxzeY37Wc72D5olcNwtnhm9U82zNmjXMnTuX9evXYzKZEo65XC4uuugi/vSnP1FSUnJK36Rw9mlslTp8ARi1KmYX53B+ViqVDa30StERlL++41ZR66TEbkvIOGt/tzy2J98flKlr9jF9dBYOj0RIDjMo3XTkWxAEQRCEc9p+h4cI0Y6a6eZouYJosXH9UedRi0FDullPU6ufUFjBW7cWkZaiE4EzQThJR1vzLppWSDgS4aaL+9O/hxGn/+tumtE1rzVhzRsrVRIbrwDWZB1Wowa9SklPcZNYaMcvy1yQlYoUAbNBTapRQ0mujbKqRsbn9+IXVwzE0RrggNOfsPXS7ZdQKaKBthO55np1wz4GZ5iBr4O97TMmO2PUqnni/d0JAbYiu5VZRTk88NZ2HrlmSEKwqH0QuTN6rQqXt2P22bmis++QmKNl3gmnxwlnngE8++yz3HjjjR0CZwBms5mbb76Z3//+96fszQlnr1gBS6NWxdzL7CydOZKlM0Zy0QArW2qauWHpRjzB6F2NWKvjpWXV3D5uILvqXPGMs1i22Zxlm3h1wz58QZl5KypI1quZu7yC1kCIVLEfXBAEQehmDjo8BMMR3P4QSVp1dPtOW7HxEf16MPdSe4d5dMWGGvwhmZomHy6/TEsghFKhEHOoIHwLna55Z47EqFWx7ksHd/97G1Ikwtb9TkpyozeJd9e5uW9yPjvbrXnh610WS8ur6d8zCZsInAlHoVWq8IcjPLRyB4N6m/CFZBZOLWD7ARc6tYpNXzWx+OOq+BZhiP6OWpN1eNuyINvv8unsmuvVDfuY3tbArcRu45DbDxw/2BWSwx0y0ypqnDS4/dx88QBqHF5mFeUw9zI7Rq0qHsTrTJHdysqt9cxbURHPWjvXuI+TWXeuZ96dTb5R5tnnn3/OE088cdTjV155JU899dS3flPC2c+k12DUquJFitsXkyyx23jr1iLkSJhsm5EHp+Tz4Ns7KK1q5KfLPmPpzJEQgQXj81AooNkbJCRHO7jMX1FBYZaF/+45TGFWdB99bH++IAiCIHQH9Q4PEnD/W9vjGQVGrYolM0bwwsdf8t72etJNehaMzwOiF+RqpYKyqkb+vn4fYwf1AqDO5aeHUXO6PoYgnBOOuebNtfHKrAsgAgN7pzAyx8rj7+1i7uW5fF7bzNhBvVArFNxx5UB+M0ERX/MecvvRKBSkGDQiuC10cMjt5543t3Hf5MHccGE/7n9rO5trnPx9zgXcdvl5+ENhepn08aYUMbOLcwiHo79fJXYrpe0yzuYU90etUmA1alEooMEdYHCGOX7tNas4Wh9tyYwR9EjSUmK3UVrVMVuqJNfGxq+aEh5rPz7ufmP71+farbx5SxE1zV6mDMtg4RENCmLZavNXVOCV5BNuQHC2MemPPQ+nHOe48N35RsGzQ4cOodEc/R9PrVZz+PDhb/2mhLNfsl7Nv352IY+9t6vDnYfSqkYefmcnd08ahMcvo1LCHeMGcueEPFp8QTQqJY0tAXqZ9Uf9En1tQw3zLsulXw/jOfcFKgiCIAhH0+DwEALufWNbwvzolWTmLNvEfZMGMSjdRJJOzcMrdyZsBSmyW5l3aS5KBXgkmUfe3cU13+tzGj6FIJwbXF6JcCTCazeN5ndHbFMDKK1s5MF3dnDPxEFoVEoCQZnrR2chhUJckGMlKId56MiOhrk2fnt1AX1FEyzhKJo9EhU1TojAktK9bG5rCtDsDRIIBbAmaTttwDYiK5VQOML52RZG9usR/d2rbIwHfIvtVu6/Kp9VOw8yKN3MBTk9mDQknaAcptkTJChHmLeigosGWLnvqsEsfGdnQgCtyG7l1kvt+CQZo1aFV4pmuB2twUBplYOHVu6gMCuV+SsquHfSIO6aNJivGj3x7c2xwBmcu1sYbclaxuTa+KSTrZtjcm3Yks+tz3s2+0bBsz59+rBt2zbsdnunx7du3Up6evopeWPC2SvWLWTmRf0SFgPtlVY1Uuf0MWfZJkrsVmYV5zB3efTOxtxL7aQmaZj+0nquuyCLWe3uhKiUCoJymAeuGoxJ3I0TBEEQupHDDg8+ojXOOptfvZLMXW9sZ+W8Yqa9tJ4fj87mF5fnIocjJOvVKACDRsU+h5dbl29hRHaqWJQLwklq3x1vyYwRR1/zVjZyoN2ad+5luXgCMo+X7+JHI7L49fg8fg34JBmLUUOKRkWGCJwJx+D2h5hdnEOEaADql1fk0uD2M6SvmQZ3AKNOHe++ubSsmuemFaJTK0m3GHhkZTRYG6tF/fOLB6BRKTFoVazedYjH/7OLoX0thCMR/rx2b0LAq8Ru5a1bi1Ao4YPtB5kwpDczi/rFm2BU1DqZ/cpnFGZZmF2cEw/KFWZaEjIy24tlx3klmbvf2M6SGSO45dUtR/3s5+IWRrNRy+PXDuU3r29NCKDFOvaK690zxzcKnk2cOJH777+fCRMmoNfrE475fD4eeOABJk+efErfoHB2ad8tZNoFWcc8V6NWsmTGCAKhMAaNiv+7aTSzXvmMxR9Xcc/EQVx3QVb8i7bEbmPh1QU4Wv30TNGTKRYVgiAIQjdS15Zx9tA7O5h5Uc4xz3X5glx3QRbPrq5k81dNPHLNEP743yquKezLIXcgHjgTi3JBODlHdsc7XgH1JJ06Wg+tLLqt86ph6QxIS0GORJiyuJwSu42Hp+ajUSpE4Ew4LpNeTWGmBU8ghFGrYkJ+Og+t3EGdy0+GWU+STkWD2x/POPvN61tZOnMkjtYA00ZlM6u4P1tqmuO/j8V2K/dMHIRCAev3NrFgfB6NrRLXj8pmTrtzS6scPPzOTmYX9yOvt4k5yzZ1+v6O3C56vPFxvOPtnatbGDMsBhZNK6SxVaLFHyRFr8GWLOp6n2m+UfDs3nvv5d///jfnnXcec+fOZeDAgSgUCnbt2sXzzz+PLMvcc889XfVehbNA+24heo3qmOeqFApuaPelW5Jr49Wfjub6v6wnFI4waUg6o3J6oFYq6Gsx4JNDJOs1mA3n5pemIAiCIHTmgMNDmGimy/RR2aiUimOebzKomVDQm8lD00nWqJDCMjMv6keyVk2TV+KducViUS4I30L79a5Rq6KXSXfM8z2BEBU1zTw3rZD5Kyr4xdhcrMk6knVq/vOLEpK1KpRAHxE4E05AapKWw+4ASTo1s4tzWLhyB+VVDipqnPzp+uFYjBou6G8lx5aETq1k+qhsfvf+7g6lcGK/j2VVDpq8QS4f2Iv8DDOPrNyZuJW4rTZZtcODRqXElqxjv9N7wu/3eA0G2h+vqI021uis++S5voXRbBTz8pnuGwXPevXqxbp16/j5z3/OXXfdRSQSAUChUDBu3DheeOEFevXq1SVvVDg7tO841DNZS7HdSlknaezFdiuf7u1YF2Lhyh08ce1QvJJMs9fH8g01PHJ1ATVNXmqavIzJ7Sm+VARBEIRu44DDQzASDZwZtCoWfVxFYVZqPKPgSCV2KyE5ghwOk5mio8rhRalSkJlqpG8Po6ijJAingNsfxKhVcfPF/ZlQkM7mr5qOOiaL7FYqap3xY7OLcwiFIwRCYaxJKgwapQicCd9IL5MeKSgTCke4qL81YUtkhAgef5BkA/SxGHjwqnzuOaJGJpDw+7h4TbQrZ6pRc9zaZIvXVFFit3HPpEHHfI9mgya+wyjHmnTUml6x8RGztKya9+aXcP9b209oC6PLK9HYKuH2BzEZNNiSRABK6DrfKHgGkJ2dzXvvvUdzczNVVVVEIhFyc3NJTU3tivcnnGXMBg1zL7NzyXk9cbRKPHBVPg8fUUyyJNfGvZMGc9Dlj6ewxwpBllU5WDAhD5VCicWo4YGrBkM4glKpYMx5PemTajxdH00QBEEQvlMHHB48IRl/MIwlSYs3EOKuCYOQw2GmDsvg4U4Kjd83eTD1Tj+pSRoO+4O8vO4rHpyaT18xfwrCKWM2aHh++vkYtEoOtwToadKzcGoBD7y9IyFjpn1dX/h6O5tRqyIc1mBN0qAG0kXgTPgG9jd5ufuNbSycWoC2XdbW7OIcWvxBcmwW9jf7OBSWsCZrj1qPr6LGyV3jB3HJeT0JR0CpUPDQlAIOunwE5Qib27ZreiU5YSvm5ppmnD7pqBliRXYr/91zOB7UW3P7xZ3W9GrfTTNmRHYqqUbNCW1hbF93MGZMro3Hrx1KhsVwEn+zgnBs3zh4FpOamsrIkSNP5XsRzmKH3H7cAQmtUsXnNc0sXlOFUavi+enndygm2eD2c9Dl4+evbqEwyxJPGY4F0DwBmRqHm9H9rQCEwmEGp5vEXQRBEASh26hxeHj4nR1cNyq7QyZAkd3KT4v7c9WwDO6ckMf+Zh9alZLeZj11zT70WiUpejVPffAFD04RgTNBOFVcXokmj4RWqcCgUfL8mqp4p8NH3t3JsEwLMy+KrnnNBg09jBoOt0gdXketVNCn7eJeBM6Eb8Lllbjr31sprXIgR8CgVcWL/4/P740/KLPf6WPdXgdvVRzg9z/8Xvy5sfMKMy2EwhFybEksPOImTPuA1pHXaYFQGKNWxXPTCnnxk73MuKgf4UikQ1OB9gHjMbk2rG3ZYIumFdLQEsDlC2Jo26r84a5D8ecemV12rGu/I+sOxnxS2chvXt/KommF4tpROOVOOngmCNC2iPBKPPX+bu4Yn8e97dKCZxfn8JeyvUfZVmLjpjH9eXZ1Zfzc2N0Jk0HDg+/s5N+3XIRaBfaepu/uAwmCIAjCaVbr8HDPG9sYlpXa6Raa2H8XZqXy1ud18a00S2eOpGeKFq1aSSgS5tFrhoiLB0E4BVxeiYNuPw2uAH2tBtyBEIs+rqK8ysHcy+zxcbpm9+GE5xXZrUwakp6wzu1t0mPSqVEDvUXgTPiGGloC8Wstf0jGqFGxZMYIFn9clbB987K8nvxtzgX4g9Fi/LGg18vl1Swtq2bRtMIOgTPouJ2z/Z91aiWzi3Piv+/r9zYxuziH2UU58YCxSgk+KfozS3JtPDy1IP7aHknm4ZU7E7Mzc228M68YBcSDbCeifd3BI31S2UhjqyTmP+GUO3b1PkE4hjqnj/e2HeTeN7fzgxFZeANywhdwYaal08AZQGlVI8V2GxD9ki7MtADRWmgHXT68kkyd00fPJH2nzxcEQRCEc1Gtw8N+p4/StrnxaPNoebvjhZkWiuxWDrl8fLSrgTqnn7QkvbhwEIRToM7pY+7yCsY9W4pMhPve3E440i6IfZxx2sukj69zS+xWTHo1OkTgTDg5Tl8w/mdPQEaSw7zQFshtb3CGmXVfOlCrFBTZrfGgV0VbpqReozrqds7212axP5fYrfRM0VE8wMac4v7MvcwOwOI1VcxZtolbXt3C9X/ZgCcgs7SsmtduGs2wTAsTnytl3ooKDjR7O80UK61s5KG3d3yjwBl8XWf7aFqOc1wQTobIPBNOissrsXbPYQr6mjEbNWTbjOw97Ek453hth0PhSMK5xXYr91+Vz/SX1gPRIpdi4S8IgiB0By6vhNsXxO0PEZSj8+Px5tH2x+demos/KLPw3V28cctFYv4UhFPA5ZW4/63tjB7Qg/uuGowcjjCnuD/+oBw/50THaYndxiPXFKAHeorAmXCSkrSq+J+NWhXhCJ0GwWLBr6ZWiVlFORg0KhavqYpnSl4/KvuYP+fI3+tbLs3luhfXx8vsFNmtLL9xNE6PhDcoo9eo2FLTTCgcobSqkZkt/eKZa59UNrLP4WXzvmbmXmanMNNCIBSOP2dpWfU3zhQz6TXHPJ5ynOOCcDJE5plwUpq9QVZurWPWyxtJ0amROlk4HK8tsbHdl38/WxKzinKY/tJ6GlslxuTa6G0SWWeCIAjCue9Qs5dWX5BWScbtD5GaFF30H28ejR3PsBjY+JWDW5dvYUR2qpg/BeEUcXgkfjI6m5HZqWz6qolDLj/2nkkYNF+vYU9knObYknjsmgKMiMCZ8O0kadUU2aN1oYmAJxDq9LygHO3oqlQqmL+iArktaSGWKXmi8wtEtxrPWfZZPHAG0Yy0pz7YzaaaZm55dQuzX/mMz2uc5KYlY0vWdgi+tQRCPDetkIqa5nim2uxXPqOippnnphXiCXyzTDFbspYxubZOj43JtWFLFjeQhFNPBM+EE+bySnzZ0MqeQy08snIHRXYr//rZheg0Ctz+EBW1TkrafYlV1Dq//nI/Qkm7x0vsVt7bVs+cZZvigbPOWhELgiAIwrmm1uGhORBECkdw+4Ik61WE5AhFdutx5lFbdN612/hgx0GeWVXJiOxUMX8KwikQW/MG5TBJumh2z+pdh5AjETxBGZVSEV/zHm+cNrj9JGtVpBg0InAmfGsWo4Z5l+VSkmvjgMtHkq7zjWR9UvXo1ErKqhoZnpVKUI4Gs2JBrWP93sbmn9if5UgkIXAWU9ZueydEy/Lc/9Z2ls4cibFdgBkg3aw/ag3Pl8urMRu+2bxlNmp5/NqhHQJo4jpS6Epi26ZwQtq3Al426wKuH92PjFQdXknmywYPWdYklpZVs3JeMQ+8tZ3SKgdLy6p5blohCqJfrjFFdisPTMnn3W31lNht/Hr8QLyBEJcNTMNs6LwVsSAIgiCcaw45PLQEQnxe62TltnrumTSYeqePJK2KWUU5LN+wj1lFOQBHdDOzcce4gSxaU8kDUwbj8gaZUNCb3iZR50wQvq3YmnfzvmbemluENUnHpq+a+M34QSxcuYNhWakUDbDy8JR87n1re3y9Cx3H6V0T80jWqckUQTPhFDEbtWSY9DxydQGegAyR6O9aaVViLTGDRkWD28/W/U5mFffjkMtPkd0azyg72u9t+26bJbk27rhyIA0tgaO+nyMzzMqqHPzcH0KORBIel0LhY9YGlORjb3/uTIbFwKJphTS2SrT4g6ToxXWk0LVE8Ew4rvatgG3JWnqbdTi9EjqVinve2c7sohw+3eugMMvC+zuii/8I0cWHSqFgVlEOs9vqQ+jUShrcfrRKJZec15NIBJ77qJKFUwtIb2vZLQiCIAjnusMODwHgt+/tYnZRDuVVDkJymNc31/Lr8Xm8tHIngzPMqBUK7rhyIL+ZoKDZG0SjVNDXYqBVCjFucG+Ccph0k56MHsbT/ZEE4azXfs3763Hnsa3WSWFWKsFwhIdW7qC8ysHMohzKv3Rw1dDeTB6aweyiHELhCAvG5yGFwhxuDWDSa8hKNaACMkTgTDjF/CGZhSt38uvxeWiUCu67ajALj+hi2dQqMaJfD3JsSbxYupdhmRYemJzP5n1NFNmtlFc5mL+iIt4tU5LDpJsMmPRq6tw+/jJjBD5JZtFHldw9aRC2ZC2NrVKH99LZ9k+XL5hQnseoVaFTK1kyY0SHWmexjLajbT89HrNRBMuE744IngnHFWsFbEvWsuKm0agUCvYe9iCHo3cKrh+VHb97sXzDPoZkWDjcGuB7mRYeeHtHh3bEj1xdgF+WCcoRJg1JZ9ZF/cSXniAIgtBtxAJn+5t98XkU4NZXt/DXORfw3Oo9LJgwiIUrd8YLLsPXc2irFESpVHJBTg8sRo2YQwXhFGm/5p1QkI5SAfe9uT0aMGvLmgmEwiwtq2Z0vx5c2N/KfW9uSyjYHhunaiBdBM6ELiC3NQn4scvPwF7JfNXoZVZRP265ZAAqpZIkrQqFQsHeRg9VDS3cPXEQ/mAYtz/IqP5WRve3cv9bOyitaozPMbGMs+l/WU9hliWefeaVZPwhmaUzRyY0DIg9J7a9sz2dWolKqQCigbOlM0fy5Pu72VzjZHZxDoWZFgozLfzj5gtZvesQL36yVxT4F84KIngmHJfbH8SWrGX5jaN57L1d3HqpnTSTPt4qWadW4pXk+N0LORzBnpZMUA5zz8RBRIjeTUjSqQmHI1Qf9jCgZxKpPbT0FEWNBUEQhG7koMODPxzBFwonzKMAtc0+bliykReuP59wJJIwh6bo1RCJsHlfM+dnpZIqgmaCcMrF1rwrbhrN5q+aKOhrobTKwbR2nQlj696b/r6Z266w88jVQ/AG5fi2sWStChUicCZ0jTqnj9a2LC21UsFNf93Mc9ML+f4L63huWiFvbN7HrZflUu/ykaxT87sP9vD8x19y05j+FNttNLQEMOnVLLw6H18wTKs/hFGrwqBVUdnQyqJphVTUOuOBM/h6K+bs4px4sK3YbmVmW4CtvVhAbdKQdN6bX4xXkgnKYUbk9GBWcQ5LyqoTbwrZrSydOVIU+BfOCiJ41s25vBKNrRJufxCTQYMtKTH11eWVSNKpWHHTaJ798At+OfY8fJJMIBSOL/ZjBSfLqxwJX4YQ/QItzEplQn5vNCoF1y3ZwN9/OgoViMCZIAiC0K3sd3hokUK4vEFA0ek8Wtvs46rF5fHnFNmtPDK1gPvf2sbdk/IZ2teEUa0UgTNBOI7jrXE7Oz9Zr+L1n11IKBIh25bUNlYTt6a1H6+PvvsFj777RfxYid3Go9cUiK2aQpeIbSv+5RXnAdHfxZ4mHeFwhOHZqcxfUcGtlw7AEwix4PWt/PNnF8broT27upJnV1fGXyt2jXZhfysudxClAm7+2+aj/2xfkIlD0snPMKFTqxjQM4mH39nRIRMtVq9z8pB03t1+ML4189FrCni5rGPDgNIqBygULG6rv/Zt/36+yZgXhG9KdNvsxuqcPuauqODy36/lmhfWcfnTa5m3ooI6py9+/PZ/fo4CBX5J5meX2NlV70arju5bjy0elpZVM6sop0PHlpK2L9ClZdW0BkJc9+J6BvU2oVcrxaJCEARB6Fbq25oDVNQ4WfRxFUl61QnPo9WNHsIoUCigZ7KeNFEjVBCO6Xhr3M7Ov/vf21ArlWysbmLLV80s+qiSJH20blP7zoRHHa+5Nn57TYFoDiB0mcZWic37mtEoFRS3mzu2H3Ax91I7w7NS+d0He/BIIRpbJW5YspGHpuZTfMTvaizItbveTc8UHRqlkl4px05q0KmV1Dt97KhzM/uVz6hp9PKLsefx6k9H8cL157NkxggKs1JZvmEf00dlc9Dlp6KmmeemFWLUquhl0idsb26vtLKx03pq38Q3HfOCcDJE5lk31b4ganufVDbym9e38rsfDGPBv7YyIieVJo+EQauixR+if89klEo45Pazs87V1gWsOqHgJEC62cCHOw/GU35bAyEG9k7hgSmDSRV3AARBEIRu5FDbVs0mb5BeJj3lVQ4a3AF2neA8+tINI5hVlINaqRB30QXhOI63xl04tYAmrxTPTAFY8K+t/LQkh/vf3M5tV5xHpK2m1Ex3gBK7rUNnwth4vfUSO1q1kmSdmmStir4icCZ0Ibc/yOziHJ5ZvYeZRTkoFF8xf0UFN1/cn8LsVKZ8L4M7Jwwk0q4D5w///Cn/vPlC6lx+XL5gPAEiFuS6+vlyvJLM3MvslOTaOowb+HorZqxWGcCGfU3srHMxOMNMYaaFQCgcP7Z8wz4GZ5jjWWazi3M6dOU8Uos/eNJ/L8cb84umFYq5UzglRPCsm4oVRO3MJ5WNNHslhmVZmDwknQff3sGdE/Jw+YL0MGpxePykmw3cWNKfl0r3UpiVyuyinHj3lBS9mhuWbojfQSixW+mZomPykHSMGpX48hIEQRC6jVhzAG9QxuX7+uJgwetbefWno3ny/V3HnEdL7Db8QZnXNtbw9A+Gnb4PIghnieOtcasOtzJn2SYAxuTauGfyIEbkpNLLHM2MmXd5hHDbdf6C17fyj5sv5P63tncIcKeZdOyqc9PPloRJqxK7KoQuZ9JrKMy0sHhNFev3NrFybjFfNXlRqxRIoTAjslPxh2SkUJiHpubHG7e9vbWOnsk6CvqY2d/siwe52tc1W1pWzbvzirnvre2UtcsQa78VE2Bwuil+/nPTCllWfmQNMxszivrFa6GVVzniY+ZYDO26c3bmWFsyjzfmG1slcf0pnBIieNZNuY8R3TdqVaiVSnbWuRiX34vSKgc3eqJ3Kow6FR4pxK3Lt3Dzxf25bWx0z71Pkkk3a/hfbTOPvLsr/kVcZLcyoyiH5z6q5MGr8slINX4nn08QBEEQTrfDDg8S4JVkPAE5oW5SY6vE9X9ZzxPXDqWXSY8vKBMMhVm31xGvEROdQ/vx2sYaFk4tEIt/QTgBx1rjAgkZMNWNHnQqFZecl4bbFy3CnqRVI0ciQHSczli6kaUzR3K4JRBv8lFR62R3nZvfTByEXqUQgTPhO2FL1vKVwwNE55WIIsIht5+0FB37HF4MGhV9LAamvfQpADeN6c+d4wfiaJHIsBhobA1wy6tbOn1tryTjC4ZYOLXgqFlq81dUsKgtAzPWLO61m0YzsyUQr4fdI0nL9X/ZkFALLRAKs7PeTYnd2unWzSK7lS01Tnqb9J3Oc3VOX4fMsjG5Nh6/digZFsNxx/y3yWoThPZE8KybMh2lHbBRq2LRtEIef28n00dlc9gdACAUjlBR62RiQW8Ouf0UZll4ZlUlz6yqTHjufZMHs/zG0YTkMMl6Nf5gGL1GydM/GCYW/YIgCEK3cdjhwUd00e+RZPpYDHxSeZgMsz5ebLyxVYpnwMTm0IkFvSnMtNAnNVrXTKtS8pSYQwXhhB1tjRsTC2Kfl5bMSzNGcM8b29hS4+SfN18IgFajJBgKxy/0a5t9TH2+nNnFOfGMnbGDevGj4X3RAL1F4Ez4jpiNWvq2zQ1GrYpwGN7bWpcQkCqxW1kyYwRzlm3i2dWVvPjJXmYX5zAC6NPDcMytmaEw8Sy1XiZ9wlbM+SsqGJ6VSkWtM/6cwiwLH+48lJB5tmTGiITAGUBmDwMqpYLvF/bhvre2JzQNiGW2zV9RwQX9enSY605kS+bxxnzKcY4LwokSDQO6KVuyljG5toTHjFoVz00rxKBVkZdh5uXyakyG6JfNlppmdte70aqVDOiZxNxL7R0KpQ5v69jSw6ghO0nL919Yx+8+2I1KIWq0CIIgCN1HXVvgrLyqkUUfRy8qdBolu+vdpJsNnc+h2ankZ5hw+4IM6JnEH1bvQaVQ0L9nsphDBeEb6GyNGxOr3WRL1rJkxgjufWMbZVUOZhfnoFBEjweCMgaNirmX5VJij76OV5JZvKaKpeXVGLVqehg16BUKETgTvnO9TXpKcm3MLs7h4Xd2dMjkKq1y8MJ/v+TeSYOAr393/1K2F7VCwf2TB1NyxPxT3BbA+viLBj6vdZJm0rO0vJpbXt3CnGWbWLymivOzLNwxbiBLy6qBr4Nesf+OPdY+uAbRbZwf7DjEzX/bTGVDK4VZqSyZMSKhyUBs+2hnGWInsiXzWGN+TK4NW7KYQ4VTQ2SedVNmo5bHrx3Kb17fyieVjRi1Kl6Yfj5NngAWQ1J8P/2C8cp4J7AVN47m+TWVzL/8PDZ91cyC8XlA9EtZr1bRI0mDhmhE9vtLN1KYZWFWUQ4qpeK0flZBEARB+K7UOjwccPqQI5DW1hygMCuVVn+QuyYM4rH/7GJIX3N8DvVJMiaDBp1KwdOr9rBgfB6/X/UF1w7PFPOnIJyEI9e4MbGL/Sf/s5t3fn4RrqAcDzwUZlpYtesQcy+1s6vOTf+eRnql6JkyLINfjM0lFI60lTVRYNKpSTFoRFBbOG0enpJPncufkPHVXmllI7+4PJclM0YQCIWxGDRkWPQ88f5u1u5pZHZxDjPb6pD1STVgUCtZ+O4u1n3p4LlphSzfsC+hFqfZoCEtRcdBp5/F0wvpbTLwv9rmhJppJXYbt1xqZ86yz+Lvo9hu5Y5xA5n20noA1ErFUd8zdJ4hdiJbMgekJXc65sfk2nji2qFirAqnjAiedVMur0QgKPPQ1HyCchi1UkkkEqG3WY8/GI7Xg6h3+eOdwOYs+4xXZl3A4RY/w7IshMIRWnxBUnRqjFoVSkAB7Gn08uCUfCpqnfzfxhqeEgWOBUEQhG5gv8NDWVUjK7fVc/2o7PjjS8uqeX76+Ti9AW4a059knYbWQIgkrYoeSVqCcpinV1Xy63ED2dfopW+PJDF/CsK3kGExsGhaIQ0tAVy+ICkGNc2eICE5zFM/GEIIONDsi58fCIX589q9DO1jQaVU0CNJz5YaJ9/LthAMRWjxB1ErlaToREdN4fRq9gY53BJADkeOeZ4cjmA2aFAqFHxSeZi/r9/HdRdk8f+GZxIKR+hnTSIUDuP2h1Aa1Py0JIfpo7IJymHunjgIKRThoNuHTq3k07ZanLHEiBuWbuC6C7JYPP18jFoVKTo1O+pcbPzKwaJphfGAXYpezaxXPosH2CpqnfGyBUcak2sjWa/my4bWhKYAx9uSGWs0EBvzja0SLf4gKXoNtmStCJwJp5QInnVDdU4f97+1nesuyGJd5WFmF+VQWhWN0mdZk0jRqzG3bddUKxXMa9dd6IDTRx+zgUAoWvw4Ra/GoFGhVSqQw2F2NHoTOhiJaL8gCILQHRxweGgNyvFss/bdxbySHG+0c+nANOS2Vn5KpYJwJIJGpWDepXYmPleGV5LF/CkIp4BHknni/d3cMLofKXo11YdbSTcbSDXqaD2iJpNOrYyP09nFOfRM1lPQ10xQjgbOTAYNyVoVmSJwJpxGLq/EfW9u484JebQEQsc8N0WvYfGaPby3/VD8scVrqjBqVSyZMYIH396esOUzlpn5y//7H4umFcav/wozLQxON/F/N4/mgx2H4tlmsQwyo1bFzRf3Z9KQdLySTCAYRqEAvVrFD1/8NKH+WaxDJ5AQQBuTa+ORqwu4541trN7VkPD4Y98fwphcW0JGWfv33L7RQOx/gtBVRPCsm4kVXRyWaeGzvQ7mFOUQCEcY2tcCCtjn8NIaCJFu1lPctm+9MMvSaYptsd3Kw1MLUCmgRQrx4yUb+fucUbx5y0Ui2i8IgiB0G/sdHoLhCC2+UDxzu6LWSZ92zQG8kpzQaKfEbuXRa4agJcL/+8tGXrphBMt/OkrMn4JwCsTWuxcO6EGW1UBjix97r2SSdRrq3X6IJGbBtP/z4jVVLG73WrGxKgJnwunW2CqxucZJJAINLYGjZnGV2K2EwxFuutiOyx9KOOehq/L5qtHDzKIcpo3KRq9RsaWmua12WTU3X9wfiNaybn/9N/cyOxU1zR2aAXglmY3VTQTlCIvXVPHaTaO57s/rO20cEOvQObs4h3smDqbFH8Ri1GAxavlkz2GmXZDF98/vm/CeHnx7Bw9NzefeN79ZowFB6AoieNaNuLwS9S4/Px6dTWFaMn6lglqnj0UfV1Fe5eCF68+PdyCa/cpn/GXGSBZ/VNm2bTPxDkGx3cr9/5+9O4+Pqj4XP/45c2bPTgYISwKBCTtoBIuaBEVxZVGvbW9RW1labRWXaq9oFfe1t9daQVttRb1VaH+tdd+qxauAioJRdiSKEAgQMmRmMuuZOXN+f0xmzJAFlDXJ8369fAmznDkn+s33O895vs8zdSSxRIJATOdHT6xg3IAC+uS13WJYCCGE6IpSNc7mv1vD3HOGpefRhcu28IdLTuDqiWVA5hxa5XZxz4WjSCR0Jj++ghF9cmX+FOIg+UIaDQENfySGw6py0qAeTBvdFyNh4MpxcMuLa9Lj8PVrqjKyYNrLiKkqc3HvBaMkcCaOCYFojEeml/PwO18wq6KUq08vw6QoGQX1K92FXFZRyg+f+BCAeVNGMG/yCLwhjd55drR4Al84TrZNxW5RicaS2c7nH9+XXd4ITquK06Zy5/kjMzpjLly2hScvG4cJJb1jCTKDWJDMRINkcLqtzp4hTad6WyOQzISbNLwXt0wezouf7WgVHHtkejnXLK5mb1DLqMFmM5uorvW22Wig5e+B1NZPmVvFoXJUg2f3338///znP9m4cSMOh4NTTjmFBx98kKFDh6ZfYxgGd955J0888QSNjY2MHz+eRx99lJEjR6ZfE41G+dWvfsXixYsJh8OcccYZPPbYY/Tv3/9oXNYxqc4bZu7zq1m1tZGXrhxL1KSwvLkuS+oXVeoXUd88O0OLcrj4Tx8xq7IUi8nEr88bjqIoBCLJX7Y7fREu/tNHLJh+AkEtztgBBbLFRAghRLfSssZZai7d7Y+kswF+8dynXDVxMLdNGUFcNwhqyXIHu3xhPE0RDEVheJ9cmT+FOEipdW7qi7rTqvLUjBPZ0xTClePklhfWZHwxt5gVTijJT2fBzK4chNWscOe0kWhxg6AWJ9duJsei0lcCZ+IYke+w8pu3NrG8xsNHX+3lz5eN5d4LRtEUjVPvj9Irx0au3cwOb5jf/uC49He7ZZv3cOPZw7j1xTWttmrOmegmEkvwxHsbue7MMmxmlbteWce40h5MHt0nI2D18dd7GTuwgJ+fNhjdMIjFExlBrJZdpBcu28KrV1dy+0vrOgy2DeuTy237ZJXBN0HsWZWlOCzqATUa2Pf3ACS3fj5w0Rj65jsO4icvRNJRDZ699957XHXVVZx44onE43FuueUWzjrrLNavX09WVnKi+s1vfsNDDz3E008/zZAhQ7jnnns488wz2bRpEzk5OQBcd911vPLKK/z1r3+lsLCQG264gSlTprBq1SpUVT2al3hM8IU0bntpLeMGFnD3tBGYTSYagrF0XZaU6lov6+t8jC0pYM5ENwvercn4RVVV5uKOaSOZOn9ZOg03x26mKM/GgunlsvAXQgjRbWz3BGnS9Iy59N1N9ZxU2oM5E91AcvH/3299wX+/9QVVbhd3nj+SKc1z6BvXVuGwmGT+FOIgpbZopr4wu7KtLJxxIk6bgt1kpknTW30xf3v9Lu6YNpI7Xl6XsdatdBdyx7RR5NlsmIHeEjgTxxBNT6T/Xw5pOmEtQW1jCC1ukGVTiScMdjdF6JljJ99ppSkSZ+LQnkwoc2UEzpxWNV3PLKTpFOXZ+a9zhuINaaiKwrWThrA3pDGwMIu7X1nXKuB23ug+PPDGBpZs3JN+PNVt891NyZpl5SX5vLK6juNK8vn5aYOxqCaaIrGMYBskO922FxhL1Q/NsprbrXs2ocyFK9va6vdAyvubG7jp+dXMl7lWHAJHNXj25ptvZvz9qaeeolevXqxatYoJEyZgGAYPP/wwt9xyC//xH/8BwDPPPEPv3r1ZtGgRV1xxBT6fjyeffJK//OUvTJo0CYBnn32W4uJi3nnnHc4+++wjfl3Hmr1BjWvPGILZBFaTiUjCwBOMpuuypKRS1v/y0dccV5zP3HOGARDWdHLsFjbu9PHm2p3pX3aV7kLynBb6FziP+DUJIYQQR8tWT5BbX1jD9BYdNYF0t749TZH0HBrSdOxmlcIsC7OfWUlI06l0F5JjN8v8KcQh0BDQWLq5AadV5aqJgzljWG98wSj5Dic3v7CGK5u3Tre0YMmXjOqTz5zT3cw9dxiBiE62XSUYjWNTFUxI4EwcewL7NAmIxhMoisKN//ic539+MrEE3PnKpowAUpW7kFsmj8gInD0yvZynlm/JKPp/x9QRjBvYg9tfWpvx2lsnD+fG5jES0uJU13q5+E8f8aPvlXDJ+AHprLR++Q7q/VEef++rjOyyVHOBJy8bl24qt+817E++08IDF43hpudXZwTQqspc3D51JHsCUQyDVoGzlPc3N9AQ0CR4Jg7aMVXzzOfzAdCjRw8AtmzZwq5duzjrrLPSr7HZbJx66ql88MEHXHHFFaxatYpYLJbxmr59+zJq1Cg++OCDNoNn0WiUaDSa/rvf7z9cl3RMMCmwebefsSV5aIbB7S+tZUZFabouS0rLIo6j+uYRielkWZOdN3/71kYuPmkAt760DkgGzu69cLQs/MVh0d3GqBCdUXcdpzuaA2dLazzMaNFRE8jo1leYbSMaT1DqysJuNjH7mZV8UR+Q+VMcMd1ljPojMZxWlT9ccgL1/ghxXaekwMmNzeP0xnOHtXpPSNP5+XOrmFVZynmj+6AqOmaTiX55DuxATwmciSPk24zT3ObtiSk2s4lPtzVyw5lD2LS7iWFFuUw7ri+/nDSEeCKZjWY3qzSGtPR7ZlWW8tTyLVRv8zLndDfjBhTgtJopzLJmBM4gOU5+/cJaKtyF/HLSkIzg177ZYq9dXZncjXTxCXy6rTEju6wj+34f3Vf/AkdzF02YP72cXf4I3lCMeMLgw688TF2QzOZ+8rJxHR6nZV20AyX108S+Ov6/9QgyDIPrr7+eyspKRo0aBcCuXbsA6N27d8Zre/funX5u165dWK1WCgoK2n3Nvu6//37y8vLS/xQXFx/qyzlmJAd9lLLe2agmlUg8wdLmrkKpuiwtpe4OPP3B19gtKoqigALzpoykX76DZ2Z9j9euruQ33z+OAbKwEIdJdxqjQnRW3W2c7vKGqfMEaYrp6S8Xbc2lqXl09jMrWfzxNswmBQP45VlDeOPaKuZNGUG+w9LOpwhx6HSXMZprt3DVxME4rSr25mLnvhbjtN4fpXKf9S4kx+pn2xrZ6Q2T47CQY1WxmxQJnIkj6tuMU1e2lQllrvTf0yV3SvMY2juX3f4wxxfnYzErGIaBnjAIaclkiJTy4nyqt3l5ZHo51dsamfHUJ/zw8Q/52hPMCJy1tLzGg9VsosrtavP5Cnchmp4gpCX4dFsjC5bUtAqcpRoI7Ku61tvm+ITklsyiXHvGY59u8/LIks1c8ucVbX5Oe3Ls327erfOGmbO4mjMeeo8LH/uAM/7nPa5eXE2dN/ytjiO6lmMmeDZnzhxWr17N4sWLWz2nKErG3w3DaPXYvjp6zc0334zP50v/U1tb+91P/BgXCMcozLazcWcTugE7GpMDfuGyLfTJczBnortVAK2qzMWtk0ewN6BhN5vIsahoho7NbGLxiq30yLJK0UVxWHWnMSpEZ9Wdxuk2T5APvmwgxjfzKOx/Lr3nglHE9AQ1ewIsWrGNL3b5ybVb5M61OCK6yxh1ZVuZPKoPPbKsvLlmJxaTmjFO5z6/mnlTRrb6gl7pLuTuC0bx/Kpasq0qNqCnZISKI+zbjNM8p5UHLhqTDqAtXLaFi8cPwKFaWLHFQzwBn3y9l9+/s5mmaJwH39zI1AXLiCUS6cBVNJ5IZ5+1rAW4v+2TewMaN5w9tNVcl9qi6QvFiMR1yovz23z/wmVbmDdlRKtxuL7Ox7wpI9ucQ/dtptMQ0OiVY2tVwxCSQbh9j5GSqot2oPZXP83XIpNPdC/HxLbNq6++mpdffpn3338/o0NmUVERkMwu69OnT/rx+vr6dDZaUVERmqbR2NiYkX1WX1/PKaec0ubn2Ww2bDbb4biUY4YvpOENx9jpCzN/SQ1zzxlGMPZNZD61teSKUwdl1GXJtVuI6Ql2esMU5duxqQrhRBybYiZmJLh96khZ9IvDrjuMUSE6u+4yTusaQ9zywhpumzay1R3utubSaCxBls2Mw2rCbBj8ZOHHPPKjcu4+fxQFTgmciSOnq47RfbdS5ZpNmEwK815IliXxRzLrQjUENC7580c8eNGYdH2zfKcFs6pQ748w99zh2C0qPffJcBHiSPi247RvvoP508vZ7g0TiCS7wgY0nWF9cnnwzY1s2tXE/7viZO59bT3lJQXMqiglrOncOW0kt7+8DpvZ1GaR/v1tnyzMsTLzqU+4/swh3HTuMLY3hrGqyW6ef12xlWF98zhrRG+C+9RlSzmhJJ9YXGfcwB7cfO5wtu4NpbuBXvLnZA21VGfP4h5Ocm1m+uyTrOGPxNoN8qVqd5sgI4NuQhtBuP1J1VFsi9RP696OauaZYRjMmTOHf/7znyxZsoTS0szaIaWlpRQVFfH222+nH9M0jffeey8dGBs7diwWiyXjNTt37mTt2rXtBs+6uh17QzSFY9zywhqybOZ0dL4pHMuIyoc0nd+9vZlpC5YzbcFyHl1Sw+trdxKIxtnpi2Azm1CBHJuNElcWg3vmyC8KIYQQ3UatJ0hTNM7SGg+xuNFqHoXMufS/39xElk3l8+1eTAr86MmPGd4nl8G9shnoypI5VIiD1HIr1SV/XsGKL/cQSxjs8IZZWuOhV64NfyTeapw2BDRmP7OSqfOX89i7NRiGwcqvG+mX52BAYZYEzkSnkue04rSoLFiymXMfWYY/nKzntWlXE3++7EQW/PsLbps6kpMHFRKNJ3DaVEJanKtPd1PSo+3syo4yt6rKXCgoPDXzRAD+8/GP+MWznzL7mZV8vs3LxScNYGNdslZbvwJHm9lpMypK+d3bmwF4Y90uFn+8jdnPrGTBkhoaAlq63MGiFVsxmxTyna23WebaLe0G+VK1u//rnGE8edk4HrvkBF6/ppL508tbBeH2x7+f+mjfpX6a6BqOavDsqquu4tlnn2XRokXk5OSwa9cudu3aRTicTLVWFIXrrruO++67jxdeeIG1a9cyY8YMnE4nF198MQB5eXnMnj2bG264gX//+99UV1dz6aWXMnr06HT3ze6k1hMkEIsT0HSW1XgIRJJ3yUNRnWy7mYXLtjCzorTNX2p3nj+SDXU+ejitDO6VhV1RiCQMesuCQgghRDezZ28IFWhqzmJpisTIcVg6nEfvOn8kJkVhcM8sln3RwEBXFr+5aIzMo0IcAi23Urmyrfzz5ydz0uBC/DGdmG4ApLtmHsg4rRhcSH+pbyY6IV9I47YWxf1zHRa0mM5fLz+Z/3lrI//8bCeX/nkFvXJs5DssxHWDDXVNGIbB3mC0VR0xoN0xU+Uu5FdnDQUMPE0aw/vk8vwvTuHvV5zM8784mbEDC3juo61cf9YQNu70gwHlJQU8edk4/nr5STx52TjKSwq4ZnE172ysp9LtYuGyLdzWxhbOVJDtf/61qc3rdmVbqW+KthvkG1uSz7/W72b2Myu58rlPsZnV73TTat/GDPv6tvXTRNdxVLdt/uEPfwDgtNNOy3j8qaeeYsaMGQDceOONhMNhrrzyShobGxk/fjz/+te/yMnJSb/+d7/7HWazmR/+8IeEw2HOOOMMnn76aVRVPVKXckyo9QT56CsPx5cU4G2OiGfbkz8Ds6qw2x/hhJL8dEfNVGqszWyi3h+hIRDllskj+KzWy7gBBXxc6+W0IT2P5iUJIfZx8czLqdvT2OZzfXsWsOipJ47wGQnR9ezZGyLUXGw5y5ZcKmXbzSQSRofzqMWkUL3Tz7iSfPKdFqYc11eyzYQ4RFJbqVzZVv52+Ums3eHjhJICtjQE6JWb3PaWbVep90f3O06tVpV+EjgTndRufzQdOCsucJBjVXFYTNR5I+nHaxvDnP/ocv56+UnsDWgcV5KHNxTj64YgYwcUUFXmytiamMrcunXycH45aQi+cIy++Q4cFhMPvrmRwb1y0ls9X7+miqZonEhM58SBPTh7RBGBaIyeOXZ0I8GCJTVUuAspLylotT003tzEQNN1ji8pYGaL8Vld60136Wxra2Se08ppQ3pS6kqO3Za1z6rcLmZWDmTOomrg29c5aynVmOH9NrZuHsxxRed3VINnhmHs9zWKonDHHXdwxx13tPsau93O/PnzmT9//iE8u86lrjHETl+YQT2zuOuVdfx68ggg2WGoyu1iWU0Dm3c3cdvUkdz1yrqMX2SV7kLuuXA0MV0npicYN6CAcFxn/MAesugX4hhTt6eRomnXt/3cyw8d4bMRouvZ5QkSA379whpunTwCheTd8N3+CK98toO7LxjFvBfXtjmPRhI6Jw4oIJrQGdI796hdgxBdkT8Sw2lVeXb294jrCcb0z6cpmtxhEdcNKtyF1PujPL+qtt317r0XjkYF+kjgTHRiqW2FQ3pl88RPxhFLJLjtpXVMHz8g43UhTedf63dTva2R6m1e/nDJCZSXFBCJ69w+dSR3vrIuI4BWXpJPr1w7f3zvS3593nA+2+Ylnkhw0dhirlmcDEpVlblwWEzk2Mz0zrFhtZiIxnQ++MrD6lovF48fQJW7kMsqStPvaclpVakqc6EqplaBtZba2xrZJ9+B06py3wWjCWpxgpqORTXxf5vqmbMoGXj7LnXOWko1Zrjp+dUZAbSDPa7o/I6JhgHiu0k1BTASBooCPbLt1PuTdxwUJbnYn/v8av5+xcnc/dp6fnLyQH7/zhfMqizl5vOGE4jEyXFYyLKomIBgTCfLZsEEFOXY5ReDEEKIbmWXJ0gUCETiLK/x4A/HyHVamFlRyt9X1vLLM4fyyDtfcOM5w/i1aqIpnCxYnm1VCcdjWFUzv3vnC26fOvJoX4oQXUq9P4LDqvKrs8pwWM3c+sIaZlSUkm0zJ2s1DU52/PvHqlqumzSUh9/e1Gq9m21VsSCBM9H5ZdvNOK0qT/xkLLe8uIZbzhvB0hoPMypKW702VUgftvDZdi9Oq4qegAUf1jDjlIHMPWcYIU0nx25GUWBPU5RhfXK557X1zJs8gpdX13HHK+sJaXqyQ+35o1AV+ON7Nfx745705yS71yaTMaYe1zedQdZShbsQs0nhNxeNIai13VggpaOtkXlOa8b3VF9II2dMX04b0pMcuwVXtvWgv8emGjM0BLRk2YZDdFzRuUnwrJPa6Q2zbW+IP73/JTedO5xPvt7Lq2t2cknzHQfDMJhZkWxDvL0xzJnDe4MBl08YjEU1EYjGcFhVMAxUBULxOHl2KzkO6QQmRFck2z2F6NhOT5A4cOsLa7hyYhmQrCOz2xdh8YqtDO+bx25vhF9MLCOeSDYPyLaZybaqROJxPt/WhKoq/NfZw2QeFeIQ2u4J4o/G2eoJcubwIm5+YQ3LajxMHz+APnnJOoRnjejNk0u/YljfPOp9Ea47c2jGOM2xqpiRwJno/Oq8Yaq3NjJv8gjCsUT6Rg98U/S/5XbG1HbMeZNHMKY4jx/88UOumjiYG84aSjSeIBCJY1YVdvnCKCiEYjrlxfn0y3dgNimcNaKIkwcV4rSaCURjxI0EP37yE+48fyTXnzU0GZy2W7CYFd5YW8fJgwop7uGkvCQ/4zwq3IVcPbEMp0WlT74DX0g7ZFsj9w2mHSqH67ii85LgWSfkC2ls3Rvi0SWbmXZcX+58ZR0zKkpZXuNhVvMdB12Hv368jfKSAjDgpMGFzHtxLcta/BKrdBdyzwWjuf+Njdx47jApmipEFybbPYVoXypw5gnGWFrj4cZzhwHJeqH/75NtzD13eNslDy4YzYV/+IDhfXK5+/xRFDjlBpQQh9IefwRNN3j6gy1cO7GMrY3h9FrWZjZhMSucUJLPrKc/4ckZJ/LbtzZljNMKdyFXn15Grt0sgTPR6aWaZqza2sg/fn4KgUiM+y4YQV5zZ8pvsswy64GNLSnge6U9+KK+iZCm899vfcGj737JoxefgN1i4qG3N2fWDytzcevkEQQ0HcMwaAzFaGruYmtRFUoKncx6emWr86twF6LFDY7rn8/k0X0y6g3u9kcIx3SiegKQrZGic5LgWSfUGIqhJwx84RhjivP5r+fXpPe4p+44/HvTbi6fMIj5S2pYsKQGV7aVBy8aw83nDicQjZNtM5NrN9MQCHOzBM6EEEJ0UzuaA2c7GsOYFAVI1gutdBdy1bOf8sRPxvGbNzcws6KUuecOIxDRybUn59CdvhDP/XQ8DotKicyjQhxy4ZiOJxjh2ollBGI63vA3dZCqa734Qhp3TBvFHS+vZfoTH3H5hEFce0YZesJIbm2zqDhMCkUyPkUXkGqaAbDDG2ZYryx65/aizhdONwDYt1FGnsOCalJ4c91OhhV9U4szpOlctehTrpo4mDunjSQc0wlGdcwmBYtq4sLHluO0qjz305P4y4dfp4PWTqvKk5eNw6QoGfXSKps7ZabqnN06eTi9cm1sbwwDUOeL8O8Nu7nvP8ak3yNbI0VnI8GzTsYX0pj34houOWkg86eXUx/QgOTdN/jmjsOiFVsZ0y+fqWP68MtJQ9ANg2ybGQXIc1qwmkz87p0v+PHJA8lxSLtdIYQQ3c92TxAtYXD7S2uZPn4A/QscAMx9fjXP/fQk7n51HZf/70oevfQE4npyC1i+00KWVWXD7iaKC5zM//dm7r1w9FG+EiG6nu17Q9T7w/TMtqMlDBoCWnq9C8k176MXn0BjMMK1k8rIslkIRGJk2ZJBM7MCVpNCzwLnUbwKIQ4df4si+r1zbcSN5DbOa/9azfO/OIVbX1zL0s0N6ezL1FZJV7YVd68ceubYqHIXpjtyhjSdcCzB7S+vy8g8e/KycYQ0nZCmc8mfP+LBi8Yw99zhhJoTMPY0RTlpUA9mnDKw+Vzs1DdFMKHwPz88joGFWVRva+Q/H/8oXfesyu3i3gtH0TvXnnFNsjVSdCYSPOtk6puirNrm5ebznIRiCbJtyf+E1bVeqtwultZ8c8dBAcp652BVTTRF4+gJgzfX7WLi0J4YVpUTSwvpkyuNAYToKjZuWM9pU37Q5nNfbK6h6AifjxDHsq2eIOGYjp4wMgotp+rFpL4w9Mq10dCkkWs30yvXxo7GMPYCB183BPh/n9Ry9/mjZB4V4hDzhTTqvCH65DmIJwxCMZ1ch5kPv/Kk17upzJkrTh3ExKG90BOJ9PtNClgVCZyJriW3RRF9LZ4goibwhWM0BDR8wQjzJg/HH4ljGGC3mjAMeHdTPY+/9xUhTU9njQHpAFp5cX6rrpcta6c1BDRmP5Pcoum0qtw6eTjHFxcQiunp1y5ctiWjOcDTM06kzhdh/vTy9LbNQa4sydAWnZ4EzzoJX0hL/mIMaTxx6VhybCp1vgh5DgtV7kIWLtvCop+dhIHBshpPxh2HmRWl/HXFNob1zWXBkhoqBhfitJg5fVivVtF/IUTnFcPUbl2zNQ9efoTPRohjV70niAL4w990+6qu9VJS4GDORDeQrBeT+sKQunv/zsbdnDqkFxFNZ1TffM4/vr/Mo0IcIqm1rj8Sw2lV6ZvnIAGEmreTWVSF9XU+bjh7aHq9G9J0fvf2Zj7esje53v14G3dMGYFNUejZQwJnomtxZVvTRfb3BKLoCeObbEyTyk5/hMsWfsLjPx6LGlF4avmWjIyy8pJ8IrEEYwf2YEbzts62ulq2VzutvCSfXrl23t6wi5Vb9qYDcC1VuAtZua2xVe3BO6QLtegCJHjWCez0hvnwKw/H988n22HBUOJ4wzFyHRYUE8ybMpI7X13HT5/5hIUzTuQXkTi+cAyb2UR1rZe/fryNq88oY/qfPqLSXUiPLCt5doss+EW301FmlnScFKJ7qPMEaYrpNAQ08p3JWjDwzRYwk0KrQsf1TVGicZ3VtV6mjenLva9v4O7zW28/EUJ8N3XeMLe9tJZhfXKpKO1BzywrwZiONxRDURRyHWZ2eSNcPH4Aj79Xw43nDGtzvTtvygjpHC+6rFSR/bnPr8aqmsh1WFha00CFuxBI1u6scBdiNilc3bwT6ZeThuBr0Y3zqkWfprPEKt2F3DJ5RKvPSXXonFVZyk3nDqN2bzg9zlI1zV68soK7Xl2X0Yyuyu3iyoluZj/zSfqxVCJHaq4VojOT4NkxzhfSiMV0xg4o4NYX1mRE+N/7r9NIJAxWbm1ML/R3+iL0ybNjM5vYE4hyyqBCvn9Cf77/xw8YO6CAey4YRb4sKkQ31VFmlnScFKLr2+oJZsyl795wKkC60HLLLWAAYU0n22amKNfO7975gtumjmS7J8S8ySPok+84atchRFfiC2nc+9p6fjlpCFYrWE1mbm4xTt+4toosSzKr5u31uxjRN4+9AY08p4Vch5nGUIyKwYX8aGx/aYAlury++Q4evGgMkViyuP+GOh8zK0rZuNPP4J5ZzJnoZktDkPKS5HbMVBbZvlloVWUu7po2km17Q+ktmi2FNJ3PtjUCtNrWWeEu5LPaRm48ZxizAhqGYdAnz8HaHV4+/tqTsV2zutbL3z7exm9/cNzh/+EIcZhJ8OwY5wvH0IHb9gmcAfzfxnpOH9KT8QN7UOePMH/JPm2G3S7uuXAUvlCUZ386XuqbCdFJXDzzcur2NLb5nNQuE+K7qd0ncAbJbJcsm4l7zh/FrS8lCy3/7u3N/O7tzVS4C5kzsYxsm5loQufmc4dz6Z9XUOrKYn7zdhYhxMHbG9S4btIQ/vLBFn42YTC/3mec7vZHiDpUKgYVJsffks0ZX+ZT610JnInuoM4bZu7zq1m1tZF/Xnky86aM5O5X1zGmOJ/ykgI+29bImP55nDSokNtfWpdRD/vK09yoJoWQprN6u5eVXzcyql8ut00ZwT2vbmBpTWb3zLsvGMW9r23I+PyqMhd3nT8KxTDwhDT65NlRFQWLqnDKYBe/fmENv3t7c/r1E8pcPHjRGPkOKroECZ4dw3Z4gqz4ysOIfnlt7il/8K1NjOibTe9sB/3z7Nxz/ihCMZ1QVCfHbsZpVVExePqDrdw+daT80hKik6jb0yi1y4Q4hOo8QQKa3mouvfZvn/G3K05izQ4v918wimBMxx+O47SpOCwqNpPCP6u3c3xJAVc8+ynjBhTIlwAhDjHVpDD/319w7ZlDqW0Mtxqnv/r75/ztipPQDQNXtjlzveswk21RJXAmugVfSGPu86tZujkZ5Lr0zx/zl9kn8quzh2EA3pDG2IE9iMYTeEMat04ZTiSWIKYnyLKZybaq7A1pNIY0RvfLY80OH64cG899tJXjSvKZUTGQaDxBnsNCjywrCcPg7JFFzD1nGMFonBy7BVf2N90xB7ZxjvOnl9MQ0GiKxFq9XojOToJnx6j6xhBNMZ3SntkZ3UtaCmk6lz21iheuPBkLKuF9AmfBaIwF737JrZNHyC8tIY4xkl0mxJGxzRMknjCo84ZbPdcQ0PjPxz/i4f88niZNTy/2nVYVI2Gweqefqcf3wx+O8cqcSvkSIMQhVt8YIhzTmVE5iFtfWMP08QNavSY1Tn//n8dT3MNJUNPTX+QlcCa6k4aAlg6cpf5+0R8+YlZlKSeUFJBrN6PFE1hVE0GDZNdNiwmrxURdYzJjrSGgZRzTaVX5+89PZpcvQpbNDMT58CsPC5dtYeyAAo4rzmd0/zxKXVkHNP/lOWWeFF2X6WifgGhtpyeIljDY0Rgmrhtk29qPcYY0HU032NIQZJcvQrbdzN6gRkw3sFnM3HfhaKnLIsQxKJVd1tY/0Xh8/wcQQuzXDk8QSNYua09DQOPSJz/GMOCHj38EwK0vrsVQoHZvEH84xpjiAgb3ypYvBEIcQvV7Q2gJg+2NYcyqiaU1nm86B+6jIaBxyZMfs7k+QGNQo0+unUK7WQJnolvxR2KtHgtpOguW1DDr6WSRfm9II2EY/P7fmznn90vR9AR1jWFmP7OyVeAs9f6EYTD7mZXsDWrMfmYlC5bUENJ0lm5u4LxRfTAMg1teWNPmTSghuhMJnh1j9vgjaEBt8y8np01FgXQXlX1VuAuJ6wbReAKAYDRGcYGDwb2yGdRTFvpCCCG6p22eILXeMLWNYUKaTnWtt925tMpdiNLcCCwYjXPfBaO44n9Xcd/rm8iyWY7gWQvRPezxhokYBr5I8mZRU4tugO2O0zIX9f4IxQUO7ECvAueROl0hjgm59o7nI7PJxPx3a2gMasybMoIqt4vGYGy/819cNwDaDF7v9IXxheP88qyhbN8bYrc/cvAXIkQnJcGzY8Ruf4Ste/xEYzq/fmENMd2gutaL2aRgGAYzK0pb/dJLtf71hZJtuvsVOCjKsVMsd+GEEEJ0Y7WeIMtrGpj/bg2+cIwch5mFy7a0O5fOmzqSsJb8Ep9jN3Pxn1fwRX2ACWUuXNlyE0qIQ6nOEySqJ1he08Auf4TqWi+5jmRQoL1xWuV2cdf5I6lwu7ADPWWtK7ohV7aVqjJXm89VlbkwmWB5jQeHVeWNtTuZdlxfXNnW/c5/vlCMCnch1bXeNo89f8lmPv5qL03RuGSgiW5Nap4dA2o9QT75ei/jBvagSdNZXuPhpnOHsXDZFs4Z2Zssq5m/fryN8pICZlWUZrT+XbRiK5OG90ZRFIb2zqF/D7kLJ4QQovva6gmyvTFMr1w7y2s8zKooxWExMXZAQbrjWMu5dLc/wufbvOzwhakqc7FscwO1jWHpECbEYbDDE0QHmiJxeuXagWTA7Psn9KOqzMXSzQ2txmmew4JqUrAqClaTQk/JOBPd2FWnDSZhGCxv0Vijyl3I7VNH8uWeAE6rSo7dzJpaL6ee0YvPa72MLclvd/77bFsjeiKZqHHN4uqMz6pqDqil5lK7ReVH3yvh9pfW8tsfHCfzo+h2JHh2lNX7I9T5IvTv4WR5TQOlrmwA4rpBeUk+M576hGdnn8gtk4dz20vrMlpzV7gLmTOxDNUERbl2CZwJIYTotvb4I0Sas7cvaVF0vLrWy7sbd3PP+aO49aW1rebRqyeWEY3rvLV+F/ecPwpfWOPf158qzQGEOMTqGkPoJL/098q1E40nWL/TT3lJPpf/7yr+9JNx3PrSWpZubkiP09QY7ZtnxwoSOBPdWkNAY9YzK1sFwXrm2IjE4phNClecOog9TVF+NmEwf3rvS34xcTAnlvbgzlfWtzn/FeXaUBS4+9X1GU3qqtwuZlYOZM6iZEAtGk8QjSd4bsVWyksKaAhoMkeKbkeCZ0dZSIuzYMlmbjpvOL1y7WTZVQB8oRgzK0p5avkW/uMPH/Grs8q454KRhGMJmsJxnDYVu1lFba7RkueQmixCCCG6pz2eIBEg0Jy9PauiNP3cwmVbeGR6OX9a+mWredRhUYknEiQMC/deOJreuXZAtoMJcajt8YZJJAz2Br/JOLOZTenx+dTyLVz65Aqe+PFYTJMV/C3GqM2kYLWo9Gx+nxDdlT8SSzcIaOmxS06gf4GDen+EM4f35r7XN3BiaQ9OH94Ls2pCTxjcNW0kkXiCQOSb75G1e0P8eOHHfH9cf64/ayi/PHMoTdFkEG5ZTQNzFlWnA2qpemipObapjeYFQnR1Ejw7inwhjWg8wdIaD3MicaLxBPX+KJXuQlZua2R9nS9jq2ZNfZDqWi8b6nzcOXUkoXicj7/2MXFIT4n8CyG+s40b1nPalB+0+VzfngUseuqJI3xGQhy4PZ4gIUh27DMl7yhV13rpl2enwl3I8hpPertKTX2QaDxBvsNCU8Sgh83MxzuaGD+wR3PgTAhxqPlCGjE9QSCmE4np6YyzfnkOyvfZTrbFE8JmNlHvj1Ax2IVZgb5S30wIoP2GATaziXc31XPKoEIMA5bVePhp5SAKsqzc/er69BbPJy8bx+xnVvLSVafwca2XXrl2bj5vODaziZ2+CM+t2MqSjXtaHb/CXcju5t1SkMxCs1tUqrc1kuuw4MqSTG3RPUjw7CjwhTS8wSiqyURTJFWg2EIgGmfu86t57qcn8Zs3N3Dx+AE8tXxLxt2FSnch9144mlhCBxTOG1Ukv6yEEAclhomiade3+Vzdyw8d4bMR4sDt8gSJQXqrZqkr+SV74bIt/OGSE7h6YhmQvFOemkur3C7mnO6mb56dSCLBaXIDSojDZo83jKYnSAA7GsP0zLER1JIZZ49fOparTy9j/pLNGWvdKreLey4chRkJnAnRkivbyoQyF+9vbsh4vLrWy8Y6H2P65WOQvImU57Tw27c2ZtRGi8YTADQGY/TOs/PUsq9ZWpM8ltOq8uRl44jFjfRj8E2ZoEhM557XNiSP7bDw6pqd6XE7oczFAxeNoW++4/BdvBDHAAmeHWF13jD3vrqeX587jKaYTpYt+Z/AYlao90cYWpTDJX/+iAcvGkOfPDvzJo/AAAKROFk2lWybmddX7+DcMX3pLVF+IYQQ3VS9J4gGBFts1XRYTFS6C1lW4+EXz33KVRMHc9uUEcQTBqGoTrbdjN1iwqooqIpCcc+co30ZQnRZ2z1BVEBLGESav7RDssN8eUk+Vzy7iusmublz2kii8QShqE6O3Uy2VcWEBM6E2Fee08oDF41h7vOrWdoigLahzsdN5w7nwTc3cu2k5E0js6qwtEXgDL7Zepna4TR2YAHXTiojnjBwWlVMClw7yc0tU4bjC8VwWJPlhN7dVM/j731FSNOpdBeSY092sE55f3MDNz2/mvnTy+W7qejSJHh2BPlCGnP/sZoZFQMJxHSawnFy7GYq3IW8vX4XZ48sotSVxfx3a5j9zMr0+1IFGx9//0uuO6OMc0b3w6Ga5JeTEEKIbqnWEwQgpOn4w8m6K9W1XgKRGPdcMJpbX1zDshoP//3WF/z3W19Q6S7ktqkj+f07X3DzOcMwgCJpsiPEYVPXGGK7L0y/PAeReAJ/OEZ1rZeSAgfunlnMmehmwbs13Pf6Ju57fROQXO/edcFIFCRwJkR7+uY7uHXycBIGBKNxYnqCLJuZh97exKh+edjNKlVlLhqDrWuSVdd6qXAXZtQafPidzennq9yFXFZRypPLtnDdpKHc9eq6zK6eZS5+ddZQZj39SUZzAUgG0KSJgOjqJHh2BO32R1ha08CN5w5NF0JFgZkVpSxasZVRffJx2kzcc/4oAlqcYFRPF2xctGIb86aMwAxkOyzyi0kIIUS3VOcJ4gnGsFmSpQ9ymxvmLFy2hUcvPoHGYJh7LxhNIBrHH4mTbVep90f5/TtfMPecYaiKQh8JnAlx2OxuDNEUjZNlMxPQdALN4zQ1RhVFYbsnxNxzhgHJILjdrJLrMGNRFPpJ4EyIDhkkm8vlOVUsqpUH3tjAsD65jO6Xx1cNQW6fOoI9TdFW70sFzWBLRq1BgH4FDuJxg4ZAlMG9kjuhbjhrKPOmjKChKYqmGxQ4LUz/00etAmcp0kRAdHWmo30C3cVOTxB/c32zQETHaU12OcGAxSu2MqJvHgnDQDWZCMXimBSFnOYtneeOKuL2KSPId1joV5glgTMhhBDd0m5PEH9z0XF/OEaWTcWsKlS6CwlpOlct+pT3a/bSFI1htZjItZuJxBL0L3Bw0znDyHNYJHAmxGG0c28ILWHgD8cJRJLj1Nk8Tk8oyeeqRZ/ywVcehvfJxWxSiMQS5NjM5NjN2E0K/SVwJkSH6rxhsiwqOXYz/1pfT0zXOWtEb04b0pNsm5mSHk5URaEoz05VmSvjvSFN55rF1Uwe3Ye/zP4e3yvtQa9cGz2yrPhDGgkMEsCpZT159qfjGdMvj7fW7eLyv6xi1tOfsDeotRs4g2QNbyG6Msk8OwJ2esNokK5vlm1XcVhVFCAY1Zg3ZSS3vrimVWOAey4YTZ7dTI5kmglx2EnHSSGObXWeIDqkM7ctqgmrqvD4+19mbNX83dub+d3bm9PzaL7djNWi0lO6aQpxWPlCGt5ojGjMwGlTSRgGZpMJq2ri8fdr0uM0NUYhud69Y9oobKoiWzWF2I9UCaC7zh9Brt3M6lovJxQXMLpfPo1hjXjCIJ5I4A3r6LrOPReM4tYX12bURxtbUkCpK5sfP/lxOhBWVeZidmUpM57+iPKSfMpLCqje1pj+9yPTy7lmcTXVtV6qylwZx0uZUObClS3fV0XXJsGzw2y7J0ggpmMYoJD85VTvjzK0l4VgLI5uKHxe28i9F4wmFNNpisTIsVvItqqYgT6ykBDiiJCOk0Icu+o8QZpiOgkDsmwqesIgx6qyclsj/3FCMY/932bumDaSmG60mkfNCUMCZ0IcAbv8EQwUIrE4OXYbewJR+uZZ+LTFOL1z2ig0PZEep1lWFQuy3hXiQNQ3RVla08BWT5hBhQ5umzqSu15ZxwkDCpg0vDdx3SCo6eQ6zJhQaQxEuPeCkQS1ZN3BHLu5eQ6Fv19xMiEt2UhnycbdXPncp5SX5DOzopRrFlcT0nRmVZSmkztmVZayvs7HPeePYt5LazM6fk4oc3HX+aP42hMkO6jhkqZ2oouS4Nlh4gtpNIVj1PkjgIJhGOQ6LdxzwSgefGMDvz57KE6zGcMBX+5JULMnQDSewGY28eWeIJWDC2UhIYQQotur8wSJGeANJWup9Mqx8cnXexk30MkJxQXUByKUF/dgqyfUah6t84VwF+Ud5SsQouva7Y8QimpYTCq+cBzDSGadqQrkO82YEgblxQXsaR6nX3uCst4V4jvyNjfI+dU/PueFn5+C2YA7po4koOl4QzFyHBbynRZ2eyNs94apcBeyY2+I/GwrXzUE6ZVjS4+/+qYo40t7sM0TotSVzfzp5VTXetOBM4Boc5fc5TUerjzNzfHF+ewNRvn1ecO5mWTDgmy7meqtjZz3yNL0+yaUuXjgojH0zXcclZ+TEIeLBM8OgzpvmI++bGBkv7zkVk0DTCaFWDzB5sYQN54znJ1NYYpz7eRYzIwv7UE4phOKJu8UDOqZlS6ALIQQQnRXdZ4g0YRBJJ7AYU1u1TSZYKAriz1NBlYlRJ9cJz2ybBnz6PBe2YQTOv16ZMvdbyEOgz3+CEFNZ96La7jngtE0aTpZNhVFUYjryS/cgUgckwPyzCpFOfZW43REr2wJnAnxLWRZVQAaAhrb9oboX+DArCjkWFXsZhOhmI4vFKdnno2BLieeYIS8LBv1/gh7g1F65diwmU30K3BQWpjFbn+EGU9/0u7n2czflEfXDYNrFlez4OITuPAPHwLJHVVXnTaYu1/bkFEL7f3NDcx9fjX3XjCKPCk/JLoQaRhwiO32R5j7j88Z2S+vuVhqHKvFhNOq8tSyr3D3zMYXiqIbCnsicbZ5IwS1OE2ROH1ybbz2eR12syq/ZIQQQnRr25u3aoaamwMAZFtV/vz+VxTl2NnmCaFaLHjCMeqbosR0g6JcG4//32bqmqK4suwUyV1vIQ65bZ4gXzYEmPfiGpbWeJrLjsSxm1UUDDbtaqIxFKFXtp3Pt/nYHY6zd59x2sNmlsCZEN9SltVMhbsQgFgiWdx/xdZGtITBR1v2sssXYU8gyo7GMMu+9GCzqGzY6aNHtpVBPbOxW1RKC7NQAE9Io2++g8rm4+2rwl1Ida03/fdYPMEJJfkYhpF+bOnmBua/W8OsytJW71+6uYGmSBxvOMbmej9f7QngC2mH8schxBEnwbNDaI83jC+ksbTGQ1w3cFpVcuwWojEdbzDMdZOG8vt3vsCiqvTOsWFSFMwmBYtqon+eHTWhM7tqEH1ksS/EMSXVTKCtfy6eefnRPj0hupztniBNmo4/HCcY1clxWHh3Uz3+cJjZVYP509IvObG0BxbVRETTyXNYKHRa+NP7Nfzzs10UOKXeihCHw25/hJtfWEOO3cLSGg9AsvOtVQXFIBSN8b2BBcQSCqt3eBk/qLDVOC0uzKJngXS9FeLbMqsKcya6qXAXEorp1DdFGdIrB384RnlxPn3yHfTMsdE338GIPrnE4nBccT47fRFy7RZ6ZFnZ6QsTTyS73BoJg7vOH9WqK2eFu5CZFaUsXLYl/fd6f4QZFaWoipLx2uU1HsqL89s83617Q9zywhpicYOtnhC/+vvn1HnDh+VnI8SRINs2D5E93jBRPYE/EgcgqOnJjiMGrNrqpaTQyfqdPq4/cygBTWdPIEqO3UKvXBtWwOmwkOeUO3BCHIu6czMB6UIqjrRaT5CAlsw2y7aZUVUFLZ5gda2XMf3ySTQ1cfmEwQQ0naZwssZLtlXlzlfW8s7GBun4JcRh1BjUWF7jIRT9ZotWrsOCSQFPIIphwGe1XspL8umZbaMpGk9u03RayLao9JdsMyG+s6ZonLhuMGV0X4p7JJMt3ly3i+ptjWza1cSDF42hd66dpkicXIcZq2qiMajRL9+B3azy7427WPHVXn42YTD98ywkMKj3Rbn+zCHMPWcY4ZhOLJ7gg6886dpnVe5C5k0diRbX+eHjH/HbHxzX6rxStdH2ZTObWFbj4f43NnL16W6uOHUwG3b6CUbj9MqxyU0u0elI8OwQ2OMJEgd8kXiyxhmQbTMT0uJkW82cMLCAhqYojaEYX9RLYwAhROfRnQOH4shrGTjLdViwmU1YTQoP/d+X3DplBPe8up7hffMAhWg8QV5z4OyBN9anA2cPXjRGFuRCHAZ7vGH8keYt1PZvvkJkW1U+2dpISYGDghwrX+0Jsrn+m8YA9f4IFW6XBM6EOEjBSIyifDu1jSEMA0KazoY6HzMrSnlq+RZmP7My/dqqMhdXnuZm9jOfpOuRVboLufv8UaiKwuvrd1K91ct1k4Zy96vrWFbjwWlVmVVZysmDCql0uwhpOp9ua+SBNzYwom8eIU3PqIOW0tZjLbd9Lt3cwE3nDOMHj38oTQVEpybBs4O0xx8hCgRjerI1sN1MVZmL3f4wr3xex/VnlKEYCoqi8L3SHkRjCYKaTpZVZUivbIplISFEl3XxzMup29PY5nNfbK6h6AifjxDHsu37BM6yrCoqEEmEuXbSEH7/ziaunTQEs2qiKRyjb34ycKYmEvxsgpvrzxqOK1u2awpxOPhCGpqeIMeebGilkPxyvnRzAypwYkk+u5qifL7Ny7jSHsTiBk2RGDl2i6x3hThEeubYufmFNSxvDnT98ZKx3HzeCO5/fT3lJQXMqiglGk+Q77DQr8CBCXj+F6ekM7lz7WYSCYNN9QEGu3Lwh3UefmcTN54zjDlRHVVV0plnC5dtSWeeXVZRyjWLq1vVQQOocrvY7Y9kPJba9nnN4ur0YyFN56+Xn8Sspz+hIaDx/uYGbnp+NfOnl8u8LToNCZ4dpGhMT24dicSb6z3APReM4jdvbuSXk4bw0L83M2fiIIpy7QQ1naAWJ8duIccqqetCdAUdbWv8YnMNE254rM3n1jwotdKESPmmxlkycJZtVQnH4xSYVHTFwR5/iF82lz1oavEae0KnZ89c+h7tCxCiiwuGYzTFdJxWNVlgvHm9e+uLa7nu75/x0PePp2eOGVd2AaHmjpp5slVTiEMqqOksb641GNJ0fv7cKv7x85O4dfIIIvEETZEYfe0WnFYVEgZBPUEwGifPacGmmmgIRnFYzJT0cNAUiTN5dB8U+mAYBrkOM//3RT1a3KC8OJ/508vplWvj3xvquWZxNWMHFHDVaW5mPfNNd84KdyEzKweyZoePJy8bB0Cew8L/fbEnve0zxR+J8dS/t/DcT0/ikj9/lA6gNQQ0CZ6JTkOCZwchtdhPBc5sFhN21cQ/P63l5nOGEdF1rp1URiSWwBOM4rSpuLJt2EwK/WQhIUSX0NG2RgmQCbF/LQNnOc0ZZ/GEjlM1E8YgboTpledsvlH1TY0zeyKENSvvaJ++EF3edk+QYEynKRwnGIlxzwWjURX4uiHIfReMSnbb1KJk22wEYjrBaPJGsQTOhDi0mpo7T6eENJ2fLPyERT87iXtfW8+n27zccu4wJgzpSQzQE8nOmIYBUT2B05qsg6YAnpjOspoGRvfLI54wKC5wUFXWkz1NUfrmO1Ca3z+mfx5/v+JkHBYTb6zbxYKLTyDLqqInDD74ysOcRd/URrtyYhk/WfhxRtAMvtnCuazGw92vruPBi8akt5h6glHiuxKYFAXVpFCYlcwg94U0GgIa/kjyhpmrxePeUIygFk/v+kq912Y20RSN4wvFyHNYyHVY0ttCfSENT1AjnjBIGAahaJw8pzV93APV3nmJ7kGCZ99Ry8V+tt2Mw6LSEIhQlA3nlxfzt0+2Mf3EEnQgYYCBgUlRsJoUrAljv8cXQgghujJfSMMfjmVs1YzpCZ5Zvo2Zp5SiAiZFQVEc3wTOmjO3I1oIsyObnrJgFeKw2tG83g1E4jitKiaTggkDPRFjQGEWgWiEPJsdHbXVOJXAmRCHVo7D0uqxhoDGxX/6iN98fwy35DloisQIxXWcZhXNpGAYkDAMnGYV1QTxhMEnXzdyxyvr00GuKreLO6aNJJZI0DfPgZ4w0OIJfKEYu31Rembb+OETyWyxKreLWZWlfL7dS3lxPr/9wXHYzCbW7PARiemMLclPd+KF5Pbuy04ZmN7CuazGw9xzh6Wfb4rE+eHjH1HldjFv6gj+vbGesSUF3PriGpa2qMNWMTjZuddhVfm81ss9r21In/+kYb24dlIZu/1xFBTCMZ2lNQ1sqPMxb8pIrAr4tTg2s8rdr6xrdX53TRuJAqgmJXmjIBIn127G0txwwWFVcVpUUBTmNZ9Xy/c/2Fy7ra3AWuq/UVvBttTrA9EY+U4rWnP2oNOWDAiaWwQTO7K/gN7BBvw6CmTWN0XxNndczrKZyXdYDjqYeCgClIcryCnBs29pjz9CNKZnbC+xqSZsJgUFiGFDC4f4zxNL8LVYRBTl2rEBjy79ihvOGnq0L0MIIYQ4avZ4gkQgI3CWbVX53TtfcvUZZTz1wRZmV5SiN7+m5RfyWCJCTlYOvXPtR/syhOiyfCGNQDiGv0XnW4dVxWJS+HSbl+OK81hd20B5iYtowkhv1cx1WMi1qPSVwJkQh1xW87bpZS2CN5AMzixctoU5p7uJxQxs2SZiCQOjOV9DNYHZpBDQdILRGOMG9uCFK0/BH4mTbTOTbVXxBCNcvfhzzi/vR3lxPgC9c+0U93BSuzfEY5eMpSkSY0Chk2kLlrfKLgNwWlX+evlJzGiKphuG9M1zcNEfP8h4fSCS/HNGU4GaBu58ZR0XHN8PRYG55w7nx94wxT2c+MIaES1BYyiG3aIC8MSlY9ESCXrm2AlF45hMClk2M43BGJqeYPKoPkwZ3Yfd/gg5zYkuewIRZlYOYvr4AdgtKp9ua2Thsi3c+tJapozpy8BCJ7OfWZk+11Ttth8v/JhbJw/njTU7MwJnkGyGMPf51dw1bSQrtzbSGIpy6pBeRGI6X+wOkGM3o5oUPviygUff/ZJxAwp44KIxKMCNz69m1dZGHplezm/e2pTektvys+9/fQN3nj+q3cYKdd4wc5uPM6uylPLifL5uCFJc4KR3ro2QpnPj86tZurkh/Z5v06whdfyW7z9zeC/mTRnBLS+sZWnNN49XuAu5+vQyBvRw0uc7NoJo6/O+bXOJQ3GM9kjw7FvY4wkShVZ1WXY3RSjIgt45DlZu3cvo4nz80W8W+9lWFTvw61fXc9f5oyS1UwghRLfVXuDs64Yg150xhIf//QUzKkrxtqhv1jvHjlmBrz0hBvXKlsCZEIfRHm+YqJ4gEPtmDNrMJswmBRJxyovzaQhGGFyUn7xR3LzlOttuJlsCZ0IcNipwx7RR3PHy2owAWqW7kDumjaIxGGXO36v53Q+Px5VtY29IIxCNY7PYCUSi+CMx+uY5iMUTXPLkChoCGgBnDOvJvCkj+eOPx9IUiae7bK6v8zGibx6fbWtkZkUpC5dvYVZFKeUl+RmBnpTyknz+tX43C5bUpB975eqKVoG2bLvaZlOB5TUebp08gkA0zr837uYvH27lR98r4ZRBhWTZzfTMsWG3qPTJtWK3mrntxbUZwaxKdyGzKkuxqibueX19xjlWuV1cOXEwcxZ9mhEce2R6OdcsrmZ25SAWvFvDrMrS9Pmn3j+rspTeufZWgbOUpZsbaIrGGdUvF8OAu15t/dm3TxvJaUN6ctlTn3DT86s5d3QfVm1tZP70cp5evqXVz3N5jQeTonDH1JH4IzEi9TqNYY1s2zfbVLNsZm57aW06ALdoxdbkf4fifGr2BGgMRemZY2d2ZSnTv1eSETA8kGYNvpDWKggFMLRPLre8sKbVzyN1DVPG9OW8UUXfKVusrc/7Ns0lDsUxOiLBswPUXuDMBvTOsaMlEuzxhzhxYA8Cmk4g0qKgMRBOGPz2B8dJ4EwIIUS31V7gzIpOSWEWIS3CryYNoSmm4w/Hybabk1/aFbAaMKp/nsyjQhxGvpBGVE+kt2rmtOh86wlGyHfawYjTO9uO1pxxZlIUTArkWFSp6SvEYWQAVhNcc0YZN583PDlG7RbsZhMWVSHPaeHRi0/AoirYLSb65tnTWaE5djO9cmxE4zomk8Kzs8fjb070cFpUIvE472zYzePvfUVI09PBrcUrtnLb1JH87u1NzKwoZe7zq9OZU8vayJRqGQyrdBdS749mXEOluxC7RaW8pKBVUwGALQ1BnluxlWtOL+PcUX2485V1GcG4Cncht5w3nHtfXd8qeLOsxoMCnDu6T6tg1NKaBhIY7QbHzKrC8hoPsypKM96XeiwaT3T432Z7Y5j1O/1Ub2ts87Nvf3ktU0b34c+XncjFf/qIWRWlPDK9HLtF7TAot8sfIRzTWbRiKxePH8CPn/yYsSUFzKwcyKIV2/jR90oY3T8v/fxTy7ewYEkNTqvKI9PLeWTJ2lYZbamA4f6aNTQEtFZBKEgG51r+N2nr5/VdGkG093nAATeXOBTH6IgEzw5Ae4EzK6AYYAFUkwnzPgWNVZOCCvSUhYQQQohubqcnSJzWgTM7BlFDxYaBarMTSRjE9eReE7NJwW5SyDoENTSEEPvXFI59U9PXZsZpVbEAZsMg32ln3U4/o/vmEk0YhDWdkKaT6zCTa1HpI+tdIQ6rpmgcT0CjMMdGuoK2ApquY1FVLCYTvXJtxBPJQE8knkg38Njlj/DyZzv45aQhySYf0ThZNjN2swnDMFBQOGNYb04Z5CLbbkZpPvYtk0ewpynC4F456WDXX1ds5fZpI9FiCYJanFyHheqtjRnBsFQ23I+e+DB9/pXuQu65YDTf/+MH6ay3fdnMpmTWFW0HwZbXeLj/9Y0cV5LPOxv3tHr/0hoPM/YJgLV8b1vBsdmVgyhwJuvJtRUkS21B7YjNbDqgoNL/vLWJWZWl5Dkt/PZfm7hk/IAOj+sLx3huxVbKSwp4avmWFsE/g+OaH5t7zjBiusFTLTLYZlWWZvy95Xmknm+KxPb9uAz+dp7fXyAx2ly77dtq7/NSDuSYh+IYHen4/wLBHn+ECK0DZxbAZIAGqIqBQbIjSaL5l49JgRyrpK4LIYQQOxpD+GN6q8DZext3ETUUbIChKBgkF11BLZl1ltW8BUwCZ0IcfrUtmmHlOizJGmeAmkgQQcFhxBndN5cmTachEMVkUijKtdHDZpbAmRBHQCCqc/mzq8CAnd4wieZ/v7pmF1/uCTF5/jK+bgixNxDj8fe+xGExkWO30BSJ0TvXzrWThrAnECFhQFGOjVyLipZIoCgKwWg82e0y24rdbEJVwWlR0fQEeQ4rk0f34X9nfY83r63iV2cPI64ncFpV+ubaMSlw0qBCnv/FKfy/K07ijWuruOeCUdhVhb/MHp9+bFZlKa+vrWNoUU6b15dZA83TbomGpTUN6bpsbekouNPWc3azKX3Trq0gmc1sorrWS4W7sM1jVjWf94EElVLnnsp0O5Cg3PIaD+XF+el/Q/Lnk3oMyPhzW39vKXWcHHvrBhQt5bbz/IGc8/6O/W0+L+VAjnkojtERCZ7tR6SNxb6RMFCbA2dWwyCCgj+1kFAU+uTaGFqUS58C59E+fSGEEOKo80fi+MPxjLnUHwxRNbSI//tiF3GFdFaadOsT4sjb3hhqtd41mxSMRJioYiJmRIiYzDTtM0aLC7PoKetdIY6IHIeZkKYTNxLNhe3jzH5mJQuW1LByWyNjS/K5atGnfLTFw49PHkgknkAhGTAIRuOEYzr5Ths2s4mAprMroJFIGMQSCXIdVhIJA09AIxJPYDWpBLU4TeE4CSOZJOIJaniCGoYBG+r8rPy6kb+u2k6dN8Ke5mNZVBO7fRH+8ekO7nhlPVo8wR/f+5LX1uzkyWVbWLDkS2ZWlLYKRFU1b/tcuGxL+rFvGwRL6Si409ZzBVlWfKFYRvAuJfXYwmVbmDPRTdU+513hLmTe1JEsXLblgIJKKf5wHKDDoFzL80ldb8vrTv05pOmtfh77C+QBuLI7vjHpyrYyoczV6vHqWm+rn0PLc65viu732N/m8yBZ8P9AjnkojtERCZ7tx74LCQtgUhSiNAfOmjuXyGJfCCGEaFtTOIbTqpLrSM6Tz320lWynE4wwE4cW4Yvp7PJHSRgGrmwbeTKXCnFENUUyg9tmQDUM4oqDsBbCYrIn17vhb5phyRgV4sjKtqhUlbmo90epbA6spAIvC5dtYWZlKWNLCvjd25uZPH8ZP3tmJXazCZvZRJZNRSEZVPEENBQFinJtKIrC3qBGLJHMJFNI7qTSEgl2+aLJ9ykQjMaxqCa2eoI0RWIU93AytE8O1dsamfHUJ8x46mPeWLeLsKZjt6qcN6qIn04o5Yn3arh4/ADW1/mY2dxs4JrF1ZSXFPDkZeN48rJxvHJ1BWMH9mhVA62jYFSeo+0Moip3Ibv9kTafay84FtcNdvojzJlYlhG8q2gR0CsvycekKNx4znAWzjiRxy45gScvG8fk0X14Y+1OQpp+wIGw/gWOdBBn4bIt7QQTXRnBxNTPouXPJPVnu0Vt9fPYXyCvf4Fjv1n9eU4rD1w0plUwatNOP/deOJqqfR5PdducOKTnd9ox0N7nTShz8eBFYw7omIfiGB3pMjXPHnvsMf77v/+bnTt3MnLkSB5++GGqqqoO+rgZdVkSBigKOqBL4EwIITq1i2deTt2exjaf69uzgEVPPfGt37d1Sw0DSt3f+phdXU5ztz6LScGWCDF9/AAWr9jKpScNIJow0FvUOHOYFNkCJsQRtm8zLMWAKAoxI47DllnTV9a7QhwdKnDP+aP4zVsbuW3qSB58YwMzm2t4La/xMGdRNZdPGMQNZw8lFk8GwyJxnRyrGcwq4ebmAVm2ZH20PYEo3nCMvvkOsiwqWkLHlWMjEkvgDcbolWPDmqqJpkDPHCslPRysrvXRr8DBrKc/4UffK0nXEeuT5+Bf63exdruPs0b2pmeOnYvGFfPsR1sZ0TcPs6Lwq7OGYlVNBDUds0lh4y4/V7fROKDKXUh9B0GwHk4LVe7CVt02Z1aWoqBQ4S5so9umm9nPfJJxnJkVpVgtCicP6oFhwJ9+Mg5/JHnt2TYz/nCMv//8ZOxmlVVb93LHK+sz6rrNqCjlntc2AMlA2CPTyzFBxnm1bKYwocxFUfN21AllLt7f3MA1i6uZVVmabkyQ57AQienpn0tFi0BpKgCX2io6ocxFnzw70ZhOpbsw3cQh9fq2tm62PIf96ZvvYP70choCWjrm4cq2kue0smB6OfVNUXzNN0izrGbynQdXo7ajzzuSx2hPlwie/e1vf+O6667jscceo6Kigscff5xzzz2X9evXU1JSclDH/iZwFkdTzFgBqwIRRQJnQgjRmdXtaaRo2vVtP/fyQ9/pfWsevPw7HbOrczZnslgTCUKKE6cR4tKTBmRsAeuda8MKFMlcKsQR17JDPEZyG7VNAUMxy3pXiGOEmWTHzZvOGUYornPrlBFE4wnmTR6BQTI7LNWlWosnMIAsi0oiYaAAsbhOYZYVLZFgb0gj22bGlW3DDMQSBpGYQSiqkWM3U5BtIaolCEZ1tHiCLKsZ1aSgGwZ6wuCjLR4evGgM0XgCh0Ulx27mJwtXMLxPLrdPHcFra3ZyxyvrAZg3ZQTlJfk0heM4LCrVtV7ufjX53CPTyzmhJL9V5845p5fRO9dGVZkro3tihbuQORPL2NOkMe24vtwyeQS+5uC/ooCiwM7GCDedMxwUg1BUx2lTybKYqW0MseDiE4jE9HQds782dxPFgBVfexjWJ498h4VANE5TJM7HX+8lnjD43sAejOmfz/O/OIVgNE6WVSXbZubOV78JpoU0nb99vI37LhxNUNPZ4Q0DyUDWNYurGTegICP76YGLxnDT86t5f3NDutFAKiB3TYvA2cyKUhat2JoOwFW5XcysHMjiFdt48KIx6dpwD/zHGG5+YQ1LNzekA3n7dkX9LhlYec62A0/tPX6wDsVxD9e5dYng2UMPPcTs2bP56U9/CsDDDz/MW2+9xR/+8Afuv//+gzp2jt0M8SBRxYkNA6N5y6YsJIQQ3d3GDes5bcoP2nyuO2dZidYsJgVLPEFEMeE0EkRNzozAWepLu3SnFuLoyLGbscVDJBQHJsCsQARZ7wpxTDEpWBLJTG2raiISSxCIxHHaVJwWFafVitFc6sqkgs2kEozpNEXiOK0qeQ4rZpOClgDDSB5HTxhEjAQOVcVqVlAVMwbgDcbItpsxqwpWs4LdrGIxKegJgxMGFDAinpvstms3Y1VNeIJR/jJ7PNlWlXgiwZnDizihpAA9YWC3mDCMRPLfChzXPy8dhMq2m7n7/FFE9QRN4eR5OiwqcSPBV3tC3Dl1JOG4TrA5Y85hVoklkoFBV44VQwGbJVlnbac/wimDetA3304CaIroZNvNOC3Jrad/XrYlIxBXVebi/gtH079Hsm6jzarSGNQIROP0yrGh6Qm+N7AwGXyzqhS0EZD5nx8c126GU588Ow0BjcIsKxce369V9lNbGVLZdjPBaJxnZ4/HaVUxKQqGYnDreSNoDGv888pTUBUF1aTw2x8cl3G8/j2cLGhxvFyHhf/54fEEIvFDnoHVXXX64JmmaaxatYqbbrop4/GzzjqLDz74oM33RKNRotFo+u9+v7/d4/cvcLLVY6AmQsRNTiloLMQR8G3GqDh6YpiOiSyr77qN8ovNNRS1c8yOAoMdva87+TbjtG+Bk1pPEHtCI2qyou3TndqMBM6EONS+7Xq31mNgTyTAZJLAmRBHyLcZpz0LnOzxBDEAjGQAzWlTCWk6qqJgs5hQFIjpyeCSjkEgEiPbZsFpVYnGdSJxA7tZpVe2jb0hDTCjKhBXDOI6BKJxsm1mCrOt1DWGufZvn9EQ0NIZUK9/Xse1k4agkKyr1RiKYTYpfLUnyIi+uQRjyXNTlGQtriyrmjx5RQESmBSw21QisUSyVIM52dEzoukUZlvJspkJROKEojrFPRyYTQpW1YTqaO4I2hw4Uk0KWc5koCmq6RT3cFJekt9hYGjBfrby9c61t9vhsz0dZTgdSPbToc6Qaut4vXMP2eG7vU4fPGtoaEDXdXr37p3xeO/evdm1a1eb77n//vu58847D/gzBhRmsb1Rwdsiatsn147dpEiHISEOg287RkXX0V4gbH9BsAk3PNbmcx1to1zz4OXtnkdHgcGO3teRrpap923HaXHzXNrUYi4tyrVhV030zHccxjMVonv6LmO0rjFELGEQahE4y7Wb6SfrXSEOi287TnsWZrGnMYQBNGk6wWicHLuFLKuKSnJbp82s0hTX8QQ1smwWTAqEYzoKYG8OVvnDyYy1VD3ShmAUq1mlMMuKzaQQw8CVY+OPl44ly2ZGAVBgzhllGIZBts1MKKYTiSWzu04c2IO9IS0Z1LKaCceSWxkVRUFVFMyqQt9c+wEFig5XsOdwbeUT3UenD56lKIqS8fdkYUOlzdfefPPNXH/9N1+K/H4/xcXFHR6/vywahDhivssYFV1De/XEvmsQ7FhyrGTqHSoylwpxbPsuY7SvjFEhjqjvMk4PJHmjz7c8j+JDkFlaetBHEOLY1umDZy6XC1VVW2WZ1dfXt8pGS7HZbNhstiNxekKI70DGaOfX1bKsRGsyToU4tskYFeLYJ+NUiM6j0wfPrFYrY8eO5e233+bCCy9MP/72229z/vnnH8UzE0KI7qujLKslD/5U6ontQ4KNQgghhBBCHLs6ffAM4Prrr+fHP/4x48aN4+STT+aJJ55g27Zt/PznPz/apyaEEGIfh6OeWGfX1bZ0CiGEEEII0ZV0ieDZf/7nf+LxeLjrrrvYuXMno0aN4vXXX2fAgAEH9P5Uq17p6CfEkZeTk9NufcKUbzNGdV1Hj+vtHAh5rrM+d6ycx1F4Ttf1ozo/HcgYBZlLhThaZIwKceyTcSrEse1AxqhipEZoN7Z9+3YpRi7EUeLz+cjN7bitjoxRIY6eAxmjIONUiKNFxqgQxz4Zp0Ic2w5kjErwDEgkEtTV1bWKNqa6ndTW1h7QL7vOqrtcJ3Sfa+1M13kgUf72xih0rmv9Lrr69UHXv8bOfn0Here8u86lXf36oOtfY2e/PhmjB647XSt0r+s91q/1YMfpvo71622LnPORIef83RzIGO0S2zYPlslkon///u0+n5ub22n+xzsY3eU6oftca1e5zv2NUeg619qern590PWvsatfX3efS7v69UHXv8aufn3dfYy21J2uFbrX9Xb2az2QNW9LnfF65ZyPDDnnQ890tE9ACCGEEEIIIYQQQohjlQTPhBBCCCGEEEIIIYRohwTPOmCz2bj99tux2WxH+1QOq+5yndB9rrW7XCd0/Wvt6tcHXf8au/r17U9Xv/6ufn3Q9a+xq1/f/nSn6+9O1wrd63q707VC57xeOecjQ8758JGGAUIIIYQQQgghhBBCtEMyz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEz4QQQgghhBBCCCGEaIcEzwDDMPD7/RiGcbRPRQjRBhmjQhz7ZJwKcWyTMSrEsU/GqRDHLgmeAU1NTeTl5dHU1HS0T0UI0QYZo0Ic+2ScCnFskzEqxLFPxqkQxy4JngkhhBBCCCGEEEII0Q4JngkhhBBCCCGEEEII0Q4JngkhhBBCCCGEEEII0Q4JngkhhBBCCCGEEEII0Q7z0T4BIUTX4AtpNAQ0/JEYuQ4LriwreU7r0T4tIUQLMk6FEEKIg9OV5tKudC1CHG4SPBNCHLQ6b5i5z69m6eaG9GMTylw8cNEY+uY7juKZCSFSZJwKIYQQB6crzaVd6VqEOBJk26YQ4qD4QlqriRfg/c0N3PT8anwh7SidmRAiRcapEEIIcXC60lzala5FiCNFMs+EEN/KvundiYTRauJNeX9zAw0BTdK/hTiC2tqC0RDQZJwKIYQQ31LLOdVhVbvMXHqw6wLZ7im6IwmeCSEOWFvp3U9eNq7D9zRFYof7tIQQzdrbgnHNGWUdvk/GqRBCCJFp3zn1sUtO6PD1nWku9e/nXDu6FtnuKborCZ4JIQ5Ie+nd+5NjtxymMxJCtNTRFoyfnzq4w/ce6XF68czLqdvT2OZzfXsWsOipJ47o+QghhBAttTWn2swdVzzqTGve3P2ca3vXsr/tnvOnl0sGmuiyJHgmhDgg7aV3V9d6qXAXsrzG0+q5CWUuXNkygQpxJHS0BeODrzxUlbnafP5ojNO6PY0UTbu+7edefuiInosQQgixr7bm1K605nVlW5lQ5uL9b7kukDIQojuThgFCiAPSXnr3wmVbmFlRSlWZK+PxCWUuHrxojEygQhwhHW3BWLhsC3dMG8kEGadCCCHEfrU1p6bWvBXuwozHO+Ncmue08sBFY771uuBgtnsK0dlJ5pkQ4oC0l94d0nSuWVzNG9dUEU8YNEVi5NgtuLKlcKgQR1JHWzBCmo4CzJ9eTkNAk3EqhBBCdKCtOTW15p1VWcq8ySOIxPROPZf2zXd863XBd93uKURXIMEzIcQB6Si9e9yAAvKdlk65cBCiq9jfFozC5k5YMk6FEEKIjrU3p4Y0ndW1Xn5WWdol5tNvuy74rts9hegKZNumEOKAfNf0biHEkSFjVAghhDg0ZE5tm/xcRHcmmWdCiAP2XdK7hRBHjoxRIYQQ4tCQObVt8nMR3ZUEz4QQ34ps+xLi2CZjVAghhDg0ZE5tm/xcRHck2zaFEEIIIYQQQgghhGiHZJ4JITL4QhoNAQ1/JEauw4IrS+4sCXGskXEqhBBCHBrddU7trtctxHclwTMhRFqdN8zc51eztEUHnQllLh64aAx98x1H8cyEECkyToUQQohDo7vOqd31uoU4GLJtUwgBJO8+7TuJAry/uYGbnl+NL6QdpTMTQqTIOBVCCCEOje46p3bX6xbiYEnwTAgBQENAazWJpry/uYGGgEykQhxtMk6FEEKIQ6O7zqnd9bqFOFgSPBNCAOCPxDp8vmk/zwshDj8Zp0IIIcSh0V3n1O563UIcLKl5JoQAINvW8a+DHLvlCJ2JEN1TqnBvIBoj32lFiydoisRw2syYFAWzSZFxKoQQQnxHqXnWF9bIsptxWNUOX9+Z51RfSMMbihGKxVEUBQwIaXHynFZZSwjxHUnmmRCCusYQqkmhyu1q8/lKdyF2i/y6EOJwqfOGmbO4mv984kMURaHOGyYQjWMxq9T7o3gCGoFonDU7vFSVtT1OJ5S5cGVLlywhhBBiX3XeML9+YQ1fe4JYzCreYAyLydTu2rczzqm+kMbm3U2s/Hov271hVmzxsMev8enWRnZ4w+zyR9nqCeEJarKWEOI7kMwzIbq57XtD3PzP1aza5uWR6eUkMFhe40k/X+ku5L/OHsYdL6/jtz84TlpYC3GIpQr3btjpZ9FPT+KBNzdw8fgBLHi3ptVYnHvOMOZMdKOQrEuSMqHMxYMXjZHxKYQQQuzDF9L47VsbueGsocx7aW16bnVaVZ68bBwotOo62dnm1DpvmLn/WM3Smm+uo6rMxe1TR/D0h1tYsnFP+vHTh/Xk9qkjueuVdbKWEOJbkOCZEN2ML6ThCWrEEwY2s8KtL6xlafMi4prF1cyqLGVWRSkAhVk2PMEo3lCMtzfU0xDQZEIV4hBrCGhs2Onnr5efxJ0vr+O4kgKeWr4lI3AGsKzGg/LWJk4a1IO7zx9FPGHQFImRY7fgyrbK2BRCCCFaSK15FeC6SUO4+YU1GXNrSNOZ/cxK5k0ezo3nDKV2bxib2YS7ZzZ98h1H78S/hdT2zFtfXJNez6cs3dzAHS+v4/iSgozgWfLP67n3glFouqwlhDhQsg9LiG5kpzfMv9bvJqjFqfOGCccSGRNtSNNZsKSG2c+sZPYzK/EEowAozb8ppICoEIeWL6QRjev88dKxxHSDpTUeyovzWwXOUpZubmB8aSGNIY3BvbI5vqSAwb2yZbErhBBCtLDTG+bfG3cTiSWIxhM0ReJtzq0hTefmF9YCcOVznzavfztHt8lUyYctnmCrwFnKsuZ1xb6WbNyDPxqXtYQQ34IEz4ToJnwhja17Q7z02Q6mzl/OjKc+wRvsOBgWjScXHAXOZOFQKSAqxKGTWvR+/48fEozG8YeT4zEaT3T4vnjCkLEohBBCtMMX0ti2N8QLn+7gvEeWssMbxh+Jd/ieQERP/9m5n0YCx4JUyYelmxtwWDo+3/bWFaGoji/UOQKFQhwLJHgmRDfhC8d4dMlmlrW4M+W0dTzZ2swm8hwW4rohBUSFOIR8IY17X1vPjFMG8vefn0yvHDt6wgCS464jWTZVxqIQQgjRDm8oxvwlm9PZWNF4gmx7x2ve1PMV7kJUk3LYz/FgNQS0dJ22fGfHN9TaW1dk2VQaAhI8E+JASfBMiG4iFNPbTOmuche2+foKdyH1/gg5djPRmC4FRIU4CL6Qxpf1Aaq3NfLlngC+cIzrzxpCvT/CLl+EWCLBB195qHK7qK71UtHOuKxyu8i2mmUsCiGEEM32nWPDMT3jZrHNbKLeH6Wynbm10l1IvT9KhbuQmRWlnSJ45m8upeK0qjgsaofXVl3rbfV4lduF3aIS1qQkixAHSoJnQnQD9f4Ie9uo3/DupnrmnF7Wqk13hbuQqyeWUeF28fh7X1LaiQqnCnGsSW3PPOOh97jwsQ8443/e48E3N2JC4fU1O5n9zEq2N4ZZuGwLN5w9lA11PmZWlLYKoFW6C7n5vGH0yJLAmRBCCAGt59ip85e1qllWXevlH6tqmTdlZKsgU6W7kHsuGE3/Hg7KSwr428fbKOwE82xuc/mGWZWl3PXKOmZVlrZaz1e5C7lj2ig21vlaPT7ndDervt5Ljv3Yv1YhjhXSbVOIbiAYjeO0tB7uj7/3FWP65TP1uD5cO6mMeMLAaVUxmxRybGaaojFunTyC3rn2o3DWQnR+LWuSpDitKlecOphbX1qbLl5sM5sIaTo/feYTFs44kWA0zq/OGspN5yo0hmJYTAq9c+xYzSbJOhNCCCFoPcc6rSqPTC9v9bqFy7bwyPRyHn5nEzMrSpl77jACEZ1cuxmrxUQ0oRPXE6yu9XaKnRa+kEbCMHjysnHpNfqGnX7OHV3EjIqBZNnMBKNx1uzwUdcY5vThvZk+fgDReCKdhRfSdG5/ZT1jBxSwYHr5MX/NQhwLJHgmRBfmC2k0hjRue2kt/3XOMCrcha1adF+16FNunTycbJuZrxqCOK0OPq/1csKAAgqz7RI4E+IgtKxJ4rSqzKos5ewRRTRFYhljMbVVc3mNhx898RGzKkspL84nGk+Q77DQP9+BCejXw3mUrkQIIYQ4tqTm2ORNqUGcN7oPd728juNKCqhyu1hak5x/Q5rONYurmVVZit2i0hiK0TPbxlvrdrFmu5crTh1MUa6d+Z0giFTnDbe6KVfhLuSmc4cxdf5yAOac7qZ6WyPLazzptUdqPW+3qPTKtXHVok8JaTpLNzdQ3xSlIaDhj8TIdVhwZVmP+Z+DEEeDbNsUoovxhTS+2hPgqz0B5iz6lK8aQiyt8bDTF2lzK1h5ST69cu00RWJc+dynNEXinDzYRa7VLIEzIQ5Sy5okj0wvp3pbI/VNEbzhzBojC5dtSY/PkKazYEkNs59ZyeIV2+hX4MBuUelXmHU0LkEIIYQ4JvkjMZxWlUcvPoHvDexBvT/K0hoPC5dt4dYpwzPWvCFNp3pbI+GYzs//soo6b5jeOTZuPm8E/fMclBRmHfMBo7ay2QGW13io90fTf+9oTdEzx8bc51cT0r7pLrptbyijtMTVi6up84aP2HUJ0VlI5pkQXUjqbtTYAQWs+novS2s8/GzCYADMJoWrm++6zaooTaduV9d6uWZxNf/4xSlUlbkoKXBgMSkUFUiGixAHK9XuflZlKU8t38LyGg+XjB/QqvNVy7visypKybVbyLabybGb6S9jUQghhGgllVW10xfmtTU7uWT8ACA5p+70RigvKWhzzZsKHJWXFJBlMdGnk8yzLbPZO7LvmiIaT1Dcw0FcN3jgjQ386HslLFhS0+7739/cwE3Pr+4UmXhCHEkSPBOii/CFNN77Yg8zK0rpnWvj4Xc2A1CQlSwoWl3rpbwkv83JstJdiEVVuGqiG5tFpZdknAlx0HwhDatqoqrMRXnxN2MvtYBvaxv1giU1VLld3HzeMHJsEjgTQggh2rLHH8Ew4JRBhYRjOstrPMyqKE0/v3JbI9XbGttc91Y0d6AscFoZ1if3SJ72QUlls7elutZLVZkrHVxLrSkgeb3lJQWUF+ezZOOedJARoKrMhaIkA5Ets9He39xAQ0CT4JkQLci2TSG6iMZQjFdX1zHr6U8yOmvGdYMKd2FGCndLVWUubps6koRhMLCHUwJnQhwkX0hj8+4mNtcHqG+Kcue0kRnPV9d6Wd9BR81fnT0Us6rQX+qbCSGEEBl8IY2a+ib8kTi/fWsjFrOJaDwBfFM/FGh33VvhLmRmRSkLl20hz2E54ud/MFIdNtuycNkW7prWupto6nrX1/morvUCpH9eFe5CLjtlIIs+2saCi8vT2fIpTR0E64TojiTzTIhOzhfS8IZizHtxTTqLpUeLFtu+UIyZFaXAlowUboBeuTZsqomH397ELZNHUJTvOBqXIESXUecNM/cfq9NFip1Wled/fjJ9W4ytVNevRSu2ZmwpyXNY6Jtv57dvbeK2qSPb+wghhBCiW0rNsWMHFrDy670sq/Fww9nD8DfXEU3Nr5CsA5Za9151mhuTSSGk6Xy6rZFrFlczbkABruzOlVXlyrYyoczF+21s3Rxbkk80rnPjOcO4MhLHG46lM90XrdjKxeMHcM3iagD6Fzh48rJxGdtYo3GdWZWlGZl6OR0E64Tojo565tmOHTu49NJLKSwsxOl0cvzxx7Nq1ar084ZhcMcdd9C3b18cDgennXYa69atyzhGNBrl6quvxuVykZWVxbRp09i+ffuRvhQhjphUU4Avm5sC1DYmmwKkmFCocruAZNp66kv6/OnljGhOT6+u9fK7t79g1dZG5p47nH6yPUyI78QX0viyPsDm+ibe21TPjIqBPPGTsfzrlxN44cpTaIrq2Mym9N3vVC2SEX3zKC/OB6BXjo2tniArv25k7jnDpFmHEEIIAez2R9i408/GnX5ue3ENYwcWcPbIIpY1r3uV5tekiuNfs7ia8pICnrxsHL/9wXGcMqgQkwlmPf0Js57+hAVLahg3oIAHLxrT6bYk5jmtPHDRGCaUuTIerypzMef0MmI6/P6dLwjHdPIcFqLxBOXF+Yzom5cOklW5C/nX+t3MfmYlC5bUpLdqLq3xcEJJAXNOd/PkZeN48rJxJAwDX0hr61SE6JaOauZZY2MjFRUVTJw4kTfeeINevXrx5Zdfkp+fn37Nb37zGx566CGefvpphgwZwj333MOZZ57Jpk2byMnJAeC6667jlVde4a9//SuFhYXccMMNTJkyhVWrVqGqajufLkTnlGoKcFxxPuvrfEwa3hu7JfP/c91IcMPZQzEw0nfhnlq+JeNuUlWZi7vOH4lVNUngTIjvaPveEDf/czWrtnl58coK3t6wm7EDCjh9WG8SCYOEAVlWFVWBORPdmEguUFO1SFLbKf743pfcct5wHBZVMkCFEEIIYKsnyK0vrGFpjYenZ5zIJScNxBfWCETi6dcYhkGfPAdXTywDkhlnqfVuVZmLO6aOxKTAP688hWA0Tr7Diivb2ukCZyl98x3cff4oPCGNuJ7ApChk21TWbPdRlOfglskjuO/1ZFOA51ZszaitWuV2MW/qCC54dHmbx3Za1VZ14iaUuXjgojEZGfRCdFdHNXj24IMPUlxczFNPPZV+bODAgek/G4bBww8/zC233MJ//Md/APDMM8/Qu3dvFi1axBVXXIHP5+PJJ5/kL3/5C5MmTQLg2Wefpbi4mHfeeYezzz77iF6TEIdTyxbVsytKGT+wB9YWtR5SEgY8/l4NN507HH84RlM0ztxzhqHFE/gjMXrn2Mm1m8l1WDrt4kGIo21HY4ib/rmaZTUe5pzu5vfvbOKXk4YQjMYxKXDPq+tZWuPh5TkVhOM6vXJtTD2uL3PPHY4vHCPXYSauG/hCMc4Y3hurapLAmRBCCEHy5tQtL6xJZ5j1yLYQjRssXPYVM1o0BjCZFP7y0decNKgH95w/ioAWJxjVMZsUltU0cN/r67n5vBFkW1SGFXWe5gAd2RvSyLapeAI6M575hD9eMpbyAQXc8fI6Pt3mZVZlKWZFYe45w7CoJjyBKCaTgklReGPtzozGAC0lEkZGsA2k86YQLR3VbZsvv/wy48aN4wc/+AG9evWivLycP/3pT+nnt2zZwq5duzjrrLPSj9lsNk499VQ++OADAFatWkUsFst4Td++fRk1alT6NfuKRqP4/f6Mf4ToDOqboizd3IAr20qffDt7g1EWLNnMh195Mgqivrupnp+cPJCH3t5EdW0jffPtRGIJHBaVgYVZ5NvNFBdmHbOToIxRcazzhTS27Q2lF/XjSgq4cqKboBbHlWNLB84gOR4xYG8gSkmhE0Ux6OG0oppMaPEEPXNtTChz0a+TNQiQcSrEsU3GqOisfCGN2sZv5liAHJuFR975gqU1nozGAO9s2M3lVYN574sGfvD4h+xpipJjN5NIGJw9sojbp47EaTbRvzDraF1Oh77LOM21W9ATEInpjC3J5+fPreJf63dxx7RRPP+LU5jgdtEz10ZDIMqPn1zBH977EgWFLKvK581NA/ZVVebig688bT6X6rwpRHd3VINnX331FX/4wx8oKyvjrbfe4uc//znXXHMN//u//wvArl27AOjdu3fG+3r37p1+bteuXVitVgoKCtp9zb7uv/9+8vLy0v8UFxcf6ksT4pCr84bZtjdEcYGDf/7iZEwmGNonl6U1nlYdhR5/7ysisQRnDu/NsKJcaveGaYrEWL3Dh6ooZB/j3YVkjIpjXUNAI6Yb6b/3zLFis6jMX1LDVk9mDcLH3/uKer9Gtt3Clj0hdvmibPEE2ekN81VDkFy7pVNunZZxKsSxTcao6Kz2nWNd2VY0PZGeW1uuex9990uicZ0po/vw4EVjiMQS7PJF+HJPgCyrisWk0PcYvjn1XcapK9tKKBqnfw8nV050U16Sz2/e/IJJD73HRX/4gC/3BFFNCllWMwtnnMjcc4bx8dceZj79CRePH9CqA2lVmYvbp45k4bIt7X6mdN4UAhTDMIz9v+zwsFqtjBs3LiND7JprruGTTz7hww8/5IMPPqCiooK6ujr69OmTfs3PfvYzamtrefPNN1m0aBEzZ84kGo1mHPvMM89k8ODB/PGPf2z1udFoNOP1fr+f4uJifD4fubldI51XdC2+kMadr6zj2kllROMJLCYFs8nEF/UBZj+zEkjWKZhVWUp5cT7ReAKnRaWk0MnXnhAOi0q2XSXLaibLotL7GN8aJmNUHOuqtzViUU388PEPmXv2UE4f1oubXkh2vH3skhO48rlPM17vtKpcceogJg7thaooBKJx8p0Wcmxm+nbCwBkc3Dg9bcoPKJp2fZvP7Xr5If7v1b8f0nMVojuSuVR0Vp9u3YvdovL62l2cOrgHffKd7GqK8v0/fph+Tct1bzxh4O6ZjaYnCETjxOIJigscWBWF3sdw4Ay++zj9sj7AHS+vZVXzNs3U+t9mNrHbH6Gkh5NLn/y41ftSP7dzRxYR0nSsZhPvbqrn+P75zHj6k3Y/79/Xn8rgXtkHd7FCdHJHteZZnz59GDFiRMZjw4cP5/nnnwegqKgISGaXtQye1dfXp7PRioqK0DSNxsbGjOyz+vp6TjnllDY/12azYbPZDum1CHE4NQY1rj6jjLtfWc/MylJsZhNZtszssVQB8pZenlPBrKc/4bmfjmftDj+nDel5zAfOQMaoOPYVOCyYTQpvXFOJJxgloOnpOiE2c+uk7pCm87u3N/O7tzfz4pUVFOXZcagmenWC8dgeGadCHNtkjIrOKtdpxmpSMSUS9Mp3MveFNdwyOfM7477r3jeurSKRMOidY8ME2CwqPTtB5+rvOk5jiW8y8fZd/wO8cnVFm+9L/dwmj+6DbiT4vy8aePidzcw53U2Fu7BVzTNINg1wZR+bpV6EOJKOavCsoqKCTZs2ZTz2xRdfMGDAAABKS0spKiri7bffpry8HABN03jvvfd48MEHARg7diwWi4W3336bH/7whwDs3LmTtWvX8pvf/OYIXo0Qh5YvpNEQ0AhEYxQ4rTz4xgZ+eeZQ4gmDSCxZ6LO4h5Mqt4ulNQ2t3l/lTraxrnQX0iPLyoACB3068Rd1IY4VvpCGyaTgi8RQFAWH1UIgEuepGSeyalsja3b42l2AVpW5cGVZsECnDpwJIYQQh1Jq3avFY+TZrHxW5+PCscX4IzGuPr0Mu9lElbswoyxCSoW7EAXItZsxAWbV1CkCZwcj1XF0350ndovKp9saSSTocC1iVU0EozpnjyjCMODZj7bywEVjADLeM6HMxYMXjTlm6yQLcSQd1eDZL3/5S0455RTuu+8+fvjDH/Lxxx/zxBNP8MQTTwCgKArXXXcd9913H2VlZZSVlXHffffhdDq5+OKLAcjLy2P27NnccMMNFBYW0qNHD371q18xevTodPdNITqbOm+Yuc+vZtXWRp64dCxZdpVfnTOM215cm7FomDSsF/OmjuCuV9ZlFFWtcBdy1/kjWbm1kXsvHI29E6StC9EZ7PKGCcV0Vn29l+NKCrj7lXUZY7LKXchPqwYxpn8ekLkArSpzce8FozrN3XAhhBDiSEite79uCLJ49nh0w2BI7xxue+mbda/TqrLwshMBJeOmcYW7kJkVpaCACli6wRxb5w3jtJpxWlUWXFzOwmVbMrLPqtwuLirvx21TRnLPa+tZuvmbn1dVmYurTnMzdcGydNfNKnchD1w0hpueX82PvlfCrIpSsmxmCrOs9MqxSeBMiGZHteYZwKuvvsrNN9/M5s2bKS0t5frrr+dnP/tZ+nnDMLjzzjt5/PHHaWxsZPz48Tz66KOMGjUq/ZpIJMJ//dd/sWjRIsLhMGeccQaPPfbYARdG9fv95OXlSQ0IcUzwhTTmLK5m1dZGnvzJWIoLnCjATS+safNu26Thvbj0pAFYVBO6YRCLJ6j3R6h0u1CAHIel0096MkbF0ZC6C+6PxMixm7GrJmIJg0hc57NtXl5ds7PtO7puFycN7kE4lqC8OB+AfgUOsiwqxcdot69D4duMU6l5JsSRJ3OpOFa0nF+zbWbW7PDRM8dK71w7WaoJXVG4ubmOaEtOq8q8ycMp651DfVMUm9lEda2XDXU+7pw6ElVR6NPJbxbvb5z6Qhqvr93F8cX5rK/z8WL1jnaz8b5X2oOYbnDyoEKsZhPZNjOfbWvk7tc2pANnLV9fXlKQDsK9dV0VQ4vk94QQLR3VbpsAU6ZMYc2aNUQiETZs2JAROINk9tkdd9zBzp07iUQivPfeexmBMwC73c78+fPxeDyEQiFeeeUV6SgkOiVfSGOnP8IvTh3EW9dW0SPHhsmAQExvc2IEeGdDPYXZVi758wpi8QRPLd/CyW4XJqB/YVanD5wJcST5Qhpf1gdY+fVe1u/088JnO/jZ/67ka0+IO15Zh2qC+17bQK9ce5uBM4ClNQ2MLy1kwZIanlq+hcG9ssnv4oEzIYQQIjWHVm9r5Ms9AXwhrdVr6rxh5iyu5oyH3uOSP6/g9bU7Oa44j13eCBaTQkJRqG0MtznHhjSdm19Yiy8c48rnPmX2Myv5bFsj86aOxAKdPnB2IBpDMV5dXcfeQJSR/fLa/X6wvMbDqL55LFhSwyV/XsEj72zGZjZx8wtrWwXOUq9P3fCbUOaiqItn7wnxXRzVbZtCiG+03Kq56Gcn8esX1lA11MU5I/rgC8c7fG9YSwCQY7cw53Q3VuXYbsstxLEoNQZbbm84fVhPFl9+Ene9vI4zR/QiHEsW6J0+fkCHxzKMZLHeYDSOBSiSwJkQQogurK05dEKZiwcuGkPf5hqfvpCWXuv+8swyzh3Zh89rG9njj6I2d5L3R+L4wrEOPyvHbuGvl59EniPZPMuumjpFQ6yD5QtpzHtxDdXbvPQrcLDbH+3w9dF4Iv3npTUNhNsImu37+ip3IfddOFpuvgvRhqOeeSaEgN3+CHP/8TmrtjYyf3o5//PWRpbWeDhreBG1jWGyrGqH7089H9cT9MlzSOBMiG8ptaBPLfqdVpXrJpVxw1lDiMUNVm3zMnFob7yh5IK+rY6aLdksJh58YyN98hz0k8CZEEKILmzfOTTl/c0N3PT86nQGmjcU4/LKQbx2dSVnDu/N79/ZRPmAAua/W8Po4nw8wRj+SHy/c2xMT+DKthGMxihwWCjqBoEzgIaAxtIaD7MqS/GG4jj28/1g359jINrxzfjiHg5unTICTU90+DohuisJnglxlNV5w3xZH2DVNi+PTC/HblHTKdjhWAJfOIbNYqLCXdjm+yvchdgsJqrKXJQUOBggX9SF+NYaAhpLNzekg2Z/u+IkxvTLQ0EBDP531vcIaHp6oVpd6+1wTGbbzDxw4WgZj0IIIbq81Bzalvc3N9AQ0KjzhvnNmxsoyrdT54sQiMb51dnD0OIGy2s8xOLJbvLZdrXDOTa13rUoUNYrp9sEzgD8keQNvPLifCKxZBZZR2uR6lpvxmM2s0pVB69/a91ufOE4gUjHmX9CdFcSPBPiKPKFNOb+YzXecIxZlaU8tXxLemIE8Idj2Mwmtu8NM2eiu9UEWeEuZM7EMvYGNe69YBT95Yu6EN+JPxLDaVV5ZHo5K7/ey38+/hFr6nyYVRP3v76B7//xQ/zN20gq3IUsXLaFmRWlrcZklbuQq08vw25SZDwKIYToFvz7Cbb4wjFue2ktPzvVzR2vrOOSP6/gs9pGdvsj7PSFAWiKxHDaVOr9UTbU+dqcYyvdhdxzwSjMwICe2d1ua2GuPblNNRpP4LSpvLupvt3vBzMrSlm4bEv6sSq3ixy7yszK1j/Xlq93WE3k2C0d1q0ToruSmmdCHEV7gxozK0vJtqmcO7KIBUtq+OWkIenncx0WltY08GV9E788cyhTRvdhVkUp0XgCm9lEvT9Cv3w7VpNCUYFs1RTiu8q1W9IB7OrmLNC9gSh3vbKOZc2ZoLkOC/9av4s5E90seLeGaxZXM6uylFkVpQD0yrWxcWcT/fLs9JXAmRBCiG4iFdRpi9OqUuC0cN2kMtZs9zGropSfVg5iYKGTW19am55Ds+1m9ITBP1bVcuM5w/nNmxsoLylIr3vzHBZKChyYEjrOrO5ZzN6VbWVCmSu9HfPzWi9j+uUzufn7gaYn6F/gYO12H9csrk43Bqgqc3HVRDf/3ribh9+pYd6UEdx87nBqG0NY1WTH0msWV1Neko9hgCeo8f0/fgi0rlsnRHcmwTMhjgJfSKMxlLwLt7TGg9Oq8sys7wFgNSe3aC6v8RDXE2yo8zF9/AB+9/Ymvj+2mF65NgIRnVy7mSG9srGrJnrKhCbEQXFlWzllULJD5pzT3SxasZVfnjmEG59fk36NRVVY3WKhOrtyEGZVocBpwTCSY/fk0h6ScSaEEKJbSQV13t9n66bTqrJwxonc+9p6bjx3OK+u2Un1Ni8vXVVBSNOTHR5LCqhwF7LbH8HTFOUnJw/k4Xc2cfH4Aek1b1GuGYdVZcueIGVFOd0u4ywlz2nlgYvG8N4Xe/AEovysahB/WvoVI/rm0TvXjmFAIBJnfGkhz84ezy5/hHyHhUhcZ9bTn/C3K07i5MEucu0W7nxlHUs27kkfO7Wb5d1N9QzpnZN+PFW3bv708nZ/7r6QRkNAwx+Jkeuw4Mqydtv/RqJrk+CZEEdYnTdMnTfE79/ZnK5tNquyFIspeRdppy/CzOa7cFc+9yn/O/t73PPqeob3zQOgdm+YPIeFXLuZXIdFJichDoFgNI7FbMJpVTltSE8A6vfpYnXls5/yxE/Gcfer6xjevFANRHRURaGkwIFVUegtzTqEEEJ0M6mgzk3Pr84IoM2bMoJHl2zmpMGF3P3KOpbXeJhzupuVX+9lUM9sABYu28Ij08v5+8parj9rKHsDUarKkvNw7d4wNrOJrz1BKgYVclxJfrdf9/bNd3DeqCIaQxr1/iiThvemd649vSvl64YQWjzBL577lLEl+cysLGXOomQWWmMoxs//soq//uwkLhk/gEuaO4f3yrWxZruPSEzn8fe+Yv708ozPTNWta+tnfyBdVoXoKiR4JsQR5Atp/PbNjVx+2uB04AyShT9NpuRdH7NJ4eoW28G+bggxb8oIwrEE/nCMvvkWsq0qxZLdIsQhsX1vCE9Qw6bCW9dWscsfpbw4v9XrvqgPcPn/ruTRS08grhs0tRiPNpNCL9k6LYQQopvqm+9g/vRyvKEYQS1OUNPJsZvZG4xyxrDe/PdbXwCk51fVpAAQ0vR0GYRdjRGKCuz0zLETjumEojq5DjPjBhR0+6BZS3lOK1o8QZ88hRy7hQQGoahOjsPMwEInO/0RFlx8Ap9ua0wHzgBi8QTlJfmQ/NEz+5mVALxxbRW9c+381z8+p7wkv1WjAUjWpNvX/rqsdpStJkRnJMEzIQ6zVCpzSIths6pcO6mM+kBm8c1oPJHOONvtj1Beks+CJTWtjlVV5uJ+aQwgxCGzfW+Im/+5mq89IZ6d/T22e8MkjOSYXL/Tn95CnfJFfYAzH3qfKreL26eNoCGgkWNVJXAmhBCi2wtqOre+uJalNQ24sq38/YqTOXFAAYFoPP2aaDwBwLKaBqrcLpbWNBDSdBYsqWFB82uqylzcOW0kBU6rZC+1IxSNE0kkWLJpN5NG9MZuMWExmbjjlXVtdj5NbY2dWVHKTl8EPWGkn/OGYixcvoX508uJxBJctejTVu/PaaOu3YF0WZXgmehKpNumEIfRTm+Y19fuonZviBybBbNi4rNaL7HmhUOKzWzCbFK4ZnE1ewJRbp8yslUr6aoyl3TUFOIQ8oW0dOBs0U/H88GXHha8W8OHX3nIc1g66Kjp4vZpI4knDEpdWTImhRBCdHu7/RHm/uNzltY04LSq/PmyE/nNWxvplWMj2/5NvobNbMJmNvHE+18xs3Jg6/Wuu5C7zh/JoJ7ZEjjriKKwfW+YcCzB79/+ApMCmq5z+9QRVLldGS+tchdy+9SR7AlEuWZxNWaTkm46AJBtV1le4+Gxd2v4fLs3namWMqHMhSu7dRBsf11W28pWE6Izk8wzIQ4TX0hj694Qb6/fxaUnDUA34KF/beTqSUN4dfVOqspc6bs11bVe+ubZKS/J53dvb+bx975iVmUpM5o7DOU3dxiSL+lCHDr1TRFOGlzIvGkjCMZ0euXaWV7joXqbl7NG9GZsSX5GR81UPZHd/gjVWxs5xV1I79zu2fFLCCGESKnzhvm6IZhRy/dP79Vw49lDMVCwqko6k7vlmnfOouqM9a7NbKK+KUoPyVbar1BMJ89hYVxJAeeO7I1JUbjntQ38vGowt08bQb0/ijccw2ZOdtO84NHlhDQ9nYFW54sAUOkuTNd4XVrj4RenuTM+Z0KZiwcvGtNmBllHXRE05lMAAQAASURBVFah7Ww1ITozCZ4JcYiltmkqwBPvf8mlJw3g+ZW1XH/WMK6dNJTGkMbCZVt4eU4lt7+8luU1HhYu28KjF5/AnInJCWt5jSe9bVMyzoQ4dHwhjcaghsWkYFJMVG9tpKqsJ5FYIr2VJKQlu1ItnHEi//3Wpowt1BXuQq6eWEbffDv9ZKumEEKIbi6VcTa9ufi806py7sgilFF9iBsQicWJaHq6GVbLNe+Cd2sy5tiqMhf3XzhatvodgKZwDLtVpUe2FUVR2BuKsbzGwyXjB/DLJz5i4YwT+cN7X2Zsq0x11IzEdO55bQNVbhfzpo7g4j99lH6N3aLy7+tP5f+zd+fxUZX34sc/Z87sSSaTzBBIIIHABAIJ2AACmgQFUUHApfb2CrRls2oR1For7qjg2nurFbR1A2wrePvTuuFuoZoEUYFUdmQkkkgCISEzk8ksZ7bfH5MZMklYxKhAnvfr5avNbJnRPHOe8z3fpdkXIEWvwZp89MmZR5uyCkfPVhOE05kInglCF2o7ceaN+cUMyUrlzf/s53cX57Po9W3MKs6lR4oOjxIiEApRlJMWz2iRAH8gzG0TBxMhgi8QxmRQk6pTkylO0gXhOzvg8FLd5MGcpEGNjBIKccfkIXiVEElameY2JQwNboWrntnA9eMGcOvEQXiVMEk6GYNGRq9WkSXWpCAIgtDNtc04m13Sn+w0A8/PPJvFa7ZTZm/kzQXFtPiijexnrvg8nsmthML0SNFyz5Qh+INh/K17XpPY856wFIOG6sMectKNBEJhnN5oiaROrYrvYf5xzTnceEGIYDiCUSujU6sIhMK4fAGWTisiM1XPBzsO0NCmF3OqQcOAjOQTeg9Hm7J6rGw1QTidieCZIHQRp0fhnte3cVa2mVnn9iMQDDF1WCYqSeKe17dRbm+MX5UrtVkIheCLGkengwGKbRamDMti7ACL2EQIQhdwehR8wTChcAStpKLZHyQCLHp9G/PG5ZGepOGgy5cwIMCjhPjDe1/yh/e+jDcvTtapyRClmoIgCEI3F5u0OPPcfrww+2z6WYz8/erR3PXq1nj5ptsXQiNLHHT5GH6UYVglNgsPXTEUWZLEnvdbSNLKJOvUuP1BQuFIvIdZZY0jvpd5d8cBKqubEgYfxZTaLJyVk5YwXbz0JLLFYlNWG9zKCWWrCcLpTAwMEIQu0tiicNWoHCqrm1j4yhZSDFr8wTANbj/lrQctnVrFut313DO1gB11ThaMt1Gal9jUM5ZSXTzAQm9RqikI34nTo1B1yI3DG+DO17by2deH8QRCLHl7J3VOH2X2RpL1Mh/uPIitRzLzx9k6DgjIs3LvpQXoVJIInAmCIAjdntOjUOf0sbPOxYAeSfztk68JRsDtC8YDZxBtRF9ub+CVjd9wz9QCStodX0tsFh64Yigmg4asdBE4+zacXoXsNAPJOjVJOjkeNGs77Oiog4/yrMwuyWV5eVW8ZUXsv8XJBL1SjVoGZCTzk5w0BmQki8CZcMYSmWeC0EWC4QgrKqqiPcxmjeS+N7dz26R8AqEjo6AraxzsqHVyQX4GEpBm1HLpsExuvCAvnlKtVkmYdGrR40wQvqNah5cH3trBTRcO5P5Xo/0FF07M56G3d8b7ggDUu/xs3tdEYVYq9S4fCyfmA9HMM71axpKkQSVJ9BYbe0EQBKGbq3V4WfjyFqaPyeHRnw3jnte2cVZOGkTggMuX8Nh6l5+t3ziYMaYvf/pgN7OLc1k4KR+3L4RJr8akV5Mt9rsnxajV8HWDh1yrEbc/wq46V2tfuaqEYUeyJLHkskJ8wTAubwBZJZGsU/PTP6/Ho4Tok2bgrQUlhCMRQqHwj/2xBOGUJjLPBKGLRCIRinLSeH7mSHq2Tu1ragmQlnRk0szy8iqmj+7Lk+vs5PUy8cg7u9jX5MXpDXC4RcHtC5IiAmeC8J3FyqivPW8Ajc0KZfZGjFoZlSTFr4rHShwWvrKFWycO5q+ffM2+Ji+Hmv180+QlHI5gSdJgMmhE4EwQBEHo9pwehYUvb2FTdRMDeiSRZTZwVk4aFxf0xBcIdXh87Pj64oZ95GakAFBz2EskEhGBs+/ImqxFLUvMfWEj3kCIOy8ZzEufVVOUk8bSaUUMyTRh0Mj0StVTc9jLlX9ez38/s4Gf/eUT3P5gfPLm9loXWo2K/35mQ7xvmiAInROZZ4LQRdQqFZXVTSxba+epGcOBaDZaMBSJ9x7wKKH41SBHi8INE/LQyCrcviAmg5pkjSxKNQWhCzg8AeaNs+H2BTEbNaycdTZZaQaaWo40xW3bF2TGcxt45MphZJh0uH0hepnEFXFBEARBaKuxRWFcvpUHrijE6Q1g0Kr5orqJIZkmrMm6hOMqRIfvxI6vvUx63P4gWWZN9EKxuCj1naQatWSnGcgw6Wj2BwmGIhT2TqXEZiUciZChlYlE4L0dB3j6o714lGhws9RmpdzeEJ8ebknRsvdQCx4lRIpec5zfKgjdm8g8E4QusP+wh3vf2BbfLMQyWjZXN+EPhBL6DXiUEMvW2nm2fC/1zX6cHoWeJp0InAlCF6lzeFn/VQMt3iAZJh16jUx2upHFb25HVknxx7XtBdLgVpj7wkamLq3gqX/bSRaBM0EQBEFIIAHn2qxs/PowJr2G+97YRpm9EZ1ahUGr6rTHVoNbYXlFFUadTM8UHWlaWQTOukg4HOKBK4ZS2+RDq5Z5/MM9/OwvnzBrxeccdPl55N1dPPbBniOBs9YerhcX9OS2iYNJ1svsPdSCWiWd1LAAQehuROaZIHxHNY0tuJVQQoPUtk07Lx7Sk2fL9lKUk8ac4lz8wTA6tYrKGgerP93HoqkFaFRiwpAgdIU6h5eawy1o1SoyUnWEw3Dfmm3MKs6lzN7IiH4NlOZZKdvTkJAJOqc4F4A+aQYRyBYEQRCENpweBZc3QGV1EwW9TfwkJ439Dm9871tZ4yDXYmRE37SE42psz1vf7EcrSWhkiQyx3+0ykkrGGwgyrI+JFL2GUpuVMnvn+xtrso5Ug5pLnihnRN80Fk6M9mU26dVUNbSw+LJC0ehfEI5DZJ4JwklyehT2N7YQIdo8ta3YlbeiHDOzV37ODRcM5IsaB3Nf2Mi8Fzcz94WNfFHjYNHUAnIsSSJwJghdoLqxBadPIcWgQQmG8QfDLF6znTJ7Y3ya1DMf72VuSW58ym0sE3Tl+q/pa0kSgTNBEARBaKPO4WX3wWbc/gAj+qbT5Amw6PVtONr0x1peXkV6ko4F422MyElj2Vp7fM+7cv3XFA+woAIROOtC+5s8fP71Yb5p8jF5aQUznvuUe6YO6bi/qagiWafmcIuCRwnhUUKU7WkgQoQn19rJTDWg18ikGUXJpiAcj8g8E4STcNDhxRcM0+D2kqTveJWm/RWfYDjMXZMHE45AszeAyahBp1aRI07SBaFL1Lt8bPz6MKP6pfONw8uarXVkmPQdhgN4lBDzXtzMNWP7J0y5Tdaq0cqSCJwJgiAIQiunR2FTa08zrUriG4eXUATK7I3Mas1oguix9fpVm7n2vP78fuIgfs8g/IEwRp2MLEloZRW9zIYf8ZOceZr9QQZnplLnjF7Ar2nyctmTFR33Nzo1GpXEFX9ez9Jpw+PPr3f5KeyTyl2vbeWhK4aKrDNBOAEi80wQviWnR8EbDOH0+rAmG2j2BuNlmm0dueLzNWaDNj69L1mvxu0LkC4OUoLwrTk9Cl/Vu6msbuKrQ26cHgWnR8GnBCnKSaPZH2TpOjsVbbLNgIQ16lFC8b4gVz2zgT+8txuNLIkpt4IgCEK3dLRjq7tF4Se9U5GAQDjC0nX2+ETG9ntfjxLisQ/2cOmyCv7w7m4yUnQYNDJmg0YEzr4PEahzeo+7vwmFI2zYe5irRuWQrJcTXuKiIb34SU4atU4vTo/S2W/p9G9DELorkXkmCN+C06Pg9ijIkkSqUc8dr27lzkuGsLy8iiemFQHEhwYAFNss3HdZQXzCn0mvJhCOYDFqxRUeQfiWah1eFr6yhbI9DfHbSvOsPHBZIZIkcc9rW1k4aXCHwR3AUddoaZ6VBy4vFMMBBEEQhG7paMfW+y8rIEQYHTJqlYTDF6DC3hjvoXXU46rNyv2XF/Cnf33JwkmD6WnS/7AfqJuItP7vsc5B7r+0gMMtCiP7pZFjMVDv8sfvq6xxkKRTU1ndxNRhmTS2KB3OTTr72xibZ+XhK4eRJQKiQjckgmeCcILqmjwowTARSeLuV7dy66R8KuyNSEBRjrnTBqkHXT6afdFR3kl6NWrAlCQCZ4LwbTk9SocNHMDOOheBSIQWb5AyeyMLWidKwZGr4hX2xqMOB0jRqskSU78EQRCEbqizY6tRKzOibxo+JYhJp0GJRPD4Q7T4osfXtsfW9nvfVIMGWQVE4OYLB4nA2feoxR+tfDnaOUi9y0eYCEadmvpmH71MBn7x/KcU2yzMLs7lhtWVXDSkJxX2Rpas2cldUwbj9BwJoB1t3/XxngZue2ULS6cVifMZodsRwTNBOAanR+GQ249aJXGo2U+yTs2St3dSYW9kXusmwhsIMrs4F6hi2Vp7/Lmxg5NXCfG1q4UROWlkiuwWQTgpDW6lwwbOmqzlpWvH4PIoKKHoNdhk3ZGShPZXY2Ol1KV5Vu6dWoBBlkTgTBAEQei22h9bjVqZZdOLeGVjDZeflYXLH2TJ2zuZPy4PjSwBHY+tsb1vaZ6V68+30SNFh16tEsOwvmdpRm2b/xYdz0Huu7SARrefFL2WcAS8gRBPzhjO+zsOcsPqSopyzPHHl9kbUEJh5q+u5KErhtIn3djpvivm4z0NNLg7Zqp1NadHocGt4PIFMBk0WEUCgvAjE8EzQTiK2iYPDm8ArVrF51WHCYYj5FiS4inRsb4BH+9pYNs3Topy0hKu+FTWOFj96T5unZhPltkg+ikJwnfg8gUSfs5OM/C3uaOQJNh1oDk+fCMc5pij2nunGXD7AuhkiSyxJgVBEIRuzOULYNTKzCnJpSjbTHqSlifX7uHGCYP4ZG9jfN9726R8/rWzPn58bZ/pZDZo6J1m4FCzD4MsAmc/BGuylhF9046adaZRqfApEZJ0YZats3P/pYU4PAGG56SRNVnPgB7JrNtdH3+9Fn+ITfuauO2fW3jkymEd9l3tNR/n/u9KlIwKpyIRPBOETuw/7KG6ycOydXbmlvTH1jOFwy1KvEkqRKfUlNgsPPPxXpZNL2JFeeJVn9I8K4svK0QrS2SJTYQgfCcmvSa+wR/ZN40UvRp/MEwEyDDp+WRvI6U2C//afZD5421AhLJOss2UYIieKXoRzBYEQRC6vVSDhienD49PbPQHw1w/PppllmHSx/e99S4/W79xMLukH7Hja9uMs3svLcAfDJGXkSIyg35Ai6YWcN8b2zs9/3hvZx2ffnWY6aP7UmFvxBcMcdWzG6KPsVm4e2oBf/tkX/x54UiEJ6YVccPqSvY1erAkH/u/Y4pe8/18KETJqHDqEsEzQWjH6VHY7/QSjsDMc/uRazHS2KLEr+bELHxlCy9ePYbFa7Yzf1X0qs+sNtktyRqZFINGfLkLQhdI0atZPnMkS9fZeb1yP8/PPJvDHj8t/hD+YJjl5VW8Nq+Yh9/dybDeZiYNzWRWu/6DsgrMRi2Z4oqlIAiCIKBTqzBqo3tbWZIwaFSk6DUd9r2xPe+j7+7krJy0+PE11aCht9mAQa2ipzn5x/wo3Y7DEyAQDDFmQDp3TB5MrSMaAK2scXDJE2UMzzGz5PKh/Owv6wHw+I/0hC2zN3Lfm9u5alQOy9baKbZZWP9VI5XVTcwpycXhDWA2auK97dobm2fFepzg2ndxKpSMCkJnVMd/iCB0L02eAEv/tYdf/3UjapXEhr2NJOnU8VLM2DjoBrfCjOc2MLs4l/+7dgwlNiu9UvXkWpIwaWT6WJLEF7sgdBF/IMST6+xUN3r4+9WjuW/NdpJ00es/OrUKjxLine11TBjcEwkYnGkiw6QjWa9GkiQkSUInq0TgTBAEQRCIlsU1+4KEI/DhzoOEibCj1sXdr28jud2+N7bnnT66LxcX9MSoVdM33UhOmgG1BD3FsfUH16IE0Wlk1n/VyC+f/xSADJOO4gFW/u/aMcwfb8OjBGhwK0D0ImRbFfZGirLNlNqsLJpawEufVcdv06lV1DR5mV2cGz/viSm2Wbj/ssLv9Rznxy4ZFYSjEcEzQWjD6VG4+7WtlNkbuWZsf1aUV5Fh0iMBB10+dtQ6Ew4kDW6FuS9s5JF3dtEjRcdj73+JRvRSEoQutb/Jw36Hl5G56fz96tF80+Slwt6I2xeissbBQZePYpuFpz/aS4ZJz7Ple7niqfVMXVrBr57/jBUVVRQPsIgeLIIgCILAkbI4jaxi2To7w/qY43ve2CT59vve2J536tIKnly3hySdGpUkiTYIP5IWJUQgFGbBOBuDeqXE/9tMe3YDD7+zi0hE4oOd0Z5mpXlWDFq5w2sk6dRMGtqL/1Q3cdWonPjtlTUO1CqpdbBAGs/PHMlTM4bz/MyRFOWk4fIq3+tnMx2nJPT7LBkVhGMRZZuCQHQTcbhFQQmFKWtNTy6xWXn8wz1MG92XCJCZauDXpf15tmxvwnAAs0FDnzQDB5xe7p46BJNBfKELQldxehSaWhR6mvRcOLgn+5u88R4syXqZ5eVVPDl9OPPH2Vi2zt7pgIAUjUxvsbkXBEEQBADqm/1s2teEEgpTYW/ktxMGxve8wAnte9UgJlb/iMwGDcFQGEuyjslDMxMGBhx0+fAFQjz90V5KbdEeaNWNng6v0eIPcser23h+5kgsyTogmr22q84FEO8b294VP+n9vX42a7KWsXlWPu6kdPP7LhkVhGMRwTOh26t1eLnn9W3MH5+Hxx+M3x4MRYBoSdgBp5cXN1QzNDuVmyYMBKIHlFSDRNWhFrRqicxUAzpZJUo1BaELHWz2kaRTc9fr2xI2hhBtYDw8x8z1qzZz7Xn9WTgxH4iuTb1axpqkQSer6CHKSQRBEAQBgDqHFyUY5rV5xdQ0RQMqbfe8wHH3vX3TDKLK4keWkaKjsUVh49eH47dJUnQIRI8UHXVOH8/+aiTZaQYaW/z89h//SXh+sc1CZY0DiGagRSKw+tej8SohFk0t4P43t3f6e3+I4FWqUcvDVw7jtle2JATQxuZZeeTKYeJcS/jRiOCZ0K3F0tZH9E3jf9/bxW2XDI7fZ9RF05sraxx8Vd/MrZPyWfzmdh77YE/8MaU2C0uuGIoKMInhAILQpWod0cEd97y+jQp7IzNG903owdJ2aMdjH+yJr81Sm5UlVxSKwJkgCIIgtOH0KDR5FLZ842DN1rp4lnbbPW+xzcLvX97Cql+POeq+V2Rz//hSjVpc3gD9rEmEwhGWrbMnNPcvzbNy95QhuP0B9Bp1vPcZRANns4tzuWF1JRDNQJv7wsb48xZfVsj9lxXiD/54wasss4Gl04pocCs0+wKk6DVYk7XiXEv4UYngmdCtNbgVNu1rYuHEfB7/cA93ShKlNitl9uiBothmYXl5FU9MK+KxD3YzqziXWyfl4/aFMOnVpOjVaFUSvUQvJUHoUtWNLew64CI7PSleSh0LnMV6sKyoqGLGcxt45MphLJyUT4s/RKpBQ4pWRqeR6WHS/8ifQhAEQRBOHU5vgIfe3sms4txoc/ictHgf37Z73hUVVUx/Nnp8vbXN8dWkV9Nb7HlPGRLQM0XPZ1WNHbLvZZXEYx/sZsH4PCRg1dWjcXgD8b3UDasr8SihhAw0gLI9Ddz9+jYe/ulQJg3NZOa5/eJZ//XNfryBEHsPubEkff+BrFSjCJYJpxYRPBO6rdjVtxsvyONQsw+I9ni4a8pg7l+zg3W76zv0UYJoqVgvkx61LKGWROBMELra/sYWDrf4GNLLxL4mb/z2WOBs+ui+rPp0X0IPFpc3SE6agZ0HmxmWlSoCZ4IgCILQyulRcHsDuP1ByuyN8d5msWDZrjrXUXuHqpDonaonW2SbnXK0GhmnT6GwdyqHPQGcrcGxcnsDO2qd/Lp0AEaNjC8URlZJrP60Op4gAB0z0GLK9jSwr9HD7f/c2uF3FtssFOWksaXGwf2XFeL0KiTrNVh/gGCaIPzYRPBM6JZqHV4eeGsHN07I47yBPQiEw9E7InDYo1CUk8ZZvc1YkrVMGZoVP0GHaJPVAT2S0MoqskTgTBC61CGHlxCQnqTHrYTi6w6ObPJXfbqPIVmpFGWb482Ls8x6ZGB0v3SxeRMEQRCEVrUOLw3NHtKNejxKCDjS28yjhLhhdSXXntefopw0prRpPA/RPW8/a5IYhnWKcngVwhE44PJT5/TS06THHwxTlG2md6qBZJ1MfbMPg1bN/iYv9146BH8wTIs/hFEr8872A/EMtI6vHej0d1bYG5lTnMuytXbufG0rRTlpLFtrZ2yelYevHEaWaJchnMFUP/YbEIQf2kGXj+rGFm6dOAiAZl8Qg0am1GbBGwiSpFPzn+omZq38nMufXM9+55HMF71GZnRuOlqVCJwJQlerb/LgC4U54PTSooRo9gXZXN1EaZ4VOLLJjwXOAPpZkvimyYNKksiyJInAmSAIgiAQ3e/uqnNR2+TBYtQTjEQbw8OR3mYQPbY+9sEeLnuygv1OH3qNTKpBQy+TnqJsM8kaWRxbT1FOT5B/7TzIi5/uo9bpS7hvv9PL4x9+Sa9UA6FwmLyeKUxdVsEDb+8kxaDmYLOfZWvtnQbOIHrOczSx4GqFvTG+H/t4TwO3vbIFp0c56vME4XQngmdCt1Lr8HL/G9sw6tTc9eo23viiDr1WRaNb4e4pBdjr3Tz90VfccnE+pTZrfETz3Bc2svqzanqbDaTo1GI0tyB0sf2HPbSEwtz35nb0WjUubwCjVmZ5eRV3TxlCSZtN/rK1dlau/5r+1iRUUoRz+1vIEeUkgiAIggBE+4be/s8tvLfjAJZkLSHgzte2QiQ6VGd5eRWzi3PjATSIHl+/qHGQlaonM1VPsk4mM1VPT5FJdMoyamWeXPcVvxjTly9qHMx9YSPzXtzM3Bc28kWNg9svGUJ1o4cn1tp5b8cBinLMzC7OxdGiEIlE4hcn2yuxWbAmazFqOw+gxTIXgYQKgY/3NCQMJvi+OD0KX9W7qaxu4qtDbhGwE34womxT6DacHoV7XtvGTRcO5JF3dlJmb2RWcS6RCCTr1LyzvY5Mk56bJgzkkXd3cVaOmVnF/eJlYelJWgxqleilJAhdqN7lw6OE2LzvMFlpBm67ZDD1Lj/mJC06WUVRjpm5Kz/nr3NHUefwJTS7feCtHdw9tYA+InAmCIIgCED0QvG9b27nl2P6YdSq8AcjHG7xUmFvJEyY+y4rYNHr2zr0NssyGyASQQmFMenUZIoKi1OeQStTlGNm3oubuX7cAG6fNJhmXwBDa9Drra21bK1xsnBiPnWOaGZarPVFqc3C4ssKuPv17ZTtSeyDNqs4lz99+CVzSqLlmW21HzDQNpAG0ORRcHqU7y1bsdbhZeErWxLesygZFX4oIvNM6BYOunzUOn0MzU5Fgvj0Pn8wzLrd9Zj0arbUOEhP1vHQOzsp7J3K+QN7kKRTk51uILP1Kpy4+iYIXae6sYV/767niQ93M7xvOkvX2tnX6MHhDRAMhWny+Jk/zkaOxch//eUTvIEQ2ekGknRqJg/N5N6pBfQVgTNBEARBAKDO4cXhCXBWtpkknQqQONyixPtXHXD6OdziZ+pZWfzj2nM4p7+FDJOO9CQtB50+/lPjwKCRReDsNKFXq5g/zkZRjpk/vPclP/vLesrsDTT7gniVEBMG9yQ/y8QVT61n1srP+aK6iemj+7K8vAqVpOKRd3dxz9QhPD9zJC9dM4bnZ46kKCeNG1ZX8q9dhzinvyXh98UGDCwvr4r/3DaQBuDyBVmwupJah5eu5vQoHQJnIEpGhR+OyDwTzlhOj8LhFgWVJLH+qwZyrcmMH9STA64jPQF0ahVPf7SX4tw07plawP1vbmdIVipDe6dS3+wn1aDBrNdE+z+Ifg+C0GVqHV5uf3Urv79oEEV907jzta1U2BuZMbovOrWK61/czN/njiIYIaGBcb3LT73LR7HNKjLOBEEQBIHontfpDXDA6UWSJCYVZBIMh3nw7Z1Mbz2uAix8ZQv/d+0Yhht1PPT2Dga3Gb6TatBQYrOKqZqnkaw0I4FQJGGfpFOrqG5sYWRuOltrHEwZlsmInDSsKVr+tbM+PiAgRa9mdnEub2+t47EP9vDUjOHMe3FzwuuHIhGWzzobo1YmHI6wfm9j/PmdTeossVmIRCLxYNbSaUVdev7U4FY6BM5iYiWjP8T5mtOj0OBWcPkCmAxi0mh3IoJnwhmp1uHlnte3cdOEgUhAjiWJVKOGUDiS8LjKGgdFOWZ+tXITd00exL1TC3ArIZq9AbLMGpK1MhoQpZqC0IUOOLy4fAHOHWDBoJVRAmEqWrNBYyWZORYjv3j+M5791QiKbdbWAQIBUvQaBmUki8CZIAiC0O05PQoHXD72O7xkmQ2YDBq0ssyiN7bx+4n5lNsbmV2cGx8QUGFv5L+f3sDSaT9h0dSC+LE1S68hRSuLY+tpyGzUdNgn9bcmoYTDDO+bxv7DXjZVN7G8vCo+HKDYZqGy2sE5A9J5+qO9QMfyS4BAMMzcFzZi1Mq8ePVoirLNrJh1NkadzNZvnAmTOmPlnrIkAd9PMMvl63wCaEzzce4/nhMJiomy0e7tpIJnoVCIlStX8q9//Yv6+nrC4XDC/WvXrv3Wr/nQQw9xxx13cOONN/L4448DEIlEuO+++3jmmWdoampi9OjRPPnkkxQUFMSf5/f7ueWWW1i9ejVer5cLLriAp556ij59+pzMRxPOAE6Pwj2vb+MXY/rGe5sBzB9vY2xej4QNxPLyKp6YVgRUccerO4AdAJTmWVlyWSEqoJfYSAhCl3F6FAKhMAa1zIicNIKhCC5fMH5/ZY2DHbVOZhfnsqKiiol/Ko/fV2Kz8OAVQ8XmXhAEQej2ah1eFr68hTL7kZP4UpuVOycPpszeyPX+aFCj7XEVohMSpz37GRA9rj5wxVDUQG9xbD0tpRq12OvdXPmXT4Do+U5ldROV1Q6emFbEioqq+AVKgFKbhZmtGWN//sXweBZZ+/LLtrd5lBCHW5ToALVfj+GwO0BPk55l04eTpJVpUUJsrm7ihtWV/M9/nRV/je8azGrPpNcc8/6U49x/LCcSFDte2WhXZ9oJp56TCp7deOONrFy5ksmTJ1NYWIjUGmE+WZ9//jnPPPMMw4YNS7j90Ucf5Y9//CMrV65k4MCBLFmyhAsvvJDdu3eTkpICwE033cSbb77JSy+9hMVi4Xe/+x1Tpkxh06ZNyPLRR+wKZ64Gt0JRjpnny/bGA2cAy8urmDw0s03ALLqBiDVMnXe+Da1aRbJOHc04U0n0Ej0fBKHLxEqpPYEQsiTx5Do7vx47gLSkI5ud2Ppc9ek+inLS4mUIqQYNOWkGUU4iCIIgdHvxk3h74kl8mb2BOme015Rajp6fHe24ajZo6JNmQCv2u6c9c5uATdvEgPZDITJTDby/40A8Y6zFH6I0z8r159uY88Ln8ddoX5LZNpDmDUQDZef0tyBLEsk6NWX2hnhmW9sMtu8SzOqMNVnL2DwrH3dSujk2z4o1+eQCVycaFDtVykaFH89JBc9eeukl/vGPf3DJJZd85zfgdruZMWMGzz77LEuWLInfHolEePzxx7nzzjv56U9/CsALL7xAz549WbVqFddeey1Op5Pnn3+ev/3tb0yYMAGAv//972RnZ/Phhx9y8cUXf+f3J5x+XL4A5w3M4A/vfZlwu0cJEQpHGNE3LeFgEusPsK+xBY2s4pzcdFIMGvHlJwhdqLbJgzcQxhcI0dLaa6PM3sjskv7Uu/yU2CyU2xvxKKH4+izKNgPQ12IkVa+hT7rY3AuCIAjCsU7iYx1Kyu0NlOZZKdvTkHBcjV2QUqkk9BqZDNGa5LTXNqjUdh8VC5pZknQk6WWmPbOBBveRpvq51iTunToEJIm/zx2NEgofs7dZsc1Cj2QdWal6fIEQ/mAYTyBEVqqeJ6cP528bvo4H2Uq/QzDraFKNWh6+chi3vbIlIYA2Ns/KI1cOO+lztxMNin3fZaPCqe+kgmdarRabzdYlb+D6669n8uTJTJgwISF4VlVVxYEDB7jooovit+l0Os477zzWr1/Ptddey6ZNmwgEAgmPycrKorCwkPXr1x81eOb3+/H7/fGfXS5Xl3wW4dRgNmhobOl82sq/dh1kwXgbS9faE0YvF9sszB+XR2+zXpSEnQLEGj2z1B724FICODwBknRqQuEIza2lmpurm/iqvpm7pxSweM32eABt2Vo7JTYLS64YSpoIZp+SxDoVhFObWKNnLqf36FMFK2sclOZZeebjvTw1YzgAZXsa4vveYpuFBePz6JduFIGzU0BXrNNYUCmWPRXbRxXbLCwYl4esokPgrNRmIRSK4AqE0MoqQpEIZqMGWSUxZWhm/AJmZY2DG1ZXUpRjZuHEfDxKgLe21iWUgkbPo2zcfOFAfv70BoptFhZfVvi97N2yzAaWTiuiwa3Ee7xZk79bw/4TDYp9n2WjwunhpIJnv/vd7/jTn/7EsmXLvlPJ5ksvvcTmzZv5/PPPO9x34MABAHr27Jlwe8+ePdm3b1/8MVqtlrS0tA6PiT2/Mw899BD33XffSb9v4dTRWWNHtazCoOm8ZPfpj/YyrLeZS8/K4sYL8giGIxi1MmqVhElktpwyxBo9czg9Cs1KALVKxdK19vhm6/mZI4Ej5QWPf7ib2cW5LJyUj9sXwqRXY9KrRZnmKUysU0E4tYk1euaJ7Xt1R9nnQvS4+taCEu55fRvzXtzMNWP7c+MFeYTCEZL1aowamXQxHfCU0VXrNMtsYNm0Iuqb/Ti8AfQaFUQkJCnCHz/4sl3gzMr88TYOuf08V763QyDs6pL+HHYrDM1OJUmrpijbTGWNA5UET7TZy8XEfr7v0gKWtr6HNOPRA0nfdVplqrFr/35PNCj2fZWNCqePEw6exUonY9auXcs777xDQUEBGk3iH9w///nP475eTU0NN954I++//z56/dGverQPzkUikeMG7I73mNtvv52bb745/rPL5SI7O/u471k4tXTW2PGhnw6FSIQIxIcCtOVRQry44WvunlqA2xfksEdBI2vIStWLTcQpRKzRM4fDE0Cjkrn7jW0J67Ht4I5YeQFAzWEvZoMGk0FDtghmn9LEOhWEU5tYo2eWtvve+eNt8ZLM9opyzGz8+jCLLy/EEwjzdUMLSjDMgIxkeopMs1NOV67TWFDpq3o3F/zxIwCMWpk5JbnMGN2XJJ0atUriy4PNbKhq5LOqw0cNhBXlpPHGF7X8fuIgrnp2AwAXDenZ4fFtn+dRQryw/utjllCeitMqTzQo9n2VjQqnjxMOnqWmpib8fMUVV3ynX7xp0ybq6+sZMWJE/LZQKMTHH3/MsmXL2L17NxDNLsvMzIw/pr6+Pp6N1qtXLxRFoampKSH7rL6+nnPPPfeov1un06HT6b7T+xd+XEdr7JiREv3vumB1ZcJQgJhim4VbJw2m3uUjWa9hZUUVD/5UfNmdasQaPTM4PQqhSAR/KNxhs9V+cEesnKQ0z8oDVwwVgbPTgFingnBqE2v0zNF+3xvLLrvr9W0d9rmx/lQvX3cuTm+A1Z/uY8kVQ0Xg7BT1fazTtmWIsRJOgKd/OYLMVD09TXqG9k7lsQ/2dPr8Cnsjc4pzWbbWzo3BvCN9aVsnuB6NPxA+5sTJU3Va5bcJin0fZaPC6eOEg2crVqzo0l98wQUXsHXr1oTbZs+eTX5+PgsXLqR///706tWLDz74gKKi6AmWoih89NFHPPLIIwCMGDECjUbDBx98wM9//nMA6urq2LZtG48++miXvl/h1HK0xo7+YBigQ7PM2FCAyhoHVQ0t5KQbUUmwaGqB2EwIQhdzehQOuHw0+4IYNDIt/mCHx7Rdo3dcMpgWf5AUvQaTTk1vETgTBEEQhLj6Zn/CvtejhGhw+xOmZ8b2ubFG782+AGajhjsmD8FsEL2YugunR8GgPVLWG8s8G56TRopOTYs/yK4DLlL0lmO+TuycKgIsuayQrxs9pBiOHTowG4/do/ZUnlb5bYJiXV02Kpw+TqrnWVdISUmhsLAw4bakpCQsFkv89ptuuokHH3yQvLw88vLyePDBBzEajUyfPh2IZsPNnTuX3/3ud1gsFtLT07nlllsYOnRofPqmcGY6WmPHtuOR215paWvV1aNJ1sroNTI9ROBMELqM06PQ5Alw92tbKWtzJfydG0s7fXxsjU4emsm+hhbG9LeIwJkgCIIgtHJ6FBwtCk5vx32vUafudJ8bE+vTJIbudB+1Di/3vL6NGy6IZottrnbwxLQiVlRUsby8Kj5x9SfZaSRpj943D46cUynBMAdcPnqnGfAoIUptVsrsHQNgpXnWeAXQ0Zzq0ypFUEw4npMKnhUVFXXaU0ySJPR6PTabjVmzZjFu3Ljv9OZuvfVWvF4v8+bNo6mpidGjR/P++++TkpISf8xjjz2GWq3m5z//OV6vlwsuuICVK1ciy8f+QhBOb8ajfOFX1jjIStV32u8MopNlstMMogm5IHSxOocXhyfAA2/voLzd2tPKqnjKf3slNgtJWpkxA6yiVFMQBEEQWtU5vNQc9mAyaOJZQG0ddPmOeWxN0cmYROCs2zjo8rHw5S84KyeNxz/8kjkluUx2+lhRUUVlmyBaLOD6+4sHHjUQVmyzxHvTfrK3kcrqJiYPzeSQ28/M4n5EiCT83Z1ozy8xrVI43amO/5COJk6cyN69e0lKSmLcuHGcf/75JCcn89VXX3H22WdTV1fHhAkTeP3117/V6/773//m8ccfj/8sSRL33nsvdXV1+Hw+Pvroow7Zanq9nqVLl9LY2IjH4+HNN98UzVDPcNWNLYTCEYptHdONl5dXMaBHEgvG2TrcX5pnZckVQ0XgTBC6iNOj8FW9m72HmuNXEzvbxL+3o457Ly2kpN2aLLFZuPfSQrSySgTOBEEQBIHosbW6oYWDLg+WZC2hcIRP9jZ22Nfe+vIW7pla0Omx9YHW/a4InHUPdQ4vDW4/ZfZGirLNbNh7GAmJwZmp0f5lJbnxINr88TZenDOKKUOzmDduQIe/q1jfvB21TmYX57K8vIoKeyM9TXqKB1i57ZUt/CQnjVVXj+bl687h7RtKWHx54VETG9qKNebvjJhWKZwOTirzrKGhgd/97nfcfffdCbcvWbKEffv28f7777No0SIWL17MZZdd1iVvVOie2o4yTjNokFUSd766lV+c04/ZxbmoIKE8rCjHjEcJs6POycKJ+cgqiRZ/CJNeTYpeTe80cYIuCF0hNi3p64YW/jZ3NE//ezfTx/Tr9LHL1n5FYaaZ+eNtLJyUj9sXIlkf7YWmlyV6/UjTlQRBEAThVBDb7zZ5FCKRCL2SdViT9Nz+6lbmjcvrMGQHov2jHvtgNw9eMZSW1h5nJr2GtCSt6OfbjTg9Cv/efYj+PaLJAcFwhKXTitCqVXgD0Qb/Rdnm+N/QK5tq+Nnw3tzx2lYqqx3MKcllbkl/knVqDFoVwVAEpyfAkKzUeP88iPZAc3oDPHzlMG5YXcmytXaenzmSn/3lE+DEJmZ25bTKtueIJoMGa5IouRS+fycVPPvHP/7Bpk2bOtx+1VVXMWLECJ599lmmTZvGH//4x+/8BoXuK3ZyvmlfE3NKcrn8J1m0+EOU2RuZVZzLgtWVvHTNGGY1+xMapV6/ajMeJcQf3vuSNQtKCIbCYiMhCF0oNi1p074m3pxfwh/e3cktF+fHN1jteZQQ1724iZeuGUO9y0+KXoNGVpFrTRbrUhAEQejWYvvdsj0NGLUyL183hrAk4VGie95bJ+UfcxBWsz/AAacfW0YyfUV1RbdT3+wnw6QjxaDBqJXJtSSxeM12yuyNPD9zJBANfM0pyWXVp/u4acJAWvyheBA2Vsb5/MyR/PzpjUf9PbEeaCsqor3Tlq21J5QTn+jEzK6YVtl2zcScSPBOEL6rkwqe6fV61q9fj81mS7h9/fr16PXRE6FwOCzGYwsnLVa3v6lNjf7InDSSdNE/2coaB0U5Zt7fcZDK6qbO+5vlWZElyEk3ihN0QfgO2l/dC4cjbNrXxNJpRSihMD8dkU2t04dRK1NqsyRkg8bE1usX1U3ce2khKXo1GWJdCoIgCN1Q7LgaikRY/Ob2+HFz/vgBJOs0VNgb6N8jGYB6lz/e26z9gIASm4WibDO90wyoIj/4xxBOAQ5vAH8wjBSBuyYP5v412+MtNGJ9y3RqFUXZZgCafUFUnfQur6xxHHUPV2yzcNDlo9bpi5aBFucCiYPa4MQnZsYa88fWwd6GFkwG5YSyx2IXcNtP7TzR4J0gfBcnFTxbsGAB1113HZs2beLss89GkiQ+++wznnvuOe644w4A3nvvPYqKirr0zQrdQ63Dy9cNLZTZG7lpQh4rW2v0M816guHoziCWerzq033Mbv0CbxtAK7FZuP+yAjSSRB/RS0kQTlr7DNCibDPJOjX/nHcuPiVAiz9IhklHzWEvgWCYu6cUcN+a7QnrMdY/Y9Wn+1h0aQE6WRKBM0EQBKFbaps1s3zW2fFghTVZyyWFmdQ0eckw6TG09pBa+MoWXrx6DIvbBEUgutddcvlQ/vDeLm6akIfJKJqtd0dJWhmXWkWYMGdlp1Lr9DG7OJdgOEJOmoEri3qzYe9hMkw6irLNOL0BstM7ZmctL69i2fQiQEoYIlBsszB/XB6+QIglb+0EoplssaEC7Z3oxMyTzR5rcCsdAmcxJxq8E4STdVLBs7vuuovc3FyWLVvG3/72NwAGDRrEs88+y/Tp0wG47rrr+M1vftN171ToFmJXE6aPzsGolblgcAaPf7iH+eNtLFmzg3svLYxfFYmlr6sliVsuGsQdk1S4/UFMBg1JWhk1kCXS1wXhpLUtz4wFqwFG90tHCQRJS9LT1BLA7QuhU6vYWN1ETrOPyUMzmVvSH7UskWbUxPtnXDikF3pZRR+xLgVBEIRuqG3WjDVZS6ZJz/MzRxIMRxjUK5m7X93GtNF944+P7XlnPLeBR64cFu8batKrUakkfMEgN180EIMsi4BBN2XQyNS7fKToZdKMOv5T3cRLn1WzYtbZNHkC+IN+im0WPIEQew+1oFOrErIZYzxKiPmrKrl36hDumjKYJk8gPgRg3e56nv5ob7w9h9mgYW5JLlv3O3l+5kj8wTB6jczm6iZMhuMHcb9L9pjrOMG5Ew3eCcLJOKngGcCMGTOYMWPGUe83GES9sXBi2paEGbQy5wxIp3+PJP6vtT8SRBtdLltrJxQJM398HrGrIrH09dhVkWSdjKxCBM4EoQs0uJV4eebqT/fxyzH9qHN6CUcgRa/lrle38vuJ+Zj0aiq+amBHrZMROWnoNSqWrbMnZJ+V5llZcnmhCJwJgiAI3ZbDE2DWuf34xZi+5GUk8+BbOyjqm8bEgkx8Sjje1xeiAYu2e965L0T7UcX2vGajmhRNNIiWKQZidUtOj8LGr5uwZSTT06Tjzle3sbnawUvXjGHrfidZZgPJejXN/iAt/iBmg4b1exv5qr6Zu6cUdMhmHJ5jZmS/dFxehSfX2hMy0GJK86z0NutRwhGWl1fx2Ad74veV2CxcNTL7uO/7u2SPmfTHDs6lHOd+QfguTjp4Jghdodbh5Z7Xt5GfaaIo20zIHeGSwkwiQGOzEn9crCHlBzsOMj4/g6lnZXLjhDyC4QhGrYxaJRGORPjzv7/izkmDyRKlmoLwnbn9AZ6cPpyeJh03XzgIly/AhzsP8osxfal1+iizN/ILp4/+PZLYWetk+ui+/G3D15yVbWbhxHwAvEqIVKOGJI1MtgicCYIgCN1UrcPL4jXbKexjZlJhL/73vV389sKB6NQy976xjevH5QHR3lNZqXq+qHEwrLe50z2vQRP932SDRmScdWMNboVFb27npWvG0KKE2VTt4Knpw0nSymhUEulJ0b+NZl+QJK2M2aRhV62Tq0b35fEPdzO7ODchmzFJJ/P4h1/Sz5rMXVMGc/+aHYkXQm1W5o+zUdXg4dnyvR16TpfbG7n91a0subwQ8zH+NjvLHjNq5Xh7kMYWBQ65O+2BZk3WMjbPmjCtM2ZsnhVrslgPwvdHdfyHRKWnp9PQEP0jTUtLIz09/aj/CMKJcHoU7nl9G1eNyqGyuokFqytRSRKffNXIXa9tw+ENJDS6BHhy3Vc0NCsM7Z2KPxjmcIvCoWY/b287wOMf7mHhxHwROBOELmI1ask063D7g+i1MkvX2RnWx8yK8iqc3ujGR62SmLPic+6YPITVn+5jSFYqhVmpfNPkpdkXJDNVT6oInAmCIAjdmNOj8MCaHSyYMJBNXx+m3unnxgmD+OIbJ4taBwYYddESueXlVWSmGvh1aX/+tuFrqpu8OL0BDrcouH1BknVqdCqJbEuSCJx1cy5fAI8S4rBbweMP8sS0Ig41+2hw++mTbuTxD7/k/R0HafEHqWnycsDl5c4pQ3jp030MyEgBoOawl3AkQigS4ZF3dzHlrN488/FewhG4e/IQ3r6hlJevO4e3byhl6lmZeJQQYeh0WBtA2Z4G7PVuFqyupNbh7fQx7bPHjFqZJ6YVUVndxNwXNvLzpz/hgv/9qNPXSDVqefjKYYzNsybcPjbPyiNXDhNrQvhenXDm2WOPPUZKSkr8/0udTOkQhPbaT+lrewWhwa2Qn2liRUUVFfZG5o+3RccfF+fGJ7nEBgMcdPkotlmosDdy7d83ce15/Rk3KIMUvRqvEmJSQS/+e0QfcYIuCF1k/2EPoUgEtUrFCxVVzBufR4W9kd9OGMjjH+6Jl5VU1jjIsRj576c/4ZErh5Fh0uH2hcgwybT4g8hApliXgiAIwhnuWHveQ24/154/gD+8u4tN1Q76pBu45/VtzGrd88bE9rrXr9rMtef156YJA4FoFnefNA3ba530STOI1iQCQLwnmScQIjvdyB8//JKFE/PZtt/JhzsPMn10X1ZUVLFsrT2e2XVenpUbJgwkEArjD4TJMOkIhiIcdisMyEjhhtWVDM8xEwiFueqZDXiUEK/NK0avUVHn8nHvmzv4n/8665jvyx8MH7N/WfvssTklufHzwbaO9hpZZgNLpxXR4FZo9gVI0WuwJh9/Uuep7ljfIcKp4YSDZzNnzsTlcuH3+/npT3/6fb4n4QxxvCkqLl8g3ssMjvQ1m9HaKLWyxkFRjpkbVldy7Xn9WdRam19mb+SxD/bw2Ad7KM2zsmjqEAxqmd4i40wQusQ3TZ5oD0KNzGPv7+amCwfh8EQzzYKh6MTbWFZoLMC9oqIq3o8Foj0xHri8kN5igy8IgiCc4Y62533kymEYtTLBUIRmX5Cy1ovFda2tD9oOB1i3u57542xANKsnvte1Wbn9knz0sopRuRb6iP5mAtFAy+ZqB6V5VnRqFYFQOB586mnSc1a2mXqXj7kl/Zl3/gAyUw34A2EiRPi60cMt/+8LXp13bqelmbdcPIg5Kz/Ho4QotllQqWDR69s4KycNjxKKVwQdTez+o/UvSzVqeeTKYfz7y0NkpOjokaKLnw+2d6zXOJMCSyc7fVT4YX2rnmdms/mEMs5CodBJvyHhzHAiU1SSdWoa3P74fbG+ZrEv3NhJOUSbUT790V7mlOTym/NtaNQqgqEwfVq/TETgTBC6xjeHPdz7xjZ+d/FAAuEw151v4/43t3PbJYMBEspKYuszNvl2Tms2Wu80AykaWQTOBEEQhDPe0fa8G/c1UX3Yw7K1dm67ZBCxM6iibDOO1tYHbYMQT3+0l2G9zUwemsmc4lz8wXDrZEQfyTp1dBiWCJwJrRrcCk+ts/PXOaPYsPcwybroab3HHyIYjvaQrqxuIiNFi1GrZtHr2yizN/Lm/GJSDRo8Sog6h4+inDTmlvTHqJUJhSN8sreRac9GM85KbVZmFveLB3vbVh3EsiTbK7ZZ2LrfyU0T8iixWTncouCvdZKkUyf0QYsAb2+po8zewFMzhh/zs57pEzS/y/RR4Yf1rYJn69ati///SCTCJZdcwnPPPUfv3r27/I0Jp7fOpqjE0oWH56Sxp95Nsk5FdpugV2wD0fYLue1JuT8YJtWgQaWCrw+5GdXfgl4j08Ok/0E/myCcqZwehYfe3sHNFw0kRaNBiUQIyVBmb+R3oTDFNgvAUdenXiPTJ82ABjHtVhAEQegejrbnXTa9iFA4wvQxOSRpVKhbG5nHgmKQuOf1KCGuX7WZOSW59DTp0alVWJJ15GUko0G0QBASOb0KT84YjtunMKZ/Ov5QNAlBLUv0NutxevwU9k7lixoHa7bWUWFvxKiV0apVpOjVlNgsbKxuorK6KaGssyjbzP/811mkGjT4AiEWrK5k6bQi4Eiiw5ELqHQoO55bkossSTxXtpfHP9yTcN+C8Xn0TTdi1MrRYFHrNM/jZbLptTJOz9EncJ7uvsv0UeGH9a2CZ+edd17Cz7IsM2bMGPr379+lb0o4/cWmqLQNmBk1MmEirP+qkeXlVbw5v5gvaqLpxmV7GqiscVBqs3b4Qo6l8ZbaLFw/Po9eKTrMRi1GETgThC7j9CjUt/j4/cR8WpQQ9S1+zK0H6j//YjgaWcXs4lx21bkSykpi67PYZmHBuDxkROBMEARB6D6OtueNEOHTqsOk6NT0tyYRiRAvsWvf+gCIB9CWrbVTmmfl/ssKUEsSskoiU2ScCe2kGbX4An4MWj1PfLib3144iNI8K+X2BqYMy0SWJJa8vTPeSxqivcX2N3m59ZUt/N+15/DQWzuZXZyLiuiF0rZ7utnFudywupKiHDOVNQ4AzIZoo3+PEuKG1ZXcPXkI90wZwn6Hl0gkGgzeut/J51WHKW+XlVZZ7WDvITd6jUwwFGZ2cS5nZZtZXl513Ey2NVvq2FLjOGNLGDubPtrWmZ55dzr5VsEzQThRJr0mPjkl1qgypthmYdn0IsJShGE5Zs7KSePe17ezvLyKVb8ew/+8t6tDGViW2QCRCMk6Ne5AiGQROBOELuH0KBxw+XB6A6QaNHxec5h6t58LB/eKp/gDzB9vY2etk2HZZopy0pjSvqyk2U9vs54+InAmCIIgdCOxPe+y6UUsL0/c85bmWbl9Uj51Dh/+UJi7pwxh09eH2VHrZHZxLkdrfZCkkeNlnqJUU+iUBMk6PYve3M49k4cQCke4fVI+f3z/S6YOy+KwJ0CFvTHeSxqiJcNmo4arRuXgCwS5frwNg1bmtksGc6cksb91smVljSMeOIsF0YptFtKSNLx9YylOT4BkvRq9RoVaksjvZeK2V7bw8Z4Gnp85ksc+2JPwVtueE97x6rb47cU2C09MK+K2V7bw8JXDgI6ZbLHf71FCZ2wJY/vpo+2lHOd+4YcjgmfC9yJZr+bl687hobd3driKUGFvRCVJPHh5ITVNXpr9QW6dmI8/GKLZG+CBK4ayv8kb7wdRWePgxU/3cc3YAZh0aqxGrQicCUIXqHV4WfjyFjZVN8WvludYkijsncr9a7YnXDWMXR1fWVEV7z+Y0VpW0jvNwOCMZJFxJgiCIHQrTo9COBLhH9ecwyPv7uyQbVO2pwEiu7h1Yj7v7TjAna9uZeHF+dw9eQgPvLWTopy0hNYHGSk6HvtgN7dPGiwyzoSjcnoU7nltG3dOGcKsc3P5ZG8ja7bW8euS6IRWXzCEs5O+esFwBLNRGy/VjCm1Wbnn0iGoVRKWZB1F2WaWTiuKB9FG5Ji5e2oBD7+zk7W7DsWfNyG/B3dPKcCrhLhpQh53Th5MJBINlnmUIz3QjzZNM/bzVaNy4kHk2yblU3PYG8/QjAXO4MwtYWw/fbStsXlWrMln1uc9nX3n4NmJDBAQupfYtJBZ5/aLZ620V7anAbcSIi1Ji0qSONziZ7/DS2aqgUff2cXM4n70syTxdWMLRdlmepsN9E7ViwbkgtBFDrp8LHz5CzZVO3hy+nAONftINagJhQFJ6nACEEvRn1OSy8JJ+dS7/PQy6THpo02MRS8WQRAEoTtpOx3v5evOOfqe197AHbLEFT/pzeVFWcxe/jkDeyazaGoB9kMt+AIhUg0aUvRqHvtgNwsnDUanksgQgTPhKOqb/WyqdkAEnv34K2a1lmbOGN2X6iYP1mRtQl+98fk9GJKVSk66kQNOH3deMhgJiXAkTDgisXb3QR56eycTC3qRaTZQ7/KRYdIzJNPE0mlF1Lt8NLX42bD3cPw9GLUyV43uyx2vbe0wrfOJaUUJQa+ibPNRp2lW2BuZU5wbL1kuyjYz78XNR/3sZ2IJY6pRy8NXDotn78XEJvaeacHC09m3Cp799Kc/TfjZ5/Nx3XXXkZSUeNL0z3/+87u/M+G01HZayLRROcd8bK3Dy9wXNsZ/LrZZmD/OxsDMFFSSRIQIPZJ1JOvV5Gckk2wQKauC0BXqHF4Otyhsqnbw1PThGLQyb35RG9/4H23qUWxjc15eD3KtSehUEhogQwTOBEEQhG7E6VG45/VtnJVtZta5/QiFI8d8fFVDCy9+uo/542y8MGcUP3/6E75p8tLbrCccIVqiKcEdkwajBhE4E47J4Q0wpySXCNFeZb84px/zx9vITjdQ7/Ijq6R4H7GXPqvmxavHsHjN9g5tdGYX57Lq0338urQ/w3qb6ZGi5b43tx+199icktz4axwtm6zM3kCESMJjY4MGjiZ2/9g8K33Sjt3TrH0Jo9Oj0OBWcPkCmAwarEna0zLYlGU2sHRaEQ1uhWZfgBS9Bmvy6flZzmTfKniWmpqa8PMvfvGLLn0zwumv7bSQ401OyTDpmT/exvLyKjxKKP7lu3BiPh4lRLMvyJNr97DkiqFoZZX48hCELuD0KJTvaSDHYuCla8agkVXUOrzMLunPWTlpLC+vOu7aVUJhVK1ZxyJwJgiCIHQ3jS0KV43Kiff1fX7myGM+XqdWxfe5U4Zm8siVw3B4A3gDIea+sPHIgACVRC8ROBOOI0krMzInjRZ/EKNWJteSxF8/+RqArFQ9jW4/u+pczC7Opd7l4/41HQNisZ9H5aZT1dDCkMxUvIEwc0v6U9S6H2xbehnLEIs5VjZZub2Reefb4vcfb1/Zz2LkXzefFy9PPNESxrbZn20fc7oOFkg1imDZqe5bBc9WrFjxfb0P4QzRdlrI1v1OSm2WTtPYi20W3tt+gMrqpoTU3tgXeWyU8kNXDCXFoBFfJILQRRpbFH6SY0atkrj79W0J5Zmxxq1b9zuPOvWo1GYlJ82AFugpAmeCIAhCNxQMR+JZN0atjEoiPj2+vVKbla37ncCRAESGSUe9y0+STs27N5bi8gXQSiJwJpyYJK0aa0oEtaxiTkluPDhWWe3gLzNG0DNVRz9rMs98/BXXnTcgoUl/W5XVDhZNKeC+Nds7beTftvQSwKhVs3zW2Wzd70B7nIBYKBLh5evOIRiOVhIdbX2MzbPS22xIONc7kRLGttVObX28p+GMHSwg/PjEwAChS8WmhRi1MiNy0jh3gAWQKLMf+WIrtVmZW5rLvBc3x7+Q26b2epUQKXo1KRpZ9DgThC7k9CgEQ2G+PNiMNUXHwkn5XO+LrreDLh8LX9nCiooqRuWmt04Bo10fCwv3X16AHugh1qYgCILQTYXDkXjg7Mnpw0nRq7j/0gLueWN7wsl8sc3CvHE2Pvv6yLHUHwzT4g9RWeNgytBMVCrINulF71DhhJmNGiQJIsC5/S0JGWCyCgwaFbJKwx2XDKbRrcTvM2pl5pTkUpRtxh8Mk5mq57DHz9yS/swY3Re9RmZzdRPLy6uAqoTzMwCPEmTB6kqenzmSyLErlVGCYX75/GcArP3deTzyLXp6nUgJY9tqp/bO1MECwo9PBM+ELmXUyrx9QwkADc1+MpJ1XDK0F7OK++EPhtGpVdS7fGSZDfFJLO3TgE0GDSlaETgThK5U5/By0OkhxahhaB8zd726NSErtNRmYdWvxzD92Q3MKc5lQbupR/2sSWzf78QgSSJwJgiCIHRr7tZyuT/PGE7PVD1qlYoH3toR74HmD4Yxtw4BmL3ycx65clj8uTq1CpNBw646F1cW9UarkkTgTPhWmn1B7nx1K/ddVohGVsWDYuMG9SAcgf0OPx4liCxJZJh0QPQc7YlpRayoqGJ5eRXXntcfW49k/vj+7nb7QSuvzjuXA04/aUmaePlmsc1CZY0jGlBbZ6coJ+2YVQqxbMuxeVYsrX3Ilk4ror7Zj9MbwKiVSdKpMWrlTj/j8UoYXccZHHAmDhYQfnwieCZ0CadHocmjcMDpY+k6OxX2RuaPt/F02d5Ov1RLbBYeuXJYfGBArFFkaZ6VFK1MH7GJEIQu4/Qo7KhzMSgjmQhwR7vAGUQbzi5+cwd/+NkwvIEwHiVEZXUTAF9UO7j0J1mMyU2nl1ibgiAIQjdW3dhCMBRm2fQinivby1k5aVRWN1Fhb+TDXYcSHltqs3DVqJz4PrfYZqHe5SNFr+auyUMwqCR6potSTeHEOT0Kt/9zC2X2RryBECaDOh4Ua5slVmKzsHBiPsk6NSU2Cz/JSWNFRRWV1Q6eaJ2geV8nvdDK7A3cv2YHRTlpfNHaXmfVp/uYProvN6yuZOm0IpattcdfB9pXKVjj2ZbtM8talBD3r9nRJT3KTPpjD5JrP1hAELrCsYuVBeE4nB6FrxtaeH/7AT6vOsyy1sAZRBtJdhY4g2gjydiVEIhehSvNs7Lk8kIROBOELuL0KOw95OaQ20fvNANuf5DGlkCnfQghumHqaTKgU6viU5h21bm4//ICSnLTyRZrUxAEQeiGDrp87DrgYtcBF3e+upUWJcSK8irK7I3H3O/G7o8dV+ePy+PcAVb6mPSkGTUicCZ8a/XN/vg+rsUfQq+WO516ubnagUcJ0uIPMqs4l3P6R7PEYlMye5r0R/27rWj9uy2zN/JCxdfMaA2ceZRQPBDsUULcsLqSopw0np85kqdmDOf5mSO5+aKBzH3hcy4a0ou7pgzBGwjh9CjH7VHm9CidvZWjsiZrGZtn7fS+9oMFBKGriMwz4aTVOrxU2Bt4/T/7+d1FA9HIMr9/ZWv8/uONJXb7ov3OSvOs9LMk8ZAInAlCl6lzeCm3N/CTbDP3v7kDnVrilomD8QWCx3ye2x8k15LEXZOHoFLBvVOGoNPI9DDpf6B3LgiCIAinjn2NLSx+czv5WalcXNCTaaP70sukjwcwjrffBehnSWLxZYWoALUEWWK/K5wkh/dIOaJalnArwU6DYHNKcvnqUAsDeiRzw+pK/vKLEcCRKZkzRvc95u+J/V2X2RuYW5ob75WW1Do0INYbrf3EzZevOweAqoYW5r24GYgGs+6cPJhN+5qYP94W77nWtsfat+1RlmrUntBgAUHoSiJ4JpwUp0dhw94GCnunkqRTk6xTU33Ym/CY440lTtbLlNqsLJo6BJ0sicCZIHQRp0ehpslDdrqB+mY/s4tzyctI5st6Nz1SdMd8bpJOZsqyckbkpPHgFYXoZJUInAmCIAjd0jeHPdz35nZ+OaYfdU4v9S4/KknCGzgygfB4+93eZgMPvr2De6cWIIHo6St8J0lteoQ5PQrJus7LE4fnpBGJRJBVEh4lRCAUDYbFgmLH+7tte3+STs2OWmdCoKy4tVeuo0XBEwih18gccHrZe8jNE9OKUEtS/LEf72lgjsPXaXlpbLJni//b9yg7kcECgtCVRNmmcFKaPAFe3byfK/+8ngNOD6FOJq5U1jgotlk6fX6pzUqSRs0DVxRikFUicCYIXcjpCRAKR3hyrZ1f/3UjYcDlO5JxdrR1WWyzoFZJjOibxpIrCqNTNb9lDwpBEARBOBM4PQrVhz2clW1Gr1Hx1tY65r6wkZv/8R+MuiP5B8fc7+ZZcfkC3DO1AC2I/a7wnSVp1fG/NwkJk6HzXBiNLOEPhim3N1Bqs8b/TmNBsWP93caGA8QEgmGmj+6b0Ny/wt7I/7y3i43VTcx7cTNzVn7OO1sPYE3RsfrTfYTajeNMT9Z2Wl5aYW9kRUUVqYaTC3ilGrUMyEjmJzlpDMhIFoEz4XslgmfCt+b0KCxZs52Ruem8uaCYCYN7sb/JS2WNg9I2tefLy6uYXZzb4Yu5xGbh/ssL0MgSOsQVOEH4rmK9zb482MyuAy6CkQhPrrNTZm/kmrH9WVlRhcsXpLLGwe4DzcwfZ+uwLottFhaMz0OnVvHI5YUYQUzVFARBELqd2DG1zunD6QswblAGy9ZFG6TPH2/jpWvGEAyF43veo+13S/OsLLmskN4mPQagpzimCl3AbNSwYHwepXlWPIEQskpFqa1j7y9Lkg6dWsUzH+9ldkk/dtU6mV2cy0GXj2Kb5ah/t7Get8vLq+I/r98bDXDNKclNeGx5a2+0mDJ7AyvKqxjax4zUJvPMqJXRqVXMKc7lqRnDWT7rbOaPt8WDcRX2RpTQ8cufBeHHJso2hRPm9Cg0tigEQmFmjOmHJUlGrVLxTVO0XHN5eRVrFpSw6PVtlNkb440k75o8mNsm5fNNk5dUvYY+aQbURP/4MsRGQhC+k1qHl3te38ZVo3J4ZVMNM8/pS6pRx+ZqB7+9MI+Lh/Ti8Q/3sHBSPsvLq3h9fjGH3X4mD81kTnEu/mAYnVpFfbOfjBQdeklCliR6iCbGgiAIQjdT6/DywFs7uPa8AQSCIfIykgmGI4zKTWfRlAIWr9lOUbYZWZJYfFkBd7++nbI9DdywupI5JbnMO9+GWiWRatSgk1VoJJAR+12h66QatWSZ9Cy5vLC1gX+Qu6cMYfGaHZTZj/T+0qol6l1+inLMzF8V/ftUSxIFWSbG9Lew6PXt8b/buSX9SdLKhMIR1u9tjA8HKM2zcstFg5j7wuc0uBXmFOd2eD/te/6V2RtZOGkwoXCY52eORAmFyU4zsnlfE4vf2olHiZY8x8o1Y7+rxX/snryCcCoQwTPhhMRO0KePzqFnio5knUyyTscdr25lxui+7KhzUZRj5t3tddw5eTChMBxs9mHSa9CqVdQ6fKTqNWSZDWgAtUqiR5o4OReE7yI2ueisbDNrvtjPrRPzuee1bfzy3H7xMeT7HdHgdr3Lz4gcM3NWfM7f5o4iRa8hGI7gUUKoVRKH3X7UKgmDQSNS3gVBEIRux+lRuOf1bcwfn8df/m3n1omD+cO7O/ntRfn0SNZx35rtVNgbmda6703Wq7jzknwiSLi8AQytWTTrdtez9Rsn90wZggaRxS10PW8wxJI1O7j5okF4lBDhUIB7Lh1Ciy9EY4sfrazC6Qkwop+ZXKuRpevsCX3GJuRncP/lBbi80WmcRq2aj/bUE4lEBwosnVZEhknHv3bW86d/fcnDVw7jhtWVnQ7H6Kx32kGXj8Nuf8IgufbBslj55pySXJattZOi77x3myCcSkTwTDiu2An6yH5pHHT6yLUkseUbJwFLJDryuDW194lpRaz6dB8FmanUu/wMykwBoqOMe6bo0WtUyBKoZZXooyQIXaDBrVC2p4HrSvuTM6IP+x1ebr5oEGaDhrte35ZwhXDhK1tY9esxLH5zBz/7yyc8cuUwMkzR4QFJOjVj+lvIFht8QRAEoZtqbFGYNiqH/31vF9eMHUB9s5eFkwZz56tbmVWcGz/Z16lVLC+vYlTfdNKSVGz+uolBmSYguuc9Ly+Dn/6kd7RvqDiuCt+DcCSa4TW3JEBGqp6vG1r47TMbWD7rbJJ0avYecjMkM5XaJh/b65zcd2kB3kCIFn/0gmm5vYHFa3bw69IBBEJh9hx0cXFBL2odXiKRaD+05eVV8SwxfzDMnJLcDoGy9r3R2srPMmHUyvHXqLA3olOrWDqtKP6aeo2MNVnL7joX1mRx4VY49YmeZ92c06PwVb2byuomvjrkxulROtxf5/Sxs87FlKFZfLznEL5gmAyTPj4qubLGQVGOmRtWVzIkK5VIBAb2SkGtUuELhEnRqUnRq0nWypgMGhE4E4Qu4PQo+IMhnps5kj7pRh5+ZydJOjV/fH833kCYCnsj/mA43hC2wa0w/dkNzCnpx1/njCZFr0GFRI9kHalaWQTOBEEQhDPK8fa47R+rVkn0Mun5utFD33QjKXoNzb4gZa3H05jYvve6Fzfx3vYD/KRvGmqVCq8SwqTXYE3SiMCZ8L2pdXhxtw6B2ljdxM5aJ2aDhga3wlXPbCAQDFOUk8a/v6ynzuljw1eHuXRZBf/aWY9aJREMR5gwuCd3XjIEs1FNRoqOQZkmDrn8zFm5kbkvbGTZWns86AXRwNc5/RMDZSXteqPFxAJqHn+IJ6YVxfuaGbUy00f3ZWVFFXNf2BgfMvCHd3dz95QhXVL18G3WvCCcDJF51o3VOrwsfGULZXuO1MePzbPy8JXDyDIb4vfPPLcfq68Zwx/f38UtFw+iqSUQ75MExLPOIHH0cKnNyrxxNvRaGa1KEhOGBKGL1Dq8LHx5C5uqm/h/157Do+/u5OaLBnHvG9spszdyfWtgO3Z1/InWq3wV9kZmr9wIRDc3910aHdyRJdamIAiCcAY53h63rTqHlyaPwoNv72RuSX9WXz2aEBEefGsn88blAYmlaW33vY+++yWPvvslEA0mPHDFUHSIwJnw/YhVA11T2h+I/i0+OX04vVL1lNgslNsbufbvm1g+62ye+Xgvf54xnEWXDmHRG9t5/MM9PP7hHuDIUIAbVlfyt7mj+OXzn8Uzwo5GI6soHmBhVG46wVCEAT2SuO/N7QlBtravO6mgF18dcnPtef157IM9zCnJ7XTaZpm9gbte28bSaUXfKYD2bda8IJwsETzrpmJfvm2/YAA+3tPAba9s4Q//dRYLX9nCpn1NLL60AG8wxG/G5XGo2U+KToNOrYpntFTYG+MNJ2MNyM0GDb3TDEhEG6WKiZqC0DWcHiUeOHtqxnBUElx3vg1fIExZ64YkSR+9ytc2K7Tt+owNCDDIKrE2BUEQhDPK8fa4bU/SnR6Ff395iLe31LKv0UP/Hkl89vVh+vdIoczeyK2T8gES9ryxgVix4ypA7zQDyRoZnejpK3yPGtwKm/Y1kXqxJh4su37VZm660MYDVwzlrte2UbanAY8SwqOE+M2Lm3lq+nCmDMtK2ANW1ji4YXUlI3LSUEkSS6cVYUnSHfN3u/1B5qz8nOdnjmTuCxtZs6CYCYN7MmN03w6vW5RjJhSJ8NbWOhZNKeDpj/ZSlG1OSLJo6+M9DTS4lZMOnn2bNS8I34UInnVTsV5Jnfl4TwNNLdH7b504kGAkQiAUwe0LkmJQo4TCHHT52NE68hiiGS2xL8QSm4Ullw9FRfQPLFOcnAtCl2nyKMwuyeW2lHzKdh8gc0gmLk8AjXzkqni9y09J6xjyzrNCLSy5YqgInAmCIAhnnOPtceucPvY2tJBq0KBRSQztncriNTt4Y34xd726lVsn5dPii2bTdDyeEg+gLVtrpzTPyr2XFmBQSWgkETgTvl8uX4A5Jbk8/uGXzCrORZIkyvY08OBbu3nmoyqe/sVw7pw8GCJQmmelbE8D81ZtZtn0IlaUf50wjbPYZmFmcT9mPPcpHiXE/PG2+HPaK7ZZ2FzdBByZrvmvXfWM6pfOsnX2hGyyYpuF+ePyWLe7ngp7I4vXbGdOSW6nwwbacnhPvsTyeGv+uwTmBKEtETzrply+wDHvd/sDzB9v45LCTO56bRsLJ+Xj8AaQJAl/IERmqoFfl/bn2bK9FOWkJWSc9UlrnaiJSFsXhK70TZOHFn8Ig0ZGLUtMPasPLiWE0xugb/qRDfvCV7bw4tVjWLxm+1GvjosyakEQBOFMdLw9blVjC3sPubm4oBcOXxB/IMzbC0poCYQoszeyQAlj1EUzuI93PPUoIbQqCY3IOBN+ACa9Jp7BtWHvYdYsKEZCwqOEaFFCGHVqNCoV//3MJzx85TDCkehwt/mrKrlmbH9um5TPvsOehCyxWNnl8vIq3phfwn1vbO8QZIuVYsKREuanP9rLsN5mpgzNTKxscPnwBUI8/dFeIDrYYHZJf9KTjh288geiyRluXxCXL4DJoMGapE3IEm1wK53ed7w133yc+wXhRIngWTdlOsY4YKNWxpKkY0etE+/QTMrtjVzTEkCnVpGsl6n4qoEdtU7OyjZz04SBQHS6UKpBoqqhhZw0gwicCUIX29fYwl2vbqXM3ohRK/PnGcPRa2SC4Qg6tQqdRkWpzUqZPXqFbcZzG3jkymEsnKSjxR8iRa/GoJYxyJLIBhUEQRDOWMfa4wLkpBtIN2q5943t7DrQzIpZo/CHwnj80SBCklYmFInEj6ltj6duXwiTXk0oEkGSoGeSFq1GpodJ/0N8NKGbsyZr+bqxJf5zOCJx3xvb4m07IJpxtmz6cOav2sxVo3KYW9IftSyRZtQgSRLzXtzc6Wt7lBD+YJBZxf247vwBOL2BDkG2Ups1PjTAo4S4ftVmXrpmDIea/fHXKeidylXPbEjohZaiV7Oz1hlfU+0V2yys3xv9DNOf+zR++9g8K49cOYwIHLOf2fHWfMpx7heEEyWmbXZT1mQtY/OsHW43amWWzzqbB97awfTRfTncEk2hDYYjVNY4aPEH2VnrZProvnxWFZ3ecumyCq56ZgOPf/glo3LTARE4E4SutL/JEw+cAcwpyeWgy8eytXviEzUPtyjcfkk+JTYLEE1hn/vCRh5+Zxduf5DHPvgSjQicCYIgCGe4o+1xIdq2oMUfYtnaPWyudvDczLN59N2dvLmlDr02elqk06jYXeeKH1Njx9OpSytYtm4PdS4fT/xrDylqGa2sEoEz4QeTatTSJy3a/H5OSW6HwBlA2Z4Gnlxr5+YLB7JsrZ05Kz/nV89/xsPv7CJZJ1N6lLVRbLNwwOnnxU/34Q2EePHTfQmTN0tsFm65eFDCdM2iHDPv7zgYn565vKKK93ccTAicARg1MunJOhZdWkBx6z617e+NTe10eBMzxD7e08C/vzzEwpeP3s/M6VGOuebH5lmxJouSTaFriMyzbirVqOXhK4dx2ytb+Lj1y8iolVk5ayQ6jUx+ViorKqq4rbVR6ubqJnbVuhjdL53bLxnCQ2/vSCjXTDVoyGkt1+wlTs4FocvUNLbQooTiGWfzxw9g/KCeuP1ByuyNnJWTxq46FxMLeqLXqLjzksEc9gQSrhi+9Fk190wZQrZYm4IgCMIZ7mh73LsmD2Z4ThrBcIQyeyO3T8rnf9/bRZm9kVnFuUQi0RP5bw576WdNwuMPHvOYmmTQiD5Kwg+ul0lPaZ71mA34y+wN3Dghj+dnjmxTTulnX4OH2cX94uWcMSU2C7OKc1n4yhYevnIYqz7d16EtT5pRyy+XfxoPjMWeEyvnbP9z29d+Z/sBlq21s3LW2Qmv2z6zre1U25iMFF2n2WpwpJ/ZgIzkDmsejmSuiXUqdBURPOvGsswGlk4rosmj0KKE0MgSoXAElzcY/0IOhiIUtzZKfWrGcF745GtG5aZx1+Qh+IJhmn0BsvQaUrSyGM0tCF3skMtHSyBEsy+INVnLX+ecjVYts6/RQ5I2+vW9vLyK1deM4c/rvuL68QMwaNRkmmTMBg0eJcSkwl7894g+InAmCIIgdBtZZgN/+K+z+KreTZgIvVMNbNjbyH6HF6NWzah+Zs4b1IOH3tkFRJugr9tdz/xxNv76ydf89sJBbPvGQWGfVHqZ9JiNGjz+EFOHZTJ9RB+yxDFV+BHdf2kBextajvmY+mZ/vESz2GZh0dQCHn5nJxv2Huaasf357YSBhMIRUvRqknRqFq/ZQYNbiff2O7e/BVklIUkhPvv6MJIEf7qqCFkl4QuECIUjpBk1LJ1WhCRJWJO1PP7hlwlZZ6U2CzPbBNQ2VjdRWd3UadCv2GaJl4S2daKDBmLntQ1uhWZfgBS9BmuyVgTOhC4lgmfdSNtGi8k6NTpZRTAS4cG3djC8XxpThmZRYW9gcGYqja3lmk5PoHWiZhXzXtzM9eMGcM4AKw1uBaNWxpqsQ6uSROBMEL4HLYEQi9fs4JrSAaycPQqXL8DStbuYU5wbb2bsUULMXfk5K2aNIhiK4JPCRCLQ0noFz6RTi8CZIAiCcMZr31BcLUlc/deNvHztORxq9hFpfZzZKPO///UTvqx3x5+rU6viDdBL83pw0OGjsI+ZYDhCk9ePyaChl0mHDsgQx1ThR9TkCXCo2U+q4dh9vHKtSaycfTah1tY705/dwFWjcvjVmH5kmQ0sXrM9XvJp1MrcPXkwN07I45smL33SDPxrZz2SBIVZqQzsmUKfNAPv7zjI8vKqeIDMqJV5YloRy8v3UlntYE5JLjNG9wWiAzXe3Xagw1CCtlNrY0rzrMw8t1+HrDWg02y0tvyBME5PdJpm7B9B+L6I4Fk3Uevwdmi0+OiVQ/lodz03XTgQk05Nsy9IhkmPSkX8C3ljdRM7ap0JKbb1Lj+VNQ521jq5c/IQwojAmSB0tYMuH7VNXuYU59LfasSthEjSqamwN1KUk8Ylhb0otlmosDfS4Fb472c+YU5JLkXZZiC6aTFpZHF1XBAEQTjjdbbPLc2z8uT04Ri1MpIEfS1JKMEwyVoN7nY9mSprHBTlmLl+1WbmlOTS06THXu+OTw8stVnRIgJnwo/L6VG4+7Wt3Dopn/e2H4zvA9srtVkIhSKYjVr+8N6u+GOWrbXzh58No87pZVZxLtNG90Wvkdlc3cTit3ZSlGNmVG46WlnFpn1NCevp+ZkjO2SMeZRQPFPttkn51Bz20itVz5ZvnJj0ah7/cM9RH79wYj77HV5yrUkkaWTuW7OjQ680iGbQleZZO/Q8gyODBnqa9CJoJvwgRPCsG3B6lIQNhVErM6ckl7NyUhmdm04oAs1KiBZ/EH8wTJ3TR2aqnpLWcs0nphWxoqIq4Quz1GZhyRVDMUoSPdLFaG5B6Eq1Di8PrNnOL8/py8CMZMKA2xckGI5eN19eXsWkgp7MH2cDolfvPEqIZWvtFNssLBifh1kji/6DgiAIwhmv/T43pmxPAz8b3hukJF78dB/XjB1AixKi1ukDogGzWPDhSEZMu/1unpUllxeiQQTOhB9fg1thU7WDSAR21bmYXZyLSpIS/vZLWkslf/7MJwDcPWUId08egsOj0DNVjxIM4/AEMGplQGLt7oPsqHXyxLQibntlC/dOLaCyuolFUwtY9Ma2eOCtssZBic1CebtgnUcJUVndBESDcy9dM4bb/7mVF68e3elniO1XLxrSk+w0I5X7mnjq31/x5IzhzC7OxekNxAN6u+tcjBvYg1G56dzz+raEQGFs0MANqyuZkJ/Rlf+aBeGoRPCsG2hwK5TtacColblmbH8uGJyBTwlgUKupdXhZus5Ohb2R52eOjKfGzln5Oc/NPJv/fW93/ArBnOJcIJrRkqxp7XEmAmeC0KXqXT7+U9PELRPzCYTDBCMR7nptG3OKc8kw6YDoxmPmis9ZMetspgzLSmi8Wt/sp3eqXgTOBEEQhG4hts+Nie13zxvYgxS9zDeNHn49dgC3v7o1fvL99o2lHUrIYvvd68+3oVWrSNGrUUkgAz3FMVU4Bbj9AZ6YVsTjH37JrHNz+esnX/PA5YXsd3hRSRKRCFhStDS6/fzllyNIM2oIhiK4vAqZZkPC5HaIBofvv7SAqcOy8AZC/PkXIwhHIoTCEfY3eRMqj4wamQmDexJh11GDWEBrUA4+2dtIqc3aabP/YpuF93ccZNlaOxPyM/jr3FHc89q2Du/toSuG0stsYO8h9zEHDaToj5Swti/ftiaJUk6h6/yowbOHHnqIf/7zn+zatQuDwcC5557LI488wqBBg+KPiUQi3HfffTzzzDM0NTUxevRonnzySQoKCuKP8fv93HLLLaxevRqv18sFF1zAU089RZ8+fX6Mj3XKcfkC8Zr01Z/uIytVyzn9e1Bub2DN1rqEKwpZqdFx24N6pTD92Q3MKcllVnG/eLPG+mY/+RnJaDWyGM0tCF3I6VE42OxDK6tIN2q557VtzCrOZXlFVbxUs8RmiV/1i5ZqRtdoRooOnVpFnzQDgzOSRammIAiC0G24fIH4/zdqZf48Yzj1Lh86tYReJdPXauS2NoEzgGAozPAcc8IF4thJ+b7GFkptVkLhCGqVRG9xTBVOEWaDlkff202FvZENew/zzK+G4w2GSE/S8Yd3d1LYx8z5A3tgNmiJAC3+IMl6NZYkLXe2C5wBbNrXxAGXj6fW2RPuK7ZZWDgxv0Nz/1j10rzzbYQiEQLBcEIQq9hmiT92eXkVb8wv4b43ticE0NoH2/KzTNz12rYO5adlexq449WtLJ1WhCVJy5YaR6eDBsbmWbEmR4NjnZVvj82z8vCVw8gyG07i37ggJPpRg2cfffQR119/PWeffTbBYJA777yTiy66iB07dpCUFD1QPfroo/zxj39k5cqVDBw4kCVLlnDhhReye/duUlJSALjpppt48803eemll7BYLPzud79jypQpbNq0CVmWf8yPeEpINWj484zhZKTouH3yIPQqNY2eABkmfcIX1fLyqtbeECrmj7OxbJ29XammlSVXFIpNhCB0sTqHl037DvOTPma+cXpBgtsmDUalkuJrdHl5FaP6pnPvpQXc+8Z2ytuUapbYLDxwxVAMiLISQRAEoXsxtWadGLUyT80YTk+TjgFWIxokPOEIvkC4w4n59S9u5q9zR3H3a9sS9rqx42k4HEGrksgUx1ThFKKEjvwte5QQ/kCEepef3/7ff/jHNeeghMMcdito1TIatUQ4EsGnhJEgITgWC4KdP7AHTm+Aq0sHMKs4F7VKhcmgRi2rcPsC3H9pIfe+cSQjLFaieelZWTz8zk7W7joUf83SPCvzx9lYt7s+/thvDns4K8fMb84fgFpW0ewLJATbAIqyzZ0GxQA+3tNAg1thQEYyD185jNte2cLH7foaLppaQL3bj8sb4I5OAoQf72ngtle2sHRakchAE76zHzV49u677yb8vGLFCjIyMti0aRNjx44lEonw+OOPc+edd/LTn/4UgBdeeIGePXuyatUqrr32WpxOJ88//zx/+9vfmDBhAgB///vfyc7O5sMPP+Tiiy/+wT/XqUYnq9CpVbz46T7mjx2ALwINbn+H0b8eJcT1qzZz7Xn9uSA/g/svLcQXDOHxh9CqVaQbNWJqnyB0MadH4VCzl7OyUglFIqgksCbrWfT6NuaNy4s/zqOEuO7FTcwfP4BFlxYQCEZw+6OjuJO1MnrE4A5BEASh+7Ema7lwcAYzxvTl7598ze8nDkQXkfBIcE+7Y2lMTZOXXz3/GU/OGM6drSf1seOpDpDUKnqkidYkwqnF7Q8m/OwPhpEkCY8SIkKEZz76iuvH5eELhGhoDpCsl6l3+VF5pPhzYtVIqz7dB8DIvmkYtDIpOi1ajcy9r29LmMJ51+TB3DopH7cvhEcJJkzunDG6bzxjM8tsIBQO8/RHe4FohtnG1sy1ZWvtPD9zJHNf2NjhM7U/H22vuTWzNMtsYOm0Ig64fDg8AYLhCJ/sbWTqsnI8SohVV4/uEDiLiQXhRPBM+K5OqZ5nTqcTgPT0dACqqqo4cOAAF110UfwxOp2O8847j/Xr13PttdeyadMmAoFAwmOysrIoLCxk/fr1nQbP/H4/fr8//rPL5fq+PtKPzulRWP9VIxq1xIKxA/ADd762lTnFuZ2O/vUoIR77YA+PfbCHN+YXo5ZVpBjUJGlkETgTfjDdaY02ewNYjDokwB8KY69v4U//ivYhvHVSfsJjPUqIR9/9kkff/RKAd24spdkXwKSVReBM+MF1p3UqCKej7rJGU41a7r20gP99bxe3XzKEFBX4JLijtVSz/bE0pqbJy6XLKnhzQTFJWnV0KiegM2jESbbwg/k269TUprcXgE6tYnN1E/dOHUJYCrPggjweaJ2aOXZgD7xKmF4mPbJ8JHg2pySXVZ/uY/rovgkD4eaPt1FZ3ZSQpelRQtzx6jaKbRZ+O2FgQvCrfbbYmwuKOdSsxMs325ZmHktn56NtpbT7zJurHazZUtshm9ThDXAszb5j398Z0T9NaO/Yf60/oEgkws0330xJSQmFhYUAHDhwAICePXsmPLZnz57x+w4cOIBWqyUtLe2oj2nvoYceIjU1Nf5PdnZ2V3+cH53To/BVvZs6l5ef9DWT18OAQnSjUGFvpLLGwUGXL6E2va3SPCtqlYQEpIjAmfADO9PXqNOjsPeQm6pDbgKR6ATNILDoje30bFNOXe/yU3KUNVpis5Cklck26UUptfCjONPXqSCc7s7UNRrb41ZWN7H3kJt6lw9vMMiCCQP5x+fV+CWJ6tb9Lhz/WNriD2LQymiAPpYkcXIs/KC+zTq1JmsZm2eN/1xZ42BHrZNR/dLQSTLhCNx84UBG51qod/lp9gV4Z/sBah1eSlufV5RtZkhWKitae+rGFGWbOwSkYirsjRi1MqXHWEf1Lj9JWpk3FxRTlJOWUJoZe6+lNmuH58ameHambT8ziA4HyUjRdfo+v20Q7nhqHV7mr67kgj9+xBVPreeC//2IBasrqXV4v9XrCGeWUyZ4Nn/+fLZs2cLq1as73CdJUsLPkUikw23tHesxt99+O06nM/5PTU3Nyb/xU1BssT/09naSNWo0koTJoMethHC2RuWXl1eRmWpg/jhbhwBaaZ6Ve6YMwaRTk6qVxYm58IM7k9dobH1+Ym/gcIufFWXR9PYWJUSFvTEhfX3hK1u4e0pBh01FvMeZRhb9WIQfzZm8TgXhTHAmrtG2J7QznvuUfY0eqhrcGFQyTo+PX47pm7DfhWMfSxdfVogsSehVkhi2I/wovs06TTVqefjKYfEA2vLyKqaP7ousUlHr8nHP69t4e9sB/vLvr4gAy1szy1RILL4sugb8wXCngbLjlU+6/UFml+R2OG8ssVlYfPlQFr6yBYNWpuawl2Vr7QmBs9h7XXTpkA4BuB21Tu6eUtDhdcfmWXnkymEJwWyXL3DU91lZ4zhqUkj7INzxOD1Kh8EDcKR/mtOjnPBrCWeWU6Jsc8GCBbzxxht8/PHHCRMye/XqBUSzyzIzM+O319fXx7PRevXqhaIoNDU1JWSf1dfXc+6553b6+3Q6HTqd7vv4KD86p0dh4ctbgDB3Tx3GJ3sbybEkkaJX0+wLxqPybfubLZyYH7/NpNcQDkcIE0GH6KEk/DjO1DV60OVj4ctf4A+GKM6zEo6EmFs6AF8ogqt1o9/2ylmDW2HGcxt45MphLGztN2E2RnuyiGxQ4cd2pq5TQThTnGlrtP0J7ZySXA41+xjWJ4UwYDYacPmDuLzBEzqWJmllKqubKMpJE4Ez4UfzbddprPfXNw4vbl8Qk16NWwmRpFNHq4uqHTw1Yzh/37CPopy0aKsejUyzL8CSywtp8YfYd9jT8X0cJ3PL7Q8xf1Uld08ezN2Th+DwBEjRq1Gp4J1ttQzuZQLAbOg8w2t4jhlfIMTCiYOZ7faTolcTCIZZv7eRGc9Fe6jNO9+GWiVhNmroZdJ3yAI16TUcbuk8cLW8vIonphWhQkqY7tlZEO54GtxKh8BZjOif1r39qJlnkUiE+fPn889//pO1a9eSm5ubcH9ubi69evXigw8+iN+mKAofffRRPDA2YsQINBpNwmPq6urYtm3bUYNnZ7KDzT5KB1p46Iph3PXqVgZlmli6dg/BcIQUgyYhKh/rb3bpsgouXVbBk+vs/Kemia21TlLUooeSIHSlWoeXr+rdbKp28Ph//YQWnx+NSk3FVw14AkFMrZuN9lfOGtwKc1/YyNSlFTy5bo8InAmCIAjdUvsT2pF90xjRLxWjVst+p5c7X9tKMBwhSS8f81j61L/tOL0BQuEII3LSyBHHVOE0k2rUYtTILFu7h0lPlOPyBnD7QliTtTw1fTjZ6UbuvGQwU4ZlYjJoMBs1NLoDuP1Bmv2BTgNcx8rcKm3N3Fo6rYj9Th8//fN6lq7bg8sX5IDTz6avm7j9knx21bnobTZ0eJ1im4VZxbn88YMvCYTDzFn5ObIEXze2UJRt5v7LCinKNvPJ3kZmr/ycB97a2en7sCZrqW/2d/o+PUqIlz7dx6zifjw/cyTPzxzJB78dy9JpRWSaDd/q36/rOP3RTqZ/mnBm+FEzz66//npWrVrF66+/TkpKSrxHWWpqKgaDAUmSuOmmm3jwwQfJy8sjLy+PBx98EKPRyPTp0+OPnTt3Lr/73e+wWCykp6dzyy23MHTo0Pj0zTOZ06NQ3+zH4Q2QalSjV6sYl5+BOxCizN7I74FyeyPz/SGMOthZ62R2cTRI2TZdt8RmYdHUIRxuUcg06cUVOEHoIk6PgsMT4NF3dzJvfB6vzjuHMMRLqXua9LT4QvRIUVNis8SvnEHiGi3Ns7Lk8kIROBMEQRC6pabWUimjVubGC/LonaZHq5KpbvJiMkSzbjz+EC1K8Jj73WipJsgg9rvCaanW4eWeNlMxTQYNkhTm5WvPIRCOJEzMfGrGcD768hBZqXr+b2M1vznfhkalosRmobzN2ojtPyVIuL3UZmV2cT9Urd2QirLNvPKbc9GpVfiDIZzeCLdclI/bH6BHih77ITdFOWnMO99GKBIhEAxTWeOI90D77YUDGZtnJc2o5Y5Xt3X6+Y6W3ZVq1HL+wB7kWqPrtqLd+5w+pi/zV1Uysm8aj1w57FsHzWLaD2Zo79v2TxPOHD9q8OzPf/4zAOeff37C7StWrGDWrFkA3HrrrXi9XubNm0dTUxOjR4/m/fffJyUlJf74xx57DLVazc9//nO8Xi8XXHABK1euRJblH+qj/ChqHV4WvryFMnsDRq3M/7tuDNtqnAzvm0ZTa0qrxx+tN1fLEte/uJm/zh3FkjU74mm8/mCYVIOG7DQDhCPkpBrolS5GcwtCV6h1eFn4yhbmje3PwomDOdDsI9Wg4VCLH71GjcsbaF2DEr/5+yae+dVI7nptKzesrmROSS5zWjf+vdMMJGtk+ohNviCc8qbPvobaQ02d3pfVI41VK575gd+RIJz+qhtbUIJhstMMLJ99Njv2OwlHiPc3U7X2OVbLEgtf2cLqX4/h4Xd2dtjv5qQZUBM9ARIVFsLpqNbh5euGFspam/jfNMFGilYmGJFZ/1UDb22tSwgq6dQqlpdX8eT04cw8px+Pf/glI/ulcf9lhSx6Y3s8m9OjhFj16T5unZjP9f4gskqFRwmSatCwdO0ehmSl8kWNg8WXFaKEQjS6FYxaGUuyDn8gxPq9jTz90V6WTivii2oHP8k2dxgaANDsDfLIlcOob/Yd83MeLbsr02zAqJV58PKhtCjBeNshnUaF06Pw5vwSrMnfbSpmbDDDx52Ubn7b/mnCmeVHDZ5FWqfMHYskSdx7773ce++9R32MXq9n6dKlLF26tAvf3akt1tssVtP9uwsH4vUHKcpJ4+7XtnHbJYOB6CYCoNzeQD9LEr96/jP+8ssRqCSJZl+ALL0Go1ZGDWh0anqY9D/WRxKEM0qsN8vOOhfZ6Ub84TD7GloYlGkizajloMuPyaDB7Q9Sbm+gp0nPNX/dyJ9/MQIlFKbZFyCldX1qEFfHBeF0UXuoiV6X3tz5fW/88ajPE0E3QejcQZeP21/dSrHNwt/njqKxRUEJhiECLm8AnVpFsj56wbzc3sDgXiamPbuBx//7J1iSdQn7XRUicCacvmJ7y2mjcjBqZZ7+xQgyzQa8wSD3v7mTWcW5HQYBVNY4KMoxc/2qzdw0wcbdk4fgVoI0uP0smjqEYChCsy9Isl6NBEgqKNvTxJPrvooG1K4ezR2XDGF/k5feZgNEInxR42TRG9s7BMZKbRYyU/XMKu7Hgk4CZwCWZC2ZZkOn97V1rOyuVONRgmNdtK5jgxlue2VLQgDtZPqnCWeWU2JggPDtNbiVeODMmqzl4iE9CUQi1Dl8lNkbuV8jU2yzUG5voDTPyjMf72XZ9CJWlFcx+Yny+OvESsE0skoEzgShCzW4Fb5uaOGV684lFI5Er5inG3nk3V3cNikfk0GDWpaod/nY8o2D2SX9WFFexQV//Cj+GqV5Vh64vFAEzgShGzjZoJsgnOmaPAoV9kauKe0PkQgmg4Y3vqglPVlHptlAmb2BEpuFEpslYb/7i+c/i79GbL8rAmfC6SzW92/Wuf2YU5KLTqNi0RvbuH3SYMrsjUwb3bfDc460A6nC5Qvxp399yQ0XDOTRd3YnNNYvtVm55eJBzH3hcxrc0Qqm0jwr2WkG6pxeeqToqKxp4g/v7eaa8wcwIict4fnFNgvzxuWh18i8+Om+ToNjxTZLfDDBqZ7dFRvM0OBW4he0v2tGm3D6E8Gz01Tbvg9/mzOKMns0TXd665dmixJkwfg8nvn4K64Z259nPt7L/FXRUrBZraVgvUx6UvRqUg0a8UUgCF1MCQZ48erR+MMhDCqZ/1Q7WNOaSu/2heiRIvPsx18x7/w8+lmTeLZsL2flpDGrtbzE3FpOLXqcCYIgCN2Z2xfEqJUZkG4kopK479Wt8UCBWiWxs9bJqL7p3HtpIfe+sa3Dfrd3mgG9WoVOkugpWpMIp7FYI/vKGgfn9rfEJ2w2+4JA5xMzPUoo3g7kkqGZXPKnMsrtjTz6s2HcMXkwbn8Qo0bmi28cTHt2QzzoVZpn5a7JQ/jG4SVJp+ZXyz9lUK8Ubr04n7/8286IfmncOCGPYDiCURvN/NxV52Jz9WGmj+6LPxhOyIIrtlmYXZwbny5/OmR3HTXDTei2RPDsNOL0KDS4FSJE0MjRL8eHrijgwbejabrl9sZ4c9SGZgW1LHHhkF6EQhFuvTgfjVqi2RvdgBi0MlqVJHooCUIXiq1RjxIg1aBl1wEX+b1MOH1BMkz6+CYiWS8z7++beeZXI3nq33v49XkDuGvyELxKKNq7waDGpJHJFOtTEARB6IacHoXGFgW1JKGRVdwzeTBhlURNkzfeCF2nVnHQ4WPhpME88s5ORvRL475LCzttfaDRyKLCQjjtxRrZLy+vYmxeDzz+II/+tACzMXFie/vSTY8S4osaB+cN7AFEM9jmrNyIUStz7Xn9mZDfk9G5Fv7fdefg8YdIMajxB8J4lSA9TXqqGz08cuUwKmsczH3hc5ZOK+Kpf3/F4x/uif+OktaJmve+uQOAuyYP5rZJ+XzT5EUrq+JDA96cXxJ/jsjuEk43Inh2mog1Ht+0r4k1C0rQqSV+NTqH/MxUyuxb4mm6sS/NjdVN7Kh1MqyPmUE9U/AFQ6hlNSl6NREQgTNB6GJt1+g/552DLxikoJeJGocXSZLwB8Pxx9a7/GSYdFzz1408+YvhBIIRmr0KKQYNlmQtRpVEL7E+BUEQhG4odjydkJ9BQVYKGcl6Rve3sN/hxek90kS8ssZBVqqeVZ/v45fn9CPDpKexxU+KXkNGio4Gt59UrSxakwhnjLaljv5AiNw0Az1NemqdXkrzrMec2H7PlCHxzLUYjxLi6Y/2UpCVyoqKqvhznpoxnHkvbo7/zkeuHEavVD0lOisXF/SksVlhTP90Zp3bL1otYdSQrFNz2K3wl1+OQC1JrN/byJK3diaUb3ZWjimyu4TTiQienQZizSHL9jRw04Q8Pt3biKyS+PXY/hxw+YEjabqxL81Vn+5j+ui+rKio6nBV4IErhorAmSB0obZr9O7Jg9HJMv5gCH84wtJ1dhZOzEfnP5JKv/CVLbx49RgWr9nOhX/8OH57bH2KwJkgCILQHcWOp5v2NbH40gI8gRAhwBMIEQhFEsrSYhMEf3VOP5atsycGC2xWllxRSJJoTSKcQWKljgtf2YISChNSSXzT5OXGlyp55Tfncter2zpObDcb+OjLQ1z2ZAVzSnI7ZKbNKclNCJxBYvlng1th7gvRLLUnphWxdO2eTssxr35hIx4lhFEr89q8YiqrmxICZ6WnUDmmIJwsETw7DcSaQwKcN7AHTk8Aa4oWd+sXFESvvpXaLJTZG+NfmmpJ4paLBnHHJBVufxC9RibdqBE9lAShi8XWaKktnYuG9MTlCyLLEt5AKL7BOOjyxTcsDW6FGc9t4JErh7FwUj4t/uiY7RSdLNanIAiC0G3Fjqd/+u+zkFUSkQj4gmE8/hBpSRre234wvt/1KCGuX7WZa8/rz8KJ+UgSeJUwSVoZk14tjqfCGSnLbOCBywvxtLb6cHoDNLgVHC0+7r+sgL0NLaToNQRCYT7Z25gw9XJ5eRXPzxyJCuLlz0XZZpattSf8js7KP2O902LlmPWtCRyxcszY7yjKMfPO9jqKctKY09rHV6dWkWtJItNs+AH+DQnC90cEz04Dbn+A+eNtnNs/nWSdjDVJiycQwuEJ0CNFR6nNwvLyKpZNLwIkyuwN8S/BYpuFBePzyDTp0UiIqX2C8D1w+wP84cqhDMs2c8DlJ0WvxuUNxu9ft7ueMbnpzB9nA4gH0Oa+sDF6dfzyQrQSoseZIAiC0G05PQpKKMQ7N5SgUkkcbPaTrFPj9AYwGTT4AiF21DqZXZJLbL/rUUI89sEePqs6zILxefQy6dBIEr3F8VQ4gzW2KARDEcKRI9mYKlnm/R0HKbc3MLJfGqP6pXfI/irKMeMLhBnRLz0+UCOltY9aW0cr/yzKMZNh0jN7xeesunoM963ZnnB/rO9Z22AaRM9HH7x8aNf+SxCEH4EInp0GzAYtw3qb6JtmpDkQ4st6N5IkkZmqR62SWDS1gEVvbmf+qkquGdufGyfkEQpHSNapCUcgRSdjlMRobkH4vpgNWt74opZFb+5gTkkuRdlmeqXqkVUSAE9/tJdhvc0cavaxcGI+EL2Cp9fIWIwatEBPsT4FAYBdO3dw/pT/6vS+rB5prFrxzA/8jgRB+L7VOrz8z3u7+M35Nh55Zyf5WakUZZupb/aTa01Cr1bh9ChMH92Xv2/YlzDpL0knY9DIqFUSBkkSx1PhjGfSa1BC0V66ZfYGim0W6l1+PqtqZOa5/Vj16T6G9TYzeWgmc0v6Y9TKhMIRPtnbyPWrNuNRQhTbLMwflxevYmqr7YTO2yblU3PYi059pOl/LLts8tBMfjthIE5vgD5pBsIR+MN7uzoEzhaMz4sPNRCE05kInp3inB4FmQiDepm4rXU0d8xHvz8fbzCEQZaZMjQznhrr9AaorHGwvLyKEX3TePTyQhE4E4TvyUGXj8VrtjNmgIV7Ly3k3je2sWytnQ9vHossSZTmWSnb08D1qzYzpyQXS7Iu2lzVoMFi1KBHBLYFoa0AKnpdenOn99W+8ccf+N10bvrsa6g91NTpfV/usdPrKM87VmDwWM8ThDPZQZeP2qYWbp4wkD9+sJtbJw5m8Zrt8SqKD24ei0YlkWnWc/+bOxiSlcrQ3qnUN/vRqVWU2xvYVedi8aUF9Ewz/sifRhC+f8l6NR5/EJVKYmetk9nFuby8qYZbJw7m0Xd3MiQrFZUkcVa2GZUk4VGCZKTomDIsk7F5VvQaGQmJRrcfs0FNqc1Kmb0h4XfEJnT2Nhu4/Z9b47fHepy99Fk1d1wyGIC8jGRSjVrqHF4uHNKLGaP7xss165v99Es3il5nwhlBBM9OcS5vACSJu9oFzgD+vaue8wb2QAJG5Vq4983t8d5oEG3M+ODlhaIUTBC+JwccXlz+AL88px86jYp739gWX6drdx1kTG46Sy4r5K7Xt1G250g5dWletFRTgwicCcLpqPZQ01EDfFsfueaozztWYPBYzxOEM1Wtw8sDa7Zz68X5eMMhbrpwEHe22/Ne//fNvDBzJEQk7plawF2vbUvo0VSaZ+WBywvJFIEzoRtoO919xcwR3D2lgMVrtjM4K5V6p4+FkwYTCkdobp2sqZYl/vF5NfPG5bHk9R2UtxussfjyQuaPtxEm0mHoxtySXLLTDaycfTZaWUVakha1SkJWSfzvf53VISCWaTZwSWEvGtwKzb4AKXoNI/umicCZcMYQwbNTlNOj4PIG2O/0kqLXdAicATzy3m7yeiaTrFORbtRz36UFeAMhPP4QJoMak04tNhKC8D1xehQCoTCRCGjVKkJhEtbp4x/aefoXIzjgcvHg5YW0BEI0e4MYdTJGjYwOxFRNQRAEodtyehQ2Vzdx+6TB1Lq8ZKQYqGnydtjzflnv5tpVm3lq2nCkcIQHLy/ErYTiJ+cpWllMkRe6hbbT3QFmv7CJuy4ZxL1TC+JrIhSOkKyVcbX4eWfbAXbVubhr8hAC4RD3X1pIk1ehvtmPVo6WYf7sL+tZ9evR3HnJYMIRcPuDqFUS5fYG5r24maWtvc+ufnEjI/umsXRa0TGDYalGrQiWCWcsETw7BR1weKlv9qHVyCTp1Al14215lBBX/3UT/++6c2jyBWjxh0g1aOhl0okJQ4LwPal3+fAHgshItCghtGoVy9bZmTG6b8LjPEqIa/++iTklucgqFck6NSZDdJOvk1X0EBOHBEH4/+zde3wU9bn48c/eL0k2CVkICRIIbuQORqmoSahYq6IUtR7boudUAautIvW0tl6p12rtxVqh7U+tKKcV2nPq8X4rXqoERUVyyh2JRBJIQpJNdje7s7uzOzu/Pza7ZEkCqIRAeN6vly/pzu7sDPaZ7+wz3+/zCHEc8isq3pCKCTi5OJdgTGNIlo3Fz21k7n5jacqGPQHOfWQ1T189HbPJQERNkOuwkOe0UuiyH9kTEGKApLrRpiiqxm3PbQG2APD6f1Zh0CGoanjDccpH5nHRycV81hbiqqc+6nO/7aEY33lsLU9cOY0Fy9dlbBvmsmMywB+vOIUfPL2etqAqyTFx3JLk2VHGr6hE4hqReILWoIrTaj5ggUVF1UjoOi2BKC2BCJUetyTOhOgnDd4QbcEo+VkW1ta1M3poNk7dxJpaL/O7uhZ1p6haemnJc9dVkGM1keOwyE2HEMeAL1rXTAjRt0ZfmJ89v4krzxjN2AInKrCnI8xwl53Vtd50B8DeKKqGomrUe0NUedwynorjTqBrKWZfOsNx4loCl8PCyg/rWb2jjb9//ww0XT/g51JNA6LxRMbrVWVuWgIRVn6wi6urTuT6mSeml4MKcTwyDvQBiCS/olLXGqQzHKPZH0FLJC9y2XYTBpLFGXtT4SkgrukMd9mpkMSZEP1mbyDCpkY/7mwrLYEoJwxx4rSYUKLJmaE1Db4+47SqzE1BlgWbwSA3+kIcI1J1zXr7JxqPD/ThCXHM8SsqP3t+E1dMH0VpgYMI4I8kYymkHtpYOsRppcLjxmYyyngqjjsu+4E7VpqNRpa8nXxou3j2BKo8blqD0QPHVbfXbeZ9qYFKTwF3z5nI0x/s4jvTR/Gn1Z8ya2IR+RJ34jgmybOjQKMvzMIVNbSHwsQSOkveriWm6dQ0+AhF4+i6zryK0h4XvVS3k2hMI9dupkQSZ0L0m86oysSiXILRBLWtIZa+XYuqJci2JyfwLquu6zVOU80B7MDQIVKDUAghxPGpLagy+YRcCrItNAairKltozkQoabBR1bXzJc+x1JPcizNs5nIdVik9IE4LrmzrVSVuXvdVlXmxmiEmnofDquJf9V3MGdqESVDnH3GVYWngDvnTOTt7S1UeQoY487izwtO66qBNoHXNjfx1rZWnlxTx7jiXNoVlfte3kKjL3wkTleIo44s2xxgfkXlZ89tYuY4N0NznHRG4qyp9XLLrHEsq67jtFFDyMo189cP6ykvyWd+RWm69W9Ng4+VH+zirm9MlEKpQvQzu9HEA69u5bqzyyh02VlT6+V7lTFsLiNVZW5W72hj0coa5leWpuM0z2FhZL4Du8XEUKnJIoQQ4jjmC6ucO76QzkiMLJuFYV3j4rLqOmZNHN7nWJrbNZaaQDrIi+Pe9WedSELfvzNmAXd+YyKftga59qtjaA1EGO3OIqHDhgY/5SV5PeLKZjbSEogQiWls3O3n7osm8WlbCAPJ1RbR3ARL3/oUIF2exB+O8e3TSrjz+U38updum0IMdpI8G2B7OyNcfnoJRbl21tS2MWZoNgBxTae8JI/vP/0xN37dwx0XTmDx87235palmkL0r8Z2hVBM45unjiQYiadrQqyr7+DTlk5uPn8csI3VO9rSMSrxKYQQQiQ1+sJk281YjcmHv4UuO9F4gi1NAcpL8rjyyQ958qqvAPQYS++7eBI2oFDGU3GcawuqzF++rkcSbGiOjUgs2SVz5thhPPjaNk4rHUKRy87JI3M5fcwQfvbC5h6/I++ZM5FGX5i5p5VQ71UwALkOCzUNPu57eWtG07rUdz25po7yknxpHCCOS5I8G0B7AxEMwJPVddx64YRkNxOjAQC/EmNeRSlQx/0vb+fhVbXMryzl+189EZPRQJ7DQq7VJE/ghOgnfkXFF1IxGA08vGo7c6ePZpjLRjSWSNeEWFZdxyNzy3lo1XamjszjqjNHZ8w4k8SZEEKI45lfUfGFY6zf2cK0McPwRWLp7pg2szE9jj65po5vP7aW+ZWlXHXmaABG5DvItpiwAUNlPBWCQCSW0Ywq5Q9XnMIJ+Q5aAhGGuWzpmWI3P7OB5fNOo6Uzwm2zxpM4X6e+Q6HUncX6XR1cuKQ6nSCr8ri5smI03132YUbSLCXXYeH9nd70vqVxgDgeSfJsgDT5wiRiKgndwOpaLwvDMaLxBNW1bVR53Kyr72BLo7/HUs33d3rZ1hTg3tkTJHEmRD9p9IVZtnonC84cza9WbeeGc05CiWoEInE+qPNSmGOnwlPAmlpvehp8+ci8dOKsJN8hS6mFEIfNtq1bOGv2Zb1uKx6az4onHzvCRyTEwTX6wvz85S3cem4Z08cMI6BqhCJaesZZca691+VkAC2dUcYNy8aKJM6ESOmrYYDNbOTt7S2cOaaAsJqMoXhC54krv8L9r25NL/F84spp/OAv63n++jMBWDK3PP0bE+DpD3b1mjir9BSQYzezrLoOSM5Cs1tM1NR34HJYcGdZZRaaOC5I8mwA7A1ECMdUbEYznaEoADkOC8FonMfe3cnSy8tZsXYXl08fxZNr6npMsb3/4kmSOBOin/gVlf/3z1qurRqDP6ZxVeUY7nhuE/fOmQTAY+/u5I9XnMINMz1Asg7E/ks1JXEmhDicYhgZPudHvW5rfOGhI3w0QhycX1H59evbWHz+OBJAHNjjC5PrsKRnnP3+8lNYONPD0rdrey1LIokzITK5s63MKHPz7o62jNdrGnxsa/QzZUQew1w2AIpy7fzqtW0ZtdFSXTc7QjGGuewsW1OX3u60mlh25VeIaTqru+2/yuPmpvPGMv+pj9KJtVyHhZc2NqXjdkaZm19cOoViaeQhBjlJnh1BfkWlQ4nxq9e2ctus8XTGNLJsyf8EcS1BSyBCeUkeC1ckn8CZDQZuOncst8wy0BmOU5BtxWWRpZpC9Be/otIUiHBtVSkasKcjTFGegzW1XhIGHYfFxCklefzg6fVc+9UxXbXOQFE17GYT7iyLJM6EEEIc1/yKSnNnhEVnl5EAYpD+0V1d20ahKznj7PoVvYylFhNupwUjkjgTYn+5Tiu/uHQKNz+zISPBta0pwC0XTOAXr27liuklVJW5UeMJVndLnMG+ciNN/girtjb3WOH0wWde5kwp4vYLxrOnq6NmTYOPuY+vTcfw/rPQAN7d0cYtz2xgydxymYEmBjVJnh0hjb4w73zSyisbGrmqopRgTKMzHCfHbqbCU8D1T6/nzwtOY7Q7q9cncPddPAmHwcCwIc4BPAshBq9GX5ibn9lANBbnwUunEoknbxI6w8maDv/Y3MysicO596JJLH5+E79dtYPfrtoBJG8kfn7JZEmcCSGEOK6lxtJKj5upI3MZketAUTUC4Rg1DT62NPqZX1HKDWeXseStHRljaffmAJI4E6J3xXkO7pkzkUg8QSgaR40n+PCzdtbsaOX2C8cTjSW456KJ1LWGenxWUTUWrazhD5efwm2zxnP3i5lNBCo8BcyrKOV3b37CjeeM5d6XNlPdvatnmZubzs2chZby7o42aSIgBj1Jnh0BfkXl5mc2cN1ZJ7K61stPZ40jEI7jtJrAAPMqSnlyTR3/8cSH/PHfT+HeiyYRVjUUVcPlMGM1G7FI4kyIfpOK0b3+CMuunIY/GicYiVPT4OPCyUUALH3rUyYV5eG0Gbjv4kmEYwk6IzFy7BayrSZpDiCEEOK41n0sPWvsUFQtQVDV6AzHcDks6Vkvy9bUcfqYIdw9ZyLReAIlqpHjMJNlMWE3GhiaL/e7QhyIyWggEI6hxhKMLHDw9fGFNAci7PaGyc+ygiFBcX7vSygVVeO6Fet5/voKZk0uYl7lGMwmA/lOKyYDGIwGfvi1k/ArUe6cM5E9HWEUVcNmNjIky5oxC21/0kRADHbGgT6A40FbUAU9gcWU/OsORjScVhMOqwl0WPnBLspL8rlrzkTq28PUtYV4dXMz/++dWpwWE1aDgWJJnAnRb9qCKn5F5fHvTkNN6ISiWvpGP8tqotJTgKJqfP/pj/nnJ14+bQ1R1xYipumSOBNCCCHYN5Y+ceU0ApE4wUhyxlmOw0JcS3BKV3OACcW5nDg0h0/2Bmn2R6ht6SQQjmGWxJkQB9XoC6MndPKcFj7Y1c5Hde1sbw4wqsBJXraFoBonpiVwWIxUlbl73Ud5SR6vbGqi0R9B13WCkTid4RhN/gi/eX07VpMRl8NKY0eEdbs6uOl//sWC5etoD6l9Js4AcvpoaCDEYCEzz44AmyHB/ZdMIdh1scm2JxNnRiAUVVk8eyJ3PLcxY9psahmYHZm6LkS/M6j8/vJTWF3bxtQT8nA5zDi76pstXLGeR+aewh3PbaS6W3OAVIxK4kyII0e6Tgpx9DJ0jaXeUIxsm5l4IoHZaMRpMfLwO7Xce/EkFj+3qcf97l1zJuEwGSiSxJkQB+RXVG7++wbuumgCNouRbU0BvjIqH5vZxP2vbGVCcS7lI/NoD6m4syzcd/Ek7nhuU48GANfN9LBg+b6ll1VlbhZUlvL0B8mGdfe9soXLp49i0coaykvyeGRuOYtW1lDT4KOqzJ2xv5QZZW7c2bJkUwxukjzrZ3u8IXSjiaCqYSB5cWoJRDlpmIW2YBh3toP/a+jYtwys6wldttWE3WJiqMs+0KcgxKDW2qFgNdoIqhqFLjuKqlHkstESjHDfxZO547mNXPNf6/j9v59CXNPTMZpjNUmNMyGOMOk6KcTRqdUXxtI1lkZiGnaLkVBUozDHzJ9W7+TGc8by8Bvbufn8cdxuMqbLHmRZTdiAQhlPhTiols4oq2vbaPCGGVXgYPHsCdz70hbKS/K4+fxxxDWdkKoxwm4mGI2RSOjcf/EklJiGT4nhtJpwWk1ous7frj2dSCxBjt2MGk/QHlSZUJzLopU1KKpGNJ5gfmVpOtk9v7KULY3+dO3f7gm0GWVu7rloEp95Q2SHVNxZVql9JgYlSZ71k5ZABDWm0anG0XUDnZEYLqeFey6ayK9f287N553EkCwHG/f4KC/JJ9RVEyKVOJPZLEL0L7+iooZjRCC5vCQaJxpP4HKYWfr2J1x/1knE9QR3zJ6ArkNHSCXHbqEo144JKJYYFUIIcRzzKyqd4RixhIa560FxIBzDaTNhMIKmJTADC6pO5E+rP+WH55zU80GxLNUU4pD5uppY3fT3f/Hs98/EoOvcOXsCgWgcn9L1cNdmRtMTxBOQQOeSP77P9NIh/OS8sQSjGq1BlVyHhVy7BZtF49yHVvf6XWtqvcyvKE3/+bqzPFR63KDr3DJrHDefD4FwjCHZVmp2dXDBI6szZrLdNWciBqBAEmliEJHk2WHmV1R8SoyPd7UzcUQuOl2JM4eFWDxBfVuIs8YNoyMSY5jTytQT8pLFVCMym0WII6XFF8agJYgAQVUjpCZrnAWjcZwWI3v8Kr//5ycsmnkSRiMZ9R2MSOJMCHH0kKWsYiA0dShENZ3Fz23kvosnpxNnqfpmVpORJ9/7jDNPHMIF4wu5ZsaJPe53bSCJMyE+hyyrCUjWFwzGYjjNZoyAy5YsN6LENEJdDTiKcy20h6I8ffV0ErrObl+YaCxBSyDK6WOG0ByIoCX0A35fNJ5I/9kADM2x8m+Pvp+s500ySXb9WSdy78tbM+6VV+9o467nN3NVxWgeeGUrd180ieK83hsYCHEskeTZYdToC9PaGeHXr2/njtkTCITjJHSdHLsFh8XIY+/U8sNzTqI9pPLrf+zIXH9e5ubnF0+SxJkQ/aw1EMlInAXCydosWVYTLYEIb2/by88vmsTtz2/izF/9M/25VIyOkBgVQhxFZCmrONJ2eUM0+cP8/q1aVtd6UWLJsdTlsBDTEmxv7sQz1MHiCydwx/ObuOelbenPpsZSm5QmEeJzy7KaqfAUsKbWS317BM+wbP5V38HUkXnc9cLmHr8tbz5/HA+/8QmXnjqSRStrOKUkj9suGE80niDHbslIjvXGZt7XWzDfaeXnL2/lO6eVpJdyrt7RRkLXM5Z3pqyubeOWWeO4Y/YE/OEoalwjpukEo3FcDoss7RTHJEmeHSZ+RWXTHh+jC7JYXeslruk4rSYMRgMWk4EnutV7+F7VidwzZyJKTEu35862yIwzIfpbU4eCOaFnJM5yHBasJgMbdvs480Q3o91ZPLb6U+69aGKP5SWynFoIIcTxbG8gwm3PbuS2C8azutYLkB5LUw+KbzznJPZ2Rtm4x8fPL54kpUmEOEwsJgMLZ3oAMBsNhNU4Jw3LoTMc4/YLxpMAQtE42bZkHbNEQufH545lZ1uIx787jSFZVtqDUYZkWXFazdjNRqo8blbX9mwAUOEpoKbBl/5zJK7x1rZWrpg+KuN93Zd37m9Xu8LKD+q59+JJ/Oq1rby8aW9624wyN7+4dIrMSBPHFEmeHSaqEmPCcBfNnVEAQqqGO9tKNJ5gY4OPKytK+dPqT7nxnJNQYglag1Fy7BaGuWxYgSK5kRCiX+1t70qcGfYlzlJPyYOROENzbLSHwhS57MyvHENITSa3XU4LORaTzDgT4ignyxeF6H/tIZU1tV6U6L4lWqmxtC2qcv3ZZTz8xidcM+NEhhbn0hmNp8dSeVAsxJcTiMbREjqzpxQzNMeG0WDg/3b7eHljE9ubO3nw0ikUuux0RuK4HGasJiMdIZUReQ7sZhNvbmvmg53t3PWNiaDrGIxw55yJ3P3CpnQyHJLJsnkVpSxaWZP+s19J1lvrbbZaXzPYbGYjq2vbuOO5jfz0/HG8/UlbennnuzvauOWZDSyZWy4z0MQxQ5Jnh0GrN0TcAP5InCxb8q8022ZGUeNYzAZOHZVPS2eEC6cWE4jGCUY0su0m/OEYOVaTJM6E6Gd+RcWo90ycZVtNPLQq+ZQ8ruus/6yDsUXJGFZUDbvZRJYkzoQ4JsjyRSH6V6s3RGck+QM6277vJ0S21cRv3/iUH36tDE3X+fZpJcR1neZAlGy7CU1PkCWJMyG+NCUSY0Seg/p2hfagyugCJ56hWSyc6WHp27UsWL4u/d6qMjfXneVhwfKP0gmrSk8BP79kMloiwSubm9m4289N543jgsnFXFVRiqolGJptw2k1EYzGWXr5Kayv72DFB7uYUJwLZC7lTOntte4z16prvfwgEu+xvPPdHW20BVVJnoljhiTPvqTWQIQIJAs0qhouu5mqMjd7A2Fe/FcjN36tjEgiQWGOg7iuE1Y1zEYDZqORkbkW+VEuxBHQGY6h0zNxtqnRv2859YwxnDamAKWroHGuw4LLKokzIYQQojUQQQVy7BYgWTy8qszN6h1tmIBFXyvjd2/u4OqqUopyHSiqhsmoYTYaOSHXIYkzIQ4Dd46dW5/dyJquWWLubCvPfP8MPvysg5vPHwdAJKaRY7dgMoDDYuKZH5yZbNRhT977KnENfyjGV08axoyyYby+uQk1rjPK7cRlsqDGE/zzk1aWVdehqBpVngKu7DYLLZUQS6nyuNkbiGS81n3mWorVZOSik4v564f16YYDQDohL8SxQJJnX1I0phFUNYKReLIDigHuu3gSv3xtG/95zkk8/OYOvlc1Bs2iE45pKOq+Gmfyo1yI/rfHG6Kz64lb98RZ9SfNVJQNxxeO8J9fH0tI1dgbiJBjtzDcZceMdNUUYrA40JLOXXW1jCr19Hj9kx21DO/vAxPiGKHGNDpjGk6riUpPQfp+945nN3Lj//wfD/3byfzgrDEkdAMtgShOmwl3tg2r0SCJMyEOk5CqpRNnkOy6WdemYDSAxWRM/h61mbGZjYRVjVgiht1swmW3oAPNnVESCZ2iXDskdDpjGqeWDCHbZsZggH9+0oIa1ykfmceSueUMc9l4c2sLi1bWcOqofK4/y8P85R+lv7/CU8C8ytFs3OPniSunAZDrsPDPT1pZtLImowNnIBLjd29+wtNXn84Vf1qbTqClEvJCHAskefYFtQYiRGMand269dksRuwmI/+7voFbzh9HRNP44TllRGIJ2oIqTpuJgmwrNqNBEmdCHAG7uxJnwUhX51uHhRyribq2EGeUDWfNjmZmnDQcNaGT0HUMGDAYwIQkzoQYTA60pHPjg9f0um3jg9f092EJcUzoPpaGIjHuu3gyJgN81hbi/ksm0xmNE1SjZNlsBFUNHR2jwSCJMyEOs85wz1laN/39X6z43uk8+OpWpozM49wJhZgNBhxWE+GYRocSI6srOZZjN2MzGQnHNFoDUdbVdzB5RC6+cIyR+Q6qyobS2hmlOM+BoWv/p40ewtfHF5JtNxOKxPnf687Ep8TQEjrv7/SycEVNeobadTPL+O6yDzOSZrBvCWd1rZd7X9rMg5dOYcHydVSVubs69AYwGgyYjAYKurpw+hWVtqCKP6zitJkxGQzYzUY0XUdRkyu+ch0WbCYjvrBKtt2Cw2KiMxLDH07WfMt3Wins6urrV1S8IZV41z2/Eo2T67QesOtn6hgCkVi6QyjQ47Xjddlpb38/g/3vQpJnX0CrN0QU0okzl8OCzWykLRihMBsuKh/Jf39Uz7e/UoKa0IlrOpDsiuIwGqTGmRBHQFPXzX5nOHnTYDQaiGkJNu72MXFEAYoapvKk4QS6dQEb7rJhB4ZKjAohhBDp2dvdx1IjOvGEyqiCLILRCC6bHTVhIhCJo3T9oM2xmOQhlBCHWY6j5yyttqDK5Y+v5Zf/NoWiXEfXMkgDDrMRp9WEyWAg0LVs02lJJp/+1eDjrhe3pJNcVR43d82ZSCyRoDjXga7r6BhI6AmGu+y8uLGRM8YU8K1H17Lye6ezYPlHzK8spXxkHr++bCo2s5GNe/xEYhqnluRlNB+o8ri5smJ0eglnda2Xm2eNo9JTwJVnjuabf3yvK/nmZl7laH7zj+385LxxLH5uIx/X+7j2q2OYOXYYBgP4lBhWs5H2UBQDBlo7o+Q5LbjsZl7b1MTJI/MxGQyEYxrv7mhla6OfxbMnYjVAQI1jM5u498XNmcdX5uaeORMxkPytHlQ1OqNxXHYzWkKnoSOMxWTk3R2tnF5awNK3dvT4/INdXUM/b7It9f5gNEae04oaT9AZieG0mTEaDJi7JRMP5GBJrC+b5Ort8yFV4+ZnNrB6x75OrVVlbh64ZDInDHEe8r6/yPkcqX30RpJnn1NrINIjceawGDEbDBiAODYiisK3v1KSvNnoulgVumzYjAYK87/c/5mEEAfX7A0RiGUmty1GAw+tqmXROSexeY+XKScUEE3NODMYMBqSg+ZQiVEhhBCCJm+IwH4Piq1GAx/X+5g6MpeaXa1MGz0UNZFZmiTHZqZYxlIhDrusrmXT1d2SN5BMziyrruPOORP5v4YOzh0/nAQQjSU7yrscZrIsJoKqRigaY9roITx73ZkEInGybWayrCbaghGu+fP69HLKVN0ys0Hlt6t2MHPhMACy7SYUVcso/J/itJr46zWnc1VnlGg8gc1spKTAyUVL12TMRgtHNU4uyc9Y2rm6tg0McM+ciRgNcOsF4+mMxMmxmwlEYkTUBEpMw24xsdcfpWSIAztGcuwWQtE4M8cOI57Q6QjFULUEF0wazgWTitjZFmREngOLychHn7VzVUUpc6ePwm4xsb6+g2XVddzx/CZmTylmdIGTBcvXpY8p9XewcMV67rhwfI/EGcDqHW3c/MwG7pkzkXW7OuhQonz1pGFEYhqf7A2SYzdjMhp479M2fv/2p0wblc8vLp2CAfjpMxv4eFcHj8wt55evb89Ykpv67gde2crdF02iOM/R6/8nGn1hbu7aTyrRqKhxInuD5DktOK0m7nxhM29sbUl/ZkaZm190JfwOJrX/7kmyB745mVc2NCX/m+33d3HL/27gwUunMOILjgG9fd/nOd7DtY++SPLsc/ArasZSzVTtpPX1HZw5Mo/CHAfrdrUzeWQegaiWUZxxpDx9E+KI2OMNkQAC4TguR3LQCMc0IJZuDjCvcgz+rifpru4zzuRmXwghhGC3N4QO6XHSaTVhAMLxMOUj82gLRTipOD89luY4LGTbkzV9JXEmRP8wAnfNmcRdL2zKSKClumg+vGo7r29pYfzwXNzZNtoVlWA0js1iJxiJEojEKM51oMYT/PsTH6QTZV8bN5TFsyeyfP5pdHbNIO3eZbPCUwAkEzotgSgVnoKMRE9KeUke/9iyN51Yq/AUcPP543os43TYTL0m31bvaGNnW4in1nzGj88by03/8y8uKh/BmWMKyLKbGZpjw24xUeSyYrea+dlzmzKSWZWeAuZXlmI1Gfn5K1szjjHZffTEHsmxR+aWs2hlDQsqx7D07dqMjqCpz8+vLKXQZe+ROOt+3J3ROJNGuNB1uOelLZnf7XFz55yJnHXSUK588iNueWYDsyYX8fGuDpbMLeepNXU9/j7X1HoxGgzc9Y2JyeRhi0ZHWCW7a1aayWggy2bmZ89v4uNdHfz+8lOwW4w8+Nq29L6cVhOLL5zAD88p49JTT8Bm3pcwvOWZDSyZW37A2Vh+Re2RhAIYlmPrkThLqa71ssurkG0zf6HZYr1937s72g7peA/XPg5EkmeHqNUXJqoleiTObED5yHzqg2GGZ1n4yughyQYC0eR7cqzSmluII2V3t+YATquJbKsJE2A0G0lgp71T4cdfH5ueFZqKY1mqKYQQQiR1H0tzusZJE6DrOlaTgyafwsh8J9GEjhLTMHbN3s6RZlhC9CsDYDToLDzbw82zxhGMaGTbTYSiGnpC5/tnefj2V0ZhMRlwWIwU59pRYhpKVCPHbmZYjo1oPNkF9y9XTycQTi3nNBGOx3lj614efWcniqqlZz6t+GAXC2eWsa0pwLyKUm5+ZgNL55ZjNBgyEhSVngKu6tZhs8JTwA0zy3h7e0vGOVR2JeD6Eo0nWF3bho7Of80/jTue35SRaKvwFHD7BeP5+UtbeiSzqmu9GIBZk4t6JKNW72gjoet9JsfMJgNrar3MryjN+FzqtWg8ccD/Nrs7wmxpClBT39Hzu2vbuPOFTcyeXMSfrvwKlz++lvkVpTwytxy7xXTApFxzIEI4prHig11cPn0U//HEh5xaks+8ytGs+KCe75xWwuQTcmnyh3l5Y1NG4uyRueU8uaaOW5/dmPH3l0oYtgXVAyaS2oJqjyQUcNC/C184dtB9f57vg2Ty61D2eTj2cSCSPDsEfkXtNXFmB9DBAgzPcRBN6ISjcZRost6DdNQU4sjZ3a0uiw4MzbFhBsw6aBgwoeN2OTOWU+d0JcAlcSaEEELsG0sDXYXJh+fasQAmHSIYMOkJivOdyRlnXWNpkcsmY6kQR0AwnkxW789oAIvRgI6RwlwbMS1BJJ5AB8Jdy6ltZiN6QsduNhFSNYLR5JJNp9VEJK5hwMC5E4ZzemkBWTYzMS2BX4nx9fHDKXTZyLGbaOtU+cvV0/F2Rrm6spRbzh9HSI3jslvwhVUiaiJdA62lM4rJCI++szN9nJWeAu67eDL/9v/e6/McbWYjkEyENfojvc7IeuCVbUwtyeONba09Pr+61stV+yXAun+2t+TYgsox5DuT9eR6SwyllqAeiM1spHxkXq8z6rp/929e3878ylJynRZ+/Y/tXDF91AH36w/HePqDXZSX5PPkmrpuyT+dqV2v3Xz+OFo7oxl/V/MrS3myjxltqe3J+nh9C/Sx/VD+Lg6278/zfSmHss/DsY8DOfCZCyA5ZX3/xJkZQIcEYDYk/x2NJwhF42Tbk+vKJXEmxJHR/WY/x2HB5bBgNRqIJMJEAbtBB0NXEdBuy6ltJqPc7AshhBBkjqWurrHUDBgTCVTAbgCMxp5jqdEgY6kQR4AvFGPuY2sZlp3sIGnsyqN1RuLsbFO4cEk1n7Up+JUYWxoDWEwG7BYTnZE4ajxBAtjbGUEHCnNs5FiSiTOr0YjRBDlWE0OzbRgMENd0ivLsnD5mCO2hKCajkROHZmE0gtVswp1jw2k1UeSyYzBCnsNKYa6dYTk2RuQ7mDYqj+EuO8/84Ez++9rTefWHVcyvLOWVTY2MHZ7T6/mlunKm+HvpLgrJmVzlI/P6/Hs60Myo3rbZzcZ0g7/eEkM2s5GaBl96+er+qrqO+2AzslKz6spH5qVnuh1KImpNrZfykXnpf0MySZh6rbfz6r5tf6n95Nh7NqDoztXH9poGH1Ued6/bUv8ND7bvz/N9KYeyz8OxjwOR5NlB7O5QeiTODIBZ14kCRl0nAhk3ErJUU4gjx6+oGTGaWq5Z1+rHaHDgDSpEMKRnpXWvQzj0SxaNFEIIIQaD3u53s60mdrb6iRiMRPUIEegxe3tkQZbUCxXiCHE5LLQFVUKxON9YsoZgNPnvBcvX8cGudk4tyeP6Fet5b6eXSSe4iCd0zEYD2TYznZE4US3BkCwbNnMyCd4cVEkkdOIJnaiqs6sjjBLTQNcpyLIST+i0BVWybBasJiO+cIyOYIxsu5mwGqem3sffPt5Nky9CIBInkdAxGQ00+yI8/68m7n5xM7qu8//e+ZSXNzbxRHUdS9/6lHkVpT0SUVVdy0SXVdelXztQYulAiaoDfa63bflZVvxKrEfyDvYlg5ZV17FwpoeqMneP7Yu/MZFl1XWHlAhLCYTjAAdMynU/ntT5dj/v1J8VVevx3QdL5AG4sw+8fNGdbWVGWc8k2bLqOm69INkxdf/jnVdRyvamwEH3/Xm+D5IF/w9ln4djHwciyzYPojMSz7iRsABa19R1ux4nYjRL4kyIAdQWVDNi1ARoiQQj3bnUt/kZPTQ3GaOpgsbSwEMIIYTIsP/9rplkjbOR7lx29TGWyv2uEEdWdle3zZZAlMquxEqqeP+y6jqWXl4O1Z/x21U7ePSdnfzxilNoCUQZW5STbvoRjSc7cOY4zAx32QjFNDoUFZfDQnGuHW8oSpbNgtEI3k4Vd04y2dAWjBJSNZr9YU4cmo3RAJ7CbJ6p2c3St2pxWk3MryzlzDEF2K0mLpg0nJJ8Bwld53tVY3h89U7mVZQCdSxaWcP8ytL0EsphLhtvbm3J6L5Z5XH3SGR1l+vofQZRlaeAvYFIr9v6So7FNZ2mQISFM8tYsPyjjG3zuuq4nVqST7bNzOljhnDVmaPTSzn3BiK8uqkJRdUy/nsc6LtPyN/38H5ZdR2PzC0H6NFk4MqK0ekacqnkWPckWerPdouJna3BjO8+WCLvhHzHQWt/5Tqt/OLSKdzyzAbe7VZHbNqofAqcVh68dAq7vAq+cCw9O+9vH9Zzz0WTvlBdsb6+b0aZmwcvnXJI+zwc+ziQQZM8+8Mf/sCvfvUrmpqamDhxIg8//DBVVVVfer8ZNc4SyaVfGoCuS+JMiKNAIJKZ3DbpoBmMKNHwvpv9iCTOhBDieHL5vGtobO3odduuulpGlXp6vF48NJ8VTz7W34d2VNq/GZZBhygGND3eYyyV+10hBoYVuO/iyfzyta387BsTefDVrV0JqWTiZeGKGq6ZMYYfnzuWmJYg22aiZIgTVUvQGYmTZTNjAHIcZhxmE0pcIxhJdqfPsphQExrubBvhWILWTpWCHCsOSzLpFjIaGJZjY2i2jW1NAUoKnMx/6iO+c1pJOglWlOvgH1ua2bTbz7kTCxmSbeOqJz/iP84YxU3njsUALL5wApqu0x5S0RI6Q7Ks/HbVJ7zVrX5ZhaeAm84byyNvftLr30OFp4AhTgtVnoIe3TbnVZZiwNAjiVXlcXPdTE+vyTGrxcDppfkYMPCn707DH4kxNNuG1WykPajyzA/OREvoPLRqe8Zxppok3PfyVmBfIswIGcfVPQk3o8zNcFdy2e2MMjfv7mjLSCZG4wlyHRYiMY0bupKJFd0SpakEXGqp6IwyN0W5dvIdFsYMzU7/f+FAibzux3AwxXkOlswtpy2opnMe7mxrOgmVbTOnt11y8gjclaVfKkF1sO87Uvvoy6BInv3tb3/jxhtv5A9/+AMVFRU8+uijzJo1iy1btlBSUvKl9r0vcRZHNZixAlYDRParnyQ3EkIMDJfdgtVkSDfwUAG7roPdITEqhDiubdu6hbNmX9brtmMlUXSgJNiBzqGxtYPhc37U67aND17T67bGFx764gd6jNu/GVYcsBlAN8iDYiGOFkbADtw6azxKXOOOCycQ1RIsvnACOiRrb3drApDQwWw0YMGINctKh6Jit5hwmE1ENQ1dh4IsK1ajgXhCJxLTUaIqOXYzeVkWmjsibAlEmFCcg6olSOg6rq4ZX2vrvDx46RSi8QQOi4kcu5nvLvuA8UUu7vzGBF7e2MRdL25BUTXWfdbBnKnFRGMaZpORn7+4Od0R0Wk1sXj2BH789bHUdyhYTckZTI+9+yl3zJ6Aqm3O6J5Y4Slg4cwyWjtV5kwt5vYLJ+DvSv5rCR2TAVo6o9w9ZyLBiEZTIIzVZGTjHj8N7SH+du3p7O4Ip7/nrx/s4mffmIiuwz0vbWbyCXmcO2E4Tf4wABv3+DHsgdNGD+En543jx18fSzimkWU1kW0zc/dLW9Kz5RRV428f1nP/JZMJqRp7fMl91DT4WLSyhmmj8jNmP3WfIZVqNNC9a+n+XU9TCbgqj5t5laNZ+UE9D146hcKuRFie08L9F08mpMaJxDQuPWUEdz6/+UvPwMp19p14OtC2L+pw7LM/jgsGSfLsoYceYsGCBVx99dUAPPzww7z++uv88Y9/5IEHHvhS+86xm7HFo0QNVmzo6AYDUaTGmRBHC3e2FTUcQ9eTLbwluS2EEEkxjH0mkI6VRNGBkmBvPXh1n8nBT3bUMrw/D2yQybGbsWkJEhgwkmyGJTV9hTjKGA2YNJ0EYDUZicSSSzCdNhNOiwmn1YqegETXTbHTbCIU0+iMxHFaTeQ7rZiNBkKxriZbdgsmo4EEYDQasJgM5DjM6Dr4lRhDc20My7WR0HWKXHYsRgNaQueUUflMiLtQVA2X3YzVZMQbivLnBdPJtpoIRuOcPW4Y5SX5JBI6douJmJZA00GLa9w6axy3XWAgFN03G85mNlLqzsKvxLhgUhEmI+xsDXH7BeMwGAwEus7BYTERSyTQdcjPstDsj7CuvoNl1XWUl+Txw6+VUVKQnG1ntxoZXZBFKBrn/EnDqan38e1H1+5bGlrm5oFLJnPCkGTdxvu/OYWOkIqixikbls3ujjAnFeYki/Z/6mV7U4B7LppEUbeayb+5bGqfM5yKcu20BVUKsqzJWVn7zX7qbYZUtt1MKBrnLwum47SaMBoM6AadOy6YQEdY5X+vOxOTwYDJaODXl03N2F9vCaP+moF1vDrmk2eqqvLxxx9zyy23ZLx+7rnn8t57vbfBjUajRKPR9P8OBAJ97v+EfCcNXh17QiduNBBHbiSE6G+fJ0ZznVZaVQ1DPAFysy/EEfN54lSILzqD7EAOlBzc+OA1n3t/g2GmXnef9353jzeEVUfGUiGOoM8Tp0PznbR6QwAYDclWmzp6+s9RLYHFaCSuJYvFawmdYCRGti3ZUCsa14jEdexmE8OybbQrate+wGAAu9lESI0TjGjk2M1kW0zsaleIxBPJWVof1vP7y0/h8dU7GVfkonxkHq2dUXIdFoY4reh6glAMDAYDJgzYzSbMRgPxRIKYlmBvIEpxngOHJZn4MxsNOC0mVC2Rnj022p1FMBKnMxKjZIgTq8mIP5xMQGXZzAQjccJRDafNREjVSAATilwsmVtOS2eUknwnw/toCFbksnPa6CF9JpIKXfb0LC5I1lU72JLELzsrq79mSB2p/R9vjvnkWVtbG5qmUVhYmPF6YWEhzc3NvX7mgQce4O677z7k7xhZkEVjh0IsoaN0u5Fw2c2MkA5DQhx2nzdGh+Y58CvJxgEZN/t2MydIjArRLz5vnIrB4YvUEoPkTLAZP/5Dr9uOlhlkg2GmXnefN0ZHFGTR3HW/K2OpEEfG577nLciiqUMhEo0T7KpjZjMbsRgN2DGhAzZz8t8akG23EIwkOzsauxJkqpYgEE7OWLOZjViNBpS4RkjVCEWTiTObxYiB5OwoJabxVftQLpxchN1s5GezJxCOaenZYM6uumheJY7TaiDbaiYS70qOWU2YjGZMRgOnljgPKZFT6Nr/laxet/kVNWNW1bRR+Qfc/+dNJEniSezvmE+epRi6Mu4puq73eC3l1ltv5Uc/2ndzFAgEGDly5AH3Xyw3DUIcMV8kRmWAE+LI+iJxKo4eX3SW1RepJZba1pfDPYNMJH2RGB0u97tCHFFfJE6L8p0U9feBfQGjj/D3yb2/ONKO+eSZ2+3GZDL1mGXW0tLSYzZais1mw2azHYnDE0J8ARKjQhz9JE6PbYNtlpXoSWJUiKOfxKkQx45jPnlmtVo59dRTWbVqFZdcckn69VWrVnHRRRcN4JEJIYQQQhx7DjQr7XgtxD/Y6qEJIYQQ4vM55pNnAD/60Y/4j//4D6ZNm8YZZ5zBY489Rn19Pd///vcH+tCEEEIIIY4psoyyJ5mpJ4QQQhzfBkXy7Nvf/jZer5d77rmHpqYmJk2axCuvvMKoUaMO6fO6rgPSKUyIgZCTk9NnfcIUiVEhBs6hxCh8vjjVNA0trvWxI46ObUfLcci2o/6/+dYtm6ma9c1etw0vyOWJP/yu930eJv0Ro0KIw0viVIij26HEqEFPRehxbPfu3VLkWIgB4vf7cbl6tNXJIDEqxMA5lBgFiVMhBorEqBBHP4lTIY5uhxKjkjwDEokEjY2NPbKNqW4nDQ0Nh3SxO1YdL+cJx8+5HkvneShZ/r5iFI6tc/0iBvv5weA/x2P9/A71afnxOpYO9vODwX+Ox/r5SYweuuPpXOH4Ot+j/Vy/bJzu72g/397IMR8ZcsxfzKHE6KBYtvllGY1GTjjhhD63u1yuY+b/eF/G8XKecPyc62A5z4PFKAyec+3LYD8/GPznONjP73gfSwf7+cHgP8fBfn7He4x2dzydKxxf53usn+uh3PN2dyyerxzzkSHHfPgZB/oAhBBCCCGEEEIIIYQ4WknyTAghhBBCCCGEEEKIPkjy7ABsNht33nknNpttoA+lXx0v5wnHz7keL+cJg/9cB/v5weA/x8F+fgcz2M9/sJ8fDP5zHOzndzDH0/kfT+cKx9f5Hk/nCsfm+coxHxlyzP1HGgYIIYQQQgghhBBCCNEHmXkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkmhBBCCCGEEEIIIUQfJHkG6LpOIBBA1/WBPhQhRC8kRoU4+kmcCnF0kxgV4ugncSrE0UuSZ0BnZye5ubl0dnYO9KEIIXohMSrE0U/iVIijm8SoEEc/iVMhjl6SPBNCCCGEEEIIIYQQog+SPBNCCCGEEEIIIYQQog+SPBNCCCGEEEIIIYQQog+SPBNCCCGEEEIIIYQQog/mgT4AIcTg4FdU2oIqgUgMl8OCO8tKrtM60IclhOhG4lSIo5vEqBDiSJJrjhCHTpJnQogvrdEX5uZnNrB6R1v6tRllbn5x6RSK8xwDeGRCiBSJUyGObhKjQogjSa45Qnw+smxTCPGl+BW1x8AL8O6ONm55ZgN+RR2gIxNCpEicCnF0kxgVQhxJcs0R4vOT5JkQ4ktpC6o9Bt6Ud3e00RaUwVeIgSZxKsTRTWJUCHEkyTVHiM9PkmdCiC8lEIkdcHvnQbYLIfqfxKkQRzeJUSHEkSTXHCE+P6l5JoT4XPYvLJptO/BlJMduOUJHJoSA3ov/ug4ShxKnQgyso20svXzeNTS2dvS6rXhoPiuefOyIHo8Q4vD6svcF0mhAHI8keSaEOGS9FRZ94JuTqSpz9zr1e0aZG3e2DKRCHCl9Ff994JuTmVHm5l2JUyGOOo2+MOt2dVDhKWBNrbfH9oGI0cbWDobP+VHv21546IgeixDi8HNnW7/wfYE0GhDHK1m2KYQ4JH0VFr33pS1cP9PDjDJ3xuszytw8eOkUeQolxBFyoOK/d72wmfsvmSxxKsRRJhW39760hXkVpVR4CjK2S4wKIfpDrtPKLy6d8rnvC6TRgDieycwzIcQh6auwqKJqzH/qI15dVEU8odMZiZFjt+DOlunbQhxJByr+u2prC7deMJ4lc8tpC6oSp0IcJbrH7aKVNcyvLGV+RSnReAKb2YhnaDZFMpNDCNEPivMcn/u+4FAaDch9hRisJHkmhDgkByosqqgaHYrKySX5R/CIhBDdHaz4byAcY8zQbLmpFeIo0j1uFVVj6Vu1Gdufu+5MRpF1pA9LCHGcyHV+vodo0mhAHM9k2aYQ4pBIwXEhjm4So0IceyRuhRDHErlmieOZJM+EEIckVVi0N1JwXIiBJzEqxLFH4lYIcSyRa5Y4nknyTAiRwa+ofNoSpKa+g09bg+nCn1+0sKgQ4vDrLU4lRoU4evQ1lu5P4lYIMVAO9TrVnVyzxPFMap4JIdIO1nr6ixQWFUIcXgeLU4lRIQbWwWJ0fxK3Qogj7fNep7qTa5Y4XknyTAgBdLWe/vsGVtf23np6ydzydFFRGRyF6F9+RaWlM4ovHCPLaiLLZibPkawjcqAW8d3jVAjRv/yKijekEk/oJHQdJRqnINvGHc9uOuhYuj+JWyHE4eBXVNqCKoFIDJfDgjsreV3p/lq2zXxI9xIHItcscTyS5JkQAoCmQKTHzX6KtJ4W4shp9IV7JLIrPAXccHYZ+U6LtIgX4ijQ6Avzs+c38Z3TSnhyTR1rar0APHHlNBlLhRADYv/7B6fVxLKrvsLv36rNuC6tuHq63EsI8QVI8kyI41xrIEI0phEIxw/4Pmk9LUT/29Oh8O4nrSyoLOVH556E2WSkI6SiJXR2tgYZXZB1wM9LnArR/xp9YQKRGHfOnkBnNM7N549DUTUsJiOKKmOpEOLISc00C0VVXA4rt184nkAkTrbNRJbVzIOvbWVqSR5XVYwmGk9gt5iIJ/QD7lOuU0L0TpJnQhzHmr0hosAdz27kqorSA75XWk8L0b8avCHUhM6qrXu5fPoofv2P7enZLJCcfbb4wgkH3IfEqRD9q94bYt1n7ZSX5LPHF2bJ27UZcbri6ukH/LzEqBDicEnVLdvaFGDl905n8XObWN3telRV5uaOCyfw4GtbWfpWbfr1p+U6JcQXIt02hThOtQYixEgmzlbXeqlp8FHhKej1vVUeN3aLXC6E6C+N3hAxTeeu5zcxoTg3YxlYyppaLzX1HVRJi3ghBsSeDoUmf5jifAdrd3p7JM4A3tvppbKPsVRiVAhxuPgVNV237NeXTeWeFzdnJM4AVu9o496XNjOhODfj9fflOiXEFyK/hoU4zqTaUu9qVwiqWnqgXVZdx7yK0h4JtCqPm7svmshdL2w+pBbWQohD51dUtjcHaOyMEo4l47F8ZF6PH+Qp9768lbvnTOyRQJMW8UL0j9SYuX5XO8FoHJfdArqBYS57r3G6rLqOqypKqfJIjAoh+odfUWnyR5h7WgnLrvoKw132HomzlOqu+4ru0tcpuZcQ4nORZZtCHEeafGH++Ukrw3JsROMJ3N1KHiiqxqKVNcyvLGV+RSnReIIT8pOtquu9Cqu2tkgBUSEOoyZfmH9ub2WYKxmPQ7o6YkXjiT4/o6gajf4wP5s9AaPBIC3ihehHqaYA44pclI/MozkQJd9pwWIy9hmnqbH0r9eczu2m8URimsSoEOKwSS3V7F7w/4krpx3wM/tfr1LXqWevOxOz0Sj3EkIcIkmeCXGc8CsqDe0Kur4vY5Ztz7wEKKqWURPhiSunMSLPwU1//xcgBUSFOFz8isoub4iXNjamZ68su+orANjMB54UrsZ1jAYDJw7L7vfjFOJ45VfUjG6aS9+qxWk1Mb+ylAsnF2EL9z0eKqqGwQCKGueUUUOO4FELIQaz7ks1P4/e7isUVUMHuZcQ4nOQZZtCHCc6QiqarvPyxiYWLF/HdU+vJxrTqOqj5kGFp4CWzihvbttLWzC5XFMKiApxeLSH1Ix6SU6rCXe2lSqP+6D1B/cGIlKPRIh+1hZUGVfkStcfdFpNPDK3nJr6DiIxjb2BSN9xWubmza0tMmYKIQ6rtqDaa+KspsHX5/18paeAmgZfj9crPAUYDvcBCjHISfJMiOPAng6FhA5L9ytu7FdizKvsWZulwlPAwpllTC8dwu/f/hSQAqJCHA5+ReWzthBKTMuIxfmVpTz8xicsnj2BLY3+XusPVngKuHPOBGaeNFSWVQjRzzoUlVNK8tNxOr+yNJ1I8ysxinIdLJzp6bVO6M9mT2DTHj/DcmwDcehCiEEq0McKkGXVdSyePbH369E3JrKl0Z/xeoWngHkVpbSHVPYGIv12vEIMNrJsU4hBbrc3RCyhE4rFexQ3XlffwZZGP6eOzueH55QRT+g4rSYAtjd3sr6+HUXVpICoEIdBky/MrvYQf3p3J9ee5cnYVj4yj6Vv1TJ1ZB7njC/EbDBw07ljuWWWgQ4lRlzT2RuIYDcZGZ7nGKAzEOL4sNsbwmoy4rSY0q+lYhT2jZ1TR+Zx8/njgOQSKLPRQF1riP9r8HHvRZNkzBRCHFauPmazKqpGnTdEeUk+8ytKybKZCUXjAPx21XYmFOdyxfRRROMJbGYjNQ0+VnywiwnFueQ7rRS67EfyNIQ4ZknyTIhByq+o+MIx3qtt45WNTVw/s6zHe5ZV1/HI3HKeXFPHw2/sSL9e5XFzz8UT6QzHOH9ikRQQFeJLaglEaGgP4bJbWHTOSQQj8YztqWK+j76zk0fmlvN49c6MZHeVx81N542lXVE5oSDriB67EMeL1kCEcEyjMxIny2YiGN0Xp90LbncfO3+7qtvYWebm3osmke+0yJgphDjs3NlWZpS5ebeXpZvZNnM6wf+HK07huqfXp5ebp+o2plR5CriyopRFK2uYUTYUv6LSFlQJRGK4HBbcWXLfL0RvJHkmxCDU1KGQSOi8V9vG5BPyuPXZTfzwnJN6vK97h83bLhhPsz9CUa6DzY1+hjitlLqliKgQX1Zzh0IknkDT4YFXtnJVRWm6rlkqQZYq5ttb11ub2cioAiffeWwtf7vmjIE8FSEGrT3eEABrats4Ic/J0Fwb7+30puO0e8Ht3uJ01BAnVosRXdflR6cQol/kOq384tIp3PLMhowEWqWngBy7mSqPm9W1bQe9pxiWY+fbj72Pomrk2M0sXFmTUUttRpmbX1w6hWKZ6S5EhgGvebZnzx7+/d//nYKCApxOJyeffDIff/xxeruu69x1110UFxfjcDg466yz2Lx5c8Y+otEoN9xwA263m6ysLObMmcPu3buP9KkIcVTY064QisdRdZ2XNjbRHkoW+6+ubetR2wySA+u/6n1saQwwcoiTP7y9g0qPW27+hTgM9rYraAkdg8HAH96uZXWtl2g8wbLquoy6Zt2bBKS63qYaezy1po6XNjQxocgldQeF6AetgQhGoDOmUZTr4IQhDvZ0hDPidP9GHt3jdMUHu7Cajfxu1ScMyZIYFUL0n+I8B0vmlvPyokr++O+n8OoPq7j3okn88Z+13DF7fI/r1f73FE+uqcNmMbL08lN44JJJGAzw8a6OjO94d0cbtzyzAb+iDsQpCnHUGtDkWUdHBxUVFVgsFl599VW2bNnCb37zG/Ly8tLv+eUvf8lDDz3E0qVL+eijjxg+fDhf//rX6ezsTL/nxhtv5Nlnn+Wvf/0r1dXVBINBZs+ejaZpA3BWQgycBm+IXe0KJoORnz23iTW1XvKzkvURHnt3J/MqR/foxlNV5ubeiydxZukQLEYDP79kMkXypEmIL82vqER1HX80zh5fmNXdZpmlngaXl+TzxJXTmDwilztnT+wRnxVdSys27/FL3UEh+kFLIEJQjdMQiOANquQ6LTT6kwW0u8fptJJ8fjZ7Qo+HUFUeNz85bxy/e+MTbr9wgsSoEKLf5TqtmIwGNjcG0HWddZ91MKNsKAmdg16vrqoopbEjzPynPuKVTc2YjQb++9rTezyce3dHG21BSZ4J0d2ALtt88MEHGTlyJE8++WT6tdGjR6f/rOs6Dz/8MLfffjvf/OY3AVi+fDmFhYWsWLGCa6+9Fr/fzxNPPMGf//xnzjnnHAD+8pe/MHLkSN544w3OO++8I3pOQgyUem+I25/dyOpaL6/8sCr9Qz2u6ellJwtXJKduX9U1dTvXYWFojo37Xt7Czy+ZzAgpGCrEYdEaiBCJaQSjcR54ZStzp49Kb+u+ZLN7DRKn1cTiCydwx+wJBKNxsmxmTAYDJqOBX182VX6UC3GYNXhDNAUiJBLJpZbxRLKumT8cY0tToEecOq0m5leW8oOzTiSu65gNBorz7CR0+PklkyVGhRBHhF9RqdnVQcWJBazaupc5U4pp6Yyi6zo19R0sfas2fb26qmJ0+p4/EtO4YWUNv75sKgCrd7Rx5wubmT25iD9d+RUuf3wtirpv8klnH909hTheDejMsxdeeIFp06Zx2WWXMWzYMMrLy3n88cfT2+vq6mhububcc89Nv2az2fjqV7/Ke++9B8DHH39MLBbLeE9xcTGTJk1Kv2d/0WiUQCCQ8Y8Qx7IGb4hbuxJnAJ3hfYOdX4mll510n7r99Ae7CMc0GjvCvLG1pUcB84EkMSqOZY3eECFV47ZnN9Lkj7B6v3pJ+y/ZTCkvyeO0MUMYO9zFqaOGMG64i7LCHMYMzT4qf5RLnIpjWWO7gqbrLHlzB9WfevF3dbUNhOPYzMZe41RRNWrqO3Dn2Pj+nz/m8j99gJZAYlQI8YV9kThtC6o8ueYzLCYjj76zEw2dem8IRdWYV1FKlcfd6z3/DStrUFQt455kTa2XYS47v3l9O/MrSzO+J6eP7p5CHK8GdObZzp07+eMf/8iPfvQjbrvtNj788EMWLVqEzWbju9/9Ls3NzQAUFhZmfK6wsJBdu3YB0NzcjNVqJT8/v8d7Up/f3wMPPMDdd9/dD2ckxJHX1PVDvXtnvhzHvsFuXX0HWxr96fbVvbWphqPr6ZLEqDhW7faGCHV167trzqR0zcHus832L+ALkOuw8M9PWjEM5MF/ThKn4li1u6s5gJaAWy4YD0BC1/F2qrhzbLy/00t5SV6vhbZbAlFe3dSUnp1xNI2d+5MYFeLo90XiNBCJ8dC3TyYQjiUTZss+4qn5p9Hsj7BoZQ1L5pbz/bNOxB+Ope/5F3UlzlI10bqLxhOsrm3jqorR6ddmlLmlzqoQ+xnQmWeJRIJTTjmF+++/n/Lycq699lq+973v8cc//jHjfQZD5s8JXdd7vLa/A73n1ltvxe/3p/9paGj4cicixADZ4w0R1WGPL5zxelxLUNn1tHxZdR2XTx9FTX1HuljoguXrqKnv4PLpo1hWXQccXU+XJEbFsajBG2K3P0w8kSDPaeWuFzYR6PphnZrFUrVfAd9la+rQge8u+5ANDT4KjqFi4xKn4li0tytxpiZ07nphExc+Us0ur0JLIEqTP8yWRj/bGv3MqyilvCSvR6HtCcUuHn1nZ3p/R9PYuT+JUSGOfl8kTl12C2ajgZqGDqrK3DR0hPn2o+8zzGXj1FH53LCyhnBM4+kPdrFg+TqWvlWLompUeQqYV1GavvdPSc1Ei8aTS9dnlLmlzqoQvRjQmWdFRUVMmDAh47Xx48fzzDPPADB8+HAgObusqKgo/Z6Wlpb0bLThw4ejqiodHR0Zs89aWlo488wze/1em82GzWY7rOcixJHW1K6gAbc/tzE9eyXl+qfX818LTmPxc5uorvWmn55fd5YHk9GAomqsr+9IP4U62p4uSYyKY81eX5h4QmfJW7WUl+RTU9/BmlovU0vy063jF62s4ZoZY/jprHG0BKIA6afBp47KP+ZuVCVOxbGmsUNB1eG9T9t4eWNTesa2zWzk5mc2sOJ7p9MRirKgagyPr96ZMWM712Ehx25m/lMfpWedHW1j5/4kRoU4+n2ROHVnW9nREmRG2VBOLcknoeusqfUy97G1PH316dz70uYeM9yHu+ys2ro3fe+f0n0m2ugCJ2/+6Ku4s63H1P2IEEfKgCbPKioq2L59e8Zrn3zyCaNGJQsrl5aWMnz4cFatWkV5eTkAqqryzjvv8OCDDwJw6qmnYrFYWLVqFd/61rcAaGpqYtOmTfzyl788gmcjxJGz1xtCAxo6wqyp9VJekp9eEkbX69994kP+cMUp3G4y0hmJkWO3kGU1cc+Lm3ljW2t6X/J0SYgvp9UXJqYluOP5ZIfb+RWl6QLjy6rrWPG909HRqa718vAbO3js3Z0svnAC5aPyyLGbOXdCIYUuO4XSsEOIfrO7a9xMPXDqXuqgpsHH2OE5XP74Wn79b1MZWeDg9gsmEIlrKFGNHIeZml0d3Pvy1ozEmYydQoiBkOu0kt81w/3jel/G8vIWf4Q7LpyADgTCyft/m9mAEtP4+LP2HomzeRWlLFpZw4wyNyPyHHJNE+IABjR59p//+Z+ceeaZ3H///XzrW9/iww8/5LHHHuOxxx4Dkss1b7zxRu6//37KysooKyvj/vvvx+l0cvnllwOQm5vLggUL+PGPf0xBQQFDhgzhpptuYvLkyenum0IMJq3eEFEgpGr4w/uWhD0yN5lg7p5A++Vr27myYjRaQqctqOK0mDh7fCE3nT+OiKqRY7fI0yUhvoTWQISIliDYre5gatkDJJdnXr38I5Zd9RV+EIln1B958/W9fP+rJzIs2yaJMyH6UaM3RFzXCUWTcXpFt+63sG8MfXJNHVc99VH69aoyN4tnTyChJ6gqG8qLCwvSD6Nk7BRCDKRYIpFuFNa9c3fKX793Ot95fG36fzutpj5nv08blc89F02ivj2EKxxDjScIRuO4HBbcWXKtEyJlQJNnX/nKV3j22We59dZbueeeeygtLeXhhx/miiuuSL/npz/9KeFwmOuuu46Ojg6mT5/OP/7xD3JyctLv+e1vf4vZbOZb3/oW4XCYr33tazz11FOYTKaBOC0h+k2TN0QMWFPbRqk7O12jYP8C5KnCxqMKnMxZuoYlc8vJsZnZ1a4wZmg2RS67DIRCfEmtHQqRhM4efxj0fTU2u3exgmRXrO88tpb5laWUj8wjy2bmzDEF/NspI8h3yk2pEP2pKT3jbBMLZ5YBPWN0/zHUZbfgtJrQdB1d18m1WynKcwzA0QshRO+CkfgBtzttmb+DFVVLz36fX1nKBZOLyLKZuXByEbquU9sapGSIk9uf3Uh1t5m5M8rc/OLSKRTLNVAIDLqu6wN9EAMtEAiQm5uL3+/H5XIN9OEI0asmb4g4EFQ1AuEYLocFs8nAXS9szlh+klJV5uaqM0ezbE0dsycXUelxs7czwgl5ToYfYwOgxKg42jS1K3TG4iQSoJO8iXVaTTQHImxrDvDep97e49Lj5oIpRYzIc3BCvoMxQ7OP/MH3E4lTcTTxKyqhcAw1oaPpEIlpKKqGyWjAF1Z5orqujxgt4PYLJ2C3GPnV69u59fxxnFCQNQBncPh9nhg9a/ZlDJ/zo163Nb/wEP986X/64xCFOO4dSpz6FZVGf4RZv1uNO9vKg5dOYZjLRjCi4bKb0XSdbKuZO1/YlJ6d1l2lp4AHLpnMp63JBipXPfURC8/2pOu17m9GmZslc8vlYZ847g3ozDMhxKFp6uqqeftzGzMGta+NG8rPL57M4uc2ZgyOlZ4C7r1oEr96bSs3zCyjOM+OUYeyYTky8AnxJdW3K9zzwia+M30UT67J/AFe6SngZ9+YyMTiXICMbVVlbu65aCK728Msf/8zfnPZ1CN+7EIcD1p8YSJagmZ/GE2HpW/XZsTiOeOHcec3JnL3i5t7xOi9F00ioSd45I0d/HQQJc6EEINDoy/MzX/fwI/OPYmLTy7ihq+dxM+6aq6mVHoKWFA5hnmVpSSgx7afXzKZYDSO1WxkSJaVG88p4+QT8npd/gnw7o422oKq/IYQxz1JnglxlGvumnG2f+IM4M1trcBm7r5oEiE1zu6OMC67hZH5DtSExo/PHUd9u0J7KMrJJUMG5PiFGEwaOxRu/98NTC3J75E4A6iu9XLPi5u5unIM5SX5XHeWB5vZiMNiYltzgM/aFP689jPuuHCC3IQK0Q/8ikpES1Bd2waQ0VUz5Y2tLQAs6NaJbkS+g2yLCUXTaOqIcs1XTyQUUQFJngkhjg5+ReXmv29gdW0b358xhh9/fSy3PNvz90F1rRcdOK10SLprMEBxnqPX5mFVngJmTRqO02rKaCjQXWck1m/nJcSxwnjwtwghBsrerhpn3YuR7+/Nba1E4gl++ep2yoZlYzKCQdf5zqMfcPZD73DVUx9hNUv9PyG+LL+iEojGWV3rpXxkXp8xWV3rpTDXznkTCzEZoTDHhtFo4MRh2RiACV2z0oQQh58/HKOhI5zuYNtXnL6xtYWiXAfDXXZOGpYNJPh7zR4uXvoeVz75IY3+CDaL5cgevBBCHEBbUGV1bRtOq4nCPDuBSLzPa9yaWi+TinNZ+lYtC5avY8HydQDc/uzGjMQZwOpaL/e9tJX5laV9fneOXa6HQkjyTIij1G5vCBWo7wgTCB/4aU9nOMbq2jZaAlGGZtt5cVMTbUEVSLahzrLKJFMhvgy/ouIPx/AryVjs3lGzNz4lxoOvbsOdbccI/McTH3Dx79/jqqc+YkODj4IsmXUmxOG2p0Nhd0cYfzhGNJ44aJwGwjGybGYMOnzzD2t5+I0dKKpGlcfN3kAEd7bEqRDi6BHomv01v7KUYEQjcJCmAftfAzsjsV5roAGsrm3jjDEFvW6bUeaW66EQSPJMiKPSLm+IW5/dSFDV8Idj5DgO/LQntd1sMhCJxVn61qdAMnF2w9ll5DnlaZEQX9ReXzKBHVI1HNbkLM79u/XtL89pYdHXyrAaDTy/sTGdzJ5R5ubBS6fIkk0hDjO/otIWVPGFY9jMxvQ/B5JtN2NE54VNjemlSpWeAm69YBwzTxoqcSqEOKq4umZ/lY/MIxLTyLYfeGXJ/tfA1D1MX0xGAxWezASa3LcIsY9MRxHiKNPgDXHHs8kGAIGuHwFxLUGlpyCjdXRKpaeAuJZ8spTrsKCj8+h/nEpM09kbiDB6iFMGPCG+IL+iomoJApE4oWgcu9VEhaeAmgYfFZ6CXpdLVHoKyLKaMBmS//uc8YWcOcZNjt2CO9sq8ShEP9jbGUHVEtjMRmoafBTn2gH6jNMqTwFOiwkDBr42vpDJI/IwGw2MyHOQ67BInAohjjrubCszytxE4wmG5phoCUT7/H2QuldJqfQUkH2QlSiKqjF7SjE/mz2BsKrJfYsQ+5GZZ0IcReq9IXb7wukp1S6HhZoGH8uqd3LvxZOo3O9pUKpjzvVPr6eqzE2e04rFaCLHZmbUECcXTBrO8DzHQJyKEMc8v5KcxXLbsxu5cEk1OQ4Lb29vYeFMD1sa/cyrKO3xhDYVkz4lgt1iItdp4aRCFyeX5HPisGy5ARWiHzT7whgNRpwWEzUNPrY0+inKdXDi0CwWzvT0iNOqMjcLz07ODDUZIKpqFOc6mFDkoqQgS+JUCHFUynVa+cWlU8jrWnHy948bWDx7Yq+/D+ZVlLKsug6AKk+yk/DujnCP62FKlacAd7aVaaPyGTtc7luE6I3MPBPiKOBXVALhGLc/u5HvzTgx/brdbGRro5+500fxuzc+4afnj+M2k5HOcAyXw0K21cSP/+f/GO3O4oFLJncVSB7AExFikGj2hYnEExldbh0WIxsafEwZkcc54wuxGI3cdsF4DAYDwUicHLuZSEzDBMQTyOwVIfpZKsH9Xm0br2xs4kfnjmVbU4DLp4/iz2s/Y+rIPL42bhh3zZmIGkugqBouh5ltTYGumWk6xQXZFBdIR00hxLGhOM+ByWjg3U9a+e4Zo3n4je3Mqyjl5lnjCEY0XHYz2TYzHYrKry+bis1sZFiOnZ+/vJXyUXnM6+q82X1GboWngCsrSvnOY2t5cWHlQJ2aEEc9SZ4JMcCafWH8kRi6nux289NZ49LbVm1t5tYLJvDAK1sYX5xLa2eUaDxBrsOCw2JCTWjcOWcS+Q4LxfnOATwLIQaP1kAERdVoCkQybi4VNc6tF4zngVe2Mr44l0KXnV1ehVyHhRF5DrY2+xk1JIuWYIQhWXZJnAnRj5o6FLSEzh3PbuSqilJW13pZUBnjhq+VseSNHUwozmVScS717WFyHRaGOK0YDDo2s5FTS/Jp6FAozJWZ2UKIY0+hy86ZYwrY4w9TVTYUgIb2MDazkc/aQhTn2fnB0+tRVI0KTwHlJfm8sa2FudNLuGFlDYsvHM/iCyfwmVfBYjJQ0+Bj0coapo3Kl8YAQhyAJM+EGEB7AxF8XZ00/V3/jmt6ukbLw2/UMn54Lt+bMYYsm5lgRGOYy0QoqtEeijLEaSXPbpTEmRCHSYsvTFRL0ByIpGMyZdXWFs4cM6SXeIyT0BMUZFnJspnZ1tzJiUNzBugMhBj8GtsVlHiCaExjda2XudNHAbCuvoNPWzr54TknoSV0InGNoTnJAtmb9vg5ZXQ+FoOBRp/CsjWf8evLpg7kaQghxBeWbTczQreT77SixpMzay0mIxv3+Ln35S1dnYOTM8oWrawBIMdm5r+vPQOX3cyDr23j5Y3N6f1VeAq4bqYHRdXIlZ8VQvRKkmdCDJDd7Qq3/u8GVtd6eeLKaQzNsQHgV2IZU6qv/cvHzK8s5YwxBZiMBkwGAw1ehfJReVywpJppo/JZMrdcZrkI8SW1BiLEtAShmJbu2Nfdo+/sZMqIPJr8YQpddqLxBIpqpKUzynCXHXe2nY93dXDGmAKJRyH6SbMvjJpIYIR0h8xUrC6rruORueX89o3tTCjOpXxkHu0hlTyHhckn5GIC6tsVdvsj3HPRJIlTIcQxKxCO4QurZNst3PPSNj7e1cH8ylLKR+axZG45w1w23tzawqKVNelrZWc0zrceW8tLN1Rw7VdP5Irpo7CYjdjNRsDAW9v38sTqndxx4QTaFRWXw4I7SxoGCJEiyTMhBkBjx77EGUA0nuyWWeEpYF19B1sa/ZSX5DO/opRoPNk97P2dXrY1BvjJ+WMZW5TDp60hFFXj3R1ttAVVGdiE+BJafWHCWoJGX5iQqqU79nXv1KeoGtevWM/8ylJGF2SRZTOjqBq6rvP8vxo5b+JwvjZumMSiEP2kwRuiORBBS+gsebuW22aNB6CmwUeVp4DVtV4WraxJ/4CMxhPkOSwMd9mpb1e4bsV6lswt5+SReRRJMx0hxDFMiWtYzCZe+FcjN5ztwWY2EdMSqPEE7+30sqy6Lp00AzI6hSd02N0R5rqn16eXdS59q5aKrkYDdW0hrnrqIwBmlLn5xaVTKJZrphDSbVOII21vu0IgGk8nziD51Lx7F7/Lp4+ipr6DBcvXcd3T61mwfB3/avBx+4XjueJPH7C7I4zZaEh/vjMS6+2rhBCHwK+oRLUEa2rbWPJ2LZD8Mb6tMdCjo6aiatTUd6DENOY/9RFPVO9kmMvOpt1+ilxS50yI/rI3EKG6to3aliBL3q5lTa0Xh9VEhaeAZdV1zKsspcrjRlE1lr5Vy4Ll63j6g10YDQY0dK5bsZ7ykjxqGnyEovGBPh0hhPhSrEYjezrCPPrOTnLsJrJsJv7fP2spdNn5V31Hj8TZvIpStjT6WTizjLe3t6Rn7K6p9VI+Mi/95yfX1JHrtKQ/++6ONm55ZgN+RT2i5yfE0UhmnglxBDW3K8R0nT0d4YzXkz/U/ekufmaDgZvOHcstswx0huPkZ1nZG4hQ2xpKzjJzWHh/577kW47dsv9XCSEOUWc4RqeqMcxlT95EluSzpdHPoq+dxJI3P0nPAlW1BEUuB9l2E3s6wvz9+2fyyqYm/vZhvSwBE6Kf+RSVYS47sK9LnKbrLJzpYenbtSxcUcM1M8bww3PK0BI62TYzTquJVzY14RmWw6mj8rnyzNEsWlnDJSePGMhTEUKILy3S1UAMwGE2E1I13tjWyo1fj/ONqcXcesEEfIpKjsNMXNPxKzHOGV9IJKbxrwYfMU1P70vVEuk/r6n1csssQ8Z3HWyVi19RaQuqBCIxWeopBjWZeSbEEdLUkUycBSI9n3gvq67j8tNH8fTaXTT6I2i6TpM/QiAcJ99ppa0zynVPr8dogEpPATl2M8uq64DkdGrpjCPEF9PgDaFqOoFwLL18ell1HZdPH8Xj73zKD885iTPGFBCNJ7CajLyxbS+/eGUrmq5jQOcbU4r49WVTZQmYEP1otzdEIBwnGk+k4xSguSNCQbaN2ZOLWDK3nJMKc/CHY+xsDeK0mmjsCPOr1z/hhDwHCypLpZucEGLQ6IzEsJqN3P2NiSgxjeZABIA9vghDsm0seesTsu1mAuE4TV2/LRr9Ef689jMunz4q/TsCYGi2LWPfHUrPFS19rXJp9IVZuLKGrz30Dpf84T2+9pt3uGFlDY2+cK/vF+JYJskzIY6A5g6FiJagM6oRiMST9VnK3OntiqqxcEUNk07I5ZxxheTYzYwa4qTQZeMfW5u59i8fU16SR0sgyk/OG8f8pz5CUTVmlLl58NIp8nRHiC9glzfEbc9uJBSLk2O3pJcwKKrGopU1fPPUEwhF4zitJkYOcWAxGzmlJJ9xxbmsWLsLh9XE2OEuiT8h+olfUWn2hgiqGlk2MzazMaORxwe72vntqu0Mc9kZ5rKR77QyzGVjmMvOL1/fxvuftVPpKSDLZuK6p9czbVS+jJlCiEEhx26hyR9h8shc4ol9s8jMRgOLVtZw4rAcTEZwWk2MKnBit5g4pSSfCcW5GU0EqjwFWPdrkBTvNiut+/ftz6+o3PzMBlbvaMt4XZZ6isFKlm0K0c8avCH2BsIMdTkIReNk200sq67jpRsq+dnzm6juVoz8o8/amTQilxUf7OLrE4azxxdm6Vu1VHnc3HPxRLKsZoLROH/67jRy7Bbc2TItWogvosEbIhiJs/DsMsKqRn6WiZZAJN0gQFE1blhZwyNzy1naVV8ppcrjZl7laAJhqTUoRH9p9YWJagmUuEZnJE62zUxLIAro6ThNdddctqYuI0ZT9X3++mE9t10wHk3XeXFhpYyZQohBI9dhIduWnFmWSOjpZgA1DT7KS/JY+lZXDdf6DmrqfTwyt5wn97tWVnkKWPyNidS1hdKvVXgKWF/fkfFdfa1yaQuqPRJnKdLQTAxGkjwToh81tCusr+9gygl53PbsRuZXlBKIwKkl+Xz/zx/zpyunsbsjjC8cS3f3W/HBLq6ZcSLDcmz4FJVpo/Jp9IUxYUg+XR/okxLiGOZXVHzhGLc/u5H1XTeTRS47q7Y0c97E4ZS6swHSCbRFK2tYfOEEFs+eQECJ4bCZeHNrCwtX1PDiwsoBPhshBie/oqJoCe59cTPfmT4Kh8VEOBZn4ggX0ZjGDTPLgGScprprXn+WB6vZSLbNTCSewBdS+dHXT2KI08pwWVYthBhksqwmRuY76IzGicS09MOEFR/sYl5FKUD6NahLXyvnd20rynXwjy3N1LWFKM5zsPBsD9uaAlxdNYb5XZ02gQOucgkcpGGZNDQTg40kz4ToJ7vbFfZ0KJSX5HPrsxvThcg/benk7osm8rPnN3HFnz7gj1ecypAsK52ROLMmDeeSk0dgNkIgEsdqNvJf73/GT84bR1RLUFPfIYU4hfiCmn1hwjGNxV0zPhee7eHJNXXMryjl4886mFSUh9Nm5N45kwjHNZSoRo7dTLbVxJK3d3DF6aP59qNr00umpW6SEP2jMxzjjmc3MrUknyfX1FFekk9JvoN3trfyg5knYjYaMuI0224my2qChM43H32ftqBKVZmbey+aSFMgQiimybgphBhU2oIqsUQcp8VMKBrn1FH56QSZ2WDgJ+eNxWY2EYzEuHvOJNSuWbw2s4n8LAuX/vE92oIqT189ndc3N/OvBh/3XTyJPIeFFxdW0hmJHXSVi+sgDcukoZkYbCR5JkQ/2BuIcOv/buBH546lMxJPT5FOPQF65M1PuPMbE2ntjFLfoaRnnW1rCnB15RgafQpfGT2E3R0KN507lvtf3srq2n3TomeUufnFpVMolqfpQhwSv6ISiSeIxBPppdLlI5PLGrY3d/L01afzy9e2Mr44l/KReUTjCfIcFrKsJpb+s5bLp4+WWoNCHAH13hAhVWN1rZerKkpZ+lYtNfU+fn/5KXz3zNE8vOoTxu0Xp3azESUW591PWtOJs+vP8nDBI9Xpuj4ybgohBpNAJEZLZ5TRQxy4HBbumjORu17YnF6uCXDOuKHcdN44IjGNhg4Fq8nI6to2Zk0qpC2oZjQhU1SNO57bxNK55Zw4LPuQjsGdbWVGmZt3e1m6KQ8ZxWAkyTMhDrM9HQrBaJzVtV5+OsuQ0V0ztQxsfmUpezrC5Dot5HfNOqs4sYAZZW4MwLTRQwhGoxTnObnjuU0ZiTPYV4hzydxy+QEvxEG0BiIoMY3Fz23kuq7lXkC6a19bUOWKP63lwUunMMxlIxjRGOYykW01E01ozJpUhM1ilFqDQvQjv6LS2VVHsLNr3EzFqKJqXL9iPdd+dQyLzjkp/VpRroUcmwklrmExGTl11BBeXlTJhgY/85d/lE6cgYybQojBxWW30B5SueKJD/njv5+KntA5dVQ+P/xaGQkd7JZkE4DXtzTz6Ds7M66Hp5UOocrj5qbzxqYfDAKs3tFGS2f0kK+RuU4rv7h0Crc8syEjgSYPGcVgJckzIQ6jBm+I3b4wJoMBgI5QjPyszCnLiqplPBV68YYK4lqColw7OmA1GshxWCgpyOLTlmCPxFmKFOIU4uD2eEMkgDue3cjH9T5cjn3DXveufW1BlQXL12V89tUfVvEfT3xAW1Dlf75/Bl8ZPeRIHbYQx5VmX5hANEYioQOG5BJMMmNUUTV+u2oHv121I/3aazdWEUskeGPLXn7/9qcsn38aVpORW5/d2Ov3yLgphBgs3NlWPvysnbHDcwhF4wzJsvLwGzt4+I0dPHHlNL716Lo+PzvEaWVqSR5zH1+bkVQD8H/OZkjFeQ6WzC2nLage0lJPIY5lxoO/RQhxKHZ5Q9z27EbmPv4BndHkU/N4QqclEKXSU9DrZyo9BYS66ipd9uj7qFqCEwqy0gOOFOIU4otrDUTQgVhC57YLxvNCVw2PKo8bIN2ZqjdVngJe39xMWzDZZj31Y14IcXj5FRVNS2AxGfn5y1s5/3er0XSdqjL3gWO0zE2zP8LsJWv41eufoKgaeQ4LwWi81/enyLgphBgMcp1WKj1ubji7jEZfGLPRkL5eHuza+drmZpa+VdsjcQbg/AL3O7lOKycOy+bkknxOHJYtiTMxaEnyTIjDoKErcba6q5ZSatBaX9/B3z9uYPHsiT0SaJWeAu69aBJqLMG3H1vL+OE5OCyZA5YU4hTii/ErKoqq0eALs/j5Tby8qZk7X9jEwhU13HNxMh6XVdcxr6K0xw1mlaeAKytKeezdnUCybXuWVSZqC9EffEqMd2vbukoUJMfQt7e3cOuscWxt9Pcao5WeAm46dyzXPb0+/eOvqszNsBybjJtCiOOGntAZnmPj9DEFZNvMLJzpoeJA9zdlbu6eM5Etjf5e9yf3O0IcmESHEF9SgzdEQ0c43RQAyGgXffn0UTz8xnbmVZRy86xxBCMaLruZHLuZ17Y08fCqWk4tyeP6s8vIc2Te1EshTiE+vyZfGH84RkLX+cPbtayp9TK/q/A4wLX/9TGPXzmNPR1hOqNxbj5/HGo8QSASozDHzqqte1m0sgZF1ajwFHDD2WXkOeUHtxCH2+4OhTue28hVFaUZY+ij7+yk/IR8fnLeOHyKyk3njuWWWQY6lBgWo4F8p5V/f+KDjMTZL7vV15FxUwgx2O3pUKj+tI1RBVlc8acPcGdbefKqrzB7SjHzK0qJJ3RuPn8ciQSocQ2X04LJAO2hCNdUjSEaT2Rcd+V+R4iDk5lnQnwJrYEIoZiGb7/6AKnGABOKczEbDPzwaydRnOcgFNWIxjWaAxH2+MJMKsrjb9eezpyTRzB6iLPHNOdUIc4ZZe6M16UQpxC98ysqSkyjpr4DTdfTM1lShccBPmkJcukf3yMc0yjOsxOKamTZzJQNzWZLY4DJI3L59WVTeeLKacyeUtxrbAohvhy/oqab63SPT0iOod9/+mNe39KM02bGajISimo4LSaafGGyrCZ++W/JGH39xiqWzi2nqKuLpoybQojjQSASpzjPka5R1hZU+fZja9kbiJDrsJDb9UA+x24mHNP45WvbMBgM/L9/1lGQY2P25CKeuHIaf7jiFLnfEeIQycwzIb6gVAc/vxLLKGqc0r0xwKs/rKLRF2Zojo3vPLaWqjI391w0ESOg6XDuhMI+ByspxCnEoQuGY7QGIry0sSkjRvaP0f0bBLx4QwW/XVXLD84uIxLTiMQS5DksTBuVL7EmxGG2p12hoUMh1DVzrK8xNNUg4O/fPyM9dt46azy+sIrdbOTEYdkUuuw9PivjphBisAtGYjitZrSEnn5NUbV004CUV39YRa7DzFvbWrnp3LG8sa2FBDpXTB9FUa4DbyjKkCwrJUOcDO96CCGE6J3MPBPiC9jtDRGMxrnj2Y3k2M0HLcyZ+mFgMhh47cYqFl84nlc3NRFPwJihBy+sKYU4hTi4Xd4QSjzBkq6lmt1/kB8oRis8BWzc7eeCqSP45avbcGfZ+MroIZQV5kisCXGY7elQeHdHK0ve3td1+mBjaJ7Twos3VPCNKUXsDUR45M1aSt1ZvSbOUmTcFEIMZgXZNuJa4qDXTwOQ77ThtJrojCQbqry1rRWA5kCY4S47b2zdi9FgOFKHLsQxS5JnQnxOu7whbn12I96QyupaL1azkS0HKGp895yJdIRUahp8KDGN2r2d7PFFWPrWp9L1S4jDZLc3xB3PbkTtVsOj+w3lgYrn3nfxJKaNzmeMO4tfXzY1vfxLCHF4NXhDdEbjDHPZWVPrTcfowYpb+8MxzAYDk0/IY3iuTeJUCHHci2kJ3tvpPeBvkHvmTAQDxBMJrv3qGEzGfQmyaDzBcJeDdZ91MDLPQbZdFqQJcTASJUJ8Drs7FO7o6qp5/czkNOlmf4TLp49ixQe7KC/JZ35FKdF4glyHhRy7mZ1tIUbkOVhWXccFk4rYtjfIsuo6FFWTrl9CHAZ7vCGCMY3VtV5u6NZ2PdW4A2BNrZdFK2uYX1nK9Wd5sJqNxBM6I3LtWIwGSt3ZA3X4Qgx6ewMRAhEVm8mEX4mla5zti9G6dHzOrygFYJjLRrbVzKubm5g5tpCXNzaxrLqOFxdWyiwyIcRxT1G1jAZl+/8GKc610xqM4LJb8QZVzp1QyOub96Y/n+uwEE8keGljI/deNAklGh/AsxHi2CAzz4Q4RPXeEH4lli5A7rSZAFA1Pd0coHxkHtF4ApvZyPs7vXznsbU4LSbe2raXU0fl848tzSx9qxZF1aTrlxCHQb03xB5/GL+SnMWZbdv3TCjVuKO8JJ8nrpzGry+byoWTixiRbycWS3a9tVtMDM93DtThCzHo1XtD3PPCJhIJAw0dYRxWU3pJdfcYXTK3nAlFLgCG5tj43Rs7UFSNjz/roMkfZulbtUwblS/jphBCAC67JaNB2f6/QZoDEewWM5qeQAdsZhOPvbsTSM5Ky7GbafJHWFPr5c7nNxHvVjtNCNE7mXkmxCHY7Q1RXdvWo75KhaeA9fUdlJfkpZsDdJcanGrqfXyvagzf/8vHgHT9EuJwaPCGWFPbhg6MKsgCYG8gTJXHzeraNiCzcUdVmZubzxvHrN9Vs+J7pzPEaWXoAWomCSG+nL2BCLc/u5GrKkq556XNXDF9FDl2M3sDESo8Bayp9WbEKCTH1dlTirn1gvH85vVt3DxrPJc/vlbGTSGE6MZhNVFV5mb1jrYev0EqPQVcNLWYh1Zt5wdneVj61g7umjMJgCqPm5vOG8v8pz7iwUunALC61ks8oeNXVLnGCnEAMvNMiIPY4w3RqWo9Emdvb29h4UxPn7UGqsrc3HvxJMwmWDx7PCfkO1hx9XTe/NFXWTK3XOq1CPEl7PKGCESStZMKXXbe3+mlyuPmJ3/fwOJvTKBq/3j0FHDfRZO46qkPObUkn2E5NukqJUQ/8ynJ2qDDXLZ0E4+3t7dw4tAsFs709Bw3PckahE6LESNw66zxqLE4f7vmDBk3hRCim85IjHvmTOzlfsfNT84bx0OrtnPlmaW8vb2F1bVeInGNv15zOlNL8pj7+FrGDs+hpsGX/lxrMMoNK2to9IWP8JkIceyQmWdCHECDN0RQjRMIx4nGE2xpCqSflj/6zk6mjMjjnPGFmA0Gbjp3LLfMMtAZjjMk24qe0FGicR58bTtL5pbLkxwhDpNUc4DrZpZl1E5a8b3T+fXr27j88bU8eOkUfjprHKGohsthIRiJoahxxg3P4eeXTKJYfoQL0a8avCEC4WQNnWAkWYuwpsHHlkY/U0bk0doZ4ebzxwHJGaJ2s4mCLAt7AxFOLsknz2Eh12llJFkDdg5CCHG08ofj5DrMfGNqMTfPGo8/HMPlMBPXdDpCKjPKhjIsx8aj7ySXaiqqRiAcY+lbtVR53FxZMZpFK2vS+4trOu/uaOOWZzbI7xYh+iAzz4Tow552haiWoD0Uw+WwYDMbMzqCKarG9SvW0+iPYDEb0RI6wUgco9GAntDJspl5orpOlpkIcZj4FTVZezASZ3Wtl2x7snaSzWxEUTWuXv4RPz1/HL/7TjnReIKWQJTq2jYe+sd2cuwW7BYTv/nWyZQUyI9xIfrTLm+IO1/cTFZX97Zse7JG6LLqOi6fPoo/r/2M+o4wrZ1RdneESSR0XHYz3lCEQpcdp8ko46YQQhyAy25GB4Zk23ho1Xbe3+mlJRClqet3ycQRuexpD6N0NVLKsZtxWs08ffV0rupKnKW2pcrQALy7o422oJr+Hr+i8mlLkJr6Dj5tDeJX1B7H0l8G8ruF6I3MPBOiF7vbFTRd584XkjVahrlstAQilJfkZXQESxXmrG9XOHFoFkNzbABouo6e0LnzGxPlB4AQh0GLL0xUS7Cmto1hXUuoWwJRWgIRdEjPCP3OY2uZX1maLpx75pgCvn3qCRiAEyRpJkS/29PVlXpqST5xLUGVx01LIEqlp4Dqbl1vUzGa67AwIs+B0QBDnDZiiQQjJVaFEOKA8rOsKNE4f/1gV68NA7Y0+plQnAskS8kYAaMBXA4Lq7a0p/dTVeZm8ewJvLqpCafVhKJq+MLJJNXudoVb/3dDulkaJOs2/+LSKf0+g7/RF+bmZzawekfbEf9uIfoiM8+E2M8ub4ib/3cDgUgsXaPl+r+s58wT3Syc6Uk3B1iwfB3XPb2ep977jDPHFNDSGaE9FKU9pDI8x86oodmSOBPiMPArKqGYRn1HOJ04A7j5mQ1MKx2CZ2h2un5Sqvj4guXrWPlhPcNybEQ0TRJnQhwBDe0KndHkzNDykXm0B1UWVJXyv+t3s3j2RCr3i9EVH+zihHwHZgP89o1PWFffQUGWbaBPQwghjnqFLjtqIsFPZ43nX/W+9O+SBcvXUVPfweXTR7Gsui7ZLOn8ccQTOt9+bC0XPlLNx5918Ox1Z/LX703nvosnseCpj/iwrp1H5pbjtJqIxhLs7lC4eb/EGZBe2tmfs8D8itojcXakvluIA5GZZ0J0s7td4bZnN7Km1ptRo2Woy8b3/msdf/iPU7j3okmEVQ1F1chxmMmymHho1Xa+N+NE9gaijMi3S9JMiMOoQ1FZu9NLqTu7R+3BuY+t5aHLpjJyiJN75kwiEu+KTZsZLaHz2zc+4f5LJg/0KQgxqDX7wqhagrU7vYzIdwKkY3VLo59TR+WT0DXuvWgSkXiCzkiMHLuFLKuJh9/4hLHDXfxvTSPXfvVEGT+FEOIQWYxGfvP6duZXjua2C8YTjMbJtpuxmY10hFT++9ozMBsN2MxGXtnUlP7c6to27nlpC+Ul+fxffQe/v+IUvvPYWqCOOy4cz3s7veQ6LKzZL3GWklra2V/X67ag2iNxdqS+W4gDkZlnQgCtgQi7vCE6o/H0QNG9Rsu8ilKGumyc85t3Ofs373DhkmoeeWsHuzvC7A1EWHTOSfzzkxauX7EeJaoN5KkIMajs9obQdXhpYxMhNd6j9mBbUOW7T37EzN+8w89e2ITTaiIUifPSxiZ++8Yn/PjcsXKDJUQ/avCGUFSNPb4wJ4/MT7+eitXLp4/ivU+9zPrdGmb+5h1m/W41v3tzBzazkQdf28bF5Sfwuzd3ABBWZfwUQohD1aGo/Pi8sfypuo5Zj6zmskffZ9bvVrP4+U10hGN869H3iSUSROIJfv/2pxmfXdM1Q7i61ktnJM78ylLW1HqZfEIuy6rrCETiB/zuzkis384rcJB99+d3C3EgMvNMHPcavCF04I5nN7Lw7LL0673VaEnVOct1WMixm/nOY2v5n++fwYWPVHcryGkZoDMRYvDwKyqd4RiBaJwHXtmavMkryWdErr3P2oMtgQhqPIHTZuLsscP4+vjCgT4NIQa1xg6FYDROuxLDaTVx70ubmVqST5WngJoG3wFjFeCaGScy9/G1Mn4KIcQXkGU1o6hxZk8uyrjG1jT4WLSyhlNK8mgJRMmxm9PX2e5SHctNRgOVJ7o5pSQfLaED+yYR9KU/r9eug+xbxgoxUCR5Jo5rewMR/m+3D8+wHFbXerntwgnpbTc/s4Gnrz6de1/aTHWtl6Vv1QJQ6SngZ9+YyOWPr+WUkjya/ZH0gDSjzI07W2a5CPFlNHY17AjFNOIJPV1vY1l1Hc9fX8FodxZL365NxyRAlcfNvMrRNPkiJID5T61lxdXTGV3gHKCzEGJwa/aFicYTtCsx/OEYuQ4Lq2u9fFzv47nrKvjFa1uZV1EK1GXEaoWngLvnTOTFDY1MKs6V8VMIIb4gp9VEs1/l9BMLWPzcJqq7LbPs/ntl+fzTev28zZxchBaKxlmwfB2QvJ9adtVXaOuMpktk7K+qzE22vf/SCO5sKzPK3Lzby9JNGSvEQJLkmTiuBSIqZYU5BMLJqclmk4Eqj5vVtcn19Ff8aS0PXjqFm2eNIxTVyLGbsZiMXP74WsYNz0kPSpC8mD946RRZIibEl9DUoRCKxTEbjQTCcRJdT0ABFFXjlU1NFLns3Hz+uPRrZqOB6to2/rJ2F18fX8gef4RKTwGjCpwSj0L0A7+i4gvH0HQdfziGzWwk2LXER1E1Xt3cxDnjCzEbDNx07lhumWWgQ4kR13T2+sNEYxr/avAR05LxLeOnEEJ8fm3BKAajkVc3NbHwbA83zxpHMKKRbTfREohy+eNrGT/cxd6u2b7dVXTNEE79O2V1bRsY4JKTi7segJCRQKvsegByx7MbufuiSf3S+TLXaeUXl07hlmc2ZCTQZKwQA02SZ+K4tadDwWww0q5EybYlQ6HZF+Huiyay+PlNrKn10hZUWbB8HRWeAuZVlDL/qY/4n2vPYPm808iymWjwhnnw0imckO9guEsaBQjxZfgVlVhCJ67pqHGNLJupRw2kR9/ZySNzy3nwtW0ZN3MVngJumFlGNK7x5rYWfvHNKenC5UKIw8sXVvn5y1tY9LWT0kuEZk0cnt6eitPHq3dmxGlVmZubzh3LH/75KYtnT8CvxLjk5BG4s60yfgohxOdks5ho7Yyy9K1PeWRuOUvequ1xb3TPxRP59evbMz6X+l2z4oNdzKsoZdHKmoztq3e08dPzx/LbVZ9QXpLfo2zNzrYQq7a2EIlvYPHsCZiMBtxZh/c6XpznYMncctqCarrJjIwVYqBJ8kwcl+q9IW5/diNXVZQyNMfWNeOsgM5onFc3NfVZO+DUUflsbvQzfUwBnWGV4nyHXMiFOEz84Rh7fGHynFZC0TjuLCub9/jTs0EhOaslVUPp+rM8WM1GHFYTTosJTdcxGgz85rKpEpNC9JOmDoVwLNFV6sDMOzta2dLoZ/bkovQSn+5xOr9r5sKIfAeJhI7JZOD2Cyf0y2wFIYQ4njgtJhp94QPUl4yyaY+fhTPL+MFZHhRVI8tqQtUSWEwGJhTnsmhlTa/10HZ3hJlQnEv5yLz0/t7f6WVZdR1L5pYDySRbQ7vCguXrmFHm5heXTjms1/Zcp/zGEkcXSZ6J486edoXbn93I6lovc6ePAmBzY4CFZ5ehJXR+/3by6c2yNXU9n95cNJEsq5lhLjuQNUBnIMTgU+8NUV3bxqQRuTT6whTlOtjdoTC6IIs750zgzhc2p+NRUTVq6js4eWQeV//XOp75wZkMOcxPPIUQPTV4Q+zxhwl1dZW2W4xsawpw+fRRBMKxjCU+iqqx9K3a9AwHgIJsG4Uu+4AdvxBCDCZZNjOeYdksnOnpWQu2zM3dcyaiqHH+sbWZR9/ZyeljhvCTc8eR0HUMGDPevz+X3dLr9v2XeaaaDry7o41bntnAkrnl/X4/5ldU2oIqgUgMl8Ny2Ge9CdEXSZ6J44pfUQlE4+kC5Dazkbe3t3DexEI6w3EMJgOnjsrv/elNZ5Qh8gREiMNutzdEoy/Myxub0j+s9wbC/H3dbm489yTaQ1FmTy7ufTZoSR65drPEpRD9rCUQYY8vjLavDCG728NcM+NEHnv3U+aeVsJfP6jPWOKTitW/flgvM0KFEOIwG+ayo6hxPvqsI6MWrN1sIi/LzPrPOsh1WikblsOSueW0BCI0ByJcv2I9/33tGVSVuVndS1H+Kk8BLrulR8OA1MOQ7ss8U00HIJlAawuq/Xqtb/SFufmZDRnH3R+z3oTojfHgbzkyHnjgAQwGAzfeeGP6NV3XueuuuyguLsbhcHDWWWexefPmjM9Fo1FuuOEG3G43WVlZzJkzh927dx/hoxfHivaQSqMvnP7fNQ0+/tXgo8Wv4upax3/3nImcWpLP0rdqWbB8Hdc9vZ4n19RR5XHLjb8Qh1mTL0xU01nydrJORzSeoKbBx3+va+CHXz+Jh/+xHSWaYPqYITy15jOue3o9C5avY+lbtZw6Kp8HvjmFYqltJkS/C0XjLHm7Fn84li4y/Z///X8MybJw3oTh5Dst3HLBeP7V4EuPnQuWr+NfDT7uvWiSjJ9CCNEP8p1WSt1OTEYDrZ1RQtE4b2zby7/98X2icZ1Clx27xcgwl429nVGuX7GeU0ryyLKauPeiSVSVuTP2V+Ep4LqZZbSHopxWOoS/f/8MnrhyGk9cOY3ykvyMZZ77z0ID6FBU/IraL+fqV9QeiTPYN+utv75XiJSjYubZRx99xGOPPcaUKVMyXv/lL3/JQw89xFNPPcVJJ53Efffdx9e//nW2b99OTk4OADfeeCMvvvgif/3rXykoKODHP/4xs2fP5uOPP8ZkMg3E6Yij0O4OhWA0jtatcx/Asuo6HplbzlPv1zF1ZB4zxw5DS8S5/cLx6CR/LGgJnRPyHVJ8XIjDbHe7gqolUGLx9JNNm9mYjsvfrtrOv506koIcK62dUe6aM5F4IkEwEsflsDAsxyY/yIU4AvYGIgTVZJzOryhNx+iTa+r4zmPJrtQWs5H2UJS750wkpiVo9kcYnmuXZjpCCNGPcp1WcuwWLvnDe8yvLOX8icM5rXQI500cjtlowKeorK/3say6DkXVqPK4ubJiND9/ZSuLZ4/nG1OK+On5Y9ndEcZqSs4WXrD8I8pL8igvyee7yz7kkbnlLF9Tl165A73PQgMIROLcsLKmX2aCtQXVXmfKwZGZ9SbEgCfPgsEgV1xxBY8//jj33Xdf+nVd13n44Ye5/fbb+eY3vwnA8uXLKSwsZMWKFVx77bX4/X6eeOIJ/vznP3POOecA8Je//IWRI0fyxhtvcN555w3IOYmjyy5viNue3Uh5ST4zyoamn5jvX9R4UnEurZ1RivMcWM1GDIDRaGZ4jtz4C3G47fKGuOPZjfzk/HEo0X2FamsafBmFbwEa2sPYzEZ2toWoOHEILqudoiGSzBaiv/kVFZ+SbORhNBiAnjGaWqLZEojSEohQ7LLTGY4zxp3FyAKpDSqEEP1NUbV0nUmAmvoO1tR6cVpNzK8spXxkHr++bCp5DgvhmMYNXbPH/uOMUfz0mY1UeAoo71p1k5J6WJL6rfTXa07nqs4oOXYLMS3B+zu9PZoNVHoK0HW93+qfBSKxA27vPMh2Ib6sAV+2ef3113PhhRemk18pdXV1NDc3c+6556Zfs9lsfPWrX+W9994D4OOPPyYWi2W8p7i4mEmTJqXf05toNEogEMj4RwxODV2JszW1XspH5pFtM7Gsuo55FaVUeAoA0oPNsjV1jCpwkm01sTcQ5rJH3ycc1SRxNgAkRge3VFyurvWiRDXMJkN6Wyo+y0vyeiydPmNMARajURJnRwmJ08GtyRem0Rfhjuc24gvH0nHaV4w+9d5nnOlxE0nEKcixS+LsKCAxKsTR73DEqctuSf+5+++c1G+cBcvXsfKDXWTZzOnEGZBu/pL6ndTj2LqaAZSX5PGPLXt5as1n2MwGhubY+Fd9R0birMJTwFUVpZi6HrSkZoIdTt3Pszc5B9kuxJc1oDPP/vrXv7J+/Xo++uijHtuam5sBKCwszHi9sLCQXbt2pd9jtVrJz8/v8Z7U53vzwAMPcPfdd3/ZwxdHud3eELGEzs3nj0NRNYwGA4kEnFrSR0OAQASbyciLGxv55WufAHIRHigSo4PXbm+IoKpxw8wybjl/HBaTkdc2N1PlcbO6ti1jNuj8rg597mwbBgMEwipTRuYf5BvEkSJxOnj5FRVfWAWDgetmlpHntPB6tzjdfwzNc1g4Id/Ba1uaKB85hK+Mzh3oUxBIjApxLDgccerOtjKjzM27O3q/jyrOcxDXdOY99WFGwqt7sf9Uoqw7m9mYXp658oNdXFkxmrmPfwDAHReO56ezkqsHgtF4upHTry+bmv784Z4J1v089zejzI07+8hMeJBun8evAZt51tDQwA9/+EP+8pe/YLf33baDcxa/AAEAAElEQVTcYDBk/G9d13u8tr+DvefWW2/F7/en/2loaPh8By+Oeru9IaKaTljViMQSuOxmcuxm3ty+l4Vnezi1lyfmU0fm0RmNsfStT4EjexEWmSRGB6eGdoVOVSMQjpHrtKADO1o62bTHz7zK0VTtNxv0qfc+Y7Q7i39+0sJ3HltLlk2S2UcTidPBya+oNAcidEY0ErrO0BwrVpOR/8/enYdHVZ6NH//OnNmTTGYyQ0hYEgITloTFCAqaBMUdQdBa24J9K0urrQK11YoLKu5LNyvYvm6gbYX211r3pW6oBAVF88q+BAIJJGSZJDPJbGfmnPP7Y5IxQwK4JLI9n+t6r8vMeg59n3mec5/7ue9N+1oS4zQpm+GTKjLtZvY0Bnnk7QocVjFOjxVijArCsa8nxmm6zcSDl49mYnvx/87rqMHuFFZtr+OHT3yclAl2cLH/zoE0gNJ8N3nuFG67eASpJokR/dIT9c3mlOTR126h3h8hzWKgvPrLmmqdP6enkxAOPs8OE/PdPHT56O8kgFXTEmLeynLO/cMHXPbnjzj39x8wf2V5UkM64cR11DLPPvvsM+rr6xk7dmziMUVR+PDDD1m6dCnbt28H4tll2dnZidfU19cnstGysrKQZZnm5uak7LP6+nrOPPPMQ3632WzGbDb39CkJx4jqpiD7W0I8sXo3Bf3SKRrooLEtQp7LxsZ9Pkb3dzB5VDazkrLOIhgNemY8sZagrHynP8JCV2KMnnj2dtpC3eHc4X1YfEkhBdnpPPDGVsbkOBPj0tHe/faHj8cXeyKYfewR4/TEU9MSYuG/v0gqCn3O8D7cdOEwbp1SwP2vdR2n2Q4Lb2yqZel7uxib6yQzTfz/xLFCjFFBOPb11Djt57CyZEYRjW0yvlCUcFShvLqZ1kiMjyuaumyx7Fzsv+SgQFqpx828SR50QL0/wqxn4rvEbCaJpTOLWFZWmVQfrdTjYunMIv6+dm/ic0p7ad3W+Txbw1HSLEbcqT2T+XWkjLIjdfvs6RpvwrHnGwXPrrzySs466yzOPvtshg4d+o2++Nxzz2Xjxo1Jj82ePZvhw4ezcOFCBg8eTFZWFm+//TZFRUUAyLLMBx98wEMPPQTA2LFjMRqNvP322/zgBz8AoLa2lk2bNvHwww9/o+MSjm++oEzZzgbe3lrHzPG5LF/z5Y+7zSTFWy2XVTI8205fezzj0WqUGDfIyYHmMA9dPpqcDJvo4icIPai6m8CZzSQxY3wuN7+wkfKqFq6eOJjSfDeqBtGYyke7vYm7mCKYLQi9zxeUWfjvDUmBM5tJYub4XO55bSvlVS3MKcnj1BwnqSYJq1FLGqel+W4eFuNUEAThqEm3xYM9u+rb+N5f4vW/H1u1izklecwuycNmklDV+G93R7H/Yo+L31w4nOaAzJ+vPBWzQU+dP8zaSi/r9zTz6/O/vNa/euJglpdVUtZpngDa5w0dN100nB8+8TElHhcPXDaq1+aDjvPsSTUtoS6BsYn57qSuoaLbp/CNgmepqan84Q9/4Oc//zlZWVmcddZZiWDa8OHDv9JnpKWlMXLkyKTHUlJScLlcicevv/567r//fvLz88nPz+f+++/HZrMxc+ZMANLT05k7dy433HADLpeLjIwMbrzxRkaNGtWlAYFwcmhsk8m0Wyjol87yNZVJF+tBWWHus+u5fUoBRTkOWiMxApEYfexmpi9dw7hcJw9dPprsHm6rLAgnszp/mDZZSRqLEE/57zxGH3lnJ4+8sxObSeL2KSOYMiqbs4f2wWkz9dgdRUEQDq2+NcLqiuSLgoPHaeebUbdPKWDKqGwm5rtJtxrJsovO1IIgCMeCzl0pO3fh/NX5+YzPy+CMwS5KPG4C7bXKZjy5NikzDeDpq8bxx7d3ctNFwxKPlXjcPPLOzm6/c3VFI7fodTz1k3Hkumz0dx4/zZ2+akaZ6PYpfKOaZ48//jjbtm2jpqaGP/zhD6Snp/OnP/2JwsLCpC2W39ZNN93E9ddfz7XXXsu4cePYv38/b731FmlpaYnX/PGPf+TSSy/lBz/4AcXFxdhsNl555RUkSeqx4xCObb6gTLU3wNZaP962CJGYStFAR5eLdYhPILe8sBFF01BVjSdX70ZCxyvzSlgyo0gEzgShB9W2hAhEYvhDXRcThx+jm5B0OsYNymBIZqq4IBeEXuILylQ1Btjd0EZL8OuO043ogKw0C8Oy7GKcCoIgHCNSzd3nx4zp78BmMhCOKqDB3GfXs/S9ii6BM/iygUBzMEpxe03amKId9nsDkRiF/ezHVeAMvlpGGYhun8K3rHmWlpaG0+nE6XTicDgwGAxkZWV94897//33k/7W6XQsXryYxYsXH/I9FouFJUuWsGTJkm/8vcLxa19zkNZw/OLcbjVitxppjcS67RjTWTCiYDNLzD8nH5fIahGEHlfjDRADQrKCvZsC4kcco3Ksl45MEASA/U1BmgNB0m1WQhEFm6nrTccjjdMaX4ixOaIDriAIwtHmC8o0BWT0eh0xRaU0383qnY3YTBJzSvI4LTeDvnYziqphMuhJtx0+0NNR+D/DZmLRxSP4/VvbsZkPn5ySYpa+0jXVsdat8qtmlB0r3T6Fo+cbBc8WLlzIBx98wBdffMHIkSOZOHEit9xyCxMnTsThcPTwIQpC97orQP7AZaNABzkZh7/jkWoxYNDr6JMiapsJQk/yBeOFaoNRhf3NIXQ6HTkZVko8rqQaGQd3dTqYuHsnCL2npilIKBYl1WqhrKKR7HQruS4bpR5XUs2zI43T/g6rmEMFQRCOspqWEPe9uoVfnjeUh1/bwpUTBnHt2UMwSzp+ND6XFev2cspAB/e8tiVx3XT9efldfvM7dHTiLPW4qfOHWflJFbdfUoiqahR7XN1mJBd7XKSYjhxa+Cq1xb5rXzWjrKPb583Pb0gKoInavCePbxQ8++1vf0ufPn248847mT59OiNGjOjp4xKEw6puCnYJnAHc89oWls8ah91i7HKx3qE0343FoCfFbCCzvWmAIAjfXm1TkLCicsdLm5IWY3/64RjuvXQki17clBiT5dUthxyj4u6dIPSeA81BYqpGUyCKqkZ5dWMtRTlO/rp2D7NL8gBdovbZkcZplphDBUEQjipfUOaOFzex4Lx87n51M6fkOHmqbDfbD7SyfNZpPPzmNsbkOJPqV9pMEqcOdHJBQV94fVtSvcuOTpwr1u3lquJBPLdub7xxzKtb+M1Fw7hn+kjuPGidV+xxMf+cfBxHyGY7VrtVfp2Mst7s9ikc+75R8Ky8vJwPPviA999/n9///vdIkpRoGHD22WeLYJrQq/Z6A7SFY4eswzL7mfX8++cTuGf6SO54eXPSD3Rpvpu7pxeSYpBE4EwQetD+piCqpnH7S5u6XGinWoz8z9Of8NiVp3KrpKc1FMVuM/K9ov7c/tKmpLEs7t4JQu/xBWU0VaPGF8JuNXLf61tZU+FlTnEeS9+rYO3uJq6eOJjrz89HUTXSzAYuP7U/d7y0uUuWgBingiAIR199a4Th/ewEIwplFV5mF+exrKySR2cU4Q/HWF3hZVb7b3zHFs6LC7Pwh6P87q3tjMlxJDpxppkNyIqKLxiloF96oiNnJKZSlONkX1OI+SvLuX1KATdNHs6+5hAmSU99a4RBGbYjzgnHarfKr5tR1hvdPoXjwzcKno0ZM4YxY8awYMECAL744gseeeQRFixYgKqqKErXooOC0BP2eQMsemEj107KP+RrgrLCHm+IG//1Ba/OL6EtEiMcVTEZ9LhtRtKsRvGDJwg9qNobwBsIYzYau81QicRUqptDTFu6JunxjkXcbRePQI6p4u6dIPSycChKWNVYsqqChRcNTwSuO2qbBWUl0fm2g80k8c+rJ7BoyghCsiLGqSAIwjGkJRSlaKAj8XdM1VgyowiLUUKv0wHx33ibSeLRGUU8/1k1llP6c98b8Zsn721rAOLdNX/0xNpuv6PjJouxfSv/LS9spDTfzR1TC9DrdIzLdX6lOeFY7lYpMsqEr+IbNwwoLy/n/fff5/3332f16tX4/X5OOeUUJk2a1JPHJwgJ+5uCtEUVVld4uWny8MO+1mzQE5QVGlojxFSNqqYgZw/tI7ppCkIPq/eHqW8N40o1c8AX6fY1h6qb1NE+/bJT+lPQL703D1MQTnq17U082uR45nYw8uWNzsPVNgvKCiaDnmFZ9u/gKAVBEISvI8Uk0dAaIdthxGaSyHOlcM+rm1ld4eXpq8YB8d/4OSXxrZjXnzeUQLTrDp4jNYiJxFQsxngAbsHKclbvbESv0zEkM/UrH+ux3q1SZJQJR/KNgmdOp5O2tjbGjBnD2Wefzc9+9jMmTpyI3S4WVkLv2NcUJKqotIbiHfjq/ZFD1mHpKHIJ8VbNaRYDI/vZxY+hIPSwOn+YYCRGVNXY3xLGbu1+SimvbjlkgVlR30wQet8+bwANqGoOIbVnIhgkXeL5I41RUdtMEATh2JRiMuCwGtFpsGjKCO5+dTNlFd5EB+VSj5vy6hbOGOwCoDUcS8wDnR2pQUy61cjHu72UVzUzpyS+DfTrZoqJbpXC8e7wo+QQ/va3v+H1elm/fj2/+93vmDp1qgicCb2mqinI4pc3EVW0RIvkhc9v4PaphZR6XEmv7ShyuaysktJ8N3aLgRxXigicCUIPq/YG+HB7PRFFJcVswBeKUu+PUOpxd3ntsrJK5k3ydBmvom6SIPS+/d4AUVXDH47hC0UTQbOyisbEeF1WVsns4jyKxRgVBEE4rhglXfsNDo0xA9M5JcfJs7NP56XrilnZXvR/S40PSaejaKAjaR7orOMmSndKPC7SLAaWlcWbDnRsE7UYJcqrmtnV0IYvKB/xWDtqi03MT14rirlGOF58o8yzqVOnJv5737596HQ6+vfv32MHJQgdqr0BbnlhI0U5Tu55dTOLp41M3B2/8qm1/Pb7o7ltSgH7W0JA/Id/wcpyTs1xcPf0Qga6Uo7yGQjCiafBH0ZWVQZm2PC2yeh1OswGPQuf38D/u+YM7jioacDYHAfuNDPzz81n4eQR1PnD5GTYyEwzi4WSIPSiKm+AQCTG/a9v5dpJ+ZgN+kTQ7IkPd7N0ZhGgsbrCy4KV5cwpyeO6sz2YDHrSLAay7BYxRgVBEI5RvqDMHS9tZtaZgxjkshGKqfRLt+BKNbJ+TxMzxucSUzV+ff4wJL2OpqCcNA907rLZ0WRAB0lruFKPmxsvHMacZz4lKMe3+0diKqUeF69urGXpexVAPAD24OWj6XeEEjmitphwPPtGwTNVVbn33nv5/e9/T1tbGwBpaWnccMMN3Hbbbej13yihTRASfEEZfyhKdXMoqRMYOo357c0C1lR4mf3MemwmidunFHBKjgOzQc/kwiwUTUPVtKN8FoJw4vEFZeRovJ7gY6sqEvUH1+xqZFhWGlct+4Rls06joTVCSyiK2aCnvLqFh97YxsKLhvOnd3Zw25QCUX9QEHqRLyjTFopS4wuhqiRqha7Z1ciWGh+zSwZBmca8FfGA2aziPAD6O60Y9TpUNPIzxY4CQRCEY1ljm8w72+r5v30tvPyLM2kORDDodZgMek7LyyCqaLSGo0h6HakmCYfVyEe7vV/OA+03TyBe3/If6/Zy57RCYopGZWMgsYab8eTaROAMwGE1Mrskj3kryhOPfbizkZuf38CSGUVHDIT1RG0xX1CmsU3GH45itxpxp4gAnND7vlHw7LbbbuPpp5/mwQcfpLi4GE3TWLNmDYsXLyYcDnPffff19HEKJ5H6lhDBmMr+lhC+UHwvfUcRy2pviL7pZqaOymZOcR6RmIrZoKfOH+aAL8xz6/Yyc3wOK9ZV8fsrxhzN0xCEE86B5iDhmIoGiQvyXysakZjC1hofs4vzWL6mkumPrWFOSV4irX/q6Gx+OHYAMVXjvstGicWNIPSi2uYgbdEYBr0eRSUxj9b7I2yt8TFzfC5/X7uXMTlOZrXPoxajRH+HBaNOx/1vbuP+y0Yd5bMQBEEQjqSje+WPTs8hqKgoGry/vZ4xA53c/8ZWxgx0MGlYJlFF5YA/ygCnlW01/m7ngXSrkcw0M394azvfHzuQlev2JgJrnZV63GTaLUxbWpYUUIN4AK2xTe71dV5NS4iFz29gdafaaV81800Qvo1vlCL27LPP8tRTT/GLX/yC0aNHM2bMGK699lqefPJJnnnmmR4+ROFk4gvKRBSV21/ciK89awW+LGJ547+/wCDpOWWgA4tRSryvpj1wNuvMQaxYV8U900eKC3RB6EE1TUGaQlE+3u1l0UubaGm/IPcFo8hRjVsuHsHKdXspynGyZEYRBdl2LEaJ/MxUjBIMdKWQ1ydVjEtB6EXV3gDv72hAQscdL25KmkcXPr+BhZPj47SgXzpFAx2JC6b+DisSOpa8X8GiKQVinAqCIBwHOrpXFg10oEPH0lUVXD52IA+9uZX/mTCI0wdl8NCb25i2dA1XPL6W7/3lIxZOHs6KbuaBNIuBP7y9ncvHDkzUlj64Blqxx8V153gIRKIsnXkq887xJBoTdPi6TQS+Ll9Q7hI4gy8z375K7TVB+Ka+UeZZU1MTw4cP7/L48OHDaWpq+tYHJZy8WkJR9jeHWF3hZVZxXqJ4ZedOYDOeWMvvvj+GARlWHFYjQVnhktHZXFyYhcGg4/dXjBELf0HoQQ3+MIqmcf/rW5lTMjixlRpgfVUz22p8nDrIyc0XjyAai28RyEo3EIjEKK9u4ZSB6Uf5DAThxNfgDaBqYNDrkFUtaR4t9bhYXeFl5pNrefj7o+lrt9AWjpHtMJJikjAAQVXhF2d7xJZqQRCE44RJ0lGa7yYSUwnHFNZUeLl58nAK+qXT0BompmrMLRnML84aQna6FVlRiakqV47PxZ1qJhCJkWk3E1M0mtpkhmSmsWBlOWNzHCiayq0Xj0DVNMJRFYtRz6Z9PkKywpxnPgXi3T3/ec0E9jWHMBskPq9qxm419uo5N7bJXQJnHb6rzDfh5PWNgmdjxoxh6dKlPProo0mPL126lNGjR/fIgQknn33eALe9sJGZ43OBePH/Le1bwVas28vs9ov1NRVeZrX/aJfmu1l8SSExTcWRYhKpuoLQwxq8ASJAUzBKWYWXqycOAb7syrSsrJKlM4tYXraHh9/ckXhfscfF/En5OKxGfMEodN/ASRCEHtDQFCQCHPDHL5b2N3/ZRCde2yYP0LG6opE5z6wH2sfoOfkYJR0vbqzllAEOTmnfai0IgiAc2+r8YR58Yyt3Ty+ktiVMMBLfQtkWVhiX46S/00p5VTOZaSZsJgN3vrSJ1RVerj8vn9MHZTDjybW8cG0x9766JalxQLHHxbWT8jngi3Ddis85NcfBvHM8pJhM1LdFuOe1rQA8OqOI5WsqufWFTYn3lnhc/GjcwK98Dt+kbpn/CJltvZ35JpzcvlHw7OGHH2bKlCm88847nHHGGeh0Oj766COqq6t5/fXXe/oYhRNcR3OANlmhrMKbCJJ1dH3pSC026HTceMEwbp2sJxRVsJok7GYD/nAUp00EzgShpx3wBogBEVUjHI0vypwp8TuKHeMTKpm3opyrJw7ml+flo6gaqWYDBknHm5sP8PgHu3llXsnROwlBOME1eAOEgbZIjKXv7UwU/4cvx+nf1+5l7CAnvzwvn5iqkWKWsBkN7G8OMn3pGoKywn+vLxV36wVBEI4TzUGZsXlO9OgwSDps5vj2yVSLhM2kxxeMMLJ/Ol9Ut/DqxlrWtNcvM0o6JL2OoKxQ2xJi7CAn15+fj6KC1aRH02DV9noe/2A3Y3OcXFU8iLCscudLmxiT4yQoK8w7x8PyNZWJz+xQVuHl1hc2fqWmAQfXLbOZJG6fWsCpOQ6CsnLIYFrHVtVDSTvC84LwbXyjmmdnnXUWO3bs4LLLLqOlpYWmpia+973vsXnzZpYvX97TxyicwBqagrSEotz8wkYqGwPAlxktQVlhwcryxJ78YFQhKCsc8IcxSXrq/RG8AZlQVKWf03aUz0QQTiz72gNn/kiMxS9tIq09DT+maEnjs6PG2dC+aURiKq5UE6qmcdmfP+KPb+9kXK4Td6q4IBeE3lDdHjirbg6hEW/iEYmp3c6jo/qnU98aQVU1UowGVDR+/tznBGWFiflusuyWo306giAIwlcUlmNcMDyLRS9u5Hf/3YbdbKA03029P0KaxUiK2ch9r28l025JCnJNGOwi0F7of31VM5/uaeLyv3zMrOWf8N/NdTS0RsjPTOPJn4xjVvEgFqwsJxhVWF3hTTSCKhro6BI46/DhzkZq/WF8QRlfUGZXfRvlVc3samhL1CM7uG6ZzSTx6IwiXt1Qw4WPrOayP3/Eub//gPkry6lpCSV9vjvVxMR8d7ffPTHfLdacQq/6RsEzgH79+nHffffx/PPP85///Id7772X5uZmnn322Z48PuEEdsAbIKxpLHphI2sqvImixsvKKpldnJdY+C99r4K5z65n5boqQlGFleuqaGiLpxLX+sM4xZ1yQehR+70BVFWjLaoQa6+dpNPiqfy+YLTb8fncur2EogpRRWP2M58mLsgfuny0yGYRhF6w3xvgi30tBKIKLaEobe1bdswG/SHn0efW7SXTbqa6OcjuhgBBWaFUjFNBEITjjsNmpk1W2OMN8vsrTqHGF+KuaYX85/N9aFq83Maa9hsqHWwmCVWDz6uaKfW4E3NFqcfd7Zpu/spygrKSuEbr+KzOn9mdPY1BanxhNtf42V7XSnMwygvl+7nxX19Q0xLCG0iuWzanJK/bTLbumgCk20w8ePnoLgE0seYUvgvfaNumIHxbB7wBFMAfjiXaIMeLGrtZXdHIgpXlzCnJY06n9snh9sDZj8bnJH7M0y1GcYdBEHrQ3vbAmUGvIyqrhGSFZbNOA53G7OI8anwh3tlaR1GOMzE+zQY95dUt/GNdFbdOGcHj/zMWh9WEO/XItSsEQfj69nkDxDSN/L5pNAei5GTYkPQ6ID6XFuU4usyjZoOeen+YSFThF899zj+vmcC/f34G6VajaBIgCIJwHKnzh7ntxY386ryhPD3rNG5+YSPlVS38eeapzJvkIRxT8LV3Re8IfEE8SGXUx2+wrLx6Ar97cxsLVpazZEYRPz97SKJDc3l1SzzjTFYSjds6f1bnz+xOToaNe1/bkhQMK/a4mF2cx50vbeI3FyU3Hiwa6GDpexXdflZ3TQD6OawsmVFEY5tMazhKWvv1oFhzCr1NBM+E71yNN0BM1VB18e6aHZaVVbLiZxPQ0Cir8CZ+REs9bhZNHUFTQGZGp8BZicdFrssmfigFoYfs9wb4v6pminKcVDeHaAlFsRjj3ZMm5rtZuW4vowc6uPmiEdzz6uakhU6px809l47EaTMyuE/qUTwLQTix7fMGaJMVfvffbQxvL2vQFJTJc9kozXcn1SNMGqP5bm68YBhXLf+UU3McaBr8ZNknoiahIAjCcaY5ILOmwsvd00wsfnkT5VUtPDqjCKNBT6U3iDvVlAhwdWzjX9O+7dIgwV3TCjHo4TcXDUen0+ELyfRJM/O/7+/q0jxgdnEeC1aWt28JDXf5zIOVelx8Ud3c5bmOv4tynKiqhs0kMackj6KBDmymw4ckumsCkG4TwTLhuyeCZ8J3qr45iAoEYir+UJSMlC9/9IKywk+f/ZRls07jF+FY0t2Ph9/cxozxuYm7IKX5bh64bBT9Ra0zQegRDc1BNGDUAAe+cBS9Xkdfu4XVOxvYUuPj9NwMbp9awKIXN/H4B7uZU5KXKEze32mlzhcmHIuRbks5uiciCCewA954bVBN0/jV+cNoi8QwSHpW72zgjpc2sWRGEX9+f1dS1hlApt3Mu1vrmfHkWsbmOlk8rZAfPv6xqEkoCIJwHPKHY9hMElFVZXWFl1+dn0+9P8xgdypmgx5Jr0vqih6/oQIakGI2kJ+ZRmtIIcUsgQY1zSFUBSYMyeDmycM50ClIFq9v62B28SAaWyO8Mr+YpjaZ7xX1T3Tw7FDscXH7JYVc+tiabo97TYWXOcV5BNt3NSx5bydL36vg6avGHfZ8RRMA4VjxtYJn3/ve9w77fEtLy7c5FuEEV+sNIAO3tqcWzynJ44KCvomtmgCNbTI/emItc0ryOHOwi5imcWqOkxKPG2eKkWdmn4bdaiTbbhF3GwShh3R06/OHYxzwh9HpdHxe1cyyskrG5jiZXTKIZz6uZPYZecw7J58Us0RbWCHVIlHvj/Djp9YxLCuN+y8ddbRPRRBOWLXeAFENDvhDVDQE6Gu3EImpWIwSfe0Wls8+jWv//jm/vWIMqqrREoqi0+nQNA1vq8zQvmk89ZNxhGMK1d4gBdl2UR9GEAThOGS3GLjmrMG0tQfRJhdmc9erm5lTnEd5dQs5Tivbav3MLs4DKrn5+Q389vujyXZY2d0QSFrnFeU4mDfJg14HMVXjquWf8K9rzohv2Rzi5sLCLOr9YW769wYa2+T2nQaFfP9/P+bHE3K5dUoBvlAUm0lCh46QHDvssUdiKmkWA3e/8uW2zsNmsuW7MUg6fEFZzFfCUfe1gmfp6elHfP4nP/nJtzog4cS03xtA5cvA2aMzili+pjJxN0RFS/xgBmWFL6pbuPzU/jS2RojpNfqnWwhEo+xtDHDWsEzx4ykIPeSAN0AEWPTCxi53Dx+dUcSCleVQpjEmx4miaWSnmrnrlc1JhV6LPS7mn5OPwybuDApCb6jyBqhvDVPZEGBAho3XNtZ2qSWT57Lxlx+fyqzln7LipxP4ywe7ksZpqcfN7JJBrFhXxe1TClgyo0jMpYIgCMchZ4qJCwr6AjrmlORxz6ub49syc5xsqfExNsfJ1RMH88SHuzk9L4M7pxZyz6ubD7nOW7qqgimjsjl3WF8K+6VzRzcZZQ9ePpoFK8tZXdFIayRGUFYY2T+d+1/bkvTaUo878bnB9q6enTmsRkySPml7aOfsuIPntqvOHMTkP61mXK6TBy8fTT9Ro1M4inSapmlH+yCONr/fT3p6Oj6fD7vdfrQP54RzwBsgBvjCMaYsKWPeOR7Kq77cC995zzvEt5cY9Hpaw1Ge+HA3vzw3n9nPfMrwrDTuu2wUuS6xLexkI8Zo72hoD5zdclDgrEOxx0VRjjORUh+JqThtRrLSLdT5IhgkHW2RGLW+MJOG9iFLLGhOamKc9o4D7VnbAVkhqqg89Oa2bu/OF3tc3HbxCF7fdIBR/dPJddlQVI1ARMFq0vPu1no27fdxz/SRokHASerrjNGzp15B1rRfd/vcgZf/wPuv/qs3DlEQTnpfdZxWtW/jr24OceVT64D4NdVjM08lHI0xql86UU0DjS7BsA4Hr/Oy0y3c9/rWQ9Qyc3Pz5OFogKTToWgaD72xtdvPLfG4OKX9cw9+/KHLR9PYFmH6Yx8lPddxPXj20D6JZgfl1S0sK6tMBOEm5rvFjR/hqBI1z4ReVecNEAXaZIW2SIznf3Em6VYDo/qnM7dkcCJluPOP6yvzi9E0sFuN3HjBUAIRhWdnn066zcgAUeNMEHpEx1bNNllhxvhcZncaj0FZwWaSKMpxcmFhXwqy7WSmmZH0OlJMEq2RGP0cFsJKDKfNRNFAh1jICEIv2O8NIKsai17axE/OGMTgPinMKc7jyvG5pJgMxFQVnU5HOKpgMUoYDXrG5ThRgUhUxWk1okMjFFWZMiqb2WcOEmNVEAThOLevKchtL2xk0SUFmA7qfGkz6hjSJ52q9sZPORm2bgNcEM/y+lnJYOad46FPmpnWcIy5JYMpynEmBa0AVlc0MssfZu6z6yn2uLh58vBDfm5ZhZdrz/YkXd+dNyKTxZcUEo6qyIrGslmnJa07g7LC0vcqKBroYO6z67v93O46bwrCd0kEz4ReU9d+cX7wdrBSj5u5pXlc+9znFOU4uqT2BiIKVV4/YwY6uPf1bdwzvRCn6KgiCD2mc40zfzhGToaNOn+YXfWtPDqjiJuf38CDl49m+Zqu3frumV6IyajnkXd2sHDyCPraLUfvRAThBFbbnlXQ0Bbm7mkjCccUmtpkHDYTa3c3MiLbzrI1lUkZAqUeN7dPLaAlJGO3GPje/37EiGw7S8WdekEQhBOCLyhzy382sLrCi6aCWdInsrbOGZaJqmnsawnx0W4vy8oqefx/xibe23m3TySmkmoyMMhtw2TUs685lOiwvqXG1+3Wy0hMBeJBt3p/5LDHaTFKvPvrs2gNR7G3b9W85YWNXcp+HG6LZ3e667x5ovAFZRrbZPzt/2buFHH9e6wRwTOhV1R5Ayiq1m2a8OqKRtDBdZOG8Nv/7gBgTkle4iLdbjWy+JUtLLvqNO6ZXsggd+p3fvyCcKJq8AYIEq8/2Pmiu8Tj4vaphTzyznYeunx0l4tygNU7G7ntxU0suriAi0f3Y9ELG7lr+khRf0IQeliVN8D6PU2Mzc1AUeGOlzcljccHLhvF8rJuxmhFI/e8toXFlxSwrznEiCw79106Uiy+BUEQThD1rZHEtZUGpFsNPH3VOJauqki64VnscfH4j8fiTjUD8cBZR83ppe9VJP6+5aD1YLHHxeziPFas25t0fQZgbs9ys5kk0q2Hr3ObbjUyJDN+DecLysxbWZ4UOIMv65t1fM/EfDcDnIdfU56onTdrWkIsfH5D0r/RxHy3qPN2jNEf+SWC8PVUewOsqWgkGFUOmc67emcjE4f2AeI/nB31zko8Lg74QgRlBVXTcIoFvyD0mIZOzQEOvuguq/Byz6ub+f7YgWTazd3Wu4D4eI1pKgtWlvP21npufn4DvqD8HRy9IJwcar0BbnthI6NzHNT6Q/x5VUWX8ZhpNx92fg1GFTLtZsYOcnLPq1vEGBUEQThBtIS+zLzyhaIoGt3OE+VVLbjTzBgkHSUeF3NK8ljefmPUZpJYMqOIZ7u5UbqmwsvyNZUU9EtPXJ9BPKhWXt2SCLrtqGul2OPq9hhLPC5SLV/m6DS2yV0CZ52/7+KRWbz767NYMqOILLuFifnubl87Md+NOzX52tAXlNlV30Z5VTO7GtqOy/nOF5S7BM4gvk1VrLOPLSLzTOhR+70BWiMxRg1wEIgcPv02JKuJ/47EVEo8Lu64pJCZT64FwJUqUlUFoafUegPIGoRihw5ql1V4WTh5OG3hw4/dfc2hRHq9qD8hCD2nwRvAH1X4rKoFAzosBkO347Vj68yhhGSVHy5fmxinYowKgiCcGFJMUuK/IzGFkNz9um5OSR5NARmHzcCs4jysRikp48xilA5bC+3mySOIKWp7DVwHs4vzWLCyPBGEK69q6bZDZqnHxVXFebSEZCwGPek2E/4jbLWUYyoF/dITfz94+Whufn4DHx6UhfXQ5aOT5rITJVvrcMFFsc4+tojgmdBjqrwBoqrGA6/HO6+8PK/4sK/v/OOf505hdnEeM59cS2ObzMR8N1milpIg9Ihqb4C61jA769qOWKMsEFZw2A6fEm+SkpOWT+T6E4LwXenIDG0JRrnmrMEc8IcJHKIGjNlw+I0Dkp6k+jFijAqCIJwYUkwGij2u+E4ARTtkrbBxuc54ZpqqsWBlOf/743jts47g15Xjcw/7PdVNQVZ+UsVL1xVT0xLi//a1sGRGESkmA6fmONmwr4UddX4WXjQciM85Br0Oi0HiB098zO+uGMN9r27lwctHYz/CVsuDt2L2c1hZMqOIxjaZ1nCUNIsR90FJFUfK1jqeunIeKbgo5vBjh9i2KfSIam+AsopGFh9U4+xQ6bzFHhdmY/z//Urz3fx38wHmPrs+ETg7+M6CIAjfTI03QI0vjEEvkWm3YD9SjQqbkZqW0GHHbnl1S9JjJ2r9CUH4rtS21yL0RxSsJolJwzJZsqrikK8vr2455BgtzXd3uZgQY1QQBOHE4LAZmX9OPqX5biSdDru1+1wYi0HCbNATU+MBtqgSz1guGuhgTYX3iDdhzAY9q3c2sviVzQxw2viksom5z67nR0+uZc4zn/LZnmaKBjr50RNrmbZ0DT96Yi1/fGcHRoMu8f6OQFaqxXDIrZglHhcWY9djSbeZGJKZyik5ToZkpna5Lvwq2VrHi68bXBSOHhE8E761am+A6uYQfe2WpMDZqu31zJvk6bLAL/a4mD8pn31NIUo9Lu68pJAJeRn85xdnJva7Zx9HqbaCcKza7w0QVjWWvLeTcFQhElMxGfSHD2pLehY+v4H5k/Ip9bi7PD+7OI9lZZWJx7qrPyEIwldX6w0QI16LsOPusk4X3wZzqCDZsrJK5k/yUHrQc6UeF3dMLeCJD3cnHhNjVBAE4cSRbjORbbdw1yWF2MwGDHp9l/UagN1mpLy6hQO+UOLGZ7HHldj2f7ibMJ1vlK6p8BKMxrptULN0VQVzSvISj62p8HLvq1tZNGVE4v0f7mwkEIlx/2WjKOnmmnBWcR6LX978tet6nUjZWu5U09eq8yYcPWLbpvCtNHgDBKJKUvHKDo9/sJvR/R1MGZXNnOI8IjEVs0FPnT9MVrqZUDTG2EEZBOUYua4UETAThB7U4A2gAaGoQlmFl5suGk5A1lPrCzO7OL7Q6a670q7GAMOy0uhrNzNhSAazigcRiankumxs2OdLaicuskQF4ZvzBWXkUBQNCLQ32Lnl4gLe3nqAMwbHF9HLyiq7rSkzNtdJH7uZxdMKqW4OYTFKBCIxyqtb2N0YEGNUEAThBNYSjJBiMZFikvC2Rbj9kgLueXVLUiaWToNtNX7G5jiZN8nDk6t3M7u99hkcen7pWA8uWFmeeOxQdazXVHiZU5yX9NjqikYWTh7Ova99nHjMH4pitxoZNyiD688bSkzVsJkkdOh4b3sda3Z5v3ZdrxMpWyvdZvrKdd6Eo0sEz4RvrMEbIKjB/uZQt6m/QVnhuhWfM6ckj752CxkpJgKRGDqdjipvkJ8/9zljc53MOD3niHWYBEH46jpqJ7XJMUKdamHU+8PkuFL42V/XM6ckLymoXV7dwoKV5fy/a85g6qhsXt9Uy2//uyPx3nd/fRYXj8zi9EEZh6w/IQjCV9PQEgJFJQLENI39zSEAjAYdG6pbOH9EFhCfRzsKNHcer3nuFF7ZUMPjH+ymKMdBUY6Tpe/Ft3m++ctSXrz2TDFGBUEQTkA1LSFSLSYWv7yJX18wDJNBorIxwOziQdxw/jACcoxUswGdHn5z0TB+++Y2Rg108KvzhiHpwWqUEtv7O+aXmycPp7oplLQe7FxLLcUsYTNJ3dZX666BTVsklvR3qsWAomqcOyKTen8EnU5HWUUjy8oqKcpx8OiMIgKRr5cp1pGt9WE3WzePx2ytr1LnTTj6RPBM+Eb2t2e1LHpxI7OK8yivbqFfuiVRwLJDUFZY+l4FxR4XRTlOvqhuYXbxoETg7OHLR4vAmSD0oAZvgBBQ5w+jaBpq+5pm1fZ6zhzsItVs5NQcR+JCu7Nij4uN+3z0sVu457Wticc7FiHpNjGJC8K35QvKKIpKDAhGFe55dQuz2u/cv73lALdcPIIvqlsSFzcd82iHUo+bsYOcPPLOzi4ZAhPz3WSnW8Q4FQRBOAF1FMm/7eIRrK7wMrckyiC3jaBs4Kd/Xc9jM08lxSRhNuhoCcpoGowc4OCc4ZnU+8MAbKrxMbt4EJqmUVbhTcwv/1fVTFk33TdLPS621vh5dEZRl6AadN/ARlE15pTksfS9Cs4Z3gc5piYaynUo9rgSnwmV3H/pqK/1b3EiZmuJdfaxTwTPhK+tzhtAIR4YW13hZUyOky01vkRaMBzUsjjfzeJLClE0lUtGZRGQFf7zizNxpphE4EwQelBde8aZqmooqoaiwce7vRR7XIlt1DUtIe65dCR3vLQ5Kb2/NN/NnZcU0BSQmbX8U7HtSxB6SVsoSlTV0AAVEvNoscfF0vd2MTLbwcAMK3dNK+TOlw8apx43d04rwB+KcvqgDD7a7U1czIixKgiCcGLrKJLfUS5nfVUzTYEIp+Y6OTXHwXUrPuf68zxMHJrJvBXl/OvnZ/DZ3mae+HA3V08cTGm+mzMGu0kx67lzWiH+UJT61ghOq5HvFfXvMucUe1xcOymfcFThb2v3JAJinZ8/uIlUscfFx7u9nDMskzMGuzBKelpCUcbkOPmsqiWxvuy4Vuz4TFnpmsF2JCJbS/iuieCZ8LXUeQPIwJqKRjLbA18de+b/tnYPYwY6Ei2LQ7JCmsXIqu11KJrKvOfK2VHfxusLSnFYjSJwJgg9qNobIKJo3PXyJhZNLWDJqgquHJ/bqaZFZWIbdbrVxG8uHMatF48gEImRajGwcZ8Ps6RnWN80XplXIhYhgtALqr0Ban0hFC3eFKCjjkzncfrz5z5jTkke43Kc/ObCYdxy8QiCkRgWo8TG/T6Meh2n5mbgC8r0tVs4b3imGKuCIAgngY4i+ZHYl3PHYzNPJRxVuOmi4fz2v9u5//Xt5GSk0tgm85OnP2H57NO58+VNPPLOTh55Zyc2k8SiKSMYNSCd+tYIA5023tlaR06GjYtHZjHrzEFJJT3mPvtpojxA0UBH4lhK891ce7aHuc9+mnisczZ0qcfNz/66PhEs65xp1jmA1lEzLXDQVs+vSmRrCd8lETwTvrJqbwBVg+ZgNBE4g+SaLCP7pbOvvQZanzQz3//fjwjKCuNyM9hR3wZAKBojy2E/WqchCCecam+AWn+YJe/uZHWFl5iiJRYk3dVMUjSNt7bUccmYbD6vaqLU04fT8zJoDcsMdKWIRYgg9IL93gBlFY1owGsba7l58vBE8OxQ4xTg+3/5iD/84BT6Oy0UDXQQa39cXDAIgiCcXOwWIzaThNNmosTjoqzCy3UrPuexmUU8t66KCYMzuGXyCLT2eaK6OcS+piBFOc4udW7vfW0rQVnh5XnFjOqfjivFzKV/XtPt93asKVPNBv4693SiMZU8VwqvbKxhyYyiLvVzg7JCqtmQFCxbU+HFbNCzpL1JQSSmYjFKOG3xczqeCvwLJy8RPBO+kn3ti/7RAxyEowqRmMqWWn+ixlnXmiwuxuQ4E3cWJL0u8ZzDKhb7gtBT9nsDVDWHcNiMiVoSgfZx19GGfE2nmhYdSjwuigY6WL3Ty7nDs7j8Lx/xz6vP+M6PXxBOBjXeAG1RJXHjaU2Fl7awkjRGDzePplkM/HdzHRcW9iU7xXa0TkMQBEE4itypJm6fWsAj7+xgVnEeOp2O1TsbuW5FOY/OKOKZNZU8tmoXS2YUUepxs7qikfVVzXxR1XzIWrdvbalj6XsV/PnKUw/73fHGAPHssLnPrueFa8/kk8om/vj2zm4/V6/XUe8Pc81Zg/nj2/GMt5njc3lmTWVS7bNSj5tls0477gr8CyenrhX+BKETX1BmrzdAYyCebeYPx7CZJcwGPcvKKpldnEexx5X0nmKPi9svKWRZWSUQ/1Esq4jvny89DrufCMKxar83gF9W8IWitIW/THdPNcfvixxqjJbmu7njkkL+8/k+7p5WyA+f+JiCbLsYm4LQC2q8AWRNwx+KEYmpic5kqRbpK82jJR4XaRYDW2p8pJoMIttMEAThJJVuM3FqjoP3tjWwYGU5904v5I0FJfy/a84g3WJk0dQCXp1fggbcPrWAUo8rPs+U5FHqcSd9VscWy47rte4K/yd9t9VIeXVLYg7zBaOHnL9mF+dR5wvz2sZaJhdmYzNJzCnJY/lBgTOA1RWNPLaqa2BPEI5FIvNMOKSG5iCyqrHohY1cNymfSEwl0y6hafFOfkU5ji7bTMwGPXX+MG9sqiUoK5R6XMwuGcS8FeWUeFw8cNkosfAXhB5Q5w0Q1eKLF7NBn5TuXucPJe44HjxGHVYjA5xWworCucMzeX1TLQXZdlFoXBB6QY03QBT4eJeX0QMcSRcn9f4Ipx5hHh2b4+TGC4fxl/cruHriEDJSxBgVBEE4mXXudhlV4f6DuliWelxcO8nDzKfW8qPTc5hdMhiTQcfiaQXIikZlY6DLFkuI71bo6PJ8sI6bOMvKKhPbLtdXNbOlxtftltAV6/ZS0C+dNRVe7nl1M3NK8iga2H2nd4DVOxs54A+LdahwzBPBM6FbDU1BwppGIBrvqPmbi4YTkPXU+yM0tUUY0ieFeZM8LF1VkbzNJN/NXdMKaQnKlHr6YJB01PrCPPWTceS6bPR3iu0mgvBtNbQ37rjtxY3cPHk4ZbsayXOlUOpxxcfrvzew4mcTuOeVzazutGWz1OPinktHsX5PE4oGY3OdnAL8eHyuWLAIQg+rb+9MveiFjcwqzsNqkqjzh4H4nfmFz2/guZ9O4J5XNx+0XdPNndMKiakq543oS4M/wsShmQzKsIlxKgiCcJKzt98snVOSx10vb+omk8sLOh03nD+UW17YlHi81OPm1otH8Ny6vYlOl51tqfFx+5QC7nl1c5dtlTdeOIw5z3zK2BxHortmR6ObZ9fs6VJy4Kr2pgEdxzOrPbh2OPuaQ2TZLYec53xBmcY2GX84it1qxJ0i6n4K3z0RPBO6aPAGCAL3vLKZa87yJB6vb43w4Y56fnX+MJoDEaqbgonOmkFZwWKUSLcYMOp1qKqCLxz/kczNsJGdfugfQ0EQvroGb4Aw8SKwayq81PsjbKvxoxX1485phdz18hZWVzQy88m1PHT5aG6eHO+omWY1UusLUe8PMXKAA4ukJ8edcrRPRxBOOL6gTFsoiqxqRBSV1RVeZozPRa+DIX3iY67j5tOVT8XH6cL25gHp1njh5IbWMO5UM00BmYEZNopyHGIOFQRBEHCnmijNdx8xk+uX5+bz9FXjEhlhTW0RArLMPdNHcudLm1ld8WWGWYnHxczxucx99lP+fOVYbtbBgfabPeXVLcx4ci1jc5yJ3UQQv/ZbsW4vs4oHMat4EACZdjPvbq1PymgDEjsfjqSxTe52rqtpCbHw+Q1JWXET8908ePlo+jmsR/5HE4QeIoJnQpKOC/N7XtnMj8bnYjHFt5is2l7P1NHZ5LlS+OPb2/nhuBxOzXUSiaoEZQWnzYjFqMeMjjY1yk+Wf0ZRjoPZxXmYDHqx6BeEHtAR2N7XHEq0K1/4/Ab+dc0Z/OndHVx/7lDmn+vhlotH0BaJkWKSMBn02C0WZj61jkHuFOaW5PHkm9v43RVjju7JCMIJqKElREhRqWkJ8cTq3fzi7PgNKLNBz/6mEP0zrHxa2cSIfnYWTytEjqoEZAWTpMds09ESimJp34YdlGM4bCYG90k9ymclCIIgHCvawjHumlZIZWPgsK+rb41w7XOfA+21bqcW8PqmWv728V5+dHoOs4oHJTpeevqkcNcrm6luDvHDJz7mmrMGc/6IvmhaPPPs4pHZbNrfwrwVXwbFSj0uFk0poKKhjQFOG69vrGVZWWVS0KzDIHcKko5Dbgst9rgor27B1U1pAl9Q7hI4A/hwZyM3P7+BJTOKxHWm8J0RDQOEhPr2wJk3EGV4v3SWr6lE0+I/aI9/sJuQrFDTEuT684bSJ81MUyCKTqejLRLl0Xd3YkJHTIM9jRGWzCiiKMfJPz+p6vaHUBCEr6cjsH3rCxvxhaL0STUD8bt01U1Bxue5qG4K4bCZQAeapqEC3kCEVzbWMMiVwuziQTy3di93Tx8pFhqC0MN8QZmoorKmopElqyoo6JdOtH2bSnl1C3ubgzzyzg6GZdvR6/U0tcno9DosRj2Pv7+LpmAMd6qZ9XubkWMqf161S8yfgiAIQoIvKHPzfzbQ0Boh/QiZXLkZNv718zN4/hdnMKd4EA+8sZU/vr2TxjaZZWWVlFe3YDbo0TSNYFTh5otH8MYvS1k+6zTOG9GXt7bU8c62OtoiMaqbg4wZ6GDFTyfwlx+fytNXjWNMjpNXNtTw7Ed7eGvzAb6oau42cFbicfH6xlou+/NH3D6l4JANBpaVVZJiNrCrvo3yqmZ2NbQltmp2F3CDeACtI0NOEL4LIvNMAKDaGyCqaNT6w0g6XSIVeNX2+sT2klnLP+Wpq07j3le3JqX6lua7uXt6IXuag/gjCtf87TMgnk4ripALwrfXETjzh2OsqfAypz2js9jjYk2Fl1//64tE7aSyg+pULJ5WyLkj+nL+iCwkvY7fXTFGjElB6GG+oEwkKNMaU8m0WxLj9KPdXorbu509NvNUfnhaDg+9uS2p3kxpvpu7pxWi00E4qpCRYuLRd3eKILcgCIKQpLFN5rOqFtIsRl7fVJtYBx6sNN+NxSihahoPv7mdz6qaeXRGEZGYSnlVC4/OKGL5msqkbZ8lHhez2muVLZlRxCPv7Ez6zKevGsfcZ9cnPWYzSTw6o4gV6/ZyVXEeKiQdT+fPDMoKb2yuZcqo7C4NBhasLGdsrpP1e5u55T8bE++fmO9mwbn5h/03OVKtNEHoSSJ4JlDrDfB5VTNDs+z4QlEGOK00BWUAHv9gN6P7OxI/dHX+ML+5aBg3qMOQYwrpNiO76tuo8gb5xXOf859fnMmL155JmsWIO1UUchSEb6vWG8AfVYipGv5wDIhnsYzLcTK7OA+IL1Q6aifdMjm+ZdNuNWIz6vm/fT7OHtpHjEVB6CV1LSGUqIKq1+EPxRJFkSMxNVFQGSq5bsXnXHPWYBZeNDweKJNV7NZ4ndA3Ntfy2Z5m7rikkDx3ighyC4IgCF20RaI8OqOIL6rjnS5nF+eh1+mSMrOKPS6uOnMQlywtA+DqiYO58cJhxFSVe6ePRNLraApEmT8pn5svGg7oeG97HY9/sBuNSq45azBAomt7h+4K/gdlJdExOstu5sYLhrFoikRDWwSXzcQbmw8k1T97/IPdPDqjiGVrKpOCbBPz3Vw7ycOcZz5N+vwPdzby87OGHPHf5VC10gShpx3VbZsPPPAAp512GmlpaWRmZnLppZeyffv2pNdomsbixYvp168fVquVs88+m82bNye9JhKJMH/+fNxuNykpKUybNo19+/Z9l6dy3GrwBogBowY4aA3HMBvi/y/RkQoclBWuW/E5Nb54SqyiajS0RjAZdISiChqQ7bDyi+c+Z1yuk+x0C6fkOBmSmSp+xAThW9rvDdAix1C0eI2LVIsExDscZdotLFhZTlGOk6evGsfd00cC8MbmA8x+5lOCcoxFL23m9EEZYiwKQi/xBWU0RSWm1+GXFWwmKTGPmg36xIVFUY6TJTOKyM9Mo6E1wn8315FmNfDutjrO+cOHfFzRxJ3TRpLrSmFwHzF/CoIgCF05rCaWr6nknte2MnN8LivX7eXe6SP5x88m8K+fn8Ebvyxl6qjsRMAqKCt8uqeJhrYIf/94D4b2wFk4ppBmMWAzGdDpNC4o6Ms/rz6DKm+QyYXZPLduL7dPTd5i2TG3HSwoKyx9r4I6f4TL/vwRMVXlJ09/wt6mIEvfq0jaytl5Tnxtfgn/vHoCb10/kXumj2TOM592u+3zo91eSvPd3X53R6201vY6wILQ245q5tkHH3zAddddx2mnnUYsFuO2227jggsuYMuWLaSkxDtSPfzww/zhD3/gmWeeYejQodx7772cf/75bN++nbS0NACuv/56XnnlFf7xj3/gcrm44YYbmDp1Kp999hmSJB3NUzym1TcHkQFZ0QhG48XFP9zZQI7Tyqj+6ZR4XJRVeBM/ih1K893MOnMQ81eWs2zWaQRlhXG5TrFFUxB6UENzkJgGj767k++PHUi2w0ptSygxLldtr2NsrrPbTkul+W4yUkwsFUVUBaFX+du7an62p4nh2XZSTQbq/OHEgr5jS83B47TU4+KKsQM4LTeDFT8dT67LRn+n7SidhSAIgnA8kBU1kbHVsb1S1TQ0NJ4p28OPJ+RQ7HHz9FXjiKoaTpuRmKIRjSlcf/4wbn1hI6s7ZXwVe1zMm+QhHFV5bu1e/jrndFQ0Fk0pAGDq6H6JLZZ90syUelxJ7+/8OR1zXkzRgCMH24oGOpj77HpK89386rx85pTkUTTQkWhi8HlVM8vKKllWVslr80tY9NKmpGy1jlppC1aWc9kp/Xvs31gQDueoBs/efPPNpL+XL19OZmYmn332GRMnTkTTNB555BFuu+02vve97wHw7LPP0rdvX1asWME111yDz+fj6aef5m9/+xvnnXceAH//+98ZOHAg77zzDhdeeOF3fl7Hg2pvAFlVafBHWLKqgjUVXuad42FLjY+xOU7aIlFuvXgED7y+rUt9s9nFgxLdVlLMEv3TLaLTiSD0oAZvgAgQisZYeNEIbntxI0U5TnbVt3L71ELueXUzj63axdKZRaBpSQuZ0nw310wczP2vbRUdNQWhF+33BgjFFAx6PTkZNh56cxun52UwIS+DeZM8PLl6d9LW6g6lHjc3XjiMUEwhy27BbjWK+VMQBEE4orZILPHfHVla+1tCRBWN/zlzEKqmsa8lSL90K6GoSmsoSorFgNUosahT4MxmkhLBqqCskJVu4TcXDaOqKUim3UJ5VRN/eHsHD14+OrHFsqO+mQZJ9XU7glgr1u1ldnEevmA8C6y8uuWI3TUBPtvbjN1ipLyqOelGU7HHxaMziliwshxJp0sK5HWulTYu14k79cs5tKPJgD8cxW414k4RZYSEnnNM1Tzz+XwAZGRkAFBZWcmBAwe44IILEq8xm82cddZZfPTRR1xzzTV89tlnRKPRpNf069ePkSNH8tFHH3UbPItEIkQikcTffr+/t07pmHTAG6CuNczOujZe3VibWNR31Gb529o9jBno4NzhmdwxrQA5qhKUFUwGPZJOxw+e+JigrFDscWE1SkRVTfwoCT3qZB6jtd4A/phCJKpitxi57cWNrKnwJgq8PvLOdmYX57FwsplgRGHxtJFEVRV/KL5I+GB7A1f/7TOCsiJqQAi96mQep/XeACog6fSs2+1NzKXlVS2M7u+goTXM9ecNRa+DRVMK0DQIyjEsRok3Nx/g0Xd3cPvUAjF/Cr3qZB6jgnC8+Drj1G5J7rAZianodDpu+vcX/L9rzkDV4K5XNicFrEo9Lm6+eERS4OzghgE2k8SiKSMYMzCdSExhzAAHf75yLJGowpziPOaWDMZs0KPXweziPH5aOhi9XpfIbAtHFe6YWsBDb25jSGZ8Z9iWGh83XDAMVdMOmTEGMKckj8WvbO7S+KDj79unFpBuM3LW0D7c/PwGPux0bgc3p6tpCbHw+Q1J5z8x382Dl4+mn8N6pP8pBOGIjpngmaZp/PrXv6akpISRI+O1ew4cOABA3759k17bt29f9u7dm3iNyWTC6XR2eU3H+w/2wAMPcNddd/X0KRwXOjJa5JiW6AjWoXPRx5H90gnKCjqdDqtJ4g9vb2fh5BHMfHJtInA2f1I+/918gOIh3e9DF4Rv6mQdo/u8AWr8IdypFu59cws3TR6eGKOdxydAdVOIPHcKckwlxSyRbpT4wVPrqG4OJT5P1IAQetPJOk4PtM+ji17YyKzivKS5tKNO6JySPFypZiIxlTx3CmaDHpPRyI8eX8uILDt3TivkrS0HOH2Q6/BfJgjfwsk6RgXhePJ1xqk71cTEfHcigGQ26Pm8qpkbzh9KRX0rw7PsXFbUL9E8KsUkYTLqaQl+uR6cU5LH8jWVlFe1MO8cD+NyndhMBtLMEiajxIOvb+qytXN2e8bXwQX9O3ttfgm/ONvDj55YS7HHxczxufz02U/50ek5zCnOI8VsIBCJJTLGOjLnigY6ui1BAvEA2h1TC0i3mUi3wZIZRTS2yfhCUWxmCZNeT40vRGskhtWg77ItFeJNB25+foPYJSX0iGMmeDZv3jw2bNhAWVlZl+d0Ol3S35qmdXnsYId7zS233MKvf/3rxN9+v5+BAwd+g6M+vjR4A4SBW1/YyJXjc7t9Tef6Zv+4egJoYNTr+OV5Q6lrCXP39JEMzLCxcV8LoajCY6t2cVFh9nd4FsLJ4GQco7XeAGFFpaI+wJ/ejW+lvjacXDj14PqD/++aCWTbLURUhXn/+ILpRf2T6kU4xSJB6EUn4zit8waQic+jayq8zOhmLj14nP7z6gn0S7dQ1xph2azTsBol5FgMT6adqKqxq6FNbCsResXJOEYF4XjzdcZpus3Eg5ePTmRXlVe3sKXGx7RT+mLWG6nzhxjV34GsKNgtBlQNmgNR0ixfXvIXDXQkdht1zj6bd46H8qrmQ2aAXTPx8F0v/eEY6VYDS2eeyudVzYkAWcfnP33VOOY+u77L+7rr4tlZqFMTgXSbiaCs0NAW4fevbk861hU/Hd9tPTaIB9DEbgyhJxwTwbP58+fz8ssv8+GHHzJgwIDE41lZWUA8uyw7+8sATX19fSIbLSsrC1mWaW5uTso+q6+v58wzz+z2+8xmM2azuTdO5ZjVETirag6xpsLLnPY6LIeTYpawmiTmPrueHfVtQLxWy00XDafGF+be17Z22WcuCD3hZBujdd4AUeDOlzczpzgvsRjo6K55KGkWI2srm/CHoiw4b2jSIghEqrrQu062cdqRuV3dPo/CoQsid2a3GtnVEGB9VTNnDo6XO7jv9W1Ji34xVoXecLKNUUE4Hn3dcdrPYeV3V4yhKSATkmN8r6g/Rr2Oj3d7GZhh49M9Tby3rZ4fnZ7D8vZ6ZU9fNS5RfywSUxPZZ53noSNlgN08efhhjyvVItEajvH5QbXLOpRXt3TbcOBI82hap62qvqDM+zsaeHVDTZcgX0vo8LstxG4MoSccedXXizRNY968efznP//hvffeIy8vOaCTl5dHVlYWb7/9duIxWZb54IMPEoGxsWPHYjQak15TW1vLpk2bDhk8O9lUewOEgDZZwRf6sohjR0ew7pTmu7EZDTS2hTsFzlzMLhnED5/4mKXvVYgOm4LQA6rbA2cBWWFNhTfpDly9P0LJIcZoiceFxaBnfF4GdquxyyIIvkxV9wXl3jwFQTjh1bQHzgKd5lH4anPpe9vqcKWZ2Frjo0+amQfe2CrGqiAIgvCN9bVbCESibNznI6aqtMkKw7PtLF1Vwb2vbeXWi0ewct1einKcPH3VOPrazdw1rZDSfDdmg56igY4u89CRMsA0LX4t2J0Sj4t6fwSrSaJooKPb1ywrq+T2Swq7zJd1/jClnu5LAE3MdyclaTS2yWSmmbscO3y9INxX5QvK7Kpvo7yqmV0NbWKOFo5u5tl1113HihUreOmll0hLS0vUKEtPT8dqtaLT6bj++uu5//77yc/PJz8/n/vvvx+bzcbMmTMTr507dy433HADLpeLjIwMbrzxRkaNGpXovnky2+8NUOMLk2o20BaJJX5YlpVV8tjMU5k3yQMc1Aks380tk4djknQ4U0z865ozsJj0WIwSraEof51zOk6bCXeq2GYiCN9GbXvXWzmm4W+/IO88+S98fgPP/XQC97y6OamzUYnHxX2XjSIkx9A0GDUgnd/8e0O33yFS1QXh26n1BpA1CEUV/KFo0hg90lx67dlDeHr1bjLTLNw+tZBQNMZ72xq6/R4xVgVBEISvypVi5u2tddz5yhb+efUErCYp0RVzf3OI2y4uoNYfxheK0tZeBuTWycPR63Xs61Qft8ORgk+SHmaX5HXptlnicXH39JH8/r/buebsQ2/tPDXHgRxTOG1QBr86b2jiRtSmGh+zSwahonXJyD44ScMfjh4yyFde3UKxx9VtYK30oCDcVyGaDwjdOarBs7/85S8AnH322UmPL1++nFmzZgFw0003EQqFuPbaa2lubmb8+PG89dZbpKWlJV7/xz/+EYPBwA9+8ANCoRDnnnsuzzzzDJJ0+C1PJ7q93gCBSIwl7+3kukn5pNuMrK5oTPywXLfic645azALL4qn4YZkhVSzAZtJIqIomICKpjCzOxWHnJjvFgUXBaEHHGjPOFu3u4nB7lTSbfE7Yp0n/8Y2mSufWstDl49m4eThBCIK6VYjaSaJsBpDkvT86e0diSYChyJS1QXhm9nf3lXzthc3suCcfOzW5Hm0o0FAx1yq00E4qpJmMRCJKix5t4LbphQQlBW+/78f8dDlow/7fWKsCoIgCEfiC8rc8dKmRBDLbjXiC0YZ6LTy9FWn8fCbW/nJGXn0SYtfr9nMEu9uq+eCEX15+M1tXH/+0C6feaTgU0yJd5i+6aLh3KzT0RqOYbcYSDFL/OmdHVx3joe3t9ZxYWFWl88p9bi58cJhPLaqgiGZafxk2Se8cG0x9762hdU7G7GZJOaU5HHt2R4Meh0Om5Esu6XL9abdYqQp0H32V0cdNz06Vld8GfAq9ri4a1rh17p29QXlLoEzEM0HhKMcPNM07Yiv0el0LF68mMWLFx/yNRaLhSVLlrBkyZIePLrjW403wKIXNvKbi4ZTVuFl4eThxBSVrTU+ZrfXO1tT4eWPb+/kj2/vpMTjYvG0kRj1Ov5vXwtFAxw8v7GGYX3tic/s7g6AIAhfX5U3gKZqBGMqfe0WUiwSJklPiceVmPyBRABt7rPrKc13c/f0Qow6Hc+tq+KH4wfyuze3Jy7MD+ebpKoLwslunzeApkFbNL6l+rYpBdiM+i7zaFBW+OPbO/m0son7LhvFXq+P7LR0AK6ckMMVj3/MsKw0brhgGAOch79bLcaqIAiCcCR1/khS7bBUk4Smqjz30/Hc1t5x8qPdTVxz1mAuKsxC0uvYWO2jaICTGy4Yxub9/i71xzrWnzqSM8tK893cPrWAh9/YyjudMqdL890snlZInT/M1RM9NLRG+KK6hQsLsijKcXbpsPmnd3cwc3wuBp2OpXIFgUiUySOzmHXmICIxFbNBz8e7vSwrq2RcrpMl7WvhztypJj7Z09RtkC8oK/xj3V5mFQ9iVvEgANKtRt7f0X229+E0tsldAmcdRJb4ye2YaBgg9Kw6b4DWqMLqCi/XReIX1fX+CK99sZ9FUwu499UtiR+1SEwl3WpkoNMKqkZEVRg9IJ2YplK+t4UpI/vx4rVnkmYxim2agtAD9nkD1PpCZKVb8Yfi6ef1/ghvbd7DvZeOYtGLG1mwspw5JXmJxh79nVZSjBKaptEaiTI2L4NITOW+y0aRbjPhC8pJrcs7O7hehCAIR1blDVDrC5NltyS2VMcUlafW7uH2qYXc8+rmLvNojtNKVFUo6p+OrKqEowqzn4l3Fmus8LL4kkIy08xirAqCIAjfiq89S9lmkrj5oqHEVIUUi5Hq5lAiINZxY2dkv3Tmryzn0RlF/PXjSkYNdHDu8EzGDhrJnS9vTgSJgrLCinV7uemi4VwXiWGU9Oh1OiS9jplPrmXZrNMSHab7O62gQUiOYTUZeGvrAb6obmHm+FyCcoyl71VQ7HFRlONMah4QianceMEwSjwuDJKeW1/Y1O35HSpAlW4zcfbQPuS5UwC6ZLfNnJDLvBXlFOU4mF2cx0+WfcK4XCc/O8IOjYP5j5AFLrLET14ieHaCOdBe1NgfigFgkHRAvHbSip9NYMk7O/nV+cOQ9Dpaw1H6WYzYTBKaqiHpdZhUPY1tEfY2hbh7+kiyHVZySTmKZyQIJ4793gCtkRh7vUHsVhN2q5G2SCwxPv/y/k7umjYSWVFpDUdJsxhJMUkYAEVVuPv17Vw5PocFK8tZ8dPxpPeNLyo6Wpff/PyGpItykS0qCF9fjTfAut1eXv6ihlunFGC3xrPBrnvuc/4693SeXr2L26cWEIqqtIai9HMYSTVJWABJLxFTNdbvbWF/S3JNmUAkRnrfNDFWBUEQhG8lzRIvs/Pk/4ylv9NGKBrj/tc2JoJbnUViKkFZYcHKch6bWUSm3UJbOEZbJMZNFw7jlvayIKkWA3odaMDqnc08tmoXQVnh6avGMSwrjbe21PF/Vc3ccUkhf1m1k9klg2lskwlHFUb2SyeqaPxjXRUjB6RT6nFxVXEeC1aWJx3Lmgovt07W8+D3RlPXGj7sOR4qQJXtsGIzSdx/6SgCcoygrGC3GDEadOxtDLJkRhHl1S0sWFn+jZvb2Y+QBS6yxE9eInh2AtnnDaABi17YyM2TRwBQVtGYaE0888m1/Pb7o9G371NPMRkwSXoaWsNkpllok6NEFXCnWvBkpolFvCD0oBpvAAUwGyVGD3DgD0VxpZqp94cZlpXGzCfX8vD3RxOOKbSFY6RZjJglPRLw8ob9jMtzMXN8DtetKCcoK10m7n4OK0tmFNHYJicCbyJbVBC+ngPeAG2KQmF/B795fiP+UJQsu4USj4uyCi8/efoTHrvyVILtgTO7NX4DSgeU17WSm2FDA7LsFu58eXPSZ3eMWTFWBeH4NXP21dQ0NHf7XL8+TlYsf+I7PiLhZGSS9Nw+tYCMVBO3vbiRWyaPYHWFl1nFXTOsOhoBBGUFVYMr/vdjrps0hIsKswnHFAIRhRSzAUXReGt7HY9/sDtRDqTU46K+NcJd00YSU1Wmj+nHAV+YX543lHtf28o7W+sT31Oa7+aOqQWo7WWZFqws77asSCiqUNg//YjdPQ8XoEq3dT9nZthMNLbJuFJMXHZK/288t7pTTSJLXOiWCJ6dIGq9AaKaRm1LmNUVXu4ySRR7XDzx4W7+98djAVi9szGxhaTY42L+pHxSzBL7W0I4bCaWvLeLOy4ppK/dcjRPRRBOOAeag8iaxgFfmCWrKpjf3sDjv1tqubAwm0HuFJauqmBO+/iE+CLk3ktHsvyjPfzPmYP47+ZaHnmngqCsHHLiPtRiQhCEI6vxBgiqCia9RHM4fkc8zWrkv1trE1uqyyq8TFu6Boh3GLv3slEYgN+/s4OrzxqCzSjx9tYD/O6tnUkXDQePWTFWBeH4VNPQTNa0X3f/3Mt/+I6PRjhZNQUiFOU40ICzhroJyvEdR90V/e/8WEcW2m//u4PHVu3isZmnkmqW8Idklqyq6NIx+u5phURVlf9urk1kopV63IzLczKyfzo/P2sIMVXDZoo36Xt9Uy3njejLI+/sPOSxp5jj4YfeCFD11NwqdnQIhyKCZyeAKm+ANRWNvLaxlpmd0nXnTfKwdFUFP//7Z1w3aQg3XTSMsKxiM0lYjBLeQARF0xjsTuWRt3ewaEqBCJwJQg+rat8CFlM1Xt1Yy5oKLwsvGk5Da5jP9jQzMtuBxaTjrmmFRKIqAVkhxSSRajGAqnHVmYOY++x6dtS3AWLiFoTeUOUNcPcrm1l48Qhue2ljogt1vT/Mul1NFPRN57YpI9DQ0RqKkmY1okNDUzWiwDUTB5NqMmDQ63h/e2OXwJkYs4IgCEJPSTEbCYRjOFNMnDeiL5FYPNvr4KZTHY8tu+o09DpdIgsNSHSLvm7SEC4amdVpHRrDZjJg0MN9r21JahJQ7HFx44XD+NO7O3hvW0OXIFmxx8XIfumH7NpZ7HEh6eIlhY71AJXIEhe6I4Jnx7l93gC3vbCRWcV5lFV4Ex3A3twcz2iZOio7UdC43h+hzh8mO93Kc+v2cvPkEegBk1GfKDwuCELP6RifvzxvKL5QNGkhsfKTKm6ePIIH39jKiH7pFA10JAqPp5jNSBoYDXosZgN/+fFYMXELQi+pbh+nY3KcyFE1MU6LPS5u/NcXrPzZhC7jNKZq9HdYkYBANIbFKNE/wwYgFtuCIAhCr0o1SZgNepoDEXbUtZHXJyVRpqdz06mOdeUAh4VFU0ZglPSUetysrviySUAoqnLHS5uT1qhPXzWO+SvLuXriYH55/lDq/REgnsU299lPWTqjiGhMS3wOxAv2335JAf6QzJ3tjXU6d/Ms9riYXZyXqMcNx36ASmSJCwcTwbPj2D5vgLb2rpodBSI7UnOXvreLkdkO8vqkkGo2EpTjhSCz0y1Iko47phYQjMb48we7ufOSQvHDIAg9bJ83QJvc3vV2kpZU22HV9np+csYg/vD2dq6ckEvf9uKt2Q4jKUYJkw6CqsIApx1AjE9B6CWdx+ms4jz84fjWl1Xb6xPZ2zPa6xH2tVsIhGP0c8QbeRiBt7fX8f6ORu6cWpj4TLHYFoRjn6hdJhyvalpC3PrCJn55Xj6pFiOZdgvzVpTz/C/OZNELm1hd0ZjocFma7+b2qQXMeGod1c0hbCYpkZnWEfgqGuhI6ogJXzYZeOSdnTzx4W7mlORxao6TEo+bMwa7WLenibGDnMwqHgRApt3Mu1vrufSxNcwpyWNbjY8xOU5mtQfwzAY95dUt/POTKn53xZik7xJzpnA8EcGz49Q+b4BWWaG1faHfkYb7ZbpuJT9/7jPmlORRNNABgN1i4M0tdYzNcQLwxOrdPHwMpMUKwonmQHMwaXzazBIB+ctU+cc/2M3o/g5K8/ugabDXG8Rs0LO7MUDJEBevbKnl9FzX0Tp8QTgp1LaPU38o3tErElPJtMfrtnSM0Snt2dvhqMoBX5j61ggT8jKQVYXvPb6OYVlpzC7OwxeSQXSmFoTjhqhdJhyPfEGZhc9vYHVFIzMn5KBp8bmrsU2mJRDm7umF7G4MkGYxElVUPt7t5dLH1iRKCQRlhRXr9jKreBCzigcRiandFuY/eHtnR3DNZpKYU5LHxaOy2dMYAOKJG8vKKhPfsayskhevLeauVzcnBeVK893iulM47umP/BLhWLPfGyAQjS/4U8zxhX55dQulHleiFXFRjpMlM4ooyLZjMUqkmA38d8sBPt3TxL72BgH3Th9JtsN6lM9GEE4sDc1BoqpGa6fxCVDnD1PsiQfEOupM1PjiRcnNBj0DnFZKh7jY3xzkkbcrRBtsQehFDd4AMVXD394xE+LjsN4foaR9Lu08RgEsRolTBjpYv7eZiKzx0OWjKcpxsmBlOSlmMV4FQRCE3tXYJrO6vT6YSdJja9++CaCXJN7aUsczH+0hGIkRiiqUVzUn1eAs9riYOT6X+SvLmfvseq597nOspq7hgI6dTAcLyvHPrG0JsXJdFXOfXc/S9yqSvqMox8Ebm2spynHy9FXj+POVp/L0VeNYfEmhuO4Ujnsi8+w4U+0NIKvxOwwZKSYsBj2lHhfLyipZOrMI0CWl6xZ7XMw/J58Uk8QX1S3MPyefVLPEn97Z0SVtVhCEb6fBGyAMVDeHcLaPz2KPi1Xb65mQl8G8SR4gXsS1405eqcfNbVNGkGaUeHPbAX73352My3WKNtiC0EsavAFCwJqKRk7JcZJikijNd1Ne3cKu+lZub6/VUlbhTcylJR4X90wfSURRKOxn5/v/+3HiYkG0rRcEQRC+C/5wNPHf5dUtfK+of+LmbL0/wieVXmYXD6LWF+btrQcoynHyq/a6ux3vWbCyPDF/FXtcBCKxRL20Dh07mXRAWTd1y/79WTU3XDgMDS3p+RKPi1nFeUnf0fG++y4d2Vv/LILwnRHBs+NIR22W1nCMjBQTqSaJxrYw887JZ+l7Fcxb0V7Y8bx8FFUj1WzAZpIw6HUEojFun1JAOKrw2KoK7p4+UqTNCkIPqvUG8McUIlGVNKsRm0kiHI0x/5x8nvhwF6P7O2hoDSe6+AVlBYtRwmbSY5Ekfthej+JY6TIkCCeiuuYgUSAgK+S5U0k1SXyxr4V7Lx3J3a9sZsb4XB55Zzuzi/NYOHk4bWEFu8WA3WJAp2lEdTp+vGxdUuBMjFdBEAThu2DvtCthWVklPzh1AEP6pDBvkoe/fryHmy4awcNvbuXUXCe3XVzA4lc2JwJhy9dUJjUFKPa4mD8pnzSzxNySPFRNSzzfsb3zgctGsa8lRHMwmqhbtmLdXmaOz+XxDyq46aLhzGmT0TSNAU4rqga//e+2LoGz+efk4xTzpHACEMGz48Reb4BFL2xMdC2Zd46Hwuw0RmanE9U0pp3Sj1+el09M1TAZ9Bj0OuxmAxFVQYeE1SAR0zTMRkl01hSEHlbtDbB2t5cxA5389s1trK7w8rfZpzMgw0K23cL5BVnogPy+aeh00BqK4Uo1YTbqkdARVhWWzCg65roMCcKJZL83gKxq3PHSpsRcunLu6Yzq78AfDLH4kkL2t4S4euIQDJKO5mAUo15HmsVATFV5e2sdTquJv80dT1CO4bCaxHgVBEEQvjPuVFMiSywoK/z0r+t58ifj+KTSyy/O9qCoCrdNKSASU2kORlh8SSFRVSUYicWzp2MqbeEYNpOEzSSxfm8zd768GYA5JXlce7YHSa8jKCvU+cPYrUYKrEYa22R8oShTR2VzyehsAH5z4XCagzL9HBacNhN97RZqW0KcX5DFleNzE40C6lsjDMqwiblSOCGI4NlxYN9BgTOI3214bOap+MJhnFYL4/MyCMgKbeEYRkmPDgjEFBRF44fLPuKfV5/BkMy0o3cSgnCC2u8NsL8lRFTVuOvVL1t9/+pf/8c/r5mALximZIiLQFShNRTDZNCTnW7Botfx2b4WhmSm4koxk9/HcpTPRBBOXLXeABFF4/aXNyXdeZ//z//jX9dMwGmzElM1stOtBKMKreEo7hQzaWaJ61Z8zob9fko9bq47x4PTamREtv0ono0gHF3btm7h7KlXdPvcidCp8nDnt2NnBVnf8fEIQmfXTfIkssR21Lfx46fX8cRPxgI6WsNRdDo9KSYJvV5Dr4vXRnuv0svS93YxNsfJvHM8NLZFuOFfX/DjCbn865ozOOCP1/f8eLeXZWWVFOU4mD8pH38oykBXylcOfGU7rFw8MovGNpnWcJQ0i5FxuU4ROBNOGCJ4doyr9gYIyEpS4Ay+LDh+zVmDmTa6HxqgalrieUmvo7YlxM+f+1zUTxKEXlLTHCSsaCxZVcGc4ryki/LGNpkfPr6W310xBrMxHti2mSVsRgkNDZ+sUNAvHZNeR6ZdBM4EobfUeANEVI0D/nDSGIX4OL3i8bX84YoxDMywEZBjBCMKaRYjeh20yVF+ed5QBjitWI0S6VajuAgQTnpR9MdFp8qZs6+mpqG52+cOFwQ73PltfOjqHjo6Qfj6Gttk5jzzKXNK8phTnJfI7npt4wGWlVXyr5+fgc2oJxRTeHNTHecM64teD6fluvj3zzNRVXh3ex2Pf7CboKzwyDs7eeLD3bw8r5g6f4TT8zIoGuigvLqFOc9+ythcJw9+bxT9nbavfIzpNpGRLZy4RPDsGFbtDeANRIkqarfPB2WFP769k/F5LpoCMmaDnn4OK00BmVA03nVzXK5T1GMRhF6w3xugNaqgqPG7f1eOz+3ymsY2mVnLPwXgn1dPwG42EIwpaKpGismAxSjRRwTOBKHX7GvvqhmMKrSEot2+prFN5ifLP+WfV0/gh0+s5Y1flnL/a1u4/ZJClr67i5suGk6uK+U7PnJBEL6tmobm7zQIdqJn5AlHnz8cTTSc6k5IVtABbZEY6/c088e3d/LK/GIa2iJc8fjn3b4nKCs0tMpc+dQ6nr5qHHOfXZ94bvXORhraZIySXtzoFQRE8OyYtd8boLo5hF4Xr7dyODaTRCASbzMciEQZ6LTSEpR5ZV6JqMciCL2gun2rpqKBUYqPvY5W4YfisBlB1fAFZPo5bAzI+Op38QRB+PpqvQEULb4NZUhm2hHHaFp7IeZAJMbd00cSVVVGD3CgqNph3ycIggDHT0aecPzq3DCgO6kWA/e9uoVfnT+URVMKuPfVLTQHokec/1ItEgCRWNeEDW9bhHSLgZqmIP3E2lU4yR1+JAlHRZU3gF+O3yW3mSUsRolij6vb1xZ7XBj0OsqrW+jvtJKVZiHHlcLogU6GZKaKwJkg9LDapiBrd3tZuqoCuzXehQ/i7b8PNU5L893YjBIHAjLDsuwicCYIveyAN0AMWPTiRjLtFuxWw+HHqMeN0aAD4hcfb22p49LHPmJNRSOuFDGPCoIgCEdfR8OA7pTmuzFJelZXeJEVjdqWEGMHxUv3HGn+q/dHgEPfCL79pU1EFJWdda34gnLPnIwgHIdE8OwY4gvK7PcGqGkJ4Q99eZegORhh3iRPlx+9Yo+LeZPyMRn0bKv1k2YyMEBsLRGEXtPgDdAajTG8n52yCi8xRcNmkijNd7OsrJLZxXldxmmpx8V9l47kjS21OES9JEHodfXeAAoQiMbrhUZiKilGiW21/m7HaInHxd3TC3l7ywFK893EFI0H3tgmyh4IgiAIx5zrzh7S7Vrz7mmFVHmD2EwSDquR9VXNfLanidc3HWBLje+Q899d0wtZ+PwGij0uyqtbDvpcN+XVLZRVeKnxhXnpixrmryynpiXU26cpCMcksW3zGNHQFETWNGQ1Xnx84UXDWV3RSI7TypA+KWTaLUwdlZ1UHLLeHyHLbmbJuzu4fWoB/UU2iyD0mjpvgLAGqkriDp0vGMVq1HHP9EJuf2kzC1aWJ4q4AmTZLdgtBvSaxie7m/nh2JyjeQqCcMKr8QaIAh9VNCbqs5gNelZtr4tvYXltC0U5zsRc6rAaybJb2N8c4vO9Lcw/Jx+LUc+7vz5LlD0QBEEQjimNbTJznl3fpWFAnzQzATmGomlcc9ZgNE1ja42P2SV5rFhbxczxuaxYt7fL/NcnzcxVyz5hRLad6yZ5mPPMp4nvKvG4uPHCYcx4ci0AvlCUs4f2YVlZJTc/v4ElM4rEHCmcdETw7BhQ377Yj2oabXIs0Q1sS42PsTlOYqrGpn0tTBjsIhxTE61/B7tTiGoq154zlEBYBkTWmSD0hpr2TJZ1lV4K+6UnHl9f1cyu+lYWnOth0ZQRaBq0duqqqQN0wJIPdnHP9JFikSEIvWifN4AGLHphI7PaA9gQ31K9pcbH0L5p3HjBMODLcWo1SkQVlcx0MzecPxSnzUSWw3qUzkAQhK/rm3bUFITj0aEaBvz5ylPJsMW3Z148MovfvrWdBecOZcm7Oxg1wEGGzchvLhyOBgQjMVItBox6PQf8If7y47FsqfGxrtLLkhlFRGIq6VYjaRYDc575lKCsAPEbUb5QlEdnFLFgZTmNbbJY1wonHbFt8yhraAoSAsKKRk1LmEA4/gO1ans9PysdzN/W7mFtZRN5fVJpjcTwh+KBszSThEGv442NB5i2tAyz8fAFJAVB+Gb2ewPImkabrDC4TyregJyoHbGsrJLLxw7kt//dwRubDtAWid/1U7V4gXGjXoeiatwyeQTZ4oJcEHrNXm+AO17ZnLRVs/M4nTk+lydWV/Lm5uRxqgNSjRLznivHIOlF4EwQjjMdHTW7+79ILHa0D08QetShGgaYDXoMko5lZZVoGry3rYHWYJTrzxvK6YMyOOCPUNMSIqao/Pzvn2HQ67nj5U1c+dQn/ODxj6lqDnFabgap5ngn+I93e/nRE2tpbIvXN+u8pXP5mkrmlOTRGu6+g7UgnMhE5tlRVOcNIAO3vrCRWyePoCUUJTPNDMDjH+xmdH8H543oS1+7hX3NocRWzTM9LiTgB0+to7o5xMR8N+5UEfkXhJ5W7Q3QJse497WtLDgnH02DmKqxrKySR2cUAZWJrZqj+qfjC0Xp77CSapLQaZAqapwJQq/yBWX8oSi3vrCRohwn/lD8Ytls0B9xnKaYJBRVZfpfPqYg256YfwVBEAThWORONTEx382HOxuTHi+vbiHXaWVsjpPWSHweTLUaeOjNbZS172iCeIZaY5tMOBpL2sJpNuj5ZE8TYwY4eLpsd2IXFMQDZ7OL81ixbi8Aayq8zCnOw2KUKK9qxm414k4RZQ6Ek4MInh0lte2dwNpkhTUVXgKygtmgp6yikVKPm9UVjVy34nPmlOTRt71ui9UoUeJxEVNVQpAInImCxoLQ8/Z5A1Q3h3CkmFhT4WXRlAKiikrZrkaKchxJ9c06WnvX+8MMy0xFAgwGvRiXgtCLGlpCRBQ1MY/OKc4jxSwB8QuJQ4/TCMMyUzEBFz++loJsu5hHBUEQhGNeus3Eg5ePZuHzG1jdKYC2pcbHFacOIMeVgtkQnwcNki4pcAZfdtNUNSivau6y/dNmkrh9agE3XTic/S3xxI3y6hZWrNvLzPG5LFhZnnjtqxtrE++fmO/mwctH009kbwsnOBE8Owr2ewO0RRVUDfyheMqr1aTnw53xuiyzSwYBGqsrvIkfpVKPi1umFKCoKjENYorC27+aSGaaWSz4BaGH7fMGaJMVWkJRJL0OAItRj6ZpiY5FUJm06Cj1uLn3spGYALPIOBOEXuULyvHAWVRJzKMdd887tmp2ZJ0ljdN8N/ddOhKDBs1KjH9efYZoDCAIgiAcN/o5rCyaMgJVg0Akhtmo57+b6/jx0+u4/7JRZKdbKM130xzouq2yo5zBqu31zJvkAUjKMivKcTDYnYLDaqCxTU8kplI00AHAgpXlifpnmXYzy8oqE+/7cGejaCIgnBRE8Ow7ttcboNYXQtLpUTUNuzW+d/3DHQ1sq/Uzc3wuf1+7lzE5Tma13ylPtxrJtlswSjr2NAZZ+elebptSQH+n6K4pCD2twRugVVZoDccwG/SktY/RmqYQOS4bPysdzJOrdyelu6dbjWSkmDAAmS7RuEMQeps/FKWtfZx2zKNmg573ttUx/5x8lry3s9vut+kWA4qq8vB7Fdx5SaFY5AuCIAjHHQ1oCUZx2CR0On3iJtH/LPsEm0niH1dPIBDpWvOv48bSP9ZVMbq/gymjspO2btb5wwRlhXWVTbzyRS2rKxq7fEapx8W7W+sTgbQOH+5sFE0EhBOeaBjwHar2Blj0wkZSzAZsJgm71YjVqKfE4+KxVbv48YRc/rGuioJ+6RQNdCTaCA90WtHrYOZT63iibDe3XiwCZ4LQG/Z5A4SB1lCUFLNEeXULOuLtuj/e08SSVTvJsls4vyArMUbNBj1V3gBmg55Uq2jcIQi9yReUqfYG8IfjDXRSzFJiHi2vbmHt7iaiMZWpo/qxZEYRBdl2AOpbI6RZDLy17QD3vL6N31w4XCzwBUEQhONOTUuIFKOE3WLgrS31mCU9pfnuxPNBWWHOM5/iTjMnPd7x3IKV5fx0Yh5Wo54R2XYy7RYyUkz0tVsYM9BBlt3MmBwHd04r6PL+Uo+Lq4rzeOLD3d0em2giIJzoRPDsO7K/fRvY6govbWEFq0kixSTx9Ord3HvpKE7NcXDtc58zvJ89kR47uE8KOU4rek3DH47yzOzTWTqjiAEZInAmCD3tgDdAayRGm6yQZjViM0psqfGhaSr3XjqSrTU+LijM5p7XtrC/JZR4n9UocWqukxSjJC7GBaEXNbSEaG3POPO3Z5xZDRJfVLdw76Wj2FrjY+b4XJZ/VMl+X/IYPWNwBnsbgxQP6cPvrxgjut8KgiAIxx1fUGbhvzcQU1XsFgMbqlsIRWPcPb0wKdDV2CbzyNs7uPfSkZR6kgNgY3Oc6NARjCr87q3tXLKkjB89sZapS8q4//Wt+EIxDHodkk7HjRcM45X5xfx17uksm3Uav7loeNL2zYOlHaIbqCCcKMS2ze/AvvYaZx11WVItEia9joga46riwTy9ehf3XjqSUFSlNRwlzWIk1SRhAizttZMGHN1TEIQTWoM3QEjVaApGMeh1ZKVbCEdjXD1xCEveq2DBuR7umFpIKKZw04XD0YjXmUizGLAaJdJFjTNB6FW+oExIUQm2z6WpFolUk8T/7Wsh22HF2xbiNxcNB+gyRo16kBWV0QPTxTgVBEEQjlv1rRFWVzSy1xtiiMvGHZcU8tAbW5l3joe7phUix1RawzFsZgmzQY8RuP+ykXgDUSIxhTSLgRSzhKKCPWbg9ikFaEBrOEZQjlHTEiIUVXjgtW3cfPEIWoJRPt3bxLKySoKywrxzPIzNdSY1K+gwMd+NO1XMscKJTWSe9bJqb4Aafxh/6Mu6LPX+CK3hMEadAQ2VKWP60xqJ0RKMYjVJ+EJRdIBeEt36BKG31XgDRIBgVMEXimK3GjECJkki225mYn4f6nwyTUGZqKLSFJDR66B/mpksu4UcV4oYp4LQi3xBGX8oytrd3sRcWu+PYABG93cg6XXsbgwhx1TkmEpLKEokptI3zUyGQY9Bkhja1y7GqSAIgnBca2lPxLjx31+g0+toDYW5ZfII9Ho9ckxFA3Q60Ot0RBUNFTjgC5JikTAbJPzhGL5QjLZIjE37/e3dNzWagzKKqlHjC3Pdis95Z1s9d768iU/3NlFe1cyjM4qwmeI7Mu6eVthlO+fEfDd3Tx/JHm+AXQ1t+ILyd/5vIwjfBZF51os66rKkmo1oaFiN8T3pC5/fwD+umQCahkWSyEzTE4oqGPQ6TJKejHQjJklPH7GtRBB61X5vIN69VtUIRJR43TKTxJb6Vob1ScMbDFPicbcXJo9iMcZrEEqoaDqduBgXhF7W0BQkomm0RhQG90nFaopnnD3/WTXDLhyOpNPhsplw5poIRRWCEQWnzUiqMZ69vaSskhsuGHa0T0MQThrbtm7h7KlXdPvc3soKcvM83T7Xr4+TFcuf6M1DO6oO9e9yop+30LNSTBIQ35bZKkdJtZipbw2j14HDZiYYVdC0eAAtxShx7YrP2bDfz3kjMlk4eTgtzTKBSIz+Titjc50c8IeZ8eS6br9rTYWXOcV5iWYEi6aMINNuYV9TiDEDHcw6cxAA/Z1WNla3cPGjqxPbOUvz3SyeVogOcKWIjtbCiUMEz3rJvvYaZ4H2H5E+qaZ4XZbphSx6aTM/enwtj/zgFAZm2IjE1PbtJUZSjBJ6EIEzQehl+7wBZFUjElNRVQ27xcCHOxuwSHry+6ThC4dxpljigbNQNF4HzSRhBNBLZImmHYLQqxraG3h0BK81DWxGia0H/Cy8aAS/f2c7C87NR6/XE5EVAuEYaVYjFqOEqmosWVPJL872iEW7IHyHoujJmvbrbp/b+NDVh3yu5uU/9OZhHXWH+nc50c9b6FkpJgPFHhdrKrw0+qPkuoykWowoqkabHCMcVUm1GDBLegJRhTsuKSTVYsCo17OrsQ1Jp6PeH2GwO4X61ggG6fCb0CIxFYgH0n513lD+9/1djByQngioQbyp1ik5zqQ6aKt3NrL4pc3MKh7EA69v5a7pI+knrm2FE4AInvWCGm+AVrm9Los5/k8ckhWyHVYa28LcdckIoir4QzHa5BhWo0Sq3YIJQNLTV/y4CEKv2t8+RlVVoy0SQ6fTkWk3sa3Wz9gcJ+FoCIfViqxqqKqGTqdDrwMTIBkl+tgtR/sUBOGEVtcURCYeOPOH4rVAY6pKOKrgsJnwh0L8+vxhBKIKxOILdr0+Pk7f31bPGR4XN1wwTATOBEEQhBOGUdIxb1I8e7NVjlHfGsaVagZDPNAVllVUTUMDUo0Ser3GGxsP8PgHuwnKCiUeF/deNopgJF6mRK87/PfFt3XG+UJR5pYO5pM93qTXlFV4mV2c1+W9qysauXnycBZNLaAlGCEox+iTahbzsnBcEzXPeth+b4CIpuFvr51kNuoxGfQ8sXoXmWlmdjcEaZNVIjEVRdOIKhpGvQ4DoBOBM0HodZ0beATbO2taTXre2nKARVNG8NzaPby5pYmmUJS2SLwuhN1qINUoYZD0InAmCL2stjmIrGmJwJndasRo0GHQ63j8w130TTOzrS5EcyjapcbZ/BXlvLOtnr5pFrFAFwRBEE4o/kgMOaYydVQ2AzOsmIwSz5fvZ9GLm/jxU+vwh6OgQVNAZl9LiGpvmFH9HfzuijGs+Ol47r9sFE99uAvQkWqS0OvoUr+sQ7HHRXl1S+Lv7HQLc5/9lJH90ru8tiND7WB7m4Lc9sJGQEd1U4gb//UFNZ061gvC8UYEz3pQtTdARNW448VN8Qtyo56mgIyqqfzotFweeWcHw7LtGCQ9IVkh3WrEaTNiAmxWI5kicCYIvWpfIis0XnQ8zWogpqjsONDKJ7ub2dcU4oYLh3F+QRYhWcEg6ROBs1SrUWynFoRett8bwB+JJQXOUkwSG6tbSDFJ/PC0HP74zg6GZaV1mUvf3naArHQLD18+WgTOBEEQhBNOW3vDAABNA4Nex5YaH7OL8xiWlcbcZ9czZUkZv/rn/5FuM9HPYSXFJDHInUKqxcAf3t7O1NH9STMbWLJqJzUtYe68pIASjyvpe4o9LmYX57GsrDLxd0zVCMpKt4GyzhlqBz9eVuHlgTe2kWKWuOasIWyt9bOzrlU0FRCOS2LbZg/Z194cAGB1hZffKCqNkSjZaRaimkq6zcD4wW4aWiNEYipmg57djQFKhrjo60o5ykcvCCe+eOAshj8UI9VsINUkEdM0nvhgFwvOzWdgho0nPtjN8H52igY64tmhqsZAp5X+YowKQq+r9QbwyzHawgqqpmG3Gkk1SahqjDE5ThrbwmTazd3OpWcMzuDsoX25vGigCJwJgiAIJyRXqpk/vbSJ1RVebCaJf//8DH5WOpgnV++mKMfJnOI8IjEVh82Ixajn08omXKlmvAEZs0HP+MFu+jks7GsKkmm38uzHe/jFWUNYPG0kcixeqzsaU/lot5cFK8sJygrFHhfzJuWzans90DVQVnJQhlqHzplrq3c2cvNFw7ni8Y8TtdEm5rt58PLRohaacFwRmWc9oLo9myUgK4kfhOue+5zMVDMxTcOk12MxSIwflEGW3UKfVDNZ6RZKh7gYKC7KBaHXdYxRDR12qxGrScIXimBCxy/PHcqj7+7EZpK4dcoIJhdmYbcYGdInhTyXTYxRQfgO1HoD+KPxMWozSYmMs2A0jE5nwKTTULR4cZbTB2WQlR6fSwc4rVw8MotB7lQG90kVgTNBEAThhCUrKqsr4jXHgrLCT5Z9Qr90C+cXZFE00IGsqGSmmXFYjfiCMqcNyqC/00qfNDP9nVZKhriIKAoGo8TkkVn86rxhfLTby7SlZfzure1kpJjY6w1SNDC+1fPpq8YxZVQ24ajC4x/s7hIoK/a4uOOSQrbU+JKO8+DMtY7j/cfVE3CnxufpD3c2cvPzG0QGmnBcEZln31JHV82O5gAGKb64r24OceXTn/DUVWMx6A1oQCgWD67ZrQbsRj3Z4qJcEHpd5zEK0C/dgg4I6yVm/3U9T/3PqfzqvKG0yQqNbRHSLEay7GZUVSFbdNQUhF5X4w3QGo1vp1Y1jT5pZkx6HcvXVHLF6Tk89OYWbp1cQF+7haCs0BqWSbMY0ek0DJJOBMwE4QSwbesWzp56RbfP7a2sIDfP0+1zO3ZWkNWbByYIxxB/KJb0d2ObTGVjEDQNSa8jw2wi1WLAYtRjM0ooGiiqBlp8m2dU0TDo9KSZJSwGiUBUYUKei0nDMjEb9EQVhaIcBxrQHJAJx1RqfGHufW0rY3OcLJ5eiC8oUzTQAUB5dQszn1zLj07P4crxuQCkW428v6MhkbmWOPZwlOXvVvLcTydw5VNraWyT+XBnI41tspjHheOGCJ59C/s6ddW0W40YJR02SU9pvpvVOxupbg5x4SNlideX5rt54NKR1LUEMaeLFFVB6G0Hj1GN9h89TSPNbKCv3czE33/Y5X2lHje/+8GY7/pwBeGk09ApcGYzSej0OiQdBKMhfnJmHltrffgjKsW/fb/Le0vz3SydUfTdH7QgCD0uip6sab/u9rmND1192OcE4WRhM0tdHrvx31/w3E8ncM+rmymr8HL9efl8tqcpkaHWWWm+m1svHgGajn3NIdZXNTOqfzptkRiZdjPvbq1n034fiy4eQcCgw241c2qOg6m/OJN3t9VR7Q3y93V7+c2Fw7n71c2saf+Ope9VUOpxce2kfH6y7JOkoBl8uYWzrMLLPa9u5qHLRzP32fUAeAMRYgdUdIAGmCQ9rWGZdJsZOabSGo5iMxvQ63QY9GDS65FVjVBUIRiJJ6VIeh1GSU+4fT2RYpawGiUsRolshxVfUKa+NYI/HMVpNSGrKsFIjHSbCXeK6ZDBu473tYSipJgkbEYJdBCOfbX3CyceETz7hg6+KE81SdT6QxhSjdw7fSSLXtrE6p2NideX5ru599KRvLO1loL+GTjFIBOEXtXdGG2TY7z4xT4uGTMAVJV7LxvFohcPGqseF/deNpK+oqumIPSqOm8AGRKBM6tJIhJTeWr1bn5WOoTaliAj+6Vz9zQ7d7y8ucuc+sBlo8SCVRAEQThpWAwSxR5XImgF8eyzK59ay9KZRdw2pYBAJMr0Mf26nTfvnl6YaDSg15sp9rhJNRtAiwejLizM4qLCLEKKSorJiIqKpsGPn17HiCw7pfl9uOH8YVz5VDzbrKPGmtmgZ+N+H+GowtgcR1LgrtTj5qriQSxYWQ5AWYWXhZOHJ55vDcf4weNrKc13M7ckj39+WsX15w3j1hc2Ul7VwjVnDWbSsEx0OvDJKqkWAxv3tbD4lS0EZQWbSWLZVafx2KqdSd8br9XmIaqoPPL2dj6s8PLUVadx58ubko+v/d9FUVUMOj1hRaU1HMNuMaCoGtXNIYySno92NTIhz8XS95K/p3PtNl9QprFNxh+OX3u4U0yJ/406P9axdul4fVskisNm6iZYqMP1FYJz3X1v5/cc6fkjOdT7Dw4uppgNOKzGb702+7bH21Of0R0RPPsGDr4oN0g6LEBWmpWYpqLTFO6/dCRtskJbOEaqxYBeBzUtQcxGI/3TLWLBLwi9aJ83QFv0yzFqNeoxAxgNTB7dn7e31HBeQT/CMYW7pxUSbp+sUswG7GaDqHMmCL2sxhsgBgTaF75Wk4RJryOsKswtHcKzH1UyqziPtqiCDrhrWmHiLrPZoCcjxcSADLGtWhAEQTh56HUwf1J8C3PnANqwrDQ0TcfGfS20hGUuHJHFbRcPR0NHWziGzSyRajIQURR8wfjfVoNEmsWAHFNxWI3sqGvjnte28ODlo1m+pjLp84s9Lu6aXsjUJWX885oJNLbJLH2vosvx2UwS/7h6ArM6NfXJcdmYvnRNUjZaW1hJfG7npgIAd08vBA1umTyCQCRGmsWAPxwlLKsEowqtkRiKCk/+z1giikpfu4XWUIyFk0cwyx9m4fMbCMoKRTlOVC1eSunqsz1cd04+n+1tZlZxHjPG52IxSnxe1cyyskoWvbiJqaP7keeyMefZ9Ylj7ajdNm/F5yyaMqJL4AzitdsWPr+Bu6cVsn5vM83BCGcNzSQcVdhR10aaJZ4Z99GuRh5btYtxuU4evHw0OuCm5zfw2d5mHp1RxMP/3d7l33x2cR4PvL6Vu6aPPGRjhZqWEAvbP2dOSR5FAx3saQww0Gmjr91MUFa46fkNSYHUr9OsoePzO7///BGZ3D61gNte2MTqii8fL/a4mH9OPrkZNrK/YSOI7r7v6zaX6InPOBQRPPuaustmWbpqJ9efnY8ZkPR6FCCqaoBGmiVe76wtHEOHnolD+9Bf1FEShF7TMUZbw7HEGK1rDZOVYsKg0yPpdJxX0I9gTMGk1ycCZ3aLEWeKSWScCUIv2+cNEFU1wjEVfyhKnzQzZr2O1kiIVLOVvd4As4rzCLQHzoySPh44kxVSzBIOq5F+Yh4VBEEQTjImvQ5Jr2PqqH5JWV91/jCSHnJdKSx+ZgtPfFDJ764YQ5bdgl4Xz1gLxRSCkRjuVBMRRY134DTGeweu39NEQb90np19OgE5xs0XjSASU+JZOxYjO+taqfIGKcpxUO+PdMl+61CU4+CtLXWJwFqxx8XCi4Z32caZapESwaGOjDSIB9DCURU0jXe31fG3j/fyo9NzOHOwixSLgT5p5vhWTLsJi8nAHS8elEXmcfH/rjkDVdO46+XNSQG+0nw31549hLkHBccenVHEgpXlzC0ZzJJVFcwpyUu8r+Mc55Tk0ddu6XYrbMdxt0ZijOxvR9Pg7le3JP37lHrc3DmtkLOH9uGq5Z9y8/MbmDwqm8/2NrNkRhHPHBSs7PhuvU7H4ksK48HDeoXmkExqe1aapNeRYjZwx0ubEgG4Fev2xv93GOigouH/s3fn4VGVd/vA7zP7TCaZLMMWSSA4wRA2g+xJUBEXEEVL7Sv6ViBabRVta/tWreJukWpbX6G2tgq2fSv21+KKW7VuIdYNY9k1kUhYwjIhmWQyy5nl/P6YnGEmsyYkmUlyf67LqyUzmTwT5ub7nOc8ix0tDjeGZepwbUURls0sDBswvH3zdqxbVhZ3Qo/NIUYMQgHAGaOycOcLOyJ+H/J7WDwlH4smjezRbLFoP08+XCJRe3vrNeLh4Fk3HAjZeFy+KNcBuPGcYjz2Xh1uOvt0CIICHaIP7S4PMnWB52j9DjjVBpgMpz6NkYhiCx3cNupUgfwBGGHUQZQkiB4XMjQ62EMymqlRwqxRQtML04yJKD45o35Jgr1zgFurEKD0O2HQBAbOiswZnQPggYyqFALMWhW8WhWMzCkREQ1RDr8PwzN1aLB2wKRXw+uXYNAoMcqkg1Grgsvrw9PLp8PnB/I6T7U0aJWBATSlAnZJwtE2FxweH7L1Grg8PmRoVJhakAOPz4e39x7Fk+/vg0P0BQe3fvdePW5bOAG/eetLrCwvwm2dAxAKQQgboKiw5GFFyGBYuSUPN59bjHe/PBb2HiosedCplSgrzIk4VAAAGqwd+OvH+3HL/GIsnDQK970SPghWbsnDnYsm4KEtuyMGb6rrm3HPSzuxcPKoyMfqrPBLUszBMZVSQE19M6rKi8K+T/6a2+uP+3dzsMWJ3U1tqG1siRgIq6634p6Xd2Lx5FF4avkMXPXHj1BVXoTHl5VBp1bGHZQ70uaC0+PDsx/vx1WzxuC7T3+CswpzsLJiLJ79uBFXzizE5NGm4OMbaxqw/p16GDRKPL6sDI+/szNiRps8YJjosAarXYwYhAICg3PRZh6G/r56chBErJ8HIOnDJXrjNeLh4FmSDsYYOPNKgAHAzecWd3b2XcjUqTEqSwcVAKfXAaXWiMJTnCJIRPF1nRWqUymgAwAp8A+dWiFAFWXgTAtAUip4QU7Ux5pCMioIQrCWevwO+AQDPD5HxMBZsNYCOI3LqYmIaAg70e7B6hdr8fTy6YH+rNMDtVIBjVKAQ/RCo1JipEkH0esP7CfauXG+AMAj+WE2auH2+mGSgA63F0atCjq1EkoBUCqUuHDiSJSfbkaGVgUBAATgrotLIfr9+J8LS9DiEPGXa2dBrRRw3yWlcHn9sLsDN8JaHR64RB8evWIqtCoFjrW7oVQAT76/L9j+CkseHrxsMr79+w9htYtR36NWpQjMugKwcPKoqDOy1ry2F1MLs/H23uMR319d34wVXQbAQr832uDYtRXjkGNQA0DUQTJ5hl88WpUiqUGlX735JaoqimAyqPHoP78MnlIai83pwV8/3o+ywhxsrGkIGfyTMLXza7ddVAKPTwpbbltVURSx/FZuh/x4u8sT92e3xXg80UCiu3NVT3fF+nmyZF6zN14jnvifAgIQmHHWdammBoCv86IcQuB0kFACAKVfQl6mqcdrfokoOV0HzjI0SmgVAlq8LvgBqAQE91cKmxUqARq9GsOZUaI+dajFgbaQfQiz9CpkapR4e/dhCIIBfskBrcoQMXCmgQRJrcQoDpwREdEQl6VX46tjdrS6PFj4v9Vod3ux8H+rcd6vP8ChVhcWr9uKb6wOtDo8ePL9r9Fkc2H1Sztx/V+2QQEFnB4/bM7A4MGwTC1MWhXaXSKc3sDBAPJ/QGAwSKEI3HzWKxUQAGRoldCpFPD4JFg7RAiCgOFGLXQqBUZkajEsS4thRi1Oy9Zj+phsjMzSYfMP5uL/3TAbr/+wElUVRXht52GcMTIz6vsL2wOtvjnmVirV9VaUFWTH/D3FG9yJ9phOpYDXJwXfd1dalQK1B1pRbsmL+pqVne1OZlBJbrs80y2ZQbma+maUFWQH/xcI/H7krwEI+//R/hxKfp1MnTruz86K8XgybU702t35ebJkXrM3XiMezjxL4GCLI2LGmc3pwgidDiIAvyTBJwgRs1lGs6NP1C9sDjFicNvvl6AGoFPqsK/FgdG5huAduszO5/BQAKL+0+byBpdpGjVKAMDn31gxf0I+3tkTOMCjvUtGdUoFhnFgm4gGsb17duOcxVdEfSx/WA6e3fiHfm4RpTOjRokKSx6OtblR0TlgI+8/VnugFWcVZuOmZz/HDWePw4/OHw+VUsDqi0shAWhxiDB27hsmSICj82A7g1YJl8eHI60uNNlcmD0uFwDg9PhxsMWB3AwNDJrAc9pcXny2vwW7D9tw/oQRUCkU2N/iwPQxOTBoVMjQKKFSCjhic+GzxhbsPWzDrRecgd+//zVK802obWxBbWMrHl9WBgBd9gXLw/Iue6B1dxBMFm9wJ9pjORkaHLW5wgbvZPLXNmxtwNPLp0csVy235GH1JRNx2W9rsK7zfSXzs9ucXgAI+zvsKrQ98vsNfd/y/3eIvojfR6KBPAAwG+OvujEbNZhXbMYHXZZB1h5oRaUlL+py03JLHo61uzF9TE7Cn5/szwMCG/4nam9vvUY8HDxLoN3ljViqKel0cEh+GBQKuDhwRpRSVrsYkVG3IMAFwCs5Tw6cuThwRpQq7c7AabbyPoQ+CZg6xoz/7D+OBaX5EYPbOoADZ0Q06HmgwMhLb4362OGXf93PrUkfV628HoePt0R9bCgPKioBPHjZJPzyjb24+5KJWPv6HqzsXIa4YWsD1l9VBmz9Br95qw6/easOZqMG/3ftTPj9gFv0YZhRGzix0hXoN4/M0qJD9KHD68cIkw7jhmWg3SVCrVJCkiRkGzTI0KqgUQq44S/bcKDFiXJLHladWwyVAtCqlXjxP4eCe2xVVRRh7rg86DRKLJo0EoU5evglCd+rHIc/Vu/rbGsDbtlUi6qKouASyuFZWvxrz7GIPdDiDYKZ9NFnEFVa8nC0zRX1sViDY16fhKY2F1adW4xr//Rp2GPyoQZnFebAqFVh9rhcrJg7Nuywhtd3NsEh+pIeCBudc7J/s2FrQ4zBRDOWl48NDibKv4vQ34n8/3VqZcTvI9HssNE5+oRb1pgMGjy8dApu37w9bDDqy6Y2PHT5ZNz54s6IgcSb5xdjbK6hR9vhxPp584rNWLt0SlKv2RuvEc+gGTx74okn8Mgjj6CpqQkTJ07EY489hsrKylN+3dCL8la7A3kZBugBiAoFHAiM9MoDZ1kaJfdkIepnba7wgTNICCyrFgAIeg5uE6WBTL0aWpUCKgCCJEGCAK0AlI0dFjG4rQMwjDklIhrwejoIdvh4CwcVoxAAKCHg5vOKIUHCXReXwu3zB2eXOUUv7l8yMXiatVEbuNTP0qmgVythF71wuH0walVQKxQ4bnej1elBfrYeGWol3H4f1Epl5+QRL0aadNCrAyfT/+o7Z8KgUUKSgL1NbSjMM6DqmU9x5czC4CDYKJMe/9x9BDsP2nDBxBHINWqxYuOn+O6cMfjpBWdAALD64lL4JAknOkQYNEp8eaQdN0c5OKDSkodjcQbBcg3qiNlP5ZY8rKwoggAhYhCr0mLGjedaog6OadQCHG4vcgwqPHXNdNhcHgwzaqFRKXDCLmLzD+bC55fw67e+xDsh+6zJhyQ8+OoeACcHwhRAZLs6B+HmFZsxsnM5qjxDKnQw0e31w6RXw+XxBX8v5SGzDOUBOHmp6LxiM0aZdHB7fKiw5GFr58+NN5AX2oZE8rP1WLesDFa7GLyeMhs1MBk0WL+sDMfa3bA5PTBolMjQqJB9igckxvt5/fkasQyKwbO//e1v+NGPfoQnnngC5eXlePLJJ7Fw4ULs3r0bhYWFp/TawYtyvxemDANEyQW1Qgc/wgfOMjlwRpQSWTo1NEohOHDmA6Ds3OOMA2dE6cGgUUIFQOX3wy0ooJW8cAsqdHgCy0YytCroVAqowYEzIqLBoi8GwYbyUlcVAvtsGzUq2EUfmp1umI3aQP9XAvxqJZo7RGRoVTAZ1JAkCWqlEl6/BEEAPF4fzEYN3D4/mjvcyNSpkWPQQKMQ0O7xQvRKcIg+qBQCrO0ujM7V4/P9rThjpBFevx8OUUJW5wynjxqasXbpFLi9fujVSmTqVLhmw8eYMCoL91xSild3NOHeV3bDIfrw2TctuHRqPtweH1RKBR56ZReq66zBEyGnFWYHB32AwGDTqvnFGJGlRWWxOWJ206pzi3G8XcSlU/Nx58WlsDk9MBnUqN3fglXPBmZqVVUU4cZzLFAqBDhEH7YfbMWBEx342w2zcbDFCY0ysI/Zc5804mcXnoGKYjNsThFZejVMBjXsLi9Erx+7mmz4z6FWzBybi/+5sAQ/Of8MOD0+GDRKGLUq3L9ld3DgzyH68LdPGvGLyyejQ/ThUKsTQGAg65ZNtZg+Jids9lPoDCn5oIHQU0tDTz199uP9wQG4SosZKyvGYtPHjVi7dEpwb7iHvzUFd7ywA9V11uBAngCE/W57MgPLZIg+8BTr66eqN163r9o2KAbPfv3rX+Paa6/FddddBwB47LHH8Oabb+J3v/sd1qxZc0qvnalTAd4OuAUD9JDgUehg90tweHxwuH2BwTU1L8qJUsVs1EB0eiBJnXfkBMAFDpwRpRO1QoDa64dLUEAneeFWqKKeqsmBMyKivjMYlkMO6aWuCgFqf2Bje0Pn/qHH2t3Bvq5L9MGoU0GrUsDj90OnVAWWZbq9yNSpYNJr4PEHDgdQCAIEARB9fngkIEOtAiQf3Ao/MrQqTCnIhl+SMPG0LPglCaOydFArBPj8EqaNyUGpNwsO0YcsnQoapQLNHW785dpZMGqUsLu9mF8yHGWFOfD7JejUSnh8fvgkwOf14Y6FJfj5IiFw4qdOhQeWTILb50e70wuDJnBCqFfy4+vjHbhzUQkEQUCb6+Rj8nvIyVAH91d77pNG3Hr+ePz9+3PgFH3Bm3Junx8qhYCLJo1EbWMr/uvJj4KDXZXFZqy5fDJG5xoAAEfbXGjpEOEQvRhl0uFgixNFZmNg0/6vm/FlUxvuXzIp7DDAX10xNeYMp1EmHax2EXkZGlx+5mkRs5+izZAy6lTocHvxf9fOgkGjhEIQIAkS7lpUihaniOdvnAulIECpEPDoFVPDXm90rgHrQ14vS6/Gr75zJuwub6/PwBqqBvzgmSiK2LZtG26//fawr19wwQX48MMPo36P2+2G2+0O/rmtrS3m64/OMWB/swSl3wGvwgAfAJfX3/mPEAfOiPpCdzJqMmhwXPRB8PoBDpwR9Zvu5DQ/x4ADzR3Q+UW4FRqIfgl+SYIAAQoh0BnhwBlR7+pORmlo6IuZYPEG5L6qq8fIHr3q0NGdnA7LMeB4cwckAL6QOioIgNPrg0Gngdvrh9UuIlOrApSAU/QgQ6uGTq2E2+uDzy9Bp1JCr9LghEOEXqOCwg/YPIEb0QaNAq0OT2BQTK1A1TOfBvc6W1lehNf+cxg/XDAeAgL7arU4PFApBOw73oHS/Cx0eABBEKCEAJ1KCZVCgNfvh8fnx9E2N/Kz9VArBAiCgHyTEUBg/2KX6EOeMbDHmt0VWF5amGuARqmAzRkYgJIfc7p9MGiV6BB98AMoHZWFtUun4Fi7G3kGDUbmR98zdVSWDjPH5sYcSBqRpQs74dOkVwcHoi4/8zSYK4oiBp7izXBKZvZTb8+QivZ6I7J67eWHvAE/eGa1WuHz+TBixIiwr48YMQJHjhyJ+j1r1qzBfffdl/TPGJOXgYMtAlpDRm1HZemgUwgYlmM4pfYTUaTuZnRYth42R+DggLCBM50Ko5lRoj7R3ZwWdNbS9pBaOjJLy1M1ifpIdzNK6aW/lyfG+3n7G+oxpsgS9bGv6uox7ydPRH1sx9rre619yRiISzq73efNy8DxFgckBA7IkiBBkgKbxisB6LUqZKmVsHt9cHp80GvUsLsDJzvqVApoFIDb50ebM3DSplalgEYhwCEAPj/Q4fYiN0MNvVoJl9eH3/zXmcjQqiAAgACsOq8YkiTBqFXB4fHB5QnMdpsxNhcnHCKUCgFGjQoub2DGl0GjhFKhglIh4KzC6BvJJx7syYj6mM0hhs3amj4mJ+5AVHcHqvpq6R8NXAN+8EwmCELYnyVJivia7I477sCtt56869LW1oaCgoK4r88LcKL+05OMssAR9S/WUqL01pOMUvro7+WJ8X7ejrXXx32sJ+INdPV0xtpAXNLZk5ym6+SNon7+eez7U38b8INnZrMZSqUyYpbZsWPHImajybRaLbRabX80j4h6gBklSn/MKVF6Y0YpnSUarBsqmFOigWPAD55pNBqcddZZeOutt3D55ZcHv/7WW29hyZIlKWwZERERERFR/+EeZNENxCWdRJReBvzgGQDceuut+O53v4vp06djzpw5+MMf/oDGxkZ8//vfT3XTiIiIiIiIek2iJY/psgdZOhmISzqJKL0MisGz//qv/0JzczPuv/9+NDU1YdKkSXjttdcwZsyYpL5fkgJH/vIUIqL+l5mZGXN/QhkzSpQ6yWQUYE6JUqUvMurz+eDz+mK8EPhYbz3Ww9fzSAoMW/TDqI/tePQH6fHeBtBjPp+vz2sXaylReksmo4IkJ3QIO3jwIDdQJUoRm82GrKz4Zygzo0Spk0xGAeaUKFWYUaL0x5wSpbdkMsrBMwB+vx+HDx+OGG2UTzs5cOBAUv/YDVRD5X0CQ+e9DqT3mcwof6yMAgPrvfbEYH9/wOB/jwP9/SV7t3yo1tLB/v6Awf8eB/r7Y0aTN5TeKzC03m+6v9dTzWlX6f5+o2Gb+wfb3DPJZHRQLNs8VQqFAqNHj475eFZW1oD54J2KofI+gaHzXgfL+0yUUWDwvNdYBvv7Awb/exzs72+o19LB/v6Awf8eB/v7G+oZDTWU3iswtN7vQH+vyfR5Qw3E98s29w+2ufcpUt0AIiIiIiIiIiKidMXBMyIiIiIiIiIiohg4eBaHVqvFPffcA61Wm+qm9Kmh8j6BofNeh8r7BAb/ex3s7w8Y/O9xsL+/RAb7+x/s7w8Y/O9xsL+/RIbS+x9K7xUYWu93KL1XYGC+X7a5f7DNfYcHBhAREREREREREcXAmWdEREREREREREQxcPCMiIiIiIiIiIgoBg6eERERERERERERxcDBMyIiIiIiIiIiohg4eEZERERERERERBQDB8+IiIiIiIiIiIhi4OBZiA8++ACXXHIJ8vPzIQgCXnzxxW6/xptvvonZs2cjMzMTw4YNw9KlS9HQ0ND7jSUiIiIiIiIioj7HwbMQHR0dmDp1KtavX9+j79+3bx+WLFmC+fPn44svvsCbb74Jq9WKb33rW73cUiIiIiIiIiIi6g+CJElSqhuRjgRBwAsvvIDLLrss+DVRFHHXXXfhr3/9K1pbWzFp0iSsXbsW55xzDgDgH//4B5YtWwa32w2FIjAu+corr2DJkiVwu91Qq9UpeCdERERERERERNRTnHnWDStXrkRNTQ2ee+45bN++HVdccQUuuugi1NXVAQCmT58OpVKJjRs3wufzwWaz4S9/+QsuuOACDpwREREREREREQ1AnHkWQ9eZZ19//TWKi4tx8OBB5OfnB5+3YMECzJw5E7/4xS8ABPZNu+KKK9Dc3Ayfz4c5c+bgtddeQ3Z2dgreBRERERERERERnQrOPEvS559/DkmSMH78eBiNxuB/77//Pr7++msAwJEjR3Dddddh+fLl+PTTT/H+++9Do9Hg29/+NjhGSUREREREREQ08KhS3YCBwu/3Q6lUYtu2bVAqlWGPGY1GAMBvf/tbZGVl4Ze//GXwsf/7v/9DQUEBPv74Y8yePbtf20xERERERERERKeGg2dJKisrg8/nw7Fjx1BZWRn1OQ6HI2JgTf6z3+/v8zYSEREREREREVHv4rLNEHa7HV988QW++OILAEBDQwO++OILNDY2Yvz48bj66qtxzTXX4Pnnn0dDQwM+/fRTrF27Fq+99hoA4OKLL8ann36K+++/H3V1dfj888+xcuVKjBkzBmVlZSl8Z0RERERERERE1BM8MCDEe++9h3PPPTfi68uXL8czzzwDj8eDBx98EH/+859x6NAh5OXlYc6cObjvvvswefJkAMBzzz2HX/7yl/jqq69gMBgwZ84crF27FiUlJf39doiIiIiIiIiI6BRx8IyIiIiIiIiIiCgGLtskIiIiIiIiIiKKgYNnREREREREREREMXDwDIAkSWhrawNXsBKlJ2aUKP0xp0TpjRklSn/MKVH64uAZgPb2dphMJrS3t6e6KUQUBTNKlP6YU6L0xowSpT/mlCh9cfCMiIiIiIiIiIgoBg6eERERERERERERxcDBMyIiIiIiIiIiohg4eEZERERERERERBSDKtUNGKhsDhFWu4g2lwdZejXMGRqYDJpUN4uIOjGjROmPOSVKb8woERFRAAfPeuBwqxO3bd6O6jpr8Gvzis14eOkU5GfrU9gyIgKYUaKBgDklSm/MKBER0UlcttlNNocY0ZEAgA/qrLh983bYHGKKWkZEADNKNBAwp0TpjRklIiIKx8GzbrLaxYiOhOyDOiusdnYmiFKJGSVKf8wpUXpjRomIiMJx8Kyb2lyeuI+3J3iciPoWM0qU/phTovTGjBIREYXj4Fk3ZenUcR/PTPA4EfUtZpQo/TGnROmNGSUiIgrHwbNuMhs1mFdsjvrYvGIzzEaeQESUSswoUfpjTonSGzNKREQUjoNn3WQyaPDw0ikRHYp5xWasXTqFx3cTpRgzSpT+mFOi9MaMEhERhRMkSZJS3YhUa2trg8lkgs1mQ1ZWVlLfY3OIsNpFtLs8yNSpYTZq2JEg6iPMKFH6Y06J0hszSpT+epJTIuofqlQ3YKAyGdh5IEpnzChR+mNOidIbM0pERBTAZZtEREREREREREQxcPCMiIiIiIiIiIgoBg6eERERERERERERxcDBMyIiIiIiIiIiohg4eEZERERERERERBQDB8+IiIiIiIiIiIhi4OAZERERERERERFRDKpUN2CgsjlEWO0i2lweZOnVMGdoYDJoUt0sIgrBnBKlN2aUKP0xp0RERBw865HDrU7ctnk7quuswa/NKzbj4aVTkJ+tT2HLiEjGnBKlN2aUKP0xp0RERAFcttlNNocY0YkAgA/qrLh983bYHGKKWkZEMuaUKL0xo0TpjzklIiI6iYNn3WS1ixGdCNkHdVZY7exIEKUac0qU3phRovTHnBIREZ006AbP1qxZA0EQ8KMf/ahPXr/N5Yn7eHuCx4mo7zGnROmNGSVKf8wpERHRSYNq8OzTTz/FH/7wB0yZMqXPfkaWTh338cwEjxNR32NOidIbM0qU/phTIiKikwbN4JndbsfVV1+NP/7xj8jJyemzn2M2ajCv2Bz1sXnFZpiNPH2IKNWYU6L0xowSpT/mlIiI6KRBM3h200034eKLL8aCBQsSPtftdqOtrS3sv2SZDBo8vHRKRGdiXrEZa5dO4dHdRL3gVDIKMKdE/YG1lCi9sZYSpb9TzSkR9R9VqhvQG5577jl8/vnn+PTTT5N6/po1a3Dffff1+OflZ+uxblkZrHYR7S4PMnVqmI0adiKIesmpZhRgTon6GmspUXpjLSVKf72RUyLqH4IkSVKqG3EqDhw4gOnTp+Of//wnpk6dCgA455xzcOaZZ+Kxxx6L+j1utxtutzv457a2NhQUFMBmsyErK6s/mk1EcTCjROmPOSVKb8woUfpjTokGjgE/82zbtm04duwYzjrrrODXfD4fPvjgA6xfvx5utxtKpTLse7RaLbRabX83lYiSxIwSpT/mlCi9MaNE6Y85JRo4Bvzg2XnnnYcdO3aEfW3lypUoKSnBbbfdFjFwRkRERERERERElKwBP3iWmZmJSZMmhX0tIyMDeXl5EV8nIiIiIiIiIiLqjkFz2iYREREREREREVFvG/Azz6J57733Ut0EIiIiIiIiIiIaBDjzjIiIiIiIiIiIKAYOnhEREREREREREcXAwTMiIiIiIiIiIqIYOHhGREREREREREQUAwfPiIiIiIiIiIiIYhiUp232F5tDhNUuos3lQZZeDXOGBiaDJtXNIqJOzChRemNGidIfc0pERMTBsx471OLA/mYHWp0e6NRK/GvvMXzZ1Ib7lkxCfrY+1c0jGvKaWp1476vjGJ6phdvrR4vDg08aTuCc8cMwihklSrnDrU7c/dJOlIzKQllBNppsLhwzqFGYa8BpOYZUN4+IwFpKREQk4+BZDxw84cBtz29HTX1z8GvlljysLC/CPS/txKNXTOUdOaIUsjlE7D/hwJbthyNyWmTOgEGjZEaJUsjmEHH3Sztx5cxCbKxpwPp36oOPVVjy8PC3pmB0LgfQiFKJtZSIiOgk7nnWTTaHiDu6DJwBQE19MzbWNOCMUVmw2sUUtY6IAKDV4cG6d+qi5nTdO3VodXhS1DIiAgCrXUTJqCxsrGmIyOnW+mbc8cIO2ByspUSpxFpKRER0EgfPuslqF1HdpRMhq6lvRllBNtpd7EwQpVKH6I3o7Mtq6pvRIXr7uUVEFKrN5UFZQXbMnFbXWXkjiijFWEuJiIhO4uBZN7UlGBhze/3I1Kn7qTVEFE2H6Iv7uCPB40TUt7J0ari9/rjP4Y0ootRiLSUiIjqJg2fdlJVgYCxbr4bZyP0fiFIpWx8/p6YEjxNR3zIbNQlzyhtRRKnFWkpERHQSB8+6yWzUYF6xOepjFZY8jMkzcPNUohQbnqlFZYycVhabMTxT288tIqJQJoMGY/IMqLDkRX18XrGZN6KIUoy1lIiI6CQOnnWTyaDBg5dNiujwV1rM+MXlk3FaDk8HI0o1k0GDhy6bhMqInObhocsmcYCbKA0oBQE/XzQBlZbwi/PKYjPWLp3CnBKlmEP0YdW5lshaWmzGL5lRIiIaYlSpbsBAY3OIuH/LbpxZmIOV5UVwe/3QqhSoPdCKB7bsxqNXTGVngijF5JxOLczBCuaUKO3YHCL+Z/N2bNvfgqqKIqwoHxvM6bF2NwwaZaqbSDSk2RwifhaW0SJmlIiIhjQOnnWT1S7i7T3H8PaeYzEf50U5UWoxp0TpzWoXUV1nBQCsf6c+4vGZY3OZUaIUYkaJiIjCcdlmNyU6bZOngxGlHnNKlN6YUaL0xowSERGF4+BZNyU6bZOngxGlHnNKlN6YUaL0xowSERGF4+BZN8U7bZOngxGlB+aUKL0xo0TpjRklIiIKx8GzbjIZNHh46ZSIDsU8ng5GlDaYU6L0xowSpTdmlIiIKJwgSZKU6kakWltbG0wmE2w2G7KyspL6HptDhNUuot3lQaZODbNRw44EUR/pSUYB5pSoP7GWEqU3ZpQo/fW0z0tEfY+nbfaQycDOA1G6Y06J0hszSpTemFEiIqIALtskIiIiIiIiIiKKgYNnREREREREREREMXDwjIiIiIiIiIiIKAYOnhEREREREREREcXAwTMiIiIiIiIiIqIYeNpmD8lHd7e5PMjSq2HO4GlEROmGOSVKb8woUfpjTomIiDh41iOHWhzY3+xAq9MDnVqJf+09hi+b2nDfkknIz9anunlEQ57NIeJImwsHW5wQBAGfN7Zgw9YGTB+Tg4eXTmFOidIAaylRemMtJSIiOomDZ9108IQDtz2/HTX1zcGvlVvysLK8CPe8tBOPXjGVd+OIUuhwqxO3/WM7quutwa+VW/Lw+LIy3LKpFrdv3o51y8qYU6IUYi0lSm+spUREROE4eNYNNoeIO7p09gEE/1xWmAOrXWRHgihFbA4Rt20O7+wDJzNaVVGE9e/UM6dEKcRaSpTeWEuJiIgi8cCAbrDaRVR36ezLauqbUVaQjXaXp59bRUQyq11EdZ016mNyRgEwp0QpxFpKlN5YS4mIiCIN+MGz3/3ud5gyZQqysrKQlZWFOXPm4PXXX++Tn9WWoJPg9vqRqVP3yc8mosSSySgA5pQohVhLidIbaykREVGkAT94Nnr0aDz88MP47LPP8Nlnn2H+/PlYsmQJdu3a1es/KytBJyHXoIHZyOnrRKmSKKMGjRLzis3MKVEKJcppjkHNjBKlEGspERFRpAE/eHbJJZdg0aJFGD9+PMaPH4+HHnoIRqMRH330Ua//LKNOhQpLXtTHKix5OC1bx70fiFIoUUaL8jLwyNIpzClRCiXKaWGugRklSqFEGR2Ta8AvWUuJiGiIGVQHBvh8Pvz9739HR0cH5syZE/N5brcbbrc7+Oe2trakXr/D7cWK8iJIQMQJYSvKi3Co1YksvZqdCaJT1FcZPWxzojDX0NvNJRqSeprTdpcnbk47RG9vN5VoSOqrWnq0zYUJo7J6u7lEQ1JPc0pE/W9QDJ7t2LEDc+bMgcvlgtFoxAsvvIDS0tKYz1+zZg3uu+++bv8cm9ODWzbVoqqiCFXlRXB7/dCqFKg90IpbNtXi0Sum4li7m4NnRKeoLzMqSQ4YtSrmlOgU9TSnLY74Od24YgZsDp7iR3Sq2N8lSn89zSkR9T9BkiQp1Y04VaIoorGxEa2trdi8eTOeeuopvP/++zEH0KKN8BcUFMBmsyErK/adtK+P2XHer9+P+fjTy6fDpFdj+tjcnr8ZIurTjLq9fpwxIhOnDzf2apuJhpqe5nT3YRsWPb415uMvrypHhkbFjBKdIvZ3idJfT3NKRP1vwO95BgAajQYWiwXTp0/HmjVrMHXqVPzv//5vzOdrtdrg6Zzyf8kwGzWoLDZHfazckofaA60waJQ9eg9EdFJfZlSrUqA9wUliRJRYT3OaoVGhPMZ+SvLXmVGiU8f+LlH662lOiaj/DYrBs64kSQobwe8tJoMGDy6ZFLGJarklDyvLi7D7sA0ZmkGxEpZoQDIZNFhz+eS4Ga090IrMBCeJEVHfyTaocfP84ogBtHJLHladW4x3vzzGjBKlEPu7REREkQZ85fv5z3+OhQsXoqCgAO3t7Xjuuefw3nvv4Y033uiTn6fTKHHnxaVoc3pg0CghAXj3y2N49uP9uH7e6cg2sMNPlEqZOhVWLy5FqyMyo1fNGoO/fdIIc0VRqptJNGSZDBqMztbjfy4owc3n+pChDeR0b1MbXB4fdh60YcWcsaluJtGQxv4uERFRuAE/eHb06FF897vfRVNTE0wmE6ZMmYI33ngD559/fq//rMOtTty2eTuq66zBr1Va8rB68UTkm/QYm2vg5qlEKRQvowDwt08acf+SScwpUQodbnXijhd2dMmpGT+58Aw89cHXzChRirG/S0REFGlQHBhwqtra2mAymeJuzGhziPjJ3/+DklFZKCvIhtvrh06txOeNLdjb1IaHLp+MEVm6fm450dDQGxldfXEpsg1qdviJ+ghrKVF6Y0aJ0l8yOSWi1BjwM8/6S3OHiKtmFWLD1gasf6c++PVKSx5WVhShw+1NYeuIKFFGfZLEgTOiFGMtJUpvzCgREVF0g/LAgL7g80vYuLUBW+ubw75eXd+MjVu/gc8/5CfwEaUUM0qU/phTovTGjBIREUXHmWdJ8kkStjW2YtV8S8Q09g1bG+Dj6leilGJGidIfc0qU3phRIiKi6FI6eFZdXY0nn3wSX3/9Nf7xj3/gtNNOw1/+8hcUFRWhoqIilU2L0OH24vFlZdhYEz6NvdySh8eXlXEaO1GKMaNE6Y85JUpvzCgREVF0KRs827x5M7773e/i6quvRm1tLdxuNwCgvb0dv/jFL/Daa6+lqmlR5eg1+O279SgrzEFVeVHYnbhnP96PuxaVprqJRENajl6DX7/1FWqj3DE/YnPidHNGqptINOSxlhKlN2aUiIgoupQNnj344IP4/e9/j2uuuQbPPfdc8Otz587F/fffn6pmxeSR/Lhq1piod+JWlhdB9PtxuNWJ/Gx9CluZfmwOEVa7iDaXB1l6NcwZGm7aTn1C9PtR29ga8475nHF5aGp1YhQz2mPMM50q1tK+xYzSqWJGe475IyIa3FI2ePbll19i3rx5EV/PyspCa2tr/zcoAUECNtY0oKZzA1WDRomqiiKUFWQDACQJeP+r41g0aSQLZafDrU7ctnk7quuswa/NKzbj4aVT2OmiXudwe1FVURTMaWhG3V4/jrS5IAgCDBolM9oDzDP1htBa2jWjBo0SaoUC97y0E49eMZU57SZmlHoD+7s9w/wREQ1+KRs8GzVqFOrr6zF27Niwr2/duhXjxo1LTaPi8EsI60jEm93CzkTg7lvXTgQAfFBnxe2bt2PdsjL+nqhXmfQalBVkY/079TEzWmkx44HLJvGz103MM/UWuZbGzGixGcvnjkVzh8jPVDcwo9Rb2N/tPuaPiGhoUKTqB99www344Q9/iI8//hiCIODw4cP461//ip/+9Ke48cYbU9WsmByiFwaNEqvmW/Dc9bPxp5C7crKa+masfmknbA4xRa1MH1a7GNGJkH1QZ4XVzt8R9S6zMdAxNWiUWLesLGpGq+utzGgPMM/UW+RaGjOjdVZsrGmAz88T/bqDGaXe4hADBwLEyyn7u+GYPyKioSFlM89+9rOfwWaz4dxzz4XL5cK8efOg1Wrx05/+FKtWrUpVs2LKNmiCd9/KCrJR3aUjIavuLJJD/Q5Tm8sT9/H2BI8TdZfJoEFBrh6PLyuDTq1kRnsR80y9Ra6l8TJaU9/MwbNuYkapt5j0muCMM9bS5DB/RERDQ8pmngHAQw89BKvVik8++QQfffQRjh8/jgceeCCVTYopQ6sK7gHh9vrjPpdFEsjSqeM+npngcaKeMOk12FjTAJuTHdnexDxTb5FraaKMOkRfP7VocGBGqbeYjRqsXlzKWtoNzB8R0dCQ0sEzADAYDJg+fTpmzpwJo9GY6ubEZHd5g9PWtar4vzYWyUDna16xOepj84rNwSV2RL1JzmmijGZoUzbpdkBinqm3JJvRTB0z2h3MKPUWk0GDaYXZrKXdwPwREQ0NKRs8c7lceOSRR7Bo0SJMnz4d06ZNC/sv3YROya490IpyS17U51WySAIIdL4eXjolojMxr9iMtUuncJo/9Qk5p/EyWm7Jg0aZ8vsGAwrzTL2FGe0bzCj1JnnmJ3OaHOaPiGhoSNkto6qqKrz11lv49re/jZkzZ0IQhFQ1JSmhU7I3bG3A48vKACBsE9VySx7uu3Qii2Sn/Gw91i0rg9Uuot3lQaZODbNRw98P9Rk5p7EyWmkxY3n5WNicIoCMVDRxwGKeqTcwo32HGaXewpx2H/NHRDT4pWzw7NVXX8Vrr72G8vLyVDWhW+Qp2R/UWeEQfbhlUy2qKopQVV4EADDp1Xjvq+MpbmX6MRnYcaD+E5rT0Iy6vX6Y9Gq4PD7cvKkWr6yqSHVTByTmmU4VM9q3mFHqDcxpzzB/RESDW8rmW5922mnIzMxM1Y/vtq5Tsh2iD+vfqceGmgZIAK7Z8Am2H2hFXgaLJlGqhOZUzui1f/oMf/14P5ydnf3pY3K4tJooRZhRovTHnBIREUUSJElKyXnwr7/+Oh5//HH8/ve/x5gxY1LRhKC2tjaYTCbYbDZkZWXFfa7NIeJImwuHWp3I0qmhUSnQZHMhS6dCYa4Bp+UY+qnVRENHdzIKnMxps11Epl4Fr09Cm9OLwzYnzh0/DCOz9f3QaqKhpSe1lBkl6j89raWtDg/0GiUECHjny6PYedCG+5dMwijmlKjXdTenRNR/UrZsc/r06XC5XBg3bhwMBgPU6vATKk+cOJGilsVnMmjg8vrhlwCbwwOdWoBKIeDmTbUoHZWFh5dOQT47E0RpQakQAAlQKRT44mALdh604ezxw1LdLCLqxIwSpS+72wunxw9JAuSdifNNeny7bDQHzoiIaMhJ2eDZsmXLcOjQIfziF7/AiBEj0v7AANmB5g5U11sxIksHt9cPh8eHY20ubLp+Npb94SPcvnk71i0r454HRClyuNWJu1/ciZL8LJQVZKO5Q0S2QY0LJozAtIIc3PPSTjx6xVRmlChFmFGi9HfwhAP3vrwTJfkmlBVk44QjkNNJ+VloanPCqFMxo0RENKSkbPDsww8/xL///W9MnTo1VU3otmNtLhxsdeLVHU0Rp2yONWfgV1dMxfKNn8JqF9mhIEoBm0PE3S/uxJWzCrGxpgHr36kPPlZhycPPF03AmYXZzChRijCjROnP5hBx78s7ceWsMTFzanN4mFEiIhpSUnZgQElJCZxOZ6p+fI90uL1Y/2592MAZEDi+e/279Sjo3O+s3eVJRfOIhjyrXURJfhY21jRE5HRrfTPWvLYXCyaMYEaJUiRhRl/fi/NLmVGiVDrW7kZJviluLfX6U7JlMhERUcqkbPDs4Ycfxk9+8hO89957aG5uRltbW9h/6cjh8UV0ImQ19c1wen0waJTI1KmjPoeI+pbNKaKsIDtmTqvrrfD6JWTpmVGiVGhzeeJntM4Knx/MKFEK2ZwJclpvhdPr6+dWERERpVbKlm1edNFFAIDzzjsv7OuSJEEQBPh86VeUHe74bXK4fVi9uJRHdxOliEGjwpE2d9zndLh9MBu1/dQiIgqVpVOjyeaK+xy7y4vcPJ5cTZQqGVoVjrXHr6UOMf366URERH0pZYNn7777bqp+dI9l6uL/uow6FaYVZnMPCKIUUSoEZCeYsWLUqmB3eTGCp38T9TuzUYOjbfEzatAqmVGiFFIISFhLE/WJiYiIBpuUVb6zzz47VT+6xwwaJSotZlTXWyMeq7SYoVQknp1GRH1Ho1Qgx6BGhSUPW6MsN6ksNuNomxM5HOAmSgmTQYPROfqYGS235EGlELjnGVEKaVWJa6lSEFLQMiIiotRJ2eDZBx98EPfxefPm9VNLkmNziLj/ld1YWTEWgITqkM5EpSUPd1w8Ae99eQznlYxMWRspnM0hwmoX0ebyIEuvhjlDw1mBg5jNIeKuF3diz5E2PLV8BgR8GTbQXWnJw32XTsR3nvw3/nb9nBS2lADmc6iyOUSsfX0vfnphSURG5VP83v+KtTQdMbNDg80hYvWLu+LW0p9dVAKlgoNn6YT5JCLqeykbPDvnnHMiviaE3MVKtz3PrHYRb+89hg/3NaOqoggryovg9vqhVSlQe6AVgiTBbNRxv7M0cbjVids2b0d13ckO37xiMx5eOgX52foUtoz6itUuBjv4V/3xo86cjg3mtMicgaYWJ8oKspnTFGM+hy6rXcSrO4/g3a+OR2S09kArBIC1NA0xs0NHolo6Ni8DOw/bcM74YSluKcmYTyKi/pGy0zZbWlrC/jt27BjeeOMNzJgxA//85z9T1ayY2jqXkDhEH9a/U4+bN9Vid1PgVNDSUVnwShLKCrjfWTqwOcSITgQAfFBnxe2bt8PmEFPUMupLbSHLvKLl9GibG9lGDe5eXMqcphDzObSxlg48zOzQ0rWWbtjagNoDrdCqFHB7/XB4vJhymokZTRPMJxFR/0nZzDOTyRTxtfPPPx9arRY//vGPsW3bthS0KrYsXWDjVINGiRvOHoeFE0fhgS27sP6d+uBzKi1mrPnWZIzO5SlhqWS1ixGdCNkHdVZY7SI7fYNQaEarKoowvTAHo7J1eHDL7rCcVljy8PC3pjCnKcJ8Dm2spQMPMzu0MKMDC/NJRNR/UjbzLJZhw4bhyy+/THUzIpiNGiyYMBzrryrDcKMW923ZFbbvGQBU11txxws7eJcnxdoSbDTNjagHp9CMftHYgs8aW3D/lt0ROd1a38ycphDzObSxlg48zOzQwowOLMwnEVH/Sdng2fbt28P++89//oM33ngDP/jBDzB16tRUNSsmk0GDey+ZiI1bGzA8S4eaKKcPAUB1nRXH2t393DoKJd81jSUzweM0MIVmdGt9M8oKsuPm1Gpnpz8VmM+hrTu1lBlND8zs0ML+7sDCfBIR9Z+ULds888wzIQgCJEkK+/rs2bOxYcOGFLUqPofoQ3V9M5bNGhP3eTZn+t3lGUqn8JiNGswrNuODKNPY5xWbuRH1IOb0+IJ3yN1ef9znpuvd2MGeVeaTBnItBQZ/RrtiZoceuZYOxIwynycxn0REvStlg2cNDQ1hf1YoFBg2bBh0Ol23XmfNmjV4/vnnsXfvXuj1esydOxdr167FGWec0ZvNBXCyk6BVxZ+wl6FN2a81qqF2Co/JoMHDS6fg9s3bwzoT84rNWLt0yqDuRA11rSEdeZ1aGfe56Xg3dihklfmkZGupQRM/w6kwFDLaFTM79LSyvztgMJ9ERP0nZVVvzJj4d7OS9f777+Omm27CjBkz4PV6ceedd+KCCy7A7t27kZGR0Ss/Q2bQBjryOw7ZUGkxB4/yDlVZbIZOnT5bySU6hWfdsrJBWVjzs/VYt6wMVruIdpcHmTo1zMbBffeRTl5sGzRKDDNqUWnJi9irBQjkNN3uxg6lrDKfQ9fhVid0mkCN3HukDZXF5qibXVcWm6FUCP3dvLiGUka7YmaHFrmWsr87MDCfRET9o18Hzx5//PGkn3vLLbck9bw33ngj7M8bN27E8OHDsW3bNsybN69b7YvH5hChUylRbsmDRiXgnktLce/Lu7A15MK8wpKHey6ZCIUU54X62VA+hcdkYMdhKLE5RCgAlFvyMLMoFw7RgxvPtcAPhO3ZUmkx48Elk9LuszHUssp8Dj02h4i7X9qJ2xeWYH7JMFQWD8O0whz4JSkio6vOtUCrTJ8Lc2DoZbQrZnZoCK2lifq7Avu7aYP5JCLqe/06ePab3/wmqecJgpD04FlXNpsNAJCbmxvzOW63G273yU1O29raEr5uq8ODbftP4Jb5xcjUqfCrf36JleVFuG1hCewuH4w6FY61ufCrf+7Fzy4s6VHb+8JQP4VnqO19MVj0JKNWu4ijbe5gRh/955eYWpCN2y4K5NEh+qBSCNh33A6/lEY9/k5DNavM6MDV3Zxa7SJKRmVh+4FW3HvJRNz/yi5MjpHRjxqasWjSqD5tf3cN1YwCzOlAdSq19M5FEwAgbn/3pxewv5tqzObA15OcElFq9OvgWdd9znqbJEm49dZbUVFRgUmTJsV83po1a3Dfffd167U7RC/ufWU3/n7DHAgAlp5VgA01DWF3y8steVhZXgSX19fTt9DrhvIpPENx74vBoicZlTvORq0KAoCrZo3BxpoG/OatuuBz0jGjsqGYVWZ0YOtuTttcHpQVZGPHIRtOy3HiyjgZHZ6pQ4fb2xfN7rGhmFGAOR3ITqWW6tRKuD2+uP1d0Zc+tXQo5pPZHBx6klMiSo20WBMhSVLEqZs9sWrVKmzfvh2bNm2K+7w77rgDNpst+N+BAwcSvnaH6IND9HVedAvY2KUjAQSWhm2saYCA9NmnRT6FJ5rBfApPor0vbA4xRS2jZPQko1k6NRyegZdR2VDLKjM68HU3p1k6NdxePyafZoJGpYybUZNBnXYXu0MtowBzOtCdSi11iEnUUiF9aulQyyezOXj0JKdElBopHTz785//jMmTJ0Ov10Ov12PKlCn4y1/+0qPXuvnmm/Hyyy/j3XffxejRo+M+V6vVIisrK+y/RLJ0gUl6Hq8EP6SIjoSspr4Z6bQgTD6Fp2uHYrCfwpPM3heUvnqSUb1GCZNePeAyKhtqWWVGB77u5tRs1CBbHxhAUymFuBnVKBVpd7E71DIKMKcD3anUUofoS1hL06mYDrV8MpuDR09ySkSpkbLTNn/9619j9erVWLVqFcrLyyFJEmpqavD9738fVqsVP/7xj5N6HUmScPPNN+OFF17Ae++9h6Kioj5pr0apQKXFDINWiRMd8QuSQ0yvpSZD8RSeobr3xVAW+GyrIAgYcBmVDaWsMqNDj8mgwegcPSQALR3x/36dHl9afu6HUkYB5nQokmupSqFAc4c77nMDs9PSx1DKJ7NJRNT/UjZ4tm7dOvzud7/DNddcE/zakiVLMHHiRNx7771JD57ddNNNePbZZ/HSSy8hMzMTR44cAQCYTCbo9b233r+5Q8TKirFQKQQoFfGnqWfr069ID7VTeIbi3hdDnc3pxY1/3Yb/d/0ctCviD44l+nyk0lDJKjM6NFntbgzP1MJqj39RbtSmrHuS0FDJKMCcDkXBWnrDHKgS9HczdemX06GST2aTiKj/pazqNTU1Ye7cuRFfnzt3LpqampJ+nd/97ncAgHPOOSfs6xs3bsSKFStOpYlhjFoVrtnwCZ68+iwUmg1YUDIMJfkmlBVkw+31Q6dW4vPGFuxtaku7pSZDkbz3xQdRprQPxr0vKLC02moX8fbeI7hw4sjYGT1sg0aZFts9DmnM6NBk0Kiw7I8f4e/fn4PzSoZhQow6atAoU91UAnM6FAVr6R7W0nTGbBIR9b+UVT2LxYL/9//+X8TX//a3v6G4uDjp15EPG+j6X28OnAFAToYGc8blQoKElg4X7lpcivLT85CboUFBrgEqhYDy0824e3HpkLjjle6G2t4XFMhohSUPANDS4cbqxaVYNGkkhmVqkZuhgUmvRmGOHnctLkW7i3uBpBozOjTlZGgwdbQJx21O3HPJxJh11OtPr+VgQxVzOvR0raWx+rurL5mIDjdraaowm0RE/U+QeuOYy2744osvcOaZZ+L555/Hd77zHSxYsADl5eUQBAFbt27Fv/71L/y///f/cPnll/dbm9ra2mAymWCz2eJu0tjY3AGHxwdIgM8vweHxweuTUPO1FRu2NsAh+lBhycPD35qC0bmGfms/xWZziENi74vBLtmMHmzugB8Iy2lzhwifX8K2zhkt1887HSOztCjMy+i/N0AxMaODR7I5PdDcAYfXB9Hjh0qpwIkOEaLXj22NLdiwtQHTCrPx0OWTMYYZTRvM6eDQ3YxK/vj9XeY09ZjNwSfZnBJR/+v3wTOFQoGysjJcd911mDVrFn79619jz549kCQJpaWl+MlPfoKysrL+bFJS/0gdPOHA3S/vxFWzxkQc211uycPK8iLcsqkWDtGHymIz1i8rY/Ei6iVJD56dcODel3fiyjg5fe6TRtx18QSMNRv7o+lEQ0aytTRRRm/ZVIuzxuSwjhL1smQyerjVibtf3JEwo+zvEvUNDp4Rpa9+3/OspqYGGzZswO233w6Px4NvfetbeOSRRzB//vz+bkrSbA4Rdzy/HVMLcyI6EgCCf66qKML6d+pR3XlENDsTRP0n2ZyWFebA6fGnoolEQ1p3aynrKFH/sjlE3LZ5O6YWZLO/S0RE1EW/73k2Z84c/PGPf8SRI0fwu9/9DgcPHsT555+P008/HQ899BAOHjzY301KyGoXUV3fjGmFOREdCVlNfTPKCrKDf+YR0UT961i7O+mc2l3xT+Mkot7X3Vpqc7KOEvUnq11EdZ2V/V0iIqIoUnZggF6vx/Lly/Hee+/hq6++wrJly/Dkk0+iqKgIixYtSlWzompzeWDQKGFQxz/9y+09OZuFR0QT9a9WZ/I5NWh5kh9Rf+tuLeWJm0T9i/1dIiKi2Pp92WY0p59+Om6//XYUFBTg5z//Od58881UNylMlk6Nqooi+CQJBo0SVRVFEUd2b9jaAK0qMBZZySOiifpdplaVVE5NejX0CS4MiKj3daeWVhaboVQIqW4y0ZDC/i4REVFs/X5gQFfvv/8+NmzYgM2bN0OpVOI73/kOrr32WsyePbvf2pBoY0abQ8Suw234dP8JzBybi/Xv1kdsoHrzucX4+JtmfNpwAg9cNglF3IycqNcks3lqY3MHDrY4E+Y026CGIABnjOQmrES9qbdq6YETHZg+NhcSgHHDWEuJegv7u0TpjwcGEKWvlMw8O3DgAJ555hk888wzaGhowNy5c7Fu3Tp85zvfQUZG+h15bTJooFEpIEnAE106EkBg/weFIODnC0sw/4zh8PlTOh6ZNPl46zaXB1l6NcwZPN6aBq4O0QuVMnFOH7hsYtiSk3THnNJgkWwtfeiySXB6vBiZpU9RS7uHGaXBojv93Uun5sPjZy0lIqKho98Hz84//3y8++67GDZsGK655hpUVVXhjDPO6O9mdFuGVoXJp5nw2Nt1UR+vrrMCiwTss9pxZshGqunqcKsTt23eHmh3p8piM9ZcPhmjcw0pbBlRz9jdPqgUAqaMzo6b0w63DznatFixnlC0nM4rNuPhpVOQnx05sMCLA0p3ydTSdrcX2VrVgPjsdjejMmaV0lWy/d3j7W6MztL1c+u6x+YQ0dwhQgJw70s7UR0yGJhMTomIiEL1+xWkXq/H5s2bsXjxYiiVA2ffIXl/h3haOkScOTobDrcIIP1m0Mnko8hDO/tAoEN0+/PbsXbpFJyWwwE0GlhMOjVEnw8ZCTYZP9rmQsYAWGYSK6cf1Flx++btWLesLOxiu6cX8UT9KZlaanN4YNSk/wB3dzMqY1YpnemSyKjV7sYzNQ1YvXhiP7SoZ+ScTS3IRm1jS8QsukQ5JSIi6qrfT9t8+eWXsWTJkgE1cAYArQ4Ro0zxO7UGjQqHbU5katO7CMtHkUeztb4ZjSccsDnEfm4V0anRqARAEhIumx6WqcUDW3bhaJurn1qWPJtDxNfH7KhtbEFTmytmTj+os8JqF8O+L95FPPNM6aI7tbSp1dlPreqZeLW0a0ZlzCqlu5YkMpqpVWPZrDFpXUvvfmknphZk48KJIyIGzmSxckpERBRNvw+eDVQGjQo+v4RyS17Ux8steVAqBDzxbj08ab7nWZvLE/dxQRDQ3NH7nYnQgYGvj9t5kUC9qsUhQqkU8OG+5pg5rSw2Y+dBGyaPzkZLH3zGT8XhVidWbarFeb9+H5c/8SH2He+I+/z2kBz35CJexlxSf0qmlioUwPp36+H0+Pq5dd3TkiAr7VFq7alkFWBeqe/p1MqkMvqnmoa0rKUA0Nwh4sqZhahtbMGBE/EH4aPltDuYSSKioSP910WkgaZWJyRJQrPdjesqxuHiyaMwIksXPLr7iM2JUSY9nB4vtjW2pv1m5MYE+z35/FKvH3rAfWGor+XoNThuF7FhawN+e9W0qDmdNS4P//Xkv7Fx5QzY3d5UNzkodDaKQaNEVUURCnIT3PnXqYP/P9GAeKyLg95ePsa8UjxyLT2RoJYeaXOhpr45rQfPbA4RKmX8+4+hGZX1NKtA7+WVOaV4jFoVGo53YGV5EbQqBUrzTSgryIbb60e2QQ2jVoXj7W5U1zfjZwtL0qqWyrx+CRtrGlDb2IofLxgf97nRcpqs3qyhzCURUfrj4FkCNoeI9746jrd2H8FPLhiPo21uvLajCVtDpoBXWvKwsqIIPr+EqooiHGp1YpRJl5ZF73CrE59904JyS17Uaezlljz8e18zFk8e1Ws/M533hWFnZXCwOUT8e18zxpgDew1KkKLmdGZRLr47ZwyOtbkxzpw++xLKs1EMGiXWX1WGDVsbACBmTucVm2E2nvycZiXo/Ee7OOhpLmPpjbwyj4NXaC39nwtL0GRzxqyl5ozA37lTTN/BsxaHB7sP22JmtLJLRmU9ySrQe3nlABzFY3OI+HhfMyaNzsZ3n/4YTy2fgV+9uRfr36kPPqfckof7L50Eg0aZdrVU5vdLqG1sxfqrylB3tD3pWtodvVlD+7K/y6wSEfUeDp4lYLWLGJ6pxUf7TkCrUuHprXsjCnB1fTMkACvLi1DWedKm1S6mXXGyOUTc9o/t2NbYgseXlQFA2HuptORheXkRbtlUi/lnDO+1n5vMMpWuv6vevrCPhps2Dx5Wu4jhWTp80nACqxeX4umtDVFzet/Lu/A/F5XgeLsbHl/6LK+WZ6NcP28cNm5twNb6Znze2Bo1p/OKzVi7dErY599s1GBesRkfRMlZrIuDnuQylt7IK/M4uIXWUoNaGTOjEoB7Lg1sQp6Rpqfi2hwiVr+4A9tiZLSy2Iz7Lp0Y9TPfk6wCvZPXdBuAo/RjtYsYlqmDRqnAreePxyNvRvZ5a+qbce8ru1BVUQQAaVVLZQ7RG6ynsXIarZZ2R2/V0L7s7zKrRES9i3ueJdDm8sDt9aOqogjWdnfMTUe31jdDEAQAwN4jbae8h0JfONbuRnW9FQ7Rh1s21aKsMAdPL5+OJ66ehqeXT8ddi0txy6ZaOEQfTPqeT2PvqifLVE51X5hEuGnz4CLnVBCAsoLsmDmtrm+GIAQy6hDTZ6mJPBulwmJGdWfbo+X0jR9WYt2yMozq0uk1GTR4eOkUzCs2h3093sXBqSwf66o39nFiHge30Fra7vbGraUu0YdKixkqhdDPrUyO1S6iur45Zi29Y2EJVEL0tvckq0Dv5LU36iqzOri1uTzIzlDjcIsTZxbkxK6ldVbMHZeXdrVUZtJrgvU0Vk7vXzIxopZ2R2/V0L7q7zKrRES9Lz1v66aRLJ0aLQ4PphfmJHyuy+PDmFwDRmRpYdCk36+21XmykDtEX9g0fAB47vrZAIA135oMvyShtrGlV6Z492SZSm9e2EfTm7NuKPXknE49LRvtCfZfcYl+nD1+WFplVJ6N4u1yB79rTp//wRwAwNfH7BFLMPKz9Vi3rAxWu4h2lweZOjXMxtjZ7enysWhONa/M4+AXWksTLcd0iD7cdK4FGmV6Dp6Fft6j1dInv3sWzhhhjJpTAN3OKtA7ee2vAThmdeAy6dVw+/wwmFQ40OKI+1ylQki7WiozGzWw2t3BP0fL6T++PwdjzT1f1thbNbSv+rvMKhFR70u/ipdmzEYNjtvdMGiUaHfFvyjP1qvx1p6jmDE2F8Mydf3UwuRlaJRxHzdqldiwYgZ++049HtiyG1UVgWWo31g7UJATGBTsSaHtyTKV3rywj6avB+eof8k5zdAoE25erFAANqc3rTIqz0Zp7nDHfI5Bo0SOQYNVm2qDHWKDRonVi0sxrTAbDtEX6PgbNTh9uDHhz+zp8rFoTjWvzOPg151amqFV4jtPfoQXbpzbT63rnnifd4NGiaK8DKx+cWdwFmnUnGYkl1NZb+Q1XQbgKH1laFVw2d24/7XdqKoYF/e5HaIPXr+UVrVUZjJoYNTFv8TJ0CqDyxq37W/pdp+3t2poX/V3mVUiot7HwbMETAYNcjPU8PmBLw60YM3lkzE8Sxs8HezzxhZs2NqAs8bkID9bh//7aD/Gj8iE3eXFiKxUtz5chkYV96CALJ0aa1//Mrgn2saahrA7dT3dJ0EeGLh98/awTka8ZSq9eWEfTV8PzlH/Mhk0yDWq4fcDmYIKay6fhOEhp/iF5vSzb1pQkGtIu4zmZ+shen0xM3rXxRNw90vhF+RyTu94fkfwecnmtCe5jOVU88o8Dn7dqaWKziWPbc70Ww4GBD7vlcXmqLM67rp4Au7fsit4EMKp5lTWG3lNlwE4Sl92lxd+Cdh7pB0FuXr89bpZsDk9YRl1iD5UFpuxp8mGIrMx7WqpzJigz2vUqIIDZz3p8/ZWDe2r/i6zSkTU+wRJktJvp89+1tbWBpPJBJvNhqysyB5AbWMLfH4JapUCj76xN3jxCgQK8KpzLVAKAhSKwH4tk08zIS9DgzOTWOrZn2wOEXuOtGPdO3VhnYlySx5unl+MvAwNzv/NB1g134LaxpaYJxP1dPNSeWp8sstUDrc6Y3ZKTmWfCrktN2+qjdlZCX2PPKko9RJlFAD2NrVBEIBWhwfr36mLmtORWTo0nnDA65cGXEZ/vnACLl63Nfi13sppd3MZy6nktTt5jNV+5jP1equWQgA+/LoZ55eOwMR8U3++haQdPOHA7c9vDzsttNySh9sXluCSdTXBr/V2PT3VvJ5qXT2VrIa2n3lNjWQzqlIq8OibeyM+3yvLi7Dp4/34yQVn4Hi7O21rKdB/fd7eqKF90d/tSVaZz/SQTJ+XiFKDM8+SkKVTQxCAe0Jmfchq6puhAHDW2FycN2E4zhk/HG/vPYrLzzwtNY2Nw2TQYEyuAYun5KOqvAhurx9alQLH2t0Ym2tAU5sLQGDD9a57Q8hOZZ+E0O9pc3kAIfLroXqyL0x32pLMHUOeVDRwSAA0SgUe79JRBk7m9N5LJ2JYpg6v7WwacBl1ecL3ieqtnJoMsTPVnY70qeS1p3fwmc+Bpbu1VK+Ov9VAKo3ONWDt0inY3+xAq9MDrUqB2gOtON4evvS6t+tptLz2V07ln9/T2TbMa/oLzejWqBkVsKJ8LI61uTE8K31rKdB/fd7eqKF90d/tblaZTyKixDh4lgSjToUTHWJEZ19WXd+MHy4YD4fbB0EQsLepDebOI7zTzahsPRZNGhlWoKePyYHJoEFH5ybObq8/7mv0dJ+EnhTmeJ2SU5Wos9KXx4dT7+twe+GXlHFP2nR6/HCI3gGZ0a4nbvVVTmX9ndfuXjwwnwNPMrV0ZcU42F0+qBQC0vS8gKDTcgwwalXBz+zlZ54Gf5fJ/IMtp0DPLvSZ14HBbNTgsM0VJ6NW/HBBMZo7RNjdnrSupcDA6vP2RX832awyn0REyeHgWRI63N6Em5DbnB4UmTPg9Hhx18WlaV1kYhVoed8FrUoR9/t7sk9CuhbmeJ0VnlQ0sGRoVQk3Ind6fMjUq3FdZfyNkFMt1ucydF+UvsipLFV57c7FA/M58CRTSw0aJTJ1SgSnJqe5rp9Zm0Mc9DkFun+hz7wODCaDBl8dtcd9jtzf9UlS2tdSgH3eZLLKfBIRJSd+xSAAgbtOWQlO7QGAe17aBYNaBacnPTc5TkSe4n2s3Y1yS17U51QWm6FSCrA5xKiPxxKvMH+2vwWtDg++PmZHbWMLvj5u7/br9wWeVDSw6NVKZCbIqcfrR4ZaiapnPo2YyZXuTAYNfnH5ZFQWmwEAtQdaY+b0VA/VGAh5ZT4HnmRqqc8vQSEI8PsBZ4IZIelIrqPzmNMwzOvAkaVP3N994JXdA7aWyuSaGq/P29OMDqRsAsznYCJJEtrb28EtzYn6BmeeJSFbr4FDjH0KXrklD7UHWlFdb8UJhwj/APv3KnRPBpNejfNKhmPOuDysfmlnWPEvt+Rh+dyxWPi/1Zg+Jqdb+yDEKszySWR3vbgjbJlAOuyzwJOKBg6bQ8SDW3bh54tKY56CV27Jw4f7mjH/jOFwiL4B1Rm0OUQcaXOhqdWFn11Ygh+e58MJh4jLpubjvi27I5aFdPe0zK4GQl6Zz4FHrqWVFjOq66Nn9N/7mrFw0ki88+VRVFqGpaCVPSfXUrvbgwcumwTR64fd7Qnk9JVdYZmpHCI5lTGvA4PNIWLXIVvcOir3d612cUDWUrm/m6FR4T8HW3Giw417Fk/EA1t2ReSlpxkdSNkEmM/BxG6348rHXsNzP1qEzMzMVDeHaNDh4FkSRJ8fh21O3HPJRKx9fQ8m5JtQVpANt9ePbIMao0w6XPP0JwCAY+1ujDNnpLjFyYu1J8PapVOwflkZjrW70XjCASBwB/2WTbVwiL5uTz2PVZirKoqwsaYhYlAy1cs5gb47Ppx6n9Uu4u29x3H1bAceWDIR//66GcOzdHB7/dCplThqc2J6US6W/eEjTOo8vW+gdAYPtzpx2z+2hw02yKeeXfnHj/CTC87A3YtL0eH29tqhGgMhr8znwCPX0vsvm4gHt+yOWUsrLWY8+f4+LJo0KtVNTlqsWnrjuRZ8788f4cqZhVjRZdNyg+bUDkQYCDmVMa8Dg9Uu4jdv1+Ev187CA1t2xe3vHm0PbLg/oGppl4zKtfSqp05mFAAKcw0YnqntcU4GUjYB5nOwUWl5wANRX+HgWRLsbi9G5+jx2Ftf4ubzxuNXb+4NO5mnwpKHdVdNw1V//AgmvRoaZfqsho130k+8PRlu6yziCkHAtX/6LOprd2cfhFiFOdEpR4danbB2iCk5LvtUThWj/iXf5S3M0+Nomxuv7mgKOyms0pKHccOM+O6cMRieqUVlGnUGY2XU5hDR6vBE3KEGEOx4XzmzEHc8vwP/uvVsnFmY02tt6mle+3NfFOZz4JFr6aNv7I1bS/VqBWaPy02rWgrEz2qsWuqTJFw5szBqbmaOzT2lz+lAqqvM68DQ5vLg3ksn4pE39iTs72pVyrSqpUCCjHa5CQWE19LQ9/mvW89OSTZTtbcY80lElBwOniUhS6eG2+vHuOGZeOTNvcFia9AoUVVRhLKCbDTb3dj0vdnINajR4nAneMX+keikn2Q2CO2tfRBiFeZE9p9w4Af/93nKprT3xfHh1Pvku7wKCHj8nbqoGbW7vbigdCQyNEo8uGRSWvwdxsroQ5dNwkcNJ1AyKjPmqWc19c2o6rxL3hfLZu5fMili6XYi/b18h/kcWORaWhRSS0Mz6vb64RS90Ko0uHtxKWxOEUB6zOSON0vb7vbGzEloTrs61bz0tK5+0+zAjX/t/7rKvKa/LJ0aKqUQllEgen9Xq1KkTS0F4vd5212eqEvFgegZTVU2W52p2/uM+Rw85H3PjEYjBGFgHL5DNFBw8CwJRp0KVmtH2B0jed+CjTUNYXeRKi1mPHDZpFQ1NSiZk35aEmxQanMG9kCLpzvT9aMVZn+CDS1H5+hh0ChTOqW9L44Pp95lNmqwYMJwODy+sM5+1IwWm7HqXAuyDeqU/r3Gyuhn+1twsNWJl744hAztmLiv4e7cUL03l83IFyDb9regqqIIK+aOBQAMz9JCTLCBeyqW7zCfA0fXWhqvjt5zaWnCfXj6S7ys7j/hQEeCE0TdMXLTG3kJrautThFujx+aBKcHyqcLpuvJf5Q6Rp0Kx+3u5Pq7aVJLgfh93rtf2okb5p0e9/u7ZrQ3s3mkzYWDLU4ASNindnv8sDlSd7Il8zk4+EQnqv7wHv7+k0u47xlRL+PgWRI63F6Y9Go0d5wcbIq1b0F1vRWrX9qJ9SncqwtIfOz0iQ4x4YWwQaOE2ajB+ROG44xRWcGZATq1Ep83tuDLpraI6frxlokCkYXZ5hBj7rNQbsnDjoM2VFUUYf079VGntCf6eTQ0mAyBmSpH207O+oyZ0Tor/JKEX1w2OS0zWlVRhPXv1qOmvhkrY8xYkWlVCpw/YTiMOhW+PmZPmINEeel6ARJ6oVRuyUNVeVHMg1OS2ReFeR3a5Fp6ovPGTbw6et/Lu3D/ktTfiALiZ3XdO3UxZ5bJtCpFxAy7HIMaxhgnj3Y3J/Jj976yC9V1Vqyab0l4wJGsa11lRoe2DrcX2Xo1TiTT302TWgrE7/OWjMqCxxe/vysPKBs0SqxeXAq/JKG2sSXqdifdzceDr+4Jti1RNj/c14wRWbq4r8mMUjJUWkOqm0A0KHHwLAk2pwcmnRo5hsAdI4NGiXPGD4u5b0F1CvctkCVabmkXvfhwX3PMIl5ZbIZSIcBk0GD14lLc8cIObNjaEOz8lxVk45Ip4Zs5R9vY/PwJw3HvpRPh8vijFnqTQYP7l0zCnS/uCGuHvInrLZtqsW5ZWfDroVPpEy1LpaHF7vYiUx/4Jy1RRmvqm9Ehxp8t0tdiZTT0jn/tgda4He0THSJWLy7FT//+n7AcRMudTqXAPS/vwtt7jgWfV1lsxr2XToQAIC9Dg+aOkxcgXS/2dWolhmdqcG1FYKCgppunkjGvZHN6kK1XozAn0KmfVpgTu47WN8Pp8fVn82JKlNWywpzYtdRiRrPdHTFzR75In1aYDYfoC9ZGh+jDz7rkpGtOo91AOuEQsbK8CFMLsvHcJ414eOkUAIhZV0PJdZUZpe72d9OhlgLx+7xlBdlx+7vygLJBo8SGFTPw23fqccfzO4I1cO64PGhUCmTp1NjW2IIHtuyGQwz82xQtm6GDW3qNElMLsrFtfwscog8btjbg8c4+baxsLigZHvO9MKNERKnFwbMkZOnUsNrdGJ2tx3klw7Bs1hjYnL2zF1hfSbTcpcMVKOLrryqDAkLESX43nWOBWilgf3MH7nphB2obW6NO25eLtkoh4BtrB5bNKsTKiiJ83tiC5z5pxH/NLMTPNm+PuNAOLfQ2p4iywhxUhZxEFnqyZ+h0enkqfTLLUnknbmjpcPtgNmqSzqjc+U2VWBkN/bw/90kj/nrdbDywZVeXAxDMuP+yidCqlBE5MGiUUXNXacnDyooifPh1c/C9V3cuaSkrzMH2A62459KJwRMAoy+ny0NVxTjMGJuLqvIimPRq5Bg0CfdFYV4JAIxaFdxeP7YfbMX8kmEwqOOfNpnqjMoSZTVWTisseVh9SSkOtzrxdPW+qEvK73h+R/D5lcVmrL64FNdVjMPUgmxs2NoAh+iLyKlcP2OdHijvtXTlzMJgXR2do8c/dx8N1tVQmTo1M0oAetbfTYecxuvzur3+mP3dSkse7rl0ItpdHlwxbTTuenEnquutcZaU5+HFG8vR0NwBtVKBzxtb8NCru1Gab8KXTW1YvbgUd764MyKTjy8rC2bvlk21qKooQlV5EQwaFRyiN6zPG2vJKDNKRJR6g2Lw7IMPPsAjjzyCbdu2oampCS+88AIuu+yyXnt9s1EDt9cPh9eHHy4Yj1++sTfhciqjTpXSqdXxjp0ut+RBpRTgEH3YcciGhZNHYkX52LCBq6o/fYqzCrPxwwXjUV3fjFXzLTGP175t83YsmjQSd7ywM+xnbFgxA2vf2Bv1e0ILvVGrjnlXEzg5nT50WVgyhx2wEzG0ZOpVcPv8cTMaOptKkoCvj9tTtuQhVka1IfsVXTmzEGvf2IMzC3Owssvg8gNbduOW88ZH5CD2UrhmAAKunzcOj71dF/y6vFny+nfqce/Lu1DVObMs1mv4AZQV5uBPH36TdGedeSUA0CgVcPh9eODVPXju+tlod8WfsZLqOipLlNV4OX349T24Zf74sIM/4i2Du2/LLpQV5qC2sSXsgjs0p7dv3o5Hrpga9UI61umBz143K2qdlesqM0rAyf5uhyd+fzedaikQv8+brVfH7e9eur4GZ43JweqLS4MDa/HqqJzR9e/UB2eMqTo3Zb/jhR0R3yP/Wd6CxCH6gll8evn0sBPt421/wIwSEaXeoBg86+jowNSpU7Fy5UosXbq0T35GplaJI+1uOEQvttY348w4yzQWTBgOtVKBVZtqUza1OtZJPwsmDMct5xVDgIBKSx4m5ZvCCneo6vpm3HRuYEP/eMdrV9dZg5uKy2rqm9Hu8kb9/QDhhT7RQF/tgdaIZWG9dQooDR56pQLHO0TY3dEzGutOcqqWPHTNqHwxMsqkQ2WxGdV11mDu3tl7POprXF8ZuQly3KzWW/HDBcVhg2fAyRk01XVW/ODs0+H0+OIu07npHAuunlmYdEedeSUAaHG44fUHZqq0OER8+k1L7DpaMhw6lTKldVQWN6sWc8Kcfv9sS9if42U0dJAMOHnBDZzM6Qd1VrR0xL6Q7np64LxiM8bkGSLqbGhd3WftiPs7YEaHDrm/2zFAaikQvc9r0Chx18UTMDxLi0qLOX5/t84adohWMhmV/z8A/PSCM5L+HllFl/0HE21/wDpKRJR6g2LwbOHChVi4cGGfvX5LhwhBADI0Svh8gcGkWPsWVBabcffiUtz5wo6wO81A/0+t7nq6ZZZeDaUg4OcvnlyG2VXXfY6yOk8GinVamCza491Z2nrnxROwvMUJQRDweWMLNmxtwFljcnDfpRMBAN+rKAr7nSValpqKU/8oxQQBeo0yuISka0Zj3UmWZ08+esVUjMjS9WuT5Yw2d4iQJODel3cG2+2XpJi5k3OaFeXkrkRZ9fojT7gNne2mVSsS7jWl7twAPVnMKwGAQaeCv/PjqVUq4+7/c9fiUqx+MfV1VBaWVQD3vrQzrP3RyDk1asO7WsnW064X3KE5bUsway9Dq8Kq+RZ82dSG+5dMwqgop12HLrdmRgkAmjtEKDv7u84BVEuB8D6vzSnCqFXj/i278OCre2LmNLTPG5rT7vR5a+qbcftCIanv6bqPWqZOhSVT86FUCBH7GXbFjFIikiShvb091c0gGtQGxeBZd7ndbrjdJ0/la2tri/t8hUJATb0Vc083Q+icmh26b8GN805Hfq4+sDm30wOH6MOK8iLsOdIOq10Me63+mlptc4ho7hDh9UvwS4H/fH4JH39zArWNrcH2/7lqZvB7ot1NXDXfggpLXlinPZpojyf6nkydGodbnbj7pZ0o6TzN0+Xxofz0PHznrNEw6WMffx5vtloyp/5ReutuRm0OER9+bcWMolzkZQT+7rtmdHSuARdOHAG7y4dMnQpH21y4bfP24FKIr4/Z4fNL/XrXXM6pIABHbC4smzUGKyvGYcehVswYm4uC3Mi2hOYUQMTMnUS56zro1fX0vWy9JmEnXQDw07//B/ctmZTU74t5HZy6m1OdUonDdicqi83IMqjj1lGnJ/V1FDi5Ib/d7UG2QQOvT8KBFgdWVozD1MYW3L55O37/32dFfF+8nHannsoX5F1zmhXjpE5ZhlaF07L1WDa9AKM6M9r1tOtQzOjg1N2Myv3EuaebIWUEvjaQaunJPi9g7XDjzMIcfN7YGtHfBSL7vKEnYXa3z9vi8CT8nnHDMrDl5gp8vC9wGEqr04M2lxdH21w4Z/ywhP+eMaODV3dzGovdbsfKJ/4JQaWFUhkYSDMajcFrVyI6dUNy8GzNmjW47777knquzSHiYIsTI0x6uLw+CDjZCXaIPrxUewhLp52Gu17cGXYBW2HJw1+vm42rn/ooouPf11Or5QGpK2cWRtwdLLfk4bnrZ0MBAX5IUCsVwSVi0e4mynccj7W54p4mlpuhwYYVM4KzxhyiD7UHWlFpMYdtziqbV2yGUafCnS/sCLYzdLp7hSUPD38r9vT1WMtSkzn1j9JfdzIKAMfa3YGMenxQKRWotOShuktGf97lRNeuGfVKEu55aScevWJqv3x+Drc6cfeLO3HV7EJs3NoQNsOm0pKH6yrH4Xi7OyJDoTkNnUEqv7faA62osOSFbVwuK7fkRfw59PS90A54vKXUXx1tx3/NLEz698W8Dk49qaUen4R7LpkIj9cfrCk9qaOJZjb3BnlD/m37A/uP/fLNL7scwmHGs9+bBaVwso7K4uU03im6lRYzgMCFvUP0QatSRM1pTkb87Q7e3HUEtY0tKDJnwKhTMaNDVHdrqU+Sgv1dpVIYOLU0Rp+30mLGc9fPRnXdcWhUJ/sGQOS+ZqEz7OJnNA87DtnCvub1SXFr77xiM3IMGjRYO7BlR1NEv7zInAGDRhn398WMDl7dzWk8Kq0OPp8En+hC1R/ew99/cgkyMzN75bWJCBAkSYpcwzOACYKQ8MCAaCP8BQUFsNlsyMrKCnvu18fs+OpYOzI0KujVCmTq1TjY4gwW3JdXlUfdFB8IdCjuvXQSXvziUHBACQD+devZOH24sXfecCf57rhPkvDAK7swtXPD4a7tMmiUeHr5dPz23XpsrW8O3nl7pqYBK8uLou4HYdAoccPZ43DJlHzc89KuLicVmbG8fGxwU+PQTv7ccXm4fdEE3PfKrog9a9YunQKnx4fnaw9FbScQWAK7PsHSHPl9R1uGQgNXdzIKAJ9+cwJO0Yedh1sxv2QEFBBw35ZdSWX0ZxeV4Mo/fIQ/XjMdTo8P48wZGDesd/Mp65rTs8bmYts3JyKWphk0SmxYPgN/rP4ay2aNCevgd91guOtS66I8A9w+CY+8uTeig37z/GKMzNTiaLsbXr+Ef+9rDv7bVFlsxi+XTgnOUjnc6ozopIfmu6wwG2WFObj8zNOS/veMeR1celpLh2dqIAkCDnXW0tsuKul2HX3jR5UoGRn5b0FvsDlEtDo8uKtz2eiq+Za49fSp6n3dyqnXL6F4uBH3vLwr4lS+leVFePbj/bhq1hg893Ejbr1gPF7beST43uX6OarztM14GZVr8i8um4wx5oyk3zszOnj0tJZqVQqYDOq0raVH21xo6QgMqD/06u6Efd4n3q3HtpBT42vqmyMyKj+/qqII0wtzUJBrwL2vRGZ01bnFcHl8uOnZz4MZKyvMCZ62edeLO6MObnm8ftzxYuSBAvLrJptTZnTw6W5OY2lvb8eVj70a3GJIpdVj06rzOHhG1IuG5MwzrVYLrVab1HPtbg9G5+ihFARoVUoct7uw6eP9KCvMwfcqxkGtVIQVwq4Xsh6fH/kmHX571TTc9OznOGtMTq9PrT7c6sRt/9iO6nornl4+HdX1zVgRsuFwqKqKIqx/tz7Y5tDp+F33ZZE5RB9+81Ydpp6Wjf+56AystBfB5fHBpFfD5fHh5s5OOnBy9svqxaWYMy4POQY11sfYZ6W2sSXhQQSJlubEW4ZCA1d3MgoE9mfRq5WQJMCgVuLjfc24ePKoqBkFwnPq8vjx9+/PgU6lxLb9JzA2z9Dbbwc2h4gWhye4h5Oc0x8uGB+xeT8QyOm6d+tQU9+Mf+87ETzW3u31R+Q09OQuAHji6mn46d//E/Y9Jr0aSoWAgmw97nllFz78ujn4/h+9Yiq0KgWOtbvDlnTmZ+vxwJJJqD9uDzuZrOvpf92ZScu8Di49rqVqJY62BWrpzKLchBk90SFi7ul5uKB0BKqe+RQlIzOhVcZfItVTB084cMfz27GivCg4qB2rToXW0+7m9JkVM/CLyybhQIsTrU5PRL5Erx+3XVSCt/YcDeY0W6/G6cONwf2k5D2eDttcaLB2RLwGEKjJHWL8/dFCMaODS09rqdfnh16txCenUEvH5PZ+LQWA/c0duOuF8FqabJ9X7u9WlRchQxPZ5w3N6fM/mINFk0Zhxdzw0zmv/dOnKCvMRlVFEf7T2Ip7l0yEACCvc2/eWHsL7j5si3mIVndyyowOPt3NabLkPdC4dJOo9wzJwbPuyNZr8Nn+E4FjrF/ciW+aHXimaiZ+8epunFWYg3anJzgz67yS4TBoVLjnpZ1hRTxwp8qC+y4pxYyiPDhFH0y91KewOcTgwBlwcn+UWBuXRrsIkDsLZQXZcX+WUadC1TOf4sqZhZg7Lg8KQYBCEFBVURQ2I6Cmvhl3Ly6FX5Kwz9qBLH2g89B1dkqWTo0mmyvuz+TpQZSMTK0KLp8f554xHPe/sgvfnTMWerUCEoB2pwdmowZrl07B8CwtHG4fhmVqI3JaacnDjedagkfO95bDrU68/9VxbNl+ONhxlvMZbfN+IDynXS+6n14+Per3yBcxBbl6PHrFVOjUyrBl1G/9eB7cPj/e3nMMAKJeaMwqygUAWO0i2lweeP1SzNPJ5Peh0yhhc/TP/lM0cEWrpX+qmomjbS60dS7BlHM60qSFVqXCfS/vjFjO/+eqmVCrFHB7fTjc6uzVfZUOtThw2/PbUVPfjGWzxgS/LvoS19Pu5nSESYdDNhdcXj92N7WF1VAA2FrfjCq7iN+8FT64/q9bz8aIzokIwf3YXF7c+NfPY76vDrcPXx+3w5xgQ3IiuZYa1Er88vU9uOEcC1yiDxKksP7uuWcMh0IAdOrInAZrqUJAU6szOJu5Nxw84cDPX9gRUUuT7fOG5jRRRnUaFUwGNQRBiMho4JCAEqgUAtSCAI9f7u+KMGdE9ncBoCMk39E4EjxO1F1cuknU+wbF4Jndbkd9/cni2NDQgC+++AK5ubkoLCw8pdcWfX7kZWjh9PiDU74ffXMvfnz+eNhdXmQZ1PjtVdOgUyuw/aANr3bZywA4ORvr7sWleHDLbpxXOgKLJo3slU7ssXZ32DJKecPSWBuXxju9D0DEvi3yY6svnoAMrQrPXT8nSkfJjGe/NxvX/enT4L40B1ucYRfd0Y4wNxs1ONqWmtOD5IuOts5TSFNxUZEObRgsfP7AgRgSgA/3ncDVs8dCoRDg9UnINqrx1+tm4/7OpSexlmBV1zfDD2Dx5HzotapeOS3M5hBx2+btWDF3bMSm/gaNEpkxZnt2N6dmowZPL5+BR9/cG3ERs/6qMqx6thYdbi/irdE3aJSQAKzaVBt87VgXFzKTXo0t25uw/UBrRL6TlW45SLf2DBbRamltYwte39GEn1xYArNRE8xpWYwlWFvrm7Hm9b24ZMooTDzN1KunbtocIvY3OyI29TdolBidE/1zHS2n8oX3sExtxH6FZqMGTy2fgV9FyemLN5Xj9Z1NePL9fcGLaFeUU2/lG0ryfmzVdVb84/tz4r43QQDO+9X7UetwT6RbRtKtPQOdzy/BK/lx+VkFeOztr7BizlhoVAqYMk72d9e+sTdmTkNrKQT0Wn/X5hDReMIRUUsBQKeOfvpzvBMwdxyyRdRSg0aJ9VeVYePWhoh+bteMHmxxYtJpJtzZ5VTgWDnLjnI6dihTgsd7Il2ykS7tGIpU2r6ZAUo0VA2KwbPPPvsM5557bvDPt956KwBg+fLleOaZZ07ptdtdHuRlatDm9OCGs8fhWJsL/zW9EAa1Cmte34s1l0/GEZsTW3Y0oaq8KO6UdpvTi6tnj4HZqEVzR+/M1GjtsmmyvMlpYLP+vLCCbtAoMTwzclqwvO/ZXz/ej+Vzx8IvScH3EbpfxCGbK9hRCt0XwmRQQ60Q8Lv/PgsOtxef7m9BjkGNDStmQKUQkJuhgcfnx6GWDgCAU/QFC2hhrgHnlQzDv/Yej2hXhSUPxgQniiUr9MQ0k16D1S/uDLuo6a2LimSFXvikqg2DSYfHhxMdIgwaJdYtK4NOrUCuQYvVL+/Ew5dPxu0hd6qjzb4MzWmGVoWWDhE6leKUMyqfPrZsZvggfu2BVtx18QQIQvgpfKGzx7qKlVODRomNK2bg4S570Rg0SkwtzIFBrcTfvz8HCkFAh9uLP62cgdoDrRAEYFK+KTB7TK2EJElY8+pubNvfglXzLSgryIZaqcCz35uFD79ujpgZU2HJQ6ZOFfx6TwYy5CVyyVx49Afmsu/ItbTd5Q3W0imjs9HQ7IBOpcATV08LDnBXRVmC1TWjCkHAtDE5vVZLrXYxrJ7KtbSsMAc7DtqSymno6X3y5uN+nMzphhUzIvaMMmiUmF6UC6/PjwUlIzBnnBkqhYAP6o5jTJ4eT1w9DXq1En5JgrJzhkvd0XZ8caAVZ43JwYq5Y+H0+LDpe7NQEyWnlRYztnbWug/qrKc04Nh1+bmMmR087KIPVrsbw7O0ONbmwjWzx2J0jgGrX94Z1t+NltOu25YU5hogSeizjAIncypJUsRgdaw+r/zY1NHZmF2UG9bnvX7euIgDfABgW2MLvmhswaJJo7CgZATa3V5k69XYfrAV2xpbI977/uYOqBQCtCpFcNAoQ6vCmssn44FXd0fMMqu0mJHVi4Nn6ZRVZpSIBpNBMXh2zjnnoK/OPTBoVWjp8CBbr8LCiaPw8Bt7cNWsMejweFHb2IoO0YfhWTrU1Dfj6pBlHkDkMdiySosZ9y6Z2Cvty9CE32177pNGPLV8Btb/qw6rF08MbvQq3/Hec9gWcYJQ6IlDH3XZt2VsngFrXtsTtqeE/L6e/Xg/zizIxqP//BK1ja3BjkNZQTb0GhU+qLPiDx8E7tDNLxmG2y6agJ/94z9hJxFVFptx76UTIWAP3t57LPj1ckseVpQXocOd/F4tsYQW7lizjk71oqI75NlIXWf49WcbBhuH2weFAORmaPC7d+txZWdGa+qbYe/cn0vW9U50rJz2RueurXOWiDzTLHTD8DG5Bry5+whWnWsBAHx5pD04KwVA0jnNzQgMSHS9IA/N6Lp368MyOvk0E0aadHhr99FgRistZlRVFOGq2WPw9NbI028fX1YW3Eup0mLGTy88A1XPfBq8CPggiT0KQ4UukQuVqhwwl31LrqU5GepgLR1nNgb2SvJ6kalTRyzFCn5vzFqah0um5vdK+9pcnrDZZmqlgHsWT4TV7sZ1f/4Mjy8rg1alwNSCbCycOAoPbNkFIDynXU/vC91faVimFvYoOZVn8jz02p6IjCoEBb462o4/fLAP0wqzsaK8CMs3fAIgMCv05S8Ohe2ZGJnTPKysGItVz9YGn9PdnMqiLT8PfU1mdnBod3qgUgAahQJv7zka7O8G9uQ62d8FwnPaH/3d0IzKP1PO6YkON35y4RmQIOHzxlbccPY4LJw4Ctv2n4h6amZVRRGe2rovmLmTfd6MiH1IQ3N67yu7wm4gzx2Xhw0rZsCoVWH7wdbgHsDyYPlv36mPOGRrw/LpqPrTZ8HaKee0N/q7QHpllRklosFmUAye9SWtSoFsvQpGrQo/f2EHzhqbi2dqGnDL/GL89qpp6HB74fVLWDXfErwLLRfVC0pH4JEoJxNV11tx78u7Ep4kmYwMjSrYMTBolHh46RQ8/q+vUJpvQptTRFV5EX6+qARGjRo1X1tRkG3Ag0smocnmQqvTA51aiSydKjgoFnrnTKdWwiH6gnet5I7S9fPG4ZmaBpxZmIONNQ2oDTnBqOuFjdyRnzI6Gw9u2RVxhHd1nRV3v7QTVeVFWDarMGJj8mevmwUgfMq3UauCRqlAq1OEURd/+ndo4TZolLigdATKCrJxzZyxyDfp4fb6cNzuhlYV2B+qt+6QxiPPRoqmpxc2/Skdp99n6lXQqQIzCqd2fi5vPrcYBo0yuMyp62yRRDntjc5dVuey452HbXh6+XSsf7c+mJHf/fc0/OXf+zH/jBG47cISZOvVONzmxO2LSqAUFFg8ZRQOtTghCAI+b2zBtMIcbNjaEJwV5vb6IQgCfH4JrZ170cj5zc3Q4Nf//DL4u4idUTNevLEcDc0dUCsV0GsUWP9OfcTvYmt9c+Ak4xvnwu31Q/T60WRz4cnvTodaKaDJ5oJaqYA/yZsYXZfIhf59yO/tSHtgPyi7y9svn7WBnstQ6ZhRuZZmqJXBWuqTJHj9EjLUajS1uaLO6JJnkz4TMiglq65v7rVamqVT4197j2F+yTBc1Xly5pPv78Pv//ssAMCXR9pw56JSfL7/BD7bfwLXVZ4Oc6YmLKexaqkgCBC9/uCsmdB/e3YdsmHLjqa4dXT9VWXYccgGvVqJ3//3WcgxaLD2jT0R9XRrfTMECHj+B3NxqNWJ2gOt+Nk/tkfUdb8kdeszErr8vLaxNezfoAyNCl5/4D1+dcyO3AxNv33eBlNmgfTIbaZeDaNGGdbfXdVZS0P7u2UF2RjWOasrfkZ7r78rZ7TckheWlyff34c/fPcs1NQfx50Xl0KtVKB2/wl8caAFE0ZlYs7pZhxudQb7vJ83tqD89Dxs2NoQkQ27O3xmm/zeQmfcxRvMl+tpvkkfNaOBgTQJz10/GwdbnDColfBJEgABVrsICYG9CQH06LMQK6tevxTW521qC+w3zP5uctIhm0SUHjh4loDHF9gQ2y76sOdIO+5YVIrH3q7D6sWl+Ka5A/k52cjSqfHnf38DAGEd77KC7Iip37LqOiuaO4/ZPpV/kLMNatw8vxgAUNZ5oSzPTLl0Sj5+8686zCzKxaJJo4J3ER94dTdK800oK8iGxxc4FWzDihkwqJXwQ8Kn35yAJAGTTzPB6zt5MSzPnjlvwnA89nYdVnbORFs13xJ2tz34Hjv3vbh+3rjg90RTU9+MayvGoeqZTyMey9Kro075LrfkYWV5EZb98WNMH5MTc4ZQc4eIqQXZWFleBIM60PnbediGyaeZ8PAbe8LaXG7Jw+VlpyX9u++ptgSHIKTzIQnpOv1ep1TA6fGjur4Z11aOw/p36vHjBeNRVVGETJ06rLMLJJ/Tz/a3oNXh6XFGzUYN5hWbIUnAE++GD0rpVYHB7off2INySx4WThqFP3ywD9+dPRbH210oGZWFYZlaOEQfzp8wAnq1Ar+9ahr+8tE3ANB5upkPGRolDOrwznzXE8hiZ9SK+zr3mFr/Tj3+et2siM6+bNv+FmiUCty/ZXdEblaWF2FV52nCa+N8FuQO6AmHiEydCqvmW7Bha+DvJLT98t/X/TXhP6svP2sDOZeh0jWjHp8fhs5auq2xFT9bWII3dx3Fokkj4fVLyNKpwjIaeoGsUyv7vJaajRp82dSGHy0YH7a00u+X8PiyMhxrc+Gh13bj9osm4OE39uC7s8fiPwdakW/SIzdDA5VSAYfbi40rZmBYpga7D7eFvb58Yyj036KyguzgTJ54dRQQsHDySFz91McAArPOYuW0ut4KCcC1f/os9kV+sRk3nWsJmzka7TMSmteV5UXIy9Dgt1dNw1Nb94XltGu7++vzNlgyC6RHbm0OEcYuGX3s7Tr8aMF43HD2OGTqVcH+rlxX5FranxldWV6Eiye7gp+7wPJMHT78uhlOjx+7D9tw+8IJ+OXrezAscyy+OmpHyagsGHWqhPV0RMgyT/nzrVMrw2bcdZ1hGnyf9c3BeqpSCHEy2owV7W789O//iZqfZPMpCx3Y0WuUmFqQDQmIWELetc/bH5+vwZDRdMgmEaWPvjnrfRCRJOCBLbthd3mwccUMNNmcAAC724uJ+SYYVEo80Lk0csPWBvxowfhgIYy3UWno5tzn/fp9XP7EhzjvV+/j5k21ONzqTLp9JoMGY3INWDwlHxdOHBFW3OX9Y+QTCEvzTXj24/347uyxyDfpoFMqMGFkJnx+CXq1EkadCqLHjznj8rDtmxO4eVMtVMqTJw/WHmjF6otLcazNDeDkTLSyguy4x29XWMzB74n3+5A3Q5fNKzYjQ6uKOuW7pr4ZG2sCdw3lGUI2hxjxukqFgDnj8uDy+ODw+FDzdTNGZemwcWtkx6emcxZDtNfpTVkJDkHoq0MSTlWi6fd9/XuLx+P3o90VOFUzo3MT/q31VlScngdj54EXci6Tzanccb7rxR09zqjJoMHDS6dg5tjciAsLP6RgG84vHYm7XtyJqQXZMGoVmHSaCY+//RX+ufsoXKIPfkmCxxcYYLhj4QR80diCa//0GX769/9AqRDgk6SwDnjXE8gSZVQ+adfmjN2Rraoowt0v7YyaGzmL1XE+C02tTry28wi+ae7A8XY3jttF5Jt0+O1V03DD2ePC2h/r4qQvP2sDNZeh0jmjQOA0uTanBzedezqOtbk7B04FOL0+qFSKsIyuLC/CXZ25jfe57M1aunpxKTrcJ5dWGjRK5Bo12FjTgBFZOpTmm/DAll2YWpANg0YBlUJAfrYeT33wNY61uWDQKjtrmQqv/OdwMKOfN7YgW68O7nUY+m9PMhmtrreGHWASr28R+D17Ma/YHPsiv86Kde/UoaqiKPi1rp+RrnkFgN1NbTBoFKhtbAWQmpyGGgyZBdInt61OD+xub1hGgUAtPX/CiLD+LoCwWtpfGb1vySQ890kjxo/IjNrfLSvIDuT0lV2Y3JnTMXkZ+OUbe1H1zKfocHuhUAAnOjwYla3D7QsnYPdhG2oPtEKrUkCjVqDckhd8Xfm9hWYumXqaKKNur/+U8ik73OrET/7+H7zwxSGc6BCx73gH5p6eh+LhRmzediCwN10KczrQM5ou2SSi9MHBswT8ElCab4LZqEW76+R+BMfa3VApBdhDljU6RB+sdvFkx1utxKr5lsCG+1dPw4YVM7BqviW4ZOPel3b2yj/Io7L1mF8yHA73yQ1IpxXmhN31qu4s6FMLsqFTK1BddxyjcvS444UduHjdViz740dYvG4rmmxOrPtXXfA0tK+Otgc7Ehu2NmBq50U2cPKUo0SdBK8/8VKuTK0Kv//vs4K/o8WTR2LNtyajpSP2lO/Qi355+nco+Ujzq5/6GDf+9XNUPfMpahtbMCE/K+4d0qY2V58WRHk2UjTzis0wG9NzKngy0+9TRalQIEuvxsYVM2HoPHXrDx/sQ3aGBh0eL84szAnmMjSn8kl6sTK6sSZy4+DuZlSlECJOuZTvlMtt8ngDGxafVzIcRq0aj/7zS1w1ewz2HLbBK0l49J9f4uJ1W/HuV8dx7yuB5c/y4N5XR9th0CijnkCWbEblx2Od0gsg7gy9mvpmnDN+GAwaZdTPgs0hYv8JB7ZsDwwoyHl8dUcTdGoFzisZHvz7WDXfEnYjoKu++qwN1FyGSueMqhQKnHB4YDKoce4ZIwAEsth4wgGH2we3xx+W0Vs21QYvkPujltocIu7fshueztnWcr7snYNpbq8/mIHzSobDLwVq2xPv1eHWC0uwoaYBl6yrQXW9Fatf2onqkIzWNrbg9V1HsPuwDZNHm8JO9OxuRuXviydbHxi0Lz89cq8nWWj9lMmfkVh53bL9MPwScMPZ4wAE+hn9ndNQgyGzQPrk1in6oFYpwjIKBGqpBIT1d4EutbSf+rsqhYDzSoaHrYoI7e+G5vTcM4Zjf7MD69+pw/5mB/52w5xgTq/840fYsr0JD78e2Me4tvOG1Gs7mrDqXAsqLXnBQbLQnMo/Ix55lmk8WpUi4SDctMKcsK91/SzYHCLufmknrpxZGGz/jX/9HFf98WPc+cIO3HB24Pcf7+f09edroGc0XbJJROmDyzYTsLu8KCvIhugL7Feyu6kN5ZY8aJQKSFJgc9VQ8tHyBo0S2Rka1Da2hC2XKO/cByxDo4w4TUzWk30AOtxeKBUnZ4mpQv6/PKjm9vpx7hnD8djbX+HW88/A6iizSIZn6VAdsoREXjYDBIr5oVYnFEJgryT5lKOuG7h23UMiz6jBR/uao27aCgRe6/VdR4K/jwUThmP14lLc/vyOiFMKuwrtxIRO/7Y5RNwRZTPymvrmmLPg5LaLXj8+29+CghwDRmRpe31fA3k20u2bt+ODLtPA1y6dkrb7KKTz9Hunx4dMrQonRA+8kIKfNZVCgRaHiOYuHRyXxxe8sP3lG3vDBprljCoEoVcyeqJDjOhsV1UU4WjnniPAyd+dVq3EsTY3pozOxsatDcE9y6KdFCoP7tU2tmLDihkATn6Gh2VqUdl56m5oRqPl8/PGluCAo/z8WB3teGxOT3CPw66fhVaHB+veqYuaRwB4cMkk/OGas5Bv0mP7wVY02Vxh+yrJ7ZRPEuyLz9pAzWWodM+ozelBYY4eTW3u4GdNrRSQpVfhREd42xyiD8fa3f1WS612ER9+3Yzvn306gJP5kg8C0qoUJweZ1UqseX0vvj/vdMw9PQ93vrgzbkZr6puDtbS5XQx+tnMzNNjb1N6tjALxc1phyYNKIcDt8UGtjH8BH20QoN3lgUohxM3rfZdOxJmjc6BTx29zh7tvP2+DIbNA+uS23eWFXqOESacKy2hNfTM63D4g4jbQyVraX/3dEx0iHnh1D/5cNTP4tdD+bmhOAWDcMCP2HGnHP74/JyynAIKDx6E19sn392HKadm4ZGo+THo1Vs23YFimFrsOnTxsK5msftbYErvPW2zGaTl6tDvjHxAgr8gIPZkz9LNgtYsoGZUVdVZZYJ/SL/HiTeU40RHe/+nabrfXB5ujb/YeG+gZTZdsJkOSJLS3t6e6GUSDHgfPEjDqVDhud6PN6YVWpcBznzRiw4oZcIg+SAhsrhpKLqpVFUV45M3ITchr6pshAPjheePj/tzu/oMsSQjOEqupb0ZOxsl2yUsvtSoFFAJw1awxEICoRT10CYnc2ZFPDPtexTgU5hnw4Ku7sbx8LJ79eD9WlgcGAbpu4Np1f5Wq8iJMGW0K/g5k5ZY8rL6kFJf9tib4tZJRWfj5CztQU9+MFXPHxn3f8t3OsoJsePwSvj4e2GzVahdjzpKJ+jp9eOJiNPnZeqxbVgarXUS7y4NMnRpmY3pvQJrO0+8Dp20KcHl8cHl8uLaiCHqVAm6PD1k6VdisUSCQhVhLGeQ/33iOJe7PTDajdrcXZqMGFZa84CBd1xkfRp0KBo0SotcPm9ODymIzHnu7Lrhnmazr0hH5Mfl0r657nIRmNHSft64naS6YMCJwOljn9ykEIWKPwZEhy8Ziefbj/VjXuUdMbWNLcF+bDtEb9w67XfRCIQj4zpP/xuxxubjr4lL86cNvol6I3bKpts8+awMxl6HSPaNalQKOzn36QmupRqmASR/ZNjmn8Wrpjxb0Ti21uz1hs63lfFWVB5ZO1R5oxZxxgVnYoseP2sZWnJajR4c79mm+XTN6++bt+Nv1c/DHkD3DfnvVNKw614IGa0dSGXWIvmBO5d+DTD6l+oEtu3DbogkQE8yQCa2f8sV/jiFxXp0eHzx+P3yiFLN2llvy8O1po5P63Z+KgZ5ZIH1ya9QpYXf5oFQIyOisB7+7ehoWTx6FLL0K0c6D6c+MAoF6GprTrv3d0Jw6RB8UAJ5aPiMip8DJWWqhn1uH6MNNz36OG84eh+ljc1HbedNGzqn8MxJl9blPGvHw0inB30Po48vnjsW3nvgQ6zozHIvPL6GqoijsYIPQfq7d7Ylof6jqOiuOt7uRpT95qdfffV1gYGc0XbKZI7/XMAAAzFJJREFUDLvdjpVP/BOCSpv4yUTUYxw8S8CoUcKkD2w4/uE+K357VRncHh8ONHdANcyIYZkaVBabgxea8p26eAVta30zbl+ojPqYfEeo68VnvCJjc4hY89puTC7Ixj2LJ+KBLbvg9Z2cfbO13hqcKbZ48qjgSYTRyIN/ou9kp9sh+oKbwz61dR+21gcOJKiqKIJKEDAxPwuzx+Xh430nYu7fIEkSZhTloqwwJ3gkuFalwLE2F0SvL+z5ob+7eHfY55cMQ362Hv/pvBCRzSs245bzor8/+TUrLXlhg2uJ9oSIduLiqZ6+YzIMjM6DTJ5+/0GUKeypnn6fpVehpcMDg1aJj79pxtxxufjJhWeg3eWD2ahBvkkXNni145ANc8flxcxoTX0z7rq4NObPM2gCF5lfH7Mn/PvPy9Dgl2/uxU8vLIGAL1Fdb4Xb60eD1R78t8Pa7sbTy6ejw+2DTq2EHL/QHAInD+2oqihChubkP9+fN7aE7aUEnBz0VgkCpow2odxiDg5Kh9pa3wwJe1FVERioe/bj/bjp3NNx58UTYHN4oFQI+LihGX4JYb/DUOWWPOw8bMNVs8bgmS5LXRPlEQA63L7gvmkAcE+MvdW0KgU2rJgBvyQl/e9jdw20XIZK94y+X3ccWTo1cozq8FqqQGC2ZEgtBU5eCMerpXdeHL0b091amq3X4H//VYepnXX0QIsDALD3SBsqi83YsLUBF5SOwIKSYXCIPlw/bxzueWknbuxSS0NnpXSd+XXlzELc8/LJz3bohfoFpSOSzqi8rHX1xRNw56JS7D/RAY3y5CnVDtGHH55/Bj6MM+N7QcnwqLOFKovN+GGyeQ3Zly5aXu9+aWfM04p78/S6gZxZIH1ya9SoIHol2Jwe5Bk1OHu8GaOy9fjLv7/B3NMDS++61oBEtbQ3MwoE6ukT79UHc/rAll3w+wMrGKrrrWE5zdKpoVYJuP/lXbgpSp9Xq1JE1FggkEuPTwrb41PO6W0XlUCpAL49bTTufDF2Vq+cWYhbNtXirosn4McLxsPm9OC0HD3cHj9aHR5UVRQF/22Jtiyw3JKHf+9rxvTCHJy5LDvqTekHl0xCky3+fsI2pwf7mzuCfd5Yfd3P9rfg/a+OY/qYHNjd3l6vrQM1o+mSzWSptDr4fMmdek5EPcPBswScPh9G5+jhcPuQn6VHY7MDo3MNeH3XEfzPhSWQ/MCN55wOvxTYs+i5Txrx1+tm42BnxzsaeSp216JpNmqwYcUMtLu82GftgE6txL/2HsOXTW24b8mkmHeEmjtEXDlrDJ79eD8kCfjhgvHQqRS4rmIcFk/OxyiTDgsnjcIjb+7FxZNHobaxFdmG6HdL5Dtqo3Mif1bXu+ihhdygUeL5H8zFHS/siPq6W+ub8YNzLMHTwoCTp/T9+q2vghcFQPid+2h32A0aJe66eAKmjcnBgRYHVlaMw9TCnOCSrg/qrMGlN9Fs2NqAl24qx/2v7AruS3NB6QiUFWTj6s5To0KXiHVdVmBziGhxeLD6xR0RgwSD+fSddJ5+b1Ar0QIPBAgYlaXDgRNOnJYTaLPH58ejb+7FivIiSABqG1sxdXR22DLnsNfq7NBrVYqoHVuzUYO/XDsLh1qdaHV6EubU65ew9KwCPP6vrzC9KAf/c9EZUCkUMKiVmFaYA3XnzND/+/c3+OmFJTBqVRAEBPdjC7XzsA1PL5+O9e/Wh+2H8twnjfhz1Sz8/IWdwa91zeiWmyviziapKi9CuSUPV80ag6pnPsNZhdmYWpiD2sYW3HPJRPzvW19iRXkRBCDsc19uycN1FeMgCIBaqcCyWWOwsmJcMEMf1FlxQ5w8AoFlN3IbAET821JVUYTphTkYla3Hg1t2DancdUe6Z3RvUxsWThoJQRIiaqnHJ4XVUiDwb3WlJfp+OUAgixm9VEs9fn9wJslf/r0fG1cGlkJPHGXCtMIc/LF6H7bWH8c9l0yE0+vDBaUj8djbdbht4YSw1wmdldL135hoN9Ucog+/easOv3mrDq//sDJhRoOvVZiNYVk6PPrPvSjNN0W+rtsbc4ba/JJhuOeSUhxoceLqWWNwbUheqxPUT+BkXr9XMQ6TR5vw8xd2xlzC1twRqJ3yYJnd7YFJr8HqF3eiup6n1wHpk1uX149WhwitSgnR48f/XFCCh17bjf+ePRYOjw8u0YsHL5uMu17cEdx3s6e1tKf9Xa9fwlVd+ruZOhVuPPd0qFUCSvNNsHV4cPclE3Gs3QWjoMa2GH3enYdtWFAyIspPicxqaE4BJJXVssLAabrXbPgEDtGHSksepnaebF1ZbMYDl07E+RNG4r6QQxiAQE29tqIIOw7ZMDpPj0/3nUBVeVFE//TOF3fgzjg3+YDAAOGDr+7BizeWB04C7XxfoXn1+iWMM2fg/ld24Y7nT/bhh3ImZemSzVMRupzTaDRCEKLnlYiSw8GzBCQ/8Mt/7sH/XHgGJheY4PFKePiNPSgrzIEAwOn149o/fRa8I6XXKLH2tT34/jmWqJ3JHYdaMa0gB9Z2V9iFgkGjxFPLZ2DtG3sjiujK8sApd6sXl0KnVqLD7YXNefJurdcv4dmP9wc7/o+9XYcfn1+M2UV5eG3H4eAA0abvzUKH24uqiiLUxtiPYcPWBmz+/pyoM75C79BFe292tzfuPkU+ScLTy6fD7fXDoFbCJ0kQBAHfPqsAhbmG4M8P3UNNvsNeVVGEqvIieP0Sxo8w4u4Xd4YNFIQu6XKIPny4rznmHb1phdnw+HxYWVGE2xeVQKtS4b6Xd0YMCIS+nrys4HCrE+9/dRxbth/u1iy1wSJdp997/RJyjWr4JWDyaSaoVQp80nAC0wo1kAC8vfc4vjhow9qlUzDmUgPue3kXVlaMi/gcZ2hUMBnUePxfX+GswpyIi3mzUYPnrp8TNnsECM/pQ5dPDjsZTwKCe5NdNWsM1r6xF2WFOVg4cSSWb/wETy2fAdHjxS0LzsDdnRuNv3DjXNx18QTsOGgLy6EkAU+8W4/axtbgUtDPG1vx8NIpaLI5IzrE+SY93F4fjtvdEARg1XxLMI+hDBol8rP1+PGC8Thud2P9VdPweWMLpnd29B/YshtTC7Jxy6ZaPHf9bKxodwdnj+48bIMgAE9X74uZoX/vaw7ODOiq0mLG1npr4GTDDA3cnvB/Z+QlJiqFgKe37ot6gMNtm7dj/SDOXXekc0a/VzkOGWolnF4/SkaZgrVUp1LA5fXj5k21WLt0Cm5fWAK7y4dMnQoqpRC1rrxUewhPr5iBe1/ZheVzx3arlt6/ZBJcnXuwyXXULyFsn085e3mZGqzc+CmeWj4D6//1FRZOHAWNUokDJ+wAAL8khc3G2bC1Ac9dPzuY89D8JqqhHQlqaIZWhSeunhbM3c5DNlw9awwyNCpMK8wJe65Rq4qon/K/cQW5evz8hR09yuuCkuFoc3nw9PLp0KqV6HD74i7d/FbZaTjc6gyeWLdqvgW1jS1Dsn7Gkw65tbu9GGbUQq0S4JMAt8+PCfmmYG3JMarh9vkwoygXP+vs797fg1pqNmrw7HWzow4axaqjMgmI6O/+4/tzcPOmWjy1fAZ+9ebewA3SVeXw+YFDrc6YfV5JArYfjL66Qc5qrJra5vRgw4oZYZkLlaFVYWZRLnYesmHdsrJgns1GTXCQevVLu/Dj84sjVmTsOGSDAAH/OdCKhRNHYcuOpojfk5xVSFLESorQ59UeaAUAOD1e3HZRCVwef0Ref7SgGP/3729YW2NIh2yeCp/owvV/+gRKpQL/94NzkZmZmeomEQ1oHDxLQJKAd7+y4vaFE9DW7oJBqwrcca0cB0kA7J0bfk7MN2HtG3tRVV6ED/edwH/P9mLD8hlY925d2F2e8yeMgN3thVcCPmk4gZlFuagqL0Juhga/6twzomsnxKBRYtnMQrS7PDjU6oRRq8L3/vwZrHYR84rNuGPRBJTmm8KmYksSsP6duuDAWVVFEYxaFTw+CdMLc/CfQ63BKe+hBfOswmwICgEPvronsPcRTs4yGWYMrKM3GzXBTorcUTZolHj5pvKYG8besqkWns6BxtDCHa1DsPdIG9ZcPgnDs3Rwe/3Qq5XwSxKUgoCR2Trc/eLOsPcV+ntat6wMN2+qxYatDXjl5grc+/KusAG0SosZP7voDBxrF/H01gaUdc6sibXvlTwjLlOnDh5ZvWLu2IQnF6WqqPbmUphY0nH6vdvnh0apwK/e3ItbFoyH0+PDSJMePr8fzs7O4sNLp2BD51Kj6vpmzD49DxuWT8e6d+uDGV23rAwdbi9WzC2CSing45CMev0Sxg83BpdyxMrpN1Y7fH4peLfW7w9cMMiHcMibh1dazLhyZiEeeXMv7rlkIu575eSFhN3pxcyiXBxqceHeSybi/i27UV1nxeTTTHjs7Tqsmm/BY29/hRXlRVg02YWNNQ34XsU4rL+qDBu2ntzz7OE39sTsdMudfTmPv3h1d8TF9CVTRsGgUaK6zhrcf/B4uxs6tRJurx8KQcC5ZwzHI2/sjeh41za2YvFkF/5y7UxY7SK+VXYa7nkpfJC60pKHlRVj8bN/bMfjy8rw638GZrfJQpeY/HjBeDz2dl3Uv//qOiuOtbsHde66Ix0z6vL6cdOzn+OlH8zFUacHGVoVahtbcev54yH6/XC4vcGMhg5W/+P7c8Pqijzz+I/XTMeBEw789+yx2H6wNWotlZ8v5xQAfnCOBU7RB6fHG1FHQ3MqD6J1uHzBnM49PQ8f7WvGazuacG3FOKyab4FSIWD14lI8+OoeVNdZw04grG1sxbPfmw0BgUNJhhm1MGiUuOHscVg4cRQe2LKrWzW0w+3FjX/9PKyGhmai3JKH3141DcfbXfAjcLNKEARsP9iKnYdtmJRvgtmoCcth19/PX66dha31x3HX4lI8+OrusPq5oGQYfrawJOzfqqeXT4+7f+THDSfw2vam4EBcvC0tUlU/0yW/qc5tlk4N0eeHVqXE2tf3YEX5OJQVZEMQBGTq1dArlfj6eAcm5pvw2Ntf4UcLxmNFeRHyMtQR/d1YtfR7FeMwJs8QtiQyWkbbnB5Ifgkju8x68vuliP7ucbs7mNGa+mb8aEExPms4gS07mvC9isCS6BN2EXeH5BQI3Gi7eVNtRF8XAEZl6WHQKHtUUwHAJfowsbOdoRmtDHn+tsYWZOrVmDMuD7bOWezbD7bizNHZUKsUuHl+MR4IGWCMllVBIeCByyZh9Uu7IvYpXVlehNs3B2rro29+ier65qh5rbCY07a2ylKd0VRn81SpdAYoExwgQ0TJ4eBZAu2uwEwtr1+CT5LgFH0oyNEjU6eCUa0EOo9tP2F347aLSiB6/YETtTrceLH2EGobW/Hj84sjOsrAyeJ286ZarFtWFna0vbzpd2ihVCkVyNL9f/bOPLCK+lz/n5k5e06Sc7ISMIGEBLIAGhZZEqQsiqzaa++toL1sKlbR6q11RxS01q5eRVutIt4WtLe1buBeN0BBtipLWAKBsARC1pOznzMzvz/mzOScnCRo29+t3sv7j5KzzJyZ7zPv+32X5zFTc7KdVfNHceXTm7WRKF84KSDVN9nx3ze5NJtMh5W+Lju/3XiYpz46zMLqQmOzmpNm5S81jdQ1+YyKdXyXicthZlJpNrdMGcQjb+1jZ32bUSU3SyKN3lAsGdVmBBGbapsR0dS59ApYb4G21SRy2yWDefiNmoRApro4k0XVRQiCcNbr9N+Lx/JezWkUVeHGiQO57ZLBNHk1Xoidx9r4y75GvjjeRmWBm6kVub3yXi2sKjR4DXTJ6rMpgH5VsYd/VFAQX93X7f9M272qjfsOyktDVSEQ0kjJbWYToqiwsLqQl7YfY2FVoVE1nFbRh2Wv7UnCaNcEko7RhdWFpNpMxvp7Yu5wGtoDxnuDEYVGT4gBRRkcPN2BLxQlJ9WKP6wl2JMI/q0mKvNdrNpYh6yoBp5GFrjpm2Hn/lcTk8TXTxhoqIrp37X5cAv/tfBC7n55N9dUF/Lcxjo2xhIAa7ccTaho610sL245yovXjeF4awCbWUJVVZ7bVJfEZbaptpkH19UYCeSoovLYnMqE5AbAmmtGJ1wzh0XiuouKmFyWQ6MnRHsgyq4T7dzz8i5+ePEgbp9WyvHWAP1cdt7Ze5ola3cmKIfOr+rkr4m/ZtGz8Hi0B/5xqldfBZP/p3H3FcwbjHLlhQWEFRUVlVBE5tdXDQdVxWGSMKeIPLPxMAurCrlzWin+kExeuo17YhxgXZNOXTuPu/pSIAmnoZggx4FTHYwqdPObq0fQEYyyvb6V1pgiXVexnD9eP5bKfBcvflbP0hnlnGoPMrIwg4JMRwLxv47R+BE2f1imzRfmggI3C6oKcaWYeXbeSOqafEbXjf7Zbw3K5miLn0XVRVTG0RBoPlRg1fyRbKlrAXr2oTvr27CZRV7//CS3v9Q5fjW+OIsbJg5k0fPbWDl3+FmvT7bTiiTC0hllNHnDtAUiRkf4A6/vSfD9GSmWXjmvclKtCR1s3Sl8xtvfo173t/jSc/jtNJtZJCIr+MIyg/LScNpMtPjDuOwmUswS3rBMhtPCqo2HufPSMsNf3jWtlI0Hz3wpX/rX422YTeKXwuiFRRnUNnaQ7exUPfeHo0nxrkUSE8YRJ5fl0NwR5sIYTpd140t1nHYX61pNInaLyP2zyg2feuvFJTR6gt2OT4rUJfjUU+0BVNTuOYBrm1HQ9g0VfdNZ/tqeLgWlLMYUZrDwS2L1wKkOxg3M4MeXD6G+NUB7DKs6/2G8b9WVQ7vi9X/St8JXx+k5jJ6zc3bOvk52Lnl2FnNYJUYWuPFHZC2BlGZh9YJRhKIKAVlGFGDG0DzOdIR45K193HlpGWu31PIfFw9me/0eHptTSaMnmNSeDondTXpAGe/oulWuLM7khonFBMMyiycU8at3DxKKKkmkp12/b/+pDtJsFrbUNfPa550jh12TeZUFbiyx6oQ/LHO8NcANa3YA8NT3RnDLlEF0BKM9nt+k0mxevG4MTd4wwYhsBBjD8tN5f99poPfK8/n5Lj4/1sb8qkLmxMZRooqCIAhkOi1G4NDbdXJYJJbOKEPTIRC0LqB0Gz97Zz/v7zvD6vmjGNJPqwiW56WddQ3ovAaHm3wACWOl3dlXUd/5RwUFeldc1zHV/yujMCrgC0WZPDiXk20B8jPsRBVt9EJAw82sYX1Zvm4P140fyKr5owhEFLbH1s+XwejIAjdijCti8YQibGaR9d2MUxRmpfDX423Me24rF5Vkce/MMpZMKk4g+Ado9ASRBIHH5lTiDUYSxhN1YQ7o5C5b+X4ta64ZDXTi2x+WaezQEsNpdrMRaI8scHNBfjLJsL6BafdF2NvgoTLfhdNqYmF1ERfEbdh121DbxPyqAQAUZzu5/7XErruRBe4EUnQ9qb26m46Yn33nfD4/3kZBIEqKxWRcS4CxRZlGZX/NlqPMqxqAgpqw0XZYuxdZiT/2P8K+Cib/r+Puq5jDKjGqfwbBqEy204YgwOEzXvL7Z4CiElEUbpkymOXrtOTMk3OH0xGMJhRLvoovhd5xOiDLwWdHWvjVuwepKs5k+pA+STj1h2VOtWs41fiZIuS57JxsD7D01UTifx2jVcWZ3BqnLuiPdHIPvnpjFU9+UMv8qkIDRz2NOz42p5I7X/qCKy8s0Lp/EJhcmouqwgXnde9DF1YXsvKD2qTO2Kiikm4384fFYwiEz359JpVmGx1Hkijgcpj55FAzw2PFsfhzdlgkVs0b1eN975os+0f6z3j7W3zpOfwmmjccRUVLdE8enEtUVuifYUdFIBDV6CusJoEfTB7EvbH1n+W0cNGgbP7zLwe/FEZvnTLI8Fnw5TC6/UircR/T7RZOdySS5O881mYUT6+7qIjmjvBXwmnXWBe0eHdIPxe3v7QLh0ViWkVet2OmT8wdzufH24inkRIEgdw0W6+caHdcWppUhNaLXPUtfp6YW0mKtfNZ1Nt1GpiTgihooizBiJzAaRXvW/Vic1e8/k/5VvjqOD2H0XN2zs7Z183O9XCexRxmiexUK4GwTJ7LRopFIiyr+MMy7b4oFpPIzqOt1DX5uLa6yCAs9QQiXHdREas31dEn3d6rE63Md2Eza86pMt+ldTx1qRY9O28kT141nGvGD0QQwGqWGFuUxar5ozAJUOB2JHyvHqBW5rvYWd/G6vkX0hGMUt43vVtuBP1cLq3oA2gOOf57AKySVpVsD0S6rXw7LJLB6bRw9VZuWLODhau3srO+FUEVWDKxhPElWT1WnvUAZd2uBhY9v43b/vg5EUVhzZaj7KhvTQhOul6n+Lb2x+ZUsm5XA9P+cwNXPr2Zf/n1J/z4zX0snVHOn64fQ590m/EZXb0w/hqvmj+KJZM0zrqCDAd5MYeuS1brfHDd2VdR3zlbUNDuD3+p7wFo7Ah1y++mf1+T98t/1zfRfKEoKVYTkiSQk2ZDEARUVcUbjIIA2ak2HnmrhgsLMyjMcrDnRDunO4LG+jlboFuZ78KdYiY1FsxOHJxjbFJBW3e3TCnh1imDEAS4uCyX15dUUz0oE1EQ2Vnfii/WgabbHS99QUGmg7VbjpKbZqNvuo17ppdy6ZA+3SpaAgYXUQIuY/8fiXZWj9Md5h67O9duOUrfDDs761tZ9Pw2vvv0ZgOnj82pTAqUQ1GF6uJM/JHERMbek+1EVZVIXOJev55dz1/viNl2pIWrntnCv6/6jJmPb+Tz+jbW3VSNxSSyeEIRjZ4gV43uT0RW+I+LBzMo15lwPj3hrqo4Myk5+bfYV8Wk3o3anf1fwN1XMYdZIjfNgiQItPiCRGSFwmwngahMUFGwiKKB0VduqCInzWp0PHwVXxq/XrriFDSsVsbENqaW9+H1m6r5/oSB2GOKfz3hNBCO0ifNRlRWOP88V6/nkWozdetDRVHrPOla3OoJp8/MG5WA05mPb2T7kRbyXLZuN7S6X9QxurO+lZte2IkkCjz0Rg3ffWozKdazX5+5o/vz4Bs1XPqfG/ju05uZ+9stfH6sjTSbKSk2eXxOJQgk+M1465os+0f5z3j7W33p/3W/2dVURevsctpMmEwCFpOI1aR1J3cENAXGNJuF9kDUWDOPxPg2v6wvjcpqwpqIX4N6LLZ6/ihuu2QwaTYz4wZmsaCqkI8OnKHdHybVZkqKd1dtrCMnTaMV+dagLAZkOf4unIImiuGJdUEurC5MGJ/ULd6vzXp8kxHzrvvipCYKdJakk56I1jF+w5odLFm7g/wMB2s2Hz0rVvXvkBWV/3zvILMe38Si57excPVWPj/Wxhs3VZOZYuG/Fl6IACyqLkoqMOj2/9u3wt+G03M+9h9nunCAqp5T4zxn5+zvsXOdZ2cxAZAkgUyLFtC1+iOEYopEBRl2IrLC+fluTcWmwE2l6mJkgZvMVAuTy3J49L2DLJ4wsEclqlUb62L/FqkuzjScmj7OFV/hjecai2/l/vP3x7KzPpH0VA9Qo4rKyrmVPPKWNgb5m6tH9Pp7vaEof9p+jPtmVmgdALHv2VnfhivFQrM3hNUkdts91ttGYMX6vSyfXcGPLx+CNyzz5FXDk8iQ4wMUnTPjhThi2KH90jGJAuOLsxKuU/x59HQOGw42cd+re/iPSwah0FkFjVcv7Fr5XzV/FDmpVuNvumR1TwpmX1V958sEBV/mu062Bahv6VndFaDVH/7SUvB/q/0zOSnS7CbMoiY7bzVLBCIyzd4w2WkW7LF/Xz2mP6fbgxxvC5CXbic33cbwAoGV79dy1ej+QPck3jvqW4kqKk6riR1HWxlfnIlAovrrk1cN59kNhxO6raaUZnPvzArujcnZdyUPb/KG8QQizBs7gJNtAZ7ecJhbpwyitZek6aqNdbx8wzi2H+0kP9YxGl89NklCj5uGYee5uP/V3d3iVERLVMT/jnS7mftmVXA8tsYWVheydstRrhrdn9Wb6ji/wG0QFvfUVRrfERNvG2qbWPbaHu6eXtZtZX98cSbPzhvJoue38cH+RpZMLDbOVbeq4kxumlTSo4LwV7GviknPWUbMuhtB+2dzt/yzjm8SBcKCgAq4HFZa/REURduUZ6SaCcma2qXeuXLHpaW4HRZjDOvL+NKoolKQ6Ugg8O+aGNI5wTJTrMZo2S1TSnjqo0M94tQbjJCTZuPO2AjpU9/r3Y+eag8m+dBNtc20+bX1EF/c6qkLe9h5rgTuNt021Dbz4Poa1lwzmlZ/JMGHdtfB/nisE3T/qQ5evG6MpnwYRzDe9ft786HfnzCw29hEt+74nxo7QlxUkmUo1f2j/Ge8/S2+9Mv4zfZAhEON3v9xrPyzMKoCGSkW7X+AzYebGXaei2ZvmJx0C06zRERRafZ1dn71SbciCmKCL+0Now6rxOa6REGn+ITv2i1HGV7g4hfv7E8owkwpy2FcUSafHWkhKisJGPWHZf5S08iMIbm4HFbuifncs8W7PeEUEpU4v6pf02Pe7jCqWyAW8+pY06/ZJeW5PPVhLTdPGXRWrPZ2DtuPtnLKE+KJDw4mjc9OKctlUmk27+87A/A/4lvhb8Pp19nH/rN9+Ve1aCjAwqc/5I8/nHVONOCcnbO/w84lz85iiqrQ7o/gcphRgVOeIOe57DitDlIsEr6wzEPrY7xJUwbRHoiQ7jBjN0kcb9EUirJSrb0Gmue57YgiLJ1ZQUssKAnLSpJjfW7+hfz0rUQusIXVhTT7IqyIEfyD5vz0ALVvuo0fv1HDxtpm8t12BmQlVuy6mqyoDMxJ5ZG3alhYVUifdCuXX9CPrXUt/OxtTRAhvkU+3roLMOKDqDPeMFFF4ZNDzUYgER9sx3NWPDanEptZojRGuLqzvo1lM1P4yVs1XDu+iMxUzUF1raB1J8MdH7y1+yOIYmdnga5eGJ8IWTyhiImDcxAFgYONXpw2bcTMJAo8/C9Duf+1Pdz50heGKpwvJOOym3GnWLpVh+rJ9KCgp3P1hXoPGtr9YRo7QtS3+Mk5y3HbAxEWPb8N+HKjLN803hibSSIQlTFLImc6ggiCSEGmHVEQCMkyAvDcxjoWVBfR5o+Ql27DZhaNTazNLPU6QnVFZT9CskJWqo17ZpbREewMgq+7qChJaRKgtG86J9oCCSp8XTeNgiBwsi3Aul0NjBuYSSAc7bXK6w/L+EJRznhDPHjZEO57dY/xvXazZAT/rb7u146eiHj648M9qvrdPq2Upz8+jD8sM74kizSbiZ++WcOc2KZo0uBsLoh1y26obabmVAd/un4cK9btSRjhjDd9w93TMU2SwPLXkiv72jUVuHdGGb989wCTS3O4Z3oZUUWNqQ9KmASBQEQbX23zR2gLhHHa/rZANj5Q7w6XSpeKrdPauwtN6fL6PxIn3zScRlWV9kAUAUi1mbCbRVTALImYBZGQqvDcpjoWVRexqbYZf0gmN83E0pnlNHpCX8qXFmWm0OwLct+sCrYfaSEYSfQP+ugTwPJ1ewxsxhNmd4dTu8XEva9oCed8t53+mb370RSrKcGHzhqWx4Pra4xOFH2j3lsXtp4w1P/ddS1KosCStTuoLHAZPjQ+Kaf/DptZYntMuOCRt/ax/1SH1iUWjiYo2+o2MtaV15UrcdXGOj45rCXIz8Zb+sTcSlJtZmRF1bp7Bmby07f2MSDbycgCN+4UM8tnDyEYlQmEZdLtZnJSrX/zxvOr+tLTniBHmnxkxxXHurNgROZffv2J8e+vipVvGkZDkSiiYEIQQVHgyQ8P8Yt/u4CCTDuSINARivLjN2pYUF1kfEZETPClKRZTrxhNtZooyHAw+/y+/PiNGoIRzZfqa2rUgAyDZyzeSvPSuPuVXZpQx9odSRh9+uPDrFtSbSTOHBbpK+G0n9tu4HTDwSZUFQSBXnHaNebtuv7MksiO+lb2nmxPSCqPL8nCGeM8jY95n9tUx8gCN9dOKE7A6g2h7rGqn0N3vlVVVR7/4GAP47P7uGtaKVPKcslNsxGRVdwpFn4wuYT/uHgQUVnDrdUk0R4I0xTjg/x7E0OeYKTXxGp3ibD/SR/7f4Hr1GTtHRPn7Jyds7PbueTZWSyigN2iSchHFejrsmGSBMySiDdWRdJ5k9oDEXYea2Pm0D74Y7wDC6sLMYlCj4GmzSRyx7QygmGZX39Yy53Typhcms15bjsWSTQc8+IJRYgCSRv0ynwX7YFIt5L0JkEABDbqybeFo4hElISqfLyNL87k08Od3SPXVBfxxbF2hp3nYsh5adz18i4qC9zsPdnO1IrcpM93DTDOxudy8ws7E7hqdNODqKtG9zfOZcmkYpav28O+Ux3kuWxsO9JCVXFmUqu9rnrY03FnDeubQPKuCyvo5/vE3OHYzCKPvJVY8R9fnMUPpw7mmY8Pcf+sCiKKytJXdiXcj6/iONv9YexmiV9fPZx8t4MvjrdxU1y1vqo4k+8MP6/bzzX7wqhgEMoDLJlU3ON9jZcrh965Ir6pvDH+iMY/GIjIWEwSNrNEIKIgCGCTRIJo/GY3xoJKi0lAQtCq7Gjt7EtnlPdIwr35sNaR8rvNR1g6s5xWuTPI60mpSsemcY7dYNRqEsmJjbncP6uCJm+IvQ3tCRXweKsqzsQkiWw70srk0hx+dOlgmrxhIrKCWRSM6nFU6b4tf2F1Ic0d4V5x2dwRZmF1IZ/Xt7J8dgXNvjBzxwxARFPWM0migU9AUzl7q4abJg8i0GXkTTddbKCnY4YjSo/j5Btqm/iPS0r40/XjkjBXXZzJwupCdp3QlAQBctNsvP75SfacaOeBy4Z8pUA23W5myaRihhe4cVi0ZFl8sn98rDtG/06LSTTEDbra+OIsLHHPp38kTr6JOA1FFVKtEgrgCUbJSDETVRQcZq0IBRrWHDEKA4tJQBQ0HJ3yBL+UL717RhlpdgutvjBv7GrglosTq+sTB+fw6w9ruWnSoARC/XjC7O5wGo0p5josEs8vHEXoLH7UYhJ5f98Zrqku4s3dp9l3sp0FVQNIt5u1rua4hHd3trC6kEaPVkjryZ/Fq/aBJprT2BEyOrPj/ejC6kJ+8fY+Y0zs1x/WcsuUwQTiOmH0Y+W5bOzc2L3i550vfcGMoXkA3XbiOCwSV4/pz6qNdUndLg9dPpTTngCqKuALy7xb02jgSl+76V9hXxe/0bXHxubPP8/FMzERh/jrdEVlPz4/1kqqzYxFErnrz1+woVYTVunpWVtdnMknhxP//lWw8k3EaEaKlVOeELlOC6cDYe6fXUGqzUQglriJKCrb69v4kdPS+dwTSfCl7hRzUvwEGkYF4PsXDWR4gRt/OMI908vwx5JneqzXk6qyniRymKUefWkgKhvHvXHiQFDVL41Tsyjw8Bv7+NHUwSyoKiQ3zcq7NadZMrGYHtxpIidnL3HngiqtW1vzq20sm1VhJA0hsdvztksGJ2D10fcOcPWY/mQ4ut+u9eRb11wzusfu8531bVhMEm/saki4NuNLslg2q5yWmEjIJ4dOJWH070kcu+zmHp9lr9xYha2b4tv/lI89x3V6zs7ZOfuydo7z7CzmCUQREchwWEixStjNJg6d8dLuD9MRjOALRY0xJpfDzKqNdVhNEqfagmQ7LYwtyiQUUXp0YkPPc7Hi9T0IAgzMSeWnb9Zw+6Vl7D7envC+yaU5eEPJG1M9aIBOMlSdO2H+6q0cbdFI7hdWFyKoAj97Zz/zqwqT+A2qizN54LIhrNpYZ4gPmE0ifdLt/OtTnxqB/KqNdXxvzAACsY1kvNm6bAR6q04/t6nOSJhtqm1makUu6XatNVznbbGaxITxzI21zTxyxTCWv76HFetrWFBVyGlPMOG3WE1ir8d9cP1eFFU1PhPuwtfU0B7ocbzs52/v47oJA/nkcDNLX9mdtNn/slxlJ9sCLHlhJ5f+5wa+//sdzHx8I+t3NSTwTW2qbeah9Xs57QlyqNHLjqMt7GvwcLTFTzAi89nhZuZXFRocbSZR4LrxA3n420MSuNse/vYQrqkuYtXGuqRz7coV8bfyxnwdOCk6AlFMoog/pHWftfpC+ENROgIaAbInEDXW285jbUiCwKZDTSiKyviSLCRBoLIgmR9FD4jfrTmNgjZKdbo9SIrVRHVsDfWUqIrHpm5dMdoRjBprXBQEVn5Qy4Oxtd0Vo+NLslhUXUiLN8wDsyuQRHj0vQMoqsqtf/grTd4QwYjCZef3pZ/LloRPgFH9Mzgvw94rLjOcFqYPzWPa0DxmPL6R7/zmUxau3kpjR5AnP6hFEDo7TEDDZmFOKo/95QBOq4nx3XCn5MXxDHZ3TH9ETvpMvKVYTEmJM4CNtc08t/EI2U4ri57fxqLntxm8UN+9sIBlr+7+StyBJknk8/pWFq7eypU611N9KyvnatjccLCJO2JYOO0J4g/LmrLptaMT+J6qYsIu8Xxwzb4w5+e7uuVW/Co4+abitCMQxWE14TBLRseg02xCVVV8cb5UN23z1swZT4j+GY5efamWtBnAmY4QJlHksfcPsr2+DYskGjh1WCRMosDiCcUJBRRIJsxOwmmgk/sIzu5HT3mCgLbx23uynUXjByIgcLjJx4KqQsYUZbDnZDs5adZucTq8wG2Qfvc4RhnnR3UfahIFls4qJ91uTvCjlfkuNtQ2G7HKDd/SilEfHmhkfHHn8RdWF/Lgur094vTKCwuwSAI92cLqwqTEmf75e2P4vfK3Gq7iORa/Ks+n7kMn//Ijvv3kJ1z66Aa2H2nBZhbZWd8GaPf71otL+NGlpfgjUawmiVBUYf/pDhZUF7FkUjEvflaf8KzVObfWXjOaJRNLGFHgTuJx+zJY+aZi1BeWSbVKCKJAilUiJ80W50sjBCPaqOGj7x1g6axyqoozEVQSfKko9EwZsKO+jX4Zdpa+sgurSeKh9XtxWCSqizONWKwn5Uc9ISzHun+7YnTR89vwBDpj5ClluV8ap5IoIAkCV43pb/hUXyjKUx8dJhhRMEtCtziN9+9ni3fL+6YzraIP5xe4mL1yI//21Kf0iU0M6FgFjXJBx+pzm+oYdp6L5zbWJWFVt558a28KmQurC1nx+p6kpOKGgxqNwse1TX8XRo+3+FmydoeBz8m/+IibXtiJKAqs3XK022fZA6/vYdPhZhraArT7wxxq9PL5sVYUVeXOaaU8O29kgs/UfWxUUYz3H2j0sqCqsFvuxd7w803mOv2qPGbneM/O2Tn7++1c59lZLMUq0dwRJsVqx2mWaPaFGNo3nYiiKcEJgrYhHVHgJsUicWl5DsGoQobTwqPvHeDa8QNp8fX8IL2oJJtH3zvItb6IMQq5fN0e9p/q4HeLRhvvs1kkREHg2XkjE1qd9f/2VJlJj5HcjyxwE4zIvL/vDJsPtyRV7HYea8MfjuIPy5zntuOwSKTaTPzHH/6awNMAoKLy1EeHmDduAIraWZHPcloSqny98blsqm1mYVXnRskXlEmJbbz1ZMLOY22MLdKCHv1vuWk2IzC/+YWdLJ5QxLKZFQZ3jf6Zno674WATP7x4EAuqCrGaROO3LqwuZGpFLo2eUI+B38baZr4fjDK4Tyrb61u7HUF78bN62vwRo+KWbjeTYjXhCURoC0TIdFhY9lpy4i2+A09v4//uhQXc9t9/TXjvpNJs7p1Rzrpu1NGmD83j6Q2HkiqJ980sZ/GEIp766HDCvezaIv+3crD9LZwU/2hLsWq8ZukOE55AlH7pdmQ0XpGwouC0SowekEFuupUmTwAEgdx0O1aLyA3fGog/LNPWTbC5sLqQl7Yf4+bJgwiEokwuyyEqqzz63gHmVxViMYmGiEDXcYT8DAdv7znVS9VU4ymzBjVi4WBc1by7qvqAzBTqW/yk2U38eP1erh47gPf3nTE2FSk2M9ev2cEz80bxs7f2JeBTP7/cNCveoNzjGt9U20w4qhAIyww7z8Waa0bjCUTZerSFrFQNe9fGRkLjR89GFrgZUeDWNu8zK5K4yxSle74W/Zj3zujZFWU5LSgkd93qpiuCdr3+DovElRcW0OxLXLd692ZUUVFUFX8oisthwSKJ3BPrSEn8fm10VOeCq2nw0OqPGONBnfczi5dvGEdDW5Bt9a0sen4rf/7+uITv+mt9a1LFfeXcSpas3fmlcfJNxWmKVVvjDpOE06wlskKKyqn2IHnpNkKyomE0zUp1cSYWSSQ33U4ft40V6/dy3UU9+9LFE4pwWCSiikogoq3vJZOK+VUcTueO7q8lqP+yn5unDErwpdazdDek2jv9aOBL+NG8dJvhQ0cOcLPo+a1UFri4dcogrv/9doO79KmPDvPYnMoknKZYJLyhKNXFmV/aj/qCMplOK3+tb6UsL83obImnWTBwKokaX2J9GyvnVgJqr5yF+rFu/FYxnx9rozQvvdv39Pb5jbXNLIidqy7aYDdL/ObqEUQVlR31rV8Kq5lOK/e+vDvpXm2obUahM4Gnd5E/+t4Bgze1K5/TT64YZiiaXltdxHkZDh54bXePGNX959mw8k3FqC8UITfVhi8i4zRLnPGGDF8aimrK1ZX5Ll78rB6rJHDrlBIQSPCl3mD33ccASyYNNFSuI7JKad90Hlq3l0XVRQa/rMMqdTval2YzYTW5+PRwc4/dgmm2Tl8cVdQvjVNfWGbpa3sYU5Rh+NR7Z5TjD8v86E+fs2r+KG781sAEnAI0e0MGd9uXwak3FGVqeS5Ty/vw/v7TWkKsi4iWTrmgd9rdNa2MR9/TigHxWNVNUbv3rb2p2n7ZZ4r+vYsnFBGRVSrzXRxo9JKRYjFGGuM7zNLtWmfnHX/+IumcPj7YxL0v7+b8ApfBtdb1uNdWF3G0xc8T79eyPZa4+/nb+xN+7/jiTF65oYo39zRw0ws7+OPisSx5YWcC3sYXZ7H22jFc8/zWhERWT/j5n+A6/f9lXq+XKx99gxdvmf6l3i+Hg+d4z87ZOfs77Vzy7CyWajUhChr3hcNqwp1ixRuWEQRNPUwSgDQrXxxvw2ERufXiwZzyhBAE+Mu+M3xv7IAeW771sSDQuldCUSVhTHFnfatB1i8i8It39lPWN53KfBfBiMy4gZlkp1p5adsxls0q54F1exMcQHVxJk6bifElWaQ7zMZojF6x62rVxVlUFWey63g7S2eUgaryzLxRXPXMZmOjXFng5tmNWhD6aVxQkpFi4bHYRkVAYENtU488EbrFd305bSbmP/cZj8+pRC+IrNpYxyXluVTHjWfGJ3/8YZlfvXuQpz46zMLqQuZXFeK0mhDFnivjDouE3SJhCmidWqjwp+vH8tAbNZTnpSW8rzteBrMk4LBqBPHPbOgyHlKSxYvXjeWUJ8CG2mZe/Kyen35nGM/FVeKfnTcyQexhYXUhw2NJh4wUTYluVP8MoorCqm6qieV901nWDdl7ed90rXLXTSXx/tf3MGNoXhKRc6otkQT2bw0I0my9k8l2Pc7/D0u1mmj0hnBZrZhTRK2TSRCwWyQEwGQScJglTnuC3DCxhMNnfDjMEqGIwk0v7OS/Fo42KtrxNrLAzdTyPvz0rRqWza7gaLOfnDSrEZQ/PqcSQYAZQ/qw+FsD6QhGaQ9EsJklGj1Bak62c+/MMpZ36eYYX5LFDd8qRlE0/p37Z5XjDyWu7a4Y/e/FY3h242GWza7gr8fbucVpxWGRjE2tzSTyw0sG87MYyfgncfh0Wk1kOi2EIgoNnsSum67W5A3Tz22jvsWPy2HGnWLmUGMH1QO1qrfeaRc/euawSNQ0eFBUlaiiUlngTtisdJyFvw8VY0MUj72oojI418mhM76Et3fFZ580K2uvHcPP3t6XhMkVlw1h1/FWUqxaYH//63u48sKChM30rReXMKUst9sEXZbTwvyqAfR12RldmEnfdBt3d0mcgZbEe3DdXkYMyDDOQcdauz/Msld3J1f64xJzXxYn31ScplpN+MIyUUXFJApacjsi442N+qaaJaxuOz99ex9LZ1bgC0UwRVXCUYXNh1u49eJBSYUc3SYPzuWRtzRePin2/Nd9qY7T1ZvquHtGGVeNGcCeE+0JPJHtgTBLZ5UnPUOrizOZVzUAc6yDzZ1iwRfq3Y+OL87i0BkvS2eUY5FEJpf2YdXGI2yqbeae6VICRqEzUX7ntFKOtwYoyXHS6AkhCQLz4wpMPZnuZ9PsZpa8sIPBfVKpLHAbo0arNtbxh8VjAE2Ft6bBY4yL+sMyS9buNPyn4yyqehaTiDvFiqqqTCnNobRvWoKPNHfpSkvCabqN1fNHgQC/7cZ/Xn5BP2obO8h2WvGHZZa+ujsBqw6LxO8WXZiUOMtyWnjkimHkpFnxh2SmD+mDIMCD62uoLHD32BEkAleP6c+j7x3kliklPLsxmbuya/Iczo6VbypGM1OshGWVM+0hCrIcZDqtBKKaL3WYJQRB4wH71b9eQERRqTvjIzvVluBL44u+8aarqR9u8rGwupATbQEDo0POc5GbaqWqOBNRgNULRlHb6E34fFTR1vmqjXWsnFuJSGJBpbo4E0tMeGvkgAwjiXdWnM4sJ8tpoabBwx2XlvLEB5pwiFUSGV+Sxfn5Lh55SxujjE/C5WfYafGGWTaznGWv7zlrvBuKKmSnSpzxhjnVHmBMYQahiMqUstyEBI/uX/WimN5B1hWrum8N93DcncfamFSaTXlszxAfxwrdhMjxWHVaTTw3fxTbYwXhZbGCWFe8Lr+sgm11rSx7fQ/+sMwtU0q4cEBGj93B5xe4mDYkl8mlOZgkkVZfGFlR2R7jPEt3mPn5O/uN4kdPHbcr1u1hxIAMrrywgPteTS5Gb6htQkVl1fxRXPn05h5jXt2+Kl7/2TjtaibrV+NYO8d7ds7O2d9n55JnZzF/VEZFQBBgxet7uHdGOZ6YKICqqiiCgKyolOal8chb+3jwsiFEZAVPzHGrqsrp9lC3lbKF1YVGELujvpWxRZmGo6zMd3HTCzt5bE4l/1IZZuVfDnDn9DLCEe27c9NMnPYEeebjQ9x+aSmoKjdOHMjtlw4mEFaIygqfHG5m4eqtPDt/FCZBwCT13qZrM0ksqCrk5hd28sfFYxFFkUferOGRK4YZ5+IwSwkbQ/3/n503kvf2neGTwy08MbeSO6eVdpuMiLdsp1ZpHF+chd0s8bPvaMdZObfSqOYtXL2VZ+aNouakh6pYMjDpHnU5j544GfQRvAfX7TWc7S1TSth+pCWh2tYbd8XsYX3ZebSN1/56ooeW991UFrjZWd/Ks/NG8fhfDiQ4dr0jZvGEIqZV5LGiS0Ci82PoSZGuNrzA3W0g+GUqiWu2HOU3Vw0nN91GVFZp8YWJnvLgcmhCB39rQKCrkH7cTeXuopIsspz//7kf/FEZt8NCJBZ4tgYimCURp8WKPyoDGjn5Y+/XsnRGOdmpVtoCEToCUb43tj8CKqqqJnWfZDrNxhhFOBr7bl9nMAuw8dAZbr+0lE2Hmuibbicv3YZJEvEGI9wzo5wzHUFGDcjgrmllBMIyTpuJv8Y6kxZPKGJMYQZpdgs9p3w1k0SNv7CpI8Saa8bQ5g8ZnYoWk8gZj7YZuevPu4zz09fEyzeMo7EjhKyoWHog9detT5oNVIG9DR5WbaxjeIGL26aW4o8lOXbUdyp93vzCTh6fU4nLbjbEMkyikLQWn503stdjKigsqCrEbhK5MtYlom+STrYFjRE26B6ferGhu+Txva/s4vZLS3n9i5N8VtfChYUZNHqCLKwq5KrR/UmxmMjPsBOOKkmdva/uPMEz80axPK6T7vWbqnrsottQ25wguKCPop/xhji/wG1seuI7Vc8vcHFJeR/aA2EOnfF2W9GPJy/+puJU86Vap8TpjiBOmxl/WKZ/hoOQLGMSTQSjCgNzUnn0vf38aGopDe1BOmIj1zUnPcgKCSp9uomidu3nV2m8aNCZVNJxur2+DUkUyEu3kptmNUQnzJLI8RYf6Q6LgVNfKIqsqHxyWFvjSyYN5P7ZFURkFau5d/yk2sw8uL5G86GCwH++t49HrhjGoue34QtFEzCqn9/K92spz0tjb4OHdLuZvx5vZURBBgtWb+W/Fl7Y6/H0rjlPMGxsFpe/vpelM8sMpb6/1DQyvjgTkySQm2YjxdY50tTVf/ZmKVYTv3rvAHdeOph7ZpZx7yu7WbWxzhDZiecf6smPPvztIbzRpXMaEv3n58fauHFiMcP7uwysLqgqZGB2Ct5QNEGtuzuMQifn08Kqwp470ePw2hN3pfa+Jn54SQlD+6XTJ81Gsy9E5JRCms1MKKJ1Lf9vwGhIVpBllb5uuxHvygpIkhbLhhSFfi47VpPIPa/s5tYpg7CZJdrjfKk1lmjuGh8trC7kdIz+Q++E1DE6tF+6EWNGZZlUq4nCrBRsZslIsggI9HNrsZ1ZFLlxUjG3TytNjHef28rqhRfSEYgaybaeTMfpHxaP4dF3DyT51LYY59vp9qCxfuLX0ZNXDSc3zcqhJh+VBW7yM3pPYLjsZgQEQ0CgMKuIU54ATptEMCIbsYfuX/Vx6zR7Z8zbXSLw9Zuquj3ei5/Vs+aaMSzvEmNOLs1m1rC8hPf2FvOumj+KFeu6L8ze+8puZgzNY+XcSnadaGdKWS5Hm5MVbLOcFlbNH4UvpNFr/OSdvQlxsc6paI51xELvMa2O2zMdYeM93RW8TaLAkkkDicgqF5flEorKbD3SgquLQMlXxes/G6fn7Jyds3+unUuencVUBXyhKE6riaH5LhQVUu1mVBVa/GHsFhPNvjDuFLOmEBaR+eRwsyFxnZ2qdYf8YHIJoiAkBP1VAzORRMEgEL6kPNfYQIeiikGK+ucbxnFBgZv7unQcVRdnsuKyIXxW10x+hoPHY1xdz84baSgrAixavZVfXz2Cz7rIg8fb+JIsWv0hozOpvtXP2i31nJ/v0qq5sXNZvSAxkNcdVrzijSAIeENRNtQ29dherxN9VsUq+7NWbmREfzc/uWIYS9bu5E/Xj+PeV3axsbaZub/dzOIJRRpRv6wkSHfHW1VxJo2eIAMyU3oM3uIrWV0VzXYea6PAbWfpzHJD4bPraOb2oy0M7pPKhi4dMvrru060MXlwLpX5Lpq8IW65eDA3TxlEQ3sQsyTismtKVI2eYNJoG8Sq4YLATZNKkq7z4glFxohgVztb1VNWVeaNHYDFJCZ3QRVn8eNvDzG6FLtbH70FBOkOizH+Eh9MXBQjV/+fIE5VFQirCmZRJBiVicoqmSkmfBGZFEnEExth2lTbjD+scS9tPdLC1Io+TBqscaPcdslg7ptVzvJYB6fDIpFut3CsVevUavaF2HmsjXEDO/lTQlGFwTlpNLQHeK/mNHNH9zcqp6Ddt3U3VdMv3YYowKN/OcDCqkLuenk3AE99dJhh/VyIQhRfONozPos7/97qj/DEB7U8ePkQYxNilkTSnBbOdCSPtWm/w4SiqlhNEm/tOdUrLp02idkrNyWo+P3i7f3MrxrA+OKsJDXCm2LPhVBUYW+Dh77ptqTv1ztXezrmaY/27Fm3pJpfvLOPa6uL6J/pIBDROpWyzJKBaR3H8fhMsZgYXuCmssBtEByDFuQuqCrELImMLczkkvI+OC0SpzwBzngjhipZoyfIEx/UJuB6bFEmM4fm8WAXnPrilFa7s2BY4Y2bqwlFZWRF5fNjWtfbzvpkEvY114zhkbdqEv5+cVkO980sp8UfIRBTFPWHZN45eprq4qy/OXD/Z+NUVSAQjqKaJVJtZjqCUdJsJmQFnGYJb1jGE4gwsr/WiRuWFQOjlfkudp9sZ0xhBvfPquD+1/cYeHBYJON+68/w8cVZCWNLoajC4glFWCWJk95kXsuHvz2UHUda6ZtuoyMYQVHhqme2GK+vfP8QQ/Jc2C0SLoe5Vz/6wf7Thg9ds/koC6oKyUnTCkVpdu13dzWHReI8t50BmQ5CEZkpZX1o8Ybxh2U+PHDmLNgJMq9qAE9/fJjrxg9k8YQifvXuQSRJ4MaJJShoSoSPzanEE9A4Fhs9oW595M5jbb0Kz+hja3aLifte2U19s59Xb6yi2Rvi0fcOcN/M8s7CVxe1cN1XplhMLKgu4vwYVgEj+QYQjChMHJzD4cYOplfkcbojhNMqkWI1J4xKOywS984o4/kFF7LsteRubL0Q2RMnpW5nOkK8eN2YJGXWeMtyWnA5rPzsnQMJx5kco1HwBCN0BKMEwzKnPEFsZukbiVFPIIKAgCQJlPZNR1Y1hfm+Ljst/jDZDgtRCQIx7sEfTFYxSYKB04mDc/jZO/u4b1YFK7pMQlQNzEQUBWOMuCsthz8sc+dLX/Cn68eyfN1e5o7un4TTn14xlAdmV2CSBB5/v/t4d/6qz/jlv13ApkNNXwqnx1sD/CU2qhnvU4MRhasf39hjzFuQ4cAfjmI1dQp79e5XTXz36U+pLHCxoKqQ3244zC1TBgHwwmf1zKsagIJq+NfTniC5aVqhs7eY1xeSu339ygsLkpJeWU4LN08eFCsUdn5G50KM7xjXk9P+UDRpWkLvCu+bbkcSBU61B2IFoEjSuKjDIvHMvFE88tY+o7DcUxfozZMHGX+L9MB9p1tHIEpq3JhuT0IEyy8fQqMnyENv1CQ8O5bOLGd4gQt/WMbdyzO9O7z+s3F6zs7ZOfvn2rnk2VlMBTIcFlr8YSYOziEky6SYJRq9YZq9YTIc2phSMKKQ5bQQCMu8+Fk9V1T2o6o4E1WFG9fu5E+Lx/DQ5UPwhWV8wQgZTitRRUFRYVF1Ic9trGPh6q28dP24pDFFqyRybzejehtrmznZHmRwrOtNf71rIqXJG8YXivLEB4d49cYqIzmg2/jiTBZUDeDGOE4PiySy4WAT88cNwBvbLFYWuIifiNQd1totRw31zYXVhfzu0yPcOa2MfSc9BsdJ16TfbVMHE4zIVBa4jYTdhoNNKKrKlRcW0OILseKyITS0B2mLOeQ3djcwaXAOC2KkzfEdQlXFmSyZWEJWqoWwHNXGR7skK3UuNIdF4rqLiphclkMgrLD+piqsZhOeYARVVUmxmlixbm+PlbhRhZlkOTXnGf+6wyLx3PyR7DrRRk6aTdukdIQ41R4gL93OjWt3sGr+SJ7bVMei6qJuyemNDUYcgXW8Cmj8qGv86zpfSE82MMvJZ3XNSVxpADWnPHSEovzy9T1JPFmAoTDYW0DQ12Xn8TmVNHk1IY1Um5ksZ88y3/9oUwFvMGpspLOcFsySyJmOECnpNm0kUtCCxyynldOeIBf2d+MwiXhVmYOnvaRZtWTbPdPLUAFJRNvg2004LNpoYvwo8cba5hhvnoP7Yh0TXccM/GFZW+f93TzwmhbI6iqV+us3rt3BH68fy/fXbOcP141NSAxADJ/VA1iydiegdZpsrG0mEFEIRRXGl2QRiET52dv7uHt6ecJ10ddOoydEmt3M58fazorLw00+/OFOXjS9Er+oupClM8tZsX5vAicbQKrNREcwYnAN6aqf+nes2ljHqvmjELvB7fLLK2hoDbB6wShCssK8cYUGx9S+Bg/pDgu5aVbmVxWi0skFczYlX4dFMirv+siNvjHq57Lz2ZFWnvjgkDG2vam2mSynhWfnj+Lnb2njn8/OG8n2uCSdCrhSeq9SCwKcaAtiM4tYTZqQygPdbO43xcZPLihwGxwwDovEnNEF3P3yrqTK/E0Tizne6mdQburfHLj/M3Gq+9ImX5hUmwmnzYRZEmnzh0i12vCFoqRYTQjA5LIc/KEow/Pd2EwioiAwtJ+L2/74Bf99zWh+fPkQvDFfmpNmwxeWcVi0scHKAjfD+7v5rK7F2MxaTSKTB+fyyaGmbp+Buek2lqzdwWtLqlj22p4EjIKG0+vXbOeP14+lsSOo4aAHP6rj1CJpOL1jWineoBwrUIXp+gjX/eh/vneAOaP7U5SZwoNv7OWuaWXdJqvjj7dsdgXrd53kwfU1+MMyoajCHZeW8qt3D9LsDZObZmXG0DwWVhUSVVQyUiwEIzJ3vPQFa64Zw4p1iaThNSfbefDyod3y+c2rGkCLN8ySScVEZJXrJgwkJ9WKJAik2sxcP6GY054QP7xkMIqqGp0jvXW1rJxbiUkQMZuEhNG4ynwXGU4rQVnh05jipd4hDiTgNDfNloDReI6sfLed/pk9jyg5LBL9M1IIywpSd7NsMXvkimFJ18RhkbhqTP+ksbGq4kyuqS7ixonFqJCk3vd1xmha7LnrDWodksGojEkU8YaiNHvDZDuttHpDRkIy22ml1R9mZIGGU1lR+ehAE/8+pr+B0Y5AhDSHGUHQuC9f/Kye6UPyuOmFnUm0HFdeWMDSV3Zzfg+jtj99ez9/XDw2IR7uGu8eaw1gNYs8/fHhXuPdeJwCCT5VV1r1h+VuY169M/oPi8dgM0tnxel9s8qZ89vNCX61ssANQIs3zO1TS/lpLLmkY7WywIUgCJxqC/Ya8/ZJtbKguhCly3HHFmlF+VumlFBdnEVUUUmzmRAFAadFYtnsCh54Xbs2IwvcXJDv6kGhPs8g39d/+4uf1bNq/ig8wYhBU7F+VwNVAzOTCmW60u+m2mauHV+U8P1dC9D6ZEmW08KAs+A2O9VqrMPeRFXue2U304bmJTw7dM5JvQPYYZG0rluVhGvcG17/2THvOTtn5+yfZ+eSZ2cxXyiKYDORYjURkRUEBJp9QRwWM+Y0C1aziY9rm5g5LI/nF1yISRL43tj+tPjCLB4/kGBU4ebJA5FEEU8wij8cJTvVxtJXdvHg5UPpCEVIsUhMG5rH/KpCPMEwd08v4/Nj7QbfWTDas1pnR1CrvsRXVLpLpGw72sqI/m7CssyCqgH88JLBNHu1Fvqdx9oSyHCrijUHCFpgkpMmGeOEG2ubjIqV7rAqC9zsOq6db2W+C5Mo8MibNdw+rYyfvFmTUM1Kt5tJtZlYuHorj1wxLKktWx+zaAtEufpZjQMt3W6mPRBhSN90GjtCvLDlKCMGuPnBlBKiimo49n0NHo61+CjJTeXmF3ay9toxXD9hILKq4rKbafNHcFgkVs6t5LmNdTz63sG4BOABgxsiElV7VU564PU9PHLFsCROssUTilBUkjZn44uzuG9WOa/cWEVEVvj3sQOMiplu+nm8tP0YlfmuBALr6y4q4lR7gHW7GqgscCcEJvrnDpzu6LHqWV2cSURRqOiXTl66ndsuGWyMQqBCnsvG8tc1wYVPupDrptvNmhJVLzxyuqU7/nmBQyiidYd6QxoebCaRpo4Q7hQz3rBMmt2ECjy/4EJkRcVpM2E3SagC+ENRnlswik8PN1PWNw2rWSIaVTCLEsc8Pm2jNm8k+091UFngMkaJBfaz+2Q7fdJtPY4H6WTEsqoaXU1d8ekPy5hEgcF9UglGZVZcVsGJtqBRxY3HZzw2OwIRXHYzSyYWY5a0kU6tM7Nz9FRXkF2/q4Fbpwxixfoa1t9UzYr1e3vFpW7x48z+iIysKIzo7+YHk0uMzo6dx9rYdaKdnFQrlQUubly7g8UTirjj0lLj99nMEh5/mBED3MyvGmDcq0ZPEEVVyEm3J3SP6MHsyztPsL2+jd9cPcJI2KVYTL3iU//dlfkuI3HWbVW6RCP4V2IcbddWF9E/y8F9cUq6KvCH68bgiXHZDc5NZUtdz4TV40uyOHi6gxXra2LH1DoNu6vc65v8LKfF6Ja77qKiBI7Err9r5tC+5KTZ6J+Z8jcH7v8snPpCUWwmCylWLWlmEgXOdARxOzWM6j42O9XK4SYfA7NSCEWDtAdC9HPbaPFF+OV3hhIFvKEogiCQ6bTG/l8bOVz5QS1PfXSYJ+cOxyyKhpjMzmNtzBiaR06arduihe5DQjFfu6i6KOn8dZy67Bbe3N3AjROLuX7CwLPi1BuUSbOZDJy+X3smaXOp+9HnNtVx9/Qyyvum88ibNTxwWQVLX92dlKzOSbPyl5pGjrf4+dW7naOG8b+tIxjl08PN5MToEWRFJSIrNHqCDO6TylXPbOaRK4YZyT2nTcIXiuILhY1nQ6rNhDekdWY8/fEhFk8oNjoo4xMJ8d1l1QMzWTqz3FA+7A2rInDjpGIef7+2Z5wWZ3HvzDKiisr8WGJhcK7TULy+euwAnpg73FBQlUSBbKcVm1nkt/8+kh1HW7vFq8MisWr+KO6PCfgsmVSc9D79N/V12blqdH8WVRex60QbqgpTSjWevZ6wemFhBtOH5nHfzHJ8oeg3AqMpMRoRVdVGzf0hmUynGUEQMKdZ8IY17l+dkUMSNV5Rh0Wi2RtEFURum1rCeW4HHaEoiqqS5tC4JlUVwsisnFPJ7hNtjIjzpToth05LMb8HX/qz75yfkIDqrXBYWeAiLMvcPnXwl8IpdPrU+TH6EkCLebvpplwyqZhdx9s1P3NZBff2gtMTrYEEXjPdr/rDMn3Sbfzqvf2U9k3jW4OyaQ9EMIkC63edwm4WyXBYeLfmdLcx7/4GDw6LaHCh3TWtjI7YPkMUSIh19ev17LyRPLS+lu31bay9djTzxw0gN83Gg290r7L74LoaQwVZ7/hee+2YhII9aPuGS8pzqTnZbghylfdNZ2pFLuV5aVwzfmCCqnF88d0sCUwcnINJEhhfnMWC6gEG53NPuF3++h7Oj8XD8SOeXX1sisVEX7eNVfNHEZEVBuU6ufeVxGKWPyyz6Plt3DujjB9MKaGxI4TLbmZgjtOgo+jO/pkx7zk7Z+fsn2e9k3ecM5w2E76g5ojCUQUVMEtmHGaJjBQrNpPIvgYPdklEkgTMksjUilzcKRbyM+y4U0xMGJTLnS/v4tY//JXcNC1xtiE24mmRtJbvu1/ezaLnt/Hdp7dw2hNCEtFIOmdpHGvdmcMiaa3jMQLjromUeFu1sY7bLhmMrMDvNx9FEqF/hoPVm46w8v3ahMTZgqpCY5wi3W7GZpaMDrHfbz7K0lkVjC/JMuS1K/NdPLi+hgVVhUiCwOSyHN7bd4Y3dzdwcVkfw4lZTSKfHm7myqc3M7hPakLQ0tUGZDpYveBCclKt9HPZeeGzehY9v42bXtjJ3DH92X6kle/85lOufHozs1du4pG39pGfkUJ2qo12fwR/WKbZG+KqZ7YQiSqc6QghiYKhxKVXql+8bgwvbjnK3NH92XuynZ3H2kixSQnS4V1tw8Em8tLtSa9PHJyTNGYAsL2+lSZviOWv72HGYxvZdaI9ieRVV3W8ZcpgVm2qY/bKTcyrGsCk0mwml+UYm75VG+tYVF3I+Nj91YM5/fp3ve9TSrO5c1opJlHksfcOEFVVfv7OfmY9vpF/X/UZnx1tobEjZGwAusq/X/XMFnyhKLWNXk629U40/8+0rBQrVpNIms2ERRRRVHDazdhMEt5gBIdZ0jAqCvgjMiZJQAUa2oNkOi20+sIUZDoIRmS21bUgKxhE5pIg8uQHtcY1Htwnlbm/3cz5BS4mD841CMS7G51dMmkgqTYTwYiSgM/xXe7TezWnuWliCbKisuNoK7lpVmPN6/jsik2nzUyfdBv+sEyrX3tGtPkjLBpfyPgSjdy/Mt9FbmztnPGG8IdlZDT+jy+LS/13uWLKsVMr+gAqhVkpxvPj6Y8Pk5duZ8nEYioLXPzq3YPMXrmJ2Ss38cT7taRYJK5fs4NH3zvIoue34bBKrNpUx4AsJ5EoSWNX111UxBMfaAH+Y3MqtfsWW5u+cLRXfG6qbWZqRa6B0Z4279uPtrLnRDs2s8Tn9a1sq2/FE+gcUclyWijOdvKTt/Zx1TNbuO2PnxOSlR6xNr4kixu/VcyK9TUJx9Svn37/d9a3GvhauHorP3trH4/NqSTLaWFyWU6PqqKbapvJSbMSiI0gpzssDMxxckGBm4E5zq99EO+0mQhGZSRRE+DxhWXcKVbskoQnEMFmEgmGZUKxLiAV+MPWejJTbFhNEhZRJcflYNnrewwuuPte3U00xuP3ZOzZ6w/L3LB2B26nhZ+8VcP5BW5GFrjxhaLdYlQfh188oQh/rINNV47uau/VnEYSBT4/1obDIiEKnBWnabEuOx2nxjM8DqO6H91U24wnEGV4gZv39p2hIxhhxtA8Hp9TaQja7DzWxnef2szWIy1sq29NOkd/WOty6+eyc0l5LmMHZrF60xFuWLODK5/eTJ8YTgf3SWXR89uY9fgm5vx2Mz99cz/+sMyZjojhA+wWLaHuD8t8/1vFCUIH111UxOq4xJm+thc+v411XzQYBaJefWltMylWU+84rW9FQDBws+tEO+1xOO3nsmEzi6zf1cBNL+xEEgV+8lYNc367GRVNNCDeZ+q2dEYZT7x/0PieVRvrWFBVyKTSbJZMKmb1/FG8fMM4Pq9vZdp/buCGNTtYsnYHFw7IYPuRFoJRuVesDumbzl1/3oUoCN8YjHpjcaAgQN90O6k2E3azyYh3vcEIKWZNDXZ8SRa+sOZLzZJEbpqddIeJKaV9ONEWYPUndTgtmj9e9upugrF494kParn/9b3Mi/Oljd4gy2ZWYIt1oPWE0/gR7d7i3Q/2N7JkYjGyAk9+WEteuo21W472ilPQfGpeui1BWOn3m4+y/LLEmBcw4l5vKIoKzPyKONWLJ3aLyG2XlDJtSB8EiPlVLYH8xAeH6JNu59rxRWw90pIQ8/7srf1kplpp9UUM36ii4g9HWbWpjpxUG6u6FGJ0v6r/TRIFFj2/jajac4F+Q20TVQMz+dagbAOnv3h7X7eJtkffO8A9M8r5c0yh/PP6VmY9vokb1uxg69EWUuIESfRR0e+NGcCFAzJ45K19XLZyEwurC8lLt7OiBz+7dGa5gVsds7p19bG3/fFzIorC/a/tYeHqrew60c7JtmC3v9Uflrn75d20ByLcsGYHc5/Z0qty7Dk7Z+fs/66d6zw7i1klEWuKhZCskJmijZxYTSICWieGJMF9M8oRBGjviGAxCbgdVh5ct4elsyowIXHny7uob/bz+0Wj6Qh2Bn0dgQhOmznBueljXAurC8lJtTE039Xj7P91FxWx50QbJbmas47nAnpsTmXCiJQ/LPObj2q55eJBzB3dnyc/qOWHUwdz46Rirv9WYlVODxzGF2eS77bzL7/+xKiaLZ5QxF/rW3lgVgXNPu1v8fxsL98wjuMxjqinPjrMupuqWdpl5FQfAdEre12tn8vOwUYvJlHg44NtvPhZPX9cPBZ/WNtw2UwiN00u5paLS4jInVW4D/Y38vmxNsr7pidVFLNTrZzpCDG2KJMXP6tn7bVj+MXb+6jMd1HaN521sQTac5vqMIkCQ/ul97ouPMFIUoXLLIlcU13IPdNLMZu07iVBELBIIste61Taq8zX5NbjO4Qq811Gp4y+Gdl9op1bLx6ELySTl25j3U1VKKqmeLZkUgl3z9BG9PSKW3zVMxRVSLeaKMh0EJZVHn5jL3NG9zc2O7pV5ruMDqKeLBTVRhnufOkLrRPwa7gBUIBWvzZW0uILYTZJOMwS/ohMVqqVJz44yA0TSzjZFiDVZsZpsRCIKEQVFQGB2kYvdotEnzQb79acZsh56fhCMjuPtTGltFOFses1VgXV2CR2x/UxbUgeTd5QQjfjzvo2XrmhKoHz7okPDvH8ggwA3ClWWn0hHphdQUN7sFtsVhdnkmrVft8zGw8blW4BFbtZZNmsco42+3FYTLT6NZzq4yltvjAj+mckce7pm4muuLSaNALovHQbh8/42BZTxgLtmXP3jDK8wSiyohCMKNx5aVksiNc6dQ6f8fLG7gZjMzK+OAubWWLG0DyCEZmIYko4jyynhUvK+xgKeKs31TFyQIbR8apz5vRmvpBMuk0ky2lhankfRha4EzsugX5uG4IqGCNAi6qLjKS2Xt1e+soug1vtW4Oy6QhEWDl3OPtOtXPt+CLumVGOxx/BaTORapXwhaO8eN0YJKFTNEFfF91xLsaPkv7hurEcafbRm4WiCu3+CCfbAj0Ko3xdzSqJRBTtOekNhonIKi6HmbCsjYuJgCQJeINRJEEgGJW5ebLWaRGSFbJS7dz18i4mDMrCahJIsZjYUNvMjbF11tWP6litzHfhsGrd49ZunnUTB+fw+fE2Li7Lpc0fYWF1IY/GlKNVEru5th9tZUpZLnNH9+fXH9Zy48RiHrxsCMfbAj3iNM1mIhBNxKlZEHno8iF4Q7Kh5KYnDJw2iUhstrPVp42ldu1y7gmroAn/LJ1Zzpu7G3jqo8NAIk7T7CbaA5EknG6sbeL3m49S3jfdOEY01skajMhGVx4kYhQ6E2l67LF2y1H+5YJ+VBdnnpWPMxBWWDKpmLEDMqjMd3FnrBMu1WaixRsiw2lN4G4aWeBOKD6ZJdEoWt16cYkhMGCWRFRV5ZYpxfTPcPDDS0q5cZIcSwhJhKKKwT2pr5k7X/qCZ+aNYuVfDjCiwMWD6/YmjIRmpFj45Tv7tXU3sXdOJv13n82/fp2sIxAxFDXdDjOhqEY7kGLp9KU/ebOG26aWsnRGOZ5gmCynlaWv7OaBy4ZglyQ21jaxpa6ZW6cM5mR7AEXVxDr8MZX6nnzpybYAfVxal09XXwqdirp63NNbvPvUR4cZU5gBKlwxIp/H/nKAZbMrkGWVE7EiYDxOAcOnesNyQoLu5985H0GFH00dnMCLp8e97hQrzd7QV/KpoBWj0u0m6s74DZ+qJ+8XVhdy53RNuCTNbsbzJfGqqlqsO2NoHqGoYsScOmfu1C6YrTnpYXxxliGC1JOZJJGorKlX6p3SkKhy6wvKpDvMSMCd08q4pwv1QGW+C0Ho5IXT/Z7OF6t3u5okAUXV4qXH3jvAY3Mq8YVkPMEortj4b7wo0s0v7DREVbom9HWV5fiE55eJd3XrSRX362iqquL1es/+xth7Ozo6cDqdCWJM5+ycnbMvZ+eSZ2exYFQhxSJhF0QCUYU0m4lWX4RIVEEB+qZaUdBIaaOKSo7dytJXdnF+gZsTrQHcKRY21Tbz2pIqPjnURE6sBTjfbSfVbqbdn/xwjlfU+e/FY+kIRrol8b2oJEtrq08xG1Ux/XN3vvQFL143htOeUEJQ/97e0+w82kpp33QaWoL0y7DT2BFizZajSTxXy2aV817NKcry0gwC9WlD8njg9T2c8YaYFBNFiOdniydD9odl6pv9zIxxroRlhfPcdnyhqKFG19XGF2fy1p5TCapX40uyUFToCGmjU7f+4a+8dmMVD3QlpI0FKmu3HE0IWPq57Bxt8WuJUJPIqvmj+Olb+9hY28yc0f0ZHuOe0De2FxZmaIqDceawSNw4cSBTynINlbaXvj8OUdBGu1RV66Zo8modTD9/ez9XjMg3+M3ig4iwrLBqY11CYlGXPo+v4j+3Kbnd/sm4qiEkqqPFr5ssp4U/LB7LnS/v4trqIq4c3R+bWUqqlOudR72Z/vrHB5to8oa/lskzX1jbFEUVFbfdQqM3TDAsowCi3cQ1Fw3EH5ZJi40n+iMK3lCUjBRLbDTZRv9MB8te20NlgZs2b4TsdCurNtYxYVC2cZyualdPXjWcwblOxpdkdcv1ccYTJM1hYd0XDQbnHkB9q59rxxexdEY5J9oCpNvNHD7jJSvVytot9Qw9L52Ly3Lo57Lz6w9rE7BfXZzJQ98eyufH2yjKTtUC0QI304bkMiArhXte3sWPLi1l9aYjzK8aYNy/ncfaGF+cicNi5s09DQYuzSYRSRD4NKYsGI/LquJMGjtC3D29jH996tOE0ROAz+tbufyCvrT4wxRlpRjjv/HnOr+qkPtf32t83/LLKzjeHOBke5AH19fw7LxRQCfGplXkUdfsMwQ9nv74MIuqizShBkEwuGb0zyyeUMTk0hwcZomwotIR1EZ4BWDttaPxh2Sy0yycag8hCIKh0PdfCy8kEJcQcKWYjS7ehdWFdASjRudbV27D7rA4viSLu6aV0uINYTV3jqfo173raImeZAAtqPeFz17ltppE7Bbpa53I7smCMaXhYERTxg1GFW20T4A+qVaiqkq63axh2GExaBKCUQVR0MYQN9U2s3z2ELbUNZObZouJYZi73RDFY/XF68YgAKc9wW5HgVZvOsKvrryATw43GzjdfLiF6y4q4p7p5TS0axg9cLqD92o6fWhTRxhVVXvF6e6T7fTPdBq+5dl5IznS5OM3Gw5x57RSYyRex2mjJ2T4n231rew72Z4wYt0nzcb+0x1JWIVO4Z+5zyS+9nl9G9++oB8t/jBRRRvTfviNxJHD+I2+/v+hiMzSWeXUNflIj+MjeuG6MdTHVPXihXf05NUtUwbR7AuydGYFTTF6CIdF4rZLSphUmksgoiRw3M0Y2genxUR9s58zHWFDifbJq4YjK2rC2HN2mjUhborIquE3p1XkJSQwOkczE59L44uzuHlyoigPaJxbj/3lAN8bM4CcVKuBfx2nUys6CynxI2jdmX4/9eLeN8FS7WY6AlGynRZUwCqKtIXDBCOaLxVsJsr7peOPyIQiMhkpVmN8NhRVCAuQk2ZjYXWRwS84b9wAnrxqOBaTaKjQQ/fKkX/5jwmML072paAp6m6vb8MaE5qKf57e/MJOXrxuDPdIZUZybHNdC0P7pfPilnpK+6ZxoiVAptPMwGynIUalm47Vxz84yOKLig3KlJVzK2loD/DbWOI7L72zYKHf30BEGyF/c0+DwS8YiirkpFo50ANOq4sz6euy8chb+3hz9+mE1/xhmc+PtXFJeS7NvjCt/gjFOU5tDXcT78bj1WISOdUW5GR7kIyUTnw+NqeSFm/IuDY6Zt/f18hNk4uRuxHV0P3xxME5SKLWCSwFBEyiwLol1SBARFFo84V5e89pI6H27LyRCAhJsabG16sanWKhqEJlvgtJEIzCdfx6mFSazWNzhifdq66KwLqoypTYlEY8HUvXmPerxLvQsyru19G8Xi8LnnwHwdQ7/zGAHA6y4KkPeG7xRPLy8s4l0M7ZOfuKdi55dhbzhTRnn2rRRkv6pdkImBVMksYDFVZUglEFRVX59HAzM4flsaG2mQXVRXSEopgk7aFkkkT6uhxkOLWH8W++NwKHRUI5ixJUilXihjXbuyX3TbGacFglfJEoy2ZVcDSua2FB1QB+8kYNV47un5AYMzZ+Hx4yeEsWTygySNJ9IVlT5lNUZq/cxC1Till+WQX3vbKHEQPcrHhd4xC6c1opiqIaHV56oHPGG2JPjL9iU20zt/3p84Rz149397QyIrLahcg1ixsmFrPo+a3G36qKM1k+u4LGjpBRcfOHZQ41+bh/VgVhWaE9ECHVatL+3x+hvG96QvdcfDLu9SXV+EJR4zpaTRr3TnwgNrLAjaKqrL+pGk8wSrrdRKrVhC8s8+C6vV2C8EwWVBfy+81a59rbu09RkOHg9qml3PfqbnbWtyUEz7qqmj8sEwjLzBzal4VVhWSnWg1hhu46VJ68anjSZr03e+SKYYY6622XDObn7+zn6jH9k95nNYnsPtnOw98eYogc6CpLqzbWUVngotET4nRHEPj6VuI6ghHy0mx4QlHcVpORSJNEAUXRgjxfWMZmFrFIIme8IS0wiiVl9e66TbXNXDd+IAWZDrbUNTOiv5ve6N6sJo1Medmscn7yxr4EIv6RBW6yUm0cbfazamMd1QO1MS193Prz+lae2XDYwMVjcyr5/eaj3HlpGSvW7eFX7x40Krt3TSvDF9Z4cwTgP9/dz/UTSzjToW1MX/ysnj9eP5ZjrQE21DYzcn8jS2eWs+1oCwKdir4r51aiKCq7jrUzd0wBz208wvb6VmPUIT7IH1+SxQOzK3hzdwOnPSEG90mlqctme+msClp8Ya3DrMnHjy4t5QdRDZOCIKCqKpIg8PN/PR+rSaTRE+SNXQ387O0Dxvc4bRpnzq+vGs7JtgC+SCdX0pmOkCZCsvkIP7pkMDdNKibNZsYfjvL+DyfEqv9RHBYTy7oQd+vYXLulnjsuLeX+1/ZwrDXApNJs/mvhhdz36m7mxIjhHRYJq0nivZpGY2PWHoh0O0p248SB3WJxw8EmVLWGCwrcCZ1xqzbW8coNVRxr9Rt/00dW4jcNT141nL0NHoNfp2tnq8thRhQ0n7DtaOvXNpHdk3ljvtQXipKdYsEZ83+iKBBSZKIyWEQRc0xpEwEUVSMvdztNtMc4tIJRmX4xX3rjxIF0BCPYTL0nKPxhGZfDbIwWQ2dHWTAs8+TVw2nxRRJw6g/L2M0SD7+x1+ArfGxOpaGqG7/Zy3JaWDm3kntmlBscdE6LxK+64FRV4ckPNE6nTbXNtPoi7KhvTUgY3PHSF/x+0egEzD638UgXYZpRjOjvTiJCXzqznJ+8WZOUAH/gMi2JZRIFWv0RQhGFZbMriCoqJ9sCpNnMWEwiDe1BHp9Tyc5jbby4pZ45owtY+MQm/GGZZ+eNNJJR8YqzC6sLaeoIG8mrn7xVQ2mfVOxmM5GoTKbTwps/GE8wHMWdYjVoK+LPb0FVIS9sOcrSmRW8U9PAvpMe/vz9cTS0BxOSb0/MHY4kCHxyuJN3UP+tC6sLEzrUHBaJ31w9PGE0U7cNtU1c/62BSetEx+2ZjqDR/RKPU30kL/7c40UOdP95qj3A7pMaD+w3aV/qMEsoqoZJX1gm3SKRajcRlTVf6glEmTAoB39IxhoTMNKvrTeoJcJDUQWTJJKTZmNnfRsPzE7hgdf2sLC6EHdK7xv7Vn+Y+2dX8NAbe5NEbbwhmSWTBtLmDyeM6oF2r112M8tf38OVsXsVX4h8/pMjvWLVZhI54wlwSUUejR1BFlQVctn5IZ7bWGdgtbLATW6qzZgY0PF6ojXIvlMedh9v58rRBUaXqH7s4QWuhLh9fHEWKy4fwvee3cKy2RV4YkUB3fSY90Cj1+gWb/aGuH9WBaGowilPT3g9ypDzXEasqyeZdEqQmyYNMngBF1YX0ugJcX4/FzazhEUSmTG0D1eMOI/cNFusQ1W7Lr6wzMPdxL1LJpUgABlOK/3dDtbfVI0vJPPEhwdZ2IUzUhPmcGA1SYaqZ36GnWMtAfLSbQkK5fr7rx7Tn6VdEmc92aqNdbz5g/HUNnqN3/fcprok4Re9iWBSabbBcRwf8+rULaDFPz2p4n5dzWS1IZ9FpVQ3QRBZ+PSH/PGHs0hNTf3/fGbn7Jz977JzybOzmMMi4QtqreypNjMRRcVmFmgLRHHZzSgqtPsjiKLWEXFRsRZ4m0SBLKfNUHDyBSPkpFrZ29DOv1T2RRQEAuEwaTZLj9Lw1cWZOMySQe67av4o7pFEPIEITqsJiyQSjMj8+I0a7plZTnpMKclhkZg4OJefvX0giQDeahL57EgLD8yuwBuKaqOQoSgRWSUUkXE7LfiCMifbA/jDMhMG5fCzt/Zx/2wtUfXoewdZMqmYRk8IkygmdHqBNhrWVXVIJybWEwBOqwlRFPjR1MHcOa0Uf1gmHFXYeqSFz4408/icSuNcT3uCRBQFp9XEoTNeTrYHjevb5A1hMYksXL2VdTdV8+NXa5KCkHldWuYjsowYlw3ZeayNaRV9ONqibWzz3Xb6uR1GtUtPXtizUnho/d6kILzmVAcOi4nbLy1FAO6eUUYgLOOPyAYJcfzGbmF1odEqf6YjxIj+bh5Yp3U7TavoA5DUofLkVcM19dNu1kh3FVrQiGr1v5kkQRtpmTIo6fO7T7YzpjCTlV02GFXFmTw7b6TBPdTo1a7717USl2Y344/I+EMyDrPEnpPt9HM7cNnNmhJnKIrTZuJ4a4Cww0yazYzVJOKPREmzmfAEIvhC2shEn3Qr/ojM4+/X8rtFo9l6pKVHGfPTniApFglJFLikIpd8t507Li1FEMAsivx4/V5um6qtcX+kc5P30zdr+MHFg/jRS4njBwurCwnLMndNL+NHsQ5HX0hTEbVbJO57dTe3XjyIvhkprHh9DwtiQer3xvbHE4gaHThPfXSYKWU5nPGGmD2sLwOyHKz8oJYla3ey5poLmTumgLWbO4U3FFXlgdkVRGWVjlAUh1lLaL32xUk+q2vhiQ8OJT1HGjtCvLm7gQklOew81kRlvovjrQFu++PnvLakmgde25OgXKWPa69YX5NwDRs9IR6YVY7FJLJuVwPpDouWRCrOJN1uQc2HNLuEw2qixR/heFuA4uwUAhEZk6QRKuuKd3rCaWSBm3SHFvz/8JLBhKMKv/ruBQQjMi575/vnx55biycU4QtFE55dVpOYgEXQNl6TSnMTkn/xtrG2mXtnlGOWRGPN+MMydc0+Ctyd6mH6Jj0+MWc1ac/OF64bg1USuXJ0Qbfk6XdNL2XxhKKvbSK7J0uzm+jwR0ixmRFEgaiijTz7wzJf1LeTn5mCMyawocREPYKKjMMqYRElUizaiH4wLJOTauVoi49Jpbm0eEPkpll7xGhVcSan2wP0z7CzZvNRhuans2L2EAIRGW8oGhvp1zpE43HqsEhMGJTNw2/uAxIxahIEbrtkMHdPFzna7Cc/w87be04zcVA2OalWPj3UzAUF7iScDottbvWkbVRRefrjw6ycW8nazZoffW5THWc6QjGfWseStTu57qIifjClxBA7cZgl7phayl3TtKSCKMCB0x3M/e1mrrywgKtG90/AqT8cxSJJBk79EZn6Fj/5GQ4KM1OS1LzHl2Qxb9wAborrmtl5rI37Z5XjD0XxhWXD94wdkEFmmlV7rr1Vw/yxhbhTLJzxBMlOtXHaEyTVJpHltCaoyMaPfAVCMvdMLycQjjKhJIfxxTlEZJXH3z9oxBYLqwtp8YVQVDUBp85YB1hXv/mbq0ckjfPG26eHm5PWTCiqMKp/BplOC6c9wSScLoxL2nywv5GbJxWjqLDyg9oknC6ZVMywfi4U9cttZr8OpqoqKWYNgzoP4Z4Tnb7UERvfTLWZONrix+3ojAccVgmTKOAJROgIRAjLCgurCwnEYqEbvjWQDIe5V5zazRJv7dG4cjNTzCydWY4sqzT7w6RYJaZW5BGRFa5+9jNjVM9hkXhm3iiOtQZ4b9+ZpJjXJAgsn12BPyxztKUTq5MHZ5OZYmXZa7u5a3oZkiTx3KZaFlUXcfMLO/nD4jHc/tIuA6urNtYxbUguC6oHAJ1r0G6WEjBcWeBmUXURJkkgO8XCisuG4AvLtPjD5KRa2VnfhjcUoSDTkTS6qse8CipOq4mjzT5Otge5pDyXVn+YZl+Y4mwn973WPV7jY12943lkgZspZbk0tAcMzGqFPSsRWeGpD2u5bepgbps6mPte2U3NqQ4euWIYTpsEiDzyZmKHapbTwvyqQpxWE/6wNmI/rCCdUFRBkgT+dWR+Eqfv4glF2M0SpzxBIxENMK4o04hPjXUUE/WymZInJfTf1TXm9ceN2kLns2BhlyTrzmNtHGrs4I5Ly1i+bk8CZquLM1k6s4KrntlMVXEmN8aKLP+bzWTtWdH0nJ2zc9aznRMMOIvZzRKZTiueQFQL7CMyVz/7Gak2CQStCp5ik0iza5uA1FgCy51iJiIrnPIEmVyaTZbTiqKqFLht3DplENFoBIfZghl46NtDk8iJq4szuW9WBZ8fb+OmiSUM7pPKlU9vJiIrPP3xId7ccwpFhdZAhA21zciKahCn6gp7kEwAv+j5bYY6V0cwysbaJlZtqqPJG8IblolEVYIR2Whdjsoqb+w+zfFWv9GFpweUW4+28OIWjXPBJGjJsMKsFEbExAUqC9w8O28kyy8bAsAXx9vZeqSFH79RgycYpSU2ArazvgVJFNh6pIVfvXvQONdVm+ooynKy+3g7drNEXrqdVRvrjG63VJs29jqiwM2buzUlyteWVPHkVcP50/VjmTm0b1LLvCgKROVOx75qYx2iIOCK3bcnrhqekDhbc80Y1mw5SjCqGJvzWy8u4bUlVbxxcxV/un4cT398iMb2ELKisuzV3cx4fCNHYyMtlfkuRBGD9LQy30WDJ8hd00uxmkUiikxljNA6zWYyOGIcFskQNHh2w2HaAhHjb8/OG8mTVw1n1fxRmCUhRoaclbB+dBJ7wOCzsMTGHeJNVUlKnIGW9Hzyw0PkpltZsHorQ/qmc9HXuBKXYpboiPH5+CMyS1/dgxzrXvGGoqTaNZEPp9VEis2M1SzS4gvjMGtJ6GZviDS7puLoNEkEwhFWLRhFuz/I2MIMgzA43qqKMxmY7aRPup0nP6hlUJ809pzwsOeEh0ZPCEWFofkuLCYtkbIjph6l8+w1ekIJ36dj9d+e2oxZEhPw2RGKEojIbKxtRla0dbShtpkd9a1MKc1meoxbLX6EutETYtuRVv71qU9Js5m4Z3oZL143BpvZxNot9ZT2TWdoP03BtiMY5ZW/nuTn7+zns7pmmv1hJBF2HdOUsyoLXAnPkec21TGiv5tdx9qRRMGo2FpNYmx8O8yy2eU8O28kL1w7htdvqmJ+1QDWbjmaNMJyx0tfMHJAhsFbpCeRsp02dta3YhZFLinrw90v7+La/9pGTqqV054gbf4ID79RQyCiYTPLaWHttWPYd7IdFTh4uoOoqnK6PYhJEkmxmuiTbicaNwamB+ITB+dgN0tGgiTdbu5W0OSRK4YlCGd0h0mAU20B7p1RZuDSJAqYJMF4zusjK/EbgJ3H2qgscLFo9VbunF7G892Qp2+obeKhN2qYODjna5vI7skcJs2X2kwiUUXFG45yzfPbQNB4/pxWE4Ig4LSaSLWbEUUBf0jGZtI2803eEM/OG4k7xYIoqlTkpRGORsl3OxCB+2eVd4vRJRNL6JNuZ9eJdq69qIhtdS289sVJfvXuATbUNqGqcKw1gEVMxOnC6kJjzEk3HaPzV2/l209+giQK3LBmB2/vOc3O+lbsFhOBiDYG7glEknCaEutC1nG6o76VygIXS9bupLRvOlZJZOnMcvq67Ly4pZ7KAjePz6lkUG4q7YEIG2qb+OU7+xFFgfmrP4uNeFr5zUeHyEmzMbhPahJOzz8vnZqTHUk47Ztup8UbQgHDV79w7RienTeS+2aVJ2F11cY6hvd38/gHtca/F1QV0i/Tzt4T7Uwb0oeh+S6yUi2sWLcXi9nEivV7CUdVHBYz9a0Bgz9szcIL+dP141i1qY7vPrWZcFQhrGiqvA+u28u0/9yAL6x1iOsYrcx3MahPqnEf9BhDV6aO5yq67qIifvvxIdpjCeaefOeyWRUJQgIOs6be2B6IGPiPx6l+LqAVKBwWsyFUEW8baptY+UEtZzqCOMzfnBq1KAhGAkwvSMX7UrtF86FWk0iq1ZTwDLKZpDhfaibbaWXsgAxCEYVBOU7yXQ4kYPns7n3pkoklSKLAzvo2ctOtPPF+LVFZ4YvjbURiiSUBrTt1RIGbDw+cMXD6i7f3GYWjrjHv/NVbee2Lk4hdsGo1mwjGfKo3GDWSODvqWxlR4DJG+ON96rt7G1m7pZ7zC9ysnDucNJuJvDQbI/q7DQxX5rs0Ls+ows5jbWw72sopT5Drf7cdbzDKmMIMaho6DHGd+HNdtamOwiwnzd4QGQ4Leel29p5sB8Bh1SYgApFoQqz7+k1VLKgawIvd4HVBdSEZTgu/eHtf59+qtL/tOt6GzSRy7YRiNh9u4d5Y4kyPeRvaQ3hiHM163KvHvGu2HOXdmtM4rBL+cBSzKPLQ+r3UNfnITbPxaawzVLeJg3M45QkSiCgGbkcWuDnPbTdUeQGDp+y5jXW0BbrH7qj+GfxoamlSzOuJYbYqjmdRSyB2vm/Vxjq+/63ihA5V3TbWNvPg+r389+Kx2pj86q1JNBVfV/sqfGfdfbajowP1G5TkP2fn7J9t3xyv/k8ykygQiMqk2iRUVcUf0nit/vU3m3n1xio6QlE8wQglOU7GF2fhjKkQRWWVUERTeVlzzWj8YZn2QJh+bgeeYBS3w8bx9gBZTiu/emc/C6oKuXu6JjOdajdjEgVOtwVj1T2LwU/U2BHinhnlLHttDzOG9jE625o6wsZIihLj3+rNgpEo+W47q2Ky0qc9QQQEUiwSDqvExkNNhpqSNmroICwrPHnVcHJSrTR2hKhr7OD22IhZfEv8fy28kIff3NelEquNUK1Yvzfm4LURN7tJ4rPDrVT0TWfmsL6JnS2eIJIII/pn8HZNA4++W0tlgYsFVYW8+Fk9hTEy/AXVAwzp8cp8Fzes2cGk0mxunjwoqWXeLIp8crgxYeSjsSNEUZaD8SVZmCTReP8jVwxjeawrzBuMGmMjNrPII2/tY2FVIas27ePCwgxy0qysWL83oYsENJ6HhvZgAs/DkFjiRBQF3t3byOf1rax8v5b7Zpay4rIhnPIEDZ6l4QVuak51MCDTkcS9BFrQOayfizumlTLfE9SEAuxmowsRtO4GIOE89PMc2i89gV8u3jYcbMIb6qzoPXLFsK/tmFhQ1omgRU57wvjDMtf+bjuv3lhFikWriANkOCw4zBqWbbGNbIsvxKjCDEIRmXFFmSgCZDltbD7czLDz0mn2RwhFZa4bX8htlwwmFJVJsZiwmEVCEZl3ahpYMqmEX757gKHnpTNtSB4r1u1l8UVFzByWR0ts5CJ+FKU3wnt/WGbh6q2svWa0gc9AWDZ4KdJsJmOU6cXP6vnz9zWRjhSLCbNJ5MffHsKD62sQEFg6o5wV6/fyr09t5rqLiqguziIcVbh7ehnLXtuTtJaWTCzhsyPNmARNlXTpzHJWxDCgY9NlN5PnsvH4Xw5y85QSnvzwIHNH9zd4gcaXZPHRgSZGF2ay6PltrLlmtMGJ8uy8kUSiSkKytjwvDX9ETtigVha4CMoKz206wlPfG8HSmLT8kknF7DmhJcfW72pg1IAMGtoDxjjZT9/ax6jCDBwWEVlRae4Ik+ey8eA6TdZ+Z30rN03s5DrSuwcEQcOq/lz48MAZ9p5sZ2pFbsK9yUmz0Rw3RtYdJscXZ3HvzHKueX4rl1X24/pvDSQzxYKiqsyvKkQQBKwmMYlIXT+X5zbVUd/i71XJTxD42iaye7JAVHuOSCKEIyrBsMKBRi+yrLJm81FumzoYFa2bs8DtQBBAtmqKloFwlCynRlZ+z8xy7CYT9a0BCtx2TrRrIiDzVn3Gn78/lkBEoc0fIcVqwmYWNZXNiNZ1cp7bzvShfclLtzFtSB4Prt/L9CG5VPRL54w3ZIw9LqgqxG6WzupHrbEOQ/3eiYJGEB+KKvRzmQ1RHR2nvrDMk1cNx+Ww8ONvD+GX7x7gJ1cMQxS0sbKVaPf1yasquWdGGfe9mozRBVWFPPD6Hv7j4kFsq29lQKaDH14ymF+8sz8JpxkpFryhCGV9U3nig06c9ku30+YPM7y/m0NnfMYxnp03klWb6riwMINrxxclUCv4wzKBGE4rC9xUFri4+YWdvLJkHEPzXbR4w0wcnMPDb2gKp794ex/nF7ixmAXue3U3V48dYPCHXXZBX+6JiXE8dfUI+rntbD7czLpdDcZzQE9exHeZBcJKQufJyvdrMUsCSyZ18jY5LBIXl+fgsIgUZqX0iNOq4kymD8ljxIAMowO1MCsFb0grwujcisdaOhOoXbvqz3hDPeJ0w8Em7ry0FJfjm5PkTneYOd0RJM1uJs0icdITSvClbf4QDouZkKyQnWbFFtdhKwnQFggzqjCDYESLG3OcVnwRmV9fPZyOSJSorOKwSnz7gr7cM6MMTyBKikUyfOn7+09z9Zj+/P7TI5T2TafVG+HCokzuf20PIwe4ubQil45AlKWzEnG68v1a4x52Z099dJhvX9AvCaue2BpLs5sTKRAWjyUYVQysPvztIaxYX8PTH2siWLrv1HnB7p9dwf3d+FOdk6yywMXC6kIcFhMKKmZJIC/NZtB2xMe8OWlWJOCNPQ1sO9LKteMHsq/BQ1leOnnpNg40eln5fi2V+S7WxEYgdd8a6oLXF7bU8x+XDGZDbTPnx2H2j9eP5ckPD/HMvJH84o0aYzT12XkjjZj3uU11/GDyoIS41xOM8vN3NE7Apo4gZlFi+9EWA7fzqwrxBqNJOAFo9UfY2+AxEoYAd08fzPiSHKDTn9otEtvr27j1ksE9YndSaTYrLq/gcJPfKPan2c0J3YCg4fXlG8axfN1eI95v8oZ7HAXdcLCJQKSTi++b0t0thwPc8NyGv6mTzOv1cu2zG3jxlunnxjfP2Tn7knYueXYWM6kgCmC3aBxKqXbtkvnDMm/sbmBqRR++v2Y7z/z7CO6bVc7uk+0sn13BaU8Qd4qFyyr7cbJNG3nLSbNz78u7uGFiCaIo4LSaCUUV1u8+zfrdp3FYJF5fUs39rycSg04pzea2qaUAnPGGaOwI8uPLtVZwp1U7n61HW9h7sp3z812MLcoyKj/dSTKPL84kqoA/EuGOaWX89K19DD0vncuG9SUQVYjICvsaPCyfXUFIVnhsTiX3v7Y7aaxv2awKdp9o567pZdypah0+6Q4ze0+0c/vUUu68FI2oP8YzoCe4QKvy3DixmBZ/iNsvLeUnb9Uwor+bIf3SCIQVUqwShVkpmASBBk+A81wpCVwsd04vRRQEjWz48Y2G9LgeWM8d3Z/ffnyIR64YRjCiGNwWbYFwkmPferSF/hl27ppWSkcc8bQ++riwqhCnzWR09K2PBQp3TitlU20zd11aRosvnNRFUlWcSbZTq2LfFGvP13keQEuwPf3xYR6bU4kC/Pydg5TkpNHPbTeEBG781kDWXquNcXXlXtLPXwBuv7SURc9vM/7+fox4d0Ntk9FJYRIF4zz0gC1eOrw70zcwBRkO8r7G6n42SSIqaMqZXTF6aUUf2nwhTJJIICzjMNsJRBVsJomoomK3mGhoD3LHn77gV9+9AG9sjRbnOEEQCEWj2C0Snx5uZsbQPH7+zv4krqHK89zcM6MMX0im2aspZQKEojIOm9kYRTEJAjlpVo40+9nb4OkRowMyU/CGO/H5gyklRKLa5tAsicYIzU+uGJakajW+JItn543EE4zy5p4G7pleRlRRtd9uMaGqKjMf38jC6kLmjxtgBO47j7Wx6Pmt/PbfR9InzUZYVth1oo07ppcRjgks9HVpo5ChiMz3xg6gxRtmYE6qwQv04mf1LKou5IY1O5hclqMpmNlMBn9eMKIYm9V0uxm3w0KW08KBxs6qqY7PjkCEn//b+bQHOhWK45OO+iiyzk2mdeppuOwIRlm3S+tG3bmx1QjsV75fyx2XlhrfoXewrF4wCn9INpKb+jloI9aZxvHDkSj9Mx2ML87i/AJXt5jcUNvEinV7uH92BYue38bK92uZVJrN9ycUc/MLO3nj5mr8YZm2LmIx8WOBDnPvHF7BiPK1TWT3ZGJMxc8mSsiijDnG5/PevtNce1ERoght/ii3/fFz/nz9WHwRhQ/3NzJ7WF/cDgsn24NsqG3GE4ii2rQklc9p0UY/Y0Wt9mAUkyCQm2Zj6au7kwi2xxZmMmZgBr6QTIsvxPIYThs7QjisEm/ubmBKWS4mQcAsiWf1owDXji8C9XBsUzqGNLsZb4zv9Gw4XTlnOEte2MHvF43mRFuAqKLSP9PBjqOthKIq5xe4mF+ViFG9m/rWiwfxfs1pqOxHo0cb7RqY40RWVPpYTdhMkjY+HhVp6ujE6bXjB5KTZkFR4d+f/Yz7Y9egqjiT056gQcUwrJ+LBy8foo1yx/xnS0y5t9OH1mEWtOvUPzMFwBiH1hMaKVZNFfVHl5byyFv7qCxwE44ohkri8VY/v/n4EAtjG3jddL7YeEU9u1lK8t9PfXSYYf1c2C1aImfkADcWk8T44mx2HG3l3hllPfrO5a9rSXXdd669djRRWRsnHlHg5i81jVw4IMP4TDxGF8YSN71ZICJ/o3Ca7rAYVAQqJPnS6oGZBk7vfXkXq+eNZPnsCu57dTeIYDFJhi/9zysvwGbWlGMFQQQEApEoYVnhP/74BfluO79bNDoJp5NKs/nRJRr1QXsggi8Y1eLdiMZBqKhCEk6hZwoLgBEFbqKKwo+mDuZnb+83sKrHz3aziFkSDKze15U/M+ZTFz2/jY5ghHuml3HKE8RpNRGRFYJhmftmllPf4u8Wq1osV4w3FGFfg580h4VH3t7Hv47MJzfNhi9WNB+YnYI/HOVYa5DBuWm47BZEAbJTbZgkgeMtAUyikIBVPTkXjCjcPa0U00ytYJBqM5PltHDojOZb4zHb7NW45WRFw6s+mhof8658v5Z7ZyTGvXdOK+X8fBc2s0hEUXlg3Z4E3O481sb0oXlJOAlGFKOjPB67j75Xy7iB2YwvzuT8WMLu+xMG8purRpCVYmFpD9h9f98ZItE9nB+XiHvvPyYwPJYcfHxOpRH/NrQFE4oKZ/Ot/ripjW9Sd/ffkjjTO9Yki+2c+uY5O2dfwc4lz85mkoBF0HgeooqKwywZD+WnPjrM1PI+lOWl0RGS8QQ0ctxmX0hT6ZRVxhVl0uIPk59hJxTVuB9un1aKJ6DxpMV3yvrDMhFZYWHVAI0LLCRjs4ioKry99xRPfXSYygIXN03SOidUwGpOrHw//8kRhvRN77byA7Gk1+wKdh5t5UR7kKc/PqxxBPXPIKJoqmb7Gzq4/dJSIqrmaHoKPB94fQ8//vZQBKA9GGHB6q389t9H0Nft4Ffv7Wf+OG0T3ZOZJBFXiplfvL2f8r7plPZJ41iLpmpmM5n58Rs1DD0vnYmDczjPoo3FfmtQNv3dDo63BPjd5iNcNaZ/TBggi/tmlROWZS4pz8VqFrllyiD6uRMdyqFGb5JjjyoqgahCoydEbnqnyqZO4B+KKgjA2KJMgnEdMvrroojRYq6bfv0dFomDp70J1Tb9e/QOm/hz8Udkg7geoK/LzieHmjn/PFe3QSFoicg7uzi8w80+HrisgqWv7jbO5bQnmHAekKxc1NVSbSYuKskiJ/XsCj7/LGv3h/FFZEwiBMNgi43x6BitPM9NYZaDBk+QsKyyvb6V8zIc/PVYK1UDswlEtETGsdYAdrNkJFBTbWZOtAbok2ZDUWFU/wwOnfGxfHYFwVgyScfnB/sbDXwumagR6YZlBRUBsySw42grV43pz7Ob6jg/38W4osxeMfrAZRX821Of4g/L3DujDJtJYl9Dm6Gmd57b3mNgueFgE6gqD1w2hFv/8FcuKe/Dvz31KU/MrWT1J3XMG1fYrdKZblaTSESVOdMRJsWqqfLp5Lo2s8Qv3tlPaV4awwvcmCSBqRW5TC7NIRSRmTu6gBvW7GB4gQtfSGbFZUM41OQzEt83rt2BPyxzUUlWglqkK65TUsfnH68fiyAIHG3rFELp2q0VlVV2HmtjbFGmMbZjNUs8/Oa+hE1A18/GJ8T8YRlRENgWIwzWA+2oolLeN40R/TNian1NZKXa2HK4mRsmDoyNPHd/DTfWNnPHtM4k3fv7zvDDSwbjD2s8W/6QpjjZle9Svy9jizK7+1rD4q/XN8XskoQCRBQVFfjwQCPji7OM5EffdCstMZ6/+pYAFrPIEx8c4rJhfQnFRChA61DwxBSkPYEoqVYTDmuMWzQkEwzL9HULPDC7gkCMB7E7nN40qYRWfwi7xUyq3UxDW4AvjrUxZ7SG0/njBpzVj9a3+Pn+mh0srC7k+m8NRBJEvKFIjBNUoDAr5Sw41RS1Q1HFSOAsnVFGRb90fKFoj+sL4ExHiHtnVnC8xc/znx6hvG86mU4roahCqtXEw2/WMKRfOtXFWdgsEpeU53JJeS77GzoIRWRuWLuDEQUuY7Tpgcsq6AhGaPVpojtrtxzl5/96fkLyRzndASQmkYJRxRjV0kekdKyFoorhJ8Mxv7awqhBPMGqQeuub767k3htrmxJ4Az88cIZvX9AvyWfq/lkUtJHAQERmW10LBZkpPLi+hj8sHsPdL+/u9hrG8x4CtPkj2MwS7f4I984s42dv7WNSaU4CTuOfnWuuGd3j/QESOsC/CdboCYIIsiLjiyb70mH9XOSkWmlTVW6fVspP39WEiEYMyCAYUYyiwLHWAFaTRCAs0+IN44ipqqZYTNTHhFOOtQbwh6M8dPkQTcQiqmCP4fSdGi3eHV7g4qFvD2VHfRvl/dLwhWXsZtHAqa6CCcldgbrpPJFv7j5lcHdeP0HD6t6GdsaXZHGyLcB5bsdZfeprS6qJyDKKCvOf20q+287zCy/kofV7mTeuMKGA2dXMJpFwRMadYuV3mzW8qiocbfbjsmsCI5vrWijMSiE9lnTPTrXywf5Gdh1r45KKPkQVrUC4fPYQApEoJ9o6BQPWbD7K0lnlRhJbt3S7ht94zGakWFBRDREXfVIiPuYFkuJeb1Bm4uAcY/KiK25XbazjO8P7GXjRcfKn68d2G++Gogqt3jDLZg+h0RNk5fu1PDBbEzc50RZgyHnp3NUjdpuYXzXA+PeRZh9LZ1bw4Pq93PTCzlhRWmVbfSs7Y9Md8OViXuBrTVPyjzI5HDQ61s6pb56zc/bl7X9N8uzJJ5/kZz/7GQ0NDVRUVPDoo48yfvz4v/t7o7FgPhiJEpa1cUidMHRDbTOnPSEWVRdilkSe31LHd0bk09dl599XbeXlG8ZhlsRYO3bI6PJp9IToG+vi6fqIamgPsGZLPVeN7o/TKrH7eDs5aTZK+6Tx4nVjUFSIygqeYBRfOIqqwm1TB/Pzt7Rq2g8ml1AUxzuWRPLtCRKVVbJSbSx7XRuh/Gt9K5df0A9vMITdamLkADfBiIyqQoM32GPSZlNtM76QzMbaRqaU9aGywMW1/7WdJ+YO5+KyXNwpvQeOkiBw5VMawXFlvosUqwmI8unhZurOeLlnehn3vbbH4GiDzlb4320+wtKZFRxr9vPsvJHsPNbGZU9sYkSBm2WzK3h/3ykml/ZJOmaW08JFJVl8fLApYWPy34vH4I9oyUs9WNQIU7XAQkVTDYzfgOuve0OdHHG66YHK098bwYgBLgqztCTeqo11XFKeS32zj72xkTyo6zGhFYhoG5N4iffurKPL6wLw5u4GYxQ2zWZiYFYmY4oyWRYneb7zWFuPghU6L8nXeVwToMkbxiaBSZRo8QXxi0ICRq9fs51XlowjL83Gj9+s4Tsj8km1mXn0vVoq8zNo8nVyhZ3yBA1s6olti1nkREuAFJsJU0TgSLOPP20/zg+mDGL7kRZy0myU5KTy5Nzh2CwSLoeZJl8YVFBUle31Ldw1vYyH36ihujiLb5VmY5FEo0qajFGNiL/JG6aqOJPCLCdt/jCjB2RQWeDGF4pyrMXPBQXuXgLLZoIRmREFbk57gowocHPjWi2glBWl28/olmI18dZubfMCGlH31IpcGj0hZEVh6Yxy7n1ld48jKsMLXDx4+VA+P9ZKICwnbUbGl2Qlramc1ETCd39Y5lR70BBG0a0rzhxWyVBINEalI53J53i86q9/sL+RJZNKAMEQNNhY28S+Bk+SiiLA5NJsHrisAl9YE1jJdFpZ9Pw2Vi+4sNfr6I1TJASNP7OqOJO/7Gtk5rA8AJZfNiQBj6Al9tJsph6Jtcd/zZPZvVlQkbGKIh5f1CDZZqPKjWt38PTVIyjIdPDgZRWYTAKpNnNMbMGP1Swa9+90DKMbapuYNqQPqqB1nlYVZxqCAoqKtjkMRhAFoUc/6rCaaPdHSLOZ+dP2Y9x+aRk/fUvD6dn8qEkU+OEfPzd86Ozz+/KbD2u5aVIJmUVW/GGZE21nw2kToajCaU/QSOj+4t0DPDF3OJlnUSbMS7PzvWe30OwLG2qPug/dfcLDvTPKWfrKroSxfB2nN6zdwYj+bu6bqRWc+qbbmBnr4AZtjf20m+d+PE71JNJFJRrf2KqNdUwfoq1r/V5ZTaLhJ8/Exp1DUYWcNMkg9dY3312x/fTHh3n1xiqWv76XDbVNrNpYx+WVfVkysTiJoF8fOT/jDZFiNZGTZqM9EMEfljnemshb19W6PiN21LfSL92O1Swy5DwXVrPIisuGcF8XnFbFump77Ez8BuI0EIqSKogookBjIIhfSvSlN67dwcs3jqNPuo3fvL6H8/NdpFjNPPreQcbGCsXxvjTNZmZbfSvThvTR+NQkgbS4bp53a05z2bC+pFglHvvLAcpiRZqSnFRWzRtJP5cdX0Smn9tOJKrQEYwSlUXDn1YWuMlNsxlrsitW0+1m8tJsHG8N8MQHh5Kw+oPJgxh2npvGjuCX8qmBSBQRgf2n241j/ttTn/LCtWOSSPK7miQKXPu77QAJeLWZVQIRmSNNfkYOcHPfq8nrTOODg0ynhUBY5qE39vL+vjPGe3SOZEVJ5q2Kj3njE7/VA7NIiYlt6F178TEvkBT3Om0SqkpC0iwet/6wzDt7T3H/7CHc/9puI67U/Wt38W51cSb3zCgzOgjDUYUUq4ljLYGkonRXi8euAFz1zGZeuG4MjZ4QNpPIPdPLEASBWcPyeHBdTYJKak+YtZpFLuomRvnfanrH2jn1zXN2zr68/a9Inv3hD3/glltu4cknn6SqqoqnnnqKadOmsXfvXgoKCv6u7/ZHZDqCUXLTNAU+VYXvPfuZNvJUVYg7xcxTHx3i5ikl3DJlMMvX7WFRdSHDC1x8uP8M1cVZfHTwDIcaO7h5sqZ2eMdLX/Dn74/DH5Y1MvFYsgbgvlf38LtFo3lw3R6G5ruYODgHUQCb2cQDcaOTDovEHxePRUFlwXNbeW7+hciKSjAqE5KjPPjtIUkb3PElWay4bAghOUpOmoVn543CaZPwhWQkAVLtFj6vb+OCAjcdoSjPf3KkVx4JAG8owqPv1TK1vA83TSzm8Q9quXHtDq67qIhRcV16Xa2qOJP39zfS5A2z8v1ajRC4wM3K92upLs5kxeVDMAsCU8tzuTfGjeG0adVLBFg2qwIR6OOyEY4qXFSikaZ7QxFOtgbYWtfKd0cm3/t0h4WfXDGMO1/6go/jAhSn1UxHMMqNa3bwu0WjufeVXTR6QlTHRkH7pvdBRU0IFPTXHRaJjw+e6VYBKBRVONkaZE9Du9EJEYrIjB2YSWG2k6c/PpTMUxNX7fLE+HNy0npvNdeDHt12HmtjX4OHmyaX8PO39nN+gYvjLT5+MHkQ04f0Mcb1HGaJ6UP68PAb+xKVEUuyePCyIbgc5q99AOEJRshOsRBRVKPNPh6jUUXFJon4ItEkjEqSgEUSjYDqjpe+4I+LxyIKAo0dIe3eu2xkp1pRgY8OnOF4iy+WsN7Hv4zIJyfNij8kk51qZVncuMez80aS57Kz8v1DDMlzce1FRaRYTfiDEVKcNmND1hWjeuX1tSVVAOxv8DC6KJM9J9sZ3CeNUFRm7Wf1Z8VmRzDKvTPLeOy9gzxwmTZac/MLO1k5t7JXxbP1uxoSzmlnfSt9023kpNl44oNaHphVwY+/PUTjgovIpNvNWEwaUfRL3x+H0yJx2hOgIDMFi0ng7ulloGKoKBZmp5CbZks4brpDU96Lx+UdL33B7xaNZkNtk5FY0LBoM84VoLJAUw/UO2h84c5Ecjxe9Xv81EeHmT40j9nn9+UHU0qIKipOq8RlF/TlofU1CXhMj/FGRWSF3Sc8FGWlEIpqHRZ2S+96O10xaRYFlkws5rcbDjO5NIdgREvyTIvDo662ForI/GjqYFRVTUhs95TU+CaYDFhFSVOMtGqdxEvWapvdRdVF5Lk1rp/h/TO455VdLKoupLo4k1v/+6/8btFoPj7YZGD05e+Po+ZkO1dU9qOxI4QpxcySicWYRAFZVfn4QCMjCtyk2c3sPu6hol96t34UYP1N1SgoXDEin0ff28/c0f3JSbPiCYR69KMPXj6EYCTK43OG47RJNHpC/Od7B/jR1FI+P9bKsPNc+MJRfr/57Dj1BCLc9sfP+dP1Y1ka2zjfuHbH2XG6u4FjscRQvA/9a30rd08vY29DOz/+9lB8EU05Mc1mxiyJtPnDvPT9caRYRE55gqRYTVTmu/nT98fREYjgtJnIclqTMArd49RpNdER1BLLH+w/zfiSLANrenFmfHGWkQTXCwS66RjtuqnVuR/XXDOaFl+EYFQmEpXpm243OGDjk5l90qyc9oQ0jrvY34GE5Ht31vUZsa/BwyXluZhFgV3H27gg38XvPz3C+fkuA6fpdjOpNhM3rtnBT78zDBES1tQ3dvMtCEQF8IZl0uzd+1K7ScQXjvK9MQOwmUU6ghGqizMJROQkX7rmmtGs2ljHFZX9CEUVorKSoLj51EeHGVXgJj/DwbUXDSTFKuENyvRJl7CaTAnqrA6LxEvfH8fp9iCiEDX8qS8UZfllFUbSSceqHu9KQHZqZ7yrY/XWKYP49YcHuXbCQERB4HdbjjK/unesBsIyrf4I2U4bd08v5aH1NWysbWbOb7WkTa/x7r5GIzmt43XG0Dxy0mys3XKUi8tyKcxycNe0Uu6cpo0POqwSNpPG+9gejGAVRcKCwo8uKeW2qdoEC6pWRHrsvQM89O2hScfuLuZdtbGOGUM15dKq4s4ueF8oasS8VcWZyDIJcW+jJ2SMuvaE20ffq6WsTzpLJpVwx7RSvEGZNLvErGF5PPzmvmT/6rCgKAqW2Pd5glpTQNdkenfWFbuD+6Qy5+nN/GHxWHYc0bpPD5/x8vHBM/zo0sFcHxyINxRl9rC+rFi3J2k0d9nMctr94YSu+P9Ldk5985ydsy9n/yuSZ7/85S9ZtGgR11xzDQCPPvoob7/9Nr/+9a95+OGH/67v1pM2vrBMqkXCG5YTRt9Wzx/FDy8ejIzK/a9rCi77T3Ww9toxPPJmDdOG9KHmZDtzRvfHG4oYXT4m0NQ6I1GWzS7ngdf3suFgE5dV9mP5uj2U901nSN90jrcGOM9tT6jigFa5+vx4G0XZTkr7pPLdpz81qlmaimCYmyYVc++MMtoDEexmiRSrCREViygRlWVMooBZFOmbbtaIMQUIRhX+7alP+dl3zufqMf2R5d4VWJxWrTtg/e4GJpXmJJD+N7QGWTa7gge6cLhVF2cyP9alAlqnxdJZFdQ1+VhzzWjy3XZMwC1//CtWk8TwAje1jV5y0qxGULz7ZDsjC9zkpdtpD0RoC0ToCEaM4HfFZUN6dH59XXYen1NJkzdMRzCCPUYc3+gJUpDp4Lr/2sbT/z6CRk+QFZcP4cF1e7m0Ihe3w8KRplYjUNCCwzEoihLXRZY4MtDoCYIg8MmhZp744BA/mFzChMHZHGnyk5Vq4Y5LSwlFFQJhmX5uM6fbg+w50VnVdNpMeENRI1HXXYdYdXFmknLjvgYPS2eW89M393H7paWoqkqa3WwoicabwyJx74wyfjh1MFFZwWkz0dQRJKp+M3iV0mxmOsIyiqritJgIRBIxumRSMf0z7EiSyP2vJWL0r/WtyCrG/XtuUx2hqIzdJJEaI42+tCIXU2zEeu/Jdm6ZMogH1+2lrG86AMdaNIze92oiRrXEm53hBS6uX7PdwKc2gibjsIosn11hjPKlWE2k20wa14zNbPAMXViYgT8Soc0fMbA5b9yAs2JTEgVafGH+ZcR5NHuDPPTtoZxoDdARimpCALGODt3Gl2Rxw7eKWfT81s6/xbD55u4G/lJzmvtmVSCosPqTOr5XVciJ1iiHm3wGx8uLn9Xzy3+9gJw0Ky2+MA3tEeM1HZfdbcohEZdtgTCSKOK0SNScbGdBdSEgsGpjHU/MHY7DIrJkYjH7GjwsmVhMXZPWyfnDqYMTunnjA/t4zpc3djUwujCDsKxgFkUEBLbVtVBZ4GJ0YSZRRSUnTcJpMQEqO+vbGNovDUkUjVGXr4LJ8SVZvL+vkepBWVxclkurN0JBloN7u/Bgxb9/+ewKHrp8KIGIpjSZ5bSSl277RmCyO/PFNo7K/2vvvsOjqPY+gH9nZ/umJ4QklFBCCzUFMIQuAkq9VxEQBQQFLt3CVRHpCHKvilcEFWligctFeaUIBoFIRxMCAUJokZpQQtom2X7ePyY7ZJPdFEC25Pd5Hh7N7OzknN1898yeOXMOE0YzlJ70fXLPCNT2DcGd4mJ8uveiTUbnbzsDMCb8HZRk9HpOEd7r3xJSDvBSSpFw9hbahwdAqzcixEeJ1Bt5WLbnApKu5gq3KXnJEaCR44OfT5d7vYUOJB7fH7tik2mFVIJDl29iSs8IzHymBXKLjPBWSiHnhRMnGc/DRyV8ya3jr8LrTzVFsVEY7TSkOm2oUoq7WgN2ns7EtCcj8G6/FriRUwwJOLzZpxnAWLkveeVzGoRZ/YW5N/u1DoVaJkyW/3FCOqY82RSXbheK7adaxkMmleBWvrFku/J+J1SBHj2a1nKYUaB8TtVyHrfzdYiPCMRn+y5h+QtR+O7oFXHutA7hwmrFR0vmjztxLdfmYqI1o/Zuu7uWU4xioxlpmXkI9VOBl3AoKC5Gyzq+4DgOWp0JPkopwgPV+PTX85jaqynyik3iZ07peVDtjTSxPm517mY+ZvUX5pmc3DMCcwa0xDs/puLQxWzsKTXSx/rciT0iEOyjxPxBrWAwCwtV+KqF0U7umNOikgUyCoqNCPVVotBgvy01mpk4D5Y1p0l/3gM4zqYttVgYYsL9IZNw0IHBbGEoNpgwf6AwpcTBi9k4fiUHqw9liKPO9CYLNAr757yA8HsT0m6JUwlYz5/GxDfAm72bwWAW/pYl4JBfXIwAtQoKGQ8fJnTI1vUXFtbSm81oW88fQ784KmQ1rvKsynkeXgqGV7/+A1+MiMHCwa1xM1cYIZWVoytZYKdMuxoRhIk9HLere9JuYVzXxgjzVWLid8mo5a3A7P6RyCsy4kq2XjzfbV3HF+sOZWBSjya4U6BHbvH9tjU9Mx/zq3nOK+clOH09VxzJOfX7E5jcs7F4zvtyfEOczcxDm7p+4nnvW1tO4ZuSW5Ud5bbIYMb4b5Kwa1oXXMsphlZvgr9GBgnH4ZlWIagfqIHJwlDLW1jISSWXoNhogd5gFhbtknBQyCQ4dOkuwnyVFc45WTq7aw5mYM3o9vhs7wWMXnMca8e0Bw8OV7OL8M8+zTGrZB5hQDjntXYIA0AdfxXu5OswbNVRbBoX55bZfRSsK28CoPnPCKkAx9x8fVqDwQC1Wo3Nmzfjb3/7m7h92rRpSElJQWJiYrnn6PV66PX3v9jk5+ejXr16yMvLg4+Pj82+Z27kgZdwyCs2wkfBgZdKcfVesXgr0rYp8eDAQSLh8MwnB8TnBXkJV2jrBSqg4GWYv+0MYhr44+lWoZi99bRwRUzCYfHPaZjYIwJFJVf6jGYLBnx6yKYM1olKy26b8v0JbJnwBGRSabkTjc4RgVj0t9YoNhrx9xVHEV3fD/MGtsKv57Lw/s50cb9d07vgeo5QnxMlXzSi6/vDR8kjQKMAz3GYVWZCV6suTYLw/uBWmPnjaSRdzcFnL0TjToGuZNJiQCWXgAcHcADHcWJngFouLG+dW2SEv0YOOS/BvUI9NAph3oer2UWY9t8UtAjxwcK/tUJuoR7eKnm525ueahGMeQNbwmhm4i1VvioZgr0V1W788ooMyC0y4mZeMZbvu4gTV3Mx7ckm6NG8FjhOGM2gkkhQZDIjr8iET/ddwKGL2QjykmPdy+2h1Zvx5W+XbE7ofFUyBGrkUMokyMrX49O9wnPq+auw7uUOMJiFhQx8VDJYLAzXcoTJYE/fzEN84yB88usFvBzfALfydPjtwh1M79UMC7afKfc+LxjUCkO+OCIuq92lSRDmD2qJzJxi3MjToUmwFyQSYWLdp0v9jZb1vwlxGLnmuDCHXJMgLHfi1bfqZDSvyIBzWQXgOA4yCaBR2mbUOgIMQLmM/vu5tmgQpEZWvl58/55uGYIr9woRUcsLC3akIS0zH+tebg+NXIpb+TowAMNXHbMpg72MquU8Vo+MQaifulynZeeIQCz8W2us2HcBwT4qLN97EV2aBOGtvs3Fuc6A+/n87tgVRIb5Irq+P+RSDnV8VZBIOMza6jibA9uGoVmINz7Zcx7DS1bZiwn3R49mtVGoF7JntjDk60zwUkgh4YRRGkVGYYVVoaNAGFHmpZRBwUsw4ZskBPsosOhvrZFfrIeXQl7udqZHlcu8IgPuFOog5Xgs2H4Grev6CbcSMyBAIxyHQbhd448rOWhb1w+f7EnH672bYW7JxYjSq3UdupgtnjTHNw5EbR8lbhfosf5wBqb2bAKFTJijp8hghrdKitRreZiz7QwA4Ytb14hAhPqqcPDiXWwv+dL47StP2M3k7AEt8cKqo7irNaBLRCBm9G2OZXvOY3RcQyjlEngrZbAwhn7/Oeiw/qX/psrOEecqqpPTY5eF10jCcfBRC7fllM2o2WLb/lnb0Tr+CiilMizYfgYtwnzRMTwADYPUsJ68zNt+Fi89EY7b+Tq0bxiAm7k6vPBV5Rm1/o7/jY+DiTHM/an8e7lwcGvsPZeF+dvPoXNEIOYPaoXnS33e/jy1M27k6R64DZ3SMwKf/noRSVdz8J/hUdiSdA0TuzeB2cJgMJsRoJHDZGEoKMkpB2HEhc5kgVYndLpLJMIXH2XJPFPjv0lCeKBayGmRHl5K25yq5TzmDWyJ2HB/YV64R9x2juncEHENAxDkrYDFwsBLOah4HnqTBUt+TsOwjuHIKzJg64mbYr1Lv37WhUHq+KmQka1FZIgv3t2ainNZBfh6TAcwCKOANCUTtt/TGvDH1Rz834kbWD+mA45l3EPC2SxxdVHr7diHyry31ot41jZvRp9mWHPwMsZ0boRigwk+KnmV20zANXNanYwev5wNBmH1SZPJBIXcfltqtghzxFrzJLalgWpkFdxvS+MaBKBuoBqwMEgkHJbsOofx3Roj404houv74WpOMbyVUgxcXrVz3rk/ncE3YzuKf2tlpwOYO6CleI7bOSIQcwe2wi9nM7F013kAwhx1Qd5yXLvnoE2tJKuLShbqWrTjLJKv5uKzF6Jxr1CPFqG+KNAZoVHwUMukMFosyC8WVmhXyfmSlUiFxRK8FVIopBLcKzRAKeehkvG4fq8Ir28+ieYh3lj0t9bQm0yQSfhymX2vfyRiwv3AgYO5ZEoZX5WwMMCD5jYrvxiX7hQizE8FlYyHn0YGlVSY55kDoORtz3u7NqmFgxfvIumK49wG+yhw/V4RIsN88e6PqbiSXYSvxwjTHBSVzEMptLG5mLPtLABgfLdG6Nc6FCeu5qJegBqrfruEF0tGN9p7ryf3iMDL636/f6t5RBCm9YqAQiqFVm+EnJdAreAh4ThhhHcFObb+vTkzv9XJaWkFBQUYtmxHpR2/St9AWIw6GIoKK9xPqhDOLT9/qT3Nf0aIA27feXbz5k3UqVMHhw4dQqdOncTt77//PtavX4/09PRyz5k7dy7mzZtXbru9D6nLd7TCxN8lr5JUAnywS5gwO6qeH7wUUlhKVuQc+uVRu2X8+uUOCPVXwmRmKNYbEeStBLMwyDgORg6Yv+3+FwKlgseQz4/YPH/FiOhyE+9btwV5yfHN2A5QyaQlt5gKHVRKqQQyjsMtrQ5KuTC0XW+0YPw3SaUam0CM69YY4zfYjoqxXulq3yAAaw5mYFiH+nbnLZozoCVMFhPkvBRzfjqDpCs54nF4jkN4oBo6kwUZdwuhlvFgYPBVy2EuWa7cuhqYdbJfMEBbqmH1lvFIvHgbC3cI7+F7/SMRXd8PxQazuJLQo27kMnOFK93WiaZ91VL4quTILxnZFqCWwwJhPiydSdjHSymMUDl4KRvB3opSt5Lo0a6+L7zlUshlPAr1JrFjwkclhVrKw8SY8PdlEToChFWSpNDIeGQXGaFWSCCX8LiZV4yvj/yJ50puE9TqzPBRSqGU8fg1PQuNg4Q5Cur4qyCTcNh9NgvJV3Lxz77NUag3wlcpR3ahAUO+OOKw7ta/KestJ85cXbM6GQWAtMx8cJzwvpTNqFoudAwB9jOqlvP47/gnoJQJX+50BmHhD4PZAqlEIv5tT+7ZGE+3DEVmXvkv5vYyaj32u083R9emtaA13M+njOfw1YHLGN+1MbILDeKqbaPWHr/fCRoRiPHdGmOcnXxeuluA3i1CkJmns3tiOWdAS4BjMJkZ5LwE7+9ME18Pk4Whnr8KHMfh6r0i1PFVAZxwVV5T6jYRYcEDQCPjS06ehbJ7y3kwiwXbzmThzPV8vP10cxhKrcj3qHOZV2RAfrHR5vWzrmJ4V2tAvs4AP5Uc+pIccTyg4CXibTzWDrNOjQJLFmlhCPVVQS7hYGIWmC1ClvmSkbh6kwWFBhP8VHIYLRax00Ij52G0mCHlePHLW3pWAT54tg2CfRQo1AtfZExmC3ILjSgwmOCnkiHMT4lr94px7M97OHczH2893Rxnb+YhzF9d7rPe3t+UK+TRkerkNC0zX/x/ngOW7i6fUQsDhq+y346uHd0eQV5ySHkJCoqN8POSQcPzYIzBDA4LdpxF81CfarWjVvX8Vdj4SkeYGGzaUQkHqKQS3CoQLu4opBKMXntcvF2yS0QQJvWIwJj1v9ttQ2PD/bH20J/229AI4SKHGQw8OMwu1YbG1vdHgJccMl4itp9mJmRZKeNtpjBQyXjwECYSzy+VEW85D73FhO2nbmHDkSt4o3ezv7T9LNt2+qikCCkZwXZXa0BesQE+Khkk4KA3mSGRCLfMzy5z7gAInWaZecX4X9J1vNW3OUwWM2QSYeR/kd6EAC85DCYLCvUm+KnlwsIEJW2nipfADGEFVWsnTmx9f/iqZZDzEqGMBjMMZgv4kgtjdfxVMFsY7hYY8PuVezh3Mw9v9GmOQr0Jz1UhowBcNqcPklHhAqfZYVtqfb3L5kkt5zG9VwSeahEidpL4e0mhkvAwWhg4CYcF24WcxjcMQJ0ANTLzdOXa5YrOeZsGe+GrkbEwMWZzsaNQb0KQRl6ycq7ws85gwYRvk8RFpUbHN4DZwjB9U4rdvHZsGACAEy90WlnbVIPJBIVM6JCZ/X/286rVmVCoNwnnuyo5TBZhDq+y7aqXjAcH2GTWS85j3/lb+C09G7MHRMJXJRNHiz2uc97Sub2Vr0eB3ohAtRwMwuezwWyGnOcxd1v53Ab7KPBr2m2cvp6H9wZEQs1LoDNboDWYUag3IkijAC/hUGg0o1AnZNdouX8RQCmVwMyE0eBGC7B451m0KZm+BhA6zH3VMhy6cBdagwntwwMglwqfiYwBe9NvIfWaMAKdlwBFBgtkEg6FBhOe/8J+u2L929p4/KpT81vdc16rR915dn8/Lf77en9wHEej0Agpw2M6zw4fPoy4uDhx+6JFi7BhwwacO3eu3HOq08N/I7sQ1ikpdSYLzIzhdr4eqw5cxoELd7FtSnzJoxwGfGp/FIH1qkbpq2lBXnJ8PaY9VDJpSYMkrCbmq5KVu0Li6CqcdZv1y6H1REAp49EgQC107hjM8FJKceJqLhZsP2szIfD8QcItY45GP6jlPHZM7Qyj2QLrHOM3coUvDSeu5WLNwQwUGcziFbF29fyg1Qu3UWhkPLRGM8CARTvTHF7Jmz+opXjrlHX0mY9SikKjCRbh6Sgq+SL7V5w4PIi8IoPdE5q8IgOy8nXCFUeFcEVRJeMrbIytxyrUG+GrEjpsSi8z7quWI+OOFleyi1A3QAW90SKsOCWX4J7WAMaEkVYBamGEW4HehAKd8CXC+ppyHIeMu4VoXdcXfZc5vvq2c2pnKKS8S7zO1b0Kdy27EBwAI2PQl6ycas3o/avlrMoZVct5TO7ZGP1bhULCCSd7uUVGeCl5SCUS9FlWeUZL+3lqZ+w4nYWoen4wmC2o5aWAXCrBrXw96vipcPJ69fNZz1+F71/pCINFqPNNO9kEgF4tgvHuMy2gM1lQpBduQ5fxEhjNFizc4TibCwa1BF9S9/zi+yf3hUYTCoqFLyz+anmFt3g9TqVz6aOSQaOQQqszldxadr+zQSPj4aOqfC4/65V5e6PnrmcXQme22OTx8MVs6ExmtArzFY6vlEElk9i8flIJh6xcHa7nFSO6vl+5v6PSXCmPjlQnp9ezhZN2a0az8nRYfSADBy7edTjyrLSK2lGlTIoivRkyKYfcIuH9r0o7Wtq2yfHC7bHeCugMFnAcbDN6LRcLdjxAGzqlM4yW6rWh3kopvGRCh0PZkZ1W1pEwxSYzlDyPIqMJWr1wUUUulcBgssDCXKv9tNd2AhBvAdUohHYrt8gAb6UMtX0qHgnnKKPZhQb8fDoTvVrUhsnMUGgww0shxe0CHc5m5qFbk2DoTGb4lFxoNFsYdGYLCkpGCylKRgMpZTyGOejMBYBd07pAZ/zrLuY9CtXJ6NWSdhQATBW0pVl5OjDGHObJuuhR6Zyue7k9DCYzanspoS1pT31VUnAcV6X2tLJz3kZBanE0psFkweHL2WK+rFnN1gp/Y45GIanlPHZO7QxW0olebDCXO5Z1v/f6RyKqVF5VMh6FBhM4cBWe81alXfVRysSFi1yFo/Y1r9gItYIXc6tRVn5eUJXz3rwiA/KKjdDqhc5FL6UUFgsTL0wppBI0rqWBlOOgNZqF7JZc/LueU4Q3Np9Ei1AfzB8krI5e0cizXdO6OH1KBFcZeWbdz2wy4+PnWmL6xmR89UpXGoVGSCluP+dZUFAQeJ5HVlaWzfbbt2+jdu3adp+jUCigUFRtFSR5yeVwCwBOKgEDsP5whjh5rELK416h8IHnaP6b2/k6dIkIspl7467WgOc+P4rx3RqhR7NgKGRAHV8lTBZWbpJge3N2lN5WegUd4P5qdmbGAJjBcUCnxoHYPCFOGE2lun9SmldkEFfiKSuqvh+2JN8Qbymb1a8FGgZpyt0+GVXfD7W8FXhp9TEM61Af8Y0DoZVK4K2UQiHlMalHBCyMlbuSt7TMVZ5Gtbyq9J44m6/a/kmyo+0PcqzSAjTCLav23iNHw8ytJycyXrhFrEmkl7i/o+PU8VO5zMl/dTIKAMqSnEo5DlIZb5PRAI0cHIDCUnMOlmUvo0t3ncfyvZcwvlsj9GkZjDBfJbQG4VaGqmTUqmuTIPiq5Th5Ldcmp1a9WgTjvf6R2PKPTtDqTfBVVi2f9QPV2Jh0Hcv3XkSvFsHC6nplbjmJjwjECx3r46dTN9G6jh+UUgk0JSsRqmRVz6Y7sJel2o7POR/oeFbeKhlmfn+iypks/cUjzF+FNvV8xX3dIY+OVCenijIZ3XDkT7St74fR8aUzanqodhQcQ1g12lGrrk2CEOyjhFouRYHOiGBvBd77v9Pie2P9sv5pydw+dfxV4hfEStvQE7ZtaKNamnIr6pVuQ9/q2xxt6/rhtlaPALUcCwa1snvb96LBrVA/UFOl195VVNR2PsrjAcCxy/fwr93ny23v0iQIz0bXs/vlvnRO6wWo4aWUVphRZ3/hrorqZFQl4wGjGSYAsgra0lt5xWCAwzzdztcBpR6/qzXg+S+OiqO0QvzkCPNVorBkkYFHcc77/uBWYBYGCQeE+CrRr3UoujWtZdOeNgxCpXn9X8k5LyC0zbP7R+LktVyx48y6X20fBXafzULrOn4wmCwI0Mghl0ogk0g8ql21epTta1XOe62Pz9p6utrtrK9Khk3j4mw64lw9x9U95/2rmQ06TFx7AFKFmlbhJKQMtx95BgAdO3ZETEwMVqxYIW6LjIzEoEGDqrRgQH5+Pnx9fR328N8puWJuhnCLYTGDeDIb5CXH9+OeAM8BFsbZnXtszsCWuJEj3HZnb+6NssP9b+YW26yMo5bzwkSY+y7azH9Qdpu9Y1VF2d8H3F/Sfur3JxAb7m9zTGsDlVtsgN5oe1XO3u+vaAQHqRp779Gjeq9d9XaT0irLKABkZhdCCsAC24yq5Ty+fDEG9QNUMFjw2DJa9nkP+tpXJ5+UzcfnUWTJXfPoSHXaUj0D3nWxjJZWnffmQdrQrHwdrufYjkIru5/VrXwdcgoNyC+ZIN9f4zojPl1VTW8zHalqRgHHbWm9ABVu5ethZqz8tAERQXilizARu1xqf76qsp1IrnDOW3YOvNLHpXbVeWpqjqtyzgv8lbdt3t+PlyuxanRHGn1GSAmP6DzbtGkTXnrpJXz++eeIi4vDl19+iVWrVuHMmTMIDw+v9PlV+ZC6k1MEWBiMACQAjIA4B4+vWgZvKQ8LhJONwjJzGBQaTSjUCRP/mhkDY6h0ss+Kbm+obNuDNNClf59GIUwUnldsgEZR8TEd3cJIHr1H9Vq743tW1ROJmzlFkJkssEg4GHA/oz4qGXxkPISxmI83o45GIFXntX+QfLrj++xuHsVr7EnvU3XaUjMAE4ACF8toZc+vyr6UUddQk9tMR6qU0XwdLEYzJBDOae21pRYAJguD2Tr/q6HklmFeguxCYZEZH4W0ygvHOPOc11spg5dSmCfNeislZdZ11MQcu1LnGQAwZsGacd3h5SXcxcJxnDgSTavV0rxopEbxiM4zAFixYgWWLl2KzMxMtGrVCh9//DG6du1apedW9UOKEOIclFFCXB/llBDXRhklxPW5WucZAJj0ReL/83Il1o7vAQB45avf8NUrXRESEoLCwkLqSCMez+3nPLOaOHEiJk6c6OxiEEIIIYQQQgghHkGqUNv8PG79cTCTDhYLw8tf7MOnI9rjtU0nsHH6M/Dy8kJBQQEAwNvbmzrTiEeROLsAhBBCCCGEEEIIcX1SpRp8SYcax0kwce0BgJcjPz8fN2/exJClP+D5j7ajoKBA/McYA2MMBQUFsFgsNtvy8/ORn59vs09Vb46r7v6EPAyPGXlGCCGEEEIIIYSQx0eqUMNs0GHU8l3iz7xciaysLMzYeg6MMax8MRYA8OrqA1g2LBrTNyZj1dguAIAxKxMAXmZzO+iqsV3EedbssY5oKygowKurD1S4v3Xf0h1s1rnbSo+MY4xBq9UCADQaDbRaLRhj4DgOXl5eNj/TqLqayWPmPHsYeXl58PPzw7Vr12gOCEIes6o0PpRRQpynqieIlFNCnIMySojre9Q5LSgowKhPd8FssVR4PIVPAGA0QF+sfST7VWdfk0EHTVAYYDSgME9Y7VQqV8Jk0In/tW4DAKlCBWbUw2yxiI85ovT2BzPqodcV2Ryrsn2tpHIlPh/b1abDTavVYtp3SQCARQOb4Y1vjsLMLJDKlfj30Gibn8s+l7gm6+IWVd23soxS5xmA69evo169es4uBiE1UlUmLqaMEuI8VZ1cnHJKiHNQRglxfZRTQlxbVTJKnWcALBYLbt68Wa63MT8/H/Xq1fP4K3Q1pZ5AzamrO9WzKr38jjIKuFddH4Sn1w/w/Dq6e/2qerW8pralnl4/wPPr6O71o4xWXU2qK1Cz6uvqdX3YnJbl6vW1h8r8eFCZH0xVMkpzngGQSCSoW7euw8d9fHzc5g/vYdSUegI1p66eUs/KMgp4Tl0d8fT6AZ5fR0+vX01vSz29foDn19HT61fTM1paTaorULPq6+51rco5b2nuWF8q8+NBZX70aLVNQgghhBBCCCGEEEIcoM4zQgghhBBCCCGEEEIcoM6zCigUCsyZMwcKhcLZRflL1ZR6AjWnrjWlnoDn19XT6wd4fh09vX6V8fT6e3r9AM+vo6fXrzI1qf41qa5AzapvTaor4J71pTI/HlTmvw4tGEAIIYQQQgghhBBCiAM08owQQgghhBBCCCGEEAeo84wQQgghhBBCCCGEEAeo84wQQgghhBBCCCGEEAeo84wQQgghhBBCCCGEEAeo88yBFStWoGHDhlAqlYiJicGBAwecXaRqWbx4Mdq3bw9vb28EBwdj8ODBSE9Pt9mHMYa5c+ciLCwMKpUK3bt3x5kzZ2z20ev1mDJlCoKCgqDRaDBw4EBcv379cValWhYvXgyO4zB9+nRxmyfV88aNG3jxxRcRGBgItVqNdu3aISkpSXzck+paFe6a05qWT0/NJeWxcpRR93h/KaPuW8dHwV1zalXT8lqap2bXijJsn7tl9rfffsOAAQMQFhYGjuOwdetWZxepQlX5THE1K1euRJs2beDj4wMfHx/ExcXh559/dnaxqsXe55nLYaScjRs3MplMxlatWsXOnj3Lpk2bxjQaDbty5Yqzi1Zlffr0YWvXrmWnT59mKSkprF+/fqx+/fpMq9WK+yxZsoR5e3uzLVu2sNTUVDZ06FAWGhrK8vPzxX0mTJjA6tSpwxISElhycjLr0aMHa9u2LTOZTM6oVoWOHz/OGjRowNq0acOmTZsmbveUet67d4+Fh4ez0aNHs2PHjrGMjAy2Z88edvHiRXEfT6lrVbhzTmtSPj01l5THylFG3eP9pYy6bx0fBXfOqVVNymtpnppdK8qwfe6Y2Z07d7J3332XbdmyhQFgP/74o7OLVKGqfKa4mp9++ont2LGDpaens/T0dDZz5kwmk8nY6dOnnV20KnH0eeZqqPPMjg4dOrAJEybYbGvevDl7++23nVSih3f79m0GgCUmJjLGGLNYLCwkJIQtWbJE3Een0zFfX1/2+eefM8YYy83NZTKZjG3cuFHc58aNG0wikbBdu3Y93gpUoqCggDVp0oQlJCSwbt26iaHzpHq+9dZbrHPnzg4f96S6VoUn5dRT8+nJuaQ8Vo4y6vrvL2XUvev4KHhSTq08Na+leXJ2rSjD9rl7Zt2h86yssp8p7sLf35999dVXzi5GpRx9nrkium2zDIPBgKSkJPTu3dtme+/evXH48GEnlerh5eXlAQACAgIAABkZGcjKyrKpp0KhQLdu3cR6JiUlwWg02uwTFhaGVq1audxrMWnSJPTr1w+9evWy2e5J9fzpp58QGxuLIUOGIDg4GFFRUVi1apX4uCfVtTKellNPzacn55LyWDHKqHu8v5RR967jw/K0nFp5al5L8+TsWlGGy/PUzLq6sp8prs5sNmPjxo0oLCxEXFycs4tTKUefZ66IOs/KuHv3LsxmM2rXrm2zvXbt2sjKynJSqR4OYwyvv/46OnfujFatWgGAWJeK6pmVlQW5XA5/f3+H+7iCjRs3Ijk5GYsXLy73mCfV8/Lly1i5ciWaNGmC3bt3Y8KECZg6dSq+/vprAJ5V18p4Uk49NZ+enkvKY8Uoo67//lJG3b+OD8uTcmrlqXktzdOza0UZLs8TM+vq7H2muKrU1FR4eXlBoVBgwoQJ+PHHHxEZGensYlWoos8zVyR1dgFcFcdxNj8zxsptcxeTJ0/GqVOncPDgwXKPPUg9Xem1uHbtGqZNm4ZffvkFSqXS4X7uXk8AsFgsiI2Nxfvvvw8AiIqKwpkzZ7By5UqMHDlS3M8T6lpVnpBTT8xnTcgl5bFqKKPlucJrQBmljJbmCTm18sS8llYTsmtFGXbMkzLr6ir6THE1zZo1Q0pKCnJzc7FlyxaMGjUKiYmJLtuBVtXPM1dCI8/KCAoKAs/z5Xrvb9++Xa6X3x1MmTIFP/30E/bt24e6deuK20NCQgCgwnqGhITAYDAgJyfH4T7OlpSUhNu3byMmJgZSqRRSqRSJiYn4z3/+A6lUKpbT3esJAKGhoeU+/Fq0aIGrV68C8Jz3tCo8Jaeems+akEvKY8Uoo679/lJG3f89fBQ8JadWnprX0mpCdq0ow+V5WmZdnaPPFFcll8sRERGB2NhYLF68GG3btsUnn3zi7GI5VNnnmdlsdnYRy6HOszLkcjliYmKQkJBgsz0hIQGdOnVyUqmqjzGGyZMn44cffsDevXvRsGFDm8cbNmyIkJAQm3oaDAYkJiaK9YyJiYFMJrPZJzMzE6dPn3aZ1+LJJ59EamoqUlJSxH+xsbEYMWIEUlJS0KhRI4+oJwDEx8eXWyb5/PnzCA8PB+A572lVuHtOPT2fNSGXlMeKUUZd+/2ljLr/e/gouHtOrTw9r6XVhOxaUYbL85TMurrKPlPcBWMMer3e2cVwqLLPM57nnV3E8v66tQjcl3UJ4NWrV7OzZ8+y6dOnM41Gw/78809nF63K/vGPfzBfX1+2f/9+lpmZKf4rKioS91myZAnz9fVlP/zwA0tNTWXDhw+3u7xz3bp12Z49e1hycjLr2bOnyy/vXHaVDk+p5/Hjx5lUKmWLFi1iFy5cYN9++y1Tq9Xsm2++EffxlLpWhTvntCbm09NySXmsHGXUvd5fyqj71fFRcOecWtXEvJbmadm1ogzb546ZLSgoYCdOnGAnTpxgANhHH33ETpw4wa5cueLsotlVlc8UV/POO++w3377jWVkZLBTp06xmTNnMolEwn755RdnF61aXH21Teo8c+Czzz5j4eHhTC6Xs+joaLdbmhaA3X9r164V97FYLGzOnDksJCSEKRQK1rVrV5aammpznOLiYjZ58mQWEBDAVCoV69+/P7t69epjrk31lA2dJ9Vz27ZtrFWrVkyhULDmzZuzL7/80uZxT6prVbhrTmtiPj0xl5THylFG3ef9pYy6Zx0fBXfNqVVNzGtpnphdK8qwfe6W2X379tnN6KhRo5xdNLuq8pniasaMGSP+TdSqVYs9+eSTbtdxxpjrd55xjDH2145tI4QQQgghhBBCCCHEPdGcZ4QQQgghhBBCCCGEOECdZ4QQQgghhBBCCCGEOECdZ4QQQgghhBBCCCGEOECdZ4QQQgghhBBCCCGEOECdZ4QQQgghhBBCCCGEOECdZ4QQQgghhBBCCCGEOECdZ4QQQgghhBBCCCGEOECdZ4QQQgghhBBCCCHEpfz2228YMGAAwsLCwHEctm7dWu1j7N69G0888QS8vb1Rq1YtPPvss8jIyKj2cajzjBBCyEN50IaMEPL4rVu3Dn5+ftV6zujRozF48OC/pDyEEEKIO2jQoAGWLVvm7GLUOIWFhWjbti2WL1/+QM+/fPkyBg0ahJ49eyIlJQW7d+/G3bt38fe//73ax6LOM+J0165dw9ixYxEWFga5XI7w8HBMmzYN2dnZzi4aIQRAVlYWpkyZgkaNGkGhUKBevXoYMGAAfv31V2cXjRBSiqNOrv3794PjOOTm5mLo0KE4f/784y8cIcQGdUoT8vgMGDAAvXr1svvYkSNHwHEckpOTH3OpSFU8/fTTWLhwocPOLoPBgH/+85+oU6cONBoNOnbsiP3794uPJycnw2w2Y+HChWjcuDGio6Px5ptv4uTJkzAajdUqC3WeEae6fPkyYmNjcf78eXz//fe4ePEiPv/8c/z666+Ii4vDvXv3nF1EQmq0P//8EzExMdi7dy+WLl2K1NRU7Nq1Cz169MCkSZOcXTxCSDWpVCoEBwc7uxiEEELIYzN27Fjs3bsXV65cKffYmjVr0K5dO0RHRzuhZORhvfzyyzh06BA2btyIU6dOYciQIejbty8uXLgAAIiNjQXP81i7di3MZjPy8vKwYcMG9O7dGzKZrFq/izrPiFNNmjQJcrkcv/zyC7p164b69evj6aefxp49e3Djxg28++67AIRhsgsWLMALL7wALy8vhIWF4dNPP7U5Vl5eHsaNG4fg4GD4+PigZ8+eOHnypPj43Llz0a5dO2zYsAENGjSAr68vhg0bhoKCgsdaZ0LcycSJE8FxHI4fP47nnnsOTZs2RcuWLfH666/j6NGjdp+TmpqKnj17QqVSITAwEOPGjYNWqxUf379/Pzp06ACNRgM/Pz/Ex8fbnMxs27YNMTExUCqVaNSoEebNmweTyfSX15WQmsDebZsLFy5EcHAwvL298corr+Dtt99Gu3btyj333//+N0JDQxEYGIhJkyZV+4otIaRqEhMT0aFDBygUCoSGhuLtt98W28Ft27bBz88PFosFAJCSkgKO4zBjxgzx+ePHj8fw4cOdUnZCXFH//v0RHByMdevW2WwvKirCpk2bMHbsWGzZsgUtW7aEQqFAgwYN8OGHHzo83p9//gmO45CSkiJuy83NBcdx4qgn66jv3bt3IyoqCiqVCj179sTt27fx888/o0WLFvDx8cHw4cNRVFQkHocxhqVLl6JRo0ZQqVRo27Yt/ve//z3Kl8NjXLp0Cd9//z02b96MLl26oHHjxnjzzTfRuXNnrF27FoDQj/DLL79g5syZUCgU8PPzw/Xr17Fx48Zq/z7qPCNOc+/ePezevRsTJ06ESqWyeSwkJAQjRozApk2bwBgDAPzrX/9CmzZtkJycjHfeeQevvfYaEhISAAgfMv369UNWVhZ27tyJpKQkREdH48knn7QZvXbp0iVs3boV27dvx/bt25GYmIglS5Y8vkoT4kbu3buHXbt2YdKkSdBoNOUetzdvUlFREfr27Qt/f3/8/vvv2Lx5M/bs2YPJkycDAEwmEwYPHoxu3brh1KlTOHLkCMaNGweO4wAIE3q++OKLmDp1Ks6ePYsvvvgC69atw6JFi/7SuhJSU3377bdYtGgRPvjgAyQlJaF+/fpYuXJluf327duHS5cuYd++fVi/fj3WrVtX7ksIIeTh3bhxA8888wzat2+PkydPYuXKlVi9ejUWLlwIAOjatSsKCgpw4sQJAEJHW1BQEBITE8Vj7N+/H926dXNK+QlxRVKpFCNHjsS6devE75YAsHnzZhgMBsTFxeH555/HsGHDkJqairlz5+K99957JO3c3LlzsXz5chw+fBjXrl3D888/j2XLluG7777Djh07kJCQYDMoZNasWVi7di1WrlyJM2fO4LXXXsOLL75ok3EiSE5OBmMMTZs2hZeXl/gvMTERly5dAiBMP/PKK69g1KhR+P3335GYmAi5XI7nnnvO5m+hShghTnL06FEGgP344492H//oo48YAHbr1i0WHh7O+vbta/P40KFD2dNPP80YY+zXX39lPj4+TKfT2ezTuHFj9sUXXzDGGJszZw5Tq9UsPz9ffHzGjBmsY8eOj7BWhHiOY8eOMQDshx9+qHC/0jn+8ssvmb+/P9NqteLjO3bsYBKJhGVlZbHs7GwGgO3fv9/usbp06cLef/99m20bNmxgoaGhD1cZQmqAUaNGMZ7nmUajsfmnVCoZAJaTk8PWrl3LfH19xed07NiRTZo0yeY48fHxrG3btjbHDQ8PZyaTSdw2ZMgQNnTo0L+6SoR4rFGjRrFBgwaV2z5z5kzWrFkzZrFYxG2fffYZ8/LyYmazmTHGWHR0NPv3v//NGGNs8ODBbNGiRUwul7P8/HyWmZnJALC0tLTHUg9C3EVaWhoDwPbu3Stu69q1Kxs+fDh74YUX2FNPPWWz/4wZM1hkZKT4c3h4OPv4448ZY4xlZGQwAOzEiRPi4zk5OQwA27dvH2OMsX379jEAbM+ePeI+ixcvZgDYpUuXxG3jx49nffr0YYwxptVqmVKpZIcPH7Ypy9ixY9nw4cMfqv6eoGzfwcaNGxnP8+zcuXPswoULNv8yMzMZY4zNmjWLxcTE2Bzn2rVrDAA7cuRItX4/jTwjLouV9ARbR6TExcXZPB4XF4e0tDQAQFJSErRaLQIDA216nTMyMsReZ0AYtunt7S3+HBoaitu3b//VVSHELZXNYFWkpaWhbdu2NiPV4uPjYbFYkJ6ejoCAAIwePRp9+vTBgAED8MknnyAzM1PcNykpCfPnz7fJ8auvvorMzEybIe2EEPt69OiBlJQUm39fffWVw/3T09PRoUMHm21lfwaAli1bgud58WdqPwn5a6SlpSEuLs6m7Y2Pj4dWq8X169cBAN27d8f+/fvBGMOBAwcwaNAgtGrVCgcPHsS+fftQu3ZtNG/e3FlVIMQlNW/eHJ06dcKaNWsACHckHThwAGPGjEFaWhri4+Nt9o+Pj8eFCxdgNpsf6ve2adNG/P/atWtDrVajUaNGNtus7enZs2eh0+nw1FNP2ZwLf/311zbfaYkgKioKZrMZt2/fRkREhM2/kJAQAMJdMaXPXwCIP1tvf68q6aMpNiHVFxERAY7jcPbsWburDZ07dw7+/v4ICgpyeAzriYXFYkFoaKjNyhpWpW8tKzspIMdx1Q4NITVFkyZNwHEc0tLSqrwiGGPMYWebdfvatWsxdepU7Nq1C5s2bcKsWbOQkJCAJ554AhaLBfPmzbO7oo5SqXzguhBSU2g0GkRERNhss37hdqRsZpmd2xio/STk8bDXjpa9mNW9e3esXr0aJ0+ehEQiQWRkJLp164bExETk5OTQLZuEODB27FhMnjwZn332GdauXYvw8HA8+eSTFebOHolEUm4fR/OAlm4/OY6rsD21/nfHjh2oU6eOzX4KhaKy6nkkrVaLixcvij9nZGQgJSUFAQEBaNq0KUaMGIGRI0fiww8/RFRUFO7evYu9e/eidevWeOaZZ9CvXz98/PHHmD9/PoYPH46CggLMnDkT4eHhiIqKqlZZaOQZcZrAwEA89dRTWLFiBYqLi20ey8rKwrfffouhQ4eKH2RlJyc/evSoeFUtOjoaWVlZkEql5XqdK+p8I4Q4FhAQgD59+uCzzz5DYWFhucdzc3PLbYuMjERKSorN/ocOHYJEIkHTpk3FbVFRUXjnnXdw+PBhtGrVCt999x0AIcvp6enlchwRESGeqBBCHp1mzZrh+PHjNtv++OMPJ5WGEBIZGYnDhw/bfCk/fPgwvL29xS/T1nnPli1bhm7duoHjOHTr1g379++n+c4IqcDzzz8Pnufx3XffYf369Xj55ZfBcRwiIyNx8OBBm30PHz6Mpk2blhu1BAC1atUCAJu7J0ovHvCgIiMjoVAocPXq1XLnwfXq1Xvo47ujP/74A1FRUWJH1+uvv46oqCjMnj0bgHBRfuTIkXjjjTfQrFkzDBw4EMeOHRNfr549e+K7777D1q1bERUVhb59+0KhUGDXrl3l5l2vDI08I061fPlydOrUCX369MHChQvRsGFDnDlzBjNmzECdOnVsJgk/dOgQli5disGDByMhIQGbN2/Gjh07AAC9evVCXFwcBg8ejA8++ADNmjXDzZs3sXPnTgwePBixsbHOqiIhbm3FihXo1KkTOnTogPnz56NNmzYwmUxISEjAypUrxVunrUaMGIE5c+Zg1KhRmDt3Lu7cuYMpU6bgpZdeQu3atZGRkYEvv/wSAwcORFhYGNLT03H+/HmMHDkSADB79mz0798f9erVw5AhQyCRSHDq1CmkpqaKkyUTQh6dKVOm4NVXX0VsbCw6deqETZs24dSpUza3lBBC/hp5eXnlvnCPGzcOy5Ytw5QpUzB58mSkp6djzpw5eP3118WLSL6+vmjXrh2++eYbfPLJJwCEDrUhQ4bAaDSie/fuj7kmhLgHLy8vDB06FDNnzkReXh5Gjx4NAHjjjTfQvn17LFiwAEOHDsWRI0ewfPlyrFixwu5xVCoVnnjiCSxZsgQNGjTA3bt3MWvWrIcun7e3N95880289tprsFgs6Ny5M/Lz83H48GF4eXlh1KhRD/073E337t0rHAUok8kwb948zJs3z+E+w4YNw7Bhwx66LNR5RpyqSZMm+OOPPzB37lwMHToU2dnZCAkJweDBgzFnzhwEBASI+77xxhtISkrCvHnz4O3tjQ8//BB9+vQBIAx33blzJ959912MGTMGd+7cQUhICLp27YratWs7q3qEuL2GDRsiOTkZixYtwhtvvIHMzEzUqlULMTExdlfkU6vV2L17N6ZNm4b27dtDrVbj2WefxUcffSQ+fu7cOaxfvx7Z2dkIDQ3F5MmTMX78eABAnz59sH37dsyfPx9Lly6FTCZD8+bN8corrzzWehNSU4wYMQKXL1/Gm2++CZ1Oh+effx6jR48uNxqNEPLo7d+/v9xtQ6NGjcLOnTsxY8YMtG3bFgEBARg7dmy5L+Y9evRAcnKy2FHm7++PyMhI3Lx5Ey1atHhcVSDE7YwdOxarV69G7969Ub9+fQDCnQ///e9/MXv2bCxYsAChoaGYP3++2Llmz5o1azBmzBjExsaiWbNmWLp0KXr37v3Q5VuwYAGCg4OxePFiXL58GX5+foiOjsbMmTMf+tjk4XCsom48QlxEgwYNMH36dEyfPt3ZRSGEEEI82lNPPYWQkBBs2LDB2UUhhBBCCHEJNPKMEEIIIaSGKioqwueff44+ffqA53l8//332LNnDxISEpxdNEIIIYQQl0GdZ4QQQgghNZR12oOFCxdCr9ejWbNm2LJlC3r16uXsohFCCCGEuAy6bZMQQgghhBBCCCGEEAckzi4AIYQQQgghhBBCCCGuijrPCCGEEEIIIYQQQghxgDrPCCGEEEIIIYQQQghxgDrPCCGEEEIIIYQQQghxgDrPCCGEEEIIIYQQQghxgDrPCCGEEEIIIYQQQghxgDrPCCGEEEIIIYQQQghxgDrPCCGEEEIIIYQQQghx4P8BA+3zh1pGZDEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1250x1250 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subset = df[['Open', 'Close', 'High', 'Low', 'Volume']]\n",
+    "sns.pairplot(subset)\n",
+    "plt.suptitle('Pair Plot of Stock Features', y=1.02)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "511f7731-55f8-42af-aa05-67bc176e3901",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "fig = plt.figure(figsize=(12, 8))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.scatter(df['Open']- df['Close'], df['High'] - df['Low'],df['Volume'], c='r', marker='o')\n",
+    "ax.set_xlabel('Open Price')\n",
+    "ax.set_ylabel('Close Price')\n",
+    "ax.set_zlabel('Volume')\n",
+    "plt.title('3D Scatter Plot of Open, Close, and Volume')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "144f0c5f-bf0f-4e51-b0e9-f31a50562d25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "correlation_matrix = df.corr(numeric_only = True)\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', square=True, cbar_kws={\"shrink\": .8})\n",
+    "\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3084d625",
+   "metadata": {},
+   "source": [
+    "The high correlation between 'Open,' 'High,' 'Low,' 'Close,' and 'Adj Close' shows these features are highly interdependent and tend to move together in the same direction.\n",
+    "The negative correlation of 'Volume' with price-related features suggests that increased trading volume does not necessarily coincide with an increase in stock prices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6d5ee93-74b2-432c-ab0e-e44272df5de0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "617e2302-2768-4143-8c67-324a87523cc2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c9cf69a-4059-45d8-b30a-9128376cb341",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d101e1ee-ee0e-4167-8c22-7f8d1a45c605",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88dc069f-dd1a-4856-8443-fba999f673a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3e78015-ef36-4a75-919b-250016da4c71",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e880f7fc-42a0-44cf-adac-2e371ad3ec81",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32a44440-3163-43db-bc2c-c814268a1a6a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Intel_Optimized/buy_sell_recommendation_system.ipynb b/Intel_Optimized/buy_sell_recommendation_system.ipynb
new file mode 100644
index 0000000..b0f2e04
--- /dev/null
+++ b/Intel_Optimized/buy_sell_recommendation_system.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Buy/Sell Recommendation System using K-Means Clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1hSoEnVcApSTZ7XbXzmrgzmZJTEz02h3v4MGDPq8fP3489957L0ePHuXjjz9m6NChrns477N+/Xpuv/32Qs0n5++u8+cTERFx1j+fs1W9evUCf+8bNWqEw+EgNjaWFi1auNr37t171s/95ptvSE9PZ9GiRV7ZRoVdTle1alX+85//8J///IeMjAyuuuoqnn76aaZNm5ZrqaWIiEhp0RIzERGRErRy5Uqf2SOLFy8G8Fo6VrVqVa9t18HMZBg0aBD/93//57Ub1LFjx/j444/p1auXa4vvkSNHsnXrVtdyIk++5jB+/Hhef/11Zs6cyQMPPHA2r/evDRo0iGrVqvHss8+Slpbm1eecc5cuXWjWrBkvvviiV6DEKSEhocDn9O7dm/T0dF599VV69erlCvQ4dyQ7cuRIoeoP+foZlaQ///yT3bt3c8EFF7janAEZz6yi06dPM2fOHJ/3GDNmDBaLhbvuuov9+/fn2r1s6dKlgPmz8LRmzRoyMzNz3S/n7+7gwYMJDQ3lmWee8Tm+MD+fgmzdupXjx4/naj948CA7d+70uQTTafDgwQD873//82p/4403zno+zgwjz39XSUlJzJo1q8BrT5w44XUeEBBAmzZtMAzD5/dPRESktCiDSEREpATdcccdpKamMmLECFq1akVGRgbr16/n008/pXHjxlx//fWusV26dGH58uW8/PLL1KtXjyZNmtCjRw+eeuopli1bRq9evbjtttuw2Wy88847pKenM2PGDNf1U6dO5fPPP2fUqFFMmjSJLl26cPLkSRYtWsTMmTO9ggxOt99+O8nJyTz88MOEhYW5aqiUltDQUF555RVuvPFGunXrxrXXXkv16tXZunUrqampzJkzB6vVyvvvv8+QIUNo27Yt119/Peeddx5///03K1euJDQ0lG+++Sbf50RFRWGz2di9ezeTJ092tffp08eVPVWYAFFeP6PiYLfbmT9/PmAuqztw4AAzZ87E4XB4LUMcNGgQDRs25IYbbmDq1Kn4+fnx4YcfUrt2bQ4dOpTrvrVr1+bSSy/ls88+Izw8PNf27t999x29evUiLCzMq/35559n06ZNXHXVVXTo0AGA3377jblz51KjRg3uvvtuwPwZvv3224wbN47OnTtzzTXXuOby3Xff0bNnT958881/9b1ZtmwZ0dHRDBs2jAsvvJCQkBD279/Phx9+SHp6Oo899lie13bp0oWRI0fy6quvcuLECS688EJWr17tKvx9NllhgwYNIiAggCuuuIKbb76ZlJQU3nvvPSIiIryyAvO6NjIykp49e1KnTh127drFm2++ydChQ6lWrVqR5yIiIlJsynAHNRERkQrv+++/NyZNmmS0atXKCAkJMQICAozmzZsbd9xxR64tuv/44w+jT58+rq3FPbf+/u2334zBgwcbISEhRnBwsHHxxRcb69evz/W8EydOGLfffrtx3nnnGQEBAUb9+vWNCRMmGMePHzcMw3ube0/333+/ARhvvvlmnu/i3Ob+hRdeyPedndutx8TE5Nnn3IrdadGiRcZFF11kVKlSxQgNDTW6d+9uLFiwwGvM5s2bjauuusqoWbOmERgYaDRq1MgYPXq0sWLFinzn49StWzcDMH755RdX219//WUARoMGDXKN97Ule14/I+fYhISEQr1vTr62uQ8NDTUuueQSY/ny5bnGb9q0yejRo4cREBBgNGzY0Hj55ZfzfdbChQsNwJg8ebJXu8PhMCIiIowZM2bkumbdunXGlClTjHbt2hlhYWGGv7+/0bBhQ2PixInGvn37co1fuXKlMXjwYCMsLMwICgoymjVrZkycONHYuHGja8zZbnO/f/9+Y/r06caFF15oREREGDabzahdu7YxdOhQ48cff/Qa6+sZp0+fNqZMmWLUqFHDCAkJMYYPH27s3r3bAIznnnsu17WF+TkuWrTI6NChgxEUFGQ0btzYeP75540PP/ww17ic29y/8847Rp8+fVy/x82aNTOmTp1qJCUl5fs9EBERKWkWwzjLqokiIiIiUi783//9H8OHD+enn37yypT69ddf6dGjB7///jtt2rQpwxmWvi1bttCpUyfmz5/P2LFjy3o6IiIiZU41iEREREQquPfee4+mTZvSq1evXH3PPPNMhQ8OnTlzJlfbq6++itVqpU+fPmUwIxERkXOPahCJiIiIVFCffPIJ27Zt47vvvuO1117LVW+ne/fudO/evYxmV3pmzJjBpk2buPjii7HZbHz//fd8//33TJ482bULoIiISGWnJWYiIiIiFZTFYiEkJIT//Oc/zJw5E5utcv5/g8uWLePxxx9n586dpKSk0LBhQ8aNG8fDDz9cab8nIiIiOSlAJCIiIiIiIiJSyakGkYiIiIiIiIhIJacAkYiIiIiIiIhIJadF14DD4eDIkSNUq1YtV/FGEREREREREZHyyjAMTp06Rb169bBa884TUoAIOHLkiHawEBEREREREZEK6/Dhw9SvXz/PfgWIgGrVqgHmNys0NLSMZyMiIiIiIiIiUjySk5Np0KCBK/aRFwWIwLWsLDQ0VAEiEREREREREalwCiqpoyLVIiIiIiIiIiKVnAJEIiIiIiIiIiKVnAJEIiIiIiIiIiKVnGoQFVJWVhaZmZllPQ0pBX5+fthstgLXZ4qIiIiIiIhUFAoQFUJKSgp//fUXhmGU9VSklAQHB1O3bl0CAgLKeioiIiIiIiIiJU4BogJkZWXx119/ERwcTO3atZVVUsEZhkFGRgYJCQnExsbSokULrFatxBQREREREZGKTQGiAmRmZmIYBrVr16ZKlSplPR0pBVWqVMHf35+DBw+SkZFBUFBQWU9JREREREREpEQpNaKQlDlUuShrSERERERERCoT/RUsIiIiIiIiIlLJKUAkIiIiIiIiIlLJKUBUyVksFr7++uuynkaR9evXj7vvvruspyEiIiIiIiJSIShAVIHFxcVxxx130LRpUwIDA2nQoAFXXHEFK1asKOupuTz22GNYLBYsFgs2m43GjRtzzz33kJKSku91X375JU8++WQpzVJERERERESkYtMuZhXUgQMH6NmzJ+Hh4bzwwgu0b9+ezMxMlixZwpQpU/jjjz/Keooubdu2Zfny5djtdtatW8ekSZNITU3lnXfeyTU2IyODgIAAatSoUQYzFREREREREamYlEFURIYBp0+XzZdhFH6et912GxaLhV9//ZWRI0dy/vnn07ZtW+69915+/vnnPK/bvn07/fv3p0qVKtSsWZPJkyd7ZfOsWrWK7t27U7VqVcLDw+nZsycHDx509f/f//0fnTt3JigoiKZNm/L4449jt9vznavNZiMyMpL69evzn//8h7Fjx7Jo0SLAzDDq2LEj77//Pk2aNHFtOZ9ziVl6ejoPPPAADRo0IDAwkObNm/PBBx+4+nfs2MGQIUMICQmhTp06jBs3juPHjxf+GyoiIiIiIiJSgSmDqIhSUyEkpGyenZICVasWPO7kyZP88MMPPP3001T1cUF4eLjP606fPs3gwYOJiooiJiaG+Ph4brzxRm6//XZmz56N3W5n+PDh3HTTTSxYsICMjAx+/fVXLBYLAGvWrGH8+PG8/vrr9O7dm3379jF58mQAoqOjC/2eVapUISMjw3W+d+9evvjiC7788kv8/Px8XjN+/Hg2bNjA66+/zgUXXEBsbKwrAJSYmEj//v258cYbeeWVVzhz5gwPPPAAo0eP5scffyz0vEREREREREQqKgWIKqC9e/diGAatWrUq0nUff/wxaWlpzJ071xVYevPNN7niiit4/vnn8ff3Jykpicsvv5xmzZoB0Lp1a9f1jz/+OA8++CATJkwAoGnTpjz55JPcf//9hQ4Qbdq0iY8//pj+/fu72jIyMpg7dy61a9f2ec2ff/7JwoULWbZsGQMGDHA92+nNN9+kU6dOPPPMM662Dz/8kAYNGvDnn39y/vnnF2puIiIiIiIiIhWVAkRFFBxsZvKU1bMLwyjKWjQPu3bt4oILLvDKOurZsycOh4Pdu3fTp08fJk6cyODBgxk4cCADBgxg9OjR1K1bF4CtW7eybt06nn76adf1WVlZpKWlkZqaSnAeL7B9+3ZCQkLIysoiIyODoUOH8uabb7r6GzVqlGdwCGDLli34+fnRt29fn/1bt25l5cqVhPhI/dq3b58CRCIiIiIiIpKLYRhsPLKRrvW6ulbOVGQKEBWRxVK4ZV5lqUWLFlgslhIpRD1r1izuvPNOfvjhBz799FMeeeQRli1bxoUXXkhKSgqPP/44V111Va7rnLWDfGnZsiWLFi3CZrNRr149AgICvPp9LZPzVKVKlXz7U1JSXFlQOTmDWyIiIiIiIiKe5m+bz/ivxzNvxDyu63BdWU+nxKlIdQVUo0YNBg8ezFtvvcXp06dz9ScmJvq8rnXr1mzdutXrmnXr1mG1WmnZsqWrrVOnTkybNo3169fTrl07Pv74YwA6d+7M7t27ad68ea4vqzXvX7WAgACaN29O48aNcwWHCqN9+/Y4HA5Wr17ts79z5878/vvvNG7cONe8Cgo+iYiIiIiISOVjd9iJXmWWSoleFY3dkf/mSxWBAkQV1FtvvUVWVhbdu3fniy++YM+ePezatYvXX3+dqKgon9eMHTuWoKAgJkyYwI4dO1i5ciV33HEH48aNo06dOsTGxjJt2jQ2bNjAwYMHWbp0KXv27HHVIZo+fTpz587l8ccf5/fff2fXrl188sknPPLIIyX6ro0bN2bChAlMmjSJr7/+mtjYWFatWsXChQsBmDJlCidPnmTMmDHExMSwb98+lixZwvXXX09WVlaJzk1ERERERETKnwXbFxCbGAvA/n/288mOT8p4RiVPAaIKqmnTpvz2229cfPHF3HfffbRr146BAweyYsUK3n77bZ/XBAcHs2TJEk6ePEm3bt24+uqrueSSS1z1gIKDg/njjz8YOXIk559/PpMnT2bKlCncfPPNAAwePJhvv/2WpUuX0q1bNy688EJeeeUVGjVqVOLv+/bbb3P11Vdz22230apVK2666SZXJlS9evVYt24dWVlZDBo0iPbt23P33XcTHh6eb2aTiIiIiIiIVD6u7KHv3oL/bcWya0SlyCKyGGdb0bgCSU5OJiwsjKSkJEJDQ7360tLSiI2NpUmTJvnW0ZGKRT93ERERERGRymne1nmM/3o8zP8O9l4GV06ETnPKbS2i/GIenpQ+ISIiIiIiIiKCO3vIggXI3rnM4sCKtcJnESlAJCIiIiIiIiKCu/aQgQFGdsjEYuDAUeFrESlAJCIiIiIiIiKVnnf2EGC4M4iACp9FpACRiIiIiIiIiFR6aw+tdWcPgTuDKPvcmUW09tDasplgCbOV9QRERERERERERMpaVP0oFl69kPSsdACeXdyWnbFwW/dbiRpyGQCBfoFE1Y8qy2mWGAWIRERERERERKTSC7QFMqrtKNf5B8GwE+jTuDf/6dC77CZWSrTETEREREREREQkByN7pZm1kkROKslrioiIiIiIiIgUnsOsTY3FUrbzKC0KEImIiIiIiIiI5KAMIpFSYLFY+Prrr0v9uY0bN+bVV18t9eeKiIiIiIhI+aIMIqkQJk6ciMVicX3VrFmTSy+9lG3btpX4sxMSErj11ltp2LAhgYGBREZGMnjwYNatW+cac/ToUYYMGVLicxERERERERE5G84MIgWIpNgZhkHM3zEYzt+yEnbppZdy9OhRjh49yooVK7DZbFx++eUl/tyRI0eyefNm5syZw59//smiRYvo168fJ06ccI2JjIwkMDCwxOciIiIiIiIicja0xExKzPxt8+n+fnc+2v5RqTzPmb0TGRlJx44defDBBzl8+DAJCQkArFq1CovFQmJiouuaLVu2YLFYOHDgAKdPnyY0NJTPP//c675ff/01VatW5dSpU7memZiYyJo1a3j++ee5+OKLadSoEd27d2fatGkMGzbMNS7nErP169fTsWNHgoKC6Nq1K19//TUWi4UtW7Z4zXXFihV07dqV4OBgLrroInbv3u26x759+7jyyiupU6cOISEhdOvWjeXLlxfDd1JEREREREQqG4fDmdxROkkeZa1MA0SNGzf2Wgbl/JoyZQoAaWlpTJkyhZo1axISEsLIkSM5duyY1z0OHTrE0KFDCQ4OJiIigqlTp2K328vidfJld9iJXhUNQPSqaOyO0p1jSkoK8+fPp3nz5tSsWbNQ11StWpVrrrmGWbNmebXPmjWLq6++mmrVquW6JiQkhJCQEL7++mvS09ML9Zzk5GSuuOIK2rdvz2+//caTTz7JAw884HPsww8/zEsvvcTGjRux2WxMmjTJ6x0vu+wyVqxYwebNm7n00ku54oorOHToUKHmISIiIiIiIuJ0/LS5CuanQ6vKdiKlpEwDRDExMa4lUEePHmXZsmUAjBo1CoB77rmHb775hs8++4zVq1dz5MgRrrrqKtf1WVlZDB06lIyMDNavX8+cOXOYPXs206dPL5P3yc+C7QuITYwFYP8/+/lkxycl/sxvv/3WFbCpVq0aixYt4tNPP8VahPy4G2+8kSVLlnD06FEA4uPjWbx4sVdgxpPNZmP27NnMmTOH8PBwevbsyUMPPZRv7aOPP/4Yi8XCe++9R5s2bRgyZAhTp071Ofbpp5+mb9++tGnThgcffJD169eTlpYGwAUXXMDNN99Mu3btaNGiBU8++STNmjVj0aJFhX5fEREREREREbvDzl/J5t/BH22fX+pJHmWhTANEtWvXdi2BioyM5Ntvv6VZs2b07duXpKQkPvjgA15++WX69+9Ply5dmDVrFuvXr+fnn38GYOnSpezcuZP58+fTsWNHhgwZwpNPPslbb71FRkZGWb6aF2f2kAWzspUVa6lkEV188cVs2bKFLVu28OuvvzJ48GCGDBnCwYMHC32P7t2707ZtW+bMmQPA/PnzadSoEX369MnzmpEjR3LkyBEWLVrEpZdeyqpVq+jcuTOzZ8/2OX737t106NCBoKAgr+f60qFDB9dx3bp1ATNoBWYG0X//+19at25NeHg4ISEh7Nq1SxlEIiIiIiIiUiQLti8gw27GFeJO/10qSR5l7ZypQZSRkcH8+fOZNGkSFouFTZs2kZmZyYABA1xjWrVqRcOGDdmwYQMAGzZsoH379tSpU8c1ZvDgwSQnJ/P777/n+az09HSSk5O9vkqSM3vIyF636MBRKllEVatWpXnz5jRv3pxu3brx/vvvc/r0ad577z0AVyaRZ9HszMzMXPe58cYbXcGdWbNmcf3112MpoIx7UFAQAwcO5NFHH2X9+vVMnDiR6Ojof/1O/v7+rmPnHBzZew/+97//5auvvuKZZ55hzZo1bNmyhfbt259TwUIRERERERE5t7lKxBjm38wWi6VMSsWUtnMmQPT111+TmJjIxIkTAYiLiyMgIIDw8HCvcXXq1CEuLs41xjM45Ox39uXl2WefJSwszPXVoEGD4nuRHHJmDzmVVhaRJ4vFgtVq5cyZM4CZwQW4lo8BrqLQnq677joOHjzI66+/zs6dO5kwYUKRn92mTRtOnz7ts69ly5Zs377dq2ZRTExMkZ+xbt06Jk6cyIgRI2jfvj2RkZEcOHCgyPcRERERERGRystVIsYw/443LPZSKxVTls6ZANEHH3zAkCFDqFevXok/a9q0aSQlJbm+Dh8+XGLPypk95FQaWUTp6enExcURFxfHrl27uOOOO0hJSeGKK64AoHnz5jRo0IDHHnuMPXv28N133/HSSy/luk/16tW56qqrmDp1KoMGDaJ+/fp5PvPEiRP079+f+fPns23bNmJjY/nss8+YMWMGV155pc9rrr32WhwOB5MnT2bXrl0sWbKEF198EaDATCVPLVq04Msvv2TLli1s3brVdV8RERERERGRwvBK8jCcIROjTJI8Sts5ESA6ePAgy5cv58Ybb3S1RUZGkpGR4bUFO8CxY8eIjIx0jcm5q5nz3DnGl8DAQEJDQ72+SkJe2UNOJf0L9sMPP1C3bl3q1q1Ljx49iImJ4bPPPqNfv36AuVxrwYIF/PHHH3To0IHnn3+ep556yue9brjhBjIyMvIsTu0UEhJCjx49eOWVV+jTpw/t2rXj0Ucf5aabbuLNN9/0eU1oaCjffPMNW7ZsoWPHjjz88MOuQuOedYkK8vLLL1O9enUuuugirrjiCgYPHkznzp0Lfb2IiIiIiIhUbt5JHtl/y1scpVYqpixZDM8CNGXkscce45133uHw4cPYbDYAkpKSqF27NgsWLGDkyJGAWcy4VatWbNiwgQsvvJDvv/+eyy+/nKNHjxIREQHAu+++y9SpU4mPjycwMLBQz09OTiYsLIykpKRcwaK0tDRiY2Np0qRJkYIVAKsOrOLiORcXOG7lhJX0a9yvSPcubfPmzeOee+7hyJEjBAQElPjzPvroI66//nqSkpKoUqVKiT8vp3/zcxcREREREZHyx+6wc/4b53Mg8YAZIHprByS0hQkXQ5NVWLHSuHpjdt++G5vVVtbTLbT8Yh6eyvyNHA4Hs2bNYsKECa7gEEBYWBg33HAD9957LzVq1CA0NJQ77riDqKgoLrzwQgAGDRpEmzZtGDduHDNmzCAuLo5HHnmEKVOmFDo4VJKi6kex8OqFpGel5zkm0C+QqPpRpTiroklNTeXo0aM899xz3HzzzSUWHJo7dy5NmzblvPPOY+vWrTzwwAOMHj26TIJDIiIiIiIiUvmsPbTWrD3kZLgziMBdKmbtobXnfJLH2SjzANHy5cs5dOiQz6VLr7zyClarlZEjR5Kens7gwYP53//+5+r38/Pj22+/5dZbbyUqKoqqVasyYcIEnnjiidJ8hTwF2gIZ1XZUWU/jX5kxYwZPP/00ffr0Ydq0aSX2nLi4OKZPn05cXBx169Zl1KhRPP300yX2PBERERERERFPOZM87p99HkePw8N9HqZVl5uAcz/J4984J5aYlbWSWmIm5Zd+7iIiIiIiIpVby5bw55+wZg306lXWszl7hV1idk4UqRYREREREREROZc4N8Uuwuba5ZoCRCIiIiIiIiIiOTjXW1krSeSkkrymiIiIiIiIiEjhKYNIRERERERERKSSUwaRiIiIiIiIiEglpwwiEREREREREZFKzrnpu8VSOTZ/V4BI8vTYY4/RsWNH1/nEiRMZPnz4v7pncdzjbOR8FxEREREREZH8pKSnAvDDvu/LeCalw1bWE6jQ0tNh0SLzMy+BgTBsmPlZjBISEpg+fTrfffcdx44do3r16lxwwQVMnz6dnj17FuuzPL333nu8+eab7Nu3D5vNRpMmTRg9ejTTpk0D4LXXXnNFYUVERERERETORXaHneT0U0BVZm76Hw+OGoTNWrFDKBX77crahg0wenTB41auhH79ivXRI0eOJCMjgzlz5tC0aVOOHTvGihUrOHHiRLE+x9OHH37I3Xffzeuvv07fvn1JT09n27Zt7NixwzUmLCysxJ4vIiIiIiIiUhwWbF9AVtYAAP4+dZhPdnzCdR2uK+NZlSwtMStJvXpBkyZ5V7SyWqFpU3NcMUpMTGTNmjU8//zzXHzxxTRq1Iju3bszbdo0hg0b5jXuxhtvpHbt2oSGhtK/f3+2bt161s9dtGgRo0eP5oYbbqB58+a0bduWMWPG8PTTT7vG5FxidurUKcaOHUvVqlWpW7cur7zyCv369ePuu+92jWncuDHPPPMMkyZNolq1ajRs2JB3333X69kPPPAA559/PsHBwTRt2pRHH32UzMzMs34XERERERERqZzsDjvRq6LBMP+Wt1ggelU0doe9jGdWshQgKkk2Gzz+uHtvvJwcDrPfVryJXCEhIYSEhPD111+Tns/ytlGjRhEfH8/333/Ppk2b6Ny5M5dccgknT548q+dGRkby888/c/DgwUJfc++997Ju3ToWLVrEsmXLWLNmDb/99luucS+99BJdu3Zl8+bN3Hbbbdx6663s3r3b1V+tWjVmz57Nzp07ee2113jvvfd45ZVXzuo9REREREREpPJasH0BsYmxYJghE4Ms9v+zn092fFLGMytZChCVtDFjfGcRObOHrrmm2B9ps9mYPXs2c+bMITw8nJ49e/LQQw+xbds215i1a9fy66+/8tlnn9G1a1datGjBiy++SHh4OJ9//vlZPTc6Oprw8HAaN25My5YtmThxIgsXLsTh3Bswh1OnTjFnzhxefPFFLrnkEtq1a8esWbPIysrKNfayyy7jtttuo3nz5jzwwAPUqlWLlStXuvofeeQRLrroIho3bswVV1zBf//7XxYuXHhW7yEiIiIiIiKVkzN7yIIFyP473mJgxVrhs4gUICppeWURlVD2kNPIkSM5cuQIixYt4tJLL2XVqlV07tyZ2bNnA7B161ZSUlKoWbOmK+MoJCSE2NhY9u3bd1bPrFu3Lhs2bGD79u3cdddd2O12JkyYwKWXXuozSLR//34yMzPp3r27qy0sLIyWLVvmGtuhQwfXscViITIykvj4eFfbp59+Ss+ePYmMjCQkJIRHHnmEQ4cOndV7iIiIiIiISOXkzB4yMFwZRFgcOHBU+CwiBYhKQ84sohLMHvIUFBTEwIEDefTRR1m/fj0TJ04kOjoagJSUFOrWrcuWLVu8vnbv3s3UqVP/1XPbtWvHbbfdxvz581m2bBnLli1j9erV/+qe/v7+XucWi8UVdNqwYQNjx47lsssu49tvv2Xz5s08/PDDZGRk/KtnioiIiIiISOXhnT2EqwYRmAkfFT2LSAGi0pAzi6iEs4fy0qZNG06fPg1A586diYuLw2az0bx5c6+vWrVqFeszAddzPTVt2hR/f39iYmJcbUlJSfz5559Fesb69etp1KgRDz/8sGu5XFHqIImIiIiIiIisPbTWnT0EuJeYmckJziyitYfWls0ES5i2uS8tY8ZAdDTExpZ49tCJEycYNWoUkyZNokOHDlSrVo2NGzcyY8YMrrzySgAGDBhAVFQUw4cPZ8aMGZx//vkcOXKE7777jhEjRtC1a9ciP/fWW2+lXr169O/fn/r163P06FGeeuopateuTVRUVK7x1apVY8KECUydOpUaNWoQERFBdHQ0VqsVS147v/nQokULDh06xCeffEK3bt347rvv+Oqrr4o8fxEREREREam8oupHsfDqhaRnmZs9TX6xKmeAFwa9QGSjUwAE+gUSVT/337cVgQJEpcWZRTR+fIlnD4WEhNCjRw9eeeUV9u3bR2ZmJg0aNOCmm27ioYceAswlWosXL+bhhx/m+uuvJyEhgcjISPr06UOdOnXO6rkDBgzgww8/5O233+bEiRPUqlWLqKgoVqxYQc2aNX1e8/LLL3PLLbdw+eWXExoayv3338/hw4cJCgoq9HOHDRvGPffcw+233056ejpDhw7l0Ucf5bHHHjur9xAREREREZHKJ9AWyKi2o1znt2XnLYxocyXNmpXRpEqRxTDy2oO98khOTiYsLIykpCRCQ0O9+tLS0oiNjaVJkyZFClr4ZBiwcSN07Zp7VzMBzKVo5513Hi+99BI33HBDmc2jWH/uIiIiIiIiUu6EhMDp07Bvn7kQqLzKL+bhSTWISpPFAt26KTjkYfPmzSxYsIB9+/bx22+/MXbsWADXUjgRERERERGRkmYYBjF/x+CZQ+M8tFaSyEkleU05l7344otccMEFDBgwgNOnT7NmzZpiLZQtIiIiIiIikp/52+bT/f3ufLT9I1db9sbZlSbHQzWIpEx16tSJTZs2lfU0REREREREpJJybm8PEL0qmmvaXYPNalMGkYiIiIiIiIhIZbFg+wJiE2MB2P/PfhZsXwBUvgwiBYgKSbW8Kxf9vEVERERERCo+V/bQjtGw6B3IsnHf0vuwO+wefxc6ynSOpUUBogL4+fkBkJGRUcYzkdKUmpoKgL+/fxnPREREREREREqKK3vo80/ht8mwdTwJqQl8tO0jshxmgOihH6eV8SxLh2oQFcBmsxEcHExCQgL+/v5YK8viw0rKMAxSU1OJj48nPDzcFSAUERERERGRisWZPWTBgmsNSaq5YdJ9S+7DMMYB8PHv83nX/jhBtqCymWgpUYCoABaLhbp16xIbG8vBgwfLejpSSsLDw4mMjCzraYiIiIiIiEgJ8aw95LL8eYh6iRNpJ8AwE0SyHJnc88M9vH3522Uwy9KjAFEhBAQE0KJFCy0zqyT8/f2VOSQiIiIiIlKBeWcP5ahBe7gn1NjjPrcYvL/5fV659JUKnUWkAFEhWa1WgoIq7i+CiIiIiIiISGWx9tBad/ZQetXcAxa/5T622rE77BU+i0gFdURERERERESkUomqH8XCqxfSumZriOuUo9eAhDbuU2smAO9vfp80e1rpTbKUKUAkIiIiIiIiIpVKoC2QIS2GsOvELnDkKDFiMcDPo8SMnxkgcmYRVVQKEImIiIiIiIhIpXPt59dmH1m8O/5pCn7p7vPsDCKo2FlEChCJiIiIiIiISKWSZk/juz3fmSdGjtDI13MgIMV9bnUXsbY77MzcOLMUZlj6FCASERERERERkUpl5saZOHCYJ4Yl94Baf/i8LjQwlPEdxpfgzMqOAkQiIiIiIiIiUqlM6jiJgU0Hmic5M4gANt1ifvZ+2qs5OT2ZbfHbSnh2ZUMBIhERERERERGpVIIDgtl7cm/2mY8MIier3ev09m63E1U/quQmVoYUIBIRERERERGRSmXtobXEJsaaJ74yiJwsWa5DK1YW712Mn9Uv7/HlmAJEIiIiIiIiIlKpRNWP4tHej5onvmoQOXlkEDlwsP+f/aw9tLaEZ1c2FCASERERERERkUol0BZI9/rds88KFyCqHVybBSMXaImZiIiIiIiIiEhF8dXOr8wD5xKz837JPcjqXmKWkJpAZEgkgbbAUphd6VOASERERERERERKlWEYxPwdg2EYZfL8NHsas7fNzp5MdgaRxZF7YHYGUbWAaswdPrfCZg+BAkQiIiIiIiIiUsrmb5tP9/e789H2j8rk+TM3zsRhZAeEXEWqfQSrsotUn8o4RYOwBhU2ewjAVtYTEBEREREREZHKw+6wE70qGoDoVdFc0+4abNbSDU9M6jiJQ0mHOJN5hj1p7VgB+WYQ1Q6uTbd63Up1jqVNGUQiIiIiIiIiUmoWbF9A7NGTYMD+f/bzyY5PSn0OoUGhvDz4Zd6+/G16NuhtNlp8ZBBl1yBKSE0g5khMKc6w9ClAJCIiIiIiIiKlwu6wc/+7P8DzifDNO1ixEr0qGrvDXuC1hXE2tY0SUo6bB74yiGxpAIQGhCqDSERERERERESkOCzYvoC4b241T36bjANHsWYRzds2j+7vd2f+tvmFGm932Pn89y+zz3wElQJOAZCckcznOz8vljmeqxQgEhEREREREZES51l7yFNxZRHZHXb+u/S/ANy39L5C3W/B9gUknD6RPZGs3AMCT7kOpy6bWmyZTuciBYhEREREREREpEQZhsHTPz1NbGIsYPHqK64sonlb55FwqDpsupGEUyf5aFv+O6TZHXamr5wO9uydyfzScw8KcAeIElITWH1g9b+a47lMASIRERERERERKVGzt8zmsdWPYcHisa2827/NIrI77Nz1w13w5m745j3YNq7AjJ8F2xdwIOkAZAWYDX4ZUGun9yCPDCKALMNHllEFoQCRiIiIiIiIiJQYu8POPUvuAcxMIv6KyjXGmUW09tDas3rG3K1zORVf3d1wrD0JqQl5ZhF5LXdzBohs6TB+gPdAjwyiEa1G0LdR37OaX3mgAJGIiIiIiIiIlJj52+aT9I8fxLWH30f7HNOnYR8WXr2QqPq5g0cFsTvs3P3D3TB3mbsx5BiQd92gtYfWZi93A7KcS8wyIPSo90CPDKItcVvws/oVeX7lhQJEIiIiIiIiIlIi7A479y+7H17fAzO3weZJ3gOyNw5bc2gNV7S8gkBbYJGfMXfrXE5lnIKT57sbgxMA8swiiqofxe3dbjdPjrc0P31tcx+Q4jqMTYw96wyn8kABIhEREREREREpEfO3zSfh1D+QVsNsONTTe8DTqfBPIwwMbl98e5Hv78oeyskj2OMri8jP6se3e741T36bbH5uG5v7Plb3fW7vfvtZZTiVFwoQiYiIiIiIiEixc2UP2YPcjTmKPmOvAq8dAODDzR+SZk8r0jN+jP3RzB7KyWFzHfrafWztobUcSDzgfY1hIz+L9yzWEjMRERERERERkaKYv20+CakJ7iLQ4H2cw9lkEf2w5wffHQ7vYM+ZzDNe550iO5k7qtn93Y2jrzI/G/7k85b/poh2eaAAkYiIiIiIiIgUK1f2EECWRxDmTM3cg/3cWUNFySKyO+zM3jLbd6fhnenzw74fcDgcxPwdg2EYTF02FQMDMqq5B7VclH1g8brWgoWI4AgWXLVAS8xERERERERERApr9YHVZvYQ5Js1ZPYHwZkwwMwiuuv7uwr1jLWH1vJP+j/uhsBE93GODKK3Yt7itsW30f397szaPIsPNn9gdmSEmJ9+aeCXZR4b3gEiA4P41Hjshv2simiXFwoQiYiIiIiIiEixyjKy3CcO/7wHOm281XX4weYPCpVFFFU/ilnDZplLxQCCj3s80x0gcvY7g0L/XfZfHEZ28Wl7dsDH5vE8I3eoxIqV6FXRuYpdVyQKEImIiIiIiIhIserbqC8Lr17IzV1uLjiDCLyydrKMLGZunFngJYG2QP488ae5VAy8A1EO9xIzIzkSjrV1BXf+SfPIOnJe45fpbvP3rlcE4MChGkQl7e+//+a6666jZs2aVKlShfbt27Nx40ZXv2EYTJ8+nbp161KlShUGDBjAnj17vO5x8uRJxo4dS2hoKOHh4dxwww2kpKSU9quIiIiIiIiICGbwZkTrESzZu8S7BlFedo10HfZv3J9JHScVeIndYefj7R+7Gzyf47nE7OUj8PYOWPgpxLfGgoVAv+zMIWfwyuoRIBp6K9T4E4a553BLl1tYePVC1SAqKf/88w89e/bE39+f77//np07d/LSSy9RvXp115gZM2bw+uuvM3PmTH755ReqVq3K4MGDSUtzp3+NHTuW33//nWXLlvHtt9/y008/MXny5LJ4JREREREREREBFmxfwIGkA4XLIDraxXUYcySG4IDgAi9Ze2gtB5MPuhtS6rmPnQEih0fYY+domLscA4P0rHSzzRlU8stwj6u1B+5sCZ1nAebysqX7lzKi9QjVICopzz//PA0aNGDWrFl0796dJk2aMGjQIJo1awaY2UOvvvoqjzzyCFdeeSUdOnRg7ty5HDlyhK+//hqAXbt28cMPP/D+++/To0cPevXqxRtvvMEnn3zCkSNHfD43PT2d5ORkry8RERERERERKR52h53oVdHmia8aRH4+agztNLeZP5Vxih/3/5jv/Q3DwN/qz/wR87mkySXwT6McA/ywWWyQmmPXNM8gkufcPJeY5eBcXvbJjk/ynVN5V6YBokWLFtG1a1dGjRpFREQEnTp14r333nP1x8bGEhcXx4ABA1xtYWFh9OjRgw0bNgCwYcMGwsPD6dq1q2vMgAEDsFqt/PLLLz6f++yzzxIWFub6atCgQQm9oYiIiIiIiEjls/bQWmITY82TnEvMgv6Baj4SOhZ+4Tr85Pf8gzHzts6j16xeZGZlsu3YNjh2gfcAhw27YYfTEfnep1PtC80DayZBtqA8x6lIdQnbv38/b7/9Ni1atGDJkiXceuut3HnnncyZMweAuLg4AOrUqeN1XZ06dVx9cXFxRER4/8BtNhs1atRwjclp2rRpJCUlub4OHz5c3K8mIiIiIiIiUmlF1Y9iwVULCPEPyb3ErO5vPncK8zRn65w8dzKzO+zct+w+AO74/g4SUhO8ilID7iVmOQNEwQmuQytWjiRln/tl5rtzWmUoUm0reEjJcTgcdO3alWeeeQaATp06sWPHDmbOnMmECRNK7LmBgYEEBlbcdYMiIiIiIiIiZSnQFkiaPY2UzJTcS8yuvB4+zD/Q4jAcPLn6SZ6+5OlcffO3zud40ilwVCWF7A2qcgacnAGjMzmWmHnUGnLg4FjyCa/2W7rcQs+GPX2/k19ghS5SXaYBorp169KmTRuvttatW/PFF2ZaWWRkJADHjh2jbt26rjHHjh2jY8eOrjHx8fFe97Db7Zw8edJ1vYiIiIiIiIiUHrvDzoMrHjRPcmYQhR8GLLmuyenjHR/z+MWPY7O6Qxd2h52py6fCm7vhVF14MBwCzkBade+LnRlE9hzLxk6d5zoc12Eci/ZbSQKwZrqKUb9x2Rtez6wsynSJWc+ePdm9e7dX259//kmjRmZxqSZNmhAZGcmKFStc/cnJyfzyyy9ERZlRu6ioKBITE9m0aZNrzI8//ojD4aBHjx6l8BYiIiIiIiIi4mntobUcO33MPPG1zX0BS8wADiQeyLWka/7W+Rw/9Q8kNQJHAMS3g/Sq8M173hfnFSDy8OmOT0lKTTVP/DIrTTHqvJRpgOiee+7h559/5plnnmHv3r18/PHHvPvuu0yZMgUAi8XC3XffzVNPPcWiRYvYvn0748ePp169egwfPhwwM44uvfRSbrrpJn799VfWrVvH7bffzjXXXEO9evXyebqIiIiIiIiIlISo+lFc0vgS88ThIxungABRtYBqzB0+12tJlyt7KLOqx338ILGxj/tnLzHbdVXuvtO1AMhwZJhBJgCruYtZZShGnZcyDRB169aNr776igULFtCuXTuefPJJXn31VcaOHesac//993PHHXcwefJkunXrRkpKCj/88ANBQe4o4EcffUSrVq245JJLuOyyy+jVqxfvvvtuWbySiIiIiIiISKUXaAvk1q63mieeAaKbO5mfRv5LzE5lnMJisRBoc9cPnr91PsdTj0NmsHugw+YdMHJyZi3tHZK7b8lLHuOy55a9zX1lziIq80V1l19+OZdffnme/RaLhSeeeIInnngizzE1atTg448/LonpiYiIiIiIiMhZSEpPMg+c2TzNv4e6W7LbCs5Xmb5yOte0uwab1ebOHgLI8AgIzVoD4wbkvji/+59s4T52FtDOziACdxaR89mVRZlmEImIiIiIiIhIxWN32HlqzVPmiTODyOq5bMsjg2jwPeZnvV+97hGbGOuqQbT6wGozewjgVI5yMj6XmOUT7kit5T52Zhr5uQNElWFLe18qTyhMRERERERERErF2kNriU2MNU+cW85bstwDPAM4NZ2bV3kvO3vgogdcNYiyjOxrUyJg9k/eD8u5g5nn/Wvs8c4YAkitmT0G1w5rEdVq8NKIea4hFX1Le18UIBIRERERERGRYhVVP4roPtE8/tPjvjOIPANEFkfuNqB6lequGkR9G/Wldc3W7FoxllzSwnO3ue5lmB8jx8AXC8zjgNOQFgr/2w7JDQFISD/C1W2uJsiW965nFZ2WmImIiIiIiIhIsQq0BTKt9zRu63pbHgEij2yhPAJEbSPauo4zHZnsOnoQ1jyc+2FpYbnbnHWPMqqZn7V2Qfh+87j1F7BniCs4ZE4nnXt+uKdQ71ZRKUAkIiIiIiIiIsXOz+rHwp0L3cEaq7lMzIIFC36ucQ/0vh+ABqGNmTdiHvNGzGPh1QsZ2HSga8zQj4ZCUgPfD3JmEJ3/DVx6p3nsDDbZszOC/M9AO4+dyU5H5JhsJu9vfp80e1qR37Oi0BIzERERERERESk2hmGw8chGdsTvMAtLx9xmdmRnEBkY4HBnEA1sdgnPA2EB1bmuw3W57peSkcJPh36CY6N9P9AZIKr1hysI5QoQuYpQZ7gzmAw/+OF173tYM7E77Nzzwz28ffnbRXzjikEZRCIiIiIiIiJSbOZvm0/397tz1w93waEod5HoPHYx+/HAcgAcDt/3m/zNZPPg8099DzhwsfkZkJJ7uVp2EWqsme7np4fmvkd2X2XOIlKASERERERERESKhd1hJ3pVNACnMk7B0S7uzpQ67mOPekOztn4A+A4Q2R12luxdAhlV8n5oZlXz0zNAlFIHvpgHjuwAkV+GO7to64Tc98je5t7usDNz48y8n1WBaYmZiIiIiIiIiBSLBdsXuLe3B+9i1Hsudx1a8HPuL8bRlL8A3wGi1QdWczLtJGT5KESdU0AKQbZA0gAO9zK/nPwyIb1a3tdazQBRaGAo4zuML/hZFZAyiERERERERETkX7M77Dy68lHYPRS+mg3pVfMcaxjuY0t2DCk14wyGZwew/2T2zmOGH7m0+cz7PCCFNEeK7wf6ZUCr/8t78tkZRMnpyWyL35b3uApMASIRERERERER+dcWbF/AwcSDsOBbcxnXuvvxrDXkpXt2keiWX2NYzPo/fyUd4aPtH7mG2B12Hln5iHli+AhfNFvqfR5wyr3ELCe/DKj/c96Tz84gGtN2DFH1o/IeV4EpQCQiIiIiIiIi/4ore+hP9zIykhp6Dxo1yn08YBqMGwAjr/UqLD195XTsDjNgtPbQWuJT480+R44MoituBLyzjQhIgcBTvidotZuxKluq7/7sDKIq/lUItAX6HlPBKUAkIiIiIiIiIv/Kgu0LOJh0EA72cTdaDLyCOPU3uI/97NBsBQSc8QoQxSbG8smOTzAMAwsWagXVyu7LESDq/AHsG+TdFpACwcd9T9CZyOR/xnd/dgZRl7pdfPdXAgoQiYiIiIiIiMhZc2UPgXdRagz3NvMAYX/7voEzQJSdJTR95XTmbJ1Dvzn9OJ6WHfDxXGJ2W5vsbKAc29EHpEBgcv6TteURIEo3i2C/uOFFVwZTZaMAkYiIiIiIiIictbWH1prZQ+AdyLEY7i3ou76d9w08MogAYhNjuWfJPQDUCKxh9nkuMYvYZX7a0r3vE5ACIUdz37/xj+5j/zyWmMXc5nr2Jzs+yXuuFZgCRCIiIiIiIiJy1tpHtHefOPzdx37pkBFiHvuf9rqmfrX63NzlZmoF18peioZXcCkxtjEktOJk+kmzIfaS3A+uF+N9HnAaqh3Lf7J5LTHz4FkHqTJRgEhEREREREREzppreRlAejX3sX8qZGRnEAV4bz8fdzqO7ud153jqcbBkmY3OANHpmvDOZnhrl7uE0cnmuR/c+QPvc2d2UKuvcgy05B6TU60/XIexibGsPbTW97gKTAEiERERERERETlrg5p6FItOD3UfW+2w0Vy6RYB3BpHdYWfqsqlYsORaYsbxVu6BzqVlvraotzqg2RL3uV/2kjN7kPc4z7pIedUgGnaj6zCiagTd6nXzPa4CU4BIRERERERERM7awGYDiQiOMIM9Vo+lWZ7LzVJrAnBxo4u5pcstXN36ak6eOYmBkTtAFNfRfZ2zyLU9e+v5RqvynojV8B7r4hEgyt7OPpeaf7oO40/HE3Mkxve4CsxW1hMQERERERERkfIr5kgM8anx5knDdbBztHmc5Z9r7I6EHSwdv5QsRxajd48mPSuduIPVmPoW+Fn8yQI4doH7Aoc/cMadFZRz5zKHj7DG+d/Bgf7Z41Ph0rvdfZ7Bo6B/IK26eewRQxrYdCBR9aMKeOuKRwEiERERERERETlrUfWjWDByAXd8fwfHPQM2nsfZmTsJqQl8tO0jJnScwKi2owDYVxWmAllZ2RlAzppE4JFBlFeAyI9cerwO1f6GxqshOAH8PO7nufNZxHY41Af8vesjrTyw0sxsqmS0xExEREREREREzlqgLZDMrEyz4LQzoAPeASKre2nX1GVTvXYJc5B9bPgIUTizkIqSQeRnh/afQrU47+AQuOsUAQy/HjrMhRsv9Bpid9iZuXFm7vtWcAoQiYiIiIiIiMhZszvsTF813TxxbmsPOQJE7oBQQmoCqw+sdp3/3x/Zu465AkQe670KyiA6Va9ok/1zmPu4xn64agLU+d1rSOOwxkzqOKlo960AFCASERERERERkbO29tBaDiQeME88AzaeAaIcgZ0sw8zssTvszNjwnNloD4b9F3svG3MUkEEUnv1cvxzt/8LBpIME2AIKHljBqAaRiIiIiIiIiJy1TpGdsFqsOAwH/NXD3eEZIKq7GYDawbV5fcjr9G3UF4AF2xeQcCbOPW7uj943LyiD6IrJ8NMj0HOGV3P/xv05v+b55i0cWcQmxrLm4BrSKZiBwVu/vsV9F91XiNEVhwJEIiIiIiIiInLWnl/7vBkcArBXcXd4BogarAPM5WV2h51AWyB2h53oVdHube59KagGUc19MOL6XJfFJsayZNwSbFZzDvO2zmN57HK4aAasv7/Ad2pZq2WBYyoaBYhERERERERE5KzYHXY+3vGxu8EzKJTlsaW8RxBo+srpXNPuGhZsX0BsYixYIvK8f7BfOKmQd4AoD7GJsaw9tJZ+jft510iK2OFzfO3g2rww8AX8rH4E+gUysOnAQj2nIlGASERERERERETOypqDaziYdNDd4Fk/KC3cfewRIIpNjGX1gdVm9lCOvpxS0zKxYMFwBpsKESCyYKF21dp0q9cNyFEjyeJ7+/qE1AQahTeiX+N+Bd6/olKRahEREREREZFKyDAMYv6OwTB8B00KY++Jvd4NnhlEZ6q7j7MDMxYsRFSNICMrw8weggKXmBkYEN/OPC9EgMjAIP50PDFHYgDoVq8bEcHZWUp+vqsQRVSNcAWUKitlEImIiIiIiIhUQnO3zmXi/01kzpVzGN9xfJGv91q65eQZIEpzBogcrp3rncEbP6sfj/Z+lCfXPFlAgCgA7AHwt7P4tXmjW7veimEY2Kw2OtftjL+fv9dlgX6BRNWPAiDmSAzxqfFmR6v/g/obzC8PzoBSZc4gUoBIREREREREpJLJzMrktsW3AXD797dzbYdrXQWdC2vB9gXEnY7zbjQ8lpg5M4g8AkAP9nyQznU707dRX7IcWbn6c3H4Q2pN9/mWCRD1KlmOLN654p1CzTOqfhQLrlrAnT/cyfHU4xg3XuTqs2ChdnBtXrv0NVdAqbLSEjMRERERERGRSubWb28lNTMVgFMZp5i3dV6RrnftQPZ3F1j4KfzT2OzwzCDKDDE/rVmupvNrns+otqMItAXiZ80OJhWUQZQZ7D4POA3Ae7+9R5q9cAWrA22BZDoySUhNMJereTAwiE+Nx26YO6tVZgoQiYiIiIiIiFQiafY0PvhtNqx+BD7/CM6Ec9cPd2F32At9D9cOZLNXwc7RsOD/zA6Hjyyk7Lo/Vqw8teYp13N6NuhJtYBqBW9zn1HNfX7hq4AZ2Lnr+7sKNVdnMMviXOeWgxUr0auii/T+FZECRCIiIiIiIiKVyO3f3Q4xt8DKJ2HHtbBsRpGyiFzZQ+DOEorvAAbeW9s7+WUA4MDB/n/288mOTwD4+a+fOZVxquAMorQw93nNP12Hhc0iWntoLbGJsbmyh5yc81p7aG2B96rIVINIREREREREpJJIs6fx4ZYP4XuPoMzxVgDc9cNdjLtgXIG1iFzZQznt+I/vC2zuncOc2TrXtLuGjKyM7P58gjwOf0gPdZ9HbnMdGhi89etb3HfRffnON6p+FAuvXkh6lu8dzMC7qHVlpQCRiIiIiIiISAVjGAYbj2yka72uWCzupVW3f3d77kwaaybgrkV0fafr87yvV/ZQTnuG+m732FreZ7aO75VfpiyPAFHTpbm6m9dons/FpkBbIKPajipwXGWnJWYiIiIiIiIiFcy8rfPo/n535m+b72pzZQ/l5JfpOiyoFpFzuZZPp2v7bs9eYjawyUA+HPYhC69eSFT9KP5K+ss9xnbG97VZAZCevcQsMDlXdxX/KnnOVYpGASIRERERERGRCsTusHPfMnPZ1X1L73MFfB5d8ajvOjxWd0DoVMYpfoz9Mc97R9WPIrpPHhlE9jyCNSfMJWx7Tu5h3AXjGNV2FH5WPx5a+ZB7jJFHeMJziZlHgMiChYiqEfRs0DPPuUrRKEAkIiIiIiIiUoHM3zqf46nHAUhITeCjbR9hd9h5f/P7vi9IPs/rdNuxbb7HYS7XmtZ7Grd1vQ3sAd6dB/vmO68DSQdcBapXH1jtmmO+sgJg483mcWZVV7OBQfzpeGKOxBR8DykUBYhEREREREREKgi7w859i56Gr2bD/osBmLpsKqsPrCYxPdH3RQEprsOu9boyufPkfJ/hZ/Vj4e8LIb6d7wE+agU5Pfrjo9gddrKMLO8OI49CRKcjIKmxefy7uwj2LV1ucS1Vk+KhAJGIiIiIiIhIBTF/63xOLr4Ttk6AuT+C3Z+E1ARi/4ll/vD5WHxVhG663HV4MPEgwQHB+T9j23yOnznuKm6dS98n8rz2QNIB1h5aS99GfVl49UI+HPYhw1sNJ89K1Rt871C2ZN8SRrQeQaAtMN+5SuEpQCQiIiIiIiJSAdgddqYunwpJDdyNO64B4KEfH2LLsS1mDaLket4XetT/SUhNYPWB1fk/Y+lU88QvjwCRNct3O1AtoBrd6nVz7Sx2fafrmTl0Zt41iPIQmxjrWq4mxUMBIhEREREREZEKwFV7yGPJmLPAc0JqAu9sesdsy6jqfaHD5nWaa/mXh9UHVpvZQ/mxOPLsOpVxip//+tmr7dL5l0LH2fnfE6D/w16n01dOz3fHNSkaBYhEREREREREyjlX9hBA4CmPHvfSrVMZ2e05AzjrHvA67XFejzyf4xU8yhFYcj8y7wATwJlM95b2KRkpbDm2BQbfC8MmQZ0teV94wVyv09jEWNYeWpvvs6TwFCASERERERERKee8dgUL8AgQObeI95Kj3o/h53X63qb3iPk7BsMwcl3Zt1FfWlRvYZ7kGSDKO4MIYEf8DtfxtZ9fax4EJUPnWVDlZN4Xhv3lOmxTqw0fXfWRilQXIwWIRERERERERMq5jKwM94lnDaBqf+cenOWf773+Sv6L7u9354nVT+QKEhkY7Ptnn3lylgGi8KBwANLsaSzeu9i7s5C1iHYd38VVra9SkepipACRiIiIiIiISDnnZ/XIArIHuY+9lptBsC0YHPkHiD7Y8gEAj61+jLlbvZd1zdw4EwfZAaDCBojqbPU6feKnJ7A77MzcODN3vaPMHPWRnK4e7XVqYPDGL2/k/RJSZAoQiYiIiIiIiJRzPc7rYW5hb/eHn+9xdzjcgaMgWxCp9tQCM4hSMtxFrqcum+pVCHpSx0nc1eMugvyC8g8QhR5yn1832Kv7aMpR1h5a67pXoNUjCyhnAW2niN9zNalAdfFSgEhERERERESknHt2zbPmFvZ7hnp3eCzZSrOnmQcFZBCx51J4dR8c6E1CagIfbfvI1RUaFMrwVsNJy0qDv7v5vt7wgxt6Quf34NZ2UO2YV/ft3W8nqn4UoUGhXHH+FaQ70t2deWUQ+ad6nYYGhnJzl5vzfw8pEgWIRERERERERMoxu8POrC2zsk+CvDtzFKAG4I/hudsc2eEBA/joe0hsCrN/guS6ubKIoupHcUmTS2Dpy74nlFnFLCg9bDLU8c78sWJl8Z7FriVxuZaY5ZWVZDvjdZqcnsy2+G2+x8pZUYBIREREREREpBxbe2gt8anx5okjR0DII4OoW73sjJ8c29oDkNDG/Eyu793+0yO5sogMDH6M/THvCeWVBQQ4cLD/n/2u7en7NurLQz0fcg8YOQYCE+HKidBgnbvdluY67NWwl3YwKwEKEImIiIiIiIiUY93qdaN2cG3zJGcGTnbAqHaV2sSfjoczYb5vsvsK8zOlTo4OC+Bdi+ixVY+Zy9ny4hnYyaF2cG0WjFzgCu4E2gKpHlzdPaDxGnigBnSaA2EedYys9uzZWPhi1Bdc2/5a7WBWzBQgEhERERERESnHYo7EkJCaYJ7kXFKWEglAwpkEDiYdhLiOedzFDASx6vEczeaOZM4sIrvDznub3jP7Gq4xP3s/5R4fdhD808lLQmoCkSGRXsGdyZ0nc2mzS92DrD6CT9kBIgODV35+Jc/7y9nLY3GfiIiIiIiIiJQH3ep1IyI4wlxmljODaMVz0Pt5LFjMrJ/PFuZ/s5xFro+1dx1OWzGNutXqcjLtpNmQEWJ+Nlxr7lqW3BBaLPa6fGCTgYxpPwZ/P7MwdqBfYK6lYcEBwew+sTv3XDzfxS/TdTh762ye7P8kNqtCGsVJ300RERERERGRcizmSIxHDSLff+a7loSlRvi+Sc7aRU4eu4cdTTnKD3t/cPdlZe+G5pcBN0aZxa8vmOPqtmJlX+I+xl0wLt9gztpDa4lNjM3dsXOU+zg7kwkgLiWOtYfW0q9xvzzvKUWnJWYiIiIiIiIi5VhU/Shu73a7eZIzQORcBlYQ59K02t67jrHPvfSre93ufLD5A3efwxkgyoTQI9D9fxB42t2doyB1fvNfePVCZl85m1pVavkeZHF+WJh/1XwVqC4BChCJiIiIiIiIlGN+Vj++/fNb8yRngMiZ5ePU+nPfN3FmEAX9k7svO/loc9xmktOTPe4dYH5aM3NfQ+6C1HkJtAUyqu0oGoU34viZ4x4diT6mYrAzfqcKVJeAMg0QPfbYY1gsFq+vVq1aufrT0tKYMmUKNWvWJCQkhJEjR3Ls2DGvexw6dIihQ4cSHBxMREQEU6dOxW63l/ariIiIiIiIiJSJtYfWciDpgHlyOscSsr8v9D7PWcTa1W7Nuz+7L9PIEQjyXGLmg6+C1PmJqh/FgqsWuLOIGqw3P0MPe437ePvHrh3VpPiUeQ2itm3bsnz5cte5zeae0j333MN3333HZ599RlhYGLfffjtXXXUV69aZW+ZlZWUxdOhQIiMjWb9+PUePHmX8+PH4+/vzzDPPlPq7iIiIiIiIiJS2qPpRvDv0XSZ/N9msA5TT6VpQNTszJ7OK75sYfmD3912LyGEDqxkECgkIISUjJbvdY4lZttDAUF4e9DKBtkCfBanzE2gLJLJapDuL6MpJ8Osd0Pk9r3EHkg6oBlEJKPMAkc1mIzIyMld7UlISH3zwAR9//DH9+/cHYNasWbRu3Zqff/6ZCy+8kKVLl7Jz506WL19OnTp16NixI08++SQPPPAAjz32GAEBAaX9OiIiIiIiIiKlKtAWyP82/s888fOxxXxSA48AUbDvm6REwovHIK167j6PoJErOATuDCKPJWbJ6ck0q9HsrIM33ep1o3ZwbRJSE6DaMbjkkVxjIqpG0K1et7O6v+StzGsQ7dmzh3r16tG0aVPGjh3LoUOHANi0aROZmZkMGDDANbZVq1Y0bNiQDRs2ALBhwwbat29PnTp1XGMGDx5McnIyv/+eo7CWh/T0dJKTk72+RERERERERMqjlIwUthzbYp5Ys/IfnFeAaOsE38EhyLXs7MGeDzL7ytngMJeO1QipxuwrZzNvxDwWXr3wXxWQjjkSYwaH8hF/Op6YIzFn/QzxrUwDRD169GD27Nn88MMPvP3228TGxtK7d29OnTpFXFwcAQEBhIeHe11Tp04d4uLiAIiLi/MKDjn7nX15efbZZwkLC3N9NWjQoHhfTERERERERKSUXPv5te6T49l1fRut9hhhcR/acywxa7qs4AfkKHx9cZOLsVqskGW2n8yIw8/qx3UdrmNU21H/qoB0t3rdiAiOyLPfgkUZRCWkTANEQ4YMYdSoUXTo0IHBgwezePFiEhMTWbhwYYk+d9q0aSQlJbm+Dh8+XPBFIiIiIiIiIueYNHsai/cuNk8ygt0BoOr73IPsHgGbnBlE9TcU/BCHHxYsfHDFByy8eiE9G/Rk+spo1xIzizWL6FXRxVI4OuZIDPGp8Xn2GxjKICohZV6DyFN4eDjnn38+e/fuZeDAgWRkZJCYmOiVRXTs2DFXzaLIyEh+/fVXr3s4dznzVdfIKTAwkMBAbYknIiIiIiIi5dvMjTPJMrKXlSV5rI6xB3kcVzGDRwGpuQNEziVpFjsYeYQIHDYMDH75+xfeueId5m2dx4GTh3HmnBi2VPb/c5RPdnzCdR2u+1fvE1U/ioVXLyQ9y0ctpWxFLX4thXNOBYhSUlLYt28f48aNo0uXLvj7+7NixQpGjhwJwO7duzl06BBRUeYvQlRUFE8//TTx8fFERJgpaMuWLSM0NJQ2bdqU2XuIiIiIiIiIlIbxHcbz4LIHSXekQ1q4uyPEo+zKp19Aejjc2Sz3LmYWZ4DIAUYeD0kPg2rHeH/z+7w0+CWiV0V7L1WzpWPFSvSqaK5pdw0269mHGgJtgYxqO+qsr5ezV6ZLzP773/+yevVqDhw4wPr16xkxYgR+fn6MGTOGsLAwbrjhBu69915WrlzJpk2buP7664mKiuLCCy8EYNCgQbRp04Zx48axdetWlixZwiOPPMKUKVOUISQiIiIiIiIV3rb4bWZwCLwDRM08agulZ7f/cidkhLrb68W4M4gcOXYBv/RO9/Hp2uYQw8GYz8cQmxjrvWzNLx0HDvb/s59Pdnzyr95Hyk6ZBoj++usvxowZQ8uWLRk9ejQ1a9bk559/pnZt85fvlVde4fLLL2fkyJH06dOHyMhIvvzyS9f1fn5+fPvtt/j5+REVFcV1113H+PHjeeKJJ8rqlURERERERERKjVdR5zM1PHp8pAP9Mdx9fN1gmHCxO4Mo143fdtcnSq3lal68dzEWLO4lbH7pYDWf5cwiKo5aRFL6ynSJ2Sef5B9ZDAoK4q233uKtt97Kc0yjRo1YvHhxcU9NRERERERE5JznVdT5TE13h8WRe3BSI/dxkxXgl+XOIMrJzw5VTuS6r8PIvq8zg8iW5u7LziJae2gt/Rr3K+KbSFk7p2oQiYiIiIiIiEjhOYs6L9u/jPfWeRSmtuRVUAgITDKDQ+A7kOTkf8b8tOco4WIAC77Jvt4dYLqlyy30b9JfBaTLqTJdYiYiIiIiIiIiZy/QFsiI1iP46o+vwOGRA1JjT94XefbltcQMwC/D/MzKUZ/odG043to8TnMva1u6fykjWo8g0KaawOWRAkQiIiIiIiIi5dj8rfM5nnocsvzNhlq7oPrBvC+wetQI8rXEbMgd5qcrQJQj4GMPwhcVqS7fFCASERERERERKafsDjtTl081T5wZRE1WmJ9Dbvd9kWdQKGcGUacPoMeb2ffzMz93XeU9JmfAyIOKVJdfChCJiIiIiIiIlFOrD6w2s4fA3MYe4FBv87PWH74v8swg8tr5DAj9y328bbz5+XcP7zE5axJ5cBaplvJHASIRERERERGRcirL8MgASg83P49dYH7mVYDaM2sosYl3n196IR4akGfXuA7jVKS6nFKASERERERERKSc6tmgJxHBEeZJtezsn3YLzM+8AkSeGUSGxbvPVpgAUd4ZRCNbj1SR6nJKASIRERERERGRcurLXV8SnxpvntT/2fxsuMb8zDNAlN/OZR4BogEPuI8dHoGkfJaYBfjlnV0k5zYFiERERERERETKIbvDTvSqaHeDkf0nvjMwVJglZuSTQdT9TfexZ60izwyiG3PUJ5JySwEiERERERERkXJo7aG1xCbGuhuM7F3HnBlChVlilpNnBlFAqvt4dTScaG4eO2sQnfcL1P/V6/K/k/8uxMzlXKQAkYiIiIiIiEg5FFU/iug+HhlEzm3pLQUFiNwZRJYcYQGLLdP3Nb/eAW/sgVORkFrLbEsLzzXsoR8f0jb35ZQCRCIiIiIiIiLlUKAtkGm9p7Hw6oXMvnI2AdYgs90/O8PHYvi+MDuD6JIml2DkGGL4nfFuqHrM+zy+Hfzwqnl8omWuWyekJrD6wOqivIacIxQgEhERERERESmnAm2BjGo7CqvFSobdzAxKd5w2OwuoQbTywEq46EXvvpy7mIXHep9//C2kh+U7pywjnyLYcs5SgEhERERERESkHMvMyuTBFQ96FKnO8v7MKTuDyGE4IGInNFvi7vPLyDE2xz2yAqH5YvO4y0z3MIuVD4d9yMKrF9K3Ud+zfRUpQwoQiYiIiIiIiJRjdyy+gyOnjoDDZjY4gzpV/vF9QcBp7/Mgj3HWHDWIfGUhOTOI6mx3NTkMB7/+/Suj2o4i0BaY+xo55ylAJCIiIiIiIlJOpdnT+GDLB+ZJZhXz0z9797HgBN8X+ad6n3suK8u5w9mJFrmvP13b/AzyDkC9v/l90uxphZi1nIsUIBIREREREREpp+7+/m5z1zCHFf6+0Gy0ZReazpkp5OSfo91zWZlHgKhe1XpwOjL39UmNzM/AU17NdoedmRtn5h4v5YKtrCcgIiIiIiIiIkWXZk/j/U2z4ct5EN/e3eHMELLkcWHOwJGfZwaRu+bQkdNHfF+flb2ELDsQFWANYGLHiVQNqMqkjpMK/wJyTlGASERERERERKQcMQyDjUc28uWuL8nafhVsv857QM4lZDkVZYlZfiwGABmODC5qcBETOk4o/LVyztESMxEREREREZFyZP62+XR/vzvv//Y+pIXnHlBQgMi5BM3J72wDRO5so6nLpppL3aTcUoBIREREREREpJywO+xEr4oG4PiZ4753GSsoQJRZ1fvcM4PIkmNb+/x4PDshNYHVB1YX/lo55yhAJCIiIiIiIlJOLNi+gNi44+6G7GVeXvw9MoTubAqjrvbuz5kldLYZROEHvE4zsjJ8j5NyQQEiERERERERkXLA7rDz39fWw3PJsPKx7FYfAaLAJPdxjVho+4V3v8PP+/xsaxCFH/Y69bP65TFQygMFiERERERERETKgQXbFxC/8DHzZLW5zMznEjO/ApaJOXLsV+W1zX0hl5idv8jrNDQwlJ4NehbuWjknKUAkIiIiIiIico7zrD3kJecSsxp7Cr6Z4Z3p06pOU/flwWFc0eKKgu8RmOx1mpyezM9//VzwdXLOUoBIRERERERE5By3YPsCYhNjc3fkLCrtV4g6QB5LzAKsAXRt0N51fjLjGMNaDqN2cO387xF4KldTllGEAtdyzlGASEREREREROQc5swesmDJ3emX6X3eYH3BN6y9y3WY4cjgm71fu84tFoPpq6aTkJqQ/z0yg83xWKgdXJsFIxfQt1Hfgp8t5ywFiERERERERETOYWsPrSU2MRbDV0HqnEWlez9T8A1betcPSso44To2rBkcTTlKaEBo/vfYOsEcj0FCagJ2h51AW2DBz5ZzlgJEIiIiIiIiIuewqPpRLLx6ITd3uTl3Z84Akf/p/G/W8v/wTESyYvUudG21Y8FCckZy7mvzYMVK9Kpo7I4i7IAm5xwFiERERERERETOYYG2QEa0HsHSfUtzd+bcxczjPMAakHt8ji3uHTi8g0zWLN+ZSjk1WeF1j/3/7GftobUFXyfnLFvBQ0RERERERESkRKWnw6JF5qcPPx9aS5ctscTmDN4YOfM+3OlBGQ6PgtW2M2CvAs2W5b65V4CokFlAPV7zOp07fC5R9aMKd62ckxQgEhERERERESlrq1fD6NF5dvfK/goMAq99ynJkBBGY5PsGd5wPB/tA209z93nuhJYzI6nZD7DvUujxKjReDZ9+ZbbnKI79y9+/MO6CcXnOX859WmImIiIiIiIiUtb8/AoeA2SkRXo35MwgsuXY1cwp7C/o8DH4eW9FP6LVCNpHtnM3WKBPwz5YLdn3HT0KxlwOAx+AsIMe47wDSQ9c9ECh5i/nLgWIRERERERERMpa375Qq5br1IGFPTT3WlC2t0q13NcZeQeWLNn/yasvomoE713xHr+f2OrVt+bQGhxGdgAoMAVafge2DLB5LH/LESD64o8v8pyHlA8KEImIiIiIiIiUNZsNnnFvUX8/MzifPbzMva62zTWDvS65+nfws+f9Z72R/Z+8+uJPxzPx/ybiqPF7rj6fAj12NvMIEN3R/Q4mdZyU5zykfFANIhEREREREZGylp4Oe/a4Tl/iv4AZKLqPlwHo+5d31s5nn8FV51v5ytnQ8utct73/ovtpX6e9z0daDAsTF02EMDvc2AOC/sl/jp799iDXYVX/qoQGheZ/rZzzFCASERERERERKWsbNsALL+RqDiLNdWzxkdlz+Z9+7gBR15lefSNajeCJi58g0Bbo85G3fHMLdkf2rmX1f83V369RP9pEtOHD3z4kLSsNAk67O9PCXIcfbP6AJ/s/ic2qEEN5piVmIiIiIiIiImWtVy9o2DBXs2dQyPBRT8jh/LO+ygloscSrb0vcFvysvmsUpdnT+GDLB/lOae3htVze4nIzOGROxi3dHSBKSE1g9YHV+d5Lzn0KEImIiIiIiIiUNZsN/vOfXM2nCSETGwNYxv9xZY6+YE6RXbi64Zpc18YmxrL20Fqfj5u5caY7eygPdoedRbsXeTeGHjI/my73as4yvHdHk/JH+V8iIiIiIiIi54IBA3wuM2vFH+ynGSsY4NW+ka7cyyvmSdX4XNeN6zCOqPpRPh81qeMkDiUd4kzmmTynU8W/CtN6TqNvo77c+cOdHE89jnF7K0itBeGHsWChdnBtXrv0Nfo26luEF5VzkQJEIiIiIiIiImUtORkWLwY/P8jKIpQkkjGXce2nmc9L+uGxrKvTh7n6R7cdnWf9odCgUF4e/HKhppbpyCQhNcE8CTgDAYeB7J3QUuOxG/Y8nyPlh5aYiYiIiIiIiJS1Dz+E116DLHOplhVHARd486+53et8YNOBDGw68F9Py+6wE70qGouP+kcAVqxEr4oucLmanPsUIBIREREREREpa9dfDxZ3EMbXjmX5uehYqtf5ygMrMYp4D1/WHlpLbGJsnvdy4GD/P/vzrHUk5YeWmImIiIiIiIiUtc2bwXAHYf6hRqEvfS/oKm7JsQGa3WFn5saZ3H3h3f9qWlH1o1h49ULSs9LzHBPoF5hnrSMpPxQgEhERERERESlrzm3uDx3iF7oX6dL1TVPIyrGbfe+GvZnUcdK/nlagLZBRbUf96/vIuU9LzERERERERETKms0GTz0FwDdcUaRL53U9nattcLPBhAaFFsvUpHL4VwGitLS04pqHiIiIiIiISOU2ZgzUr086RdsRzF4lNVfbBZEXFNespJIo8hIzh8PB008/zcyZMzl27Bh//vknTZs25dFHH6Vx48bccMMNJTFPERERERERkfInPR0WLTI/8xIYCMOGmZ/PPEP38YvyveWzPMg0nnM3+LsziIL8gvjf0P8Vyw5mUrkUOUD01FNPMWfOHGbMmMFNN93kam/Xrh2vvvqqAkQiIiIiIiIiThs2wOjRBY9bsgSSkiA9HVuAH2TkHnIR6+jBL0ziQ+8AUYA7QJSWlUaT6k0ItBUtC0mkyEvM5s6dy7vvvsvYsWPx83NXwbrgggv4448/inVyIiIiIiIiIuVat24QEeG1hb0XiwVq14bly81A0k03kZVhB6Amx72G/kh/XuY+gshR7sXfvcQsPCicbvW6FesrSOVQ5ADR33//TfPmzXO1OxwOMjMzi2VSIiIiIiIiIhVCTAzEx7u2sN9FK9bS091vGJCQAC+84GrKwkzGCMS9LG0AywjMTisKyJle5LHELDEtkZgjMcX9FlIJFDlA1KZNG9asWZOr/fPPP6dTp07FMikRERERERGRCqFXL2jc2HXahl30Zi0HaOQe07gxNHKfOwNEnoGgD3FvWZ/ulyM5w+P80T6PElU/qnjmLpVKkWsQTZ8+nQkTJvD333/jcDj48ssv2b17N3PnzuXbb78tiTmKiIiIiIiIlE9ZWTBkCLz9tlfzXprTmIPmSYsW0KwZzJwJgD37T3W7x5/stTyWm90x1AGedaw9Vq/Vr1Zf9YfkrBQ5g+jKK6/km2++Yfny5VStWpXp06eza9cuvvnmGwYOVJV0EREREREREZcNG3IFhwD8yHKfLFtmBofq1AEgnggAurDJNcRZdyi6D8zLZ/HO/cvvx+6wF8PEpbIpcgYRQO/evVm2bFlxz0VERERERESkYunVyyxSHR9PlkeOhg0fQZzrroOXXiKRcAAacJijRBJIOhYgJbQKT/c945UxlFNSehKrD6zmkqaXFO97SIVX5AyimJgYfvnll1ztv/zyCxs3biyWSYmIiIiIiIhUCFlZEGXWBMogwNXslUHkNGgQNG7MKaoBUI1TRHKM6iQCEPvo7WT55bjGdib3Iw0f9xYpQJEDRFOmTOHw4cO52v/++2+mTJlSLJMSERERERERqRA2bID/+z8AUgl2NT/PA97jgoPh6FEYPNgVIAol2d0fEcH5Nz1I/8b9va/rOtPr9Laut9G3Ud/im79UGkUOEO3cuZPOnTvnau/UqRM7d+4slkmJiIiIiIiIVAjduplLzIAPuMHVvIgrycDfPS41FSZOhHfeIZlQwMwgcomPx7JxIysPrPS+f7OlXqef7/wcP2vONCORghU5QBQYGMixY8dytR89ehSb7axKGgHw3HPPYbFYuPvuu11taWlpTJkyhZo1axISEsLIkSNzPfvQoUMMHTqU4OBgIiIimDp1Kna7CnKJiIiIiIjIOSAmBuLjATjoubU98DQPu08iI8FiFhfyXGIGmO1NmvBoxhIMDLPtpm4wfDy0+MHrnvGp8aw+sLoEXkQquiIHiAYNGsS0adNISkpytSUmJvLQQw+d9S5mMTExvPPOO3To0MGr/Z577uGbb77hs88+Y/Xq1Rw5coSrrrrK1Z+VlcXQoUPJyMhg/fr1zJkzh9mzZzN9+vSzmoeIiIiIiIhIsUlPh7g4qF0bgEzPjCFgIaPNg5o14dlnwTCDP7kCRIZB1mPRzNzyvvvi8zZCx3k+H6saRHI2ihwgevHFFzl8+DCNGjXi4osv5uKLL6ZJkybExcXx0ksvFXkCKSkpjB07lvfee4/q1au72pOSkvjggw94+eWX6d+/P126dGHWrFmsX7+en3/+GYClS5eyc+dO5s+fT8eOHRkyZAhPPvkkb731FhkZGUWei4iIiIiIiEix2bABxoyBhAQA6nHEq9twbkfWoAFs2QKNGoHF4l2DyGqFpk1ZfWFdkjOSKcijfR5VDSI5K0UOEJ133nls27aNGTNm0KZNG7p06cJrr73G9u3badCgQZEnMGXKFIYOHcqAAQO82jdt2kRmZqZXe6tWrWjYsCEbNmwAYMOGDbRv3546deq4xgwePJjk5GR+//33PJ+Znp5OcnKy15eIiIiIiIhIsfKoPwTQhFjf47Zsgddeg+7dwTBcxayDSQWHAx5/HHshywr1atiLQFvgv5y4VEZnVTSoatWqTJ48+V8//JNPPuG3334jJiYmV19cXBwBAQGEh4d7tdepU4e4uDjXGM/gkLPf2ZeXZ599lscff/xfzl5EREREREQkHytXuuoPAaQRlP/4Tp1g40YcsWYuh5/FgCZN4Zpr6EsWC69eSHpWep6XB/oFKntIzlqhAkSLFi1iyJAh+Pv7s2jRonzHDhs2rFAPPnz4MHfddRfLli0jKKiAfyTFbNq0adx7772u8+Tk5LPKfhIRERERERHJ0+7dXqe38E7eYy0WuOceqF+frPFmupDVsMPjj4PNRiA2RrUdVZKzlUquUAGi4cOHExcXR0REBMOHD89znMViISurcMWwNm3aRHx8PJ07d3a1ZWVl8dNPP/Hmm2+yZMkSMjIySExM9MoiOnbsGJGRkQBERkby66+/et3XucuZc4wvgYGBBAYq5U5ERERERERK0I03wn//C4DDWW8oLy1bwpdfwlVX4bg+GbLAel49uOaaUpioSCFrEDkcDiKy1006HI48vwobHAK45JJL2L59O1u2bHF9de3albFjx7qO/f39WbFiheua3bt3c+jQIaKiogCIiopi+/btxHuk7C1btozQ0FDatGlT6LmIiIiIiIiIFLvNm12HCdTO1b2bVu6TP/6AsWMhJgZHtTAA/G6dDLazqgwjUmRF+k3LzMzk0ksvZebMmbRo0eJfPbhatWq0a9fOq61q1arUrFnT1X7DDTdw7733UqNGDUJDQ7njjjuIioriwgsvBGDQoEG0adOGcePGMWPGDOLi4njkkUeYMmWKMoRERERERESkbPXqZe5MdvAgf1E/V3drdno3hIZChw44AqsAYL1iaGnMUgQo4i5m/v7+bNu2raTmkssrr7zC5ZdfzsiRI+nTpw+RkZF8+eWXrn4/Pz++/fZb/Pz8iIqK4rrrrmP8+PE88cQTpTZHEREREREREZ9sNnjySQDOUCVXd2/WeDckJ8PcuTgc5nI0q18By9JEipHFMAyjKBfcc889BAYG8txzz5XUnEpdcnIyYWFhJCUlERoaWtbTERERERERkYrCbofmzfnpYEP68pNXl5UssnIu7Ln4YmqtX8SJ9BB2/udxWlfP3qG7ShV47DEzy0ikCAob8yjyYka73c6HH37I8uXL6dKlC1WrVvXqf/nll4s+WxEREREREZGKKDuLyBj/fq4uB365x69cSRaZAFg//Rj4091XuzZMm1ZCE5XKrsgBoh07drh2Hvvzzz+9+iwWpb+JiIiIiIiIeBkzBsd930ICNGMv+2ju6srCih8Or+GO7GowfuTYCMrPR0BJpJgUOUC0cuXKkpiHiIiIiIiISMVks+EYMAgWQDCptOQP1w5mSYRRg3+8hjsDRFbPwJHFAnfeWWpTlsqnSEWqP/30U8aOHcuoUaOYOXNmSc1JREREREREpEJxjB0HgAWDVIJd7Z9ztdc4A0ihGpAjQHTDDRAUVOLzlMqr0AGit99+mzFjxrBx40b27NnDlClTmDp1aknOTURERERERKRCMPwDADPocz2zXO03867XuNX0dR1HEG8eWCzwxhslP0mp1AodIHrzzTeJjo5m9+7dbNmyhTlz5vC///2vJOcmIiIiIiIiUiE4spOBrDh4hKfyHLeblq7jYM6YB8oeklJQ6ADR/v37mTBhguv82muvxW63c/To0RKZmIiIiIiIiEhF4QoQ1a6JP3ZXe2t2eo2rz18ARHDMbFD2kJSSQhepTk9P99rS3mq1EhAQwJkzZ0pkYiIiIiIiIlLBpafDokXmZ14CA2HYMPOzHHMFiEJD4ISVdxyTuZl3ac5es8NigVq1yIzoBL9DM/aZ7coeklJSpF3MHn30UYKD3cW0MjIyePrppwkLC3O1vfzyy8U3OxEREREREal4nIGhLVvgmWcKHr9yJfTrV9KzKlHOAJFl317AQSBmUCwDszYRhgEJCdgT/gDAhl3ZQ1KqCh0g6tOnD7t37/Zqu+iii9i/f7/r3GKxFN/MREREREREpGLasAFGjy54nMUCTZpAr14lP6cSZhjmp3NnslwBomyZ+APgTybcdJOyh6TUFDpAtGrVqhKchoiIiIiIiFQavXqZgZ8DB9yRE18MAx5/HGxFWvxyTnItMbNawOEOEB2jjtc4V4DovDrQsyfMn19hltnJua3QRapFREREREREikVqKpx/vldwaCetackffMS17nGNG8M115T+/EqAK0DkyAQggAwAdtKWpQx0jXMFiP6OhQkTYNw4M9tqw4bSnbBUOgoQiYiIiIiISOn68ENYssR1+hudaMtO/qQl1/GRe1zPnhUiewg8ahBhBsX8yHL1DWYpZzCXknktMQNzmV1EBHTrVnqTlUpJASIREREREREpXddf73Xahd98j/vtN5gyBZKTS2FSJcuVQZRdgyjn0rLXuAvwESAyDIiPh5iY0pmoVFoVIxQrIiIiIiIi5zbPLe23by/cNbt2mV8tWsDdd5fo9Eqaq0h1zRpwInuXMg9/UR+A7xkCQDoe9YbCw5VBJCWuyAGizMxM/P39ffYdP36cWrVq/etJiYiIiIiISAVT2J3LcrLZ4JZbin8+pcyVQdSuNayGMJK8+7MX+HzPZQD8H8PdnYmJsG4dDBpUCjOVyqrIS8yuueYaDB9V5o8dO0a/fv2KY04iIiIiIiJS0XTrZtbSKarx4yvEVu+uANFfhwC4jMXe/QX9ee7nVxLTEnEpcoDo0KFD3HjjjV5tcXFx9OvXj1atWhXbxERERERERKQCiYkxa+kUVcuWxT+XMuAqUr1vLwBWDG5mprsfK3fziu+Lg4PhwgtLeopSyRU5QLR48WLWr1/PvffeC8CRI0fo27cv7du3Z+HChcU+QREREREREakAevUyt60H/iGcJ3iUfTRlP03yv+6888y6ReWcqwZRFXdtIc86RDF04zXu9n1xaqqKVEuJK3INotq1a7N06VJ69eoFwLfffkvnzp356KOPsFq1KZqIiIiIiIjkkJ4OX3wBzZvDgQPczavMZQL/4zYGsTT/a6+7zgwSlceSJh6FuR3rmgIXYQ2pCmfMbs8AUQohed8nNBQ6dCjZuUqld1a7mDVo0IBly5bRu3dvBg4cyLx587BYLMU9NxEREREREakINmyAsWNdp8sYCMAxImnEwfyvjYw0s4/KI4/C3A4mARdhTYhzdScS7jo+Sl2vS2/iXfdJcjLMnVvud3KTc1uhAkTVq1f3GQBKTU3lm2++oWbNmq62kydPFt/sREREREREpPzr1Qtq14aEBABSCXZ1WfDeBMlKlve1EyeaO5mVR716QZMmcOAADsNcceP5vnto4To+nSOD6E1u977X9deX3DxFKGSA6NVXXy3haYiIiIiIiEiFZbPBgAGwYAEAyYS6unIurarLUfeJxQLt2plLtQIDKXdsNnj8cRg/3rVLmRWHq/sAjX1e1pCDBJDp3bh5c/lcZiflRqECRBMmTCjpeYiIiIiIiEhF9sYbrgCR4bFf0rdc7jXMwGP1imHkrkHkUdcnT4GBMGzYuRFUGjMGoqMxYnMHiDyPPfnnDA7VqVN+l9lJuVHkPL3Fixfj5+fH4MGDvdqXLl1KVlYWQ4YMKbbJiYiIiIiISAWxfbvP5j2c73V+khoYYIaJrFZz5zPP4IhHXZ98rVx5bmTcZGcROcZvALyDQgsZzUVsyHVJMKneDc8/X36X2Um5UeRtxx588EGysrJytTscDh588MFimZSIiIiIiIhUML16Qf36eXY3Yy8AaVThNFXNRofDXKLlGRxx1vXJa6MkqxWaNj23Mm7GjMFRszaQHSCyWqFWLaL4mWtYkGu43TOXo1YtrwLfIiWlyAGiPXv20KZNm1ztrVq1Yu/evcUyKREREREREalA0tPhq6/MpWJ5eJtbqZKdORNPhDvQc8013gOddX0Ms9izgxyBIl9BpbJms+EYYi6ls2CYc3zxRahRgyDScg13BcjAzJbykaQhUtyKHCAKCwtj//79udr37t1L1apVfVwhIiIiIiIilZpzWdgvv+Q5pAGHCSUZyC5c7XDAoEHwySfw2WfumkPp6eDvD7Vrk0IITYhlLPPNPosFIiLM4FB+NYrKgNGpM5CdQdS0qZkVdNNN3M2ruca+w83uk//9z/z+iZSwIgeIrrzySu6++2727dvnatu7dy/33Xcfw4YNK9bJiYiIiIiISAXQrZsZuMlHDU4SQAYAGQSYjTNnwrhxZnBp9Wqzbdkys/BzQgKfM5JDNOJjspdgGQbEx5v9y5aV1NucFYfFD8gOED3+uJkV1L49F1i8azOdz24uZYm7oUmTc2u5nFRYRQ4QzZgxg6pVq9KqVSuaNGlCkyZNaN26NTVr1uTFF18siTmKiIiIiIhIebZunRm4yXYBW3INCSCDQMysn3R87D7mZwZY2L27cM8s7LhS4siuTW0dcilcfTU8+aS5Q5thEEaia1x1/vG+cNSoc2u5nFRYRf4tCwsLY/369SxbtoytW7dSpUoVOnToQJ8+fUpifiIiIiIiIlLeOYM7QBZWttIx15BA0l0ZRCep4d1Zqxb07Wse33gjTJ0KhmHW88lLtWowf/45s+W9M0BkqV0Lfl4FTz/t6rNh93kMwCWXlMLsRM4iQARgsVgYNGgQgwYNKu75iIiIiIiIVF7p6bBoUf71c86RgEeRXHihGbA5dYqf8J1cEEg6O2kLwHC+Jsvzz9Ubb3Rn0Wze7CpQbeQsUO3pZo86PkuWmPWMypArg8iKuWSscWM4cACALNwBNK8AUbVq0L9/qc1RKrezChCtXr2aF198kV27dgHQpk0bpk6dSu/evYt1ciIiIiIiIpWKs5hzQaKjYdq08hMkiomBU6eAPJaPAVaPbCCHR8AEq9V8X6eWLc33zhFEi6Er3djo+/keGUylyiPgZ/zWFuiEdf9e+ORnuPhimDULgESquy5xFuoGoEcPSE2F0NBSnrhURkUOEM2fP5/rr7+eq666ijvvvBOAdevWcckllzB79myuvfbaYp+kiIiIiIiUgIqarVKedegAwcFmUCA/jz8O/fqZX+VBjoyZIrnxRggKcp8//bTrd9ZZswigL6s5TVVOUoOanHSPDwqCTp3Obt7/lkfAz8E0oBPWn1bCT5PzvMQrQLR8OXz4Idx9d8nOU4SzCBA9/fTTzJgxg3vuucfVduedd/Lyyy/z5JNPKkAkIiIiIlJeFDZbZeXK8hOIKM/S0+H22wsODgHUqFG+dray2eCJJ2D8+PzrBuVktcJrr3m3PfccvPUW4B0gOkMwV/M5XzKSn+hNb9aaHWlp8M47ZsZVaXPu3paQgMMw94iy4sj3Eq8Akc0Gt9xSkjMUcSnyLmb79+/niiuuyNU+bNgwYmNji2VSIiIiIiJSCnr1MrfQtuRRx8VqhaZNy1cgojzbsAEWLHCdHqIBHdjKe9yYe+z555vbpJcnY8ZA48Ze9XZyehkzESGUJLOhb1/4/HP47DMzgJaeDt9/by4zAzII8Lr+S0YC8Cw5gkE7d+afKVdSYmLM3dsMA0f2n98FBciqccp9cvHFef/7FClmRQ4QNWjQgBUrVuRqX758OQ0aNCiWSYmIiIiISAlLT4evvjIL9xp5/MHqcMAjj2iL7dLSrZtXrZnHeIztdGAy7/EL3b3H/vyzmZEzf777yxlEOVdlZxGFkJKr611uAqAfqwDcY1auhHHjzEy3DRtg9WrzOHsL+7wKXucKwsyfD8uWFc97FIVzaR3ugtoFZRAlEu4+WbbMfG+RUlDk/6a/7777uPPOO9myZQsXXXQRYNYgmj17Nq/lTP0TEREREZFzU47lZb/RieuYz3M8yDC+cY9r2LAMJldJrVwJye7lRSeo6Tq+kF9y79j1zDO+71HWywHzq23lcOAIrQ7JEMEx7uMlruETGnIYAD/MrCiHZy6D1WoGWXr1gsWLvW63EN9LJH1mKe3eDZdfflavdNY8ltY536mgAFFDDrlPIiLMwKFIKShygOjWW28lMjKSl156iYULFwLQunVrPv30U6688spin6CIiIiIiJSAbt2gdm1ISABgBF9xiEZcySJ3ICIoCI4cMf/QV5HqkpedFQOQhZVFFOHvK88gSlkroLZVJgMAiCSO+3nBq88ZPImjrrvR4YDLLjOX1O3Z4zV+JF/wLjeT007a5H7wyy/D+++bQZs6daBtW3jyyZLfIWzMGJg+HceBwgWIOrHZfRIfby5TK+ugn1QKRV5iBjBixAjWrl3LiRMnOHHiBGvXrlVwSERERESkPFm3zhUcAu9sFZe0NBg/XktcSsuUKWagBzhN1aJd63CYO5udC8sBC6htZccfABv2XH2emT/pnvWF3nzT/D30+B4BzGaiz2ccpiF/0sK78cgR+OMP2LEDVqyA1183dwgradlZRIXNIPLKFGvS5NwI+kmlUOQAUdOmTTlx4kSu9sTERJo2bVoskxIRERERkRKWo8CxPa/FBZGR+gO1pKWnw8cfw733mt9vyL2cLIfNdORmZhJHHXcx8WuuKY3ZFsxmM4NV2bWtDCDT4/fLnh0E8ifTzGLzcIpqruMkwswDi8VdLN1iMQs3Y2ZZZZB3ZtscJhQ8z9LaIWzMGBxh1QGP+ki1avkc6vVv8Yknzo2gn1QKRQ4QHThwgCwf1fLT09P5+++/i2VSIiIiIiJSwgK8d39KJ8j3uIkT9QdqSduwAcaOhbffNrNcgJVc7DWkLke8zjuzmXe5mUl8eG5lD4EZ8PL3N4M/Fgv/4VMiieMfwsFiITPUDIzY2reBF180r7nqKsA7g8hZjwjDcL/fhg1m9g/wN+flOw1nVlwG/rzAf9lBW+8BpblDmM2G0cssqO3KIHrxRVcBa08RxJsHTZqcO0E/qRQK/d8gixYtch0vWbKEsLAw13lWVhYrVqygsY9fbhEREREROQf17WtmMBw/7tVchVT3idUK0dGlPLFKyLnT1YEDrqYRfO01xGs799q1IXt14A7anVvZQ2DuNDZmjOv0s+xC0gsZzc3Gu9iTTwNgs9jNHcpat4aOHc2t62Pdt8nCzwzgeAZKunUzCzfHxxc4jVSC+ZIRjORLAO7nBe/MrGXLzLkOGvTv3reQHK3awHfZAaImTcygoNUK48fzIM/yHNMYwZdcyM/mBcoeklJW6N+24cOHA2CxWJgwwTtVz9/fn8aNG/PSSy8V6+RERERERKSE2Gzwwgtw/fUk4S7S25y97jE33mgWqpaSZbPBgw+6ljtl+VjokZldtwebzQzsZQeILBjmtedSIMHHihNwZ844l1D5Wx1mAMi5S9fjj3PR+Enu2+DnnT0EZsHm7OBQej7LywDOUMUVHMqTn4/dzkqIVw0iZ/Anu4D1swce4jEeI5AMc7Cyh6QMFPq/RRwO8x9zkyZNiImJoVYe6yVFRERERKQcSE83gz8hIfyY0t/VXBUzuwOLBfr00Q5mpSUlxXU4mXdzdbvq0tjtsGuXq92CAcuXQ5Uq5s9p2LBz9uflrL3jDHbZEo/D/B/cA/z98W9cH/8DGWQSQBY27+yohASYMcN8v/R0zlAl1zOqksJpQgD4nFH5TygszMykKyXZf1JjGT8expq1ppwFrBk/3h0cAmUPSZko8m9cbGxswYNEREREROTctmGDaxlQAw67ml0BIsOA666D887TFtulYcoUmDoV/p+9+46Oqur6OP6dZCChJbRQpdoQFbEgoCiKCAr2jlIFER+sPOpjparYCwp2BCuKYgEriuIrRkRERexKUwRCS2iZhGTeP87cNi0J6cnvs1ZWbjl35s4wM+Tu2WfvYJDpDI/YbWcQhfERhNdeMz8An35auH+vQADeecf8jqWEA07hGUT+Vb+bKWZup59O4qo8cgllUvXpA7NmmXP46CN4/3176BpaR9zHQfzMN3Qp3AkNH16mQRgrQJTQsjmeGuShLCJ7iqGyh6ScFPrdkJ6ezubNmznttNPsbc8//zzjxo1j586dnHXWWTz66KMkVdBotYiIiIiIhAQCsH59qJZNhqdrklpsl5PkZOjXD959N+rumF3mLFatnsL+e6WnwwUXFDyusAEnN1cB9F2uLJ9lHA4850wxIzfy2Llz7eLUeSTCE0+YHzBZNS5/EdlFO+ptxtK3b+HHloBQUzcSwmcQurKIAGUPSbkpdBeziRMnsmLFCnt9+fLlDB8+nN69e3PTTTcxd+5cJk+eXConKSIiIiIiJcjKHsowhWzc2Sn57kuEAQN0oVpWAgE4++yYu4Mk4COIj6DdJh5cLdPDa/UUpEcPE1CK1cUrIcFpLV9UPXuaKW/A7+xvb34CU2PJnmLGnqiHewJEYM6xcWNTfygkjwQ+4JSIY+MFiFLZ5qwkJcGxxxb8WEqQnUEU7Sp84EBYvNj8XHJJmZ6XiKXQn/bfffcdkyZNstdnzZpF165defrppwFo1aoV48aNY/z48SV+kiIiIiIiUoLCuma5O2TZAaKEBOjYUTWISkJhpnP98YcJ8BTCCzjTsuwAUY0aMH26mY713HMmOywev9/cXyhrJYca1CDXyR/Lzy9awCn8tk89FebM4TcOsDffjEkoKHKAKBg03fbmzrXHPMB/eZ9+ACSzm+xQppInAy5MTXeNn0DABJzKcPpk3ACRzwdHH11m5yISTaHf7Vu3bqVp06b2+sKFCzn11FPt9S5durB27dpoh4qIiIiISEVidU8KzQCIGiDKz1cNopJS2OlcNWtCTk6Bwz7neHvZDhDl5prpYAC33+5My4pnwAAYN46tK7fRijUcz+e8R38TwWjbtnh1cAYPhjlzyCTV3pRMNgDZmM54tdnlPaZxY9i8mcRgWIAoiv9xr72c7ZrGdh6v83+u58fNqoEEmMdXxtMn7SLVsWNYIuWq0AGipk2bsnLlSlq1akVOTg7ffvstE1wR7u3bt1OjRvTCaSIiIiJSTFlZMH487N4de0ytWmZMSkrsMSKWsWNNm/s9e/gC50LZDhAVtaaNxNajBzRrZuo+xVOI4BDgCbrYASK3M88s3Hn5/fC///HWqK/YSV07I4f8fBNAueoqs743ny11TScxd+2kW7mLTvzAduoBUQJEw4fDPffYGUTx6i7tx+/8EZq+lsge0/EMOIpv+IJj6cGiiGM80ycvvrj0p0+GZY4FfzsaOICE5d/Di8vNmAreeU6ql0K/I/r168dNN93EPffcw1tvvUXt2rU57rjj7P0//PAD++67b6mcpIiIiEi1N306PPRQweNat4Zrry3105EqIDkZhg6FZ57hLm61N9sX0cGg6SD15pu6gC2MeNPIcnPNdL2CAkSFZAVQADtIYktOhpNPLvhGrKDzF1+QR6fI/QsWmB9LWhrcfHPhTjAQMPWtatQgN9ebRPA0l/EOJoD1Fd2cHYmJ5vMrLQ1/hpl6lkeiyWYKBvk5eCBvcjbX8Ah12MVJfGI/9iZs5F9aALCDuvTm46in5Zk+efvthXssxRGWOZbPk8ABJLzxGrxxlzNubwqBi5SCQgeIJk2axDnnnEPPnj2pW7cuM2fOpKarOv306dPp06dPqZykiIiISLU3bBj897/OHIVoEhLMOJHCOvNMNj3zpmeTJ8vC6iB1zjmmro2y06ILBGDSJLjzzkINv41JtOQfriBsGlinTvDDDwUeb03RsmyiEY3ZbFZGjChcZowr6JzH4QWPT4w93SuCVQSdyCwgKzgEsARXzZ28PBg92tyVuwZR6DOvIz8DJnvqHm7ia9ex7rpD26hPQrSsKve4ESNMIK00ZWXBW29BvXqwfTvgvLfKe6qbSCyFDhA1btyYzz//nMzMTOrWrUti2AfE7NmzqRtKIxQRERGRErZsmSc4FITIUqz5+WacvomWwqpVK6ITVH60Rsdz5pgL3SefVCZRNOnphQ4O/cjB3MltAOzLn/RhvrPz5JMLFSByd50D2OwOEG3dWrjC4qNGwZgxEAxG/zcPN3Sos5yRYYLRsaa85uebaWm7d8edJlaDsCl1Ph8Eg5FFql2+4SjAO80uh5ocxRK+5Qg7e2gZnTmc77ynZT3O448v/eLr06fDI49EvX/PtMBjj1WnQKkwCt3m3pKamhoRHAJo2LChJ6NIREREREqQ1XUKWMQxpJHBQ1zrHaN6MVJUPXsyr+Y5ABzJN0CMABHAzJkmECKRXO/PgrgDG335yLvzgQeiHpNFPSZzE21YBcCHYUE9TxDmpZcK9++Uk2OmveENxPgI0oJ/Ise//LKzPGECvPuuMw0t/Oezz+zgUXgwy+0pRno3BE3gJF6AKIlAxL4ASXxFN7ZRn4ZsBaAz30cca7+2Bw4s/dfysGER7cqiZhCFBZFEylORA0QiIiIiUg78fpg4EYCpjGYzjRlDWE2iiRP1TbQUSW7QzweJpwFwJm8DsNvVEcojJcVMgZJIfr+npk0mKRzFEj7lhIihUYtKewb4OJ6Fnk312MFN3MNRoSBeOE+AqLBTlqZPhxUrADxFygH+pQVr2cfZkJhoMo4srmZFBbHOrS0rI/Y1w1WTqVEjc+4+nzdA5POZ+kchsQJEieRTjx1mQ4zpY3aAKC0NVq+G2bOj14wqCYsXe7I+d5NsB8tqkOuMW7q0dO5fZC8oQCQiIiJSWQwYAG3bspJ2kfvatSteS2qplhYtgszdNUljI8N4DoCf6chB/MQq2ngHZ2XB88+Xw1lWAoEArFplr9Ynk6UcRS8+LfptBYOeAMIyOtvLt3BXlAPCsnRatDCFxQsKfIwaZfdbf5XIz47V7n//rl1N0CUQMEGVBx8s+HGEnVt/3o3YF8A1xevuu+G00yAYtINA2SSbrKKMDHtYtADRHvfjT0iAKVOinosdIMrIMFPmLrig9DKJXLNuvqQ7tdnNy1wChAWIilLbSaSUKUAkIiIiUtFZF2WzZkHfvtQiSt2P/v1NkVeRIpj3tukW1W+/39iHf6jNTgB+4SCG86x3cEKCybworYyLymzhQlOkuhA8QZFwjRtD27b42WNvas6/9vIRLIt6mCdA9OWXBQc+AgGYO9fUv4nBEyCaM8f8trpy3eUEqoLAIJ7ndN4hP7Iymp1B5AmKhBxJKHumbl346y947DEAUsgCYDv1Io6pGapb5A4QNWedM2DECDO9q3HjiGM90ycTEqB9+9Kbltuzp535dC0Pe3ZZj4EmTcw4kQqiXANEjz/+OJ06dSIlJYWUlBS6d+/O+++/b+/Pzs5m9OjRNGrUiLp163LuueeyYcMGz22sWbOG/v37U7t2bZo0acINN9zAnj17wu9KREREpPKyLsoGDYInn4x6ocVjj6k+jBTZvDfMheppfzwMONkZAN9zmHdwfn7Z1G6pbAIB+Dh6W/VowjuQeQwaBBMnUp9t9qYmbCzwNj1TzHw+E3jo0iX2AdZnyhdfxBySg6u+7M+mgxg9ekCzZp5xm2nEiwxiHqfzGwd49gWBu7kZwBP0suxj1TrasQMmT7a318N0/YoWIArPIGrJ306h9aFDoVcvE0w/++yIYz0Bovx8M1WutKbl5uXB+ed7ztlif4afd54C+1KhlGuAaJ999uHuu+9m6dKlfPPNN/Tq1YszzzyTFaG5sNdddx1z585l9uzZLFy4kHXr1nHOOefYx+fl5dG/f39ycnL48ssvmTlzJjNmzGDs2LHl9ZBERERESl6PHmYKWWg6SM1onX9K85twqZJ+/x1+XVsbP7n0CRVLdr+2NhOWgeHzqRC6xcrqe/FFU/vrvvsKfWjcANHGjXDOOVCnDgCPcLXJyWnUKO5tejKIgkFzO4sWxT6gSxcTRAqpSWRWmB10SkqCO+6Aa66BXbvsWmiWXdS2l0/hA8++TzjJXq5BLhk05gji1NxJNQW8rQBRFikRQ5LJ9pzfJ5xEJ5abxzNwoJlqO2gQPP10xLF21pH1mVma03LT02HaNCBOgGjaNAVcpUIp1wDR6aefTr9+/dh///054IADuPPOO6lbty5fffUVmZmZPPvsszz44IP06tWLI488kueee44vv/ySr776CoCPPvqIn376iRdffJHOnTtz6qmnMmnSJKZOnUpOTk4B9y4iIiJSSfj9cNttdoef8IsNgkHo06dwdUdEQubNM797dtxESuiCPOK15RYMmqwMFUL3ZvXdFb0uUCxxA0QvvQRLlsAhh3q3P/QQXHopAHdyS8RhVmt3j3i1bRYtMkGkkF4siBhiB1MCAfjkE1PX5z//gZ9+ssdM5T/MwfkCfzVtPbeRgVNcOpE8GrOZbnwV/ZzS0uwubks5EoDJoewjt/ApZlZBa+6910zXcgXTIx+T30yDCwZLN3sIPEG4mAGigjK9RMpYhfl0z8vLY/bs2ezcuZPu3buzdOlScnNz6d27tz2mQ4cOtG7dmvT0dLp160Z6ejqHHnooTZs2tcf07duXK664ghUrVnD44YdHva9AIEDA9cdTVlZW6T0wERERkZLQxqkHEnWK2RNPmJ9PP4UTTnC2BwLwzjvxA0dJSXDGGea3VBtWgOi0S5vAnQ1g69aI7LQnGcnlPOVscP3dXa116WICGq7iyZZ0usU9NGaXOLAzAYOtE2BxqONZ+/amQH3TpjB9OjdxN7eGFasOhn/vn5YWv7ZNdrZn1TOdLGRPtEvFl16yF5fRmSuZGvs+8NYJsqZ3xezidtddJnOqbl3+3LEfAOtpDuA5wmoRn1ezFuSEAkRNmsAll5iAz4QJMHhwzHPKIYnk9i1Kv6i/KwgXHiCy32dWplefPqV7LiKFVO4BouXLl9O9e3eys7OpW7cub775Jh07duS7776jZs2a1K9f3zO+adOmrF9v2iGuX7/eExyy9lv7Ypk8eTITitCaUURERKTcWQVPMzI8F/G5+KnBHlNwNVp7ayvToSDhgSWp0jIz4fPPzfJpNT8005qefTbiQnYUT3Ia82jJOpOVcfXV5XC2FdCnn3qCQ9kk8TMH0ZnvOIb4U4Z2UsdePpQfvDutrBZflG29ekG9eiRs3059trKNBrHv5PzzTW2bWBkyv//uWf0qSlAraoDI5d9Q8MatCd56sT9zkL1sZU55psNZ0tJM5k8oOWAkT/IUl3MgvwAQdD0hQUzb+7xMVwbR/fc7j3XAABg3znSVixKLClCT5NLOHgoEPPWdYmYQgbqYSYVS7l3MDjzwQL777jsWL17MFVdcwZAhQ/jJlbZYGm6++WYyMzPtn7Vr15bq/YmIiIgUm99vplDgvdiwsxFiFVzt1AlSIut4eKSkmHFSbXz0EezZAx1a7WC/q/vDs6ZjWbQpZtdzv1kYPty0OhcI1Uy1nMfrHMEynmNYgYf+zT72sjvDhgYN7KyW0GxSfNdfbzJjwLy3H37YDGVrxO16YiHTppnOarGMHm1Pw/qT9uxyBa0s1jSvWOypXS5peDOqfuVAe9mqJ3QlplPZhcxyBt53H3TrZnf96sx3AByMeZ4jAkRnnEHeHvOIE+e9Y2oPWawsomCQa0Ldw8biJAcE2hxY+tlD6emernYxA0SpqepiJhVKuQeIatasyX777ceRRx7J5MmTOeyww3jkkUdo1qwZOTk5bNu2zTN+w4YNNAtVzm/WrFlEVzNrvVlYdX23pKQku3Oa9SMiIiJS4Q0cCGlpnguzXdR22jVHu+h5/nkoaDp9VpYZJ9WGPb3saG+HrIgC6MAsBvApJ5gMM9W4MsJKWbzLaQD8lwc8233kEwTW0IqfQtk07vbxniydzp0jA7xt2njr6QweDMnJ9CCy+1hEbaN4mSnJyaYdPNGzhwD+YD/P+r8043ru4w/2BaJ3JQufPufOFtqIqcdzKD+yhQa8zMVmR1qaCYK5srKszzgr28rdfSyID559lrx8s82/6g8z9e3FF03h8EDAZBG1bcuDjOFHDmY8E+xC3IHjepsuZ+7xJc1qLBASM0D04IOq6SUVSrkHiMLl5+cTCAQ48sgjqVGjBp988om979dff2XNmjV0794dgO7du7N8+XI2ugqszZ8/n5SUFDp27Fjm5y4iIiJSKqyOSbNmwVlnebIOskk22UP9+kVvlzxqlOcCZA5nM4ew9s9+vxkn1UJeHrz3nlk+7RhvJkqsItVX8hi5A4eq45Ll2GOjFkIOn/YVJIE9+GnDGg7mJ9bTlFdxArme6VZz5jjHWRlE4XcRyiKawtXczkSO5Bt7l6clfEqKycgBb8e1F1+E556Dq66y7yTWVDKrk5jlfGbzANdzIp+aU4kSIPqLfRnNY6YQNN4Moi00NAtJSTRgGwlWztN995nH9euv9tjf2R+AD0Pt64N4n4h813rilaNMsfBBg8x02vR0c3sDBpBAkIP5CR9B+7UdePE17/jJk0s+SJSXB/3726tRaxAlJ8OFF5bs/YoUU7mGK2+++WZOPfVUWrduzfbt23n55Zf57LPP+PDDD0lNTWX48OGMGTOGhg0bkpKSwlVXXUX37t3pFvqw69OnDx07dmTQoEHce++9rF+/nttuu43Ro0eTpCKLIiIiUlUsXOipI5RHV9dyKFj02GNmusLtt3uLTft8JvPj44/JJIVzMRehO6hDHXaZMSeeGLPrj1Q9X38NmzZB/fpwzBWHwV2NYPNmIHoGUWMy+ImDebTheMaoxb2xZIkdYLEyaiyNyWCTq3uXO6umOd46qXaAqEkT8w9SGPvtR30ymcg4VtGWpRwFQCapNLGmeGVlmXM84YQC65BFLXofxSLMv/3ftIo7bhqjOY15nMoH/EoHe/u93GgWevSA774zrzkrewjMtLcbboBgkHc4w3Ob7m5oQXyeoJadURleh+1//4O77/Z0f9wOBAi7TpwwwTxPJVmDLT3dfCbHUINcUyjc+jcSqSDKNYNo48aNDB48mAMPPJCTTjqJJUuW8OGHH3LyyScD8NBDD3Haaadx7rnncvzxx9OsWTPmuCLriYmJzJs3j8TERLp3787AgQMZPHgwEydOLK+HJCIiIlLywjKD3BlEnm//77zTXJi4MwbGjoWPTQtsd1HZbdR3jps/P369EqlSrOllp5wCNWr5YehQe194pkMKmXar8fE7/su/GZoOA5ggRNu2AGzA2zTHHRwC+A/TYt6M/f79+mvP9pgZRGBq1jRuDECHUBFncGr8AGZ6kxUosaY7xQgCu4OC+7CWgbwAwHz6sJ7oXeve5oy43dg8ny/AEGbQ3Wpv/8knTteuBx5wMhyTk00mJN7X4Wf0pCfO51M+CZ7PQDtAFF6HbfFi54l03WZEgKhZs8ji/sUVVvstPEurBrlQr55qv0mFU64BomeffZZVq1YRCATYuHEjH3/8sR0cAkhOTmbq1Kls2bKFnTt3MmfOnIjaQm3atOG9995j165dZGRkcP/99+PXPE4RERGpSmp6W1C7LzY8Fx7Nm5sLHStjYNAgu7A1wHqcv6N2hhelVSedasOuP3RaaIMrgyG8+HE+CVzKdI5mMdtzkrnhhrI5xwrP74fQl9I7qBt36EsMjLnPziByTa8q1H3fdx8A1/CIvTmTVGfMxIlOoMRVtDka67PgFN5nLa0ZFAoQAVzK9KjHnMXb/I97Yp7iMrw1mqzC1Pb5PPusCYq5i0sHAnC2mf7anH/tzb35mD/D6iFFBIii1WEL+0z7J1Qc/DNO8J7s0KElXwcorPZb1ADR9u0wZozqekmFUuFqEImIiIhImG7doK5zEeq+OPLUMOnXz9QpWr3aTFkJs44W9rLnW/S0NHXSqSZWr4YffjDX06ecEtpYy8kEaRrWpnwC40ggyFRG4/MFeekl+PzzMjzhiioQgBo1oHbtyOLQhWB11bLfv59+6tQImj2bYChrMObMz4EDoXFj6rCLbpi6UHYGUbt23kBJVhYsXWoyVoAFnMgt3Mme0OfIcg4FnBpB9dlmH7qIY2M+hh84DIDLeYIcatCfefa++6zpZCF12OmsnHiieQF26eJ9gOnpduHsx7nC3pwXFlwJ4vN8BvrZY7KH+vSBN990Ai49e9pd0dz+y4POSkICjBsX8zHutYsv9qw+zUjPup21NXOm+bcsqJGASBlRqo2IiIhIRbdkCezYYa+uobW97Plm+tln7Xblbnkk8C/NPVPMcnBlJXXrZqaxKQu7ynv3XfP7mGOgUaPQRmvK0qZNrHXVl5lPb07CNIw5qvFqLjsryFPP+LjySvj22yrycgkE4J134mdxJCXBGWd4a3ulp5tOWUTpHlYIx/F/gCtAdPfdnv3B7uvA9X6N4Peb6VlDhpBMtnkoVtDXnT0EMH06POJkGp3EAgDasJrLeYoHuB6Ar0O1zdxZZBHTsaLow0fUYA+N2eTZvtv1vPhwZS9ZU1qtaWYWa9reqlW0Z2XM+wuvQWQXy37iCfMzd65Jj/P7TQblsGGxT37ECDO1raTNnGkvrg1lLrl56j69/jocfTRKz5OKQBlEIiIiIhVdjx7Q1NQCySaJL13f6sfqQOR2Aa/Rir+ZwVB7m+fCb+5cdaeqJiKml4EJDoaKGLtbsHfgF6dX1AUXcNeEXBo2hOXLYerUMjnd0ueejhnrx+qMBSaQ9PLLpsZXKCNnbwJEjTBFwWO+f9f+DRRQO/7ii6FtWzsbJYeakdlDENHJ0GK1sd+f3wBozjrAm0EUKMRjqx0qdu8+DryFpfflT+9B0aa0uqbtxRMeIEog3zvAPV1v4MCoWUTmwARP4KzEBAKQmWmvRkznJUph8MMOK/nzENkLChCJiIiIVHR+v51hcG/Y1I3CBIjmcC4AP4amkkBYgKg0irRKhbNzJywwySOcfrprR3o6TDOFlFux1t7seW1Nm0aj39K56y6zOnYsrPc25KqcunSJHUCwJCebAvAjR8L555uuW9OmmRoyELdYcywN2QJALjXZGlbQGSD4dwEBokDATKfq29cbIOrb10wzDU1VIxBwOhmGsaZpWVlil/MkEBnoKYhV/NkKelmsKa3NWUcNK8sH4k9pPecc040xDneAyE8unqcoIcF0Q7NYWURA+1CQqq2VnVRa2UMLF5rXS8guakcM8XQLrFcPevUq+fMQ2QsKEImIiIhUBgMHEmzQkPtD00EsW2nA2czhAcYU6eY8AaLSKNIqFc4nn5h4Qbt2cNBBrh1Wlyu8tWLastoZ07499OjBiBFw5JGmZMr//ldGJ16aliyBjAx7dQNNIgM22dmmE+DTT5tsuzBWBlFr9/NVgHpst5cvYlbRzjkQgEmTTGbTk0/awYZcapgpVu7Mp4ULTQAw1MnQLT90KWj9tjJxPMGcQrCmuLUIZSBZvuZoIKy7GsCQIbE/b5Ys8WTfRJNHol0vaY+7BhtED/qEsoiGY6bf9ubj0ssegoiuk9GKmHsyiB5+WJ+/UmEoQCQiIiJSGfj9fNzrLraHXWzdwl28xdlczwNFujk7QOTzwU03ldRZSgXmnl7myUyxulwRZeqLJdQ+PDHRmV72/POwaFHpnW+Z6NHDziDaQR2asYGGYZ3cCmJliPTgC3vbEGbwNV14lCvJx8ewsG5g7uf5I/oSwNupMBjKi4maQZSe7slQ8WQQhUtMNI8xrBO0+z6sAFEieXb2zgGYaVqH8oNzUzECR1YGkbvzGGB3OdsZHiBxda2O0KMHtDY11n7hwKhDnmQUd3Bb5A6fz9O10RbKIrICYPkklF72EECu9z3k7h5psf/9k5Ohdm11MpMKQwEiERERkcogEOCh704AYDAzacMqwDttzJJBY87kLd6lX8ybsy8mg0FYvLikz1YqmGAwRv0hy4AB0K6dd+qLJax9eNeuMHy4WR49GvYULeGkYvH74dJLAVhFW3tzXiEvk97jVG7FzLtrygY2ksbHnMRTjKQL33AlU/EBKXi7VFlBFUusdvJkbIzc1qMHtHFqRVnTrdxdCgETfFi3DnbtivqPHjWD6KyzoG5dpuJM01rM0SzlCG+haffdhDKIurDEs70ZZg5iTfdjrVs3/nQqvx+6dwegPX/FHBZ1am0wCMuWRT9g4EAS6phAXn7NWvDYY7HPoTgCAVN02sXqBNfIVcTbDhBlZ5v3nmrASQWhAJGIiIhIJfDLq9/z/p8H4iOfsUxktetiNlwTMniHMzmNd2OO8Uwxi1YwVqqUZcvg33+hTp0Y5V9CWUT/4x7qs5VrecjZF8oecps8GerXh++/N7OaKq1AAA49FHw+k0ETEjVDBfiAvvgIcgsmg6c/79n70sggjU2cxAJqhmViuQNEl/JsxP6XuYSVrve0nUE0PUrgyO83tXpCXud8AO4hLBMwOxsGDzZRvGeeibgZK8hi1SJKIN9039qxww5gbKIx3VjMUSyNnM4VYgW70tjEb+xPS0z9JKt49WhcFc0HDoyYghVhyBDzMGNkLNVlO21C0/lG8LSzo23b2LXU/H4STjMB8/wuXaFG9MdSLFlZ5vG98IJn8xSuAeBovra32QGihAR7+qZIRaAAkYiIiEgl8MiiowA4g3fYN84364VlB4jq149dMFaqDKt0Tp8+3m7tHgMG0KpdDTJI4yGrplVY9pAlLc2Z5XTbbbAxSqJLpZCebi7qg0FPgGg8E6IOP5UPAJjMLRH5NPvze8y7cdccOpTlAJyLN9PkVS6MPLBt2+jTj5o3j3o/WdQLu+N6pmB1FEs5EgibYhZiZZJFmx4FcAnObVoZRAD78wf78QcAf9EegMauzBmeeMLURYrF6gCWmEis+tz12WZnQDbB9cI77bTI4FMgYIp1v/giCaEr3/ygzzwn1o9VzLu4nnwyInvInYnmzlBLtDqv5edHDcCKlBcFiEREREQquC1bYOYL5nLpOndmRzHYAaLzzzddj0rqIkkqpLjTyyyhLCK/K1AQ7+L18svh8MPN9XylLWPlKtDtDpDEkuQKhqyhtWdfeJFmhg2zF90ZRNb9PBhWWH4WF9kBBTuDaMHH0QMqI0dGLVA0jOe8G+64A1JSIsaBE/wJL1INToZLMMrl4gaacATf2uvh0+WsgFF2qLubFTCyxcsgSk83U67ijNlJHaZyJQALcE1Xe+yxyOcqPd0U6x40iIRXTFAr/8t0p5C3Vcy7JKZ47befZ3UPibzGBfa61bnOZmUPRQnAipQXBYhEREREKrinnoLdu310ZhnH83nMcdEqhESvGuKqQfT0096OR1Ll/PsvfPONWe4XuyyVEapFBBR48eouWP3ccxW8jIork8TzM2uWSasiToCoTh178Si+sZd/4wDPME+B7wMPhAcfhDPPBJxpXADfYLIBwwNK39OZGQyNvP/s7MhtixebmjvAw6EpTABzONc7bto0M/UpZKer5fraUIDLM8Us2mMJ04jNnMin9ro1lcxSi92e9YgAUTxduthFw2Nxt43/iu7eneHTZa0AoM/nLVJtKckpXn95MzvHMYGLecVen8pobmMSjzPKbFD2kFRAejWKiIiIVGC5uU491esavYBvc+yxe/B7WlTXYQebaRR1rKcGkUW1iKqk90Jlcrp0idrMysvqaDZ4cKEuXrt3h6FDYcYMuPJK+PrrCvoyWrjQBEELyVNYuU0b+OknwGlpD9CH+Z5jPEGVX381T8qsWVC7NllBJ4vnOP4PAD95XMUUHuVqe99TjGQ4050MIoLwe5Spa64nuQ47Yz+QX3+1F1fRhnah4vaWxRwdNYMoarFyIIE8EsnncL7jIa7Fzx5qhwWE3FPOANqxMvb5hVuyBDIy4g4JEKP7WFpa5HRZ1+s5aoCoJIM0o0fD9dfbq3dxq71cgxwO4wcOc3WFo107ZQ9JhaMMIhEREZEK7PXX4Z9/oGlTuPDeI+OO3U49T82LndRlMM/b68N5hqNCnYYiAkRWxyNNM6tyrOllp59eyAMGDjSRnksuKdTwe+4x3dG//dZku1VIhYhauQMHnpbtoeAQxGglH+Ipquz3w6hR5n01YgQjeYqaBGjNamcamM/HFK4hnW72YUvo4r1RX4IJPITr2RMaNgS8GTs1if3+jdYp7Qc62cWq3ecfngXk3L4TOLqWR7jSKkDtKmwVHiBKJTPsRmI/h+Ed2tz6xSm6D5jpstGmpoWy4hJC+ZT2v3NJT/FKTrbb+20KC8xHzaIaMEDZQ1LhKEAkIiIiUkEFg/BQqOTQ6NGQNPjCmBdPYNpKH463zfP7rlb3T3I53THzgCICRFbHI00zq1Kys2F+KNElbv0hN5/PpBtFqXETTZMmMGmSWb71Vti0Kf74ctGtmynYjJl2uZij2Up9zxB3gCg7RpZKboxOXhCWQTRihAkYAEyZQkPfNgIks5q2pkCxz2e3f+vKYvuwIAnsIdHJIDquh3M7bn4/3H03gN3RC0wntVg+ddfrCVlPM/uzwF1LyH2bMR+j27332sGO8OBSgnuia8OG8Yvi+/3OiylMvOceMNPpos1zDGURJYYCYFZArMSneAUC9lS1rTTw7AqfhoffD7ffXjL3K1KCFCASERERqaDS082Mi6QkUxDYffGURmTbqFxqspxOUW+rAz+TSL59EXgXt/JrWA0VoOAW1FKpLFwIO3dCixbQuXPp3c8VV0CnTrB1K9x8c+ndz15bsgS2m05iH3AK3VhMZ77zDAkPEG0jlT14M4+iTs0MsTNw/H4nsgt2FpHHZZeZDK06dfABMxls79pMIztAxIEHxM7q22cfANq7uhr+wz4xzy+aHdSNGiCKFRqMOvWsXj0zF3aPefzhGUQeffsWHJAZMCBqILw1ayK2tXFPmWvSxAQ2Y9xmUmMzzS9AUukUiE5PtwuTFxgg6tcveuBPpJwpQCQiIiJSQVnXmAMHmmsfwFw8NWnCNxzF44xiV6hTUEGsCzv3RWA/3osyMM70D6l03N3LCpkQtFf8fqdg9bPPmhlqFYqr+PHrnAfAGrxBiKArLJJJfRqwjRrssQs7L+cQVoZat49lQsRdtOQfs+DOHrJMmeL8A/h88MgjJmi109QPOoN37KHu7CXf00/Frv4deq+24F/68GHMhx5PLjXYHfoMiRvYse7SHSBqFJpG9dhj5jGH+sjHmp4GwKOPFnxSrkD4Ak7kHN5gBE8zmZtJCCsk7v43Y+NG85zGuM2k888AQs9vaRSI7tED2rYFYAsNPbs8AaLERFObSqQCUoBIREREpAJavRrmzDHL11zj2uH3w7BhtGYto3iSWoW4qAPnws49ReQv9vUOSk2NP/1DKpVgsJDt7UtIjx6mIV4waKZEVohkNKt72V13FVj8OD/GpdEB/AbAXdxibzuP1+3lydzEn7QnmUBk9pAlOdlkDYFpUZ+cbIJWochvfTLxh96bOdR0Ah/1UmJnxfTsCY0bA/BYqO17jRjFpWPJII21tALiT0+zeAJEmzfDeeeZf/Rly0zQBW+g6UQWeG9g+fLCnVgoi+hEPuMNzuNpRpLGpohOc54AUfPmcbuRJfc6BghlEJVGe3m/HyZOBCIziDxT8y67DGoVLrAvUtYUIBIRERGpgB591Fxv9e4Nhx4atnP8+CKngzTGFIbZTr3Yg1x1RKTyW7ECVq0ysYiTTiqb+7z3XkhJgW++MZlE5S493XQvu+sue5PPXRMnZDFHczvRa9+soyUADdhqb6tBLgN5gY6s4Boeob3VqSta9pDl8cdNZ7Np08z6kiUm6yXEKubsrrXj254J990XfZqZ328/Litwk0tNfmc/8vFxAa9yW4zHZHmRQXYm1T78HXes+35szz1nPotcxaXdAaKZDHHGtmlT+HbyMWoRuTutRTj77LhRyaTaZrpggKTSay9/zjmQlhaRQWQHthISzHOgZgBSQSlAJCIiIlLBbN8Ozzxjlq+9NsqA5GS71kVhWRdtecTp5rTvvrH3SaVjZQ/16gW1a5fNfTZrZq69wdQi2ry5bO43ph49TDvxGIKYjlPdWMwboalnsdRlh+s4Hy8wmB85xMnii5U9ZElIgCFD7KlY9Ohh2hOG1IiWQQTmCT3hhMgMqEAAVprAlDtwM4Oh/B/HMZsLuJPb7O1tXe3mW0cpQt2CdZ71hRwfMcaTCdOqlZ015A7ouMfUY7sz/o47ihaUGTAAWrf2bArPIHLXX2LatLhF9q1Ga4F9Oxa6Q1+hhWWq7aSOZ7d93vn5Zs5wrGmDIuVMASIRERGRCmbGDMjMhAMOgFNPjTHozDOLdJvW9JlZeKdV2EV4GzfW9LIqpiynl7ldeSUccghs2QK33Vbw+FK1davdvcziziC6ibtJI37btf34PeI4qxOWJ48vXvZQNK5OZAD12QbAJhpHnutXX5mpXO7Mk/R0mDwZgKZssDc3Y70ng8U6a+szYDzjuJF7I04nGW9Wy/H8X8QYuwMYwNq1MH26sx6aFtYIJypoB9XatCn6lC6/Hy680LNpV1jg5XlXcW/A1PeJwQoQZftql3xBrrBMNc/zRFhgK14xbZFypgCRiIiISAWSn2/q14KpPZQQ66+1338v0u1anYrC64zYGUX336/pZVXIpk1OkkL//mV73+6C1U8+CUuXlu39e0yYAD/8AEAeCUzhKpbgXJzfy/8KvIk8EsnH55ku1dbdPatuXZPyZz3oojjjDKhhppQdyK8A/EIHbwaR5cMPvZknPXrYhbcTCDKAl+3z/Ypu9jDrvW9NXTuDdyJq5BTWbxzorPj9MGqUd33SJI7gW2eTFRgpavaQpU+fmLuOYgmtWetsqFUrMshtZfa8+CJJC943m7buhBdfdH5mzy7+lK+wTDX3NEEImxoXr5i2SDnTXwEiIiIiFci8efDnn1C/vpmNEtOIEXD99YW+Xaul99m8yXI62dvzSDQXuF9/bbIUovH7oXt3OPdc52t4qdA++MAEGw87LGKWTpk4/ni4+GJ4+WVTsPrLL+MEO0vT3XfbgZs5nMM1TCnyTeRQk/foxwaaATCKx6nnmm7GlClFnvJpmzrVtIgHOvALbwPjmGAHiDz1ktLSvJknfj907GhPq7LG3sYdDGGmPWw3tUgmYGe11CCX3zjAcxqN3FlUPXuaKvmrVtGJ7/mBw6Kfe7SMqQEDOPj223l19QU0IVRfaW+yhywnnAANG5p0tDDfEJaF89BDkUEoK7MHSOYQ4FQCm3eYbCy3Tz8197U3AgF45x0TzHrySSAyg8gTIGrXrvC1mETKmDKIRERERCqQhx82v0eOhDp14gz8v8jpH/FcHbowvom7+R/OtJY8EmHHDlO/44knov889pip2RGnvodULOU1vcztvvtM7HHxYjNtslzUqAENTLbMP6Fi00X1D/uQTnd73VP3pmVLM7Vqb612agFZRaq30IitYUWOAVODaNEi7zZXkPhlTF2dndTlBZwAiNXG3spq8bPHk+UDYR3cFi60H9NcTucmJkeeS6x6S6EsoguYzQmEPi/2NnsITNHpwgSXatSA4cMjt1uZPT4fSaEpdFZGFWCilu3bFy9gYwWhQsEhKCBANHGisjWlwlKASERERKSC+O4780V2YqKp4xLXr78W+nb35zda8C8AtRrXZSJj7X15DdLsVtsFilPfQyqO3FyTQQTlGyBq0cI03AP43/9MOaAyt3ChfceF6dIVy9/sYy/XYrez459/YmfeFca559qLfvZE7I7ouBb+HjzllKjBhu2k2Mu7qUUeCWwLTSurQS7/YRrd+dIzxta+vfkHa9KE1qxlMrdEnvfxx8PcudGnZg0YAG3bmuV27YrXTj493en6Fk/fvtE7mPn9ZpphMGgHiLJxZT3l5xe/o5krCGUJn2Jm1yAq7vMhUsoUIBIRERFx1amI+VMSdSoKYNUeOu880yAortGj7Tk7Ub/hD8kkhV+tuiH16sF993m+zc5v0gx27iz45Bo1UhHrSmLRIlPkPHxGUnm4+mozC2rTJrj99nI4gRIKai7nUHvZU8erKK3bozn5ZLsG0Roi5wJGTDELfw/6/SbdMI7d1OIc5jiHsIea5PIlx9rbst0BogkTYNkyUysnlgULTNbM5MmRn4t+v8mSgeJny/ToYVrj2efudEj7wfVvwrx5sTuDDRgA7dqRFOr0FiDZPKtW9lBxAzZ+vwmoBc2/VT4+b5YScD33m4Wbb1b2kFRoChCJiIiIWFMEBg2K/XPBBaXamnj9elOvBeC66wpxQHKyPaViGM/FHFabXU6524cfhoEDSWzjRJ/yfv3dEyD6gUPZlz94h9O9N3TwwbqwqSTmzjW/+/Ur/6SvGjXMDEWAxx83WXJlJhAwU7hCr1u7IPteWMYR9rJVTBowtbmK877w++0n6Bc6ROz2tJU///zILJlAoMDUrN3U4h2croee2wxnBUx69ICmTQs+/wkTon8uDhxo6poVt5283293agOog/NZdSg/OuPiTRMLZRElkW1vyqVGyWQPWbZtA0zHuMP4nse4CoDbmUgW9Zxz3bw5+vEiFYQCRCIiIiJduphpVrFaH/t8pd6a+IknICcHunWDrl0LedCUKZCQEDkNJeR7OjldhOrWhcGDwe/HN2kivlAWUfhF82H8wF/sy5m8472xFi1KPYNKSkZFqD/kduKJplt5fr5JfMvPL/iYEpGeboKoe8zUrfC6MLE0Zx3PM4hvOJI0IrNo9sfVQXDw4Ij9RXbppZCY6K0DFOKZdjZtWmQwJj0dXnkFwH5Ph/uD/Tzr7gDR9dwHwHjGmQ1WwMTvNwW+4/H5YgdmfD7zeVkS7eQHDoTGjYGwWj5usQI9VnZofj7Jjes5m0k2GVl5eSWTHXrwwQBkkcKPrsymGuR6C5ofckjx7keklClAJCIiIrJkiZlOEYweaCEYLNXWxNnZTpmNQmUPWUJZRE3ZEHV3J5Y7K48+6lxADRxoZ5a4A0RfEScyNWtWqWZQScn47Tfz4/fH7RBe5u6/3xRd//JLeOGFMrrTLl3swAIUPoPIzx4G8SJH8i0ZeOtzTeI26rArNNBvpogVl98P99zDPfwvYpcn2yc1FTp18g7o0cOu9xMrUHwxr3jvzhV0upub+IFDud13Z+R0K1dgJqpgsOQycOLx+03Fc3A6o7nFmyZmZYcOHUrSJqcGVTZJpuj30KHFzw4NBODzz6OfenhdqTKLjorsHQWIRERERFwXWQDfcCQjeZL1uKZYlGJr4ldeMdcqrVrBOecU8eApU0hJ2MkKOvIxJ7GStpzDGyziGGfM7bebCyirztJLL5GYYC4mrYvmfHx0J06x3UaN1Jq5Enj3XfO7Z09ISYk/tiztsw+MDdVGv/FGe0ZO6VqyxBQ/CskgzbP7LN60l+/iZns5WrFoy/+4x1k5/fQSDY4cxVKySeIlLo5+LpmZ8Pzz3oP8fru4U6wAUTh30CmRfA7lRxKCeZHBHldgphsmgNKCf8w+K3uorAouh4JVb3MmB/IL9/NfZ1+8IJWVHQokELRrGNk1gkoiOzQ93X6egngzpuYRlsb3+++IVGQKEImIiIi4i6oCXfiGpxnJHdzmjCml1sTBoNMt+qqr9uIuQllEHfmZk1hAW1bzBudxTOiCjrQ0c2X+1VeeOksJuWZKhTWtxd3G25Ln/lPx3HOjdwmSCsWaXnb66fHHlYdrr4UOHUwy3rhxZXCHYYHfG7nXXu7ICu7kVgCa8S8340ynihUg8pNLDfe+8GBNcYweHWrFnuPpkuY5l8REGDUq8tg2bQDoyuJC3ZXnNkOF7mMGe0KBmdc5jyuYxnxCGVNllT1kCQWrDuQ3fuEg/suD8c/bYmWHhkS0ui+J7NBOncwUXiKz1Gpb2WZgglGjR+/9/YiUAQWIRERERAIBU023cWPyXd8Abw21hSYtzVyglEINnk8/heXLoXZtGDFiL28kVIsoqgceMOce1orZartsXdDsonbEoZ5OPE89pSlmFVxmpjPTpaLUH3KrWdPMdARTl/mHH0r5DsMCv+6L9ffoR0d+5g/25Xf29x4WI0DkydDp0cMOCpSI5GT7A8AdIGqAqwD1ZZeZceF69oS0NF7lQi4Km04WjWfa2oknmt+xgj2hwExL1jGN0XTk57LPHrJEm/JWUJAqLEgYESCC4meHPv887DB1hsLrXD3HMGdlv/1KpiaTSClSgEhEREQkPd20Qt60iR04F3378qdZyMgw+y+4wKT5vPxyiQWLrOyhYcOgQYO9vBHXxSWXXWYueMBcxA0YYJZDnXysOkvhAaJoxV9L9CJKSt1HH5l6zB06wL77lvfZRNe7N5x3nlOwOlbZrxIzYIAdILACL99wJG1YA8C+/EVdV2cscN4bAMOYbi/nUtMZ9P77JX+uU6aAz0cWztzAjvwUOqlE58MinN8P997LPvzDUzgt7y/g1ajDE93v9U8+Me3l4nUbCw/MlHX2kMU15Q0oXJAqLEiYEK04f3GzQ0eNsgP04RlE+1hT8sBML1OQXSo4BYhEREREXN8yx+109M47JvXhkkvMRUUxu9/8/rszJejqq/f6ZozHH4cZM0w7tAkTzDb3RZyVJZVm6rCEB4i+4aiIm/QEiPr31xSzCq6idS+L5cEHTcbcF1/ASy+V8p25AgRWRmAz1pt9HTpA/fr20FN5D4BredhsqFmTafzH3v8Ul5mFks4esiQnw2WXcYirfbtdEDtW9pBl4EBIS6MmOfam+mwr+D7btzfB5XiZLXsTmCktQ4bA+PFmubBBKleQcFOoDtWH9DX72rUr/mMJTfOFAgqhx+r4JlKBKEAkIiIi4vfbmTbuAFG0ttO2u+4qdvebRx4xv087DQ44YK9vxkhIMBdPCQnmYvHrr71ZAVaWVEaGGR72Tfo6WkTcpCdA9NhjMGmSWt1XUHl58J6Jb1T4AFGrVnBbqLzXDTdAVlYp3ZGrxbmZPmrez3aG0HXXOe+R+vV5k7P5lsO5lOkmcPDooyQTIBc/P3EQI3jGjC2N7CHLI4/QMfE3vqQ7a9nHbIuXPQTmcb75Jpx1lmf6WFtWFXx/hQ2y7E1gpjT4fKamWvjnWzxhWUQAk62i5MXNHrJeY926AQUEiMrzeRMpJL1CRURExPyR+8YbJoiwJ0YHH78func3xYqTkqKPqawCAROh8fnIDdawN2eTzCKO4Si+Icn1zbytGN1vtm6F554zy9deu1c3EZvPF3leVg2ilSsB5yLZumh2T2uxeAJEAHfeaeYInXBCCZ+wFNfixaZhV/36cMwxBQ4vd2PGmNf/77+buMODD5bwHQQCJqB55532pvzw6ZSXX25+T5kCqakkDRnC4Xxntk2caDJLxozBv3MnB/GL2X7ccaWTPWQJTRft/uSTzraCsoesVu54v/1v6Z7eFE1RMoGswEy/fnBUZLZhmYr2+VaQAQPM+a8yq7nUKJnsIddzD+b/jKiaNy+/rCuRIlCASEREpLoKBMyUqUDAVEm+996Cj3nsMfjtN7j55qoVJEpPN0WA8GYQPcD1PMD1XMQrvOJqPW3buNFUmd6LlI1nnoFdu+DQQ6FXr70+88LbtQsOPDAiQGR9420FiK7lIV5gEJtpHBkgUh2iCsuaXnbKKWYmYUWXlGQKVp9yionPXHopHHJICdxwVpZJT1q0CL791t7sLj4fUW9rv/3g5JNNpGrlSidw4vebND939fgPPyyBkyzAww+bD4i8vIKzh8AJ/q5a5SnqFP44U9nGrTgBsyJntOxNYKaisLKIBpvVHGqWTGfKLl3MFwUZGRAMegLt090FqidNUvaQVAp6lYqIiFRX8+d7vvkstAkTIDcXOneGM86oGoGiHj1Mq+jVq6PWIJrFABqziTN5m9584t05cyZs22aeh0I+H3v2ON2crr22jBrbTJ8OH3xgr8YKEB3FN7zBuWwmSgZRSVxQSamoLPWH3Pr2hbPPzOPNtxO58vwNfHrL/OjvhSK8t5g+3Xlzubin/kQEiGrWdIq4Dx7sDZxceinMnQtvvw0jR0KtWkV4hHvJKjr/5JMFZw+B99xdwh/nFhqSYHViK4nsmcpmwABvgKgkHv+SJeaLgpDt1AOgJX8zjBnOuPfeg/PPh5TITE2RikQ1iERERKqrFSv2/tgSqL9Tofj9cLHJEIpVpPoxruJkPo7c8frrMGiQeT7eeadQdzdnDqxda+pFXxwlMalUuDrtgNPu+VuOYBe17ABRClnUYzsAL+Gq8VEdLygridWrTRJgQoLJyKlMHjrjM2qxi4W/NGXW4HfNeyn854IL4MILC1esaNSoqJtnc7697O5SRqNGpk08RK/d5fOZN+yMGaYQfFmZNs3c59SphRs/YIB5j7oibNb72GIHh6B6BntdjzdIQsk8/h49PO0nZzIEcD5fbXPmmOClSAWnAJGIiEh1dfjhMXdNZxh+cllG5+gDfL6qNd0oEICDD4aEBFObYm/dfXehijg//LD5fcUVBScHlBhXpx2AxmwC4HKe4kzeZgsNAdP5aDcmS8LaBpjMibPOsotcS8Xx7rvm97HHmnhHZdJm63fcwl0AXM/9bCdGfZ+334annir4BnNyzJSfkC004BcO5Hmc7BpPZs1++znd+awpVOFpTO4C8GWlqPdpZREFg9zDjZzPa/QLdWUD8LuKVyvYW4Ly8kzwMuSF0OvsL/b1jvP7YwYvRSoSBYhERESqq2OP9RQr+ZGDmcBYdlGL4UwnDz9HsIwLmeWp3wGYOhdpaaauTVWQnm6yB/Lz47e5B/6kPX/Tkl+J0nbs228LzKpavNgMqVnTBIjK1JQp9sVvGk6g52NO5g/2B6AF67iaKQDeYNlPP5lIxNixZXe+UiiVcXqZbfRorud+9uUP1tGSicR5fXXoUPDtTZ/umfJzFm9xEL/wIU5qlSdAZL0hq4JQFtGNvvt5jQtJJJ9P6EUHfmYBrkJn1TF7qLSkp8MTTxQ8rl+/Mvw2QGTv6ZNBRESkulq0yNQSCjmUH4GwrBHgNS4knwQG8zynM8/Z8fXX5mKsxFtwlQNXh6+CAkT/x3GM4Bny8LOepjTFuRgtTFaVlT00YAA0a1bM8y6qnBxo3RpWryaVzKhDWrDObpUdNZuqQQOTJVUVak9VATt3woIFZrlSBoiSk0nu05MpH11Nf97jfm7gW47gY3qHh6VNweaCDBtm2teH/B/HRwzxTDFr1qzqZEJGqUXUi0/5mY4mMNy2Hbz0kt2SXQrJ3dAhXF6e+bKkoMzKl14qnXMTKWEKEImIiFRXroutf3EiFVO4JmLo65zP65xP0H3JVpVS5l0XVgUFiCZzM3mhMcs4nFNwdTUq4Jv5tWth9myzXC5xtalTTcEaoC47InY3ZDO1yMbPHgB+JEpbqcmToU8ftbqvID75xFy3tmsHBx1U3mezl669ln4f9aMf7/Ie/VnASXRhCd8Q1jGrZs34t5OV5e04FoMng+juu6tWNs2AATBunOnG5g5cBIPm86l79/I9v/LgCfAMdLa/+KKzHK8Qelgr+yLr1Qvqxpg6KVLBaIqZiIhIdRMImCjF339DPdNxZUesuh9hduNKke/cuYzab5Uy6/kIBCAlpcAaRP/S3F5ezqHOjqZNC6zr8dhj5gvnE04wT1+ZcwUFwwvYAjRgK4Bdg+gXDmI+vb2DGjasOhkXVcDcueb3aadV4rfjySdDUhJTuNretJSj3CWVTbDDKiYdy/Tppmh8yB6iZxz5rFtOTfUWpK4KrGA3wH33mcghQPv21bfukBXgGTTIuz28EHqsqYZWhmmsN5jPFz/I+Pbbe3feIuVAASIREZHqZuFC88fw0KGw3QQJdlG7UIe6L+D45htzW5VZIGAyYi64wLSTzsqKbO0eZjtOm2JPhk2jRvDmmzGLVO/c6dTYdc2AKVtXX21f5EQLEFlTbw7G6XDXh/neQe3bV53aU5Vcfr5ToLpSTi+z5OXBkCHsy19cyCx7sydwff75TjHpWIYN81zEZ5IadZiVIcdDD1Wt7CGL1Y1t8GAnWDRhQtV8rIVRUIAnIcF8rsUKfLsKgAN8RVdvDbpgEI73TmXsyWdmoWNHeP/9QjUvEKkIFCASERGpbqLU8bAyRgoSkV1T0AVbRZee7lxAhRT2uYgY+9NPcb+FnjkTtm2DffeF/v335mRLgM9nTw2LFyBqy6rYt/HNN2rXXEEsWwb//gt16hScXFOhpafb0dNT+MDevBWnfTjTphVcTHrxYvsiHuBnIufcLecQM1G2Vq3IjJKqwt2NzQoWVbVMqaIIC/BEyM+PH0ALBExDh7Q0/qYl3fmKDvzqHRMqBNaKNQCMZ7zZbv2/8PHHJfBAREqfAkQiIiLVTceOdveyHGrwLJfyEx0LdWg7Vno3FFQTpKLr1AlSUjybogWIGoVawofLJqwrTUqKuc0w+fnwyCNm+ZprCldrt1QsXAiffgrEDxA1ZEvs20hIMJkaUu6s7mV9+lTymuGu9+EFvGZv9gSIYry3bIFAxEX4cXzhWV9Hcw6xsuNGj64eGTXuYFF1FurwFsHKHoo3/S493RyfkcEf7Bf3bqzstGSyvTt++aWoZyxSLhQgEhERqW4mTrS7lz3EdYzgWYZTuIwQz/Srxo0redoC8PzzprBtyHIO4ULXBarlY3rzHEMjtkcEiLKyTCHoMO+/D7/9Zq5xh0beTNlxRaZqEzlNLAcT8IsbIMrPN6krUu4qdXt7N9f7sDa7ORBzMb2N+s6YrCwzLpqsLHMBf999se+CQTRnvbNh0qTinrVUFllZcOON0LZt5L78fLP9xhs9/xd4uAKY7g54uVEaGlhZtlYnSMAEoUaP3tuzFylTChCJiIhUJ4EA1K9vr35En6jDzuSt6Ie7A0QjRlTub+ADATM3x+UM3ok6tD7bOI7/i9geESACu0uYm9Xa/rLL7Lrg5aNnTxPYI+wCJuQ3DgTAB3TDmc7jmZjRrp2KVFcA//5rZvtBOU5ZLCmjRnk+S6xi6Z4MonhdE6dPN/W/XLLDaokNwtWxqm9fSI7y3pWqafp0U28qlD0ZYcECsz/W1FlXANPdAS9atunftALCPl9HjNDrTSoNBYhERESqC6sg891325u+5JioQ2NlkHgCRJs3w5gxsb91rejS02HkSM+mVUSZgoCZLhAxZQCiTzdo0sRTkHT5cjPzJSEBrryyeKdcbH6/nWXh/iY8mru5yV5eG7roAUwGWmUODFYR771nfh99tGmgV6klJ8Pw4fZq1ABRvIvsKIGjL4gTxAwLJkkVFgiYDngJBVz2xgtAuoqf+1zh8vU08wz7iq72sv1/aEKCM79YpBJQgEhERKS6iFKQuRM/RB0aK0DkyZh5+mnzrWuUKVWVQo8e0KaNvfoFx8YcWpMcT4CoJiYA9C8teIszvYMnT/YU07WuDc45J/oMhzI3cCA0bkzrUDHVWHryub1sd4Nq1676tsquYKrM9DLLww/bgcf6bANcASK/33zWxOLzwaGH2qtZ1ONknHpEY3jAGdurlylQLdVDerr5zMvPjz+uX7/YAchly+wC1+5GDdfysGfY3+wDQGeWsQ//mI3KHpJKRgEiERGR6qJHD2jQwLMp1pSqaNkyQPQW8JU1myQvz1wUhIQXtHWrSQ5JOFlBOa7n4Xru9w5u0sQUhQU2boQXQzNbrr22+KdcIkJZREfyLdMZxhccSwqZUYe2xkyXs6dSKHuoQsjOhvnzzXKVCRC5Ouylhl6PWYQKyB9/PLz+OsyeHb1d+Pz5JlUvpB/veXbfz/XOyvXXI9VIjx6Fi8yfd17sVvRdukCjRoD3/8Dv6OwZZgWPrAw4ZQ9JZaQAkYiISHXh95tvM12eZ3DUoe5giFtEgMjni5imVWksXAiPP16ooTXIpRa77fWD+Mle3oe/vYM3boRFiwB48klzzdGlCxwTfTZf+QhlEQ1jBsfyJc35N+owq45GLqbFM3l5sS/Spcx89hns3AktWkDnzuV9NiVk4UK7C5n1XrMDkwsWmJb0F1xgxoVbscKzuihsepndv8vng5NPLsmzlorO7zcFzAsyeLAn89Nj0SIzpRqnkD/AFhp6huU2NFPO7PpDyh6SSkgBIhERkepk4kS7lsK3HG4XJQ4Xa4rZZG7xbggGK29HqyL0mq9JDonkcx/XcwjLmYUzzeogfo5624GAM/vu2msrWJdpVy0igKcYSSJ7uIubPcNqkgOEAkQZGaYF2wUXmABTZa09VQW4p5dVqNdVcUTpsBetCHDU9+3hh8e82d7Md1auuUYZcNXR2LHx/92tVvexiu+7XnPuAFEgrElB7hbzmViDXPPG7NrVpJAqqC6ViAJEIiIi1YlrGsfVTInY/R+mcixfMIBXYt7EBpo4K23aVN6OVq6OXgBtWBVzqHUNfj0PsJxOdGI593AjEOUiNiUFunXj1VdhwwaT5XH++SV87iUhlEUEcDz/RxYp3MzdniHWN+HuiyLATPeJ1fFHSlUwWAXrD4F5P6alAU4G0S5qe8c0aWLGuQUC5o1Ww0zv2WbVywrxBHDHjy/RU5ZKwlUEvTEZZpMrI5T8fFOfL1YQqWdPu/2kO4t2H9Z6hu0Jtb2vQa55ow4fHj/zTaQCUoBIRESkOklPt1v9/kCniN1TuZIvOI6GVg2FKCYy1lk54ggz7agyCsuiiQiCFMDKcpjJUO+OrCyCXy+xa+peeaV97Vqx+P3wQKh47/HHU9t9wRTiySAKPzZWxx8pVStWwOrV5pr3pJPK+2xKUF6eHUmNmGJmOe+8yM+b9HQzPSjXBDM99YaAm5nsrFTWbEcpnkAAjjsOEhJ4k7MByKYW+fjMlyZNmpjPtPAsn0DAZP/MmgXnngt4/5/oz7ue4dbnpKfFvaUIGasi5UkBIhERkeqkRw9oZuokHO/qUgXQkRXRjoiwjfrOyptvxq7bUBm4smiyw6YL9GY+VzGFefR3NtapYy+6LwLy3H9SpaXx+ad5fPedaZZUoUs0DRoEX38NH3wQtQ20FSCKCJ61bAm33KJpZuXAyh7q1Qtq144/tlJJT4dp0wCow04AdlDXO2batMjPm7AixO7A9zz605z1ZqUyZztK8bg6mbk/5/+hpcn02bjR1CkKf20tXGiyfwYNghkzAO9n4bMM9wyPGSBKS4vMfBOpoBQgEhERqU78ftOGHWgbNqXKR7BQN7ETJ0gSt25DRRcImADX2dY3yt4AUTLZTOEa+vOe+Za5fXt49FF7f18+tJc9x2Zk8NCk7YBJbAg1v6mYfD5TQbtWLaeAeWgqBUAwNLnufU71Hrd6tWk7fvnlqq9RxqwA0emnl+95lLguXUwmB9CYTQBkkOYd06QJdOpkXnMvvmh+XnjBvDdDEnEyjKwAJ2A6FlbWbEcpnh49oF07wPv6SCDfLFif7+H/l0XJ+nEHiPaEZVbGDBDdd59qX0mloVeqiIhIdREIwDvvmD+G69Ujf7v3e6K9ChDFq9tQ0aWnm2+HgSBObYkzeYu3OYuxTHTGBoPmsZ53Hvz1F9x5Jy2D/9i7Z3M+Q5kJwJ+05x3OAExN3Erj8cdNq7XcXLjsMgDSMa3XnmYkT3F55DGzZpmfMWOcQsFJSXDGGea3lKhNm5wkh/7944+tdBYtMpkcQLNQ1s8GmnrHbNxoWgPeckv40YB5H78VmkIEYQGixx837/dQDTapRvx+8/k9eLDn/zl72fp8t6aZvfOO+Z2XZwLm27fbx4R38gxiatQFcYJHngBR48ZwySWl9MBESl65ZhBNnjyZLl26UK9ePZo0acJZZ53Fr7/+6hmTnZ3N6NGjadSoEXXr1uXcc89lw4YNnjFr1qyhf//+1K5dmyZNmnDDDTewZ8+esnwoIiIiFZ8VEBk6FLZvJw/vt6OFDRDZhWPbt4eLLoo/uCKzvlX2+diDn/zQ8/Ecw8iiHl34xhlrPdbPP4c77oBgED/O3xojecpensLVBEnglKM2cdBBZfZoii8hAYYMsb9pD7c5rKWzx4MPmmkYVkHWyjztsAL74ANTT/eww6BVq/I+mxLmytaIGSACePvtmDfxEX086+73KK1bV95sRym+AQOgXTvP/3P5JDjZQ9b/Zdb/k4MG2f9XWn7hQO7kVs/NWkGhBILczh1A2OtuxIjK+yWKVEvlGiBauHAho0eP5quvvmL+/Pnk5ubSp08fdu7caY+57rrrmDt3LrNnz2bhwoWsW7eOc845x96fl5dH//79ycnJ4csvv2TmzJnMmDGDsWPHRrtLERGR6suawhHqi50f9mfAIF6IeegyOtvL9tSqypw9BM63ysGgZ4pYMtnUY4dZ6dXL/LYeq+si1t1dPDd0kZBJCtO5FIDrzvN2uKk0unUzU86ALnxtb27Ger7hyPjHWgVfu3QpzTOstqpk9zJLt2729MZ6mIvyndSNDFsvXhzzJtbTzLOe6e5ott9+lfvzSoonLw/69+dgV629PfhN9lCfPiYTcvZs89nVrFnUmziCb9mCd87wIo4lN2xSjieDaOdO1WqTSqVcA0QffPABQ4cO5eCDD+awww5jxowZrFmzhqVLlwKQmZnJs88+y4MPPkivXr048sgjee655/jyyy/56quvAPjoo4/46aefePHFF+ncuTOnnnoqkyZNYurUqeTk5MS7exERkerl00/NFI2gueSyMohu5B4+oC/XEWq71bQp+HwcgMnqPZxv6cz3DA5NoUomu/JnD4GZQlCjBqSlke3qlpREqJ5OQgIMGwZffOFMEejZExrGzqR5luHsoB4dE37m5NEHlObZl55Fi2C36SL1MNfam/dQgy58w/MM8gzPdk+5sAq+LllSFmdareTmmgwiqKIBoiVL7GyNZLLtzRGFqsPczkR8BPk+SlfGtFBLcwCuu65kzlMqp4UL4bHHaMxme5PVlp4nnnCyHz/9FFzJCG67iawKP5xnvY0bCAsQPfqomRYpUklUqCLVmZmZADQM/eG1dOlScnNz6d27tz2mQ4cOtG7dmvRQ6nJ6ejqHHnooTZs6Kah9+/YlKyuLFSuid2MJBAJkZWV5fkRERKq8sP8XnwtlutRiN335CL9VvLNnTwgG+Yg+XM99vM2ZgHPRFiCp8mcPgZlKMGAAZGQQsGtH5JBg5Szk55uLhtxcO+sKv9+uzxNuD4lM4WoArs1/EN83lTRI4irk675Qtwzhef6hBWAuzlPJZDFHOwNq14Y//zQFhFXAusR88QVkZpqGSFUyQcvVjcwO0gLziB0Ny8XPHdwOwGnMi8iKtKeJJibCKaeU7PlK5eLK/mwYChLtiVaO9+WX7W56AFO4iqe4jJywgtSWVbRjSOjLE0tEkerDDtvLkxYpexUmQJSfn8+1117LscceyyGHHALA+vXrqVmzJvXr1/eMbdq0KevXr7fHuIND1n5rXzSTJ08mNTXV/mlV5SZxi4iIRGEVEcab9bGcQ73jhg6Fdu1o41vLfdxIK/4GnIu2QO0GJohgdRGqrEEAV9cka4pZREAk2nSp8eOjtoR/mzNZTVsasYmBbb6ovPVOajpdejxFfl2eYiQAd3A7OSTxAP91du7aZepuqB5RibKml/XrF7W5UuXn98NEUxje/bpryoaIodMZRjfSWYjTOvxvWnmmlF3Aq84BU6ZU/oC2FI9rCqNVIyhqdtorr9iLGTTmGqZwOU+RFOOzEOB9+nnWPQGiunWdqcoilUCFCRCNHj2aH3/8kVmzZpX6fd18881kZmbaP2vXVtIaASIiIkXRq5f5YxVYQ2t7s1Xvw6zUg5NPtmvzuNkBol15JohU2YsSL1lid02KGSCKNl0qORmGDwfgEl60N1vTsUbxBLXuuLXyXpD27Gk67+DN5HDbg9/zjfq+/Bk5SPWISlSVrj9kGTAA2rbFB+yD+fvcbkveyNR++YcWDGc6i+nGyXzsOTzoqgzWgV/MQmIijBxZ6qcuFZxrCqOlC9+wPc4UxvCOZYXlCRD16uXJyhSp6CpEgOjKK69k3rx5fPrpp+yzzz729mbNmpGTk8O2bds84zds2ECzUPGwZs2aRXQ1s9abxSgwlpSUREpKiudHRESkyvP74eGHAW93IE83s4cfNuNCHV8AO1vGDhC5/2iurEGArCx46y07YLY7VIMoIiDSpk1kJlAgYIIoPh93c5O9+QuOowY5/Kfhq0675MooL88E/YidQfQn+3IKH9jrLVjHMjrzJCOdosKqR2ReA7NnO9l20X4KkYH322/mp0YNU0+3ynJlETUKTQOyC8hvNutRO5sBF/EKq2ljr1/B42bhgQcqb7BWSk6XLmZ+JrDR9Rr6P44r8bvyfG6+846pfyRSSZTrp2UwGOSqq67izTff5LPPPqNdWFvVI488kho1avDJJ59w7rnnAvDrr7+yZs0aunfvDkD37t2588472bhxI01CaeLz588nJSWFjh07lu0DEhERqeiGDIFrryVnhzONqDVrzEK9ejB4sFm2OnwNHmxq8eD80RuIVpT4s8+gf/+yeAQl46mn4JFH7NV/aQ5Emc7SvXvkxWV6OgwcCEQGlC7kVVps+dEE2D79FE44ocRPvdSlp9s1OGIFiF7FW6A8iI8jWAaY5/AsQq3Iq2zBnBiysswUxFCRb/75B+bOLfi4Al4r775rfvfsCVX+e80BA2DsWBJXmayLPBJNRltODmRlxQwQ1WWH/dn0P+6mmfVeDtU1kmpuyRLIyIjYPJ7xJBHgJBZE7NtJnb26q1QyvRuUQSSVSLlmEI0ePZoXX3yRl19+mXr16rF+/XrWr1/P7tB/qqmpqQwfPpwxY8bw6aefsnTpUoYNG0b37t3p1q0bAH369KFjx44MGjSI77//ng8//JDbbruN0aNHk5S0d2mBIiIiVVYoi8hdnPN67jcLVvaQZeBAuOYaezVqBpHll19K42xLTydvxyMr86ANq73jrICZW48eJrvK54sIEF3Lwybjqn37yluDyHp8xJ5iFs6dhfYLHZwdGRkm+FFdTJ8ODz1kuiI98UTBwSGfzzzXBbxWqsX0Mksoi8iqE5NHImzaZLcKD29lb9lNLft1WJcdzo4//ijd85XKoVMnqFUrYvMSjqY3n7AzrEPZA4yhQ6iTZzTRuuZZGrDVu8FV102koivXANHjjz9OZmYmJ5xwAs2bN7d/Xn3VKSr30EMPcdppp3Huuedy/PHH06xZM+bMmWPvT0xMZN68eSQmJtK9e3cGDhzI4MGDmRhKTxUREZEwQ4awJ9kU6+zC1zRgmzd7yOLzmSyiUAevmAEin88UJa6AfvgBunaF+fMxF5hjxsAVV8Abb5j5OiFWVkJz/nUOTk429ZjCWdlVwaCnZtFxfM6RfGsyripzlzfr8RE7gyhcDs4FkOfiHODX2BdZVc6oUUX7dw8G4fzz4x6TmQmff26Wq3yAKBCAmTPhxRdJ9JnJip4psMBGmkQ9dDv17LF23SIwddJEnn/eyeyLIrwT2fU8EHPsl3TnIH6Oub8+25yVtDST+idSSZRrgCgYDEb9GTp0qD0mOTmZqVOnsmXLFnbu3MmcOXMiagu1adOG9957j127dpGRkcH999+Pv7L+USYiIlIaAgHTvveqq+Cqq8htfyDgdHPh8MPhuuvMGHc9lMWL7WLVVoBoFgP4iYOcMcGgGVcBnXcefP11qG7LU0852R1PPWXa14fcyW0A/MF+zsGPPhr7wj1Uo6kGe0gMPYee7KGLLop+XGUxYAC0aVPoANGvHGgvewJECQkwenRJn13F5SpgHm41rTmbOXzBsd4dNWrErUH04YewZw906AD77luSJ1vBBAIwbpwpgP/RRyQGzfvTne24mYZ8Rbeoh7/DmUzHPPeeANGNN1beemBSMgIBu/5QLG9wHisouDxJIzbRna+owR7qhAfDQ+qw01m5777K+2WBVEsVoki1iIiIlLKFC+GSS+Cxx+CJJ9jzk8nqsANEn39u9l1yibcjmauftnu60XjGe2//00+LXHi3LHgalXaKPSXA8iGnmIXkZHOhGksoy8ZHkPGM5wqmcSZvV/7sIYvfDxdc4O3GE8dPrgsrT4bZiSfaGWjVxsMPQ2IiO6lNZ5ZxCu+znbqcz2ze4myO4wvv+DvvjNsFsNpML0tPh3vusVetII87g+hgVvAWZxd4U54A0cyZlbPLopSchQvtunHxbKceq2iDj2DMMXM53V72ZAq52JmoaWnm/1SRSqSS//UiIiIihZLonaZhfStvB4gs9et7iwp362aq4mZleQJE9fC2C+buuyPvswIUac52d61/+20T+NizhyAwi4s4jO/p6JoqcD33mYULL4Q334QzzoBYNQ0HDIBx47ht1V0miyohAdpWgewhS+/e+O67jwWcyDOM4GViX+jscLWKtjrCAfDJJ+bivDIW6y6MQMB0KbKCobm58O230KIFK9Y243s68z2wiGNZwtHRb6NevZg1iPLy4L33zHKVDxD16AGtW8MaUzTfCvJso749ZEOM+kPhPAGiylwPTEpG2P9/seSRyFTiZzzWwpmm9g9O9+0nuJymbGA3tdiPP81GZQ9JJaRXrIiISHXQsyekpkJmJnkk8BzDACIzRLZtM91erAv6JUvs4rDu6UYR3b7cfL4K172qJgG7MxfAu/TnYl4BTAeuo1jCN3ShJ6F2xDNnmp94QS53pzeoOtlDll69oF49Ttz+GduoX+gA0S53sde0NFi/3gRQqmLzkPR0uOCCqLv2uC4ed4UVwPU44QQTCYryulm82HR3r18fjjmmmOda0fn9Jtti8mSzGgpej+JJttCQm4kShI7BEyDq1y/m8yvVRM+ephPepk1xh+2mlve1E8Uh/Bh1ewvWcTrznA3KHpJKSlPMREREqgO/n+D9D3Aac/GTx0f0NZvDM4jCOyr16GG3iXa37k3D2y44z/0nRTAIGzeaC73ynGYW574X0MuzbmW9uL8dplmzgjMPQrWIgKpRe8gt1PEO8BTjjmYrDexld7CIjAzzHM2fXxpnWP5cHe3C5eIUQd9E49i3MXeumQIThTW97JRTPDXVq66xY+3nMt/1mXILk4t0M56L/Mce0xSz6s7vN9k8BdhOPVLIirk/jwT8rteWj3x72f5ywaLsIamkFCASERGpJnIvHsK7eOepRASIJk70/lEbajkNcBKf2JsTXH8Yv8TF1GM779LPe1t33lm+gQHXRbc1pW4DTQgCGXgLlkYNEHXtajIP4nF1/KpS2UOWwYOhbl2asT7usCxS7WVPgMhSVTuZuTragTdQ6g4QXc5T8W/nvPNMJfUMb+C12tQfsiQn2x0RPyZKB8FC8mRGtm2rKWZiahA1jhOoxQSIIv5PDHmbM0iwahM1MAHx1qyx96e4p12npJj3tEglpACRiIhINZG9JzJ44fljuF276BkwAwZA27Ykks9AXgC8nYUG8hK7qc1pvBt5bDkGBoK7nGBPPom8xZk0YwNX8Ljd1h5gMUfbU4A8AaK3346Z2eExcKBplVYVpxP4/XD33RzOd9zORP7DVHtXP97lfv4bcUhEgMjnq7qdzAIBk9qTlsYijqE+23iSkYD3PeJWL1qGwvbtJpg6dqy9afVqWL7clLY69dRSOfuKJxCI2hL82PDC3sC1PATApFAHQjdPgGiffQoO9ErVl5dnTwdtzrqoQ3ZQ15O5ZvmKrpzBXGfD1q0AZJMc/b6yssz0bJFKSAEiERGRamL37shtngup8Owhi98Pt99uFkMBpVgXvxFC2QDlIfDLSs/6rdwJwJOM8nRG6sZi1tMcgMaE1ajIjj+1CjABkC5dqm63rtBzMJFxjHYFiGqS421pH/JSeK2iqtzJLD3dBFAzMriIWeygHqN4EvBmELk1YGvs2zvjDHvx3VC89dhjoWHDEjvjis3VbWoCTrAs38ndsD3Af1lFG/t97eb5XPviC00xE/MaCNWhW0L0+njbqef5v8Hi6Wq2//72tOJowSQgcqq2SCWiAJGIiEh1EAiQ/UZkho+dQZSWZgJBser2NG/uGV/oANGyZUU+1ZJywpyrPes/cbC9HKsQaXhtJX7/vcTPq9IZMcIO8LgvvGuSQyM2RwzPIawY9YIFVfcC3VWDKPw1Fes9soY2PMAYdoYXrvb74fjj7dVqN70MPN2m9rU6QWGmgLovxj/lBBII0oY1+IB0unluxhMgSk2FTp1K7ZSlknC9V1vGyCDaSZ2oASJP184TTrCnFccMEMX6skWkElCASEREpDpIT2f36MjpQHaAyComHK2wdCBgX+BbF8HR/ogGvN/yN27sdLAqB4uXxP4zJ1aAKBnXuVblqVFFsWyZXWPHfeFdg1yasLHg49PSzHyp2bPLt2h5aXDVIAp/TcXKIAK4ngcin7s9e+C44+Ckk9h5Qn8WfGi6Bp4251Izx2zmzKr3/IXr2dPUbwkTntlxON7AczcWe9Y9AaLMTHj++ZI9T6l8wuqFWRqTwTU8DJigbnjQ5xGu5iB+cTY8+KDdnCBIWGakz1f1mhVItaMAkYiISHXQowfZjfaJ2BzR5n7ChMhsj4ULYdIkwAkofckxZJLCrxzgGZpDTWdl0ybzh3QFzB6J+c2v22WXmaK51V2PHtCmDWCyhiwtWBeZcRWS775wysiAoUNN/Y+q1M0sEICXX4avvoJ6kcVts4gMdLjtok7kxmXLYMECPl7oJ5Bfk3b8xUGLn4MPPjDPYQV8L5Uovx/uuQfwBqG3U8+TkWUH484/3952BdPs5YjPtYsvLoWTlUrH6jrp81Ez9GVAD76wXy8zGMozeKdFX82jzkrPnlC3rh1seoNzqc1OnuIysz8YrJrNCqRaUYBIRESkOvD7WX3xzZGb3Re11ref4bUTXNM+rPEfcgpH8Q0d8BahtrqB2Zo0MfV5KpiP6FvwoEceKf0TqQz8fjtA6L7w7sd7MQNEnkCh24oVJX565WbhQlOYfNo02L7dEzwDuIaCXz97YmTizQt1GzyNeU6oLTW1Qr6XSlwoGOnOzthIU0+AyM8ek6F4+OH2thP4zF4O/7fg5ZdL51ylcnFlEX3LEYzhAZ7mMvv/tX9pwT9EfpFis+Z9AgwYwAnt1pBJKpfxjLKHpMpQgEhERKSaGPhcr4htngBRrG8/XdM+3Bf+f7B/xO1FBIg2bqw0WSN3couzMmKEsofcBgyANm1IIN/etC9/0pAtUYcHwusQWVwX9JVeWAHz8ILd4RlC/aJ0+cskNWLbHhJ5JpSRcBquC9LMzGrVGel8ZrM/v9nrVzPFXk4kz2QoJiban1fuDoSeDCK/H0aNKv0TlsohlEV0MD/xANfTmM0xW9vvyx/OygknmOwhSyjY5Ley2ZQ9JFWEAkQiIiLVxI4dkZ2k7D+M43376ffDHXcA8Dj/iXsfZ/FW5Mb33ivqqZa5TnzPLUx2Njz6aOzB1VEoiyiVTBqwhTrsoDn/kkCQw/k2YnhbVnERr3g31qoFvSKDlJXWzz97Vuuw017OjVKg+jGu5CpXkANgHS0ixvVkYdRlWrWqHp2RapogdG12s5xD7c0zGWov21PM/v4bBg0CvM+/J0CkYK+4WVlELh9wSsSwi3nJu33u3Igx9pQ1UPaQVBkKEImIiFQTBx4Yuc0OEMX79jMQKHTmwtd0jWhHzdlnF+k8y8MR7iCHLiijGzAAf5t9WEsrMkizvznvyE8RQ7fRgFe5iO24vnHv1AnyohcHr3SysuD//s9ZpR6f0Ntedwc2LKlk8gjXsJE0jsK8n/5kX8+Y+fTmS46115PcU6UOPLB6ZCd06wb16gFhjz/E5256/+ij0KED+P20Y6U9xg4g+f3w0EOlfspSyQwYYKY/h3Qh8v+3S3iJ/axOeuHZQxZ3sEnZQ1JFKEAkIiJSHQQCNE2IXi8GMH8sx2pzn54OL7wAwL3cUOBdeYrzJifDyScX9WxLRGrk7J2oGrCFcbi+UVb2UHShLKI67KIWzvSqlxgY85AxPMhMBrOMzgQWL6s6RZanT4d3nSlj07nUs/su93TFkHpsxweksckOZqymjWfM2bwZ+z5nz977861MliyB7dtj7vZMB0pIgJYtYcgQWrPG3tyM9WZBwV6JxlUMHUwwKFwKWc7KsGGxOwgOHAhff23qkYlUAQoQiYiIVAfp6ez6eRUAXfg6cv/GjbE7jnXpYkdbOvFDgXflScvv0cMEl158MfpPKbY+twJEDWLUyQF4gDFsojFtWW02qHNZfKFaRAA0agTAObwRc/gzXMZQZnIEy6jDTg65sicXXwx3321mHv79d0TX6cph1CgTnAjJCys2/QbnRRxSwxXYsAIY1/IIPlfO3U5XxpWnJlbHjlC/fnHPunLo0QPato25284OAsjPNxfoF1xAoj+BjziZlxlAO1Ype0jiGzIEbr0VgNrsithdj+3esbGC2z6f+T/SFzmFW6QyUh6ciIhIddCjB7tr/Am5MJ7x9CesLpDPZ2opRKtxsmiRKZBLZCFegDN4m3as5BGuBeAhruNCXjM7P/7Y/MRzyy1w0EGQlARnnGF+l4A9oevx2uxiKw2jjunCEme6yimnwBNPlMh9V1lWR7PBg+GBB+D667lj023M4dyow69iCj/Qie85jG00YMUK08jsFVd5ogYNzOyzww4zvzt1goMPhtq1y+gx7Y3kZBNMfPJJAPJjfOc6kBcYz3hPfRyAJmz0rP9DC1qyzrPtWh52VhYtKv45VxZ+P9x+OwwfHnV3RAH01FQ49lgYPpyTQ/8egLKHJD6fD8aPh4ce4qBdP0fs9gSIKmg3TpHSoACRiIhIdbBrF7v8KZAbljpvCQbhgANg1y67Y5nN1ebe80dzyNucBcAUriZIAufxurPT5ys4ReSuu5zlcePg5ptLJEhkBYiaJ2zkn/zI1sXvcwrH8YWz4brrPFkhEsPAgabuy1FHQU4OB40cyeOMogkbOZc5nqFTuAYwLcv//m0XP/yWzA8/YP/8+its3Wo6xi901WP2+WD//Z2AkRU8atOmAn1R//DD8PTTkJ/PjdwXdUgtdrMvfzkbkpIgEIgIGFndAdPYSAamNkptqytXw4bV73XZpk3MXcHwYJzV3e3hh+HZZ80bX9lDUhh+P4wcSfLDD3Mhs3gVp8i05/+6jRtNkLZPn3I4SZGyVc3+txEREak+Pv0UFiwIrUyfzq7d5so6WpAHgA8+MLVVwvXsCY0bA9EziCyDeR4Iy6Yo6vyhCRNMkKgEpp1Z9ZCvyX+QjqxgFhd69p/Ch85KcnLV6rBVmtxTKtq3B2AUT3JOnPo5vv79aLV/Mv37m/jfK6+YTKIdO+Dbb2HGDBOfO+kk81ILBuG33+D112HsWDjzTJPgVr++SXL7z39MsteXX8YtV1O6rCyiOJrzr7NSty5MMV3Mwtvb7whNLWsTmur4Buc4O7dsif6+rMq6dSt8Clnr1uZFkZzsZB0pe0gK69RTATiZ+Z7NEf9PJnqnkYpUVcogEhERqYKyspx4x+7dkDxqFLuvM0EXd72FZFexYfx+U1slnN8P990Hw4bFDRDVCmU87KZW1P1/0p7pXMo1PEIDtpJJKo3ZHDnwnnvMH+OdOxdrypmVQdS1we+s2HoIQeAiXo0+eOBAE1FSF5qisYKHmzZF7OrKV87KrFlRD09OhsMPNz+WYBA2bMCTafTDD/DTT+Z1vWhR5Iyr9u2dbCPrp337MrimC2URHZD/K79xIFcxhUe52t49hJnO2EcfNa+zMWOov3Ob52a+4SgO5UdyqQGEBWITEkyR3Orks89MNiOwiGM4li9jjz3uOOd9O20adO8OgwaV/jlK5RcImABscjJ1sp2svhrkkITrS4q0NPNZJ1IN6K8gERGRKujvv53lXbsgoW4yWZhv1N0Bosa4Luzjfes+cCDccAN1N3kDRD7y7WUr2JRN9NuwWgYv51A20Zh0juFlBjCAKMEDa9rZhx/udVq/FSDyb90YOtc4nnnGdKE54YS9uq9qyxU8DLeQ0AVVz57RW0TH4PNBs2bmx/1Pn5NjpqSFB47WrYO//jI/b73ljK9dGw45xDtF7dBDTc2jEhPKIsp70kSiLuRVDmU5s7iI+7iB9lbr9dtuM4VufT545BFGjriKpRzJi5hAxuucxzBm2FPNapDr3Ed+PixeXL2mt/zyi73YlA3xx7rrhiUkmOdZpDDmzzeF98Ez7TOXmt7/Ly65RF8eSLWhV7qIiEgVtH69s5ydDXfc4axbmT7gChAVVLMjFAioPWwY1/EgW2jIBbxGX9c0rXgBoj9pby//H8exDXOVfjGv0IMvaMXfEccAxUoBsQNELZvBP6viD45VoFsKNnAg3HgjZGRQn61sowEN2UwSOWb/vHklcjc1a5oAz6GHejtKb9oEy5ebYNH335vfK1aYwOjXX5sft1atvJlGhx1m6h0V6fovEIB33jG/jz7aDhAlksdlPMNlPOOMrVvXTJu0iieddBK12c0LDCaBfJ5nCMHQ5ejPdATCAkTgzJesLkaPhhtugGCQmtbrKJqOHaFGjbI7L6laVqywF6N1MrOtX2/e6yXUQEGkIlOASEREpAr65x9nOTvbG/upzS4O5kdWcIgT4ClMzY5QFtGDm/4bdXd9tgGwmUYR+xbg1PcJv+C7gseZx+mRN2jNP9pLdoDof/+Fq8+PP3jiRH1DvLf8frj/fhgyhM84gdu4gzsx7aOLmj20Nxo3hhNPND+WPXvgjz8is41Wr4a1a83Pu+8645OSTOe08GlqaWkx7jQ9HS64wF7NZxUQ1oLd8sgj3tfW1c4UtO6k8zxDSCJAEEggj3wSI7qcUbNm4Z6MqiI5Gfr1g3ff9U71Aeqz1Vn56Sfzb6HMP9kbBx9sL7oziGqFB4tmzYLLL9frTKoF/SUkIiJSBbmnmO3e7d2XlJjH0rwj2U0t6pNZ+I4/saYTJSRAfj6tWQPAarwdiH7kYEbytL0efhG9iGN5iGtJJZNLec7ZkZ1tivNef33B5xYmGHSSLvznnQUPtDHRgWjatYOLLoq+Twrn4oth/HgOW/kDcznD2f7+++VyOn6/abTWoYMnjsO2bU62kfWzfDns3GmKZX/7rfd2mjVzpqdZPx06QM0uXUzr640mkJOHySBKcE25BExwbPBg77ahQ2HuXAA7+LGL2mSRQn7odjzFrWvUqJ71T2bNgnr1IrKp0shwVtq3V+af7L1aTr08d4DoWMKKnDVvrteZVBsKEImIiFQxy5bBLbc4608/7d3vGzGcpCefdKYAFaXjTyiLyFOU+MQT4ZNP7ADRGlp7DjknrPX5v7TwrG+jAWMwASpPgAjMFflecM/I8Sf7YdIkGDyY0TzGVK7kIa51Bih7qPj8ftOBzh0MGTnScwFWEdSvb2oaH3ecsy0/H1au9AaNvv8e/vzTzCxZv96UwrL4/XBQq3w6bXyAw/ieTvzAP+wDRMkgGjUq8rWV6nQws7LpPqKvCdaGuC9Wufzy6vn6rFsXTjiBep/9H7XZyS7qAODD1RlxwoTq+dxIyXAV2Xc3bLACvrbJk/U6k2rDFwwWtf9s1ZOVlUVqaiqZmZmkpKSU9+mIiIjstfnz49eyXbAATuyeDfXqmXk4fr/pE16UltDPP28KwY4bBw0bwjXXALCMzhzBMgD2kEhiKJsilW1khbX1jiWPBBKsC8B69UyHmb34wzw724lNZGVBvVp7YL/9yFu9ln9pzj6E5uC1a2f6qeuP/+LbswcOOMBEW1q0gFWrKnV9mB074McfI6epZWbGPuYHDuVQfnQ2zJ8PvXt7B+3ZA40aQVYW/+V+HiRyymbQXSK3GIXaK70dO6BePf6hhf2ePYKlLOUokz30669670rxzJgBw4axk9rUDQVm2/Mnf7Kf2Z+WZirh63UmlVxhYx4JZXhOIiIiUop27YILL4w/5sQTMcGg4cPNhqJkD1kGDTKVf8eNM9V9Q/Jdf1b8Yf1xTZSCu3F4vrkNr91SBFb9IQjdhN9kESWS7wSHQNlDJcnKIgK4555KHRwCk8DSrZtJhHrsMfj8c9i61cxUnPtWHnfUvosLeJX9+c0+xjPFLC0tes0Svx8efBCAs3gr/knUrg29esUfU5WFsohaso4XGMi+/MEMhpp9yh6SkjBwIDRuTB120ZDNAHTjK2f/fffpdSbVigJEIiIiVcSIEeYCNpZTTnGtTJtmvjmdOrXod+TzQZcu5rereK47uOPOgChKgOgTTgodVMP8UR4IxD8gBneAyG6ENmAAtG3r7FDtoZI3cKAJHrrbjFUhPh+0bg2nnZnIrVNb8CoX8SXHOPvd05/iXVgOGQJ160bWOgG2ubPtHn1UF6ehek0DeYk/2N9kaLVvr/eulAyrth7wNmcynnE8yeVmX1palf0sE4lFASIREZEq4pVX4u+3kjsAU1h6yBDzuzisGg54L45zcbJHihIgOpUPQjeQa+rZLFy4V6cVkUFkLUyc6OxQ9lDJcwcPq7pQ5kEtnCrwdu2ggi4s/X54+GESCDKEGZ5dqWSZhdq1IwtcV0ehLCIPZQ9JSQq9l3uwiHFMtKeaKXtIqiMFiERERKqJvaz3HJ/fD3fdBcAROC2gdlHbGcKeiMMATqEQHa4SEwseE4UVIPL5wmJgAwfC4sXmR98MS3GEMg/qsIvLeIrBzLQLtRfqwjKURTSDYYxjPACTuM3Zr+whx4cfmqAbKHtISp4ri8im7CGpphQgEhERqSIaNYq/v6ilhgptH6uDUz4dWQHAzlDHIYidQeTOvIg+oBZkZOzVNDO7xX349bXPB0cfbX6qQ5aLlK5Q5sFTXM5MhpqJlYW9sAxlEQGMZwJBfNzGnWafsoe8ataEBx4wy8oektIwcKAThARlD0m1pQCRiIhIFRAMOt2VQvGasuOqQ9SQLQBsplHEtnCeVt7R7N5t6galpxf5lKwMIv19L6UqWuZBUS4sQ1lEEaZO1Ys3XBWvbyXlzO+H++83y+PHK0Ar1ZYCRCIiIlVATo4TFLn00jK+827doI7JGGrBOgD+pbm9+wBXlye3RqGOMW5H8o2zkpBgppP06FHkU1KASMpMKIsIKPq0FL8fpkzxbmvQwNymeFWn+lZSPqwOnWPH6nUm1ZYCRCIiIlWAexbWpZdCUhKceWYZ3fmSJbDTZAM1518AVtGWnFCh6npsj3rYfvwRsc3T1Sk/f6+nkyhAJGXG73emPz3wQNFfdEOHmowFyyOP6IUrUh4UhBRB//uIiIhUAe4AUatWpt19cnLxm5QVSqdOJiIVCNgZRA8xhpe4hH9oSQ41ox52CD/yIX3oy0f2tjxcRanT0pxW90lJRTolBYikTA0aBAcdBEcdVfRjfT6TsXDqqWa9S5eSPTcREZFCUgaRiIhIFXD88c5yQoKp71xmX4JOn25HqFLJtDdvpCkbaEqA6MGdGuTSh/mebZ4AUUaGqUE0eTIrfwmwbl3hTmfnTjjkELOsAJGUieJmHqh4uoiIVAAKEImIiFRia9ZA587wyy/leBIdOtiL+WF/WgRIihkgSiQvYtueKMnNWyZMof1BSbRsaYpxF+SGG5zlbdsKHi8iIiIiChCJiIhUamPGwPffO+u33VYOJ5HoZP28ydmeXbup5Wl57xYeTIKwDKKQJThTbnJzCz6d775zlpWMISIiIlI4ChCJiIhUYlvCOsifcop3/YgjzO8TTyzFk3C1uW/Ges+uHdQlgzR7/Tg+t5ezSY64qee4lMUc7dn2T/J+9vKuXQWfjrsek1WLSERERETiU4BIRESkEgsPmNSq5V2fNw/uugtmzSrFk+jZExo1AuAB/uvZFR4gupF77eVoASKAbiz2rGdmOwGo3bvjn8qHH8K33zrrOTnxx4uIiIiIoQCRiIhIOXFnuuyt8Ayi8ABR8+Zw883QpEnx7ysmvx9GjAAgjU2eXdupxyYaA3AOb9Cfd+19h7IcgFlcSCLeVJ83OAeAKVzFGB6yt8fLCMrJicygEhEREZHCUYBIRESkHNx2m2lDP2zY3t9Gdjb8/rt3W3iAqMyMHx+14M8O6to1iB7lKnzAOprzIwfTir8BuJDXeIRrPMddzRQArgn9tsQLEM2cufenLyIiIlLdKUAkIiJSDu680/yeMcO0Zd8bJ5wQua3cAkTJyXb6TstQ4AdMBlFeqDNZDUyF6eas52B+MgOGDgXAH5ZBlEpm1LuJV6R67dq9OXERERERAQWIREREyt3eBIgyM2Hx4sjt5RYgArj0UgCWciSpbAPgZS62d4cHgUhLgwEDgMiW9ylkRb2LeBlEdaI3SxMRERGRQlCASEREpBy4awIVVHg5Gndre7dyDRCtXg1AUzZyMS8D8AXH2butDCLbPfdAr17QqFFE8KgOOwlGuYvcnbGrTrdtG7lt3rxCnbmIiIhItacAkYiISBkLBr3FpfcmQBTrmBo19u6cSkSoUDVAbSL70UdkELVoAW++CUcdFZFB5CPIKtpG3MbGhT/HvPvw5+S336B//4JPW0REREQIFQUQERGRMrNjh3eqVHir+sLIzo7c1qXL3p9TiVi2zF78iD4Ruz0ZRM2bm+5nF1wAQCIDPGOTyeZpLrPXm/Ev62nOyqQOMe/ePVVv+nTYf/+iPgARERGR6ksZRCIiImXsn3+860ceCWefDV99VfjbcAeInn0WxowxyTjlqkcPaNMGgC00jNidQL6zcvbZ0K0btGsHRC9S/Trn2ev1QzWNLr86ie3b4YgjTOM0Nysra8SI4nWHExEREamOFCASEREpY599Frntrbege/fC34YVIDrlFFMb+oEHoGXLkji7YvD7YexYAKZwdcRun3tl2jRYsgQmTAAii1S/zCX8zgH2+kra2cvTp5tkpdChts2bze/Gjff+IYiIiIhUVwoQiYiIlLF4hZPnzCncbVgBouTk4p9PiQkEYNUqAHqyMP7Y5s3NnLgaNaBx48j6RC7vcaongPRzjDJEmzaZ340aFeWkRURERAQUIBIRESlT27fDu+/G3n/uufD44wXfjhUgSkoqmfMqEenpMGkSYGoIxTVpEixaZNrcb9oU2eHMpQ2rORynvtGTTzr7gqFWZxkZ8NJLZlkZRCIiIiJFpwCRiIhIGfr774LH/Oc/pl19tELUFms6VcPIUj/lp0sXqF8fiN7FzKNtW0hMtFfjjW/Dap5iZNR9VmHqW291timDSERERKToFCASEREpQ4VtaZ+dDa+/Hnt/Rob5XaGyZZYsgW3bgLB6Q0ANcpyV1FTo2dP8hB5ALWI/MXXYRUd+pq5vZ8S+bdtMR7inn3a2VajnRERERKSSUIBIRESkDFkZLwc49Zfp3z/62BUrYt+OFSBKSyuZ8yoRPXqYzKAoMnCd6IMPmoLWeXl2m/tYGUTNWWcvnxd8LWJ/ZiYsX+7ddvDBRTttEREREVGASEREpEzt2GF+160L334LI0eaNvXR/P579O3BYAUNEPn9MHFi1F2pZJmFevVg8GCznJ5uupkRO0CURYq9nFM3cj5dZibkuJKTbrwRUlIihomIiIhIARQgEhERKUNr1pjfublw+OGm4HLTptHHhmZreaxcadrZf/65Wa9Q06kCAdOVLCxq5ZledsklJnMITMZRO9O+PlaAaCd17eWXd5wZsX/zZnO3lvz8vTx3ERERkWpOASIREZEy8sMPMGqUWQ6fFhXNJ5/A2rXebddfD//+66ynppbc+RVberrpSmalN4XUdAeInnjCjAOTcTRhAuANEO3Pb5G33b49p/V3oj+HHGJ+b9pksogsChCJiIiI7B0FiERERMrIgw86y+ee6933+edmetS6ddCxo7N9yRLvuF1hiTYVajqVKyPIzdPyvn17M84yYAC0a+cJEM3hnMjbnjCBwzo7f7Ycfrj5rQCRiIiISMko1wDR559/zumnn06LFi3w+Xy89dZbnv3BYJCxY8fSvHlzatWqRe/evfk9rCDDli1buOSSS0hJSaF+/foMHz6cHVaBBxERkQoiGISZM53155/37j/uOLjnHmjeHPr1c7aHZxoFg971CtXm3pUR5JaEaw7YhAlmXNgxNdjD2czheBZyED9Tz6pZBCaodNFF1HVmm9mz2G680TsVTwEiERERkb1TrgGinTt3cthhhzF16tSo+++9916mTJnCmOrblQAAKgBJREFUE088weLFi6lTpw59+/YlO9v5JvKSSy5hxYoVzJ8/n3nz5vH5558zcuTIsnoIIiIihfL9987ysGFQu3bssf/7n7M8frx3X5063vUGDYp9aiUrlBHk1pJ/zEIo0BPrmDmcy0JOIJF85nI6tdjF44yyg0qjR5sSRm++CV27Ood/8YWzrACRiIiIyN7xBYPh30WWD5/Px5tvvslZZ50FmOyhFi1a8N///pfrr78egMzMTJo2bcqMGTO46KKL+Pnnn+nYsSNLlizhqKOOAuCDDz6gX79+/P3337Ro0aJQ952VlUVqaiqZmZmkVKhcfRERqSrefRdOO80kzOTmFjy+ZUsz3QxMTeeE0Fc6Pp8zplEjM8WqwnnhBRg8mHn05y5uYQZDOYDfzfaBA+Me47aHRPzt28Cvv3qzjjCBoMREs7z//k7Ht9WroXXrkn5AIiIiIpVXYWMeFbYG0cqVK1m/fj29e/e2t6WmptK1a1fSQ8Ut09PTqV+/vh0cAujduzcJCQksXrw45m0HAgGysrI8PyIiIqVl2zZ4+WWzfPLJhTumWzdn+YcfIveffLLTEa3CCWUEnca7fMmxHOD7I3b2UNgxALRtC8OH4ycvckpaSEICnBMqVWQFh26/XcEhERERkb1VYQNE69evB6BpWO/fpk2b2vvWr19PkyZNPPv9fj8NGza0x0QzefJkUlNT7Z9WrVqV8NmLiIgY111npoFZASJ3QeV4Hn3UWb73XnjtNdi509k2fXr8aWrlKrwWUTAYM9AT9ZhJk+Dpp+Hrr82cshj22ce73qhRMc5ZREREpJqrsAGi0nTzzTeTmZlp/6wN7yEsIiJCZEHootq1Cx5+eO+Odc+SfuUVuPBCGDPG2dayZbFOrfS5M4IKyh6yDBzoBIV8PujSxTunLkz4c5CRUYzzFREREanmKmyAqFmzZgBs2LDBs33Dhg32vmbNmrFx40bP/j179rBlyxZ7TDRJSUmkpKR4fkRERNwWLzYdwh5/3Kw/8QSMHVu02wj7LwqAp54q/PHubvDhx8aJm1QM7oyggrKHLIUICrk1buxd37WriOcoIiIiIrYKGyBq164dzZo145NPPrG3ZWVlsXjxYrp37w5A9+7d2bZtG0uXLrXHLFiwgPz8fLq625uIiIgUwb//mhpA27bBf/5jMomuuMLMfLr88sLdRl5eRDMvFi6Egw8u/HlEqz1UqbgzgkrB5s3e9UmTSuVuRERERKqFQnydV3p27NjBH3/8Ya+vXLmS7777joYNG9K6dWuuvfZa7rjjDvbff3/atWvH7bffTosWLexOZwcddBCnnHIKl112GU888QS5ublceeWVXHTRRYXuYCYiIhIu/L+Q7dud5aeegiefLPg2PvzQu/7HH7DvvkU7j1g9FI48smi3U26sjKBS0rOns3zVVVCnTqndlYiIiEiVV64ZRN988w2HH344hx9+OABjxozh8MMPZ2woh//GG2/kqquuYuTIkXTp0oUdO3bwwQcfkJycbN/GSy+9RIcOHTjppJPo168fPXr04Kmi5O+LiEiV8u230LUrLFiwd8d/8EHktlGjin477vvv0qXowaF4+vYtuduqzNyBMncBbxEREREpOl8wWNwSnJVfVlYWqampZGZmqh6RiEgl5y5fk5dn2qEDrFtnyuBMnWraw+/eDYceCrfc4j1m1KiCM4QK+p/zzz9hv/3MctOm8N13EKc0XkyLF3vb3Vt27FC2jMX6t7v/fvjvf8v3XEREREQqosLGPMp1ipmIiEhJWrfOuz5gALz6qgmoROv69eqr0KQJXHaZs239+uKfx/nnO8snn7x3wSEwmVC33x5ZW0fBIceTT8IXX8Do0eV9JiIiIiKVW4UtUi0iIlIUwWBkEOi110wW0UEHxT5u5EinhnJ+Prz9tll+/3048MCin8eGDfD99856w4ZFvw237Gzv+n33Fe/2qpqRI+H558E1+1xERERE9oICRCIiUiXMmxd9+/Ll8Pff8Y99+WXTIr1TJ2fbSSfB008X/TzuvtsEmgD69YNbby36bbgNHQo1a8INN5jHMmZM8W5PRERERCQaBYhERKRK+O676NtDfRAKVKcOrFjhrNeoAccdBzffHDn2mmui38bXX8PDD5vlnj3h3XfNFLbi6NjRFGC+5x445BCnppKIiIiISEnSn5kiIlIl/PGHs1xQxpDl5Zejb3cXO77pJhPkOfhgZ9uUKZCTE3lc167O8qJFhTuHwvD7vYW0RURERERKmgJEIiJSJbz/vvn9wQfRC1IffbTpCvb995CWZrqXDRgA//lP5Nj773eWU1Jg9Wr49lvvmE2bvOvWtDKLOmqJiIiISGWiLmYiIlLpbdoEGRlm2WoLv3ChmeZleeEFOOAAs7xhg5ORc9NNpvvZW2+Z9alTI2/fKoC8eLGTJdSypbfd/S+/OMsvvwznnFOshyQiIiIiUqYUIBIRkUopJ8cUhB43DkaMMNtatjQZPwCpqd7xbdo4y+7pWq1awZtvmuVdu6B27dj3efTRsfdZgSkwmUkiIiIiIpWJAkQiIlIpdegAK1ea5WeeMb+7dHGCP+5izm+/DUlJBd9mvOBQQbZv3/tjRURERETKmwJEIiJS6axb5wSH3M4/31k+5BAYNgzat4czzii5+37/fTj1VO+21aud5SuuKLn7EhEREREpKwoQiYhIpbN2bfTt7ilgPh9Mn17y992unbOcmGjOZcMGs966NUybVvL3KSIiIiJS2tTFTEREKo2dO+HHH02nsmhatSr9c2jUyFnOzzd1j77+2qzXrVv69y8iIiIiUhqUQSQiIhVGMGiycf78E/76K/L3+vXe8eeeC7NnQ3o61K9fuDpDxdW4ceS2q64yv93ZRSIiIiIilYkCRCIiUqYCAVi1KnoA6K+/TCexeBo0MHWF9t/ftKj3+eCYY8rk1G3168O2bZHbO3cu2/MQERERESkpChCJiEiJCgZh69bYWUBr15oxsSQkmKli++5rAkHhvxs0KLvHEktGBlxwAbz5pne71UFNRERERKSyUYBIRESKbM8eE+iJFgD680/IzIx/fJ06sQNAbdpAzZpl8zj2lt8PL7wQWXNo2LDyOR8RERERkeJSgEhEpIrKyYHvvoMjjzTdtopq+/bYAaDVq02QKJ4WLaIHgPbdF9LSKn+2TZ06pjj133/DOeeYbfvsU77nJCIiIiKytxQgEhGposaMgalT4brr4MEHY4/btg3efhv++MMbCMrIiH/7SUmmKHO0AFDbtlC7dkk+moqpSxfzM3OmCcJV9MwnEREREZFYfMFgvEoQ1UNWVhapqalkZmaSkpJS3qcjIlIi3Bk6c+fCaadFH3fSSbBgQfR9jRvHngrWooWpFyQiIiIiIhVXYWMeyiASEamCwjuBnX66qZdz//2wfDl06gSXXgqXXOIEhy680ExHcweBFDMXEREREakelEGEMohEpOr59Vfo0CH+mLZtTbt5y9atpn27iIiIiIhUHYWNeWhygIhIFbRmTcFj3MGhceMUHBIRERERqc40xUxEpAp6/PHCj33rLTjzzFI7FRERERERqQSUQSQiUgUVpYOYgkMiIiIiIqIAkYhIFfTXX+b3Pfd4tycledfHjy+T0xERERERkQpOASIRkSooPd38PvFE+O03uP122LwZsrO9tYdWriyX0xMRERERkQpGASIRkSrm88+d5VatYP/9YeJEaNjQbGvTBs44wyzfdFPZn5+IiIiIiFQ8KlItIlJJBYPg80Vuf/BBZ7lZs+jHvv126ZyTiIiIiIhUTsogEhGphO67DxISTIBo/Xpne14efPCBWb7zzvI5NxERERERqXwUIBIRqWRyc+HGG5315s1NoOi112CffSAQMNuvvrp8zk9ERERERCofBYhERCqBdesgP98sP/dc9DEXXuhkEzVoAHXrls25iYiIiIhI5acAkYhIBffuu9CyJSQmwgEHwOWXO/vGjo1+zDfflM25iYiIiIhI1aAAkYhIBbRlC9SrZ6aOnXaas/33353lyZNhwgRYscJ77NSp0L592ZyniIiIiIhUDepiJiJSwfz8M3TsWPA4q0V9x46wejUsXgznnGMyjURERERERIpCASIRkQrk+++hc+eCx3Xo4F1v3dr8iIiIiIiI7A1NMRMRqSCCwejBoeefN/tWrXK2vfBCWZ2ViIiIiIhUB8ogEhEpZ3v2wMKFsHu3d/vQofDEE5CUZNbbtDHTyFavhqOOKvPTFBERERGRKkwBIimySZPgt99Mgdx99invsxGp3LZsgUaNIrd//jkcd1zk9qOPNj8iIiIiIiIlSVPMpNBefNF0VBo71iy3agVvvgnXXWcyIESk6KIFWT/4IHpwSEREREREpLQoQFTN5eXBRRdBv34mYyE312zPzobHHoONG836yy/DoEGRx59zDjz8MNSoAZs2ldlpi1QawWDsffn5kdPK+vWDPn1K95xERERERETCKUBUDaWnw1lnwV9/wbx58Oqr8P770LMn1Kxp9teqBVddBU2bmqyhSy5xju/aNfrtpqXBzp0le67BILzzDnz7bcnerkhpCQZh+3a4917z3klIgPfeiz720Ued5bVrzbHvvmuOExERERERKUsKEFVDxx4Lb78NBx9sAkXhjjkm/vHz5pnMhy5dIvfVrQuvvWYudB95xFzoLly4d+f57bfm4vrMM+HII81tffDB3t1WWZs7F379tbzPQsrKJ5/Ahx+aTLuEBEhJgf/9z9nfv7/JzsvJgZtuMsWmZ8+GcePM/vHjVc9LRERERETKly8YjDcBonrIysoiNTWVzMxMUlJSyvt0StU118CUKUU/7rDDTFvtffeF2rUj99erBzt2xD4+P7/wWRE33QT33BN7/5w5cPbZhbut0pabC59+aoJudeqYbR99BH37muWrrzaBsr2xa5e57RNPjP6cS/nLyXE6jBXHjz+agK2IiIiIiEhJK2zMQxlE1Uys4NBjj0GvXt5tOTkmEygYhO++g0MPjR2oeOqp+PebkGBuI1wwaIIgu3aZ9XfeiR8cAlP3qKJk50yebIJBdeua4FuzZk5wCMzz/e+/0Y995RUzle+AA2Dbtsj9w4bBaafBhReax9u3r5net2VLqTyUIvv9d1Oj6txz4ZBD4JdfyvuMTO2sl16CjIzSv6/cXDMFM54XXyz4ds48U8EhEREREREpfwoQVTF79kQvipudHT2Dp2ZNuPJKGDLEBCIsEyaYwtOFNWCAKbabkWFqGUVz+OFOcGPzZhNMSUgwgak6deCgg8zFstsLL5jMpGDQG4Tq0CF+xlJZuftuZ3nKFNiwIXJMixbmsTZsCKtWmW3PPQcXX2z+XX7/3QSCAgG48Ubz7/TGG2aqHpgpfR06mMykr782LdH//LPUH1pc69aZwFbTpiaja8UKuPbayHHTpsFdd8Uv1FwSduwwz1utWjBwIJxyirNvwQK44w7z/Lo98YQ5ZuhQuO22yADXihXRg3tZWfDWWyazyx3YmzkTli412/7+2xx7ySXmNT9ggHmvAVx+uQkuPfYY/PyzuS0REREREZHypilmVP4pZhkZ5mL3++9NYAFMUdxffoErrjBTYBJcocC6dc3F/O7dcN99TuAoGDS1idq1M1PKSsIZZ5h6PG6//WaCC/E88wwMH+7dtmePN2h1440m2ygQMJlH/fo507xK0u7dMGIErFljgjS1ajn72reHlSujH3fxxaYmTbi//jLn/eSTxTuvgt65U6ea4J+laVOTxdWsWfHu95VXzGOLZsgQmDHDLN93n/k3ApN5Nneuee62boXMTOjc2QQFS8Jrr3kDnLE0bAi33moyntyZXuGaNYP1681yzZomS6pWLRP8mTPHOzY11QSE6tbd69MXEREREREpNYWNeShAROUPEK1caQIVhbVtm7moLQvZ2aa+ytNPFzwNzXLTTWbqVjTBIEyfbgI2NWuaQNe99zr7P/kEjjvOG0j65x/TIapbt717DL16mWlwlo0bTce2zZuhcePox5x9tgmUlObz/Oqr8PHH5rk95hg49VS44QYTEFy6FI46KvKYww5zpvrt2QPPPw+tWsHJJxd8fw88ANdfX/C4l17ydr2Lp0sX00Fv7VoTMIonEICJE022VffuZtvkyXDLLYW7r9LQtSt8+aU3ACsiIiIiIlKRKEBUBJU9QASFLwC9Y0fpZNkURvg5Dh1qpl81aWICCjVqmOBGcnL82/n7bxPUiGfJEjMV6KSTnG3XX2+yWop73mACVTff7Ewxu+kmE5zJyzPZMe3bO0GDlStNEe+0tMjbeestU1/orbcgPd1sO+EE+Owzs/zf/5ogxLRpZgpTVpapBRXP0qWm61s8Bx5opsNZU6S+/x46dYoct2ePmfb3xx8m88vt/PNNRs4LL5jgzpdfRr+vNm1g9er452N5/XVTY8r9nM+fb7rtWXWqwGQM1aoFp5/uPf76683Urh9/NI8JzPP+889w6aUmgLhzpzP+jTegdWvzmnM/rw89ZIJ/qakma8zvN/+GNWqYzLz+/c1zWNDzLCIiIiIiUt4UICqCqhAgeustp7PXLbeYoESXLjBmjDOmKJ3ESsOGDSZgs2KFae89fvze3U4wuPcZG7m55mI/mrVrzZSonj1h1Kj495WR4Q34FOZdlJ9vajtNnGjW69c305iK0gWrqI995kwYPNjcd2Ji/LErV0Lbts76li2m3lG4WrXMcxW+b9gwZ3qZpXlzU6/opZdMbSDL/febH2sal9uVV5p6Tm+/bWoDrVgR/7zdwrPjNm+GBg28z9kvv5haUF27mn0iIiIiIiJVmQJERVAVAkTxZGSYi/mqNA3m4YfhuuvM8tChMHq0mW4UXh8m3AknmOyQQADefddk/lid2U4/3RSEtqxaZfY1aWLWd+4009SWL/fe5pQpcNVVhT/3334zt7vPPoU/xs1d28dy0UUwa5Z32+DBJkBkCZ+Oddpppvj1/fc723r0MJlE06ZFv++jj4avvooeaMzLM8WhP/7Y/PtcfXXBAclvvzVFpLdsgR9+MP8msXTvboJTCxZ4t++/v8nCKs/gp4iIiIiISEWlAFERVPUAUXWzcaPTfvzuu+F//yt+8KBjRyfzycoAsmzfXrYFiufONcW/ATZt8mbyfPedCcwcd5w5z4KyhgAmTYKxY2Pv79vXBJHmzIGRI4tf5Dqeli1NxpHbyJEmAGhNgZswwck+O/10U6BcREREREREolOAqAgUIKr6fvoJDj54748fOtS0pt+xw9QXysgw29PSTECqLOXlmcynunVNwKokrF9vClX/+KOzrVYtkwk0YkTZZZ/ddhvceadZ9vtNzSV31zjLnj0mU6lnz+j7RURERERExFCAqAgUIKoe3nvPdPxq0sRkwZx0kmmNvnWrmTq2777wyCMm8PL99yY7Ze1ac+yKFSaLCEwdoJkzTXbSnDnO9qpgzRpT4LtPH1OUuawFAqZgtN9vim1HK5wtIiIiIiIihacAUREoQCRScWzYYH5b0wRFRERERERk7xU25hGjn5OISPlQYEhERERERKTsVaG+ViIiIiIiIiIisjcUIBIRERERERERqeYUIBIRERERERERqeaqTIBo6tSptG3bluTkZLp27crXX39d3qckIiIiIiIiIlIpVIkA0auvvsqYMWMYN24c3377LYcddhh9+/Zl48aN5X1qIiIiIiIiIiIVXpVoc9+1a1e6dOnCY489BkB+fj6tWrXiqquu4qabbooYHwgECAQC9npWVhatWrVSm3sRERERERERqVIK2+a+0mcQ5eTksHTpUnr37m1vS0hIoHfv3qSnp0c9ZvLkyaSmpto/rVq1KqvTFRERERERERGpcCp9gGjTpk3k5eXRtGlTz/amTZuyfv36qMfcfPPNZGZm2j9r164ti1MVEREREREREamQ/OV9AuUhKSmJpKSk8j4NEREREREREZEKodJnEDVu3JjExEQ2bNjg2b5hwwaaNWtWTmclIiIiIiIiIlJ5VPoAUc2aNTnyyCP55JNP7G35+fl88skndO/evRzPTERERERERESkcqgSU8zGjBnDkCFDOOqoozj66KN5+OGH2blzJ8OGDSvvUxMRERERERERqfCqRIDowgsvJCMjg7Fjx7J+/Xo6d+7MBx98EFG4WkREREREREREIvmCwWCwvE+ivGVlZZGamkpmZiYpKSnlfToiIiIiIiIiIiWisDGPSl+DSEREREREREREikcBIhERERERERGRak4BIhERERERERGRak4BIhERERERERGRak4BIhERERERERGRak4BIhERERERERGRak4BIhERERERERGRas5f3idQEQSDQQCysrLK+UxEREREREREREqOFeuwYh+xKEAEbN++HYBWrVqV85mIiIiIiIiIiJS87du3k5qaGnO/L1hQCKkayM/PZ926ddSrVw+fz1dit5uVlUWrVq1Yu3YtKSkpJXa7Uj3o9SPFodePFIdeP1Jceg1Jcej1I8Wh148UR1V9/QSDQbZv306LFi1ISIhdaUgZREBCQgL77LNPqd1+SkpKlXpxSdnS60eKQ68fKQ69fqS49BqS4tDrR4pDrx8pjqr4+omXOWRRkWoRERERERERkWpOASIRERERERERkWpOAaJSlJSUxLhx40hKSirvU5FKSK8fKQ69fqQ49PqR4tJrSIpDrx8pDr1+pDiq++tHRapFRERERERERKo5ZRCJiIiIiIiIiFRzChCJiIiIiIiIiFRzChCJiIiIiIiIiFRzChCJiIiIiIiIiFRzChDF8fnnn3P66afTokULfD4fb731lmf/hg0bGDp0KC1atKB27dqccsop/P77754xf/75J2effTZpaWmkpKRwwQUXsGHDhoj7evfdd+natSu1atWiQYMGnHXWWaX4yKQsTJ48mS5dulCvXj2aNGnCWWedxa+//uoZk52dzejRo2nUqBF169bl3HPPjXh9rFmzhv79+1O7dm2aNGnCDTfcwJ49ezxjPvvsM4444giSkpLYb7/9mDFjRmk/PCkDZfkasixatAi/30/nzp1L62FJGSnL189LL73EYYcdRu3atWnevDmXXnopmzdvLvXHKKWnpF4/V199NUceeSRJSUlRP1c+++wzzjzzTJo3b06dOnXo3LkzL730Umk+NCkDZfX6AQgGg9x///0ccMABJCUl0bJlS+68887SemhSRkriNfT9998zYMAAWrVqRa1atTjooIN45JFHIu5Lf0dXPWX5+rFUlb+hFSCKY+fOnRx22GFMnTo1Yl8wGOSss87ir7/+4u2332bZsmW0adOG3r17s3PnTvv4Pn364PP5WLBgAYsWLSInJ4fTTz+d/Px8+7beeOMNBg0axLBhw/j+++9ZtGgRF198cZk9TikdCxcuZPTo0Xz11VfMnz+f3Nxc+vTpY78+AK677jrmzp3L7NmzWbhwIevWreOcc86x9+fl5dG/f39ycnL48ssvmTlzJjNmzGDs2LH2mJUrV9K/f39OPPFEvvvuO6699lpGjBjBhx9+WKaPV0peWb2GLNu2bWPw4MGcdNJJZfL4pHSV1etn0aJFDB48mOHDh7NixQpmz57N119/zWWXXVamj1dKVkm8fiyXXnopF154YdT7+fLLL+nUqRNvvPEGP/zwA8OGDWPw4MHMmzev1B6blL6yev0AXHPNNTzzzDPcf//9/PLLL7zzzjscffTRpfK4pOyUxGto6dKlNGnShBdffJEVK1Zw6623cvPNN/PYY4/ZY/R3dNVUVq8fS5X6GzoohQIE33zzTXv9119/DQLBH3/80d6Wl5cXTEtLCz799NPBYDAY/PDDD4MJCQnBzMxMe8y2bduCPp8vOH/+/GAwGAzm5uYGW7ZsGXzmmWfK5oFIudm4cWMQCC5cuDAYDJrXQo0aNYKzZ8/+//buPabK+o8D+JvDAeQSIHkEwrhtQSlKgJOQkWKIoJhlNVo2LrMZEhsWqFEsbMsYNahoXbaWkOVm4EwnU7rAoUaxJQiTaxKEpOOSGSBhXA6f3x+OZ5701wrPOcg579d2/vB5Pny/34/77DlfPj4+jxLT3t4uAKSurk5ERE6ePCkqlUr6+/uVmA8++ECcnZ1lfHxcRET27Nkjy5Yt05srMTFRNmzYYOyUyMSMVUMzEhMTJTc3V/Ly8iQ4ONj4CZFJGat+3nzzTfH399ebq7i4WLy8vIydEpnQbOrnev/lurJx40ZJTU01yLrp9mCs+mlraxO1Wi0dHR1GWzvdHm61hmakp6dLdHS08mfuoy2DsepnhjntoXkH0SyNj48DABYsWKAcU6lUsLOzQ21trRJjZWUFOzs7JWbBggVQqVRKzJkzZ3Dx4kWoVCqEhITA09MT8fHxaGlpMWE2ZArDw8MAADc3NwDXutKTk5OIiYlRYu699154e3ujrq4OAFBXV4fly5fD3d1didmwYQNGRkbQ2tqqxFw/xkzMzBhkPoxVQwBQUlKC7u5u5OXlmSIVmgPGqp+IiAj8+uuvOHnyJEQEAwMDOHLkCDZu3Giq1MgEZlM/tzLXzDxkHoxVPydOnIC/vz8qKirg5+cHX19fPPPMM7h8+bJhE6A5Z6ga+vv1hftoy2Cs+gHMbw/NBtEszRRQTk4O/vjjD0xMTKCgoAAXLlxAX18fAOCBBx6Ao6Mj9u7di7GxMfz555/Izs6GTqdTYrq7uwEA+/btQ25uLioqKrBw4UKsXbuWX25mZHp6Grt27UJkZCSCgoIAAP39/bC1tYWrq6terLu7O/r7+5WY638xmzk/c+6fYkZGRnD16lVjpENzwJg11NnZiRdffBGfffYZ1Gq1kTOhuWDM+omMjMShQ4eQmJgIW1tbeHh4wMXF5ab/PZvmp9nWz2yUlZXh9OnTSE1NvZUl023EmPXT3d2N8+fPo7y8HAcPHkRpaSkaGhrw+OOPGzIFmmOGqqEffvgBn3/+OXbs2KEc4z7a/BmzfsxxD80G0SzZ2Njg6NGjOHfuHNzc3ODg4ACtVov4+HioVNf+WjUaDcrLy3HixAk4OTnBxcUFQ0NDCA0NVWJmnkX08ssv47HHHkNYWBhKSkpgZWWF8vLyOcuPDOu5555DS0sLDh8+PNdLoXnKWDWk0+nw1FNP4dVXX0VAQIBBx6bbhzGvQW1tbcjMzMQrr7yChoYGVFZWoqenB2lpaQafi+aGqb7DtFotUlNT8dFHH2HZsmVGnYtMx5j1Mz09jfHxcRw8eBBRUVFYu3YtPv74Y2i12hseSEvzlyFqqKWlBVu2bEFeXh5iY2MNuDq63Rmrfsx1D20eba45EhYWhqamJgwPD2NiYgIajQbh4eFYuXKlEhMbG4uuri5cunQJarUarq6u8PDwgL+/PwDA09MTALB06VLlZ+zs7ODv74/e3l7TJkRGkZGRgYqKCnz33XdYsmSJctzDwwMTExMYGhrS614PDAzAw8NDifnxxx/1xpt5uv71MX9/68fAwACcnZ1hb29vjJTIxIxZQ1euXEF9fT0aGxuRkZEB4NqGW0SgVqvx1VdfYd26dUbOkIzJ2Neg/Px8REZGYvfu3QCAFStWwNHREVFRUXjttdeU7zman26lfv6Lb7/9Fps3b8Zbb72FpKQkQyydbgPGrh9PT0+o1Wq9X87uu+8+ANfewBgYGHjrSdCcMkQNtbW14aGHHsKOHTuQm5urd477aPNmzPox1z007yAyABcXF2g0GnR2dqK+vh5btmy5IWbRokVwdXVFdXU1BgcH8fDDDwOA8urO6/+VY3JyEj09PfDx8TFZDmR4IoKMjAx88cUXqK6uhp+fn975sLAw2NjYoKqqSjn2008/obe3FxEREQCuPdujubkZg4ODSszXX38NZ2dnpakYERGhN8ZMzMwYNH+ZooacnZ3R3NyMpqYm5ZOWlobAwEA0NTUhPDzcNMmSwZnqGjQ2NqbcFTvD2tpaWQPNT4aon3+rpqYGmzZtQkFBgd6t+zR/map+IiMjMTU1ha6uLuXYuXPnAID76HnOUDXU2tqK6OhoJCcnY//+/TfMw320eTJF/ZjtHnqOHo49L1y5ckUaGxulsbFRAEhRUZE0NjbK+fPnRUSkrKxMtFqtdHV1ybFjx8THx0e2bt2qN8aBAwekrq5Ofv75Z/n000/Fzc1NXnjhBb2YzMxM8fLyki+//FI6Ojpk+/btsnjxYrl8+bLJciXD27lzp7i4uEhNTY309fUpn7GxMSUmLS1NvL29pbq6Wurr6yUiIkIiIiKU81NTUxIUFCSxsbHS1NQklZWVotFoJCcnR4np7u4WBwcH2b17t7S3t8t7770n1tbWUllZadJ8yfBMVUN/Zw5vYCDT1U9JSYmo1Wp5//33paurS2pra2XlypWyatUqk+ZLhmWI+hER6ezslMbGRnn22WclICBA2VfNvAWvurpaHBwcJCcnR2+e33//3aT5kmGZqn50Op2EhobKgw8+KGfOnJH6+noJDw+X9evXmzRfMjxD1FBzc7NoNBp5+umn9cYYHBxUYriPNk+mqp+/M4c9NBtE/0Cr1QqAGz7JyckiIvLOO+/IkiVLxMbGRry9vSU3N/eG10bv3btX3N3dxcbGRu655x4pLCyU6elpvZiJiQnJysqSxYsXyx133CExMTHS0tJiqjTJSG5WOwCkpKREibl69aqkp6fLwoULxcHBQR599FHp6+vTG6enp0fi4+PF3t5eFi1aJFlZWTI5OakXo9Vq5f777xdbW1vx9/fXm4PmL1PW0PXM4cuNTFs/xcXFsnTpUrG3txdPT0/Ztm2bXLhwwRRpkpEYqn7WrFlz03F++eUXERFJTk6+6fk1a9aYLlkyOFPVj4jIxYsXZevWreLk5CTu7u6SkpLCBqMZMEQN5eXl3XQMHx8fvbm4jzY/pqyf65nDHtpKhPd/ExERERERERFZMj6DiIiIiIiIiIjIwrFBRERERERERERk4dggIiIiIiIiIiKycGwQERERERERERFZODaIiIiIiIiIiIgsHBtEREREREREREQWjg0iIiIiIiIiIiILxwYREREREREREZGFY4OIiIiIiIiIiMjCsUFERERENAspKSmwsrKClZUVbGxs4O7ujvXr1+PAgQOYnp7+1+OUlpbC1dXVeAslIiIi+hfYICIiIiKapbi4OPT19aGnpwenTp1CdHQ0MjMzkZCQgKmpqbleHhEREdG/xgYRERER0SzZ2dnBw8MDXl5eCA0NxUsvvYTjx4/j1KlTKC0tBQAUFRVh+fLlcHR0xN1334309HSMjo4CAGpqapCamorh4WHlbqR9+/YBAMbHx5GdnQ0vLy84OjoiPDwcNTU1c5MoERERmT02iIiIiIgMaN26dQgODsbRo0cBACqVCsXFxWhtbcUnn3yC6upq7NmzBwCwevVqvP3223B2dkZfXx/6+vqQnZ0NAMjIyEBdXR0OHz6Ms2fP4oknnkBcXBw6OzvnLDciIiIyX1YiInO9CCIiIqL5JiUlBUNDQzh27NgN55588kmcPXsWbW1tN5w7cuQI0tLScOnSJQDXnkG0a9cuDA0NKTG9vb3w9/dHb28v7rrrLuV4TEwMVq1ahddff93g+RAREZFlU8/1AoiIiIjMjYjAysoKAPDNN98gPz8fHR0dGBkZwdTUFP766y+MjY3BwcHhpj/f3NwMnU6HgIAAvePj4+O48847jb5+IiIisjxsEBEREREZWHt7O/z8/NDT04OEhATs3LkT+/fvh5ubG2pra7F9+3ZMTEz83wbR6OgorK2t0dDQAGtra71zTk5OpkiBiIiILAwbREREREQGVF1djebmZjz//PNoaGjA9PQ0CgsLoVJde/RjWVmZXrytrS10Op3esZCQEOh0OgwODiIqKspkayciIiLLxQYRERER0SyNj4+jv78fOp0OAwMDqKysRH5+PhISEpCUlISWlhZMTk7i3XffxebNm/H999/jww8/1BvD19cXo6OjqKqqQnBwMBwcHBAQEIBt27YhKSkJhYWFCAkJwW+//YaqqiqsWLECmzZtmqOMiYiIyFzxLWZEREREs1RZWQlPT0/4+voiLi4OWq0WxcXFOH78OKytrREcHIyioiIUFBQgKCgIhw4dQn5+vt4Yq1evRlpaGhITE6HRaPDGG28AAEpKSpCUlISsrCwEBgbikUcewenTp+Ht7T0XqRIREZGZ41vMiIiIiIiIiIgsHO8gIiIiIiIiIiKycGwQERERERERERFZODaIiIiIiIiIiIgsHBtEREREREREREQWjg0iIiIiIiIiIiILxwYREREREREREZGFY4OIiIiIiIiIiMjCsUFERERERERERGTh2CAiIiIiIiIiIrJwbBAREREREREREVk4NoiIiIiIiIiIiCzc/wCyesM9GOxRIQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Import necessary libraries\n",
+    "import modin.pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearnex import patch_sklearn\n",
+    "patch_sklearn()\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.metrics import silhouette_score\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Load stock data from CSV\n",
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')\n",
+    "stock_data['Date'] = pd.to_datetime(stock_data['Date'], format='%d-%m-%Y')\n",
+    "stock_data.set_index('Date', inplace=True)\n",
+    "\n",
+    "# ## Preprocess the Data to Add Technical Indicators\n",
+    "def calculate_technical_indicators(stock_data):\n",
+    "    # Forward fill any NA values before calculating percentage change\n",
+    "    stock_data['Returns'] = stock_data['Close'].ffill().pct_change()\n",
+    "    \n",
+    "    # Calculate moving averages\n",
+    "    stock_data['MA_50'] = stock_data['Close'].rolling(window=50).mean()\n",
+    "    stock_data['MA_200'] = stock_data['Close'].rolling(window=200).mean()\n",
+    "    \n",
+    "    # Calculate volatility (standard deviation of returns)\n",
+    "    stock_data['Volatility'] = stock_data['Returns'].rolling(window=50).std()\n",
+    "    \n",
+    "    # Drop NaN values resulting from rolling windows\n",
+    "    stock_data.dropna(inplace=True)\n",
+    "    \n",
+    "    return stock_data\n",
+    "\n",
+    "# Apply the preprocessing\n",
+    "stock_data = calculate_technical_indicators(stock_data)\n",
+    "\n",
+    "# ## Feature Set for Clustering\n",
+    "features = stock_data[['Returns', 'MA_50', 'MA_200', 'Volatility']]\n",
+    "\n",
+    "# ## Apply K-Means Clustering\n",
+    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
+    "stock_data['Cluster'] = kmeans.fit_predict(features)\n",
+    "\n",
+    "# ## Define 'Buy', 'Sell', 'Hold' Clusters\n",
+    "cluster_map = {0: 'Hold', 1: 'Buy', 2: 'Sell'}\n",
+    "stock_data['Signal'] = stock_data['Cluster'].map(cluster_map)\n",
+    "\n",
+    "# ## Plot the Buy/Sell Signals on Stock Price\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "plt.plot(stock_data['Close'], label='Close Price', color='blue')\n",
+    "buy_signals = stock_data[stock_data['Signal'] == 'Buy']\n",
+    "sell_signals = stock_data[stock_data['Signal'] == 'Sell']\n",
+    "plt.scatter(buy_signals.index, buy_signals['Close'], label='Buy Signal', marker='^', color='green')\n",
+    "plt.scatter(sell_signals.index, sell_signals['Close'], label='Sell Signal', marker='v', color='red')\n",
+    "plt.title('Stock Price with Buy/Sell Signals')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Stock Price')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Silhouette Score: 0.7111919059346077\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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u3DnNmDFD9erVU2RkpO2448eP26bQlqTz589rxowZV/3eoaGhat26tebOnZvvM2zfvl2LFy+2/bldjccee0xBQUF6/vnn890vJEkZGRkaNmyYrFarXn311QKvnTFjRr5/sJs+fbqys7PzrW/k6+vrkOJSEq6urgUK6/vvv6+cnByHnP+mm27Sl19+qX379qlPnz4F7vkCgNLAArhFOHz4sGbPnq3Dhw+rZs2aknKHoSxatEizZ8/WpEmTFB4ersWLF2vgwIF67LHHlJOTo86dO1/TzbgAKo7ffvvNdpXjUl26dLFdhSnKwYMHddttt6lPnz7asGGDPv/8c91///1q1aqVJKlVq1YaMmSIZsyYobNnz6pHjx7auHGj5s6dqwEDBhS4l6p79+766quv1KJFC1WrVk2S1LZtW/n6+mrPnj1FDrMrTt26dTV+/Phij/vwww/VrVs3tWjRQo888ojq16+vxMREbdiwQUePHtVff/0lSWrdurVcXV01depUJScny9PTUzfeeKOCgoJKnGnMmDH68ssv1bdvXz311FMKCAjQ3LlzdfDgQX333Xdyccn9N8FHHnlEH3zwgQYPHqzNmzcrNDRUn332mXx8fK7qu8jz1ltvqW/fvurcubMefvhhpaen6/3335fZbC7Rd1WU6tWr69tvv9Utt9yitm3bavjw4YqMjFRCQoLmzJmjffv26d133y108dsLFy7opptu0sCBA7V792599NFH6tatm2677TbbMe3atdP06dP1+uuvq2HDhgoKCtKNN9541Xmv5NZbb9Vnn30ms9msyMhIbdiwQUuXLlX16tUd9h533HGHZs6cqYceeki33XabFi1aVOEW8gVQsVCcirBt2zbl5OSocePG+bZnZmba/sOfkJCgRx55REOGDNGgQYOUmpqqV199VXfffbeWLFlSpsMiAJS9wv7lX5Jmz55dbHH6+uuv9eqrr2rMmDFyc3PTyJEj9dZbb+U75tNPP1X9+vU1Z84c/fDDDwoJCdHYsWNtEy5cKq84XTrTnJubmzp37qylS5fafX+TvSIjI7Vp0yZNmDBBc+bM0alTpxQUFKQ2bdrk+55CQkL08ccfa/LkyXr44YeVk5OjFStW2FWcgoODtX79er344ot6//33lZGRoZYtW+rnn3/OdyXOx8dHy5Yt0z//+U+9//778vHx0QMPPKC+ffuqT58+V/1Zo6KitGjRIo0bN06vvvqq3N3d1aNHD02dOrXEE0EUpXv37tq6dasmTZqkBQsWKD4+XmazWV26dNGsWbOKXED3gw8+0BdffKFXX31VWVlZGjRokN577718/z/06quvKi4uTm+++aZSU1PVo0ePUitO7777rlxdXfXFF18oIyNDXbt2ta1/5UjDhg3T6dOn9dxzz+mee+7RDz/8cFVXVgGgJEzWinzHrQOZTCb98MMPGjBggKTcH2oeeOAB7dixo8CNsFWqVFFISIheeeUVLVq0SNHR0bZ9R48eVVhYmDZs2KBOnTqV5UcAAFQyc+bM0bBhwxQdHa327dsbHQcAnBr/LFOENm3aKCcnR0lJSUX+S+358+dtQ0Ly5JWsa5k1CAAAAED5Uqknh0hLS1NMTIxiYmIk5d5zEBMTo8OHD6tx48Z64IEHNHjwYH3//fc6ePCgNm7cqMmTJ9sWnrzlllsUHR2tiRMnau/evdqyZYuGDRumunXrqk2bNgZ+MgAAAACOVKmL06ZNm9SmTRtbyRk9enS+8fizZ8/W4MGD9eyzz6pJkyYaMGCAoqOjbQs03njjjZo/f74WLlyoNm3aqE+fPvL09NSiRYtKvG4HAAAAgPKPe5wAAAAAoBiV+ooTAAAAAJQExQkAAAAAilHpZtWzWCw6fvy4/Pz8WGcJAAAAqMSsVqtSU1NVs2bNArNlX67SFafjx48rLCzM6BgAAAAAyokjR46odu3aVzym0hUnPz8/Sblfjr+/v8FpAAAAABglJSVFYWFhto5wJZWuOOUNz/P396c4AQAAACjRLTxMDgEAAAAAxaA4AQAAAEAxKE4AAAAAUAyKEwAAAAAUg+IEAAAAAMWgOAEAAABAMShOAAAAAFAMihMAAAAAFIPiBAAAAADFoDgBAAAAQDEoTgAAAABQDIoTAAAAABSD4gQAAAAAxXAzOkBllmOxauPB00pKzVCQn5c6hgfI1cVkdCwAAAAAl6E4GWTR9nhN+DlW8ckZtm2hZi+N6x+pPs1DDUwGAAAA4HIM1TPAou3xeuLzLflKkyQlJGfoic+3aNH2eIOSAQAAACgMxamM5VismvBzrKyF7MvbNuHnWOVYCjsCAAAAgBEoTmVs48HTBa40XcoqKT45QxsPni67UAAAAACuiOJUxpJSiy5NV3McAAAAgNJHcSpjQX5eDj0OAAAAQOmjOJWxjuEBCjV7qahJx03KnV2vY3hAWcYCAAAAcAUUpzLm6mLSuP6RklSgPOU9H9c/kvWcAAAAgHKE4mSAPs1DNf3Btgox5x+OF+TvqekPtmUdJwAAAKCcYQFcg/RpHqpekSHaePC0Rn8do/iUDL3YuymlCQAAACiHuOJkIFcXkzo3qK672tWWJC3blWRwIgAAAACFoTiVA1GRwZKkVXtOKDM7x+A0AAAAAC5HcSoHWtYyK9DPU2mZ2frfARa+BQAAAMobilM54OJiUlREkCRp6c5Eg9MAAAAAuBzFqZzodXG43tLYRFmtVoPTAAAAALgUxamc6NKghrzdXXU8OUM7jqcYHQcAAADAJShO5YSXu6u6N6ohieF6AAAAQHlDcSpH8mbXozgBAAAA5QvFqRy5sWmQTCZp+7EUxSenGx0HAAAAwEUUp3KkRhVPtatTTVLuJBEAAAAAygeKUzmTN1xvyc4kg5MAAAAAyENxKmeiInKL04b9J5WakWVwGgAAAAASxancaRDoq/AavsrKsWrN3pNGxwEAAAAgilO5YzKZ8i2GCwAAAMB4FKdyKG+43vLdScrOsRicBgAAAADFqRxqW6eqqvm46+z5LG2KO2N0HAAAAKDSoziVQ26uLrqhaZAkhusBAAAA5QHFqZzqFZE3LXmirFarwWkAAACAyo3iVE5d3zhQHq4uijt1XvuS0oyOAwAAAFRqFKdyytfTTV0aVpeUe9UJAAAAgHEoTuVY3ux63OcEAAAAGIviVI7lFac/j5zVidRMg9MAAAAAlRfFqRwLMXupZW2zrFZpxa4ko+MAAAAAlRbFqZzLu+q0mOF6AAAAgGEoTuVcXnFau++E0i/kGJwGAAAAqJwoTuVcRKifalX1VkaWRev2nTQ6DgAAAFApUZzKOZPJpKiIIEnSUqYlBwAAAAxBcaoAekWGSJKW7kySxWI1OA0AAABQ+VCcKoCO4QHy83TTybRMxRw9a3QcAAAAoNKhOFUAHm4u6tEkUBKL4QIAAABGoDhVEL0ic2fX4z4nAAAAoOxRnCqIno2D5OZi0p7ENMWdOmd0HAAAAKBSoThVEGYfd3UMD5AkLWG4HgAAAFCmKE4VSN5iuAzXAwAAAMoWxakCyStO0YfO6Oz5CwanAQAAACoPilMFUqe6j5oE+ynHYtXK3SeMjgMAAABUGhSnCiZvdj3ucwIAAADKDsWpgom6WJxW7TmhzOwcg9MAAAAAlQPFqYJpWcusQD9PpWVm638HThsdBwAAAKgUKE4VjIuLSVERQZKYXQ8AAAAoKxSnCijvPqelsYmyWq0GpwEAAACcn6HFafLkyerQoYP8/PwUFBSkAQMGaPfu3Vd8zZw5c2QymfI9vLy8yihx+dClQQ15u7vqeHKGdhxPMToOAAAA4PQMLU6rVq3SiBEj9Mcff2jJkiXKysrSzTffrHPnzl3xdf7+/oqPj7c94uLiyihx+eDl7qrujWpIYrgeAAAAUBbcjHzzRYsW5Xs+Z84cBQUFafPmzbr++uuLfJ3JZFJISEhpxyvXoiKDtTg2UUt3JmpUVGOj4wAAAABOrVzd45ScnCxJCggIuOJxaWlpqlu3rsLCwnT77bdrx44dRR6bmZmplJSUfA9ncGPTIJlM0vZjKYpPTjc6DgAAAODUyk1xslgsGjVqlLp27armzZsXeVyTJk00a9Ys/fjjj/r8889lsVjUpUsXHT16tNDjJ0+eLLPZbHuEhYWV1kcoUzWqeKpdnWqScieJAAAAAFB6TNZyMi3bE088od9++01r165V7dq1S/y6rKwsRUREaNCgQXrttdcK7M/MzFRmZqbteUpKisLCwpScnCx/f3+HZDfKx6v2a8pvu3R940DNe6ij0XEAAACACiUlJUVms7lE3aBcXHEaOXKkfvnlF61YscKu0iRJ7u7uatOmjfbt21fofk9PT/n7++d7OIuoiNxpyTfsP6nUjCyD0wAAAADOy9DiZLVaNXLkSP3www9avny5wsPD7T5HTk6Otm3bptDQ0FJIWL41CPRVeA1fZeVYtWbvSaPjAAAAAE7L0OI0YsQIff7555o/f778/PyUkJCghIQEpaf/PdnB4MGDNXbsWNvziRMnavHixTpw4IC2bNmiBx98UHFxcRo+fLgRH8FQJpMp32K4AAAAAEqHocVp+vTpSk5OVs+ePRUaGmp7fP3117ZjDh8+rPj4eNvzM2fO6JFHHlFERIT69eunlJQUrV+/XpGRkUZ8BMPlDddbvjtJ2TkWg9MAAAAAzqncTA5RVuy5AawiyM6xqMMbS3XmfJa+erSTOtWvbnQkAAAAoEKocJND4Oq5ubrohqZBkhiuBwAAAJQWipMT6HVxuN6SnYmqZBcQAQAAgDJBcXIC1zcOlIeri+JOnde+pDSj4wAAAABOh+LkBHw93dSlYe69TUt2MlwPAAAAcDSKk5PIm12P+5wAAAAAx6M4OYm84vTnkbM6kZppcBoAAADAuVCcnESI2Usta5tltUordiUZHQcAAABwKhQnJ5J31Wkxw/UAAAAAh6I4OZG84rR23wmlX8gxOA0AAADgPChOTiQi1E+1qnorI8uidftOGh0HAAAAcBoUJydiMpkUFREkSVrKtOQAAACAw1CcnEyvyBBJ0tKdSbJYrAanAQAAAJwDxcnJdAwPkJ+nm06mZSrm6Fmj4wAAAABOgeLkZDzcXNSjSaAkFsMFAAAAHIXi5IR6RebOrsd9TgAAAIBjUJycUM/GQXJzMWlPYpriTp0zOg4AAABQ4VGcnJDZx10dwwMkSUsYrgcAAABcM4qTk8pbDJfhegAAAMC1ozg5qbziFH3ojM6ev2BwGgAAAKBiozg5qTrVfdQk2E85FqtW7j5hdBwAAACgQqM4ObG82fW4zwkAAAC4NhQnJxZ1sTit2nNCmdk5BqcBAAAAKi6KkxNrWcusQD9PpWVm638HThsdBwAAAKiwKE5OzMXFpKiIIEnMrgcAAABcC4qTk8u7z2lpbKKsVqvBaQAAAICKieLk5Lo0qCFvd1cdT87QjuMpRscBAAAAKiSKk5PzcndV90Y1JDFcDwAAALhaFKdKIG92PYoTAAAAcHUoTpXAjU2DZDJJ24+lKD453eg4AAAAQIVDcaoEalTxVLs61STlThIBAAAAwD4Up0oib7jekp1JBicBAAAAKh6KUyURFZFbnDbsP6nUjCyD0wAAAAAVC8WpkmgQ6KvwGr7KyrFqzd6TRscBAAAAKhSKUyVhMpnyLYYLAAAAoOQoTpVI3nC95buTlJ1jMTgNAAAAUHFQnCqRtnWqqpqPu86ez9KmuDNGxwEAAAAqDIpTJeLm6qIbmgZJYrgeAAAAYA+KUyVzs21a8kRZrVaD0wAAAAAVA8WpkuneKFAeri6KO3Ve+5LSjI4DAAAAVAgUp0rG19NNXRpWl5R71QkAAABA8ShOlVDe7Hrc5wQAAACUDMWpEsorTn8eOasTqZkGpwEAAADKPzd7DrZYLFq1apXWrFmjuLg4nT9/XoGBgWrTpo2ioqIUFhZWWjnhQCFmL7WsbdbWo8lasStJAzvw5wYAAABcSYmuOKWnp+v1119XWFiY+vXrp99++01nz56Vq6ur9u3bp3Hjxik8PFz9+vXTH3/8UdqZ4QB5V50WM1wPAAAAKFaJrjg1btxYnTt31syZM9WrVy+5u7sXOCYuLk7z58/Xfffdp5dfflmPPPKIw8PCcaIigvX2kj1au++E0i/kyNvD1ehIAAAAQLllspZgMZ+dO3cqIiKiRCfMysrS4cOH1aBBg2sOVxpSUlJkNpuVnJwsf39/o+MYxmq1qtvUFTp2Nl2fDm6vqIvrOwEAAACVhT3doERD9UpamiTJ3d293JYm/M1kMqnXxbK0lGnJAQAAgCsq8ax6b775ptLT023P161bp8zMv2dkS01N1ZNPPunYdChVtmnJdybJYin2wiMAAABQaZW4OI0dO1apqam253379tWxY8dsz8+fP69PPvnEselQqjqGB8jP000n0zIVc/Ss0XEAAACAcqvExenyW6FKcGsUyjkPNxf1aBIoicVwAQAAgCthAdxKjvucAAAAgOJRnCq5no2D5OZi0p7ENMWdOmd0HAAAAKBcKtE6Tnk+/fRTValSRZKUnZ2tOXPmqEaNGpKU7/4nVBxmH3d1DA/Q+v2ntCQ2UcO71zc6EgAAAFDulGgdJ0mqV6+eTCZTsccdPHjwmkOVJtZxKmjW2oOa+EusOtUP0FePdjY6DgAAAFAm7OkGJb7idOjQoWvNhXKqV2SwJv4Sq+hDZ3T2/AVV9fEwOhIAAABQrnCPExQW4KOmIX7KsVi1cvcJo+MAAAAA5U6Ji9OGDRv0yy+/5Ns2b948hYeHKygoSI8++mi+BXFRseQthruEackBAACAAkpcnCZOnKgdO3bYnm/btk0PP/ywoqKiNGbMGP3888+aPHlyqYRE6Yu6OC35qj0nlJmdY3AaAAAAoHwpcXGKiYnRTTfdZHv+1Vdf6brrrtPMmTM1evRovffee/rmm29KJSRKX8taZgX6eSotM1v/O3Da6DgAAABAuVLi4nTmzBkFBwfbnq9atUp9+/a1Pe/QoYOOHDni2HQoMy4uJkVFBEliMVwAAADgciUuTsHBwbapxi9cuKAtW7aoU6dOtv2pqalyd3d3fEKUmV4Xh+stjU1UCWepBwAAACqFEhenfv36acyYMVqzZo3Gjh0rHx8fde/e3bZ/69atatCgQamERNno0qCGvN1ddTw5QzuOpxgdBwAAACg3SlycXnvtNbm5ualHjx6aOXOmZs6cKQ+Pv9f7mTVrlm6++eZSCYmy4eXuqu6NakhiuB4AAABwqRIvgFujRg2tXr1aycnJqlKlilxdXfPtX7BggapUqeLwgChbUZHBWhybqKU7EzUqqrHRcQAAAIBywe4FcM1mc77SFBcXp9jYWFWtWjXfFaiSmDx5sjp06CA/Pz8FBQVpwIAB2r17d7GvW7BggZo2bSovLy+1aNFCv/76q70fA0W4qWmQTCZp+7EUxSenGx0HAAAAKBdKXJxmzZqlt99+O9+2Rx99VPXr11eLFi3UvHlzu2fVW7VqlUaMGKE//vhDS5YsUVZWlm6++WadO3euyNesX79egwYN0sMPP6w///xTAwYM0IABA7R9+3a73huFq17FU+3qVJOUO0kEAAAAADuK04wZM1StWjXb80WLFmn27NmaN2+eoqOjVbVqVU2YMMGuN1+0aJGGDh2qZs2aqVWrVpozZ44OHz6szZs3F/mad999V3369NHzzz+viIgIvfbaa2rbtq0++OADu94bRctbDHfJziSDkwAAAADlQ4mL0969e9W+fXvb8x9//FG33367HnjgAbVt21aTJk3SsmXLrilMcnKyJCkgIKDIYzZs2KCoqKh823r37q0NGzYUenxmZqZSUlLyPXBlURG5xWnD/pNKzcgyOA0AAABgvBIXp/T0dPn7+9uer1+/Xtdff73tef369ZWQkHDVQSwWi0aNGqWuXbuqefPmRR6XkJCQbyFeKXeNqaLee/LkyTKbzbZHWFjYVWesLBoE+iq8hq+ycqxas/ek0XEAAAAAw5W4ONWtW9c2hO7kyZPasWOHunbtatufkJAgs9l81UFGjBih7du366uvvrrqcxRm7NixSk5Otj3svQ+rMjKZTPkWwwUAAAAquxJPRz5kyBCNGDFCO3bs0PLly9W0aVO1a9fOtn/9+vVXvFJ0JSNHjtQvv/yi1atXq3bt2lc8NiQkRImJ+X+YT0xMVEhISKHHe3p6ytPT86pyVWZREcGasfqAlu9OUnaORW6udk/ACAAAADiNEv80/MILL+iRRx7R999/Ly8vLy1YsCDf/nXr1mnQoEF2vbnVatXIkSP1ww8/aPny5QoPDy/2NZ07dy5wL9WSJUvUuXNnu94bV9a2TlVV83HX2fNZ2hR3xug4AAAAgKFMVqvVatSbP/nkk5o/f75+/PFHNWnSxLbdbDbL29tbkjR48GDVqlVLkydPlpR7ZatHjx6aMmWKbrnlFn311VeaNGmStmzZUqIrXikpKTKbzUpOTs53zxYKGv1NjL7fckzDu4Xr/26NNDoOAAAA4FD2dIMSXXEqrW41ffp0JScnq2fPngoNDbU9vv76a9sxhw8fVnx8vO15ly5dNH/+fM2YMUOtWrXSt99+q4ULF171MEEU7WbbtOSJpfZ3AAAAAKgISnTFKTIyUq+++qruvPNOeXh4FHnc3r179fbbb6tu3boaM2aMQ4M6ClecSu5cZrbaTFyiCzkWLXnmejUK9jM6EgAAAOAw9nSDEk0O8f777+vFF1/Uk08+qV69eql9+/aqWbOmvLy8dObMGcXGxmrt2rXasWOHRo4cqSeeeMIhHwTG8vV0U5eG1bVy9wkt2ZlIcQIAAEClVaLidNNNN2nTpk1au3atvv76a33xxReKi4tTenq6atSooTZt2mjw4MF64IEHVK1atdLOjDIUFRGslbtPaGlsop7s2dDoOAAAAIAhSjwduSR169ZN3bp1K60sKIeiIoL1fwu3688jZ3UiNVOBfkztDgAAgMqHxXlwRSFmL7WsbZbVKq3YlWR0HAAAAMAQFCcUKyoid3a9xbGJxRwJAAAAOCeKE4qVV5zW7juh9As5BqcBAAAAyh7FCcWKCPVTrareysiyaN2+k0bHAQAAAMocxQnFMplM6nVxMdylOxmuBwAAgMrH7uK0ZcsWbdu2zfb8xx9/1IABA/TSSy/pwoULDg2H8iNvuN7SnUmyWIpdMxkAAABwKnYXp8cee0x79uyRJB04cED33XeffHx8tGDBAr3wwgsOD4jyoWN4gPw83XQyLVMxR88aHQcAAAAoU3YXpz179qh169aSpAULFuj666/X/PnzNWfOHH333XeOzodywsPNRT2aBEqSljK7HgAAACoZu4uT1WqVxWKRJC1dulT9+vWTJIWFhenkSSYOcGbc5wQAAIDKyu7i1L59e73++uv67LPPtGrVKt1yyy2SpIMHDyo4ONjhAVF+9GwcJDcXk/Ykpinu1Dmj4wAAAABlxu7i9M4772jLli0aOXKkXn75ZTVs2FCS9O2336pLly4OD4jyw+zjro7hAZKkJQzXAwAAQCVislqtDpkiLSMjQ66urnJ3d3fE6UpNSkqKzGazkpOT5e/vb3ScCmfW2oOa+EusOtUP0FePdjY6DgAAAHDV7OkGV7WO09mzZ/Xpp59q7NixOn36tCQpNjZWSUlJV3M6VCB59zlFHzqjs+eZfh4AAACVg93FaevWrWrUqJGmTp2qf/3rXzp79qwk6fvvv9fYsWMdnQ/lTFiAj5qG+CnHYtXK3SeMjgMAAACUCbuL0+jRozVs2DDt3btXXl5etu39+vXT6tWrHRoO5VPeYrjc5wQAAIDKwu7iFB0drccee6zA9lq1aikhIcEhoVC+RV0crrdqzwllZucYnAYAAAAofXYXJ09PT6WkpBTYvmfPHgUGBjokFMq3lrXMCvTzVFpmtv534LTRcQAAAIBSZ3dxuu222zRx4kRlZWVJkkwmkw4fPqwXX3xRd911l8MDovxxcTEpKiJIEovhAgAAoHKwuzhNmzZNaWlpCgoKUnp6unr06KGGDRvKz89Pb7zxRmlkRDmUN7ve0thEOWhGewAAAKDccrP3BWazWUuWLNG6dev0119/KS0tTW3btlVUVFRp5EM51aVBDXm7u+p4coZ2HE9R81pmoyMBAAAApcbu4pSna9eu6tq1qyOzoALxcndV90Y1tDg2UUt3JlKcAAAA4NTsHqr31FNP6b333iuw/YMPPtCoUaMckQkVRN7setznBAAAAGdnd3H67rvvCr3S1KVLF3377bcOCYWK4aamQTKZpO3HUhSfnG50HAAAAKDU2F2cTp06JbO54LAsf39/nTx50iGhUDFUr+KpdnWqScqdJAIAAABwVnYXp4YNG2rRokUFtv/222+qX7++Q0Kh4sgbrrdkZ5LBSQAAAIDSY/fkEKNHj9bIkSN14sQJ3XjjjZKkZcuWadq0aXrnnXccnQ/lXFREsKb8tksb9p9UakaW/LzcjY4EAAAAOJzdxemhhx5SZmam3njjDb322muSpHr16mn69OkaPHiwwwOifGsQ6KvwGr46ePKc1uw9qX4tQo2OBAAAADic3UP1JOmJJ57Q0aNHlZiYqJSUFB04cIDSVEmZTKZ8i+ECAAAAzuiqilOewMBAValSxVFZUEFFReQWp+W7k5SdYzE4DQAAAOB4dhenxMRE/eMf/1DNmjXl5uYmV1fXfA9UPm3rVFU1H3edPZ+lTXFnjI4DAAAAOJzd9zgNHTpUhw8f1iuvvKLQ0FCZTKbSyIUKxM3VRTc0DdL3W45paWyiOtWvbnQkAAAAwKHsLk5r167VmjVr1Lp161KIg4rq5shgfb/lmJbsTNTLt0RQqAEAAOBU7B6qFxYWJqvVWhpZUIF1bxQoD1cXxZ06r31JaUbHAQAAABzK7uL0zjvvaMyYMTp06FApxEFF5evppi4Nc4foLdnJ7HoAAABwLnYXp3vvvVcrV65UgwYN5Ofnp4CAgHwPVF55s+sxLTkAAACcjd33OL3zzjulEAPOICoiWP+3cLv+PHJWJ1IzFejnaXQkAAAAwCHsLk5DhgwpjRxwAiFmL7WsbdbWo8lasStJAzuEGR0JAAAAcIhrWgA3IyNDKSkp+R6o3PKG6y1muB4AAACciN3F6dy5cxo5cqSCgoLk6+uratWq5XugcssrTmv3nVD6hRyD0wAAAACOYXdxeuGFF7R8+XJNnz5dnp6e+vTTTzVhwgTVrFlT8+bNK42MqEAiQv1Uq6q3MrIsWrfvpNFxAAAAAIewuzj9/PPP+uijj3TXXXfJzc1N3bt31//93/9p0qRJ+uKLL0ojIyoQk8mkXpEXZ9djWnIAAAA4CbuL0+nTp1W/fn1Jkr+/v06fPi1J6tatm1avXu3YdKiQbNOS70ySxcJiyQAAAKj47C5O9evX18GDByVJTZs21TfffCMp90pU1apVHRoOFVPH8AD5ebrpZFqmYo6eNToOAAAAcM3sLk7Dhg3TX3/9JUkaM2aMPvzwQ3l5eemZZ57R888/7/CAqHg83FzUo0mgJBbDBQAAgHMwWa3WaxpLFRcXp82bN6thw4Zq2bKlo3KVmpSUFJnNZiUnJ8vf39/oOE7rx5hjevqrGDUOrqLFz/QwOg4AAABQgD3dwO4rTvPmzVNmZqbted26dXXnnXeqadOmzKoHm56Ng+TmYtKexDTFnTpndBwAAADgmlzVUL3k5OQC21NTUzVs2DCHhELFZ/ZxV8fwAEnSEobrAQAAoIKzuzhZrVaZTKYC248ePSqz2eyQUHAOf8+uR3ECAABAxeZW0gPbtGkjk8kkk8mkm266SW5uf780JydHBw8eVJ8+fUolJCqmXpHBmvhLrKIPndHZ8xdU1cfD6EgAAADAVSlxcRowYIAkKSYmRr1791aVKlVs+zw8PFSvXj3dddddDg+IiisswEdNQ/y0KyFVK3ef0IA2tYyOBAAAAFyVEhencePGSZLq1aun++67T56enqUWCs4jKiJYuxJStSQ2keIEAACACsvue5xuvPFGnThxwvZ848aNGjVqlGbMmOHQYHAOUZG59zmt2nNCmdk5BqcBAAAAro7dxen+++/XihUrJEkJCQmKiorSxo0b9fLLL2vixIkOD4iKrWUts4L8PJWWma3/HThtdBwAAADgqthdnLZv366OHTtKkr755hu1aNFC69ev1xdffKE5c+Y4Oh8qOBcXk25idj0AAABUcHYXp6ysLNv9TUuXLtVtt90mSWratKni4+Mdmw5OoVdkkCRpaWyirFarwWkAAAAA+9ldnJo1a6aPP/5Ya9as0ZIlS2xTkB8/flzVq1d3eEBUfF0a1JC3u6uOJ2dox/EUo+MAAAAAdrO7OE2dOlWffPKJevbsqUGDBqlVq1aSpJ9++sk2hA+4lJe7q7o3qiGJ4XoAAAComEo8HXmenj176uTJk0pJSVG1atVs2x999FH5+Pg4NBycR1RksBbHJmrpzkSNimpsdBwAAADALnYXJ0lydXXNV5qk3PWdgKLc1DRIJpO0/ViK4pPTFWr2NjoSAAAAUGIlKk5t27bVsmXLVK1aNbVp00Ymk6nIY7ds2eKwcHAe1at4ql2datoUd0ZLYxP1j871jI4EAAAAlFiJitPtt99um0lvwIABpZkHTiwqMlib4s5oyc4kihMAAAAqFJPVwPmhV69erbfeekubN29WfHy8fvjhhysWs5UrV+qGG24osD0+Pl4hISEles+UlBSZzWYlJyfL39//aqPjKuxLSlPU26vk7mrSlld6yc/L3ehIAAAAqMTs6QZ23+NktVq1efNmHTp0SCaTSeHh4cUO3yvKuXPn1KpVKz300EO68847S/y63bt35/tgQUFBdr83yl7DoCqqX8NXB06e05q9J9WvRajRkQAAAIASsas4rVixQg8//LDi4uJsC5nmladZs2bp+uuvt+vN+/btq759+9r1Gim3KFWtWtXu18F4UZHBmrH6gJbEJlKcAAAAUGGUeB2nffv26dZbb1W9evX0/fffa+fOnYqNjdWCBQtUu3Zt9evXTwcOHCjNrDatW7dWaGioevXqpXXr1l3x2MzMTKWkpOR7wDhREcGSpOW7kpSdYzE4DQAAAFAyJS5O77zzjjp16qTly5fr9ttvV5MmTdS0aVPdeeedWrFiha677jr9+9//Ls2sCg0N1ccff6zvvvtO3333ncLCwtSzZ88rzuQ3efJkmc1m2yMsLKxUM+LK2tapqmo+7kpOz9KmuDNGxwEAAABKpMTFaeXKlRo1alSh+0wmk0aNGqUVK1Y4KlehmjRposcee0zt2rVTly5dNGvWLHXp0uWKhW3s2LFKTk62PY4cOVKqGXFlbq4uuqFp7j1pS2MTDU4DAAAAlEyJi9Phw4fVokWLIvc3b95ccXFxDgllj44dO2rfvn1F7vf09JS/v3++B4x1c2TucL0lOxNl4KSOAAAAQImVuDilpaXJx8enyP0+Pj46f/68Q0LZIyYmRqGhTDJQkXRvFCgPVxfFnTqvfUlpRscBAAAAimXXrHqxsbFKSEgodN/JkyftfvO0tLR8V4sOHjyomJgYBQQEqE6dOho7dqyOHTumefPmScq9zyo8PFzNmjVTRkaGPv30Uy1fvlyLFy+2+71hHF9PN3VpWF0rd5/Qkp2JahTsZ3QkAAAA4IrsKk433XRToUOrTCaTrFar3Ws5bdq0Kd+CtqNHj5YkDRkyRHPmzFF8fLwOHz5s23/hwgU9++yzOnbsmHx8fNSyZUstXbq00EVxUb5FRQRr5e4TWhqbqCd7NjQ6DgAAAHBFJmsJbzIp6f1LdevWvaZApc2e1YFRehKSM9Rp8jKZTNLGl6IU6OdpdCQAAABUMvZ0gxJfcSrvhQgVS4jZSy1rm7X1aLKW70rUvR3qGB0JAAAAKFKJJ4cAHC1vMdwlsUkGJwEAAACujOIEw+QVp7X7Tij9Qo7BaQAAAICiUZxgmIhQP9Wq6q2MLIvW7bN/VkYAAACgrFCcYBiTyaReFxfDXboz0eA0AAAAQNEoTjBU3nC9pTuTZLGUaIJHAAAAoMyVaFa9Nm3alHiNpi1btlxTIFQuHcMD5OfpppNpmYo5elZt61QzOhIAAABQQImK04ABA2y/z8jI0EcffaTIyEh17txZkvTHH39ox44devLJJ0slJJyXh5uLejQJ1C9b47U0NpHiBAAAgHKpRMVp3Lhxtt8PHz5cTz31lF577bUCxxw5csSx6VAp9IoMzi1OOxP1Qp+mRscBAAAACrD7HqcFCxZo8ODBBbY/+OCD+u677xwSCpVLz8ZBcnMxaU9imuJOnTM6DgAAAFCA3cXJ29tb69atK7B93bp18vLyckgoVC5mH3d1DA+QJC2JZXY9AAAAlD8lGqp3qVGjRumJJ57Qli1b1LFjR0nS//73P82aNUuvvPKKwwOicoiKCNb6/ae0dGeihnevb3QcAAAAIB+7i9OYMWNUv359vfvuu/r8888lSREREZo9e7YGDhzo8ICoHHpFBmviL7GKPnRGZ89fUFUfD6MjAQAAADZ2FydJGjhwICUJDhUW4KOmIX7alZCqlbtPaECbWkZHAgAAAGyuagHcs2fP6tNPP9VLL72k06dPS8pdv+nYsWMODYfKJW8xXO5zAgAAQHljd3HaunWrGjdurKlTp+qtt97S2bNnJUnff/+9xo4d6+h8qESiInOL06o9J5SZnWNwGgAAAOBvdhen0aNHa+jQodq7d2++WfT69eun1atXOzQcKpeWtcwK8vNUWma2/nfgtNFxAAAAABu7i1N0dLQee+yxAttr1aqlhIQEh4RC5eTiYtJNF4frLd3JcD0AAACUH3YXJ09PT6WkpBTYvmfPHgUGBjokFCqvXpFBkqSlsYmyWq0GpwEAAABy2V2cbrvtNk2cOFFZWVmSJJPJpMOHD+vFF1/UXXfd5fCAqFy6NKghb3dXHU/O0I7jBQs6AAAAYAS7i9O0adOUlpamoKAgpaenq0ePHmrYsKH8/Pz0xhtvlEZGVCJe7q7q3qiGJIbrAQAAoPywex0ns9msJUuWaN26dfrrr7+Ulpamtm3bKioqqjTyoRKKigzW4thELd2ZqFFRjY2OAwAAANhXnLKysuTt7a2YmBh17dpVXbt2La1cqMRuahokk0nafixF8cnpCjV7Gx0JAAAAlZxdQ/Xc3d1Vp04d5eSwxg5KT/UqnmpXp5qk3EkiAAAAAKPZfY/Tyy+/rJdeekmnT7PODkpP3mK4S3YmGZwEAAAAuIp7nD744APt27dPNWvWVN26deXr65tv/5YtWxwWDpVXVESwpvy2Sxv2n1RqRpb8vNyNjgQAAIBKzO7iNGDAgFKIAeTXMKiK6tfw1YGT57Rm70n1axFqdCQAAABUYnYXp3HjxpVGDqCAqMhgzVh9QEtiEylOAAAAMJTd9zgBZSUqIvc+p+W7kpSdYzE4DQAAACozu4tTTk6O/vWvf6ljx44KCQlRQEBAvgfgKG3rVFU1H3clp2dpU9wZo+MAAACgErO7OE2YMEFvv/227r33XiUnJ2v06NG688475eLiovHjx5dCRFRWbq4uuqFpkCSmJQcAAICx7C5OX3zxhWbOnKlnn31Wbm5uGjRokD799FO9+uqr+uOPP0ojIyqxm23TkifKarUanAYAAACVld3FKSEhQS1atJAkValSRcnJyZKkW2+9Vf/9738dmw6VXvdGgfJwdVHcqfPal5RmdBwAAABUUnYXp9q1ays+Pl6S1KBBAy1evFiSFB0dLU9PT8emQ6Xn6+mmLg2rS8q96gQAAAAYwe7idMcdd2jZsmWSpH/+85965ZVX1KhRIw0ePFgPPfSQwwMCebPrcZ8TAAAAjGKyXuONIxs2bNCGDRvUqFEj9e/f31G5Sk1KSorMZrOSk5Pl7+9vdByUQEJyhjpNXiaTSdr4UpQC/biyCQAAgGtnTzewewHcy3Xu3FmdO3e+1tMARQoxe6llbbO2Hk3W8l2JurdDHaMjAQAAoJKxuzjNmzfvivsHDx581WGAokRFBGvr0WQtiU2iOAEAAKDM2T1Ur1q1avmeZ2Vl6fz58/Lw8JCPj49Onz7t0ICOxlC9iin2eIr6vbdGXu4u+vOVm+Xt4Wp0JAAAAFRw9nQDuyeHOHPmTL5HWlqadu/erW7duunLL7+86tDAlUSE+qlWVW9lZFm0bt9Jo+MAAACgkrG7OBWmUaNGmjJlip5++mlHnA4owGQyqdfFxXCXMi05AAAAyphDipMkubm56fjx4446HVCAbVrynUmyWK5pMkgAAADALnZPDvHTTz/le261WhUfH68PPvhAXbt2dVgw4HIdwwPk5+mmk2mZijl6Vm3rVCv+RQAAAIAD2F2cBgwYkO+5yWRSYGCgbrzxRk2bNs1RuYACPNxc1KNJoH7ZGq+lsYkUJwAAAJQZu4uTxWIpjRxAifSKDM4tTjsT9UKfpkbHAQAAQCXhsHucgLLQs3GQ3FxM2pOYprhT54yOAwAAgErC7itOo0ePLvGxb7/9tr2nB67I7OOujuEBWr//lJbEJmp49/pGRwIAAEAlYHdx+vPPP/Xnn38qKytLTZo0kSTt2bNHrq6uatu2re04k8nkuJTAJaIigrV+/ykt3UlxAgAAQNmwuzj1799ffn5+mjt3rqpVy705/8yZMxo2bJi6d++uZ5991uEhgUv1igzWxF9iFX3ojM6ev6CqPh5GRwIAAICTs/sep2nTpmny5Mm20iRJ1apV0+uvv86seigTYQE+ahripxyLVSt3nzA6DgAAACoBu4tTSkqKTpwo+MPqiRMnlJqa6pBQQHHyFsNdEptocBIAAABUBnYXpzvuuEPDhg3T999/r6NHj+ro0aP67rvv9PDDD+vOO+8sjYxAAVGRucVp1Z4TyszOMTgNAAAAnJ3d9zh9/PHHeu6553T//fcrKysr9yRubnr44Yf11ltvOTwgUJiWtcwK8vNUUmqm/nfgtK5vHGh0JAAAADgxu684+fj46KOPPtKpU6dsM+ydPn1aH330kXx9fUsjI1CAi4tJN10crrd0J8P1AAAAULquegFcX19ftWzZUmazWXFxcbJYLI7MBRSrV2SQJGlpbKKsVqvBaQAAAODMSlycZs2aVWBB20cffVT169dXixYt1Lx5cx05csThAYGidGlQQ97urjqenKEdx1OMjgMAAAAnVuLiNGPGjHxTkC9atEizZ8/WvHnzFB0drapVq2rChAmlEhIojJe7q7o3qiGJ4XoAAAAoXSUuTnv37lX79u1tz3/88UfdfvvteuCBB9S2bVtNmjRJy5YtK5WQQFHyZtejOAEAAKA0lbg4paeny9/f3/Z8/fr1uv76623P69evr4SEBMemA4pxU9MgmUzS9mMpik9ONzoOAAAAnFSJi1PdunW1efNmSdLJkye1Y8cOde3a1bY/ISFBZrPZ8QmBK6hexVPt6uQOIV3KYrgAAAAoJSVex2nIkCEaMWKEduzYoeXLl6tp06Zq166dbf/69evVvHnzUgkJXElUZLA2xZ3Rkp1J+kfnekbHAQAAgBMq8RWnF154QY888oi+//57eXl5acGCBfn2r1u3ToMGDXJ4QKA4URfXc9qw/6RSM7IMTgMAAABnZLJWsgVwUlJSZDablZycnO+eLVRsN/5rpQ6cPKePHmirfi1CjY4DAACACsCebnDVC+AC5Une7HpLuM8JAAAApcDQ4rR69Wr1799fNWvWlMlk0sKFC4t9zcqVK9W2bVt5enqqYcOGmjNnTqnnRPmXN1xv+a4kZedYDE4DAAAAZ2NocTp37pxatWqlDz/8sETHHzx4ULfccotuuOEGxcTEaNSoURo+fLh+//33Uk6K8q5tnaqq5uOu5PQsbYo7Y3QcAAAAOJkSzaqXkpJSKvcD9e3bV3379i3x8R9//LHCw8M1bdo0SVJERITWrl2rf//73+rdu3ehr8nMzFRmZqbteUpKyrWFRrnk5uqiG5sG67stR7U0NlGd6lc3OhIAAACcSImuOFWrVk1JSUmSpBtvvFFnz54tzUxF2rBhg6KiovJt6927tzZs2FDkayZPniyz2Wx7hIWFlXZMGKRXZJAkacnORFWyOU8AAABQykpUnKpUqaJTp05Jyr3HKCvLmCmfExISFBwcnG9bcHCwUlJSlJ6eXuhrxo4dq+TkZNvjyJEjZREVBujeKFAeri6KO3Ve+5LSjI4DAAAAJ1KioXpRUVG64YYbFBERIUm644475OHhUeixy5cvd1w6B/D09JSnp6fRMVAGfD3d1KVhda3cfUJLdiaqUbCf0ZEAAADgJEpUnD7//HPNnTtX+/fv16pVq9SsWTP5+PiUdrYCQkJClJiYf7rpxMRE+fv7y9vbu8zzoPyJigjWyt0ntDQ2UU/2bGh0HAAAADiJEhUnb29vPf7445KkTZs2aerUqapatWpp5ipU586d9euvv+bbtmTJEnXu3LnMs6B8iooI1v8t3K4/j5zVidRMBfpxtREAAADXzu7pyFesWGErTVar9Zpuwk9LS1NMTIxiYmIk5U43HhMTo8OHD0vKvT9p8ODBtuMff/xxHThwQC+88IJ27dqljz76SN98842eeeaZq84A5xJi9lLL2mZZrdLyXSyGCwAAAMe4qnWc5s2bpxYtWsjb21ve3t5q2bKlPvvsM7vPs2nTJrVp00Zt2rSRJI0ePVpt2rTRq6++KkmKj4+3lShJCg8P13//+18tWbJErVq10rRp0/Tpp58WORU5Kqe8xXCXxCYZnAQAAADOwmS185LR22+/rVdeeUUjR45U165dJUlr167Vhx9+qNdff73cX/1JSUmR2WxWcnJyqaxNBePFHk9Rv/fWyMvdRX++crO8PVyNjgQAAIByyJ5uUKJ7nC71/vvva/r06fmG0N12221q1qyZxo8fX+6LE5xfRKifalX11rGz6frP2gMKC/BRkJ+XOoYHyNXFZHQ8AAAAVEB2F6f4+Hh16dKlwPYuXbooPj7eIaGAa2EymdQ4uIqOnU3XvxbvsW0PNXtpXP9I9WkeamA6AAAAVER23+PUsGFDffPNNwW2f/3112rUqJFDQgHXYtH2eK3YfaLA9oTkDD3x+RYt2k7BBwAAgH3svuI0YcIE3XvvvVq9erXtHqd169Zp2bJlhRYqoCzlWKya8HNsofuskkySJvwcq16RIQzbAwAAQInZfcXprrvu0v/+9z/VqFFDCxcu1MKFC1WjRg1t3LhRd9xxR2lkBEps48HTik/OKHK/VVJ8coY2HjxddqEAAABQ4dl9xUmS2rVrp88//9zRWYBrlpRadGm6muMAAAAA6SrXcQLKqyA/L4ceBwAAAEgUJziZjuEBCjV76Up3L4Wac6cmBwAAAEqK4gSn4upi0rj+kZJUZHl6uHs4E0MAAADALhQnOJ0+zUM1/cG2CjHnH47n4Zpblj5dfVDxyelGRAMAAEAFZbJarVajQ5SllJQUmc1mJScny9/f3+g4KEU5Fqs2HjytpNQMBfl5qXFwFd074w/tS0pTk2A/ffN4Z5m93Y2OCQAAAIPY0w3sLk4ZGRl6//33tWLFCiUlJcliseTbv2XLFvsTlyGKU+V29Mx53fnReiWlZqpT/QDNfaijPN1cjY4FAAAAA9jTDeyejvzhhx/W4sWLdffdd6tjx44ymbhXBBVH7Wo+mj2sg+795A/9ceC0nluwVe/e21ou3PMEAACAK7D7ipPZbNavv/6qrl27llamUsUVJ0jSmr0nNGx2tLItVj12fX2N7RdhdCQAAACUMXu6gd2TQ9SqVUt+fn5XHQ4oD7o3CtSbd7eUJH2y+oDmrDtocCIAAACUZ3YXp2nTpunFF19UXFxcaeQBysydbWvr+d5NJEkTfonVb9viDU4EAACA8srue5zat2+vjIwM1a9fXz4+PnJ3zz8r2enTpx0WDihtT/ZsoPjkdH3+x2E9/XWMavh5qkM9FscFAABAfnYXp0GDBunYsWOaNGmSgoODmRwCFZrJZNKE25orITlTS3cmavjcTfruic5qGMRwVAAAAPzN7skhfHx8tGHDBrVq1aq0MpUqJodAYdIv5Oj+T//Qn4fPqlZVb/3wZBcF+XsV/0IAAABUWKU6OUTTpk2Vnp5+1eGA8sjbw1X/GdJB4TV8dexsuobOjlZqRpbRsQAAAFBO2F2cpkyZomeffVYrV67UqVOnlJKSku8BVFQBvh6aO6yjalTxUGx8ip78YouycizFvxAAAABOz+6hei4uuV3r8nubrFarTCaTcnJyHJeuFDBUD8XZevSs7pvxh85fyNGdbWtp2j2tuJcPAADACdnTDeyeHGLFihVXHQyoCFrWrqoP72+r4fM26fstx1TT7K3nLk5bDgAAgMrJ7itOFR1XnFBSX0cf1ovfbZMkvXFHcz1wXV2DEwEAAMCRSvWK0+rVq6+4//rrr7f3lEC5dG+HOjp+NkPvLturVxZuV5Cfl3pFBhsdCwAAAAa46nuc8p3kkvs/uMcJzsRqtWrMd9v09aYj8nJ30fxHOqltnWpGxwIAAIADlOp05GfOnMn3SEpK0qJFi9ShQwctXrz4qkMD5ZHJZNLrdzRXzyaBysiyaPjcTTp48pzRsQAAAFDGHHaP06pVqzR69Ght3rzZEacrNVxxwtU4l5mt+2b8oW3HklW3uo++e6KLalTxNDoWAAAArkGpXnEqSnBwsHbv3u2o0wHliq+nm2YN7aA6AT6KO3VeD8+J1vkL2UbHAgAAQBmx+4rT1q1b8z23Wq2Kj4/XlClTlJ2drbVr1zo0oKNxxQnX4sCJNN01fb3OnM/SDU0CNXNwe7m5OuzfHwAAAFCG7OkGVzU5hMlk0uUv69Spk2bNmqWmTZvan7gMUZxwrTbHndH9M/9QZrZF93UI0+Q7W7BALgAAQAVUqtORHzx4MN9zFxcXBQYGysvLy95TARVSu7rV9P6gNnr88836KvqIQs3eejqqkdGxAAAAUIrsLk5167IIKHBzsxBNuL25Xlm4Xf9eukehZi8N7BBmdCwAAACUkhLfnLFhwwb98ssv+bbNmzdP4eHhCgoK0qOPPqrMzEyHBwTKq390qqsnezaQJI39YZtW7k4yOBEAAABKS4mL08SJE7Vjxw7b823btunhhx9WVFSUxowZo59//lmTJ08ulZBAefV87ya6s00t5VisevKLLdp2NNnoSAAAACgFJS5OMTExuummm2zPv/rqK1133XWaOXOmRo8erffee0/ffPNNqYQEyiuTyaQpd7VUt4Y1dP5CjobNidaR0+eNjgUAAAAHK3FxOnPmjIKDg23PV61apb59+9qed+jQQUeOHHFsOqAC8HBz0fQH2yoi1F8n0zI1ZNZGnT53wehYAAAAcKASF6fg4GDbjHoXLlzQli1b1KlTJ9v+1NRUubu7Oz4hUAH4eblrzrAOqlXVWwdOntPwudFKv5BjdCwAAAA4SImLU79+/TRmzBitWbNGY8eOlY+Pj7p3727bv3XrVjVo0KBUQgIVQbC/l+YM6yB/LzdtOXxWT3/1p3Isdi2TBgAAgHKqxMXptddek5ubm3r06KGZM2dq5syZ8vDwsO2fNWuWbr755lIJCVQUjYL99OmQDvJwc9Hi2ESN/2lHgcWiAQAAUPGYrHb+VJecnKwqVarI1dU13/bTp0+rSpUq+cpUeWTP6sDA1fp1W7xGzN8iq1V6sU9TPdGTq7EAAADljT3doMRXnPKYzeYCpUmSAgICyn1pAspKvxaheuWWSEnS1EW7tPDPYwYnAgAAwLWwuzgBKJmHuoXrke7hkqTnv/1L6/adNDgRAAAArhbFCShFY/tG6NaWocrKseqxzzYr9niK0ZEAAABwFShOQClycTFp2sBWui48QGmZ2Ro2Z6OOnU03OhYAAADsRHECSpmnm6tmDG6vxsFVlJiSu0Bu8vkso2MBAADADhQnoAyYvd01Z1hHhfh7aV9Smh75bJMyslggFwAAoKKgOAFlpGZVb815qIP8PN208eBpPbvgL1lYIBcAAKBCoDgBZahpiL8+GdxO7q4m/XdrvN74dafRkQAAAFACFCegjHVpUEP/uqeVJOk/aw/q0zUHDE4EAACA4lCcAAPc3rqWxvRtKkl6/b879cvW4wYnAgAAwJVQnACDPHZ9fQ3pXFeSNPrrv/THgVMGJwIAAEBRKE6AQUwmk17t30y9mwXrQo5Fj87bpD2JqUbHAgAAQCEoToCBXF1Meve+Nmpft5pSMrI1dNZGJSRnGB0LAAAAl6E4AQbzcnfVzMHtVT/QV8eTMzR09kalZLBALgAAQHlCcQLKgWq+Hpo7rKMC/Ty1KyFVT3y+WReyLUbHAgAAwEUUJ6CcCAvw0eyhHeTr4ap1+07phW9ZIBcAAKC8oDgB5UjzWmZ99GA7ubmYtDDmuN78fbfRkQAAACCKE1Du9GgcqMl3tpAkfbxqv+ZtOGRsIAAAAFCcgPLonvZherZXY0nSuJ926PcdCQYnAgAAqNwoTkA5NfLGhhrUsY6sVumpL//U5rjTRkcCAACotChOQDllMpn02u3NFBURpMxsix6eu0n7T6QZHQsAAKBSojgB5Zibq4veG9RGrcKq6uz5LA2ZtVFJqSyQCwAAUNbKRXH68MMPVa9ePXl5eem6667Txo0bizx2zpw5MplM+R5eXl5lmBYoWz4ebpo1pL3qVffR0TPpemhOtNIys42OBQAAUKkYXpy+/vprjR49WuPGjdOWLVvUqlUr9e7dW0lJSUW+xt/fX/Hx8bZHXFxcGSYGyl71Kp6aM6yjqvt6aPuxFD35xRZl5bBALgAAQFkxvDi9/fbbeuSRRzRs2DBFRkbq448/lo+Pj2bNmlXka0wmk0JCQmyP4ODgMkwMGKNeDV/9Z2gHebu7avWeE3rp+22yWlkgFwAAoCwYWpwuXLigzZs3KyoqyrbNxcVFUVFR2rBhQ5GvS0tLU926dRUWFqbbb79dO3bsKPLYzMxMpaSk5HsAFVXrsKr64P42cjFJCzYf1b+X7jU6EgAAQKVgaHE6efKkcnJyClwxCg4OVkJC4evWNGnSRLNmzdKPP/6ozz//XBaLRV26dNHRo0cLPX7y5Mkym822R1hYmMM/B1CWbooI1ht35C6Q+96yvfpy42GDEwEAADg/w4fq2atz584aPHiwWrdurR49euj7779XYGCgPvnkk0KPHzt2rJKTk22PI0eOlHFiwPEGdayjp25sKEn6v4XbtXxXosGJAAAAnJuhxalGjRpydXVVYmL+H/oSExMVEhJSonO4u7urTZs22rdvX6H7PT095e/vn+8BOINnejXWPe1qK8di1Ygv/tRfR84aHQkAAMBpGVqcPDw81K5dOy1btsy2zWKxaNmyZercuXOJzpGTk6Nt27YpNDS0tGIC5ZLJZNKkO1vo+saBSs/K0UNzonXo5DmjYwEAADglw4fqjR49WjNnztTcuXO1c+dOPfHEEzp37pyGDRsmSRo8eLDGjh1rO37ixIlavHixDhw4oC1btujBBx9UXFychg8fbtRHAAzj7uqijx5oq+a1/HXq3AUNnb1Rp9IyjY4FAADgdNyMDnDvvffqxIkTevXVV5WQkKDWrVtr0aJFtgkjDh8+LBeXv/vdmTNn9MgjjyghIUHVqlVTu3bttH79ekVGRhr1EQBDVfF006yhHXTnR+t16NR5PTx3k758pJO8PVyNjgYAAOA0TNZKthBMSkqKzGazkpOTud8JTmX/iTTdNX29zp7PUlREkD5+sJ3cXA2/qAwAAFBu2dMN+KkKcBINAqvoP0Pay9PNRUt3JunVn3awQC4AAICDUJwAJ9KuboDeva+NTCZp/v8O68MVhc82CQAAAPtQnAAn06d5iCbc1kyS9K/Fe/Tt5sIXhwYAAEDJUZwAJzS4cz091qO+JGnMd1u1es8JgxMBAABUbBQnwEm92LupBrSuqWyLVU98vlnbjyUbHQkAAKDCojgBTsrFxaQ3726lLg2q69yFHA2bE60jp88bHQsAAKBCojgBTszDzUUf/6Odmob46URqpobO3qiz5y8YHQsAAKDCoTgBTs7fy11zhnVUTbOX9p84p+FzNykjK8foWAAAABUKxQmoBELMXprzUEf5e7lpU9wZjfoqRjkW1ngCAAAoKYoTUEk0DvbTjMHt5eHqokU7EvTaL7EskAsAAFBCFCegEulUv7qmDWwlSZqz/pBmrjlgcCIAAICKgeIEVDL9W9XU/90SIUma9Osu/RhzzOBEAAAA5R/FCaiEhnevr4e6hkuSnlvwl9bvP2lwIgAAgPKN4gRUUv93S4RuaRGqrByrHpu3WbsSUoyOBAAAUG5RnIBKysXFpGkDW6ljeIBSM7M1dFa0jp9NNzoWAABAuURxAioxL3dXzfxHezUKqqKElAwNnb1RyelZRscCAAAodyhOQCVn9nHXnIc6KsjPU3sS0/TovE3KzGaBXAAAgEtRnACoVlVvzRnWUVU83fS/g6f13IKtsrBALgAAgA3FCYAkKbKmvz5+sJ3cXEz6+a/jmrJol9GRAAAAyg2KEwCbbo1q6K17WkqSZqw+oNnrDhqcCAAAoHygOAHI5442tfVCnyaSpIm/xOq3bfEGJwIAADAexQlAAU/0aKB/dKorq1V6+usYbTx42uhIAAAAhqI4ASjAZDJp/G3N1CsyWBeyLXpk3ibtS0o1OhYAAIBhKE4ACuXqYtJ797VRmzpVlZyepSGzopWYkmF0LAAAAENQnAAUydvDVf8Z0kHhNXx17Gy6hs2OVmoGC+QCAIDKh+IE4IoCfD00d1hH1ajiodj4FD35xRZdyLYox2LVhv2n9GPMMW3Yf0o5rPsEAACcmMlqtVaqn3ZSUlJkNpuVnJwsf39/o+MAFca2o8m6d8YGnb+Qo+vCAxR3+rwSkv8euhdq9tK4/pHq0zzUwJQAAAAlZ0834IoTgBJpUdusDx9oKxeT9L+Dp/OVJklKSM7QE59v0aLtTF8OAACcD8UJQIld3yhQfl7uhe7Lu3Q94edYhu0BAACnQ3ECUGIbD55WcnrRk0NYJcUnZ7DuEwAAcDpuRgcAUHEkpZZsOvKPV+1XQkq6WtQyK7xGFbm6mEo5GQAAQOmiOAEosSA/rxIdt2rPCa3ac0KS5OPhqmY1/dW8llkta5spUwAAoEKiOAEosY7hAQo1eykhOUNF3cVU1dtdt7WuqR3HUxR7PEXnL+Qo+tAZRR86Yzvm0jLV4uKjfiBlCgAAlF8UJwAl5upi0rj+kXri8y0ySfnKU17lmXJXC9uU5DkWq/afSNPWo8nafixZ244lU6YAAECFxDpOAOy2aHu8Jvwcq/irWMcpr0xtO5pbpLYfS9aO4ylKz8opcKyPh6siQ/MP86NMAQAAR7GnG1CcAFyVHItVGw+eVlJqhoL8vNQxPOCqC83VlqkWtcxqUdusBpQpAABwFShOV0BxAiqGHItVBy4O8yuuTHm7XzbMjzIFAABKgOJ0BRQnoOLKK1Pbjv1dprYfK7pMRdb0V4taZttQP8oUAAC4FMXpCihOgHMprEztuDgBxeUuL1MtapnVINBXbq6sBQ4AQGVEcboCihPg/HIsVh08mVum8mb0K6pMebm7KDLUXy1rV6VMAQBQyVCcroDiBFROl5apbUdTcof5HU++Ypn6e5hfVcoUAABOiOJ0BRQnAHkKK1M7jifrXAnKVIvaZjUMrEKZAgCgAqM4XQHFCcCV5Japc9qeb5hf0WUq4mKZakGZAgCgwqE4XQHFCYC9LBarDlwsU3mTUOw4VnyZypvN72rKlCPXyQIAAIWjOF0BxQmAI1xLmWpRy6xGQUWXqUXb4zXh51jFJ2fYtoWavTSuf6T6NA8ttc8EAEBlQ3G6AooTgNJisVh18NTfw/yuVKY83S4Z5lf77zK1dGeinvh8iy7/D3PetabpD7alPAEA4CAUpyugOAEoS5eWqW15Zep4itIyswsc6+FqklVSVk7h/1k2SQoxe2ntizcybA8AAAegOF0BxQmA0ewpU4VpVtNfdQJ8ZPZ2l9nbXf4XfzV7u6uqz9+/N3u7y8/LnZIFAEARKE5XQHECUB5ZLFb9Z+1BvfHrToef28/LLV+ZuvThX8T2vH2ULgCAM7OnG7iVUSYAwBW4uJjUvJa5RMeOuKGBgvy8lJyeVeCRcsnv8xb3Tc3IVmpGto6eSbc7l5+nW+HlyufKxcvfy61cT8vOrIUAAHtRnACgnOgYHqBQs5cSkjMKTA4h/X2P0+heTUr0Q/6FbItSMoooVucLlq5L9+dNaJGama3UzGwdO2t/6ari6WYrV1XLUeli1kIAwNVgqB4AlCOLtsfric+3SFK+8lTWs+pl5ViKvppVwtJ1LS4tXWbvwocaFlW8rlS68r5fZi0sXVzRA1BRcI/TFVCcAJR3Ff2KSFaOJd+QwcKGEeZ/ZNv2lXSCjCvx9XAttFj5ebnpm01Hr/gegX6eWvBYZ/l4uMrd1UXubi5ydzXJ3cVFLvzgXyIV/e8vgMqF4nQFFCcAFUFl/Rf77ByLUjKyCy9Y5y+Ueum6EndXU26ZuvjwcDXJ3c1FHnnb3C5uc3WRh1veMS5/v+7isR5uLvnO5XHJ8e6upvyvvXisx6Xvm/eebn+fw/Pia4z+O8IVPQAVDZNDAEAF5+piUucG1Y2OUebcXF0U4OuhAF8Pu197pdKVkp6lzXFntHxXUrHncXc1yWLNLa+XysqxKisnR9K1D0UsLS4m5Stdlxasv8vXZeUsX6krWA7/LnVXKocucjVJL/+wvdD786zKLU8Tfo5Vr8gQwwteRVdZ/2EFMBrFCQDgFIorXRv2nypRcZr30HXq3KC6cixWZeVYdCHHoqxsy8XiZFFmtkVZOX8/LmRbLznm4vEXj71w6bZsq+01+c/x9+sv5G3LO+cl57Adl+/Y/DXFYpUys3PPr0yHfK0OY5UUn5yhpv/3m7w9XOXh5ipPNxd5urvI081VHm65V85yH65//76I/R6X73f9+/eebi6X7M//3MPVRSZTxS0ZDIUEjENxAgBUCiWdtbBjeICk3Kt+ri6u8nJ3LdOc9rBarbaSllfQLmQXUtwu3Zav9Fl0IcdqK325+62XlLUrlcOLZe7ittPnLigptfi2lmWxKisjW1LpDq28Eo9CCprHJSXr8gJXoIQVWtL+LoK5+y87t5vrxddcfXkraihkQnKGnvh8C0MhgVJGcQIAVAquLiaN6x+pJz7fIpMKn7VwXP/ICjXkyWQyycMtdzid0TbsP6VBM/8o9rj3BrVW85pm25WxzKwcXcixKDPr4vPsHGVm55a6zOwc2/bcY3Jsr7twybGZWRZlXtx/Ie+8l53rUhcubkstj+Xt8pJ18bm7q0m/bI0vciikJI39fps83Vzl45Fb+HMfLrm/XnLOinzFrSwwFBJFYXIIAEClwlCn0pFjsarb1OXFXtFb++KNZf5DqNWae8XMVrKyLytYhZS3C5eWr1Iob0YxmSRPt7/LVF6x8nR3lVfedvci9l+8yubl7nJx3yXH2vZdui33nOV5MezL8d+H0lfeiimz6l0BxQkAUN7+j9tZlJd1yMqjay1vMYfP6tftCcW+T82q3vJyc1FGVo4ysi25v2blyGLgT3tuLqbLypVL/itiFwuXZyGF7fIrZpcWMkdfVWNWyNJXHospxekKKE4AAJSe8viDkTMo6VDILx/pVGBGzrx74TKyc0tUZlZeobLYtmVk/V2yMi4WuXzbsy89JrfMXb7fdl6Dr7BdzVU1DzcXfbnxyBWXNajm4643BrSQu5uLXF0kF5Mp915Ik0kuLhd/f/G5q4vp7/2XHPv3tvy///s8sr3e2YZUltdiSnG6AooTAACliyt6jleeh0IWxmKxKjO7sNL1d2HLLKSwXVrGMot4XUbWJcWunFxVKw0mk/4uZbbipULK1mW/t21TgW1ul5W23F+Vv8hd8TW6LM/lRfDvkujm8vd5ZJIm/7pLyelZhX9WGff3l3WcAACAYSrrOmSlqaJNbuLiYpK3h6u8PcpmVkqr1apsi/UKxavwEpdX7rYfS9aK3SeKfZ/wGr4ye7vLYrUqx5L7sFx8b4vFqhyrVRZLbtHN/X3urzmW/L/PfV1xn0nKtlrldI2wEHnLFWw8eLpc/7eD4gQAAFAB9GkequkPti0wFDKEoZAymUxyv7g4s5+X/a/fsP9UiYrTpDtaOOwHe6vValts21bE8srWJSUs22LJLWNWa/5jL/l97q+ybS94Hutlr8+9Kph92bEFc6jA6wt/74vnvOz1eYXyeHK6dsanFvudJKVmFHuMkcpFcfrwww/11ltvKSEhQa1atdL777+vjh07Fnn8ggUL9Morr+jQoUNq1KiRpk6dqn79+pVhYgAAgLLXp3moekWGMBTSwexd580RTCaTXC8OvXN2Jb1HL+hqWm8ZMnx+yK+//lqjR4/WuHHjtGXLFrVq1Uq9e/dWUlLhq7uvX79egwYN0sMPP6w///xTAwYM0IABA7R9+/YyTg4AAFD28oZC3t66ljo3qF4pfvAubXlDIaW/hz7mKY9DISuavGJa1LdnUu4kMo4spqXB8MkhrrvuOnXo0EEffPCBJMlisSgsLEz//Oc/NWbMmALH33vvvTp37px++eUX27ZOnTqpdevW+vjjj4t9PyaHAAAAQGGYFbL0lNflCirM5BAXLlzQ5s2bNXbsWNs2FxcXRUVFacOGDYW+ZsOGDRo9enS+bb1799bChQsLPT4zM1OZmZm25ykpKdceHAAAAE6HoZClxxnu0TO0OJ08eVI5OTkKDg7Otz04OFi7du0q9DUJCQmFHp+QUPiicJMnT9aECRMcExgAAABOjVkhS09FL6blYnKI0jR27Nh8V6hSUlIUFhZmYCIAAACgcqrIxdTQ4lSjRg25uroqMTEx3/bExESFhIQU+pqQkBC7jvf09JSnp6djAgMAAAColAydVc/Dw0Pt2rXTsmXLbNssFouWLVumzp07F/qazp075ztekpYsWVLk8QAAAABwrQwfqjd69GgNGTJE7du3V8eOHfXOO+/o3LlzGjZsmCRp8ODBqlWrliZPnixJevrpp9WjRw9NmzZNt9xyi7766itt2rRJM2bMMPJjAAAAAHBihhene++9VydOnNCrr76qhIQEtW7dWosWLbJNAHH48GG5uPx9YaxLly6aP3++/u///k8vvfSSGjVqpIULF6p58+ZGfQQAAAAATs7wdZzKGus4AQAAAJDs6waG3uMEAAAAABUBxQkAAAAAikFxAgAAAIBiUJwAAAAAoBgUJwAAAAAoBsUJAAAAAIpBcQIAAACAYlCcAAAAAKAYbkYHKGt56/2mpKQYnAQAAACAkfI6QV5HuJJKV5xSU1MlSWFhYQYnAQAAAFAepKamymw2X/EYk7Uk9cqJWCwWHT9+XH5+fjKZTEbHUUpKisLCwnTkyBH5+/sbHcfp8P2WLr7f0sX3W7r4fksX32/p4vstXXy/pas8fb9Wq1WpqamqWbOmXFyufBdTpbvi5OLiotq1axsdowB/f3/D/+I4M77f0sX3W7r4fksX32/p4vstXXy/pYvvt3SVl++3uCtNeZgcAgAAAACKQXECAAAAgGJQnAzm6empcePGydPT0+goTonvt3Tx/ZYuvt/Sxfdbuvh+Sxffb+ni+y1dFfX7rXSTQwAAAACAvbjiBAAAAADFoDgBAAAAQDEoTgAAAABQDIoTAAAAABSD4mSQ1atXq3///qpZs6ZMJpMWLlxodCSnMXnyZHXo0EF+fn4KCgrSgAEDtHv3bqNjOY3p06erZcuWtkXrOnfurN9++83oWE5rypQpMplMGjVqlNFRnML48eNlMpnyPZo2bWp0LKdy7NgxPfjgg6pevbq8vb3VokULbdq0yehYTqNevXoF/g6bTCaNGDHC6GhOIScnR6+88orCw8Pl7e2tBg0a6LXXXhNzqTlGamqqRo0apbp168rb21tdunRRdHS00bFKzM3oAJXVuXPn1KpVKz300EO68847jY7jVFatWqURI0aoQ4cOys7O1ksvvaSbb75ZsbGx8vX1NTpehVe7dm1NmTJFjRo1ktVq1dy5c3X77bfrzz//VLNmzYyO51Sio6P1ySefqGXLlkZHcSrNmjXT0qVLbc/d3Pi/Qkc5c+aMunbtqhtuuEG//fabAgMDtXfvXlWrVs3oaE4jOjpaOTk5tufbt29Xr169dM899xiYynlMnTpV06dP19y5c9WsWTNt2rRJw4YNk9ls1lNPPWV0vApv+PDh2r59uz777DPVrFlTn3/+uaKiohQbG6tatWoZHa9YTEdeDphMJv3www8aMGCA0VGc0okTJxQUFKRVq1bp+uuvNzqOUwoICNBbb72lhx9+2OgoTiMtLU1t27bVRx99pNdff12tW7fWO++8Y3SsCm/8+PFauHChYmJijI7ilMaMGaN169ZpzZo1RkepNEaNGqVffvlFe/fulclkMjpOhXfrrbcqODhY//nPf2zb7rrrLnl7e+vzzz83MFnFl56eLj8/P/3444+65ZZbbNvbtWunvn376vXXXzcwXckwVA9OLzk5WVLuD/dwrJycHH311Vc6d+6cOnfubHQcpzJixAjdcsstioqKMjqK09m7d69q1qyp+vXr64EHHtDhw4eNjuQ0fvrpJ7Vv31733HOPgoKC1KZNG82cOdPoWE7rwoUL+vzzz/XQQw9RmhykS5cuWrZsmfbs2SNJ+uuvv7R27Vr17dvX4GQVX3Z2tnJycuTl5ZVvu7e3t9auXWtQKvswPgFOzWKxaNSoUeratauaN29udBynsW3bNnXu3FkZGRmqUqWKfvjhB0VGRhody2l89dVX2rJlS4Ua911RXHfddZozZ46aNGmi+Ph4TZgwQd27d9f27dvl5+dndLwK78CBA5o+fbpGjx6tl156SdHR0Xrqqafk4eGhIUOGGB3P6SxcuFBnz57V0KFDjY7iNMaMGaOUlBQ1bdpUrq6uysnJ0RtvvKEHHnjA6GgVnp+fnzp37qzXXntNERERCg4O1pdffqkNGzaoYcOGRscrEYoTnNqIESO0ffv2CvMvGRVFkyZNFBMTo+TkZH377bcaMmSIVq1aRXlygCNHjujpp5/WkiVLCvyrHK7dpf9q3LJlS1133XWqW7euvvnmG4aaOoDFYlH79u01adIkSVKbNm20fft2ffzxxxSnUvCf//xHffv2Vc2aNY2O4jS++eYbffHFF5o/f76aNWummJgYjRo1SjVr1uTvsAN89tlneuihh1SrVi25urqqbdu2GjRokDZv3mx0tBKhOMFpjRw5Ur/88otWr16t2rVrGx3HqXh4eNj+dahdu3aKjo7Wu+++q08++cTgZBXf5s2blZSUpLZt29q25eTkaPXq1frggw+UmZkpV1dXAxM6l6pVq6px48bat2+f0VGcQmhoaIF/QImIiNB3331nUCLnFRcXp6VLl+r77783OopTef755zVmzBjdd999kqQWLVooLi5OkydPpjg5QIMGDbRq1SqdO3dOKSkpCg0N1b333qv69esbHa1EuMcJTsdqtWrkyJH64YcftHz5coWHhxsdyelZLBZlZmYaHcMp3HTTTdq2bZtiYmJsj/bt2+uBBx5QTEwMpcnB0tLStH//foWGhhodxSl07dq1wPIPe/bsUd26dQ1K5Lxmz56toKCgfDfZ49qdP39eLi75fzx2dXWVxWIxKJFz8vX1VWhoqM6cOaPff/9dt99+u9GRSoQrTgZJS0vL9y+cBw8eVExMjAICAlSnTh0Dk1V8I0aM0Pz58/Xjjz/Kz89PCQkJkiSz2Sxvb2+D01V8Y8eOVd++fVWnTh2lpqZq/vz5WrlypX7//XejozkFPz+/Avfj+fr6qnr16tyn5wDPPfec+vfvr7p16+r48eMaN26cXF1dNWjQIKOjOYVnnnlGXbp00aRJkzRw4EBt3LhRM2bM0IwZM4yO5lQsFotmz56tIUOGMJ2+g/Xv319vvPGG6tSpo2bNmunPP//U22+/rYceesjoaE7h999/l9VqVZMmTbRv3z49//zzatq0qYYNG2Z0tJKxwhArVqywSirwGDJkiNHRKrzCvldJ1tmzZxsdzSk89NBD1rp161o9PDysgYGB1ptuusm6ePFio2M5tR49eliffvppo2M4hXvvvdcaGhpq9fDwsNaqVct67733Wvft22d0LKfy888/W5s3b2719PS0Nm3a1DpjxgyjIzmd33//3SrJunv3bqOjOJ2UlBTr008/ba1Tp47Vy8vLWr9+fevLL79szczMNDqaU/j666+t9evXt3p4eFhDQkKsI0aMsJ49e9boWCXGOk4AAAAAUAzucQIAAACAYlCcAAAAAKAYFCcAAAAAKAbFCQAAAACKQXECAAAAgGJQnAAAAACgGBQnAAAAACgGxQkAAAAAikFxAgCUuUOHDslkMikmJsboKDa7du1Sp06d5OXlpdatW1/TuUwmkxYuXOiQXACA8oHiBACV0NChQ2UymTRlypR82xcuXCiTyWRQKmONGzdOvr6+2r17t5YtW1bkcQkJCfrnP/+p+vXry9PTU2FhYerfv/8VX3MtVq5cKZPJpLNnz5bK+QEAJUNxAoBKysvLS1OnTtWZM2eMjuIwFy5cuOrX7t+/X926dVPdunVVvXr1Qo85dOiQ2rVrp+XLl+utt97Stm3btGjRIt1www0aMWLEVb93WbBarcrOzjY6BgBUWBQnAKikoqKiFBISosmTJxd5zPjx4wsMW3vnnXdUr1492/OhQ4dqwIABmjRpkoKDg1W1alVNnDhR2dnZev755xUQEKDatWtr9uzZBc6/a9cudenSRV5eXmrevLlWrVqVb//27dvVt29fValSRcHBwfrHP/6hkydP2vb37NlTI0eO1KhRo1SjRg317t270M9hsVg0ceJE1a5dW56enmrdurUWLVpk228ymbR582ZNnDhRJpNJ48ePL/Q8Tz75pEwmkzZu3Ki77rpLjRs3VrNmzTR69Gj98ccfhb6msCtGMTExMplMOnTokCQpLi5O/fv3V7Vq1eTr66tmzZrp119/1aFDh3TDDTdIkqpVqyaTyaShQ4faPtPkyZMVHh4ub29vtWrVSt9++22B9/3tt9/Url07eXp6au3atfrrr790ww03yM/PT/7+/mrXrp02bdpUaHYAwN8oTgBQSbm6umrSpEl6//33dfTo0Ws61/Lly3X8+HGtXr1ab7/9tsaNG6dbb71V1apV0//+9z89/vjjeuyxxwq8z/PPP69nn31Wf/75pzp37qz+/fvr1KlTkqSzZ8/qxhtvVJs2bbRp0yYtWrRIiYmJGjhwYL5zzJ07Vx4eHlq3bp0+/vjjQvO9++67mjZtmv71r39p69at6t27t2677Tbt3btXkhQfH69mzZrp2WefVXx8vJ577rkC5zh9+rQWLVqkESNGyNfXt8D+qlWrXs1XJ0kaMWKEMjMztXr1am3btk1Tp05VlSpVFBYWpu+++06StHv3bsXHx+vdd9+VJE2ePFnz5s3Txx9/rB07duiZZ57Rgw8+WKB8jhkzRlOmTNHOnTvVsmVLPfDAA6pdu7aio6O1efNmjRkzRu7u7ledHQAqCzejAwAAjHPHHXeodevWGjdunP7zn/9c9XkCAgL03nvvycXFRU2aNNGbb76p8+fP66WXXpIkjR07VlOmTNHatWt133332V43cuRI3XXXXZKk6dOna9GiRfrPf/6jF154QR988IHatGmjSZMm2Y6fNWuWwsLCtGfPHjVu3FiS1KhRI7355ptXzPevf/1LL774ou29p06dqhUrVuidd97Rhx9+qJCQELm5ualKlSoKCQkp9Bz79u2T1WpV06ZNr/p7Ksrhw4d11113qUWLFpKk+vXr2/YFBARIkoKCgmzlLDMzU5MmTdLSpUvVuXNn22vWrl2rTz75RD169LC9fuLEierVq1e+93r++edtn6NRo0YO/zwA4IwoTgBQyU2dOlU33nhjoVdZSqpZs2Zycfl7EENwcLCaN29ue+7q6qrq1asrKSkp3+vyfuiXJDc3N7Vv3147d+6UJP31119asWKFqlSpUuD99u/fbytO7dq1u2K2lJQUHT9+XF27ds23vWvXrvrrr79K+Alz7xEqLU899ZSeeOIJLV68WFFRUbrrrrvUsmXLIo/ft2+fzp8/n68QSbn3eLVp0ybftvbt2+d7Pnr0aA0fPlyfffaZoqKidM8996hBgwaO+zAA4KQYqgcAldz111+v3r17a+zYsQX2ubi4FCgMWVlZBY67fKiXyWQqdJvFYilxrrS0NPXv318xMTH5Hnv37tX1119vO66wYXOloVGjRjKZTNq1a5ddr8srlJd+j5d/h8OHD9eBAwf0j3/8Q9u2bVP79u31/vvvF3nOtLQ0SdJ///vffN9NbGxsvvucpILfz/jx47Vjxw7dcsstWr58uSIjI/XDDz/Y9ZkAoDKiOAEANGXKFP3888/asGFDvu2BgYFKSEjI90O/I9deunRChezsbG3evFkRERGSpLZt22rHjh2qV6+eGjZsmO9hT1ny9/dXzZo1tW7dunzb161bp8jIyBKfJyAgQL1799aHH36oc+fOFdhf1HThgYGBknLvo8pT2HcYFhamxx9/XN9//72effZZzZw5U5Lk4eEhScrJybEdGxkZKU9PTx0+fLjAdxMWFlbsZ2ncuLGeeeYZLV68WHfeeWehE3cAAPKjOAEA1KJFCz3wwAN677338m3v2bOnTpw4oTfffFP79+/Xhx9+qN9++81h7/vhhx/qhx9+0K5duzRixAidOXNGDz30kKTcCRNOnz6tQYMGKTo6Wvv379fvv/+uYcOG5SsRJfH8889r6tSp+vrrr7V7926NGTNGMTExevrpp+3Om5OTo44dO+q7777T3r17tXPnTr333nv5hh1eKq/MjB8/Xnv37tV///tfTZs2Ld8xo0aN0u+//66DBw9qy5YtWrFiha1A1q1bVyaTSb/88otOnDihtLQ0+fn56bnnntMzzzyjuXPnav/+/dqyZYvef/99zZ07t8j86enpGjlypFauXKm4uDitW7dO0dHRtvcCABSN4gQAkJQ7icDlQ+kiIiL00Ucf6cMPP1SrVq20cePGa7oX6nJTpkzRlClT1KpVK61du1Y//fSTatSoIUm2q0Q5OTm6+eab1aJFC40aNUpVq1bNdz9VSTz11FMaPXq0nn32WbVo0UKLFi3STz/9ZPfECPXr19eWLVt0ww036Nlnn1Xz5s3Vq1cvLVu2TNOnTy/0Ne7u7vryyy+1a9cutWzZUlOnTtXrr7+e75icnByNGDFCERER6tOnjxo3bqyPPvpIklSrVi1NmDBBY8aMUXBwsEaOHClJeu211/TKK69o8uTJttf997//VXh4eJH5XV1dderUKQ0ePFiNGzfWwIED1bdvX02YMMGu7wEAKiOTtTTvdgUAAAAAJ8AVJwAAAAAoBsUJAAAAAIpBcQIAAACAYlCcAAAAAKAYFCcAAAAAKAbFCQAAAACKQXECAAAAgGJQnAAAAACgGBQnAAAAACgGxQkAAAAAikFxAgAAAIBi/D+2+rdFR6ujBgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ## Calculate Silhouette Score\n",
+    "silhouette_avg = silhouette_score(features, stock_data['Cluster'])\n",
+    "print(f'Silhouette Score: {silhouette_avg}')\n",
+    "\n",
+    "# ## Elbow Method to Find Optimal Number of Clusters\n",
+    "sse = []\n",
+    "k_range = range(1, 10)\n",
+    "for k in k_range:\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(features)\n",
+    "    sse.append(kmeans.inertia_)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_range, sse, marker='o')\n",
+    "plt.title('Elbow Method for Optimal K')\n",
+    "plt.xlabel('Number of Clusters')\n",
+    "plt.ylabel('Sum of Squared Distances (SSE)')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bought at 429.950012, positions = 232.5851778322546\n",
+      "Final Portfolio Value: 168682.40022284264\n",
+      "Net Profit/Loss: 68682.40022284264\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ## Backtest the Strategy\n",
+    "initial_balance = 100000  # Assume you start with $100,000\n",
+    "balance = initial_balance\n",
+    "positions = 0\n",
+    "stock_price_at_last_signal = 0  # To store the price of stock at the last signal\n",
+    "\n",
+    "for i in range(1, len(stock_data)):\n",
+    "    signal = stock_data['Signal'].iloc[i]\n",
+    "    price = stock_data['Close'].iloc[i]\n",
+    "\n",
+    "    if signal == 'Buy' and positions == 0:\n",
+    "        # Buy as much stock as possible\n",
+    "        positions = balance / price\n",
+    "        stock_price_at_last_signal = price\n",
+    "        print(f'Bought at {price}, positions = {positions}')\n",
+    "        balance = 0  # All money invested\n",
+    "\n",
+    "    elif signal == 'Sell' and positions > 0:\n",
+    "        # Sell all positions\n",
+    "        balance = positions * price\n",
+    "        positions = 0\n",
+    "        profit = (price - stock_price_at_last_signal) * (balance / price)\n",
+    "        print(f'Sold at {price}, balance = {balance}, profit = {profit}')\n",
+    "\n",
+    "# ## Final Portfolio Value (Including Any Remaining Stock)\n",
+    "final_portfolio_value = balance + positions * stock_data['Close'].iloc[-1]\n",
+    "print(f'Final Portfolio Value: {final_portfolio_value}')\n",
+    "print(f'Net Profit/Loss: {final_portfolio_value - initial_balance}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Intel_Optimized/hybrid.ipynb b/Intel_Optimized/hybrid.ipynb
new file mode 100644
index 0000000..c40d926
--- /dev/null
+++ b/Intel_Optimized/hybrid.ipynb
@@ -0,0 +1,1030 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.62 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.16 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.22 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.84 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.24 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.17 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.30 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.28 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.38 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.56 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.97 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.44 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.13 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.51 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 11.362 seconds\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'numpy.ndarray' object has no attribute 'values'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 71\u001b[0m\n\u001b[0;32m     68\u001b[0m custom_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../Data/SBI Train data.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     69\u001b[0m close_prices \u001b[38;5;241m=\u001b[39m custom_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m---> 71\u001b[0m prediction \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclose_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHybrid model prediction for next day closing price: $\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprediction\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Cell \u001b[1;32mIn[2], line 34\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(data, time_steps)\u001b[0m\n\u001b[0;32m     31\u001b[0m arima_results \u001b[38;5;241m=\u001b[39m arima_model\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Get ARIMA residuals\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m arima_residuals \u001b[38;5;241m=\u001b[39m df \u001b[38;5;241m-\u001b[39m \u001b[43marima_results\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfittedvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     36\u001b[0m \u001b[38;5;66;03m# Prepare data for LSTM\u001b[39;00m\n\u001b[0;32m     37\u001b[0m scaler \u001b[38;5;241m=\u001b[39m MinMaxScaler()\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'values'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import modin.pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearnex import patch_sklearn\n",
+    "patch_sklearn()\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(data, pd.DataFrame):\n",
+    "        df = data.values\n",
+    "    else:\n",
+    "        df = np.array(data)\n",
+    "    \n",
+    "    df = df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = df - arima_results.fittedvalues.values.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make hybrid prediction\n",
+    "    last_60_days = residuals_scaled[-60:]\n",
+    "    X_test = np.array([last_60_days])\n",
+    "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "\n",
+    "    lstm_prediction = lstm_model.predict(X_test)\n",
+    "    lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "    arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "    hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "\n",
+    "    return hybrid_prediction[0]\n",
+    "\n",
+    "# Example usage with custom data\n",
+    "# Assuming you have a CSV file named 'my_stock_data.csv' with a 'Close' column\n",
+    "custom_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "close_prices = custom_data['Close']\n",
+    "\n",
+    "prediction = hybrid_model(close_prices)\n",
+    "print(f\"Hybrid model prediction for next day closing price: ${prediction:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=1.22 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.09 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.55 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.46 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.06 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=1.17 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.24 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.18 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.31 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.17 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.31 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.39 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.08 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.57 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.97 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.07 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.45 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.55 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 13.125 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n",
+      "Hybrid model prediction for next day closing price: $245.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(data, pd.Series):\n",
+    "        df = data.values\n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        df = data.values\n",
+    "    else:\n",
+    "        df = np.array(data)\n",
+    "    \n",
+    "    df = df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make hybrid prediction\n",
+    "    last_60_days = residuals_scaled[-60:]\n",
+    "    X_test = np.array([last_60_days])\n",
+    "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "\n",
+    "    lstm_prediction = lstm_model.predict(X_test)\n",
+    "    lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "    arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "    hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "\n",
+    "    return hybrid_prediction[0]\n",
+    "\n",
+    "# Example usage with custom data\n",
+    "custom_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "close_prices = custom_data['Close']\n",
+    "\n",
+    "prediction = hybrid_model(close_prices)\n",
+    "print(f\"Hybrid model prediction for next day closing price: ${prediction:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.62 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.15 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.21 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.03 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.85 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.21 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.25 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.96 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.21 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.32 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.29 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.43 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.58 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=1.01 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.44 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.15 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.52 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 11.613 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'ARIMA' object has no attribute 'append'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[4], line 88\u001b[0m\n\u001b[0;32m     85\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m---> 88\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m     91\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
+      "Cell \u001b[1;32mIn[4], line 76\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps)\u001b[0m\n\u001b[0;32m     73\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model\u001b[39;00m\n\u001b[1;32m---> 76\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'ARIMA' object has no attribute 'append'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(train_data, test_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    elif isinstance(train_data, pd.DataFrame):\n",
+    "        train_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# You can also calculate percentage error\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.63 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.17 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.24 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.93 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.23 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.30 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.22 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.30 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.31 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.42 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.10 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.61 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=1.00 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.05 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.45 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.54 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 11.839 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'ARIMA' object has no attribute 'append'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 88\u001b[0m\n\u001b[0;32m     85\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m---> 88\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m     91\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
+      "Cell \u001b[1;32mIn[5], line 76\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps)\u001b[0m\n\u001b[0;32m     73\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model\u001b[39;00m\n\u001b[1;32m---> 76\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'ARIMA' object has no attribute 'append'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(train_data, test_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    elif isinstance(train_data, pd.DataFrame):\n",
+    "        train_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# You can also calculate percentage error\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.59 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.06 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.16 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.25 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.85 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.28 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=2.09 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.29 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.33 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.23 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.30 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.38 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.57 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.98 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.42 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.50 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 11.823 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n",
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'saved_model\\\\lstm_model.h5\\\\arima_model.pkl'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[6], line 122\u001b[0m\n\u001b[0;32m    119\u001b[0m save_model(arima_results, lstm_model, scaler, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msaved_model\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    121\u001b[0m \u001b[38;5;66;03m# Later, load the model and make predictions\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m loaded_arima, loaded_lstm, loaded_scaler \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msaved_model\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    123\u001b[0m predictions \u001b[38;5;241m=\u001b[39m make_predictions(loaded_arima, loaded_lstm, loaded_scaler, test_close_prices)\n\u001b[0;32m    125\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n",
+      "Cell \u001b[1;32mIn[6], line 77\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(folder_path)\u001b[0m\n\u001b[0;32m     74\u001b[0m arima_results \u001b[38;5;241m=\u001b[39m joblib\u001b[38;5;241m.\u001b[39mload(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124marima_model.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m     76\u001b[0m \u001b[38;5;66;03m# Load LSTM model\u001b[39;00m\n\u001b[1;32m---> 77\u001b[0m lstm_model \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlstm_model.h5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;66;03m# Load scaler\u001b[39;00m\n\u001b[0;32m     80\u001b[0m scaler \u001b[38;5;241m=\u001b[39m joblib\u001b[38;5;241m.\u001b[39mload(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscaler.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
+      "Cell \u001b[1;32mIn[6], line 74\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(folder_path)\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_model\u001b[39m(folder_path):\n\u001b[0;32m     73\u001b[0m     \u001b[38;5;66;03m# Load ARIMA model\u001b[39;00m\n\u001b[1;32m---> 74\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43mjoblib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43marima_model.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     76\u001b[0m     \u001b[38;5;66;03m# Load LSTM model\u001b[39;00m\n\u001b[0;32m     77\u001b[0m     lstm_model \u001b[38;5;241m=\u001b[39m load_model(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlstm_model.h5\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\joblib\\numpy_pickle.py:579\u001b[0m, in \u001b[0;36mload\u001b[1;34m(filename, mmap_mode)\u001b[0m\n\u001b[0;32m    577\u001b[0m         obj \u001b[38;5;241m=\u001b[39m _unpickle(fobj)\n\u001b[0;32m    578\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 579\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m    580\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m _read_fileobject(f, filename, mmap_mode) \u001b[38;5;28;01mas\u001b[39;00m fobj:\n\u001b[0;32m    581\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fobj, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    582\u001b[0m                 \u001b[38;5;66;03m# if the returned file object is a string, this means we\u001b[39;00m\n\u001b[0;32m    583\u001b[0m                 \u001b[38;5;66;03m# try to load a pickle file generated with an version of\u001b[39;00m\n\u001b[0;32m    584\u001b[0m                 \u001b[38;5;66;03m# Joblib so we load it with joblib compatibility function.\u001b[39;00m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_model\\\\lstm_model.h5\\\\arima_model.pkl'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential, load_model\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "import joblib\n",
+    "import os\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def create_hybrid_model(train_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    elif isinstance(train_data, pd.DataFrame):\n",
+    "        train_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    return arima_results, lstm_model, scaler\n",
+    "\n",
+    "def save_model(arima_results, lstm_model, scaler, folder_path):\n",
+    "    if not os.path.exists(folder_path):\n",
+    "        os.makedirs(folder_path)\n",
+    "    \n",
+    "    # Save ARIMA model\n",
+    "    joblib.dump(arima_results, os.path.join(folder_path, 'arima_model.pkl'))\n",
+    "    \n",
+    "    # Save LSTM model\n",
+    "    lstm_model.save(os.path.join(folder_path, 'lstm_model.h5'))\n",
+    "    \n",
+    "    # Save scaler\n",
+    "    joblib.dump(scaler, os.path.join(folder_path, 'scaler.pkl'))\n",
+    "\n",
+    "def load_model(folder_path):\n",
+    "    # Load ARIMA model\n",
+    "    arima_results = joblib.load(os.path.join(folder_path, 'arima_model.pkl'))\n",
+    "    \n",
+    "    # Load LSTM model\n",
+    "    lstm_model = load_model(os.path.join(folder_path, 'lstm_model.h5'))\n",
+    "    \n",
+    "    # Load scaler\n",
+    "    scaler = joblib.load(os.path.join(folder_path, 'scaler.pkl'))\n",
+    "    \n",
+    "    return arima_results, lstm_model, scaler\n",
+    "\n",
+    "def make_predictions(arima_results, lstm_model, scaler, test_data, time_steps=60):\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(test_data[i:i+time_steps])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_results.append(test_data[i])\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Example usage\n",
+    "if __name__ == \"__main__\":\n",
+    "    # Load data\n",
+    "    train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "    test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "    train_close_prices = train_data['Close']\n",
+    "    test_close_prices = test_data['Close']\n",
+    "\n",
+    "    # Create and save the model\n",
+    "    arima_results, lstm_model, scaler = create_hybrid_model(train_close_prices)\n",
+    "    save_model(arima_results, lstm_model, scaler, 'saved_model')\n",
+    "\n",
+    "    # Later, load the model and make predictions\n",
+    "    loaded_arima, loaded_lstm, loaded_scaler = load_model('saved_model')\n",
+    "    predictions = make_predictions(loaded_arima, loaded_lstm, loaded_scaler, test_close_prices)\n",
+    "\n",
+    "    # Calculate accuracy metrics\n",
+    "    mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "    rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "    mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "\n",
+    "    print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "    print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "    print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "    # Plot results\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    plt.figure(figsize=(12,6))\n",
+    "    plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "    plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "    plt.title('Actual vs Predicted Stock Prices')\n",
+    "    plt.xlabel('Date')\n",
+    "    plt.ylabel('Price')\n",
+    "    plt.legend()\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000002DD2831BCE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000002DD2831BCE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n"
+     ]
+    },
+    {
+     "ename": "UnboundLocalError",
+     "evalue": "cannot access local variable 'arima_model' where it is not associated with a value",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 107\u001b[0m\n\u001b[0;32m    104\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m    106\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m--> 107\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    109\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m    110\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
+      "Cell \u001b[1;32mIn[9], line 95\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps, model_dir)\u001b[0m\n\u001b[0;32m     92\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model with test data\u001b[39;00m\n\u001b[1;32m---> 95\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241m.\u001b[39mappend(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
+      "\u001b[1;31mUnboundLocalError\u001b[0m: cannot access local variable 'arima_model' where it is not associated with a value"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential, load_model\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "import joblib  # For saving the ARIMA model\n",
+    "import os\n",
+    "\n",
+    "# Data preparation\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "# Hybrid ARIMA-LSTM Model\n",
+    "def hybrid_model(train_data, test_data, time_steps=60, model_dir='./model'):\n",
+    "    # Ensure data is a numpy array\n",
+    "    train_df = np.array(train_data).reshape(-1, 1)\n",
+    "\n",
+    "    # Create a directory to save models if it doesn't exist\n",
+    "    if not os.path.exists(model_dir):\n",
+    "        os.makedirs(model_dir)\n",
+    "\n",
+    "    # ARIMA Model\n",
+    "    arima_model_path = os.path.join(model_dir, 'arima_model.pkl')\n",
+    "    if not os.path.exists(arima_model_path):\n",
+    "        model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                                d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                                error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "        arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "        arima_results = arima_model.fit()\n",
+    "        # Save ARIMA model\n",
+    "        joblib.dump(arima_results, arima_model_path)\n",
+    "    else:\n",
+    "        arima_results = joblib.load(arima_model_path)\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM Model\n",
+    "    lstm_model_path = os.path.join(model_dir, 'lstm_model.keras')  # Updated file extension\n",
+    "    if not os.path.exists(lstm_model_path):\n",
+    "        lstm_model = Sequential([\n",
+    "            LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "            LSTM(units=50),\n",
+    "            Dense(units=1)\n",
+    "        ])\n",
+    "        lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "        # Early stopping and model checkpoint\n",
+    "        early_stopping = EarlyStopping(monitor='loss', patience=10, restore_best_weights=True)\n",
+    "        model_checkpoint = ModelCheckpoint(lstm_model_path, save_best_only=True, monitor='loss')\n",
+    "\n",
+    "        # Train LSTM model\n",
+    "        lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=1, callbacks=[early_stopping, model_checkpoint])\n",
+    "\n",
+    "    else:\n",
+    "        lstm_model = load_model(lstm_model_path)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model with test data\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# Calculate Mean Absolute Percentage Error (MAPE)\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Intel_Optimized/readme.md b/Intel_Optimized/readme.md
new file mode 100644
index 0000000..dee59d2
--- /dev/null
+++ b/Intel_Optimized/readme.md
@@ -0,0 +1,102 @@
+# Stock Price Prediction - Intel Optimized
+
+Welcome to the Intel Optimized version of the Stock Price Prediction project! This repository leverages the Intel AI Analytics Toolkit, specifically the Intel Distribution of Scikit-Learn and Modin Pandas, to enhance performance and scalability.
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Features](#features)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Introduction
+
+This project aims to predict stock prices using advanced machine learning techniques optimized for Intel hardware. By utilizing the Intel AI Analytics Toolkit, we achieve faster computations and efficient data handling, making the prediction process more robust and scalable.
+
+## Features
+
+- **Intel Distribution of Scikit-Learn**: Enhanced performance for machine learning algorithms.
+- **Modin Pandas**: Accelerated data manipulation and analysis.
+- **Optimized Data Preprocessing**: Efficient handling of large datasets.
+- **Advanced Prediction Models**: Implementation of ARIMA, SARIMAX, and hybrid models.
+
+## Installation
+
+To get started, clone the repository and install the required dependencies:
+
+```bash
+git clone https://github.com/rohitinu6/Stock-Price-Prediction.git
+cd Stock-Price-Prediction/Intel_Optimized
+pip install -r requirements.txt
+```
+
+Ensure you have the Intel AI Analytics Toolkit installed. You can find the installation guide [here](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html).
+
+## Usage
+
+1. **Data Preparation**: Place your stock price data in the `Data` directory.
+2. **Run the Models**: Execute the Jupyter notebooks or Python scripts to train and test the models.
+
+Example run the following notebook:
+```
+jupyter notebook Intel_Optimized/Stock_prediction_Data_Analysis.ipynb
+```
+
+## Contributing
+
+We welcome contributions! Please read our [Contributing Guidelines](CONTRIBUTING.md) for more details.
+```
+\Stock-Price-Prediction\Contributing.md
+```
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+```
+\Stock-Price-Prediction\code-of-conduct.md
+```
+
+## How Modin Uses Parallel Processing for Fast Computation
+
+Modin leverages parallel processing to speed up data manipulation tasks. By using Ray or Dask as the execution backend, Modin distributes operations across multiple CPU cores, allowing for efficient handling of large datasets. This parallelism ensures that data loading, cleaning, and transformation tasks are performed much faster compared to the traditional pandas library.
+
+### Key Benefits of Modin:
+
+- **Seamless Integration**: Modin's API is designed to be compatible with pandas, so you can switch to Modin with minimal code changes.
+- **Scalability**: Efficiently processes large datasets by utilizing all available CPU cores.
+- **Performance**: Significant speedup in data manipulation tasks, making it ideal for big data applications.
+
+To use Modin in your project, simply replace the pandas import with Modin:
+
+```python
+import modin.pandas as pd
+```
+
+This simple change allows you to take advantage of Modin's parallel processing capabilities without modifying your existing pandas code.
+
+## How Intel Scikit-Learn Uses Parallel Processing for Fast Model Training and Evaluation
+
+Intel Scikit-Learn leverages parallel processing to accelerate machine learning tasks. By utilizing Intel's oneAPI Data Analytics Library (oneDAL), it optimizes algorithms to take full advantage of Intel hardware features such as vectorization and multithreading. This results in significant performance improvements for model training, evaluation, and hyperparameter tuning.
+
+### Key Benefits of Intel Scikit-Learn:
+
+- **Speedup**: Faster training and prediction times due to optimized algorithms.
+- **Multithreading**: Efficient use of multiple CPU cores to parallelize computations.
+- **Vectorization**: Enhanced performance through SIMD (Single Instruction, Multiple Data) operations.
+- **Compatibility**: Seamless integration with existing Scikit-Learn code, requiring minimal changes.
+
+To use Intel Scikit-Learn in your project, install the Intel extension for Scikit-Learn:
+
+```bash
+pip install scikit-learn-intelex
+```
+
+Then, enable the Intel optimizations in your code:
+
+```python
+from sklearnex import patch_sklearn
+patch_sklearn()
+```
+
+This simple setup allows you to benefit from Intel's hardware acceleration, making your machine learning workflows more efficient and scalable.
\ No newline at end of file
diff --git a/Intel_Optimized/reduced_redundancy_stock_price_prediction.ipynb b/Intel_Optimized/reduced_redundancy_stock_price_prediction.ipynb
new file mode 100644
index 0000000..32643ed
--- /dev/null
+++ b/Intel_Optimized/reduced_redundancy_stock_price_prediction.ipynb
@@ -0,0 +1,303 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d6a21042",
+   "metadata": {},
+   "source": [
+    "DEPENDENCIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "15cc2438",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'xgboost'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[1], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MinMaxScaler\n\u001b[1;32m---> 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mxgboost\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxgb\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'xgboost'"
+     ]
+    }
+   ],
+   "source": [
+    "import modin.pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearnex import patch_sklearn\n",
+    "patch_sklearn()\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "import xgboost as xgb\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1a47080a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the dataset\n",
+    "df = pd.read_csv('SBIN.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8b945263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data preprocessing\n",
+    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "imputer = SimpleImputer(strategy='mean')\n",
+    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "af430abe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select features and target variable\n",
+    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
+    "y = df['Close']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "763efa5e",
+   "metadata": {},
+   "source": [
+    "TRAINING AND TESTING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2dddc0b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1c523190",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale the features using Min-Max scaling\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "73b110e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to train and evaluate model\n",
+    "def train_and_evaluate_model(model, X_train, y_train, X_test, y_test, params=None):\n",
+    "    if params:\n",
+    "        model = GridSearchCV(model, params, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n",
+    "    model.fit(X_train, y_train)\n",
+    "    predictions = model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate evaluation metrics\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
+    "    mae = mean_absolute_error(y_test, predictions)\n",
+    "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
+    "    accuracy = accuracy_score(y_test > predictions, y_test > predictions.round())\n",
+    "    precision = precision_score(y_test > predictions, y_test > predictions.round())\n",
+    "    confusion = confusion_matrix(y_test > predictions, y_test > predictions.round())\n",
+    "    recall = recall_score(y_test > predictions, y_test > predictions.round())\n",
+    "    f1 = f1_score(y_test > predictions, y_test > predictions.round())\n",
+    "\n",
+    "    # Print the evaluation metrics\n",
+    "    print(f\"RMSE: {rmse}, MAE: {mae}, MAPE: {mape}, Accuracy: {accuracy}, Precision: {precision}, Confusion Matrix:\\n{confusion}, Recall: {recall}, F1 Score: {f1}\")\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "68f21263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define models and their hyperparameters\n",
+    "models_params = {\n",
+    "    'Linear Regression': (LinearRegression(), None),\n",
+    "    'SVR': (SVR(), {'C': [0.1, 1], 'epsilon': [0.01, 0.1, 0.5], 'kernel': ['sigmoid']}),\n",
+    "    'Random Forest': (RandomForestRegressor(), None),\n",
+    "    'Gradient Boosting': (GradientBoostingRegressor(), None),\n",
+    "    'XGBoost': (xgb.XGBRegressor(), None),\n",
+    "    'AdaBoost': (AdaBoostRegressor(), None),\n",
+    "    'Decision Tree': (DecisionTreeRegressor(), None),\n",
+    "    'KNeighbors': (KNeighborsRegressor(), {'n_neighbors': [3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]})\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "af1992c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Linear Regression Model:\n",
+      "RMSE: 1.6881364651923592, MAE: 0.9433353486266863, MAPE: 0.006085435968276418, Accuracy: 0.8296819787985866, Precision: 0.8623595505617978, Confusion Matrix:\n",
+      "[[560  98]\n",
+      " [143 614]], Recall: 0.8110964332892999, F1 Score: 0.8359428182437032\n",
+      "\n",
+      "SVR Model:\n",
+      "RMSE: 134.5606234817444, MAE: 76.05976804010935, MAPE: 0.39705639844596075, Accuracy: 0.9978798586572438, Precision: 0.9970544918998527, Confusion Matrix:\n",
+      "[[735   2]\n",
+      " [  1 677]], Recall: 0.9985250737463127, F1 Score: 0.997789240972734\n",
+      "\n",
+      "Random Forest Model:\n",
+      "RMSE: 2.191031983578823, MAE: 1.254384854932792, MAPE: 0.007957611370512454, Accuracy: 0.8551236749116607, Precision: 0.8556851311953353, Confusion Matrix:\n",
+      "[[623  99]\n",
+      " [106 587]], Recall: 0.847041847041847, F1 Score: 0.851341551849166\n",
+      "\n",
+      "Gradient Boosting Model:\n",
+      "RMSE: 2.697993647984777, MAE: 1.6922021460047614, MAPE: 0.011881429758725463, Accuracy: 0.9017667844522969, Precision: 0.8982300884955752, Confusion Matrix:\n",
+      "[[667  69]\n",
+      " [ 70 609]], Recall: 0.8969072164948454, F1 Score: 0.8975681650700075\n",
+      "\n",
+      "XGBoost Model:\n",
+      "RMSE: 2.733930065274145, MAE: 1.502457380471909, MAPE: 0.010026410639661481, Accuracy: 0.8840989399293286, Precision: 0.8948106591865358, Confusion Matrix:\n",
+      "[[613  75]\n",
+      " [ 89 638]], Recall: 0.8775790921595599, F1 Score: 0.8861111111111112\n",
+      "\n",
+      "AdaBoost Model:\n",
+      "RMSE: 9.915909571695305, MAE: 8.180488940261938, MAPE: 0.1986601163272193, Accuracy: 0.992226148409894, Precision: 0.986, Confusion Matrix:\n",
+      "[[911   7]\n",
+      " [  4 493]], Recall: 0.9919517102615694, F1 Score: 0.9889669007021062\n",
+      "\n",
+      "Decision Tree Model:\n",
+      "RMSE: 3.0678929179050054, MAE: 1.6473595220251132, MAPE: 0.010260672105066157, Accuracy: 0.8572438162544169, Precision: 0.8650137741046832, Confusion Matrix:\n",
+      "[[585  98]\n",
+      " [104 628]], Recall: 0.8579234972677595, F1 Score: 0.8614540466392319\n",
+      "\n",
+      "KNeighbors Model:\n",
+      "RMSE: 3.261285106039988, MAE: 1.8071640369846882, MAPE: 0.014187783844224503, Accuracy: 0.8968197879858657, Precision: 0.9097744360902256, Confusion Matrix:\n",
+      "[[664  60]\n",
+      " [ 86 605]], Recall: 0.8755426917510853, F1 Score: 0.8923303834808259\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train and evaluate all models\n",
+    "for name, (model, params) in models_params.items():\n",
+    "    print(f\"\\n{name} Model:\")\n",
+    "    train_and_evaluate_model(model, X_train_scaled, y_train, X_test_scaled, y_test, params)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "fb66a3c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GJgAqqmq44tF5AGkvvnSoUURERLq1qdMXbS+6WtQ0NDF1+qK056LCS0RERLq1lVU1HdqeSjrUKCIiIt3Sx+u38ZunF+Ix7h9aUpjWfECFl4iIiHQzm2oa+OuMD7jjlWXk5hgnHFTGzMVrqW1o3r5PYV4uUyaNSHtuKrxERESkW2hoaub+1z/mj/95n43b6jnjkN340WdHsGv/gh3Paiwp1FmNIiIiIolwd2YsquRXTyxgydqtHLHXAK466UBGlvffvs/kseVMHlvOzJkzmTBhQsZyVeElIiIiXdaCVdX86okFvPzBOvYc1JdbvjyezxxQipllOrWoVHiJiIhIl1O5uZbfP7OYB99cTnFBHlefciDnHr4HvXtl94QNKrxEREQka7Wecf7S4/dlTXUtf3thCQ1NzXz1qD35znH7UNKnd6ZTjYsKLxEREclK0Wac//EjcwGYdFAZl3/uAPYc1DeTKXaYCi8RERHJStFmnAcY1K83N58/PgMZdV52HwgVERGRHivWzPLrt9SnOZPkUeElIiIiWWfBqmpyc6KfmZiJGeeTRYcaRUREJGs0NTu3vLSU3z+zmIK8HOobnfqmzM84nywqvERERCQrfLR+Kz98aA5vfrSREw7alV99fiQvvb8uK2acTxYVXiIiIpJR7s79ry/nl0+8R26O8YcvjmbymHLMLGtmnE8WFV4iIiKSMZXVtfz4kbnMXLSWo/YZyNQzR3fpMVztUeElIiIiGfH43JVcNW0+tQ1N/PzUgzj/iD3IiTGgvrtQ4SUiIiJpVbWtnp899i7/mrOS0cNK+P0XRrP34H6ZTistVHiJiIhI2ryweC0/fngO67fU84P/2Y9vTtibXrk9Z3YrFV4iIiKSEpHrLA7573PsNagPryzZwL6l/bjtgkMZWd4/0ymmnQovERERSbrW6yyu2lTLqk21TBwxiL+dN56CvNwMZ5gZKevbM7PbzazSzOZHbPuFmc01s3fM7BkzG5qq5xcREZHMibXO4uI1W3ts0QWpXTLoTuCEVtumuvsodx8DPA78LIXPLyIiImnW3Oy8uHgtFTHWWYy1/mJPkbJDje7+opkNb7WtOuJmX8BT9fwiIiKSPqs21fCPN1fw4BvLqaiqIcegOcqvfHeeoyse5p662icsvB5395ER234FfBnYBEx097UxHnsxcDFAWVnZuAceeCBleQJs2bKFfv06fyprMuIoF+WiXJSLclEuXSGXxmZnztomXlzRyNy1TThw0MAcjt0tj/qmZu55r4H6T5ZZpHcOXDiyN0cOzUt6LumO05aJEye+5e7jo97p7im7AMOB+THuuwL4eTxxxo0b56k2Y8aMrImjXFIXI1lxlEvqYiQrjnJJXYxkxVEuqYuRrDixYny4dotf/9QCH//LZ32Pyx73w371rE99eqF/tG7rDvv98+0VfuR1z/kelz3uR173nP/z7RVJzyVTcdoCvOkxappMntV4H/AEcHUGcxAREZFWIqeBKJ/1PFMmjeCEkbsy/d3VPPD6cv67dD25OcbEEaV86dBhTBgxOOpcXN1tncVkSGvhZWb7uvv74c1TgYXpfH4RERFpW+tpICqqavjhP+ZwxaNzqWloZtiAQqZMGsGZ43ajrLggw9l2PSkrvMzsfmACMMjMVhD0bJ1oZiOAZuAj4JJUPb+IiIh0XLRpIJqaHXJz+L+vHc6n9hrY7ddTTKVUntV4dpTNt6Xq+URERKTzYk33UNvQxFH7DEpzNt1Pz1kcSURERNpU29AUc3LTnj4NRLKo8BIREREqN9dyzi2zqGloolerQ4mFeblMmTQiQ5l1L1qrUUREpId7d+Umvn7Xm2zYVs9fzz2E+sbmT85qLAkG008eW57pNLsFFV4iIiI92NPzV/H9B+dQ0iePhy85kpHl/QE0DUSKqPASERHpgdydv8z4gN89s5gxw0r4+/njKNX0ECmnwktERKSHqW1o4scPz+Vfc1YyecxQrj9jVMxB9ZJcKrxERER6kMrqWr5+z1vMWV7FlEkj+OaEvTHTvFzposJLRESkh5hfsYmv3fUm1bUN3Hz+OCYdtGumU+pxVHiJiIj0AE/OW8UPHnqHAX168/AlR3Lg0OJMp9QjqfASERHpxtydPz33AX/4z2IO2b2Em88fz+Ci/Eyn1WOp8BIREemmauqb+NHDc3hi7ipOP6Sc604/mPxeGkSfSSq8REREupFpsyu2T36a9+x0Gpqcyz+3P/977F4aRJ8FVHiJiIh0E9NmV3DFo/OoaWgCoKHJ6Z2bw67FBSq6soTWahQREekmfv3kgu1FV4v6pmD5H8kO6vESERHp4t76aCM3v7CEys11Ue9fWVWT5owkFhVeIiIiXVBzs/P8wkpufnEJbyzbSP/CPIrye7G5rnGnfYeWFGYgQ4lGhZeIiEgXUtfYxGOzV3Lzi0tYsnYr5SWFXH3KgXxh/DCefW/NDmO8AArzcpkyaUQGM5ZIKrxERES6gOraBu577WNuf/lDKjfXceCQYv7fl8Zw0sFD6JUbDNmePLYcYPtZjeUlhUyZNGL7dsk8FV4iIiJZbPWmWm5/5UPue+1jttQ1cvQ+g7jhC6M5ep9BUc9UnDy2nMljy5k5cyYTJkxIf8LSJhVeIiIiWSBy/q3yWc9z3hG7s2TtVh57p4Jmh5MOHsLFx+7FyPL+mU5VOkGFl4iISIa1nn+roqqG3zy9iLwc49wj9uCio/dk2IA+Gc5SkkGFl4iISIZNnb5op/m3AAYV5XPNqQdlICNJFU2gKiIikmGx5tlavak2zZlIqqnwEhERyaDVm2rJzYm+nI/m3+p+VHiJiIhkyAeVWzjjb6+Sa9A7d8efZM2/1T2p8BIREcmA2R9v5KybXqWusYlHvnkUvz1zFOVhD1d5SSHXnX6w5t/qhjS4XkREJM1mLKrkm/e+TWlxPnd/9TD2GNiXkeX9Nf9WD6DCS0REJI0efmsFlz0ylwOGFHHHhYcxuCg/0ylJGqnwEhERSQN35+YXl3L9Uws5ep9B3HT+OPrl62e4p9H/uIiISIo1Nzu/fGIBt7/yIaeMHsoNZ42mdy8Ns+6JVHiJiIikUH1jMz/6xxz+NWclXzlqOD896UByYkwfId2fCi8REZEU2VLXyDfufYuX3l/HZSfszyWf3ivqwtbSc6jwEhERSYF1W+r4yh1v8N6qaqaeOYqzxg/LdEqSBVR4iYiIJNnH67fx5dtfY3V1Lbd8eRzH7V+W6ZQkS6jwEhERSaL5FZu48I43aGxu5r6vH8Ehu++S6ZQki6jwEhER6aRpsyuYOn0RFVU12NMv07+wFw9/40j2KS3KdGqSZXQuq4iISCdMm13BFY/Oo6KqBgAHahuamV9RndnEJCup8BIREemEqdMXUtPQtMO22sZmpk5flKGMJJup8BIREUlQTX0TFVW1Ue9bGfaAiURS4SUiIpKAlVU1nHXzqzHvH1pSmMZspKtQ4SUiItJBb320gVNvfIVl67bx9WP2pDAvd4f7C/NymTJpRIayk2ymsxpFREQ64KE3lnPltHmUlxTywMWHs09pEQcN7b/9rMbykkKmTBrB5LHlmU5VspAKLxERkTg0NjXzqycXcMcryzhm30HcePYh9O+TB8DkseVMHlvOzJkzmTBhQmYTlaymwktERKQdVdvq+fZ9s3n5g3VcdPSeXPG5/emVq9E60nEqvERERNrwQeVmvnbXm6ysquW3Z47iC1pzUTpBhZeIiEgMzy1Yw6UPvENBXi73X3w44/YYkOmUpItT4SUiItKKu3PTC0v57fSFjBzan5vPH6fpISQpVHiJiIhEqG1o4rJH5vLYOys5ZfRQfnvGKAp757b/QJE4qPASEREJrdpUw8V3v8X8lZuYMmkE35ywN2aW6bSkG1HhJSIiPda02RXb598a/PJ/qG1oxDFuOX88nzmwLNPpSTekwktERHqkabMruOLRedsXuF67pQ4DLjthhIouSRlNQiIiIl3StNkVHHX981z49FaOuv55ps2uaHP/5mZn9aZaXlu6nn+8uZyfTpu/vehq4cA9sz5OYdbS06nHS0REupzWvVUVVTVc8eg8GpqaGTOshI/Wb+PjDTtelm/YRl1jc7uxV1bVpDp96cFUeImISNwix0SVz3o+oTUJOxOjudlZs7mWXz7x3k69VTUNTUx5eO4O2/r2zmX3gX3ZZ3A/jtu/lGED+rDHgD7sPqAP59w6i5VVtTs9h6aNkFRS4SUiInGJ1csExF04tRfD3dm4rYHlG7axfOM2lm+oCf/dxoqNNVRsrKG+qe1eq//3pTHsHhZXA/r2jnlW4o8n7b9DLgCFeblMmTQirtcikoh2Cy8zKwWOAoYCNcB84E13b7+/VkREuoXKzbX8/N/vxuhlmsNDby6nMC+Xgt65wb95ORTm5W7fVtArl8Leufz26YVRY1z+yFxuemEJyzdsY2v9jvfv0iePYQP6cOCQYj57UBm77dKHPz67mPVb63fKs7ykkNPGxFcEthSL23vfSgoT6sET6YiYhZeZTQQuBwYAs4FKoACYDOxtZg8DN7h7dRryFBGRNKptaOKNZRt46f11vLh4LQtXb465b0OTU9/YzKaaBmobmqhtaKamoYnahiZqGppwj+P5GpvZbZc+fGrvgey2Sx+G7VLIsAF9GDagD/3yd/6pKsrvlZTeqsljy5k8tpyZM2cyYcKEDj1WJBFt9XidCHzd3Xc6vcPMegEnA/8DPBLtwWZ2e7hPpbuPDLdNBU4B6oElwFfcvaozL0BERDrP3Vm4ejMvvb+Wl95fx+sfbqCusZm8XGP8HgP48QkjuOOVZazdXLfTY8tLCnn4G0fGjFvX2Ly9IDvtLy+zpjp6jFsvGB93vuqtkq4qZuHl7lPauK8RmNZO7DuBG4G7I7Y9C1zh7o1m9hvgCuCyeJMVEZHERBvQfuQ+A3nlg3W8tHgdL32wbntRtW9pP849fA+O2W8Qh+85gD69g5+Kof0LO9zLZGYU5OVSkBcsuXPF5w5I2rgq9VZJV9TWocY/uvv3wuuXuvv/i7jvTne/sK3A7v6imQ1vte2ZiJuzgDMTyFlERDog2oD27z/4Di1HAAf07c3R+wzi6H0Hccy+gxjSP/pZfcnoZVJPlfR0bR1qPDbi+gXA/4u4PSoJz/1V4MEkxBERkTb8JsqAdgeKC3px39eP4MAhxeTkxLceYTJ6mdRTJT2ZeYxRj2Y2293Htr4e3n7b3Q9pN3jQ4/V4yxiviO1XAuOB0z1GAmZ2MXAxQFlZ2bgHHnggvleUoC1bttCvX7+siKNclItyUS7JiLOpznn2owYeX9oQc587T+ibllxSFUO5KJdMxWnLxIkT33L36IMW3T3qBZgD7AIMjLg+ILzMifW4VjGGA/NbbbsA+C/QJ54Y7s64ceM81WbMmJE1cZRL6mIkK45ySV2MZMXpybl8tG6rX/nPub7flU/68Msf9xFXPel7XPb4Tpcjr3su5bmkOkay4iiX1MVIVpxsyqU9BNNuRa1p2jrU2B94C2jpf347sl5LpAI0sxMIBtN/2t23JRJDRESie3flJm56YSlPzF1Jr5wcTj+knK8fuxfzVmzSRKEiWaKtsxqHdyawmd0PTAAGmdkK4GqCsxjzgWfDmYRnufslnXkeEZGezN2ZtXQDf3thCS8uXku//F58/Zi9+OrRe1JWXADA3oODwyoa0C6SeW2d1bgHUOXum8LbEwkmT10G/MXdd54yOIK7nx1l820JZyoiIts1NzvPvLeGv72whDnLqxjUrzdTJo3gvCP2oH9h3k77a0C7SHZo61DjQ8DngU1mNgb4B3AdMAb4K/C1VCcnIiI7zsE19L/Pcex+g3l92QaWrt3K7gP68MvJIzlz3G7b58oSkezVVuFV6O4rw+vnAbe7+w1mlgO8k/LMRERkpzm4Vm6q5YE3ljO0fwF/Pnssnxu5K71yczKcpYjEq61Pa+SkLscBzwG4FscWEUmb66PMwQVgBqeMHqqiS6SLaavH63kzewhYRTCVxPMAZjaEYK1FERFJkY/Xb+PvLy1h9abaqPevrIq+XUSyW1uF1/eALwJDgKPdvWUGvl2BK1Ocl4hIjzS/YhM3vbCEJ+etoldODn1657Ktfucer6El0Zf1EZHs1tZ0Eg7sNF28u89OaUYiIj2Mu/PqkvXc9MISXnp/XTAlxLF78dWj9uS/S9ZrDi6RbqSt6SQ2s+NEqRbeNoK6rDjFuYmIdGtNzc7T81dz0wtLmFexicFF+Vx2wv6ce8TuFBcEU0JoUWmR7qWtQ43PERxWfBR4wN0/Tk9KIiLdW21DE4+8vYJbXlzKsvXbGD6wD9edfjCfH1sedUoIzcEl0n20dahxspn1B04HbjGzAuBBgiJsQ7oSFBHpqiLn3yqf9TzfOm5vNm5t4I5XlrFuSx2jduvPX889hEkH7UpujrUfUES6vLZ6vAhnrb/DzO4iGGj/Z6AA+H0achMR6bJaz79VUVXDTx6dD8Ax+w7iG58ew6f2Hki4fJqI9BBtFl5mdiRwNnAM8DLweXd/KR2JiYh0ZVOnL4o6/9bgonzuuejwDGQkItmgrcH1y4AqgjMbLwYaw+2HALj726lPT0Ska1pZVRN1+7rNdWnORESySVs9XssIzmKcBHyWHWeyd4LZ7EVEpJWtdY307pVDXePOC31o/i2Rnq2twfUT0piHiEi3sHZzHRfd9QZ1jc3k5RoNTZ/MyqP5t0Qk5iJfZnZ0Ww80s2IzG5n8lEREuqala7dwxt9eZfGazdz65fFMPXM05WEPV3lJIdedfrDm3xLp4do61HiGmf0WeBp4C1hLcEbjPsBEYA/ghynPUESkC3j7441cdOcbmBn3f/0Ixu6+C4Dm3xKRHbR1qPH7ZrYLcCZwFsGajTXAAuBmd385PSmKiGS3Z99bw3fuf5uy4gLu/Mph7Dmob6ZTEpEs1d48XhuBW8KLiIi0cs+sj7j6sfkcXN6f2y48lEH98jOdkohksTYLLxERic7dmTp9EX+duYTj9i/lxnPG0qe3vlJFpG36lhAR6aD6xmYuf2Quj86u4OzDhvGL00bSKzfmuUoiItu1N3N9DnCEu7+apnxERLLa5toGvnHv27z8wTp+8D/78Z3j9tGyPyISt/bGeDWb2Q3Ap9KUj4hI1lpTXcuFd7zB4jWb+e2Zo/jC+GGZTklEuph4+safMbMzTH/SiUgP9v6azZz+11f5aP1WbrtgvIouEUlIPGO8fgD0BZrMrIZg6SB39+KUZpYm02ZXMHX6Iiqqaiif9TxTJo3QBIcissN3w6CXn2VLbQP9Cnrz4MWf4uDd+mc6PRHpototvNy9KB2JZMK02RVc8eg8ahqaAKioquGKR+cBqPgS6cFafzes21KPAVMm7KWiS0Q6Ja7TcMzsVDP7XXg5OdVJpcvU6Yu2f7G2qGloYur0RRnKSESyQbTvBgduf3lZRvIRke6j3cLLzK4HLgXeCy+Xhtu6vJVVNR3aLiI9g74bRCRV4hnjdSIwxt2bAczsLmA2cHkqE0uHoSWFVET5Ih0aLmorIj1L5eZapj69CI9xv74bRKSz4p3xryTiercZ4DBl0ggK83J32FaYl8uUSSMylJGIZEJdYxM3v7CE4373AtPeqeC4EYMpyNvx61HfDSKSDPH0eP0amG1mMwjOaDwWuCKlWaVJywD63zy9kFWbaiku6MW1p43UwHqRHsLdeW5BJb984j2Wrd/G8fuXctXJB7LnoL47nvFcUqgznkUkKeKZub4ZOAI4lKDwuszdV6cht7SYPLacyWPLGXHlE5w5bpi+WEV6iA8qN3Pt4wt4cfFa9h7clzu/cigTRpRuv7/lu2HmzJlMmDAhc4mKSLcSz8z133b3h4B/pSmnjCjJNyo312Y6DRFJsU01DfzxP4u5+78f0ad3Lj89+UC+/Kk9yNNaiyKSBvEcanzWzH4EPAhsbdno7htSllUGlOQbldV1mU5DRFKkqdl54I2PueGZxWzcVs+XDt2dH312Pwb2y890aiLSg8RTeH01/PdbEdsc2Cv56WROSb6xRj1eIt3SrKXr+fm/32PBqmoOGz6Aq089kIOGdpvzhESkC4lnjNfl7v5gmvLJmJICY+76OtwdLUsp0nVFDoove/U/DCku4J0Vmxjav4AbzxnLSQcP0WdcRDImnjFe3yI4zNitleTnUNNQz5a6RooK8jKdjogkoPVSP2uq61hTXccJB+3KH744hsLeue1EEBFJrXhGkz5rZj8ys2FmNqDlkvLM0qwkP/gLeI3GeYl0WdGW+gGYV7FJRZeIZAWN8Qq1FF6Vm2vZp7RfhrMRkURoqR8RyXbtFl7uvmc6Esm07YWXerxEuqxd+xewatPOJ8loqR8RyRYxDzWa2Y8jrp/V6r5fpzKpTCgpaDnUqDMbRbqqvQf33WmblvoRkWzS1hivL0Vcb71E0AkpyCWjCnKhT+9cKjerx0ukK3rro428smQ9x+w7iPKwh6u8pJDrTj9YK1KISNZo61Cjxbge7XaXZ2aUFuWrx0ukC6pvbOaKR+cypLiAv503jn75vbTUj4hkpbYKL49xPdrtbqG0uEA9XiJd0E0vLGHxmi3cdsF4+uXHc86QiEhmtPUNNdrMqgl6twrD64S3C1KeWQaUFuUzv2JTptMQkQ74oHILNz7/ASePGsLxB5RlOh0RkTbFLLzcvcdNelNWXMDzCys1e71IF9Hc7Pzk0XkU9s7l6lMOynQ6IiLtimcC1R6jtCifbfVNbKlrzHQqIhKH+9/4mNeXbeDKEw9gcJEWuxaR7KfCK0JZcXAEVeO8RLLfmuparn9yIZ/aayBnjd8t0+mIiMRFhVeE0vAvZp3ZKJL9rn7sXeqbmvn16QdraICIdBkqvCKUhj1ea9XjJZLVpr+7mqffXc2ln9mXPQftPGmqiEi2ijm43sw208a0Ee5enJKMMqi0WD1eItmuuraBnz02nwOGFPP1Y7rVkrEi0gO0dVZjEYCZXQusBu4hmEriXKAoLdmlWVF+LwrzcrVeo0gW++3TC1m7uY6/nz+evFx12otI1xLPt9Ykd/+ru29292p3/xtwRqoTywQzo6w4nzU61CiSld5YtoF7Z33MhUfuyehhJZlOR0Skw+IpvJrM7FwzyzWzHDM7F2hKdWKZUlpUQKUONYpknbrGJi5/ZC7lJYX88LP7ZTodEZGExFN4nQN8AVgTXs4Kt3VLpcX5mk5CJAv9dcYSlqzdyi8/P5K+WhZIRLqodr+93H0ZcFrqU8kOpUUFzKiuzHQaIhLh/TWb+evMDzhtzFAmjijNdDoiIglrt8fLzPYzs+fMbH54e5SZXZX61DKjrDifrZq9XiRrNDc7lz86j775vfjpyQdmOh0RkU6J51DjLcAVQAOAu88FvpTKpDJJU0qIZJf/e/1j3vpoI1eddCCD+mlZIBHp2uIpvPq4++uttnXb7qCyonDZIE0pIZJxqzfV8punFnL0PoM445DyTKcjItJp8RRe68xsb8LJVM3sTGBVew8ys9vNrLLlEGW47Swze9fMms1sfMJZp1BLj1flZvV4iWSSu/PTx+bT2NzMrz4/UssCiUi3EE/h9S3gZmB/M6sAvgdcEsfj7gROaLVtPnA68GL8KaZXy7JB6vESyayn56/m2ffW8P3P7MceA7UskIh0D22e1WhmucA33P0zZtYXyHH3zfEEdvcXzWx4q20LwrgJppt6Rfm9KMjL0RgvkQza2uBc8693OWhoMRcdvWem0xERSRpzj7kcY7CD2fPuflxCwYPC63F3H9lq+0zgR+7+ZhuPvRi4GKCsrGzcAw88kEgKcduyZQv9+vUD4McvbmOv/jlcMrqgU3GSkUum4ygX5ZLOGK+ubOCRxQ2sr20GjNP36cWp+yQ2oL47vS/KRbkol+zKpT0TJ058y92jD6ly9zYvwA3Av4DzCQ4Tng6c3t7jwscOB+ZH2T4TGB9PDHdn3LhxnmozZszYfv3Mv73iX7jp1U7HSUYumY6jXFIXI1lxuksu/3x7he9/1VO+x2WPb7/sf9VT/s+3V6Q9l2THUS6pi5GsOMoldTGSFSebcmkP8KbHqGniGeM1AFgPHAecEl5OTrgM7AJKiwtYq9nrRdJq6vRF1DTsuBpZTUMTU6cvylBGIiLJF8/M9V9JRyLZpLQon5ka4yWSNhu31lNRVRP1vpUxtouIdEXtFl5mVgBcBBwEbB/05O5fbedx9wMTgEFmtgK4GtgA/BkYDDxhZu+4+6SEs0+RsuKC7bPX99OacCIp09Ts3Pf6x9zwTOxeraElhWnMSEQkteKpKu4BFgKTgGuBc4EF7T3I3c+Ocdc/484uQ0qLwrm8qmvpNzi1A/BEeqo3lm3g6sfe5b1V1Ryx1wCO3W8wf37ugx0ONxbm5TJl0ogMZikiklzxFF77uPtZZnaau99lZvcB01OdWCaVtczltbmOvVR4iSTV6k21XP/UAqa9s5Ih/Qu48ZyxnHTwEMyMof0LmTp9ERVVNZSXFDJl0ggmj9WM9SLSfcRTeDWE/1aZ2UhgNcHZit1WmdZrFEm6usYmbn95GX9+/n0am5xvT9yHb07cmz69P/kamjy2nMljy5k5cyYTJkzIXLIiIikST+H1dzPbBfgpwbQS/YCfpTSrDBscrteoMxtFkmPGwkquffw9Ply3lc8cUMZPTz5As9GLSI8Uz1mNt4ZXXwD2Sm062aG4QLPXiyTDsnVb+cXj7/Hcwkr2GtSXO79yKBNGlGY6LRGRjInnrMaovVvufm3y08kOZkZpUQGV6vESicu02RWfjM2a9TyXHr8PH23Yxi0vfkhernHF5/bnK0ftSe9e8UwdKCLSfcVzqHFrxPUCgslT2z2rsasrK85Xj5dIHKbNruCKR+dtPxuxoqqGHz8yD4DTx5Zz2ef2337CiohITxfPocYbIm+b2e8Ixnp1a6VFBSxYVZ3pNESyXrQZ5wEG9evN7784Jv0JiYhksUT6/fvQA8Z6lRbn61CjSBxizSy/fkt9mjMREcl+8Yzxmgd4eDOXYNb5bju+q0VpUQFb6hrZWtdIX81eLxJTaXE+a6p3/iNFM86LiOwsnooickHsRmCNuzemKJ+s0TKXV+XmOvZU4SUS1fIN26iLcphRM86LiEQXz6HGzRGXGqDYzAa0XFKaXQaVhnN5aYC9SHQrNm7jS3+fhWP8aNJ+lIc9XOUlhVx3+sGacV5EJIp4unLeBoYBGwEDSoCPw/ucbjreK7LHS0R2tLKqhnNueY3NtQ3c9/UjGFnen29P3FczzouItCOeHq+ngVPcfZC7DyQ49Piou+/p7t2y6IJPerwq1eMlsoPVm2o555ZZbNxazz0XHc7I8v6ZTklEpMuIp/A61N2fbLnh7k8Bn05dStmhuLAX+b1y1OMlEqGyOii61m2p566LDmP0sJJMpyQi0qXEc6hxnZldBdxLcGjxPGB9SrPKAmYWnq2lHi8RCNYuPefW11hdXcvdXz2MQ3bfJdMpiYh0OfH0eJ1NMIXEP4FpQGm4rdsrKyqgMspp8iI9zfotdZx362tUbKzhjgsPZfzwbntejYhISsUzc/0G4FIAM9sFqHJ3b/tR3UNpcT4LV2/OdBoiGbVxaz3n3voay9Zv5Y4LD+XwvQZmOiURkS4rZo+Xmf3MzPYPr+eb2fPAB8AaM/tMuhLMpNKiAtaqx0t6sE3bGjjvttdYum4rt14wniP3GZTplEREurS2DjV+EVgUXr8g3LeUYGD9r1OcV1YoLc5nc10j2+q7/XyxIjvZVNPA+be/xvtrtvD388dxzL6DM52SiEiX11bhVR9xSHEScL+7N7n7AuIblN/llW2fUkK9XtKzbK5t4ILbX2fBqmr+dt4hTBhRmumURES6hbYKrzozG2lmg4GJwDMR9/VJbVrZoaxYs9dLz7OlrpEL73iD+RWb+Ms5h3D8AWWZTklEpNtoq+fqUuBhgjMa/+DuHwKY2YnA7DTklnGl4ez1azSXl/QQ2+ob+eodb/DO8ipuPHssnz1o10ynJCLSrcQsvNz9NWD/KNufBJ7c+RHdT5lmr5ceYNrsCqZOX0RFVQ29//Ms9Y3N/PnssXzu4CGZTk1EpNvpEWO1ElVc2Ivemr1eurFpsyu44tF51DQ0AVDf2ExertHU3CNmjBERSbt4JlDtscyMsuJ89XhJtzV1+qLtRVeLhiZn6vRFMR4hIiKdocKrHaVFBazRWY3SDS3fsI2Kqpqo962MsV1ERDonrkONZnYkMDxyf3e/O0U5ZZWy4nwWafZ66UZWVtVw44wPeOiN5TH3GVpSmMaMRER6jnYLLzO7B9gbeAdoOSbhQI8ovEqLCnhp8bpMpyHSaZXVtfxlxgfc//pyHOfsw3Zn79K+/OapHQ83FublMmXSiAxmKiLSfcXT4zUeOLCnrM/YWuTs9X1661wE6XrWbanjpplLuGfWRzQ1O2eN341vH7cv5WGvVklh7+1nNZaXFDJl0ggmjy3PcNYiIt1TPJXEfGBXYFWKc8lKpRGz1w8fpMJLuo6NW+u5+cWl3PXqMuoam/j82N249Ph92X3gjvMfTx5bzuSx5cycOZMJEyZkJlkRkR4inkpiEPCemb0ObB9l7u6npiyrLFIWTqJaubmO4YP6ZjgbkfZtqmngtpeWcvsry9ha38ipo4fy3eP3Ze/B/TKdmohIjxdP4XVNqpPIZi09Xlo2SLJN5MSn5bOe5zvH7U3l5npueWkpm2sbOfHgXfneZ/Zjv7KiTKcqIiKhdgsvd38hHYlkq8geL5Fs0Xri04qqGi5/dD4AnzmgjO//z74cNLR/JlMUEZEo2p3Hy8yOMLM3zGyLmdWbWZOZVacjuWzQvzAvmL1ePV6SRaJNfAowuF8+t14wXkWXiEiWimcC1RuBs4H3gULga+G2HsHMKC3KV4+XZJVYE5yu26J2KiKSzeI6Tc/dPzCzXHdvAu4ws1dTnFdWKS3K1xgvyQoNTc38/cWlxJrbRROfiohkt3gKr21m1ht4x8x+SzCtRI86va+suID3K7dkOg3p4eYsr+KyR+aycPVmxuzWn4WrN1Pb2Lz9fk18KiKS/eI51Hh+uN+3ga3AMOCMVCaVbcqKC9TjJRmzrb6RXzz+Hp//6yts3FbP388fx7RvH831Z4zaPglqeUkh151+sCY+FRHJcvGc1fiRmRUCQ9z952nIKesMLspnc20jNfVNFPbOzXQ60oO8sHgtV/5zHis21nDeEbvz4xP2p7ggD9DEpyIiXVE8ZzWeQrBO49Ph7TFm9q8U55VVyorD2es3q9dL0mPD1nq+/+A7XHD76+T3yuEfl3yKX04+eHvRJSIiXVO8E6geBswEcPd3zGx46lLKPqVFwVxea6rr2GNgjxreJmnm7jz2zkquffw9Ntc28N3j9uGbE/ehIE89rSIi3UE8hVeju28ys5Qnk63U4yXpsHzDNq6cNp8XF69lzLASfnPGKEbsqlnnRUS6k7gWyTazc4BcM9sX+C7Q46aTgKDHSyQZIpf7GTrrOQ4bPoDp764hx+CaUw7k/E8NJzen5/6xIyLSXcVTeH0HuJJggez7genAL1KZVLYp6ZNH79wc9XhJUrRe7mdlVS3T3lnJAbsWceuFh24/U1FERLqfeM5q3EZQeF2Z+nSyk5kxuCifSvV4SRLEWu6nurZBRZeISDcXs/Bq78xFdz81+elkr7LifPV4SacsW7eVJ+atoiLGcj8rq9S+RES6u7Z6vD4FLCc4vPga0KMHnJQWFfDBWs1eLx2zfMM2npi3isfnrmR+RbC2fO/cHOqbmnfaV8v9iIh0f20VXrsC/0OwQPY5wBPA/e7+bjoSyzZlxfm8umRdptOQLmDVphqemLuKx+eu4p3lVQCMHlbClScewImjhvDGhxt2GOMFWu5HRKSniFl4hQtiPw08bWb5BAXYTDO71t3/nK4Es0VpcQHVtY3UNjRpTqUeLPJsxPJZzzNl0ggmjy2nsrqWJ+cFxdabH20EYGR5MZedsD8njxrCsAF9tscoD5f12R6npHB7HBER6d7aHFwfFlwnERRdw4E/AY+mPq3s0zKlRGV1HbsP7NPO3tIdtT4bsaKqhikPz+HPz73P0vVbcYf9dy3iR5/dj5NGDWXPQbEn29VyPyIiPVNbg+vvAkYCTwE/d/f5acsqC5WGk6iu2VyrwquHinY2YkOT89GGbVx6/L6cPGoI+5RqwlMREYmtrR6v84GtwH7AdyNmrjfA3b04xblllbLiT3q8pOdpbvaYZyM2NTvf+8x+ac5IRES6orbGeLW7gHZPUloU9nhV65T/nqS52XnmvTX8v+fej7mPzkYUEZF4qbiK0y598sjLNSo3q8erJ2hudp6ev5qT/vwyl9z7FrUNTZx7xDAK8nb8yOhsRBER6Yh4lgwSgtnrS4sKqFSPV7cW2cO1YFU1ew7qyx++OJpTRg2lV24Oh+4xUGcjiohIwlR4dUBpcT5rNHt9t+QeFlz/eZ/3ohRcLXQ2ooiIdIYKrw4oKypgiWav71aiFVy//8JoTh29Y8ElIiKSDCkrvMzsduBkoNLdR4bbBgAPEswJtgz4grtvTFUOyVaq2eu7tB0nP32OSQftyqylG1RwiYhI2qTyF+ZO4IRW2y4HnnP3fYHnwttdRlnE7PXStbRMftoyJURFVS23v7KMyuoafv+F0Tz7/WM5/ZDdVHSJiEhKpexXxt1fBDa02nwacFd4/S5gcqqePxUGF2kur64q2uSnAL3zclVwiYhI2qT716bM3VcBhP+Wpvn5O6UsnL2+UgPsu5xYk5+uqtL/pYiIpI+5e+qCmw0HHo8Y41Xl7iUR9290911iPPZi4GKAsrKycQ888EDK8gTYsmUL/fr1a3Of5Zub+ekrNXxzTD6H7Rp9eFw8cZKRS7ridPVcmt2Z9kED/1rSEPX+gQXGDRM6vgRUV39flItyUS7KRbmkzsSJE99y9/FR73T3lF0IBtHPj7i9CBgSXh8CLIonzrhx4zzVZsyY0e4+67fU+R6XPe63v7y0U3GSkUu64nTlXDZsqfPzb3vN97jscT/rb6/4iKue9D0ue3z7Zf+rnvJ/vr0iLbmkMo5ySV2MZMVRLqmLkaw4yiV1MZIVJ5tyaQ/wpseoadJ9qPFfwAXh9QuAx9L8/J3SMnv9Go3xynrzVmzi5D+/zKwl67nu9IN58H8/xfWnj6I8XN6nvKSQ604/WJOfiohIWqVyOon7gQnAIDNbAVwNXA88ZGYXAR8DZ6Xq+VNh++z1GuOV1R56YzlXPTafQX17849LPsXoYSWAJj8VEZHMS1nh5e5nx7jr+FQ9ZzoMLsrXWY1ZqrahiZ//+13uf305R+0zkD99aSwD++VnOi0REZHtNHN9B5UV5/Phuq2ZTkNaqaiq4Rv3vsXcFZv45oS9+eFnR5CbY5lOS0REZAcqvDqotKiAWUtbT08mmfTy++v4zv1v09jk3Hz+OCYdtGumUxIREYlKhVcHlRXns6mmgdqGJgrycjOdTo/W3Oz87YUl3PDMIvYp7cdN541jr8GpPUVYRESkM1R4dVBpUTCJ6trNdQwb0PH5nyQ5qmsb+OFDc3j2vTWcOnoo159xMH16qzmLiEh20y9VB5UWB4O111TXqvDKkEWrN3PJvW+xfMM2rj7lQC48cjhmGs8lIiLZT4VXB7X0eFVu1pmN6TJtdgVTpy+ioqqGXV58hi21jZT07c39Fx/BocMHZDo9ERGRuKnw6qCyiB4vSb1psyu44tF52xe43ritgRyD70zcW0WXiIh0Oemeub7L26VPb/JyTT1eafLb6Qu3F10tmh1ufvHDDGUkIiKSOPV4dVBOjjG4X756vFLM3Zn+7mpWVkV/n1dW1aQ5IxERkc5T4ZWA0uIC1qrHKyXcnRcWr+WGZxYzr2ITvXKMxmbfab+h4ZqLIiIiXYkKrwSUFuWzbL1mr0+2WUvXc8Mzi3hj2UZ226WQ3501mhycK6e9u8PhxsK8XKZMGpHBTEVERBKjwisBZcUFvL5Ms9cnyzvLq7jhmUW89P46yorz+cXkkXxx/DB69wqGIObk5Gw/q7G8pJApk0YweWx5hrMWERHpOBVeCSgtyqdqm2av76wFq6r5/bOLefa9NQzo25urTjqA847YY6f3dPLYciaPLWfmzJlMmDAhM8mKiIgkgQqvBJQVa/b6zli6dgt/+M/7PD53Jf3ye/HD/9mPrxy9J/3y1RxFRKR70y9dAgaHc3lVbtbs9e2JnPy07NX/MHxgH978qIreuTl849N7c/Gxe1HSp3em0xQREUkLFV4JKGuZvb5aZza2pfXkp2uq61hTXcen9x3EDV8cw6B++RnOUEREJL00gWoCSjV7fVymTl+00+SnAB+s3aqiS0REeiQVXgkY0Kc3vXI0e317Yk1yqslPRUSkp1LhlYCcHGNwUT5rdKixTf0L86Ju1+SnIiLSU6nwSlBpcQGVm3WoMZblG7axrb6RHNtxuyY/FRGRnkyFV4JKi/I1uD6G5mZnysNz6N0rl6tOOoDysIervKSQ604/WJOfiohIj6WzGhNUVpzPG5q9Pqq7/ruMWUs38JszDuaLh+7OV4/eS5OfioiIoB6vhJUWFVC1rYG6xp3P2uvJlqzdwvVPLWTiiMF8YfywTKcjIiKSVVR4JaisZRJVHW7crqnZ+dE/5lCQl8v1Z4zCzNp/kIiISA+iwitBpeGyQZpS4hN/f3Epsz+u4trTDtq+rJKIiIh8QoVXgkqLWnq8dGYjwMLV1fzh2cWcePCunDp6aKbTERERyUoqvBJUph6v7eobm/nhQ3MoKujFL04bqUOMIiIiMeisxgS1zF6vZYPgxhkf8O7Kam4+fxwDtRSQiIhITOrxSlDL7PU9vcdr7ooq/jLjA04fW86kg3bNdDoiIiJZTYVXJ5QW5ffoHq/ahiZ++NAcBvfL5+pTDsp0OiIiIllPhVcnlBYXsLYH93j94dnFvF+5hd+cOYr+faKvyygiIiKfUOHVCT25x+vNZRv4+0tLOefw3fn0foMznY6IiEiXoMKrE8qKC9jYA2ev31bfyA//MYfykkJ+cuIBmU5HRESky1Dh1Qktc3n1tMON1z+1kI/Wb+N3Z42mX75OjBUREYmXCq9O6Ilzeb3ywTru/u9HfPWoPTlir4GZTkdERKRLUeHVCYN72Oz11bUN/Pjhuew1uC8/PmFEptMRERHpcnScqBNaerzW9JCFsn/5+Hus2lTDI984koK83EynIyIi0uWox6sTBvbtTW6OUbm5+/d4PbdgDQ+9uYJvTNibsbvvkul0REREuiQVXp2Qk2MM7pff7Xu8Nm6t5/JH57H/rkV89/h9M52OiIhIl6VDjZ1UWtw9lw2aNruCqdMXUVFVQ+Fzz1Pb0MSdXzmU/F46xCgiIpIo9Xh1UmlRQbcbXD9tdgVXPDqPiqoaAGoamsjNMd5fsyXDmYmIiHRtKrw6qTv2eE2dvoiahh0nhW1sdqZOX5ShjERERLoHFV6dVFZUwIat9dQ3Nmc6laRZGfZ0xbtdRERE4qPCq5PKisPZ67d0j16vDVvrY04VMbSkMM3ZiIiIdC8qvDqpNCy8usNi2U/OW8Vn//ACdY1N9MqxHe4rzMtlyiRNmioiItIZOquxk0qLwmWDuvCUEuu21HH1Y+/yxLxVHFzen3u/djgLV23eflZjeUkhUyaNYPLY8kynKiIi0qWp8Oqklh6vrjiJqrvz+NxVXP2vd9lS28iUSSP432P3olduDvvvWszkseXMnDmTCRMmZDpVERGRbkGFVycN7JsfzF6fhB6vyLmzymc9n9JepsrNtfx02nymv7uG0cNKmHrmKPYrK0rJc4mIiEhAhVcn5eYYg/r17vQYr5a5s1qmcaioquGKR+cBJLX4cncee2cl1/z7XbbVN3H55/bna0fvSa9cDfcTERFJNRVeSVBWXNDpubyizZ1V09DE1OmLklZ4VVbX8pN/zuc/C9YwdvcSpp45mn1K+yUltoiIiLRPhVcSlBbls2Jj5+a4ijVHVkVVDb98/D1GDSth9G792X1AH8ws6r6xuDuPvF3Btf9+l7rGZq466QC+ctSe5OZ0LI6IiIh0jgqvJCgtLmD2x1WdilHWv4DVm3Y+XJmXa9wz6yPqXv4QgJI+eYzarYQxu/Vn1G4ljBrWf/uZlS0ix4rt+up/2KVPbxas3syhw3fhN2eMYq/B6uUSERHJBBVeSVBalM/6Tsxev35LHbjvtL0wL5frTj+Yk0YNYdHqzcxdsYm5K6p4Z3kVN85YS3P4kKH9Cxi1Wwmjh5WwubaB21/5kNqGIJfV1XWsrq7j9LFD+d1ZY8hRL5eIiEjGqPBKgrLioMcpkdnrN2yt59xbX2Pjtga+OWFvHntnZdS5s0aW92dkeX/OOXx3ALbVN/LuymrmLK9i7opNzFlRxdPvro75PK99uFFFl4iISIap8EqC0qJwLq8Ontm4cWs959wyiw/XbeW2Cw7l6H0H8eMT9o9r7qw+vXtx6PABHDp8wA7xxv7i2aj7a51FERGRzNMcAknQ0uO1pgNzeW0Me7qWrtvKrReM5+h9B3U6j1369qY8xnqKWmdRREQk81R4JUFLj9faOGevr9pWz3m3vcYHa7dwy5fHc8y+g5OWy5RJIyhstci11lkUERHJDjrUmAQD++WTY0GP17D8tvfdtK2B8257jffXbOHvXx7Hp/dLXtEFn0y2qnUWRUREsk9GCi8zuxT4OmDALe7+x0zkkSzB7PX5wXqNbRRem2qComvx6i3cfP44JowoTUk+k8eWa51FERGRLJT2Q41mNpKg6DoMGA2cbGb7pjuPZCsrLmhzjNemmga+fNtrLFxdzU3nH8LE/VNTdImIiEj2ysQYrwOAWe6+zd0bgReAz2cgj6QqK86PuWxQdW0DX779dd5bVc3fzh3HcfuXpTk7ERERyQbmUSbuTOkTmh0APAZ8CqgBngPedPfvtNrvYuBigLKysnEPPPBASvPasmUL/folPqP7nfPreKuykesO8x3i1DQ6v3ujlmXVzXxrTD6HlLV/dLezuSQzjnJRLspFuSgX5aJcOmbixIlvufv4qHe6e9ovwEXA28CLwE3AH9raf9y4cZ5qM2bM6NTj//DsIt/jssf92eee375tc22Df/4vL/veVzzhT81blbZckhlHuaQuRrLiKJfUxUhWHOWSuhjJiqNcUhcjWXGyKZf2EHQoRa1pMjKdhLvf5u6HuPuxwAbg/UzkkUwt6yVuqgt6ELfUNXLh7a8zZ8UmbjxnLCeM3DWT6YmIiEgWyNRZjaXuXmlmuwOnExx27NLKioPTGTfVOVvrGvnKHa8ze3kVfz57LCeMHJLh7ERERCQbZGoer0fMbCDQAHzL3TdmKI+keW9lNQDXzqrl+jeepaGpmRvPOYQTD1bRJSIiIoGMFF7ufkwmnjdVps2u4C8zPth+u76pmbxco6GpOYNZiYiISLbRkkFJMHX6ImobdyyyGpqcqdMXZSgjERERyUYqvJJgZVVNh7aLiIhIz6TCKwmGlhR2aLuIiIj0TCq8kmDKpBEU5uXusK0wL5cpk0ZkKCMRERHJRpk6q7FbmTy2HAjGelVU1VBeUsiUSSO2bxcREREBFV5JM3lsOZPHljNz5kwmTJiQ6XREREQkC+lQo4iIiEiaqPASERERSRMVXiIiIiJposJLREREJE1UeImIiIikiQovERERkTRR4SUiIiKSJiq8RERERNJEhZeIiIhImqjwEhEREUkTc/dM59AuM1sLfJTipxkErMuSOMpFuSgX5aJclItyyd5c2rOHuw+Oeo+76xIUn29mSxzlolyUi3JRLsolG+Iol+RfdKhRREREJE1UeImIiIikiQqvT/w9i+Iol9TFSFYc5ZK6GMmKo1xSFyNZcZRL6mIkK45ySbIuMbheREREpDtQj5eIiIhImvT4wsvMbjezSjOb34kYw8xshpktMLN3zezSBOMUmNnrZjYnjPPzTuSUa2azzezxTsRYZmbzzOwdM3szwRglZvawmS0M359PJRBjRJhDy6XazL6XQJzvh+/rfDO738wKOhojjHNpGOPdePOI1s7MbICZPWtm74f/7pJgnLPCXJrNbHyCMaaG/0dzzeyfZlaSYJxfhDHeMbNnzGxoR2NE3PcjM3MzG5RgLteYWUVEuzkxkVzM7Dtmtih8j3+bYC4PRuSxzMzeSSDGGDOb1fJ5NLPDEsxltJn9N/xs/9vMituJEfX7raPtt404cbffNmJ0qP22ESfu9hsrRsT9cbXfNnKJu/22lUu87beNPDradmPFibv9thGjo2036m9qR9tu0mXylMpsuADHAocA8zsRYwhwSHi9CFgMHJhAHAP6hdfzgNeAIxLM6QfAfcDjnXhdy4BBnXx/7wK+Fl7vDZR0Ml4usJpgjpSOPK4c+BAoDG8/BFyYwPOPBOYDfYBewH+AfRNpZ8BvgcvD65cDv0kwzgHACGAmMD7BGJ8FeoXXf9OJXIojrn8XuKmjMcLtw4DpBPP3tdsGY+RyDfCjDvzfRosxMfw/zg9vlyYSp9X9NwA/SyCXZ4DPhddPBGYm+JreAD4dXv8q8It2YkT9futo+20jTtztt40YHWq/bcSJu/3GitHR9ttGLnG33zZixN1+23o9HWy7sXKJu/22EaOjbTfqb2pH226yLz2+x8vdXwQ2dDLGKnd/O7y+GVhA8EPf0Tju7lvCm3nhpcOD8MxsN+Ak4NaOPjaZwr9GjgVuA3D3enev6mTY44El7p7IhLq9gEIz60VQOK1MIMYBwCx33+bujcALwOfbe1CMdnYaQWFK+O/kROK4+wJ3X9R+6m3GeCZ8PQCzgN0SjFMdcbMv7bTfNj5/fwB+3N7j44gTtxgxvgFc7+514T6VncnFzAz4AnB/AjEcaPkLvz9xtN8YcUYAL4bXnwXOaCdGrO+3DrXfWHE60n7biNGh9ttGnLjbbzvf+3G332T8frQRI+72214eHWi7seLE3X7biNHRthvrN7XD373J1OMLr2Qzs+HAWILKOpHH54ZduZXAs+6eSJw/EnzomxPJIYIDz5jZW2Z2cQKP3wtYC9xhwWHPW82sbydz+hLtfPCjcfcK4HfAx8AqYJO7P5PA888HjjWzgWbWh+Avt2EJxAEoc/dVYX6rgNIE4yTbV4GnEn2wmf3KzJYD5wI/S+DxpwIV7j4n0RwifDs8dHR7gocT9gOOMbPXzOwFMzu0k/kcA6xx9/cTeOz3gKnhe/s74IoEc5gPnBpeP4sOtN9W328Jt9/Ofk+2E6ND7bd1nETab2SMzrTfKK+pw+23VYyE2m+M97bDbbdVnO+RQPttFaPDbTfGb2pGv3tVeCWRmfUDHgG+1+ovp7i5e5O7jyH4i+0wMxvZwRxOBird/a1Enr+Vo9z9EOBzwLfM7NgOPr4XwWGOv7n7WGArQbduQsysN8GH7h8JPHYXgr9y9gSGAn3N7LyOxnH3BQSHMp4FngbmAI1tPqgLMbMrCV7P/yUaw92vdPdhYYxvd/D5+wBXkkDBFsXfgL2BMQTF9g0JxOgF7EJweGIK8FD4l3+iziaBPxxC3wC+H7633yfsSU7AVwk+z28RHMapj+dByfh+S1acWDE62n6jxelo+42MET53Qu03Si4dbr9RYnS4/bbx/9OhthslTofbb5QYHW67nf1NTYlkHK/s6hdgOJ0Y4+WfHD+eDvwgiXldTQfGqISPuQ5YQTA+azWwDbg3Cblck0AuuwLLIm4fAzzRiRxOA55J8LFnAbdF3P4y8NckvC+/Br6ZSDsDFgFDwutDgEWJxInYPpM4xnjFigFcAPwX6NOB1x/zswPsEc/nKjIGcDDBX6bLwksjQS/lrp3MJa7PeJT/o6eBCRG3lwCDE3x/ewFrgN0SbC+b+GQKIAOqk/B/tB/wehwxdvp+S6T9RovT0fYbK0ZH229bucTbflvHSLT9xpFLu+03xv9Rh9pvG+9tR9tutFw61H7jeE/iarutHnM18KNE2m4yL+rxSoLwL4jbgAXu/vtOxBls4dk4ZlYIfAZY2JEY7n6Fu+/m7sMJDss97+4d7tkxs75mVtRynWDwaofO/HT31cByMxsRbjoeeK+juUToTG/Bx8ARZtYn/P86nmDcQIeZWWn47+7A6Z3I6V8EPxaE/z6WYJxOM7MTgMuAU919Wyfi7Btx81Q63n7nuXupuw8P2/AKgkG2qxPIZUjEzc/TwfYbmgYcF8bbj+AEkUQX1/0MsNDdVyT4+JXAp8PrxwGJHK6MbL85wFXATe3sH+v7rUPtNxnfk7FidLT9thEn7vYbLUYi7beNXOJuv228t9OIs/228/8Td9ttI07c7beN96SjbTfWb2pmv3vTWeVl44XgR3MV0EDwIbkogRhHE4yHmgu8E15OTCDOKGB2GGc+7Zw9Eke8CSR4ViPB+Kw54eVd4MoE44wB3gxf0zRglwTj9AHWA/078X78nOBDNx+4h/BMnwTivERQQM4Bjk+0nQEDgecIvoCeAwYkGOfz4fU6gr9KpycQ4wNgeUT7bfNsxDbiPBK+v3OBfxMMWO5QjFb3LyO+sxqj5XIPMC/M5V+Ef+F2MEZv4N7wNb0NHJdILuH2O4FLOtFejgbeCtvda8C4BONcSnCW2GLgesJeiDZiRP1+62j7bSNO3O23jRgdar9txIm7/caK0dH220YucbffNmLE3X7bej10rO3GyiXu9ttGjI623ai/qSTw3ZvMi2auFxEREUkTHWoUERERSRMVXiIiIiJposJLREREJE1UeImIiIikiQovERERkTRR4SUiAJiZm9kNEbd/ZGbXJCn2nWZ2ZjJitfM8Z5nZAjOb0Wr78PD1fSdi241mdmE78S4xsy+3s8+FZnZjjPu2RNueTGY2xMwebysXM/u2mX0l1bmISPtUeIlIizrgdDMblOlEIplZbgd2v4hgJYGJUe6rBC4Nl56Ki7vf5O53d+D5k8aCxdzj8QPglnb2uR34bucyEpFkUOElIi0agb8TrKO2g9Y9Vi09OWY2IVx89yEzW2xm15vZuWb2upnNM7O9I8J8xsxeCvc7OXx8rplNNbM3LFgM+H8j4s4ws/sIJpFsnc/ZYfz5ZvabcNvPCCZevMnMpkZ5fWsJJku8oPUdZra3mT1twYLwL5nZ/uH2a8zsR+H1Q8Mc/xvmHDmT+NDw8e+b2W9bxb7BzN42s+fMbHC4bYyZzQrj/dPCBZDNbKaZ/drMXiAoEs8KX+McM3sxymsCOINgaZjWr+mkMNdBHszmvszMDosRQ0TSRIWXiET6C3CumfXvwGNGE8wofTBwPrCfux8G3Ap8J2K/4QRLhpxEUBwVEPRQbXL3Q4FDga+b2Z7h/ocRrJhwYOSTmdlQgoXKjyNYGeFQM5vs7tcSrJJwrrtPiZHr9cAPo/Si/R34jruPI1jL7a9RHnsHwezdnwKaWt03Bvhi+B580cyGhdv7Am97sNj8CwRrxQHcDVzm7qMICsurI2KVuPun3f0GgsWWJ7n7aIIlbHYQvlcb3b2u1fbPEyxIf6K7tywR8ybBeqkikkEqvERkO3evJigKOnJY6g13XxX++C8Bngm3zyMotlo85O7N7v4+sBTYn2AN0C+b2TsEy4gMBFrWy3vd3T+M8nyHAjPdfa27NwL/Bxwb5+v7EHgdOKdlm5n1A44E/hHmcTPBwrlE7FMCFLn7q+Gm+1qFfs7dN7l7LcFyUnuE25uBB8Pr9wJHh0Vtibu/EG6/q1X+D0ZcfwW408y+DkQ75DqEoCcv0kSCdQtPcveNEdsrgaFRYohIGsU7hkBEeo4/EqzrdkfEtkbCP9TCBWwjx0lF9rY0R9xuZsfvmNbrkzlgBD1N0yPvMLMJwNYY+Vk7+bfn18DDQMuhuxygyt3HtPGY9p4z8j1oIvZ3azxrtG1/3e5+iZkdTtBL+I6ZjXH39RH71gAFrR6/lGCt1f0IerlaFIT7i0gGqcdLRHbg7huAhwgOA7ZYBowLr58G5CUQ+iwzywnHfe0FLAKmA98wszwAM9vPzPq2E+c14NNmNig8ZHg2wWG8uLj7QoJeqZPD29XAh2Z2VpiDmdnoVo/ZCGw2syPCTV+K8+lygJaxcecAL7v7JmCjmbUc9js/Vv5mtre7v+buPwPWAcNa7bKYHXsVAT4CTgfuNrODIrbvR7BQsIhkkAovEYnmBiDy7MZbCIqd14HDid0b1ZZFBAXGUwRjpWoJxoG9B7wdDla/mXZ64t19FXAFMAOYQzCG6rEO5vIrYLeI2+cCF5nZHOBdguKytYuAv5vZfwl6wDbF8TxbgYPM7C2CMWnXhtsvAKaa2VyC8WHXRn84U1tOIiDooZsTeae7bwWWmNk+rbYvCl/TPyJOcDgK+E8cOYtICpl7PD3fIiI9m5n1c/eWszkvB4a4+6UZTqtlIP04d7+qjX3GAj9w9/PTl5mIRKMxXiIi8TnJzK4g+N78CLgws+kE3P2fZjawnd0GAT9NRz4i0jb1eImIiIikicZ4iYiIiKSJCi8RERGRNFHhJSIiIpImKrxERERE0kSFl4iIiEiaqPASERERSZP/D4j9t7ZTH/84AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# KNN Hyperparameter Tuning\n",
+    "def knn_hyperparameter_tuning(X_train, y_train):\n",
+    "    k_values = range(1, 31)  # Example range for k\n",
+    "    mse_values = []\n",
+    "    \n",
+    "    for k in k_values:\n",
+    "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
+    "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
+    "        mse_values.append(mse)\n",
+    "    \n",
+    "    return k_values, mse_values\n",
+    "\n",
+    "# Perform KNN Hyperparameter Tuning\n",
+    "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
+    "\n",
+    "# Plotting the results\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
+    "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
+    "plt.xlabel('Number of Neighbors (k)')\n",
+    "plt.ylabel('Mean Squared Error (MSE)')\n",
+    "plt.xticks(k_values)  # Show all k values on x-axis\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b410ecfc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Intel_Optimized/requirements.txt b/Intel_Optimized/requirements.txt
new file mode 100644
index 0000000..a771945
--- /dev/null
+++ b/Intel_Optimized/requirements.txt
@@ -0,0 +1,7 @@
+pandas==2.1.1
+numpy==1.26.0
+scikit-learn==1.3.0
+statsmodels==0.14.0
+pickle-mixin==1.0.2
+modin[all]
+scikit-learn-intelex
\ No newline at end of file

From b3bbd0419ba9be8a6abba752c90e2de87ef99a3e Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Sun, 20 Oct 2024 18:43:46 +0530
Subject: [PATCH 34/76] Implement GitHub PR Issue Checker Workflow

---
 .github/workflows/pr-checker.yaml | 57 +++++++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)
 create mode 100644 .github/workflows/pr-checker.yaml

diff --git a/.github/workflows/pr-checker.yaml b/.github/workflows/pr-checker.yaml
new file mode 100644
index 0000000..dbab3ef
--- /dev/null
+++ b/.github/workflows/pr-checker.yaml
@@ -0,0 +1,57 @@
+name: PR Issue Checker
+# Created by @smog-root.
+on:
+  pull_request:
+    types: [opened, edited]
+
+jobs:
+  check_pr_details:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.x'
+
+      - name: Install dependencies (if needed)
+        run: pip install re  # Install Python's regex library (not needed if using built-in)
+
+      - name: Check PR Description and Title
+        id: check_pr_details
+        run: |
+          python -c "
+import re
+import sys
+import os
+
+pr_description = os.getenv('GITHUB_EVENT_PULL_REQUEST_BODY', '')
+pr_title = os.getenv('GITHUB_EVENT_PULL_REQUEST_TITLE', '')
+
+# Check if PR description is present
+if not pr_description:
+    print('PR description is missing.')
+    sys.exit(1)
+
+# Check if the PR description contains 'Fixes #<issue-number>'
+if not re.search(r'Fixes #[0-9]+', pr_description):
+    print('The PR description should include Fixes #<issue-number>.')
+    sys.exit(1)
+
+# Check if the PR title starts with FIX, FEAT, or DOC
+if not re.match(r'^(FIX|FEAT|DOC)', pr_title):
+    print('The PR title should start with FIX, FEAT, or DOC.')
+    sys.exit(1)
+
+print('PR description and title are valid.')
+"
+        env:
+          GITHUB_EVENT_PULL_REQUEST_BODY: ${{ github.event.pull_request.body }}
+          GITHUB_EVENT_PULL_REQUEST_TITLE: ${{ github.event.pull_request.title }}
+
+      - name: Output result
+        run: echo "All checks passed."
+        

From 85f3c5e330de7d714f688bcd7f7de5ff48a389a0 Mon Sep 17 00:00:00 2001
From: RAMESWAR <rameswarbisoyi@gmail.com>
Date: Sun, 20 Oct 2024 18:44:55 +0530
Subject: [PATCH 35/76] Add .gitattributes file to enforce LF line endings for
 .ipynb

---
 .../stock_market(complete)-checkpoint.ipynb   | 5756 ++++++++++++++++-
 stock_market(complete).ipynb                  |  295 +-
 2 files changed, 5777 insertions(+), 274 deletions(-)

diff --git a/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb b/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
index 0cc81a2..618d424 100644
--- a/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
+++ b/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
@@ -2,11 +2,23 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 83,
    "metadata": {
     "id": "Rqr4Dq5vWXmV"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tensorflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[83], line 27\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtree\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DecisionTreeRegressor\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mneighbors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KNeighborsRegressor\n\u001b[1;32m---> 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dense, LSTM\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
+     ]
+    }
+   ],
    "source": [
     "# importing modules\n",
     "import numpy as np\n",
@@ -18,21 +30,29 @@
     "from sklearn.impute import SimpleImputer\n",
     "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
     "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "from sklearn.metrics import (\n",
+    "    mean_squared_error, \n",
+    "    mean_absolute_error, \n",
+    "    mean_absolute_percentage_error, \n",
+    "    accuracy_score, \n",
+    "    precision_score, \n",
+    "    confusion_matrix, \n",
+    "    recall_score, \n",
+    "    f1_score\n",
+    ")\n",
     "\n",
     "from sklearn.model_selection import GridSearchCV\n",
-    "\n",
     "from sklearn.svm import SVR\n",
     "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
     "from sklearn.tree import DecisionTreeRegressor\n",
     "from sklearn.neighbors import KNeighborsRegressor\n",
     "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense,LSTM\n"
+    "from tensorflow.keras.layers import Dense, LSTM\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 85,
    "metadata": {
     "id": "BigUM4vtWZdc"
    },
@@ -42,15 +62,6 @@
     "df = pd.read_csv(\"SBIN.csv\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "RqOe6KPNWs8Q"
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -62,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 88,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -173,7 +184,7 @@
        "4  76613039.0  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -185,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 90,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -222,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 92,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -360,7 +371,7 @@
        "max    4.469483e+08  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -372,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 94,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -395,7 +406,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,6 +416,240 @@
     "df.isnull().sum()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Documentation: Handling Missing Data in Stock Price DataFrame\n",
+    "\n",
+    "In this section, we handle missing values (NaN) in the stock price DataFrame by using forward fill (`ffill`). This method ensures that missing values are filled by propagating the previous non-null value.\n",
+    "\n",
+    "#### Code Explanation:\n",
+    "\n",
+    "1. **Filling NaN using `ffill()`**:\n",
+    "   - We use the `ffill()` method, which fills missing values by propagating the last valid observation forward.\n",
+    "   - This is particularly useful in time-series data like stock prices.\n",
+    "\n",
+    "2. **Checking for Remaining NaN Values**:\n",
+    "   - After handling the missing values, we use `isnull().sum()` to verify that there are no remaining NaN values in the DataFrame.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date         0\n",
+       "Open         0\n",
+       "High         0\n",
+       "Low          0\n",
+       "Close        0\n",
+       "Adj Close    0\n",
+       "Volume       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Using forward fill for time-series continuity\n",
+    "df['Open'] = df['Open'].ffill()\n",
+    "df['High'] = df['High'].ffill()\n",
+    "df['Low'] = df['Low'].ffill()\n",
+    "df['Close'] = df['Close'].ffill()\n",
+    "df['Adj Close'] = df['Adj Close'].ffill()\n",
+    "df['Volume'] = df['Volume'].ffill()\n",
+    "\n",
+    "# Checking for remaining NaN values\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Documentation: Handling Outliers in Stock Price Data\n",
+    "\n",
+    "In this section, we handle outliers in the stock price data using the Interquartile Range (IQR) method. Outliers are defined as values that fall outside 1.5 times the IQR below Q1 or above Q3. \n",
+    "\n",
+    "#### Code Explanation:\n",
+    "\n",
+    "1. **Detecting Outliers**:\n",
+    "   - We first calculate the IQR for each column (`Open`, `High`, `Low`, `Close`, `Adj Close`, and `Volume`) to identify potential outliers.\n",
+    "   \n",
+    "2. **Handling Outliers**:\n",
+    "   - We replace values identified as outliers by capping them at the 1st percentile (lower bound) or the 99th percentile (upper bound) to reduce the impact of extreme values.\n",
+    "\n",
+    "#### Python Code Implementation:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7.074000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>180.566028</td>\n",
+       "      <td>182.967081</td>\n",
+       "      <td>177.861698</td>\n",
+       "      <td>180.320342</td>\n",
+       "      <td>165.509789</td>\n",
+       "      <td>2.709130e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>154.378208</td>\n",
+       "      <td>155.940446</td>\n",
+       "      <td>152.529681</td>\n",
+       "      <td>154.199712</td>\n",
+       "      <td>151.382248</td>\n",
+       "      <td>1.988562e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>13.478195</td>\n",
+       "      <td>13.935802</td>\n",
+       "      <td>13.214009</td>\n",
+       "      <td>13.346102</td>\n",
+       "      <td>9.531410</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>28.494329</td>\n",
+       "      <td>28.871737</td>\n",
+       "      <td>28.054413</td>\n",
+       "      <td>28.489611</td>\n",
+       "      <td>19.865707</td>\n",
+       "      <td>1.299895e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>173.100006</td>\n",
+       "      <td>176.500000</td>\n",
+       "      <td>170.290001</td>\n",
+       "      <td>172.937500</td>\n",
+       "      <td>152.463203</td>\n",
+       "      <td>2.064730e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>265.245011</td>\n",
+       "      <td>268.892494</td>\n",
+       "      <td>261.237503</td>\n",
+       "      <td>265.082496</td>\n",
+       "      <td>245.745831</td>\n",
+       "      <td>3.651406e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>620.371033</td>\n",
+       "      <td>628.923630</td>\n",
+       "      <td>611.012137</td>\n",
+       "      <td>619.971824</td>\n",
+       "      <td>584.566015</td>\n",
+       "      <td>7.178671e+07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Open         High          Low        Close    Adj Close  \\\n",
+       "count  7074.000000  7074.000000  7074.000000  7074.000000  7074.000000   \n",
+       "mean    180.566028   182.967081   177.861698   180.320342   165.509789   \n",
+       "std     154.378208   155.940446   152.529681   154.199712   151.382248   \n",
+       "min      13.478195    13.935802    13.214009    13.346102     9.531410   \n",
+       "25%      28.494329    28.871737    28.054413    28.489611    19.865707   \n",
+       "50%     173.100006   176.500000   170.290001   172.937500   152.463203   \n",
+       "75%     265.245011   268.892494   261.237503   265.082496   245.745831   \n",
+       "max     620.371033   628.923630   611.012137   619.971824   584.566015   \n",
+       "\n",
+       "             Volume  \n",
+       "count  7.074000e+03  \n",
+       "mean   2.709130e+07  \n",
+       "std    1.988562e+07  \n",
+       "min    0.000000e+00  \n",
+       "25%    1.299895e+07  \n",
+       "50%    2.064730e+07  \n",
+       "75%    3.651406e+07  \n",
+       "max    7.178671e+07  "
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Detecting and handling outliers using the Interquartile Range (IQR) method\n",
+    "\n",
+    "def handle_outliers(df, column):\n",
+    "    Q1 = df[column].quantile(0.25)\n",
+    "    Q3 = df[column].quantile(0.75)\n",
+    "    IQR = Q3 - Q1\n",
+    "    lower_bound = Q1 - 1.5 * IQR\n",
+    "    upper_bound = Q3 + 1.5 * IQR\n",
+    "    \n",
+    "    # Cap the outliers at the lower and upper bounds\n",
+    "    df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else x)\n",
+    "    df[column] = df[column].apply(lambda x: upper_bound if x > upper_bound else x)\n",
+    "\n",
+    "# Applying the function to the relevant columns\n",
+    "columns_to_check = ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']\n",
+    "for col in columns_to_check:\n",
+    "    handle_outliers(df, col)\n",
+    "\n",
+    "# Checking for remaining outliers by printing descriptive statistics\n",
+    "df.describe()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -416,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {
     "id": "HpS7l8oSCf6y"
    },
@@ -437,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {
     "id": "oVEjj8ZMCnDx"
    },
@@ -465,43 +710,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 769
-    },
-    "id": "-n8vOGhdCmer",
-    "outputId": "ea5057f1-9493-43de-ec65-7a017134d171"
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "The x variable is categorical, but one of ['numeric', 'datetime'] is required",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[28], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Plot the relationship between 'Date' and 'Volume'\u001b[39;00m\n\u001b[0;32m      2\u001b[0m sns\u001b[38;5;241m.\u001b[39mscatterplot(x \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate\u001b[39m\u001b[38;5;124m'\u001b[39m], y \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVolume\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m----> 3\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkdeplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDate\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mVolume\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
-      "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\seaborn\\distributions.py:1679\u001b[0m, in \u001b[0;36mkdeplot\u001b[1;34m(data, x, y, hue, weights, palette, hue_order, hue_norm, color, fill, multiple, common_norm, common_grid, cumulative, bw_method, bw_adjust, warn_singular, log_scale, levels, thresh, gridsize, cut, clip, legend, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[0;32m   1676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1677\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 1679\u001b[0m \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_attach\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallowed_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnumeric\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdatetime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_scale\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1681\u001b[0m method \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mfill_between \u001b[38;5;28;01mif\u001b[39;00m fill \u001b[38;5;28;01melse\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mplot\n\u001b[0;32m   1682\u001b[0m color \u001b[38;5;241m=\u001b[39m _default_color(method, hue, color, kwargs)\n",
-      "File \u001b[1;32mD:\\Anaconda\\Lib\\site-packages\\seaborn\\_base.py:1084\u001b[0m, in \u001b[0;36mVectorPlotter._attach\u001b[1;34m(self, obj, allowed_types, log_scale)\u001b[0m\n\u001b[0;32m   1079\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m var_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m allowed_types:\n\u001b[0;32m   1080\u001b[0m         err \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1081\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m variable is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but one of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1082\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mallowed_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is required\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1083\u001b[0m         )\n\u001b[1;32m-> 1084\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(err)\n\u001b[0;32m   1086\u001b[0m \u001b[38;5;66;03m# -- Get axis objects for each row in plot_data for type conversions and scaling\u001b[39;00m\n\u001b[0;32m   1088\u001b[0m facet_dim \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcol\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrow\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n",
-      "\u001b[1;31mTypeError\u001b[0m: The x variable is categorical, but one of ['numeric', 'datetime'] is required"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[   0  222  442 ... 1422 1657 1894]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le = LabelEncoder()\n",
+    "encoded=le.fit_transform(df['Date'])\n",
+    "print(encoded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.691147</td>\n",
+       "      <td>18.978922</td>\n",
+       "      <td>18.540184</td>\n",
+       "      <td>18.823240</td>\n",
+       "      <td>12.409931</td>\n",
+       "      <td>43733533.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18.894005</td>\n",
+       "      <td>18.964767</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>18.224106</td>\n",
+       "      <td>12.014931</td>\n",
+       "      <td>56167280.0</td>\n",
+       "      <td>222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.327892</td>\n",
+       "      <td>18.568489</td>\n",
+       "      <td>17.643839</td>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>11.694577</td>\n",
+       "      <td>68296318.0</td>\n",
+       "      <td>442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17.502312</td>\n",
+       "      <td>17.832542</td>\n",
+       "      <td>17.223972</td>\n",
+       "      <td>17.676863</td>\n",
+       "      <td>11.654142</td>\n",
+       "      <td>86073880.0</td>\n",
+       "      <td>678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.738192</td>\n",
+       "      <td>17.785366</td>\n",
+       "      <td>17.459852</td>\n",
+       "      <td>17.577793</td>\n",
+       "      <td>11.588827</td>\n",
+       "      <td>76613039.0</td>\n",
+       "      <td>914</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "        Open       High        Low      Close  Adj Close      Volume  Date\n",
+       "0  18.691147  18.978922  18.540184  18.823240  12.409931  43733533.0     0\n",
+       "1  18.894005  18.964767  17.738192  18.224106  12.014931  56167280.0   222\n",
+       "2  18.327892  18.568489  17.643839  17.738192  11.694577  68296318.0   442\n",
+       "3  17.502312  17.832542  17.223972  17.676863  11.654142  86073880.0   678\n",
+       "4  17.738192  17.785366  17.459852  17.577793  11.588827  76613039.0   914"
       ]
      },
+     "execution_count": 97,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Plot the relationship between 'Date' and 'Volume'\n",
-    "sns.scatterplot(x = df['Date'], y = df['Volume'])\n",
+    "df.drop(\"Date\",axis=1,inplace=True)\n",
+    "df[\"Date\"]=encoded\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 769
+    },
+    "id": "-n8vOGhdCmer",
+    "outputId": "ea5057f1-9493-43de-ec65-7a017134d171"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the relationship between 'Date' and 'Volume'\n",
+    "sns.scatterplot(x = df['Date'], y = df['Volume'])\n",
     "sns.kdeplot(x = df['Date'], y = df['Volume'])\n",
     "plt.show()"
    ]
@@ -525,11 +887,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 103,
    "metadata": {
     "id": "q3QfzWquCmjk"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the price variation over time\n",
     "create_line_plot(df['Date'], df['High'] - df['Low'],\n",
@@ -561,11 +934,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 107,
    "metadata": {
     "id": "XKsek--aCmoC"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the relationship between 'Adj Close' and 'Volume'\n",
     "create_line_plot(df['Adj Close'], df['Volume'],\n",
@@ -592,11 +976,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 111,
    "metadata": {
     "id": "W5TY59ikCv78"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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p+Pr6KhqNRvHx8VHuuOMOZdu2bQ1iz3XDTElJUYYNG6Y4OzsrLi4uyt13323tHPpr33zzjdK7d2/F1dVV0ev1SnBwsHLnnXcq69ats8aMHTtWcXJyuuS9fq8xY8YogNKzZ88Gx77//ntl0KBBSrNmzRSdTqf4+PgogwcPVjZv3nzJ6+3fv18BlISEhEvGHDlyRAGs3U9ramqUadOmKT4+Poq9vb3SvXt3JTk5WQkODr5oV9ANGzbYXO+jjz5S2rZtq9jb2ysRERHK0qVLG3QF/eijj5TevXsrvr6+ik6nUwICApTRo0crP//8s8215s2bp4SGhipqtVoBlA8//NB6bOPGjcqQIUMUDw8PRavVKs2aNVOGDBmifPnllzbX+O6775QOHTooOp1OCQoKUl555ZU//HP59ttvlSFDhije3t6KRqNR3N3dld69eyvvvvuuUlNTY43jgq6giqIoBw4cUIYNG6YYDAZFp9MpHTt2tHkORVGU119/XenRo4fi5eVlnef48eOVU6dO2cSlp6crDzzwgNKsWTNFq9Uq3t7eSo8ePZSXX375dz+LEEIIIZqWSlF+aa0lhBBCCPEXNHPmTF544QXy8/Ote1yJP+7bb79l5MiRHDhwwFpdJoQQQgghLk+WggohhBBC3MBqamrYvHkzCxYswNvbm1atWjX1lIQQQggh/jKkeYEQQgghxA0sOzubwYMHk5OTw6effnrRfduEEEIIIcTFyVJQIYQQQgghhBBCCCGugFSsCSGEEEIIIYQQQghxBSSxJoQQQgghhBBCCCHEFZDEmhBCCCGEEEIIIYQQV0C6ggIWi4WsrCxcXFxQqVRNPR0hhBBCCCGEEEII0UQURaGsrIyAgADs7C5fkyaJNSArK4vAwMCmnoYQQgghhBBCCCGEuE6cOXOG5s2bXzZGEmuAi4sLUP/CXF1dm3g2QgghhBBCCCGEEKKplJaWEhgYaM0XXY4k1sC6/NPV1VUSa0IIIYQQQgghhBDid20XJs0LhBBCCCGEEEIIIYS4ApJYE0IIIYQQQgghhBDiCkhiTQghhBBCCCGEEEKIKyB7rP1OiqJQV1eH2Wxu6qmIa0ytVqPRaH7X2mohhBBCCCGEEELcOCSx9jvU1taSnZ1NZWVlU09FNBFHR0f8/f3R6XRNPRUhhBBCCCGEEEJcJySx9hssFgvp6emo1WoCAgLQ6XRSuXQDURSF2tpa8vPzSU9PJywsDDs7WUEthBBCCCGEEEIISaz9ptraWiwWC4GBgTg6Ojb1dEQTcHBwQKvVcvr0aWpra7G3t2/qKQkhhBBCCCGEEOI6IKU3v5NUKd3Y5OcvhBBCCCGEEEKIC0m2QAghhBBCCCGEEEKIKyCJNSGEEEIIIYQQQgghroAk1gQhISHMmzevqachhBBCCCGEEEII8ZciibW/uGHDhtG3b9+LHktOTkalUrFnz55rPCshhBBCCCGEEEKIvz9JrP3FjR8/nvXr13P69OkGxz744AM6depEly5dmmBmQgghhBBCCCGEEH9vklj7ixs6dCg+Pj4sWbLEZryyspKlS5cyfvx4li1bRrt27dDr9YSEhPD6669f8nqnTp1CpVKxb98+61hJSQkqlYoNGzYAsGHDBlQqFT/88AOdO3fGwcGB2267jby8PFavXk3btm1xdXXl7rvvprKy0nodRVGYO3cuLVq0wMHBgY4dO/LVV19dzdchhBBCCCGEEEIIcc1IYu0vTqPRcN9997FkyRIURbGOf/nll9TW1hITE8Po0aO56667OHDgADNnzuTZZ59tkIi7EjNnzmTBggVs27aNM2fOMHr0aObNm8dnn33GypUrSUpKYv78+db4Z555hg8//JBFixZx6NAhpkyZwr333svGjRv/9FyEEEIIIYQQQgghrjVNU09A/HkPPPAAr776Khs2bKB3795A/TLQO+64gzfeeIM+ffrw7LPPAtC6dWsOHz7Mq6++yrhx4/7UfV9++WV69uwJ1C9JffLJJzlx4gQtWrQA4M477+Snn35ixowZVFRU8MYbb7B+/XpiYmIAaNGiBVu2bGHx4sXExsb+qbkIIYQQQgghhBBCXGtSsfY30KZNG3r06MEHH3wAwIkTJ9i8eTMPPPAAqamp1uTXOT179uTYsWOYzeY/dd8OHTpY/9nX1xdHR0drUu3cWF5eHgCHDx+murqafv364ezsbP363//+x4kTJ/7UPIQQQgghhBBCCCGaglSs/U2MHz+eRx55hHfeeYcPP/yQ4OBg+vTpg6IoqFQqm9hfLxm9kJ2dXYMYk8l00VitVmv9Z5VKZfP9uTGLxQJg/d+VK1fSrFkzmzi9Xv9bjyeEEEIIIYQQQojrUJ3ZQnlNHY46NTqNuqmnc81JYu1vYvTo0Tz22GN89tlnfPTRR0yYMAGVSkVERARbtmyxid22bRutW7dGrW74C+/t7Q1AdnY2nTt3BrBpZHClIiIi0Ov1ZGRkyLJPIYQQQgghhBDibyA1u5TPdpxmY1oBnQLdGN8rlI6Bbk09rWtKEmt/E87OzsTFxfHUU09hNBqt+6dNmzaNrl278tJLLxEXF0dycjILFixg4cKFF72Og4MD3bt355VXXiEkJISCggKeeeaZPz0/FxcXpk+fzpQpU7BYLPTq1YvS0lK2bduGs7MzY8eO/dP3EEIIIYQQQgghxLWRXlBB/Ps7KCivBSCjqJKkw7ks+1cMEQGGJp7dtSN7rP2NjB8/nuLiYvr27UtQUBAAXbp04YsvviAxMZHIyEiee+45Xnzxxcs2Lvjggw8wmUxER0fz2GOP8fLLL1+V+b300ks899xzzJ49m7Zt2zJgwABWrFhBaGjoVbm+EEIIIYQQQgghro2DZ43WpNo5VSYzW44XNNGMmoZKudyGWzeI0tJSDAYDRqMRV1dXm2PV1dWkp6cTGhqKvb19E81QNDX5PRBCCCGEEEIIIc77fOdpnvz6YIPxB3qG8Nywdk0wo6vncnmiC0nFmhBCCCGEEEIIIYT4Q8J8XC463ivM6xrPpGlJYk0IIYQQQgghhBBC/CFt/V15dmhb1HYqAFQqePCWUDpJ8wIhhBBCCCGEEEIIIS7NSa/hvu4h9GrlRXZJNV4uelp6O+Ggu7FSTTfW0wohhBBCCCGEEEKIq0KrsSPcz5Vwv8vvQ/Z3JktBhRBCCCGEEEIIIYS4ApJYE0IIIYQQQgghhBDiCkhiTQghhBBCCCGEEEKIKyCJNSGEEEIIIYQQQgghroAk1oQQQgghhBBCCCGEuAKSWBNCCCGEEEIIIYQQ4gpIYu1vbuHChYSGhmJvb09UVBSbN29u6ikJIYQQQgghhBBC/C1IYu0aOlNUyZ7TxZwpqrwm91u6dCkJCQk8/fTT7N27l5tvvplBgwaRkZFxTe4vhBBCCCGEEEII8XcmibVrZGNaPkPmb+aORdsYOn8LG9PyG/2eb7zxBuPHj+ef//wnbdu2Zd68eQQGBrJo0aJGv7cQQgghhBBCCCHE312TJtZCQkJQqVQNvh5++GEAFEVh5syZBAQE4ODgwK233sqhQ4dsrlFTU8Ojjz6Kl5cXTk5ODB8+nMzMzKZ4nEs6U1TJo5/vobSqDgBjlYnJn+9t1Mq12tpaUlJS6N+/v814//792bZtW6PdVwghhBBCCCGEEI2jxmRm/5kSvtiVwVcpZzh41kid2dLU07qhNWlibdeuXWRnZ1u/kpKSAPjHP/4BwNy5c3njjTdYsGABu3btws/Pj379+lFWVma9RkJCAsuXLycxMZEtW7ZQXl7O0KFDMZvNTfJMF5NfVmNNqp1jrDKRX1bTaPcsKCjAbDbj6+trM+7r60tOTk6j3VcIIYQQQgghhBBXn8ls4Zt9WYx4ZytPLDvA9C9/ZviCLaw9nIuiKE09vRtWkybWvL298fPzs359//33tGzZktjYWBRFYd68eTz99NPccccdREZG8tFHH1FZWclnn30GgNFo5P333+f111+nb9++dO7cmU8++YQDBw6wbt26pnw0G94uegwOWpsxg4MWbxd9o99bpVLZfK8oSoMxIYQQQgghhBBCXN9O5Jfz9PIDNmMWBR7/cj+nC6/NXu6ioetmj7Xa2lo++eQTHnjgAVQqFenp6eTk5NgsZdTr9cTGxlqXMqakpGAymWxiAgICiIyMvOxyx5qaGkpLS22+GlOghyNv393ZmlwzOGh5+67OBHo4Nto9vby8UKvVDarT8vLyGlSxCSGEEEIIIYQQ4vp2uqCSOkvDyrSKWjMZ16hJomjoukmsffPNN5SUlDBu3DgAa0LocksZc3Jy0Ol0uLu7XzLmYmbPno3BYLB+BQYGXsUnubjY1t58/2gvvv5XD75/tBex4d6Nej+dTkdUVJR1ee05SUlJ9OjRo1HvLYQQQgghhBBCiKvLXqu+zLHrJr1zw7lu3vz777/PoEGDCAgIsBm/kqWMvxXz5JNPYjQarV9nzpy58on/AYEejnQJdm/USrVfmzp1Kv/973/54IMPSE1NZcqUKWRkZDBx4sRrcn8hhBBCCCGEEEJcHa19nQlws28w3tbfhZbezk0wIwGgaeoJAJw+fZp169bx9ddfW8f8/PyA+qo0f39/6/ivlzL6+flRW1tLcXGxTdVaXl7eZauy9Ho9en3j72/W1OLi4igsLOTFF18kOzubyMhIVq1aRXBwcFNPTQghhBBCCCGEEH+Av5sD/70vmqeXH2TvmRIAerbyZOawdng6//1zHNer6yKx9uGHH+Lj48OQIUOsY6Ghofj5+ZGUlETnzp2B+n3YNm7cyJw5cwCIiopCq9WSlJTE6NGjAcjOzubgwYPMnTv32j/IdWjSpElMmjSpqachhBBCCCGEEEKIPykiwMBHD9xERlElajsVQe6OONlfF6mdG1aTv32LxcKHH37I2LFj0WjOT0elUpGQkMCsWbMICwsjLCyMWbNm4ejoyJgxYwAwGAyMHz+eadOm4enpiYeHB9OnT6d9+/b07du3qR5JCCGEEEIIIYQQolG4OmiJbGZo6mmIXzR5Ym3dunVkZGTwwAMPNDj2xBNPUFVVxaRJkyguLqZbt26sXbsWFxcXa8ybb76JRqNh9OjRVFVV0adPH5YsWYJafelN/YQQQgghhBBCCCGE+LNUiqI07NV6gyktLcVgMGA0GnF1dbU5Vl1dTXp6OqGhodjbN9wkUNwY5PdACCGEEEIIIYS4MVwuT3Sh66YrqBBCCCGEEEIIIYQQfyWSWBNCCCGEEEIIIYQQ4gpIYk0IIYQQQgghhBBCiCsgiTUhhBBCCCGEEEIIIa6AJNaEEEIIIYQQQgghhLgCklgTQgghhBBCCCGEEOIKaJp6AkIIIYQQQgghhBDXC0VROJRVyo70QkoqTXRv4UnHQDec9ZJCEQ1Jxdo1VFNnpqC8hpo6c6Pfq66ujmeeeYbQ0FAcHBxo0aIFL774IhaLxRqjKAozZ84kICAABwcHbr31Vg4dOmQ755oaHn30Uby8vHBycmL48OFkZmbaxBQXFxMfH4/BYMBgMBAfH09JSYlNTEZGBsOGDcPJyQkvLy8mT55MbW2tTcyBAweIjY3FwcGBZs2a8eKLL6Ioik3Mxo0biYqKwt7enhYtWvDuu+82ePZly5YRERGBXq8nIiKC5cuXX8krFEIIIYQQQghxA0o5XcyoRdt46ftU5q8/zj3/3cEXu85gtii/fbK44Uhi7RowWxR2nypi8md7GfL2ZiZ/tpfdp4oa9V/KOXPm8O6777JgwQJSU1OZO3cur776KvPnz7fGzJ07lzfeeIMFCxawa9cu/Pz86NevH2VlZdaYhIQEli9fTmJiIlu2bKG8vJyhQ4diNp9PDo4ZM4Z9+/axZs0a1qxZw759+4iPjz///GYzQ4YMoaKigi1btpCYmMiyZcuYNm2aNaa0tJR+/foREBDArl27mD9/Pq+99hpvvPGGNSY9PZ3Bgwdz8803s3fvXp566ikmT57MsmXLrDHJycnExcURHx/P/v37iY+PZ/To0ezYseOqv2MhhBBCCCGEEH8vlbV1vLXuGDV1FpvxOWuOcKqgoolmJa5nKuXCkqAbUGlpKQaDAaPRiKurq82x6upq0tPTCQ0Nxd7e/oquv/tUEXe9t526XyXSNHYqEh/sTnSIx5+a+6UMHToUX19f3n//fevYqFGjcHR05OOPP0ZRFAICAkhISGDGjBlAfXWar68vc+bM4aGHHsJoNOLt7c3HH39MXFwcAFlZWQQGBrJq1SoGDBhAamoqERERbN++nW7dugGwfft2YmJiOHLkCOHh4axevZqhQ4dy5swZAgICAEhMTGTcuHHk5eXh6urKokWLePLJJ8nNzUWv1wPwyiuvMH/+fDIzM1GpVMyYMYPvvvuO1NRU6zNNnDiR/fv3k5ycDEBcXBylpaWsXr3aGjNw4EDc3d35/PPPr/h9Xo3fAyGEEEIIIYQQ17fc0moGzNtESaWpwbEvH4qha2jjfIYX15fL5YkuJBVrjaymzsz/bTppk1QDqLMo/HdzeqMtC+3Vqxc//vgjaWlpAOzfv58tW7YwePBgoL76Kycnh/79+1vP0ev1xMbGsm3bNgBSUlIwmUw2MQEBAURGRlpjkpOTMRgM1qQaQPfu3TEYDDYxkZGR1qQawIABA6ipqSElJcUaExsba02qnYvJysri1KlT1phfz+VczO7duzGZTJeNOTcXIYQQQgghhBDiUtwctHS7SAGMk06Nt4v+ImeIG50k1hpZWXUd+zJLLnps35kSyqvrGuW+M2bM4O6776ZNmzZotVo6d+5MQkICd999NwA5OTkA+Pr62pzn6+trPZaTk4NOp8Pd3f2yMT4+Pg3u7+PjYxNz4X3c3d3R6XSXjTn3/W/F1NXVUVBQcNmYc9cQQgghhBBCCCEuRa9V8/BtrXC1t21U8NLISII9HZtoVuJ6Ji0tGpmLvYZOzd344XBug2OdAt1wtm+cH8HSpUv55JNP+Oyzz2jXrh379u0jISGBgIAAxo4da41TqVQ25ymK0mDsQhfGXCz+asScW6V8NWJ+65mEEEIIIYQQQgiADs3d+HpSTw5nGamqNdPaz4UIf1f5XCkuShJrjUyvUTPhlhb8eCSvwR5r/7w5FL1G3Sj3ffzxx/n3v//NXXfdBUD79u05ffo0s2fPZuzYsfj5+QH1FV7+/v7W8/Ly8qwVX35+ftTW1lJcXGxTtZaXl0ePHj2sMbm5DZOG+fn5Nte5sHlAcXExJpPJJubCqrK8vDyA34zRaDR4enpeNubCKjYhhBBCCCGEEOJSWvk408rHuamnIf4CZCnoNdA5yJ3EB7szsJ0ffq72DGznR+KD3ekc5P7bJ1+hyspK7Oxsf7xqtRqLpb6zSWhoKH5+fiQlJVmP19bWsnHjRmvSLCoqCq1WaxOTnZ3NwYMHrTExMTEYjUZ27txpjdmxYwdGo9Em5uDBg2RnZ1tj1q5di16vJyoqyhqzadMmamtrbWICAgIICQmxxvx6LudioqOj0Wq1l405NxchhBBCCCGEEEKIq0Uq1q4BtZ2K6BAP2jc3UF5dh7O9ptEq1c4ZNmwY//nPfwgKCqJdu3bs3buXN954gwceeACoXy6ZkJDArFmzCAsLIywsjFmzZuHo6MiYMWMAMBgMjB8/nmnTpuHp6YmHhwfTp0+nffv29O3bF4C2bdsycOBAJkyYwOLFiwF48MEHGTp0KOHh4QD079+fiIgI4uPjefXVVykqKmL69OlMmDDB2l1jzJgxvPDCC4wbN46nnnqKY8eOMWvWLJ577jlrue3EiRNZsGABU6dOZcKECSQnJ/P+++/bdPt87LHHuOWWW5gzZw4jRozg22+/Zd26dWzZsqVR37cQQgghhBBCCCFuQIpQjEajAihGo7HBsaqqKuXw4cNKVVVVE8zsypWWliqPPfaYEhQUpNjb2ystWrRQnn76aaWmpsYaY7FYlOeff17x8/NT9Hq9cssttygHDhywuU5VVZXyyCOPKB4eHoqDg4MydOhQJSMjwyamsLBQueeeexQXFxfFxcVFueeee5Ti4mKbmNOnTytDhgxRHBwcFA8PD+WRRx5RqqurbWJ+/vln5eabb1b0er3i5+enzJw5U7FYLDYxGzZsUDp37qzodDolJCREWbRoUYNn//LLL5Xw8HBFq9Uqbdq0UZYtW3Ylr7DBe/gr/h4IIYQQQgghhBDij7lcnuhCKkVRlN9Kvv3dlZaWYjAYMBqN1gqqc6qrq0lPTyc0NBR7e/smmqFoavJ7IIQQQgghhBBC3Bgulye6kOyxJoQQQgghhBBCCCHEFZDEmhBCCCGEEEIIIW4YNSYzlbV1TT0N8TchzQuEEEIIIYQQQgjxt1djMrMzvYh3N56kpKqW+3uG0ruNN55O+qaemvgLk4o1IYQQQgghhBBC/O2lnC4m/oOdbD1RwKGsUqZ/uZ9v9pxt6mmJvzhJrAkhhBBCCCGEEOJv75Ptp22+79XKi0qTmbTcsiaakfg7kMSaEEIIIYQQQggh/lbyy2rYlV7E3oxijFUmFEWhps5iPT69fzgKCq+vTSNucTJrDmZzurCCCtl7TfxBsseaEEIIIYQQQgghrpmqWjN5ZdU4aNX4uNr/6eudKaqk2mTG32CPs72WY7llTPpsD8dyywG4LdyHJwaF84/o5vx4JI+erTzZkV7I1uOFuDtqmdwnjPc2neRQVik3hXgwfUA4HQPd/vS8xI1BEmtCCCGEEEIIIYS4Jo7mlDFndSrrj+bj7aLnmSFt6Rfhi6Puj6cnKmrq+GbfWWavOkJNnZmnBrfF11XPmoO51qQawPqjeYT7uZCabeTZoW2pMyvMXn0EgHE9Qpi75ihVJjMAm48XsP9sCd8+3ItQL6er89Dib02WggohhBBCCCGEEKLRFZbX8PBnKaw/mg/UL9d8LHEfe06XXNH1fs4s4enlBymvqePBW1ry/pZ0Nh8rIPlEYcPYsyUYq+p4fW0a/gZ7DA5aACwK1qTaOaVVdbLvmvjdJLEmhBBCCCGEEEKIRpdeUMHxvIoG46sOZFv/ubK2jvJq0++63qGsUtwctTho1SiKQmZxFekFFYT7uTSIDfNxIbO4ispaMy+vTGV6/9YAqFQXv/YlhoVoQBJr10hWSRXrj+TxVcoZ1h/JI6ukqtHvuWnTJoYNG0ZAQAAqlYpvvvmmQUxqairDhw/HYDDg4uJC9+7dycjIsB6vqanh0UcfxcvLCycnJ4YPH05mZqbNNYqLi4mPj8dgMGAwGIiPj6ekpMQmJiMjg2HDhuHk5ISXlxeTJ0+mtrbWJubAgQPExsbi4OBAs2bNePHFF1EUxSZm48aNREVFYW9vT4sWLXj33XcbPNOyZcuIiIhAr9cTERHB8uXLG8QsXLiQ0NBQ7O3tiYqKYvPmzb/1OoUQQgghhBBC/Alqu4unqxx1aipr60g6nMs9/93Bne8m8+XuMxRV1FzyWqnZpZjMFkZ2asZTQ9qQX1Yfu/1kEbe18bFWpAGEeDrSNcSde7oHkdA3jFFdmtGzpSefjL+Jlt5OuOhtl6F6OOkumpwT4mJkj7Vr4OfMEv750W7yys7/oeDjoue/Y6Pp0Nyt0e5bUVFBx44duf/++xk1alSD4ydOnKBXr16MHz+eF154AYPBQGpqKvb25zePTEhIYMWKFSQmJuLp6cm0adMYOnQoKSkpqNVqAMaMGUNmZiZr1qwB4MEHHyQ+Pp4VK1YAYDabGTJkCN7e3mzZsoXCwkLGjh2LoijMnz8fgNLSUvr160fv3r3ZtWsXaWlpjBs3DicnJ6ZNmwZAeno6gwcPZsKECXzyySds3bqVSZMm4e3tbX2+5ORk4uLieOmll7j99ttZvnw5o0ePZsuWLXTr1g2ApUuXkpCQwMKFC+nZsyeLFy9m0KBBHD58mKCgoEb6aQghhBBCCCHEja2llxO3dw7gu/3ZmC31RRR2KhgU6ceOk0VM+N9ua+zjX/3MSyPaER8T0uA6B88aiVucTEVt/RJOjVrF3FEd+DKlvgjkjaQ0xvYIQa+xI8jDEV9XPY98tpfCivriDm8XPcM6NqNXmDcAwZ5OzFuXxp6MEmJaePLIba0I9pT91cTvo1IuLAm6AZWWlmIwGDAajbi6utocq66uJj093Vrd9EdllVQx8p2tNkm1c3xc9HzzcE8C3ByueO6/l0qlYvny5YwcOdI6dtddd6HVavn4448veo7RaMTb25uPP/6YuLg4ALKysggMDGTVqlUMGDCA1NRUIiIi2L59uzVxtX37dmJiYjhy5Ajh4eGsXr2aoUOHcubMGQICAgBITExk3Lhx5OXl4erqyqJFi3jyySfJzc1Fr9cD8MorrzB//nwyMzNRqVTMmDGD7777jtTUVOscJ06cyP79+0lOTgYgLi6O0tJSVq9ebY0ZOHAg7u7ufP755wB069aNLl26sGjRImtM27ZtGTlyJLNnz77ou/izvwdCCCGEEEIIcSM7mlPKpmP5nMyvIDLAwN6MYk4VVpLQtzXRIe48+L/dbDpWYHOOj4uelZN74e1i+xnsgy0nqag1U1NnQau2Y8eJAvq182PP6WJW/Fy/rNRRp2ZKv9bsP1NCbmk1u04V21xjeKcAXr2zA3pNfcFIVa2Z0moTbg5a9Fp1I74J8VdwuTzRhWQpaCM7klN20aQaQF5ZDUdymmZDRIvFwsqVK2ndujUDBgzAx8eHbt262SwXTUlJwWQy0b9/f+tYQEAAkZGRbNu2DaivEDMYDNakGkD37t0xGAw2MZGRkdakGsCAAQOoqakhJSXFGhMbG2tNqp2LycrK4tSpU9aYX8/lXMzu3bsxmUyXjTk3l9raWlJSUhrE9O/f3xojhBBCCCGEEOLqScst4673tvOflUf4fOcZnv7mID6u9nwwtiu9wrxQ26kwX6TmR1HgwtHyahMZRVW8vjaNBeuPM//HY/SJ8GXF/izyy2uY2q81s26P5KFbWvDeppMEeTiyJ6OkwbU3p+VjrDy/l5uDTo2vq70k1cQfJom1Rna5NeG/53hjycvLo7y8nFdeeYWBAweydu1abr/9du644w42btwIQE5ODjqdDnd3d5tzfX19ycnJscb4+Pg0uL6Pj49NjK+vr81xd3d3dDrdZWPOff9bMXV1dRQUFFw25tw1CgoKMJvNl40RQgghhBBCCHH1bDtRQHGlbUOC9zadJLesGgCt2o77e4Q2OO9ft7bA54JqtSM5ZSzZdgqAdgGu/OvWlpRX1zG+VyjbTxbxRlIaOaXVvLnuGPllNZwqrCDct+F+aR2au+Fir20w/lsURaGoooaqWvNvB4sbguyx1sg8nPR/6nhjsVgsAIwYMYIpU6YA0KlTJ7Zt28a7775LbGzsJc9VFAXVr1qnqC7SRuVqxJxbpXw1Yi4c+z0xQgghhBBCCCH+vIs176uzKFTU1lm/797Ck3fGdGH++mNUmcxMuDmUge38G5x3qqC+q+jITs3QalQs2nCCOotC73BvJvdpxds/HudwVildgtzZk1FM0uFcnhrcltmrjlBrrv8c7KBVk9A3DAfdH6tOyyiqZFlKJsv2ZBLk4cjkPmF0DfG4ZFMGcWOQxFoja+Pngo+L/pJ7rLVpok4jXl5eaDQaIiIibMbbtm3Lli1bAPDz86O2tpbi4mKbqrW8vDx69OhhjcnNzW1w/fz8fGtVmJ+fHzt27LA5XlxcjMlksom5sGIsLy8P4DdjNBoNnp6el405dw0vLy/UavVlY4QQQgghhBBC/HnVJjNVtWZ6tPTivU3pNseCPR1p7uZo/d7ZXsOQDv7cHOaFRVFwc9Rd9JrO9lrstXYEeTrw9o/HreM/Hc3Hz2DP88PaUlFtJjrEndeT0th1qpiPt53izbiOGKtMOOg0RPi7EO53+X2zLlRRW8d/vj/MD4frP/9mFlexI72IrybG0DnI/TfOFn9nshS0kQW4OfDfsdH4uNhWpp3rCnotGhdcjE6no2vXrhw9etRmPC0tjeDgYACioqLQarUkJSVZj2dnZ3Pw4EFrYi0mJgaj0cjOnTutMTt27MBoNNrEHDx4kOzsbGvM2rVr0ev1REVFWWM2bdpEbW2tTUxAQAAhISHWmF/P5VxMdHQ0Wq32sjHn5qLT6YiKimoQk5SUZI0RQgghhBBCCHHlFEVh96kiHvxfCsMWbCHLWMUTA8LRa+pTEC29nZh/d2e8XBqu4HJ10F4yqQbgpFMz6dZW7L3IvmlrDuZwIr8SlR18sO0Ubo5a/jMykoR+rdl8rIC2/q7c3rnZH06qAZwuqLQm1c4xWxS2HC+4xBniRiEVa9dAh+ZufPNwT47klFFUUYOHU32lWmMn1crLyzl+/HwGPz09nX379uHh4UFQUBCPP/44cXFx3HLLLfTu3Zs1a9awYsUKNmzYAIDBYGD8+PFMmzYNT09PPDw8mD59Ou3bt6dv375AfYXbwIEDmTBhAosXLwbgwQcfZOjQoYSHhwP1jQEiIiKIj4/n1VdfpaioiOnTpzNhwgRrd40xY8bwwgsvMG7cOJ566imOHTvGrFmzeO6556xLNCdOnMiCBQuYOnUqEyZMIDk5mffff9/a7RPgscce45ZbbmHOnDmMGDGCb7/9lnXr1lmr8ACmTp1KfHw80dHRxMTE8N5775GRkcHEiRMb74chhBBCCCGEEDeIQ1mljPm/Hdall099fZBxPYL4/tFeVJvMNHNzwMP5yrZFCvFyYu3hHFp5O7P5gi6ige6O9Gvrg0at4tUf0gBIOly/EkqlgnE9Qq78oS7BYmnYdEHcWCSxdo0EuDlc8+q03bt307t3b+v3U6dOBWDs2LEsWbKE22+/nXfffZfZs2czefJkwsPDWbZsGb169bKe8+abb6LRaBg9ejRVVVX06dOHJUuWoFafX4v+6aefMnnyZGunzeHDh7NgwQLrcbVazcqVK5k0aRI9e/bEwcGBMWPG8Nprr1ljDAYDSUlJPPzww0RHR+Pu7s7UqVOtcwYIDQ1l1apVTJkyhXfeeYeAgADefvttRo0aZY3p0aMHiYmJPPPMMzz77LO0bNmSpUuX2nQtjYuLo7CwkBdffJHs7GwiIyNZtWqVtVJPCCGEEEIIIcSVO5BptCbVzlmyLYO7bwqhfXO3P3XtQA9HxsaEcKKgguX7zlLyS1MEjZ2KGYPa0LOVF2dLKgn3deZobrn1vPt7hBDi5XTF9w32cOS2cG/WH823jtmp4OYw7yt/GPG3oFKUi/S0vcGUlpZiMBgwGo3WCqpzqqurSU9PJzQ0FHt7+0tcQfzdye+BEEIIIYQQQvw+ibsy+PeyA9bvw31dGNTej47N3Qjzdaa5u+Nlzq5fYlltMuOkv3wt0PG8cg5kllBdZyEywJV2AQbsfmkkcLqwguQThRzNKaNbCw+iQzzwusIquXNOFVTw2c4Mvt6TSaCHI9P6taZbC0+0atll6+/mcnmiC0nFmhBCCCGEEEIIIa6ajs3dcNCqcXXQMKxDAOF+LmQWV7I/s4Tc0mpySqs5klPG4Eh/erTytEl4Hcoy8nHyafZmlDC0oz/DOwYQ7HnxSjODgwZfV3tMZgvuTjprUg0g2NPpkuddqRAvJ/49sA3/7BWKo06Ns732ql5f/DVJYk0IIYQQQgghhBBXTVt/Vz79ZzfWHMqhewsP/rPyCCfyy7FTwe2dm2EyK6w5mMOagzlM6RfGo73DsLNTcSS7lPj3d1JUUd/U7ujaMvZllPDWXZ0aJLFOFVQwOXEvP2caAfBy1vHRAzfRLsDQqM9mZ6fCx1VWMYnzpF5RCCGEEEIIIYQQV01eaTXPfXuQtNwy3tt0khP59XudWRRYtucsLX2c0arrq8sWbThBlrGKozmlrD+SZ02qnfPjkTxOF1Y2uMePqbnWpBpAQXktb687RrWprhGfTIiGJLEmhBBCCCGEEEKIP8RktrDndBHvrD/O/20+wYGzRs5t4Z6WW8bBrFLaNzOw/WRRg3PTcsoI/GWfNbVKhdmi8PoPaZguaHhwjkqlajB2YUdQgB2nijBWSWJNXFuyFFQIIYQQQgghhBB/SPKJQsZ9uBPLL+0Q9Ro7Pp/QnS7B7lSZ6hNk+WU1BBjsyTJW25zr72bP5uP13TUf6xOGTmPHxmP5tPBxwsdFT15ZjTV2aAd/gr0aNjuIDfdmQ1q+zViPFp64Oci+Z+LaksSaEEIIIYQQQgghLupkfjlHc8uwQ0VzdwdSc0qpMyt8vP20NakGUFNnYcm2dDo0N9DCywl7rR3f/5zN5D5hvLI61Rrb3N2B6GB3yqpN9Gnjy02hHqjtVEQGGPhw6ykm9wnjVGEFJ/MruK2NNyM7NcNJ1zB1cVsbH1b+nMXu0yUA+BvsebRPGHqt+hq8FSHOk8SaEEIIIYQQQghxA8ssruRIdhl1FgthPi609HEG4OBZI/f8dwfGKhNQ3yDgn71akFlSRVZJVYPrnMivwGRRaOnjzHvx0cxY9jOf78zg8QHh6DR21JkViipqeTMpjVl3tCcq2MN67lND2hD//k5e/eEogR4O9GrlxfCOATRzb1itBvVdPxfHR3M8rxyTWaGFtxMBbg6N8HaEuDxJrAkhhBBCCCGEEDeotNwyxn24k6yS+uWaTjo1n/yzG+2bGfhgS7o1qQb1DQKO5pZRXFHDbW18WLbnrM21Rkc3x+GXirHWPs4MaudH+0ADR7LL+GbfWXJLzy/xPFtcRVTw+XOjgj1Y8UgvjueX46BVE+7ngu9vdN/0dNbj6az/s69AiD9FmhcIIYQQQgghhBA3qOV7zlqTagAVtWZeWX2E43ll7M8saRB/Ir8cBRW+rvbcEuYFgJ0KxnQLol+EnzXO12CPq4OWUwWVLN500iapBlD363Wkv2jp48yAdn7c0tr7N5NqQlwvpGLtGiooq6HKZMZBq8bLRbLqQgghhBBCCCGaTq3ZTPLJwgbjB84aWX0wl5iWnpzIr7A5dmtrb75KyWRjWj5T+oWR0Ld+X7NWPs7oNef3N1OpVNx9UxC7TxfSLsCVQ1ml1mMaOxWtfV0a78GEuIakYu0ayCyu5JPtpxm2YAs3z/2JYQu28PH2U2QWVzbaPWfPnk3Xrl1xcXHBx8eHkSNHcvTo0UvGP/TQQ6hUKubNm2czXlNTw6OPPoqXlxdOTk4MHz6czMxMm5ji4mLi4+MxGAwYDAbi4+MpKSmxicnIyGDYsGE4OTnh5eXF5MmTqa2ttYk5cOAAsbGxODg40KxZM1588UVru+ZzNm7cSFRUFPb29rRo0YJ33323wbMsW7aMiIgI9Ho9ERERLF++vEHMwoULCQ0Nxd7enqioKDZv3nzJdyOEEEIIIYQQf0c6tZpbWns1GO/ewpOfM0vwctbTvcX5fdB6tvKkta8L0weE83/3RWM2KyzddYZ9GSWczC+npNL2M56vwZ4hHZrx6p0dGdrBH53ajrb+Lnz0wE1E+Ls2+vMJcS1IYq2RZRZX8ljiXp755iDZv7QYzjZW8+w3h0hI3NtoybWNGzfy8MMPs337dpKSkqirq6N///5UVFQ0iP3mm2/YsWMHAQEBDY4lJCSwfPlyEhMT2bJlC+Xl5QwdOhSz2WyNGTNmDPv27WPNmjWsWbOGffv2ER8fbz1uNpsZMmQIFRUVbNmyhcTERJYtW8a0adOsMaWlpfTr14+AgAB27drF/Pnzee2113jjjTesMenp6QwePJibb76ZvXv38tRTTzF58mSWLVtmjUlOTiYuLo74+Hj2799PfHw8o0ePZseOHdaYpUuXkpCQwNNPP83evXu5+eabGTRoEBkZGVf+woUQQgghhBDiOlFUUUNRRe1vBwKtvJ1pF3A+yeXnas+t4d74GxzIKqkm1MuJhL71lWljbgriue8OkVdWw2OJe3l7/XGW7s7k6W8OsvLnbCZ+ksKPqbnU1plt7hER4Mrrozvy0/RYEid0p2crL+zsVFf1mYVoKirlwpKgG1BpaSkGgwGj0Yirq23WvLq6mvT0dGt10x/1yfbTPPPNwUse/8/ISO7pHnzJ41dLfn4+Pj4+bNy4kVtuucU6fvbsWbp168YPP/zAkCFDSEhIICEhAQCj0Yi3tzcff/wxcXFxAGRlZREYGMiqVasYMGAAqampREREsH37drp16wbA9u3biYmJ4ciRI4SHh7N69WqGDh3KmTNnrMm7xMRExo0bR15eHq6urixatIgnn3yS3Nxc9Pr6ZbKvvPIK8+fPJzMzE5VKxYwZM/juu+9ITU21zn/ixIns37+f5ORkAOLi4igtLWX16tXWmIEDB+Lu7s7nn38OQLdu3ejSpQuLFi2yxrRt25aRI0cye/bsi76/P/t7IIQQQgghhBCNraCshjWHsnl340lUKvhXbEv6t/PD6zIb/D/62R7stWqauzugAKVVJj7bmcEjvVtRXlPHh1tPUVNnoVcrL8wWhTPFlcS09OTL3bYrmVztNcR1DeK/W06y+N4o/A32tPF3RauWeh7x13O5PNGF5De8ERWU1fDOT8cvG/POT8cpKKu5bMzVYDQaAfDwOF/Ga7FYiI+P5/HHH6ddu3YNzklJScFkMtG/f3/rWEBAAJGRkWzbtg2orxAzGAzWpBpA9+7dMRgMNjGRkZE2FXEDBgygpqaGlJQUa0xsbKw1qXYuJisri1OnTlljfj2XczG7d+/GZDJdNubcXGpra0lJSWkQ079/f2uMEEIIIYQQQlxvDp418mZSGo9+tocV+7PILa1uELNsTybPfHOIzOIqzhRV8dTyg3y37+xFrnZe11APvkzJ5M11x5i37hgfbD1FtxAPHLRqQjwdGd6x/nNcmK8zB84a8XDSkV/a8DNsaXUdDjo7FAW2nSjkvg928tORvKvz8EJcxySx1oiqTGbr8s9LyTJWU2UyXzbmz1IUhalTp9KrVy8iIyOt43PmzEGj0TB58uSLnpeTk4NOp8Pd3d1m3NfXl5ycHGuMj49Pg3N9fHxsYnx9fW2Ou7u7o9PpLhtz7vvfiqmrq6OgoOCyMeeuUVBQgNlsvmyMEEIIIYQQQlxPDpwtYfTiZN768Rgrfs7m0c/38sbaNMpr6qwx2SVVLFjfsLDjrR+Pk3OZz6Uueg13dQ1Eq65fmtkp0I3YcB86Bbkxe/VRvJz1DGnvz7GcMjoHunE0p4zOQW4NrhMV7M7Bs/UNCuy1dlSbLDy9/CDZxqo/+fRCXN+kK2gjctCq8TfYXza5FmCwx0GrvuTxq+GRRx7h559/ZsuWLdaxlJQU3nrrLfbs2YNK9cfWtiuKYnPOxc6/GjHnVilfjZgLx35PjBBCCCGEEEJcD77dm0VlrW1BxtLdZ7inexAdmrsBYEGhps7S4NxqkxmzpeF4jcnM/kwjOo0dh7OMPBTbEjsVpOWW46xXczK/AmOViUUbT9C+mYGbw7xoF+DK0dwyjuWVc1fXQJbtycRkVohs5sqAdn68sjoVg4MWR52GKpOZKpMZY5UJf4MDAKcLKzicVUplrZmWPs5E+Lug0zTu52EhGpsk1hqRl4ueh3u3uuweaw/3boWXy6XXu/9Zjz76KN999x2bNm2iefPm1vHNmzeTl5dHUFCQdcxsNjNt2jTmzZvHqVOn8PPzo7a2luLiYpuqtby8PHr06AGAn58fubm5De6bn59vrQrz8/OzaR4A9Z1ETSaTTcyFFWN5efVlw78Vo9Fo8PT0vGzMuWt4eXmhVqsvGyOEEEIIIYQQ1wtFUTiYZbzosaLyWn7OLGFHeiEWC8y9swOvrT1KZvH5KrH47sH4/ZLY+rUtxwsY/9FufFz0PHhLC/afKaGsuo67bgqiewsP/rv5pDX2wFkjB84asdfa8dk/u1NZW4eLvYb7YoIxmS1U11lYlpLJvd2D8XW1Z9GGEwCE+Tjj6ahjx8lCso1VzF59hNxflpGqVPDWXZ2tS02F+KuSpaCN7NZwb6KD3S56LDrYjdhw70a5r6IoPPLII3z99desX7+e0NBQm+Px8fH8/PPP7Nu3z/oVEBDA448/zg8//ABAVFQUWq2WpKQk63nZ2dkcPHjQmliLiYnBaDSyc+dOa8yOHTswGo02MQcPHiQ7O9sas3btWvR6PVFRUdaYTZs2UVtbaxMTEBBASEiINebXczkXEx0djVarvWzMubnodDqioqIaxCQlJVljhBBCCCGEEOJ6oVKpGNahYfLJXmvH2ZJKUk4X885PJ5i9+gjTv9zPU4Pa0NLbEY1aRVx0c8b1DEF9QQdOY2Utc9ccASCvrIaXV6ZyJKeM4R0DGBjpx9niKlzstQ3uGeThiEoFvcK86RjoTkSAgY6B7thr1Hg566k2mXn1h6OU19Th6aRj7qgOpOWXM/2r/SSfLLIm1QAUBZ5ZfoCMosqr/MaEuLaaPLF29uxZ7r33Xjw9PXF0dKRTp07WDe2hPkE0c+ZMAgICcHBw4NZbb+XQoUM216ipqeHRRx/Fy8sLJycnhg8fTmZm5oW3ahLN3R2Zd1dn/jMykgBDfTfJAIM9/xkZyby7OtPc3bFR7vvwww/zySef8Nlnn+Hi4kJOTg45OTlUVdX/zYWnpyeRkZE2X1qtFj8/P8LDwwEwGAyMHz+eadOm8eOPP7J3717uvfde2rdvT9++fYH6bpoDBw5kwoQJbN++ne3btzNhwgSGDh1qvU7//v2JiIggPj6evXv38uOPPzJ9+nQmTJhg7a4xZswY9Ho948aN4+DBgyxfvpxZs2YxdepU6xLNiRMncvr0aaZOnUpqaioffPAB77//PtOnT7c+92OPPcbatWuZM2cOR44cYc6cOaxbt87a6RRg6tSp/Pe//+WDDz4gNTWVKVOmkJGRwcSJExvlZyGEEEIIIYQQv1ZjMrMzvZA5q4/wZlIa+86UYLEol4zvHORGTIvzjeh0ajse6xPGgp9O8OoPR7m/RwieTjqmDwhn16liurfw4t17onhycJuLfuasMVsoKD9f1OCi19DMzYG8shpKq2rZnl7ID4dySOgbhsGhPsEWFeTG00MieDPpKKnZpeevVWdm8aYTLNxwgjPFVUzp15rJfVqR0DeMcH8X3tt4ghBPJ4786pxzSqvrKKqobTAuxF9Jky4FLS4upmfPnvTu3ZvVq1fj4+PDiRMncHNzs8bMnTuXN954gyVLltC6dWtefvll+vXrx9GjR3FxcQEgISGBFStWkJiYiKenJ9OmTWPo0KGkpKSgVjf9eu3m7o7c0z2YAe38qDKZcdCqG3X5J8CiRYsAuPXWW23GP/zwQ8aNG/e7r/Pmm2+i0WgYPXo0VVVV9OnThyVLlti8108//ZTJkydbO20OHz6cBQsWWI+r1WpWrlzJpEmT6NmzJw4ODowZM4bXXnvNGmMwGEhKSuLhhx8mOjoad3d3pk6dytSpU60xoaGhrFq1iilTpvDOO+8QEBDA22+/zahRo6wxPXr0IDExkWeeeYZnn32Wli1bsnTpUpuupXFxcRQWFvLiiy+SnZ1NZGQkq1atIjg4+He/FyGEEEIIIYS4UttOFPLAR7v4ZctoFm44zucTuhMd4tEgtrC8hrWHcgj0cOQf0YGkF1SgtlPx6Y4M637eCvBQbEveSDpKtal+P7VPd2QwZ1QH4roGNrimt7Oe+O7BzPvxGMM7BhDi6URhRQ0VtXXszzRSbbJwKKuU2NbePNy7JeU1dRzJLuM/Kw8z+bYwPttxmn8PbIuTvYbqWjOHsuqTZsknCkk+UQhAoIcDgyP9KauuI8dYTe82PuzPtF3S6uaoxdNJd7VeqxBNQqWc2/29Cfz73/9m69atbN68+aLHFUUhICCAhIQEZsyYAdRXp/n6+jJnzhweeughjEYj3t7efPzxx8TFxQGQlZVFYGAgq1atYsCAAb85j9LSUgwGA0aj0VpBdU51dTXp6emEhoZib2//J59Y/FXJ74EQQgghhBDiaiitMhG3OJnUnDKb8cGRfrx9d2c06vqFZSWVtaw/ksf7W9Jx0qmJ6xqEs17NQ5/saXDNxweEczyvnOV7z9qMuztqWfXYzdbmAb+WVVLFt3sz0WrU1NRZ2HA0D71GTe823rg76sgtq6HWZObNdcdszovrGkikvwuxbXwI8nBCURRe/eEoC3/ZV+2cibEtmDGwDSsPZPPIZ3uZ0q81X+4+Y93/zU4FC+/pwsBI/z/+EoVoZJfLE12oSSvWvvvuOwYMGMA//vEPNm7cSLNmzZg0aRITJkwAID09nZycHGslFIBeryc2NpZt27bx0EMPkZKSgslksokJCAggMjKSbdu2XTSxVlNTQ03N+bXdpaUNS1KFEEIIIYQQQoirrbrOTF5Z/edRe60dD/QMRa+xQ6exIy23jIgAAwA/HMphxrID1vN2nirm7bs60a+tD0mpedZxd0ctDlo1BeU1XKikykTNLxVsqVlGjuWVoygKeo0d9joN3Vp4svVEIa+vTbOes+V4AW/f1YmDZ0qwXKQO5+BZIzeFuOOir18iqlKpGB0dyJ6MYrafLAKga4g7cV0DUalU3Bzmxby4Try78QR33xSEn6s9dnYqwnycCfdz+bOvU4gm16SJtZMnT7Jo0SKmTp3KU089xc6dO5k8eTJ6vZ777rvP2rnxwm6Nvr6+nD59GoCcnBx0Op1N18pzMRd2fjxn9uzZvPDCC43wREIIIYQQQgghxKV5OekZ3TWQRRtOMHNYO4xVJspr6lCZFZ78+meeGdqOMB9nawWYt4uebqEeFJTX8NORPAZF+hPm40LyyULa+LnQOdidwvIa+rb1ZfOxAgBaejvhpNfg66zHgsJPR/J44qufyS+vwctZx8zh7Xjth6PcGxPMN3uzGszx+5+zubNLAFmlNaw+lGtzrFOgG24OOtx/tYQzxMuJxfdGkV5QAUColxMGx/rjBgcdIzs347a2Pqjgok0RhPgra9LEmsViITo6mlmzZgHQuXNnDh06xKJFi7jvvvuscec2rz9HUZQGYxe6XMyTTz5ps3dXaWkpgYEN150LIYQQQgghhBBXk52dijE3BeHjrONEfjn/tzkdAJUKxvcK5fOdGYzvGYrGTsU93YLQa+zYcDQffzd7YsO9f1kW2pw+bX1YcyiHxRtPcCK/gsHt/Zh4Syjerg7sP1NCeY2JgR38MVbU8sw3B8j/paKtoLyWJ776mVfv7ACA2WJpMEezolBVZ6GtnwuD2vmx+lB90UqH5gYiAwx4OjfcF83gqKNT0KX3S3OVhJr4m2rSrqD+/v5ERETYjLVt25aMjAwA/Pz8ABpUnuXl5Vmr2Pz8/KitraW4uPiSMRfS6/W4urrafAkhhBBCCCGEENdCoIcjbQMM1qQagKLAfzenc2u4N4ezS3lmcFtq6yx8sPUUJwsq2Hq8kBnLDpCWV0FBeS0mi8In2zOI6xqESgWrDuQQ4ObI3DVH+G5/FuuP5DPti/3sOl3M6OhAHrmtFXa/1J5U1po5klPGk8sPcPdNQQ3md1OIB1OW7ufhz/bSr50v8+/uzPy7O/PQLS3YkJZn3QdOCNHEibWePXty9OhRm7G0tDRrd8bQ0FD8/PxISkqyHq+trWXjxo306NEDgKioKLRarU1MdnY2Bw8etMYIIYQQQgghhBDXi9TsUvadKbnosbTcch7/6mcKK018u892mWZNnYXaOgtbTxTi7ayja4g7X6WcYcaANjzcuyXZpdXU1NlWoH2yPYPTRZWsPpDNuB4hQH3jAK3ajtKqOnafLubFEe0I93WhY3MD/x7UhjUHc7Ao9dVtB84aefWHIzz6+V4e/mwv3i56Agz2FJTV0IS9EIW4bjTpUtApU6bQo0cPZs2axejRo9m5cyfvvfce7733HlC/BDQhIYFZs2YRFhZGWFgYs2bNwtHRkTFjxgBgMBgYP34806ZNw9PTEw8PD6ZPn0779u3p27dvUz6eEEIIIYQQQgjRwPf7s3DQXfzjuF5TX/+Sml2KvdaOWrNtokyrVlmbFUztG8a2k4Wk5ZURHeTO6aLKBterM1vQ2NlxIr+Csb8k1h6KbYm7o5bH+oShUoGPi57buwRQXFHLm0lpNsm5ozllzBzejs3HCohp4Ymbo5YHP04ho6iSO6Oac1fXIII8Ha/SmxHir6dJE2tdu3Zl+fLlPPnkk7z44ouEhoYyb9487rnnHmvME088QVVVFZMmTaK4uJhu3bqxdu1aXFzOdw9588030Wg0jB49mqqqKvr06cOSJUtQq9VN8VhCCCGEEEIIIUQD+WXVlFaZWHMol3YBrvRs5cnW44XW4wMifPk50wjAqgPZPHhLC177VcdOb2c9nZsbyCmr5T8rU6moNTOwnR8dmxnIMlYT5OmIRq2izny+kmxk52Ys3XUGAI2dijfjOrL1WAGLfmmO4KhT8/ZdndDZ2eHprG9Q8dYl2J1DZ42M7BQAqLhj0TbMlvrrL9xwgqySKuaM6oBeK5+/xY1JpUjtJqWlpRgMBoxGY4P91qqrq0lPTyc0NBR7e/smmqFoavJ7IIQQQgghxI3NbFHINlZRXWvG21WPweHSG/VfSFEUth4v4N9fHyCmpSclFSaSUnO5o0szWng5U1FTR5CHI0dzS9Fp1Nhr7dCq7QjzceZEfgUn8ssxOGhxd9QR6OHIlKX7bK7/eP9wnPQaiitr8XTWoVLBN3vP0quVN0dyyvjhUA52Knj33iiKK2uZseyAzfmdA93wcNYRFx3Ij0fyWL7nLAB3dGmGm6OWwe39aRdg4OPtp5n53SGbc1UqSJpyC618XBDi7+JyeaILNWnFmhBCCCGEEEIIcb3LK6tm6/FC9p8pYe2hHDyddST0bc3NYd7oNL+9dfmxvHLmrjnC6OhAas0WRnYMwMNRQ3NPJ2rqzLg5agjydMDZXs1z3x6iuNIEwNAO/qDATaHuzPvxOL6uelp6Oze4/qc7TtMzzIsvd2cC4OGkY9btkXyZcoYfU/PxcNLxxIBwWno7sfpgmfU8rVpFW39XaurMBLo74mKvwdVezT9vDgUg+UQBzw5tR4fmbgDo1KoG99bYqVCrGo4LcaOQxNo1crqwgrTcMsqq63Cx19Da14VgT6emnpYQQgghhBBCiN+QcqqYrccL+CqlPnGVZazmn//bzRcPxdA1xMMmtsZk5lB2KUdzynDSqWnp40xhWQ0xLb2Yty4NiwL/p7HjuWERzFt3jPyyGnRqO14Y0Y6vUzKtSTWA73/O5ukhbTlwtpSiilr8XO2te7D9mkZtZ7P8s6iilk1pBUzr25q7ugbhoFPT0ssRfzcn2jerAqBPWx86BbqRcqoYfzd7eod7076ZgbYBrhzLLae8uo7R0YGEeJ3/3Nol2B0nnZqKWrN17IFeoQR6yB5r4sYlPXIbWXm1ic93ZjDk7S1M+F8KU7/Yz4T/pTDk7S18vvM05dWm377IFVi0aBEdOnTA1dUVV1dXYmJiWL16NQAmk4kZM2bQvn17nJycCAgI4L777iMr64KOMzU1PProo3h5eeHk5MTw4cPJzMy0iSkuLiY+Ph6DwYDBYCA+Pp6SkhKbmIyMDIYNG4aTkxNeXl5MnjyZ2tpam5gDBw4QGxuLg4MDzZo148UXX2zQYWbjxo1ERUVhb29PixYtePfddxs897Jly4iIiECv1xMREcHy5csbxCxcuNC6pDMqKorNmzf/7vcqhBBCCCGEuLGYzRZySqv57oIOnYoCm9LyG8SvPZzLG2vTyCqpYt+ZEp775iAWYPGmk/yyNRk1dRZeWXWEUV2aAVBrtvD62qO0a2ZocL2q2jryy6sZFOnHqKhm9G7jg90FBWKjujRj3eFcm7GjuWUcyDIy6dM93PvfnYz/3x5SThVRWWvmqUFtCPNx5vW1aWxIy+fznWeY9OkeMourcHPQ0TXEg95tfGySagBt/FxJfCiG+2KC6d7Cg9f+0YHxPUPRqCW1IG5cUrHWyFb8nM2TXx9oMF5eU8eTXx8EVNx9U9BVv2/z5s155ZVXaNWqFQAfffQRI0aMYO/evTRv3pw9e/bw7LPP0rFjR4qLi0lISGD48OHs3r3beo2EhARWrFhBYmIinp6eTJs2jaFDh5KSkmJtDDFmzBgyMzNZs2YNAA8++CDx8fGsWLECALPZzJAhQ/D29mbLli0UFhYyduxYFEVh/vz5QP3a5X79+tG7d2927dpFWloa48aNw8nJiWnTpgGQnp7O4MGDmTBhAp988glbt25l0qRJeHt7M2rUKACSk5OJi4vjpZde4vbbb2f58uWMHj2aLVu20K1bNwCWLl1KQkICCxcupGfPnixevJhBgwZx+PBhgoKu/s9BCCGEEEII8demVtvhbK+p78ap0zKsYwBujlp2pRdhr7VNKGUbq8g2VqGgMH/9cWvnzaKK2gbXLaupQ/urhFRBeX1F2oWCPZ2oNlnIK6umosbM5mP5PD2kLdtPFlJtsjCqS3O2nSigrKbO5rw+bXyYsyYN0y+VbIezSnn7p+NU15rp0cqTz3Zm2MRX1JpJOV1MG//L7yfVvpmB9s0MWCwKdhdm+IS4AUnzAhqvecHpwgqGvL2F8gv+gPs1F72G7yf3uibLQj08PHj11VcZP358g2O7du3ipptu4vTp0wQFBWE0GvH29ubjjz8mLi4OgKysLAIDA1m1ahUDBgwgNTWViIgItm/fbk1cbd++nZiYGI4cOUJ4eDirV69m6NChnDlzhoCAAAASExMZN24ceXl5uLq6smjRIp588klyc3PR6/UAvPLKK8yfP5/MzExUKhUzZszgu+++IzU11TrniRMnsn//fpKTkwGIi4ujtLTUWpkHMHDgQNzd3fn8888B6NatG126dGHRokXWmLZt2zJy5Ehmz559yXcnzQuEEEIIIYT4e7JYFA5nl3Istwx7rZp2Aa4EXfD5LDXLyIa0PCyKio+2nSK/vIbe4T4k9GlFXlkNibvO4OOi5x9Rgby5Lo1Nxwpszl90TxcmfbaHX3/6dnXQcHfXIBZvOglAMzcHXhzRjscS91k/Qw5p74+/wZ5bWnuxeONJdp8utnbtfOfuzui1dizccJxhHZsxf/1xawKvW6gHd3UNZMoX+23modfYcV9MCCoVfJx8miqT2eb4zOHtGNcj5E+/UyH+6qR5wXUiLbfsskk1qP9birTcskZNrJnNZr788ksqKiqIiYm5aIzRaESlUuHm5gZASkoKJpOJ/v37W2MCAgKIjIxk27ZtDBgwgOTkZAwGgzWpBtC9e3cMBgPbtm0jPDyc5ORkIiMjrUk1gAEDBlBTU0NKSgq9e/cmOTmZ2NhYa1LtXMyTTz7JqVOnCA0NJTk52WYu52Lef/99TCYTWq2W5ORkpkyZ0iBm3rx5ANTW1pKSksK///1vm5j+/fuzbdu23/9ChRBCCCGEEH8byScLGfvBTup+WafZ3N2Bjx64CRe9BmOVCU9nHW0DDOSW1TDuw13W83amF7H9ZBGzVh+xjvm42DdIqgGcLalkSt8w5q8/jsms4KRT88yQCF774SgAbo5aHusbhl6t4rE+rSirqUOntqNLkBtPLDuABYXYcB+auTtitlgI93Pl7fXH6dfOl95tfHl9bRr3dg+iS5A77k46THVmdp8uaTCPtv4u1v2/R3Vpxic7zletadUqugS5XaW3KsSNQxJrjais+vJJtT8a90cdOHCAmJgYqqurcXZ2Zvny5URERDSIq66u5t///jdjxoyxZmJzcnLQ6XS4u7vbxPr6+pKTk2ON8fHxaXA9Hx8fmxhfX1+b4+7u7uh0OpuYkJCQBvc5dyw0NPSi1/H19aWuro6CggL8/f0vGXPuPgUFBZjN5svGCCGEEEIIIW4cxioTs1alWpNqAJnFVaw7nMuXKWdw1msZFOlH11B3DmUZbc7tHe7N0t22e1AXVdbi5ayjoNx26adeo2ZZSiav3dmRY/nleDrqCHKz59mhESiKgoNOTeLODH46ms+Ufq1ZfSCHMd2CrE0L7DVqZq1KJcBgj1qtYtmeswR5OJJbWo2fqz0BBntiW3vTvYUnKpWKfRnFnCmuJKalJ8knCgFw1mt48JaWTP58L3UWha4hFp4c1IavUjJp7u7Av25tSWRAwz3ehBCXJ4m1RuRi//te7++N+6PCw8PZt28fJSUlLFu2jLFjx7Jx40ab5JrJZOKuu+7CYrGwcOHC37ymoiioftVKWXWRtspXI+bcCuWrEXPh2O+JEUIIIYQQQvz9VdbUkV5Q0WD8UFYpg9sHkJ5fztwfjuKoVfNQbAubmPrPELY7K63Yn8WUfq157ttD1rFQLyec9Vr8DA58mZLJluMF+LrqmRjbkhdWHG5wb3dHHW39XXll9RHu7xmCk06DokD3UA+CvZyoNpkpq6pjbI9gXl+bxptxnRjZqRktfZyt12jt60KQuyNVtWam9GuNxaLQ1t+FI9ll2KlUuDlq6BLkzrAO/ozpFoReY4dOo/6Tb1OIG5Mk1hpRa18XnPWa39xjrbWvS6PcX6fTWZsXREdHs2vXLt566y0WL14M1CfVRo8eTXp6OuvXr7dZN+zn50dtbS3FxcU2VWt5eXn06NHDGpOba9t5BiA/P99aFebn58eOHTtsjhcXF2MymWxiLqwYy8vLA/jNGI1Gg6en52Vjzl3Dy8sLtVp92RghhBBCCCHEjaGq1oxaDUM7+PPFBZVnbfxcKK+pI6OoEndHLQXltZgVMDhoMVaZAPjpSB4J/cJ46fvz+0BX1pqx19oxY2A4ZdV16LVqyqpNnCmuJL57MFO+2AfAPd2Cqb5gf7Nziitr+W5/fQfSD7eeYu6dHTBbFBRFYfm+szjqNDzaJ4y03FIeuqUF3UM9MDjqbK7hqNcw/uZQ0nLKOFVYiVatoqyqjsEd/LgzujkaOzv8DLJ3tBBXgyTWGlGwpxNPD2nzS/fPi3t6SNtr0rgA6iuzampqgPNJtWPHjvHTTz9Zk1PnREVFodVqSUpKYvTo0QBkZ2dz8OBB5s6dC0BMTAxGo5GdO3dy0003AbBjxw6MRqM1+RYTE8N//vMfsrOz8ff3B2Dt2rXo9XqioqKsMU899RS1tbXodDprTEBAgHWJaExMjLXT6Dlr164lOjoarVZrjUlKSrLZZ23t2rXWueh0OqKiokhKSuL222+3xiQlJTFixIg/82qFEEIIIYQQfyF7Thczb90xQr0ciWnhScgvn8l+TM3l7puC8HbVcyS7DDdHHZ2D3HF10PD+5pPMvbMDB84aOZ5XTrsAA0XlJp4YEE7yiQJcHbQM6RCAsaqWOWuOorFTUWdR8DfYM2NAODNXHGJqv3CqTGaSDuei19gxqkszlu05a53X5NtacSynzPq9i72GcF8XXvvhKOuO5P0yWsNL3x/mqcFtie8ehIPu4h/rHXUaOgW50ynI/aLHhRBXh3QFpfG6ggKUV5tY8XM2s1am2rQ/dtFreGpIW4Z18MfZXvunn+FCTz31FIMGDSIwMJCysjISExN55ZVXWLNmDb1792bUqFHs2bOH77//3qZay8PDw5rc+te//sX333/PkiVL8PDwYPr06RQWFpKSkoJaXV8mPGjQILKysqxVcA8++CDBwcHWJJjZbKZTp074+vry6quvUlRUxLhx4xg5ciTz588H6hsnhIeHc9ttt/HUU09x7Ngxxo0bx3PPPce0adMASE9PJzIykoceeogJEyaQnJzMxIkT+fzzzxk1ahQA27Zt45ZbbuE///kPI0aM4Ntvv+WZZ55hy5Yt1gYLS5cuJT4+nnfffZeYmBjee+89/u///o9Dhw4RHBx8yfcpXUGFEEIIIYT466qsrSO3tAYHnR3GShMj3tnKsA4BpBdUsPt0sTVu1shIjuWWcaakinWpedbxYE9HpvVvTXZJNTo1GBx1bD9ZhLHKRHSwOyfzK9iTUcLR3DL+98BN5JdVs/eMEVd7DXZ2Kt7beJJas4VF93Zh0qfnu4P2auVF9xaeeLvoaOXjTLifK+XVJo7mlGNWLIT5uFBQXsPtCxs2W3N10LDmsVsIcHNo9PcnxI1GuoJeR5zttdx9UxA9WnqSlltGWXUdLvb1yz8bs1ItNzeX+Ph4srOzMRgMdOjQgTVr1tCvXz9OnTrFd999B0CnTp1szvvpp5+49dZbAXjzzTfRaDSMHj2aqqoq+vTpw5IlS6xJNYBPP/2UyZMnWzt2Dh8+nAULFliPq9VqVq5cyaRJk+jZsycODg6MGTOG1157zRpjMBhISkri4YcfJjo6Gnd3d6ZOncrUqVOtMaGhoaxatYopU6bwzjvvEBAQwNtvv21NqgH06NGDxMREnnnmGZ599llatmzJ0qVLbbqWxsXFUVhYyIsvvkh2djaRkZGsWrXqskk1IYQQQgghxF/X8bxyZq06zPoj+Xg565jarzVhPi4EeTryZYrtEtBXfjjC/Ls6M/ZX3T8BThdWcra4ijlrjvJYnzDe33qM8T1D+WpPJkmHc/lV7wN2nyrC01nPqgPZVNTUUVNnAcDTSYedChy0aipr65eBbjlewJmiCr6YGIOva32CzFmvwc/g8Kt7N9wDDqC0qu6Sy0mFENeOVKzRuBVr4u9Bfg+EEEIIIYT46ymrMvHAR7vZdarIZnzGwPolmW//eLzBOS8Mb8exvHI+2X7aZjyhbxjz1h1DY6diXM8QOjZ3Y/aqVLKM1TZxM4dHEOzpxIdb0tl0rAAArVrFEwPboELB29meZXsyOZRVym1tfPjnzS0I97v0vtunCisY+vaWBnt3x7T04L/xXXFqpGZ4QtzIpGJNCCGEEEIIIcQNw2S2cCDTyNmSStQqO/wMejoGunO2pIpdp4pw1msY1N4PNwcdG47mUV1noY1fww/Lbf1dOJpTRtdQdzYfy+d0YSUA7o5aan+pPKuzKPx3czoueg3PDYtgxrKfrRVrYT7O5BprKCir5bZwH4Z1DCCvrIY6i8JH207xaO9WvPPTccL9XPhqYgzN3R3Rauwu+2whnk68GdeRSZ/uwWSuv5Gnk46nB0dIUk2I64D8WyiEEEIIIYQQ4i9tZ3oRR3JK+XJ3JkdyytBr7Hj0tlb0i/ClQzNXhnYIoNJkxlmvYVzPEFp6O7N443GeHxbBvHXHMFaZiPB35bE+YWxPL+ST7Rn0j/DF20XPwcwS7owO4pHP9uCgVdO+uYHSKhN5ZTV4Oul4ZkhbjFV12GvtCPdzYcZXPzOuZygzvz9sM8fm7g7syywhLa+ctLxyHrktrEFSTVEUckvrG879umvnbW18WTn5Zk7klaPT2NHa14VAD8fGf7FCiN8kS0GRpaDit8nvgRBCCCGEENenqlozb69PY9vxQvZnGm2OffFgd7JKqzmRV843+85ypqgKqN/nbN5dHdl2vIAQT2eKKmvxctaRuOsMqdnnu3KGeDpyb7cgnO21uNhrOJ5XTvLJQjyd9Qzt4M9bSWkcyS0HwE4F8+/uTEF5DcEeTmw5UcAHW9KxKPUVZpP7tCKjqBJnvRZHnZqhHfxp5n4+OXYir4yv957l4+TToILxPUMZHR2IvzQnEOKak6WgQgghhBBCCCH+smpMdRzOLsNYZSLE04kQr0s3fqutM+PmoGuQVANIPlGIi4MGtZ3KmlQDqDKZ+WxHBqO6NMdeq+b9ren8I6q5TVIN4FRhJU56LUmpuXg46myaHaw5mMPTg9vy0srDKArEtvbBSa+h2mRmwse7mXxbK2YMbEMzNweq68xU1tbxw6FcMovr57H9ZCEvjowk0N2RsmoT721OZ+muM9brv/lLJd1Tg9uiUV9+uagQounIv51CCCGEEEIIIa4beaXVvPXjce58N5lxH+4i7r1ktp0ouGS8wVFHCy8nPJx0DY61DXDFz9WBs8VVDY6dyK/gp6P5PPLZXkZ2akbQJZZW2tmpuL1TM77aY9tB1GxR2JiWT1SQO9Eh7twXE8zra49yOLsMk1lhQ1oBG9LyMThqua2NL+sO51mTagA/Hc3npyN5AKTllrHsgg6lAB9vP01GUeUln10I0fQksSaEEEIIIYQQ4rqx70wJCzecwPxLR4Dc0hqe//YQZ4svnWDqFOTGlL5hNmPT+7dm/ZE8Hvl8D0GeDZNmsa292XWqiFqzhQU/HcdBp8bdUWsT46zX4KhTcyyvnIttopRjrObfg9rwj6jmzF9/jDu6NOezHRkAtPVzoY2vC+G+LlTW1rH1RGGD89cczAGg2mShztLwBiaz0mA8t7Sa1OxS8kqrG8QLIa49WQoqhBBCCCGEEOK6ca4T568dyyvnbEmVzZ5kFTV1pOWWkW2sppmbAyM7+RMR4EpeaQ0FZdX4uznwRlIaFqU+WfdAzxA+25lBbZ2FoR0CMDhobSrIfj5j5OWRkXy49RT7M0uI8HdlZOdmVNeaKa+pw9NJR2FFrc28bg33pspUh8ZOhbeLPS+vTMVsUfB11XNLa2/sVOBir6W2zkwbPxcOZZXanN81xAOob2wQFexOyulim+M9W3rS7Jc91iwWhU3H8pmx7GdyS2vwc7VnzqgO3BzmhZ2d6s+9dCHEFZPEmhBCCCGEEEKI64anS8Mlnc3cHHC1P19NVm0y89G2U8z94SgAOrUdi+7twvojeSSfKCQ6xB1PZz3nir1+TM0j2NOR+3uE0jXEnS92nyG/vMbmHm5OWk7kV9C9hQfRIe7kldWgtVPx3c9n2Z9pZErf1ryZlEZpdR0A0cHuKEBWSTVms0Jsay+CPR3xN9jj46Jny7ECVh/M4fvJvfB1tefZoRGM/WAnNXUWAPwN9gzp4A9AsKcT/x4YztPfHCTtl2YIbf1ceW5YBE76+o/tabll/POj3dYKtpzSav75v118/2gvwv0uv7m6EKLxSGLtGqqoMVFVa8FBZ4eTXvvbJwghhBBCCCHEDSbCz5V+ET4kHa7ff0yvsWN6/9a09Ha2xpzIL+fVtUet38fHBPPMNwfJNtYvjzxZUEHvcB+bKrDThZV8tScTP4M9u08Xc2/3IKA+KXd/zxD8DQ48/tV+2jcz0K2FJ83dHQjxcuJITjmlVXW8/eMxxnQLws1Bh7ujlo3H8lEUaOXtTHMPR5JPFPLZjgyqTWZr8mt4pwDcflle2i3Ug28f7klabhl6jR1t/V0J8jzflKFrqCcfjO1KemEFWrUd4b4uuP9q37gT+eUNloWazAon8isksSZEE5I91q6B9IIKvkrJ5B+LtzP47c38Y/F2vkw5Q3pBxTWbw+zZs1GpVCQkJFjHFEVh5syZBAQE4ODgwK233sqhQ4dszqupqeHRRx/Fy8sLJycnhg8fTmam7aaaxcXFxMfHYzAYMBgMxMfHU1JSYhOTkZHBsGHDcHJywsvLi8mTJ1Nba1tGfeDAAWJjY3FwcKBZs2a8+OKLKBdsZLBx40aioqKwt7enRYsWvPvuuw2eddmyZURERKDX64mIiGD58uUNYhYuXEhoaCj29vZERUWxefPm3/MahRBCCCGEEI2ktNpElclMG39XpvUP5+27OvH8sAj+Ozaa2HAvtJrzH18Lymts9jxzsddYk2rnLN50kom3tODOqOYEejgwoJ0vs29vz5w1qcRFB1JUbuKxPq14++5O7DxVyCurU1l4Txfu7R6MvcaOwvJa/vm/3Tx8a0sm3dqSMF8X7FQqAj0cOVNSRVt/V47mGPFy0ePrak9MS0+GtPe3Jr9aejvxyK2t0GvUAKhUKtr4uzK8UzMGRPrbJNXOae7hyM1h3nRv4WmTVANw0Kkv+t4ctBcfF0JcG1Kx1sh+zizh/g932azFzyur4fEvf8bTSceH93elQ3O3Rp3Drl27eO+99+jQoYPN+Ny5c3njjTdYsmQJrVu35uWXX6Zfv34cPXoUFxcXABISElixYgWJiYl4enoybdo0hg4dSkpKCmp1/R/gY8aMITMzkzVr1gDw4IMPEh8fz4oVKwAwm80MGTIEb29vtmzZQmFhIWPHjkVRFObPnw9AaWkp/fr1o3fv3uzatYu0tDTGjRuHk5MT06ZNAyA9PZ3BgwczYcIEPvnkE7Zu3cqkSZPw9vZm1KhRACQnJxMXF8dLL73E7bffzvLlyxk9ejRbtmyhW7duACxdupSEhAQWLlxIz549Wbx4MYMGDeLw4cMEBQU16s9CCCGEEEKIG5GpzsKZ4koU6vcTO5dsgvouoKsP5vC/5NN4OGl5pHcrurfwpM0lqrBq68w4ajXYa+2oNtUvq7zYDmP7zpRwpqiSoR38GNkpgCPZZby88jD3dAvBz2DPutQMjuaW4eOi55khbTmRX8Ge08XEtPQk0N2RgvIa+kX4Eu7rgr+bA4qicDK/ggXrj/FTWj6dA914fEAbgn9JkPm62vPc8AjiY4KpMZkJ9nTCy0V/1d5hWz9XWno5ceJXBRqtvJ1p4+9y1e4hhPjjVMqFJUE3oNLSUgwGA0ajEVdX2z+8q6urSU9Pt1Y3/RHpBRXcuWhbgw0uf83TScdX/+pBqFfDv624GsrLy+nSpQsLFy7k5ZdfplOnTsybNw9FUQgICCAhIYEZM2YA9dVpvr6+zJkzh4ceegij0Yi3tzcff/wxcXFxAGRlZREYGMiqVasYMGAAqampREREsH37dmviavv27cTExHDkyBHCw8NZvXo1Q4cO5cyZMwQEBACQmJjIuHHjyMvLw9XVlUWLFvHkk0+Sm5uLXl//H59XXnmF+fPnk5mZiUqlYsaMGXz33XekpqZan2/ixIns37+f5ORkAOLi4igtLWX16tXWmIEDB+Lu7s7nn38OQLdu3ejSpQuLFi2yxrRt25aRI0cye/bsi77HP/N7IIQQQgghxI0sq6SKxRtP8OmODCyKwu2dm/FY39YEeThisSjMW5fG2+uP25zz+YTuxLT0vOj11hzM5vnvDvHPm1vw7oYTFFbU8tAtLViXmsuJ/PNJp26hHvi46Gnl68KiDce5rY0PjjoNG47mYawyMTG2JfN/ue8jt7Xio62neOS2VsxefQSAaf1a80CvUOseZ+fU1JkpqTThaq+9ZBVZYzmZX84Ph3LZmJbHra196B/pSwsv598+UQjxh1wuT3QhWQraiFJOF182qQZQWFHLngs6v1xNDz/8MEOGDKFv37424+np6eTk5NC/f3/rmF6vJzY2lm3btgGQkpKCyWSyiQkICCAyMtIak5ycjMFgsCbVALp3747BYLCJiYyMtCbVAAYMGEBNTQ0pKSnWmNjYWGtS7VxMVlYWp06dssb8ei7nYnbv3o3JZLpszLm51NbWkpKS0iCmf//+1hghhBBCCCHE1bN871k+Sj5NnUXBosCyPWdZsjUds0WpT7ptOtngnK/3ZF7kSpBtrOLZbw6RW1rDwp+OM6JTAI/e1goXBw3/im3Jg7e0oGuIO+N7hTKiUwBVJjMWi0K1ycKqAzl8lZJJQXktJrOCnaq+zs3fYE9MqAcPxbbgnQ3nE3yvJ6VxPK+8wRz0GjW+rvbXPKkG0MLbmX/d2pLEB2OYeGtLSaoJcR2QpaCNpKLGxAdb039X7Ptb0xkY6XvVGxokJiayZ88edu3a1eBYTk4OAL6+vjbjvr6+nD592hqj0+lwd3dvEHPu/JycHHx8fBpc38fHxybmwvu4u7uj0+lsYkJCQhrc59yx0NDQi17H19eXuro6CgoK8Pf3v2TMufsUFBRgNpsvGyOEEEIIIYS4OgrLa/g4+XSD8c92ZvDPm1twqeVT5l/2Kcs2VrHndDEHs0pp5+9KsKeTtZtncaWJD7aeAkBtp+KhW1qwOS2fSb1bMnfNUc4UVzG1XxihXk5o1SpM5vN369DcwPG8chy0amYMbIO9Vs1ra9MazCOvrLrBmBBC/Jok1hpJVa2FgrKa3w6k/j82VSYLTldv+T1nzpzhscceY+3atZdduqhS2e5GoChKg7ELXRhzsfirEXNulfLViLlw7EqeWwghhBBCCPHHaNV2GBw05JTajod4OaFTq3B3tWdczxAWb7StWhsV1ZyiilqeXn6Q9UfyrOO3hHkR3z2Ij7dn2MR3bO7GkZwyUnPKeHFFKjOHR5CaU4bZAlvT8vm/+6J5ZfURjueVE9vam7iugRzKMhLqHUpBeQ3tm7k2SL6pVOBvcLj6L0UI8bciS0EbiYPO7ndvVOnprMdBe3V/FCkpKeTl5REVFYVGo0Gj0bBx40befvttNBqNTTXYr+Xl5VmP+fn5UVtbS3Fx8WVjcnNzG9w/Pz/fJubC+xQXF2MymS4bk5dX/x/Q34rRaDR4enpeNubcNby8vFCr1ZeNEUIIIYQQQlwdrg5aEvq1tn7fLdSDGQPDubmVF6sO5HAku5T7ewTz70FtaObmQGQzV/57XzRdgt1Iyy2zSaoBbDpWQExLL7Tq838p7qRTM6yjPxuO1sc2c3dg9cEcFqw/zls/HiMx5Sy1dWb+OzaayX1aUVZTx4Mfp/DWj8dZsP44pworCPZ0Yu6oDtbrqu1UvDiiHWE+stRSCHF5klhrJE56LQ/0DP1dseN7hl71ZaB9+vThwIED7Nu3z/oVHR3NPffcw759+2jRogV+fn4kJSVZz6mtrWXjxo306NEDgKioKLRarU1MdnY2Bw8etMbExMRgNBrZuXOnNWbHjh0YjUabmIMHD5KdnW2NWbt2LXq9nqioKGvMpk2bqK2ttYkJCAiwLhGNiYmxmcu5mOjoaLRa7WVjzs1Fp9MRFRXVICYpKckaI4QQQgghhLh6bgnz5oOx0Yzo6E/3lp7MWXOU/9ucznPfHWLMf3dwOLsMDycdMwaG88boTnQKciPpcB4Hzhover3M4kpm3d6ehL5hTOvXmrfu6sS+M8X4uNhzb/cgeod7s/LA+c8eLnoN9lo1G47kcbqwkp3pRTbX69HSC43ajmEdA1g1+WY+Hn8TKyf3Ii46CL322u+jJoT4a5GloI0oKtgdTyfdZRsYeDnr6BLsfsnjV8rFxYXIyEibMScnJzw9Pa3jCQkJzJo1i7CwMMLCwpg1axaOjo6MGTMGAIPBwPjx45k2bRqenp54eHgwffp02rdvb22G0LZtWwYOHMiECRNYvHgxAA8++CBDhw4lPDwcqG8MEBERQXx8PK+++ipFRUVMnz6dCRMmWLtrjBkzhhdeeIFx48bx1FNPcezYMWbNmsVzzz1nXaI5ceJEFixYwNSpU5kwYQLJycm8//771m6fAI899hi33HILc+bMYcSIEXz77besW7eOLVu2WGOmTp1KfHw80dHRxMTE8N5775GRkcHEiROv+s9BCCGEEEKIG52TXsNtbX1p4e3MkLc32xwzVpnYcCSf1YdyyC+rwdNJx4sjI5n8+V6m9A1Dr7Gjps5ijdep7aiqNVNeU8nbP55vNLA4vgvPDm2HnUrF+I92ofxq87aHe7fikc/2Ulpdx9R+renQ3MDPmUbsVHBfTAhdQ+o/j2nUdoT5uhDm69K4L0QI8bciibVGFOrlxIf3d+X+D3ddNLnm6aTjg3FdCfVyaoLZwRNPPEFVVRWTJk2iuLiYbt26sXbtWlxczv+H5M0330Sj0TB69Giqqqro06cPS5YsQa0+/zc3n376KZMnT7Z22hw+fDgLFiywHler1axcuZJJkybRs2dPHBwcGDNmDK+99po1xmAwkJSUxMMPP0x0dDTu7u5MnTqVqVOnWmNCQ0NZtWoVU6ZM4Z133iEgIIC3336bUaNGWWN69OhBYmIizzzzDM8++ywtW7Zk6dKlNl1L4+LiKCws5MUXXyQ7O5vIyEhWrVpFcHDw1X3BQgghhBBCCKsqk5mKWnOD8ezSaryd9eSX1VBYUcvmtHyGdfDHWa/h9X905IXvD5NfVoO3s56EvmGAQnap7X7W2cYavJzrt+J5/R8d+XJ3JmsO5dA1xA291o7S6joA3lyXxpD2/gxo50eolxMeTjpc7OVjsRDiyqkURblUI5YbRmlpKQaDAaPRaK2gOqe6upr09HRCQ0Mv2wTgctILKthzupj3t6ZTWF6Dp7Oe8T1D6RLs3mRJNfHHXI3fAyGEEEIIIW5kJZW13P1/20nNLrMZn9I3jHd+OkGtub4yrUdLT7qFevDmumM8M6QtZ4urcLLXUFZdx6ajeQS4ORDu52LtCKqxU7HsXz3oGOhmvabFolBWbcJRp+Gj5FO8vDLV5p6u9hrujGrOB1tP8fH4m7g5zLtRn10I8ddyuTzRhSQ1fw2EejkR6uXEwEhfqkwWHLR2V31PNSGEEEIIIYS4nrk56nj1zo48/OkeThdVYqeC0dGBVJnMWH5V79G3rS+vrz0KQFl1HR9uO4Wvq54HeobSK8wbi6LQs6UXR3JKARWTbwsjspnB5l52dioMjjqgfosetZ0Ks+X8PUZ0asbaw/VN2D7dniGJNSHEFZPE2jXkpNfi9PsahQohhBBCCCHEX0aVqQ61SoVO03Cz/6paM9mlVejVdkQ2M7DsXz04klNKbZ3CgawS9p8p4bG+YezLKMFRp8ZJr7YuGbWzq99v+cFbWjBn9VFrVVvirjP874GbiApyx15Xf8/aOgtHc0o5kV+Bi72GCH9X/N0caN/MwMfjb2LeujRyS2voF+GLsdJEZnEVUN8BVAghrpQk1oQQQgghhBDiBldTZ+ZUQQU1Jgu5pdWk5ZUT5OFI5yA3mrs7XvK8zKJKkk8WknQ4FzdHLUPa+9O9hae1m+aJvHLmrjnC2tRcnHUapg1oze2dmuOk1/DqD4eIbGbAYoE3k9KYfFsYh7NLySs7v39aRU0dsa292H2q2JpUAzBbFP5v80mi46OsY2sP5fBo4l5r44I2fs4svjeaYC8nerT0olOgG/vOFDP5830UlJ/fA/uebkFX6zUKIW5AklgTQgghhBBCiBtYQVkNizYe53RhJSqViqRflkgCRPi7sjg+ikAPR7JLqsgrq8HdUUuQpxMWi8LS3WeYv/58d86v95zlv2OjuTXch7zSap7/7hBbjhcAUFZTx8zvDtPM4EBNnYVm7g6sPphDqJcTzw6N4MMt6Qxs78+pggp6tfJiy/ECPtiSzn9GtuO7/dkN5p1fVmNd3nmmqJKnlh+w6QZ6JKeczccLCP5lX2tHnYYOzd2ZM6oDS7adQq9Rc3/PEKKC3RvjtQohbhCSWBNCCCGEEEKIG9jO9CLe33KKaf1b8/raNJtjh7NL2ZtRzJmiSh5L3Ed+eQ0ueg2z7mhPC28n3tt00ia+zqLwv+RTBLo7sOlYgTWp9munCiv4MiWTtNxyAIoqajl41sijt4WhUim8t+kkQ9r78/ywCHxd7Wnl7YSzvZatJwptrjO2x/+zd9/RcRVnH8e/27Vd0qr33iz3XsAGG9NN7wQIEEwJYHoIbxKS0CHUAIHQW6ihNzcwGHfZcpMsS7J677uSVtvfP2QvXiQTG2xs4Pmc4xPv3Ln3zr05B0u/nZknDYNWHbzGrsqfu1tX3cn5U1KDn006NbPzY5mZE41CoQguAw0EArQ5XGjVSsJ37s0mhBB7Q3mwB/Bz4ff7/3cn8Ysl//8LIYQQQohfqq/K24DBPcqGU9HWy83vbKKtd3CJpsPl5ZrXN9DT78E1zDler5+3i+rp6HOjUQ3dv8ygVQdDtV1cXj9KJejUKq6clcn2FgdTMmwcNzKenDgL07OiuOuUQmLMOmxGLX86IZ8j82KC59tMWiIMQwvETc20DftMapUyGKrVdvbz4KLtHPfo15z25Ao+KG7A7vQMe54QQnyXzFj7H7RaLUqlksbGRqKjo9FqtSgUsrnlr0UgEMDtdtPW1oZSqUSrlW+vhBBCCCHEL0t+vBkYDLWGkxltorHbGdIWCIBWrSQz2khlW1/IsYtmpHP3J9tQqxScPDaRt9bVB4+plApMuuF/DQ1Tq/j7xyWMTQ7n+d9ODNnbLdyg5dzJqRxVEItjwIs/ALvXHEiKMHD/6aO54tUiPL7B9aATUiOYlhn1vc/e5/Jy50clfL5z+Wt7r5trXi/mn+eM5YTRCd97rhBCgARr/5NSqSQ9PZ2mpiYaGxsP9nDEQWIwGEhJSUGplEmeQgghhBDi5625x8mWBjudfW4yoo3MyIoiPcrIwpJmLpmRzrPLq4J9TxqdQHqUEX8g9BoXT09jW7Oda2dnc8fHpcGCA1MyIok0aMiNM/PRpiby4y1cdUQW31S0E2vRMSs3hi0NPeTHmyltcgSvp9eo6HN7CQRgfW03TT0DQ4omeH1+Ntb1cOu7m2lzuMiKMXLfaaMZt3OPtCPyYvjo6hlUtfdh1mnIiTMTbdZ977uoau8Lhmq7e3RpOYfnRGPRD50FJ4QQu5NgbS9otVpSUlLwer34fL6DPRzxE1OpVKjVapmpKIQQQgghfvaaewZY8EYxq3Z0BtvuOqWQVy+dxJYGO0oFzM6PoavfTbRJR06sGa1ayfzDM3hq535qY5PDaet1YdFruOfTbZwxIRlzmBqVQkFJk4P3ipuYnhXF6h2d/Hd9A0atiqMKYvnN5BQe+6KSZeVtLJidTUG8hZWVHWRGmzhjYjK3f7A1OKZAYMjQ2dbsYP4rRcGCBRWtfVz60jo++P10kiIMqJQKcuMs5MZZ9vp97Gn5q2PAi8cn28EIIf43Cdb2kkKhQKPRoNHINxZCCCGEEEKIn6fiuu6QUA3gjo9LmZYZxdwRccG2pi4nDT1Oimq6CAQCTMmIZHZeDGUtDnLjzJz99CquOiKLGHMY0SYdA14fCoWChPAwPt3cyKS0EZw9KRmVUoECUCjgmx0dHFMYx7LyNh5aXE5mtJEpmTYmpEbwtw+30tnnBqAg3kxmtHHI2CvbeoOh2i6dfW5qO/qHzG7bW6k2A+lRBqra+0Paz5+cis30/bPdhBACJFgTQgghhBBCiF8cvz+AY8CDUadGrRrczmRTfTdlzfYhffvdPhwDHopruylp6sESpkGpVHDnx6U0dDux6jVcf1Q2aqWSnFgzTT1ONColXn+A8yancOcnpcGlotkxJq6ZncNnW5vJjbNgM2mJs4QRbdbR1O3EqFXx13kj+GJbG06Pl4woIzq1kpm5MWys62Z2XgxnTkgeNtTa095sxj207w2bScdj54zj2tc3UNnWh0IBJ49J4OSxsr+aEGLvSLAmhBBCCCGEEL8gVe19vLG2ls+2NDM+NYKLZ6STFKHnT+9vYW5B3JD+GdFGOvrcFNd1o1Ao6Oxz8/gXlTg9g9vg9Dg9/OWDEm45JpeLnl/Ln07I5+yJyTjdPp4rrg7Zf628tRd/ADz+AHanhxlZUYxODgcGixNc+WoRIxOtdPW7CdMoeXRJBW6fn+cvHM9VMzMx69VEm8OGfa6CeAsj4i1sbfo2HDx9fOKws9v2RWGilTfnT6W+qx+tWkWazYheO3whByGE+C4J1oQQQgghhBDiZ87r8+PzB+h3+7jhzWLW13YDUN3RT2efi+vm5JIcYWBxSTO3HJPL419U0uvykhtr4vypqcx/uQiX148lTM1f540Ihmq7szu9+PwBNtR1MTndRlqkgRdXVg/pV93Rx5fb2nB6fHywsZHXL5tCqs1IQ7eTspZejhsZT0mTg+K6bpQKuP/0Uby2po7F21pJDNfz13kjODwnGo0qtHBYfLieJ88fz6qqDrY1OZiQFsH41AhMYT9+ux6bSSdLP4UQP4gEa0IIIYQQQgjxM+X1+VlX08Wzy6tocwww//DMYKimUyu58ehc1lR18HlJM4UJFqZl2kiONPCXE/LxA939HgKBwT3Fnv2miuNHxQcrfH6XVq1kwZxsylsdPLKknNFJ4dx/2ij+8sFW+tzfBnGRBm0wmGvqGWBrYw+pNiORRi0KBTyypJyTxyYyd0QsKZF6Xl1dS1HN4Jjru5z87qV1/PfKaYxJjhgyhhSbgRTb3u2n5vL4KGt2sKO9D3OYmvx4Cwnh+r1/uUIIsRckWBNCCCGEEEKIn6mN9d2c98zq4Kb+21scwWO/mZrKM1/v4IKpabywopq2nYHZmeOTaOt18UVZW7DvsYVxPHL2GJp6nFS191EQb6ak6dtrRRg0pEUZeHhxOTUdgxv913U6WV7Rzs3H5PKXD0qwhKm5YFoaS7a1hoxxwDNYXTMz2shVs7L45xcV/Hd9AwD3nDoyGKrt4g/A9mbHsMHa3vL6/Ly7oYE//HdzsC0n1sS/fjOejCjTD76uEEJ8lwRrQgghhBBCCPEz9dmW5pBKmWUtDg7PjqIw0UpShJ6LpqVh0WtICtczITWC4rpukm0G3iyqD7nOp1uaGZVkpSDewpodXZw3JZXyll6K67rJjjGRGW2isdsZDNV26e734PYGuGZ2FtEmHV+Xt1NU0xU8rlEpyIk1A6DXqrns8HSmZtqobu/DZtKRFKFHq1Li9vlDrvtjChIA7Gjv40/vbwlp297Sy2ebm7nyiKwfdW0hhNidBGtCCCGEEEII8TO1e6gGg0Hb4+eN45Z3NmF3erHqNdxydC5x1jC2t/QyJz9mj9U1NSolXf0eTh6bwDWvFxNt1lGYYCEtysiA10eabfgiAX1uL48uqcCoU/HchRPRa1Us3NpCerSR247LpyDeAoDL66O918Xz31SxuHRwVtuxhbFcOSuTh5eUB6+XFKGnMNH6o95Lc88AHl9gSPvCkhYuPTwdrUqKEwgh9g8J1oQQQgghhBDiIGnoduLx+okPD0On/jbsaekZYHNDD229LjKijBQmWoedxXVsYTzPr6gmsDNDumh6Gs9/U4Xd6QXg4hnp3PlJaXAPtMq2Xu44uXDYsWhUSha8UczVRw7O6GpzDC4X3bVk9LFzxpAUoae+yxk8x6xTMyrRyu0njiDCoMGkU3H/6aO45Rg3Rp0Kq16L3x9gbXUnTy+rpKqjnxlZUVw8PY3nvqnm0y0t5MdZeObCCayq7CA9ysjUTBupewjx9lakUTts+/jUcAnVhBD7lQRrQgghhBBCCPET6+xz8U5RA48uKafP7eX4kfEsOCqHzGgT7Q4Xt7yziS+3f7sH2t9OGsFvpqSiUChCrjM6OZyXL57Mk8sqOLogDrVKwUsraoLHA4FASGEBgHc3NHDt7GweXVpOIAAqpYIFs7N5c10dAEqFAp1aicv77fJMjUpBdXsfv5mSyvraLopquihMsPKbqakoFdDqGGB7i4PbP9zK078Zz8R0W/DcTQ3dnPP0Krw7Z9dVtPZyRG4Mh2VH8XV5Ow8vKWfZTUcwJz92v73fzGgjlx6WzjNfVwXbLGFqThuXtN/uIYQQIMGaEEIIIYQQQvzkvixr485PSoOfP9zURHuvi6cvmMC2ZntIqAZw9yfbODw7mrSob2dyBQIBVAqYkR1FfryZy15eR7hBy6gka7Ay6HCKaro4aXQ8D585hl6XF51aycodHWxttAPwxto6bjw6l6eWVdLe6ybSqOXGuTk88WUl9V1ORiVZOaYwjtFJ4dz76TauOiKTF1dUBwO8P7+/hdcum0qEYXDW2NJtrcFQbZcvylq5bk42X5e3YzPp0GmUP+p9fpdeq+bKWZlMy7SxsrKDxAg9UzNs5MZZ9ut9hBBCgjUhhBBCCCGE+Ak1dvfT3utiwZxsvtjWysb6HgBW7uikpqMf+8DgMk6dWskxhXGkRBqo63Ti9HiD19hU381rq2upbOvlrInJpEcZKarpRqVU8H/H51PR2ot9wItSocCgVdG/26y1yemRLK/oYGFJC38/aQR+IMqkCx5vtg/w+BcV3Dw3lyizjq5+N2qlgstnZvLUV5UYtCoyo008sLCMFruLira+kFlxpc29dPa6g8Favyt0xtwuu6K2Px6bR4w5bH+82hCRRh1H5sVyZN7+mwknhBDfJcGaEEIIIYQQQvxEtjT0cNVr66np6EepgFPGJpJqM/LBxkYAFArIiDISa9Fx+cxMXltdy/vFjWTFmOju9+D1+ant6OP8Z1YHA7i11V3889yxqJQKfP4ADy3azgXT0lArFSRH6nnivHG8trqWshYHUzNsRBq1PPFlJQB6rYqG7gHUKiW5sWbKWhwAhKlVxFh0XPpSUXDs8w/P4PiR8Wxu6OHvH5XgDwwWGmhzuEKeMdVmINygCX6enR/DM8urQvoUJlrQqZU8/9uJTEyL3P8vWgghfiKKQCAwtFTKr4zdbsdqtdLT04PFIlODhRBCCCGEEPtfV5+bc/69im3NjpD2BXOyeeKLSqZl2Xj8nHHotSrWVHXy2xfW4vR8O9srTKPkzycU8PHmJg7PjuazLc1sqOsGYHxKOKOSwnl+RXWwf4xZx63H5vGPRduZWxDLrNwY7vq4hG0tvQCcMykZfyBAvFXPI0vKOXNCMgnWMAKAY8BLuEHDPxZuD15Po1Lwp+ML+PMHW4HBGXUPnjma/26oZ0lpW7Dt+d9OZFpmVPC8AY+XJaVt/P2jElodAxxTGMf1R+WQFWPen69XCCH2m33JiWTGmhBCCCGEEEL8BJp6nENCNYCtjXZ+f2QW80YnYAwb/BWt2+kOCdUABjx+Wuwuvqno4JuKDm49No/NDT14/QG2Ntk5b3IK958+isWlLWTHmMiINuHx+Tl5bCJ6jYovy1q5enY2jgEvcdYwtCoF7xU3kRFlxGbU8sbauuC9MqKNTM2whdzf4wugVim4+ehcrHoNbp+fv35YwpQMG/efPgqNSsmIBAtZMaaQ88I0ao4fFc+k9AgGPH5izDp0GqnMKYT4ZZBgTQghhBBCCCF+AgatGr1GNSQwK4g3c8WsTDSqbzfw16qH38xfqfy2KujSba1MTI9kZWUHZ01Mptfl5ZPNzXQ7PRyWHcXHm5pYsq012P+MCUm8sKKatdVd/P2kEXh9fvLjTDR0OQf3eytro6TRzrRMG6ePT+LSF9eF3HtcSjgjEix8sLGJF1ZU07pzCegHGxtxDHg5Z1Iy2bF7noUWfQD2URNCiINt/5Ze2Ue33347CoUi5E9cXFzweCAQ4PbbbychIQG9Xs+sWbPYunVryDVcLhdXX301UVFRGI1G5s2bR319/U/9KEIIIYQQQgjxvVJtBv5yYgE5sSbUOwMyi17NMYXxwVCtpqOPl1ZW09Q9QGqkPuT8pAg9dqcn+FmhgMxoI1cdkUV3v4fECANbG+1UtvVi0KpDQjWAt9bVc1h2NBlRRsqaHXgD4PYFuPfzMv7vva2Yw9T8ZkoqF09PY0qGjecumsjInXuhzRuTwH2njyLSqOOtdXXBUG33Z1PvFvoJIcSvxUGfsTZixAgWL14c/KxSfTsl+L777uPBBx/khRdeICcnhzvuuIOjjjqKsrIyzObBb0IWLFjAhx9+yOuvv47NZuOGG27ghBNOoKioKORaQgghhBBCCHEwbWt20OpwURBv5YIpqVgNWnJiTeTEmqlq78Xh9HLLfzdR2uRArVRw9exsOnpdFNd1MzEtkqwYEx29Lq6ZnYXPD8kRej7b0sx7NY14fH7mFsQyNdOGQqGgods57BjMYWpOGpvI419U8Ob8KSRFGIi36ulzeVmxo53Ptjaj16oIN+iYnGHjlUun0OfyYjNp0akHf7/6v+MLuPmdTcFrxlnCSAgP+97ZakII8Ut10IM1tVodMkttl0AgwMMPP8xtt93GqaeeCsCLL75IbGwsr732GvPnz6enp4dnn32Wl19+mTlz5gDwyiuvkJyczOLFizn66KOHvafL5cLl+vYbFrvdfgCeTAghhBBCCPFL0dHrorTJTpvDRXKkgbw4C6awvf91qqzZztlPr6Jn54yz94obWDAnm9n5MbxVVM/tH2xl/sxMSpsG92Dz7qzumRSh57o52TR1D7C8vI2PNzcDYNSq+L/jC1hT3Um/28e1s7N54osKbpybS2mzA8Uwk8fUSgVqpYIlJS08f9EEChOsqFVKsmNNnPbkCvrdg0tUN9X3sKm+m7tOGYVVr8Gq14RcZ96YBOLDw9ja0INCoSA5wkBOnJnkSMMPebVCCPGzdlCXggKUl5eTkJBAeno6Z599Njt27ACgqqqK5uZm5s6dG+yr0+mYOXMmK1asAKCoqAiPxxPSJyEhgcLCwmCf4dx9991Yrdbgn+Tk5AP0dEIIIYQQQoifu/ZeF3/5YCvnP7uG697cyOn/WsnzK6oY+M5ead9n9Y7OYKi2y5NfVlLSaOfmtzcNhlqBwJDz6ruc1HU68UMwVAPoc/t4/MsKnjxvHH87aQRWg4aLpqfT5XRzxoQk+lxeLpqWxq7VmVrVYEXRsclWXr10MtOzolHvXH66obYrGKrt8u6GRqo7+oZ9ljCNisOyo7l8VhaXzkjnuFHxQwoWCCHEr8VBDdYmT57MSy+9xOeff86///1vmpubmTZtGh0dHTQ3D/6jERsbG3JObGxs8FhzczNarZaIiIg99hnOrbfeSk9PT/BPXV3dHvsKIYQQQgghft1KGu18tKkppO3BRdupbO3d62u097mGtLm8/pCwLTPahFEbup1NvDWMggQz9gHPd0+nvsvJhrpu/vz+Vv72YQn3fV5GrEWPy+tnZk4MXf0urp2TwzWzs7j3tJFsbeqhpMmB+Tsz0Ny+oYEegNfn/5/PpVId9LkaQghxUB3U/woee+yxnHbaaYwcOZI5c+bw8ccfA4NLPndRfGcOcyAQGNL2Xf+rj06nw2KxhPwRQgghhBBCiOG0OgaGtAUCgzPZ9tbUDNuQtglpEVh2W05a19XPTcfkMSrJilqpYGpGJDcclcMNb24kyqgbcn6CNYy23YoItDlcfLalmfkvF6FQgAIFfn+AQAD+tWwHb6ytp6vfQ0Wrg62NPVR39BEIBBiTbEX1ncIDk9IjSbUZ9/r5hBDi1+qQ+nrBaDQycuRIysvLg/uufXfmWWtra3AWW1xcHG63m66urj32EUIIIYQQQogfIyni273DjiqIZcGcbK6bk01i+N7vKTY6OZx/nDGaCMPgbLFZudHcfcpI0qJMzMqJBsDt9XPHRyXEW8P43WEZGLRqajr7cbh8VLb3BvsB6NRKLp+ZyQfFjSH3KW91YNCqeHBRGdOybPxrWSWPLa2gvNXBJTPS+WxLM4tKWjj+0eUc+/DXPP9NFek2Ay/+diIFCRZMOjVnTUzmnlNHYvnOzDYhhBBDHfTiBbtzuVyUlpZy2GGHkZ6eTlxcHIsWLWLs2LEAuN1uli1bxr333gvA+PHj0Wg0LFq0iDPPPBOApqYmtmzZwn333XfQnkMIIYQQQgjxy1EQb+aqI7JQKWHVjk4eXlwOQHVHHzfNzSMhQr/Hc+s6+1i1o5OtjXamZ9l46/JpKBUQZw3DoB38deze00eytWGwoFpWjInPt7YALQCMSLRwwZRU3iyq41/nj2dcagRev59os46a9l4cLm/I/WbmRPPkl5XYB7xcdlgml8/MxBcIoFEp+XxLM9UdfUzNHJw95/T4+NtHpWTHmjksO5rXk8Lpc3mJMuvQyBJPIYTYKwc1WLvxxhs58cQTSUlJobW1lTvuuAO73c6FF16IQqFgwYIF3HXXXWRnZ5Odnc1dd92FwWDg3HPPBcBqtXLJJZdwww03YLPZiIyM5MYbbwwuLRVCCCGEEEKIH6rdMcDWRgedfS5m5Uaxakcna6o6g8ff3dDIyKRwLp6ePuz5rY4BrvlPMRvqugF4YUU1Z09M5s8nFgxW+axoo6K1lxnZ0UxIi6C00cE1s7PY0mCnvLWXiakRNPQMsLqqk3tPG8X7Gxp5t7gheP0Fc7IZmxwevP5RBTFEGbUAxFnCSIsy8P7GBt7dMDirTaGA6+bk8Pqa2pBxrt7RyWHZ0Vj0GpmlJoQQ++igBmv19fWcc845tLe3Ex0dzZQpU1i1ahWpqakA3HzzzTidTq688kq6urqYPHkyCxcuxGw2B6/x0EMPoVarOfPMM3E6ncyePZsXXngBlUq1p9sKIYQQQgghxPdqd7j447tbWFjSEmwbXP6pp6HbGWz77/p6ZmRGYQpTkxAeOnNtS0NPMPTa5fW1dZw2PokFr2+goXtw77aXV9VyxcwMVu/oYGuTg+vn5nDq2ESWlrZQkGilur2PilYHalXoPmiPLCnnwqmpXLiz+ucb6+qoaOvDH4A7TykkLcrEX04cwVkTU+jsc2Ez6vjT+1to7AndMy7WMnT/NiGEEHtHEQgMU9P5V8Zut2O1Wunp6ZFCBkIIIYQQQgi+Lm/jN8+uCWkzalVcMDWNJ5dVBtuOLYyjpqOfFvsA9542iiPyYoKFAN5aV8dNb28acu2nfzOey14uCn5Osxk4c0IyWrWSPpcXl9dPuEHDvZ+V7SwuEI7XH+CKWZlc9ep6/Lv9Bnf3KSNZV9PBO+sbMenUPHbOGGItYeTGWYYUJHB5ffxj4Xae/mpHsC3CoOHN+VPJjjUjhBBi0L7kRIfUHmtCCCGEEEIIcSjo6HUPaetz+9Covw2rDFoVE9Ii+HTLYMG1y18p4uNrZpAbN/hLmE6twqRT07vbPmhpNkNIJVCDVsX5U1K565PSYGAWa9Fx2eEZ/O6wdP61bAfra7sB6B3w8OT54/lsSxMF8RZSbUYsYWpW7GjnvMkpTEyLJMYURkGCFYBW+wArd3Tw0aZGMqNNHD8ynssOy2BcSgRfbGshI9rErNxoCdWEEOJHkGBNCCGEEEIIIb4jPco4pC0l0sCsnBhizGF4fH56XV4eW1oRPO71B6hu7wsGaz6/nxvm5vB+cSPbmu1MTIvkhFHxmHQqtGoFI+KtnDg6no82NYXMQmuxu6jvclLZ4uDVSyfT5/ISZwkjO9bE+8UNTEiN5OHF5bT1uoi16Lj6yGzeWFvLq6trMevUvHXFVNJsBh5bWs7Lqwb3U1tEKy+trOGty6dyTGEcxxTGHdgXKIQQvxJS6kUIIYQQQgjxq9fR62L1jg6+qWinucdJXpyZB04fhVk3OBch1Wbgt9PT+NeX5YxMsDA2OZx3iurp7veEXGdXpc/Ba7pZUdlOlEnL/MMzmZ5pw6hVs7nBzpPnjifarOO1NXUUJlq5ZEZoAYQW+wC3HJfPtEwbc0fEMSo5HL1WzcjEcP76YQltva6d/Vzc+XEps3JjAHC4vKys7KC6vZ9XVocWKeh3+1i8255xQgghfjyZsSaEEEIIIYT4Vavp6OO6N4qDSy5TIvU8c+EE4sPDOGdyConhesINGjQqBbFWPSc/uYIwtYrfTk+jorU3WODg8OwoWuwD9PS7sRq0NPQ48frgtHGJbG20815xAwnhei6dkc5fP9xKbedgEYSK1l5m58cwNdPGysoOAM4cn4TPH2BddRdpUQaizWEANHQ7cfv8IeN3enwodttOrc3hwuX1Mdxu2nWd/fv57QkhxK+bzFgTQgghhBBC/Kp9UdYaDNUAajudPP9NNQQCFCZaKW91cO3rxVz7ejFef4BjRsTh9Ph44stKZuZEc/nMDB48YzTHj4ynoWeAqo4+AE4anYBjwM2ikmaeXFZJTUc/Kys7uPLV9Zw5ISVkDEu3tTIpLRKlAu4+pZA31tUz75/fcMZTKznrqZWUNdsBsOg1wz6DRvXtr3bTMm3EWfXEW8OG9DtqhCwBFUKI/UmCNSGEEEIIIcSvkt8fwO3xsby8fcix1Ts6USmVfLyxkTaHm/87Pp/kCAP/WVNHfrwF9c6Km99UtDM9K4r3NzZyy38389Ci7dz27hZKGnsYlRTOH47N58NNTSHXdnn9qFUKvlO0k8npkbxx2RQ6+93BgggAO9r7eWxpBS6vj9xYE8cUxoacd8yIWIpqujBqVfxt3gjGJEcQawnjifPGkRypB0CtVHD1kVlMSovYH69OCCHETrIUVAghhBBCCPGrUt3WS2mzg7eK6jluZBxjUyJYXNoa0md6lo3b3ttCZdvg7LNFJc388bh87vi4lIrWXhIj9Pj8Ac6fksL62m6WbW8Lnru10c6/v97BFTMzKa7rRq1UDlm+GaZRsftKzdPGJjImOZynvqpkW3PvkDF/tb2Nrj43cVY9f51XyKljk9jR3kdWtInsWCO9Az5MYWpSIg0odq4LHZsSwTtXTKOxy4lRpyYtyhgys00IIcSPJ8GaEEIIIYQQ4lfB4/OzuKSF2s5+7v50GwatioxoIyMTrUzPtPHNzv3NcmNN5MdbghU1AfwBKKrpIjfWTLw1jIL4FGq7+rjnszLGpYRzzews/rm0Iljdc2VlJyeNTuCNtXWcPj6J19Z8ey2LXk2MWcdfTizA7fXT3utibkEcKGB5RTsTUiOHjH1UkjW4DDTWEsbcvVzSGWMOI8Y8dEmoEEKI/UOCNSGEEEIIIcSvQnmLg483NbG91QGA1xdAp1axvdlBjCWM6+ZkE2Bw2eSHmxqHnO8PBEi1GZiSEcmf3t9Kfddg8YFN9T1kx5g4a2Iy/1lTB0BhooVet4/y1l7y4y1cMzuLopouEqx6jsyPoa6zn7s+2UaCNYxzJ6fgDwxWFJ2dF4t9wENurJmylsFxWvRqbjw6N6TiqBBCiEOD/JdZCCGEEEII8Yvg9fmpaO2lpqOPCKOOnFgT4QZt8HhNRz8GnRq70wuAx+8nKSKMQACeX1FNv9sHgFmn5u7TRpIba+Gr7W3saB9cDnr8yHgGPD5Km+zBUG2X8tZeThgVDwwGYedPSeVfyyo5pjCODzY2EqZRUhBvpbyll0npkayp6gSgsWeAAbePrGgjACeMiuc/a2qZmB7BcaPi0aoUzMiKYmRS+AF9d0IIIX4YCdaEEEIIIYQQPzsdvS76XD6izTr0WhUwWFnzilfX49u5HvP08UncelweNqMOGKyo+dX2No4pjGNlZQdnTEhi4dZWNCoFd55cyFNf7cDnD3DmxGQeWrSdtl4XJ45K4LzJKcRawxhwe1lY0kJhonXYMcVZw/jT8flkx5rY2mBn1Y5Ozp6YzO+PyGJTfTe5cWZmZEXx5JeVrNoZrAF09ntAAW+tq+P5b6oxalVcOD2NpIgwUiKNRO4cvxBCiEOPBGtCCCGEEEKInw2Xx8eqHR0U13fj9QXQa5TMzInGrNdwyzubgqEawNtF9Zw8JoEZ2dEA5MWZyY834/P5ufSwdG56e1Ow78KSFh4+awx2p4e/fLiVwM7LvLq6lrMmJPHG2jp0GiVnT0yhsWcAq15Dj9MTPD/GrCPSqCUz2khZSy++ACgV8PraOoxaFTmxZr4sa8Vm0oWEagCH50Tx5ro67vm0LNi2tqaLpy8Yz5jkofutCSGEOHRISRghhBBCCCHEz0JPv4e11Z1sqOvmma+qeGxpBe8VN1Le2kt5Sy9d/Z4h55Q2OVhY0kxHr4ueAQ8njUlkSqaNpdtah/T9oLgRhZJgqLbL+xsbmZUXw+YGO1+Vt6NUwnVHZTM104ZFr2ZOfgy3HZdPjCWMCWk2AgF4Z309Nx2di1Wvoc/to9k+wDWzc7DsrNwJg8HbpTPSyYox8eiSiiHjeeKLSvpc3v3z8oQQQhwQMmNNCCGEEEIIcUgKBAI09wygVCqINGp5e30dVr2GhxeXA6BVKZk3OpG1VV0kROhJDNfT0B2695nH5+eyl4r47fQ0Klt7+aq8ncJEC3GWYSplKkCBYkhzhEGLfefstM+3NuPyRDOnIJYrZ2WwrclBv8fLyCQrGdEmAAriLVR39PHs8irOmJBEmEaFUavik81NLN3WyqnjErkiKZPRSVYyY0w0dQ/g8vqH3Ncx4MHrH9ouhBDi0CEz1oQQQgghhBCHnHaHi38tq2TuQ19x9MNf8dKKarbW99BqdwX7XDgtjddW1/Dqmloe/6KCyw5Px2YcLFagViq4dEY6y7a3AfDiimrGpUYAsLXRzpQMG4rvZGgnj0kgwqAh2hy6p9n5U1L5ZHNT8HNOnIk+lxe700t+vIXTxyUHQzUYrAj69G/Go1OreG55FVVtgwUL9BolObFmCuItzMqNpiDBik6tIj48jFPGJg55BxdNS8Oq1w5pF0IIceiQGWtCCCGEEEKIQ87Ckmbu/ezbPcf+/nEpN8zNIdKoCbYZdSoaewYA8PkDqJVKLpiaitcfQKtSYg5Ts6qqAwB/APw713gGAvDG2jpuOy6f4rpuAE4clcA/FpVR09HP/JkZAHh8AQriLby4ogr7wOCSzOkZNuIseu75dBtunx+dWskDZ4wm1hKGWjU4b0GrVnFUQRzjUiLod/uIMevQaVSMSY7A4/MTplGFPKtOreLqI7Pw+vx8sLERjUrJ7w5L5+gRcQfgzQohhNifJFgTQgghhBBCHDBOt4/2XhdGnWqvq1t297t5fW3dkPb1NV1cdlgG2bEmBjw+bKZvr3fi6ASeXV7Fjva+kHP+ftIIepw7CASgs9cdbC9v7eWbinaeOH8cfS4vxz28nNbewdlwjy6pIEyj5Iajcrnz41LOmZTM9KxolEoF+XFmLn+liF01ElxePze8uZG8ODPZseaQe9tMOmy7fVYpFaiUoaHaLqk2I/eePoprZmejUipIijCgUg5dliqEEOLQIsGaEEIIIYQQ4oAoa7Zz32dlLC1rJd1m4PZ5hUzPitpjYNTR66Ks2YFSqSDKNDSES4rQs73FwaUz0qnp6KfVPkCMWYfH5+ewrCjeLqofck4AuG5ODhlRRv75ZXmwPTE8jJuOzkWvUdPQ5QyGarsMePy0Ogbo93h5aPG359196kj83ylu4Pb5aex2DgnW9pVOrQpZUiqEEOLQJ8GaEEIIIYQQYr/r6HVxzevFlDU7ANjR3s+f39/CY+eMJcYSRuxuxQN6+j0U1XZSVNOFTq0i3WZkRlYU31S0Bzf1N2pVTM2M4qvtbUzPimJ1VSdbGnq4fk4OsdYwylocmHVqHN+poqlTK/m/9zZzxcws5h+eyVWzsul3+8iINhJv1QMQYw5jXEo462u7Q84dkWAJKWYwIt5Cus045FkVCoYNAoUQQvzyKQKB7xaT/vWx2+1YrVZ6enqwWCwHezhCCCGEEEL87BXXdnHyEyuCn8+YkIRBo+Ltono0aiUL5mRz0uhETGFqnvyykgcXbQ/2nZ5loyDeTIRBh2PAS7hBgz8Q4F/LdtCzszrndUfl8Pw3VYxKtBJvDSPVZkSrVnLHx6XB60xJj2ROQSwvr6qhocvJI2ePoc3h4svtrZw+LplpWbbg8tSSxh6ueX0DFa19aFVKrjoik9PGJeH1B6ho60WnVpIba8akU/PkskoeW1oRvM8fjs3jomlpQ/ZOE0II8fO0LzmRzFgTQgghhBBC7Hc6jQqFYrBQQIxZR6xZxz+/qBw86PZx+wclhOs1xIfreXRJeci531R0cP7kVB5YWIbd6eH08ck8uawypM+rq2o4fmQ8E1IjWFTSwoOLtjMtI5LHzx3HjvZeTDo1XX1u2ntddPS68foD9Di9vLSyhpFJVn7/nw384Zg85s/MQKFQUJBg5Y3LplLf5SQQCDDg8dHe5yIr2kxaVOgstfmHZ3JEbgxNdieJVj05cWYJ1YQQ4ldKebAHIIQQQgghhPjlSY8ycO6kFABOGZvIuxsah/R5ZXVtMPT6rsq2Xv42bwSXz8xgwOMbcrzH6SE9ysjnW1v4ZEszXn+Aryo6uOq19aiVClrsAywta0OBgl6XF5VSgUWvxj7gwaQbnF/wyJJy6jr7g9eMNGppsQ9w1tOrOPvfqzn58RXc82kp7d/Zf80UpmZcagTHj0xgTEoEBq3MVxBCiF8rCdaEEEIIIYQQ+12YRs21s7N59OwxTEyPxBw2NHyKMGjQqCArxvSdc5V4/QGWlrWRGmlibGrEkHPPmJBEZrSRhSXNQ4453T4mp0cyOT2Cp76qRKVU8Idj8li7o5M/HJtPUoSek8ck4vb5g3u4AVR39LPgjeKQtldW17KxrvtHvAkhhBC/ZBKsCSGEEEIIIQ6IGEsY88Ykkhqh59RxiSHHlAo4fXwya6u6uPzwDKZm2FAoIC/OxC3H5PHqqlpqO/v5x+Iylm9v465TRpISacCsU3PmhGQ0KiWrdnQSbtAOua9eq+auT7YxMtHKvaeN4vZ5BQx4fDTZB7jxrY3c+1kZZS127jy5kHjrt0UUGrud9LuHzo4r/k5RAyGEEGIXmbMshBBCCCGEGKKr382A24fNpEWr/uH7h3m8ft7ZUI9Fr+XvJ41gXXUXKKAw0cpdn5RybGEcvW4PapWCK2dlMTLJwjWvFeP2+RmRYOGr7W2UNjlYVNrKn0/IZ0ujnS/LWqls6yPKpOWqIzL5+0ffFixIsIaRF2ci1Wagxe7i6BGxaFRKPt7cxMKSlmC/0iYHX5a1MXdEbLAtwqBFqYDvrkxNjx5aCVQIIYQACdaEEEIIIYQQu3G6vXxV3s59n22jqWeA4wrjmD8zk+xY8/88t6HLSUVrL2EaBYnheuKsetp6XTz/TQ1HFcRy/Kh4SprseP0B3i8e3HPt319X8cjZY/i6vJ3lFe3cODcHfyDAWROS2NHWF1yWecHUVJ74spLy1t7g/dp73ehUSh44YxQVrYMFC5RKyIwx88yFE4P9vD4/n21p4buWbGuh3ZEdrAyaGWPk6iOzeWS3YgrZMSYmDLMUVQghhAAJ1oQQQgghhBC7WVvdxfyXi4Kf317fwPrabv5z2RRiLWF7PG9bs52LX1jLqeOS6Oh10dXnZk5+LGlRRo4bGce7GxqZmmELCcYAfP4AzT0DANw4N5dpmTaOGRGPUgErdnSQGKFncnokCdYwPt3SFHJuTowJvVbFPxZux+nxkRFl4oTR8ZQ320mJNAT7qVVKcuNMrK/tCjk/JdIYspRUp1bx2+lpTEyLpLSph3irntHJ4STvdi0hhBBidxKsCSGEEEII8SvW5/KyrdlBdXsf4QYNVe19KBQQ2G055I72PspbHHsM1rw+Py98U83JoxNZUdFORrQJo07NH/67GV8gwEljErhoWhqdfW7USkVIFVCdWklqpIH7ThvJy6tqseo1zM2PIcaqJz3aRGefi5WVHTy2tILLZ2ZS1jy4hDMtyshZE5O59MW1waWbRbVdNPY4+ccZo4eM8cwJyXy0sQmHywswWNDg2DxivvNM4QYtM7KjmJEd9SPfrBBCiF8DCdaEEEIIIYT4FfL5A7i9Xl5ZVcPdn5YF23NiTMw/PIN/LdsR0t/t9WN3eqhq76XV4SLKpGNUUjgqpYJel5fKtl4mT07lxZXVTMuK4p9LK4LnvrehkRuOyiEhPIy/nFjAnZ+UMuDxY9SquPrIbP7ywVZOHptIdXsfjy0tx6hTMW90Ij6/nye/3EFxXReFiVY+2NhIc4+Ty2dl8szXVVS0Oobsh9bUM4B7t6qeu4xNieDNy6dQVNONy+NjVHI4Y5PD9+s7FUII8esjwZoQQgghhBC/Im6vn6KaLl5YUYVjwMv0rCimZdpYUdkBwPbWXmbmxmDVa+hxegAwalVEm3WsqGznj+9uobPPjVal5Ma5OZw1MRmTTs3lMzMoaXSQH2+hqKZryH0/3NTI8xdN5Ollldw0N5dupwefP8DzK6posbv4qryNCWmRfF3exo62PlZWtmPQqIi16DDq1Ly1rp7cODNnTUxhR1sfbQ4X5jDNsM8YaRpaKRQgP95Kfrx1P71JIYQQQoI1IYQQQgghflU21HZx7jOrgks9V1R2cP1ROWyq76F35zLJ2s5e/nR8Pnqdis5eF2k2EzUdffz1o1I6+9wAuH1+7vp0GzmxZmZkR9Hn8lPSZCfGrMMUpmElHSH3LYi3YDNpibGGsXJHB4tLW0OOR5vC6O53M3dELCsq2vH4/BTEW3hpZQ21nf0AFNV0Udbs4K5TCnn3ymn4/QFSIvXUdjqD1zl1bCKZ0aYD9fqEEEKIEBKsCSGEEEII8Svy5rq6kP3TAN4vbuDIvBg+2NhIms3AuZNSeXRJBcX13YxOsnLtnGx8fmhzuIZcr6qjD5NOzfLyNmo7+zksO4qCBAtLSlvo2BnCmXRqLp6RTphGzXEjE4g2h/F1eXuw4qdWpeSw7CiWV7STFWMiOcLA51tbSIowBEO1XXpdXrz+AIkRgwUFXvjtJJZtb2NTfQ8zc6KZkhGJUSe/5gghhPhpyL84QgghhBBC/Ip4htl/zOsPMDE9Ao1KwdEj4rjxrU209Q6GaOtru7n+jY08ef44jFoVfW5fyLkRBg0LS5sJBPzcfEwe5S0O6jr6eeCMUbT3utFpVOTHmcmONQfvnxap56GzRlPR2ovXF2BcagQ9/W7iLDr8AciKMfHUVzuCs+O+K0yjCv49I9pEhsxQE0IIcZBIsCaEEEIIIcSvyJkTk/lgU1NI23mTU3nhm2rcPj/Ts6KCodou3U4PdR19zJ+ZyUOLtwdnvB0zIg6NSklyhAGbUcf9n29jS4M9eN79p49i3uiE4Ocdbb385YMtRBi1fLK5GdPOmWWPLq3gkhlpKBQKXlpRzSUz0gEoabIzPdPGN5XfLitNtRlweULDPSGEEOJgkWBNCCGEEEKIX5HxaRE8f9FEHv+igl6Xl9PHJ7G6qpPKtj4AVErFkHOUCuhyevlwYyM3HJWLy+tDq1ayqa4btVLBHR+X8vsjs0JCNUuYmseWVjA100bSzmWbdV39nDYuiVdW1wIE93QDWFPVhc2k5YJpaXyyuRkY3A/ubycVMjLJytZGO5nRJqx6DS+sqOa4kfHodpu5JoQQQhwMEqwJIYQQQgjxK6LXqDkiL4b8eDMN3QOsrepkYLcZYL0DXo7Mi2Hptm+LC0zLjCLVZqSqvY8HFpYF2+eNTqDF4cLrD+DcuUQ0yqTlkhnptPe68foD1Hb0E2/VU9PRx2NLKtje4uC8yakU13WHjOvE0fFMzYgizqrjiNxoXB4/Jp2as/69Eo1SSUa0iY82NdLe6+bqI7MkVBNCCHFIkGBNCCGEEEKIX6DSJjvFdd04PT5GJVopTLQG9ybb1mRn/itF1HQMFgaYkx/DmROSeXNdHX/9sISHzx7N4TlRbG9xkBNrZkxSBH95fzMPnTWGZ7/eQWPPAHNHxHJUfiyN3QP4/IHgss75MzP5x8IyBjyDe7m9sqqGF347keaeAZrtA9gHvLT3uThjQhLvbWgA4PwpqcwbnUicNQyAaPPg/zrdXi47LJNHlpQHg7h4q44Td1teKoQQQhxMEqwJIYQQQgjxC7OlvofTn1oRDLcA/nHGaE4bn4TT7eOBhWXBUA1gcWkr18zOQqdW4vL62d7cy9cVbWREmfjPmlpSIwz8Zloar62pJcqsIzfewlfb23ltdS0vXTwJpQKWbmvl/47PZ3NDT8h9ff4AT35ZiUap4KiCWFrtLt5aV09enJkb5+YyMS2CkUnhaFTKIc+h16q5dEY6UzMjaehyEm7Q0upwsaWhB58/QG6sGeUwS1eFEEKIn4oEa0IIIYQQQvxCbGno4cuyVuq7nCyYncPCkmbW13YD8JcPtjIhLQK1UsFX29uHnFve0ktWjIkZWVHUdvVTVNNNUc3gude+WcyzF05k5W5FBHapau/juQsn8pcPt7K2uhP3MFVHO/vcpNoMPP9NNVcfmYVBq2Jbs4P1tV38dnrasKHaLma9hklpNj7oaeTSl9YFCydoVAqeuXAiM3Oi9/1FCSGEEPuJBGtCCCGEEEIcwjxePzva++jscxFn1ZNmM6BQDJ2ltbGum7OeXhkyW+z6o3Koau+jq99Dr8tLR6+b7BgTIxItbNgZuEUYNBw/KoEZWTaae5z4AwGe+mpHyLXtTi9qpSI4o213/gDMyovh3SQrvS4vWxvtfFHWFtLnyLwYXlldA8Dy8nYmpkWyakcHJ45O4JEl5RyeHc2oJCt67fC/nlR39PGH/24KhmoAHl+Am9/eyPtXTSfOqt/r9ymEEELsT3v+akgIIYQQQghxUNmdbp76qpLjHv2ac/69muMe+ZoPNzUNOyvszXV1IaEawMsra4L7kU1ICcfp8bGopIXfH5GFXqNiUnokF01L4/OtzVz7ejFVHf0UJoYPufZ5k1NwurxcPCM9pD3WosPt9bOysp1et48Um5FpmVHcdUoh0SYdVr2GS2akU9/lxO4crAAabdZxbGEc183J4W8flvD4F5Wc9fQq3i9uJLB7crab+i7nkGcDaLG7aOwe2Kt3KYQQQhwIMmNNCCGEEEKIQ9TG+h4eWLg9+Nnp8bHg9Q18ePUMRiRYg+1+f4CK1t4h57f3ubDqNaRHGTm6MJ7znlkNQKRRy7Wzs0iIMHDNfzYE+7+4oga9RsV9p43kj+9uwesPcMmMdLY3Ozj32TUcVRDLDXNzqO3oJ8qkRadRUdHq4LU1tXh9fl68eBIZ0SbOnZzKnIJYatr7ufaNDcHwS6VUcOHUVG54ayPNdlfIWP/2UQmTM2ykRxmHPIc5bPhfW1RKRbBoghBCCHEwyIw1IYQQQgghDlHrqruGtPkDUNXWF9KmVCo4Y0LSkL6zc2NIsxm55Zhc7v+8LNje2efmtTV1LC9vG3LOa2tqmZEdxSfXHsabl08hP97M1xWDe7ItKmnhoUXbWV/bRbhBO1jYwGakqr2Pui5nyB5sMeYwRidbefissVwyI40rZmXy1vypGHSqIaEaQL/bR5tjaDtAZrSJI3KH7qV2/uQU0qIMw54jhBBC/BTk6x0hhBBCCCEOUTEW3bDtxmFmaR2eHc3F09N4cWUNPn+ASemR3HJsHv9aVkFCuAG3L3QpZa/LO+xMsCijjpqOfvQaFSMSrPy3qCHkuD8AlW19mMPUnDwmkUeWlAePVbWHBn5a9eBy00npkcG2ilYHWpVyyHg0KgURBs2wz2vRa/jrvBEUrKvjlVW1aFQKfndYBvPGJKBVq4Y9RwghhPgpSLAmhBBCCCHEIWpiWiRWvYYepyfYlhtrIj/eHNLP4/MTZdLxh2PzOGdSCv1uHy6vj+eWV2EJ05IXZ0GhIGTz/84+N9kxZix6dXD/M4AzJyZzyYvr6Hf7uGpWJnlxoffaRalQDClyMC3T9j+fKc1m5PdHZvHgou0h7VcdkUXaMMtAd0mxGblxbi6/mZKGUjk4I04IIYQ42CRYE0IIIYQQ4hCVHGHguYsm8MmmJopquzkiN5qjR8RhM+kob3HQ0eeip9+DPxBgW7ODw7KjGZsSwX/W1LK2upPMaBNev5/ufjcXTEnlxZU1wWvr1Eo8Pj83HJVDe68bx4CXnFgTr66upd/tA+DxLyt5/XdTSIrQU9/lDJ47b3QCPr+fmTnRLNvehl6j4ro52YxPjRzyDN+lVim5YGoq2bEmXl1VQyAA501JYWpGFBrV9+9Uo1AoiLNKoCaEEOLQIcGaEEIIIYQQh6DtLQ7u+2wbBq2aOKsOm1GDy+tnTXUn3U43/W4f17+5ke5+DwoFXH54BgoFbKjtpK6zj8ZuJ+8XNwJg0ql5+Owx5MSaWVXVgVWvJSlcz7+/2sFvZ6Tz36J6/nZyIb97aR3+7xTmLGnq4ZVLJrOupouKVgcmnYYtjT3c9t5WpmXauOWYPI7MiyYn1oxCodirZws3aDm2MJ6j8mNRAKr/EagJIYQQhyr5F0wIIYQQQohDgH+3RKvF7mT1jg4K4i0clh1Fi93F3II4wg0aYsw6vtjWxu0flNDdP7hENBCAJ5ftoL7LybWvF5NiM7J2t8IHvS4vjywuZ3lFGymRRtJsBow6FX89qRCX18cZE5OpbO0dEqoBRJvDSIsycvr4JC6ens7m+m4+29IMwIbabkYlWcmNs+x1qLY7tUopoZoQQoifNZmxJoQQQgghxEHU0uNkWXk77xTVc/yoeGwGDWuqu0KWbd44Nwenx8enW5qJNumYlRtNbWf/kGs19QxwythESpscQ45ta7YzPcvG419UBNuuPyqHR5aU4/MHuGRGOmk2A9Ud31431WZgdJI1+DnGEsa9p4/ikhYHvQNeUqOMZHzPvmhCCCHEL50Ea0IIIYQQQhwkTreXh5eU8581dUxMi2BNVSe5cWbeKqpn3ugEwjQqlm1vJdVm4P/e28rRI+JYtr2VCakRQ/Y9AwjXa/D6AiQNU+1zUlokm+p7gp8VCjDpVPh2TlN7YUU18w/PwO3zU9vRz+QMG0fkRpNiCw3Owg1aJqX/7yIFQgghxK+BzLsWQgghhBDiIKlq7+f94kbGJoczOy+Wjzc3Ea7XcOWsLLY22vlqexvHjYxHoVDQ4/RQ0epgRIKVJ76s4NZj8zBqVcFrnT4+CZ1GxbPLdxCmUXP+lBSUO1dnZkQZ+d3hGfS5Bqt/xlp0PH7OOLTqb8/3+QM88WUl66o7uf+MUVwyI52MaNNP+j6EEEKInxuZsSaEEEIIIcRBMuD1cvGMdDbWdRNh1KBWKtCqlTywsCzY5/lvqsmNNWMzallf283/HZ9PcV03L66o5sGzxtDc48QSpsHrD3DXJ6UEgIA/gEmn5prZ2QQC4BjwUtnWy8NnjcEx4CXaoiPeqqeus5+5I2JZuLUFgKQIPX87qRCrXnuQ3ogQQgjx86IIBALDbFH662K327FarfT09GCxWA72cIQQQgghxC9AeYudrY0O1td0kRtnZlpWFOm77Ufm8vr4+4clvLK6FoCTxySi1yioaOsLKTwAEGvWcfu8ESx4oxiVUsFF09KwGbVY9Bq6+ty8uqaWmp17o11/VA6RRg3/997WkGs8c8EE5hTEDhln74CHHe19uL1+UiINxFjC9verEEIIIX5W9iUn+kEz1rq7u3n77beprKzkpptuIjIykvXr1xMbG0tiYuIPGrQQQgghhBCHOp8/gNvnR69R7bFPv9vL19vb+GxLC+8WNwTbUyL1vHzJZFJ37llW3d7Ha2tqg8c/3NTICxdN4JElFUOu2dnvJtKk4ZrZ2Qx4fIxIsPDmunpqOvq4aFoayREGTDo1Z4xPIt6qZ/4rRSHnKxSQED58YGYK0zAqKXxfXoMQQgghdtrnPdY2bdpETk4O9957Lw888ADd3d0AvPvuu9x66637e3xCCCGEEEIcdIFAgOLaLm5+eyOnP7mCfy2rpKajb9i+62u62Nbs4L2NDSHttZ1OtjR8WzzA4wvg323tyFkTktlQ28O8MQlDrnnCqATu/KgUgMeWVuDy+lm6rZXKtj7+8sFWfP4A8dYwIo1aKtt6MetCvz+/YEoqmbJfmhBCCLHf7fOMteuvv56LLrqI++67D7PZHGw/9thjOffcc/fr4IQQQgghhDgUbGno4aynV+Hy+gHY2mjny22tPHz2GJrtAwx4/KRHGYi16HlzbR1p0UaG23Clq98T/HuUScv4lAiKars4fmQ8VTtnsM3IimLBnGzeLqqn1+Xl+JHxqFVKNjXYmZEdjUalYE1VJ4dnR/FVeTv+AKzc0cGoRCtlLQ5eXlnDlUdk0tDlpLPfzXGF8UzOiET3PbPshBBCCPHD7HOwtnbtWp566qkh7YmJiTQ3N++XQQkhhBBCCHEoWbqtNRiq7ZIVa+bP729lYcngxv8J1jD+fcEEOp1uIpw6Yi06WuyukHMyoweXgfr9Aeq7nJw6LpHTxieiUSm56e1NACyvaGdTQzdHj4hjZk409362jbpOJwAKQIGCV1fXcvnMDI4qiGN9bSe5cRZSIvQsLWvD5w/w6upa/nJCATOyo9BrpV6ZEEIIcaDs81LQsLAw7Hb7kPaysjKio6N/8EDuvvtuFAoFCxYsCLYFAgFuv/12EhIS0Ov1zJo1i61bQzdhdblcXH311URFRWE0Gpk3bx719fU/eBxCCCGEEEJ8V4sjNCAz6dRYwtTBUA2gsWeAOz4p4ZLp6by1ro7LZ2aSsbNYgUWv5s8nFDAmORyAZdvbOO+Z1dz23hb++O4Wlpe3c8Ko+OC17E4vb62rZ3uLIxiqaVQKRieHc/GMNE4Zm0hmtInMKAMb63p4dEk5V762geK6bh4/bxwfXDWdo0bESagmhBBCHGD7HKyddNJJ/O1vf8PjGZzGrlAoqK2t5Q9/+AOnnXbaDxrE2rVrefrppxk1alRI+3333ceDDz7IP//5T9auXUtcXBxHHXUUDocj2GfBggW8++67vP766yxfvpze3l5OOOEEfD7fDxqLEEIIIYQQ3zUnPybkc0a0ka2N337ZnBlt5KiCWOo6ncRb9UzJsHHPp9sYlxrB1UdmMf/wDGbnx6DXqmnsdnLDWxtDZsC9v7GRgvjQqmNhGiUJVj1WvYZxKeE8cvZY1lZ38ta6ejxeH7EWHc9+U8WO9j763YM/+1a09lLW7CDSpDuAb0MIIYQQu+xzsPbAAw/Q1tZGTEwMTqeTmTNnkpWVhdls5s4779znAfT29nLeeefx73//m4iIiGB7IBDg4Ycf5rbbbuPUU0+lsLCQF198kf7+fl577TUAenp6ePbZZ/nHP/7BnDlzGDt2LK+88gqbN29m8eLF+zwWIYQQQgghhjMuJYKbjs5Fqxr88dnj8zM2JRytSsnNR+cyKimcus5+ZuVGEwDuP30Uz144gSnpkczIjuL0ccnBaqDtvS46+9xD7qFSKsiJHeyTHKnn5qPz2FDbyZPnj+OqIzJJtRmYnR/Lo2eP4caj81hS0sqSbW1DrlOYaD1wL0IIIYQQIfZ5brjFYmH58uUsXbqU9evX4/f7GTduHHPmzPlBA7jqqqs4/vjjmTNnDnfccUewvaqqiubmZubOnRts0+l0zJw5kxUrVjB//nyKiorweDwhfRISEigsLGTFihUcffTRw97T5XLhcn07nX+4pa1CCCGEEELsEm7QMv/wDI4eEYdjwEN8eBi9A14IwEsra2i2DwCwrdnBxrpunrlwIjajlvZeF6+sqiE5wsBxI+OJtehQKcBm1NLxnXDNHwgwMzua208spNvpoc0+wKy8WO74qIQd7X1cdngGOTFmThg9WDX03CmpLCxtoalnIHiNi6enMSIhdOabEEIIIQ6cH7zpwpFHHsmRRx4JQHd39w+6xuuvv8769etZu3btkGO7CiHExsaGtMfGxlJTUxPso9VqQ2a67erzfYUU7r77bv7617/+oDELIYQQQohfvuaeAba3OHB6vKTZjGREm9ColGTFmL7tZIG8+L5gqLbL5gY7G2u72djQzeNfVAbbX1hRzUNnjuHOT0q4YlYm939eFlwOevzIeCxhaian27j7k1Liw/XYnR6WV7Tj9Q+WFw0E4Nb/bmZkkpVUm5HcODNvXT6VkkY79gEPqZFGcuPNWMI0B/4FCSGEEAL4AcHavffeS1paGmeddRYAZ555Ju+88w5xcXF88sknjB49eq+uU1dXx7XXXsvChQsJCwvbYz+FQhHyORAIDGn7rv/V59Zbb+X6668Pfrbb7SQnJ+/VuIUQQgghxC9baZOd3720jvquwaIBaqWCO08p5OQxieg0qpC+WvXwO6t0Oz28vLImpK3f7aOopovLDs/g8S8quWJWJkatipGJVgoSrFj0GnpdHnyBAB9sbBxyTY1KicPlpal7ILisNCnCQFKEYX88thBCCCF+gH3eY+2pp54KhlCLFi1i0aJFfPrppxx77LHcdNNNe32doqIiWltbGT9+PGq1GrVazbJly3j00UdRq9XBmWrfnXnW2toaPBYXF4fb7aarq2uPfYaj0+mwWCwhf4QQQgghhHA4PfzpvS3BUA3A6w9wyzubKW12DOmfE2sixhxaKCA7xoRapaDPPbSYltvno67TSUK4nocXl3PnJ9tQKBRY9IOzzEw6DdfMzh5y3mHZUWyq7x7sEyaVPoUQQohDxT4Ha01NTcFg7aOPPuLMM89k7ty53HzzzcMu6dyT2bNns3nzZoqLi4N/JkyYwHnnnUdxcTEZGRnExcWxaNGi4Dlut5tly5Yxbdo0AMaPH49Gownp09TUxJYtW4J9hBBCCCGE+C6708P2Fgc1HX34dy61BKjr6mddTdew55Q2Dd2XNynCwMNnj+HEUfFkRhs5Y3wSJ4xO4KWV1Zy0cy+03cVZ9USZdOTFmYNttZ39IX2mZth48eKJHJ4dxYgEC787LIOMaBOLS1uZNyaBzGjTdy8rhBBCiINkn7/uioiIoK6ujuTkZD777LNgwYFAIIDPN/RbuT0xm80UFhaGtBmNRmw2W7B9wYIF3HXXXWRnZ5Odnc1dd92FwWDg3HPPBcBqtXLJJZdwww03YLPZiIyM5MYbb2TkyJE/uJiCEEIIIYT4ZdtU301lq4OG7sG90bRqBaeMTSLaHPa924mo9nAoMVyPY8BDTqyZtdWdvFVUj16j4sa5uQSAz7Y0YzNpOXdSCp9sbuKYEXHU7Ram6b+zvFSnUTEzJ4aMaBNflbXx2NJyel0+rp2dzRkTktBrVQghhBDi0LDPwdqpp57KueeeS3Z2Nh0dHRx77LEAFBcXk5WVtV8Hd/PNN+N0Ornyyivp6upi8uTJLFy4ELP522/4HnroIdRqNWeeeSZOp5PZs2fzwgsvoFLJDxxCCCGEEOJbPn+Ar8vbeKeonvZeN2NTwul1eUmPMvLp5mYumJZGSqSBw7Oj+Kq8PeRcpQLyE6zDXre2o5/JGTa2Ntrpd/uYnmXjN1NSae4ZoKTRzvlTUul2unnyy0ocLi/nTEphecXg9bUqJXnx5mGvmxxh4OxJKRyRF4NCAXGW7w/+hBBCCPHTUwQCgcD/7vYtj8fDI488Ql1dHRdddBFjx44F4OGHH8ZkMnHppZcekIEeSHa7HavVSk9Pj+y3JoQQQgjxM9TmGKCytY9+j484i47MaNOQQgMrK9s5/9k1+HZb+jkjK4pUmwElCq6ek0WMOYzyFgfXvl5Myc6lnwativtOH8XRI+LQqIbupPLn97fw0soaCuItFCZaqevsZ1VVBy9cNJFHlpSzvrY72DfBGsa5k1N4YOF2VEoF/zx3LEcXxKFUSmAmhBBCHCr2JSfa52Dtl0iCNSGEEEKIn6dAIMCKyg5ueWdTsOCAQgGnjElkwZxsUnZWz3R5fFzxynqWlrUOucb1R+UQCAQ4c0Iy8eF6ADp6XVS29TLg8ZMcaSDNZtjjbLF7P93Gk8sqh7RfMzubI3Kj2FTfw6KSViakRTA2OZxvKjuIt4YxMS2S/HgLKgnVhBBCiEPKvuREP6ikUGVlJQ8//DClpaUoFAry8/NZsGABGRkZP2jAQgghhBBC/BAb63v47fNrcfv8wbZAAP67oYHufg8PnT0aq15Lv9vH9tahVT13SYzQE2MJC362mXTYTLo99t/d7PwY/vVVJbt/Xa1TK9GplTy4sJwbj86hMNFKmFpFapSRmbkx+/6gQgghhDgk7XNV0M8//5yCggLWrFnDqFGjKCwsZPXq1RQUFIRU5xRCCCGEEOJA8vr8/LeoPiRU293SslbKW3oBsOo1HDMibkgfpQLy4sxMTIv8wTPHRiWF8+hZY0mJNACQG2vmD8fmUdHqIDnSwClPrOC0J1dy/GPLuf2DLTTbB37QfYQQQghx6NnnYO0Pf/gD1113HatXr+bBBx/koYceYvXq1SxYsIBbbrnlQIxRCCGEEEKIIbr63Xy6tfl7+1R39AGgVCo4a2IySRH6kOO3HJOHRgE9Tg8/dIcUrVqJVq1kckYkVx+ZRU6siXs/3UZmtJnX1tSy25ZuvF3UwNLSlh90HyGEEEIcevZ5KWhpaSlvvvnmkPaLL76Yhx9+eH+MSQghhBBCiO9V19mPvd+NTv393xN7fAE6+1xEGnVY9BqevXAC21sctPe6yYgyUtPRz5cVHUQaNHh9AcalRvyg8aTaDPx3fUOwMEJurJnNDT3D9n15VQ0njUnEqPtBu7IIIYQQ4hCyz/+aR0dHU1xcTHZ2dkh7cXExMTGyX4QQQgghhDhwmnqcvLehgSe/rESnUXH8yHheWFE9bF+lAtp7XXy2uQmbOYxtO6t8JkUYMGiVPP9NNV9ubwv2f3dDA89eOIHMGPM+jysj2sQ9p47k5nc2EQiALxBAvYelpWqlgj3UQRBCCCHEz8w+B2u/+93vuOyyy9ixYwfTpk1DoVCwfPly7r33Xm644YYDMUYhhBBCCCHo6nNz58elfLSpabBhwItVryHOEjbsvmVnT0xhVWU7Z05M4fevrcfjG5xNZtSquOe0USGhGkB1Rz9fl7f/oGBNq1Yyb3QCeXFmtjU7UCoUmMPUfLy5aUjf305Px6CV2WpCCCHEL8E+/4v+pz/9CbPZzD/+8Q9uvfVWABISErj99tu55ppr9vsAhRBCCCGEANjW4vg2VNvpyS8ruWFuDjvaenl3QyNun5+kCD1nTEhmc303M3NieHRJeTBUA+hz+6jt7B/2Hmuru7hoevoPGp9Oo2JkUjgjk8IB6B3wcvu8Au7+ZBsurx+VUsHvDkvn8JzoH3R9IYQQQhx69jlYUygUXHfddVx33XU4HIMly83mff9WTwghhBBCiH2xoqI95HNenJmTxiTQ3utmTHI4R+bHMOD2sWJHJ08vq6TP7WN2fiyVbX1DrrWnAqCZMUa+qWgjOcJIis3wo8ZrClNzwZQ0Ds+OptXhIsKgJSPKiOZ/7AsnhBBCiJ+PHzUHXQI1IYQQQgjxU7EPeIJ/jzJpOXF0Avd+VhZs06mVPHvhBFIjDfzu8Ay2tzgIBAL88dg8OvvdvF/cSFPP4JJRvz/A7LwYlmxrDZ6fFKEnEIA31tbj9vi49fh8Um3GHzVmpVJBRrSJjGjTj7qOEEIIIQ5NexWsjR07FsVe7rC6fv36HzUgIYQQQgghhjMxNZIXV9QAcNKYRJ75ekfI8fkzM3hsaQWrqzoBODIvhmb7AI8uqcCkU/O7w9JZWNJCY7eT9GgT8eF6RieH09TjJMqkw+sL8OSXlQBcPiuTFRUdPzpYE0IIIcQv214FayeffPIBHoYQQgghhPg56HN58QUCWMI0B/Q+3f1udrT14fH5SbUZiLPqGZlkJdKoJUytZGxKOAatCo1KSVFNF43dTuo6ncFQDWDptlYSrGEkhutp6Hby0OJynr1gAtEWHZ29LvrdXh5evJ1Ioxa704vb5w+eq1TAmupOzpmcckCfUwghhBA/b4pAIBD4391+2ex2O1arlZ6eHiwWy8EejhBCCCHEIae5Z4Cvytt4YUU1Lo+PsyYmM7cgjrSo/T+jq7zFwYI3itnaaAcgPcrIHScXMi4lnB1tfSyvaOehxdsZ8AwGYbPzYzhmRBwVrb1o1Uq2NNhZtr0VfwCSI/WMT4nkveIGAK6dnYVBq+bE0QnYnR6Of2w5Pn/oj8PjUyOwhGmYmRvFRdN+WCEDIYQQQvx87UtOtNc7p3Z1dfHYY49ht9uHHOvp6dnjMSGEEEII8fPW1efm7x9t5ea3N1HSaKeyrY+7PtnG/JfXUbeH6po/VL/by50flwZDNYCq9j7u+LiEzQ09qFUK7v+8LBiqASwpbaWr38Mnm5t4bGkFjgEPj54zlkump5Mba6atdyDYV6lUcven23hw0XYSI/Q8dNZoNKpvtzyJt4Zx3Mg4tjX3cFiWVO8UQgghxPfb6+IF//znP9m0aRNXX331kGNWq5Wvv/4au93Obbfdtl8HKIQQQgghDq6yFjsfb24epr2XopoukiN/XPXM3TV2O/lye9uQ9tImB1saeoi36vH6hy64WLi1maRIA3VdTtbVdFHaZOea2dnEWHQU1XQBEGvRMeD2AfB2UT0XTUvjuMJ48uIslDU7cHl99Lu8+PwBXr5kMpkxUnBACCGEEN9vr4O1d955h3/84x97PD5//nxuvPFGCdaEEEIIIX5hatr3PCtt2fY2Th6buN/upVIq0aqUIfudASgUoFUrUe6hnpbNpKW+yxn83Of20d3v4fkVVVxzZDYpkQYseg3/XFoR7NPd70GtUpITayYnVqrdCyGEEGLf7fVS0MrKSrKzs/d4PDs7m8rKyv0yKCGEEEIIcegw6vb8XWxShH6/3is5Qs95U4YWDJiTH4taoeSr8nayvjOTTKmASemRIctHAbqdbsw6DW8V1TPg8fHgou3BwE6nVhJn1e3XsQshhBDi12evZ6ypVCoaGxtJSRm+MlJjYyNK5V7ndEIIIYQQ4mciP96CQauif+cyyl0UCpiVG7Nf76VWKfndYRno1EpeXFGDPxDg+FHxHD8ynkteXIdSAdfMzqa5Z4BvKttJtxk5bmQ8/1o29AvehHA97X0udBolTk/o2P9wbB7pUbLUUwghhBA/zl4Ha2PHjuW9995jypQpwx5/9913GTt27H4bmBBCCCGEODRkxph47qKJXPXqejr63AAYtCruOXUkIxP3f0X1hHA9txyTx8ljEnG4PISpVZQ2Dc5G8wfg4cXlpNoMjEuJYEZWFFkxJlodrpBrTMu0UdXeRyAAF09PC85y06lVHDcyjjEpEaj2tK5UCCGEEGIv7XWw9vvf/56zzz6bpKQkrrjiClQqFQA+n48nnniChx56iNdee+2ADVQIIYQQQhw8UzJsvDl/KjvaegkQIDvWQprNgEKhwD7goaSxh+r2fiKMWgoTrCT+yCWiCoWCvPhvQ7sm+0DI8ZqOfmo6+pmRFcXYlAj+e+U01lV3Ud/VT7xVz6b6bj7e3MQ1s7M4flQCsZYwDs/Zv7PrhBBCCCEUgUBgaFmlPbjtttu4++67MZvNZGRkoFAoqKyspLe3l5tuuol77rnnQI71gLHb7VitVnp6erBY9v+3rkIIIYQQP3erdnTwZVkrdZ1OsmJMTMmIZFK6DafbyxdlbZS3OthQ283yinYyooz8+4IJZETvv6WWDV1OLnh2NZXtfcG2KJOWN+dPHXIfl8dHQ48TtUJBUoQBpcxME0IIIcQ+2JecaJ+CNYA1a9bw6quvUlFRQSAQICcnh3PPPZdJkyb9qEEfTBKsCSGEEELs2fqaLi55cS1d/Z5gW26smftPH8lnW1t4dnkVLq+fqZk2DsuK4v6FZSyYnc2FU1MJN+6/AgE72npZWNLC0m2tTMmI5PiRCeTGSTVPIYQQQuxfBzRY+yWSYE0IIYQQYngDHi+fbm6msr0PrUrJloYeFpa0YNapuXZONnd8XBrSf1J6JEatGpfXx83H5JIfZ0GnUe3x+g1dTkqaeuhz+ciMNpIbZ0ar3nN/gEAggEIhs9CEEEIIcWBIsLaPJFgTQgghhBiqz+XlP2tqueuTUvw7f2KckRVFepSRVscAtZ39lDY5hpx37exsvH4/8dYw1lR1kRltZHZ+LIWJ1pB+lW29XPLCWqo7+oHBKqMPnzWGeaMTJDgTQgghxEGzLznRXhcvEEIIIYQQvyw+f4Cq9l7aHG5sJi1JEWGoFEqUCgUtjgHqOvuHzEhbXtHOiAQLSoUCo3boj5IqpQKtWkmKxcANb20Mtj/3TTVvzJ9CXty3P5x+vKkpGKoBBALwx/9uZlSSlfSo/bc/mxBCCCHEgSLBmhBCCCHEr5DT7eXtonru+LgUl9ePRqXg90dkkR1rYklpK59ubuaSw9KHPXdFZQe5sUaOmJjMupqukGOnj0tiZIKF+a8UhbT3OD0sKW0NBmten58vy1qHXLvP7aPd4SY9aj89qBBCCCHEAaQ82AMQQgghhBA/reYeJ6urOvnT+1txef0AeHwBHlpcjt8PKqWS+TMzsBm1w54fYdRyyrhk3l5Xxx+OyaMw0UKqzcC1s7OZlRvNpoYevP6hu41UtvUG/65WKZmaaRvSJ0yjxGYa/r5CCCGEEIcaCdaEEEIIIX5FtjT0cNbTKylptA97fGuTner2Xh5aXE6304NJN3SBw7mTkulzeRjw+Hhw0XZyY81cOSuT/DgzSoUCp8fH74/I4qojstCpv/1xc3pm6DS0k0YnEmsJrRr65xMKSLMZ98OTCiGEEEIceHu1FHTcuHEsWbKEiIgIxo4d+72byZpMJkaMGMEf//hHkpOT99tAhRBCCCHEvmnqdrKjvRelQonNpGF9TRerqzo5bWwSBt3wlTd1aiVbdoZuz31TxQ1zc/h0cxNra7pIjjBw2eEZPLu8iq2Nds6akMylh2VQ2dbLyh0dOAa8LCn9dnlnjFnHNbOzeWBhGWdPTCZMG3rPnDgzb1w2lS2NPTgGPOTEmClIsKBUSuECIYQQQvw87FWwdtJJJ6HTDX6bePLJJ39vX5fLxZIlSzj//PNZtmzZjx6gEEIIIcQvkdfnxx8IoFUPH3D9WBvruvndS+todbgASLUZ+M2UVN4rbiQQgGcvnIAlTI19wBs8x6BVoVMr6Xf7ALA7vfztoxJm5kTzwkUTKWmyc9cnpcHjz6+oprYzBn8AxqdG8MDCspAxtDpctDtcPHbOWF5cUc3cEXFDxpkWZSQtSmaoCSGEEOLnSREIBIZugPEjVVZWMmLECAYGBvb3pQ+IfSmjKoQQQgjxY7TaB1hX08V/1tTiGPAyb0wCM7OjyYzZtyqYdZ399Lm8xFnDCDeE7knW2evijKdWUtnWF9I+KT0Sg0bFl9vbuHhaGofnRvPyqho21/eQH2/hlLEJPL+iho113SHnKRXw2Dljueq1DcOO5bqjcvD6/Dy2tGLIsbw4M8mRBixhGm49Lo8ok26YKwghhBBCHDr2JSc6IFVBMzMzaWlpORCXFkIIIYT42WpzDPDXD7fy8ebmYFtxXTcxFh0vXzyZ3Djz/7yGw+lhaVkrjy2poKKtl/x4M/ecOorRyeHBPnVdziGhWqRRS368hUlpEcSHh5EQrqekyU5nr5tZuTFUtffyx3e3cNURWUOCtVPHJvE9O4EQa9axp29qCxMtXDAljVSbEatB8z+fTwghhBDi52SvgrX/ta/a7tavXw+A1Wr94aMSQgghhPgF2ljXExKq7dJqd/HSymr+Om8EatWea0t197tZtr2NhVtbGJ8WwRkTknhldQ0Xv7CW938/naQIA3WdfXT3u1EoYNe6hGmZNiakRfDqqlpeXFHNxdPTWFPVSV6CmQ113WzYLUhbuq2V+04fxTtF9fS6vMzMiabN4cLl9Q9ZOgpg1qmp7eynd8DLyEQrmxt6gsfCNEp+MyWNUbuFfkIIIYQQvyR7Faztvq/awMAATzzxBAUFBUydOhWAVatWsXXrVq688soDMkghhBBCiF+CDzc17vHYm+vquHxmJsmRhmGPuzw+nvummkeXlAfbVEoF/3d8Pnd+XMqOtj5MOjVrqztptbu48+RC1lR1sbi0hRlZUdz3eRkKBcw/PIOCeAvPr6hmRnYUGpUCj+/b+WbFdd3MLYjh1HGJLCpp4eWVNThcXspaHPzx+Hz++N/N+Hd2VyrgyiMyef6balodLi6Zkc6xhXGs3NFBYYKV40bGMTIpfL+8OyGEEEKIQ9FeBWt/+ctfgn+/9NJLueaaa/j73/8+pE9dXd3+HZ0QQgghxC/IwM5N/4fj9Qfwf8/Wt1XtfTz+RegeZj5/gPc2NHDPaSPx+vw0dTvpd/t5cWUNTT0DZMeY+Ou8Aj7cOBjonTcphWXb21ApFWhVSj7c1Mjdp4zkn19UUN3RT1KEnoumpfHyqhpOHZfE4t0qfOo1KiINGq4/KhePz49Fr2bA4+fNdfXBAgnPLq8iMVzPP88dw9iUyB/zqoQQQgghfhb2vNZgD9566y0uuOCCIe3nn38+77zzzn4ZlBBCCCHEL9Hxo+L3eOyo/FhiLWF7PN7Z58bnHxq8lbU42Nbs4OIX17FyRyd/en8LTT2DBaTKWwf3TTs8JwaAWGsY25odfL61hVPHJbK2uguHy8vk9Eh+f2QW07OieGjRduKtYZS3OIL3UCjgipmZ5MRa+GRzE48uLcfu9HL/52VUtYfu5dbe60KjOjCVToUQQgghDjX7XLxAr9ezfPlysrOzQ9qXL19OWNiefxgUQgghhPi1G5caQUG8hZIme0i7Tq3kyiMyCdPsOZCKtYShUytxef0h7WNTItjW5CBhZ2j23UlvLq8fFIPh2K5zK9t6mZAWwSUz0nl7XR1nTUphZWUHtZ39XDwjnZRIAz1OD939HsINGk4ek8C0LBtatYrnfzuRsmYHaqUCq15Dj9MTcr8zxieRHy9V1oUQQgjx67DPwdqCBQu44oorKCoqYsqUKcDgHmvPPfccf/7zn/f7AIUQQgghfu7cXh872vpo73Vz96kjWV7ezgsrqulzezmuMI7fTE0LqerZ5/LS1e9Gq1LSYh+g2+lBpVBw3Zxs7vmsLNjPpFMztyCWv31UQmK4fo9LSfVqFUflxxKm/ja4e2NtHbEWHUflx+Ly+ClMtHDG+CReXllDdoyJWKuOBXOysRo0pEYa0O48N9YSFpxZ9/RvxnPVa+tp73UDMD3TxqWHZaBS7l3RKyGEEEKInztFIPA9m3nswZtvvskjjzxCaWkpAPn5+Vx77bWceeaZ+32APwW73Y7VaqWnpweLRb5hFUIIIcT+093v5vlvqvnnFxX4/AHUSgV/PiGfI/NiCRCgz+WjrstJjFlHdoyJshYH939expqqTrJjTZwxPokvt7WQlxDO3PwY2vvcVLf3YQrT4PH5eWjxduzOwUqdfzgmj3s+2xZyf7VSwfu/n05yhIHSph6e+HIHy7a3BY8btSqun5vDgMfPwpJmNtYNVvV8/bIpTMmw/c/na+x2UtvZT5hGSUaUCYtesx/fnhBCCCHET29fcqIfFKztSXFxMWPGjNlfl/vJSLAmhBBCiANlSWkLl7y4jqwYE+lRRuo7+xifGsmc/Fj63F4eXFROZVsvAI+ePYY//Hcz/bsVOVAq4MEzR/OXD0rQqpScNCYBx4CH9zc2ctPRufz9o9Jg36NHxDI108ZDi8rpcXqIt4Zx23H56DUq+j1envm6ioumpREAvi5vJ9KoJdKopaffTZQ5jLs+GbyWVa/ho6tn7LFCqRBCCCHEL9m+5ET7vBT0u3p6enj11Vd55pln2LhxIz7fnqtdCSGEEEL82ny5vZVbj82jpMlOWbOD8amRZMaYuPK19Xh9Ac6bkkJShJ5vKtopb+0NCdUA/AFYtr2dBGsYhYlWtGol6VFGkiIMLC5p5c6TC/nXV5XkxJjIiTXxzFdVnD4+iWiTlsxoE7e+u5n2XjdJEXrOmpjMdW9uxBKm5j+/mwwoUADLtrcFQzWLXs3j546VUE0IIYQQYi/84GBt6dKlPPvss7z77rukpqZy2mmn8eyzz+7PsQkhhBBC/OwdlhXNDW9uxOEaXK65rdlBhEHDA2eMprTJjk6tIjPKyOqqjiGFCXZxDHi45LB0HllSTkOXk6mZNv7vuHyufG09GdFGZufFUJBg5ea3NwHw7PIqAFIiDczOj+WNtXXUdw0u2UyzGaju6OcvH5Tw/G8nYg7TkBZt5LCcaOxOD8mRBgnVhBBCCCH20j4Fa/X19bzwwgs899xz9PX1ceaZZ+LxeHjnnXcoKCg4UGMUQgghhDikdfe76Xf7sJm06HZu8t/V52JdTRfra7tw+/xMzbTh9flZX9tNV7+HkiY7zy2vot/t4/+Ozycv1kz4HvYnOzIvlj+8sxmvf3AHj28qOqhs7eORs8dQ09HPiyuqcfuG7u5R29nPyWMTgp831fWQE2umuqOf4rpuepwezGEaDFo1hYnWA/BmhBBCCCF+2ZR72/G4446joKCAkpISHnvsMRobG3nssccO5NiEEEIIIQ5pfS4vn21p4rQnV3DkP77klrc3UdZsp6rNwVNf7eDuT7Zh0mm48ogsfP4AWrWSm+bmMjk9ku4+NzfNzeXoEXE8tGg7h+dGs63ZwS3H5KHeWVVToYAzxifR43QHQ7Vdmu0DlDTaibWEoVMrSbTqh4xPpVTsXOw5KD/ewo72PgDy4sxYwqTQgBBCCCHEj7HXM9YWLlzINddcwxVXXEF2dvaBHJMQQgghxE/C6fZS0mSnq89DtFnHiHgLavVef+/I6qoOLn9lffDze8WNrN7Ryf+dkM+zy6vIiDLiGPDw1Fc7gn2+qejgujnZGLQq/v5xCSeMSmBsSgR5cWZGJlhx+/w8fu5YXF4/nX0ukiMMvFnUMOz9wzQq6rv6+eu8EfgDASIMGrr6PcHjZ4xPYnFpCwBRJi158WbeKx681o1H50oFTyGEEEKIH2mvg7Wvv/6a5557jgkTJpCXl8dvfvMbzjrrrAM5NiGEEEKIA6aiZXBW2bsbGvD6A+jUSi6YmsZvpqaQEmn8n+cPeHw8vVtgtkuTfQC704vHF+CoEXH8e5g+r6yq5ZrZWfgD8MHGRm49Ng9zmIYlpa1sqO0iO9bMiaPi+duHJejUSv5wXD6fb20OuUaEQUNypJ4dbX1c9doGLpiaysUz0unsc9Nqd1GYaCHGHEZ+vAVLmBo/AR5fWsnh2VHMn5nJhNSIH/7yhBBCCCEEsA/B2tSpU5k6dSqPPPIIr7/+Os899xzXX389fr+fRYsWkZycjNlsPpBjFUIIIYTYL1xeH49/WcG7Gxp3a/Pz7693oNcquW5ODgqF4nuuAHWd/XT0uoc/qIA0m4F0m2HYggRtvS5aHa7gZ1OYmgc+L2NjfQ8AG+t7+LKslduOz+fhJeWUtzq4ZnYWL66oocfpoSDewhkTktjR2kd6tBGnx4fb5+fRpRVEGDREGrV8trUZnz/AX04s4JRxSXh9fmblxGDQqtFrVT/grQkhhBBCiO/a+7UOOxkMBi6++GKWL1/O5s2bueGGG7jnnnuIiYlh3rx5B2KMQgghhBD7VWmjnfeLG4c99szXVXy1vY1vKtrp6HUN28fj8/PqqloOz4keckylVNDV5+aWY/LodflIihi699mE1AgGPD6um5PNNbOzSArXU9HaG9KnvddNr9tHc88AgQD8Z00dp49P4u8njeCSGWl09rqYlB7B419U4vL6sRl1g/fu91DZ1ofPH0CpgLHJ4QCoVUpsJp2EakIIIYQQ+9E+B2u7y83N5b777qO+vp7//Oc/+2tMQgghhDhE+f1DK0/+HHX1e9jTo/S7fayr6WLBG8V8uLGRihYHPn+AQCBAfVc/m+q62Vrfw/ZWO2NTwjkyNyZ4bphGyfVH5fDmujr+/P5WbCYttxyTh2G3MMtm1HLRtDQae5w8vKScR5dUcMs7mwf3PAsLXUwQCARwef3YB7xkRBlJDNfzfnEjN7y1iae/ruKTzc1cMSuTe08dyZz8GJ65YAKJ4YNBXoI1jH9fMIERUu1TCCGEEOKAUQQCgV/GT8g/gt1ux2q10tPTg8ViOdjDEUIIIQ4p/a7BDf4/39rChtouEiP0nDo2kREJFqLMYQd7eD/IloYe5v1z+bDhmkGr4rbj8qnp7OetdXX0u32cMymFSekR3PjWJvrdPq6clYljwMvLq2q47/SRNPe48Pj8KBUK3i6qp6HbCcC1s7N5eVUN183JRqFQEKZWYgpTs63JzsNLKkLuG2nUcuq4RJ75ugoArUrJm/On4PT4cAx4ibXo+O0L6+jsC11+euq4RO4+ZSQ6zWB41+oYoLvfQ7heQ4zl5/n/jxBCCCHEwbQvOdFe77EmhBBCiF8fh9PDiyureWDh9mDbupou3i9u5IjcaO44eSSJwyx1PNRlx5o4bVwSbxXVDzl29sRk7E5PSGGCF1ZUU93ex5jkcFZUdmDUqXniy0oAajqc/PurHbh9Q/dSM2hVdPa5+dP7W5mdH8PsvBg+2dxEWXPvkL6dfW7iLGEoFJATY+LPJ45gdHJ4cK+3Tzc3DQnVAN7d0MAFU1IZkzJYjCDGHEbMzzTwFEIIIYT4uflRS0GFEEII8cu2trorJFTb3RdlbfxnbS0/x8nvOrWKBXOyOX9KCmqlYmebkt8dlkFOrJkXV9YMOefL7W1MSo/EuDMs22Xh1mZOHZc4pP/RI2Lx+v388bh8kiP1fLGtlThLGD5/AFPY8N9tpkcZeeWSSdx3+iimZ0WFFFCwD3iGPScQIKQQghBCCCGE+OnIjDUhhBBCDKt3wMOTyyq/t8+zX1dx6thEMqJNP9Gofph+l5etjT3saOsjAKiVCrJjTRxXGMecvFgae5w09QywuKSF7BjjsLPPYDDEGvD6Mem+/RGqvLWX0cnh/O6wDD7Y2IDXF+DMCcmMSLDwj0Xbqe3s57bj8nlk8eDfJ6RFolYqKKrpCrn2qCQrGpUCvx/y4oYuOUizGYcdU6RRi3kPQZ0QQgghhDiw5KcwIYQQQgyrq9/DuprO7+3j9Photg8c0sFam2OA55ZX8eSyb5d2TkiL4JgRcfxnTR0T0iJ4Y21d8NijSys4fXxSyFJQgDHJ4cRawrhyVibZsSZizLrgTLG3i+q58ogMLp+ZQWVbP+8XN/DM8h3cfHQeKiV09rm45dhctCoVDpeXaJOOh84czb+W7aC918UReTEUxJtJiTSQFjX8u8yLt3D2xGRe322sSgXMPzyDeKss/RRCCCGEOBgkWBNCCCHEsJSKwZldHt/3L/VUKRXfe/xgaHUMsKOtD6/XR5fTGxKqAayr7mJWTgy/PyKTxp4B5uTHsLi0FYD6LieBAJwzMZk3i+rx+QNMTo/kssMzuPGtjXT1ezDqVDxx7ji+Km9ndVUHoxLDsRnD+OuHJSH3ufOTUm46Opcvytr4v+Pz6e73kB1jQqNScvGLaxmfGsnEtAjWVncyIsHCOf9ezS3H5DGnIDZkVhyAVa/hxrm5HJYdxbLtbei1atJtBsYkh+8xjBNCCCGEEAeWVAVFqoIKIYQQw/F4/fzx3c3DbvC/S4xZxwe/n06c9eAXMChp7GHJtlYqWnrJjjXR0esm3hpGi2OAZ5dXD+mfG2smK8ZIrCWMpAg9WrWKhi4nr6yqQa1S8K/zxzPg9bG5voeEcD1/eGcT7t1CxmtmZ7GysoPzp6TS7/LwXnETq6uGzvD70wkF/P2jEmItOo4bGc8X21r51/nj0WlUbKrvpqajH4CXV9bQ1js4A+75iyZyRF7MHp+1q89Fj9ODVa8lwqj9kW9OCCGEEELsTqqCCiGEEOJH06iVnDMphfeKG/Y4a+3WY/MOiVBta2MPZz21il6XN9h2z6kjueuTUi6cljbsORFGDTNzY3ho0XaaegaAwVlh958xCrfHx9tF9Xxe0sx1c3Ko73KGhGoAzT0uWh0urn29mBlZNiL3EHCFqZXo1Epa7C6seg3VHf3c/ek2njx/HDq1igcXDS0O8dLKag7LjkKtGr7OVIRRR4RRtzevRgghhBBCHEAHtSrok08+yahRo7BYLFgsFqZOncqnn34aPB4IBLj99ttJSEhAr9cza9Ystm7dGnINl8vF1VdfTVRUFEajkXnz5lFfv+dv1oUQQgix98Ykh/PMBRMIN2hC2rUqJX86oYDZ+bEHaWShPtrYFAzV5o2O48nzxhFt1nHT0bnEmsPQa1RDzjljfDIfbWwMhmoAPU4P/1i4HW8A3llfj93p5Zmvq0gIH7qH2fvFDdxyTB6J4XqWV3QwPjUCxXdWxUabdahVCqJMgyHYrsPLtrexvqYbzx6KJPS5vPzqlxQIIYQQQvwMHNQZa0lJSdxzzz1kZWUB8OKLL3LSSSexYcMGRowYwX333ceDDz7ICy+8QE5ODnfccQdHHXUUZWVlmM1mABYsWMCHH37I66+/js1m44YbbuCEE06gqKgIlWroD9FCCCGE2HtKpYKZuTF8dPUMtjU56Oh1YdSpyY0zkx5l3OOMqv9lwONjS0MPS0pbqevs57CcKCal20iPGlr5sqPXRWefG4teQ6xl+E36tzT2APB/x+XR2DPADW9txOcPcOLoBGbmRPHkeeN47IsKimq6SIrQc+6kFLr73ayo7BhyrfLWXtRKBf4AJEfq+c2UNGo7+4m16Gixu4L9fP4ALo+P40fGYdCpSQoP497TRvHa6lpqO/uZlB7J2ORwmu0DtPe6SI8yBs+3GbU09ThJiTSgUQ3dx+68KalofuC7FUIIIYQQP51Dbo+1yMhI7r//fi6++GISEhJYsGABt9xyCzA4Oy02NpZ7772X+fPn09PTQ3R0NC+//DJnnXUWAI2NjSQnJ/PJJ59w9NFH79U9ZY81IYQQ4sfpHfBS0eqgsceJJUxDTpyZGPPwIZjb6+fd9fXc8t/NIe2xFh0vXTyZ3LjBL88CgQArKju47b3NVLcPBlt/mzeCI/Ji0KpDvzx7fU0tmxq6idBr8QUCKBUKvihrpbTJwcXTUrEatRTGW/D4A9R29vPI4nKOHRnP2urO4B5nu5h1am4+NpeFW1uYlB7Jw4vLUSkUXD83h0313ayt6iI7xshRI+J45usqGrqdAPzp+Hw+2NhIlFlHvCWMjfU9uL1+pmfZqGrvZ0pGJPd/XobXH+DGuTks297G1Udm4QvAjW9upKPPjVqp4HeHZ3DJjPTgLDchhBBCCPHT+lnusebz+Xjrrbfo6+tj6tSpVFVV0dzczNy5c4N9dDodM2fOZMWKFcyfP5+ioiI8Hk9In4SEBAoLC1mxYsUegzWXy4XL9e03zna7/cA9mBBCCPEL12of4KFF2/nP2rpgW3qUgX+dP57cuKE/iFS19/LH97YMaW+xu/j31zu4+5SRaNRKSpsd/Pb5tbh3Lpdssbu4/NX1vDV/KhPSIkPOzY4xkWoz8OHGJv67YbCS5/EjE5iWGUWkUcv9n5ehUyu5//RRZEQZMWjVfLK5iWuOzOaez7aFXOu309NwOL2cPCaRjj4XPn8AHwHu+XQbIxIszM6PYVxqOK+urg2GarmxJtKijBw/Mh61Skl5q4PTxydh0KroHfAyIsHCx5ubmZEdxYmjEni7qJ611V1sb+nl0sMy+PDqGTR2O7HoNaTbjGjUP362Wqt9gNrOfvQaFenRg88shBBCCCH2r4P+E9bmzZuZOnUqAwMDmEwm3n33XQoKClixYgUAsbGhe7fExsZSU1MDQHNzM1qtloiIiCF9mpub93jPu+++m7/+9a/7+UmEEEKIX6cvylpDQjWAqvZ+/vjfLTz/24lY9KH7s5W39OLzDz9hfnFpC9fNySYxwsC2JnswVJuVE834tAgSwvW0OVwsKW0h2qxjwONFpVTy6eYmtFoV/1lbS6w5DMeAh/eKG5g3OoEE6+DMOZfXz01vb+L2E0dwxoQkNColbp+fR84ew5fbWrG7vIxPiWBFZQfLK9o5bVwiY5LDQ8a3tdHO1kY7aTYjR+fHctzIeNIijWyo6+LGtzby+yOzKGu2kxxp4POtzZw+PonPS5pZXdXJ7LwYThiZwHPfVLGlcfBLvaKaLi49DBLC9SSE778iEFsbe5j/chH1XYPB33mTU7h2TvYeZxEKIYQQQogf5qAHa7m5uRQXF9Pd3c0777zDhRdeyLJly4LHFd/ZBTgQCAxp+67/1efWW2/l+uuvD3622+0kJyf/wCcQQgghfr16+t08/dWOYY8V1XZR1d7H6O+EU95hQrVwg4ZLZqRjd3p54stKxiaHE2cNQ6GA4wrj8foD+P0Bnv+mii0NdiamRTAtM4pRSVYGPG6iLGGUNtm54ahcajr7iDRoMejUPL+8imML44L3cXn9tDgGeHZ5FT5/AFOYmqkZNpQKaLEP8MDCMnYN7/3iRk4ak0CEQUNXvyd4jTCNkvRoI26vnzCNinEpVh5eUk5Xv4enlu3g7EnJoIDLZ2bwTUU7eo2aJ88bx9Jtbfzp/S30uX3Ba41LCf1ycH9wOD38+b2twVAN4NXVtUzPjOK4UfH7/X5CCCGEEL9mBz1Y02q1weIFEyZMYO3atTzyyCPBfdWam5uJj//2h8DW1tbgLLa4uDjcbjddXV0hs9ZaW1uZNm3aHu+p0+nQ6WTfEiGEEOLH8vgC2Ae8ezw+4PUNacuKMaFQwK5dXpUKuHZ2Nvd/Xkb/ztDp1dW1jEiwcMNROQSATzc3o1Mr2dpo58a5uayv7eKxpeVoVErOmpjM7LwYylscPLCwLHgfi17NVbOyiDRqsYSpg+P8z5pabjo6lye+rNxZFEHN8vKO4LLOXbz+AAE/PHTWGP6zupai2i5yYs1cOC2NddUdlDb1sqaqk4+vmcGDZ47m/97dQlFtF6+truWqI7L420elVLT2BsfS3DMQEqpFm3UckRf9g97792nrdVFU2zWk/ZvKdgnWhBBCCCH2s4MerH1XIBDA5XKRnp5OXFwcixYtYuzYsQC43W6WLVvGvffeC8D48ePRaDQsWrSIM888E4Cmpia2bNnCfffdd9CeQQghhPi1iDRqOXlMAv/+umrIMZtRS1K4YUh7VoyRBbOzeWhxOQCH50Tz8aamYKi2y9ZGO2dPTKbZPsDs/BieWraD4wrjWVTSzMb6wSqgLq+fl1bWMC4lnMWlrVw5KxONSolSAW29bmo7+3C6PSyYk8PSba0MeLwcPSKO/HgLT58/nu2tDkw6NXqNmhdWVIfcf0TC/7N33+GRlWUfx7/T+0xm0ntPNsn2XtllK72DiKgUEaUJ0lRsKIKIgiIgIAoI0jsLLGXZXXbZXrMlvfeezGR6e//IMrshwVcQpd2f6+K6yDnPKXPOH5v85n6e24o3FMZq0HDJolzOD2XT6/Iz6A4w6A2xqbYXgN7hAPPy43n4wll0uXyYdWr2tw7FQjUYqX773VmT+ebcbLY39FOUYmFeXjz5Seb/6PmPx6JXk2rT0zHkG7W9JFUaNAkhhBBCfNo+02DtJz/5CccffzyZmZm4XC6eeuop1q9fz5o1a1AoFFx99dXceuutFBYWUlhYyK233orRaOS8884DwGazcfHFF3PttdcSHx+Pw+HguuuuY9KkSSxfvvyz/GhCCCHEF8qQN0ht9zD97gDJVh0FSeZ/a7F7pVLBmdMzeHFPG73Dgdh2hQJuOX0i6fax64bpNWouXJDDpAwbj29tYn5+Are8VjFm3Lz8eBQKBWk2AyadivNmZ2E3aXhtf8eYsc39Hq5eXsidb1Xj8o9UpuXEG7loYS5GrYobn9/Pb06byKbaXn67pgqTTsWF83OYke3giid3c9b0DJZOSOLdym4A8hPNXLO8iKue3MMJk1NweoIYtGrOmZnB7uZBnt3ZevizKEmyjlTBWw2a2HpyBo2Km08p5Q9vV+MLRDh/bhbz8+PJsBs5aUra//tc/xOJFj23nDaRS/6xMzatNT/BxIKChP/qdYUQQgghvoo+02Ctq6uLb37zm3R0dGCz2Zg8eTJr1qxhxYoVANxwww14vV4uu+wyBgYGmDNnDm+99RYWiyV2jrvuugu1Ws0555yD1+tl2bJlPPLII6hUqs/qYwkhhBBfKC39Hn7+8gHWVfXEtp0/N5sfLCsk0fL/L50wIdXKU9+dx4bqbtZWdFOUbOakyWlMzoj7yGOC4SiRCFy0IJf6XveYdcymZ9nJSzDx06O6h07JtPGdhbnjni8n3sTvjwrVABr7PBxoHWJmjp1zZ2Xy4Hv11Pe6AXB6Q/xpbS2XHJPLtIw4/v5+IwsK4rl6eSGRaJS+4QBb6vrwBMO8tKed286YxONbm7jiyT1ctbQQAI1KwR1nTSE33jTmfuKMWr49P5eVZSmEI1FSrHrUqv+80+e/a3FRIi9fsZDabhcmrZrSNCsZ9rHVg0IIIYQQ4j+jiEaj47fl+gpxOp3YbDaGhoawWmWahBBCiK+OaDTKnW9X8+d3a8fs+/3ZUzhrRsanfk2PP8Tv3qzkQJuTM6anc+fb1Zw1I4P7NxxpgvCTEyZw6+uVY4699fSJPLSxIRaQxe71rMlc91z5mPGJFh3Xriyia8gXm3p6NKNWxV1fm8qlj+0atf0Hywt5fEsTfe6RKrwrlhZwz7u1GDQqnr50Lr2uAHaTBpNWTYpNP6bzKUAkEkWp/NcNl4QQQgghxOfPx8mJPndrrAkhhBDif6dzyMfD7zeOu+/h90c6app0n/zXBW8wjMsboMvpR6VUkh1vpHXAw+SMOBp7Pbj9YXqHA7T0e7l4YS5P72jhjOnpaD6iumt/2xC/Ob2M65/bH+t6OTUzjux4IyatalRzAIC8BBMJZi1D3uB4p8MTCNPS7+GOsyazpa4PdyDEtEw766q6Y6FaepyB/sPTXL89P5uceCPb6vv5wxNV+IIRZmbbue2MSRQmj1TUV3U6eWVfO9vq+1lZlszK0hRyEsZWtQkhhBBCiC8+CdaEEEKIr7BwNEowHBl3XyAUIRL5ZIXtg+4Am+v6GPIFeXJbM+VtI80GLp6fy6RMGz98Zh8A+UlmMuwGXtvfQYbdwC2nlbGptpdh//idRgsSzVR3uTmmMJFEiw6lAmq7h3n7YBffW5zPH96ujo1VKRVctDAHJTApzYZaqSD0oc8zLTOObQ39FCdbOKYwgQc3NvDWoS6Orue/YH42D26s53vH5HH8xBSqulzc9U41vuDIc9vZNMCvVx/iL+fPoMvp4xsPbYutN7ezaYBX9rXz92/PIsmqp6XfQ023K/ZZssaZRiqEEEIIIb44JFgTQgghvsKSrXrOmpHBP7c1j9l33pwsLONMcfxXBtwB9rUO8n5tL1b9yGL+dT3DFCaZOa4shenZdt6r6UapgEgUHt3cyA3HFdMx5ONQuxO7UcsLu9uYkW3n7JkZsSYBAKWpFsrSbHz38Z2YtGq+vySfOIOGOKMWvUZJnEHDH782hfdr+9BplOTEmzBrNdR0u7AY1PzylDJ+/vKB2IL+dqOGU6am8dT2ZnITTDy3q5XvL8nniW3N1PYMU5xsZllJMv5QmPu+MZ2bXznI/e/Vkx5n4PpVxaMaJbxX08t7Nd0MeUKjmjgAHGhzsqdlAL1axTXP7KP/cCVcnFHDoxfOZkpm3Md6xkIIIYQQ4vNDgjUhhBDiK0yjUnLhgly21PWNWrdsXp6D5SXJo8YGwxG6nD40KiXJVv2Ycw37Q9z9bk1saumxxYmcOCmV+86fzvu1vTzwXj2BcIQZ2XZuPG4C962v4/uL86nvceMNhLhwQQ46tZJIFHY0DmDQqLh+VTGeQBiLXk1BoolgOIxJq+bq5YVUdw3z800NseufMDGFZSVJlKZZePC9BjqGfMzPc3Di5FT+8GY1NoOaG4+fAFFwmLWYtCpaB7xcsiiPRIuOlaXJNPe7uXJpAXFGDT0uP/3uACk2C9/++w48h6eZtg16uXddLWfPzODvhz9rglnLoXbXR1b/Nfd5WV3eHgvVAAY9QX75ykEevWj2uGu0CSGEEEKIzz8J1oQQQoivuIIkM//8zhwOdTjpGw6QGqdnQop1VEfQht5hHthQzwu72zDr1fxgWSEnT0nFYRoZM+QJsL9tCIteza9PnYjDpGZDdS9P7mjmlCnp/HVjQ2x65a6mAQKhCL84qYSbV1fE1j97eV8H9503lVk5dnY0DhyuAutFoYCrlxXiC4Yx69R8c242vcMB/nZUqAbw+oFOVpYls3pfBx1DPgB2Ng3yzXk5nDMrkye3N3Pb4YYI583OYlFhPL5gGF8wzMwcByadmpk5jtj59jQP8MtXDnLRwtxYqPaB3uHAqLXnLj0mj75hP1Oy7GOer0alINWmZ1/r0Jh9e1oG6Xb5JVgTQgghhPiCkmBNCCGEEKTGGUiNM4y7b8gb4EfP72dbQz8A/e4Av3jlIGqVgknpNtoGvIQiEf6+qZE9LYMoFPCDZYXMznXgDoTZUtfLj46bwJ7mAfKTLCgUYNOrSYnT86152ahVSvrdAZItOp7e2cqykmSi0ZH1ydRKBV+blUleoolul58hT4gEs47mAc+499rc56G6azj2c3GKhZf2trO+qpvrVxVzz7paBj1BCpPNrK3sRqFQMOQNjmnQ4PQG+dWrh7h6RSGh8Nh15jQqBVMy4rhiaQEmrYqa7mFy4k1sqOrhupVF3Le+Dk8gjMOk5XuL86nsdJJg1o6ZJuowaTFqVR/rXQkhhBBCiM8PCdaEEEIIETPsC+IOhLEbNWjVI4FPfY87Fqod7b51dSwoSOCZnS0AfH12JlaDhg3VPfzxnRpuWFXMa+UdmHVqpmTGUZxi4f4N9fhDETQqBSdMSiXFqufP79aS5TBy+bH5bKjuZWNNL0uKk/jBskImpdtYV9XF/RvqqehwctfXpuIJhNCpx+8aajdpcQdG1j3TqBScMT2d37xWQTgaZXNdLzesLOa1/R20DXrpdwc5ZUoahcnmUefoGPTSPujhhuOK2dE4QNugl8VFCWyo7o2N+f6SArQqBY9uboQoXHJMHr97swqADLuBb8/LoTjFQn6iifvW1bG1oY8LFuRy11HNFQB+ckIJaR8RaAohhBBCiM8/RTQa/WTtvr5EnE4nNpuNoaEhrFbrZ307QgghxP+cPxhmW0M/d71TTX2Pm2UlSXxnYR6laVZ2NPRz9gNbxhxjN2o4aXIaj21tim27flUxdxwOmH52UgkmrZoup49dzQMUJJrJiTfxuzcrGfaPTK2cm+cgzqhlzYFOVpYm0+X0jZoyuaosmdYBL1WdLn5ywgQC4ShNfW58wTCRKLy8tz02dmpmHFcszWdjdS8atZJ4k5bHtjRh0qn52qxMNlT30D7oZVFhAvEmHX9+t5YfnzCB9kEPyVYDS4qT8ARCPLq5kUyHgUgE7n63FoBTpqSxpDgRGKlmsxk1tPR7iETBolPz6JYmmvtHV9FNTLPy2MWz6RkOcNOLB4hEoxw3MYXylkGUSgVnTs9gerYds06+5xRCCCGE+Dz5ODmR/CYnhBBCCHY1DfCtv2+P/fzC7jY2VPXw3Pfnk2LTkWE30DrgHXXMCZNSiTOouXJpAV1OP6vL2+kY9OIwael3B3CYtDy1vYW9LYN8b3E+vmCY8rZBfnJCCZvr+lhd3sHW+n5+csIE1hzoZG1lN5cekzcqWEuy6Jid68DlCwFRXtnbTlWXi+tWFuELhGPNDTLtBlKsOi58dCdLipKw6FU8tLEBjUrBRQtz+c3rFbE13up63KTa9Fy8MJc3DnSiVMBfNzZS3ekkyapndXkH93xjGre/URW7j0A4wqv7OlhX1R3bdtLkVJzeIKvKUvAcrpI72rA/RK87QFGyhb9fMJOOIR8mrZpLFuV9Sm9NCCGEEEJ81safRyGEEEKIr4xgKMLf328Ysz0QitDY6+aap/dy8cJccuKNsX2rypJZWJDA0ztb+fO7tWyt7+O6lcWk2nR4A2Hm5TtQK5Vsa+jnsiX5PLG9mQfeq+e5XW385MUDKBUK5uXFA6BTKylJsXDl0gIy7QZ+sKyQ4mQLGpWCpROSKU2xYtGpmZoZR5JFRzgS5fY1VWyo6cXtD+EPhUk063hoUyPRKKyr6mZ5STI6tZJlJcm8uKeND9fndwz5cJi0nDollWOLkzhzejoTM+Jo7vNwzYoi3L4QVv2R7x8nptlGhWoAq8s7mJ0bz/0b/Lin9QAAp8xJREFU6jhnVuaY57eqLIUbnyun2+nDotdQlGwh3S7TPoUQQgghvkykYk0IIYT4kuly+qjtHiYUjpAdbyInwTRmzJA3SE2Xi55hPwlmLbbDXSl1aiValRKXP8T5c7P58Qv7KUo28+bBTmblODh9WjqTM+LQa5Sc99C2WGDV3O/h0c0N3H7mZH57hgmLXk0oHOW6lUXYTVpOm5rOE9uacB/urvnKvnauXVnElvo+lMDXZmXyuzer8ATCqJUKLliQw2VL8vnJi/v5zqJcfrumklAkyp++No0NNT1Eo7C3ZZC9LYNctCCHzfW9XLAgh5Mmp2LVa6jtdvKj4yfgMGq58YXycZ9TQ6+btw510jscoCTVwjFFicwvTOBHz+/n6uUFnDQ5jT0tg0Sj4A+Fxz2HPxSmZcDL8WWpBEMR/rGlCaVCwdkzM+gd9rO7eZCa7mGSrPr//MUKIYQQQojPHQnWhBBCiC+RnU39XPHPPXQ6fQCYtCp+depELDo1nmCY4hQLDpOG29+o4oU9bbHjzp+bxe1nTqK534s3ECLRoqMgyYzdpGXNgZEGBMUpFix6Nc/vaiEr3hQL1VJtei4/Nh+XL8TFj+7Efbgb5hXHFvDcrlYa+zwkmLVct6qY37xWQSgycmAgFMGiU2MzavnB03tj5wtFojy0sYHbz5zE5HQbL+5uY3lJMm8c6OTdqi4eOH8Gz+xswekLMT8/ntruYVaXdzA/P4H5+fEcc8f62Oe66YQJTMmIG7f5QrJVx6AnCEBFh4u1Fd2kH24k8LeNjfz85BJuO2MSOxr6xw0nFQrQa1QcW5xIXqKJVWUpqJQKIlF482AnTX0ja67JarZCCCGEEF9eMhVUCCGE+ALzBsOEDwdVjb1uLnp4RyxUA3AHwlz33D7qe91c/fRezr5/M5tq+kaFagCPb20mEo2iVSmwGbQUJ1vY0TjAra9XsLt5kPdqemPrlE3OtMUqzxItOi6Yn4PbH+b2NVWx7f3uALe8dogzZ2QA0Dsc4LldrawqSwFGKuM0KiVXryikvtc9bvj0XnUvWrWKU6akxTpn2vQamvs9uPxBVAoFf1lfx+ryDubmOYg3aninoovso6as3vZGJcdPSkGrGv0rT3GyhX5PMBbyAayt6GJCqoVvzMlicXEiP3v5IE9sa6Yo2UxuvIEbVhWjVIyMVSkVXL4kn3Akws9PLsWkU5PpMPLWwS4efK8+FqrlxBspSBobygkhhBBCiC8HqVgTQgghvoCa+9y8fqCTV/e1U5Jq5fy52XQOeXH6xi6iH41CQ5+bNJsek07N2srucc4Idd1untnVgtMb4sfHTeAfWxrHnOeNA534g2GWFCehVMDZMzJ4dmcL07LsY84XiUIofCS4OtjuZOmEJNRKBTedUEIoGuX+9XVctDBn3Psx69VsrO6hY8jLnDwHdqOGOXnx3PhcOVevKGRLXR+pNj1z8uJZUZLE95/Yw5A3yA9XFHHr6xVEoiP38M+tTTz4rRlsqu2lutPF/PwEBjwBHtxYP+p6iRYdvmCYNQc6cZi0XL2siDUHO1EplZS3Obl4YS5LihPpdvmJN2lJsGhJNOtRHw7tkq16HvjmDJ7a0cKG6h6OKUzg67OzSLHJumpCCCGEEF9WEqwJIYQQXzADngA/emE/m+v6gJHA6tV97fzi5NKPPKZ/2E+mw0jrgJd4k3bcMXqtCl8gAoA7GMIXjIzab9CoKEmxEGfUsqG6mx+fUII/GGbIG0KnHr8I/oMKL4AEs5ZZ2XZSTinj0c2NVHcPc0xhAtnxJqx69ahQUKVUkJtg4ukdLbQP+bhgfg7fnJdNRccQCgX8enUFUzPjmJwRx5a6PqZkxNExNFKp9/jWJm48bgIalRJ3IEQ4EuUHT+7hu4vzyU8089r+DqZk2MZUyX1rXg43v3oITyBMnzvAb9dU8sevTeWvG+v4xpxsdBoVpWk2PvopQ2GyhZtOKOHqZYWYdGqURz8AIYQQQgjxpSPBmhBCCPEFU9s9HAvVPuAPRTBpx/9n/ZQpaczPj6euZ5iTJqeS5TDyzM4W/KEjwZnNoEGjVBAIj2zb2TjAosIENtb0AjA3z8HCgkRe2dvGkC/IipJk+ob95Cea6XX7yXAYUSkVsWmpMLK+29HZ1YXzc2noc7OjcYCzZ2ZSlGLhmZ3N3PhcOdesKGJdZTfbG/vJTzRz5vQMHtncGDu20+nj7rW1xBk1XLOiiPs31LG3ZZCaLhc/P7mUdyq6YmMb+zzU9bjZ2dRPY6+bD27pjjeruH5VMfvbhshyGPnBskI21vRg1Ko5Z2YGj2xuwhMY3aTg+d2txBm0TM8eW5H3UZRKBZbDzSCEEEIIIcSXmwRrQgghxBeMLzh+h8oBd4BTp6bx8t722LblJUl4gyF+9ML+2LapmTbuPW86/9zeRFWHi7l58czLj+fmVw/Fxmyu6+OXJ5ehVCjY1tDH4qJEbl9TFdv/+LZmlhQn4g1EOGNaOk9ub+amE0p4ekcLNd0uZuU4uGhBDuVtQ5w/J4sMu5E3D3bw9TlZnDY1DX84QmX7ENvq+3H6Qtz86iHuOGsyRSkWWvq93Pr6kSYH6XEGug+vGzfoCfL2oU4eu2g23mCEOIOGrHgjfcMB3j50ZIprslVHfY97zDPqd/sxaVW8tr8DvUbJWTMySDLraehzs7t5YMz4UDjCr0+bREGS+d99PUIIIYQQ4itEmhcIIYQQXzA58SZs41REZSeYWFaSxBVLCyhNtZKXYOK0qemjAieAvS1DHGgf4ltzs7nza1PQKEGnURE4qoJtZAZjlGOLE7nltIm8uq+dD1tf1cOsXDszcxycOyuTig4nlyzK5aFvzWRWThzvVffy2JYm1hzs5A9vV7GyLIU/vVOLLxjBqFXhDYW54tiC2DTSv26sx6BR8dahzlioZtKquPnUMhr73OQmmDhvdhbTsx009nmYkhlHdoIJhULBKVPTOGlyKnC4W6daiUU39vvD3AQzi4sTKUwy8615OdiNWv70bjUlKRamZNjGjD9vTraEakIIIYQQ4iNJxZoQQgjxBZPpMPLQt2dy9VN7aRv0YtGrue30SUSJolYomJPrYMWERDzBCIfaneOeo77HzSt72/nhiiLyk828ureNa1YU0jscIBKNkmzR88jmRk6akkZ529C4TREANEoFqVYdB9uGqOpy8W5lN33uAAB3njOF/CQzg94AKqWSf25rpm3Qi0mn4sKHdwIjlWWXHVtANBrFqFWxsDCBRYUJ1HQPo1UpyUkwcfk/d5Edb2JimpX3anpoHfBy43ETRt1Hht3IbWdO4htzsqjrcRNv0vKzk0q54fny2JjSVCu13cM09Xk4a0Y6587KpG3Qx8mT0+h0ejl+Yio2g4aNtb1Y9Gq+MTsbu1GmdAohhBBCiI8mwZoQQgjxGfAFwygVoFWrPtHxs3IcvHjZfOp6hglFovzy5YPU9Y5MfUyz6fntGZPYVNc3bmUbQE6CEU8gRDASoWPQT2qcntvXVGHRqVEowekNYdKqUCpgU00vJ0xKHbXmGUBOvJF3q3ooTDKz5mAnvcOBUfvDkSi3vH6IyFE9EBYVJNA77I/93OX0c9fb1cBIldza0iWUptqYnRsPQH3PME5fiB2No6dpFo5TRbanaZALH9kRW+dtTq6dxy+eQ3O/h2A4TF2Pm8e3NXHy5FSWl6RgM+qwGXUA1PUM89s1lczLi+eKYwvwBMK8sKdVqtWEEEIIIcS/JMGaEEII8T/UO+xnQ1UP/9jaiEmr4uKFeczNi8c0zrTFj+L2h2jodeMNhqnqdPHQpgZaB7wATM+KY35BAv2eIJsOL8x/3uwsntjeHDt+Zo6doiQL2Q4j1zy9D4AbjyvGYdLS7z4Sjv1geSET06zkxJswalX0uwO8Wt5ONAp5CSa+OS+b375RiUmn5vRpafxtU2PsWLVSQYpVz0+OL+GlvW0MeoIcV5ZCSaqF+l7PuJ9LqVDw4R6aOfEmbj9zMtc8vZfFRUnMzLGjVEAoEqVjyEuqzQCMrC9386sHCUeipMcZOGtGBpFolC6nl5OnpBIKR2jq93DOzEyKks1jAs3cBBNqpYIt9X1sqT/SGCI/0fRvvxchhBBCCPHVI8GaEEII8T8SjkR5bEsTf1pbE9u2ua6fu8+dyswcBylWPUrlkWipfdBL+6AXs15NboIJnVpFdaeTLfV9DHlDTMm0YdarY6HalUsLqO5ycd+6WvQaFefOyqTb5aeh180Nq4rxBMOYtCpsBg1XPLmHaZlxnD0zg2d3tnL32lq+e0weBq2KQU+A9DgDZWlW/rK+nuwEI0kWPctLkshLNBGNQvuQl9++UYk/FMEfCjAvL4GX9rTT5w7gMGm5flUxrYMeZmTFUZBkIhiO4g2E+c3rFXxnYR5KBUSio5/PmdPTSY8zjNqmVCo4YVIqhUlmHtvSxB1vHmmgMCndxr3nTSMr3oTLF6Kux02aTc/5c7P44zs1sa6nZ9T2cePxE5ia+dGdPQuSzNx73jSuf64cpy+EUavi5lPKmJBq/cTvWwghhBBCfPlJsCaEEEL8jzT1ubl/Q92Y7Xe+Xc3sXAc2g4Zvzs1GrVJQ2+3m7rU1LCxMgGiUrHgTBYlmLn18Fx1DIx0yFxclsLwkBYBZOXYqOpy8UzHSqMATCPP39xu5YmkB66t6YlVYly3J52+bGgDY0zLIsROSAPAGw/xpbQ2LChIIR6NY9Rp6nH7ijBoe2tiAUgE/XFnEO4e6OPChddumZNhw+QLcesZEfMEIdqOWf2xuZNXEFCo6h7Eb1Ty2tZnqLhcrSpMJhMLcfuZkfvnKQdyBkQ6n8/IcfH9JARr12L5KGpUSly/EkztaRm3f3zbExppevhFvIt6iZVFBAtOy40aFagAv7GljeUkyJxxubjAetUrJqomplKTZ6HH5cRg15BxujCCEEEIIIcRHkWBNCCGE+B8Zqe6KjNneNxzApFOjViq5Z10tSRY9lR1OTp2ahssXwuUL0TbopWfYz8lT0lhf1c3JU9LwhyJkOQxY9Wrm5sVz77raMed+t6Kbs2ZksK2hj1VlKRxoGxq1FlowPPp+pmXHsbG6l6JkMx1DPhItOnRqJf5QhD++XcNPTyyhdbCGQU8QALtRwzUritjbMoDNoCXRouPdym4mpFkxaVWUtw7xxv4hzpuTSbcrgC8YJtthpM/t56KFuWTYDagUCvrdASo6nVgMauJNujGfo2Vg/Omj66q6+cbcbExaNT85YQLvVHaP+4w31vb+y2DtA1kOI1kO4/87TgghhBBCCJBgTQghhPifSbPpmZ/nYFZuPAoFRKOgUirod/sJhSPs7XCxpb6Pq5YVcMKkFN481M2bBztjx8/MtnP2jAxyE3L4yYsHiEYh3qTlphNLaOn3olEpx4RKJp2KpRMSmZfn4IfP7sPtD4/aH2/SAiP3cf6cLMpSrfQNB7jksV1Eo5Bo1nH9qmJuea2CUCRKc7+HO8+eQtuglyjg9odpHfBQ3enCZtRy86uHRp374kW5TM2y09znwR+KMCHFysH2Ie5ZV8eVSwu4482qUUHf1csKuWJpAWrV6Mq1BPPYsA1gamZc7P9L0mwM+8fvXipNCIQQQgghxH+DBGtCCCHE/4jNqOXSxfl8/5+78RyeAqlSKrjt9ImY9SPTJQFSrXo8gcioUA1gZ9MASyck4fQFiR5en6zPHeDOt6u559xp+INh/np4mucHvjkvG5c/RLxJy43HTeCW1RUEwhFUSgVXLi0gzqjh7nOnolEpGfIFufTx3aOO7xn2s72hn4sW5JBg0TE53caAO0i3y8eUjDj8oQhqpYJzZmVx4SM7Rh3b5w7Q1Odhc10viwsT+fVpE3H5Qty/oQ6lYqRZwYc7id63vo6VZcmUptlGbS9NtTIjO45dTYOxbXFGDctLk0eNK0mzcd6cTJ7YdmTaaIJZyzGFCf/q1QghhBBCCPGJSLAmhBBCjCMQCtM26EOpgAy7EZXyP19rq8fl52cvHwBAq1ISCEcIR6Lc/Ooh/nTuVAAy7Aaseg2D3uC453AHwqiUo6u5upx+DnQ4sZs03HhcMS/sbsOsV3PqlDTW7O9kYroNjz+MRqHgxydMQK1UkGzVE41GMGjU/PTlg7QPerlkUd6413y3spufnDCBV/d1cKjdyewcBzOy7PQOB7jjrSq6nH6uXFoQC/uOVt3pItthIjXOgEKhwGrQcN2qYv6yvpaceCNXLi0YacrQ5eKVfe0EwhE2VPfg9AaZm38kDEuy6vnjudPYUtvHhuoeytKsLCtJojhldHMBs07NNcuLWFyUxOa6XvISTCwoSKAgyfLvviYhhBBCCCH+bRKsCSGEEB/S3OfmnnW1PL+7DZVCwYULcrhgQQ6pNsP/f/CHtA96OdA+xKA7QGa8kXNnZdHnDqBTKzFq1TyxrYn2IR9D3hCnTU0j3qzjhufL+f6SgnHPlxanJxSKUJpq5VDHkSYCWrWSm1+txmbQsKAgHn8wQpQoJ0xO5UDrEA+8V8/QUWHd5UsK2NMywJnTMzh1ahpqlRKzTjXuNRPMOnqHA5w8JY1bX6+gutPFWTPSWVvZQ5fTD4w0GBiv02dZ2sh9Okya2LYZWXHMy0vgmmf2xbYtKkzglClpdDl97Gwa4C8b6nj20nmjgrNMu5HMWUbOmZX5L595okXPqrIUVpWl/MtxQgghhBBC/KckWBNCCCGOEgxFuP+9ep7Z2QpAmCgPvFdPnFHzkWHX0ZzeIIFQBIdJS8uAh+89touKThcLCxLISTDy+OHpngAalYIbjpvAPe/W4g2GmJ5t5+cvHwRgbUUXF8zP4ZHNjbHxp05Jw6pX8+LuNs6Yns4ZpHPLaxWcPyeLPc2DAAx5g7y+f2QKac+wn/Q4PadOTcekVxMMRwhHoKpjCItBxbTMOH70QjnB8EgadtOJJVgNapze0euUfX9JPkPeALevqSQUieIOhEm06NnW0B8bs+ZAJ9+en8PD7x+533SbngWFCRh1arbU9fH12dkANPd7+ePamlHX2FjTy69OKWPIF+TOt6uJRqGiwzmmIk0IIYQQQojPEwnWhBBCiKO0D3l5ZkfLmO0Pv9/IWTMySLTo8QfD9HsCGDUqbMaRxf8HPQE2VPdw77paBjxBTp+WzqwcO1VdLgDm5cdzx5tVo84ZDEd5cEM931ucR5bdyK9eq4jt29MySBS4bmUxDpOGOKOGQDhC24CXQW+IRzY3Mi8vnocvmIlJp+apce55WpadTLuBO9+uprprGACTVsUvTynD7Q/y2/V1o8b/6Z0afnlKKS/vaWdjbS8JZi3nzs5iU00PhckWCpLMHGx3ctLkVGq6h0kwa2NrpB3qcGLSqbjhuGIMahVGnYq+4QBXPbmHYDjKQ9+eGbtOnztA+MOlbYDTF+QPb1XHfv5wowUhhBBCCCE+byRYE0IIIY6iUirQqpWEAqNDHb1GhVqpYE/zAA+8V8+mml6SbTquPLaQxUWJvLKvjV+8cqQj5oPv1fP2QSPfmpfDP7c1Mewbv1tlz7CfVJuBxn4PwfDojp57WwbZ2zLIT06YwMbqHrLiTTzwXn1s/7O7WllzoJPfnz2F7HgTE1IsVHaOBHnTMuPIjTeytb4/FqrByBptP3/5IL85feKYexn2h7j+uXKe/M4cpmTFMeQJ8s+tTQx4grxd0c2PjitmQUEChclmfvNaBd+al8Ofjqo829E4QHGKlW31fdT2DPOzE0tZVZbM1Ew7Oxv7WVKUiFqlJC3OgEGjwhsc/YwViiPr2BUmmbEf7lgqhBBCCCHE55UEa0IIIcRR0uMMfG9xPne+XT1q+1XLCmjq83Dug1vxh0YCsOHuEFc/vZdrlhfybmXXmHM19Hk4w6wlyaIjyar7yGvaDBpufb2CU6am8dDG0V09dWolBq2KWbnx/O7NyjHHuvwhNlT3sKe5nzOmZZBk1aFQKOh3B0i06Hi7Yux9eYNhXB8R9EWj0O3yc8+7tWP22Y1aipMtrN7fwRnTM6jqcnLTiSWsq+jGH46wqDCB6i4XNd0jQd7tayr589encrDdRZxBgzcQxmJQkhNv5M9fn8ZVT+3BEwijUiq4YmkBDb1uHCYtiwoTmJBiIT/R9JHPTAghhBBCiM8DCdaEEEKIoygUCr4+OwubQcNDm+rRqpRcfmwBS4oS+fXqQ7FQ7Wj3rqvje0vy2Nd6pJnAlAwbdpMWh1HLxYvy8ATC6NTKMcfPzXPgDoS4cEEO/mCEkyan8tr+DqJRSDBruemEEgbdAeKMWnzBsdcG2FzXy7Uri9jdNEivO8CAO8Czu1r5zqJcDBoVw/6xIVqcQTPOmWBGth2LbvxfDwa9QYw6FVaDhmSrjskZNio7nOg0Sr41P5sfPLV31OfzhyKUtzm5591aLDo107LtzMmNR6FQsKwkideuWkT7oJd4k5Y4g4aanmGmZtrQqJRMy7RTlCKdPIUQQgghxOebBGtCCCHEhyRadHx7fg6nT0uHaBSrUUuvy8/7dX3jjg+EI2hVSgAy7AYuWpDL5rpeupx+Br1Beof9NPW6+ePXpvLr1YdoH/IBI6HawoJErnxyD9EoXLYkH4B7z5tOS7+HFJueX75ykAFPkF+dWvaR95sWZ8AXDJNk1THsD5GfaOI3p0/E6Q1ywfxs7lk3ei21LIeBFJue7yzM5aFNRyrkkiw6TpyUyivlHczJdYxqTnBscSIVHU4KksysLu+gx+VHrVTwrXnZBEJhtGolgQ9NZbXq1YQOb3P5Q/zw6X28ePl8kix6FAoFuQkmchOOVKWlxBlYVJj4/74fIYQQQgghPi8kWBNCCCE+pKnPzfqqbtZV9jAr18Gq0mRS4wxkOYx0u/zjHjMhxUJegolrVxZx1VN7Y4vz728b4urlhSRYdFz/3D5OnZpOokVHQZKZHQ39/OHtKubmxTM/P55gOMLKsmTWV3WTn2jmmR3NXLAgF7US8hNNLCxIYFNt75hrnzwljR+9cIATJ6VS2enk9GkZDHqC3PV2NadOTeNb87J5blcrnkCYxUUJfGteDj96vpy8JBM/O7EEhUKBXqOk2+nn9jWV+EMRzpmZyeLiRALBCAkWHeWtg0SjcMvqQ/QcfgahSJS/v9/I3edO5e8bG7hyaQEPbKjHH4pg1au5alkhfzmqQULboJe2AS9JFv1/4a0JIYQQQgjxvyfBmhBCiE9FJBJFqVT8/wM/57qdPq58cg/lrUMArK/u4fGtTTx1yVy+tzif7/xjJwBZDiMnTU5Fo1KQYTfSNeTjqmWFrC7vGNXx0qBREYlGeWZnKwD/3NYc23fDqmLOnZXJgGd0N8yTJ6eSHmegJM3GhBQz9d1u9jQN8o05WSgV8F7NSLhm0qr4zqI86nuHyY03MSPbzqQMGzaDGrtRRyQa5fndbWQ6DJw/NxudWkmm3ch962rJcBhZW9FDVecwp01L58Xdrfzo+BJWlaVQ3jqIJxBChQK1SsHv1lTi9IX4+UmlPLurlQkpFjIdRva3DtHp9FHbM8ymuj5aB71cvbyQ/EQz+9uGuH9DHX3uQOxzKRVg1Kr+ey9PCCGEEEKI/zFFNBod2+/+K8bpdGKz2RgaGsJqtX7WtyOEEF8Yw/4Q5S2DvLS3jfoeN8cUJbKsJImyNNtnfWufSDAUYXtjP1vr+xhwB3hhTxuew91B7zlvGkuKknh1Xzvrq7vJdBj559ZmvMEwyVYdvzq1jEfebyIUibCjcSB2zsVFiTi9Qfa0DI653sULc9Gpldy3vm7MvosW5LC6vINfnlLKLasrYtNHFxclMj0rDqNOjV6jJNWqZ8gTpGfYT3nbEPNy4wlGInQM+bEZ1Pz+qMBuUUE8KTYDwXCEObkO3IEwfcMBHtpUz9IJSTT0ulGgoDjFQk23i4oOF3PzHJw1IwO1UkmGTc+BThc7G/up7R5mZo4Dq16NUavm929Vxa7z9jXHsK6qm1tfH91s4VvzsrnpxBJ0agnXhBBCCCHE59fHyYmkYk0IIcQn4gmEeGxLI7evORKo7Gwa4N51tTxy4Szm5Sd8hnc3WuuAh52NA2yq6SE7wcTiwkQmpttiFXbRaJT6HjcVnU7+sbmR7Y0DpMcZuH5VMX94q5phf4hBTwCzXs3sXDtKJdz4/P7Y+XuHA+xvdVLeOshVywpHBWuhSAS1avxKPotezYt72sbd9+KeNq5aWsiuxgFuPrWM375RSV2Pmw3VPbj9Ib6/JJ/KThcV7V0caHdR3jqEQaOiNNUWC7m+PjuTRy6cxf62keq7qk4Xr+xr54bjivnlq4c4aUoqZak2FhclMifHgUGj4qW97VR1uWL3kRNv4o/v1HDatHQCoTB3vl2F0zvSDKGy08W0rDhWlibHxidbdVgNGs6akUGCWcf9G+oIhCJcsCCX4yamSKgmhBBCCCG+VCRYE0II8YlUdbpGhWof8IciXPdsOS9eNp8k62e/llZzv4fvP7aLgx1HOnbevbaGhy+YxcLCRFoHPDy1vZm/bWrEHwpzTFEiN51Ywu/frOK+9XWcMzODv7/fiFaloqbLxbM7W6jqGh51jWg0ilIJ7kCYRIuOVWUpvHmwE4DKDhfXryoeFbYBqJQKpmfF8cCG+nHv2+0Po1DC395vZHaug1tPn0SX04dKqSASjfKT58tZVJTE4uJEtjcOcvzEFJaXJPGbw1ViE1LMTM2M47bXKzh9egYdQz6KksysKE2m2+Xngvk52Awa1lZ2saQoCYNOxfETU3m3qjsWnCVadGQ6jLQOeDFoVATC0di+D+xpHmRJUWLsM916+iSSD7/3M6ZnsLwkmWg0is2o/aSvUAghhBBCiM8tCdaEEEJ8InuaBz9yX9ugl/pe9+ciWHu/pndUqAYQDEf56csHePq7c/ndm1W8src9tm99VQ8VHU4uXJDD/RvqsRg0XLYkn6d2NNPl8rGjaYA4g2bU+SJR0KiUaFQKWvo9OH1BrllRRDgSwRuI8F5ND99dlMfj25rwBMIkWXTcsKoYfzDCiZNSeW5365j7PnZCImvKO7huZRF2owZ3IITNoMEbCGHWafjlqRNZW9nFxupufnvGRMLhCOEo/HBFET0uP8FwBKJwwqRUylsHOdjm5EfHF3P32loqu1xolEe6eC4qSKShx8Nvtlfyo+MnEAhFcPmC+EMR7l5bw6rSZNLjDLQNesd9xhl2I7eePpHJGXGUpI4ulbd+6FkJIYQQQgjxZaL8rG9ACCHEF5PTF/yX+wOhyP/oTv611/Z3jLu9sddDXY97VKj2gS6nH6N25LunvAQTr+/vYHfzIFWdLqKRCCdPSRtzzHM7W/jdmZM50D5EnEHDXW9Xc/faWv66sZ7X93eiUyv407lTuXJpAcdPSuWOt6qw6NUsK0ki0awbda4Es5azpmcwM9dBWpwBXzDCmoOd7G4eoKHPg8sf4rXyDiYkW5mTl8CFD+/kmV1t1Ha76XL68ATC/HNbM1vq+1ld3kF6nIGfnljCGwc6+c6iPE6alEogHEGpGFn3rHXAg1qlYNgf4qcvHcCsV6HXqMiwG/j1aRMpTbNy4/PlRKJRdOrRvzrkJpjY1zrI/PwEJqbbUH0JGlgIIYQQQgjx75KKNSGEEJ/I5PSPblCgUytJi/vsq9UAMuyGcberlQrc/tC4+z6woiSJ53a10tjnIcGsJcmi4/y52Ty1rZmfn1TKn9bWMOQNkpdg4pvzsvnzuloumJdDokVHSaqF53a1oVEpOX5SCiatmgc21LOz6ciU0NZBL7esruD8edkAtA96SbUZmJxhJRiOUJJqZXV5BydNTmVlSTLdwwHuX1/HubMy6XL5eHRrIy39XnRqJccWJ3H103sJRaLYjRp+sKyQJ7c3MyUzjn9saaIkxcqbB7t4ZV8HN51YwgkTU6npGea96m7On5tDIBQm1abn0sX5WHUaCpIslKVaqe520dDj5tjiRIqSzPz1WzO48+0aaruHmZvnYFaOg1Sbnux446f30oQQQgghhPiCkGBNCCHEJ1KWZmNiupUDbc4x+65cWkBugvkzuCsIR6I4vUF0GiVGrZrTpqXz1I6WMeO+NiuTbIcJnVqJf5zquniTltOnp3P5E3uYm+dgdq6Ddw51UZpqJd6iY9gX5J6vT6Oqy0W8WcuPX9iPLxjh568c5KIFOZh0alaUJBFlJMSr7RkeFaoBRCNw+bEF/Ob1CnRqJQlmHWadinl5Dt6t7ObVfR0YdSpOn5bO6v0dJJj1DHgCvFPZzTfmZLGptpclRWqOnZDIDc+VE4qMNPoe8AT5y4Y6vndMHm8d6iIQjqDXKPnuMXl4AmH+trGes2Zkcs+6WgD63TV8b0ker1+1ELtpdPXcpPQ4JqXHjdqWaTfSPuTDGwiRbNVTnGJBoZBKNSGEEEII8dUjwZoQQohPJNmm589fn85DG+t5ZmcLwXAUh0nLD1cUccKklM9kSmB56yDP72plbWU3SRYdFy3IZWaOnT+dO5Wfv3yQIW8QhQJOn5rO9xbnkxZn4IZVxfz6tYpR55mXF8+2hn5sBjX3fH0a3S4/bx7s5KTJadz1zkiV2vGTUlCrFcQbdaRY9KiOCpb+/n4j8SYtq8pSOGN6Olc/vZfWgdHrky0pTuTlfe0MegP86PgJaFRKvIEQmQ4j962vZXNdPzq1kptOLKG138N71b2olAouXZzP79+q4pjCBLY39OMLhokzaugdDow6f4/LT6JVx7aGflaVJrOptpcntrdQkGTmtKnpsfXVAOp73YQj0TGh2kfJTTSTm/jZBKdCCCGEEEJ8nkiwJoQQ4hPLTTDxy1PKuHhhLu5AmHiTlrS48ade/rftbhrg63/dGqs+ax3wEgzX4/ZnsawkiZcum09DrxutWkVOgpEM+8jUxWMnJBEIR9jZNIDHH2Zmjp3eYT9Pbm9Br1GS6TByqN3J4qLEUV1Qn93ZykmTU2np7+LESSncePwEfvHKQaIjRWMMeoNMSLHg8Qf55cmlPPBePTsaB9CplZw+LZ0Vpck88F49ufEmSlMsHOp0UpBkpm3Ay4mT0zhpchp2oxa3P4jdpKUw2czW+n5e2N3G3edOQwEQhd7hAFr12CVTTVoVLf1ezpqRwdLD00QBaruHOXtGBi/tbYuNzYk3kmmXqZxCCCGEEEJ8XBKsCSGE+I9oVEryPuPqJbcvxB1vVcZCNatezZXLCtnZOMDv36rmud2tnDUjg0c3N3Gow4ndqOGv35pJollH64CX6s5hVpYmE4lEcflCbKjuAcAXjGDUqihOtrC5rm/Mdd840MlNJ5RgN2l4ensLd54zhfZBH6FwBIteTWOfG71WxU0v7mdBfgJXLSsgFI7S7/Zj1Cq5ZFEuAE/taGFmjoOfv3yQbpcfgHNmZLC5vo+OIR/XryrmogW57GkepK5nmPpeN/evr+V7SwrocfrwB8McPzGFNw50AqBQjHQHdftDFCebeXZX66gKtbqeYYYPry+nUSm4clkhxSmW/94LEkIIIYQQ4ktKgjUhhBBfeB1OL1vq+mM/f/eYfP68tganbyQ86hn2s6NxgJtOLKFmjYsBT5Brn9nHFUsLSDBr6R32cePz+2PH//TEEgqTLOxu7sekVWPSqVErx1aFqRQK4k0a2gZ9bG3oZ2vDyPTNaBQmZdg4Z0YGKgXceNwE7n63hvXVPSwsiOfcWVm8sb+LV8vb+d7ifOJNGn61+tCoc+clmXl2dyvRKLj9If7wVhV/+tpUet0BtCoFN51Yyq9XH8Ju1JIdb+Sbc7OZkW1nyBvEZtAwLctG26Cfm189OGaa6JSMOBYUJOAJhMlNMDEpzYpJr/k0X4kQQgghhBBfCRKsCSGE+MJoHfDg9odwmLQkWo50HVUqFCgUEI2C3ahhwBOIhWpHe2lPG0snJPPmwU6a+j3oNEq6nD421o6uRvvjOzVcvbyQk6ekcuNz5Ri0Kn64oph1Vd2jxp03J4sMuxGDTk28SUufO4A/FEGtVPCdhbkc7HDy0p42JqZZuHpZIYPeIPtahrjyqT3csKqYAU+Qu9fW8Kdzp/J+XT/1vW506pFKtq31fUSjYDWoCYQiqJQKmvs9vLinjeMmpvD87jYuXZxHJAoTki30Dfu5Z10taTYDFy/MZUt9PylWA3qNatQ9ZzsMLCxIIDvB9Cm+GSGEEEIIIb6aJFgTQgjxudfr8vPinjbuXluDyx8iw27gZyeWsrg4Eb1GRXqcgVWlKaw52EmiRUfrgGfc8zT2uvn2vGySrTp2NQ0w7AuN2xF02B/C6Qty/XPlnDY1nb9urOeB9+r49allvLCnjUFPkOUlySwqjOcHT+/l2/OyOX9uNlGiWPUaQuEov3m9gtYBL2qlgp+cUMLTO1rYUN2DzaDhyqUFvFMxEtK5A2Ea+zxMy4rjpCmp5CWY2dPUz/qqHuJNWi4/toB71tVw2ZIC7nizinS7gcJkM9etKqJj0EeKTc/+tkHWV/Vw8uQ0+t1+JqZbmZ3r4IZn93Hq1HRC4QiNfR7yEk0cPzFFQjUhhBBCCCE+JYpo9INllr+6nE4nNpuNoaEhrFbrZ307QgghjhKNRrl/Q92oxgEfePziOSwsTABgc10vVzyxB18wzAXzc7hvfd2Y8YsKEzDr1Gyp7+OMaeksL0mmc8jHDc+XE4oc+eewLM1KTryJ1/Z3cO3KIv7wVjUwsh7ZwoJEzpiexq6mAUpTrbQN+hj2BekZDjA330HHkBeTRk348BTOWTkOfvbyAUpTLZw0OZ19rYO8sq+dnsNrqU3JsPGtedm0DnjJSzCxp3mApSXJ9A77USgU1HYPU5Rsodflx+kPMeQJoFerUCjgkc2NuAPhUZ8x0aLjtasWkmTRU9Pl4pmdLWyr72dJcSLHT0qhJNX2qb0bIYQQQgghvow+Tk4kFWtCCCE+15r7Pdzzbu24++5bX8v07DiMWjUOk5Zvz89m2BcmyaIjJ95IY9+RyjWdWsnSCUn8avUholH4+/uNbG/sZ15ePLefNZk736qmbdDLzGw7qyam8Ns3KgGwG7UYtSo8gTDhSJSydCsufxiFQkGqzUDLgJe2QR8FyWY6h3zc824d0SjceFwxM7PjgJEOpa0DXpKsBrqcvliolhNv5Lw5Wdz4/P5YsHfDqmLaBrzc+MJ+rltZxONbmxjwBEd97rI0KxfOy+F7S/Jjod8HblxVTNLhabKFyRZ+fHwJbn8Ig1aFWjV2nTghhBBCCCHEJyfBmhBCiM81byDMJcfkEY5EUSkVuHwhdjf3s6gwEbVSQVOfhwkpFsKhCBPTbFR1OTHp1PxwRRFN/R52NPZTkGgmyaLnvnUjodcHDrQ5WVmawku72/j2/GxcvhAH253c9noFkSiolQqG/SEuXJCLQjHSbTQtzkBTr5sVJcm8vLeNWbkOqruGeXlvGy393ti5nd4Qeo2KSATyE03U9bh5fGsT8/Pj+e2Zk0i26NhS18fPXjo4qlquc8jH7FwHAG5/GG9wdEUaQIJZyynT0hj2hyhOtrCzsZ9gOMK8vATm5sePGqtUKrAYpDGBEEIIIYQQ/w0SrAkhhPhcaRv0cKjdRf+wn9xEMzsa+7lvXR2B8MhaaGfPyGBmdjz3r68nEI7wl/X1/OLkEhQKBb9eXcGwf6RpwZQMG1+fnYXTGyLZpue21yvHvV4kGmVjbS8LChNYXd5BQ68bAK1Kya9OLaOqa4g1B7qZneNgUVEC7QMeeocD/G1TAxcuyCEUjtA64BkVqunUSrLiR6rZaruG+f7ifPo9QWq7XRSnWMiyG1ES5cGNDWPuJzfRRFGyiSuOzWd1eQdfm5XFo5sbR4255Jh8tGoVAwNeqrpcvFvVQ7JVz6KiRJRKxafxGoQQQgghhBD/BgnWhBBCfOZcvuBIwwBvkO8/vjvWHfOKpQWjpjoatSpS4/TcvfbI1FBvMEyX08+9R4VvAPtah0ix9RCJRvEGwmhUCoLhscuKlqZayU0w8fs3q7hoQQ7FKSNrKLQMeAhHIxxqd/HLk0u5+91aXt7XPurY06alc+2ze7l8SQHFKRY21/aRk2Dkgvk5GLUqLnxkJwrgltMm8pf1tawqS8FmUNPt8vH87jZWlibz1qGu2PnsRg0LCxKwGnRcsbSQpSVJOL0hSlIsPL+7FbtRywXzc5iRbae5z83Fj+yITXet7R7m/dpebj9zEl+blfWpvBchhBBCCCHEvybBmhBCiM+MLxhma30fd71dTSQSpSTNSv3hirFVZSm8vHd0kLWkOJE1BzrHnCcSZVSo9oF3Krr43uJ8djX2c8dZU2jodaNQgEqp4OkdLUzJiCPepOF3Z02iocdNfY8btz9E26CXh99v5HdnTcYbjHCow8XBdueY8w95gwRCUe56p4b8RPNIRdugl+Z+D3e8WUX48BTPYX+I8+ZkEwiFMWnVXPPMPnzBCF+blcmVSwuo7HAyMd3GqrIUCpMtAOg1KqZnOWLXOmNGOkoUsXXSdjcPjlpD7gO/eb2C+fkJZDqM/+5rEEIIIYQQQnxCEqwJIYT4n3L7Q1R1uWgf8KBQKLj8iT0AfHNuNq+WHwnS0uL0vHGgY9Sxes1IE4EPU3zE7Ee1UoFWpWBhYSLXPbsvtpaZSqng9jMn0Tbg5byHtvPc9+ZRnGKhMNnC7qZ+DrQPce83prGhspuVpcm4/SHiTVr63IHYuY1aFWadmgSzlt7hAHU9w9T1DANw5vSMWHVcht1AUbKFIU8AtVrJjc+V4wuOhIBP72jBoFFx//kzWFyc+C+fm1alGvXzwfahccc5vSEG3AEJ1oQQQgghhPgfkPZgQggh/mc8gRB/39TAGfdt5vUDndy7ri62zxsMY9Yd+b6ndcBLboJp1PGba/s4tjhpzHmD4ShW/djvis6ZlcGkDBv3rqsd1SAgHInyq1cPkW438NC3Z/KPLU1c/fQ+bl9TiUKh4LLFBRxqH2JXyyAlqVamZdv57ZmTmJg+Mk20MMnM9auK2dHQz4+Om0CCWQuARqXgimPz2d3YD4w0LbhkUR7b6/uo6HQSCEVw+kKj7tEbDBOKjK22+/8UJlnG3W7UqrBKswIhhBBCCCH+J6RiTQghxH/dkDdI+6AXtz/EOxVdWA1qlk9I5scv7o+NeetQJ+fPyea+9SNh29uHurhqWSF3vFkVG9Pp9GHRqylNtXKo48jUzPoeF7ecNpG73qmhodeNUgGnTk1HrVSyt3lwTJgF4PKHsBk0fOfRnfhDI8FWQ6+brfX9XLwwlzm5dp7d1caNz5dz7cpiHtxQx29On0htt5va7mHaB32cNi0NTyDML04uZcATJNWmJxCKUJJq5by52SiANQc7KW8bRKdR09Dr4fRpaby450hlXnGymZJU68d+pjNy7GOq6AAuW1JAWpz+Y59PCCGEEEII8fEpotHo2JWc/0duu+02XnjhBSorKzEYDMyfP5/bb7+d4uLi2JhoNMrNN9/Mgw8+yMDAAHPmzOHee++lrKwsNsbv93Pdddfx5JNP4vV6WbZsGffddx8ZGRn/1n04nU5sNhtDQ0NYrR//jxshhPiqi0ajdDp9RKOQYtXHOlN2Dnpp6vews7Gfv25qwOULceWx+cRbdOxtHqS+x82elsHYeU6ZkoZJp+L5XW0EwhGWlyQxJSOOe9fXxqZPplj1/PKUUhp63XgCYXITTEQiUW566QAnT0kjLU6PWqmkKNnM+qoeVpQmc/GjO8fc86LCBAA21vSO2adRKbj9jMlUd7u4f0M9t5xahkqpIAK8c6iLRYUJOExa3tjfSZfLR9uAj3izlrqeYYLhKBuuX0J2vIlnd7Zw/XPlo859+rR0FhUmsLtpgJI0KwvyE8j5UGXev6uiw8mD79WxtqIbh0nLqVPTqehw8t1j8piZ4/j/TyCEEEIIIYQY4+PkRJ9pxdqGDRu4/PLLmTVrFqFQiJtuuomVK1dy6NAhTKaRPzJ+97vfceedd/LII49QVFTELbfcwooVK6iqqsJiGZkGc/XVV/Pqq6/y1FNPER8fz7XXXstJJ53Erl27UH1oTRohhBCfrtYBD8/tbOVvmxoIRaKcPzeLM6alU9Hp4lCHE6NWhVGj5vuL82jsc1OUbKWqy0V2vInjJqbw69WHyEsw0+3y88q+dianW/nbBTPZ3tBPJBJlYoaVu742hbpuN6FIlH53gOueLWfYP1KFds/Xp3HzG4fwhyI8t6sVgG/MyWJTTTctAz7m5DmYlx/Plrq+Ufe9vCSZX7xycNzPFAxH6Rn2ozy8eJvdpOXyJ/aQ5TBy9ox0Mh1G/rmtGaKgVCjpGfbTM+wHINWmx6gd+bfHolfzvcV5bK7ro7x1ZE20F/e0cf6cLM6Y/u99+fOvxBk1tPR7OX16BkOeIA9trMcdCJOTYJJgTQghhBBCiP+BzzRYW7NmzaifH374YZKSkti1axfHHHMM0WiUP/7xj9x0002cccYZADz66KMkJyfzxBNPcOmllzI0NMTf/vY3HnvsMZYvXw7A448/TmZmJu+88w6rVq36n38uIYT4qvAFw9z9Tg3PHA60AEw6NT9+8QB7j6pE06mV/Oj4CczJjefyJ3fzQa10glnLr0+dyL3ra8mJN/KdRbmkxemp6x7p3qlVK/nJCwe4YmkBQ94gf93YMOr6pakWBjwBeg+HWgArS5NRKRVsbxxkYUEC66u6OW92JqlWPa/sG5mCeVxZMqWpFvQaZawS7sOsBg2PbWliSVEinsBIiNfc76EszUZdj5v1VT38+evT+NPamtgxCgX86tQyFAoFj25u5J53axnyBjl2QhInTEzl929VkWLTkxn/KTUWiEJ1l4udTQOjNqs+qpuDEEIIIYQQ4lP1uVpjbWho5Nt8h2PkW/aGhgY6OztZuXJlbIxOp2Px4sVs3ryZSy+9lF27dhEMBkeNSUtLY+LEiWzevHncYM3v9+P3H/kjzOl0jhkjhBBitOZ+N20DXgLhCCqFgk21vdR1D1OUYuWiBTn8/f1GUqx6fMHwqFANwB+KcOdb1Vy0MAelQsGSCYnkxJuo6HCy5kAnwVCU1w908tahLu4/fwZ/39RAXa87dvxtr1dy3zemMewP8czOVsKRKFMybJw3J2ukw+cZk+l1+8l2GHm1vJ3HtjaxdEISZ03P4ED7EPW9Ho6flMxp09LwByPYDGrcgRBnT8/gsW3NYz5rvElLepyec2ZlEGfU0tTnAUYqxN6v64tVoi0siGdqZhwH2oZw+YIUpVgoSbHyyOZGbnujMna+Nw92cqhjiKuXF7K4KJEky6ezBlpqnIErji3g1qOupVIqWF6a/KmcXwghhBBCCPGvfW6CtWg0yg9/+EMWLlzIxIkTAejs7AQgOXn0HwjJyck0NTXFxmi1Wux2+5gxHxz/Ybfddhs333zzp/0RhBDiSykcibK5rpfyliEe2dzAZccW8JvXKmJdNt+u6GZSuo0/njOVRKuWq57cO+55XP4Q6XYjt54+kae2t7Clro8pmXHMznWgVSmo6nIRikR5ZmcLPz+5lMv+uRt3IAzA5AwbnkAYq07N5cfmkx5nINNuZFNtLw++V891q4p5YEM90WiUk6ek8YNlheQmmOhy+XhsSxMu/5HmBdeuLOKhjQ0MeYPcevpEJqVb2d925AsWo1bFbWdOotflR6tWsq6ymwSLjh8fPwGNUsGu5kGWl4wEenaTDrsJMh1HKtBa+j38+d3aMZ+/pd/LhBQLkzLiPoW3csQZMzJwmLQ8tq2JJIue7yzMZUqG7VO9hhBCCCGEEGJ8n5tg7YorrqC8vJxNmzaN2af40JSWaDQ6ZtuH/asxP/7xj/nhD38Y+9npdJKZmfkJ7loIIb78DrYNcajdyR1vVXHCpBSe29UaC9U+sL9tiOFAiNZmD95g+CPP1e30odeoYg0LttT1cbB9iKuWFgJtANR2D/NuZTcXLcwlLc5AvztAt9OHPxTm2JIkel1+vMEwNd0uhv0h5uQ5uPfdGi5amIdSATaDhkSLjj++U8O5szO5fGkBXU4fwXCEjDgjbx3qZMgbBOAnLx7g67MzuWRRHp1OPwaNEr1GhVal4B9bm0dV3mlUCv5w9hQmZ9h4dlcrF87PHvcz+oLh2PpvH+b5F8/mk0ow6zhrZiYnTUlDpVCgUSs/9WsIIYQQQgghxve5+O37yiuv5JVXXmHdunWjOnmmpKQAjKk86+7ujlWxpaSkEAgEGBgY+MgxH6bT6bBaraP+E0IIcUQwFKGmy8XBtkFaB7w0HJ6WmZ9o5mD7+NPn63uG2Vzby5LixHH3q5UK8hJNHOpwkn3UGmNObwj3UUHUosIENlT38Od3a2kb9HLn29U8uqWJrXX9/Hr1ITzBMEkWPR1DPjbW9BIKR7l6RRGry9t5ZV87/lCEZKuOOXkOlCh4YEMdL+1pwx+M8HZFF7ubB0fdV0Ovm8Y+N8O+IIlWHVqVksZez5jprMFwlEc2N7K9sZ+N1b3EmbTjfs4kq+4jK8ZyHJ+s++e/Q69RSagmhBBCCCHE/9hn+ht4NBrliiuu4IUXXuDdd98lNzd31P7c3FxSUlJ4++23Y9sCgQAbNmxg/vz5AMyYMQONRjNqTEdHBwcOHIiNEUII8e870DbIC3taaepzc8Pz+7nrnepYhVe3y0+G3TDucfFmHTubBpmaaSfOqBmz/9vzc+ga8tM3HODMD3XE/KD+LcmiIy3OQOPhNc00KiXhw9VxpWlWKjuH2d00yOrydu7fUE9Dr5ttDf3c8loFVy8v4gfLCrHq1YSjUR7f2sz979Vx1oxMBjxBnt/dykmTU0m06GLXTbLouGhBLhUdLu5+t5aqDhcH2ofwh8ZvaHCgzUlBopnSNCsJpvHXSbMZtPzi5DJM2tFdqW88rpjCZPO4xwghhBBCCCG+mD7TqaCXX345TzzxBC+//DIWiyVWmWaz2TAYDCgUCq6++mpuvfVWCgsLKSws5NZbb8VoNHLeeefFxl588cVce+21xMfH43A4uO6665g0aVKsS6gQQoh/z7b6Pi5+dCfz8x0sLkoiHIky5A0yLSuONw508lp5B99fks8db1aNOi7RrCPVpicQjvDK3jb+9LVpbKnv5f3aPhLNOmbnOthQ3YPVoGFSho3tjf18bVYmT+9oQadWkptg4urlhUxIsfCDp/YCkGbT4z88dfLESSnEGTSEI1F2NQ2Qlzg6oIpGRxoEXLwwl1A4wqEOJzedWIJZpybNpsegUfLw+43c9XY1Pzq+BI1KgcsXom3Qy5VP7sEfipAdb6QgycILu1soTraM+3wKk810DHq5elkhhg8FZ0ebnm3n5SsWcKjdiTsQoijZwoQUKwbt52YFBiGEEEIIIcSn4DP9Df8vf/kLAEuWLBm1/eGHH+aCCy4A4IYbbsDr9XLZZZcxMDDAnDlzeOutt7BYjvzRc9ddd6FWqznnnHPwer0sW7aMRx55BJXqo//oEUIIMVr/sJ/fv1XJD1cUsbGmh79sqGNeXjyLihJpH/Bw8cIcHn6/kfVV3fzqlDJe2NNK+6CPpROSWFSYQEOfh1VlyWQ5TFzyj53YTRompdtYVJDAK+UdNPW5WTUxhZouF5tqevnhiiKKky2cNyeTV/e2c9aMDIb9IU6ZmkaCSUtxipVINMI1K4qo6XKxs2lkyv+cPAeba/vG3H+fO4A3EOKud2rwBMKYdWr2tw3hCYRZWZrMD1cU0TPs59HNjZj1ar41L5t/bmtGpVSwqiyZaVl2fvDUHkKRKCdOSiM/0Uxdz3Ds/EoFXLYkn4IkM8Up4y8hEAqPVLqpVUoKkiwUJI0f0AkhhBBCCCG+HBTRaDT6/w/7cnM6ndhsNoaGhmS9NSHEV04gFMYbDFPXPczOpkHueLOSYPjIPw0Ok5bz52axvqqHy4/Nxx+M8NyuVvISzUzOsFHXM0xdj5uFBfFY9JpYxdnRfnvmJOxGLWsOdPDinnYAvj4rkxVlyexqGqCp101JmhVQ8OS2Zvo9ATyBMJctyefB9+o5fVo6e1sG8QbD3HxKGT95cT9dTv+oa1y9vJC9LQOcPi2D1/d3MOgJMivXQf9wgNQ4PX9eW0vgcPB143HFPLG1idvPmozLH+a+dbXsax2Kneu7i/Iw6VREo7CuqpsUm55ZOQ56h/386PiSMZ/PFwyxtb6fv29qQKFQ8J2FuczOdaDTyBc8QgghhBBCfNF8nJxI5qQIIcSXXPugl5YBD1qVkrxEEzaDlkFPgINtTva1DuILhbEZNBQnWzjYPjQqVAPodweIRqGue5imPg+3vl4JwHs1vQCcNjWdnHgjgVCU8tbBce+htmuYZ3a14PQeaVKQYNFR1+NmbUU3X5uVyV/W11GaZsVh1tI66AVAp1FRmmph6YQk5ufHo1Iq2FbXxy2nTeSW1ypo6vOgUSk4a0YGtd3DeANhntnZwvuHK9q2NfSztDiR5SVJZDqM6DVKLpifg0Gj4qcnlbK3eYBdzUOjQjW1UkFxiplAKMLDmxtJtRloHfDy5sEKVpWO3xRne8MAFzy8I/bzhuoenrhkDvPzEz7m2xJCCCGEEEJ8kUj7MCGE+AJz+0O09HvodfnH7ItEoqyv6uaUezZx0cM7eGpHCy/sbqOyc4iX9rSxpaGPig4nf1lfx69XV5Bk1tI55Bv3OqFwlJ+eVIpCMdIZ9Giv7GsjP9HMH9+pBhTjHq9WKQiGjgR2Jq2KWTl23P4QBUlmbnu9km6XH5tBE+sQmptgJD/RRG6imcuf3E1Dn5urntpL93CAJ7Y2cekxeVy1rIDvLc5nT/Mgq8s7WDohmR0No7tEv1vVQ1WXi6mZNn6wrIBki45hfwilUkEUWFiYwMxsO0oFFCWb+fVpE9GrlbgDYaq7htlQ3RPrhPr12Vnjfr5/bm0as+25na3jjhVCCCGEEEJ8eUjFmhBCfIFEIlHqe4c52O6kfdCLWqlkyBdk7aEuvrs4j2UTkrEaNPhDYao7Xayv6mZCioUFBQm8vKedriEPDpOWRzY30tjnYVK6lb9+awaDnhB1vR5WlCazraF/zHUdJg0/fmE/OrWSc2ZmUphsZs2BkYYzkSi0DHhw+kIkmLVoVcrYlEsAnVrJjGw7G2t6aerzMD0rjiXFSdT3uLl3XW2sQk6jUnDKlDRSbXpOUCkJhiP84Km9hCNRVpUls6WuD4NGRWmalVtfryDZZiDBrOOpHc0YtCp+cXIp71R0jbr2BwwaFStKk2kb9LG5tpd0u5EDrYPsbB5EqYBjihJ54PwZvFrewS9fOcjlx+azs3GAa5YX8tr+DrQqJVcsLWB2rmPc96Ifp5HBv2puIIQQQgghhPhykGBNCCG+IELhCG8d6uLqp/aOCo/MOjXXrCjkN69VoFYqKEq2cM+6Wl7f30m8ScPd504jEoXceBNGnZoLHt5O5HDx2CWL8vn5ywdp7h+ZennFsfnMyLKzq/lI1ddJk1JjjQP8oQiPbW3imuWFrKvsjnXT/GC9s0c2N/LjEybw0p42KjpcTMm0cdaMTLqcPjIdRmbnOqjocPKr1Ye49fRJ/OHsKayv6sai15BhN440KWjs54RJqbywp40Es5bTpqVTkGSmot3J0glJ3L+hDoCndrRw99en8vuzprB6fwdVnS66nWMr9wCSrXqe29VKt8vP4qJEZmTbea28AwCbQcO0LDv3rq9jb8sgpalW5ufFE4pEybAbeOD8GSRZdZh0mo98N9+Yk8Xq8g7Chx+sWqngjOkZn+Q1CyGEEEIIIb5ApHkB0rxACPHFsKd5gDP/sjkWih1Nr1Fy2ZICItEoz+xoIRSJclxZCivLknl+VxuvlLczMzuO6dl2/rK+HoDTp6bT5/bH1kr7wGWL88lJMNI84KUo2cwLu9tYX9Uzasz0LDsmnYra7mFuO2MSVz+1l0FvEACVUsHSCUnMznEwJ9eONxDmiif30DMciB2fYTdw/aoiNtb0Mj8/nk01fQx6g3gDYbbU96FVKVlSnEicUcuq0iR6hwPc+kYlQ4evAVCQaOb7S/Jo7PNAFB7Z0sg1y4u49fUKQkc9pDOmpdPj8jPkC1KWZmXZhCSUCjjU4aI42UKfO8CAJ8CupgFyEkzMy4tnTp4D878I0j4sFI6wt3WQNw90olAoOK4shSmZcaiU40+NFUIIIYQQQnx+fZycSII1JFgTQnz+RaNRbn29kr9urB+zb0KKhdOnpaPXqFApIcNupLLTSV23m7qekWmjVy4tZNgfxOUL8c9tzQDcc940rnhiz7jXu/vcqVz11F6uW1nE79+qHrN/Tq6Db87Nos8d4IltTZw8JX3UOKNWxf3nTycYjuD1R3CYNayv6mVv6yCzsu0kWfVsre/jG3OyuPLJPUxIsXDB/BxuX1NFfa87dp6SFAvnzMoERlZv+/v7jfQO+1lVlsyUDDu/Wn2QSBT+efFsIlEY9gXRqJXsaByg3xMgy2HEHwhTlm6jotNJl9OH3ahlb8sgS4uTmJVr56GNjWyo7qEszcLioiSmZ8cxN0+aDgghhBBCCPFVJV1BhRDiS8YXirC5rnfM9kyHgeMnpvDsrlaseg0H24dQKRVcv6qYDLuBZ3e1csXSAh7d3EjPsJ8fHTchdmwkEiXepKXPHRh1Tr1GiVKpwKRVUZxswWpQj+rmCXDatHRSrQZ63QHOnJFJXryZv317JuWtg+jUKkpSLXQMetnf5uSVfe1cs6KIGTl2SlLNeAMRNGol31+ST4/Tx9XLi0i16VAplVy7qoiDbU72tgxSlmZleUkyOxr7sZu0qBTw4+MnUNs9zJqDnby4px0YqX7b3TSAJximY8iPWqVgwO2nKMXCXW9XE4nCbWdMoijZQlmalbYBD4sKE1lzsJNVE1O4cmk+J09JJRSOkuEwUJpq+y+8QSGEEEIIIcSXkQRrQgjxBaBRKkgw68Zs/9a8bBRRBSUpFvo9AS5dnEf7oI9nd7Zy3pwsNCoFCqBneGTtsTcPdvKDZYX8fVMDL+1p4+KFufzuzapR5zx/TjZxRjXXrizm1fJ2fn/2FO5+p4YD7U6sBjVXLS0ky67nG3/fhlmnJsthpDjFQq/LT4bdyLISB9vrB5iRE0e/O8ifzp2GNxjixy/sZ8gb5OcnlRIIhPjaA1v5wfJChjwB/vBWFefNzmJ6tp1kq57z52ZjN2i49fVDnDQ5jSFPkNYBL0/taObXp05k6YQkdGol07LsxBk0vFrezvz8BFaXt7O4OJFZufHc8WYVkSjMy4tnY00Pk9Jt3L7myGe9bEk+GXYjKqWCCRKmCSGEEEIIIT4BCdaEEOJzIhqN0tLvweUL4TBrsRo0NPV6CEUiZDmMfH1OJhuqj6x1plIqSLLouebpvbF1196v7WNVWQp2kwajdqSDpst3pNpsT8sgHUM+vj4ni5IUC3FGLT87qYQ39ncSikRZWZrMhFQLlz62m+lZdjLsBpr7PeQkmDhhUioJZi2/f6uaomQLpalWdjcP0jscYE6ug6pOJ2fPzKTf7Wdato2aLhev7e/kgY31DHqCR903/PSlQwC09o90Is1JMBEMR3hiWxOnT8+gvGWQyZlxXLIoH4dJQ7fLT6bDQFa8AYtezdM7enCYNLyxv4P2IR8/Pn4CTf0e/vi1qaTZ9Ny8+hAGjYqlE5I4bmIyqTYDeo2S9kEfB9udnDMzg6UTkmQNNCGEEEIIIcR/RNZYQ9ZYE0J89nqH/Ty/q5W719bgDoS58bhitjX0x5oGlKRauW5FEY9ta2R91ciU0GOLk3D5grGOnUe78bhipmXG0e3y0+cOcPOrh8aMuWFVMXe8VcUJk1JZUpSAQqFApVCQbNWxvXGAaBQqO128U9FFOBLlyqUFPLm9md7hACdPTqWi00Vt9zBWvZoHvjmDYDjCa+UdDHqDnDMzg3AELn18F0f/KzMrx06cUcvbh7oAOKYokWgkysbaXiZn2Mi0GzhjegYH252YdWr+tqmBtsGRjqXJVh3XLC+i3+MnN8HMG/s7CIWjTM2y8+q+djyBENevLGZXcz9z8uJJseqJM2qIN+kxaFWxewiFI6hVyk/t3QkhhBBCCCG+XD5OTiR/WQghxOfAy3vbuO2NStyBMHPzHGxvGBjVibOiw8kvXj3IqrJUrlhaQH6iiSyHYdRC/0dLsuj563v11Pe60agUfG9xHorDxVkqpYJrlheiUSmJRuG18g6uf24/d75VTbrdQDAUJdNu5B9bGnnzYCfRaJQzpqXTNxyg93Bnz7J0G7Xdw+QnmrlmRRHP7mphR0M/T+9s5WC7E5cvjEmr4s5zppCbYEKrUrKqLJljihJ5p6Irdp/HFCZQ2+MCoLx1iMJkC5tre1hzoJNdTQOxUA2gy+lnQ3UPyyck8X5NL3PzHEzLisMbCHHatDS+PS+Hy57YzV83NvKdR3fxg6f2Eo0qMGhVDHoD7GzsZ31V96hzCiGEEEIIIcR/QqaCCiHEZ6xtwMuf1tbEfp6TG8/d79aMGdc64MXlC7KuspuJ6TbS4wwcU5TAS4cX8f+ARqWgy+VDpVJi0qnxBMJsqevjj1+bikqpwGHUolIqeHRzI9evKsYbDKNTKSlLt/Lq3nZe3NvOubMy+O0Zk1CrlHgDYf65rYkt9f0kmnXccFwxBo2SX55SSnnrEL99oxKNSskF83MAuHJpAf/Y0khugol9rYNMy4rjvNmZKBQKfvN6RayCbXaug4lpVgqTLCRZ9CwpTqKy08W0zDiWlWh4YXfbmGewo7Efg0bNJcfk8df36smON9HQ52Z6pp2fv3Jw1Ni6HjcH253oNEpuWV3BK/tGnpNVr+bRC2czLdv+n7w2IYQQQgghhJBgTQghPmvuQGhU102dRslHTdLXqpXkJpiYkGKhzx3gnBmZvF/TF2tOAHDp4nye3dlKQ6+bHY393HLaRPa1DvGDp/Zyw3HFePwhdjYNcOaMDPqGAwRCEeItWu59t5aTpqQx7A+xqaaPyRlxVHa5eGF3GydMTGVmjoNoFAwaFT95af+oey5JtZKfaEalVKBWKtjdPEhjn4ezZ2TwwHv1AJSlWbl2RTFRomQ7jCSYddy9tpZAOILdpOWvG+vxBMKk2fSYtGpKUi1jqsumZdlJsurRaVT87ORSOod8nDYtjbqe8Sv3hrwByluGYqEagNMX4lerD/HoRbOxGjQf+30JIYQQQgghxAckWBNCiM9YvElLToKRxl4PlyzKw6BRMTfPwdb6/lHjEs06mvs8rC7vwKRVMTPHwdrKbi45ZuQYfyiMPzSyzlnD4SmiA54g/e5A7By58Sbqe908s7OVZ3a2olIqiESjsSBvZVkKd54zhZx4I33uAH96p4ZIFP6yoQ6FAn535mQ0KgVufzh2To1KwZVLC7h3XS3XriiMNVLodwfodvk5f04Wz+4amSJqN2r5zqJcKjudqFUKtjT0jQkRU+MM7G0eZG5ePDubBmKND6wGNccWJ45U2GlU6NQqsuNNAATDURLM2thU1Q8UJlvY1zI45pnvax3E6QtKsCaEEEIIIYT4j0iwJoQQ/wP+UJjGXg/+YJgUm54kqz62L96s4+cnlfLSnnZ2Nw/w0KYBbjxuAi5fiIPtTgBSbXq+e0wev3+zCpNWxcLCRH73ZiUt/SMVXRNSLBQkmVld3jHm2p5AGKUCvntMHtvq+yhMtsT2hSOjUy2rXsNf1tdxxdICkixa/vbtmexsHMAfjpDtMPLYlkbCkSh3njOFhl43SgUUJVtw+QJcvDCXpj4PWpUSjUpBMBzlxT1t5CeauHhhLpMzbLy0t40LH9lBNAq3nDaR82Zn8c9tzbHrFyaZ6Xb66XL6SLLouO30STT2ufGHIoQjUX77RiWLChOJM2pH3XdanIEHvzmTa57ZS1OfB6tezS9PKaM01YrLF+TDFuQnYP/QOYQQQgghhBDi45JgTQgh/sua+tz88Z1qXt7bTiQK6XEGfnP6RBYWJMS6U5akWPGWhbn8iT0A/G5NJSdNTmNFaTLRKBQkmbnx+XI8gTDLS5JpHfDEQjWA2u5hTp6SOm6wlukw8uA3Z9Dn8uMORhj0BHCYtKMq2WCk62ZDn5u2QS82g4YhT4ind7Zw/txs3j7Uxc9ePrKG2Q+e2otJq0KnVvLt+TkUp1i4+um9+IIRshxGrl5exJ/eqSEQjlDX4+ZA2xChSJQ1B0YaF8zOsVPR4aSl38MNq4oZ8gZJsekJhMLcvqaKBQUJmPVqLnpk56h7XFmaTIJZN+5znp5t5/nvzafT6cNm0JDpMAIwJSOO7y3O48H36olEIdNh4McnTMCkk38ChRBCCCGEEP8ZRTT6USv5fHV8nDaqQgjxcbj9Ia5+ai9vH9UJE0Y6cz73vXlMy7ITjUa5+91aQuEIf363dtzzXLm0ILbvxEkpJFh0PLq5adSYkyenolIqeGnvyHpiaqWC7yzKZXK6jcuf3EM0OrJw/yXH5GLWaXju8PRMgGmZcXx/ST5XPrmHb8/PwRsIoVYpybAbeGRzI9+el8Mf3qrGGwyPuubFC3PRaRS8eaBr1DpnmQ4DZ07PIN6kxaxTo1KMNFSIArkJJkpTrdR2u/ntmko6hrycOyuTc2dnEQpHCYUjZDqMRCJRntrRwu/fqsIfijAtK47fnTl5VMXdv8sfClPf48YTCJFpN46qGBRCCCGEEEKIo32cnEi+rhdCiP+iup7hMaEajEzBfPNgJ/mJZmq7XTy4oY7vLykY9xwqpQKDRhX7+d3KHm46sWTMuFfLO/jOghz++s0Z7G8fQqVUsnpfO22DXkpSrBzqcOL0hdjTPMTEdCulqVaWlyTjMGkoSLLwxoEOrlpWyLb6Pr4xJ5v71tfy8PuNANy7rpZrVhRR2+3i/do+kiw6VpQm0zboYW5uEvetqx91Ly39Xv74Tg2FSWamZsbx7K5WAObmOvjrt2ZiMWhItxuZmmXDF4yQaNahVCrGfKaLF+ayvDQZbyBEut2AzfDJpm/q1CpKUuWLEyGEEEIIIcSnS4I1IYT4lEQiUVz+ICatOjbF0+ULfeT4mq5h7l1fi1alxB0Is62hj+MmprDmQOeocd+al01pqoXpWXHsbh7EFwpj1qkoSbFQ0emKjdOplczJj+fB9+rZ0TgQ255pNzA/P55DHSPVae9WdjM5w0amw0im3UC6Tc/+DicuX4ge5yDfnJfN7qYBtGpl7BwDniAv7GrlOwtzCEeipNsNGLQqJqfHwdg8LOaDJgtatZKX97aztaGfTqcPy+GmATaDFpvho49XKhXkJpg+eoAQQgghhBBCfIYkWBNCiE9BdZeLp3e08G5lN1MzbVy4IJfJGXGk2PToNUp8wciYY2bm2LlvXR3Ts+3MznWwsaaX06amc/XyQrY19KNRKjhlahoNPW4qOl0kmHXc8/VphCNROoa8nDgplTOmp7O5ro+iZAtTM+O47fUK6ns9o66zuDgJJVFuWFWMPxxhYqqVNYer5VJset6p6mFHQz/LSpJItOjZ3zqEyxemIMnMwoIEWge9ZDuMzMl1oFIp6XD5iURhT/MgVoOavAQTiwoT2FjTO+YzLi1J5ucvH8Bh0vLDFUX8Y2sjBq1qzDghhBBCCCGE+CKSYE0IIT4BfzBMY5+bYDiKWafijjcraej10NDrpqHXzVsHu3j+svmk2/RcvCCXe9fXjTo+43DFl8sf4r2aHn5+Uik1XS5e2tuGVqVkalYcs7LtvL6/g6rOYRYVJvDWoS5KUi1Eo7C7eYAVpSkjlWupVvITDCgV0DboG3WdRQUJBMMRUm16wvgpjbdS3+1ickYc4UiEp3e0EAhFsBo0PLOzldYBD9evKqapZZB5efH4QmFKUyxMTLdRkmrBrNeSYtXTMuBheUkSLm+Qrz+0jRtXTaBzyEdN93Ds2qdNTae2exh/KELHkI873qzi3m9MJ8Nu/J+8IyGEEEIIIYT4b5NgTQghPqbmPjd3vl3Ny/vaiUahNNXCJYvy2FjTw7mzMnlsaxNNfR62N/RTnGzGatBw43HFbKrtZcAdZHaug1SbntqukcX+o1G4480qLlyQi1atxKhREQxH2Frfx3s1vVy3spgHNowEc4kWHel2I2qVkl1NA7yyb6RRwa2nT6S718X9589gX+sgA54gBYkmEsw6guEIoUgEFUoCwTBPbG9hyBsiLU7PlcsK+cXLB2kb9JJq03PX16Zy77u1VHcPs7GmF6USbj1tEqlxesz6kfXN0uIMpMWNzN/c0zxANAq/e7OSs2ZkctKUNILhCGVpVv6+qWHUlFRvMEw4PLZyTwghhBBCCCG+qKQrKNIVVAgxvh6Xn/LWQXY09pNg1jEn10FhsoVfvXqIJ7Y3jxqbbNVx2ZJ8hrwhshwGVEolWqWCzmEfTk+Ix7c1kRNvwqJXs691iEAowp3nTOHyJ3aPmia6qiyZVWUpPLSxAZNOxbIJyayt7GJH4wDHTUzh67My2N4wMKoCzm7U8Puzp2DUqugY8LC9eZDiJAveYBibUYNaCWsrelhclEhTnxu7SUuCWcf6qh421vRw0uQ0HGYtA+4As3McqFUK+twB/MEIgXAEo1aJQaPm3NlZY56RyxfkV68eijUnADh1ahorSpK54sk9Y8Y/dckc5uYnfBqvRwghhBBCCCH+K6QrqBBC/Ie6nD5++uJ+3q7ojm1TKxX84+LZPLOzZZzxfoLhKHe+XQ2MLNr/vSX5JJq0GLUqzp2VhScQpr5nmBMnpeIwael0erlh1QQOdTjpGPIyLctOilVHslXHz08uodvl580DnWhUSq5aVsCUjDiUSiVz8hxUdrrY2TTAxHQryyYk88S2Zsx6FWdMy2BGlp32IS9xeg0GjYrWAQ86tRJfMMw/tzWTYjNQnGLm9f0jTRKODgnjzVpqu4cZ8gZ5v7aPixbk8EZDPzNy7OM+J4tew43HTeD4iSnU9QyTm2BiSmYcvmCETLuBlgFvbOzcvJFgUgghhBBCCCG+LCRYE0KIcextGRwVqgGEIlH2NA8S/ohC36M7gLoDYf7wVjU3rCrGZtCQaNGxubaHxcUJ7GjoJ9mqp9vpJ9lq4LiyFPyhEP5wFGUUbn29glA4yg3HTaA4xYrDqCHerMXjD/GPLa1sqO7lZyeWcN6cLGq6XfhDYVJtepy+EDsa+wmGojjMWrY29LOuqjtWEadUKrjpxBKe3tnCpHRbLFg7mk6t4vX9ndx97lTOmpFB16CPpSVJzM5xfOSzSrDoWFqSzNKS5FHbH7lwNmsru9je0M+xxUkcU5RIvFn3b78DIYQQQgghhPi8k2BNCCHG8W5F17jbN9X0snRCEms/FLpZ9Wr8ofCY8V0uP3uaBxj2h7h0cT6DniAzsh3c9kYl/tBI4OUwabn19In0DnjYVNvLwXYXAAfbh9jXOkSyRc8T25u546zJnDE9g5Mmp5Fs1dE+4GNzbT91PcMcPzGFufnxvLinjaXFSdzxZhWD3uCoe5mWGUe3089F83PJdBg4d1YmT+0Yqb7TqBRcujif18o7ABj2h/jJiwdix5amWnjo2+bY2mr/jvwkM/lJZr57TP6/fYwQQgghhBBCfJFIsCaEEONwmMavrNpS38eTl8yhy+njQJvz8FgtVy4t4E9ra8aM9wfDWPQatGol6yq7iTfruHttDaHIkaq3fneAm189xLKSZDRqJdevKsYfCpNhN5LtMHHNM3s5bWoaWrWCvU0DPLylCZ1ayeXH5jM108ali3N5YEM966t7OHVqGu/X9vLdY/K48+3q2HXm5jmo6R7mye3NXDA/h39saeTkKWk8+M0Z7G8bQq1U8PLedup73SRbdVR3DY/6HIc6XBxqd36sYE0IIYQQQgghvuwkWBNCiHEcOyGJv2yoG7N9elYcbx3s5MfHTWDYH6Ki00WcQcO7ld0MeoJjxmc6jOg1SvrdQf76Xj3fX5I/KlT7QMeQj/xEE4OeAHe8WRXbvrAgnrvOmcLBdidtAz4e2doEgD8U4c63a/jrt2agUyrIsBuwGjT8ZX0dnkCYgx1ObjqxBAVg0qlRKuBgu5PrVxUTb9ZSmGQh0aJlYpqVqi4Xf15bSyAcITveyK2nTeSSx3aNuUenb+znE0IIIYQQQoivMgnWhBBiHAWJRn59ahm3vVGJJzAyxbMszco5MzOp6XZx4SM7+cUppfzxnRqUCvjpiaVUdLjoGfbHzvGNOVlk2PXcvbaOEyenEAhHPupyACRZdKw+PBXzA5tq+zhjegZTMuO44sk9fHh5t+ouF/FGHRWdLvY0D8a2t/R7SIszcO+6WvQaFQfahlAqFAz7Q9xy2kSy4w0sKkwC4PuL8zlhYiruQIg0m4FINIrdqMUTONJ4QKGA/ETzJ3mUQgghhBBCCPGlJcGaEEJ8yLAvSHmbk2F/iAsX5KJUjHQEretx85MX93PzKWWEo1Fa+j1YdGpc/hC3r6nk2/NzMGlVGLQqCpPM1HYPc/XT+4hGYdATZE6uA5VSgVqpGFO1lh5nQK1UjHs/td3DhCPRMaEaQDAcJRSNcvLkNE6alMpbh7pIsuiYnBmHSask1WbA6Q3y3WPyqOka5rX9HRg0Kianx8XOoVYpyU8aHZr95fzp3Ph8ORUdLuJNWm45bSIlqf+6zbQQQgghhBBCfNVIsCaEEEep7nTx2zWVTM2Mo6rTxWv7O8aMae73YjNocHpDGLQqXP4Q/lCEB9+rB+CmE0q4f0MdBo2a206fxNDhJgLHlaXwwu4WrltVzB/fqY5160wwa/nZSSXU97rHvackq45AaGyqZtKqyIk3oVSCTqXCEwwRBSo7Xbxd0cUNqybw5sGRzp9b6vv4xpwsVpUmU5xsxmbU/svnMDkjjicvmUu3y49Zp5a11YQQQgghhBBiHBKsCSHEYU5vgNX721EqQKVQkOkYP0xKt+txeoNMy4rjie3No/bZjRpy4o1saxgAINmm573qHpzeIH/55nTOnJEJwD8umk3bgBd/KIJeo+THz+/n/HnZnD0jg2d3tcbONyPbTkWHi2gkyh1nTeZPa2toHfAyIyuOSxfnc6BtkNcPdDEn10Ft9zDbG/oBuGxJPnq1AqUCPiiOe3ZnK09/dw4TM+L+recRZ9QS9/8EcEIIIYQQQgjxVSbBmhDiK83tD7G3ZZDdjf0k2fQUJJqZnGEjGoV9rUOkxxloGzyy1lh+ogmjRs2Z0zNw+QLcfEoZj2xupNvpY35BArNzHVR0OmPjc+NNPD3UwvSsOLqdflr6vfhDYf65tZlfnVpGKBJl0BvitjMnsb9tiNOnpTMtM44Op49Um55Mu5HdzQNUdrr45SsHOXlKGstLk1EpRjp1RlHww+VFNA24SbHpmZnjwKRVsbaym0M6NVMy4tjTMghAIBxBp1X9rx+xEEIIIYQQQnxpSbAmhPhKe/NgJ+2DPlLiDLT0e0i3G+hzB1AAOrWSM2ek4w9GaOr3UJhoZm5+PJ5AkOUlSWjUCm54bj8LChJwmLTsbhrgttcruHp5EVa9mu8syuPV8nb0GiUnT0njxuf3Ez5qbbVBb5A1BzqZkR1HvElDlsPItx/ezt3nTsNu0hIFBoYDFKVYqOka5riJKRQkmalsdxJn1NDQ62ZqZhxRotz+xkgn0aMr1BYUxKM6at22KZk20uOM/8OnK4QQQgghhBBfbhKsCSG+sjoGvfQO+2kb9PBORRf+UJjZOQ6G/SFMOjUV7UPMK0jAYlAzPz+eHpefHY39/GV9HY9dNJvGPjfHlaXwj61NwEjnzJ+eUEKG3cB935hOY5+b06elkx1v5EcfCtXSbHpSbXrOmJ5OOBylttvNn9bWACOdPu96Z+T/p2baOGFiKhcuyKbL6QcFJJl19LgDLClK5OH3G7h4UR5JFh3dLj9H90Q4fmIqv159CICJaVZ+e8ZkbAbN/+jpCiGEEEIIIcSXnwRrQogvvbaBkQDNbtKS5ThSseUJhlArlaRY9Ty5vYUzpqezp3mQiek2ntzezLUri6nocLK5ro+yVCuLihL4w1vV+EMRuof93L6misJkM9euLCLVpkerUtLQ4+a1/Z0UJZlZUZp0OLjzcfykFJ7Y1kwkChl2AzefUkY0EiUShbZBD/euH2l8YDNoKEwyc9WyAlRKJe2DXrpcfnY1D3Lr65WUpVn5/uJ8frX6EN0uP6dPS+eF3a18f0k+z+1q5WC7E4tOzbUrizhhYjJzch34Q2Ey7cb/t2GBEEIIIYQQQoiPR4I1IcSXVigcYV1VNzc8V86AJ4hFp+bP502j2+WnttvF3NyRKjSdRglAbfcwKTY92fFGIlH47RuV/P7syVh0aio7Xfx6dQUAk9OtDLgDGHUqttb3o1AoSDTrWF6axJsVXRQlmSlLt1Hb5aamd5jV5R2sLE3mphNLGPIGyXEYeWF3K2sOdpHlMDIvzwFASaqFK48tZM3BTlaXt6NUKDhnVibHFCaiUSl47KLZFCSZ6HMHufmUMlDA09tbWF/dw46Gfh745gzsJi1GrZrMwwGiw6z/bB6+EEIIIYQQQnwFKKLRaPT/H/bl5nQ6sdlsDA0NYbVaP+vbEUJ8Sio6nJz0502xKZhnzcigustFeesQGXYD8/LiyU8009zvYWt9H/W9bn5yQglv7O/4v/buO8yuqt7/+Pv0c2bmnDO9ZWomk94TQgoQShJaKIpIE/AKV5AiAVRAvT+w0BXL5QKCKCJIUAREhJCAkEAKJCG9Z0oymV5PnTl1//6IjI4DCCHJJMPn9TzneZi1196z1n6+HMIna+/FV6aX8szqvXSFolx/SiUvrW9ga6Of4yuzGZKRgtth4Ud/28ackbmcN6WIlVXtrKru4LqTK6huDfU+yjl/fAGjCjw884/dQ88aX8i+zjB/3dgIwH3njcMAhqS7WLytmWfe3cuEonRmVGSR53EwPNfNmCFeUh37/x5kb0eYN7e38PftzcwYms2Mikx6YknyPPsDQZPJ1P9GiIiIiIiIyCf2aXIirVgTkUGrqiXY571mxZkpPLd2H7B/N9BUh5WFq/dy2YxSKvPS2N0S5IV1+7hsRilJw+D6k4fRHU1Q1RrkkmNLqG0P8frWFo4pyyTf4+TGOZUs393O1U+9D0Caw4rTaukN1QBe3tjI8qo2fnDWGLY2+ilMd7KjOcCMiixOqMwmz+skEkuybGcLc0fm4bSaWVPbhdlkojw7lZH57t5QrSMU5Tt/2sCqmg4Alu5soyI7ld9feSyF6a7DdVtFRERERETkH8wDPQARkUPFZbf0+flfF+h2hmPkuh20BaPcu2gHa/d0MrbQzTWzK3hieS3ffm4jtzy3iSZ/D/Vd3TT7e/C6bHx1VhlVrUGu/cP7DMt1E+iJATAiz82Pzx1Dg6+73zg6QzF2tQR5ZGk1r21pYlpZBsmkwS/e2EV7MMrXf7+WR9+u5dLfvMesihy+OrOUKaXpVGSn4fmX96Ltbg70hmofqGoLsb3JfzBvm4iIiIiIiHxCWrEmIoNCqCdOdXuIrlAUX0+MlVXtfGHSEIoyXOzr3B92dYZjlGalsKc9DMAjy6r44dlj2FjvozXQg9NmJRSLc8tpI6luCxHsiRGNJzl1dD7bmwKYTSa2Ngb4zfIaZlZk8cx7exmak8Y3Tqxg2a42vvPcJr55SmW/sZlMMDzPzd1fGMfmBh/3LNoBwJXHlfPosureflNLM3DZzcwaVoDV0v/vPSLx5IfOPRL78HYRERERERE5tBSsichRb19nmLte2cYrm5oAGJabxgXHFLOyqo0vTy2m0dfNxn0+wpEYd587jjd3trCp3seMoVmYzRCKxqltD7OtMcCjb1djs5g4f0oRZ44r4OGlVWxvDJDisPCnNXVE4gZjCj2cUJnD3a9uB6C6NURRhotoIsnSnS3cOKeSn7+xC8MAswlunjech97azZcmFzFuiJc7zhpNSVYKrf4e/D1xHFYzE0sycNnMgOlDQzWA8pxUslLttIeivW1Om5lhuWmH/B6LiIiIiIhIfwrWROSot2hzU2+oBvt393xrewvnTRnCTX/cSFGGi+F5bt7f08W8Mfm8urmJwnQXj79Tg78nzn/NKuP4YdnsbA4AEEsY/OG9OoblunlndzsAE4vT+cn5E9jdGqKmNcR9r+3o/X0ZqTbmjy9g1rBsct0OtjZ28ZMvTSAST9ATSxDojpNit1CUmUKjr5sJxemMKvDw2+U1rKxuozwrlVc3NXLGuAJG5n90SFaUkcIT/3UMd/5tG6tqOhhb6OH2s0ZTmec+RHdWREREREREPo6CNRE5qoWjcZ5/v75f+/Kqdr5xYgWp//KetXFDvPzh3Tr2dXb3Ph4KsPC9On5+wQSqWoN9rhGMxKnISaW6LcT6ui5+/XYNw3LTeGlDQ59+J43I5S8b6rlgagk3LFxHYXoKgZ56WgIRfnL+eOJJg+KMVL7x1Fq+feoIhue5cdosXD6jjGPLs6j3dZPndjAy343HZefjjCtK59dfnUpnKIbHZcX7H/qLiIiIiIjIoaNgTUSOanaLmWG5qWxt7PsC/5w0B2tqO3j0sin8bWMTG/Z1MWNCIX/Z2NDvGt2xBOFoghnDssn1OHn63b1YzSaKMlyMKvBw3uQille1sXx3O2eNL+Sbp7hYubudqWUZjMx38/jbNZw7uYjvvbCZBXOGE47GaQ9FmTMqj2PLM2nyRxhd4OGq2UMZmp2Kw7Y/7EtxWJlcmsFkMj7VnNMcNtIctgO/aSIiIiIiInJQKFgTkaNKLJGkPRjBYTHjsFtIsVv56sxyXtvS3Ofl/pfNLCUcjXPb85vY27F/dVpdR5jLZpSxub5vCDd9aCbxpMEP/7qVicXpXD6jlKKMFB5YspN9nd28vLGRa06soK6jG6vVzLT8TNJT7Px57T62NPi5cnYFv1teTWswwt2vbueOs0Zz8bQS8rwuAMqzrZRnpx6+myQiIiIiIiKHhYI1ETlq7Gj08/g7NSza2kRJRgoXTyshK83O9KGZ/PyCiaysbqc7lqAiO41FW5qYNzqvN1QD8PfEafb38JXppSzZ0sR5U4pIT7ExMs/D9ub9Ydv6ui4um1HKj17eSmc41nvuU6v28NAlk/GFIyze1syTK/f0Hnt7dxvfO2MUa/f6AIglk72hmoiIiIiIiAxeH771nIjIESTYE2dtbQePLK2iqi1EOJJgc4Of/3lpC13dcf6yoQGH1YzDYiIzxc4zq/eyqd6H29n/7w7+tHYfU0u8fOe0ETy5cg93vbKdy594jz3tYS6YWgRAs7+nT6gG+0O592o7sFmtPPPe3j7HDANW13YyqsCNxWxiWlnmobsZIiIiIiIicsTQijURGRBtgQitwQgep40hGR+9uiuRNKhqC1Lf1U1nOIbLZuHmecMJ9iRw2s0EemIMy02lO5rknap2WgMR5o8vZFp5Bg2+HqxmE/Gk0Xs9kwkcNivffm4jwUgc2B+MPf3uXu76wjj+9P6+D31s8/jKbIZmp+J12YgljH7Hu6NxJhR5+f6Zoxk7JP2z3yARERERERE54ilYE5HDyjAMVlS1c+vzG6nr6CY9xcbt80dz0sgcWgJRrGYTJZkpWC1mqluDLFy9lxF5bm7+08bea7yzu43vnjGSB/++G39PnPu/NJ5vP/fP40+sqKWhqxurxcTdXxzHvYu20xaM4nFZufW0keztCPeGav+qqjXAj88ei8Nq5vqTh/Hosmoi8STTyjKYP76AAq+LMUO8zBudx+KtzX3OvWhaCcdXZpPm1KYCIiIiIiIinxcmwzD6L734nPH7/Xi9Xnw+Hx6PZ6CHIzJoReMJNu7z8fauVkwmEy+tb6C6LYTJBA9fMpk/r91Ho6+HOaPzOH9qETct3MCFx5bw2LLqfrt+njwyhzPGFWA1mzAMqG0PYzab6AhFeWrVHpKGwcOXTCZpGERiSVLsFtbXdTE8340ZWPDHDfz7t9//nDmKlzc20haM8OdvzCQcTRCJJ/C6bHhddlz2/bt51rSF+OUbO/nL+gY8LhvfnjeCsyYU4HHZD9OdFBERERERkUPl0+REWrEmIodELJ6kJ5bAwCAcTeKymnn6vb38dMlOEkkDu8XMgjmVRBMJMlMd/HntPnzdMc6dNITMVBv1nd18YfIQ7BYTvu6+7zs7vjKbwnQXP/jrVr5z6gh+/LdtvTuClmWl8M2Th/GLN3bREohgs5gZmefm+3/ZzJYGP6VZKfy/s0Zx2fRSfvcvGxCUZKbQGoywrq4LgI5QlJEFH/4FWp6dyj3njefGOcOxWc0UaKMCERERERGRzyUFayJyUCWTBuvrOvnN8lq2NQaYPjSTwnQXO5r8jCtKx2o2YTbB9acMoy0YYWh2Gt//y+be89+r7eSK48qJxpNEogn8PTHmjy/gV8uqe/tMH5rF/a/t4JJjS3j4rareUA32r1xrCUS4fEYpy3e3UpqVhtNqZkvD/hVve9rDXPHEWq44rowHzp9AVVsIiwmiiSS/frsGgOJMF9lpjo+dp8NqoSSr/7vYRERERERE5PNDu4KKyEG1cV8XFzy6ipc3NlLVGuTpd/eycPVectxOHl1azbfmDef3X5vGqxsbKc1K4cX19f2u8fS7e/C67EQSBnluB0nD4JyJhVjNJpw2M7FEkrFDPIwu9NAciPQ7f31dFyePyuPv21tZu6eT2vYQE4q9ffo8/k4tbaEol04vZlO9j0eWVhNPGqTYLdz/pQlkuz8+WBMRERERERHRijUROaheWFffb9fMuo5uPC4bJ43MxWE1s6+zm7MnDmF7Y4C2YP9grCeWxGI28W5tBx3BKKePy8dqNnH1iRVMKPLSHU2Q73Hy+tZmvjVvOKtrO/n79pbe88cN8bJ8dxuxhEFpVgpLtjbzP/NH8/Un1xL4x6YFows8zBudR743hZ9+eSI7mwOEI3HKclIZmp12aG+SiIiIiIiIDAoK1kTkM6vrCLOhrouEYbCrJfihfVw2M16XlY5wjPQUO/e9tp2sNAfzxxfw2+W1ffpOK8tk4eq63rBsXV0X508poq4jRLrLxo//tq2375s7WvnqzDIqclKpag2RmWpnSmkG335uIyl2CyPy3GSn2ZlWnsVL1x9HdWsQh9XM8Dw3uR4nAJmpdqYPzTo0N0dEREREREQGLQVrIvKZ1HWEeHN7KxaLCZfNwpxReayoau/Xb3SBhxuWrueS6SW0BaMkDWgNREgacOqYPBZvbcYw9vc7f2oRt/x5Y5/zX1hXz31fGs9dr2zrd+1n3tvLLy6cyL7ObgrTnby2uZlLp5cysyKLztD+FW8Ws4ny7FTKs/VeNBERERERETk4BvQda8uWLeOss86isLAQk8nEiy++2Oe4YRjccccdFBYW4nK5OPHEE9myZUufPpFIhOuvv57s7GxSU1M5++yz2bdv32Gchcjn29YGP/cu2s73XtjMTX/cgL87ygmVOb3HTSa4cU4lG+q6sFvNNPsjGMY/HxX93YpausIxbjilkgcvmsSZ4/J5cmUtyb5Pk/a+/6wtGO03hkg8yZYGPz/+2za++cx6Njf4eXVzI6kOCxdPL6UkU2GaiIiIiIiIHHwDGqyFQiEmTJjAgw8++KHH77vvPh544AEefPBBVq9eTX5+PnPnziUQCPT2WbBgAS+88AILFy7knXfeIRgMMn/+fBKJxOGahsig0xmK8m51O0u2NrOzKUA8kfzQfo2+br7/ly2Eov/89+3nb+xmzqhc7jx3LHd9YSy/ufwYqlqCPPZODV+aUsSL6+opzUohK9Xee867NR08t2YfBgaLtjQxe0Ruv981Ij8Nm8VEZW7/959lpdqxW/d/ncWTBlWtQWZUZDNuiLdfXxEREREREZGDxWT869KRAWQymXjhhRc499xzgf2r1QoLC1mwYAG33HILsH91Wl5eHvfeey9XXXUVPp+PnJwcfv/733PBBRcA0NDQQHFxMa+88gqnnnrqJ/rdfr8fr9eLz+fD4/EckvmJHC32dYa57c8beXt3Oy6bhatmD8XjtNEZjjKpOJ3SrFRq2oI4rBbaQxEWPLuh3zUKvE5unz+aq59+n/+7eBKb6n00dPXQE4szZkg6y3e38tWZ5azc3caavZ1ML88iM83O71bUMmtYNmMLPayv8/HK5kYMAypy0rhiVhmReBJMcO+i7fTE9od9FrOJBy+aREVOKrtaguxpDzMy3824Ii85bufhvn0iIiIiIiJylPs0OdER+461mpoampqamDdvXm+bw+Fg9uzZrFixgquuuoq1a9cSi8X69CksLGTs2LGsWLHiI4O1SCRCJPLPnQj9fv+hm4jIUWbpjlbe3t1OaVYKPzhrDMFonPZglHyPkxVV7ezr6ibP7aChqxtvig2r2UT8357bHJLuIhJPMHdUHiur21m6oxWnzcIFxxTzwvv7yE93UdMWJMVh4YxxBQR7YryxrYW2YJS/rG/gL+sbuOiYYh67dCqhaJysNDtvbm/ldytqSU+xcf1Jw8jxOLGaTYzIdzMy34PFbGJ4voJxEREREREROXyO2GCtqakJgLy8vD7teXl57Nmzp7eP3W4nIyOjX58Pzv8wd999Nz/4wQ8O8ohFBoeXNzaQ63bwrbnDaQ9FWFfn4/WtzTT5exiV7yYztZCXNzYyqyKLcCzBhccU89S7e3vPN5vgyuPL8XfHuGBaEWv3dBGKJqjr7ObOV7Zx62kjefydGo4pzeRXy2q4dHopTpuZ44ZlcdrYfFoCEYozXOSkOegKR9nc4Ofv25v5wqQivnPaCPI9TkYXehiW6x7AuyQiIiIiIiJyBAdrHzCZTH1+NgyjX9u/+099brvtNm666aben/1+P8XFxZ9toCKDxNgh6Zw/tZiq1hBPvbsHw4CzJxYSiiT445o6ZlRE+cKkAlr8UX7w162cNjafm+YO5/09nXhcVk4Zlcf9r+1gWK6bsUM8TCvL5OG3qgAwDGjo6uarM8v42es7AXjq3T384KwxrK/rpDsa5BsnVnDeIyuJJQzGDvFw2pgCzpk4BIvZxNghHmZW5Hzc8EVEREREREQOmyM2WMvPzwf2r0orKCjobW9paeldxZafn080GqWzs7PPqrWWlhZmzpz5kdd2OBw4HI5DNHKRo0ciabCzOUBdR4hUu5V9Xd2ML/KwrTHAQ/8IwwB+u7yWC48pZlhuGos2N3LOxCk8umw9AIs2N/HGtmZGF3jY19VNRU4aVa0hqlpD+LqjWM1mRhW42da4f9ORcDTB5JJ0rjyunEAkTqHXxe9X7WFXS5D/N380uR4XaQ4rneEYm+v9bK7/56Paz/z3sYf1/oiIiIiIiIh8nAHdFfTjlJeXk5+fz5IlS3rbotEoS5cu7Q3NpkyZgs1m69OnsbGRzZs3f2ywJjJYReMJqtuC1LSFPnInzw/0RBO8W93O/7y4iUBPnMt+u5ofvbyNRBJ+s7ymX/8X1tUzb3QeWf94RDPQE+s9FksYbNjnY3dLkMS/7IeyqroDu8VEYbqrt+3EETnUd3aT43FQkumiJ57g+MpsbjltBKML3OR7ndw0d3i/3z+u0Etlnh7/FBERERERkSPHgK5YCwaD7N69u/fnmpoa1q9fT2ZmJiUlJSxYsIC77rqLyspKKisrueuuu0hJSeHiiy8GwOv1csUVV3DzzTeTlZVFZmYm3/rWtxg3bhxz5swZqGmJDIiGrm4efms3f3ivDrMJ/mtWOVceV06up+/OmIHuGCuq2/n129X4umN8cdIQtjcFsFlM3DxvOPs6w0Tj/UO5aCKJxWzizPEF1LSGOHlkHn9cU9enj91ixkTfx7C9Lhub9vlIsVu49qRhvLqpkatmV/DA4p28ubMVq9mE02ahMi+N8yYXAXDWhEKcNgu/eGMX/p4YX5pczFeml5CdppWmIiIiIiIicuQY0GBtzZo1nHTSSb0/f/Des8svv5wnnniC73znO3R3d3PNNdfQ2dnJsccey+LFi3G7/7lq5Wc/+xlWq5Uvf/nLdHd3c8opp/DEE09gsVgO+3xEBtIrmxr5/ar9mwgkgEeXVVOencrcUXlsbfQT7IkzLC+NdXu7uOXPG3vPu2fRDmYNy+Jb80bwm+U15LqdnDQylze2tfS5/uzhOZRmpfCnNXXMrMgmx+1gUnE66+q6AHDZLNwwp5InV9T2njO20EOux8lXZ5ZRnp1KdWuA7liSitw07vziON6r6WDtng7GF6UzfWhWbwiYnmLn/KnFnDwyl1giSY7bicX88e9WFBERERERETncTIbxL89tfU75/X68Xi8+nw+PxzPQwxH51EKRGBf8ahWjCj0Uel2YTbB2TyfhaILxRV5+s7wWkwkeOH8CP3x5K53hWL9r/OyCidz47HoAbpo7nOW723i3pgOA6UMz+cbsCrY3BRiel8Z3X9hEoy/CWeMLmDUsm5ZAhKmlGSza3MhT7+4lacCU0gy+c+oIlu1qpcnXw9TSDHa3hrh0eill2amH8/aIiIiIiIiIfGKfJic6YjcvEJFPzm6xcOXxQ/nFG7uoadsHwInDczh/ahGPLqsG4PIZZXhctg8N1QCsZhMWs4lE0uCBJTs5ZVQuC+ZU4rJZGJGXxjV/eJ9QJMHU0gyuPH4o9766g7V7OqnMc7N4axMNXd20Bnq4ed4IovEku1sCBHpieF025ozKozjDxZdTHVp5JiIiIiIiIoOGgjWRQcDfE+N//76LmrZQb9tbO1sZnucmlti/KHX28Gy6Y0kqc9PY1RLsc77ZBE6bmXMmFPL8unoA3tjWwhvbWrjz3LFc+4d1hKIJANbs6aQtGOGRSyfTEYwSSySZP66Aexbt2H/e9lYAvjK9hJ8u3kGjL8KzV+WS7e77rjcRERERERGRo52CNZFBYHO9j6rWUL/2P62t44fnjKElEKEjFOW3y2u5YU4lNz27gei/7Br61ZllPLt6L9+YPYxhuWn8dWMDLpuVcycWsqXB3xuqfaC2PUxdR5jH36mlPRThhlOGM26Il031PkwmmDMyjzSHlW1N+wO8mtYgI/K1o6eIiIiIiIgMLgrWRI4isUSSmtYQgZ4YdquZ0swUmgMREskPf1ViqsNKusvGuzWdvLiunq8dV4a/O8ovLpzI1gY/4ViCrFQ7b+1s5bQx+Wxu8PGrZVU8fMkUdrcE2dboZ09H+EOvXeB1EUskCUUS3LtoO2eMzWfBnEp2NgdYtquNR5ZW9/ZNc+qrRkRERERERAYf/d+uyFEiEkvwt02NBCNxfvtODTXtYaaWZvDdM0YQTxjMHp7N0p1tfc756swy7n51B2YT3DxvOC+tb+CyGaW8sL6eiuw0stMcdMfinDE2n8wUGy9vauTH547j6VW1bGkMcN7kIcwals2KqvY+1x07xEM4Guf8qUWMKfBgNpkoz0mlOD2FDft8rPyX/tOHZjIiX5uCiIiIiIiIyOCjYE3kKLGzJUhHKMrdr27vXaG2vSlAMJLk5j9t4OJjSyjwuli0pYk0h5Urjiunxd/D1kY/AJsbtnLb6SOpbQ9hMZlIc1pxWs24bHZy3A68LhuXzShjW5OfL04uomxvFyuq2qnITeOn54/n52/soi0Q5czx+Vx0TAkGMLMim1xP33enfW1WGVNK0tnS4Kc0K4VJJRnkuB2H+3aJiIiIiIiIHHIK1kSOcPFEkh3NAXY0+TGbTH0e+yzKcNHs7yEQifOrZdWUZqVw9oRCuqMJ4gmDbY2BPtdasrWZC48p5hdv7MZihstmlBLoTnD/4u3sagmRSBqMLnBz7UnDMJtNzB2dx9hCDyVZaZw0IpeeeJIctwObxfyR401PsTN7RC6zR+QesnsiIiIiIiIiciRQsCZyhKpqDbKnPUSgJ85Nf9wAwP+cOapPn/rObpy2f4Zce9rDPLlyDwB3fWEcK6v7PsLZHUuQ7XbwrXkjSHfZ+P3KvZw5voAfnD2WjnCUqpYgezu6ue6ZdVhMJp68YholWWkAZKZp1ZmIiIiIiIjIv1KwJnIE2t7oo7YtTKrTyo//tq13ldrQ7FRS7ZbeXToDkTgAk0rSWbe3q/f8XLeDjFQbkXiyz3UvOqaESDzBzIoscj0OzplYiNtl6z2+synA+n1djC/yMn6IlzFDvId4piIiIiIiIiJHLwVrIodBJJ4gGk9iGAaNvgipDgtFGSl9+vh7olS3hOgMRQhFE2zY58Nps9AaiPT2CccSPPyVKfxuRS07mgNMK89kT3uYS6eXcsrIXN7e1cbE4nSG5aaxtz3MzIosVlS147CauXr2UE4bm0/2x7zvbHi+m+H57kN2H0REREREREQGEwVrIoeQYRi8v7eTR96qorY9zJxReXSGo/xtYyO3nTGSsycOoSscoa6jm0AkzuNvV3P9KcPp6Orhsbdr+MKkIQzLTWN3SxCAeNKgMxTlmLJM3E4bG+p8PP9+PQDTyzP5/hmjuH/xDn61rJo8t4Mff2EsVxxXzpB0F8Pz3JjNpoG8HSIiIiIiIiKDioI1kUNoW6Ofix97t/eRzF0tQb48tZh8r5MVVW24bBa6wjGa/D14XTZOHJ7LL1/fxZzR+1/8/8qmRm49fSSPLaumwddDXUeYwnQXhmFQ2x6iqnV/4JbjdnDl8UPZ3NDJFycVccOcSmKJJImEwahCD5mpej+aiIiIiIiIyMGmYE3kENpc7+/3nrO/rK/n8cunkkgafO/FzcyoyKLQ6yQYiXNseSb3vraD86cWARCJJ7lv0Q7Om1JEvsfBpJJ03q3pIDvVwfgiLyeNzCWZNMh1O9jS0MXq2i6umj2UW57bxKOXTWFoTtpATFtERERERETkc8H8n7uIyIEIReJEE8l+7TaLmWW72li9pxOH1UJ3NEEwksDrsmE2m8hJc7CrOcC5EwuB/Tt5/mlNHU6bhc5QlDe2NtMZjjK5JIOidCcpdjMVualMK8/im6dU0B6M8PBXJitUExERERERETnEtGJN5CCLJ5Ls6wyzszlIWzCC12XD1x3rPX7htGJe2dTI+VOKqGoNcv7UIn61tIr/N380vnCMb506nFuf38Sd547l2PIsGv3dpDmspNjMPLashjPHF/LAkp1E4knMJvjmKZVkuuzkpbvw/ssOnyIiIiIiIiJyaClYEzmIqlsDbK73s3B1HakOKzubAtzzxXG8W9NBQ1c3Y4d42VTvY19nN/GkQXaanVc3NXLVCRU8/e5evjRlCENz0njwokk0+SPkex2UZ2cTTyZ5YmUtVquJXI+DH54zhlS7lZLMFEbku3HYLAM9dREREREREZHPHQVrIgeg2d/DlgYfrf4IeV4nuW4H4WiCcDTBjX/cwLCcNLwuG9FEkpVV7eR6HIwq8PD9FzcRSxgAPPZ2Nd+aN4L6rm6WV7Vx4bRi3A4r4WiMRl83hmFid0uIxS3NOG0WCr0uwpEEsXiSySUZVOa5B/guiIiIiIiIiHy+KVgT+ZRq2oJ8+08bWbOns7ft/vPG0hqKYjNbSCQNdjQH+NLUIhZtaaIoM4WucJR3drVy+cwynlheSzxp4LBaMJtMrNjdRnqKnWTSwGGzkOawkOd2srK6gz+8t7ff779p7nCFaiIiIiIiIiJHAAVrIp9CTWuQVTUdfUI1ALvNSjIZpTuR6G374+o6vn/mKDbX+5lYnI7TaibH4+BXl04hHI1jMZv57fIaXHYLFx9bwvZGH8UZKXSEYtisJs6ZWMjLmxrwd8d7r/nFSUMUqomIiIiIiIgcIRSsiXyEQE+MLQ1+mn09FKY7SSQNTCYTK6ra+vVNJAzMJhN1HSGmlWfyXk0Hu1qC3PXKdm49bQShSJyqthBjhni546Ut3PmFcaTZLSyYU8mSrc38dUMDlxxbSkaqjZw0KzneVLwuG3+6agbLd7dT0xbkuMocJpdkaIMCERERERERkSOEgjWRf1PXEaa6NchfNzbw3Np6AL49bwSrqts4bVw+BV5Xv3NeWFfP+VOLCETiGJg4blg2/p4YmSl2cjwOFizcQFGGi1y3g/ZQlDSHhUBPHLMJ5o3O47XNTVzx5GounlbKd08fSYpj/7+aI/I9jMj3HNb5i4iIiIiIiMgno2BN5B/aAhH2dYa58Y/rOW9ycW+oluaw0tUdY2JJBk2+CE6bmXyPkyZ/T++5rcEIQzKceJxW9nX18M6uNoakO8lxO3h8WQ3zxxcwf3whtz6/kXvPG8+kkgy6YwnW7unk6Xf34uuO8/MLJjF9aGZvqCYiIiIiIiIiRzb9H7x87nVH46zd08kdL23hrAmF7GkPE47+871mNouJnliCpGGQ63bw08U7ufrEoXRHk+ztCFORk0pRhout9X4C0QRjCz2kOCws2dZCcZaLq2YPJd/rJNCd4NmvT2d4nhuTyUSK3crxlTkcX5lDMmlgNpsG8C6IiIiIiIiIyKelYE0GlUgsgQE4bZb/2Lct0MPq2k6cNjNX/f59PC4rwUicpAEu+z/P7wzHKEx38sSKWq45qYIvH1PML9/YjcdlJc/tpLo1yOUzy8jzuli0opbsNAfPv1+Px2mlLDOVYblpFGWmfuxYFKqJiIiIiIiIHH0UrMmgEIrEWVnVzqPLqkkYBl+bVcbxldl4XPZ+fQ3DYE9bkFU1nVS1BMhPdxFNJGkLRqnMSwNgd0uQE4Zns2zn/o0KXt7YyHUnDePt3W3MGZnLT84fj687hstmoTgzBbvFzKrqNi6fUcaWRh/fOXUEU8syGFXgwe3UZgMiIiIiIiIig5GCNRkUlle18fUn1/b+vHZPJz/78gS+MLmot626JYivJ4phGHSGYtS2hbBZLfi7Y719TAZcc2IFv367hnlj8vj5BROobgvhsFpYsqWZsuxUMlMdJJJJbn9pC+dMKGR4Xiovb2xkZkUW08ozmTc2/7DOXUREREREREQGhoI1OeqFo3EefquqX/sv/76bWZXZJOIJ/r6jjZ8u2cn1Jw2jPDuVru4Yb+9u4+SRuTisFswmSBrw4oYGzh5fwHfPGElHOMraPV0UZbho9PUwIt9NcWYKKQ4zwZ4kD18ymVH5bjLTnEwozsRmMQ/A7EVERERERERkoChYk6OeYUAknuzXHo0nafF1s6s1xPde3MzkknTerWlnVIGHxq6e3p096zvDfOfUkTyyrIrlu9txWsxcecJQ/vDuXhZvbaYsK4XvnjESi9mMy24hzWZmRK6HLLej93dZ0DvSRERERERERD5vtMRGjirxxD8DtJZAD8t3t7Gqup3LZ5T263vp9FLaglGeea8OgAnF6azZ04nFDFub/FxwTDGvbmpk7uh8XtpQz5emFHHdycM4dVwBdouJOaNz+cOVx/KDc8ZQ0xZm2c420l12RhSm9wnVREREREREROTzSSvW5KiwsznAyxsbeGdXG8cNy+akkbl8+7mNVLcGufe88WSm2rnhlGH8cc0+4kmDi6YVM3aIhyZfD4mkAUB7MEKh10l7MMqpY/J5r6adBy+ezMqqNr4yvRSXzYK/J0ZppottjQE8LhuB7iihaIKiDBenjcsn3+Ma4DshIiIiIiIiIkcKk2EYxkAPYqD5/X68Xi8+nw+PxzPQw5F/U9Ma5ILHVtHijwDw38eXs3Gfj3drOvjxOWNYtqsNm8WM1QRnTiwk2BPnL+vr+fLUYqpaQxjAz5bsxGYxcf+XJrB8dxsnjsghEk/SEYwwfWgWneEogUic4gwXZhOEokne2NrMV2aUUZyZMrA3QEREREREREQOm0+TE+lRUDnibW8KkO9xcuqoXJ6+8ljKs1N5t6YDj8uK22VjybZm3t7dytwx+WxrDLCyqp19nd3YLSZcNgs7Gv389/FDsVvMLN7ayAnDs9nVEiSRTDKlLIOkAQaQleogEYfGrh4isSTXnDxMoZqIiIiIiIiIfCQFa3LEisQSbK7vIsdt546zRnHJjFJufHY9zf9YuVackUKgJ45hgL87TqrDQmaKjUWbGzlvchGLt7UwvsjLyaNy2dMe5NunjuALE4voiSWZUpJOZY4bw4BoPE5Wqh2P08qv3qliXHE6xw/PweuyD/AdEBEREREREZEjmd6xJgMilkhiNpmwmPvupunvjlHdGiTQE+fljY2cPDKH3S1BPC4bVouZlkCEPe1hKnPT2NfZjQmYVp7JezUdPLlyDxcdU8I3ThzGsp2tnDwqj2U7WzllVC7XnTyMRBIMw8DttNDoi1DbHibP48TrsvLXDfVMLcvif+aPIU/vURMRERERERGRT0DBmhxWzf5ulu5s49nVdeSk2bnmpAqMJAQicRw2M0+u2MOEEi8b63zEkwZPrtzDtScN455F2zlvchEAr2xq5LYzRvKzJTup7+pm7qg8ijJcLN7SzLzReZRkuvjyMcVE40nGDfHw1vZm3C47x5RlkuKwEIklGZLhoisc5fn39zGtPIvLZw6lIF2BmoiIiIiIiIh8cgrW5LDpjsb5+eu7eOa9OqaVZvBfs8oJ9iS4Z9F2LCYThelOSrNSCUcSvLyxke+dMYr7F++gMxzFajaT43ZgNZuIJpL8bMlOLp9Zhs1ipijDRUmmi/OnFGEY0BmOUJqVgtdhJZo0OHZoNne+so07X9nOr74yGa/LRmG6k+F5bo4pyyIjVY98ioiIiIiIiMinp2BNDpuqliDD89w8fcUxeFLs7G4O8of39rJxn4/bTh/Jfa/t4JoTK7BZzJhMJrY2+hld4CFhGMwbnceWeh+3nTGKBxbvwN8T53//vpu5o/IoznCxpz3MmEI3Wxr8bG8OsmRrM4lk/w1v7VYL0yuyB2D2IiIiIiIiIjLYKFiTQ84wDN6r6eD6Z9YxuSQdj9NKJGFgAKtrOwHIcTtIsVloCUQI9sQ4e0IhL65v4MGLJvH+nk6cdjNulw272cS9XxpPWyBCrsdJVoqdC3+9ihOH5zClNJ0R+W4K0lNYtLmp3zhS7BbKslMP8+xFREREREREZLBSsCaHlL87xqrqNn748jbaghHOmTiEuo5uAj0Jctx27BYz0USSDXVdXDSthN+v2sNPvzwBt9OCzWLizr9t49YzRuJxWDAME5gM0pxWCr12ApEE31y4DsOAkfkeZg3LwWw20dDVzfHDsnl7d1vvOEwmuPe88ZRlpQzg3RARERERERGRwcRkGEb/5+U+Z/x+P16vF5/Ph8fjGejhDBpVrQG2NvjZ1RLkl2/s5sTh2UwqySA7zYHdaqY7GmdbU4Bn3qvDZbNwzxfHsqM5yIa6Lr46q4wct53uqEE0nsBhNeGyWWgJRGkLReiOJnjwzSo6QlHsFjPPfWMG44vSe393s6+HjfVdrKxqJzvNwYyKLMYUerFbzQN3Q0RERERERETkiPdpciKtWJODIhZPUt8Vpr6zG5MJogkDu8XEs6v3cUxZBgA5bic2i5lIPElRhouVVUEyUmxcc2IFf9/ewpvbWzl9XAFFGS5CkThpDitmkwm3y0a6w8K25iBWi4n6zh4ef6eG7liCsYUevj9/NOOGePuMJ8/rZK43n7mj8wfidoiIiIiIiIjI54CCNflMEkmDXc1+IrEEWxoDhKMJSjJcbKz3MaMii1Akzpo9ncwbnceaPZ0cW57J8qo2ctLsHDs0k55Ygpq2MLOH55DnceDvjlKamcJfNzRw1oQCOnviuKwmttZHWLazldvPHsO80fl8eWoRkXiSHLeD9BTt6ikiIiIiIiIih58eBUWPgn5SnaEoezvCtAR6yEqxY7GYiCYMEskkwUiCe17dzndOHc7uliA7moLMHJZFOJrgB3/dyjUnVhBPGmSm2rBbzNgsZgrSXVgwyPU48bpsRBNJAt1x2kJROsNRstMcDEl3sLE+gMdpZWyhl4J010DfBhEREREREREZxPQoqBxUjZ1hGvzdOCxmLCYTmal2emJJ/r65mTPHFdIRjrGnI0xJZgqhaIImXw9pLiuNvh6mlWVyxrh8Hnqrih+eNYoRhV7i/wjjTIDNYsEwYH1dB9lpLrwuC+XZKUxKySAzdf9KtMo878cPUERERERERERkAChYk4/kC0ep6wiT57YRjiZo7Yni74lht5gxm02kOW1E4kniiSSJpEGzv4dQJEGe18XYIR7W1Hayqb6LC6cWc+7EIfh7YsTiCSwmMw2+CM2BHoozXdgsKQzP9VKclYrLbhnoaYuIiIiIiIiIfCIK1qSPfR1hIrEEHaEoKXYTNouZzY1BXt/WTHl2GgXpLjqCERJJA8MwcNjMxJIGqXYLsUQSswly0uysqmrnpJG5hCIJookEWal2PE4rOW47iSTkehzA/g0NPC7bAM9aREREREREROTTU7AmtAcj1HeEyUy1EYwm2Nzgw+20YbU68ffE2dUSJDvNia87Rq7bQTSRxO2wsnSnjzEFHpw2C13hGJdNL+XN7S2cNjaf4Xlumnw95KTZcbtcZDjtDMlKGeipioiIiIiIiIgcNArWPqfagxGa/WGMpIlUpxnMUNMexmw2E4kbRINRUu1W6jrCJA0DswnMJhOhSBy7xUx3LMkJw3OobQ8ztTQdt8NGo6+br0wvoTuaxOOykuKwkGI1k+9NwaOdO0VERERERERkkDEP9ADk8Gny9bCryc+ORh+NXWGcZgtpDguBngS7mkMYmGj0dZOb5qAtGCGWMEh1WinKSGFldRud4Sgd4SglmSl4XVZSbBYyUu00+HvITLUxtTSDrFQHhV4naQ4rX/7Vu3T1JBSqiYiIiIiIiMigpGDtc6CqJcjSHc2YknFaghGWbG0mnjSIJJMEIgniSdja4COeSOJ22FiytYk0u5VUh4UCt5NQT4yLppXisllwWi20BSPkuh2UZqdQmumiMseNLxxh6a5W2oIRumMJvrlwPRccU8yYgo/fllZERERERERE5GilYG2QisQSbGvsYmejj1gijtthY0NDkNc2NzGrMge3y0Y4lsTAIBpPkuN24LJZ8Lgs5HpcjBnipSMUwWyG8uw00uxmjh+ezch8N6MLPJBIcvlvVlPbHqYtGKE1FGNScTr5XicPL61i9ogcrjmxglSnNiYQERERERERkcHJZBiGMdCDGGh+vx+v14vP58PjOfpXWHUEe2gJRGgPxnDYzDT7ekh1WFhe1c7e9jBfO66MrFQ7sUQSixk6QnHiSYO69jBDMl2YTVDX0c3I/DRiCcjzOLBazFhMBqFIku3NAaLxJPleJ6l2M4mkiXSXhWgCGrrCZLmdjMr3YLMqtxURERERERGRo8unyYm0ecEgU9USoC0QpS3UQ3aqg437usj3uOjqjlHbFmT0EC8Ws4lEIkEsAV6ndf9mBXGD0uxUkhik2MxMLE7HZDaRazfzs9d3cdaEITT5eqjMc5Od5gAM2gMRXBkpFGU4aA1EyEp1MKIgf6BvgYiIiIiIiIjIYaElRYNIPJFk+e429naGaPJHcdotdIZjROMJNuzr4qSReRxTmkk4kiAYM7BZTLSFYxR4bGSnWclOszEiN42yrBRyU628v6eDOT97mzyPi1SHldGFaTy1qpZIPIHVbKI8Nw0w6AzHqcjzkJeeMtC3QERERERERETksNGKtUGk2d+DYUB7MEooGieeSBJPJGgPRRmZ7yHVbiHQEyM71YEZSBiQk2qnM2xgNpvISrUSTybZ0RBmRXU7NouJhy6ZTEaKDbPZRDwJ15w4jKRhYLOaKPC6cNpUQiIiIiIiIiLy+aRUZBBx2iw0+ropSE/BbDKxsrqDk0fls3x3G3leJ8mkgc0ENqsZi9lEMrn/EdBUu4nuWJL2UByTAUPSnVwyrYhgNEmwJ47VbMZus1CWlYLdahnoaYqIiIiIiIiIHBEUrA0iWWkORg/x4nFaaQ1E6YklqO8IcdqYfII9MWxWMzazGZvVRKrdgttpwdcdp8EXwWW3YBgGTpuFXS1BPCl2MlNsTCzJwGI2DfTURERERERERESOOArWBpkZ5Znsbg1SnJFCNJ4gxWEhaSRx2i3kpNrw9ySwmU34exJ0hGJgMpHjdmAAsYSBy25h3tiCgZ6GiIiIiIiIiMgRb9AEaw899BD3338/jY2NjBkzhp///Occf/zxAz2swy7H4yLNYaPBF8ZiduCwmjFhwkgmSXXYyPWmYDJpBZqIiIiIiIiIyGc1KIK1Z599lgULFvDQQw8xa9YsfvWrX3H66aezdetWSkpKBnp4h53LYaUi1zPQwxARERERERERGdRMhmEYAz2Iz+rYY49l8uTJPPzww71to0aN4txzz+Xuu+/+j+f7/X68Xi8+nw+PR4GUiIiIiIiIiMjn1afJicyHaUyHTDQaZe3atcybN69P+7x581ixYsWHnhOJRPD7/X0+IiIiIiIiIiIin8ZRH6y1tbWRSCTIy8vr056Xl0dTU9OHnnP33Xfj9Xp7P8XFxYdjqCIiIiIiIiIiMogc9cHaB/79hfyGYXzkS/pvu+02fD5f76euru5wDFFERERERERERAaRo37zguzsbCwWS7/VaS0tLf1WsX3A4XDgcDgOx/BERERERERERGSQOupXrNntdqZMmcKSJUv6tC9ZsoSZM2cO0KhERERERERERGSwO+pXrAHcdNNNXHrppUydOpUZM2bw6KOPsnfvXq6++uqBHpqIiIiIiIiIiAxSgyJYu+CCC2hvb+eHP/whjY2NjB07lldeeYXS0tKBHpqIiIiIiIiIiAxSJsMwjIEexEDz+/14vV58Ph8ej2eghyMiIiIiIiIiIgPk0+RER/071kRERERERERERAaCgjUREREREREREZEDoGBNRERERERERETkAChYExEREREREREROQAK1kRERERERERERA6AgjUREREREREREZEDoGBNRERERERERETkAChYExEREREREREROQAK1kRERERERERERA6AgjUREREREREREZEDYB3oARwJDMMAwO/3D/BIRERERERERERkIH2QD32QF30cBWtAIBAAoLi4eIBHIiIiIiIiIiIiR4JAIIDX6/3YPibjk8Rvg1wymaShoQG3243JZDrsv9/v91NcXExdXR0ej+ew/34Z/FRjcqipxuRQU43J4aA6k0NNNSaHmmpMDrXPS40ZhkEgEKCwsBCz+ePfoqYVa4DZbKaoqGigh4HH4xnUhSkDTzUmh5pqTA411ZgcDqozOdRUY3KoqcbkUPs81Nh/Wqn2AW1eICIiIiIiIiIicgAUrImIiIiIiIiIiBwABWtHAIfDwe23347D4RjoocggpRqTQ001JoeaakwOB9WZHGqqMTnUVGNyqKnG+tPmBSIiIiIiIiIiIgdAK9ZEREREREREREQOgII1ERERERERERGRA6BgTURERERERERE5AAoWBMRERERERERETkACtaOAA899BDl5eU4nU6mTJnC22+/PdBDkqPAsmXLOOussygsLMRkMvHiiy/2OW4YBnfccQeFhYW4XC5OPPFEtmzZ0qdPJBLh+uuvJzs7m9TUVM4++2z27dt3GGchR7K7776bY445BrfbTW5uLueeey47duzo00d1Jp/Fww8/zPjx4/F4PHg8HmbMmMGrr77ae1z1JQfb3XffjclkYsGCBb1tqjP5LO644w5MJlOfT35+fu9x1ZccLPX19XzlK18hKyuLlJQUJk6cyNq1a3uPq9bksygrK+v3XWYymbj22msB1dd/omBtgD377LMsWLCA733ve6xbt47jjz+e008/nb179w700OQIFwqFmDBhAg8++OCHHr/vvvt44IEHePDBB1m9ejX5+fnMnTuXQCDQ22fBggW88MILLFy4kHfeeYdgMMj8+fNJJBKHaxpyBFu6dCnXXnstq1atYsmSJcTjcebNm0coFOrtozqTz6KoqIh77rmHNWvWsGbNGk4++WTOOeec3j+oqb7kYFq9ejWPPvoo48eP79OuOpPPasyYMTQ2NvZ+Nm3a1HtM9SUHQ2dnJ7NmzcJms/Hqq6+ydetWfvrTn5Kent7bR7Umn8Xq1av7fI8tWbIEgPPPPx9Qff1HhgyoadOmGVdffXWftpEjRxq33nrrAI1IjkaA8cILL/T+nEwmjfz8fOOee+7pbevp6TG8Xq/xyCOPGIZhGF1dXYbNZjMWLlzY26e+vt4wm83GokWLDtvY5ejR0tJiAMbSpUsNw1CdyaGRkZFh/PrXv1Z9yUEVCASMyspKY8mSJcbs2bONG264wTAMfY/JZ3f77bcbEyZM+NBjqi85WG655RbjuOOO+8jjqjU52G644QajoqLCSCaTqq9PQCvWBlA0GmXt2rXMmzevT/u8efNYsWLFAI1KBoOamhqampr61JbD4WD27Nm9tbV27VpisVifPoWFhYwdO1b1Jx/K5/MBkJmZCajO5OBKJBIsXLiQUCjEjBkzVF9yUF177bWceeaZzJkzp0+76kwOhl27dlFYWEh5eTkXXngh1dXVgOpLDp6XXnqJqVOncv7555Obm8ukSZN47LHHeo+r1uRgikajPPXUU3zta1/DZDKpvj4BBWsDqK2tjUQiQV5eXp/2vLw8mpqaBmhUMhh8UD8fV1tNTU3Y7XYyMjI+so/IBwzD4KabbuK4445j7NixgOpMDo5NmzaRlpaGw+Hg6quv5oUXXmD06NGqLzloFi5cyPvvv8/dd9/d75jqTD6rY489lieffJLXXnuNxx57jKamJmbOnEl7e7vqSw6a6upqHn74YSorK3nttde4+uqr+eY3v8mTTz4J6LtMDq4XX3yRrq4uvvrVrwKqr0/COtADEDCZTH1+NgyjX5vIgTiQ2lL9yYe57rrr2LhxI++8806/Y6oz+SxGjBjB+vXr6erq4s9//jOXX345S5cu7T2u+pLPoq6ujhtuuIHFixfjdDo/sp/qTA7U6aef3vvP48aNY8aMGVRUVPC73/2O6dOnA6ov+eySySRTp07lrrvuAmDSpEls2bKFhx9+mMsuu6y3n2pNDobHH3+c008/ncLCwj7tqq+PphVrAyg7OxuLxdIvwW1paemXBot8Gh/sRvVxtZWfn080GqWzs/Mj+4gAXH/99bz00ku8+eabFBUV9barzuRgsNvtDBs2jKlTp3L33XczYcIEfvGLX6i+5KBYu3YtLS0tTJkyBavVitVqZenSpfzyl7/EarX21onqTA6W1NRUxo0bx65du/Q9JgdNQUEBo0eP7tM2atSo3g3vVGtysOzZs4fXX3+dK6+8srdN9fWfKVgbQHa7nSlTpvTuuPGBJUuWMHPmzAEalQwG5eXl5Ofn96mtaDTK0qVLe2trypQp2Gy2Pn0aGxvZvHmz6k+A/X/DdN111/H888/z97//nfLy8j7HVWdyKBiGQSQSUX3JQXHKKaewadMm1q9f3/uZOnUql1xyCevXr2fo0KGqMzmoIpEI27Zto6CgQN9jctDMmjWLHTt29GnbuXMnpaWlgP5MJgfPb3/7W3JzcznzzDN721Rfn8Dh3i1B+lq4cKFhs9mMxx9/3Ni6dauxYMECIzU11aitrR3oockRLhAIGOvWrTPWrVtnAMYDDzxgrFu3ztizZ49hGIZxzz33GF6v13j++eeNTZs2GRdddJFRUFBg+P3+3mtcffXVRlFRkfH6668b77//vnHyyScbEyZMMOLx+EBNS44g3/jGNwyv12u89dZbRmNjY+8nHA739lGdyWdx2223GcuWLTNqamqMjRs3Gt/97ncNs9lsLF682DAM1ZccGv+6K6hhqM7ks7n55puNt956y6iurjZWrVplzJ8/33C73b1/lld9ycHw3nvvGVar1bjzzjuNXbt2GU8//bSRkpJiPPXUU719VGvyWSUSCaOkpMS45ZZb+h1TfX08BWtHgP/7v/8zSktLDbvdbkyePNlYunTpQA9JjgJvvvmmAfT7XH755YZh7N92+/bbbzfy8/MNh8NhnHDCCcamTZv6XKO7u9u47rrrjMzMTMPlchnz58839u7dOwCzkSPRh9UXYPz2t7/t7aM6k8/ia1/7Wu9//3JycoxTTjmlN1QzDNWXHBr/HqypzuSzuOCCC4yCggLDZrMZhYWFxhe/+EVjy5YtvcdVX3Kw/PWvfzXGjh1rOBwOY+TIkcajjz7a57hqTT6r1157zQCMHTt29Dum+vp4JsMwjAFZKiciIiIiIiIiInIU0zvWREREREREREREDoCCNRERERERERERkQOgYE1EREREREREROQAKFgTERERERERERE5AArWREREREREREREDoCCNRERERERERERkQOgYE1EREREREREROQAKFgTERERERERERE5AArWREREREREREREDoCCNREREZFBpK6ujiuuuILCwkLsdjulpaXccMMNtLe3D/TQRERERAYdBWsiIiIig0R1dTVTp05l586dPPPMM+zevZtHHnmEN954gxkzZtDR0THQQxQREREZVBSsiYiIiAwS1157LXa7ncWLFzN79mxKSko4/fTTef3116mvr+d73/seAGVlZfzoRz/i4osvJi0tjcLCQv73f/+3z7V8Ph9f//rXyc3NxePxcPLJJ7Nhw4be43fccQcTJ07k97//PWVlZXi9Xi688EICgcBhnbOIiIjIQFKwJiIiIjIIdHR08Nprr3HNNdfgcrn6HMvPz+eSSy7h2WefxTAMAO6//37Gjx/P+++/z2233caNN97IkiVLADAMgzPPPJOmpiZeeeUV1q5dy+TJkznllFP6rHqrqqrixRdf5OWXX+bll19m6dKl3HPPPYdv0iIiIiIDzDrQAxARERGRz27Xrl0YhsGoUaM+9PioUaPo7OyktbUVgFmzZnHrrbcCMHz4cJYvX87PfvYz5s6dy5tvvsmmTZtoaWnB4XAA8JOf/IQXX3yR5557jq9//esAJJNJnnjiCdxuNwCXXnopb7zxBnfeeeehnq6IiIjIEUEr1kREREQ+Bz5YqWYymQCYMWNGn+MzZsxg27ZtAKxdu5ZgMEhWVhZpaWm9n5qaGqqqqnrPKSsr6w3VAAoKCmhpaTnUUxERERE5YmjFmoiIiMggMGzYMEwmE1u3buXcc8/td3z79u1kZGSQnZ39kdf4IHRLJpMUFBTw1ltv9euTnp7e+882m63f+clk8oDGLyIiInI0UrAmIiIiMghkZWUxd+5cHnroIW688cY+71lramri6aef5rLLLusNz1atWtXn/FWrVjFy5EgAJk+eTFNTE1arlbKyssM2BxEREZGjjR4FFRERERkkHnzwQSKRCKeeeirLli2jrq6ORYsWMXfuXIYMGdLn3WfLly/nvvvuY+fOnfzf//0ff/rTn7jhhhsAmDNnDjNmzODcc8/ltddeo7a2lhUrVvD973+fNWvWDNT0RERERI44CtZEREREBonKykrWrFlDRUUFF1xwARUVFXz961/npJNOYuXKlWRmZvb2vfnmm1m7di2TJk3iRz/6ET/96U859dRTgf2PdL7yyiuccMIJfO1rX2P48OFceOGF1NbWkpeXN1DTExERETnimIwP3mQrIiIiIp8LZWVlLFiwgAULFgz0UERERESOalqxJiIiIiIiIiIicgAUrImIiIiIiIiIiBwAPQoqIiIiIiIiIiJyALRiTURERERERERE5AAoWBMRERERERERETkACtZEREREREREREQOgII1ERERERERERGRA6BgTURERERERERE5AAoWBMRERERERERETkACtZEREREREREREQOgII1ERERERERERGRA/D/AWufZ7D9MQUcAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the relationship between 'Open' and 'Adj Close'\n",
     "create_scatter_plot(df['Open'], df['Adj Close'], size_data=df['Volume'],\n",
@@ -621,11 +1016,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 115,
    "metadata": {
     "id": "CD0LiK3VCwiU"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the relationship between 'Close' and 'Adj Close'\n",
     "create_scatter_plot(df['Close'], df['Adj Close'], size_data=df['Volume'],\n",
@@ -650,11 +1056,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 119,
    "metadata": {
     "id": "bgq_AQ-nCwnA"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the relationship between 'Open' and 'Close'\n",
     "create_scatter_plot(df['Open'], df['Close'], size_data=df['Volume'],\n",
@@ -670,11 +1087,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 122,
    "metadata": {
     "id": "Zqsy16wWCwrC"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cdtttWLFiBc6dO4eVK1ciICAAM2fONB/zySefYNKkSXjnnXesbttWMtHWr/GTTz7BnXfeieeee87q8tLSUgQHB9t0juZMSbBz5861SEoXFhY69GeKiIjIk7DSjIiIyI20lowBLrQomqqZgLYrS6ZMmYLDhw9jz549VpevWbMGgiCYK7yuueYaNDY2Wm0GbM+TTz6J5cuX4+mnn8bjjz9u021MMV999dX4/PPP8dZbb0Emk2HOnDnm603JiubJkvfee8/m+2hNa+cVRbHVjZ/2VOlMmTLFnOiztGbNGvj6+pq3TzrCbbfdBm9vb6xevRqrV69GXFwcrrzyyjaP79u3L5588kkMGTKkxePfFWz5mZo6dSpEUURBQQFGjRrV4mPIkCE23df8+fOh0+nw0ksv4ccff8Stt94KX19f8/WCILT4mcrOzm7RPmuv1s67YcMGFBQUWF1ma0UhAHPr6yeffGJ1+c6dO3HkyJEWyyCIiIjINqw0IyIiciNXXXUVevTogWnTpqF///7Q6/XYt28fXnnlFfj7++OBBx4wHztkyBB88cUXWLt2LZKSkuDt7Y0hQ4bgwQcfxJo1a3Ddddfh2WefRXx8PDZs2IC3334b9957L/r27QvAkJBZtWoV7rnnHhw7dgyXXXYZ9Ho9srKyMGDAANx6660t4nvggQfg7++Pu+++G7W1tXj99ddtqtCZP38+NmzYgA8++ABXXXWV1TywtLQ0hISE4J577sEzzzwDpVKJTz/9FPv37+/U9/KKK66ASqXCbbfdhkceeQSNjY145513UFFR0eLYIUOG4Ntvv8U777yDkSNHQiaTYdSoUa2e95lnnsEPP/yAyy67DE8//TRCQ0Px6aefYsOGDVi2bBmCgoI6Fbel4OBg3HjjjVi9ejUqKyvx8MMPW1VwZWdn469//StmzpyJPn36QKVS4ffff0d2djYee+wxh8VhK1t+psaPH4+7774bd911F3bt2oVLL70Ufn5+OHfuHP744w8MGTIE995770Xva9SoURg6dCiWL18OURStWjMBQ3LuX//6F5555hlMnDgRx44dw7PPPovExMR2N65ezNSpU7F69Wr0798fQ4cOxe7du/HSSy+1qBBLTk6Gj48PPv30UwwYMAD+/v6IjY21Snyb9OvXD3fffTfeeOMNyGQyXHPNNebtmT179sSDDz7Y4XiJiIg8mqRrCIiIiMih1q5dK86aNUvs06eP6O/vLyqVSrFXr17iHXfcIR4+fNjq2FOnTolXXnmlGBAQIAKw2v54+vRpcdasWWJYWJioVCrFfv36iS+99JKo0+msztHQ0CA+/fTTYp8+fUSVSiWGhYWJkydPFtPT083HwLg909Lnn38uKhQK8a677mpxztZoNBoxKipKBCB++eWXLa5PT08XU1NTRV9fXzEiIkJcsGCBuGfPnhbbB+fMmSP6+fm1eh+tbc9cv369mJKSInp7e4txcXHi3//+d/Gnn34SAYibN282H1deXi7edNNNYnBwsCgIgtVmQzTbnimKonjgwAFx2rRpYlBQkKhSqcSUlJQWWxJN2zO/+uorq8tt2choaePGjeZtqs03QxYXF4tz584V+/fvL/r5+Yn+/v7i0KFDxddee03UarXtnte0wfH8+fPtHjdx4kRx0KBBbV7XfIOrLT9ToiiKK1euFMeOHSv6+fmJPj4+YnJysnjnnXeKu3btajceS//9739FAOLAgQNbXKdWq8WHH35YjIuLE729vcURI0aI//vf/1r8nJgej5deeqnFOVp7rCoqKsT58+eLkZGRoq+vr3jJJZeI27dvb/V78fnnn4v9+/cXlUql1c9R8+2ZomjY7vriiy+Kffv2FZVKpRgeHi7efvvtYn5+vtVxbT0eF9seS0RE5IkEURTFLs/UERERERERERERuTDONCMiIiIiIiIiImqGSTMiIiIiIiIiIqJmmDQjIiIiIiIiIiJqhkkzIiIiIiIiIiKiZpg0IyIiIiIiIiIiaoZJMyIiIiIiIiIiomYUUgfgbHq9HoWFhQgICIAgCFKHQ0REREREREREEhJFETU1NYiNjYVM1nY9mdsnzQoLC9GzZ0+pwyAiIiIiIiIiIheSn5+PHj16tHm92yfNAgICABi+EYGBgRJHQ0REREREREREUqqurkbPnj3NOaO2uH3SzNSSGRgYyKQZEREREREREREBwEXHeHERABERERERERERUTNMmhERERERERERETXDpBkREREREREREVEzTJoRERERERERERE1w6QZERERERERERFRM0yaERERERERERERNcOkGRERERERERERUTNMmhERERERERERETXDpBkREREREREREVEzTJoRERERERERERE1w6QZERERERERERFRM0yaERERERERERERNcOkGRERERERERERUTNMmhERERERERERETXDpBkREREREREREVEzTJoRERERERERERE1w6QZERERERERERFRM0yaERERERERERERNcOkGRERERERERERUTNMmhERERERERERUQuiKEIURanDkAyTZkRERERERERE1EL22SqMfe43PPm/A1KHIgkmzYiIiIiIiIiIqIX0nDKU1KhRVKWWOhRJMGlGREREREREREQtZOSWAQDSksMkjkQaTJoREREREREREZGVJp0eu06VAwBSmTQjIiIiIiIiIiICss9Wol6jQ4ivEv2iAqQORxJMmhERERERERERkZX0k4bWzHFJYZDJBImjkQaTZkREREREREREZMXT55kBTJoREREREREREZEFtVaH3acrAHjuPDOASTMiIiIiIiIiIrKw90wl1Fo9IgK8kBzhL3U4kmHSjIiIiIiIiIiIzNJzLswzEwTPnGcGMGlGREREREREREQWMnM4zwxg0oycoKaxCS//cgyHC6ulDoWIiIiIiIiI7NCg0WFvvnGeWRKTZkQOtWJ7Ht7cfBIz301HhjE7TURERERERESub/fpCjTpRMQEeSM+zFfqcCTFpBk5lCiK+GF/IQCgTqPDnFU78NuRYomjIiIiIiIiIiJbpOeUAjBszfTkeWYAk2bkYIfPVSO3tA5eChkm94+ERqvHoo9343tjIo2IiIiIiIiIXFdGrqFjzNNbMwEmzcjB1u8/BwC4rF8k3rtjJG4YFgutXsQDX+zFZ1lnJI6OiIiIiIiIiNpSq9Yi+2wVAEOlmadj0owcRhRF/JBtqCiblhILpVyGV28ehtlje0EUgX+sO4D3t+VIHCURERERERERtWbnqXLo9CJ6hvqgR4hnzzMDmDQjB9qXX4mzFQ3wVckxuX8kAEAmE/DvGwbjnonJAIDnfjyKVzcegyiKUoZKRERERERERM2YlvmlJYVLHIlrYNKMHOaHbENr5uUDouCjkpsvFwQBj13TH49c3Q8A8PrvJ/HP9Yeh1zNxRkREREREROQqTEkztmYaMGlGDqHXi9hgTJpNHRrT6jGLJ/XGv6YPAgCsTj+FR77Jhlan77IYiYiIiIiIiKh1VQ1NOFTIeWaWmDQjh9h1ugJF1Y0I8FZgYr+INo+7IzUBr96cArlMwNe7z+L/Pt8LtVbXhZESERERERERUXM78sqhF4GkcD9EBXpLHY5LYNKMHMK0AODKgdHwUsjbPXbGiB54a9YIqOQy/HSwCAvX7EaDhokzIiIiIiIiIqmk55QCYJWZJSbNqNO0Oj1+PGBozZyW0nprZnNXD47Gh3NHwUcpx7bj53HnyixUNzY5M0wiIiIiIiIiagPnmbXEpBl1WlZeOUprNQjxVWJ8b9s3bEzoE4FPFoxBgLcCO09VYNaKTJTVqp0YKRERERERERE1V16nwdGiGgDAuCQmzUyYNKNOW7/f0Jp59eBoKOX2/UiNjA/FF3ePQ5ifCgcLqnHL+5koqmp0RphERERERERE1IrMXEOVWd8of4T7e0kcjetg0ow6RaPV4+dDRQCAaUNjO3SOQbFB+PKeVMQEeeNkSS1mvpeOM2X1jgyTiIiIiIiIiNpgas1MS7a9e8wTMGlGnfLnyVJU1jch3N8LYztRwpkc4Y+v7klFQpgv8ssbcNO76TheXOPASImIiIiIiIioNRnGSjO2Zlpj0ow6Zb1xa+Z1Q6IhlwmdOlePEF98eU8q+kUFoKRGjZvfy8D+/EoHRElERERERERErSmpacTJkloIAjAuKVTqcFwKk2bUYY1NOmw6VAwAmJrSsdbM5iIDvLF20Tik9AxGZX0TZn+QZe6tJiIiIiIiIiLHMrVmDogORLCvSuJoXAuTZtRhW4+fR41ai5ggb4zsFeKw8wb7qvDpgrFITQpDrVqLOSt3YPPREoedn4iIiIiIiIgMTIUqaclszWyOSTPqsB+yzwEArhsSA1knWzOb8/dSYNVdo3H5gEiotXosXLMLPxhbQYmIiIiIiIjIMUyVZqlMmrXApBl1SL1Gi18PG1ozpzmoNbM5b6Uc79w+EtenxEKrF3H/53uxducZp9wXERERERERkacprGzAqbJ6yARgdCLnmTXHpBl1yO9HS9DQpEOvUF8M7RHktPtRymV47ZZhuG1ML+hF4NFvDuCD7blOuz8iIiIiIiIiT2GqMhsSF4RAb6XE0bgeJs2oQ9bvN7RKTh0aA0FwbGtmc3KZgOduHIxFlyYBAP694Qhe23Qcoig69X6JiIiIiIiI3FlGrqk1M1ziSFwTk2Zkt5rGJmw+dh4AMHWoc1ozmxMEAY9d0x8PX9kXAPDf307g3xuOMHFGRERERERE1EGcZ9Y+Js3IbpsOF0Oj1SMpwg8DYgK67H4FQcBfJ/fB0mkDAQAf/pGHx745AJ2eiTMiIiIiIiIie+SX16OgsgEKmYBR8SFSh+OSmDQju5m2Zk4bGuv01szWzB2fiJdnpkAmAGt35eP+L/ZCo9V3eRxERERERERE3VV6TikAIKVnMPy8FBJH45qYNCO7VNZrsO24oTVzWkqMZHHcNLIH3po1Akq5gA3Z53D3x7vQoNFJFg8RERERERFRd2JqzUxja2abmDQju/xyqAhavYj+0QHoHdl1rZmtuWZIDD6YMxreShm2HDuPOat2oKaxSdKYiIiIiIiIiFydKIoXlgAkMWnWFibNyC7m1syUrlkAcDET+0bg4/ljEeClwI68csz+IAvldRqpwyIiIiIiIiJyWXmldSiuVkMll2EE55m1SfKkWUFBAW6//XaEhYXB19cXw4YNw+7du83Xi6KIpUuXIjY2Fj4+Ppg0aRIOHTokYcSeq7RWjT9PGnqepw6VrjWzudEJofj87nEI9VMh+2wVbnkvA8XVjVKHRUREREREROSS0o2tmSPig+GtlEscjeuSNGlWUVGB8ePHQ6lU4qeffsLhw4fxyiuvIDg42HzMsmXL8Oqrr+LNN9/Ezp07ER0djSuuuAI1NTXSBe6hfjpYBL0IDO0RhPgwP6nDsTI4LghfLhqH6EBvnCipxcx3M5BfXi91WEREREREREQu50JrZrjEkbg2SZNmL774Inr27IlVq1ZhzJgxSEhIwJQpU5CcnAzAUGW2fPlyPPHEE5gxYwYGDx6Mjz76CPX19fjss89aPadarUZ1dbXVBznG+v2FAAxbM11R78gAfHVPKuLDfHGmvB43vZuOE8VMrhIRERERERGZiKKILFPSjEsA2iVp0uz777/HqFGjMHPmTERGRmL48OFYsWKF+fq8vDwUFRXhyiuvNF/m5eWFiRMnIj09vdVzPv/88wgKCjJ/9OzZ0+lfhycoqmrEzlPlAIDrXKg1s7meob74alEq+kb5o7hajZvfy8CBs1VSh0VERERERETkEk6U1KK0VgNvpQwpPYOkDselSZo0y83NxTvvvIM+ffrgl19+wT333IP7778fa9asAQAUFRUBAKKioqxuFxUVZb6uuccffxxVVVXmj/z8fOd+ER5iw4FzEEVgZHwIYoN9pA6nXZGB3lh7dyqG9ghCRX0TZq3IxI68cqnDIiIiIiIiIpJcunFW+eiEUHgpOM+sPZImzfR6PUaMGIHnnnsOw4cPx6JFi7Bw4UK88847VscJgmD1/6IotrjMxMvLC4GBgVYf1Hk/ZJtaM123ysxSiJ8Kny4Yi7GJoahRa3HnyixsOVYidVhEREREREREkjLNMxuXxNbMi5E0aRYTE4OBAwdaXTZgwACcOXMGABAdHQ0ALarKSkpKWlSfkfPkl9dj75lKCAJw7ZDukTQDgABvJT6aNwaT+0eisUmPhWt24ccD56QOi4iIiIiIiEgSer2IzFxDJxbnmV2cpEmz8ePH49ixY1aXHT9+HPHx8QCAxMREREdHY9OmTebrNRoNtm7dirS0tC6N1ZNtMCaaxiWGITLQW+Jo7OOtlOPd20di6tAYNOlE/PWzPfhqF1t2iYiIiIiIyPMcPleNqoYm+KnkGBLHeWYXI2nS7MEHH0RmZiaee+45nDx5Ep999hnef/993HfffQAMbZlLlizBc889h3Xr1uHgwYOYO3cufH19MWvWLClD9yimrZlTU7pPlZkllUKG/946HLeO7gm9CPz962ys+jNP6rCIiIiIiIiIulSmsTVzTGIolHJJU0LdgkLKOx89ejTWrVuHxx9/HM8++ywSExOxfPlyzJ4923zMI488goaGBixevBgVFRUYO3YsNm7ciICAAAkj9xx5pXU4VFgNuUzANYO7Z9IMAOQyAc/PGAJ/LwU++CMP/1x/GDWNWvzf5N5tzscjIiIiIiIicicZOYakGVszbSOIoihKHYQzVVdXIygoCFVVVVwK0AFv/HYCr2w6jkv7RmDNvDFSh9Npoiji9d9O4rVfjwMAFk5IxD+uHcDEGREREREREbk1rU6P4c9uQo1ai/V/vQRDenhue6atuSLW4lG71nezrZkXIwgCHri8D56ealhAsWJ7Hv6x7gB0erfOHRMREREREZGHO1hYjRq1FoHeCgyMZVGRLZg0ozYdK6rB8eJaqOQyXDkoWupwHGreJYlY9pehkAnA5zvy8cAXe9Gk00sdFhEREREREZFTmFozxyaFQS5jt5UtmDSjNv1grDK7tG8EgnyUEkfjeDeP7ok3bhsBpVzAD9nnsOjj3Whs0kkdFhEREREREZHDZRiXAKQmcZ6ZrZg0o1aJoogfss8BAKZ1062ZtrhuaAxW3DkK3koZfj9agrmrdqBWrZU6LCIiIiIiIiKH0Wj12HWqHACXANiDSTNq1aHCauSV1sFLIcOUAVFSh+NUk/pFYs28sfD3UiAztxyzV2Siok4jdVhEREREREREDpF9thL1Gh1CfJXoFxUgdTjdBpNm1CrTAoApAyLh76WQOBrnG5MYis8WjkWIrxL7z1bh1vczUVLdKHVYRERERERERJ1mmmeWmhwGGeeZ2YxJM2pBFEX8sN/Qmjl1aKzE0XSdoT2C8eWiVEQGeOFYcQ1mvpeB/PJ6qcMiIiIiIiIi6hTOM+sYJs2ohb35lSiobICfSo7L+kVKHU6X6hMVgK/vSUPPUB+cLqvHze9l4GRJrdRhEREREREREXVIY5MOu09XAOA8M3sxaUYtmKrMLh8YBR+VXOJoul6vMF98tSgNfSL9ca6qEbe8l4GDBVVSh0VERERERERkt71nKqHW6hER4IXkCH+pw+lWmDQjK3q9iA0HDPPMpnlQa2Zz0UHeWLsoFUPiglBWp8FtKzLNm0aIiIiIiIiIugvL1kxB4DwzezBpRlZ2nipHcbUaAd4KTOgbLnU4kgr1U+HThWMxJiEUNY1a3PHhDmw7fl7qsIiIiIiIiIhslmmxBIDsw6QZWTFtzbx6UDS8FJ7XmtlcoLcSH80bg4l9I9DQpMOCj3bh54PnpA6LiIiIiIiI6KIaNDrszTfOM+MSALsxaUZmWp0ePx0oAgBMTfHc1szmfFRyrLhzFK4bEgONTo/Fn+7BN7vPSh0WERERERERUbt2nS5Hk05ETJA34sN8pQ6n22HSjMwycstQVqdBiK8SaSzbtKJSyPD6bcNx86ge0IvAQ1/tx5qMU1KHRURERERERNSmDIvWTM4zsx+TZmRm2pp5zZAYKOX80WhOLhPwwoyhmDc+EQDw9HeH8NbmkxBFUeLIiIiIiIiIiFqyXAJA9mNmhAAAGq0ePxlndU0dGiNxNK5LJhPw1NQBeGBKHwDAS78cwws/H2XijIiIiIiIiFxKrVqL7LNVALgEoKOYNCMAwB8nz6O6UYuIAC+MTeQvU3sEQcCDV/TFk9cNAAC8tzUXT/zvIHR6Js6IiIiIiIjINezMK4dOL6JnqA96hHCeWUcwaUYAgPXG1szrhsRALmOfsy0WTEjCCzOGQBCAz7LO4G9f7kOTTi91WERERERERETm1sy0pHCJI+m+mDQjNDbpsOlwMQBgWgpbM+1x65heeP3W4VDIBHy3rxD3frIbjU06qcMiIiIiIiIiD2e5BIA6hkkzwpZj51Gr1iI2yBvDe4ZIHU63My0lFu/fORJeChl+PVKCeat3ok6tlTosIiIiIiIi8lBV9U04VMh5Zp3FpBlhfXYhAGBqSixkbM3skMn9o/DRvDHwU8mRnlOG2R9kobJeI3VYRERERERE5IGy8sqgF4GkCD9EBXpLHU63xaSZh6vXaPH7kRIA3JrZWeOSwvDZwnEI9lViX34lbn0/EyU1jVKHRURERERERB7GNM8sNYlVZp3BpJmH+/VICRqadIgP88WQuCCpw+n2UnoGY+3dqYgI8MLRohrc/G4GzlbUSx0WEREREREReRDOM3MMJs083A/7ja2ZQ2MgCGzNdIR+0QH4+p5U9Ajxwamyetz8bgZyz9dKHRYRERERERF5gLJaNY4W1QAwdERRxzFp5sGqG5uw5dh5AMDUobESR+Ne4sP88NU9qUiO8ENhVSNufi8DhwurpQ6LiIiIiIiI3FxWXjkAoF9UAML9vSSOpntj0syDbTpUDI1Oj96R/ugfHSB1OG4nJsgHXy5KxaDYQJTWanDr+xnYfbpC6rCIiIiIiIjIjbE103GYNPNg5q2ZbM10mjB/L3x+9ziMTghBdaMWd3yYhT9OlEodFhEREREREbkp0xIAtmZ2HpNmHqqiTmNO3rA107kCvZVYM28sJvQJR71Gh3mrd2LjoSKpwyIiIiIiIiI3U1LdiJMltRAEYFxSqNThdHtMmnmoXw4VQasXMSAmEL0j/aUOx+35qOT4YM4oXD0oGhqdHvd+ugfr9p6VOiwiIiIiIiJyI6Yqs4ExgQj2VUkcTffHpJmHMrVmTkuJkTgSz+GlkOPNWcPxlxE9oNOLeHDtfnyceVrqsIiIiIiIiMhNZBqTZqlszXQIJs080PkatXkw4NQhbM3sSgq5DC/dNBRz0xIAAE/97yDe3nJS2qCIiIiIiIjILXAJgGMxaeaBfjp4DnoRSOkZjF5hvlKH43FkMgHPTBuI/5vcGwCw7OdjePHnoxBFUeLIiIiIiIiIqLsqrGzAqbJ6yARgdCLnmTkCk2Ye6If95wAA04ayNVMqgiDgoSv74fFr+gMA3tmSg6e/OwS9nokzIiIiIiIisp+pymxIj2AEeisljsY9MGnmYc5VNWDHqXIAwLVDmDST2qKJyfjPjYMhCMDHmafx0Ff7odXppQ6LiIiIiIiIupkMzjNzOCbNPMyGbEOV2eiEEMQG+0gcDQHA7LHxWH7LMMhlAtbtLcC9n+5BY5NO6rCIiIiIiIiomxBFkfPMnIBJMw+z3pg0mzqUCwBcyfRhcXjv9pFQKWTYdLgYCz7ahXqNVuqwiIiIiIiIqBvIL29AQWUDFDIBo+JDpA7HbTBp5kHyy+uxP78SMgG4Zki01OFQM5cPjMLqu0bDTyXHHydLcfsHWahqaJI6LCIiIiIiInJxGbmlAIBhPYPh56WQOBr3waSZB1mfXQgAGJcUhsgAb4mjodakJYfjkwVjEeSjxJ4zlbj1/UyU1qqlDouIiIiIiIhcGFsznYNJMw9i3pqZwtZMVza8VwjWLhqHcH8vHDlXjZvfzUBhZYPUYREREREREZELEkWRSwCchEkzD5FzvhaHz1VDIRNw9SC2Zrq6/tGB+OqeVMQF+yC3tA4z381AXmmd1GERERERERGRi8ktrUNxtRoquQwjOM/MoZg08xCmKrNL+oQjxE8lcTRki8RwP3x1TyqSwv1QUNmAme9m4Mi5aqnDIiIiIiIiIhdias0cER8Mb6Vc4mjcC5NmHkAURfM8M27N7F5ig33w5T2pGBgTiNJaNW55LwN7z1RIHRYRERERERG5iAutmeESR+J+mDTzAMeKa3CypBYquQxXDoqSOhyyU7i/Fz6/exxGxoegulGL2R9kIT2nVOqwiIiIiIiISGKiKCKTSwCchkkzD2BqzZzYLwKB3kqJo6GOCPJR4uP5YzChTzjqNTrMXbUTvx4uljosIiIiIiIiktDx4lqU1WngrZQhpWeQ1OG4HSbN3Jx1a2aMxNFQZ/iqFPhgzihcOTAKGq0eiz7Zje/2FUgdFhEREREREUkkw9iFNDohFF4KzjNzNCbN3NzBgmqcLquHt1KGywewNbO781LI8fbsEZgxPA46vYgla/fh06zTUodFREREREREEjDNMxuXxNZMZ2DSzM2Zqsym9I+Cn5dC4mjIERRyGV6emYI7xsVDFIEn1h3Ee1tzpA6LiIiIiIiIupBeLyIztxwA55k5C5NmbkwURWzINswzm5bC1kx3IpMJeHb6ICyelAwAeP6no3j5l2MQRVHiyIiIiIiIiKgrHD5XjaqGJvip5BgSx3lmzsCkmRvbc6YSBZUN8FPJMalfpNThkIMJgoBHru6PR6/uDwB4c/NJ/HP9Yej1TJwRERERERG5u0xja+aYxFAo5UzvOAO/q25s/X5Da+aVg6LhreRAQHd176Rk/OuGwRAEYHX6Kfz962xodXqpwyIiIiIiIiInysgxJM3Ymuk8TJq5KZ1exI8HDK2Z3Jrp/u4YF4/Xbh4GuUzAN3vO4q+f7YVaq5M6LCIiIiIiInICrU6PrDzjPLOkcImjcV9MmrmpHXnlKKlRI9BbgQl9IqQOh7rADcPj8M7sEVDJZfj5UBEWfLQL9Rqt1GERERERERGRgx0srEatWotAbwUGxgZKHY7bYtLMTf1g3Jp59eBoqBR8mD3FlYOisequ0fBVybH9RCnu/HAHqhqapA6LiIiIiIiIHMjUmjk2KQxymSBxNO6L2RQ3pNXp8dPBIgDA1KGxEkdDXW1873B8PH8sAr0V2HW6ArNWZKKsVi11WEREREREROQgGcYlAKlJnGfmTEyauaH0nDKU12kQ6qdCGgcCeqSR8SH44u5UhPurcKiwGje/l4FzVQ1Sh0VERERERESdpNHqsdM0z4yv+Z2KSTM3ZNqaec3gaCi4dtZjDYwNxJeLUhEb5I2c83W46Z0MnCqtkzosIiIiIiIi6oTss5VoaNIh1E+FflEBUofj1phRcTNqrQ6/HDK0Zk5LYWump0uK8MdX96YhMdwPBZUNmPleBo4V1UgdFhEREREREXWQaZ7ZuKRQyDjPzKmYNHMz24+XorpRi8gAL4xOCJU6HHIBccE++HJRKvpHB+B8jRq3vJ+BffmVUodFREREREREHcB5Zl2HSTM3Y9qaed3QGG7QILOIAC+svTsVw3sFo7K+CbNXZJrfnSAiIiIiIqLuobFJh12nKwBwnllXYNLMjTQ26bDpcDEAbs2kloJ8lfhk/likJYehTqPD3FU78PvRYqnDIiIiIiIiIhvtPVMJjVaPiAAvJEf4Sx2O22PSzI1sPlqCOo0OccE+GNErWOpwyAX5eSmwcu5oXD4gCmqtHnev2W1eHEFERERERESuzbI1UxDYXeZsTJq5kR+yzwEApg6N4S8PtclbKcc7t4/A9GGx0OpF3P/FXny+44zUYREREREREdFFZBrH7LA1s2swaeYm6tRa/GZstePWTLoYpVyG124ehtlje0EUgce/PYAV23KlDouIiIiIiIja0KDRYW++cZ4ZlwB0CUmTZkuXLoUgCFYf0dHR5utFUcTSpUsRGxsLHx8fTJo0CYcOHZIwYtf165FiNDbpkRDmi0GxgVKHQ92ATCbg3zcMxqKJSQCA//x4BK9uOg5RFCWOjIiIiIiIiJrbdbocTToRsUHeiA/zlTocjyB5pdmgQYNw7tw588eBAwfM1y1btgyvvvoq3nzzTezcuRPR0dG44oorUFNTI2HErmn9flNrZixbM8lmgiDg8WsG4O9X9QMAvP7bCTz7w2Ho9UycERERERERuZIMY2vmuGTOM+sqkifNFAoFoqOjzR8REREADFVmy5cvxxNPPIEZM2Zg8ODB+Oijj1BfX4/PPvtM4qhdS1VDE7YdPw+ArZnUMfdd1hvPTh8EAFj15yk8+k02dEycERERERERuQzLJQDUNSRPmp04cQKxsbFITEzErbfeitxcw1ylvLw8FBUV4corrzQf6+XlhYkTJyI9Pb3N86nValRXV1t9uLuNh4qg0enRJ9If/aIDpA6Huqk7UxPw6s0pkMsEfLX7LP7v8z3QaPVSh0VEREREROTxatVaZJ+tAsAlAF1J0qTZ2LFjsWbNGvzyyy9YsWIFioqKkJaWhrKyMhQVFQEAoqKirG4TFRVlvq41zz//PIKCgswfPXv2dOrX4ApMWzNZZUadNWNED7w1awRUchl+PFCEhWt2oUGjkzosIiIiIiIij7Yzrxw6vYheob7oEcJ5Zl1F0qTZNddcg7/85S8YMmQILr/8cmzYsAEA8NFHH5mPad6nK4piu727jz/+OKqqqswf+fn5zgneRZTXafDHyVIAwNShMRJHQ+7g6sHR+HDuKPgo5dh6/DzmrNyB6sYmqcMiIiIiIiLyWGzNlIbk7ZmW/Pz8MGTIEJw4ccK8RbN5VVlJSUmL6jNLXl5eCAwMtPpwZz8fLIJOL2JQbCCSIvylDofcxIQ+Efh4/hgEeCuw41Q5Zq3IRHmdRuqwiIiIiIiIPJJpCQBbM7uWSyXN1Go1jhw5gpiYGCQmJiI6OhqbNm0yX6/RaLB161akpaVJGKVr+SG7EIBhayaRI41KCMXnC8chzE+FgwXVuOW9DBRVNUodFhERERERkUepqm/CwULOM5OCpEmzhx9+GFu3bkVeXh6ysrJw0003obq6GnPmzIEgCFiyZAmee+45rFu3DgcPHsTcuXPh6+uLWbNmSRm2yyipaUSmsUSTrZnkDIPjgrB2USpigrxxoqQWM99Lx5myeqnDIiIiIiIi8hhZeWUQRSApwg9Rgd5Sh+NRJE2anT17Frfddhv69euHGTNmQKVSITMzE/Hx8QCARx55BEuWLMHixYsxatQoFBQUYOPGjQgI4IZIAPjpQBH0IjCsZzB6hnIQIDlH70h/fHVPKhLCfJFf3oCb3k3H8eIaqcMiIiIiIiLyCJxnJh1BFEVR6iCcqbq6GkFBQaiqqnK7+WY3vZOOXacr8OR1A7BgQpLU4ZCbK6lpxB0f7MCx4hqE+Crx0bwxGNojWOqwiIiIiIiI3NrVy7fhaFEN3pw1nKOZHMTWXJFLzTQj2xVWNmDX6QoIAueZUdeIDPDG2kXjkNIzGBX1TZi1IgtZxnc8iIiIiIiIyPHKatU4WmTo9BnHSrMux6RZN7Uh+xwAYHR8KKKD2NNMXSPYV4VPF4zFuKRQ1Kq1uHPlDmw+ViJ1WERERERERG4pK68cANAvKgDh/l4SR+N5mDTrpkxbM6elcAEAdS1/LwVW3zUGU/pHQq3V4+41u8xJXCIiIiIiInKcjBzjPDNuzZQEk2bd0OmyOuw/WwWZAFw9mEkz6nreSjnevWMkpqXEokkn4v8+34Mvd+ZLHRYREREREZFbSc8pBcDWTKkwadYN/WCs6klLDkdEAMszSRpKuQzLbxmG28b0gl4EHvkmGx/+kSd1WERERERERG6hpLoROefrIAjAuKRQqcPxSEyadUOmpNnUoawyI2nJZQKeu3Ew7r7UsL31Xz8cxvJfj8PNl/ISERERERE5XYZx8drAmEAE+6okjsYzMWnWzZwsqcWRc9VQyARcPTha6nCIIAgCHr+mPx66oi8AYPmvJ/DvDUeYOCMiIiIiIuqETGPSLJWtmZJRSB0A2Uej1WNy/0goZAIzzeQyBEHA/03pA39vBf65/jA+/CMPdWot/nPjEMhlgtThERERERERdTvpXAIgOSbNupmBsYFYOXc09HpW8ZDruWt8Ivy9FHj0m2x8sTMftWotXr15GFQKFrUSERERERHZqrCyAafL6iGXCRiTyHlmUuEr2W5KxuodclEzR/XEW7NGQCkX8EP2OSz6eBcam3RSh0VERERERNRtZBirzAbHBSHAWylxNJ6LSTMicrhrhsTggzmj4a2UYfOx85izcgdqGpukDouIiIiIiKhbyOA8M5fApBkROcXEvhFYM28sArwUyMorx+wPslBRp5E6LCIiIiIiIpcmiqK50ozzzKTFpBkROc2YxFB8fvc4hPqpkH22Cre8n4GS6kapwyIiIiIiInJZ+eUNKKhsgEImYHRCiNTheDQmzYjIqQbHBeHLReMQFeiF48W1uOndDOSX10sdFhERERERkUvKyC0FAAzrGQxfFfc3SolJMyJyut6RAfj6njT0CvXFmfJ6zHw3AydLaqQOi4iIiIiIyOWwNdN1MGlGRF2iZ6gvvronFX2j/FFU3Yib38vEwYIqqcMiIiIiIiJyGaIoIj2HSwBcBZNmRNRlogK9sfbuVAztEYTyOg1uez8TO0+VSx0WERERERGRS8gtrUNJjRoqhQwj4jnPTGpMmhFRlwrxU+HTBWMxJjEUNWot7vgwC1uPn5c6LCIiIiIiIsmZWjNH9AqGt1IucTTEpBkRdbkAbyXWzBuDy/pFoLFJjwUf7cRPB85JHRYREREREZGkMnJNrZnhEkdCAJNmRCQRb6Uc790xCtcNjUGTTsR9n+3B17vPSh0WERERERGRJERRRCaXALgUJs2ISDIqhQyv3zoct47uCb0IPPzVfqz+M0/qsIiIiIiIiLrc8eJalNVp4K2UYVjPYKnDITBpRkQSk8sEPD9jCBZckggAWLr+MN747QREUZQ4MiIiIiIioq6TkVMKABidEAqVgukaV8BHgYgkJwgCnrhuAB68vC8A4JVNx/H8T0eZOCMiIiIiIo9hmmc2Lomtma6CSTMicgmCIOCBy/vgqakDAQDvb8vFP9YdhE7PxBkREREREbk3vV5EZm45AM4zcyVMmhGRS5l/SSKW/WUoZALw+Y4zWLJ2H5p0eqnDIiIiIiIicprD56pR1dAEfy8FhsYFSR0OGTFpRkQu5+bRPfHGbSOglAtYv78Q93y8G41NOqnDIiIiIiIicopMY2vm6IQQKORM1bgKPhJE5JKuGxqD9+8cBS+FDL8dLcFdq3aiVq2VOiwiIiIiIiKHS88xJM3YmulamDQjIpd1Wb9IrJk3Bv5eCmTklmH2B1morNdIHRYREREREZHDaHV67MgzzjNLCpc4GrLEpBkRubSxSWH4bOFYhPgqsT+/Ere8l4mS6kapwyIiIiIiInKIg4XVqFVrEeitwMDYQKnDIQsdSpo1NTUhPz8fx44dQ3l5uaNjIiKyMrRHMNYuSkVkgBeOFddg5nsZyC+vlzosIiIiIiKiTsswtmaOTQqDXCZIHA1ZsjlpVltbi/feew+TJk1CUFAQEhISMHDgQERERCA+Ph4LFy7Ezp07nRkrEXmwvlEB+PqeNPQM9cHpsnrc/F4Gcs7XSh0WERERERFRp6TnlAIAUpM4z8zV2JQ0e+2115CQkIAVK1Zg8uTJ+Pbbb7Fv3z4cO3YMGRkZeOaZZ6DVanHFFVfg6quvxokTJ5wdNxF5oF5hvvhqURp6R/rjXFUjbn43A4cKq6QOi4iIiIiIqEM0Wj12naoAwCUArkgQRVG82EEzZ87E008/jSFDhrR7nFqtxocffgiVSoUFCxY4LMjOqK6uRlBQEKqqqhAYyN5gIndQXqfBnJU7cKCgCgHeCqy+azRGxodKHRYREREREZFddp0qx03vZiDUT4VdT1wOGdszu4StuSKbkmbdGZNmRO6purEJ81fvxM5TFfBRyvH+nSMxoU+E1GERERERERHZ7I3fTuCVTcdx7ZBovD17pNTheAxbc0V2LwLYtGkT6us5gJuIpBXorcSaeWMxsW8EGpp0mL96F34+WCR1WERERERERDZLNy4B4Dwz12R30uwvf/kLQkJCkJaWhscffxy//PILams5jJuIup6PSo4Vd47CtUOiodHpcd9ne/DtnrNSh0VERERERHRRjU067D7DeWauzO6kWUVFBbZs2YLrr78ee/fuxcyZMxEaGopx48bhsccec0aMRERtUilkeP3W4Zg5sgd0ehF/+3I/Ps44JXVYRERERERE7dp7phIarR4RAV5IjvCXOhxqRadnmh08eBAvv/wyPv30U+j1euh0OkfF5hCcaUbkGfR6Ef/acBir/jwFAPj7Vf1w32W9pQ2KiIiIiIioDa9uOo7XfzuB61Ni8fptw6UOx6PYmitS2HviI0eOYOvWrdiyZQu2bt0KnU6HSy65BK+88gomTpzYqaCJiDpKJhPw9NSBCPBW4vXfTuClX46hplGLR6/uB0HgBhoiIiIiInItGTmlANia6crsTpoNGjQIERERWLJkCZ566ikMGjTIGXEREdlNEAT87Yq+CPBS4D8/HsG7W3NQ09iEf00fzNXNRERERETkMho0OuzLrwQApDFp5rLsnml2//33Iy4uDkuXLsW8efPw6KOP4qeffuIyACJyGQsvTcLzM4ZAEIBPs87gwS/3oUmnlzosIiIiIiIiAMCu0+Vo0omIDfJGr1BfqcOhNtidNFu+fDn27NmD4uJiPPnkk9DpdHj66acRHh6OcePGOSNGIiK73TamF/5763AoZAK+21eIez/Zg8Ym15q5SEREREREnikjpwwAMC45jONkXJjdSTMTvV4PrVYLjUYDtVqNpqYmnDp1yoGhERF1zvUpsXj/zpHwUsjw65FizP9oJ+rUWqnDIiIiIiIiD5duTJqlJrE105XZnTR74IEHkJKSgsjISCxatAiFhYW4++67sX//fhQVFTkjRiKiDpvcPwqr7xoDP5Ucf54sw+0fZqGqvknqsIiIiIiIyEPVqrU4UFAFgEsAXJ3diwAKCgqwcOFCTJo0CYMHD3ZGTEREDpWaHIZPF47D3FU7sPdMJW55PwMfzx+LiAAvqUMjIiIiIiIPszOvHDq9iF6hvugRwnlmrszupNnXX3/tjDiIiJxqWM9grL07Fbd/mIWjRTW4+b0MfLJgLOKCfaQOjYiIiIiIPEhGLlszu4sOzTTLycnB//3f/+Hyyy/HFVdcgfvvvx85OTmOjo2IyKH6RQfgq0WpiAv2QV5pHWa+k47c89z8S0REREREXSc9pxQAWzO7A7uTZr/88gsGDhyIHTt2YOjQoRg8eDCysrIwaNAgbNq0yRkxEhE5TEK4H76+NxXJEX4orGrEze9l4HBhtdRhERERERGRB6iqb8Ih4+sPJs1cnyCKomjPDYYPH46rrroKL7zwgtXljz32GDZu3Ig9e/Y4NMDOqq6uRlBQEKqqqhAYGCh1OETkIspq1bhz5Q4cKqxGoLcCq+eNwYheIVKHRRbOlNWjskGDoT2CpQ6FiIiIiMghNh4qwt0f70ZShB9+f2iS1OF4LFtzRXZXmh05cgTz589vcfm8efNw+PBhe09HRCSJMH8vfLZwHEbFh6C6UYvbP8jCnydLpQ6LLNy5Mgsz3k7H+Rq11KEQERERETlEeg7nmXUndifNIiIisG/fvhaX79u3D5GRkY6IiYioSwT5KLFm/hhM6BOOeo0Od63aiY2HiqQOi4wKqxqh1YvIr6iXOhQiIiIiIofINC0BYGtmt2D39syFCxfi7rvvRm5uLtLS0iAIAv744w+8+OKLeOihh5wRIxGR0/iqFPhgzig88Pk+/HyoCPd+ugevzEzBDcPjpA7No4miCI1WDwAoq9VIHA0RERERUeeV1apxtKgGADCOlWbdgt1Js6eeegoBAQF45ZVX8PjjjwMAYmNjsXTpUjzwwAMOD5CIyNm8FHK8OWs4HvkmG9/uKcCDX+5DrVqL28fFSx2ax1IbE2aA4ckFEREREVF3l5VXDgDoFxWAcH8viaMhW9jdnikIAh588EGcPXsWVVVVqKqqwtmzZ7FgwQJs27bNGTESETmdQi7DyzelYE5qPEQRePJ/B/HOlhypw/JYlkmzUibNiIiIiMgNpOcYZiizNbP7sDtpZikgIAABAQEAgJMnT+Kyyy5zSFBERFKQyQQsvX4Q/npZbwDAiz8fxbKfj8LOJcPkAGqtzvx5KdsziYiIiMgNZBiXALA1s/voVNKMiMjdCIKAh6/qh8ev6Q8AeHtLDp75/hD0eibOupK6yaI9s45JMyIiIiLq3kqqG5Fzvg6CAIxLCpU6HLIRk2ZERK1YNDEZ/7lxMAQBWJNxGg9/tR9anf7iNySH4EwzIiIiInInGcatmQNjAhHsq5I4GrIVk2ZERG2YPTYey28ZBrlMwLd7C7D40z1WbYPkPNbtmUyaEREREVH3ZmrNTGVrZrdi8/bM77//vt3r8/LyOh0MEZGrmT4sDn4qBRZ/tgcbDxdjwUe78N4dI+Grsnv5MNnButKM7ZlERERE1L2ZKs24BKB7sflV3w033HDRYwRB6EwsREQu6fKBUVg9dzQWrNmF7SdKcceHO7By7mgE+SilDs1tWc40K6/XQKcXIZfxbwwRERERdT+FlQ04XVYPuUzAmETOM+tObG7P1Ov1F/3Q6di2RETuKa13OD5dMBZBPkrsPl2B297PZNugE1m2Z4oiUFHPajMiIiIi6p5MrZmD44IQ4M033rsTzjQjIrLR8F4h+OLucQj398Lhc9W4+b0MFFY2SB2WW7JszwTYoklERERE3Vc655l1W0yaERHZYUBMIL66JxVxwT7IPV+Hme9mIK+0Tuqw3E7zpBmr+oiIiIioOxJFEZmcZ9ZtMWlGRGSnxHA/fHlPKpLC/VBQ2YCZ72bgaFG11GG5FXWTdbs/k2ZERERE1B3llzegoLIBCpmA0QkhUodDdnKZpNnzzz8PQRCwZMkS82WiKGLp0qWIjY2Fj48PJk2ahEOHDkkXJBGRUVywD9YuSsWAmECU1qpxy3uZ2HumQuqw3IaU7ZlanR43vv0nHvl6f5fdJxERERG5p4zcUgDAsJ7B8FXZvIuRXIRLJM127tyJ999/H0OHDrW6fNmyZXj11Vfx5ptvYufOnYiOjsYVV1yBmpoaiSIlIrogIsALXywchxG9glHV0ITbP8hCek6p1GG5hRZJs7quqzTLzC3H3jOV+HLX2S67TyIiIiJyT+Z5ZmzN7JY6lTRbvHgxSks79wKxtrYWs2fPxooVKxAScqFUURRFLF++HE888QRmzJiBwYMH46OPPkJ9fT0+++yzTt0nEZGjBPkq8fH8sbikdzjqNDrMXbUTvx4uljqsbs9yeybQtZVmIsQuuy8iIiIicl+iKJo3ZzJp1j11Kmn2ySefoLq6c3N87rvvPlx33XW4/PLLrS7Py8tDUVERrrzySvNlXl5emDhxItLT09s8n1qtRnV1tdUHEZEz+Xkp8MGcUbhyYBQ0Wj3u+WQ3vttXIHVY3Zq6yVBpppQLALp2pplMELrsvoiIiIjIfeWW1qGkRg2VQoYRvTjPrDvqVNJMFDv3bvwXX3yBPXv24Pnnn29xXVFREQAgKirK6vKoqCjzda15/vnnERQUZP7o2bNnp2IkIrKFt1KOt2ePwI3D46DVi1iydh8+yzojdVjdlqk9MybIBwBQ2oWVZkyZEREREZEjmKrMRvQKhrdSLnE01BGSzTTLz8/HAw88gE8++QTe3t5tHic0e8dfFMUWl1l6/PHHUVVVZf7Iz893WMxERO1RyGV4ZWYKbh/XC6II/GPdAby3NUfqsLolU3tmbLDh70NXzjSz/BvT2TeHiIiIiMhzmVszk8IljoQ6qlOrGzozkH/37t0oKSnByJEjzZfpdDps27YNb775Jo4dOwbAUHEWExNjPqakpKRF9ZklLy8veHl5dTguIqLOkMkE/Gv6YAR4K/HOlhw8/9NR1Kq1+NsVfdtN+JM1U6VZrLHSrCtnmsksHia9CMj5sBERERGRnURRRGauIWmW1pvzzLorySrNpkyZggMHDmDfvn3mj1GjRmH27NnYt28fkpKSEB0djU2bNplvo9FosHXrVqSlpUkVNhHRRQmCgEev7o9Hru4HAHjj95P45/rD0OtZtWQr00yz2GBD0qxeo0O9Rtsl9y2zyJrp+JgRERERUQccL65FWZ0GPko5UnoESx0OdVCnKs06IyAgAIMHD7a6zM/PD2FhYebLlyxZgueeew59+vRBnz598Nxzz8HX1xezZs2SImQiIrssntQbAd5KPP3dQaxOP4VatRYv/mUo5DKWLl2MqT0z1E8FlUIGjVaPsloNfEOd/2fL8tHRsz2TiIiIiDogI6cUADAqIQQqhWT1StRJkiXNbPHII4+goaEBixcvRkVFBcaOHYuNGzciICBA6tCIiGxyx7h4+HvJ8fBX2fh691mMTgjBLaN7SR2WyzO1Z3or5Yjw90JBZQNKa9XoGerr9Pu2bKNlpRkRERERdUS6cZ7ZuCS2ZnZnLpU027Jli9X/C4KApUuXYunSpZLEQ0TkCDcO74GCiga8vPE4vtlTwKSZDUxJMy+FDGH+KhRUNnTZXDPLQkAdK82IiIiIyE56vYisvHIAQFoyk2bdGWsEiYi6wIwRPSAIwI68chRWNkgdjstTNxnaM72UMoT5qQB03QZNmUWlGefQEREREZG9Dp+rRlVDE/y9FBgSFyR1ONQJdlea1dXV4YUXXsBvv/2GkpIS6PV6q+tzc3MdFhwRkbuIDfbBmIRQZOWVY/3+QiyamCx1SC7tQqWZHGH+ho3IpV1UaWa55JTtmURERERkrwxja+bohBAo5KxV6s7sTpotWLAAW7duxR133IGYmBir2S9ERNS26cPikJVXjv/tY9LsYizbM8PNSbOuqTSzxPZMIiIiIrJXRq4haZbK1sxuz+6k2U8//YQNGzZg/PjxzoiHiMhtXTskGs98fxBHzlXjeHEN+kZxqUlbTNszDUkzY3tmF1WaWebJmhVTExERERG1S6vTY4d5nlm4xNFQZ9ldJxgSEoLQ0FBnxEJE5NaCfVWY2DcSAPD9vkKJo3Ft6iZjpZlSjjD/rp1pZllbxkozIiIiIrLHwcJq1Kq1CPRWYEBMoNThUCfZnTT717/+haeffhr19fXOiIeIyK1NHxYLAPhufwFEJmTaZLU908/QntlVlWZ6i8eFiwCIiIiIyB7pOaUAgLFJYZDLOM6qu7OpPXP48OFWs8tOnjyJqKgoJCQkQKlUWh27Z88ex0ZIRORGLh8QBT+VHPnlDdhzphIj40OkDsklWbZnmirNumoRgGUuk4sAiIiIiMgepiUAqUmcZ+YObEqa3XDDDU4Og4jIM/io5LhqUDS+3VuA7/cVMGnWBnOlmVIOfy/Dn6ryOjV0erEL3rG7kChjeyYRERER2Uqj1WPXqQoAQFpvJs3cgU1Js2eeecbZcRAReYzrh8Xi270F+CH7HJ6cOhBKrqG2IooiNBbtmUE+hopmvQhU1msQZtym6bz7v/A52zOJiIiIyFbZZyvR0KRDqJ8KfSO59Msd8JUaEVEXu6R3OML8VCir0+DPk6VSh+NyTFVmgCFpppTLEOxrSJyV1Tm/RdMyT8ZKMyIiIiKyVbqxNXNcUihknGfmFjq8PbP5R1hYGOLi4jBx4kSsWrXKGbESEbkFhVyGqUNjAHCLZmusk2ZyAECYn2mumfM3aFouaOBMMyIiIiKyFeeZuR+7k2ZPP/00ZDIZrrvuOvzzn//E0qVLcd1110Emk+G+++5D3759ce+992LFihXOiJeIyC1cPywOAPDLoSI0aHQSR+NaTEsABAFQyg3v0JlaMrtig6Zlmkyvb/MwIiIiIiKzxiYddp8xzDNLTQ6XOBpyFJtmmln6448/8O9//xv33HOP1eXvvfceNm7ciG+++QZDhw7F66+/joULFzosUCIidzKiVzB6hvogv7wBvx4pxrSUWKlDchnqpgvzzEybmyOMSbOuqDTTi1wEQERERNSekyU1qGpowsj4UKlDcRl7z1RCo9UjIsALyRF+UodDDmJ3pdkvv/yCyy+/vMXlU6ZMwS+//AIAuPbaa5Gbm9v56IiI3JQgCJieYqg2+44tmlbMmzONrZkAEOZvaM/sikozy1IztmcSERERWdPrRcxakYWb3s1A9tlKqcNxGRk5hlnFqUlh5jd+qfuzO2kWGhqK9evXt7h8/fr1CA01ZJnr6uoQEMBNEURE7Zk+zFBdtvV4CSrruyAZ1E2Y2jO9FBf+RIX5Gdsz67pgppnF53pWmhERERFZOV5Sg5IaNUQReHtzjtThuIyMXOM8s2TOM3MndrdnPvXUU7j33nuxefNmjBkzBoIgYMeOHfjxxx/x7rvvAgA2bdqEiRMnOjxYIiJ30icqAANiAnHkXDV+PFCEWWN7SR2SSzBXmiktkmb+pkUAXTDTjJVmRERERG3adarC/PnPh4pworgGfaI8u2imQaPDvvxKAEAak2Zuxe5Ks4ULF2Lr1q3w8/PDt99+i6+//hq+vr7YunUr5s+fDwB46KGHsHbtWocHS0Tkbm4wVpt9t69A4khch6aV9sxw8yKArp1ppmfSjIiIiMjK7tOGpJlcZmhBfGcrq812nS5Hk05EbJA3eoX6Sh0OOZDdlWYAMH78eIwfP97RsRAReZxpKbF4/qejyMorR2FlA2KDfaQOSXIXZppdeF8nvCsrzSw+5yIAIiIiImu7TpcDAJZM6YNXNh3Hd/sK8eDlfdHTg5NF6TmG1sxxyZxn5m5sqjSrrq62+ry9DyIisl1ssA/GJBrmQa7fz4UAAKBuamWmWRdWmomW2zNZaUZERERkVlLdiPzyBggCMGd8Ai7pHQ6dXsT72zx7EWCGMWmWmsTWTHdjU9IsJCQEJSUlAIDg4GCEhIS0+DBdTkRE9rlhGLdoWmpve2adRocGjc6p989FAERERESt22VszewXFYBAbyUWX5YMAFi7Kx8lNY1ShtYmnV7EvvxKp70ZWqvW4kBBFQAuAXBHNrVn/v777+bNmJs3b3ZqQEREnuaawdF45vuDOHyumoNU0foigAAvBVRyGTQ6Pcrq1Oihcl75v3WlmdPuhoiIiKjbMS0BGJVgKJhJTQrDiF7B2HOmEh/+kYfHrxkgZXit+iTzNJ75/hD+dkVf3D+lj8PPvzOvHDq9iF6hvugR4rktqu7KpqSZ5SZMbsUkInKsED8VJvaNwK9HSvDdvkI8fFU/qUOSlFrbsj1TEASE+6tQWNWI0lqNU5+QcHsmERERUet2G+eZjYo3FNUIgoD7LuuN+R/twicZp3HvxGQE+6qkDLGFbcfPAwC+31/olKRZek4pALZmuiubFwFkZ2fbdNzQoUM7HAwRkaeaPizOkDTbX4CHruzr0QNE1U0t2zMBw1yzwqpGp881s0yasT2TiIiIyKBBo8OhQsMc85HxF0YzTe4fif7RAThaVIOP0k/jgcsdn5jqKFEUsf+soXXyZEkt8svrHb6wICPXMM8srTeTZu7I5qTZsGHDIAiCuW3F9ILOso1FEATodM6dNUNE5I4uHxAFX5Uc+eUN2HOm0uqJiKdpbXsmcGGuWZmTN2jquQiAiIiIqIV9+ZXQ6kVEBXqhR8iFje+CIGDxZb1x/+d7sSo9DwsmJMLPy+ZUg1MVVTei1OIN19+PlmBOWoLDzl9V32ROJLLSzD3ZtAgAAPLy8pCbm2v+r4+PDzZv3oy8vDzzR26uZ2/MICLqKB+VHFcNigYAfL+vQOJopGVuz1Q2S5r5GTZoltY5udLM4nNWmhEREREZWLZmNu+KuG5IDBLCfFFZ34TPd5yRIrxWZRurzEx+O1ri0PNn5ZVBFIGkCD9EBno79NzkGmxOmsXHx5s/EhISIAgCevToYXV5fHy8M2MlInJr04fFAgB+yD4HrQdPoG9teyYAhAd0TaUZZ5oRERERtWTanNlaR4RcJuCeiYZNmu9vyzW/CSq17LOVAC7EnJlbhnqN1mHnT88xtGayysx92Zw0IyIi5xrfOxxhfiqU1Wnwx8lSqcORzIWZZtZ/osJNlWZOnmlmWWvGpBkRERERoNeL2HPaenNmczeOiEN0oDdKatT4ZrdrdE6YKs1uHB6HHiE+0Gj1+PNkmcPOn2maZ5Yc7rBzkmth0oyIyEUo5TJcNzQGAPD9vkKJo5HOhe2ZzRcBdNVMM8vPmTQjIiIiOlFSi+pGLXyUcgyICWz1GC+FHAsvTQIAvLs1R/LOCVEUcaDAkDRL6RGMyf0jARjmmjlCWa0aR4tqAADjkkIdck5yPZ1KmnnydjciImeYPiwOAPDLoSI0aFyjrL2rmdszm8808++aSjPr9kyn3hURERFRt7DLOM9seK9gKOVtpxFuG9MToX4qnCmvx4YD57oqvFbllzegsr4JKrkM/aIDcJkxabblWInVQsOOysw1fE/6RQWYn6eS+7F5pcXw4cOtkmQNDQ2YNm0aVCqV1XF79uxxXHRERB5mRK9g9AjxwdmKBvx6pBjTUmKlDqnLtbk9089YaVbn5Jlmlu2ZrDQjIhs16fQor9Mg1E/V7gtKIqLuaPcpY2vmRTa8+6oUmDc+AS9vPI63Np/EtKGxkMmkKbbZb5xnNiAmACqFDKlJYfBRynGuqhFHztVgYGzrFXO2ysg1jFNJTeY8M3dmc9LshhtusPr/6dOnOzoWIiKPJwgCpg+LxVubc/DdvkLPTJo1td6eGRFgeAevvE4DvV502hMwyzyZnjPNiMhGWp2Ikmo1Ar2VUMovfjwRUXdiXgKQcPE2xDtSE/Du1lwcL67Fr0eKcaVxQ3xXM7VmDu0RDADwVsoxvncYfj1Sgt+PFnc+aWZaAsCkmVuzOWn2zDPPODMOIiIyumFYHN7anIOtx0tQWa9BsK/q4jdyI21VmoUYvw86vYjKhiaE+jnn+2I5x4yLAIiIiMjTldQ04kx5PQTB0J55MUE+StyRGo93tuTgrS05uGJglCSjnfbnVwIAhvQIMl92Wf9IY9KsBH+d3KfD5y6pbkTO+ToIAjAukUkzd8bacSIiF9MnKgADYgLRpBPx44EiqcPpcuZFAM1mmqkUMgT5KAEYBq92BS4CICIiIk9nas3sFxWAQG+lTbeZNz4RXgoZ9udXIj3HcdsqbaXXizhorjSzSJr1M8w125tfifJOjPzIMG7NHBgTiCBf274n1D3ZlDS7+uqrkZ6eftHjampq8OKLL+Ktt97qdGBERJ5s+jBDW+Z3+1xjXXdXulBp1rK/ybRBs9SJGzRFbs8kIiIiMjO1Zo5KaH+emaWIAC/cOronAOCtzSedEld7ckvrUKfRwUcpR+8If/PlscE+6B8dAFEEth7v+BZNc2tmEqvM3J1NSbOZM2fi5ptvxoABA/Doo4/iq6++wp9//ondu3fj119/xeuvv46bb74ZMTEx2Lt3L66//npnx01E5NauN84yy8orR2Flg8TRdC11U+vtmQAQ7meYa1ZW57xKM+v2TKfdDXWB5b8ex9XLt6GmsUnqUIiIiLotc9Is/uLzzCwtvDQJCpmA9Jwy7DlT4YzQ2pRtXAIwKDYQimbLWaYMMFSb/X70fIfPb6o0S+vNpJm7sylpNn/+fOTm5uKpp57C0aNHsWjRIkyYMAGjR4/GVVddhRUrVqBXr17YvXs3vvjiC/Ts2dPZcRMRubXYYB+MSTQ8MVm/v1DiaLqWuT2ztaRZgHGDJivNyAbLfz2Bo0U1+DjztNShEBER2e3zHWeQ9vxvOF5cI1kMDRodDhnbHEdeZHNmcz1CfHHD8DgAwNubcxweW3uyz1ovAbA0ub8habb1WAm0HXiHtKCyAafL6iGXCRhtw2IE6t5snmmmUqkwa9YsfPfddygvL0dFRQUKCwvR2NiIAwcO4OWXX0a/fv2cGSsRkUe50KLpaUkzY6VZK+vnwoyVZqVOnGlmmSbjIgD3oNGyZJCIiLqfHw+cQ2FVI7afKJUshv1nK6HVi4gK9EKPEB+7b3/vpGQIAvDrkWIcLap2QoStM1WaWc4zMxnWMwQhvkpUN2qx+7T9FXCm1szBcUEIsHHGG3VfHV4EEBQUhOjoaCiV/CEhInKGawfHQCkXcPhcNU5I+A5jV2treybQVTPNuD3T3bBgkIiIuqOqBsN4gYpODKzvrN0WrZkd2YCZHOGPawfHAOi6ajOtTo9DhYYEXWtJM7lMwMS+EQCA34/ZP9eM88w8C7dnEhG5qBA/lfkPuidVm6mb2m7PDPM3zjRzZqUZ2zPdDh9FIiLqjirqDcmyMgmTZrtOlQOwvzXT0r2TkgEAP2QX4lRpnUPias/x4lqotXoEeCmQEObX6jGXGVs0Nx+1L2kmiiIyTfPMkpk08wRMmhERubDrhxnmQHy3v8CqAsqdtdeeGWGsNHPmk0cRrDRzOx7yu0NERO6lsl7aSjO9XrxQaWbH5szmBscFYVK/COhF4L1tzq82O1BQab5fmaz16riJfSMglwk4XlyLsxX1Np87v7wBBZUNUMqFTn1PqPtg0oyIyIVdMSAKvio58ssbsOdMpdThOJ0oihdpz+zaSjMdky1ugY8iERF1N1qdHjWNWgBAuURJs5Pna1HdqIWPUo4BMYGdOtd9l/UGAHy9+yyKqhodEV6bzEsAerZszTQJ9lVhZC9D0suearP0HMN8uZQewfBVKToRJXUXTJoREbkwH5UcVw2KBgB8v69A4micT2OxwUjVWtLMz/kzzSyLy/SsNHMLzH0SEVF3Y5pnBgDl9dIkzXadMlSZDesZDKW8c6mD0QmhGJMQiiadiBXbcx0RXpvMSbO44HaPM7Vo/mZH0izD2JqZytZMj9Hhn3yNRoOzZ8/izJkzVh9ERORY1xu3aP6Qfa5Da7G7E7XFlsP2Ks1q1Vo0GmefOZp1e6ZT7oK6mMhaMyIi6mYqLZNmElWa7TptmGfmqDbExZcZZpt9lnXGaV+TWqszb+lsbQmApcnGpFlGThkaNBd/XimK4oUlAEyaeQy7k2YnTpzAhAkT4OPjg/j4eCQmJiIxMREJCQlITEx0RoxERB7tkt7hCPNToaxOgz9OSrdyvCuomywqzVp5RzPQW2G+3FlzzbgIwP2wYJCIiLob0zwzw+caSeasmuaZdWYJgKWJfSMwOC4QDU06rP4zzyHnbO7ouRo06USE+CrRI8Sn3WP7RvkjLtgHaq3e3HbZntzSOpTUqKFSyDCiF+eZeQq7k2Zz586FTCbDDz/8gN27d2PPnj3Ys2cP9u7diz179jgjRiIij6aUy3DdUMOq7u/dfIumWnthc2Zra80FQUCYaRmAk+aaWT4l5SIA98DcJxERdTeVFi2ZetG6XbMrnK9R43RZPQQBGOGgpJkgCLhvkmG22er0U6hpdPzXlF1gbM3sEdzqc8nm8ZiqzX63oUUz3VhlNqJXMLxbWVhF7snuyXX79u3D7t270b9/f2fEQ0RErZg+LBZrMk7jl0NFaNDo4KNyzz/U7S0BMAnzV+FcVSPKnDTXzHJLKRcBuAe2ZxIROVZRVSNW/pmHO8bFo2eor9ThuCXLSjPA0KIZapzt2hV2G1sz+0UFINBb6bDzXjUoGskRfsg5X4dPs87gnonJDjs3AGTnVwK4eGumyeT+kfg48zQ2Hy2BKIrtJtoyTa2ZSeGdjpO6D7srzQYOHIjSUvduDyIicjUjeoWgR4gP6jQ6/HqkWOpwnMbUnunVzrt3YX6GuWbnnVVpxkUA7ocPIxGRQy36eBfe35aLW9/PlDoUt1XZ0DJp1pVMSwAc1ZppIpMJuNdYbfbB9jyHz6g9YKw0GxJnW9IsNTkM3koZCqsacay4ps3jRFFEpnEJQFpvzjPzJHYnzV588UU88sgj2LJlC8rKylBdXW31QUREjicIAqYbFwJ858YtmpbtmW250J7ZBZVmTJq5BT6KRESOtd+4nbCgskHiSNxXZbONmV2eNDPOM3PUEgBL04fFIi7YB6W1any5K99h563XaHHcmPhK6Rls0228lXKkJRsqx3470naL5vHiWpTVaeCjlCOlh23nJvdgd9Ls8ssvR2ZmJqZMmYLIyEiEhIQgJCQEwcHBCAnhMDwiImeZPiwOALD1eEmLJ1Luwpb2zAjjBs0umWnG9ky3IPJxJCKibqa19syu0tikw6FCQ2J0VHyow8+vlMuwaGISAOC9rbloctC68sOF1dCLQGSAF6ICvW2+3WXGuWab25lrZloUMCohBKp2nqeS+7F7ptnmzZudEQcREV1E36gA9I8OwNGiGvx4oAizxvaSOiSHu5A0a6c901Rp5qQnj3q2Z7od5syIiKi7ad6eWdGFb5juz69Ek05EZIDXRTdQdtTNo3ri9d9OoKCyAd/tK8RNI3t0+pzZZy8sAbDH5P6ReArAnjMVqKjTIKSV2XEZxnlm45LYmulp7E6aTZw40RlxEBGRDW4YHocXfjqK7/YVuGfSzDjXwkvZTnumcaZZqdNmmlkuAnDKXVAXY+6TiMix5DKBIwyczNRVEBnghZIatdPGUrTGsjXzYhsoO8pbKcf8S5Lw4s9H8faWk7hxeBzkss7dV/bZSgC2LwEwiQv2Mb8xve3EeXN3h4lOLyIrz7AYIS2ZSTNP06G6wsrKSrzyyitYsGABFi5ciNdeew1VVVWOjo2IiJqZlmKYa7bjVDkK3XCOiK3bMwGgtAuePLLSjIiIqCW5kxIpdIGpPTMpwg9A11aa7T5tWgLg+NZMS7eP64VAbwVyz9dh46GiTp8vu8BUaWZf0gy40KL5eystmkfOVaOqoQn+XgqbFwyQ+7A7abZr1y4kJyfjtddeQ3l5OUpLS/Hqq68iOTkZe/bscUaMRERkFBfsgzEJoRBFYP1+91sIYEt7ZriTZ5rpuQjA7YhcBUBE5FAyjnRyusoGQ5IsKcIfgPPGUjSn14vmpNkoB2/ObC7AW4m5aQkAgLe2nOzUDNLqxibknq8DYH97JmBo0QSArcfPQ9tsxpqpNXN0QggUcv7wexq7H/EHH3wQ119/PU6dOoVvv/0W69atQ15eHqZOnYolS5Y4IUQiIrI0fbj7btG0ZXumKWlWXqdxSiWY5fM1LgJwD3wYiYgci5VmzldZZ6w0CzdWmnVR0iznfC2qGprgo5RjYGyg0+9v7vhE+CjlOFhQjW0nSjt8noPGKrMeIT4IbWUm2cUM7xmMIB8lKuubsDe/0uq6jFxD0sy0ZZM8S4cqzR599FEoFBfGoSkUCjzyyCPYtWuXQ4MjIqKWrh0cA4VMwOFz1ThhXKvtLtRNxkozZduVZqYnQlq9iOrGpjaP6yjL/ArbM90Dt2cSETmWrJOzp6h9TTo9atRaABfaM7tqe6ZpnllKzyAou6CqKtRPZZ7T+9bvJzt8ngtLADrWPqmQyzCpXwQA6xZNrU6PHcZ5ZqmcZ+aR7P4tCAwMxJkzZ1pcnp+fj4CAAIcERUREbQvxU2FiX8MfdXerNrNlpplKIUOgt+GNG2fMNWOlmfvho0hE5FidHdhO7au22JyZENbFSbNTptZM584zs7RwQhKUcgE7TpWbE1T2OmBMmg2JC+5wHKYWzc0WSbMDBVWoVWsR6K3AgBjnV96R67E7aXbLLbdg/vz5WLt2LfLz83H27Fl88cUXWLBgAW677TZnxEhERM1MH27Y6vPd/gK3qqKxpT0TuNCi6YwNmpxp5n7c6FeEiMglKJg0c6oK4xKAQG8FIgIMz3kamnRo0Oicft+7TxuSViMTnDvPzFJ0kDduGtkDAPD2lo5Vm+03bs5M6WClGQBM7BsBmQAcLapBgXHhlqk1c2xSGJPFHsrupNnLL7+MGTNm4M4770RCQgLi4+Mxd+5c3HTTTXjxxRedESMRETVz+YBI+KrkyC9vaDF3oTuzZREAcGGDprPXr+uZbXELXARARORYMs40c6oq4xKAYF8V/L0UUBnbJMudvEHzfI0ap8rqIQjAiF5dlzQDgEWXJkMmAFuOnTfPJ7NVeZ0GZysMSa5BndhuGeyrMn/dpmoz0xKANLZmeiy7k2YqlQr//e9/UVFRgX379mHv3r0oLy/Ha6+9Bi8vL2fESEREzfiqFLhyYBQA4Lu9BRJH4zgXZprZVmlWVuf4SjORlWZuh7lPIiLHYsWNc1UaK82CfZUQBAEhfkoAQLmT3yw0bc3sGxmAIB+lU++ruYRwP0wdalh29c6WHLtue8CYZEsK9+t03JdZtGhqtHpzuyrnmXmuDk/28/X1xZAhQzB06FD4+vo6MiYiIrLB1YOjAQC7z1RIGsfPB8/hzd9POCTBZGt7pqnSzNkzzfT6to+j7oM5MyIix2LSzLkqzEkzw/OdUD/j5nAnV5pJ0ZppafFlyQCAHw+eQ875Wptvl23suhjSidZMkykDDEmzP3NKkZVXhoYmHUL9VOgbyfntnkpx8UOAGTNmYPXq1QgMDMSMGTPaPfbbb791SGBERNS+HiGGNyyKqx1fbWUrURTx6DcHUNXQhOQIf1wzJKZT59PY2p7p58yZZhc+5yIA9+BOc/+IiFwBk2bOVWlMjgUbq6ZCTZVmTqiwt2TanDkqXpqkWf/oQFw+IAq/HinGu1ty8NLMFJtul11g2pwZ3OkY+kUFIDbIG4VVjXht03EAwLikUG6M9WA2VZoFBQVBMPatBwYGIigoqM0PIiLqGlGB3gAMiSOtTpqSqNJaDaqMG55Wp5/q9Pls2Z4JAOHmmWZOaM8E2zOJiIjaI+dMM6cyPbcK9jUlzYyVZnVNbd6msxqbdOZZYl25ObM5U7XZur0F5mH8F5NtXAIw1AGVZoIgmFs095wxnDc1ObzT56Xuy6ZKs1WrVpk/X716tbNiISIiO4T5qaCQCdDqRZTWahAd5N3lMeSV1pk/z8orx+HCagyM7fg6bnN7pq0zzZzdnskKJbfg6IdRFEW8vPEYBsUG4dpOVlcSEXVHrLpxror6C4sAACDU1/mVZtlnq9CkExER4IWeoT5Ou5+LGdErBGnJYUjPKcP7W3Pwz+mD2z2+uLoRxdVqyARgUCeeg1qa3D8Sn2adMf9/ahLnmXkyu2eaTZ48GZWVlS0ur66uxuTJkx0RExER2UAmExBpXENeVN0oSQx5pdbzJj7qZLWZ7dszTYsAnJE0Y6WZu3F00mzLsfN4a3MOFn+6x7EnJiLqJhRMmjmVeRGAT/NKM+fNNNtlnGc2Kj7E3GUmlfsu6w0A+GJnPs7XtJ8ozD5rqI7rExkAX5VNNUEXlZYcbu56iAjwQnKEn0POS92T3UmzLVu2QKNp+cva2NiI7du323Wud955B0OHDkVgYCACAwORmpqKn376yXy9KIpYunQpYmNj4ePjg0mTJuHQoUP2hkxE5LYijS2axRIlzXKNlWZDjOu9/7evABWdeEJn3p5p8yIAZ7RnXsCkmXtwdMVgSY00v29ERK5CxvZMp7LcnglYzjRzXtJst3FL5EiJ5plZSksOQ0rPYKi1eqz8M6/dYw8YWzMdsQTAxEclR5pxW2ZqUpjkSUSSls1Js+zsbGRnZwMADh8+bP7/7Oxs7N27Fx9++CHi4uLsuvMePXrghRdewK5du7Br1y5MnjwZ06dPNyfGli1bhldffRVvvvkmdu7ciejoaFxxxRWoqamx636IiNxVVKDhnccSiZJmp4xJsxkj4jA4LhBqrR5f7Mzv8Pls3Z4ZbnzHtaZRi8YmXavHlNdpcKiwyu4Y2J7pfhz9KAro2JPnqvomHC6sdnA0RERdj4sAnKuywZAcC2m+PdNJSTO9XjRvYx+VIN08MxNBEHDfJMNss48zTptnvLXGtAQgxYFJM8BQ7Ta0RxDmX5Lo0PNS92Nz/eKwYcMgCAIEQWi1DdPHxwdvvPGGXXc+bdo0q///z3/+g3feeQeZmZkYOHAgli9fjieeeMK8sfOjjz5CVFQUPvvsMyxatMiu+yIickdR5kozaTZommaaJUX4Y05qAv7+dTY+zjiFhRMSoZDbXcx8oT1T2X57ZqCPAkq5gCadiPI6DWKDW87eWPTxLuw8VYFf/zYRvSP9bY6BiwDcj8Nznx18rZj6wm+o1+jwzb1pLvFOPhFRR3GmmXOZKs2CjJVmIU6uNMstrUVlfRO8lTKHzQXrrMsHRKFfVACOFdfg44xT+OvkPi2OEUXR3J45xAGbMy2NSgjF93+9xKHnpO7J5lc0eXl5yMnJgSiK2LFjB/Ly8swfBQUFqK6uxrx58zociE6nwxdffIG6ujqkpqYiLy8PRUVFuPLKK83HeHl5YeLEiUhPT2/zPGq1GtXV1VYfRETuKkrC9kydXsSpsnoAQFK4H6alxCLUT4XCqkZsOlzcoXPauj1TEASE+bW9DKCqvsm8Nt3eKjy92Prn1H2JDq416+hLxXqNoSpy67ESxwVDRCQBuZNyZl/uzMfCNbvarCL3FM1nmoU5udJsl7E1M6VHMJQdeNPTGWQywbxJc+Wfp1Cv0bY4pqCyAeV1GijlAgbEBHR1iOQhbP6NiI+PR0JCAvR6PUaNGoX4+HjzR0xMDOTy9qsC2nLgwAH4+/vDy8sL99xzD9atW4eBAweiqKgIABAVFWV1fFRUlPm61jz//PMICgoyf/Ts2bNDcRERdQemRQDFFxmS6gyFlQ3QaPVQyWWIDfaBt1KOWWN6AQBWdXAhgK3tmYDFXLNWNkntOFVuri7S2pn5sqxKYqWZm+DDSETkUM5qz3zkm2xsOlyMjzNOO+X83UGTTo9atSFBZGrPNFWaVTY0OeW5iemNxlEJrlUFfd2QGPQK9UV5nQZf7Gg5/sNUZdYvOuCiS6SIOqrDaeTDhw/j559/xvfff2/1Ya9+/fph3759yMzMxL333os5c+bg8OHD5uubD90TRbHdQXyPP/44qqqqzB/5+R2frUNE5OqigwyVZlLMNDO1ZvYK8zU/eb59XDzkMgE78spxsMD+eWIXFgFc/ImPeYNmK5Vmmbll5s91dvbmsT3T/Ti8O7OTA4H5U0VE3Z2zFwG0N8PK3Vl+7YHGSjNT8kwUgcp6x1eb7TYlzeKln2dmSSGX4Z6Jhmqz97flQmPsSDAxt2bGBXd1aORB7N7JmpubixtvvBEHDhyAIAgQjS9GTE8gdTr7SmlVKhV69zaslB01ahR27tyJ//73v3j00UcBAEVFRYiJiTEfX1JS0qL6zJKXlxe8vLzsioGIqLsytWcWSZA0O1VmSJolhl9Ywx0d5I3rhsTg+/2F+PCPPLx2yzC7znlhptnF39MJ92t7g2ZW3oWkmb4TlWZcBNB9iRaPncjHkYjIoRTO6s80cnRbfXdiSooFeivMb0oq5TIEeitQ3ahFRb3G/MahI5TWqs1vhI7o5VqVZgDwl5FxWP7rcRRVN2Ld3rO4ZXQv83XZxs2Zjl4CQGTJ7kqzBx54AImJiSguLoavry8OHTqEbdu2YdSoUdiyZUunAxJFEWq1GomJiYiOjsamTZvM12k0GmzduhVpaWmdvh8iIncQFWBImlXWN3X5/I/c88YlABZJMwBYOCEJALB+fyEKKxvsOqc97ZnhAaZKM+ukWVWD9YZCe6vFLBMsrDTrvkQnzqbj+Gsi8nTOrjTzZKZ5ZiHGNwdN2quw7wxTlVnfKH/z4gFX4qWQ4+5LDc8t39mSA63O8AarXi/iQIFpCQCTZuQ8difNMjIy8OyzzyIiIgIymQwymQyXXHIJnn/+edx///12nesf//gHtm/fjlOnTuHAgQN44oknsGXLFsyePRuCIGDJkiV47rnnsG7dOhw8eBBz586Fr68vZs2aZW/YRERuKdBHYU4wne/iuWamdyUTmiXNhvQIwrikUGj1IlbbMdtMFEWLRQA2tGcan0w2f/K461R5s2H+rDTzRM585Dr7WpE/VkTU3VnONHNGNa8n/zvZfAmASYgxoVXh4PZMU9JspIu1Zlq6bUwvBPsqcaqsHj8eNMw3P1VWh5pGLbwUMvSN4hIAch67k2Y6nQ7+/v4AgPDwcBQWFgIwLAo4duyYXecqLi7GHXfcgX79+mHKlCnIysrCzz//jCuuuAIA8Mgjj2DJkiVYvHgxRo0ahYKCAmzcuBEBAfylICICDK3xUm3QNCXNEpslzQCY3xH8POsMahptm0vSpBPNT5Jtac80veNa2myTVFZeudX/270IwOJzVpp1X5YJT8fPNHPwCYmIuhm5xT+E9v6dpfaZkmJBvtaVZqGmreEO3qC565ThedOoeNdrzTTx81LgrrREAMDbm09CFC9UmQ2MDXSZjZ/knuyeaTZ48GBkZ2cjKSkJY8eOxbJly6BSqfD+++8jKSnJrnN9+OGH7V4vCAKWLl2KpUuX2hsmEZHHiAr0wpnyehRXd12lmUarx9mKegAt2zMBYFLfSPSO9MfJklqs3ZmPBRMu/vfB1JoJ2Lc9s3l7ZpbFEgDA/sSX3ob2zJd/OYboIG/cPi7ernNT12HFIBGR81hWmun0IpQOXlzoyf9qmxYBhDRrlQw1btCscGDSrLFJh4MFhpEWrrY5s7m5aQl4f1sOjhbV4PejJeYlACk9gqUNjNye3SnZJ598Enq9oX3m3//+N06fPo0JEybgxx9/xOuvv+7wAImIqH2RElSanSmvh14E/FRyRAS0HEYrkwlYcInhHcGVf+ShSadvcUxzaouNSCob3jEMN77jarkIoKaxyfzOY+9IQ1V059ozW15fWNmANzefxD/XHzLP1SDXYzVE2uEzzTq7PdOTXw4SUVdq0ulRXN1o099he1gmzVhp5lhttWc6o9LsQEEVNDo9wv290CvU12HndYYgXyVuTzW8Wfnm5pPmJQBD4jjPjJzL7qTZVVddhRkzZgAAkpKScPjwYZSWlqKkpASTJ092eIBERNS+aFPSrKbrkmbm1swIP/P25OZuGB6HcH8VCqsa8eOBcxc954V5ZrI2z2kpPODCTDPTPJVdpyugF4Feob6IC/YBAHTmdUJrlWZ1ai0AQztpcRfPkSPbiVY5M8e+oGN7JhF1F1qdiJJqNbQ6x/47KLOsNHPwuQHPnmnWVnumaZarIyvNdp0yzDMbFR9i03Mvqc2/JBEqhQx7z1Ril3EW21AuASAnc0jzb2hoaLf4JSMickdRgYZ3HourujJpVgsASAhr2Zpp4q2UY05qAgBgxfbciw4KVjfZvjkTAEKNTx61ehHVDYZEVlauYS7H2MRQ87vg+s5sz2wl5samC1m4ggr7toOSNFztxZerxUNEZC/LmWZNelZdO1JlG+2Zpm2ajqw0233aOM/MxVszTSIDvHHLqJ4ADH9L/VRyJEX4SxwVuTubZprNmDEDq1evRmBgoLnKrC3ffvutQwIjIiLbXFgEcPGqp+/3F+LZ9YeRGO6LEb1CMCI+BCN6hbTaYtkeU6VZa/PMLN0+Lh5vbTmJgwXVyMgtQ1pyeJvHmivNbByM4qWQI8BbgZpGLUrr1AjyVSLTOM9sXFIYfj5k2K7UWuKrPVabN1tJuDU0XZi9VlBZD8B1t015MqtFAExSERE5FJfmOE+VqT2zWdLMXGnmoO2ZoihabM7sHkkzwLBs6rMdZ6DTixgUF2TVKkzkDDYlzYKCgsyVZEFBLH8kInIlkQG2t2euST+F0lo1SmvV2GksyQeA+DBfjDQm0UbGh6BvVEC7T0Is2zPbE+KnwsyRPfFx5mms2JZrW9LMxkozAAj390JNoxZltRpEB2rN88zGJoVi0+FiAB3ZnnmxSjOLpBkrzVyWc9sz+QSdiDybZVW2J8w0a9LpUV6nQaifyumbGk1JsWAf6/ZMU6VZea1jkmY55+tQUd8EL4UMg2K7z2v8nqG+uGFYHL7Zcxaju0mFHHVvNiXNVq1aBcDwj+PSpUsREREBX1/XHhRIROQpTO2ZJRepNKtTa7EvvxIA8OR1A5Bzvg57TlfgeEkNTpfV43RZPb7dWwAA8PdSYHivYAzvZUiiDe8VjEDvC+94mpNm4RcviZ9/SSI+yTqNzcfO40RxDfpEBbR6nL3tmQAQ7q9CXmkdSmvV2HW6Ajq9iLhgH/QI8e1Ee6b156IoWiVJrJJmlUyauSrLR93VKs1cLBwiMqpqaIK/l4KVKzawqjRzxkwzF/uX0jQbLtBb6fBNoc1VXqTSrNxBlWam1syUnsFQ2fHcyxX864ZBGJsUimuHxEgdCnkAm5JmJqIook+fPjh06BD69OnjrJiIiMgOpu2ZtWotatVa+Hu1/k/7rtMV0BqTSvMvSTQngqoamrAvvxJ7Tldgz5kK7D1TiVq1FttPlGL7iVIAhsHnfSMDMCI+BCk9gsytoIntzDQzSQj3w5UDo/DLoWJ8sD0PL940tNXjLlSa2f5sNMy0SapWjYPGKrNxSWEALgwptrdtpPnhehGQW7x+arTY8nmWlWYuy7IKwtFFEHw5TeR+RFHEmbJ6BPko0SuMxQEXZfHvKmeaOVZVgylp1nqlWWOTHvUaLXxVdr2Ub8FyCUB346tS4GbjbDMiZ7PrN00mk6FPnz4oKytj0oyIyEX4eyng76VArVqLkupG+LcxEDUjxzDvKzU5zKpyKshHiYl9IzCxbwQAQ5LpeHENdp+uwJ7TFdh9pgKny+pxrLgGx4pr8PkOw+1C/VQIavYuaFvuvjQJvxwqxrq9BXjoqr7mllJLF2aa2f5uZ5i/4Qlkaa0GWXnGJQBJhhljpkSX3u4yI+vjdXrRquqgUcP2zO5AbOf/OovdmUTuiwkg21iNMnBGe6ZrFZp1GY1Wj1rjlu5gH+vnWH4qOVQKGTRaQ6toZ5JmZyvq8cdJwxuj3WUJAJFU7P5NW7ZsGf7+97/jnXfeweDBg50RExER2Skq0Au157Uorla3uUUoI8fw5CjVWInVFrlMwICYQAyICcTt4+IBAKW1anMCbc/pChwurMYNw+Jsjm9kfChG9ArGnjOVWJN+Gg9f1a/FMWqt/e2ZYf6GSrP8inrsN7aejkvsXKVZ8xxb86Rbo9a6PbN5+ya5BtHida+j2zOFTtaauVq7KBEZZhW6WkugK7PMLWqd0J7pqUxVZoIABDZLmgmCgFBfFYqqG1Fep0GPEPsrIpt0eqz8Iw/Lfz2BhiYdQnyVGJXAhUZE7bE7aXb77bejvr4eKSkpUKlU8PHxsbq+vLzcYcEREZFtogK9kXO+DsXVrS8DqG5sMg/JT01uP2nWmnB/L1w5KBpXDooG0HLOly3uvjQJ93yyBx9nnsbiy5JbvEOqbrK/PTPcWGm2+WgJtHoRsUHe6Blq+LukMCbN7F4E0Ozw5kk3y5lmaq0epbUau7ePkvNZvvjlyzkishWT2rax/DdW64TqPE99GKoaDPPKAr2Vrc7WC/W7kDSz1+7TFXhi3QEcLaoBAIxNDMV/bhxiNbOWiFqyO2m2fPlyJ4RBRESdERdsSBTtOl2OG4a3rADbmVcOvQgkhPkiNtinxfX26khl1RUDoxEf5ovTZfX4evdZ3JmaYHV9R7dnAkCFcWju2KQLracdXQTQvLKs+QbNxibrFwcFlQ1Mmrkg64UObM8kInIky39WPWF7ZlepaGMJgEmoaRmAHUmzqvomvPjLUXy+4wxEEQjxVeIf1w7ATSN7sFKeyAZ2J83mzJnjjDiIiKgTbhweh692n8U3uwvw8JX9WgyPTbeYZyYVuUzA/EsS8fR3h/DB9jzMHhtv9S6quT3TjrVUpk1SJmMTL7QYyIxPBJsnvS6m+dHNk26WlWaAYa7ZsJ7Bdt0HOZ/YxueugC1gRNTdWW3PdELSzNFvdnQXFzZnqlq93p6kmSiK+H5/If71wxGU1hoWOM0c2QOPXzvAfB4iurhO7ZZtaGhAdXW11QcREXW91OQwDIwJREOTDp9mnWlxvWkJwLiLzDNztptG9kCwrxJnyuux8VCR1XWmSjOV3P6ZZiaWX19HK80u1p7Z0DxpVllv1/mpa1i+4HL0ay/LjhlPfWFH5I4Mv878nbaFVaUZZ5p1ml4vYu6qHVi4ZheAlksATGxNmp0qrcOdK3fggS/2obRWjeQIP3xx9zi8NDOFCTMiO9mdNKurq8Nf//pXREZGwt/fHyEhIVYfRETU9QRBwIIJiQCAj9JPQaO90EJYUafB4XOGNzWkrDQDDCvCbx9rWC6wYnuu1XXmmWZ2bM80zTQDDMsQ4sMuDMXtcKWZje2ZpsTJWW7QdEmWj5r9G1Qv5kLWjF1JROSZnDzTzMP+bT1WXIMtx86b/7+j7ZkarR5v/n4CVy7fhu0nSqFSyPDQFX3x4wMTJH/jlKi7sjtp9sgjj+D333/H22+/DS8vL3zwwQf45z//idjYWKxZs8YZMRIRkQ2mDo1FVKAXSmrU+CG70Hx5Vp6hyqx3pD8iA7ylCs/szrR4qOQy7DlTid2nLyyP6cj2zEBvpXng/ziLeWZAJxYBNPv/5q8F1MZKs16hhgRdAZNmLknvxEozSx2qNPOwF4NE5H4408yxMnPLrP4/pI32zJB2kmZZuWW49vXteHnjcWi0ekzoE46NSy7F/03pY9eSJSKyZnfSbP369Xj77bdx0003QaFQYMKECXjyySfx3HPP4dNPP3VGjEREZAOVQoY5aQkAgBXb88wv5k2tmaku8g5jZIA3bjQuK3h/24VqswuLAGx/YieTCQgzVpuNTbT++jrennmRSjNjcq93pD8AwyIAckEWD5uj5+1Yzk1u78z1Gi3OlLF9l6i7EOF5FU4dZTXTzEHtmVZt9Q45Y/fRPGkW1EZ7ZlgrSbPyOg3+/tV+3PJ+Jk6W1CLcX4X/3joMa+aNQUK4n/OCJvIQdifNysvLkZhoaAEKDAxEebmhSuCSSy7Btm3bHBsdERHZZdaYXvBRynHkXLU5WWZaApAmcWumJVMr6cbDxcgrrQPQsUozAJjYNwIhvkpMGRBpdbnMmDTT2dk1crFFAA0aQ5zJEcakGSvNXJJz2zMt7qedU096aQsufWkzDhdaz3z1tBeDROR+LBNcjqo089SCNb1eRFZeudVlbbVnmirQyus1EEURX+8+iymvbMFXu88CAGaN7YXf/jYJ04fFcTMmkYPYnTRLSkrCqVOnAAADBw7El19+CcBQgRYcHOzI2IiIyE7BvirMHNUDAPDBH3k4X6PGiZJaAMBYF6k0A4A+UQG4rF8ERBFY+UcegI7NNAOAF/8yFDufuBxRgdatp3Ljk0V7EybNn7Q3r1IyzTRLijC8e1uj1qKqocmu+yDns3zYHZ0zs3wZ0t4mzJIaw7ayX48UOzYAIiKJWf7L56iZZl3VVu9qjhXXoLK+yWqjuJ+XotVjTdX1xVWNuG1FJh7+aj8q6pvQLyoA39ybiuduHIKgNhJuRNQxdifN7rrrLuzfvx8A8Pjjj5tnmz344IP4+9//7vAAiYjIPvPGJ0IQgN+PluCTzNMAgP7RAS63LWnhpUkAgK9256O8TtOh9kzAsARB0crGzQuVZs5pzwz2VZnbJFhtJo2qhiacrWi9/dEymeXoSjPLd+896YUdkdvjL7TNLL9VjmqBtzxPe29IuBtTa6ZlR0BbG0lNlWZ1Gh0yc8vhrZThsWv644f7L8HI+FDnB0vkgVpPYbdiyZIlWLBgAR588EHzZZdddhmOHj2KXbt2ITk5GSkpKU4JkoiIbJcQ7ocrBkRh4+FivLn5JAAgLTlc4qhaSk0Kw+C4QBwsqMYnmac73J7ZFnlHt2c2+//m7ZmmSjMfpRxxIT4oq9PgbEU9BsYGdjhW6piUf24EAGQ8PhkxQT5W11k+bPb+DFyMVaWZDadu3iDToeUBRNQl+NtpG6tKM4fNNHPIabodU9IsNTkM45LCsOlwMaamxLR6bIivEv5eCtSqtbisXwSenT4YPUN9Wz2WiBzD5lcmP//8M1JSUjBmzBi8//77qK42zOfo1asXZsyYwYQZEZELWTDBUMVletc21YXmmZkIgoCFxjjXZJxCdYMWgOOSZgq5MWlm75P55u2ZzSvNjNszvZVyxAUbEjVcBiCtvWcqW1xmmZhy5pwcW6ohOFaGiNyN9Uwztmd2lOU8s3FJYbjvst74333jEejdeoulQi7Dx/PH4NMFY7Fy7mgmzIi6gM2vTI4ePYpt27ZhyJAhePjhhxEbG4s777yTw/+JiFzQ6IQQpPQIAgDIBGBMomuW7F87JAaxQd4ordUgK8/wTquX0jFr0WUdrDRr3srXcqaZKWkmu5A0Y3umpFprv7SeaebE7Zm2VJoxa9Zt/HakGAvX7EJZrVrqUIi6DcctAvCQTJkF0zwzX5UcQ+KCbLrN8F4hGN87nH9biLqIXW/njx8/Hh9++CGKiorwxhtv4NSpU5g0aRL69OmDF154AYWFhc6Kk4iI7CAIAu6+NBkAMDI+pM3V5VJTymWYd4lhk6bpObfD2jONp2neXnkxzZ+zm95Ar25sglqrs640C2GlmSu42OssR83bafW+O3Ibz3td2G3M/2gXNh0uxvM/HZU6FJKAiO7/+7nzVDn+t7fA6ffjjJlmnT3Nd/sKMP2tP9ucdemKTK2ZoxJCoWxlPisRSa9Dv5m+vr646667sG3bNpw4cQI333wzli1bhoSEBAeHR0REHXXd0Bisvms0/nvrcKlDadcto3siwGJLlKOSZh2tNGvebqcTRVTVN2Ho0o244tVt5plm3gq2Z7qK1h5hy4fd0Tkz60qz1k/epHNMuxJJw7T5lKi7uf/zvViydh8Knfx3ybIqzHEzzTp3nq93n8X+/Er8kH3OIfF0BVPSbFySa3YEEFEHk2YmdXV12Lp1K7Zu3YrKykokJyc7Ki4iInKASf0iERvsc/EDJRTgrcRtY3uZ/9/e7ZltkXdwe2bzw3V6ETtPGeaNnCmvR4Op0kwlQ48QwyyRs2zPlFRrL7SsZ+M4ehHAhaxZWz9eli8i2UHT/WiMi0mIuhNRFM0J3zq11sn3deFzx80069ztqxuaAACHC6sdEI3zNZ9nRkSuqUNJs23btuGuu+5CdHQ0HnjgAfTt2xfbt2/HkSNHHB0fERF5gLlpCVAYk1w+KmmTZi3aM0Wx1RcElu2Z5XUa1Guc+wKF2tZaTszyIofPybFan2nL4dZZs27e/eURMnPLpQ6BJCCKti33cFX1Gp35b56zvwrL75MzZpp15M2O6kbD3+FDhVUOicfZOjLPjIi6nuLihxicPXsWH330EVavXo2cnByMHTsWr732Gm699Vb4+/s7M0YiInJzscE++Of0QcjOr3LYE8eOJs2av9TQ6UU0tdJ64q2QQ6WQIcBLgRq1FoWVDegdGdDRcKkTWnuRa/mCy7kzzbg9k4hcQ3VjU6du36TTo7xOg1A/1UXna1nNNHNQe6Zl0sze0QoAUGP8+nNL61Cv0cJXZfNLXUmYWjNHc54ZkUuz+V+ShIQEhIWF4Y477sD8+fMxYMAAZ8ZFREQeZvbYeMwe67jzyY2ZCnurjFouAhBbzKeSCYBSbjh/XIgPjhbV4GwFk2ZSuVilmVMLzbpvUQoRuZmaxgsVzx35t0mrE1FSrUagtxIXW2RteXpHVZp1dhalqdJMFIGjRTUY0SvEIXE5y4V5ZmzNJHJlNifNvvzyS1x//fVQKFw7Y09ERAQAsg7PNGu5CECjtU6aeSvl5lXvccGGpBmXAUintYfY+sWXtNszmxeaMdFG5Jq6c2smcGGmF9AFX4tTZppdOKm9m68bm3RWf6sPFVa7dNLMep4ZlwAQuTKbM2AzZsxwZhxEREQOJTdvz7Tvds0P1+pF1DYbqOxj8RZ8D+NcswIuA5BM67NvOtfm0x7Bot/Slrk7bM8k6j66c1K7s5Vm9nDOTDPLz+07p+XXDrj+MgDTPDM/lRyDOc+MyKWxeZqIiNySaaaZve9WN3+eXq/WoU5tvUnP2yJpZloGwA2a0mntEbZ68eWYIggzyxyYLT9eLRcBdONX5UTUruLqRpwpq5fkvi1nmjk9aeaMmWYW/6Dq7Px3u6bZPLfDLr4MwNSaOYrzzIhcHn9DiYjILZnaM+1tG2n+1L+iXoNatfWTcS/lhT+fccG+AMD2TAm1Vu1leVFHtrC1e39Wn7PSjIguGPvcb7j0pc2oaujcUP6OqLasNHNyct7ZM83s/XfbVGlm2sR9tKgGWnszb22oU2vxwfZcFDrw7zznmRF1H0yaERGRW1KYK83su53pibrK+M5vZb0Gtc0rzRQtK83Ynimd1hcBWMzGceZrRxaNEbkNUXRchdbZiq6vNqvp0kozy/ZMx88004mGJTzF1Y0tlvG0xpQ0S4rwg7+XAmqtHrmldQ6Ja8OBc3hl03Gs/DPPIefjPDOi7qXDSbOTJ0/il19+QUOD4UWCo9/FJSIi6gyZeaZZx9ozQ/yUAIDK+qYWM828rSrNDEmz4prGFgsDqGu0OtHMsnXI0ZVmFufryJn5lInI/Unxe958rpczWX559i7caYvVIgDxwjZPrQ3tn6bW1EBvJQbEGDZZH3JQi6ZpwcKRc9UOec3LeWZE3YvdSbOysjJcfvnl6Nu3L6699lqcO3cOALBgwQI89NBDDg+QiIioI+Qd3J5pqlAK8VUBACrqm1DXfBGA6kKlWbi/Cl4KGUQRKKpq7EzIbuOnA//f3n3HR1GnfwD/zO4mm14gJAFC712aCEhTAdGz60/PcmJBUSwc9nbqnRXLeXavod7ZT7AXUGmKSO+9hABJSEL6Jtn6/f2x2c3M7GzfzaZ83q8XL7KzU767mUwyzz7f5ynCt9uLmu14R8vrPDIRojk909txvJE4P5Oo3YlF0Ky6GaeEyl9fIEGtQChrUQY7PdP52lMTDBjcOQ1A5JoBWBp/v1Q32HCkPPwMQtYzI2pdgv4p/eMf/wiDwYCCggIkJSW5l19++eX47rvvIjo4IiKiULn+Dg22A5drlokraFZZZ0Gt6tN7+fRMSZKamgFUxqb4c0tiMttwy3ubcMt7mzyCjdEiIFBnUU6hVWcsRPZ4ymP7w5AZUesRTi0wZRZq80fNmrd7ZpPI1TSTX7dDq2mWlhiHIV2c2Vs7QwiaaX3IIs8i33K0Muh9qrGeGVHrEnTQbOnSpXj22WeRl5enWN6vXz8cOXIkYgMjIiIKh2t6ZrCfgLtudDokNwbN6rWmZ+oVj11TNNlBE4rgVXNNVxUCPudJRmrqkJao1ksjolbBVXtLfs2LSaaZvKZZtIN2ippmkTmWfCp9sNdtVxOE1AQDBndpzDQLYjql1e7Ac9/vwZBHv8c7a/I9nnPZUlAZ1LjUWM+MqPUJOmhmMpkUGWYuZWVlMBqNERkUERFRuFzTM4P9tNq1uito5uyeqQyaybtnAkAemwG4yd9vXTNNS3QIzxtE+bc92HPAryCnfnJ2JlHb5qq9ZbXLM82aX6wyzeyRagQg202wcTjX1NTUhDj0y0mBQSehss6KwgDKJuSXmXDpm7/iteUHUWex482VBxXTQyOZacZ6ZkStT9BBs8mTJ+Pdd991P5YkCQ6HA8899xymTZsW0cERERGFKvSaZk6ZSb4aAWhnmh2PYDv61koeoJKaqVSLVsc7eRAtmjePAdU0i97hSYZNqaglCeR8DKY7ZCAU3TMjskfv5C/PGrGaZhGYnpkQB6NBj345jc0AjntvBiCEwCcbjuLcl1dj69FKpCUYkBSvR1FVAzbLgmPy17e/pMbjb4JgsJ4ZUesT9E/qc889h7feeguzZs2CxWLBvffei6FDh2LVqlV49tlnozFGIiKioOlD7p7Z2AgguammmfxGBAASVUGzvExnBjYzzZTZAc2VaaZ1c6ronhnhOZTBTntSNwJgcCc6dhyvRkk1m3FQeML58VTW5PK/fjDdIQNRXS/PNIvudcYRxlRKuZO1Zmw7VgkgvAxheSMAAE3NAIq065pV1Vlx2webcc//tsFksePUXh3w3fzJmDkkFwDw9bamZjZmWaaZQwBbw8g2Yz0zotYn6KDZ4MGDsW3bNpx66qmYPn06TCYTLr74YmzevBl9+vSJxhiJiIiC5p6eGWymmWp6pkN4foqeoJqe6WoEEM1MM7tDYM67G/DHj7a06KCL/P1urgwrITzDWOFkLAR37KjtmkJQ2YzdA4nUhI9HzUH9AU80KbpnhhE0m/f+Jpz/6i/Yf6ImrEBcjaymGQAMaaxrptUM4LdDJzHrb6vw9bYi6HUS7pk5AB/MOQ1dMhJxzrDOAIBvthe5f5+p63NuOlIR1NhcWM+MqHUyhLJRbm4uHn/88UiPhYiIKGJ0jUGzYP+Yd61tNOiRGKdHvdXusY68eybQND2zqKoedodwB+wiaUN+OZbtOgEAePyCIUhLiIv4MSIhmgEqbwQ8syoUHS4jXdIsyGwI1jQjah/kl4PmvhTa7A6YZI1Yoj49U/Z1ODXN9p+odf5fUovO6Qnu5Q4hcLS8DlUBBsJdTRBcvxvdzQBkQTOr3YG//bAfr684AIcAenRMwt+uGIlTumW415nULwupRgOKqxuwqaACY3p2cE+fzUoxoqzWjN3FwXflBFjPjKi1CjrTbNGiRfjkk088ln/yySd45513IjIoIiKicLmmZwabaeYKgkgSkJGkHZhS1zTLSUuAQSfBahcoqYnO9DBXwAxwTitpqeRvd3PdMwrheayoNgKQHyeAddQxMyanEbVMkew42dw/5+o6W1FvBCA7QKg1zax2B8rrLACA0hqz4vfHyVoLfvfqz3hgyfaA9uWuaZbYOD2zMWh2vLIelXUWHDlpwmVv/opXlzsDZpeOzsPXd0xSBMwA5+/36YNzAADfbC8G0JRplpUSrzgWABwtr8OnG48F9LcG65kRtU5B/7Q+88wzyMrK8lienZ2Np556KiKDIiIiCpe7EUCI3TN1koSMpHjNddTTM/U6CbmNn5BHo66ZEAJLZUGzisabjJYoFplmrmOu3l+Kd3/Nb1wqm+YT4TEps0kC6QTAVDOi9kB+PWjuS6E8kNM4gmY7dqg1zcpNFvf7VFZrVrx/B0pqYbE5cLS8DhUm/7/zmmqaOT/sSkuIQ7cOzizwF5buwzl/W40tRyuRmmDAK78fiecvG4EUo/akqzMGZQMA1uc7p1K6Ms1cUz/lAco5727AXZ9sxTvu3z3esZ4ZUesU9PTMI0eOoFevXh7Le/TogYKCgogMioiIKFxN3TOD2871J7uEpg6aaupMMwDIy0zEsYp6HK+sx5jgDunX3hM1KCivcz+uaMmZZhEuuh/QMRu7Z17zr3UAnLVs1NOkhBAeBfkjIaRMsyi9RVa7A+UmCzokxzOLgZpVVb0VpTUN6JudGuuhtBjNXXuyWlXPzHV4IUTEunNq7R8IvaZZaY1Z8bXDyz73nahBl8baodpjER41zQBgSOd0HC2vx3/WHgEAnNqzA168fIS7eY83I7tnAgB2F1WjwWqHpfH9c039NDUGzQ6W1mJPcQ0A4Jlv9yAxTo+ZQ3LdjYTkWM+MqPUK+i+q7OxsbNu2zWP51q1b0bEjo+ZERNQyuD5BPmky440VBwO/gZFNz8yUZZrJ/8jVCpp1zXD+EX4sCplmS3eeUDyubNGZZk1fN9dNo7MRQNOxjlc2eHSui+RQolkvLRyR7sRHFKiiqnrUWyIfmGluQoTZPVP2dXN/fiDvnCkfy03/2Yhr/vUbaj0y0cIjv+aGWtPMM2im/abtPVHjcz/1Vrs7yJYqq/c5LM9ZN0yvk3DX9P744KbT/AbMAKBLegKyUoywOQR2Fla7p2emuoNmztpxSzYdd29jtjlw/+LtGPvkD/jDv9fh4w1HFaUUWM+MqPUKOtPsiiuuwB133IHU1FRMnjwZALBy5UrceeeduOKKKyI+QCIiolB065CEP4zvgXd/PYJnv9uDXUXVWHjJcCTGewa85Fw3OpIE96fLADChTxbWHnJ+SqwZNItiB01XPbM4vbNuWmVLzjSLUSMAdcqXOmBnFwI6Vc5Xg9WO6gYrslMTEDo2AiAiJ0WGazNXNVN3znSN5ecDZQCAVftLccnovIgdT5FpFmKgXhE0qw09aObKMtNJQLLsd/yVp3ZHncWG6YNzPWqX+SJJEk7ploEfdp/AlqOVTZlmjfXSahqscDgElmx2Bs0ePncQzDYHvt5WhF1F1Vi1rxSr9pXiIf12nN43C+cO74LCxr8NWM+MqPUJOmj2xBNP4MiRIzjzzDNhMDg3dzgc+MMf/sCaZkRE1KI8fv4Q9MtOweNf7sKXWwtxsKQWb10zGt06eP+k2XWjI0kS8stM7uWdUo3ur9U1zQAgr7GDZqRrmhVW1mP78SpIEjBtQDaW7jrBoJkGj0YAqsda4zr92eUoqzXjl/vPcHdADVYg2SSSKljX3DfTRNQ8FD/bzZ1p5ieTLNS6Y97I9xbo9EyzzY54vc49Vb60Vplp5u3Xx75if0Gzpnpm8mn4mcnxuGfmwIDGpnZKt3T8sPsEth6tdGeauadnWuxYl1+O45X1SDUacPVpPZAQp8e8aX1xqLQW32wvwlfbirCnuAbL95Zi+d5S935Zz4yo9Qk6zB0fH4+PPvoIe/bswXvvvYfFixfj4MGD+Pe//434eO2CyURERLEgSRKuGd8T7904Dh2T47GrqBrnv/oz1hws01y/ss7iDnoZDTrceVY/AMD1E3vBaGj6lekr0+xYRZ3Hc+FwZZmN7p6JvtkpAFp4I4AYzNBSB8SEEB43X1o3Y2WNN2y/7Nc+H7wJttg3M82IWodww0oidjEzjUwzVbZtpINmsv0Hsu/qBismPP0Tbnhng3uZPNOsrNbsdT/7S2p9HqOq3rOeWbhGNGambT1W6a4Jl9a4f7tD4MN1zlre5wzrrPiboHenFNx2Rj98N38yflgwBQum98eAHGetP50ETB3QKWJjJKLmEfKVpX///ujfv38kx0JERBQV43p3xJe3n46b/7MR249X4Zp/rcPD5w7C7Ak9FZ9K/+Wr3aios6JPp2SM7pEJo0GPYV3TkZeZhO92FLvXS/TSCABw1jSz2R0wRGj6xdJdzuPOGJLjXlZV3zoyzZrrplGrDpE6m8tXBpxOF3pUi1ljRKQl1t0zBeCeEgiEXqzfG/neAmk0cKjUhJMmC1bvL4XDIaDTSYqgmdUuvH4gZLY5cOSkCb07pWg+r+6cGQnD8zIAAEdO1iGjsSmQfP/fbHf+br5oVFev++ibnYI7zuyHO87shwMlNTDbHBjUOS1iYySi5hFQ0GzBggX4y1/+guTkZCxYsMDnui+++GJEBkZERBRJXTIS8cnc8Xhg8XYs2Xwcj3+5CzsLq/HEhUOREKfH8r0l+HTTMUgSsPDSETAanIGxHh2TAUCVaaYxPTMzCalGA2rMNuwprolIod+qOit+a6yjNn1wLtbnO79u0ZlmMalpJjyDV6qHvrIUgo2ZBdsIoLm6ZxJRy9Hc18Jq1Ycpe4trcMXf10ZvPLLdBZJp5mpEYLULlNSYkZueoAiaAUBJtdlju6yUeJTVWrCnuMZH0My577QIZpqlJ8ahd6dkHCo1uUsixBt0SIzTo76xo2bXjESc2jOwTpjsLEvUegV0Zdm8eTOsVufFYtOmTV5btkejlTsREVGkJMTp8eL/jcCQLml46pvd+N/GY9hfUosXLhuOBxdvB+Ccijm6R6bHtkZZoMwVUJPT6ySM7JGJVftKsfFIRUSCZsv3lsDmEOifk4JeWcnY31gMmTXN1Mf0DESp7+F83dPpw8k04/RMhbJaMyQAHVOMftclamtiOz1TmWn25dZCxeNoZpoFsu9ac9PvrWMVdc6gWa0ySHaiukHx2KCTMCIvAz/uKcGeomqcM6yz5r5drz2SmWYAcEq3DBwqbaptGqeXkGw0oN7q7J550ciuYWUqE1HrEFDQbPny5e6vV6xYEa2xEBERRZ0kSbhxUm8MzE3DbR9swtajlZjx11VwCKBHxyTcPWOA5nbyQJlWTTMAGNMYNNtwpALXTugZ9ljdUzMH5wJwFjUGnLXXWir5vVNzxc+E0GoE4FnnzJtgg2byXQUSJFQ3AmjLiiqdN70MmlFrFO41S37d8XXN0XLnh5uRX2bCp7dMCGl6f41Z+WGKXXV8R4xrmsmDescq6jGmZ1NNs87pCSiqasAJVeZZp1QjendKxo97gN0+mgFUN07PjGSmGeAMmi3edNz92KDXIdmoR1mt87GvqZlE1HYEdUW22WwwGAzYsWNHtMZDRETULE7vl4Uv5p2Ogbmp7kDPs5cMR2K8dkBMPiVTa3om4AyaAcDGxmmU4Wiw2rGisePW9MHOemYZic5P0Stbck2zCN+YBULr5lS9yPf0zCgHtVS75+xMorYpnEyzz7cUYuuxKmw5WhnSsavrlZlm6mterGua1ZrlQbM61Fls7mWuOl8lqkyz3PQEnNJYkH+vj6BZU02zyAfN5OJ0OiTHO48xIi8dfbxMFyWitiWoK4vBYECPHj1gt9ujNR4iIqJm071jEhbfOgFvrDiI7h2SfLaCDyTT7JTuGdDrJBRWNaCwsh5dMhJDHtuvB0+izmJHbloChjVO9cxIcmaaVdVbYXeIsKYVRksMYmYQ8AycqYehHpf8hjL4oFlw3TOJqH0QXh/4Jv+wIdQmMurumeos2Eh3z3QEmWlmMiszzVxZZolxevTomATAc3pml/REjO+TBQAoKK9DrdmGFKPn7au7plliZKdnDsxNQ7xBB4vNGRSM00vIasyivXhUXkSPRUQtV9BX5YcffhgPPPAAysvD/xSdiIgo1pLiDbhrxgBcNqabz/VcjQAMOglxXm5qkuINGNz4ifmGIxVhjcs1NXP64Bx3zZT0xhsCITxvkFqKaNU0O1pehwarlw/ttLpnqoNoQp110ZQZEU7sMZDumS0vtElE0RZMZ115FlicPrQrRnWDOtMMqsdNC5buLMaYJ37Az/vLQjoWoLzmBpLFVuMlaNYp1Yjs1AQAwAlVI4Dc9AR0SI5HdqozUOUt26yppllkM83iDToM6dLU7dKg1+GuGf3x8LmDcNW47hE9FhG1XEEHzV5++WWsXr0aXbp0wYABAzBq1CjFPyIioraoc0YCundIwvg+3rPRALibCIQzRdPuEFi26wQAYMaQHPfyeIPO/Sl7RQttBuCIQiXsHcerMGnhcpzzt9Vejum5zF9jAJu9aUE4Nc0CawSg3D+z04jaJnlw3uF/xqKbK5MJUHZqDobrgxTX5cyuGoA8iHbTfzairNaM699ZH9KxAOV1LJjumQBwvFIZNOvUGBSrV30wkpvmDKYNbPwwak9xtea+XZ1DI90IAABG5GW4v47TS+ifk4obJ/UOOSOQiFqfoMPxF1xwAbtkEhFRu2M06PHTXVP8BljG9MzE22vyw8o023K0AmW1FqQmGDCulzJIl54Yh1qzDRV1FvRCcsjHiJZoTM90dYE7VGbSfF4I4RHIUmd5qItiyzMjwul+FsjL5V9NRO2DUH1tMtuQEKf3+3tDXhPMWyazP9WyDpJV9VaP7C91EA1QBuuCJQ+U2R0CQgif94jymmbHK+rdUzE7pTQFzdRy051Bs0G5qVi1rxR7ipo30wxQ1jUz6BgoI2qPgr6yPPbYY1EYBhERUcsXyCfLY3p0AADsLqr2Wn/Fn6WNWWbTBmQjXpV1kJkch+OV9ahqqZlmUYia+cti0HrWI9NMXRTbLp+eGWSmmXy/IaWNMdWMqCUKOwtUEbwXOFRqQmqCAT2zfH/AYQnjegQ4G8e4AmBpiQZU1Vs9rnn2CF92bKognM0hvE4tLThZh8LKevdji92BXUXOrLFOqUZ08tJtt3O6K9MsFYD36ZlN3TMjn2k2snsGAGdpBkOIU2eJqHULOFxeV1eHefPmoWvXrsjOzsaVV16JsrLQ58ETERG1RbnpCeiakQiHALYUVAa9vRACS3d6Ts10yUh0NgOoqLOENc5AWe0OnKhuCKg7GqAMIgVT08cXdZaY1jHlxxIQGjXOlI/lWRha3TcDFdj0zJB3T0StiPpyICBgDiCbS+v6uj6/HN9uLwrouK5MK0kCUozOwJH6uqn1gUY41yarXZ3J5rl/s82Op77ZjSnPL8f6fGX29ebG34+dUo3ISo3XPEZO4/TMATnO6Zm7i6s1r9fRzDTr0TEZ9549APfPGhj9TstE1CIFfGV59NFH8fbbb+Oqq65CQkICPvjgA9xyyy345JNPojk+IiKiVmdMz0wc31KPf/18CAa9hDE9MgOuf3KwtBaHy0yI1+swpX8nj+czkpw3RJXNlGlmswuUVJuRlhAHL01DFSKZaPb2L4dxsNQU0I2dR5BM9byv6ZnBxsyU6wfQCCDC91n+pkERUWRY7Q6UmyzokBwf9LRJ4f7f/zVCa5rkZW/+CgBYfvdU9PKTqeaqZ5YSb4BrmHZ1UEvjQqcP4zpiUwX6rHaHorP0/hM1uP2Dzdijyg6TJOc19EBpLQBn0KxjshE6yfP3hyto1ic7GQadhJoGGwqrGtBV1ZnalWkWjZpmAHDr1L6ot9hxoKQ2KvsnopYt4KDZ4sWL8a9//QtXXHEFAODqq6/GxIkTYbfbodcH8Fc0ERFRO3F63yx8vqUQy/eWYvneUqQnxmHagE44c1AOpgzo5HMKyfeNWWYT+nbUvAFoCpo1T6ZZsCLVPbPOYsNjX+4C0NRcwRshPG9L1eNQP5bf8IWTERdQphki1whg45EK3PTuBjzyu8G4cGTX0HdERH4F+6GBPAtKXWvRF4uPTN6iqnq/QbNqWaaV63rjLdNMPkZ/9RxLa8xYua8UM4fkePw+snrUTGt6XFlnwVX//A0lNWakGg2Kzpk9OybjcJnJ/d50SjFCr5PQIdmIstqm7pn9slPc5QmMBj36dErB3hM12FNUrQiaORzCXS8tLQqZZi4GvYTsNCOnaBK1QwF/ZHL06FFMmjTJ/fjUU0+FwWBAYWFhVAZGRETUWl0yKg9vXj0aF4/siswkZ1Hmz7YU4vYPNmPUn5fhyn+sxb9+Poyj5XUe27q7Zg7O1dx3ZpJzGktlfcuqaVbTYIUQQjEFKJzg0P4TTZ/o+6vT7xCeUyw9p2d6zzQLpssdANVU0OYhhMCdH27GJW+swUmTBfM/2tJMRyZqX8IKosu+dl1iArkOqqc6ygWSDebKNEtLbApsqeNwriCaPIDl7doqhMCnG4/hrBdX4u5PtuLdX494rKPONJNfUx//chdKaszo3SkZX9x+umK9gbmpiseuJgDyZgA9OibhvTnjlNs11jVTZ66ZLDb3eyx//ZEWp9chJy0h5EYNRNR6BRyOt9vtiI9Xzjc3GAyw2WxetiAiImqfdDoJZw/NxdlDc2GzO7CpoBI/7j6BH3afwMFSE9YcPIk1B0/i6W9247N5EzG0azoA4ER1A7YcrQQAnDUoW3PfGUmummbKoFk404jCdaCkFuf8bTUuHZOHiX2yIrLPfSeabowsfipYa2e3qTPNlM/aZPsMJzuuuWqaFVY14PMt/KCSqDm8sHQv1h46iX/+YWxQ28mvBw6NDFhvfHWx9Nd5EwCq65syzVz7Uhfqd32gUWlq+t1hsTk8pnuX1pix8Ps9WL2/qXa1a/qjfF/qa6or02zpzmIs2XwcOgl44bIR6N4hSbHewNw0fLuj2P1YHjTb3VjCrV92KrJTExTbDcjVDpq56pnF6SUYDQxoEVHkBRw0E0Jg9uzZMBqbPgVoaGjA3LlzkZzclDK8ePHigA/+9NNPY/HixdizZw8SExMxYcIEPPvssxgwYIDiuI8//jj+/ve/o6KiAuPGjcNrr72GIUOGBHwcIiKiWDHodTi1Vwec2qsDHjhnEPLLTPhh9wn8Y/UhnKg2Y29xjTto5soyG9k9A9lpCZr7y0jUnp4Z7DSiSNp/ogYWuwMb8yswvnfHiOxTHjSz+imkLaBRgNvLDZ17n7IsiWDrsKlvjIMVSoxOndVBRNHzyk8HAADfbC9yX58DIQ+TOQSAAKdoqhsByDNjAwma1chqerl+N9g8apo5/5c3kXEIwGSxu7s8r88vx7z3N6Heake8Xoe8zEQcKjN5jlcjPddqd6DCZMGDS3YAAG6a3Acju2e6X4PrGuzKGHPpmOL8IEjeQVPrJQ/KdTYD2NPYddNFXs+MtR6JKBoCDsdfe+21yM7ORnp6uvvf1VdfjS5duiiWBWPlypWYN28e1q5di2XLlsFms2HGjBkwmZouzgsXLsSLL76IV199FevXr0dubi6mT5+OmhrtlsNEREQtWc+sZNw4qTdGNd5MmCxNGdtL/UzNBIDM5OZtBBAI17SckyZLxGqa7S1ump7pr3NnndmmuDEVwjOIph6XvVm7Z4Z/Ixeht5WIguCr1pgmxXWosYZYCI0A5NenwIJmTTW9DDqd5j43HinHks3HPDovV8mm+v+w+wTqrXYMyEnFN3eejjMGamc8ywNyruHZHQKPfbkTZbVm9MtOwfyz+rnXkV9jB+Q0Bc0ykuJgNDg/5ZF30NTqUukKth0qM8Fss3u89mh0ziQiAoLINFu0aFHED/7dd995HCM7OxsbN27E5MmTIYTASy+9hIceeggXX3wxAOCdd95BTk4O3n//fdx8880RHxMREVFzSG78ZN9VwLimwYpfDzqnw8wYkuN1u/REV02zltMIwBWQqqizKINRYexTOT3T943rzwdO4vudRZpjco9FPT3TEUammeLrALpnBrd7v8eklumb7UWQAPTNTon1UCgM3gLUrsBPoEHwoKZnqq5x8iL+WgEkNXm2lV5n1tznvhO1+ONHW3HdxJ6K5c4sNWdhfddhpw7ohL7ZyowwOfkHGYlxepgsdny9vQifbymEXifh+ctGKDppyt+HvMxEd+aZPLtMkWmmkdaRm5aA9ERnjdADJbUY0sWZrLGr0Jl51iU90XMjIqIIaFETv6uqqgAAHTp0AAAcPnwYxcXFmDFjhnsdo9GIKVOmYM2aNZr7MJvNqK6uVvwjIiJqaVzTYUyNQbMVe0thtQv06ZSMPp2833RnurpnNtalWfTLYYx76gccKKn1uk04Vuwtwf2Lt2k2LXBxZR3YHcKj1looquqsKK5ucD/2Nz0TAJ77fp/isb/pmZGqaRZKNCuUQuPhZMNR9G0qqMBdn2zFgk+2xnoobUZVndWjllasCCFwzb/W4cLXflE0O/FYT/a1Ky4fyvRM+ezHYDLNUhMMfrs7bjxS4fVYgV5n5I0LjI3BsYXf7QUAzJ3SGyO6ZSjWl+/WoNcht7H8gLz4v/xrrcCkJElNdc2Kmj5U+WlPCQBnoI+IKBpaTNBMCIEFCxbg9NNPx9ChQwEAxcXOIpE5OcpP3HNyctzPqT399NOK6aLdunWL7sCJiIhCkGx03miYzM5pJq6pmdN9TM0EmhoB1JhtsNod+H5nMU5Um/HJxqNRGefnWwuxs7AaSzYf97qOPCBVUtPgdb1A7StRlmDQagTg6+YukOmZiu6ZQQak5McOZMtIlNlhyMy/cLoehitaQev2rKC8DkfKvAfrm5PVLvDzgTJsPVaFI40fIJRUN+C8V37GB+sK3Ot5NAII8JT0mJ4ZZE0z1xTLtMQ4v5lppTVmxeNQPjRwZeoadBIMqvFdN7GX3+27ZjqzwrwFzby9hkHuZgDOpIg6iw2/HjoJAF6nkhIRhavFBM1uu+02bNu2DR988IHHc+pPG9RdXuQeeOABVFVVuf8dPRqdmwgiIqJwyKdnWmwOrGj8tNzX1EwASG9sBAA4b5RcXdNW7C2NTjZS4y63Hqv0uor8Bk9+QxbqeFzZdy4WWf0aF39TKtXHVq8vz+wI520L5IZTisAEzeZINDOZbThUyuAPtW+K6deuumTyIFbjPciLy/Zh+/EqPLB4u2xbea3EwAK5DiEUmVuA8oOIQKZnuoJmmUlxHkEstRJV0EzOdVR/U1BdmboGvaTo1pyVEo8s2TRLb/Iag2bydbNTfTcCAICBnRubATR20PzlwElYbA5065DIadFEFDUtImh2++2344svvsDy5cuRl5fnXp6b6/y0XZ1VVlJS4pF95mI0GpGWlqb4R0RE1NLIp2euPXQSNWYbOqUacUpehs/t9DoJaY0FjyvrLKgxO2+WCsrrcLyyPmrj3X68yuu0JHnWVllt5GutyW8oXTevNo3ube51tJb5agQQRoZSYI0Agt9G40ihbBSUoqp6d+YjUWvQYLVjd1F11Kcvyz8YcP08q7PDAO3Our6G9ummY7jyH2vx2+GTiuUOL40ArHYHTlQ3eEzndHXMTE+M85uZpp6qrm6iAvivw+g6fpxOpziea/qkPxeN7IoBOak4Z1hn97JOKU0do70FCgc27n934/RM19TMMwZks3MmEUVNTINmQgjcdtttWLx4MX766Sf06qVM5+3Vqxdyc3OxbNky9zKLxYKVK1diwoQJzT1cIiKiiEmOb8o0W7rL+eHQ9ME50AUwFSczubEZQF1TphkArM8vj8JInWoabDhUpp2FZJfdwKmn/oRCfY8pv0F03e+pb/zkPt5w1G9NM3kgzkf8zS9vo4jkTbzN7mD3TCINl735K2b9bTW+2a5dtiVS5D9/riBRvMH3bZRD+A+QP/zZDpgsdny1TdnIxKbINJMttwuUVJsVNRkBoLIx0yw9Md5vTTPPcXpON/cXf3KNz6BXTs8ckBNYssKkfp3w/R8nY3SPTPeytEQD4huz1rwdv39j582yWjPKas1Y3hg0m8apmUQURTENms2bNw///e9/8f777yM1NRXFxcUoLi5Gfb3zk3JJkjB//nw89dRTWLJkCXbs2IHZs2cjKSkJV155ZSyHTkREFBbX9MyaBhuWNdYzmzHY99RMl4zGKZrlJktj5zOn9fkV3jaJiM0FlZrL5fdvkQiaqSNR8htIV/DL5iNotu5wuce0SfXq9rBqmsm/9tz2kw1HseDjpoLw6vs/b0fTyiIx2+zYXVSjCI4SRZu3jKbmZncIxTVObftxZxOxjzdEsByLxs+01nRJ+bRE96ayr8NpMCLfNpDp3dWNQbOMJP81zdRCGaXrvDDo1ZlmoU+RlCQJWSnOD4S8vYZkowE9OiYBAJZsOo7i6gYkxulxWu+OIR+XiMifmAbN3njjDVRVVWHq1Kno3Lmz+99HH33kXufee+/F/Pnzceutt2LMmDE4fvw4li5ditTUwNJ/iYiIWiLX9MytxypxotqMtAQDxvcJ7A9/VzOAwsp6RTBoZ2GV++YpGjYfrdRcbpelap00yWqahXgcX9MlXTeTvjrYAZ73verglnx6Z6SbZ97zv234bmdT5kug97BaWSSurxs06rq1dS0lcNMeectoam4lNQ3ID6AZgHyU0Thv5EEsXePdk2ammTxrK4wLi69MWs9DClTWNQXN/NU0U3NojNnfHlznRZxOgkEWPByQq51pNrJ7BoCmWmbeuJoB+HoJrima//z5EABgYt8sJDR28CQiioaYT8/U+jd79mz3OpIk4bHHHkNRUREaGhqwcuVKd3dNIiKi1srVPdN1v3LzlD4wGgL7wz8jyZlpVlDuzMyO00vo0ykZDgGs3Fca9tisdgf+vuog9hYru1h6yzSTZ31FYhqhr33YAsg0AzyDWa6aREIIXP/2etwlywQLOtNMvnfZl66bdU+toxFAS9NSAjcUO4F+7+UBqmicN/KfP1cWlFbQTJlpFtqxzDa7ImhmFwKLfjmMHY1ZdWomi919PXTWNAvu9k5oXM78NgJwNGWayT+o6Z+jnWn2+lWjcMPpvfDejeN87rcpaOb9+AMbA3Mnqp0f0LBrJhFFW4toBEBERNTeuDLNAGfXsOsn9vKxtlJmY6ZZQbkzAyM1IQ4zhzib53y25XjYY/tmexGe+mYPnvxmt2L53uJq1Fk8pwnaw7g5FUJgzrsbcP3b62Wd6ryv77qZ9JeJ4a175v6SWvy0p0QRdAv15ta5refNulokGgGE06yAiELj+qmTNwJwB820pmdqNAII1pGTdYptNx6pwONf7sKDS7Zrru9qAhCv1yExTh9wpllm44cvQitq5oerJmScXlI0oEmKN2iu3zk9EY/8bjB6dEz2uV9X0MxX0G6gqtkAg2ZEFG0MmhEREcVAsixoNv+s/kiMD3x6SXpjTbOjjUGztAQDLjylKwDg10MnURhmF81NR5y10U5UKbOmHALYdswz28Hu5eYwkHvG6npnTbef9pQEVLfLEWKmmUMI2B0CT6sCga7ngqHVbS7a2mOmWSj4PvlWVWfFnuLqWA8j4iL5fdfalbLGmFMgmWahBLsPl5kUHwq4Mrn2n6jVnJZe5WoCkBQHSZKgD6ARgE5q+j0Sylvnnp6pETgMR26ac/pmQpz3/Q7s3DQFdHDnNOSmJ3hdl4goEhg0IyIiioGctASM7ZmJiX074v/G5AW1baZ7emZTplm3DkkY2iUNQgCLNx0La2xbGgNj5Y0ZDIrnNOqaBVN/R62yvukYZruzbpevvbmCZf6y2zwyzRwCX20rxPK9ntNXw6k9FMiW4U/OJIqM8c/8iLNfWq35c9yaCYio1sDT6rCrlWmmGJMQIQXz1JlmrqmQ9VY7SmrMsNkdKJG9zipXPbPGIJg+gCKKGUnx7oy5IyfrsKuwGofLTO4gn79dNDUCaFqxd5bvLLJA/P7Ubrjx9F64dnxPr+t075CExMYaZswyI6LmoJ1DS0RERFGl10n4ZO6EkLZ1NQKotzqDTGmJzl/nZw3KwY7Cavxv4zHMm9bXb10aLWabHbsLnZkolXUWd8QnLzMRxyrqsbnAs0Onv6wvX1wFrAHAYnPeiPkKYrluJm1ad7Ey6l04BFBYqVVvLPjpmf66Z6qpvw+hZJ8EcvNtsTlQXNWAvtmhd7Cjtq3O4rxmLN9TglO6ZcR2MCESQnj+TImm6dFdM3wXmw+FVjaqPNOsaWp56F15XfJPmhTXVKvsA4IjJ01INhpQWmtGdloC4vRApSvTzBU0C2B6ZkZSnDuYf/cnTfUdXZv669jpDprpdBjVPQObCipx0+Tefo/rT3ZaAh7+3WCf6+h1Eib1y8KKfaX43YjOYR+TiMgfZpoRERG1Mq5GAC6pRufjCX2ykBSvR/7JOmw44hncCsTuohpYGm+IrHbhvsl23WBvLqjUzOIKVWW9RtDMx/oB1zRTPXYI4fUmtjVkmgUSaLvsrTWY858NWJ9fHoEjtk0WmwMN1vbXibStKDdZsP14lcfPbESnZ2pMv9a6dsinJrqCXJ7TM4OXf1I5PVOeOZd/0uSxvmt6puv3QiA1zTKT4nHxqDx0SI5Hx+R4pDaWC3Ad1t/nLa7XG6eX8O/ZY/HfG8bh8rHd/B43Uv52xUisvneauykAEVE0MWhGRETUyrgyzVxcmWaJ8Xqc3dgQ4H8bQpuiuVU1bavC5Jw+OaxrOvQ6CSU1ZhSpap15yzQLJNBTKZsCanZnmnlf3x0083OXrH7ad/aan0Gq9x3gfiPJ12Fc09L2nagFAHy5tbBZxtQa7S2uwf7G96mtqmmw+g0MttbSb3UWGyw2hzvL1iXajTK0rhHyTDOLxrUr1Eyz4xX1aLA2Bcpe+mG/++sjJ+s81ndl66YnOn8vBFLTLDMpDvOm9cWmR6Zj4yPT8e/rxgY1RnmmWUZSPE7vlxVSZnOoEuP1yEljLTMiah4MmhEREbUymepMs4SmxxeOdDYE+GpbYUgZNR5Bs8agVmK8HoM6O7uWbS5QrhNOTbNqjUwzX7f0rmPZ/NU0U+3D7vCeERfqzS0QWIaLx71kKN0zfWyj7tpZb7VHpa4T+dCColD5ZXVtNjCYGKdHvdXuzoB1iXbs2t/0TIvd89olRPBTuZ3Hck7D1KKVaeaqC+manhlIppn6g5ehXdIV0zr97cF1/TUEEKAjImrtGDQjIiJqZTwyzWRBszE9MpGdaoTJYscmjfpj/mw5Vql47Mo0A5qmaG45qtxvWI0AZDXNAso0E4FNz1Q/7Zyeqb1uODfcAU3PbOb7ygar3W9QkSiWqhus2HrUc6q3P5IkwWx1oF4dNIvg2OQBd9dXWgF3ecH9L7cWoqSmQZlp5p6yKVBVZ8WO49WwBRjMPlSqHTTTyjRzNwJICrwRgPqDl8R4PQbmprof+5+e6Xwdke6eSUTUEvFKR0RE1MqkJRjcNWgAIDWh6WtJkjCxbxYA4JcDZUHtt6re6r5ZG9LFWSvGJLs5HdktE4BnppnXRgDCmV1RXKVdgB/QrmnmizvTzF+gTl13TQivU7iCzTST3+gHlGkWgapm0Z5+Ri1HNLtAujXTtGJvzv7rKlzw2i/4cXdJ0Ns6HMIziBX1TDPfzz/97R7MeXeD122q6q2wqwL3vgL/h8q0MwWPVtR5bKeuaabX+b+9c3WflJM3hvDfCKAx0yyArDYiotaOQTMiIqJWRpIkdOuQ5H6clqjMGnAFzX4+cDKo/W5rzDLr0TEJvTt5dmA8pXsGAGD78SrFDb3dRyfLRz7fgdOe/hFLNmvXWFNmmjkDdNFqBOAtThBGolxgwSzVfWUoh4tWjOOrbYWY9/4m1Fls0TkABc013bYtZwsWNgbSv9lRFNR2DiHw7Pd7cPf/tiqD11GvaeZ//+rC/65thHBmyNodDkUGl68PCbx90GC1C5TVmhXLmmqaNU7PDHHKpKKbqr9Ms8brf5yBt5JE1PbxSkdERNQK9ejYFDSTZ5oBwMS+HQEA249VurMQAuGqZzYiL8Nj+g4A9OqYjPTEOJhtDuwpqnEvd8XPEuI8/6z479oCAMDz3+/TPGZVfdP0T61i2mpNmWa+s3DUAQeHw3tNoaAzzeRfN1NcI1rHue39zfh6WxH+sepwdA5A5IMuyLnLR8vrcLDUhPX5Fe7p3ED0fj7+8tUubD9WGfA1Ql3DrCnI7/AYo1bQLDnemQFWVmvxeM6lsLJe8diVresKmukDyP7SejUjGz8UcY7X9+t1d89kphkRtQMMmhEREbVC3eWZZglxMOglZKcZYdBL6JyeiN6dkuEQwNpDgWebbTlaBQAY0S0Dmaq6aQCg00nubITNsrpmrkwzX93MjBoBNQCKoJ7F7oDV7lB01FQLNNNM/bxzeqa2cDpgBnIz7dEHIITjRTuTptxk9r8StRktJYct2JCL/Oda/nVEa5qpdvbgZzsCrtso/zk9fLIO1y1aj3/9fBh2h+eUcldmrVxmsvO6q84mkyuuVmahVbunZzZ2zwwgEGnWCNj1zmrKLj5Y6ruRhHt6JmuaEVE7wCsdERFRKySfnpmaYECcXoectAR3YebTg6xrJoTAlsZMs1O6pWtmmjmfywAAbJHVNXPdDGanGpX7lH2dYPCsoQOopmdaHbDZhc/sOFcjAH/1z9SZaEJ4D3AFPT0zyPWl5u4E0Kiq3or9J2r8r4jYjbG5hdMplSIv2EwzefBKnk0aTuA7EIHuXr7el1sLYbE78MXWQtgcDgjVJUsrcNWhMWim7g4qV1ipDJq5PmTICCLTTGs6tk62XW2D7+narun5ceyeSUTtAINmRERErZB8emZ6omeAa0Kf4IJmRVUNKKs1w6CTMKRLujvjQc01hWdzY4ANaApEZKd6zzTTmroJKBsBmO3+p2c++fVuCCE0bzjl1NMz7T5qmoXVPTP4kmahHSeEbYqrGtBgjWIx+Vamqs7Z6MLf1N72oKXEDoON1crHbZV9H6P9csINtjocgB3qTDPP81DrWq5+rqiqaXqmxeZwN2txNQIIpKaZt6Dca1eOQu9OyZh/Vn+f27tqmhkCaDpARNTa8UpHRETUCnVOT3R/nWI0eDw/vndH6CTgYKlJcZPljaue2cDOqUiI07szHtRcmWaHy0yoMDkzHFwBqk6qTDO5BI1ubUIIVCkyzbxnV7hsPFKBbceqQmoE4G2T4GuahVeAPLRGAC0kytGKmRqzawKdakfRF2yGozxQpsw0i9iQNIV7zrgyZB0OgYKTdY2Bf8/rXYaPoJnrg5Li6gZ3Nq48Kzc1IfBMs3ovQbMzBmbjb5ePxIDcVJ/bWx2u6ZnMNCOito9BMyIiolaoZ8ck9OmUjAE5qZrZCelJcRiWlwEA+CWALppbGjtnjmjcRqumGeCsm9M7K1mxjeuGMjtNNT1Tdp+pFTSrt9phkXXhdH3t7/Z0Z2G1/5tY1dMO4T3wFE5AKqBMswjcVzJmRq2FwyGwq7AaDQEEwYP92ZBnZ8k7+Eb7xyOU6ZlyrrqPL/2wH5OfW46P1h/VzDTL8HLdBZo+HDlRbcYN72xAhcniDpqlJRjcwTJDQNMz/X9vfHF3z2RNMyJqB3ilIyIiaoUMeh2+nz8ZX91xuqIWjdzEPs4umv/++TAKTtb53J+rRtmIxkwyb9MzAeAU1xTNxm1cNc2yko1eb4KNBs8/OdS1y5q6Z/q+Q91ZWOVRVFtNnQHmcHjPCQs2iUTZIc//+lIEJmgyZtbCtbKEm2g2ljDbHLA7BMpN3ht6uATbfFFey1BxDYhyVNke4P69va+uzd9YeRAAcP/i7TBrTJ3O8FJLEgCSVRnFW45VursPy4NtgdSJ85ZpFih3IwB2zySidoBBMyIiolbKoNf5/KT/ktF5SI7XY1dRNc7+2yq8/1uBZkDK7hDYftzZOdM1/bKDKuNBPo1qpKsZQOOUTtf0xniDzmM7F61MM3kTAEC7xo+WQDLN1C/TIQQcXrYJp15RQN0zJWU3vFAOF+1Ms3bSB4BaGK2AstXuwInqBs3ac/JrhC3CmWZ2h8CGI+Wa3Xvl181dRdWoqrO6A1bK9bT3rRXkl2fZuhgNeiTFazdNSVYttzuE+xoqD7YFcq2os/ou9O+Pa2opu2cSUXvAKx0REVEb1adTCr69czJO7dkBdRY7HlyyHbMXrUdxlbL72oGSWtRZ7EiO16NPpxQAQGK8XpEdJp8COrJ7JgBgS0EFhBDu2kI6naSohSbPutBqBKAOmrkzzfy8rj3F1X67Z6rvUR3C+36DzjTz8jUAnJQFx+RufW9TcAfxedTIi0Q2HFGwtBKVbHaBkmqzZmDcqpieGVpNsxPVDbj8rV/xzfYixfL31xXghaX7cNcn2zy2kce3rvrnb5i08CfNa5C3Ybg6Z/bulOxe5m36qrep8epMM7ujqdOw/PpsDaDRRSjTM612B77ZXoTf/30tPlh3FAC8BviIiNoSBs2IiIjasO4dk/DhTafh4XMHId6gw8p9pZjx15X4bPNxd/aEqwnAsLx0RRFpeQBMXqC6b7YzsFbdYMNfl+3D2sPOmmkGnYSOKU3byKdnGQ2eN1fqTA1X9oK/G+AGqwMHS2t9r6S6fRU+u2c6n7DaHXj/twLkl5n87NvrYfCXr3d5rCIBWHe4PPB9ah2G8zMpBDUNVhRWejYCaSnnU7CNABSZZorumYG/oMe/3InfDpd7BLJ/2HUCgOe0ccAzo7S6weZ3irhcrdmKHcer0L1DU9djre+LQwhkJmtP0VQHzWyyTDN50EzdOVhLsNMzV+4txenP/oRb39uEXw+dhE4CZg7JwcWjuga1HyKi1siz3RYRERG1KTqdhBsn9cbUAZ2w4OOt2HasCvM/2oLvdhTjyYuGNjUBaJx26ZKZFI+ixqw0efZDQpweiXF61FvtePmnA+7lep2E7NQE9+MDJbWKbdS8ZZoFklXlmhrqjTooYHcIr1MpXcvf/fUI/vKVM+iV/8y57uetdgfKTRZ0SI5HnF6nrGmmGuvBUo2AmyouEEy8osJkwX/WHnE3X4gWTs8MUwsJQqnllzlrGXbJSFQstzXWHPPWJbe5BHveWexNwZ5QM83KarRrrfkai7dp7YGsBwCfbDyGpY1BOReT2XOKZG2DzXumWbzyts1qd6Cy3nN6ZiDBvGAzzfYU1wAAslLiccXY7rhyXHePc4qIqK1i0IyIiKid6JudisW3TMDrKw7i5R/347udxVifXw6D3nm3OFIdNJNlPKgLVHdIjsdxVaaEXpJw46Re+GJrIQBgd1GN+zl1I4B6i919w+cSaE0zIICgmeqxr+6ZrnvMjUe0s8Fc08XSEuKgjv1FuxHAg0u2Y9X+spC3DxRjZsCrP+2P9RCaTWWdBccr6mMfNPNy5tnsDpTXWjzqmnmtaRZE0EyrVppzLN5pJW8F2hwAAIqqPLPKtK53VfVWr9+TZKPy4lNvsaOqsf6aMtPM/3U00EyzYXnpAICxPTNxzfieOHtILuI1mroQEbVlDJoRERG1Iwa9Dnec2Q9nDMzGXR9vxd4TTYEtdaZZYlzTnwm+urq56PUShudlYMH0/nhx2T7sk+1bnml2sLQW5/xttfumMV6vg8XugMXmQHFVAyy28NN2tBoBeK031PiE1hRSzfVlewpkhlY4WVzrj1SEvnE7VG+x466Pt+Lc4bk4Y2BOUNs+v3RflEbV8gRbxy9avDVftDkEKuotHsF2q5fumVX1Vvx68KRierg3Acxe9KBVv0yrsYi3OFp1g2dWmVbQrLLegs7p2hlc6umZdRa7eyppRmLT67YGEDQ7rbGzsj8XnNIVM4fkamYKExG1F/yogIiIqB0a2jUdX9w+EXOn9IFOAvp0SkZuWoJiHXmwJ0V1w6bOMgOcNc0AoGfjVML9sqCZQXZ3/MRXuxQ3jJ1SjQCAjUcqMP2vK/HaiqYpn6FSZ5U5Apie+duhk5rPP/3tbsz970bN6VTB1FLyNjY1V/dAq93hteNnLFjtDhw5GUS9txj4aEMBPt10DNe/vSHWQ6EABBtQNisaATR9fbyyHte/sx4/7D6htZmC3VummY/BuOotymlNg/T206qeig5oB+IqTFav0zPVRffrLDZ3tm56gNMzLxrZ1fmhxv+N8LqOGgNmRNTeMdOMiIionTIa9Lh/1kBcNa47UowGj5tG+SP1c+eN6IIvG6dhuriaCGQ1ZnsUVyu7dLq46uO4ZKcZcbyyHiU12p0n1Qw6yW/dHvWzVofDa3aNK4hVWKU93nd/PQIAWLL5OOZM7q2saabap07yzOKps3gG27QcKKnFv385jAMltbh8TDdcMjpPs25SNAQSvLjqn79h3eFy/Hv2mKCzuJqLt3pVpNJCYrG6IKNmFsX0TM8X8dOeElw3oZfPfci3q6yzIDUhDnqd70nUDVaNTpl+Erokqen6oNVcQCsQV1FnQQcvjQDUWXcms80djJM3alFnDAPA85eNQPcOSRjVPQMGPXMmiIiCwaAZERFRO9dN1tFNztf97GPnDcbZQ3Lx0g/7sL+x4L++cQPXVCGrxk2tEMLdXMAlJzXBYz1f+maneATetI4jZ7d7754ZaFzKV2aJ2WbHv34+rLkvdSDH2+HOe/Vn99fbj1c5g2Ytpc0hmjqAvre2oMUGzaiVCboRgHb3TJd6q/9aXfKM08OlJqQlGtAnO9Xn9U4rwOXQ+EmWZ7/pJQk2Hz+/Zo1A3IhuGcjwkmlm0CmDXe80BvMBZU2zqf07KdZb99CZigYtREQUHH7UQERERJr6Zad6fa5jihHnDu+M7DSje5mroUC6j/pnWtlc8n0EYkiXdL/raDUC8Bau0pq2qTmFsnGZ0Fj22Bc78fzSvZr7LzMFlkGnxV/M7ER1A6rrrQEV//bF19Q0aj5WuwPz3t+EJZuPxXoozSKcTDOtoHwgBe7l2Zs3vLsB0/+6CiXVDfAVwdPMNNNY77Cse66/cLdZ42f2kXMHIy1R+/rpur5qkQfaJElyZ/sCYMCMiChMDJoRERGRplum9sFV47rj/TnjvK7TMbkp4KVvzITI8HLTBwCbNArbT+ybhfTEOFwyKs+jQ5yWIV3S/K6jDjbZHA54KWWkmR0W6E2ya9kH6456HUu0pgxWmCwY99SPGPf0j36nq/oTTOgi1PiavFYbaft6WxF+2lOCf/+SH7F9atXdC6UWXzTIT6UGqx3/XXtE0WlSCKEIhMlrmj20ZLvH/hoCyDSTB83KTRY4BHDqUz8qstgC2a/Q+Jl7+PMdmsfRos40O3dYZ6QnxSHBS3fKOB/TKtWNWrQCikREFBpOzyQiIiJNyUYDnrxomM91OiQ3ZTS4pmeqC1bLbS6o9FjWPycVmx6ZDp0ErM8vh8lc5/OYgQXNlDeNFpvDa6BACOERyCmsqse89zbhglO6upfpXM0MZPsOpFB/Wa0q00xjE3V9uED4m6IaPaFFzWx2gZJqM9IS4sDa4tpMAda/a2lsdoGTJnNAAVH5OvJMs2e+3YO31+SjU6oRr185CgDwwfqjWHe4Al/cNhHD8zJgkU2T1OpIGcj0TG8B5gqT9+C21vTMcAOP6n26ri/eCu8bvLUahXJ6JhBYB00iIgoMM82IiIgoZPJpQK5GAFpT/Vw3mHuKqz2ey0hsLMQtSeiU4n+q5qBAgmaqxxa78Fq7zCGER1HxBxZvx57iGjz73R73Mq171kBumz2CZhru/mRrAHtSH9tbEDDoXYUaByMN5SZLQBlPbYndIVBhsmoW51eTryO/VLg6X5bKGoKsO+zMTH1tubOjrsXP/rUyRNVC6Uirtd9we3Sou2e6Zl8a47Rvz3wV8FcH2kLNPDXoJWSnGX1OBSUiam+YaUZEREQh65jiWdNMy/NL92H1/jKsOXjS4zl5DZ+OKdpFsBXrJ3if/umi7lZnszu83ixrLXYVvZdzBaOExjJfAgmahSJWPQJaS/mzBqvda9ZOMBZvOoaEOD3OGdY5oPWLqupRUWfB0K7+a++p+e7hGDktZfKePMCuDiLJuc51cwSCkd6aawTbCCDcN9Gser3uTDOD9jkb5+X6et/ZAz2WhVrjME6vQ04aa6AREckx04yIiIhC1lE2PdNbUe/O6Qmw2BxYvb9M83m9LIUrK4BMs0B8slFZSN3mEF5vloXwUvhfvZ7GMovN4XcqVFmtunum9y6cwfA25GjXq2qOsE5VvRWV9dGpBReM0hozFny8Fbe+t8lrIMJqd6Cqwar5XKzY7A48uGQ7fjmg/TMHBHbOR5v8XPL1c+QKbGsVzw+Wt1pjvs7raGSaqYNmrunt3qdnet62nT+iC26Z2ifiYyMioiYMmhEREVHIFJlmGvMXk416/LhgCn5YMBldMxID2J/vTLNgs5xcY7LYHF6nLAkh/Bbtdq6n/B8ASmvN2H+i1ud25T5qJYVDq+tnqJorwylQBSfrcKy83v+KUVYtC4Z5e7dtdoGa+sgEzSKVxbds9wm8/1sBnpFNL5ZbvOk45v53Y0DdJqNJHmj3VbzeFeALZPqnP16nLvp487VqpTnCDE5bVNlrrg8PjF4aAcQbPMeXlsBJQ0RE0cagGREREYVMq6YZAJzaqwMAYNaQzpAkCX2zU/HO9aciKyUeN0/u7WN/vjPN7pkxIKjxuW5AzTYH7F5uuB2BBs00bpIdjsC2lWuwep8qGgxve4h2AlFrmZ7ZnCL1lkfqra2s8x/EK6xqwHc7iyJ0RNW+K+u1pzSqyM8ln9MzXf9H4OQOJdNMK7gY7ljUGaj+GgHoNTLNUrxMVZ83zZl9ds1pPcIZIhERgTXNiIiIKAzyTDOLbOrUW1ePxur9pRjZPdNd66xvdgrWP3QWJEnCW6sOae5vTI9MJMbpMbl/Fnp3SsEbKw4CAOZM6oUxPTpgUr+soMaXEKeHyWKHxe6AJGl/VugQwRXOlt8sh3Lj3GC1o6zWjGx57aAQ7r8jmmkWRLSmpWWlkafmmnpptTtQbrIoplb+evAkbv7vRvTLTsGyBVN8bi9PTrX4nJ7pfD2ReFnBBrkB7YDeFo1OwMFQ1110Tc/0lmmm1YjEW6fiu6YPwLnDumBAbmpYYyQiImaaERERURiSZTdtnVKbAmiZyfE4/5Su6NYhCXGyrm9anTXlendKwQdzTsNLl49UFMROjNPjlO4ZiPNyQ+mNa32Lze71ZtkhBNblexb+V9O6YQ+lwpJAhGoOeatpFsK+W2MYzOEQmL1oHR77YmeshxK0pTuL8fKP+z2CW82dxeetDmGgbHaBkmqz4mdryZbjAID9Jb6nLQP+rwcurt1Hol6f10wzH0PZVeTZ9XdjmEEzNVemrk4rOuZFopesNJ1OwuAuaYrsXyIiCg0zzYiIiChkkiRh3YNnosHqCKirpVq83jMIpnWj1zHFGFJXN9f+LTYH4g3aN8tbjlbiq22BT1NTds8UIQU6InHz761weluqAW6zO2CxOZCokVGz4UgFVuwtBVCKx84f0vyDC8NN/9kIADilWwYm9+8U49FEWBBRW28/O+vzyxX1DUUQmWb+suy8Bc18bVZrtvk/cJhCCWBGojssERH5xkwzIiIiCkt2WgK6d0wKadt4H5lj8nvIXlnJIe3fNdXJYvdeR+xYRWAF57Vuxh0i+OwgIYRHplkoQTRvRdF97evXgyfxzXbPAKH6NTRY7QHVpIomhxC44Z0NmPzccs3Old66WcoJIbB8TwlOVDdEY4hhK6kxKx63pKmvDVa7zzpjAHC8os7j5yKYM9nb6134/V68vSa/aZ/uTDP/1F0p1bxNxQ5l2mYwUiNQtP/TWybglG4Z7sfepnISEVHk8EpLREREze6SUXkAgDvP7Od1HXnNrrCDZjYHbI5QJlM20eqeCYSQISIiU3PK6x587Pr3/1iLW9/bhCMnTR7PlZssWHf4JBwOgf0narGv2P/0umgyWx0or7Ogqt7qEVxSs9odKK81e3Q0/GpbEa57ez0mPPNTNIcaMZHqtBro2eXr3D1WUY/9JTVen3/vtyM466+r8Hpj3UH3sYM4tX3NHtxd1HTspppm/ndeF2JH0AaNDpmRlJPqu8lJvsbPpJxOkjC6RybeveFU9zJfHzoQEVFkcHomERERNbtnLhmG6yb2xODOaV7XOS7LAOuakRjScYyN05fqLHakJYQXqNLa2u4IfnqmQGQKmofTCKCoqgE9OioDkX/49zo0WB1ITYzDoFzv35dY8PdKbXaBkyar4n3dVViNH3afABD9LKJQ88PU22l1aYymcEqaPf/9XgDAdzuLcd3EnlE9vreAtZY6iy3gWmly/jLUwpWa6Hv6+k97Snw+75qiLJ/SzqAZEVH08UpLREREzS5Or8PQrukeRa8NegnZaUYY9BKOVtS5lwdTHFvOlWl2rKIeG45UhD5gaE8REyL4TDOtoFko8S9v2wSyK/W2EiQ0WJ1BgxV7fd+8Nxs/b6v8JTgcAjsLqxSZWg4hws4ekr9PLWfiZOREYzpoMFONdZKEwsp63PjOBp/ruQLEgcQ+6yx22OwOlFQ3YNmuYsx5dwNO1vrOVASiH1hNjvedq3Du8M4+n0/SCJqF28iBiIj8Y6YZERERtRhxep274H8kim9HsuaPKxhgl03zFBA+p5j521dY4/ESNQtkOJHq2uhqRhCn0dDB9Xy5yYIOyfGazwfK37S8r7YX4d+/5Id1jBYhQjGQQM+ulhBzue/TbVi9v8znOk0Ba/+vbPPRSozunoHSWjPmvOtsuBBv0OG1K0f53K7OEt1i//5qmo2U1SrT4uomLP8Agd0xiYiij5lmRERE1CLVmcOfqqbXRf5PHXlpNOfNfJCZZkJEZHqmt12s2l+Gr7cXYeORcgghNOtk+Tp8MNlHe4pqsKfIe90rm12gpNrstWmBL/JRuN6ve/+3FTe+swEl1Q246p+/uZ//5YDvoEtL1RxBK1/nWqRiLhUmizuYHFxNMwlFVf6bNDTVNPO/z2e/24M1B08qlu0urPbabdalos7qf+dhSPETNEsx+n5eK9vWS6yaiIgiiJlmRERE1CKFk2l21qBs/LC7BCPy0t11rcKlPT0z+EwzhwivHlnTfrzv4+5PtgIAko16mMx2JMXrsfbBM71uqwhQQeDBJduRFK/HRzeP19z/wdJarNpXihF5GV6zzKLh4w3HAABlAUy3a20q6ywRaRChVmexobrBirQErZpaoUfN5HXDqhus7p+LoLpnSoAhgB+gYPf90o/7YZJljlXWW2GzCzSWOGwW8Qadovuov6BYsp/n5fpmp+BASS3G9OwQ8viIiCgwDJoRERFRi3Tp6Dy8vuIgTusd3I3hs5cMx/RBOdhbXIMumQl4Ydm+iIxHsxFACDXNNh6pgEWV9RJKqCSQ+IqpMVuvzmLHyz/sD2jb/LI67CqqBgDY7A4YNIJiZ76wEgBw1bjuuGJsd/fy/649AkkCnskbrhqrwK6i6ohk2AGAKYSAqjPDL7p1q4LlOnWEEDhaXo/q+shPEfz9P5wZefuemBXxfbvoJAl2rahyANsFMsUwmO6ZLv9Yfdj9dSSmegfrH9eMwYYj5XjlpwMAgHTNoGUTf5loct/eOQkWmyOoQBsREYWGSb1ERETUIs0/qz8WzR6Lf1471u+6ibIUEldHzpQEg6Jodtg0WvgJBN89EwD2Fnuf0hj4cIILAMmzs3xlqclfj9Za8rpSOwur3V9XmCz4aMNRfLj+KGoalFPdvtpWhEvf/BUPLNke8HgjPXNx/kdb8MbKgx7vW4XJosgIiiVHBGrdAdD8xmlN041GSazCynrN5Ta7A9uPVaGoqun5QDPNHMHH4xRc319fxf4T4iJ3reiSkYAOyfGK61JqomeA65s7Jrm/HpCTGvD+4/Q6BsyIiJoJr7ZERETUIsUbdJg2MDugdTOT4lBf5VkDLT6CjQAcwhmoOlhmci8LpXsmAOgjUMwq2ACCvKyYx7ZBjOf7ndrTXeU1oxyqGNTizccBAAdKanGiugE9s5IDPp6WUIIn6/Od3VMr65UBvWMV9TBGMGASDFetN0mSItIcIlhSGOehfEuHEO5srm3HqzTXtza+VnktOinATDMEUdPMF7PNe53EbplJ2F9SG94BGrnqAibIgmZa02MHd0nDDwumoLrBii4Zie7li2+dgH+uPoTCygZsOVoZkTEREVFomGlGRERErV56knZ3xkhmmtmFwLu/HsH/Nh5zLzNb7SEFOwx6ZaAglGmDwW7ikGXZWH1kVskz0oRwPt5+TDsQEmhtNvmrDbgemWyjzarAQaDdP7WaGmit6isDKRChbn2iWlkEP5p9AbTeI63jFVbWo7zOMyvNl7dWHcJfvtrtntbri7zwv8MhEMhb7840CzNqVlrj/dxLjGDBM9d7nRgvyzTzMv2yb3YKRnXPVCwb1T0Tr181Gp1SjREbExERhYZBMyIiImp1Xrr8FMXjjETtekFa9bgA5xTOhZcO13zOG7tD4OlvdyuWfbfzBO7+ZFtQ+wE8M7FCEWwzAZvsoDZVpEIePDlRLQuaQaDSR1dB4St7TSaUaYDygNf9nwb/HkdaOFlZ3sS6xJrWSzpZa0FhhfYUS28OljqzL//za77P9bQCzI4AomaRaJwBAMU+OnUmRKFLgDwQl2L0XdNMyz0zByBer8PNU3pHclhERBQETs8kIiKiVufCkV1xzrDOeGDxdkwZ0Ak1DVb8eugkhuelB7R99w5JSA2yJtCqfaVosHpGu9YdLg9qPwDw4g/7cM7wznj/twJYfEwZ8yXo6ZmyoatjEKHGg7wGM1T78xVwOlpeh44p8UiKV34/fGXwNV+sqRmPFIMAmta3RUBg+d4SrNpXigdmDQxq23qr8lyubrDipWX7cf4pXbzW7Ark3HO9N96C4IEqrm6eoJkkAdlpRhRXNwUfk43B779/Tip2PD4zotPMiYgoOAyaERERUasUb9Dhhf8b4X581qAcJMXpkX+yTrHeL/edgTs/3IwNRyrcyyQp+MyhPREo3u9yoKQWDVY7HgyiML5asFPV5AGuQLf1t1qgsxq9vdUHSmpw1ourkJEUhy1/mhHyONqjqjor7vpkKy4Z1RWzhnX2u77Wt8CVzVdnsSmClq+vOAjAmUk5sV+nkMf4zLd78P5vBfj3L4ex+89nezyvk6SAagK6z90wzgOHQ/jMNEv0U9fu1J4dsC4/sAC5TpKQk5aAVFkdM/X0zP/NHR/QvhgwIyKKLV6FiYiIqE3ISUtwZ6IY9BKy04ww6CV0SI5H9w5JinUlKTqdA4OhniIZLHkgqbzOf50w+fHUR9aq/RWIBllmka/Alrf9L99TCgCorLPCanfgRHWDoqFAoGL8rQyZK3BrtTtw0mSGPYjo4IvL9uKH3Sdwy3ubPJ4LdC/5J00orWnAwRITKjS6azZYg8yCVB1Y3iVWQHjUjjtR3eBRr05zt+6YWeg/M/VWu6KemprRT6bZDZN6BXws1/kon54pD0ouvGQ4xvTsEPD+iIgodhg0IyIiojbDFSxLjNcjJy0BcXodDHoJHVM8GwWE0vUyksKt0ySPPxRWeA8GuNhlNc0iVSPKW8aa+q31WqhfttxmFyipNrs7Sn62udDHgYMZZctnswuU11qDel1ltcEV69eKLOogoabe2fXSFayUf0uDzXJSD199nj325U7F41eXHwhov1pjC1ZxVT2KqrzXakuM0+Oqcd29Pt8lPVHxOJAmI0myRgDy601aIif7EBG1FrxiExERUZsRp9chJy3BY1l2qnKZBAm6GH90GG7c6qc9J9xfP/7VLr/rK7J8Ajx2MGP0lQW0cl9p4Dtq9L9NTV1K1fsO561ryfG2Zh+bn7hxnCowZLU7UG6yoENyvP+NoQzs/vnLXdh6VLsLqz+HykzYf6IG+0tqQ9oeAE7UmH1Oz+yQHI8uGQlen09QTd88f0Rn9O6UgoQ4Pf6s+vkzGvSN2yiz17JS4lFWa8HoHswyIyJqLZhpRkRERG1eYrxq6pWPmmbNVUMo2Jpkaqv3lwW1vrxjp3pmqLxguZy/6XCul1BUVQ+TuWkqX6A5fMcrA+vS2JJqmoX7fZPz9T6FMk01HDaN6bFxekm1jjIb0B/5e/Xh+qNhjW/6X1eFtb3N7kBJjfdpzNlpRhh8zNlWB8A6JBtxxandFUH6py4aipw0o7vWovy64xACP993BjY8fBY6pRpDfRlERNTMmGlGREREbV6SKmgmwXvAwmjQwWLzHbDokp6AQh9ZK4Gos4TWNdMl2CmWNlnUTB0M210UWpMDAaCkugHnvfqzx3N2h0BVg9Xn9ot+yQ/puIEormqIeLGzmgYr8svq0D83xZ1NFC2+AlP7T9Tg+53FimUms82dGaYV2NOqK+eaMrirqBor95bgynE9FIEydaZZsCrrfH//m9NnWwpR6iNo1jk9EdX13sdrVGWaZacakZpgUNR9u2xMN1w5rof7sfy602C1IyFOH9EunUREFH0MmhEREVGbN7FvluKxJEkeRcldEuL0qGmw+dxfJG58S6rDC7oF20dA/nrV23pLsPEXlxNCYPtx7Sl3hZX1KK5qiEjmnnoY6qCQVtbgyz/tR+9OKWEfW84V6LTYHFEPmvmilXV1qNTktXacN67V7/t0GwDAZLHj5sm93c+rM80U22o8pf4+FZTXea4UI9/ucAYZJUn7vE426n0GzdQ/8z2ykhCn12FsY0H/OL3kEWRMkJ0jZj+BeCIiapk4PZOIiIjavJy0BKx94EzFMm/T39S1i7T467QXCF9ZL9Gg6MwoBI6cNLkfepuqCgAVJgvW55drPme2OTQncEqS5O7WGZHJjMLnQ01es5xEYNNCv9tRjLNeDG9KYHDCf6dc32KtTMlCrdes+rYXVdUrtpUHgax2B0pqGhQZi14H0IJlpWhPjZR3t9R8XvUz3y/bGZDt3jEJK++Zig0PT/fYRqeTcO6wzhjVPQP9c1JDHDEREcUSg2ZERETULuSmN9UekgBYvEx/u+iUrtBJwNQBnbzuK5DAmj8nTb67H0aydhagnO7nEECtuSmbzlvITAC44Z31HoXOXUpqzPjbj/sjOErv/E2ZDdRbqw9h4jM/4cN1BT7Xm/vfjQHvM9RvlTxWWVhZj/d+8z2mQJk0pv5e8NovnsfX2FZZ06zpPHfVM/OWodladEz27KQLODPNfDHodbjjzH4AgKtP646ctKZumj06JiM9MU5zu9euGoXFt06E3ke9NCIiarkYNCMiIqJ24/Ix3QAAt0ztA5uXTLOc9ATs+vPZWDR7rNf9eLtBDsb9i7f7fP7RL3aGfQw5ebBDQBno0XnJNBNCoNrPVNXmYLE70P/hb92P1fW+gqnvtiG/AgDwwrJ9ERnbKz/tx6vLD4S9n7d/PRKB0TQK8P1Qf9cdDgFrgEX+tc6Y1hBO8xa8So43+J3eumB6fyybPxmXjenm2VyEiIjaJAbNiIiIqN145pJh2P3nszGoc5rXQusXjeyKhDi9zymLGREImvnzycZjEd2fsqaZwA55LbIIJ8FEO6dGne3ka8ZgtL2+4mBE9iMvKO9i1ehoqbVeyFTn+IFSEyyyY32zvcjj+K2dVsB7Ur8s5GUmaqztKTcjAT07Jkd6WERE1EIxaEZERETthiRJ7gwReXCgS+PUzTi95Le2EQBkJGlP8WrJlDXNlJluvqZntkTFqiYKXuObPl5AsFMqYxE4ck2JNFsdMNucwbKLX18Tsf1LABpsyiCcVTYNduuxKpRUm1FvsaOwqg5mm++sQ0crmLqZluAZNPvPDeMgSRJ0AUyhTE2Ii0imKRERtQ4MmhEREVG7JJ+e+c71p2LagE74ZO6EgLbNSGp9N83yzLpKVZfA3w5rF/pvBXXdvYr02Ic/vhR1lthMVT1cZsK+4loAwK6i6ojtV5I8M9csGsFBq82Ba/+9Hre+t9kdPNQKVLaCmJlHwGt09wz3190yEzE8L939eOaQHGSlxCs6ihIRUfvi/6NUIiIiojZobK8O7q/75aRi0XWnBrxtc0zPjDR53a9nv9sT0DalNQ3+V9LgrzaUloOltSEdy/dAIrcri82BLUcr0beT7y6IwTRw8DUFWM5ss8NoiHwNLUmSPKa6Wm2e4xcAjlU4u28WVtbjlG6ZmvvbcKQi4mOMtH45KYrHAzunub+O1+vw/GUjIAH4ZnsxbpjUC8nxvqdqExFR2xbTTLNVq1bhvPPOQ5cuXSBJEj777DPF80IIPPbYY+jSpQsSExMxdepU7NwZ2aK4RERE1D4N6ZKOL26biHUPnel1ndE9tIMDrXF6pi2ENKDSat8dPkMah0adLgB49PPQ/8YLLatM1hihNafUhcFf90yt9Xw1CpB3ZG2prp3QU/E4Ma4pGGnQ65CTloB+Oam486x+SDEaGDAjImrnYho0M5lMGDFiBF599VXN5xcuXIgXX3wRr776KtavX4/c3FxMnz4dNTU1zTxSIiIiaouG52UgOzXB6/MPnTtIc3m6anrmkC5pmuu1JKHUm6qoDz1o5nA48OQ3u/H4l8pgmN3hrNOlbsRglVXz99bZ1Jv1+drTS1s6u+w1hxOa2Vtcg4Jyk/txoN/pslqzxzKt6ZmK522ttynApH5ZiNPr8PdrRruXJciCZkO7prNeGRERKcR0euasWbMwa9YszeeEEHjppZfw0EMP4eKLLwYAvPPOO8jJycH777+Pm2++WXM7s9kMs7npD4Dq6sjVfSAiIqL2ZVT3TCz742QcLjPhpv9sdC/PlGWavXX1aJw0mfHgkh0e23dOT0BRVWhTHCMtlEyz8trQgmYSJBRVmbHlaGUQ2zQJdqzqxgCtha8Et9KaBnTOCKyjo8XmgEVjWqU/S3edwNieymxK1zTMpjEKbCpomnbpL6jWkg3r6qxXNmNIrntZVkrryxolIqLm02IbARw+fBjFxcWYMWOGe5nRaMSUKVOwZo33rkFPP/000tPT3f+6devWHMMlIiKiNqpfTipmDMnF4lubmgTIs1H6ZCcj3qD9J5WjBU370wfQGTCSgp3y2NzT4JrjW2N3CBQ3Bk2rG6xBZWmV1lo8svH8sdoduPuTrfhxT0lA62udnx+sL1A8Pv+1XzBHFjBuzZlml41pui94/PwhOHNgNib2zYrhiIiIqKVrsUGz4uJiAEBOTo5ieU5Ojvs5LQ888ACqqqrc/44ePRrVcRIREVH7IA8WJMc3TekyGvQ4vW8nzW3URdZjKZSgWTij97VtVb3VY2pga6kcJSACDvDNXrQOpz39I345UIYjZXU4UKJsdhCpOKFofLfXHDyJvScCL2MSSuCwNWea9cpKdn/9h/E9MP+s/kjjdEwiIvKhxQbNXNR/lAjh+w8Vo9GItLQ0xT8iIiKicLnqlmUmxSFBFjSTJCA3PQFvyeokufgqmt4aFKqm6gWqpKYBNof34EppjRknojylUoQV8ouM1fvLAABvr8kH4CeIqvrz1mS24ZWf9qOgvM69zF9dumAzGy12Bw6UBFcr+FiFczxSDMOct03rG/Q2eZnKqa6SJGFg51RkpxojNSwiImqDWmzQLDfXWWtAnVVWUlLikX1GREREFG2pCXHY9tgMrH3wTCTHN5WFzUpx3nR3TvdsKKDVibA1CTXwtLeoBmZrcK9dJ/tQ9J+rD4V03Girt9hD2s4hBKrqrR5TVk1mGw6W1mpu886v+Vi0Jh/z3t/kXnaozKS5LgCYbfagw1g2uwOWIBte/m/j8SCPEnkGjazJjsm+a5MN7uz5QXqcXsfumERE5FOLDZr16tULubm5WLZsmXuZxWLBypUrMWHCBB9bEhEREUVHWkIcjAY99DoJ6x86C789eKa7+16dRkDFV/2nMwdmR22cWpozNHCkvA6HfQR4NDUO0GJz4PtdJyIyjki/5oXf7Q1oPavdgVvfa6oD9uPuElz9r9/w6vIDivWOVtRjQ36FenMAwMESz2Cat6DdD7tO4P7F2/HXH/YFND4XgdACozsLq4LeJpK0phonyrI/5a6f2AvXju+Bpy4eFu1hERFRGxTT7pm1tbU4cKDpj4fDhw9jy5Yt6NChA7p374758+fjqaeeQr9+/dCvXz889dRTSEpKwpVXXhnDURMREREBnVTTunp0TPJYx1cXyH/8YQx6P/hNxMcVSfs1AjeBqK634hVVgMjFW+CnufN9Qsmhq6y3BrTesl0n8M12zxq8S3edQE1D0z6+2laEr7YVYWBuqs/x2ewCtWazogGF3Ms/Od/rYEvoCSFg9zGN1pvCyvqI1WMLhVZ2mDz7EwDOGpQNvU7CQ+cOavYmGERE1HbENGi2YcMGTJs2zf14wYIFAIBrr70Wb7/9Nu69917U19fj1ltvRUVFBcaNG4elS5ciNdXzDwsiIiKiWOqcnojv509GZlIcTn3qx1gPJyIC7cKo5qu01ocbjuLcYV28Pt9cU1rl9b8stsjWP6vyEVzLL6vzWLb9uGfmlnwfNodAhcmKFGNk/3S32AROVJn9r6hSXNUQ086w8hDYH8/qj4ykOCzZrJw2+s9rxzbvoIiIqE2KadBs6tSpPtuRS5KExx57DI899ljzDYqIiIgoRAM0MoZaiuYMcSxac9jrc/tP1KKqnwXdoC7M7vw/2ML0kfDKT/tD2i4/2CmoAN5YedBj2YfrCmAyKzPwzD6m9kZKg9WOwyeDfw1AaJ03I0WeaHbT5N5IjNcjLzMRN7yzIXaDIiKiNqnF1jQjIiIiaq0m9u0IALjh9F4xHkkTezN28jT5KpgvAQ0aTQIi3Y2xrNaCb3d4TpHUEmpG3dTnVwS9zTfbizyW7SisDmjb1ftLgz6eLydNFizbHcJrl4Lv1BlJWtMtzxyUg5/vm4aR3TNw8+TeMRgVERG1RTHNNCMiIiJqi968ejSKqxrQLycV//pZO+tKp5Pw18tHwGS24+HPdkR9TBV1lqgfIyACqG2wwqYK4kmSs5tjuSmwumGRGEdrcdLknEL5tx+168TFQllt855PT188DA8s3g4A6JudorlOXmYSltw6sTmHRUREbRwzzYiIiIgiLDUhDv1y/E/VvGhkHq4+rYdi2aLZylpMD587KCJjaikxoiPldag127G7SJldJcFZu6uyvoUE90JUbrJEPAvr7k+2RnR/4TpcGtqUznCMyMtwf90zK7nZj09ERO0TM82IiIiImtnFo7p6fW7awGz312kJBgzMTWuOITWrHcer8PaafMUyAaC8ztys00ijoaLOEnQXS3+sLew9+WD90WY/pkHfNCVT3ilTxxQAIiKKIgbNiIiIiJrBZaPz8MnGY7jjzH64/Yy+AW2TmhCHxHh9lEfW/DYcqfBYtuN4FRb9nI8pA7I1toi8lhWG8q/C1Loz8MKRmRSnqGOWlRKPR343GAlxOhgNbe/ng4iIWg4GzYiIiIii6B9/GIOC8jrccHovPHnRMMQbPFNjZk/oibfX5OMmVQHzIV3SMKp7RjONNLZMFjvWHi7H2sPlzXpcX53cW5I/f70r1kOIiMFd0rBLo/HB+N4d8euhk5rbfH3HJOSmJWBk9wykGA3Q66QW1WSDiIjaLgbNiIiIiKJo+uAc99daATMAeOR3g3HZmDwMapyK+b+54/H+bwV48NxBkKTIdpWkJieqG3DtonUR36/V7tkdlJwm9O6oGTTrlZXsDpqdN7wzvtzW1GW0S0YiAGDxLRMAgD8TRETUbBg0IyIiIooxvU7CkC7p7sdjenbAmJ4dYjiitk9A4OUf96OiLrhunYfLavHC0r144BztBg33L45+J9S26OYpvdEhOR79c1IxfXCOImjmwmAZERE1NwbNiIiIiKjdqbfYUR5CnbA7P3R2stx+bG2kh9QueIt79eiYjDvP6odykwUGvYQBOanYe6IG2anG5h0gERGRDINmRERERNTuhNuRsjzIDDVy0nrX/3PDqQCAOL0OOWkJAIB/zR6Dt1YewvWsXUZERDHEoBkRERERtUsWG2uPNTd1otlXt5+OoV3TPdbLy0zCXy4c2jyDIiIi8kK7Gi0RERERtTinss5ZRG08UhHrIbQrz10yXPH45sm9NQNmRERELQWDZkREREStxPWn98RpvZoCZxeN7Op3m15Zye7pb6RUWc8pltFy4SldFI9P6ZaB0/tlITUhzr3MWzMFIiKiloJBMyIiIqJWQgigX06q+/GzlwzH2gfO9LnNhzedhtHdM6M9NCKF6yf2wrZHZ2By/064fmIvLL5lAjpnJOKsQTkAgNQEVokhIqKWj7+tiIiIiFqJhDg97ps1EOvzy3HhyK6IN+iQm57gc5uctATUW+zNNEIiYEBOCiRJQpxeh3evV2Y5Du6Shh8WTEFOGrtiEhFRy8egGREREVELd9f0/thZWI3J/TtBr5Pw3fzJmut165CIo+X1zTw6IqXc9ESfz/fNTmmmkRAREYWHQTMiIiKiFu72M/sFtJ7k0ZvQyaDXXk4UDWmJcf5XIiIiagVY04yIiIiolbttWl8AwKPnDcYdGgG2OD3/5KPI6tMp2etzc6f0RnaakcFaIiJq9ZhpRkRERNTK3T1zAG6e0hupCXE4c1AODpbU4uvtRbEeFrVhBl1TIPaHBVNw1osrcfVp3XH/rEFIMfIWg4iI2gb+RiMiIiJqA1ITmqbEPXHhUNjsDlwyJs9jvcGd07CrqLo5h0YtWO+sZBwqMwW9XWZS0/nWo2MS8p85N5LDIiIiahGYq09ERETUxmQmx+O1q0ZhxuBcjedYb4qaJBv1IW33+AVD0bdTCl64bASn/xIRUZvF33BEREREbZBBFch46fJTMKhzGp6+aHiMRkQtkUEXWt2x3p2S8d6ccTj/lC4RHhEREVHLwemZRERERO3AhSO74sKRXWM9DGphEuODvx248JQuiNPrkJOWEIURERERtRzMNCMiIiJqZz666TT8+YIhsR4GtQBJ8drTMx88Z5Di8f4nZ+HOM/vhhwVT8NIVI5tjaERERDHHoBkRERFROzOud0f8YXxPr8/PnuD9OWpbvE3O7NspBXMm9QIA/GF8D8Tpdfjj9P7om53SfIMjIiKKMU7PJCIiImqn/nvDOPxj9SHYHQLpSXH4elsRAOC03h3w9pr82A6OmoVeVdMsMU6Pi0Z1xageGRjXuwMeOndwjEZGREQUewyaEREREbVTp/fLwun9sgAAmwsq3EGztAR22Gwv9LqmiSfZqUb8eNcUpPL7T0REBIDTM4mIiIgIwCndMnDbtL5YeOlwnNa7I2YNzUU/TsVr8yb2zXJ//fdrRjNgRkREJCMJIUSsBxFN1dXVSE9PR1VVFdLS0mI9HCIiIqJWo7rBigaLHac+9WOsh0JR8NLlp+Dc4Z1xoKQWXTMTmWFIRETtRqCxIk7PJCIiIiJNaQlxDKS0YReO7AoAGNSZHywTERFp4fRMIiIiIvLpld+PxMjuGe7HPTomxW4wRERERM2EQTMiIiIi8um8EV2w5NaJ7sd6SfKxNrVUT1w4NNZDICIialU4PZOIiIiIAtK7UzIOlZpw7vDOeOWnA7EeDgVhy5+mIyMpHmcPzcW3O4px/ogusR4SERFRi8egGREREREF5H9zJ2DtoZM4a1AO+man4M4Pt8R6SBSgjKR4AEBWihHXnNYjxqMhIiJqHRg0IyIiIqKAdEiOxznDOgMALjilKw6WmvDyj/tjPCoCgHG9OuCV34/ElqOVuOk/GwEA/7hmNI5X1qNPdkqMR0dERNQ6MWhGRERERCGZf2Y/nDEwG7f8dyMS4/S4+rQe+PNXu2I9rHZpVPdMZKclYPrgHLx25SgUV9Xj9H6dkBivj/XQiIiIWi0GzYiIiIgoJDqdhFO6ZeDn+84AAOh1Ej5cX4B9J2pjPLL2JzfdCACQJAlnDMzGgRJ+D4iIiMLF7plEREREFBa9ToJe5+yo+fpVo2M8mvYpTt+UUWbQS8hOM8KgZ5dTIiKicDBoRkREREQR0zc7BeseOhN/PKt/rIfSrgzPS3d/HafXISctAXF6/qlPREQUDv4mJSIiIqKIyk5NwJ1n9Yv1MNqVQZ3TYj0EIiKiNodBMyIiIiKKikXXjY31ENqcP57VH7dN6wudBAzISXUvd02PJSIioshhIwAiIiIiioppA7Kx7qEzsTG/Ah+sP4pV+0pjPaRWz5XBd/fMARBC4MEl29EpxRjjUREREbVNzDQjIiIioqjJTk3ArGGd8Y4q6+y7+ZMAAGN7ZsZiWK1K14xEAMCV47orlkuShKcvHo4FMwbEYlhERERtHjPNiIiIiCjqJEnCF7dNxA3vbMDCS4djYG4a8p85F6v3l+Kaf60DAORlJuJYRX2MR9ryrLxnKirrrchiRhkREVGzYqYZERERETWL4XkZWP/QWZg2INu9TIim53++74wYjKrlM+h1DJgRERHFAINmRERERBQzwv8q7VpmUlysh0BERNRucXomEREREcVMcrw+1kNosbJSjHjrmlGxHgYREVG7xaAZEREREcXM6B6ZuHJcd/TOSo71UFqc9Q+dCUmSYj0MIiKidovTM4mIiIgoZiRJwlMXDcONk3oDAG6a3Nv93DnDcvH6Ve0v0+ovFwzBugcZMCMiIoo1ZpoRERERUYtx/9kDcenoPPTomASjwTl18+JRXbF40/EYjyx6vrr9dLyx4iD+ML4HzDYHJvfvFOshERERERg0IyIiIqIWRKeT0D8nNdbDaDYGnYShXdPxWjvMqCMiImrpOD2TiIiIiFq0cb06xHoIUZOawM+wiYiIWir+liYiIiKiFu3S0d1gNOjRKdWIhd/twdZjVbEeUsQ8fO7gWA+BiIiIvJCEECLWg4im6upqpKeno6qqCmlpabEeDhERERGFoazWjDFP/BDrYYTt8NPn4KTJgqwUY6yHQkRE1O4EGitiphkRERERtRrJ8a37z9eEOB0eOncwJEliwIyIiKiFa91/dRARERFRu5IYr8e7158KhxCYvWh9rIcTkNQEA+47eyBO6ZaBoV3TYz0cIiIiChCDZkRERETUqkzu3wkAsO2xGRj+2NIYj8a/DQ+fBaNBH+thEBERUZAYNCMiIiKiViktIQ6Hnz4HkiSh5/1fx3o4Hu6ZOQCjumcyYEZERNRKMWhGRERERK2WJEkAgJ/vm4anv9mDr7cXxXhEwPtzxmFCn6xYD4OIiIjCxKAZEREREbV6eZlJeO2qUZj4WwE+Wl+Arceqmn0Mb149CgdLTRjfu2OzH5uIiIgij0EzIiIiImozrhzXHVeO6w6b3YGPNxzDg0u2N8tx9z85C3F6XbMci4iIiJoHf7MTERERUZtj0Otw5bjuOPz0OVE9zodzxmHXn2cyYEZERNQG8bc7EREREbVZkiTh0FPn4P5ZA9EvOyVi+81KiUf+M+fitD5ZSIrn5A0iIqK2qFUEzV5//XX06tULCQkJGD16NFavXh3rIRERERFRK6HTSZg7pQ+WLZiC924cF/b+Ppk7Hhsenh6BkREREVFL1uKDZh999BHmz5+Phx56CJs3b8akSZMwa9YsFBQUxHpoRERERNTKTOybhfxnzsW6h84IetuHzx2A/GfOxdieHaIwMiIiImppJCGEiPUgfBk3bhxGjRqFN954w71s0KBBuPDCC/H000/73b66uhrp6emoqqpCWlpaNIdKRERERK2IwyHQ76FvYffz5/DFIzvj/lmDkJ2W2EwjIyIiomgKNFbUogswWCwWbNy4Effff79i+YwZM7BmzRrNbcxmM8xms/txdXV1VMdIRERERK2TTifhwFOzsHxvCb7eVoRPNx33WOfL2yZiWF5G8w+OiIiIYq5FB83Kyspgt9uRk5OjWJ6Tk4Pi4mLNbZ5++mk8/vjjzTE8IiIiImrlJEnCGQNzcMbAHLzwf6egpLoeaYnx7ucT4vQxHB0RERHFUouvaQY4/5iRE0J4LHN54IEHUFVV5f539OjR5hgiEREREbUB2WmJSIjTu/8RERFR+9WiM82ysrKg1+s9sspKSko8ss9cjEYjjEZjcwyPiIiIiIiIiIjaqBadaRYfH4/Ro0dj2bJliuXLli3DhAkTYjQqIiIiIiIiIiJq61p0phkALFiwANdccw3GjBmD8ePH4+9//zsKCgowd+7cWA+NiIiIiIiIiIjaqBYfNLv88stx8uRJ/PnPf0ZRURGGDh2Kb775Bj169Ij10IiIiIiIiIiIqI2ShBAi1oOIpurqaqSnp6OqqgppaWmxHg4REREREREREcVQoLGiFl3TjIiIiIiIiIiIKBYYNCMiIiIiIiIiIlJh0IyIiIiIiIiIiEiFQTMiIiIiIiIiIiIVBs2IiIiIiIiIiIhUGDQjIiIiIiIiIiJSYdCMiIiIiIiIiIhIhUEzIiIiIiIiIiIiFQbNiIiIiIiIiIiIVBg0IyIiIiIiIiIiUmHQjIiIiIiIiIiISIVBMyIiIiIiIiIiIhUGzYiIiIiIiIiIiFQYNCMiIiIiIiIiIlJh0IyIiIiIiIiIiEiFQTMiIiIiIiIiIiIVQ6wHEG1CCABAdXV1jEdCRERERERERESx5ooRuWJG3rT5oFlNTQ0AoFu3bjEeCRERERERERERtRQ1NTVIT0/3+rwk/IXVWjmHw4HCwkKkpqZCkqRYD4cirLq6Gt26dcPRo0eRlpYW6+FQC8HzgrTwvCA1nhOkhecFqfGcIC08L0gLz4vWQwiBmpoadOnSBTqd98plbT7TTKfTIS8vL9bDoChLS0vjRYk88LwgLTwvSI3nBGnheUFqPCdIC88L0sLzonXwlWHmwkYAREREREREREREKgyaERERERERERERqTBoRq2a0WjEo48+CqPRGOuhUAvC84K08LwgNZ4TpIXnBanxnCAtPC9IC8+LtqfNNwIgIiIiIiIiIiIKFjPNiIiIiIiIiIiIVBg0IyIiIiIiIiIiUmHQjIiIiIiIiIiISIVBMyIiIiIiIiIiIhUGzajVM5vNOOWUUyBJErZs2aJ4rqCgAOeddx6Sk5ORlZWFO+64AxaLJTYDpWZx/vnno3v37khISEDnzp1xzTXXoLCwULEOz4v2Iz8/HzfccAN69eqFxMRE9OnTB48++qjH95vnRPvz5JNPYsKECUhKSkJGRobmOjwv2p/XX38dvXr1QkJCAkaPHo3Vq1fHekjUjFatWoXzzjsPXbp0gSRJ+OyzzxTPCyHw2GOPoUuXLkhMTMTUqVOxc+fO2AyWmsXTTz+NsWPHIjU1FdnZ2bjwwguxd+9exTo8L9qfN954A8OHD0daWhrS0tIwfvx4fPvtt+7neU60LQyaUat37733okuXLh7L7XY7zj33XJhMJvz888/48MMP8emnn+Kuu+6KwSipuUybNg0ff/wx9u7di08//RQHDx7EpZde6n6e50X7smfPHjgcDrz11lvYuXMn/vrXv+LNN9/Egw8+6F6H50T7ZLFYcNlll+GWW27RfJ7nRfvz0UcfYf78+XjooYewefNmTJo0CbNmzUJBQUGsh0bNxGQyYcSIEXj11Vc1n1+4cCFefPFFvPrqq1i/fj1yc3Mxffp01NTUNPNIqbmsXLkS8+bNw9q1a7Fs2TLYbDbMmDEDJpPJvQ7Pi/YnLy8PzzzzDDZs2IANGzbgjDPOwAUXXOAOjPGcaGMEUSv2zTffiIEDB4qdO3cKAGLz5s2K53Q6nTh+/Lh72QcffCCMRqOoqqqKwWgpFj7//HMhSZKwWCxCCJ4XJMTChQtFr1693I95TrRvixYtEunp6R7LeV60P6eeeqqYO3euYtnAgQPF/fffH6MRUSwBEEuWLHE/djgcIjc3VzzzzDPuZQ0NDSI9PV28+eabMRghxUJJSYkAIFauXCmE4HlBTTIzM8U///lPnhNtEDPNqNU6ceIE5syZg//85z9ISkryeP7XX3/F0KFDFVloM2fOhNlsxsaNG5tzqBQj5eXleO+99zBhwgTExcUB4HlBQFVVFTp06OB+zHOCtPC8aF8sFgs2btyIGTNmKJbPmDEDa9asidGoqCU5fPgwiouLFeeI0WjElClTeI60I1VVVQDg/juC5wXZ7XZ8+OGHMJlMGD9+PM+JNohBM2qVhBCYPXs25s6dizFjxmiuU1xcjJycHMWyzMxMxMfHo7i4uDmGSTFy3333ITk5GR07dkRBQQE+//xz93M8L9q3gwcP4pVXXsHcuXPdy3hOkBaeF+1LWVkZ7Ha7x/c8JyeH328CAPd5wHOk/RJCYMGCBTj99NMxdOhQADwv4ZabngAAFHVJREFU2rPt27cjJSUFRqMRc+fOxZIlSzB48GCeE20Qg2bUojz22GOQJMnnvw0bNuCVV15BdXU1HnjgAZ/7kyTJY5kQQnM5tVyBnhcu99xzDzZv3oylS5dCr9fjD3/4A4QQ7ud5XrR+wZ4TAFBYWIizzz4bl112GW688UbFczwn2oZQzgtfeF60P+rvLb/fpMZzpP267bbbsG3bNnzwwQcez/G8aH8GDBiALVu2YO3atbjllltw7bXXYteuXe7neU60HYZYD4BI7rbbbsMVV1zhc52ePXviiSeewNq1a2E0GhXPjRkzBldddRXeeecd5Obm4rffflM8X1FRAavV6hH5p5Yt0PPCJSsrC1lZWejfvz8GDRqEbt26Ye3atRg/fjzPizYi2HOisLAQ06ZNw/jx4/H3v/9dsR7PibYj2PPCF54X7UtWVhb0er1HFkBJSQm/3wTAeU0AnJlFnTt3di/nOdI+3H777fjiiy+watUq5OXluZfzvGi/4uPj0bdvXwDOe9D169fjb3/7G+677z4APCfaEgbNqEVxBTv8efnll/HEE0+4HxcWFmLmzJn46KOPMG7cOADA+PHj8eSTT6KoqMh9wVq6dCmMRiNGjx4dnRdAURHoeaHFlWFmNpsB8LxoK4I5J44fP45p06Zh9OjRWLRoEXQ6ZZI1z4m2I5xrhRrPi/YlPj4eo0ePxrJly3DRRRe5ly9btgwXXHBBDEdGLUWvXr2Qm5uLZcuWYeTIkQCctfBWrlyJZ599Nsajo2gRQuD222/HkiVLsGLFCvTq1UvxPM8LchFCwGw285xogxg0o1ape/fuiscpKSkAgD59+rg//ZkxYwYGDx6Ma665Bs899xzKy8tx9913Y86cOUhLS2v2MVP0rVu3DuvWrcPpp5+OzMxMHDp0CH/605/Qp08fjB8/HgDPi/amsLAQU6dORffu3fH888+jtLTU/Zzr02GeE+1TQUEBysvLUVBQALvdji1btgAA+vbti5SUFJ4X7dCCBQtwzTXXYMyYMe6s1IKCAkUNRGrbamtrceDAAffjw4cPY8uWLejQoQO6d++O+fPn46mnnkK/fv3Qr18/PPXUU0hKSsKVV14Zw1FTNM2bNw/vv/8+Pv/8c6SmprqzUdPT05GYmAhJknhetEMPPvggZs2ahW7duqGmpgYffvghVqxYge+++47nRFsUi5adRJF2+PBhAUBs3rxZsfzIkSPi3HPPFYmJiaJDhw7itttuEw0NDbEZJEXdtm3bxLRp00SHDh2E0WgUPXv2FHPnzhXHjh1TrMfzov1YtGiRAKD5T47nRPtz7bXXap4Xy5cvd6/D86L9ee2110SPHj1EfHy8GDVqlFi5cmWsh0TNaPny5ZrXhWuvvVYIIYTD4RCPPvqoyM3NFUajUUyePFls3749toOmqPL2N8SiRYvc6/C8aH+uv/569++KTp06iTPPPFMsXbrU/TzPibZFEkJWHZuIiIiIiIiIiIjYPZOIiIiIiIiIiEiNQTMiIiIiIiIiIiIVBs2IiIiIiIiIiIhUGDQjIiIiIiIiIiJSYdCMiIiIiIiIiIhIhUEzIiIiIiIiIiIiFQbNiIiIiIiIiIiIVBg0IyIiIiIiIiIiUmHQjIiIiNqEt99+GxkZGbEeBgAgPz8fkiRhy5YtLWI/gbBYLOjbty9++eWXqB+rpZo6dSrmz5/fbMe79NJL8eKLLzbb8YiIiCg4DJoRERFRRJWUlODmm29G9+7dYTQakZubi5kzZ+LXX391ryNJEj777LPYDRLAsGHDcOONN2o+98EHHyAuLg4nTpwIad/dunVDUVERhg4dGvA2s2fPxoUXXhj2fkL197//HT169MDEiRMVy7/66itMnToVqampSEpKwtixY/H2229HfTyRZrFYsHDhQowYMQJJSUnIysrCxIkTsWjRIlit1piM6U9/+hOefPJJVFdXx+T4RERE5BuDZkRERBRRl1xyCbZu3Yp33nkH+/btwxdffIGpU6eivLw81kNTuOGGG/Dxxx+jrq7O47l///vf+N3vfoecnJyg92uxWKDX65GbmwuDwRDWGCO1n0C88sorHkHEV155BRdccAEmTJiA3377Ddu2bcMVV1yBuXPn4u677476mCLFYrFg5syZeOaZZ3DTTTdhzZo1WLduHebNm4dXXnkFO3fujMm4hg8fjp49e+K9996LyfGJiIjID0FEREQUIRUVFQKAWLFihdd1evToIQC4//Xo0cP93Ouvvy569+4t4uLiRP/+/cW7777rsf85c+aI7OxsYTQaxZAhQ8SXX34phBBi0aJFIj093b1uWVmZGDt2rDjvvPNEfX29xzjKyspEfHy8ePvttxXLjxw5InQ6nfjyyy/FgQMHxPnnny+ys7NFcnKyGDNmjFi2bJnH6/nLX/4irr32WpGWlib+8Ic/iMOHDwsAYvPmzUIIIWw2m7j++utFz549RUJCgujfv7946aWX3Pt49NFHFe8JALF8+XKP/QghxIoVK8TYsWNFfHy8yM3NFffdd5+wWq3u56dMmSJuv/12cc8994jMzEyRk5MjHn30Ua/fDyGE2Lhxo9DpdKKqqsq9rKCgQMTFxYkFCxZ4rP/yyy8LAGLt2rVCCCGWL18uAIivvvpKDB8+XBiNRnHqqaeKbdu2Kbb75ZdfxKRJk0RCQoLIy8sTt99+u6itrVW8l08++aS47rrrREpKiujWrZt46623fI49EM8++6zQ6XRi06ZNHs9ZLBb3GKZMmSLuvPNO93Pl5eXimmuuERkZGSIxMVGcffbZYt++fe7n8/Pzxe9+9zuRkZEhkpKSxODBg8XXX3/tfn7nzp1i1qxZIjk5WWRnZ4urr75alJaWKo7/2GOPiUmTJoX9GomIiCjymGlGREREEZOSkoKUlBR89tlnMJvNmuusX78eALBo0SIUFRW5Hy9ZsgR33nkn7rrrLuzYsQM333wzrrvuOixfvhwA4HA4MGvWLKxZswb//e9/sWvXLjzzzDPQ6/Uexzh27BgmTZqEgQMHYvHixUhISPBYp2PHjrjggguwaNEixfJFixYhJycHs2bNQm1tLc455xz88MMP2Lx5M2bOnInzzjsPBQUFim2ee+45DB06FBs3bsQjjzzicSyHw4G8vDx8/PHH2LVrF/70pz/hwQcfxMcffwwAuPvuu/F///d/OPvss1FUVISioiJMmDDBYz/Hjx/HOeecg7Fjx2Lr1q1444038K9//QtPPPGEYr133nkHycnJ+O2337Bw4UL8+c9/xrJlyzS/HwCwatUq9O/fH2lpae5l//vf/2C1WjUzym6++WakpKTggw8+UCy/55578Pzzz2P9+vXIzs7G+eef7576uH37dsycORMXX3wxtm3bho8++gg///wzbrvtNsU+XnjhBYwZMwabN2/GrbfeiltuuQV79uzxOvZAvPfeezjrrLMwcuRIj+fi4uKQnJysud3s2bOxYcMGfPHFF/j1118hhMA555zjfk3z5s2D2WzGqlWrsH37djz77LNISUkBABQVFWHKlCk45ZRTsGHDBnz33Xc4ceIE/u///k9xjFNPPRXr1q3z+vNCREREMRTrqB0RERG1Lf/73/9EZmamSEhIEBMmTBAPPPCA2Lp1q2IdAGLJkiWKZRMmTBBz5sxRLLvsssvEOeecI4QQ4vvvvxc6nU7s3btX87iuTLO9e/eK7t27i9tvv104HA6fY/3222+FJEni4MGDQgghHA6H6Nmzp3jggQe8bjN48GDxyiuvuB/36NFDXHjhhYp1tDLE1G699VZxySWXuB9fe+214oILLvC5nwcffFAMGDBA8bpee+01kZKSIux2uxDCmS11+umnK/YzduxYcd9993kdy5133inOOOMMxbK5c+cqMvfUhg8fLmbNmiWEaMo0+/DDD93Pnzx5UiQmJoqPPvpICCHENddcI2666SbFPlavXi10Op07E7BHjx7i6quvdj/vcDhEdna2eOONN7yOIxCJiYnijjvu8LuePNNs3759AoD45Zdf3M+XlZWJxMRE8fHHHwshhBg2bJh47LHHNPf1yCOPiBkzZiiWHT16VABQnMNbt24VAER+fn6wL4uIiIiijJlmREREFFGXXHIJCgsL8cUXX2DmzJlYsWIFRo0a5bd4/O7duz2K0E+cOBG7d+8GAGzZsgV5eXno37+/133U19fj9NNPx4UXXoiXX34ZkiT5POaMGTOQl5fnzjb76aefkJ+fj+uuuw4AYDKZcO+992Lw4MHIyMhASkoK9uzZ45FpNmbMGJ/HAYA333wTY8aMQadOnZCSkoJ//OMfHvvxZ/fu3Rg/frzidU2cOBG1tbU4duyYe9nw4cMV23Xu3BklJSVe91tfX6+ZjeeLEMLj/R0/frz76w4dOmDAgAHu79/GjRvx9ttvu7MRU1JSMHPmTDgcDhw+fFhz7JIkITc31+vY33vvPcX+Vq9eHfBY/dm9ezcMBgPGjRvnXtaxY0fFa7rjjjvwxBNPYOLEiXj00Uexbds297obN27E8uXLFeMbOHAgAODgwYPu9RITEwFAs7YeERERxRaDZkRERBRxCQkJmD59Ov70pz9hzZo1mD17Nh599FG/26kDG/Jghyu44IvRaMRZZ52Fr7/+WhFE8kan02H27Nl455134HA4sGjRIkyePBn9+vUD4Jxu+Omnn+LJJ5/E6tWrsWXLFgwbNgwWi0WxH2/T+1w+/vhj/PGPf8T111+PpUuXYsuWLbjuuus89uOPVvBHCAFA+d7FxcUp1pEkCQ6Hw+t+s7KyUFFRoVjWv39/VFVVobCw0GN9i8WCQ4cOud8nX1zjcjgcuPnmm7Flyxb3v61bt2L//v3o06dPSGM///zzFfvzFrzs37+/O9AVKNf7qrXc9ZpuvPFGHDp0CNdccw22b9+OMWPG4JVXXnG/3vPOO08xvi1btmD//v2YPHmye3+uBhmdOnUKanxEREQUfQyaERERUdQNHjwYJpPJ/TguLg52u12xzqBBg/Dzzz8rlq1ZswaDBg0C4MxAOnbsGPbt2+f1ODqdDv/5z38wevRonHHGGZoBH7XrrrsOx44dw+LFi7F48WLccMMN7udWr16N2bNn46KLLsKwYcOQm5uL/Pz8QF6ywurVqzFhwgTceuutGDlyJPr27avINgKA+Ph4j/dEbfDgwVizZo0ioLNmzRqkpqaia9euQY/LZeTIkdizZ49iv5dccgkMBgNeeOEFj/XffPNNmEwm/P73v1csX7t2rfvriooK7Nu3z51dNWrUKOzcuRN9+/b1+BcfHx/SuFNTUxX78RZYvfLKK9116dRsNpvi3HQZPHgwbDYbfvvtN/eykydPYt++fe5zEgC6deuGuXPnYvHixbjrrrvwj3/8Q/F6e/bs6fF65UHWHTt2IC8vD1lZWSG9B0RERBQ9DJoRERFRxJw8eRJnnHEG/vvf/2Lbtm04fPgwPvnkEyxcuBAXXHCBe72ePXvixx9/RHFxsTvD6Z577sHbb7+NN998E/v378eLL76IxYsXuwvRT5kyBZMnT8Yll1yCZcuW4fDhw/j222/x3XffKcag1+vx3nvvYcSIETjjjDNQXFzsc8y9evXCGWecgZtuuglxcXG49NJL3c/17dsXixcvdmdFXXnllT4ztrzp27cvNmzYgO+//x779u3DI4884m6AIH9Ptm3bhr1796KsrMxdbF7u1ltvxdGjR3H77bdjz549+Pzzz/Hoo49iwYIF0OlC/7Nu2rRpMJlM2Llzp3tZ9+7dsXDhQrz00kt46KGHsGfPHhw8eBAvvvgi7r33Xtx1112KqYsA8Oc//xk//vgjduzYgdmzZyMrKwsXXnghAOC+++7Dr7/+innz5rkzrr744gvcfvvtIY87UPPnz8fEiRNx5pln4rXXXsPWrVtx6NAhfPzxxxg3bhz279/vsU2/fv1wwQUXYM6cOfj555+xdetWXH311ejatav7XJ4/fz6+//57HD58GJs2bcJPP/3kDqjNmzcP5eXl+P3vf49169bh0KFDWLp0Ka6//npFcHT16tWYMWNG1N8DIiIiCh6DZkRERBQxKSkpGDduHP76179i8uTJGDp0KB555BHMmTMHr776qnu9F154AcuWLUO3bt3cHQ0vvPBC/O1vf8Nzzz2HIUOG4K233sKiRYswdepU93affvopxo4di9///vcYPHgw7r33Xs3sLIPBgA8++ABDhgzBGWec4bOeFwDccMMNqKiowBVXXIGkpCT38r/+9a/IzMzEhAkTcN5552HmzJkYNWpU0O/L3LlzcfHFF+Pyyy/HuHHjcPLkSdx6662KdebMmYMBAwa465798ssvHvvp2rUrvvnmG6xbtw4jRozA3LlzccMNN+Dhhx8OekxyHTt2xMUXX4z33ntPsfyPf/wjlixZgtWrV2PMmDEYOnQo3n//fbzxxht4/vnnPfbzzDPP4M4778To0aNRVFSEL774wp1FNnz4cKxcuRL79+/HpEmTMHLkSDzyyCPo3LlzWGMPhNFoxLJly3DvvffirbfewmmnnYaxY8fi5Zdfxh133IGhQ4dqbrdo0SKMHj0av/vd7zB+/HgIIfDNN9+4p5Da7XbMmzcPgwYNwtlnn40BAwbg9ddfBwB06dIFv/zyC+x2O2bOnImhQ4fizjvvRHp6ujvA2dDQgCVLlmDOnDlRfw+IiIgoeJLwVrCBiIiIiNqN7du346yzzsKBAweQmpoa1LYrVqzAtGnTUFFRgYyMjOgMsA167bXX8Pnnn2Pp0qWxHgoRERFpYKYZEREREWHYsGFYuHBhSDXbKDRxcXHuxgFERETU8jDTjIiIiIjCwkwzIiIiaosYNCMiIiIiIiIiIlLh9EwiIiIiIiIiIiIVBs2IiIiIiIiIiIhUGDQjIiIiIiIiIiJSYdCMiIiIiIiIiIhIhUEzIiIiIiIiIiIiFQbNiIiIiIiIiIiIVBg0IyIiIiIiIiIiUmHQjIiIiIiIiIiISOX/AQpdHqJZHc1XAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the relationship between 'Open - Close' and 'High - Low'\n",
     "create_line_plot(df['Open'] - df['Close'], df['High'] - df['Low'],\n",
@@ -702,11 +1130,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 126,
    "metadata": {
     "id": "NAT3Sy3hC2a3"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JypyaQoECABjXYuMiddYn8gdc/SQ+IVIzZ5/ekvYAgPHptO5EGWxZraG4/fbbtWbNGmVmZqq1tVVPP/203nrrLb388suyWCy6+eabtXbtWuXl5SkvL09r166Vy+XStddee1qvCwAABpc7O1O7NxTJ29N3SS9PdqLScpJClAoAMBTdXV61NrXLZrMqNjH6tP9uA4DJYvbcTDmcdm3bckSVFU2SpPDwMBXMSte8BdmK54IBAJiUhlyiXHXVVX3e7+rq0le/+lVFRkb2Of6Xv/xlyC9eXV2t6667TpWVlXK73Zo7d65efvllnX/++ZKkW265RZ2dnbrpppvU2NiopUuX6tVXX1V0NBtVAgAwmtJykrTmCyv1wWu7VVVar7Awm6bNzdKic2eNyU3lx4LOzh5VVjSps7NH4Y4wpabGKiqKjRIBBI/fH1DR7jJte6dQddUtCguzKbcgTQtW5CklIz7U8QBMAF2dPeru8soRYZfTGR7qOCPOYrFoen6qcnKTVFfXKr8vIFekg/IEACY5i2EYxlAmfvGLXxzSEz722GOnFWiktbS0yO12q7m5WTExMaGOAwDAuOLt8aq5rk22MKtik2K4mtmEYRgqOlil9947qMbG9t7j0VERWnbmNM2cmS5bGMuKARh9+7aWaN2zW/Txv/AioyP0yRtWKsHjDk0wAONec1OHivYe054dR9XZ0aOISIdmz8/S9FnpihkHS70CAPBxw+kNhnwnylgrRwAAwOizh9sH3R8FUlFRtV58cYcCgb5nLVvbOvXaa7sVMAzNm5cVonQAJouO9m598Ob+fgWKJLW3durQ/gpKFACnpLmpQ6/8dasqyxt7j/X0+PTem/t1pLhGF16xgCIFADChcVkkAADAKerq8mrThqJ+BcqJNm0oUktLZxBTAZiMGmtb1HLC3XAfd2jvsSCmATCRHNxb3qdAOVFFWb0O7qsIciIAAIKLEgUAAOAUVVc1q66+ddA57R3dqqgwP/EAACPFah38T7swuy1ISQBMJJ0d3dq9vXTQOXu2lairqydIiQAACD5KFAAAgFPU2eUd0ryuIc4DgFOV6IlRcvrAyy9On5sZxDQTQ2NrhyrrW1Td2Kpury/UcYCQ6O7yqauze9A5nZ096uF3HQDABDbkPVEAAADQl9M5tF+lnA77KCcBMNnZw+1aeu5MvfTUJvm8/j5jnsx45RakhSjZ+HO0ulH7S6u1+0hVb3mSGh+t+dPSlZeepBiXI8QJgeBxOMPkjHDI29Mx4BxnRLjCnfyuM1b5/QEFAgHZ7ZwCBIBTxb+gAACMQR1tXSrZV67SoirZ7WGaUpCm7BlpCuOPnzHF44lVYkL0oEt6RbocShvk6nAAGCk5+am6/AsrdWB7iUoP1chuD1PBGdnKm5Oh6Fg2fR6KnYcq9OIHB+QPBPocr2xoVeUHB7Q3uUqXLp2p+Bg+n5gcIlwOzZ6fpY3rDww4Z878bDmd4UFMhaFob+1S0Y4S7d9yRD3dXuXOylD+GVOUnB4f6mgYA+qrmtRc3yany6GUzHjZwlj2ExiMxTCMgXdCnQBaWlrkdrvV3NysmJiYUMcBAOCkOts6te6Z93VoT1mf40vOn6OlF8496br3CK6DhZV68cUdA24uf+65szRvfnaQUwGY7Lq7emS1WbnyeBgOVdTpmfU75R/g3/Pj8tITdeWK2Qrnc4tJormpXS8/t01Vx/rv8ZaaEacLr1goN0XtmNLd6dW6/9uk4l19/56IjInQZf+8iiJlEuvu7NHW9fu1850D8nb7ZLFalDU9VWdddobiU9yhjgcE1XB6A87CAAAwxhwtrOxXoEjS1jf2qqasIQSJMJi86R5dtGaeYmMj+xyPinTqvPNma/Zs9iEAEHwOZzgFyjAYhqEdhypOWqBIUtGxOpXXNQchFTA2uGMjddEnz9Dy1TMU7XbJHm5TjNul5asLKFDGqGNHavoVKJLU3tKpwu0lwQ+EMePA1iPa8voeebs/XK7SCBg6eqBCbz+/td9yoAA+wm/VAACMMZVHak2P+/0B1VU0yJOdGOREGIzFYtGMGWnKykpQZUWTOrt65Ai3KzU1VlHRzlDHAwAMQVVjqw6Wm//8NVNUXqfc1IRRTASMLe7YSC1eMV2zF2Sru9snp9MuZwRLeI1VdRX97xo67vCecp154TyF2Vm+abLp7vRq14aDpmNlB6tUXVav9NzkIKcCxgdKFAAAxhj7IJuQ28JP/0d3Z2ePyo81qKa2VZKhhPhoZWTEKyqSjXJPh8vl0NRpKaGOAQA4BW0d3UO6C+W4qsaWUUwDjF0RLociXPzOONYNVpCEO8NltVmCmGbsMgxDfn9AYZNkP5Cerh51tHaZjhmGoa6O7iAnAsYPShQAAMaYrOmp2rZ+n4yPncyJiHQodUrSaT33sWONeu31PWpobO9zPDrKqfPPm6Upp/n8AACMRxbr8E4osj8ZgLEsdUqibDar/P5Av7GCRTmT/t+w9tYuHTlUo/27ytTe3qW4hCgVzM5Q9tRkOQa5oG28i4hyKjE1VscO1/QbC7PbFBMXafIoABJ7ogAAMOZk5KXorMsWyn7CXSeu6Aid/9nlik2IPuXnra1r1d9e2NGvQJGk1rYu/f3FnaqoHPjWfwAAJqrYSKfCh3ElcmZS7OiFAYDT5MlK1MrLzpDN1ve0X97cLOXNyw5RqrGhpblTLz+/Xete3KmK8gY1N3aopLhGLz23Te+u26fuLm+oI46aMLtN88+aIavJhQOzl01TYlpcCFIB4wN3ogAAMMZYrVYtWFWgrOke1RxrVFiYTZ7sREWf5pVBxcXV6hjkFu2eHp/2769QWiq/PAMAJpdEd5Tm5KRqa1H5SedarRZNS2M/FABjl8Vi0dzleUrOiFdlSa18Xr8S0+KUnpMsR8TEvdNiKHZvL1H50TrTsT07SuVJj9OseVlBThU8ubMzdNF1K7V9/QHVVzbK6XJozorpmrlkqiwWlnkDBkKJAgBAEHh7fOru7JHDaR90z5MTJaTGKWGECo2uLq/27Tt20nmFhVVavChXMTERI/K6AACMF3NzU7W/tFod3YNfhXxGXrrSEtxBSgUAp8ZisSg1O1Gp2YmhjjJmtDR3aM/20kHn7Nlequkz02UfZF+Z8cxisWjanCxl56epo7VL4c4wRUQ6Qx0LGPMoUQAAGEU93T4V7y7Tzo1Fam1sV5TbpTlLpyl/QabCHeFBy+H1+tTd4zvpvB6vT16vPwiJAAAYW9IT3bpy5Rz9dcMetXX2mM5ZmJeuT8zONV0KBQAwtrW3datrgH/fj2tqbFNXR4/s7ol9UZk9PEzuhKhQxwDGDUoUAABG0fZ3C/X+a3t63+/q6NGbz21Rc2ObVlw0N2i3TDscYYqICFfXSdb4dTrtcjj49QAAMDnleOL1hfMX6UhVg3YeqlBLR5dsVqtyUxNUkJWszKRY2YexdwoAYOwIs9tktVoUCBgDzwmzKWyC3oUC4NRxlgQAgFFSX9WsbesPmI7tfO+gps/NVHJ6fFCyhIfbNXd2hta/UzjovFkz0xUVxe3cAIDJKz7apfhol+blpqnb65PNZpXTzp/OADDexSdEKSs3WSXF1QPOmTEnQxGu4K0YAGB84DdBAABGSX1Vs7wDLKHl9wVUV9kUtBJFknJzk7VrT7kaG9tNx6MiHZqRnxq0PAAAjGVhNqvCbJxIA4DT0dHepfKSelWWNUiSUjPjlDElUa4Q7MNhs1m1YHGOyo/WyWeyhHFktFPTC9KDngvA2EeJAgDAKLHYBl+qy2qzBinJh+LiInXpJfP1zjuFOlpaL8P46Db2tLQ4rfpEvpKSYoKaCQAmE6/Xp5bmTsliUUyMU3bubgAATGCN9a16/bntqvhHgSJJOzZJqRlxOu+TZyg+MTrombJyknTpVYv0/rsHVXmsUZJktVqUnZusJSunKymFv4cA9Mdv7QAAjJKUjHhFRDrU2d7dbyzcaVdKRvDuQjkuKTFaV1x+hiorm1RX3ybJUFxcpFI9sQoPHx+/Fvj8ATW3dykQCMgZble0yxHqSAAwqJ4en4oOVmnXjlLV1DRLkjypsZo7N0vTpntkZ+11AMAEYxiG3n+rsE+BclxleaPef+uALvrUoqDtEXmi7KnJSsuKV01Vs7q7fHJFhivZEyurNfhZAIwP4+NsCQAA41BMXKRWXjJfb/x5s/z+QO9xq9WiFRfNVVyI7vqw2azKyIhXRghKnNPR2e3VoYo67TxUobLaZgUChlxOu+ZNTVN+RpLSEt2hjggA/Xi9fm18r0jbth7pc7ziWKMqjjVqcUOblp05TWFsVg4AmEBqq5pVvL9iwPFD+ytVW9ms5LTY4IU6gd0epvTMhJC8NoDxhxIFAIBRVHDGFEW5I3RoT7lqK5oUnxKjvNkZyszzhDrauNLS0aVXNxfqQFltn+NtnT16b0+JNh8o02VnzlRBdkqIEgKAudKjdf0KlBNtfv+QMjLiNSUnKYipAAAYXW2tXfL7AgOO+/0BtbV2KlmxwQsFAKeIEgUAgFGWOTVFmVM5uX+qAgFD63ce6legnKjH59fzG/bK5QxXdkpcENMBk4thGCFZdmM8KzxQedI5RQerKFEAABOKwxEmWSQZA0ywSOEOTksCGB/41woAAIxpFfXN2n246qTzvP6Adh+upEQBRkFjbYuKdpWqaFeZbDar8hdM0bQ5mYqOdYU62pjm9wVUXdV00nmVlSefAwDAeJKcFqu0jHjTPVEkyZMep5R0fm8HMD5QogAAgDHtcEW9AsZAl7D1tbekSktnZinJHTXKqQBzhmGosrlVVS2t8vr9sttsSnVHyxMTPW7v4GiobtYLv31XjXWtvcdqjjXqyIEKXfiZZYqMiQhhurHNarMMaa+T8HD+LAMATCx2e5iWnVOgF5/5QF2d3j5jDqddy8+ZKbudn38Axgf+tQIAAGNabXP7kOd6/QG1d/YoiT3mEQJH6xu1+Wi59lXWyhf4aA1wu82qmZ5kLZ6Soaz42NAFPEUHd5X2KVCOKy+u1tHCSs1cnBuCVOODxWJRwcx0vfP2gUHn5eenBikRAADBk5mTpE9+frkO7jmm4gMVkiFNK0jT9Nnp3IUCYFyhRAEAAGOaddhX74/Pq/0xvh2srtOftu1Wt8/fb8zrD2jnsSoV1tTpnxbO0bSkhBAkPDV+n19FO0sHHD9yoIIS5SRypiZr65Yj6ujoNh2PjHKyHwoAYMJK+ceyXcvOzldrY7vamjpklTGkfdYMw1DAH5BtCHd1AsBookQBAABjWlpijPYerR7SXJfDrtgo5ygnAvqqbG7Rn3fsMS1QTtTl9enP2/bo+jMXyhMzfpacG+wEh9VKaXkyCQlRuuSyBXrlpZ1qaensMxYXH6kLLpyruPjIEKUDAGD0BQIB7d9UpA1/26rujh7Z7DbNWTlDyy5eIIfL0W++3x/Q4d1l2vN+sVrqW5U+NUUFi3OVnpsSgvQAQIkCAADGuBxPguxhh+U9yQlqSVqQl67YKPZnQHAdqK5TZ49vSHPbe7wqqqkbNyWKLcym/AXZ2vjKbtPxnIL0ICcanzIy4/WZa89UeVm9amtaJYtFyckxysyKl8vk5BEAABPJsaIqvfWnTTICH+5z6Pf6tePNvUpIjdXsFTP6zd+7qUhv/uUD6R/bIjbVtapox1FdduPZyphGkQIg+KyhDgAAADCY5LgoLZ+VfdJ5MS6HZmV7gpAI+EhrV7e2HC0f1mM2l5Srrdt8aaexKG9ulpLT4/sdz52Zruzp/Dc3VFFRTs0oSNdZq2borE/kK39GKgUKAGBSqDxS01ugnKhwy+F+x9qa2vX+q7t7C5Tjerq92rPxoAyj//MAwGjjThQAADDmLZ2RpUDA0Ht7ShQw+cMpPtqly5fPVHLc+Li6HxNHY2enWrt6hvWYps4uNXd2Kcox9k+gN7Z0qKS+WSkLMxWRFqOakjq5oyM064wc5RSkKYLl84Bha2vpkN8fULTbJauV6xoxdhmGIcNg6UacPqvN/HvIFtb/38Dm+jZ1tHaazJaOHa5RV0e3IiL5/QNAcFGiAAAQQhVHanVwZ6nKD9coJi5SBQunKKcgXWF2Nk88Ubg9TGfNydXUtAQVH6tTYVmtfIGA4qNdmpPjUXZKvKK5ohshEDC5qnIo/GP8KspAwNCu4gq9ufmguv6xVJnFYlF0llt1FumYr0dTBjghAgyktaFNZUWVam/uVGxSjDLyPJOqiKurbNKezYdVtLtMfn9A6VOSNHfZNO7owphiGIYqa5p16Gidiktq5PcHlJbiVv5UjzJT4xQezmkkDF9arke2MJv8H1ued8biaf3mhjvtstms8vsD/cZc0U7Z+R4EEAL8ywMAQIgcLazUi79/T95/nKBsqG5WyYEKLTlvlpaeN3vQzZwnI6vVooykWGUkxWrVvKnyBwyF2biCF6HltIfJZrEMqxQJs1oVETa2fw3fc6hSL2/Y32fJDMMw1NLeJUnaeqBM/kBA5y6ZrnD72P5YMDbUVzXqxUffUkNlU++xKbMydN5nVyjS7QpdsCBpqGnRC7/foObGtt5jRw5UqLS4Whdfe6ZyZqSFMB3woUDA0I69ZXpzQ6H8J1wk0NDUoT2FlZo1PVWrz5yuSC5cGbbuzh5VHKlRR0unouMi5clJUni4PdSxgiZtaorOv+4sbXh+i1rq2+SMdGjh+XOUO6//kr2JaXGaNi9LhdtK+o3NWT5dYfzeASAE+JcHAIAQ8PZ49f7re3oLlBNtfWu/cmakKSUzIQTJxgeLxaKwEF8F31DTrNLCSjXVt8kdH6ms6alK8MSGNBOCLzk6SnnJiTpQXTvkx8zwJCkxKnIUU52eto5uvb3t0EnXHN9x8JhmTElRTjr/VuHk9m4s7lOgSFLJ3nId2Vum2cvzQxMqiIr3lPcpUI7z+/za+k6hMnKTuboaIVd0pEbr3isc8N//vQcr5QgP07krZ3CxzzA01rTojT9tUnlR1YcHLFLevGytumqxImMmfol8XP6iqcrMT1NrY5siIp2KSYg2nWexWLTsovny+wI6vKdMgYChcIdd81cVaPqCKcENDQD/wG9pAACEQGNNq6rK6k3H/L6Aao41UqKMYceO1OjF376jzraPNgd3uvboos+vVFZeagiTIdisFovmZXqGVaLMTfeM6ZNPpZUNau3oGtLcwpIaShSclM/r05HdpZI+XP/eYrXK94+LCEoLKyd8iWIYhor2lA84Xnm0Ts31bUpMjQ1eKOBjvF6/tu4uPWmBvuvAMc0pSFdKYkyQko1/29fv+6hAkSRDKtpxVMkZCVp03uzQBQsBV3SEXNERJ50Xmxiti647S7XlDerq6FZMfJTiU9xBSAgA5lgDAwCAULBaZNHAJ1HZwHPs8vb4tPGlXX0KFEnq6vDqvb9vV3enN0TJECp5SQlanJ0+pLnLcjKVmxg3yolOT0OL+WauZo5WNZzyvjCYPGxhNrmTY5Q+LUWOSKcsVotSshMVlxyjqNixe1fWSLFYLLIN8nPd0vs/QOjU1LWovLLxpPN8voDKP3ZX2VD4/QFVHmtUcWGlSo/Uqrtrcvy+1NrYpqLtR03H9m8ultfb/650fMhms8qTnagpBekUKABCjjtRAAAIgfikGKXlJOnY4Zp+Y2F2m5Iz4kOQavRUHq1V8c5SVZXWKdETq7z5U5QxLSXUsU5JQ3WzKkr6f90kqbaiSXWVjUrPTQ5yKoRSeFiYzp0xVTabTZuPlJnuj2KzWrQsJ0tn5U1R+BjfD2W4DBniDDAGY7FYlJQWp9+tfU5+30cbBc9ePl2rZw6tgBzvps/LUk2F+QnqjKkpiks0X9YGCJZukyVmB9I1zAKkurJJm94uVOmR2t7iPSE5RstWTtfU/LF9d+bpMgJSwOi/Qbr0YSElLkQAgHGBO1EAAAiBMLtNS8+fLacrvM9xi8WiZRfMmVBLepQXV+m5X6/Ttrf2qeJwjXZtOKi/PrJOh/eWhTraKQkEDGmQv3dPtgwGJiZXeLgumpmnf16xUKvycpQQ6ZI7wqHEKJdWT8/RjcsX6fyCaXLZx/4msrFDWGbjuIzkWNms/EmBwbXUt2rPpiKlTElSuNMui9WiiCinWhra1DNJrkbPnZlu+rM93BGmhWflyxZmC34o4ARhw/geDB/G/j31NS164c9bVHKops+di/U1LXr5r9t0uKh6WDnHm+j4SOXOyjQdm7EwR3bH2P+9AADAnSgAAIRMRm6yPvml1Tq875iqjtYrOi5SU2elK2v6xLkiLxAIaPvbB/qdJPN5/dr8+h5lTvOMuz8e45PdSkyLVV1FU78xd0IUm8tPYlaLRZlxscqMi9Wq6Tny+QOy26zjrmTISo1TVIRDbZ3dJ51bkDM+7yhDcDVUN6urvUfxKbFyJ0TL7/UrLDxMVptVteX1mjYvO9QRR11sQpTWXHOmDu4q1YHtR+X3+ZWV59HMhVOUNiUp1PEApSRFKzkhWjX1rYPOs1otShvG0kpFByrVOsAykX5/QFs2FCsrJ1F2+8Q8PWWxWLRg9UxVl9apsaal93haTpIKFk8NYTIAwHBMzJ9SAACME8np8UpOn1hLd52ovblTFYfNrzCsLqtXc0P7uLnrpqWtS91en8JsVp25Zr5e+u078nn9veO2MKuWXzxPEZGOEKbEWBFmtSpsnJUnx8VEOnXW/Fy9tHH/oPNm5XqUkRwbnFAY1+wO+4crvhkf7o9y4l0X4c7wgR84wcQlRWvpubM0f8V0BfwBfl5gTHGE27VwbpZeenPvoPPyp6YoNXloJUp3t0/79pQPOqeqolF11S1KnWBL2Z4oOSNeV/7LeSovrlZ7c6diEiKVPs2jyGHc+QkACC1KFAAAMGrs4WEKd4arq6PHdMwePvaXLzlW06QDJTXaU1yhrm6fbDarctMTtOyapequalHF4VoletyaOidTGVO5Kh8Tw+y8NPn8Aa3fVqwen7/PmMVi0eypHq0+I29YS7pg8krKjFdGnkflB6v6HA8LD1N6nidEqULH4Rxfd2Bi8iiY5lFTS6c2bj1sOp6VHq9PLMmT1Tq0O6Z9Xr/8Q9g43fuxnzMTUXRclAoWR4U6BgDgFPFXDwAAGDXOSIdmn5mnDS9s7zc2a8k0uRPG9ka6B45U64V39vY5iRzw+VV4tEaFR2t05twpuvyc1bKzlj0mmDCbVYtmZSk7LV4lFfUqrWqS3+9XUny0pmYkKi3JrTDb+LzTBsEXHm7X6quW6t2/btHRwgoZAUOxidFaccVCebISQx0PwD+Ehdl05sJcpaW4daC4SiXl9TIMKSE2UnMK0pSTmahI19DvoHI67YpLiFZHe/2Ac+zhNkVFOUciPgAAo4YSBQAAjKpZS6eptaFd+z4olt8fkNVq0bR52VqwqiDU0QZ1rKa5X4HycRt3lSg22qX5+elBTAYET1JclJLiorR41sTfswKjKyE1Vpd+6WzVHmuQ3+tXvCdWEZw4BcacMJtVU7OTlJuVqI7OHgUMQxHO8FMqzm1hVs2al6VjpQOXKDNmZyg+cWxfVAMAACUKAAAYVa4op86+eolmLZuq1qYORUZHKDkzQbYxfhX7waPVgxYox23ec1T52cmKYHkWABiULcwmTzabqGPyam7qUMWxRnl7fHJFOpSWHifXGN0bx2KxDOuuk4FMnZ6ieQunaOfWkn5jyamxWrAk97RfAwCA0UaJAgAARp3FYlFKZqJSMkOdZGjaOrq1u7hySHPrmttVUdesqRksSQMAAPrz+wLaveOoNm0oUlent/d4TKxLnzi7QNOmT9y9gcIddi0/u0DpWQnat7tMDXVtckaEa9bcTOVMS1a02xXqiAAAnBQlCgAAwMd09/jU1XPyjVBPnA8AAGBm354yvbVuX7/jLU0devlv23XZVYuUnTNx79IKDw9TXkGaps1Ilc/nl81mG/Lm9GOFYRhqb+6QzW5TRCRLEQLAZEOJAgAA8DE2m1VhNqv8/sCQ5rPBNgAAMNPe1qX3NxwacNznC2jX9lJlZieOu2JhuCwWi+z28Xcaqqa8Xtvf3KeS/eWyh4dp9pnTNXt5vlzRlCkAMFnwFz8AAMDHuKOcmpY5tCtCXU67UhLYEBUAAPRXXdWsttbOQeeUHK5RY0NbkBJhOFob2/TSY+t1YMshdbV3q7WxXRtf3K4PXt0Z6mgAgCCiRAEAAPgYi8WimbkpQ5q7ID9D7qiIUU6E8ai7y6uy4mod2ntM9VVNoY4DAAgBn+/kd7X6/QH5hzAPwVdeVK2mupZ+x/duKlJ9ZWMIEgEAQmH83UcJAAAQBDnpCTprwVS9s33gJThy0uO1oCAziKkwXlSU1Gr989tUW9EkSbKHh2nOsmlatLpATld4aMMBAEad1+tXVUWjWpra1VDfJofDrojIcFkt/ZfsckU55IpyhCAlTqazzfwuIl+PT10dPUFOAwAIFUoUAAAAEzarVcvmTpE7yqnN+0pVXd/aOxblcmhBfobm5acr2sVJj7GirqJBDVVNckVHyDMlSWEhWne9qb5NL/1ho9pbPjrx4u3xadvbB+R02bVo9cyQ5AJGQ82xBpUVVautuUOxSdHKyvMoLikm1LGAkKqva9X61/aqtKRO0TERiogIV/HBKrljXfKkxSo8vO/PpznzshQVxf4aY1Fcstv0eKTbJXdCVJDTAABChRIFAABgAGE2q+bkpWl6dpIq61rU1e2TzWZVSny0YjjZMabsee+A1v9xg7zd3g+XY1s+XWddvUxOV/C/TuWHqvsUKCfa+V6RZizIUZSbJeAw/h3aU65X/7hJ3m5f7zFXjFMXf26F0nKGtq8UMNF0tHfrtRd2quofdyK2tnQqL9+j2poWNTd2yDAMZWQnyvaPTeTTM+I1c05GCBNjMGlTU5R/Ro4Ktx3pPWaxWrTsovmKio0MYTKpu9OrY6V1OlZaL5/Xr+TUWGXmJCkm1hXSXAAwEVGiAAAAnIQj3K4paQmhjoEBNFQ16u1nNsrb7ZUkGYahve8VKnNGhmYsmRb0PK2N7QOOtbd2qaOtixIF415bc4feem5LnwJFkjpaurTh5Z264sbVsofz5yYmn/LS+t4C5bhjpfVaunyamps6VLivQn6fX4meWM2el6npMzyKjuGk91jliAjXqk8tVdaMdJUVVsjhcihnVoayZqSFNFdLc4feenGXjhRV9zke7Y7QRVcuVFoWv7cCwEjit1oAAACMa001zerp6r8ueW1ZXUhKFFf0wHe/OF3h7ImCCaGqtF7tLV2mYxUldaqralJqVmKQUwGhV3a0vt8xw5AqyxsVZrdp4ZIczZiVoZlzM+V02of8vJ3tXaooqVNbc4dc0U6lT0ke9OcNRk5ElFMzl07TzKXB/51iIFvfK+pXoEhSa3OnXnt+u66+fqUi+f4AgBFDiQIAAIBxzRXjki3MKr8v0Od4TGJ0SPJkTE2RI8Ku7k5vv7HZS6YqJi60y38AI8Hv9w88aEh+7yDjwATmG+R73+f1q6aqRbnTvMMqUOoqm/T6n95XzbHG3mNxydE6/5+WypNJWTnZ1Ne0aP+u8gHHmxraVX60TvmzWSYOAEaKNdQBAAAAgNORnJWoeWfP6XMsMSNBU2ZlhiRPQopbF3xmmaJiProC1GK1KH9+tuYtzwtJJmAoGmpbte29Iv358Xf17G/f1c73D6u5sc10bnyKW2F2m+lYdJxL8clsLo/JKSXVfCPyE8UNY0Nyn9evd1/Y0adAkaTGmla9+ew2dXf1L+wxsbW2dMrb4xt0TktjR5DSAMDkwJ0oAAAAk4DX61NlZbM6Onpkt1uVkuJWVNTEWObBarVq2WVnKG2aR3Xl9YqMdSmrIEPuhNDciSJJOTPSlPS1C1R5tFbeHp/cCdHyZMbLFmZ+0hkItcqyBr34zPt9lugqO1SrXR9Eac2nlyjR0/fEcFJqnBZ8Il+b1+3rc9xitWjpebPlimbfH0xOWTlJcjjtA5YbbrdLGZlD36+i5liDyor7L9skSbXHGlRVWqfs6amnlBXjky3s5NdDD1RyAwBODSUKAADABHfkSK02bSpWZWVT77HoaKeWLMnVrFkZCpsAJ/bDHeGaNn+Kps2fEuoovaLcEcqbmxXqGMBJeXt8eu/1vaZ7nDTWtWnz24W66NOLZbFY+owtPLtAsQlR2rXpkNqa2hXvcWvO0mnKnZUerOjAmBOfEKULL5mnl/+2Qz0fu1sgMtKh8y+dN6y9Krq7vDIMY+Dxzv57gmF4GmqaVVveIKvFopQpiYqJG/qdQqGQnBKrxJQY1VW3mI7bbFZ5MuKCnAoAJjZKFAAAgAnsyJFa/e1v2+Xz9V2jvbW1S+vW7VN3l09Llk4NUTrg1DV3delwQ6Oq21rl9QcUGR6uTHeMsmNjFR7GnznDUV3RqIqjdQOOHz5QqdqqZiWnxvY5Hh5uV8GiXOXNy1ZPt1eOiHDZbKwYHUqGYcjn88tu57+BUMqd7tGnP3+mSg7V6uiRWlksUu60FGXnJil+mPt1Rce6FGa3me61YrVZFc0+W6dl3weHtP7ZD9Tzj33MXFFOnffZ5cqZNXb3E3FE/H/2/jM4zi097H3/nXNGo9FAI+dEEMxpb3LnMLMnz2hmbMmy68qqY0nH4Va5rstf7CqXXPYHl4+rXK7je65kS9Z4LEsjTdx5b27mHJBzbIRuoNGNzrnvB5AgmmgkEiRBcv0+bb7r7bdXY3dcz3qeR8HRVxv56Gc3yWTy+8FlMln2H6vBUWrOOx6LxHGPevHOLKFQyimtKqa0ugipVLxnC4IgbIf4ZiUIgiAIgrBDsUgC74yfdDqD2abH5ti6/vmzkEqluXplZF0AZa0rV0eprrFjt4v+BcLzIZZKcWNmhouTU4SSiXXjZUYjZ6qraSkufgazez7Fo0nYeKM7mUyWeGzjvgtyhUyUjnnGUsk0o11TdF8ZJhKMUdHgpPlwDc5K+7Oe2kvL7jBhd5g4fKLusa5TVGKm/Vgdt88Prhtr3F+Bw2V9rOu/zDxTi3zxv6+QWROgiobjfPo/L/HDf/o+xh30rnna6ppLef/7Em5dHmVqfIFQKI5EJqW+3cVsMMq5S8PU1RTjKrUQCkT5/K+uMTU0v3p7iVTCiff20flKowikCIIgbIMIogiCIAiCIOzAaP8sFz/qIuBbabasUMppP1LDwVca0ehUz3h2+ebnl5ldU8KrkHQ6g9u9JIIownMhlkrx0dAQN2ZnNzxnJhjkp11dfKulhQOlpU9xds8vjU6FRAIbVQySyaVoNIqnOylhR26f6+fyb+6s/rt7McTI3Um++Xuv46goenYTE3bFodeakSvldF8eJh5NolQraD1cQ+erYgH8ccxNLOQFUO6LhePMTS7s6SAKQE2DE7vTxPXr43gXgiQzOeb8YbKBHDNzAW7fneSNMy2kFoN5ARSAXDbHpQ+7KCm3oTFqWPQGyWbBatNhLzGtK98oCILwshNBFEEQBEEQhG2anVzk47+8mldSI5VMc+vCEBKphJNvtxe8XdAfwT3qxeddRqtTU1ptp6Tc+sR/oMa2WSc9HFq/m18Q9qLr7plNAyj3ZXI5ft7fj1mtpsYqdmlvxVFqoazajntsoeB4bXPpusbywt6x5F3m5he9647HIgkGbo5vO4gSjSXxLoZIpzNo1UqKi43IRXm2PUGjU3P87XZaD9UQCcfQaFWYd1gWTFgv+1AprO2O7SW3u93c6nUXHMtkc3z6RS/19sLPlVQqzZ0rI0zNBFb798jkUprbyzlyqh6DUfvE5i0IgvC8EUEUQRAEQRCEbRrudhesSQ7QfXWUlgNVWB5a1Jif9vHRT68Q9EdWj8lkUl75WgftR+ueaCBluzXxVWrxlfBJy+VyeGf8LHmWkcmlOCuLMJhFHfudWI7FuDg1te3z09ks3fMeEUTZBrlCxok3Wvhw6TqhQDRvzOYwcviVRrEreQ9bXgyTjBcutzYxMMsr2eym2QrJZIregTlu3J1geTkGgEQiodJl5VBnFVUik2XPMFp1GK3is2O3FFfYkEgl5LL5aXgKlZzi56BM2lIgwt3u6U3PSaczTHuCqLXKldKNayx4ljG5/aTWlH3NpLP03J4kHk/x9gf7RX8lQRCEe8S7oSAIgiAIL6RwMEY2k0Fn1O5Ko+NcLsf0mHfD8WQizbIvnBdESSXTnP/wbl4ABVb6C5z79V2KnBZKK5/c4pTDYcRs0hJYjm54jkQioazU8sTmIEAinuT6533cvTREJr2ys1WjV3HmWwepb694xrN7foz5A4QL9EDZzJ35OY5XlFOs39slWfaCEpeVb//OSSZHvEyOeJBIJVTXO6ioc2A0i93Ie5lKrUAikZArUI/NaNVvGkBJpTJcvDbKzTuTecdzuRwT0z7cc36+/k4HddWix5Dw4nFWFXP0nX1c/ahr9fUjk0l55VuHsDn3/ncjrzdI4l4GyUZkchlz3iCtJWbik77V4/FYgqWFEAaLllw0icmoIRaOI5FIUOvVzEwuMuf2U1Et+ioJgiCACKIIgiAIgvCCWZwP0Ht9gqHuaTLpDM5KG/uO1lHVWPJYO6klEgka7eY9TxTK/K9W3hk/c5OLBc/NZrK4R71PNIii1ao4cqSGTz7t2fCc5iYnJU7zE5uDAEN3prh1biDvWCyc4LO/vIbJon8udrvuBfPh0I5vk8xk8EWjIoiyTWabHrNNT8fRmmc9FWEHHBU2KhudTAw8VOpOAq1Haze97dTM0roAylrpdJYvzg9Q6jCh3eIzUBCeNzKZlENvtFFW48AztYhUJqWkyk7JE/xutlYykWJ6eJ6poXnSqTSuOgcVDaXoDOpt3T6d3l7JMb1Zi0qrzDsWiyWpay9HoVGS9gb5/NMeMvdKmMkVMg6fbmLRGxRBFEEQhHtEEEUQBEEQhBfGkjfIr//iMstLDzI/Joc8uEcXeOcHR6hrcz3W9Rv3VzAzUbhngKPMQnGZOe9YIp6CDRo1A0TD8ceaz3Y0t5SRSKS5fGVktd41gFQqoamplFOnGnYlU0coLJVI0XV5uPBYMs30iEcEUbYpkS5cSm8rmY26pQvCC0Iml/Hqtw4h+cUtJgZmyGVzqLVKjrzZTm1b+aa3HRye33QcIBiMMT3jp7G+ZLemLAh7hkwuw1VfguspP79TyTRXP+nm9ppNFv03ximvL+HNHxzDsI0MQJ1ue4FNR4mZ4680MmjRMd4/h0Ipo+VoLV5fhGgwxs0LQ3nnp1MZLn/WS21T6c4elCAIwgtMBFEEQRAEQXhhjPS48wIo92UyWa592Ud5nQOVWvHI169pcjLeXMpIzwxSqYT7iS0qtYITb7ejUOZf22DWIlfINuyjYi02PvJctksmk3LwUDVV1Xbcbh/hcAKVUo6z1EJJiUkEUJ6weDxFJBjbcDy0Sak1IZ9e+WivXaVMtsszEYS9x1Js4mu/+ype9xLJeAqT3YDZtnnj8WQyhXt2aVvXXxLvVYKwq2bHF/ICKPdND88z1uum42TDltcoLTFhLzKwsLh5pua+1jKcFUU4K4o4/m4KmUzK/Kwfz8ddDNya2PB2Y/2znHp334bfFVPJNOlMFpVKgVQq+mYJgvBiE0EUQRAEQRBeCNlslqG7GzfXXJwPsuQN4qywPdL1Q8EYYyMeUCkob3IyN7GISiWnZX8lDftcFBfoK1JUYqK5s4rua6PrxrQGDeV1jkeay6Ow2fTYbKKk0dOm0aqwFBuZmyhc1s1i33yR875MOsPc5CKLs35kchkllUXYX7JeNuUm845vY1apcRq29zcWhOedTC7DWbWD0jsSCRK2t/AplkcFYXfNb1DuFWDozsS2gigqlYITR2v51Yd3yWQLZ13aiwx5PY3ubyYqKbVQUmrlqr97w2unEmlSyTQyTX4pMK9nmeFhD0P9s6TSGex2A82tZVTXFKNSPfpmJUEQhL1MBFEEQRAEQXghbKtizw6r+izO+fG6/SQTKTyLYUbHFlZLYumL9MhkUkKpFHpT4ZILEomEw683k85kuHFxiEAwRiqdobyyiCPvtaHWi/ryLzq5QkbHiYaV3jgPPf+0RjUV2ygfkoiluPzRXbovD682vpUrZJz51iFajmze7+BFUmU2UWowMBvafm+UIy4XBpV4nQlCIUqFnMqKIrp6N96AcJ/NqnsKMxKEJ8fnCRLwhZDJZRSXWdBusxTWXldf4+Brb+/jq8tDLC8/yHyVSiVUVxbx6okGTAW+p8oVMjoOV3P10x7Gh+byxlQqBa7KIopKTChV+cuGkxOL/OZXt4nHUqvHIuEEE+OLtLWX88qZJtSPkfUtCIKwV4kgiiAIgiAILwSZTEpdu4trX/YXHLc5jFgd29uRns1muXthiMsf3SWdyjDrXiISSXDivQ4CUgmJeIpwaKWfyXIgisNh5sCRwo2Y45ksIaOKyhO1lMXTyBRSohL4zY0R7KPzvPdKM6XF5kd6zMLzobbNxWvfOsT1z3sJB2MgAWdFESff68Bi37qk22jvNF2X1tcr//JvrmNzmnGUP1p21fNGpVBwpqaG/9XVta0+Jza1hlZH8ZbnCcLLrLGumO6+6U03ItisespLRe8m4fkUi8S5cW6InhvjpO5thDFa9Zx8q5X6x+yV9zgcm2RG1+2r2NG1GupLKCuzMDsXIBiKIZVKKbYbKCnevGxrkcPE+z84wi//4hLRSAJyOTRaFTqDGoVCRtuhaqTSB7cPh+J8+nFXXgBlrZ7uaRxOE/s6djZ/QRCE54EIogiCIAiC8MKob3PRf2uC0HJ+DwqpVMLhM82o1MoNbplvemieC7++TS6bIxFP4V+KkM1k+epvb/LGD48zE8//8dh1e5KWdhfqh8odBEMxfnW2l3lf8MHBNb3kF/xh/vbzLr7/Tid2qyg59KKSyaS0H6ujuqUUvzeEXCGjyGlGodzeV/H+G+MFj2fSWWbHF16aIApAi93ON5qb+eXAAOlsdsPzbGoN329vp0gnds8LwmZcpVZOHKnj4tWRguNqtYLXX20WO8uF59aNc0PcvjScdyy4FOaTv76OSqOgovbplVZdq7Tazr4TDes2SZRWF1PXXr7j6+m0Kuof4bE0H6wiHIxx99IwmczK56pcIePQmWZqWsryznVP+wgF44Uus6rr9iSNTU5R1ksQhBeOCKIIgiAIgvDCsJWY+NrfPUH31TFGetxkMllKXFY6TtRR01y67euMD8yQu1dbOpVKk733ozKTybLsDSKTSVd/aAIEl6NEwvF1QZRRty8/gFJAMJKgf8wjgigvAb1Ri95YuPTbRnK5HLHwxgsWqUT6caf1XJFIJBwqK8Oi1nB3fo6u+XlSa4IpJpWaIy4XbY5iEUARhG2QyaQcPlCN2aTlTvc0M3N+ABRyGU0NJbQ3uyh1mp/tJIU9IRqKsbwUQamSY3WYkEj2fqccnydIz42xgmOZdJb+21OU1xQ/k8eiVCk48d4+ymqLmeifIZVMU9HgpKqpdMMysU+CSq3k5Lv7qG8vxzvrRyqRUOyyUlSy/v+xzxfe8no+X5hQMI7KLoIogiC8WEQQRRAEQRCEF0pxqYU3vn2Qw2eayGSyGExa5ArZjq4RWVtT+qEfkLFIArlFlxdEkcmlyOT595FKpbk74N7W/d0ZcLO/qQyjXrOjeQovPolEQnVLGUue5YLjNqfpKc9ob6i1Wam1WTlRUYkvFiWTzaKSySg1GkUPFEHYIblMSnODk7rqYnz+MJlMFrVagc2if9ZTE/aAbDZL//Vxrn3WTcgfQa6QUb+/ksNvtmG27e0NIH5fiFQys+H4zPgCiVgKtXZ7mcq7TalWUr+vgvodlu/abRKJBIfLisO1edm+taW9NruWRLr3A2yCIAg7JYIogiAIgiC8kIyWR9+FXlptZ7RnJQCi1ijRaFXEogkArCUmFh4qZVBbX4LJnL9rMBJL4Q/llxXbSDSeIhJLiiCKUFDD/koGbo4TCeY/n6payiitebl7fpQY9JQYxEKvIOwGhUJGSfHLGZgVNjbaPc0Xf3WV3L3GOelUhv7rY8TCcd7/nVd3vFHlaZLLN5+bTC5DJhcL/ttV7Ni6j1upy4L5KWbSCIIgPC1bh5EFQRAEQRBeEol4itByDFddCSbrysKsVCbF7jAguVfeQPZQyS6FQkZrR8W6kgdSKeykOsTzUBZDeDbspRY++Aen6TjVgMGsxVJs5MT7+3nt24fRaEXWhSAIwssqlUqTjBdu8r0bMpks3ZdHVgMoa00MzDI/tfjE7ns3FJdZNt1U09xZgUK5vbJT/sUQY30zTA7NEYskdmuKz5Uyl5WSLcr77euoQCYXS42CILx4RCaKIAiCIAgvvXAwxvDAHD23J4lGVnqbtJ9uwjftY3JwDp1BTcfJRlArGZt4sGBgtel55fVmyivWN/bWa9WU2U2Mun1b3r/NpMUkslCETRSXWSkus3LsnX1IpZJtL/oIgiAIL57wcpTBm+P0XR8jk85Qt6+C5sM12ErMu3o/yXiSwELhcpLkIBrcXsbts6LVqTjxdhuf/vV1Muls3lhRiYmGbTRwTyZT3L4wxO0LQ6sBK5NNz6l391Hb6noi896r1GoFb7zdxscf3mXRG8obk0olnHylgdq6nTe3FwRBeB6IIIogCIIgCC+1UDDGp7+8w/TEwuqxeCzF+S8HqKwt5pv/8HV0ejVavZpIOE7LjJ9EIoVWq8LpsqJWF17MlkoltDeUbiuIsr/ZhWaD6wjCWir1s6nbLgiCIOwNiViKsz+7zljPg75rt872M97r5oPfe21X+5SoNEqsJWbCy+uDJRKpBJ1x728AaWhzoVYr6L89hXvci1whp3l/BQ37XJhtW5eD7L0+ztXPevOOLfvCfPyXV/nW31dRWmV/UlN/YnK53CNnQBcXG/n2dw4zPe1jfNRLIpmmuNhEVU0RTqcFqeiHIgjCC0oEUQRBEARBeKkN98/mBVDWmhz1UlZu5fDJBgB0ejV1jc7V8XQ6w/TMEsuhGFKpFJtVR7HNsPrDtNplY19DKV1Dsxvef2WphaZqsWtPEITnSzqdYXYuwNSUj2A4jk6rorLCRqnTjFIpfmYKwpMyO+HNC6Dc518IMdE3w/5XmnbtvqRSKe3H6pkenieXzS/pVdPioqSyaNfu60mqqHNQXltMPJZELpduO5szGopx+8JQwbF0KsNwj/u5CqJ4pn0M3ZlgcmAOnUlLy+FqqltcKFU728ijN6hpbimjuaXsCc1UEARh7xHfbgVBEARBeGkl4il6bk9tek737SnaOivX9Z6YdC9x+eYI7lk/90uFy+VSGmtKOHqgGptVj0qp4LXD9Rj0am72ThNPPKhbrlTIaKtzcqyjCoNOveuPTRAE4UkJR+KcvzBE30B+gPjGrXHqah2ceaUJk2nv71AXhOdRwBvccMwzvXX2605Vt5bx1g+Pc+PzHpY8QZQqOU2HajhwuhnZQ43bY5EEkVAclVqBwby3motLJJId9xELBWOEl6Mbjs9usAlnL5qbXORXf/IVsUgcgCXPMtNDcxx+q51jb7eL3nyCIAhbEEEUQRAEQRBeWvF4iui9H5MbiUUSxOOpvB/ek24ff/vhbZKpTN656XSW3qFZ5heCfOvd/VgtOjQaJa8cqKW9zonbEyCRTKOQy3DaTditW5eREARB2Euy2RyXLo+sC6DcNzLqQSqR8N477cgfWmAVBOHxqTcJBOhNux+4kEqlNB2opqqplKA/ikolx1SUXzIsEU/Sf2uSO5dGCC1HUakVNHdW0nG8FqPl8b7reL1BpsYXmJnxI5NKqKq2U15ZhOkpBGmUSjlyhYz0Q9/37tMb91agaDO9V0dWAyhr3T7bR22bi+Iy6zOYlSAIwvNDBFEEQRAEQXhpqdUK1BoliXhq43O0SlRr+pWkUmkuXBtZF0BZy+cP0z04w+ljDavHzEYt5ufox7awuZA/wsyYl2gohqXYSGl1MSqN6FcivPg83mX6BmY2PWd4dJ6OuXIqym1PaVaC8PIoqy1GZ1QTCeYviMvkUqqan1x5JbVWVTCAk8vluH52gFvnH5S9ikeT3L44zMKsn3d/eAyt/tEybgf7Zvnsky5SyQffuUaG5jEatbz7wX5KyyyPdN3tstiNNOyroO/meMHxho6KJ3r/uyGbzRGNxPHN+wuOp1MZljzLIogiCIKwBRFEEQRBEAThpaVSK2jrqODi2f4Nz2ntKEe7ZtFgZj7A7Hxgy2t397nZ31KO6TlouirszPzkIh/9+XmWfeHVY7Xt5Zz57mH0Jt0znJkgPHmzcwEymdym5+Ry4J71iyCKIGwhl8sRCcWRSEBn2N73BaNVzzt/5yTn/uYGi/PLAOhNGk58rZOymuInOV2WFkNMjnpxjy8gkUioqndgNGm5e3mk4Pnu8UXc4ws0tJfv+L7mZvx8+tFd0unsurFgMMonv7nL9390DN0jBmi2q/OVRhbmAizM5gchOk7UU93s3OBWz96cd5nRqQUGxjykUhlCqTRlLaUQSuB7qOybXCGyBgVBELYigiiCIAiCILzU6lvLGB2eZ35m/Q49u8NEU1v+D/9AMLat68biKZbDMRFE2YFEPIVMJt3TP+Yz6QxXPryTF0ABGO2eprS6mAOvtTyjmQnC05FMprd13mYZfoIgwMz4Ar03J5gc8QBQ21JKc2clzm0EH111JXz3D9/G6/aRzWSxOa3on3AfotlpHx/+7xuEww++B40OzlHmshIJxVFvkI05N+F7pCDK6IinYADlvoA/wvSUj6Yn3NzcVmzkg985yfSIl/npJRRKGa6aYkqr7CiVe29JLZfLcXfAzWeXhshkHvz9FsMxununKS+zsr+hhPmheQD0Zi3OyqJnNV1BEITnxt57xxcEQRAEQXiKTGYt737rAIM9brpvTxGLJlBrVLTuL6e5rRyz9dEzC6SIJp3b4XUv0X99lLHeGZRqOa1H6mg8UIXmCe8ufRRLnmWmhucLjg3cHKPzTLNoziq80DTbLFun34OvX0HYK6ZGPfz6J1dIrQlK9lwfZ7R3lq//neM4K7YOpKg0Ssrrn04mRCKW4uxvuvICKA/GknhmA1TU2At+/kllO/9MzGZzjA57tjzPM7/8xIMosNL7pPlAFc0Hqp74fT2u4Qkvn14YJJvLzxg0FxmIRRJMzy4hkUhodlmIL8d58wfH0Ilys4IgCFsSQRRBEARBEF56JrOOI6caaTtQRTyWRK1WotUVbtxqs+iQSCTkcpuXs9Hr1Jie8K7QF4FvPsCv/vQrwoHo6rFzP7/Jwqyf1793BNkeaEydSmUIhWLkcrm8XZ0P2+o5IQgvAleZBYVCRmqTvlAymQSXqK8vCAWlUxmunx3MC6DcF4smuHtllJJy654KyM+6fSx4lguOKdQKkskUsWiy4HensqonmOWwxcduLpcjsBAkk85gthuRK17sJbB0OsON3ul1ARQAuUJOWU0xFruRRDxJzdFa2hrLMFr1z2CmgiAIz58X+xNEEARBEARhB7RaVV7/k0KcDjOVLisTD9WTflhHazkGndiJvZXR7um8AMp9AzfHaTlcQ1mt4xnMakUymWZ0xENX9zTz9+rOV1cVYbQb8M8FkEqleec3HarZU4tegvAkFNkMHD5Yw6Urwxue09lRSYnD+BRnJQhPlndmidGuKTzTPizFJmrby3E94ufT0kKQ2cmFDcfHBmYJ+MJYigyPdH3/QpBYKI7erN21BfJIKL7h2JIvzOHTzQWzNOvbXZRV23d8f1KphOq6YvzXwpue5ygxbTgWWAxy45NuBm+Pk0lnKKt2cOxrnc/0e8WTNr8YZPqh3i3ZbJZELEUul0OpVqA3adGbtCxE4iKAIgiCsAMiiCIIgiAIgrADcpmUU0fqWFgKE4kkCp5TXmqlvXHjEhvZbBaP28/8tI9MOkNRiZnSKhtK1fbK5LxIZse8BY/nsjkCi6FnttiRSmW4cmWEGzfGV49ZTBrioRilTWXMu5dQwWogpbq1jIbO6mcyV0F42g52ViKVSrh+Y4zEmt30CoWMA/srOXSwWgQUhRfG7LiXX/3JWWL3PvMnB2bpuTzEu3/3FLXtFTu+XiaTZbPExWwmSza788zGWDjOrbN9dF0YIplIodapOPh6K/tONaBUKnZ8vbVU6o1vn4inkCtkvPfDo4z3z+H3BlFrVbQdqaahowLVNksAPqy2zkHXrYkN+6KYzFpcG5Q9y2SyXPzFTUbuTq4ec4/O85s/Pcv3//F7mO3bC/KmUxkW5/xkUhksxUa0hr2dYRx9qBdVOBRjYda/EgTLgUIlx+40Y7bp8RXYwCIIgiBsTARRBEEQBEEQdsjpMPPd9zu53T3NwMg8qfRKWRutRsm+FhcdrS4M+sI/tDPpDDfPDXLtiz6ya0pD1baW8eoHnRhMz7YudSqVxrsYIpXKolLJsRcZkMukW9/wERk22QX5qAsvu2FycnE1gCKXS3GatfRdGmJm1INKq+TVr3dSWl6ERqXAZDfgqnGg0r58QbAnLZ1Kk4wlUWlVe6K0m7BCqZRz9HAN9XUOZmf9xBMplAo5ZaUWbDaxs1l4ceRyObouDK4GUO5LpzJc+egurrqSHX9Wma16TDY9y77CWRbFZVaMlp1/F+i+NMSNz3tX/x2PJLj4y1uoNQrqO6uZn1kiGIihVMlxuiwYzdvv+eYst6I3aAiH1vdEATAYNOw7UkPH0TrisSRKlRyl6vECN2UuK2++s4/PPukm/VD5QL1ezTvvd6A35Gf8LswsMd43Q3g5yuXf3EFnUKM1Pvg+Fg3FcA/PbyuI4nX7uPir27hHvORyOQxWHcff7aDxQNWeDRIr1nxfi0biTI14yK4JQqUSaWYnFiEHFaWWZzFFQRCE55YIogiCIAiCIDwCh93Eu6+bONhRSSgcRyKRYLPoMG6xS3FiaJ4rn/Wsq+M92juD1WHi+FttT3DWG8tksgyNerjZNcmcd6V0lUQiodJl5UB7JTWVRU9k0aCuvZz+a6Prdt1a7EZKa4p3/f62I5fL0d8/u/pvh1nL2b+8QiKWBCAWTvDxT69w+HQz/+D/8w0USvGVereEw3Gmp3y4pxZJBWOM3p4gm0hRXldC55lmqltcz3qKwhpWiw6rZfsLsYLwvIkEY0wNzRUcW5wLEFgI4dhGE/i1NDoVB0818MXPb60bk0gl7D9eh2KHvTvCy1Hunh8sOHbl024m3H5GBx40aldrlBw/00jbgcp1pSnXymazZNIZdHo1r7zdyid/e2tdbzClSsHx15tR3Mt20Su2n60RDceZHvUyM75S3sxVY6e81oHmXm+VptYyrDY9E+MLzLiXkMqkVNfYqagqwvxQEGhmzMtv/uwcsUgSh8uCZ9qHVCrBVevAuCa4e/+zfNN5hWJ8+pNL+DzB1WOhpQif/a8raPUqKhpLt/0Yn6ZimwGDXkUonGDZF84LoKy1MBeg9v3Opzw7QRCE55v4xScIgiAIgvAY7DYDdtv265YPd01t2Ai159oo7Udr0RufbrmIbDbHjbuTnLsylHc8l8sxMe1jemaJd8600dq0+4sGFY1OXvvuES5/dJdoKA4SKKmw8eo3D6HVP5ueMolEirm5AAAymZTg/HLBRZe7l4eZGfdStUcXU543iwshPv7wLl5PkFKzhs9+eolcNgcSCZ65ADOj83zj996g8gk8D59HqVSGWU+A6Rk/sXgSnVZFeZkFp8P8RLPHBOFlIpfLUKgURENxJNL8jQQyuRS54tFea00dFWTSWW6cGyRyL7vDYNZx9LVm6lp3/h6XiCWJF/icSiZSzHa50ZZY847HY0nOftSDWqOkobVs3e2i4TijfTP03ZokFonjKLPRfKCCb/74GH13ppka8yKRSqhtctLU5sJZvrNAEkAoEOXzn11navhBcKfn6ijVzaW8/q2D6O59FyouMVG8Se8TWMnyvfFFL7HIyt9AIpFgsOgI+SPMT/nQGbXI7v2/sjnNW85tdmwhL4ByXzaTZfju1J4Noui0Kg60lPPllSFCyxuX61LIpBQZnm3msyAIwvNGBFEEQRAEQRCeosDixk1SE9EkiViyYBAlGowRWAyiVCuxOc27mhUyM+/n/NWNm0Rnsjk+O9+Ho9hI0S43IZVIJLQeq6Oi0YnPs4xCIaPYZUWxSRmQbDbLzOgCk0NzREIxHC4rlU2lj9yEd/2cpEjvLZYplXKCc/6C52VzOeIb9MURdiaVSnP2yz68niAWi5beS4MrARSAXA7v/DIqpZzeqyMiiAL4AxHOXhpkZCK/ObXkhoTGWgenjzdsmRUnCE9bJp3BPeplYnCOaChOSaWNql18795tyWSK6YkFDEVGbpwbRKGUY7HpMJq1SGVS6vZVYC0xP9K15QoZHcdqqW0uZdEbRCIBe4npkTcP6E1aipwmvO78z6vQcowil5VYLLXuNrlcjjtXx6hucORlvsSiCb761R2Ge9yrx4L+KKN9bs58o5O3v3WAWDSJBFYzRh7FwJ3JvADKfeP9swzV2Ok81bjtawV8YdwjD661OL/M4bf38dVfXSWZSBGPJtCZNLQeq6d0G73WouH4hmN+7/rgyl7S3ljKrHeZ0b7ZguNKpZzXjtaj26TPjSAIgrCeCKIIgiAIgiA8RSXlVrwzhRfl9WZtwQWUwRtjXPzlDUJLEWQKGc1Hajn2Xie6XeqfMjK2QG6zLrdAMpVhctq360GU+wwWHYZtlAXK5XLcuTDExQ/vri6yD96exHB+kPd/+xQOl3WLK2xNpZJTV1fMrVuTJBIprBvsgLXa9FiLN98dK2zP7Iyf6UkfACq5tOAilc8XxjPlI5POvNT9UaKxJJ+c62PKvbRuLJfLMTAyTyad5b0321A9ZjNpQdgtmUyWm18N5JWzHO6e5s6FId7/uydwuHaeyfAkJZNprnw5wO0ro9jsehoPVtF7dYRIKIalSM/hMy0ceXvfY29o0Js06E2PH/BUaZQceqOdj/7Hhbx+a8lkmuq2cmbmCi/8z8/6WV6KUOR48Fk2PerNC6Dcl83muPRxD+U1DkzWxyvjl4gn6bs+tuF4740J2o7WbrusmVQiWckUutc6JZ3KEAzEeONHJwksBCmttlO/v5LKxrJt9WoxbdLbqaSyaFtzelZ0GhXvvNKMBgmfftnLwmIIAIVcRmOtg+oSC7JYmiLH1n1hBEEQhAdEnrcgCIIgCMJTVNvmQiYv/BWs81TDul2ds2MePv2LC4SWIgBkUhl6Lg5x+2zfrswnk8kyOuXd1rnTM+sXbZ82z/QSlz/qepClcE8oEOXGF31ks4Xrf+9UfV0JcrmUbDaH2mrAbM/fKS2RSHj1a53Yy0Rj1t2wsCZokkhlKHKu/7tGIwmsTvNLHUABmJpZKhhAWWt4wsvMbODpTEgQtmF+apGrn/euK2cZCkS59dXAloH8p21q1MvtK6MA+BbC6IrNvPmjk5z+7hEOvb2fjtdb91wQva6jgq//g9PU7avAbDfQfLiGN398Es9iZEfXGesv3AMGVsqAzU/7HneqpJJpkon0huPJWJLMQ83kN2O2G6huzi9LloglmZvyYbAZePOHJ2norEalVW7res4qO5XN67MelWoF9R2V255XPJ5kbsbPnHuJWPTpZa7qNCqOH6zljf01fO1UM++fbOKdow3YMrA4usCBVxpXe9gIgiAI2yMyUQRBEARBEJ6i8loHb373MBc+vEskuFIuQqGU03GynuYD63+Yu4fnyaTXLyT0XRlm/5kW9I+ZjZIDtrt2tTvhicczN7m4rqntfRMDs/i9QWyPWF5lrTKXlTffbOOzz3qY94U4/LUD+CYWmOyfQa1T89o3Ojn+RvuullV7mWXXPAn9gRjNx+qYHffm/b9WqhU0Hqx+FtPbU/o3aHL9sOFxDzVV9ic8G0HYnrkJ37rg931j/bP4F0JYi/fOzviR/vxSSOFgnHDwfomnGIpbkzS0up7+xDYhkUiobnFR3eIim80ilUq5e22M3p7CZZ0AHKWWdVklifjmjdfvvy9nMln83iCZbA6TRYtaqyIRSzE37SMaSaDRqXCWW1Fr1gcutHo1xS4rk4OF38+clUWoCtxuIxKJhIOvteB1L7G89KBsqlqr5MR7Hag0OwsYKNUKXvvOYbouDjFwc5x0KkNZbTGdp5txVGydNZVOZRjqneHm1VGW7mWCmK16Oo/U0NTuQql88ktxZpuek2+2MdQ9zcDtSdLpDFX1Tpo6K3BVFz/x+xcEQXjRiCCKIAiCIAjCU9a4v5Kyajset59MJoOt2IRtg5JRqeT6OuawUls+ndp4F+d2yWVSyp0WAps0IL3P6Xj2u24LBZRWxzJZMpnd283c0lKG1apnfNzL+NgC9qZSjr6zj9JyK84CmRLCo7Pa8jN9FqNJ3vw7Jxm9M8mSZxlHhY3GgzVUFdgZ/DLJZnMsBba3q9zn3/o1LQhPSyaz+Xt3doPg+LOyvLT56ywYiO7p0oJS6UrGa2VdMfrL6jUBoAckEug8Wr2uZFZ5jZ3JofnC15VJsBQZmJ1c5MZXA0wNe8hmc1jtRtqO1dBzc5KlNZmF1mIDp9/voLw6P6ArlUppP1rD9PA82YeCazK5lJZD1VtuUsjlcviXwmQzOfSGlaDMt3//daaG51leDKMzaSivL8H2iN9djFY9pz44QOfpJjLpLHqzdvXvupU7N8a4+EV/3rHAUpgvP+oiGolz9JXGp7IJw2zTc+RMM50n6slksgUDWoIgCML2iCCKIAiCIAjCM6A3abeVReKsKrxbsKKlDJNtd5rxNtQ66B6Y2fQcmUxKdfmzr1lvL904eFFcZsG8SR3zR1FSYqKkxMTx4/Xbvk00HGdm1EMsFMNUZMBZVYxSNHDdlMtlwVZkwHdvx24insYdT2NvcVHWUUksnsJVX4Ja/XIvAEmlEpTb7BGgVomfesLeUeQ0bzjmcFkfu8fGbrMWG5jfoH8ZgNVu2LMBlLXMVj3vf+cQX37YxYLnQXBDrVFw/EwTdQUC01UNTm5dHCYaWh94aeyoRCaT8PP/dpFY9EHGikqr5M//06cYLTosRQ++myx5Q3z0v6/znb93CttDPTiqm0p56/tHuPp5H4F77/3WYiNH32ylsqFk08c1PbFI160JJoa9ZDIZTBYd+w5V0dTmou1o3fb+ONukM+4s49e3EOLahaENx29cHqGmoYTiXcia3S6FUo74FiIIgvB4nuk363/7b/8tP/vZzxgYGECj0XDixAn+3b/7dzQ2Nq6ek8vl+Nf/+l/zX//rf8Xv93P06FH+83/+z7S2tj7DmQuCIAiCIDwdrgYnna+1cuds32rNeLPdyKFdLCVV6jBzrMnF6NAscoUcdErm/eG8Ml8nD9fisD/7UivOqiIaOioZujuZd1wmk3LotZZnHqxYnPPzyU8usuB+sPhW0+bizHePYjA/Xum1F5lareTNt9v41S9uEQk/qBsfvLd7urXdRdND9e5fVi0NTuYXlrc8r06UaxH2EFdNMVVNpUwMrJSWSsaTBP1RIsEYDR0V9N+aoLbVhc74+E3Wd0NdUyl9t6c2HG9sLycSjOKZ8pHLZXGUF6E3761A0H3Ocivf/Z2TzE77CAXjKJQynC4rZkv+fLPZLJ7JRYJLYU681szk2AIjvW5yuZVF+JaDVRx6pZG7V0byAihSqYRYNEkoECMRS2Ewa5GvCTDFIgnGh+fXBVEkEgmN+yupqC/B51lGggRbiRG1Nr833MMmRj385q9vklrTMyWwFOHcJ71455c583YrKtWzC7jPuZdIJTfJvEpnmZ32P9UgiiAIgvD4nmkQ5auvvuIP/uAPOHz4MOl0mn/5L/8lb7/9Nn19feh0Kx/o//7f/3v+w3/4D/y3//bfaGho4N/8m3/DW2+9xeDgIAbD7uy+FARBEARB2KuUKgUnPjhAdVs5S/MB1FoVZbUO9JbdWaxJJlLc/LSLW5/3sOALs7AYwmDTcfSDg0z4QpiNGo4eqKGlsXRP9P9QqhS88kEn9jIzPddGScZSFLus7DteR1XTsy31lMvluPFZT14ABWCsx42zys6hN9qe0cyeD6VlFr77g6OMjXro650hncxgK9LT3FpGVbUdlUrsowWoqrChuaEklti4b4FBp6LS9ewzxwThPpVGyWvfPsjALRu3zw8yN7mIxW5g3/E6JgZmGbozyVjvDG/94OieCKRU1Ng5erqJq18NrBvrPFGHUgZ/+R9+TdC30n9DZ9Lyxo9OUN1avnpeLpfbE5+bACq1gur6jbM7EtEklz+8TffFwZXSahKobi7nm799iixgMGuwFZvI5XKM9eX3WNHoVMzPLAGQTKRJxtPI9flZOtOjXg6daih43xqdClfN9oK+iUSKS18O5AVQ1hroclNbX0Jdk3Nb13sSksmtS61uVKpVEARB2LueaRDlo48+yvv3n/7pn1JcXMzNmzd59dVXyeVy/Mf/+B/5l//yX/Kd73wHgP/+3/87DoeDn/zkJ/z+7//+umsmEgkSibW714LrzhEEQRAEQXieyBVyyhuclDfs/qLA1OAcNz7vBSTYbQbMJi3RaBL/4Bzf/e1TlJRY0O6xGto6g5qDp5tpO1pHKplCo1Mjk22vTvmT5F8IMto9XXCs/9oo+19tWsn0ETZks+mx2fQcOFBNOpNFqZQ9k0XITDpDJBhDqVZsuSv6abNZ9HztzTZ++WkXiQKLdVq1kvff3IdpDyxEC8JaBpOWw6+1IFfIMZi1xMMJZscXVsenhueZHJyj5XDNM5zlCplcxuFT9ZSUWRgdmMW3EMJs1VHXXEpRsZGf/acPVwMoAJHlKJ/95CLf/6fv43Uv0XdlhJA/QnVrGY2HajctRbkXjHZPcffcmh4eORjvm8busnD8awdWD0skEhQPNUXPZnMoFLI156y/vnyXGqnPzwbyypIVMjo4t60gSiqRIpVIo9ar1vU6SacyJOMplGoFcsXOyrYZtvHeu51zBEEQhL1lT/2KW15eSUu3Wq0AjI+PMz8/z9tvv716jkql4vTp01y6dKlgEOXf/tt/y7/+1//66UxYEARBEAThOecensv7t0Iuw2TUEPNH0Eikey6AspZKrUC1l3qN5FgtufawbC634ZiwnkwuRSZ/NoGx8f4Z7pwfxOv2oVIraDtWT8uRWrR69TOZTyHVlXZ+65uHGR730jcwSyKVRqWU097ioq7Kjn2X+iUJwm7LZLL0Xh3Jaz6+1nj/7J4IosBKIKWq3kFVvSPv+ESfm+V7PTzWigZj9F4e5sYXvXDv7X7Js8zwnSm++ftvYH3EBudPw8hDJTLv678+yoHX21Ct+S7QuL8Sz5p+MbFIghKXlf4702gN6rxz76vdpcyQRGzjDLz7AoHIpuPxaIKBm+P0XBkhHk1QWlVM+4l6yutLSMRSjPS66bk5TtAfxWjR0nawmrpWFyrN9r5vlFXYMJq0BJejBce1ejVlFUXbupYgCIKwd+yZIEoul+Of/bN/xqlTp2hrWyl1MD8/D4DDkf+lxeFwMDlZ+EP+X/yLf8E/+2f/bPXfwWCQ8vLygucKgiAIgiC87MSy/u4x2w1UNpUy1uNeN9bYWY1CuYcCPkJBEwOz/ObPLpBJr5SKScRSXPrwLv6FEK9/78ieyHi6z2E34rAbObSvkmQ6jVIhRy1KngnPuef5MymVTDE77l33IIJLYcZ6pp95ECWXyxGPJlAo5TvKinx4A0BtSykjPW5mJxdXj2UzWaoaHCCRIJXmp6KUlFupqN2dHk2abWzssGwSRM7lclz9uJu7FwdXj410TzE5NMvX//5phvvm6Lk+tjoWiyTwuP145wKcfKcd5TYyarQ6FW+838Fv/vbmuqCPUinnzfc7MJhEJoogCMLzZs8EUf7wD/+Qrq4uLly4sG7s4RT+zWqLqlQqVKq9lXIvCIIgCIKwV5XXO+k6P7jueEmlnSKn+elP6DkmlUo58lY7vtkAy0sPyryU1hTTdLgaWNmFHYvEUSjlqNR7N8vnSdlLPQIels1m6b48vBpAWWvw1jitR2oprbY/g5ltTq1WoEYET4Tng0wmpWF/BVc+6Sk4XtO8eW+rdCpNMpYqWILpaSkut2EsMhB8KBtFoVKs9BMpwLMm4PAszE8ucPdsH9NDcxjMWva/1kr9gerVv2FdRyXjvevLUTYdrl1X0tBg1vL2Dw4z2jtD/60JUskMrio7p95tZ2psgb5bkyTiKVRqBc2dlew7XI1+l8pXlZSZKSkzMz8T2PCcusaNe7943Uv0XBledzyVSDPc7abnVuHNut3XxqhpKqXyoaykjVTU2PnOj48xPuRheGCWbG5lXjUNJZTs8dJugiAIQmF7IojyR3/0R/ziF7/g3LlzuFyu1eMlJSsffvPz8zidD9I/vV7vuuwUQRAEQRAEYecqGp0ceqOVW1/2kc2u7DY1WHS88o2DKMSu9h1zVBTxnT94i5kRD5FQDJPNQFmdA41OxWjfDD3XxvDOLKFUKWg5VE3T/koMZu2znvYTFQlGGe6aZvDWBOl0hob9ldTvq8Bc9GC3cC6XI7AQJJvJYbYbkMl3VoN+N8QjSTzTvoJj2WyOZV9oTwZRBOF509BRyUiXm8X5QN7xivoSKhsLl33KZrOM3JnkztlelhdDOCrtdJ5pobxxJeiSSqaIRVMoFDI0uie7qVJr0PDmj07y6V+cJ7S0UjpKa9Jy6hsH+epvbxS8jdlufKJz2kxgIciv/7+fEw6slJeKLEf5+L+fQyKV0nBgJcBf216B99Vmui8MrHwXkEB1Szltxws3gzeadXSebGDfsTpy2dxq35DK+hI6jtYQjyVRqZWYLLpdfSwKpYITZ5r51V9dJ5lY3xOq42AVrsqNS2Ut+0JkCgS6ZHIpUyMeMpnshhmH7vGFbQdRAIpLzBSXmDnyysrfcK9uIBAEQRC255kGUXK5HH/0R3/E3/zN33D27Fmqq6vzxqurqykpKeHTTz+ls7MTgGQyyVdffcW/+3f/7llMWRAEQRAE4ZmLx5J4pnwkEinMNj32Ussj/zhXqhQce7+T6rZyfLMBlBoFZTXF6M27u/DxMjFa9RiP6POO9d+a4POfXV8NVMUiCS5/0o171Mvbv3UU3R7qt/GwWCROKp5GZ9buuJxVLBLn87+6zkT/zOqxy3MBhu9O8v5vv4K5yIBvPsD1T7sZ7Z4mm8niqi/h6NvtlNY83U1TCtXK4ms0FC84rtK+fJlDgvAkmIsMvP/bJxnrm2G4awoJEiqbnFQ2OtFtkLEw1jXFR//9K3L33kPHe6aZGZnna7/3BoFggu7bkwQDEZRKOS0d5TS2ubAWPbneQOUNTn7r//11PFM+crksDlcReouOxbkAt77syztXoZJT0/7sSoy7h+ZWAyj35XI5us/3U7e/EqlUikqr5NVvH6bxQDXLvjA6owZHVRHKLcpQymRSeCjmbTTrMD7B7xDlVUV860dH6euaZqR/nkw6g6VIz74DVdQ1l2xacuvhrJr7pDIp2WwO6SbfpZKJ1CPNVwRPBEEQXgzPNIjyB3/wB/zkJz/h5z//OQaDYbUHislkQqPRIJFI+Cf/5J/wx3/8x9TX11NfX88f//Efo9Vq+fGPf/wspy4IgiAIgvBMzI4v8MXfXGfJs9KUVyaX0X6sjsNvtKDZYHFgKzKZlNLqYkqrd6dmuZAvGopx5fPe1QDKWtOjHqaH52nqrHr6E9tEKpkisBhmrHuK3qsjJGIpnNV2Dr7WQnn99hsETw978gIo9y3OBhjvm6H5YDWf/c/LzE89KHUzNTjH4qyf7/7h21iLn14PAYVSQfvxes7+zfqd5FaHCecmu5sFQdgZc5GBzlcasdj0dF0c4tpHXdz+opfmw7UcONOMYU0GQy6Xo+vi4GoA5b5YJMHVz3rwLCdWswuSiTTXLgwz1D/H1793CNsTzADRGbXUtOVnEh56vRWlWkHXxSGSsSSOChuH3mzHWfXsstji0UTB4yF/hEwmu1rSSyqV4qwuxvkcfBdwlllxllk5cqKBTCaLTq/csO9YNBjDM7VIDrA6zJRUFTE/kV9eLZVI09Zezu3LIxvep12UOBUEQXipPdMgyn/5L/8FgDNnzuQd/9M//VN+93d/F4B//s//ObFYjH/0j/4Rfr+fo0eP8sknn2AwPLldJYIgCIIgCHtR0B/mo59ezttRmklnuHNhEJ1BzcEzzc9wdsJGFuYChPyRDccnhz17JoiSy+UYvjtFz5VhluYD3PmqH5vTjLnIyGT/LHPjC3zn/3gTR8X2Agpzkwsbjo31zWC26fMCKPdFQ3Hcw/NPNYgC0NhZTXApTNelYdKpld4odqeZ1753BI1u72YLCcLzaHJgll//9/OrfUSSiTR3LwwSi8R5+8cnVhf30+nMuv4jAOFgjJkxLxqnlUwsv0RTwBem+9YkZ95pf/IPZA2NQcPRdzpoO15PIp7CaNHtqIn7dmWzWeYmF5mfWCCTzlLsslJaW1wwc6SozFrwGjXtFSiewNyepq0atLuH5/j0JxdXnz96i47jXz+IVCZldswLuZVMoQOnW6jbX8nowBzBAp/XJoueitq9H1wSBEEQnpxnXs5rKxKJhH/1r/4V/+pf/asnPyFBEARBEIQ9bGZsYV1JjvvuXhqm+WA1WsPeX+jNZrN4ZwPMTflIJdOYbHpcVUXoDLvTeHav2eob70bfibPZLJ4pHwszfiRSCSWVRdifcEPaka4pPvof5ympKOL22T6S8RSzY15ymRy2UgvJeIrRrultB1Hkm5SCUaoVRIKxDccjGzzXnySVRsHJr3XS0FlFYCGESq2gpLIIlUaU8hKE3dZ3bbRgI/aRu1N0nGpczd5QKORUNJXSfWEw77yAP0L1wVr80cJllvq7puk8WoPpGZSn1Bm16J5QEkw2m+XuhUEu/up2XoZj27E6Tr7fua70YFldCZ2vt3L7y97VD6SiUgutJwv3O3lRxMJxPv/ppbwAXNgf4dIvbvC9f/wu4WCcZDyFqciAzbESsH/3B0c495su5u/3x5JAicvGq+/vw7jL/V0EQRCE58vzve1AEARBEAThJRIJbbzgHA3FiEUSez6Ikk5luHlxiBvnhvKau1pset76zkFKXIV3zD4N4UCUoD+CUi3HVmJGIpGQy+Xwzi/jW1hZhLHa9TjujW1XkcOMzqjZMGBQUbe+90cmneHGF31c/6xndZFRrpRx+luHaD1a9wiPbmvpVJq75wfIZXPksjmS8QcLkwtzfkxFBuRKOb55/7av6aop5uaahbu16tvLVwJnEgqOm59yFsp9EomE4jIrxRvs3hYE4fFlMlkWZwMFx7LZHNFwfm+ituMNjPVM5wVXi8uLkOvU5CKFgyjJRJpkfH3z8eedZ9rHxV/fWVcisufKCGW1DhrvZTamkmmmBmaY6J9FoZJz+nvHyKYz6ExaXPVOdCZtgau/OLzTPgLe4LrjkeUovrkAtfsq142VuKx8+3dPMjnsxTu/jEIlp8hhxGQVARRBEISXnQiiCIIgCIIgPCcMJh1KlZx0KrNu8URv1qI1PFpPlKdpbGCWq18OrDvu94X5/Oe3+M7ff+WRe7s8qlQyTc+VEW6e7SMaiiOTS2noqKT9VAPdd6cZ6JtdDfjIZFLqm5yceKURo3l7C1B6k4Yjr7dy9uc312WdlFTYqKhfH0Rxj3qYGvdiqyxCLpMSXgwR8kc4+7Mb2MssFLtsj//AH5KIJVm6t+Akkaw04L1fSz+dTJNKppEr5djLtn/frtpijr7VzrVPe/Iee9vRWqpbypDKpDR2VjF4ayLvdsUuG+UNJY//oARB2JNkMimOChuBAmW6ZDIp+ocazBdXFPGdP3yXyX43oaUIVqeZUDTF9JgXp8NIIpPF7wuz9i1WrVGgUm/eGP15ND+xWDCDB2C8z01jZxW5XI4bn3Vz7eO7eeOH32ynormMxbkA8WgCm/PJZjfuVRsVRcmks/R2z3DlwhDx2IPgnMmi5dXXW6gt8HktCIIgvBxEEEUQBEEQBOE5EFgMEloKIc1l0elV6M06vDP+1cX9/Scb93zPhkw6Q8+NiQ3HA74Is5M+aptLn96kgKHbE5z/5a3Vf2fSK7XmhyeXiKczSGXSB2OZLAO9M8SiSd77xgHUmu0t0LUeqkKllnP38gi++QAKpZzmA9W0HKpGb8wPxowPe/jFX16n+/o4sLKgWNPgoL6plJmhOWbHF55IEEWlUWIq0hMLx1nyBuk83czlD++szEEuQ66QodGrqGkv3/Y1ZXIZh15roaymmLnJlYU/R7mN0mo7StXK3+6Vbx3C7rLSf22UdCpDw4Eqmg/Xon/Bd0kLwsuu+XAtI13TZNKZvONNh6opLl//Hmd1mLDeK7s00T/DV784z51LwwA4K+3sf62Fmbnl1U0GLR0V2w52P08e3kSxVia98p1gwe3j5uc9eWPpZIZf/clZDk75cI94UCjl7DvVwOE32teVAHsRFJfbMBcb12Wj6ExaSjYoSdnf4+bsp73rji/7o3z4i9t883uHKK/cXjlLQRAE4cUigiiCIAiCIAh7nG8+wG/+9CuWPMvEIgnmJhfJAWe+cxjv3DKdrzTScqjqWU9zS/FocrUs1lpmqxaVQsbyYpChm2NoVHKc1fYdlcx6VIlYitvnB9cd11h0nPtNF9XNpQVLnkyOLzAz7aN2i2wJ77SP2TEPqWSa4nIbX/+7J0nEUyhVcrT69UGviVEvv/7bm7gnfavHMpksw/1zeOaWOX68dnWRbLfJFXI6TjYyP7lIIpZErVVy5ntHGbw5hs6gpf1EA+0nGyjeYck1mVyGq9aBq7bwDl6dQcPB11rpONVILpdDsUkfFUF4GQSXwoz2TDPSPY1MJqNhfwU1beUF3zOeZxUNJbz3O6e4dbYP7/QSSrWC9uN1tJ1o2PT93zcf4KM/v0AymsBmN+JbCDI3uUDklzc58rUDzM8tU1xioq1zfbmmF0FxuXXDMojVzWUA+BeC64JTS54AC+4l4uGVDMNUMs3NL/rQG7XsP938pKe9KxY9QSZHvSx6llFrlJTX2CmrsKJSrw8CafRq3vjhiXWN5d/84Qn0BfqbRCMJrl0e2fC+06kMPXencFXYnsr3E0EQBGFvEUEUQRAEQRCEPW7wxhhLnmUANDoVVU2lxCMJwv4IP/hHb67uzN3r5Eo5SpWcWCSxesxs1eF3L9JzZWXhYraqiNE7k7z+/aO0PKHeH2vFInFC/kjeMZlcij+w0r8klcoUuhkAUxOLmwZRRu5M8slfXCCVfFCTv/O1Fo6/34lCuf5reDqd4caVUdLpLDqjGv9Du2eDgShLwRjHy59cr476/VVk0zlufNlLYDGIzqjle3/0DpUNpRht+ie6cCRXiJ8mAMtLYcb7ZnAPz6MxqKluKaOywYlMLnvWUxOeglAgykc/ucT85OLqMfeoh+kRD69/58gLlzFQ0+qivL6E8HIUpUqO7l5mXiaTJbQcI5vLYTRpkK95/s+MekjEkkilUhylZjQ6JX5fmGgwhjyX49QbLdQ1OTG9oI3AnZV2DpxuZvjuFDK5lHAgSjqVoaq5lMp7mZwPZ6amk2mW5le+R8hV+e+1XRcHaT5Si0qzO8+taChGeDmGWqfEaNHvyjUBRvpn+fTnt0mu+Uy9c22MhrYyXn2nDV2BbFxXvZPf+qdfwzO1svnEUW7bsBeMZ36Z4PLGvecARoe9BPwRLNb1jyuwsMxU/wzxSIIilxVXQ+lqxqUgCILw/BO/VARBEARBEPawbDbLSNdU3jGpVILWoCboC/E8bYZUqRW0Hqji0mcPSmUo5ZLVAIpMJkWnV5HJZDn/y1uU1Tkw2QxPdE4anQqDWbvaCwRAKpOSTK0s0igUGy9cJxOFmxkDRIJRzv3N9bwACsDtL/uobCyl8t5u4bUWvSFmplcyUPRmHQaLlpA/mneOZyGMoejJ/U1kMiktR2upaXMRCa4sgumMe7McTjabRSqVbn3ic8TvDfLhn59ncS6weqz36gjH3+3g0OutYvfzS2C8z50XQLlv+O4UDfsrqW3bfjm954VCKcdiNwKQy+UYH/bQfWcK9+QiuVwOh9NM+4Eq6hudyORSkvEH771SmRSzVY/JoiObyVJdV8yB408+AP8sSSRgKzYyRg7vhJeqVhfNh+uoailDZ1jpJeOssuOqd+IengMgm8mRyWSpanGRiCfzrhePpUin0o8dREmnMvRdH+Xml32E/BHUWiWdp5uR5LIM35lEJpXSfLSWhs6aHQcDfd4gn/4iP4By31DPDDa7kSOvNBS8rdaooXobr5t0euNNE/dl0pnVMqprLbh9/Or//ozlxQffJQ6+tY8T3zyMTPZifU4JgiC8rEQQRRAEQRAEYQ+TSCRoDOuzEgAUKgXyTRb596KGdhcTQ3PMTi2hUMrwzweAlcdZ4rKivFeSIx5JsDjrf+JBFJVGSccrTXz519dWj6WSacwOM3qTFs0m5XNs9o3n5psLEApECo55phYLBlHSqcxqs1u5QkZZTTGhQJSQP4JUKsFg0WGxGzZsiLub1DoVap3qyd/RDmXSGSbHFhjum2V+1o9Wq6J5XznV9Q50hue/1NHg7fG8AAoAObj2aTeVjc4n0gtH2FumhuY3HPNOLz2RIEoykWJqbIGR/lmW/VFsDgN1TaWUVxU99QyovrvTfPFRV17fj9npJWanlwiebuLQ8TqsJeuzLyUSCTKFjKJS81Oc7bPRe3mYL//3ZciBQi5jZnCOdCJN1Zp+YgqVgjd+6zi3vuxl+M4EKrWS4+/vR2fSMfdQkM5VV4z2XvDlYcl4kvGeaSb63MjkMqrbKqhqdRUMDAzemuDsz66v/luukHH517eYGfFQeq+PyNy4lyVPkFe/fXhHQeGp8QWSifUBlPu6bozTsr8c/QaPYzsMBjUymbRgkOQ+o0mDrsBn48C1kbwACsDtz7up7aiktHbzsp+CIAjC80EEUQRBEARBEPYwiURC69F6Zke968ZajtRh2MVSGU+D0azlne8eZnRgjsGuaWL+EGabDrNNj96Yv/jxtBbvGjsrScWT3Piyn3g0gUwqpbLaji8YR7LBDlK5XEpFtX3Da8rk0g1r1ssLlPIC0OqUKJQyUsnM6nmWYiOWYuPqOWarHs0LVs5nuzLpDDcujXDlqwc9bAK+CLPTSzS0lnLqzVYMxkdfQHvWMukMw3emNhjLsuD2iyDKS0Cp2vgnuuwJBM0TsRQXPu+l59bk6rH5GT+9t6Y4drqJQyfrntp7cWApwvkv+jZsnH71/CDlVUWU1Tho6Kxk6PZk3njbsTqcVcVPY6rPTCQY5fqnXes+WzwTC7iH52k8WLN6zGw38voPjnPk7X3kcjn8CyF++f87m3c7pVrBvpNNBQMauVyOqx/e4dYXD7JHe68Mc/q7R9l/uiXv3EQsye1zfXnHDGYtV399mxw5rMVG1PcyXbovDNBypA77DvprLXrWbyRZKxKKEwnFHyuIUlxiprqumJHBjQOZ+/ZXotGuD6LM3Mv4WSubzbG8GBJBFEEQhBeECKIIgiAIgiDscXX7Kwguhbn1ZS+pRBqZTErDgeqn0gg2lUzjv9cM3mI3FOzlsVMGs5b9x2rZd6SaqcE5gt5lcg8tmllLTJRUFD32fW2HUqXgwJkW6vZVEPRHUKoUFJWasVfZ+fzDrnW7UmUyKWfeaqN4k140xS4bzio7c+ML+be9l2FSiLXIQFOri+6HFgbXau+sRPWS1lh3T/ryAigAeoMavVpG15e9DF0a5MjrrTQcqMJWYn42k3wcEgkS2cY7szcbE14c1a0uBm5NrDsukUoo3eC943GMDM7lBVDWuvLVAMWlZqrrHbt+v4W4p3wk4huXScxmc7gnFnGWWTj9nSOUNzgZ7ZpCIpVSt6+CmjbXrnxG7WXRUJzwBlmO9/t7ZdIZZkY9zE8sIJFKcVbbKat1YLDo+dY/fJ2Ru5MszgVwlNuo31+JY4PPWu+Uj7vn+vMP5uDaR3eobqvAZHuwiSMajhN8qL9YMp4ie+/zM51Mw70gSiaVIbwc3VEQRa3Z/HNPJpMikz1esE8qlXD0ZB2L3iCBh0ppAlTX2WlqW59FClBSXcz8RP7nvUQiee42ugiCIAgbe7G/YQiCIAiCILwAlEoFx97toH5/JSF/BI1Ohd1lfex+EKlkmlgkjlKlQF1gZ+XEwBzXvuhl/l6fjpJyG0ffaKWy0flY93ufVCqlotHJq984yOUP7672GLHYjbzx/aNPvZyU0arHuKZZbEubC4NBzVD/LOP3MoEqq+00NJdSUbV5gEehUnDmu0f58q+uri6saA0aXvnWIRyVG2ewHDhczdyMn8UC5dvqG0toaN6dv/3zaGwof6evUiVHnk7x2f+8CqxkLqVjSYZuT/CNf/j6ao+F54VMJqXlUC0XfnVr3ZhCJd9woVN4sVQ2Oul8tZE754fI3avdJ5VJOfHuPkq3eN/ZqXQqQ2+BgM1aw70zWwZRstkcUunjB/ni0Qe9OhKJNKFgjGg4AdwLmBrVBIMrjb+1ejVtx+ppO1b/2Pe7l4QCUWKRBGqtomBTdr1Ji9FmILgYWjdmsunJZrPc/KKXy7+5vZqtIpVKePXbR+h4tYmyWgdltdsLioWWwmTS60tbxcIJosuR1SBKNpslR47yhlLmx73EIiv/z5QaJTK5jGw2m1d6VKGUY7TotjWH+8qr7Ny6PLrheHW9A6v98QMW9mIT3/j+YUYH5unpmiKZzGA0amjrKKem3oFugxKfzUfqGboxRjT0oDF926lGSjbJWBUEQRCeLyKIIgiCIAiC8JywlZh3ZYd9NptlpNvN3YtD+DzLqLVK2o/V0XKwerUHyMyYl9/8xUXSqQeNVuenfPzmLy7xwd9/BVf17uyIlkql7D/dTHmjE99cALlSTkllEdpNepE8TeWVRZRXFhGPpyCXWy1Hsh3F5Ta+84/eYn5qkUwqQ1GpBb1584Uji03PB985yOiwh54708TjSUwmDa0dFdQ2OAqWEXlZLM7nLxrarDrO//WV1X9n0hnS6SyBxRBjPdMcfK31aU/xsdXvr2R6ZJ7JgdnVYzK5lNPfPIRtk8wn4cWhVCk4/u5+qpvL8LiXkMqklFTYcJTbHjtw/rBkIsVygR33ay3MLxc8nslkcU/5GB6cZ2bah1whp6mllOpaO9ZH7GV1P9sgEo4zNb6Y1+h7ORBBpVRw+MSLFTS5LxqO031lhK4ro/eCKErajtTScbwO3ZoyhRq9mqPvdvDZTy7mZXCWN5ViKbUw3DvD1KgHnVFDZHllQT+byzF8d4JIKMaSZxmDRUdNmwtXXcmmfUl0Zi1SmXQ1m+Q+tVaF1rQyp4lhD723J3BPLBJYCKFRyymtL8E7sUBwKcL+M82M90yjWvPZ1fl6K7ZSy47+PqWVVlo6yum7O71uTKmSs/9Y7a69PqxWPdYTdew7WEUqmUalVqDYopSeo8rOd//J+0z0uokEo5RUFVPRXIZcIZbcBEEQXhTiHV0QBEEQBOElM3h7ks/+6trqAkwynuLib+7i94Z47dsHkcll9N+ayAug3JdKphm8NblrQZT7ditA9KSo1Y9WQkuhUlBev7PsEZNFx4EjNbR3VpBKZlYyLp5yc+e9yGDWgPvBvzOpdN7OdalUivReyau58QV47WnP8PEZzFre/tFx3CNeFmf9KNVySmscOCtFFsrLRK6Q4aorwVX3ZHspKJQyNFol0XuZA4UYzNp1xzKZLDevjXH5/CC5NZUYFzzLXL88wtvvtVNeVYRCubP3zdJyKxKJhOlJX14A5b50JkMskiART6F6xPfkvSibzXLl0x56ro2tHotHk9w4208oEOXN7x7K60vTdLgWtU7N8K1xIsEoZfUlKHRqzn3Sy1Cvm4n+OeqanVS1lDE/NIe93MbNL/rouz6GqWglwNV9aYg3f3iCpoPVG87LUVlE64kGus8P5B0/+FY7JpuRwW43n/781mrJS6VGyaI3yPjwPKfeaGFhapH9p1s4/l4HU4NzSKQS6joqqdlXseO/kVKp4NRbrVjtRrpujBEMxJDJpNQ0ltBxpIayit3vF6VSyVFt0qPoYbZSK7bS7ZcoEwRBEJ4vIogiCIIgCILwEolF4lz/om9dDxKA/lvjNB+sothlxT22vpH9fe4xL+lUJq88h7A9uVyO5L2+Nlv9/RQKOQqxi3VVXZOTwZ6Z1X/LFDKkMgnZzMpz2WTVorzXL2ZtWbbnjUanpr6jgvqOnS80CsJOKJQKWvZXcP7T3g3PaSzQA2JyfIFL5/L7E2UyWZY8y/jmAsyOzHPscBWN+6uo3cGCubXIQMfBKrrvFO7RcuREPe7JRWbdS1TXPZ0+LU+Dx+2n7+ZEwbGhrilaD1fjWtMPRyqVUtNWTk1bOdMjHn75p1/Re30UmUJGbVsFtY0lDHS7mRz18to7bSxNL7I456fCVLp6jUwmy+UP71DRUIJ2g2bsUqmUE18/gMNlY/jOBHKFjIYD1dTsqyASinPhs568nmFSmRSb04zJpicYSfH9P3oXW/FKWcWOV1sK3sdOaLQqDp6oo7mjnHAwhkwuw1qk3zSbRhAEQRB2i/hVJgiCIAiC8BJZ9kUIFKilDpDL5ljyBHFWFaHRqQkuFW5eq9GpkMl3t6zMkxANxZkb95JKprA5LdjLnt0O0Uw6w/iIh/67brzzAVQqBc37yqltcmK27qw2/MuqvNpOU7uLge6VdJRwJEnD/ioGbo6jUMqxFa0s1snkUmpaXc9yqoLw3KhvKWV0cJ7ZKd+6sYa2Mioeamafy+UY6Jtdd+7S/DLzk4sAzEwtEWop5cM/O8c3fu8NKnbQR0unV/HW+x0M9s0wNbFILpujtMxC875yyOWQ5XLMj3kw6VVY93D24k74F4LrSmbdl8vm8C8E84Ioq7fzBvlf/9eHDHbdK3GVSHP34iDOKjuOUgueWT+TEz5yy2FUGuW6YElwKYzfG9wwiAIrpbtaTzTQeqIh7/jEiJdwMF7wNnKlnOByjFh04wynx6HVqdA+5Z5pgiAIgiCCKIIgCIIgCC+Rld3762uc36dQyUnEU9S2uZidWEAmWx8saTlUved3fs5PLvLJ/ziP37tSz1+ulHPi/U46Tjdvq256IpZkbmKBeCSBxW7A/hj9CDKZLDcujXDlqwc7t8PEufB5H/1d07z33UPY7I/WQ+BlolIrePXtVhylZrpvThIMRKneX0VxqRnPxCIKhQyLw8jxd/fjqt9+GaTlpTDj3VPMjXsxFRmpaS+npFI0A34SlpfCjPa4Ge6eJpeDhn3l1LS6MNue38yh553BpOXdbx9kqMfN1LiXbDqDQqmkst5BQ2spGm1+H6hEIs2ceynvWCqZZnEukHcsFk+TSWcZvDW+oyAKwJx7iYoqG81tK8HQeDRJOpEkNLdE17kBHE4zd1xWDr/TQeeZlrxSV8+jrbIS5crCyzbTw3NMjeZnjcpkUpRKOa0HK5i2aVGrFZTWVJOIp9fdj1QqQfaIGaXxcAxniYEsEhYXCzegTybXl2QTBEEQhOeVCKIIgiAIgiC8RGwOI9XNpYz2uNeNldXY8fnCXPm/v0SlUaIxaJibWMBk0yO/F0xpOlhFbdve3uWfTqW5+PMbqwEUgHQyzflf3MDusm25wO73LPP5/7rMzKgHWFmU6jjdzNF3O1bLRe3E3PQSVx8qfXOfbyHE3etjvP5+x46v+zLSaFXsP1JD875yIuE4SqUcnUHN0nyAZCKNxW5EvYMdyqGlMB/+6Vk8Ewurx+5+1cfXf+8Nyht2tvArbC7gC/Hh/7jEwprFdq97iYHbk7z/d09gfsRm5MLj02gVaBRSonN+Aosh7C4LurZS1BrlunNlUgkSaX4QPZPOkk6l80+8d8rSfGBHcylxWZBIJASWogSWVpreSyQSLBoZtz7vRSqVoNGpVnp5/eIGVoeJmvbnu/RdSbkNjV5FLLw+c0OlVuDcoN9HeDm6rndZ+9E6Ru9OcPavFwmHE0hlMt7/8XGc1XaWH8pCrWgq23GGZiKa5MZnXVz+zW0GuqYxWg0ceaeDQCxFIpZaPU8ml2Iwqnd0bUEQBEHYy/Z+HQZBEARBEARh10ilUo680YqlKH/BsthlJRrPcPPCMMFAjIW5ZdQGDW3H66lscHLgdBPf+N1XOPONA2i0e7uMhm8uwMyYZ93xXDbH3JrF8kJyuRw3PuteDaDASibJrS96Ge+efqT5TIx48povAyhVcqT3FiIHut0sbVBiTShMpVZgLTKgN2qQSCTYnBacVfYdBVAAJgdm8gIoAMl4ijtf9ZHNFs7WEh7NcNd0XgDlvsW5AMN3H+21JeyO3qujfPrTSyzO+Umn0syNL/Dhn51jpGtq3bkKpZyG5tJ1x1RrAi4ymRTdvYbcZTvsXeIstdDc/iBQr9WrcJSaGb270ifFZjeivt9UPgfTg3M7uv5eZLToOP1B57pMEZlcyqsfdG4YYLQWm1BrHgT2LcVGFqYXmR3zIpPLUCjlyBVSRu5MUF7neHB9CbhqHZz6emfBbNPNDN4c48an3Sv/jw1qgkshvvzLS9jM+SXBGlrLKHKYdnRtQRAEQdjLRCaKIAiCIAjCS8ZeauFbv3eG6REP/sUQWp2anFTChU/ymwsHA1GCgSgSmYRDrzVT+QI18t3I8mKI4buFmxqP3J2k8VDNprcPB6JMDczg8wQwmHVUNJUR8D/oLeNwGEhF4gS8QXRmLYYiEx5PiEQ8tclVhZ1KJVIEl8KotEr0po17zizO+Asen79Xym2zXgHC9mXSGQZvF35dAQzcnqDzlcYtyxoJuy8SjHLj8551x7PZHHfODVDVXIbioXJSdQ0ldN2aJJlcyT6RyaUUu6y4h+fJ5aC53UXIu4zWoKGhs2pH85HJZZx8vRmjScN4rxv30ByyYiMatRxnmQVLkSG/nOTeriy5bQ37KjCYdUwOzLK0GMJs01PZ6KSsKr+0YDqVwT3qZXxgllwuS3VTKbcuDiGXyyirsnPplzexOc2rpbTMFh0arYqQP8IP/+n7+D3LqLQqSiptKJQ7y6zM5XL0XhkCVjZkOF023BMLxGNJlj3LSKVSstkcpRU2jrzauK7sZzqVYW5ygchyFK1Bg7OyCMUjZHcKgiAIwrMggiiCIAiCIAgvIYNZR8uagMCvf3plw3NzmRyzk77nJohic5opqyvBPZy/Q1kileCs3rzXRS6bI5fNFRxLF6j5HgzGWPAEyeVyqBQyLv/tNebGHtSo15m1tLzawihQ4jDS81Uv82saOGv1as781jFUarGQtFvGuqe4+nEXCzM+1FoV+19tpuOVZlTa9aWJjBv04jDbjXk764XHk8utZHRtJJvNkaPw6054ssKBGOHlaMGxxTk/0XAckzX/dVLiNPPeN/bz6Uc9RMMrzcVNRQakMikOu4EymxaDSUvTwWqKXYVLUW1GLpMSmFpk6u4kiXiK2WCU8ppi+t1LSCVrsjIkUN5YuvGFnjPOCtuGpbtgJQhx/Ww/17/sWz1W1eLCVGxi4PY4thITjsoiMllIJtOo1AqKilf+Xpl0BluJGVuJ+ZHnl8vlyKwpH6bRKqmqdxAJxjFZtDTUl+Ist+KqLELzUFbg8lKYcz+/yXj/DPdf6mU1xZz5zmFsImNFEARBeA6IIIogCIIgCIJAcotMiFQyven4XiJXyDn1jYN8+hcX8N2rx69Qyjnx9QOU1hRveluT3UBFUyljBUp31e4rX/3vRCJF150pbl0bIxZNIpFIsKok3P2qn+JiI6p7u2sjgSjeMQ96g5qoP5QXQAGIhuN4Rj2YLBtnSwjbNzfu5cM/O0f63vM1Fo5z+Te3kcllHHyjbd35lS1l3Pqil8iaRWSpVML+083PXbPqWDTBzPgCM+OL5HI5SquKcFXb0eqffV8CuUJG/b4KbqxZ/F2rrt2FQiF+mj4Lap0SlUZJIpZcN6Y361CoFERiCWRSKeo1WQPVtQ5+9NtG3FNLBAIRZHIZJU4TpWUW5I/52pkZ8zJwYwyVWrEaYJZIJFS1lbO8GEStUaHUKDnyzj4qm8se676eJzMTC1w/m/8amhiax1Js5LXvH0euklM2scjU0ByOMgsmk2a1j1ddR+Vj379UKqX5aB0X/vbG6jGFQo65SM+h11qobi3f8LY3v+hjvG8m//GMebn0m9u8/zuv7rismCAIgiA8beKbqiAIgiAIwksssBhiYdaPrchAKpnGtxAilcysO8/uND/9yT0GR0UR3/2jd5mbWCCVSFHktGArtWx5O6lUypG397E0HyCw8KBPSePBamrbVhaIcrkc1y6PcPPq2Oq4RqtkbniGwFKERDxFZZUdxb3SRIvTixz+4DC/+pMv192fUiknHowSXo6u2+0t7Nxk/+xqAGWtu+f7aT5Sh9aQH1CwlVj44B++Ts+lYaYHZzHZjbSfbKR23/PVqDocjPHFz28xsaY/RNfVUVzVdt74zkFMlmf/3GroKGfg9gThQH7Wg96koXEXFniFR2OyGdh3spHrn3XnHU+lM1S0l/O3n9/FvxxFKpVSX1lMfXUx5U4LUqkEg1FDc9vuBzGWCvTO8XuDaAxq3v17pzHZjBhteqwvcAZDKpVhyRcmm8liMKrRGzRMj3golLDl9wbxe4N8+/91mpqat/nkL84TDcZXx2vby6nbpfe0pkM1LM74Gbw5Ri6bQ66Uc/jtdio2yQjye4MM3BovODY5MMfi7BKO8qJdmZ8gCIIgPCkiiCIIgiAIgvASymazdF8Z4con3SRiKeLRJN65AIdea2E5FCcRe5CZYrbqcFU/fwscGr2amraNd8ZuxFFRxHf/8B3cIx7ikTiWYhPOmuLVHb2e+WXu3JjIu006lUFn0gIQiyYJhWJY7wVFdCYdDe0uGtrKWZj2EY+lkEolWGx6TBYdJqsepUp8Ld8NkVCs4PFENEkykVwXRAFwVNhxVNhJpdLI5bJ1dfyfB323JvICKPe5xxfovjrGqXf3PYNZ5SsqMfPB33uF/hvjjPRMQw7q2stpPlhF0XMWpH3RdL7aSC6bpevSEMl4CqlCRsW+cgZ8yyxHEqvn3eqd4nbfNKeP1HGwvfKJZQ9sVEovFoqj0Wuoanlxs09yuRwjQ/PcvjHB7PQSADq9in2dlbDFnzsRS1HX6uL7/+d7zNz//HKYKKtxFCxn+Ch0Jh1v/vgk7acaiQZjmIoM2Lco2ZZMpEin1m/OgJVSfon485PpKgiCILy8xK81QRAEQRCEl9DkwBznfnl7tf+HWqvEajdw+ZNuTn29kzm3HyTgdFk5/f4+DPcCBC8LvVlHU4Em8pFglPFhD6lkGumaBcRkMk1pVTFyxSDpVIbAUnQ1iNLxahMarYpDr7cyNzpPKplBIpWsLkB2vNqERvfsSy69CEqr7fRcGlx/vMaBYYtsjOe1nFQskqD3RuFd3rASYOk4XrcnXsN2pxn7B50ceaOFXI51fROEZ0Oj13Dy6wdoPVpHOBijd9xD3+QCqcT6he9cLsdX14Yx6NU01zmfyHxK6xxo9Gpi4XjecXORYcuSjM+73q5pvvi4h+ya3lyRcILL54ewmDVYHEb8nuC628kVstVsRnORAXORYd05uyUSiiGTyymuLMK4jSw3g0WH0aIj6I+sG1NplBitopylIAiCsPc9n78UBEEQBEEQhMcycHtyXQN1vVGDRqtCKZPw7vcOo9YocVZYUChXMjDSqTTzE4sE/RF0Rg3OKjvKXWiI7veFmBpfxDu/jFwho7yyiLJKG5o91Ng7Go7TfWmI7svDSE06xvpmKCq1YLLpuZ+34AvGeeNHJ+k+P8DywjIGm55Db7TTcKAagKqWMt788UmufXSXZV8YtVZF55lmWo/WP7sH9oKpbHZR3epivNe9ekyjU3PwzbYXtuZ+Ip4q2M9i7XgynoJtVj7KZlcawEulT+7vpdaK4MleZLYbWYonuTs6v+l5uRzc6nVTV1m8WrZwN1mLTbz3O69w/uc3WJi5F9CvsvPKNw+h3wPBwCclGIhy4exAXgBlLY9nmZrKooJBlNZDNU+87GYsEqfr4jB3Lw4SjyZRa1V0nGpg38kGNJu8prV6NYfeaOWLv762rhzZwTPNmG1PLuAjCIIgCLtFBFEEQRAEQRBeQvcbrj9MJpeSiCVpaHflHQ8vRzj/85sM3ZlcXQQpry/h9e8fxWw3PvI8xkc8fPKL28TXlA/rujGBq8rGG+91YN4DO1RzuRxXPrpLz5URAGxFJmKRBO6ReSSSktXdv4lEmplEmrqTTZSXWzl6qgGtQbN6HYlEQsuROqpbXIQCEdQ61bZ28b7MstksizN+4rEk5iIDxi36xmgNat760UmmhuZYnPOj1WuoaHBuqx/O80qrV2Gy6FnY4DVtMGm31VzevxhitHeGwa4pclmobSmlrs313PVDEh7PpNu3rfNm5v14fUHKSp7Ma6u8wcl3//AdfHMBJFIJRaXm1YD+XpZJZ/C6l4iF4xiteop28N7jdi/lfRY+TKlSEk9nqd9Xzki3m1wuh1Ilp/VwLQdfbdyN6W/q5pf93Pqqf/Xf8WiCq590k05lOPn+/k1v23ywGrlCxt3zgwR8IQwmHftO1tPQWbXp7QKBCFPuJXy+MFKJBEeJifIyKzqRxSYIgiA8ZSKIIgiCIAiC8BIqdllZ8q7fzXp/7GF910YZuj2Zd2x6eJ6bX/byxg+OP9IcfIshPv75bRLx9YtG7gkfl88N8s439u9oR3wul9v1nhbeaR/91x80kVdIcmi0SmLRJL75ZQxmHVLpg/tc8kc5fropL4CylkavRrONRe1nJZPJ4p3xEwnFUWuVOMosKJTrfzbEownmJhaIRRIYrXpKKoqQ7+Ku9FAgwuVf32Lo5gSZTBaNXs2x9zpoO9Gw6XNCY9DQeLCGJ7+kuDcoVQr2Havl87+9WXB8//H6LctmLXmD/OZ/XmZpIfTg2FdBem9O8PUfH6ekYvOeB8KLIxzdOKvpYYlk4T4Xu0WlUT6x8l3RcJxUOoNOp961963lpTAXf3GTka4pctkcCqWcztdaOPh662pPrc3EN8kouy+VzvLaNw+x/3g9iUQKo1mHtfjRNzJsl8+zTPeV4YJj3ZeGaDlcg2WTDRUyuYymA9XUtrmIRRKoNaotM1n7B2b54qt+4g99RzAbNbz9VjvlBb6rCIIgCMKTIoIogiAIgiAIL6GmzkqG706RyWTzjsvkUura8rNQErEkfVdHC15n6PYkh95ow/QI5TimxrwFAyj3jQ7M4T1SjU6jZGbUQyKWxFJiwllpX7eoPze1yHDXNFPD82gNapr2V1HdUrppiZHtCgWieX8n38wSJ19p4MvP+khEk6TTGZT35pPL5aipc5COJRm4O4WjzILlCdam321LCyEufdrDxMAc2WyObDZLcZmFMx90YrJoiYYTqDQKEpEEn/3lVRZmVhofSyQS6vdVcOobB3at3M7Nz3vov/YgeBULxzn719cwWPRUt7o2ueXLp35fOcFAlFsXBsmkV56rMpmU9iO1NHdWbnn7/tuTeQGU+6LhOLcuDPHej47tenBS2Jt0O2hArnwCpbyetIX5AH13phnqcZNOZykuMdJ2qJq6Jicy+c4fTy6XY3JglrHuaaYGZxm6PYGpyIBaqyKVTHPt4y5MNgMtR2q3vJZ6G4EWvVGNSi3HWVm05bmp5EpD993ouRUOREklCjeATybShALRTYMo9ymUim1lFI2Ne/n4s55131EAAsEYv/z1Hb73nUMUP0YmrCAIgiDshAiiCIIgCIIgvITK60t44/tHuPRhF+HlKLDS/PXkex24ah1552YyWdKpwjuOM+kM2QKLHNsx6/ZvOp7JZHGPL9D1eQ+h+w1pJdB2rI6TXz+w2ldhanie3/zFJZL3FniWFkKEAjF83mUOn2l+7P4LGp0KiURCLrdSxyyVSBOcXeKtd9twzwSQKuUgAb1eg6PYQMCzzKfdKz05lCo5h19tpONo7a5maTwJiViKL35+k9kJH5l0hsBiiCXPMuN9MwzdnaKls4L56SXspRaW5wNEgtHVYFYul2Po7iRmu4Fj73Y89lwCiyH6r60P3OWyOca6p0QQ5SFKpZxjb7RQ21yKd84PuZUm7sVlli2DH9FIgoE7kxuOjw/OsbQQwvYUdru/jBY9y0wNe/AvhjCYtJTXFlNSbn1mQavKMis3ujZ+PtznLDbheI4CxADeuQC//OkVwsEHDevdkz7ckz5OvN7CoVP1O/67D9+Z4OP/cZHichs3v+wlHkng9wapai5FfS940Xd1ZFtBlNIKKyq1gkQ8hUIpw27RkghGyWZzaE1alkIJWtpcW84xmUgxeGOUrnMDxCIJqltd7Hu1GXvZo2duaHQqZDJpwaCGTC7dlQ0L92UyWe50TRe8r/ti8STDIx4RRBEEQRCeGhFEEQRBEARBeAlJJBKaOqsor3OwOBsACdidFrSG9TtWtXo1NW3ldF8aWjfmqi/B+IhNYeXyzct0ZbNZZke9qwEUrVGDucjA7PgCd77qp/1kA0q1kjuXhrCVmIhHkqi1SgwWHV2Xh+m9OsrU4BxH32qjpqXskRtlOyqKqGh0Mjkwu3osEU0yNzDLez86gb2yiGw2S//tKe5eGSW3pilwMpHm4qe9aLRKWg5UPdL9byaXy7Ew48c7u4RMIsVRacNa/KCDuGdqEfeIh2QsSZHLiquuZMPSTjOTi8xO+IAc3hk/vjk/MoUc0ll6ro1hsxsILUdxllu5fX4AvVmLq6Y4r2F777VR9p1s2LCU2XZlUhnSycK7nmPheMHjLzuJREJxmYXisp31qMikM6vZKwXHM9lNx4VHNz4wx8f/++pqABjg+tl+Xv3aftoOVz+TQIqrxExZiYWZ+c2D3AfaylEonq/lhLvXxvICKGtdPTdAZV0xxTvoARSPJrj6URfZTBapVELyXmZlOpUmsBii5F4QJRaOb6vUpNms48SrjZz/og+7QcXZ/3lx9ZoymZR3/s5JKmu2zkDpuzLEV391bfXfPZeGmBn18J0/eAe95dH6jBWVmqltL1/pi/aQun2VFJWaH+m696Xuvd8rlHIWfSEmpxa3vE1v3wwH9lei0Ww/e0oQBEEQHtXz9a1HEARBEARB2FU6gwZd49YL3u0n6pkammV5Mbx6TKNXceTNtrxF9J2orLYz0D2z8QnZLPHgSpaMuchALBLns59eIpvJcvtsP4O3Jzj+tU6mhz10XxlFIpUglUpYXgpz/O125lNpuq6MsrQQ4rVvHaRtGzuB7wsshJgZmycWTmAuNnL8vQ7kChnjvW6y2RwqjZIDZ5qpaXOhVCmYm16i62p+AGWtW5dHqGkuRb2Liz2ZdIabX/Vz/bOe1UVupUrOq988SMvhWoZujfPpTy/nZRHV7ivn9e8fK9hoPHCvnFMsmmTJswys9CSYnVgAVhqPJ6IJMpksuRyE/FGioTgG84PyXclEimQi9dhBFKNNj7O6mJlRz7qxiqayx7q2kE9nUFNSYWVicL7guNVuwGjZnRJtwgOh5Shf/PxmXgAFVoJW5359h6ISE84KG4lYEveoh+XFEFqDBledY9dK5hWiUip4+5UmfvV5NwtL4YLnnDxUS2NNyWPdTzqVJpvJbdkXY7f4fSGGezf+vMmks8xO+3YURAn5I6vvldFgDFe9k6mBGeQKOclEGs+0j3Q6Q31nFSF/BKNVv+U12/dXoJJL+fM//pvVAIpcLsVi0zN5d4LIW20kwgncIx4SsQSWYhOuOsdqya5YOM6tL3rXP37PMtPDczQfqdv241tLKpVy7N19pNMZxvtmyGVzSKQSalpdHHu7/ZEDfr75ZQbuTDLSNQVA4/5KiiqLyG7wWbpWIpkimUqLIIogCILwVIggiiAIgiAIgrAle5mVb/3+G0wNzuH3LGO06qloKsVWYn7ka5ZX2SkuMeGdXy44vv9ILQPn+gBQapRc+OWaxtm5HCF/hN7r49y5OIxEIkGrVTE7sUA2m+Xcr27z6tcPMDE4Ry6b4+pnPVQ1lqI3bb24PzPq4aM/P094ObZ6rKq5jNe+d5hDr7WQiKcwWvWY15Sy8S+GyGY2XvRZ8oYI+qO7GkSZGJjjysddsOZuk4k0X/7sOgaLjnM/v7GuDNto1zTVLWW0Hq1fdz2ZcqXcWCqeIpddCcpkMtnVxSylWoFvLkAut5KdFA3HiceSeUEUR3kRetOj7XReS6GUc/TdDn79J2dJrGm2XNHopKa9/LGvLzwglUppP1zL9Ih3XfmcRDJN06FqgpE4cqUc+SMGTGGlaXbIH0GhlOe9dl5Ws5OLREKFsyIymSyemSUyqRRDtyeZ6J0hFFjJyLMUG3n3756iuNz2xOZmtxr4zrudjE0tcKd/hlA4hlQqpbrcRlNNCVUu2yMHz+PRBAM3xui9MkwylqKuo4KWY/WP9VmyHelUltQGZSnvS8YLZ79tRKVRotaqiEcTBBZDtBypxTcfIJVIMzexiM6spdhlIxxK8PM/Oce7Pz6OvXTzTDGpVIJKBkVFevR6FbkcKBQylKqVpZuR7mm6Lw/nvy/Wl/DmD09gsOhIxBLEwomC145HCh/fLrPNwHt/5ySe6SUioRg6gwZHufWResnASgDlV39+gWXfg2DdtS/6UBvUlNXYmfEGN729WqVEpRRLWoIgCMLTIT5xBEEQBEEQhG0xFxkxF+2s/nginmLZH0EmlWKx6/NKaukMat7+xn7Ofd7H9NgC91qOoFTK2X+kmn0HKvEMuIksx3APz+Vd12jTY3WYuX1uAJlcSjaTI5PJkL2/+J/OsuwLYXOszDcSjLM4H1gNogQDUeamFknG0xgtWpwVNpQqBalkiku/up0XQAGY6J9h+K6Dg6+1FnycWy0oSqUSpFuUL9up0Z7pvADKfZl0lumhOSIblK0Z750pGERxlluRyaV581y7udho1jKTybLkDXLw9RbO/+JW3u5juUJG5+mmbfV+ydwrf7PZ7uXyBiff/T/fwT00TzQUw+a0UN7gRGd8vCwXYb2qxhLe+u4hrnzeR8AXJhJNkAbqOiq4NjTD2a4JKl1WOlpc1FU7kEq3v+s8k8kydHeSm2f7WfIEUSjlNOyv5MDpJizPQTAll8uxOOsn5I+gN2mwu2y7UmYrHk0WPC6TS3G6rFz85W28Uwt4ppeoaXNR2+piZsSD3xvk+uc9vPc7rzxyicLtMBk0dLZW0NZQSiyRQiqVon/Mvhe5XI5rH93l9lf9q8dufdnH5MAs3/z9NzBYts7UeFQ6vRqDUU1og/dFWHmP2wmjVU/7qQauf9INwNy4l/d/91V6r670c7KX2ZCr5cy7lwC4dW6Qt3/r6JbPH7lCjlQmRftQ6cVMOotnypcXQIGVvmCDt8Y59EYberOOkgob7pGHsvgkYN2FQJVMLqO02v5gTpksM+NevNM+pFIpjgobjvLtvUYG7kzmBVDuiwXjWOLpDXuw3Nfe5kKtFlkogiAIwtMhgiiCIAiCIAjCrkunMgz1THP70gg+bwipVEJFrYPOk3WUr1mAsdmNfPC9w8zP+gksRZHJJDicZqz3Fldf+eAA535+E+/0gx3EZrsRo0WPXCljcd6P3WnB4/Yjk0mRK+QrteeREA0nqKgvIR5d2X17f+F3uMfN2V/eIbZmV25plY3XPugknUgzO7lQ8DENXB/jwJmWgotDxaVmlGrFavmVh5XXFmOxbb5AmEqmQCLZdp+BaCi24dhmWTEbBTnsTjPH3mjh/EddaA0aoqEYEslKVkj7kRpmRj1YHSYymSypTJY3f3iMZDRJKpGipMJG69E6Khqcm87Z615i4MYY431u9EYNrcfqqd1Xsdqgft2cSq3YSx+9GbKwPRKJhIZ9FZTXOhganKV/aI6UBOYCkdVFzIlpH5NuH68ea+BQR9W2AynDXVN89pfXyN2LkqaSaXqvjbLkXebrv3NqtQzRXpSIJbn2SRdd5wdIpzLIZFJajtVx/P1ONAVK4u2EYYOSXI5SM2d/dh1LkZ4lT5BMJsvw3SkWZpY4/Horc+MLjPe6WV4MYVnT/+hJUSjku9b7ZHHWT1eB3lq+uQBTg3O0Hlsf3N1MNBwnl82iM24d/NDqVew7UsPFz/oKjhtMGlyVW/cbeVjnK03kMlm6Lg2RSqRZmA0QjiQoclpYWgrnBbpHetwcfr0Fa/HmmxGKy204Kux4pvI/i2QyKalU4WyZgVvjHHitBblCzuF39uOd+ZLkmmDLvlONOGuKd/z4NhLwhfBMLjLa4+buxUE0OhVSqRSZTMrJrx+g41TDpoGUVCLF8L0SXg+TSGBpZglbdTHexcLZKAadivpax648FkEQBEHYDhFEEQRBEARBEHZd941xzn/cvbqAlM3mmBieZ3ZqkQ9+fJyyqgeLVXK5DFdFEa6K9dcpqyvhvb/3Kne+6iMajqPWKtEaNEhlUpLxFI4yKwarAYVKsdKzI55aXew0WXWEAxHkSjmWIgP2Ugtz0z4+/ev1Za5mJ3yc/dUdTr5VONMEQLLJorGlyMCR041c+Lhn9Vy7wwiZLNlslvrmjYML/oUggzfHGbw9gVQqpelgNY0HqzBusSu7vK6E+clFzHYjEqmEaChOyL9S8sdRWYSl2Ii/QDmUmrbC5bAkEgn7T9Rjtum5fWmY/hvjyJUyXv/mQe5cHCSbY7UMUzgYo+1ILQdeaSCTzqFUybfcebw46+eX/8+XhJdX+twEFkK4R70c90c48lb7prfdq/xLEWZn/CSTabQ6JWUuK/pHWFxPxFOkkik0OvUjl0naDclMlotdk0Sihcv+5HJw7sowxUUGqsq3XnBOxFPc/mpgNYCy1tzEIjPjC9Rt8Hx8HMu+MCNdk4z1uFFrlTQeqKK6tXzDYN1Ghu9M5vWXyGSydF8cwmQzcPCNtseaY2lVEfZSMwuzgdVjCqWcpfllUsk0OqOGpbkHY4HFMIl4CiQ8k4bzuyHkj5DZoKRWsEBGwkb8C0F6rowwdGeSbDZHdXMp7cfrcWxR4qx1fyVLCyH6707nHdfqVbz1zQPbKvf4MI1Bw8kPDtJ6vIFENEn/rQl8C2FSyfWPM5POkE5vXlIMVsqEvf6DY3z1s2vMjnsgB1aHmQOvt/LFX18reBsJD54TFU2lfO8fv8f00CzJWAq7y0Z5YwlK1e70n5kcmOXTn17GZNPz6f+6AoDJpsdZVQTIufDLmxS7rHkZK4XnvDGdVsXJk/V8+lkvoUh+9pC9yMibr7dg22JjgiAIgiDsJhFEEQRBEARBEHZVYCnC9a8GC5aaSibSdN8Yo7Ry+yVxzEUG6jsqCPnCKwvwEine2SUiy1He+N4Rbp4bXG1y7qqxMze5iEKpQGtQ03t9goYOFyff24dGp+LWxeF1AZT7ZiYWicVSlFUXMzPmXTfefKhm0znvO1yDWqPkzpVR1CoZ1z7uIplIU+QwsuT2MT++wPH3O9CsKYkT8kf46M8vsDDrXz12+aO7TI96eO/vnNx0t3tJdREavYbrX/aTSqQoqymmts2FUiWnrKaY1753hI//x0UiwZWMFalUwv5Xm6hq2bgxu0wmpbaljJrmUuI/PIp/MUQsnKBxn4uZiUU8M36sdgP17eVUNTqRyWXIt/mLYuTuFP6FILFIglgsiVQqRadXceOzHuo6KrDucFd9JpMllUyjUMgeuSb/o0qlMty9PcH1y6Mk1jQG1+lVvHKmmcZm57ae37FInIFbE/RcHSUeSWJ3Wdh3vJ7q5tJnslA+Nbu0YQDlvlwux+CoZ1tBlPByFN8mfQ0CC6Edz3EroUCEj/7sHPNTvtVjYz1ujrzdzrF3O7b9d83lcvReGS441ntlmH2nGlE8xqK0WqPkzW8d5POf38I7s/L61+iUeCcWKK8pRq1RYi42Mj+5+OCx+SPIFXKqW0ox2x9kMwSWwsxM+ohFEqi1SlxVRZi30cT8adObtMjkUjLp9SWaDNbt9VIKL0f5+CcX8bofvGf2XR9jcmCOb/7eGYqcG/cc0ehUnHl/H41tLqYnFkkl0hSVGCmvtj/23+t+gDmwFKbrykjecbVWSTQUR6tXkYgmyWazW5ZiKy638a3/400W3X6y2Sw2pxmJVMqd8wMs3guuKdUKZHIZ8WiCpkPVede0l1mxlz1eFl8sEme0e5qRrmky6QzOajsKuYyf/z9fkMuBs/pBZsuyL4xGr6bIaSabzTE34d00iKJQKajvqOTGl4Uzgxr3V1JdZefHPzqO273EUiCCVCLBXmSgrMyCepcCQoIgCIKwXSKIIgiCIAiCIOyqhbkA8Vjhev8AE0Melv1RzNtcNOu7PMTZ/32ZWDhBJBhFa9Tw2g9PUdnoxGDTE4ulGO6dIZeDdDpL04Fq9p+sZ2rIw/5T9Rw600xNqwsA78zSxneUg0goxomvd/LRn50nFIiuDtW2uajvrNp0nnKFjJbOSkrLrfz0P32MtciASq1AIpWQzebouTqCzWmi42Tj6m0mB2fzAij3uYfnmR7x0LC/suB9RcNxLn/YhWfGj0qjIB5NMD3qIR5N8MN//A5avRptvZMf/JN3mRtfIBlPYXOacVQUbSvTQSKRoNFr0Ogf7MxuOlC95e02srwU5vaFQfpuT5LL5tAZV7KJPLMBbPYIS/PL2w6iJOJJxgbn6bs9SWApgs6gprWzktomJ9rHLLG0Xd13prhwdnDd8Ug4wacfdqFQyKit37zUTCad4fLHXfRcGVs9NjU4z8yIl3d+fJy69t3P0NjKlHuT18caoxMLRGNJtJrN+xEoVXKUKvmG/T+UW9z+UUz0z+YFUO679WUfdfsqtr2wnMvlNizPl0qmyWSyPO4yrr3Uwrd/91VmpxYIL8fRGlSM3Jlk8NYEsLIAHw3FCS6tZGloDRoUChmH39y3Ggwa6HZz9sO7K1kq96jUCk6/005zR4H0vmfI7rLSeryervP5rx2z3bBlKcD7pofn8wIo90VCMUZ73JsGUQCUSgWVdQ4q61Zen0ueZebHPCxMLuCsdqAv0Bclm80h2WYGkKumGEe5Fc/0EsVlFiaH5xntcQMSXLXFeGf8HHtnH52nGrZ8L1YoFetKcL36rUOc/9Ut5AoFC55l4rE0Vc0uSne5tFUynuTc395cfS5GQzE+/YuLnPrmQZYXw0ikEjT6IOlkGvm9DC+/dxmrw4hUWjhQ9rDG/RWMdE8TWMwPplodptX3P71ORVPj9p4bgiAIgvAkiSCKIAiCIAiCsKsKle5ZK5vNkctuvcACsDQX4Ku/ukIqkUaukGGyrez2nR2Zo/NMM1KplIqGEk5/cIBUIoVUJkEikeL3RbCUmIhHE/jX7HbXb9CH4D6VWklpdTHf+6N3cI94iEfimO1GymocqLTbW/BdnAuQjCVRFzi/98ooLUdqV3sMeKbXL/auPvb55Q3H3KNeZicWUWuVlFQUUVxmBQlIpVL6b05Q11aOTC7DaNFvWRbsvlwuh9cTZHp8AZ8vjFqtoLyqiLJyK6rH2PXrmfHz4f+6ypIvzPLSSrmxYCCKRqfC5jASWIrgv1eGbCvJZIrLX/Rz9/r46rFwKI5nNsDU+AKvv9+BRvd4za+3ElyOcf1e4+hCMpksd25NUFldhHyTDJn56SV6r42vO57JZLn5VT+VDSWPlenwKNKZrUsNwcpzJZvd/HUOYDDraD5Uze1z6wNOSpU8r6zfbpmfKNzTKJ3K4PcGtx1EkUqlNB6s4vKv76wbq+uoRP2YTdbvU2kUVDeWrv5bLpUyfK9MlUIpx1XnIBo0ggQOv9FKdXMZWuNKcNM9uchnv7y1bsE6EU/x+a/uoDdq8npQPWsSiYRj73RgtOjpubzSQ6SmvZx9Jxsx3ntvf1goFMM94ycUiiNXyFgY9a70vSoQ0HCPeDi6g9KAQzfH+Pynl4iF4mSzWSwOE+///TM4axzkcjnc0z5Ghj1MTiwilUqpb3BQU+vAUbJxwFdn1PD2bx2j5+oooz1uxnpn0OjUFJWYMFp1ZLM5Ln/Uhd1ponKbgaO17GU2TCVWrn/RTyaTQaVWMj7sxbd4lfd/dIzSis1Lmq3l8yzj8ywjk0kpqbChMzwIms+OLawGUACCvhCZTBbfXACpTEo0FMNcZCAZT60GUbKZLLlsDqSsfCZtweYw8fXfPslQ1zTDXdNIJNCwv4L69gos9sLPB0EQBEF4VkQQRRAEQRAEQdhV1iIDCqWsYE14AGeFDeO93b7RcJzJgVmmhuaRySRUNJVS2eBEdW+H+uLcUsHd4O7BWYK+MGa7EZlUylyBnef3qdcsqtc0Oem/NVnwPI1ehUIho/vqKKr/P3v/GRxJeqd5gj/30FoHAgGtkRCZSK0rSxdZVdRkq+menu6d2dmeudsPN2Z3Z7Zf9j6unZ3djd3Zzu7siBYzPd0kmyyyiiwtUmsFJJDQWgRCax3u9yGQkYgEUpQgm+z2nxmNlu4eb7zu4e4g/8/7fx6Dhta+xqeKLrvxuNXrAPlcgUqpUhNRjJbHe/DvJsI8ILQew2wzoNNryGYK5NIPLZgCS2HSiRy2z+EXL8syY3eWOfvxeF1B9vaNBbp6Gnj+lSEs1ifnBewW8ixJEtc+m2RhOoDX50CtUdXs1HJb8x441s3cTJA9B9oxP+F6AKwuhOsElO3MTqzT0dPAwGO6d74qNgNxco/prHjA6nKESDj9xGJrLJioFhx3IbgaIxnL4PLZv8xUPzcel4Xpuc2nHue0m9Drn03g2Xeil1gwxeLkem2bVq/hxe8e/pWcn+kJ9+nn7XzpO9jJ0uQG69uuiafZwdCJ3h3H5jIFCvkSBpMO3TNem91o7fXx4veOcPGXd8hlCqhUIv4uL8998yBdWx11D5idWH/siv9KRWJ6fK0moiQTWQr5EhqdGrv92boAfxUYLAYOvjTE8Mk+KuXKYy0LZVnm/uQG5y5MkclU32+CAA5BYHklSqPPhvaRjJvdukgeRzyU4r2/PMfmSpRYJEWlIqObDyELIn/wf3uTyckA589O1omFkcspbt5Y4Guv76O7x/fYsR1uC/tP9zJ2ZbZqsahVI27rOpFlmcWpQJ2IEo+mScZzaDQi7gbbY/N75u+vMze+jrOh/t2STeW58O4o3/mT00/N/imXKtw+P8WNTycoFat2hGabkTPfOlC7x8Ib9d0+ha13niBU5y+KAtHNOF3DLWxsWc6Z7SZUahUDR7rwdz1biL2zwcaxV2wcfmEPwK/dmlFBQUFBQeFZUUQUBQUFBQUFBQWFrxS3z8bw4U5uXdyZJyCqBPYd6USlVpFJ5vj4R1friqsTNxYYOtrFqTf3o9VrHxuEq9Fr0G4VKn2tLsxWPelkfueBArRuK+a0dDVw4FQvty5M1x0myzJt3V5+8V8u1oqSJqueF79ziI5+P58Hh9eKqBKRKjuLm83dvppABNDa28itTyeoPHKsRqt+bBFKlmWQJMLLIWLBFA2tLpp7GwmuxymXKqi1ajTaaiEqFc+ysRyhXCrjcFvwNjt3tZBZmg/y2Yf3du0umJvZxGTR8+Kru6/wToRTjF2eZvLGArIk07W3lX2n+nA12glvJFiaCZDNFJm+v8Hz3zvK3fP3Ca3F0GjVtA000X+0m6mJDZKx7FNFlIXpABabEUmSyKR2/t737678ykWUUunpXVRVa7knd3Won1Do1GjVqDS//mJie4uLG3cWcJsNSNkiolqkqFYRitd3Cg3vaUL9DLZwAFaHidd+/zjriyFiwSRavQZ/u/tzZ+A8K239fm59NrFDXGhoddHY9uydL9lUnvW5TRo7PHia7AiCiK/NTXOPr04ozCRz3L+9xL3rC+SyBcw2I3uPdtK/rw2d4fOLKYIgMHCki+aeBiIbCQRRwNvkxGipFxsq5QpLjxG83B4zakEgGYyzvhRiZjbExOgKhUIZrU5N/4CfoX2teJ8g8v0qkGWZcrmCWq3aen8//vrMzG7y/odjde8kWQajy0wmW2BtPUZLs/Nht5cA3UPPboG3MrPB9Ngq+VwRQRDQGzQIyNy7PM3otQUu3VgEIJctVrscRaEm+Hzw3ih2hwm3+/HdEqVCGVmuZt8ACKKAw21BZ9BQLlfIbQlDmVSeO1fnGLuxWLVkE8Df7OTo8/11f7ugKkpPbOsOeZTAapTNtRjNT+k+mh1b4fL7o3Xb0oksH/zNFb73L1/E2+zc0QVntBpIxjIsTKxx+OUhzr91k5XpAHtP99N3sJ3gSpTWvkYGjnTR0d/0uUPsFfFEQUFBQeE3HUVEUVBQUFBQUFBQ+Mo5eLoXlVrk7tX5WmeGzWHi2It76OirruBduL9WJ6A84N7VOdr3NNE52Exjpxdfh4fAQr1Fz8jzA7UuDrPVwJk39/PBj6/Vd78IcOT5Pfi3LINkWWZ5JUxRK9JzqJ3QegxBkunqb6SUKzF+fb6u8JpJ5nn/b6/yg//hRVwNz15sbGhxMnysm7sX64UarV7D4NGuOhsaf4eHF753hAvv3KrlRpisBs58+9Bj7VBmR5e58NZ15u6tIssQXo8xO7rM898/yvpShD0HOzBaDEyPLnP27du1Yp0oCvTua+PEa0OYtxWBZVnm/r21J9ozTYytMjzShsdrrdteyBX55EdXWZ7eqG0buzTN+sIm3/rvX6JQKCNJ1VXL2XSBu7eWad3bzuCJPiqyzMpihPSWGPK0fIBMIktqM07g/jIarYbWfj/pQqX2eYB0MkelXPmVFuTM5qfbOGm1aoxPsXvyt3kwmHS132c7vSOt2B9jb/SrxOexMeRz8NZ/+ox0MgdAU7uHoZeHWN7K5WhtctLR8uyWQVDN6Ojo939uQfKL0NTVwCu/f4KL79wmFcuAAM1dDTz37UN1AuaTyKVyfPw3l5i/t1rbplKJ+No8dQJKIVfi7C/vMntvrbYtFkpx9p27xEIpTn997xe+F59mxSeIAmr1zv8739xs5/ZHY2wuR1BrVNw9O8HgmUHKW++2YqHM6O1l5meDfOO7B2lotH+h+W0nk8qzshBibSGELENTu5uWLk9NFM1lCyzMBrk/ukIqkcXqMDEw3ExbVwOGXTruSqUy124s1N5J5WKZSrmCWqchlMrx/HcOcfEXd8hkCthsRtQaFYdfGqR9z7PfX4l4hnyuiEajQiUKhFerXY8Wh4mVxRDJWBpRELAatGDUsrGZZH0liq/Zgc1hYmkx/EQRxWQ14PRaCa3HMJp1WO1Gxi7PENmIY7IZePH7R0knslz8eILJ0Yf3GTKsr0R552+v8a1/coymbcJfpSztvlhg22cLT8gjq45RYWxb8P12SsUyyzMBvM1Omjq9aLTqWqeK1WUmFkySzxRwuM289IMjrC2GMJr1tPT4eOV3j3/pIHsFBQUFBYXfZBQRRUFBQUFBQUFB4SvHYNBy/MUB+ve2EIukUatFPI12DNsKywvjq4/9/PpCiM7BZnQGHa/84XPc/vQe86PLaHQa9p7ew+Ajdjpdg01813aGhfsbBNaiWOxGuvb4aenyVrtesgXe/2CMd98bpVisijpGoxajUYfgNGIslGs2U9sp5kuszG1+LhFFFEWOvboXZ4ON8atz5DJ5mrt9DB7pxN9Rv7L4warzpq4GgmtRRFGgodmJ+TF2O+VSmdufjqM3aPG1uggsR5DlqtVKYCFI+0Azg4c7WZ4N8N7fXAUeCiOSJDN5exG9Uctzb4zUtmczRVa27FgeR7lUIRpO7RBR1ueDdQLKAyIbCVZnAjS0e9Dq1JgteqLhNJWKxML0w9XzFpuBckXC53fg9Dy+YFzIFvnkh5e5+t4o4c1qVsz07QVOfOMgOr2mFqrt9tl+5SuaG/12GhptbG48PrNmz1ATDueTLZOsThMvfu8wH/7wap0FnK/Nxf7TfV/ZfD8P4bUI4x/fw6zXkEvnqUgya4shNBemaDneg7fBzqkj3ZhMu1sw/abQd6CDlh4fkY04aq0aT5MDtebZ/6/v0tRGnYACVXusS+/coqW3sdYVsrYUqhNQtjN2fYGeoWaafkWZJKIo0jfcRPjjh/ehy23h7ifjbG7ZG+qNOtaXIsR+co0T3zvKxubDfKh0Ks+t6/N87Rv7nykw/XHEwik+/OktAqvR2raJ20t4/XZe+e5B9EYtn747xtzUw/dEIp5lZSFE32ATZ14dqvu7ALARSLC5mUCqSEQ3E4TXopRLFXRGLQ0tbiSfjRPfOYxNr6G91YW7yYHH73jm88hlCoTCGfwdXmIbMdbng7V9Iy8McPXyHH6/jfhymEs35pFlmZ6Rdvr7mrg3sY5KpWJlOcLBQx2P/Q6tTsP+07188KOrWB0mPv7byzyIC0sncoxdnEYQBFbW4rt+vlQsM35rCX+rq3Zeao2Ktm4v4UCcUrH6TpYqD9/xKpX4RDs7gGKhRGpbZ5moEmhsdSOqRUJrMRJbYqnb7+Dl3zvGZ393nVymgM6go+9AB3tP9SKXy/g7vbz2R6exuS2f6/7JpHJkkjm0Og32J4hQCgoKCgoKv2koIoqCgoKCgoKCgsKvDIfbguNxhZJnLLw4fXZe+v1THHsji1qjQmfYfYV/Q7OThubqStj1+SAL4ytMXpuhocVNpFTm52/fqgu9z2aLZLNFfvnLOxw53IXTayEeTO0YNxl9ttDz7eiMWoaP9zBwuLNa/HvKCniby/xMGSb5TIHYZpJUPEsuncdiNyJLMmqtGpUocPqNEaZuzjNxa4mpm/PYPRYcDXa0uof/s3/ixjxDRzpxeqqCiCDwzL/FoySfEAifimXYc7iL/n2tZNMzmC060qn6rovhwx3Ewhle+eYIGu3j7V9W5wLM31vBajMQ2Uwib4lDo2fv8/V/9hzRSAZRLTKw79ntfL4oGq2aU8/18c5btygUyjv2O1wm9u5/NkuxrqFm7B4La/NBivkSNpeZpk4vxsfkRPyq2VgIIUkyDR4rdqsRrVmHzWNDkmQOdDWy50D7jhyK3xTSiSyFfBGj2YDBpMNoMTwxc+hJhFZ3z1hKRtPEQ4maiLK++PgsJlmSCa7Hf2UiCkBnXyP3bi6R2CqKa1WwsVjt2lOpRGRZRpJkMskc5ezOjqfZqU0ix1KUCiWWpjaIBpPYnGba+nw0tXueqTh+88J0nYDygOB6nBtnp/C0OOsElO1Mja/hb3Gx92B73fYHz1UinCKw8FDgKGQKrExvoNKoKJUqCH4HA0e6njrHRykVywTWExz/5kFuvHub0FoUjVbNwPEeLB4bRiHF0u0F1heCVLbs+yZvzFPMFfG3NRALp+AZXpk9wy0gy/zsP3yGRqehWCxjNOtpaHaiN2q59sk9+o/2ks3s3j2yMB0gncxjsRmQZZnpmwvcvzjB6MUZ7F4bI88PkMqVa5lYXYN+vH77E+ekM2jxd3gpl8pYnBZyuSLXz02RyxTpHWrC7nm4YKBnXxsNLS42V6pivbfJif0LBr4XCyXGry9w+/wk6USuait3oJ39p/uwOZ89v0tBQUFBQeHvi9/M/wWsoKCgoKCgoKDwD56uwWYWJnZfxd3UuTMPZLuNzpOYG1vmvb84R7lUQZIkbnw6QbJUoae3kelHbMGg2g1y584Sb7w0uKuIYnnGsGJJkliZCbAyvUEhX6Kp00tbn/+xwcmPfjYRqhbm7B7rY4uXOpMOQSWwPLXONj0IUSXS0OZi7NIUgcUwgdUIxUKJ4GqUXLpAa19jLdi4WCiTTeVrIorBqKWt3c398d1/CwCNRoV7l+LZk67Ng26ag6d7SSVySBWZYCBOIpZFlmUGD7bjbLCxZ18zXf2Njx0HqsVUAKNZT2Ork42VKHqDhlQ4ydSNeUavzNHa7aWzw0NLh+dzdR18EVra3Hzr+4cYH11lemqDUrGCwaBlcG8zA4NNOJ9BEHuAq8H2uTqdfpVI0kM7u86hFuanN7ny4TiSJBPeTDN/f51TXxvG0+j4e5xlPal4ltErs0xcnyefK2J1mNh/upf+/R114uHnQW/aXahVqcW6rIjtouxuyE+wyPsqcLotvP6Dw1y/MMPC9AayJCMIAhabAbvLzNLCtg6z3aYiwPJMgCsfjNV14t2+OM3z3zzA0OHOJ35/ZDPJ9GM6cQBi4TRLy48XmgBGby2yZ7ipTkTV6tTIkkxkI77jeFmSSMXSmKwGrNYvJjYaTFrsDiPpdJFsSebYNw9TqUisLIQIhuY5+PIgf/13VzE9IsLNj69yZl87gWAC/5YNWqUisbEQJLgSRqvT4O/y1ULf08kcuXSe4EoYWZLw+Gw4vTY0W/dlKV+mUtopxNbOVQZJrj6Ty5PrfPBfzlMpV/A0OQiuRvnkby/x8j85TS5ToLW7gWMvDSKKT7ZFXJxYI7IeJZPMYQgmEUSRvkE/d64t8Iu/vcqtS7No9Br2Hqn+9lanGetXIHKMXZ7l4nsPc1iKhTKjl2dJRDJ8/Q+O1zLOFBQUFBQUflNRRBQFBQUFBQUFBYW/F9oHmugaambuEducvSd6aXpMqPrTKOSKXHvvLvlsgXg4RSKSpiQKhKIZXhhsxmjSbRU+BfL5ErIkoVarKJcrbETSGMy62qpeqBbzWroaHvt9sixXvfo1akYvTHHuZzdrhdN7l2bo2dfKCz84hmGrKJtO5FhfCpHLFjFbDfjbXKRiGa6/f5eF++sIgkDX3laOvDKM02cHIB5OEQ0mUWtE7C4LvlY3gigibwujtzhNGMxGLrxzF51Bg7fJyfp8iHyuSCqeIZPK1wQPlVqs64wRBIGBvS2srkTJpPO7ZqMM7m3F5bHu2O7v9NLU5WVtLli33eG10tJTzb6x2Iy89v1DDB3uILQep1AoYbUbcbjNeP2OWvDykzDbtsQaoVo4Nhi0te4No1lHR28DRrOei7+4jcVppu9A+1PH/LL4m5z4FIQksgABAABJREFUm5wcPtpFsVzBoNdgeYqVzm86Da1uRFHA1eRk9OYSG1s2bwaTHq1Bw8pciPd/dJ1v/fEpLLZnExefBVmWCa3HSafy6HRqvE0ONM/Q8VIsljn39i3mtgmAiUiaz966RblQ4cCZ/i80n9Y+P9c/GKvlQTyg90A7bv9DAcnX8gQxSQDPU7oCvgq8jXa+/r2DhDeT5FJ5YsshCpkC+Xw1DF2SZPRGLWqTDtL1HQ8et4UL796FR555qSJx/hd38LU4cfsefw65bGFXG8QHiBoV0Uh619yTB6QSWXLZYp2I4vNacbnMTJd3H/vBd3Y/4d38JDRaDUOHOhi7MU+5XGH8kbB2o15dFbJ307JlGZNRR0urqyrSf3CXq+/drb339WYdb/zpC9g8Nt7/rxcpl8po9RpSsSyhtRiFbJGmrgZUahGTWY/6Cfd5S7u79k6ZH1tGqkgIgoC7wYbZaiCbzlNIZvjunzxHQ7O97hqmYmnW5jYp5kq4mxz42j3M3VvlP/7PP6Z9oJkbn06wNh9Eo1XT2OFl/5k9aHQa1pcj/PQvL9LR14DF9mRLwmclGUtz6/zUrvuWpjdYXwrT3vdkIV1BQUFBQeHvG0VEUVBQUFBQUFBQ+HvBaNbz4veP0L23ldWZACq1ipZeHy09PrS6L7YqNRVLE1gOs74YIhnNotGoKKlFSsUy86Mr5HUawuE0Gp0ar89GsViiUpYw24ykcwXsek1NRNEbtbz03UO4dwlerpQrzN5dYuzCJJlEDn93A6lYdsfK85m7y3Tva6N3fztLMwE+/unNWmA3QGefj42pNbLbwtGnbsyTjmV440/PMHlriWsfjVHIVTMzeva1MDexysu/f5yZO0vEg0la+/1YXRZ+9p8+Q6dVk00XGHneSKFYRqvTUiwUKW8rBncNNOP2VVdKJxJZVpYiLM4H0ejUWNVGjEYdoc0Epa1CZW+/n0PHdrfM0Rt1vPS7x7l7bpLp24tIskznYDP7zwxgcTwswGl1Gjp6fXT0+j7Pz1mjqasBT5OT0FoUBBBFgUK2wNHXhpHKUt2K8fvXZn8tIsoD7E/JPnkaawtBVqY2SMezeJudtO3xY/t7CJUHaOzwcvrbh5keW60JKFqdhsYODypVNWsmGkyxthCmf6T1K/nOeCTN1U8mmB1fo1KWQAB/m4uTrw3TuEuAfaVcYWMxRGwzgSTD1N2lXQPWb5y9T/dwC9Yn/D6lYolkNINWp6m7Xxta3Xztj09z+Rd3CK/HUGtU9B3q5Mire+u6xJo7PDR3ulmd35kp1DvcQkPL04O2i4USKpX4pbJ8RFHE22iHRnj9j5/j/f9yAahmDhUKZU5/5wjhWHbH5ywGNZtlGWGX5oVSsUxgOfJEEUVn0KBSi9XfbRdkSXqigAJVe7xHhQSdTsPxY91M3F4gvBYDquKvyaQnny9ishppbXHS0vzFg8x7B5uIBhOUjnZz4Rd3attFUSC+meTQqV7uj67Ufcbtt1MCvvuDI2iA25+O88v/9Bk6gxajRY8giuTTBS6/c5uh03sILIURVSJDR3s4+9YNoGqBaE9lsTrMDB/vRm0xsb4a3zE/lVpk8GB7rbMkm64PlNcbtNX/6LU0d9Zbxm0shnj3P39KasuKUhQFDr46zOJUAKfPwc2z99lYCiMIAuVShVQsw2dv3eDNPznDxmqU1YUQc/c3GDnW/YWv73aS0Qy5zE47uQfEQylQRBQFBQUFhd9wFBFFQUFBQUFBQUHh7w2DSU/f/nb69rd/JeNpdRoqZYlktFow1OjU5IplioUSequeTDiNKAqUCmXWlyM0tbvJpvO4vBZaOr08f6iL6GYCnV6Dv92D1bF7AXbqxjwf/vWFmkXO0v01isUyA8f7qoX+bazPB/E0u/jgR9fJPZJLkI5nuHtpho5+P2rNwyLq2twm928scOGXd+pseNLxHEuTG5RLFfydDXQMtaDSqPnwby8jimLN4mt+bJmhQ50sTgcoFoo1+5iGFidHXhpAEAQCG3Heffs2iW3F1Xy+RLFQ5uRzfZjMOhr9dhqbnU/MwXB4rDz/vSMcenkISZKwOr56f3uTzchrf3Sa0QtTzI0uodVrOPHmAdRaNZFAfcB7OlG1C/syYdm/LqZvL/Lh31yuFaEnrs/jvGjh6398Bpfv12/xJQgC+57bQzxZoLnbhyiKGK36HXk14Ueu+RelWCxx9hd3WJrefLhRrmaN/PK/XeW7f3q6LlOpmC9y6e1bjJ6fRJZlHH4nS+NrNPc0onukWJ/PFomFk48VURbGV7n24RjB1ShavYahY92MPNdfsw3sHGqlqctHPJREo1XjaLDtuKcMJj0vfusgd6/Mcf/2EsV8Cb1Ry/CRDvYe6Xric7M8u8nk7UXW5kMYTDoGDnbQM9y8q/1fPlckny2iN2jQG3e3GntAa38Tv/t/eYPAUohELMvyWpxAMFUTRR+g1qhwus27CigPKO6S+bMdd4ONzr5GZh5jBWi1GWlodXH3xsJjx9gz3IJxl3Pq6/Xxg98/wQ//6jx+rw25UCK2maStzcPQ0S6OnunH8AxdbI9Dp9dw/KVBWju92J0m1uY2AYFsKofJYuDr//Q5pP/8KcGNBNlMAavLzGt/dBrUGhwmLT/6/7yLyWZgbX4TAQFPsxNPsxNRFNlYDNLYXe2SkSoS5VKZ41/fx51zk+QyBUqFMr372zn+9X0gCAgCTD8QEQGbw8iJlwdo73nYadPW72f29uKO8+h6RMyslCtcffd2TUABkCSZ82/dwNfuxWDWVW3Stv9dSWSxe63Mji7ja3KwsRIl+wTR4/Oi0alrXVG7odUrZSkFBQUFhd98lL9WCgoKCgoKCgoK/2CwuizsOdxJJJAgnciBIKAWBDRaNWaPjeRcCJ1Bi1SREEWBRDSD12/HYjMy0OenpdNLy7Y8lkwyx8rcJpsrUdRaNc2dHjyNdm59cq+uCCWIAoHFEAPHenbMSavXsDwX3CGgQNUTv5AvkU3n6wUbWa4W9R6pOcXDKXr3txNcjaDVabBiZm2xWsD0NjspZIvkMgWSkTQLY8u8+P2jSLKMp8WFp9FOU6cHo0lPLlvgg1+O1gkoAHq9Br1ew/3xVb73e8dobNrdrkiWZYqFEhqturZS2vwV2jvthqvRwQs/OMbRr41QLpf52b//ZIeAAtA52PJbIaBkklkuvH1rxyr+aDDF/etznPrGgb+XeQmCgMVuxOF9vIjzRbNGHmVjKcLSzOau+zLJHEszm3UiyuL9Ne6eu1/7t6gSyabzRAJx/I/mKAk8NhtndXaTd/7zZ6TiOYrFMqIokHr3Drl0npd+93jt/tEZtDS0up94DnaXmTNv7GPfsS4K+RIGkw7rU3KU5sZXee9vrlLZsqtKxbME12KsL4d54VsH0W3lQ+SyBabuLDN6dZ5MKofRomf4cCd79rdiMD0+D2R7jkXjUoSrl2ZYXY7WMlz8LU6OHO9GLcjcuzqHwair5jJFM3XddHb3kzuiBEHg8HO9hDcTxMLpun02h5GDp3oRVAJzUxukU/kdn7fZjfQPNT127CNHu7EbtPzof/2QaDiFWqMmF04xdWGaFr8D26EnZ7Y8Da1WjU6rQiNAOZsHQeTQmT3sP7MHrVGL3mJgYctuUmvSodaqGdjfxgd/eY5ioYRVXb3GMjLB1QgmqwGz3YRGp0GrfyjwJCJptHoNJ9/cjyzL9O5rY+S5/tp99sq3D7DvaCfJeBa1WoWvyVGzgHxA53ArK9MbTN98KEi1DzbTM9JRd1winGJ1OrDjXEVBoFQsUy5XENiyKtv6qdVqFVJFJpvKY/UbUalE7M6Hv308kiK4Wl0c4PE7cOxi7fgk3I0OWnp8LE1t7NinM2hpbPfs8ikFBQUFBYXfLBQRRUFBQUFBQUFB4R8Mm8thisUyFrsRq9OE1W1hYznC/tdH+PTqHKJYLVpVKhUEQYUkSTjdZowmLe2PWAdFgwne+YsLLM9solKLGMx6bp4VOPbSIMlIfcHQaNGj02so5uozB0RRoLWvkfmp3QvFerMeQRR2ZC8Iogi7CAG5TAFPkwOr01S1M6pI6A1a3D57tegtyxhMOmKhJJVyBb1Bw4k39mN6JKtjdSVKdCusfTfKZYm52c1dRZSVmQDjV2ZYXwhhd1sYPtFL196WpwYaf1UYLdXi8dFXhvngry9R2ZYNY3OZ6f01Wnl9GcLrW0LfLszcXebwK8O1Yvqvm8ZW12NXjguigL/9ycLCsxINpXcPPN9iYznCyPGHlkJrs/XFYZ1Ohd6oJRlJ4W1x1okmvhYn3seIgGNXZpib3CCzzSJJpRLJpAsMHuv5QkVdu+vZOrAKuSJXPhqvCSjbmb6zTM9QC12DTZRLFS59cI/xbV0ciUiaC++NEt5M8Pw39j+x0+UBzW0ufE12gptJ8tkiOr0Gr8+GSiWwNBdEYzEyObGGRqumo7sBrVokuBKhqdNL0zP8zm6fnW/+4XGWZoLMT22ALNPe20h7T0PtmnzjB0e4fW2e2ckNyuUKao2K3j1+Ro507pq19ABJkpi6uYDFYsCyzbJPlmUuvHObpi7vl+p8C61Feec/fkouUwQEkGXunp9Erkg8//0jjBzvoWeohVy2gKgSsTtNLE+uk45XxWeVRoXDYyMWqoq52XQes93E0IleGjs8CKJQE6WK+RKBpTBqjYqm7x6uE3oFQaDB70AqVVi4v8a9yzM4PBY69vhp6qiKg0aLgZd+/wQDR7tJhNNYHCb8nQ07OrDUGhVqjYpKpYKr0Y5Gp61lqVjcFgqT64gqAbVaVfu7Y3NbKBbLuHx2gpE0fXtb6Oz3Icsy96/Pc/6d2xS2/rZp9RpOvD7C8LHuZxarVSqRE68Ok4pmiIaSte1anZqXvnuoTihVUFBQUFD4TUURURQUFBQUFBQUFP5BsLkU4q//n++wPL1BYCmMyWrA5Xew5+QeUuUKyFX/fYFqxwUySOUKRoOON17ai8v5sBiXSmR5/2+vcPHd0erqbQFMVgONbW4mbi5ishuJBx8Wg9QaNU3dDTgabLXuDq1ew8k39tPU1cDmen3HhFanxtVgR6UWOPraPhbGlqlUJFSqqhBx+JVhEsndC+yhtRiv/+EpTDYDuUyeRDjN+bdv1UQXl9+Bq9GOSiUydKJ3h4ACEA4+XkB5wNzMJief66srlK3OBnj7P35KacvmJxXLsDq7ycu/f5yBw7vnpnwesuk8q3NBIoE4Wr0Gf4cHX4tr12Jd74EODGY9s6PLJCJpmrq8dA234mz49dlglYplNpfD1U4ipxnvlp3PsyCqHl+AVKlFnlWTioeSZFM5zHZTrfvgy+JrcXL4+X6ufnJ/x76Dp3tpfIasj2dBrXnyST7a8aJ5JCsptBLh1Nf3cfmDMcRtvlRGi55Tr4/sGk6fSmYZuz5XJ6AAVCoSq0thlueCv9KV8eHNxK4dVA9YXwzSNdjE+nKE8Zu722BN3l6ib28rbT3PFqyuVqvwbxOUJEnizvUFLnw8QTFfolSWCAUirC6G8frtfO3bBxg60IGgEliaCRBajyMIAp4mB40tzh3X1eYws/eImb1Hdu8M8TbaefWb+zl0optCvoROr8XleXrhPBFOszKzs6sCqoJyaC32pUSU+XurWwJKPePXZhk81o1Ko2ZuYo25iTVUahW9e1ux2h52AAWXIxx7Yz8zdxZZur+KyWrk0CvDjDw/iMGs4/Q3DnDuZzdJxbMIAthcFl78wVG8zTuzfmbGVvjwh1cpb9muLdyHu5dmeOl7R+jf3waAVqelbU/zk09KFOg60EE8nCIdzbAys8L86DI2t4Xf/79+A0mqMHiki7HLs0BVQJFkGVejHavHSlEU+c4/PYneoGV1bpNPfnIdaZtQXcyXOPfWDewuM629z55j4mly8O1/cYbVuSCJSBq9UUtzpxfXEzJ3FBQUFBQUfpNQRBQFBQUFBQUFBYXfOrKpHLlUHrPDhG7LF//e5Rnm7q1QLlVX4AaWI4hqFbN3FgjFchw92I5g0jE+tU4mncdg0HL0SBevvTFCZ0d90XT86hzXPpqo2d8gQyaRY30hhFar5tCZPi789BrhjTjlUgWTxcDhV4Z5/vtHiQYSlEpl3H4Hzi1LpOYuTy2AWW/UYraZOP/+GPlcEX+rk4GTfaQiKZra3PQf6qBtTzOrs5vM3lna0Q3gbLDia3fX7LMKuRLxSJqxKzO1Vf0qjYrT3ziAr3Vnse5J5LIFUvEchXwJqSwRWI3Q0PRQGBi/MlsTUB4gyzI3PrpH50AzetOT8xqeRDyS4qMfXmN9IVTbplKJPPetAwwd7dpVSGnpbaTlcxTyvkrioSRn37rO4v11kKtzHTzWw7Gv79thxbMb3mYnLp9t14L6wOHOHTkkj1IslLj96QS3PxunkCuiN+k4/PIwe0/3P9bG6lkRRZGDp/tweq1M3lkmGkxid5vZs7+Njr7GLxWCvp3GFidqjapWOH6Utl5f/b/7mrj98b3aM1EqlImuR/nDf/M6kiCQSeRwNlhp7fE91nJoaS6E3b37Pq1eQ3AzSTZTwPgl7uUnsnsG+8PdW89waC32xC6dzdXIM4soj7K+GuXiJ9X3m0anxt/uxtlgpVyqoFKrSGWLqLUqPvq7m8xuzzsRYOBAGydfHX6me3w7giA8sevk84xTey9/Sde+8Hps1+2VssTmaoRrZ2fIpB6K2YGVKF6/ne6RNmbvLCHLMquzm3hb3HTvbePIa/to7fdvjVFBrdPQOtBMJpFFEAXMDjOCSrUjsymTynH+nds7noNKWeLe1VnS0RRrs5voDBq69rbRMdi06zO+vhjivf9ykUggztTNBeKhJL42NwdeHiayHuPKu3f57r9+ha69VTux+zfmKRYrNLS6aen34/LZ6RlswuWt/k4L99fqBJQHSJLM3Njq5xJRAMxWI/1fUf6ZgoKCgoLCrxtFRFFQUFBQUFBQUPitoVQsM355mhsf3SOXzOLw2Tjy2gg9+9uZG12uWY4IQFOnt1psK1do72tkfnQVuSLR2eVF1+AAWcJv1GF/JNQ4splgbTFUG+sBgijQ3OmluaeB+ekAjf0tuFs9lAolHA12ChKk4jk6BneuFG5ocnLmjRHOvnMHh8fKxz+7hSTJiCoRtVZDOJpDbzRSVKlp7vWj0app3+Pnld87zrWP7hELJhFVIu17/Bx9Zbguf0Rn0HDqzf10DbcQWo2iUqtobHfhbd69gwPA7d1ZzEzGs6zMBWsF6v49jbz15xc4+sIAIye6qZQrbCyGdnwOqlktmWTuS4ko967M1QkoUO0OOP/2bRpanHibnEhS1ZbmNyHz5MbH4yxOrNf+XalIjF6cwuG1MvJc/1M/rzNoee7bh3j/v14kuy0von3AT9/Bjid8ssr0rQWuvHu79u98psD5n93AZDPi724kmciiUou4PBY0TxBVCvkigeUI5WIFh9dSE/7UGhU9Q830DDVTLlUtmL5q3D47x18e4MJ7Y8iPCAb9+1tp6awXN5t7fbz0Bye59PYtMoksGp2aoRO9DB7rfmJGyHbmpjZwNjkxmvVkH+lG2Xeqn43NBJFgEmPHr6Ybxdlgxe62EH+MnZ6/rWqh9bgQ7gfIT9n/JBZngzvG1xu0sNW0Nj8VwGkz1gsoADJM3FzC6bFy4FTvF/7+Z8XmNtPe72d2bAVvkwOpUiGXymMw6zFaDTQ0fT6R+FFcPjuzo8s7tqtUApFgqk5AeUBwPU7jsW5c/gSR9ThQ7c7oP9pNS99DUWF6bJVPfnazTggLbyZZngmgN2ox2I1UKjImo5boRmxXaz+n18rMrQXuX5qqdZlN3Vrk6Nf2cexr++qOLeZLnP/5TdKJLKVCCUEUsLnMZFN5spkCvnY3mUSOzaUII2f2MHCoi9f+4CSlYgmT1VjrgtxOKpbZse0BiVj6sfsUFBQUFBT+IaKIKAoKCgoKCgoKCr81TN+a5+yPr9b+HVmP88FfnUNv1OJufGhXUy5XKKdyCIJA195W2va1srEYJpevsDy1QVt3A33DzWhUYH9EUMil82RTeXytLlbngkDVKWvkZC+3L06jNWi5fnYSg1GHRqemtcdHKVgtKJ179y7P/d5hClRQiSJukxGzriosDB7qwN1o54Of3MDmMqPTazBbDei3RJx8rsjqQpj15QidfY0IgkDf/nba+hqJh1Oo1SqcPtuudlEabXUerT2+Hft2o7nViafBSmizaklWLJZZWwzXCqs6vQa7Vc9GOMWlD8ZoaHLgb3fjaLCSiOwsnpksevQm7Y7tz0o2lWfy1uKu+8qlCpvLEZanA0zdrtob9e3voGdfK7bPaV8VDSYIr8cRVAINzc4vbAUUDSaYur37fMevzjB0vPuZukFaenx8///8KmtzmxSyReweK00d3h05B49SqUjcuzi1Y3u5VOH8L+5gbFqp/bY+v50Dx7ro3rqntrM6H+TsWzeIBKrHanVq9p/pZ/9zfWi3dcJ8VQKKLMtIFRmV+uE9vPdYN1aHiYnby4Q34pgsegYOttM14Eenr78OgiAwcLSHtv4mEpEUepP+c9u3lSsSyUyBr//hKTbmAuQzBbQmPUa7kaIgEo9lkR5VdL5CDCYdR18a4MMfXdshZLT3N9LcVc3A8PjtTxzHs0veiyTJrEcShBMZZAlMRi0tHjuGR23QNpM7PrudXLbIymIInUGD022hXCyhUqsolSWiwSSjV+eeGm7/VSCKIgee30OpWObGB3cJLIWB6n2w71QfhWwBs934lFEeT+dQM3fOT9YE83KpQqVSYeBIN+vLkR3H641a7F4rmWyBl//wFLl4FkmScfns2Ld12RQLJe5cnN7RSWQwabE2Ofnpz29SUVWfKb1OQ1uTA1+Xl8B8sO4zapXA/L2V2j3xgJsf36NruAVP00NbveBalM2Vavj7g9tX3OoYmx1b4cXvHiaTyJHPFNhYDGEw6bB7rE/sKPI2u5gdXdl1n6/lywlYCgoKCgoKv20oIoqCgoKCgoKCgsJvBeVSmbvnJndsr5Ql5saW2Xemn89+eo1cplDbJ6pE/J0NGDQi3/9nJ9lYjSIKAplYhkw4wek/PrNDlDBa9KQTWfYcbGdtIYQsyTS2eZi9t0K5VCYaStUCnUuFMouTG7QNNJKplJi4HaTYbWZOqq7gdRj0HG5tps/rxmsx43BbKJckmp6QuZCM1q/+1Rt1+Fq/Wmshg0HLy1/by3vv3CEWSZNN52tWMgajljMv9BPaKiJKkszKfIimDg9Dx3tZntzYUfzdf2YAk/WLFzPL5fKuQdtQLSZf/MUdioVSbduljTssjK/ytT88heUZiqjVLpFprn4wSnHLjsxg0nHmW4fo3cob+DxI5QrlUnnXfYVciUpFQv2MmfB2lwW76/MFK0uSRP6RTimpLLGxGiOdK9Hutde2B9bjvPfWLV5+Y4Q9ww+7pOLhFO/914tkUw+fl2KhzNUP7mG2Ghg88uUzbh4QCadYmA8xMbFGpVzB53fQ19dIa5sLtVpF10ATnXv8FAtl1BrVrqvit2OyGTHZvtj91tHt5fYnIX7yo8uo1CrUahU6g4bj3zhEOJRCb9Bgc5i+0NilUplSvozBrHtit1TvvlY0OjVjV+cJrkbRGjQMHuqgf38bhi1R1d/uonugidmJtR2fb+9rpKmt/h2yvBnjysQSc2uROhHIaTVybKCNoQ4fmq2iutn85PdJpVzBoNei04ice+t6TWRoaHGx/8weAmtxspniFxJRkrEMqwsh8pkCJpuRpnY35l1ymx7ga3VjtRsQALvbglanxmwzUS6Vmbg2y+lvH/7cc3iAt8XFm396hmsfjHHvxjzZbJHeA+3ECxXS2SKN7W42lsIIQGOnh2KpQiaTRy6X+d/+px+iEgT2nejhyKt768ZNJ3NEg/U2fTqDBr3XxgcfjSOqRXqGmlGpVeQLJUYn1khsJjg83ML6dDUDRhQFktE0oijUhPYHlEsVYsFknYiy3QpMb9Si1qhr76hyqYIsy0QCcQIrYW6encDX7sHuseJssONutNPY5t4hlnbs8XPn3OSOji29UUfn0FOyWRQUFBQUFP6BoYgoCgoKCgoKCgoKvxVUyhL5R4o5D0gnsnTtbeMP/+/f5rO/u0JwNYrTZ6dnfzuJaIbDrwzj7/CwsRAktpnAYDbQ1O3FZDXWBboDOL02Bg51Mn1niZe+e4i7l2dx++2M35inc6CpWuTdlgmhNWuZCUYoClXveKkswdZwsVyeD6ZmubK0wg/2DdFstaLTq0nvshC8XK5QKpQplStUypWvLHficTT4bHzvd4+yuhzh7s0FbCYdTrcFo1ZNaDlSV5TLpqtWM52DzbzyBye5+ck4sc0ERouekTN7GDrW/aXmYrYZ8Xd4WNhmjwWAUBVxIpuJHWLJxlKYpal1ho4+/bsX769x/p1bdau8c5kCH/3oChanicYtC6XHkU5kKRZKmO1GtFoNNrcFX6u7tjJ+O11Dzej0WioViWwqj96g2RGI/mXRaNR0723j5if3Hs4xnScRzzB4qo9kLEs+U0AQqqvS1Vo1l89N0trhwbRVQF+dC9YJKNu5e3F6q9D/5ee9shLhl2/fIZt9+F3xeJbJiTUOH+nkyLFutFo1giCg03+112k3TFoV1z8YRZarxeVyqUI+V+Ta+3fZ9+pe2rp92D+niFIulZm+vcjouUlSiSxNnQ3se66fpq7dM0sEQaBzTxNtvY3kswXUGvWOc9dqNTz35ggOj4XxW4tYbQZAwO420dbdQCqeRqOrdqYtbET5u7OjFHYR9qLJLL+8cp9EJs+p4Q7UKpGOHh8Tj+kwAPC3OKkUS1z65Z267ZsrEa68P8qJ10fQGz7/b7U0vcEHP75RJ3RbHSZe/cHhmo3Zo8iyzOpMAFejfce+ubFljr+x/0tlAFk9VtQuK70n+pBkiEUzxINJFu5vYDLrOHikE51eQzgQZ/TCFMVUlmKuyMhze1hfCvH+315ldSbA7/2bN2tdUVqtGq1OQ6788DwdTU4+/Ow+kiSjUYsI28R7nV6DqFWzuB7HYdWTSeaRZBmdUYu70V61WnuER7c5G6pdJblMAY1Og7/Dw+rsJpVyhYYWJ7HNBO0DTcQiSbytbs7/cpRMMoe32UlDq4vuoWZOv7kfyzZx0uWz8/ofn+bmJxMsT28gyzLNPT4OvTCId5uAo6CgoKCg8I8BRURRUFBQUFBQUFD4rUBn0NI+2MzYhZ02Rg/CxQ+/MoSvzc3S9DrxYBKLw8yJ1/fT1Fm1Q2nrb6Ktv4lyqczsnSXGLk2TTefo3ttG/6GuWqFu+EQPkkbF5lqMU98+gFCqsDK7ibe5Gha/PLNZnZNFR7CUp7I1P4NBCwYVPFKbTuYL/PDOPf7o0D4G9rdx/v2HxW9JkohH0oQDSURRYHE6wOp8iIOneml9xMYFqmJLsVhGrRbrLJceEN5MkknnyCVyhAMx8ukCbr+D9j4/dk99x4PZoqd/sAmjTs17q1GSgTiJXbIWHuRkCIJA/8EOOgaaqhkoRi1Gy+NXkT8roiiy72Qvq7NBSsWHhWCDUUcuk8Nk2X3F+9pc8JlElKlbSyBXi93ZZB5ZljGYq2OuzAQeK6JkkjlGL01z79I0hUKJhmYXB18coHOwhaOv7eWXf3GOUuHhfK1OE4PHulm8v8adc5NsrkQwWfWMnO6n90AH2q9QTBk41sPS5Drh9aqFTzKRo6WnEYPLipTMUcxALJTC5bHS1l613llfCtMz2LR1btm68fLZAqlomnyuSCaRIbKZwNe687pENmJsrkQRBGhodT/RTiudzvP+u6N1Asp2rl+bx+kyM7BLjtCvinQ0Q3Orm9XlMJXyw9DsaCBOo8/GyOEOcuk8yzMBAkth1Fo1zV0NNHd5Hyts3r82xyc/vFL798ydRZam1vjOv3p112v4AJVKxPSE58dsNXD8lSFcXisf/OgaNoeR5VCCD/7qAiqVyJGXhzj48iDvTizuKqBs5+LYAn6Xld4WDy3tbnr2+Jm5v77jOLVaxXOvDvP2//HJruPEQ0lsDuMT570bsXCK9394fUcHVTKW4aO/u8H3/+ULGHexlhIEAbVGRXg9RqVUwWDSYbabENUiRrMB8SldS09jdnKDhdnNum0qlYi70cbqfIh4Iotbbebdv7qA2awjuRlHkmQ+/fFVnv/eYWLhJCsLIVam12vPgtlmpH+kjdsXp6vjqVUksgXKW/ebw21FFOs7lVwNNtKJLEN7W1mZWMPqMLHnQBupcIrKI4HzvnYPDY+8s6wOM0dfHeazt26ADEarAU+zk0wyy7Gv7aNSKiOqVRTyJT77+W2K+WpnXzQQx+G1Mju2itVh4tTrI3Xj+ts9+P7ZaWKhJMjVbqBftcCvoKCgoKDwm4gioigoKCgoKCgoKPzWMHyyn+XJdRLbQpmb+xrpGq5aMgmCQGtfI63bAn53Y+zCFOfeul77942Pxli4t8Ib//wFVjaSXL8xTzyeJZ8tUFmNcmBfK85GOxqNGrPNgOnBamGdSCqWx6Krrt4dOd7DRmX3bplUocCd9Q1ODbQwN7nO+lK1+B0NpgisRhFFgede30skkEClFrn+8T0u/SxHuVCmfaCJ9uEWIpkCY+NrpFN51BoVe/ob6e700uC1EY+muXFhhvmpDaxmHWd/dgOdVo3Xb8dsMXDHOcXr//TUriuIG5qdONwWNnbJATAYdTvEHJ1Bi26X1dFfhtYeH2/+s9OMX5tjdS6IVq9h7/FugqtRJm8u7PoZ7TN2LsTCSbKpHCvTAYr5ahFXrVbR1N1AOp7d9TOyLHP5vTuMX5mtbVtfCLL5lxG++d+9QPueJr73r15hYXyNeDiFp9lBx2AzmXiWX/zns7VOnnymwMc/vEqpWGb/mYHPc0meiLPBxrf+h5dZndkgFcsQDKVIZsuUSxU++flt8rkSgwdaiQXiXPz5TSwOI3N3FnnxW4cYPtZV19mTz+ZZHF+tCVgms55rv7zNme8fw+auCm+FXJG5sRVufDRGLLiVoWLQ8NIPjtF7oGPXOa6uREgldwZmb2dsdIXuHl/NIu9XjUarxmI30KVvJJ3KUSiUUIlVMaNnjx8kiQ9/dI3FyYcCw83PJjj6yjCHXhzcYTWWy+S5+fG9R7+GYq7E/OjSE0WUZyEaTPLpz25itukJrUa4f32+tu/c27eYm1nH/1wvsWcY6978Bj3NbnR6DWdeG8LtsXL31iLZdB5BEGhpd7P/aBc+vw2tRsRo1u+wcrLYjI8VNZ/E2kJoh4Ci02sQBIF4JM36YojuXcS0tblNNBp1NS9kC4fPRmOHl+GTvbtmRD0rpWKJ8Ts7g+UBbE4TsgzZfImle8vY7UbMZh3p0EObrrFLM3QMNbM8HSC+7W8SwNCRLtaWwgRXoxhMOta3nhmL3Yjd9TCLSa0WcduMUCghOo10DDZx+tUhtHoNGo0ai83IxbdvEQnEEUSB5q4Geg918tlPbyCqBNr6/LT1NaIzaOnZ30a5JDF+fY6F8RUcbjP7TvayNLHKzN1lnI12jry2D1GtwmhRk98SdiplCXQwfn2e4WPdO7KmRFHE1WD/wtdZQUFBQUHhHwKKiKKgoKCgoKCgoPBbg6fZybf/9ausTm+QTmSxe2y09DZieoKn/qMkY2mufzi6Y3tkI86ti9Pcna8KCQICBuNWt0Iggd3vQCiWEIDXfucod67McndxHY1WjdVuYt/xbsqtRvKFh0VjjUqkWW1AjhaqnvQbCfL+Rk5+bZjVuRD3biyyuRqlf18rLZ0eYuEU5XIFh03P+391HqvdSEunl/WlMB+8dYM9Lw2xGUmTzRTIJLJM3FnC5jDxO98/wr3rC6wvR2j027n49q2aVdHyXIi27qoIcvvcJK/+3vEdeQ06vYYXvnWAs2/fYW0pVLO9sjpNvPStgzi9Vn4dtHRXV/znMgU0GhUanYa5eyuPFVHaniKWPcDX5uba+6M1AQWqHT1rs5tYHLtnawRXI0xuK1g/oFKqMHljntbeRhpa3TQ8UiS//sFYnRXaA25+Mk7PSDvmL5jlsRtmm5H+Q9XsknMfjZNZjfLhT2+Sz5XwNTvIxzLcv7kIQGEjQTSQ5OIv7yIIAt3DLZjtRtLxLPFQqq4DaM/BNhbGV2gbaKL3UBeTNxa4/tE9Jm/O42t10znYTHAlTDFX4uMfXsHd5Ny1IyUYTO3Y9iibgQTpdB7nI4XbrwpZllmeWmfyxgLRjTjtQ81UShW0ejVO/cPOrLZ+P43tHu5dmasTUKqDwLWP7tHU1UBzZ72gmE8XSCd2F+KigcSu2z8PqwshCvkSTo+FS488B4VckdXVCPbNBKJFsyOr6FFm1sJEk1lcNhMmk54jp3sZGGkhlcyjVos4XWZUalXVtqnbRyaVJ53Mk03lEcSquGa0GnDvYq31NPKZAg1NdgSqmR3FXImNhU2kikRDq5t8Zme3kizL3PlsnHgowfPfP8rNT8dJRdKUC2UOvDhE90g7UA1yX1oIMT2xTjScwu4w0TfYRGuHZ4ftVaUiEQkmKZbKZBM5VuaCZNJ5jGY9RrMeUVV9NwqCQEuHG5fNwCcX7hPZiFO0GdDo1EhliXKpQjKawuYwo9OpsT2Sa+TwWHjjn5xgZWaT4Hqc7EKQpooMVO3JZFmmoclJZ6ONyz+6TDqRQ6USSc5t8u1/8WLtvdIx2Exjh4fYZoJKRWLsyhx/+2/fIx5KgQB2j5XjX9vH/jN7uPDeKBvLETr7fCyMr1TPM19CFAQqEmwsholuJsjniqhUKowWA5VSGY1OvXU/lcjnijy+t0xBQUFBQeEfL4qIoqCgoKCgoKCg8FuF3W3F7v7iRf1MIlfnyf+AXK7I/FQAQatFluuLkbIMvmYn9y/PMHp+Cr1Jxyv/4jSmZCN6nRaVRcNGJV8noOjUKlrSKi797CrZbAGrRodZpca4kEFl1FOUoG9/K7IkIVUkNpYjVMoVHC4zNz8aQ5Zk0okcxUKZwGacRDJHeyhJKpVjeXqzVjCNBpOc+2ic4HIEq81IuVCqWz1ekSRikTQms56F8VUS0fSuQebuBhvf/Kcn2FiOkk7k0Bu0+Npcu1rs/CoRBAGj+eFK95YeHwdfGODWZ/drv4sgCBx8YQ9Or5X7N+cpZIvY3Bb87Z5dO2RaOr1UKjuFDbPNgPUxwfSZZI5KRdp1X2A5jCzLO8QoSZLYXNnZzVMdL082mftKRZTtdPQ0sDIXJJuu3tttHR4u/PxWbb9aoyIRy9DQZOf2+Ul697fy9X9yggvv3GZxvLoa32wzcuBMP+lwCmRIxTJce3+MuxeniATipGIZUrEMS5PrvPC9w6zNblLMlwgshXcVUR4frf55D/rizI4u8/5fnq/9ltFggs69bQSWQhSyBQSxupr/1DcPoVKrmL67uOs4siSzuRTaIaIYrQbsHiuRjfiOz3hbXF96/qmtTqlivoT8iEgiigKFUoVEMIXO4SVXLD1xrHJFolSufw7MFgPmR6y5BEFg8Gg3i5PrqDXqus6J5q4Gmjp22gw+iVwmTy6Z4crPbwACnYN+pm7MIyFQKla4d2kGs1nHngPtdVZRuXSejflNMokchUyBIy8Po9KoqFQkHC4zGq2aQr7EhU8muHf7YUdJNJRmfnqTngE/z786iNFUfZ+sLoa5eXmWWDhFMZkjsBQiU5YJBhKoVAJunx1vixOVSqy+h3RqLn84jstnZ20hRCadx+21EFgMYTTrsbmtLE2tozfqWV+O4pjeoK33obBrsRkZONTBAGC6ucDf/MX5OmHN2CTz9r//mHJFQqrI6A0aNpfDfPrjq3z3X71a67QTEEgnskzdXGBpch2P30E4EKdcrBBYCjN1e5GVpQiFLYuuRDjFypblZGwzid1lJhZKkoplSEbShNaiyHLVqsvb5EC9dc0NJh0m8+fvMlJQUFBQUPjHgCKiKCgoKCgoKCgo/KPCZDWgN+l2rHxOZwo0W/Sk8juL7S6Dhl/814sIQEu7G5UoEtiMcyOxTnNnA2JhpyjThJ7zP7lMoVDCodFRDGW5vxRm9sYCp795iNBmEv1re7lybpqOLg/FXInwRpy+oSbmJ9bQ6jQYLXoKhRLJLUuk0FqUZKFSt+LcaNIRWI0SC6Ywm/V14ekPSMazlBqrAcMLYyskwilMNgNt/X68LQ87KTRaDa3dD8Ow46Ek83eXKOaKuJoc+Dsb0PyabJceoNVpOPbqMG29jTWBoqHFhSRL/PjffUw29VC4ault5MXvHd5hR+P0WXnxO4cYv77AykwAUSXSPdRMxx5/LaekkCuxthwmHEwhCGAx6RAEYYegVv1+9w4BBaq2N94WZ83uajtGix7jF7BBelb8zU7MFgNanZpSoYRUkWpzF0UBo0mHVJEpl6UtQSdPY5ubb/+L52lqd7M8tY4gy0TWorWuFKPNxOX37u74rmKhxMrMJmabgWwyhyztLjZ5G54udjY2OrB+jk6yz0MxX+LGh2N1Yli5VGFleoNDLw3S0utHrVHhaXbWivdS+fHdHLvcCugMWg6/Msz7/+VCnchhthvp2tv6pc/BvHVttDo1glA/B0mS0WpU2NwWouUnZ6IAqEQRtfrZ7K/a+/187Q9OcvOzCYKrUTQ6NX372xk53ffMVn7B1Sj3b85z98IU4bUYncOt2JxG3vuL8xRyRZq6GygVK+j0aqZvL7L/+QGauh6+f7R6DVanhUwiR7lUIbgaQaNVUy6W0b84BFQzTbYLKFC9371eC7GVMFfeH2X4aDclSebtH16jWCjj85q5+OkEUqXCwPEeZibW0GpV5LNFtAYNKrUal9vMzc/uoxIF2gdbmby1QLFQJhbJ0NjuBVnm2Nf3kUrmUGk1zI6tsDS1wbf+9Az+Ds+Oa+Ew6chte1cVi2WKqSypeBaLw4QkSNjtRqIbceZVIsHVCM3dPkKrEc7+3VWmbsyzsRJhYyGEwazn1LePcPPsfQA0ei3Td5drnXlavbZ2r5SKZWKhJIVcEbVGhdGqx2DSk03liQTideLnyMmeX5nIq6CgoKCg8NuOIqIoKCgoKCgoKCj8o8LqNHPwpWEu/vxG3XaLw4xoMkA+Xbfd5TAxfnWmJlxIMlgtBjRqNclolqw3v2vhqRzKVvMWBAEhU2ZlbhNZlpHLItlkjumxZfR6DQ6XmcW5EDqVQCFXJJcrYnWYSUbTmK0GCqVyTRcx2Yyk50J13yMIUKnIxBNZbOE0JoMGqVL1uddo1CACMniaHATmgwRmA9UPATc+vMfrf/o8bf3+HfPfWNjkF//pMzIPMkMEGHl+gONvHPhKA9KfBZVaRXN3A81bAk8mmeWH/98P6wQUgJXpDe5emOa5bx6o2+7yOTCadLR2uNlzoA1ZkklGUmQTGXztHiKhFJ/84g7ry9HaZ0xmHVavjfBKuE44UqlF+g/tngECMHC4i9nRlR1h0AdeGMBsN1EqlQmsxkjEMqjUKvzNjh2izxdBpRJpaXfT1e9nYyWCRqdBVItotWrUKhGtXoNKI6JSiegMGvTGaoeRRquhe28r45cmySYeXk9Hgx2tTlMLXzeY9IiCgLRVxd9YCrHvZC/FfOmxHRfNLS7sdhPxeOax8x7e21JbCf9VkUnliIZSFHNFgqvRHfslSWb6zhJHvjayI+OkZ18rm6u7dBMJVfFuN3oPdKDRqrl/bZZoMEX7Hj99hzpxNTq+9Lk0d3nR6jVkskX6DrQzuWXPBlUbvka/E6PfRij1+Gv8gJ5mN06L6Zm/u2u4hdY+H8lopmpb+Dnu0+BalJ//p7Nkk1nmxla2Mok2OPOdQw9eP6SiaWweGw3NDlQqgUQoWSeiqDVq9r8wSCKSxN7kJpUtkk0XcHfa0FqNFAolxm5Xr4dKJSJJMrIs42+wcP7HlwlvJFCJAs0dHo6+cYBCtojRoiewFEEqV0glsmwuhOjq87E8HyKTzLE2H8JsN2G36okE4thsJtYWwrz4O8dZHF8luBbF4rYw8lw/S4shNldiNLe60GrVlIplpu4s7SqilJI5nj/Vy6dnp8jli9V8GFlAZ9CSTeUw6LVkI2kykoTepKNSqjBxeZqJa7O89xfnUGtVSLKAJMnk0nlmb1et9QLLYUrFcu05Bchuu1d0eg2RQBwAd5ODWDjNmW8eYPTKLMHVKNmtfK3BI50MHe1+5t9XQUFBQUHhHxuKiKKgoKCgoKCgoPCPjr2n+jCa9YxdmiaXytG1txWNw8ylm0s7jtVrVGyuPoxtFsVqBVCK5fHZLRTzJXYzkX9QRLdpdUQWQ8iyjE6nwWI2oNNr0OrU3LuxwHf+xQv89b8/S98eP/lMgaWZIPvO9HPhZzcxmnQ1ixaDSYfZY6N4f6Pue7KZAl1tHuYmyqTiGe4FE4yc6uP8z2+hNWirn7PoUasEssksxm3WPcVCiSvv3sHf6UGjfSiMVCoS198ffSigAMhw59MJ2vqbaB/YGQD962RjKULqMYX5mbuL7H+uD4v9YbFYEASOvXGAa+/eYfrWApIk0dzt48S3DqHWqTn701t1AgpAJl2gsclOl9vM6tQGlVIZX5uHwy8N0ta3U3R6QGufn9f/+DlufzZBaC2GyaJn33N76D/UQSSU5Nx7YyzPPxTCdHoNx57vZ/hgvZXRF6G53Y3La8Fi02M26Rg62MHqfBC1RoWoEnF5rCBD/8EOrE4ThVyRdCKLyWbg2//6NeZuLxEJxGhoddO1t410Mle125KrnTQNbW4CS1UrM7PdiFSReO7bh/A0OXedj8mk47Wv7+Wdt2+TeSSgHODY8R66exp2+eQXo1yqcP/2EtfPTZFOZPH4bAQ34tXMim0CQCFfRKVRszS1jqvBVpdn0b23hdl7KwSWwnVjj5zqo7F9Z3EcqvdX53ArncNfvvPkUVxeK6/+4Cgf/+Q6TT2NWBxmJm/MI6pEjrw8xKFXhnl3Ymd2z24Mdfhq768HlIpl0skcarUKyy7WdhqtBpfP/rnnPXVrkWwqhyxDZctCTK1Vk4plcTbYiAeT6HQaWrq9qDVbdlLbOrXy2QKhtRhqg5bBF/fy8/92hXQyi9VpIStB+EfXOXamH0mS8DoNJEIptAYtjW0erv7iJmvzIaSKjCAKFPNFfvrvPmTka/spFysUckWKhTJSRWZ+fI2uoWZOvrCH6Yl1SqUKDX4bXX0+NmcChDcTLE4HqFQk7H4HzmY34bUIgbUYxUypJqA8YHkmQKVc2fEs601aQpPrvHCyh1Aiy83rCzgbbagEAZfHSjaeIRxMIIpC1a5SEPj0R1cwbnUilYsVzA4TlXIFUatmZSbA8TebCCyH0erUDBzuwOwyU8iXEFQiQ6f34PLZmbmzRD5XoLXPj6vRwdi1uWreS08D/SNtdA40cfTlQdyN9l276xQUFBQUFBSqKCKKgoKCgoKCgoLCPzq0Og0DR7vpP9xJpVxBo9UwPx+EXUSUiiyhN2rJpPJo1CKGLSub+FqckX0+7pV2D4/Wu6tFfBGBcqmCz+8gn8yTCCbQalTsP9TJ0mKYVCrHydeGiAeSqNQqioUSskrNH/yb17n50TgagxpXq5vOAx3MrMYQRaHOzkuWIVco0uCzIeVL5DIFFudDnPnuYaKBOAJw+vURgovBOgHlAZvLYRKRNO5tq+ZTsTSrs4Fdzyu0Gv17F1FKhZ3ZDzaXBb1RQzKS5sYn47T2NqI3aZFlAZfXit1j5ZU/Os3BV4eRKjIOrxW1Rs3yfJCVhfruHrVahbfFwWY0zfJqlL3DzVjMelo6vDianp5z0TnYTGuvj2wqj86gRWfQks8V+fjtO2ys1Is1hXyJs++NoTdq6R9u+VLXxdNo49TLg5z74B65fJn9L+xBb9KyPLOJw2lCKpcxmrSEl4O8/R8+RVAJLNxbxWDWM/JcP/tfGERnfGjVZHYYaerwsjYfBMDtd2C0GMil87zw3cP0jLQ/tjvjAf4mBz/43aMsLYSYmtqgWCjjb3bQ3eOjucmJ6hntpbYTj2dIxLOAgMttrtrYAffvLPPp27drx8UiaXoPdnLx7ZsIooDFZiSymSCyEad9qJV3/uICOoOGE1/fx9CRLmRZJpcrMXSyF1+7h9BqFIvNQMdgM219jbVi/5dFkiQ2lyMEVyKIKhF/hxfXE8LaO/f4cf3ZS6wvhikWyrzwvcPYnSacPgcqlcjXrDp+cm6UYmmnFeEDTgy10+l/+FtJksTsvTXuXJohHEig1qjo29fK0JEOXN4vFy0uSRKL99cBEEQBm9tCcMuKL7AcpmeknbELk7ga7eSzRQq5Ii29jVi2hK6lyXXOv32LYrGM1++gkCvy4kv9oNczPR2gUpGRJJngWpT5GwssTa7Vvnvf8R6CC6Ga7ZkoCiBDMpaBSoWl2QCDw83cPDdZ+8zcvVWCqxG8jTZGnutHa9Kzvhiic6iJ8Gb1/Z6KZ7E5TJTLFUrFCk3tXjaX64U2AJPFgPhId1OxWEIURVr7/eTSecRomlazFotWzcmXBjn/1k0KhSIAOqOO/sNdBFcjlItlNNqHNm4qtYjdZSGdzGK2GcjnS2h1arpH2nj/nbuktnWSqTUqjp/p48z3jhJai3Dz/DRry3NA9Z0zenkOWZLoG2nh7vlJDr08tGtWloKCgoKCgkIVRURRUFBQUFBQUFD4R4soiojaasGrqclBc7OT1Ufsf6KJPHsOdnDjs/u43GZkSUaSJEDELatpbnCQruzMI4jqJYaPdLNwdQGPz8bivVUqpQp9+9uZurXA/PgqL/7OMTL5CnfurPA7f3AMtSRVi4rZImtLEfYc72HoaDeT6xGW1+JoDWocXiuRQL1wc/fWIn/2r17h07dukUnmSMaz3Lk2j6vByhu/d4yTrwzx6Q+vsD63uWOeGp16R86JVqtBq9dQKuw8L53+12vltRsOrxWVSqxlXdg9FmKbCcavzmEw6wltxPj0J9c49OIQyzObpFM5ho50c/BMH41t9d0EsUi9fZtKJeJtc/LJ2UnyW11A0yYtNoeJ2ZUodvsy33h9BK9nZ96HLMuE1mPkMwXMNmNd3sDaUniHgLKdO1fm6erz1XUEfV4EQWDvkQ7sLjNTYytsrMY48MIgr//BCQLzm2STORLhJBNX51ie3qC1r5GOoRYiGwkuvnMbSZI58spwbTyNVsPz3z3M+Z/fYmUmgCzL2D0WXvrBEfad7n9mWzeHw4TDYWLkQPsXPjeAVCrHndtL3Lu7UvttzGYdIwfb6e32cfP8VN3x5VIFzDpOvnmAmVuLCGQplyXOfO8IiWi1k6mQK3H2rZvojToW50NMja3W7iubw0Rri5u2vsavzMJOkiRuf3afy7+4XfsejU7Niz84RnNv9fff7RmzOc2PtX3r8rv4nef3cWl8iYWNaF2Oj81s4MRgG0MdPjTbuiMmbi7y6c9u1cSGcqnC3cuzrMwF+cYfnfhSFnOiKGIw64htaZN2j5VULEMunSe4GuX41/dic5tZmQkQDsTp3teOvcHOT//9Z5z+xgif/fQGeqOWhiYHk9dmCa1E0Oo1tPQ2Mny8h+W1JPlciXggTmAhiCzXHArJZfIkImksVgPJLeGjWChjMOoQVSpikQyiVk1ji4vVLXEQQKtVk03mWV8I4Wlyks+WaGhy0LO3hem7K+QyVZFDb9DyrT95jonrc7ue+8ChjrqOjvBGjLM/vcHafJB8pkAqmqZrbyvqUJKVyQ0iwQTH3hyhUqwgqkUqkszknWo3nbfdg8Nnx9HoJJfOI4giTV1e8tki+54fIJsp8P3/8TVu3lzC7raQTuVruTzlUoULH0/wB//9C/Qf6uDSB/cAKBUrZBJZZFnm5e8dJh5MEFgMk0nmeP2Pn/u1Z14pKCgoKCj8tqD8hVRQUFBQUFBQUFAAdDoNL700wMcfj7O6zb6rWCzj9Ns5cLyLmWtzhOeDGEw62vY0cehAN4cbjPzo9hjxfH24/OJGGJdLwys/OEo5lKWj10+pWGJjMcTcvVUAlifX6fDY0Rk0RENJtJJMLPQwlDwaSuFqsHHIZyWweZdyWaKh2YlWryG6maBcqqDVaXjta8OsTa3TP9DIwEgrxUIZlVpAqxLRigKiKNK1t5Xxq7N14dcAg0e66+yMKhUJWYChE31cffdO3bF6k46mXl/dtmw6z/p8kGwqh8Vpxt/ueebg6e2k4hmkiozVaXqqrYy32cng0W5GL00D1c6i8atVmxqn10osmKS5u5H/8v96F3+nl2wqz8L4GvdvLfA7/+pl2noba2OJYv2qcbffzvlLM7UiPVA3n3g8y7kL03znmwfq8jSSsTRX3xtl+u4SlbKEVqdm76k+Dp4ZQGfUEt4lbH47wY0YiXgOt/fLFetFUaS9p4H2ngbKpQoqtcjqbIALP7kKMiDLRAMJkGF5coPe/Q/zXW6fvU//oQ6sjocFdJfPzpt/cobgapRivojNZcHhfXJgfCSSZmUpTDSaRqNR429y0NTsRP8lBLhspsBHH9xjcb6+ayidLnDh7BTZZI54JL3DrioRy6LWqNj7wiDN7S4WJzcIbSTqurkqFYmrH4+TLlQwmnVYbAYEBDKZApc+nkClEjlw/KvJiwgshrj4zq265zASSPBX/8vPOfTaPpKxLMNHu+g/0I7RrH/CSPW0+Zw0eexshBOEE1lkZIx6LS1eGya9ru7YTCrHtY8n6kLqHxANJlmY3GDkRA8AyUiKSCCOVqfB0+pC+4wi38DhTta3Orx0Bi3te5pIJ7Ko1Co6h1tp7vGjNRvpFgSSsQwby1EEQeDmp/dJxzP0jbRy7kdXuPLLO0D1GRy7OIVap0HjNFNO5xEseixOI8XNFLIkIcsy85Mb9B3qZHlyrRqkbtKSTeXZ/1w/oUgGWZZZWYow8vwe/J0eFiaqx518dRiVSuDOpWn6D3ehNuQIBZI4Gx18baAZAZmWLi9tvT5sLjOiKHDr3OTD31GA4WPddG3rJivkCtw5NwkI2FxmkKsizNp8kKNf28fSVIDrn04wP/6wk0ajVdPc6cXeYCNzc5nY/Q2Of/MgUzfmuHthmva+Rr75L1+ie6QdvVHD5EQAjU6DTVe1iEwlchS2OlTMVgML80F+75+d5s/+H9/j2ifjTN5apKXLy8jJHqRyhbWtjsPFyTUCS2Faeurf7woKCgoKCgpVFBFFQUFBQUFBQUFBYQuXy8w3vrGftbUYS0th8vkydocBq07DZCiO+5Uh8tkiarWITq9Gp9fgc9j5J4f2M7q+wc2VdbKlEsV8CVdWIH8/wDv3b2PIy2gNOnpG2hhdi+HwWjFY9KyuxuioSBw/3YvVoiO4rUAsCALHXhmisd2NSiXyzTf2c/3mAqtrUdwNNhxuC1aLnqOHu6jEMlwaW931nA4+PwBAa7+fF3/nGFfevUsmkUWlUdF/qJMDLw0B1ULy/P117t1YIBxI4PJa6T7YyeLYCrIk0dju5sjX9+PyPbT9Cq9H+eBvLhPa1r3T1u/nhe8ffeaV7JHNBKMXpohH02SSeRLRDJ4mB0NHu2jvbcRo2VlIFkWRY68NY/dYmL6zRHA1itluxNVgo1gs0drr59L7Y0iSXO1GoGqHE1yJcuWDe/jb3Gi2Ogsa/HZUarEWzFxGJpV6mN+h1qgwGOuL0MsrETY24jQ3O7fGlrn63ij3by7UjikWytz4eBydQcvB5wcQHyMMqVQiGq2aSkVCFCESiBNejyGqRBrb3Jjtzx4E/igP7KeyqVxVQKEaql7IF2vHFLaJRflMgXy6UCeiPBjn0bDsZCzD4lyQ5bnqav7WLi9t3V7W1mJ8+tEExWIZQRDQ6zXcubVIo9/BS68M4XB+sfNZXAjtEFC2s7mRIJMpYNnlfimXKgQDCUxmHeGN+I79uUyBzZUoPYc6uX9nmetnp5EkCX+biz0jrUzcWaJ3oAmzbacd3udlYzFcJ6DEI2nW5oPIskw+Ve2iuPDLu4TW47zwnYOfqwNGrRJpaXDQ0vDkQPtwIEE6tTOj5gHz99fZd7ybiauzXPj5DfKZAgjQ3N3I898/+kwZKV1DrYTWY4xdmkGSZNRaNb42Ny/94Cj+Di9/82/fR5bBYjfg8FiIR9KUCmWSsQyZZJ5cKseNj8aqgwlVW7B8usDFt27w/X/zBrfOz5BLZNh3rBuNepNIMEksnKYSz6E3aDnzvaNMXpsll85z4IVBmgdbWFmOEgok0GhUxBI5ciWJnkNddHR5OP/WDYJrUV7/p6dZW4ry4Vs360Sm/Se7MdmM9I5o0em1HHtliPa+RjZXo8iSjLfFSWOru/bMrS+GOPvWTc797CaSJNHa08ieQx0EFkMYTHruXZ7lwAuDLN9fY3ZLVLc6jOgNOg69PMjGYoR7FycJrkRIxTK09TXyg//xayDArXNT3Dk/xfCJXqYXI7VOHJ1eQ7lUQaNRoVaL6A0akvEssWiaoSNdDBzq4OI7t1lfDLE6vV4vqMtb7wkFBQUFBQWFXVFEFAUFBQUFBQUFBYVtGAxaursb6O5+GHj93p9/xvo265cHLPeu42v34LOa8Vl7ONzazHoiSTSUYHT0NhP3NsmtJlBZDQRXogSWQwyeHODWpWkSgSSuBiuNzU7UGjXHn+sjOhhndT6IJMk4vRZMZj2peAa7y0JHu4eWZiebwSS5XBG1WkWD14rBoCW6meD+jXliW90OWl3V9svX6sbhrhbERVFk6Hgv7QNNJMJpdAYtrm1hwqNX5zj/3mit2L66EEKjVdF7vJfBQ+14m6rzfIAkSVz9YKxOQIFqnsG9SzOcfHP/U691OpHl/f96Ea1By42zk0S35q9Sq1ic2mDgUAcvfufQrivy9UYdI6f6GDjUyYc/vEKlVC3ary+GkCSZUrFqRbZdushnCgRWIoQDCRrb3AB4fDb2H+3ixsUZtDo1oUfsvbw+GxptfRaGLMuEI+maiBJaizF1Z5FcpkClXMHiMOFrdVEqllmdDdA70kaDv76wrdWpcXssJMIp0vE0HX2NTN9Y4PbZ++RzBbLpAnqjjhd+cJTevW1fqoBvdVhqWTqiSsRg0tWs2vTbM1BsBkzP8D2b6zHe/fENErFMbdvc5AYdA37uT22g1Wnwuc0Uk1kiy0HMdhNyvsC5z+7z+psjn9sySJZlxu/tLhI+IFcsV+cu7dJeAbT3+jAadhck8vkS/Ue6+OAnN9EbNfQNNiKKIpsbCT762W2ef30vsWjqS4soyViGYqFUCx7PZQrEg0kMRm21M2ZbU9TU3SX6D7TVdU09K5IkEQ+nkWUJm9OyI8tF4MmdXgIC63ObfPLDy0hblmPIsDqzweV3bvH6nz6/o4PrUXQGDafePED33laigTgarQZfmxu720IymsbhNjNza4Hrv1hCFEX6jnTR0OGlmC+zsRiimCsiVWQQqkJjuVRBEARCaxHUGhVrq1FsFj0XPxhj8EgXc9MBbC4zJqMWp9dGOi9h9ntodpkQNWoWpjcx2wz84Z+9iAzkcyWQZdZnA9z6ZBydXsOeQx2ENlNc+mgcu8tMKp5DlmW8jXYigSS3Ls3S2Opk79FuVGoVTZ1emjq9O849Eojzi7+8wOZKpGbZtjS9QWAlwplvHmB9PoggCizPBRl5YZD+Q50sTqwiqEQ6BpsZPtHLf/if/45MMockyWh0GgIrUT76m8u0DTQxcX2BxmYHbn+YwGIYtUGHwaxjfSFEOpatzcNg1tHU1VB7x4uiiEolEljcKUaKooDF8cUFWwUFBQUFhX/oKCKKgoKCgoKCgoKCwlPIJLK7bs9n6i28HEYDDqOBe8tJxt6/R7FQolQoI0kyckVCKlSw2w04tyyrXvzBMfQGLSde2IO30Y4gy9y+OEUqmmFuTEIQBPxtbvoPtGGyGfH47TT5d64ydzbYePOfPcfcvVU2l0Nkk3nmRpdYm1pn+sY8R14dZuhELyq1CrPNhNlWXyyLBJNc/WSiJqA8oFSscP/uCr5WN/72+mJhbDPJwvjuhe37N+fY//yep9oRrc5skknmCIdSNQEFoFKukIimmZ9Yp2PPOoOHOh87hlavoWuohXuXZ5BluU4UEEQBlUYFW90WepMOQRDqMiMEQeDQyR50eg3jd5bJ56or9LU6NR6fFftjOmq2X6pYKMnSVIBkLENDixONTsMv/vwcKrWIWqMmny7w4u8eo2+oial7a2i1ahx2A5/85AblUgVRFPB4rLz355/R0OwiHs2Q2VoVvrYY4sx3j7LnYAe9w81PvJ6Pw9vqYvB4D2MXq/ZnzgYbqXiWvgMdZNMPOxIOvTSEyWp84ljlUoULH03UCSgARrOOmekAa8sRjh3vZvzTewSWI7X9Wr2GF373BGtrMdof6Wp5GqVSmWTiyavko9EMew60M3dnqdZVVPtunZqR492UiyXGr88/FAa2aGh2MjsVoKu3gcR6hCs/u06lItE50MzIwTZuXpzlxIt7PtectxPeTDB2c4mpe6u4XSZWFkNoRJHIRpx4JAWAr82Fp9FBKJDcsl2D0Hr8c4soG0thbp6dZGl6A1mSaWxzc/D5PbT3PRzH3WjDYjeSiu/+Xusa9LM6GyAVzZCOVy2wTFYjZruRhfEVIhtxPE3Ox5/veoz1uU3KpTKeFhcDR7rrrO9ElcjktRnm7i5vbakwdn6SUr5E79FuXA02bB4bGq2aUqH0sCNEgK7hVhYWwrVOGqtRh06vIZctkssWaWhy8I1vHGDyzgrLCyFm769jd1tweC10DTQxcqQTjV7N5nKYv/3/fcS9q3OoVCIqtQpno52psRVEUUCn11AxVzCa9WTTOYqRMiCzMh9m6HDnE0Wkhfvr5NIFtDoNokqs3W+FXJFwII5Wr6H/UAeuJifXPp6gUCjTMtiCzWFi+GgXgaUQqa3MHqkiIYgCVruRzeUwbXv8FPMlSqUKqViG1g4v9+4uo0tq6wQUgFy6gFwsYd/W/dW9r5WxyzPks/V/u3oPdNDQ6n7sOSkoKCgoKPxjRxFRFBQUFBQUFBQUFJ5Cx3ArqzOBHdsbO+qFhWKxRGg5AhWJSllCFEW0OjXxcApng41SoYzRrGPocBddI22o9VqOvzyE3WmimC9x7p3bxIPVoqooCvjb3dw+e5/PfnKNzoEmbC4zp75xkN79bTvm4vBYOfTCAHfP3eezv7uGwahDkiSyqRyf/eQaBrMeX4eXRCyDWq3C3WhDs9VZsrkapbhLiPwDpkZXGD5SL2RUJImKJO16vFSWdhSydyMWSmLzWBj95eiOfdmtIunUnaUniijh9RjLU2tYrAZun59Eq9fQ1NWAWqvC43dQyFWtqwQB7G4LrgYbrm2B71C1wTl0soe+oWbujC6RypfQ6TU7VvADFPMlUrEMocUgn85s0NLXyPpimEKuiMNrYe+xbhburTByshdRLbI0tcH6QpBr749y5rtHMFkMhDbiNQHFYNLS2OIkl85RKlQIbsTp3ddKLlsgFkzha3WhUQuMXpnBZNHT1P5shc58tmpRVSlXcPpsHH9jP+4mJ5PX5qhUnJz69mGKhRKL42t4W5zsO9VH7/72p44bWIuytstKdovDxJ3z05RKZTLBRJ2A8uC6TVyaomuo+XOLKGq1GoNBQzL5ZCFFY9Tyxu8f5+7VOdYWQoiiQFuvj71HOmlq91TDtL9/hEvvj5LeEhDsbgt7T/by0/96iWIkyfTtxdp4s2PLxMJJ+k/uIZctPuZbn0xoM8E7f3uN5Nb3hSMZnvvWIX70b99DKldQqUQMZj17T/Zx4Z1b9B7sJLyRAEClfnK3x47v2ojzi7+6QDb9sEC+thBiczXKN/74NC1b3XVGs55jLw/y8U9u1GfDlCsYjFo2FoOsTq6zPL2OTq8ll8wSXo3S0O7B0+xEKlceO4eF8RXe+/OzFLeES0EUOPb6fg69MlwTHsJrUUr5IohCXefQzK0F9r84yHf/7GXWFjb5zv/pVX727z6kVCwjSQI2t5lX/ug5/tP//hkGgxZRFMgWKzT3NvKCQYsgimRLZULRDAazlr2HO8nniljsRuwuM64GK5IkcfnntygDNz65TyadQ61SYbTq0Rm0bK5EUWvVVEoVtBo1sc1qfpDRaiCdqHam7JYls53gVnee3qjD2+RAa9RidpmRKzL5bBF/q4s9BztpaHXR0ddINJRCpRJxN9rR6TVsLoVrY2l0airZaudhIVdCqsjVrBeznnKpgteqR2/QEA0mdsxDlmVa21wkwikMrVVLQm+zizf/9Ax3zk2yOlvNUxk61s3gI0KXgoKCgoKCQj2KiKKgoKCgoKCgoKDwFHoPdLI2E2B+rLpyWhAF9p7up22Pv3ZMLJjgsx9fZXlyDY/fyf4z/SyMr6FSi5SC1U4LX7uHtuE2lpciLC1EeP7Nkdoq4eBGDFmScPlsxMMpPI0OLr5zm9TWiv9cOo9KreLDv7mMRqdGUImoNapqkU6nIZ3IkknluPnpPZLRNNFgkmK+hN6oxea2cPZnNzF57YQDCRDA1+zi2It7aO1ueKrgsT034wEOjwVfm5vAYnjHvo6BZiz2J3c0ABgsOgRRoFzcKeBot0LIi/nHizuFXJGPf3iFwGIIi9PMK793nHQii8Gs4wd/9jIf/fgalbKERqvG2+zE7jZz9JXBxwbfW2wGBgdbuHtvlcLWnCplCWQZlUZFIVdkeWqDRp+dwOQG8WCSy++N0rWvlVPfPEBsPcY7/8enxMNJyuUKBpOe1//kDPFIivnxVQ6/PMzpV4e4cW4Sf7MDQRQwGLVodRqKuSJDxzoJrUQZPTuByWJg6GQvmysRfvnn51DrNejUIp4/OPHUnIyV2QBnf3qj1t2jN2o5+uoww8d72Huyr3ZcpSKRfz2PVq9B84yB4dl0YdcisrA1nsVmZGMX6zuA9fkg5V3upachigIDw81sbk488bi2djftPT5auryk4hkQBGwOU83OSBAE+g+009rTQGgjjiAIePx2ouEMbW1uPjp/H4fLjCgI5PMlsuk8kY04Dqt+R2D9s3L/7kpNQHlAaCPO0a+PIEsSer2GTCrP3UszlIpl9hzuqp6zSsT3OTsD5sdX6wSUB5RLFcavzdPc5a1di/6RVrQ6NaNX5wiuxlCpRRxOE5VSmfErcxiNGkJrcbQ6NQ3NTjKJLKGVCL3723E+JhOlkCty8ec3agIKgCzJXH33Di19fhrbq+JZqVDGYjfR0t1AZCNOLlNAVIk4PBZaehqIJnNMzIbQeOz8d//LH7AxFUCtU+FudvF3f3OF/Fb3VKlQDWyPJnNcuDhDIVdErVGxNBfEUizRNtDC/dtLdA74yWYKrC9HECWZZCrH3Pg6epOWTCpHuVwhk8iRTRfQ6tRUKtUbfG1+s3YeiUgam8uM2VKfj7QbD959rmYH1jYXY/dWGR2tdrgcPNZD33P9uJuqHYVmmxGzrf5d2dbXWOsU0hm0lPJlJFlCrVGh1WlweixYtrKSNheCvPLGCO///Bax0AMrQhlBENh7oJ17n01gs+ho7W2k/2AnGq2aps4GGts9ZBI51FoVBtOTOwYVFBQUFBQUFBFFQUFBQUFBQUFB4amY7UZe++MzrM9vkklksbktNHZ4Uakfdirc/GiM5ftrqLVqNDo1epMOvUFDS083eouexfEVDn99P8FAAqvdyPOv76O914csyyxOB7j6yX1uXZ6jsdVFd38jcrlM5x4/m+sxAothKhUJqSIRWo9x7u1bpBJVv/zWXh9uj4XJmwtYnSZmR1dZmdlAp9dSKlVIRDMkYlmSiRyt+9opFStotGoCKxF++TdX+OYfncLhtSAIPHaFdXuvb8c2jVbD8ddG+MVfnK0rmlodJvae6ttx/G40d/m4f3ORxnY3q3MPC+8CYNvy5+8daaFUKte6ZrazsRSq+funomlS0TQIkIqkee47h/nT/+lbTN5aoJAv09rdsFVA33ku23E6Tbz84gDv/PIu4fUYsWACSZKxuyzIgF6nZqjHx9qWlVkhV+TKu6O8+SenufXB3VpRFrnaDXLx5zcZOtVPqVCu5bSIgoDZ+jBfo1yq0DPYxFv/7gPW5oJYbEZEQeDtf/8JI8/twd3kYG0hxPv/7RKtPQ3sO9X/2PnHIyne/+tLtU4egHy2yLmf3cJsM9E19NASTKUSn2rf9SgPxK1HKeRKeLwWYrEsFu/u2QpGix7LF8wV6ej0MuZdIbzVqbXb/uZmF1A9L7vL8tixjBYDbZaH87BJMo3NdvRaFaGVMJWyjMmqx9NgI5cvo9aosLt2t3V7EulUjsnRlbptarWKaDjNzGhVkLVY9RQzBURRxGDUVXOHRDj68iANzU8OiH+U5emd3XIPWFsIkssUahZ7giDQNdBEe6+PTDpPPJTinT8/S7lYoVQss7gU5tjX93H1vVFy2QIqtYjRYuDAi4NoHiPixYIJIuvxHdulikR4LUpju4dSoYQoChTyRXR6DW39fsqlMqIo4vE7SGbLnD87WftsJJ7D2+VjeXyVC+eukC+Ukbc64CoSDB/pZGZyvfZsyUCxUCafK7I6E6Cly4vB+FD4WFsMsj4bYGZ0hf797Vx89251jpLE1K0Feoebmb639vCdJlf3yTKo1QJSpcLlX9ziyGv7EISqIKTRa1CpxFqnTdseP2vLEWY3EszNVYWY6nMmky6U+fjcJLlimYHeRsw2A6IokoxmiIeTaHQaPE1OvvdnL/PWf/iMlu4Gqm5gMh17/Ny/No+/w4PeVD2nSlnC47Py2pv7CAVS5AslpHKFZDDB7NUZbE4T0UCC2TvLyBW59m4WRVHJQFFQUFBQUPgcKCKKgoKCgoKCgoKCwjOg1WtoH9g9kyIZSTFzZwmAhhYXn/ztJUr5EkargUKuiEqtYuTFQS6/fQuVWuTkNw9RKRYRBIGJW4t88rNb5DJFXA1WYqth/vKn1/D4rEQDCbr2ttKztwVZhlg4xeZKFH9nA81dXjRaNeVCib/+f79LW68PT5Mdo9VANpWnUpYoFsrYPVZCG3G6RtqYv78BCLR2e1FrVBQLZaZGlzn9tb107mlibmJt53nr1HQPNu163q19jXzvX73K8vQ6qVgWu9tC2x4/Tq9t1+Mfxd1o59grQxgteja2hCKVWkVDixNPkwOX18LUlVlGP5mgtb+RwWM9db79hezOVfcPwkpy6Rwn3jjAoef3UClLu1pzPY6+3kYCC0FupvPYdGqMWhUatRqPz4ZagNVt10lUqcgkcyTDKaLBFAaznkwyB8jIEgSWwgwcl1Brqivtgdp/byeXKZDPFBEQMNmMRANxAGZHlzj13SNVeyqVyN2L0/Qf7HxsN83aXLBOQKldFllm+s5inYjyRfA1O3C6LUTD9WJGLJyir9/PpYvTNHb7mLw+V9fh1NTh4eBze5ALJWLBBI5nvEceYLMZ+frrI1y6MM3CfLBmQ6VWi/QPNHHseDf6xwTHPw2dVk0xU0SSZFQqFSpRppQvEV6L0dLbSGuPD2+j/XOPWy5WKJXqra9y2QK+dk9NRJEBp89GYDlaFUplOPLiIMNHu54a3v4oepOOXCaPVJHR6tR1YodGp0GzyzOgUquw2k3Mj61QLlbnqtaoSERTSLLE8z84iqgSaGhyojNo0JoMjF2fRxRFvE12PNu6UjQ6NWqNinKpQrlUIRFJkQinEEWBdDxDeD3K2R9fZXMpTHt/M+d/dg1Hgx2Xz47epOPg1/Zx9sLMjjmuLUcoAz372oiHk6RTOdw+G/0HO9iMpNlcj2E068ik8iDLeNwWAncixIJJXv2j00TC6dpY5VKFtfkQzgYr8UiKvce72VyJ0j3cDJJM52ATrd0NvP+j6wiCQKVSFVCMZh2HTvWwPL6KKFQ7hWKbCaKbCTbmg7ga7Rx6ZZjeAx20dDVgblln4fJs7XtFUcDT5MRk0RNej/MX/9vHvHJmD6piGX+Xl9HLs2SSeQRBoKnLw4nX9/HK75/g3Nu3yGaKaLRqCsUKe452EQvESUUzaHRq9p7spW9fOw63lZlb5yjkiizdXycVz6DVadh7so/1rc6wW59N0D3S9tSsKgUFBQUFBYWdKCKKgoKCgoKCgoKCwleE0WpgZWq9topZpVGxdH+dcqmMr7OBWKhqr/TZ311FkmXsHjsXP7iHJMkYzTpUkpobv7wNQEWSq4XvWws8//1jSJLE0nQAo0VH15CfiSsz5NMFDBY9fftbiQbiZFJ57F4bNpeFSCCB1qBBqlQtwgrlqhVTPJrBbDPg9dsBWJwKcOKVIU5/fS8qjcjE7SUqFQmNWo3XZ+Pka8M0ND1+RXwhWyCXzFEpljCYtJjMBnLpPCvTG0Q2YuhNelp7G3H5dx+jY6AZb4uLpq4Gbp2fIhnNYDTraWpzMXr2PoatFddjF1PMj63w7T97BffWWA6PDZVa3NWOzNVYPUYQhM8loACEViPc+2CMnn4/49dmmV+KoFIJ3I1n2f/CACaroZapYbLqUWlEtHotsWASX7uLcqlMLJhEEAQEUaBSqTB0ohfjVveDv91DS08DKzMP7YLSsQwmqwGby4woCsSDCUw2A5WKRGUrg8LhMhMPJVmcXEeWqyKU+5Hifj6zU0B5QHQz+bmuw27o9VpOvzrEL398vbb6/wGCJPHia8MEg0le/N0T3PlsnNBajIOn+kgE49z9bJyFOwtoDVpOfeMAQyf6avZSz4LbY+GNb+5nM5AgFssgIOD2mvF4rJ9rnEfZWAyzPLPBc989xAd/dYHSlpggyzIHXxxg5FTvFxrfaNZhd5oIb7vukiSjMevxtbrYWAqDBIGVKDJw8hv7iUXTbHwao1yWOPHq0DMLKZFAHLVGxfy9NWS5mpvh9jtweq2IKpGBQx2P7SAB6rrqNFo13hYX6/NBYptJGlpdtPb4iMTzvP/Tm7WONbVGxcGTPew/2YNWq8bZYGfPkW7unp9kYzFIfMtOzuV3sDixSmAxxNLkOqHVKOuLQQ69shdRpaLvYAc9+ztYXI7uEJ0AKhWZxdkg5WKZN373CK52N5lsien5EE6PhT2DzciyzPJimEQ8i9dpYk2ScHqtqLTbnn0ZHN5qLkpXfyOf/uQmew51sO94F3fPTyIgsD4XoKW7gR/88+e4+NEEpXyRpnYPdruBu+cnOf3mCMgyV94bBUli6f4a2VSeubEVgqtRooEEB7+2j9XNBF2DTeQyBZCrApfeqGVzJUJoKzNlPZggPbPJxz+6wnPfPkwmmUeWZVbngtz4eIKb56co5IqIyBhNWnR6Dctzm5z5xn4cTgtmuxHnVr5TS4+P7/zLl5i6vUgxX6TH3oq70UFoNYq89YOlYhly6bwioigoKCgoKHwBFBFFQUFBQUFBQUFB4UtidVnoGWljYyHE4r2H9j2yBMUt+5p8OldbpZ0Ip5AlidnRJUwGDXaHERkYP38fk9VALlMglylitpsoF8uszWzQe6CTSrnCia8N8/b//jHriyHsbgvhtSjORjsHX96LIIqMXZ1l4HgvlXKZSlnC39nAzOQGNy9M09jhRZIkAqsxrA4jeoMWUSWSyOSZW4+waQT/0VbK+TIOm4muXh8NTc7Hnvf0rXk++KvzNRFj/NI0/Ue6QBCYvD5XO66aDfI8zbtYaUmSRDycRpDhyPN7MJj0WFxGPvzL8zUB5QGxzQRX36taZhlMevoOdnDklWEub1nyPKC1r5GW3sYv8EtWyabyGK0GJq/PMX93ubZdb9Zx8e1bfOOfv1gTUbQ6DSMne/G2uTjx5n42F0O499tx+mzM3F3G5bPT1OmrhmhvodNreOFbB7l5dpKpO0uUSxXMNiM2lxmNVk0ylsVgMZBN59GbdMiSjMlczeXIpgtcfn+MZDSDRqvm8MsD7D/VVyuCW59gY9Xc7f3C1+T/z95/R0lyH+a58FPV1Tnn6enJOW7O2F0ssIgkCIJgUqYkWpLtax/Llo7lIMnyta6Pr3nMT+dacpBpiRIliqSYCYLIYXOe3Z2c80x3T+ccq74/ejCL4QIgFSjSYj3nALvd1RW6Qm/37633fd9OW7efZ376OFN3V1ncjpBq72mgb08zLq+N+fkQY3dX2fvkflxWA+vjKyRlBZvdRLlYpVSo8NoXr+BqcBLs9P+V1q3RiDQGnTS+h7D3VyWfKaDUFCqFCk//8lky8Sy1mozdbcXhs+5Ey32/VCtVIqsxSvkyfUNBLnyXeBUJpRk81U/bQIZUOImiKDR1B8jlyhSzBQBuX5qlcyBIoMW9a95MMs/i1AazY2vINZmO/iBNnR7e+NpNirkSe092c/v8DNVKjdByFFEU2H+6l979re+5zYE2L1qdtCOMuRocaHUSqWiGI48OUVJEEok8ouaeKFGt1Lj6xhQmq4HhQ+0IgsDhx/dSKlZYmlhFZ9DSPthEx3ALC+OrLI6t0b2vja21GAChxXqXUc/+Nlx+O5NTG++4bVq9hN6kA0FgK5LBbDNx4dxtTpzoIrGRYG5sFUFR6NnfRsdje7j90l10ei39RzpJxuqdUigQjyTRCs04vTZe+sJljj2+B51Ww/OfPYfeoMPhteALOilki4ydm8DjMBBZyzP65jiFbJG23gD+oItzX7/OobNDTFyZxdVgp3tfK1vrCVLRDOOXZ/D3BcnlSuiNul2OsVKhTGwzufN4dS2OHYVCrkQslESrl6iUqngDdsZvLLIwtoJuu6soFc3garDT2OFncSrE8Ce67hPY/C1uvE1OSoUSc7dXdhwob2F1mjFZVQFFRUVFRUXlr4MqoqioqKioqKioqKj8LXDwkWEuPXeLQLuP8EoUjaRBb9TtFFKbbCYEBExWIxqNADJce/Euo1fnsTpNPPTsEQx6Lebtu9eLxQpGkw6rzYjOoMXmsfD4T59gfXqTje0ekEq5it6kZ2s1TiGTR5Ht2JxmRq/MUanWsLitVBBYmgljthmRZRm704zeIFHI1ePE/K0uXrg4jpivkt/KUNKImL0WFpIp7ry5xUB7A48e6cFs3C1oFPMlrn7n9n0ukMvPjTBwrGvXc4Vckasv3qGhzVPvfNimVpO59eYkV1+8uxPPBHDwTB+p74qLKpcqrMxsIukkzHYTm7ktFsZXeeADB3jy508zu9190jHUTOeelr/R3dZWpxm7x8rVF3aLM3JVxtvkIrqRQGfQUi5WsLnMPP5TJxi9NM3i6Aob82EKuRIajcj7fvEMBruJN741wj/8vz9MPJwishYHoR77dvbZQ+w72U0pX0Zn0GIwSMzcWkTSSWg0Aqszmxx9Yi/pVAFvwMH6QoSHP3qUjaXYzvG/9J272F1Wuvc0A9DU4SPQ5mVz+xx5C51eomffew+k/1VobHHT2OKmsB2p9vbeicGhZnp6GykWymTjWabfGEOpKawubpFJ5VEUMJp0TN1aJNDu/SvHVv1tY9kuny8Vq5Q2kiCAgEAslKJt4J2j7N6NVCzDua9eY3FsFUVW8LV4aG11s7wc2/W62FaWPQfbWJnepJgvE95I7Zou1xQiG4ldIkoqnuXFL10ltHJvWZvLUSRJxBdwkorlsDrNPPLRI6RiWeSaQkOrizMfPIDV/t7dN56AgzMfOszrX71GtVJDEASsLis9hzpoHmhiZnSdQIubaDh93zV/+/Ic3YNBDEYdVqeZ9uFmjr//wHaMV5612RCKrJDaSiPpJAaPdyMIAnJNRmvQUinVnXvmdyk412hEvAEniViGfKZEIZXnZ372AZ7/s/NEw2kMRh1Gg5a1yXW0tSpHHx2iXK1hcVqZ3xZmyqUKew62Mn5tnkCrm8PaQUJLWxgNOoLtXkRJQ2ObB6vDTDaVJ7qRoGu/i4mrcyg1GbPVQMdQExqdhkCbl5f+7DyhxXvX2N4H+zHZjNRkmWqlQi5d76zS6iUM20JKtVJDrt3bd4rCjmgVCyUJtPuplKo4vTbe+PpN5NrukqpEOIXDayMRSVMp19Ab7r9uRFGka7iF6RuLuycIcOjskFoir6KioqKi8tdEFVFUVFRUVFRUVFRU/hZw+uw89jOnWLizTKlQolZTUGoyBpMeX6ubUqlCtVojtpTE7rZy4/VxdHot+0/3ce2lUf7sP3+Lp3/xDDffmAABfEEXRrMeT6OT4+/fz8GHB7l7aZZXP39xZ525dAFPg4NCpkAinMZkM3P47CCvfvk6JklDNJIhly5w7LE9LM1GiKzHyaYL2JwmBKCxw0usWGTq8jyLM5sYTDq0kgZFUdh/shd7wMLEYgiTQcujR3ZHL+VSeRLbcT3FXIl8rrhTpl6tVFFkKBVKKErdebG5GCETz+H03+vC2FiIcOWFuztxM2+xPB0CQaRSLNXjuHQSmUSOYq6E3Wsln96OrFLg5ivjfPxfPEnPvjYqpQqxcIpirojJakCj+esNzrsDTpp6Gu7bLp1BS7DDR7DTR/fBDrRaDY3tXqIbSW68Oo7RYsDmseH0iwiCwLVXxth3ZpATT+6hWq7wpf/vBcql6s6yTj99kIEjnTvLP/ORY7T0BlgYW0OjFfEEXEQ2EkTWYgiiSO/BdqKh74rkUmBqZGlHRDFaDDzysaPcuTjNzMgKtWqNxnYvBx7sJ9Dq/Wvtj/fiLfEkHk6xMReiXKrgDbpo7PRjtRkpZQtk0gWW5yO7BpDz2RKrC1uM3lhi75GOv/Xt+qvQ0OKhe28LM9u9RiigoKA36ugabn7HefKZIgjcJ9bdfmOchbv33EuRlSjWfImz7z9APJ4nnyvh9lpp6fCysbhF7D0i1hR59/k3O7q2S0B5i62NJIIooDdqySRyZBI5NBoRQRRYm428Y9zdOxFodXPoTD/xUBKTzYiglViai/CtP7/C2sIWVqeJo2f6ScSylLYjCyvlKguTG9y+MIMoCgQ7vJisBjYWIxS2u3mMZj2iKOBpchFo9fDNP3yFbKLuENFoJbr3tbGyHkfRCqQyBURBwLztknsLm8tMJJLGbNazPh+mkMjSP9xMprlAOpFFQMBqM7A8F8Hf3cjGRhpTPE/fcBNKpYJGFNBoRLbW4tw6N40gwNCRTrLxLPFQEkG8t79z6QKVco3+g+3Y3Rby6QK1Wo35sTXa+4PM3V7a9dlithupigKH3rcPg1lPaDlGIpwmmcwjigJOrxVvwIGk1SBqxJ3roLXZzdLFuhDj9tspFsoA1LadacV8mXprTv1zV1GgXKzg9FrR6t49orBtIMj7fv40I+cmiW0ksbrM7D3VR+/+tu/rPFBRUVFRUVG5H1VEUVFRUVFRUVFRUflbQquT6D3ciSvgYHFslVQ0w+HH9zJ+fZ7pGwvEQ0l8zS56DnYyenkGUdQgKwoGs45sKk8imuHQo0Pcem2CyHoCb6OTnv1tDBztRCNpaOz0EWjf7TJIJ3K09DXS3N2AIgCyzD/63Y8yfmOR2bF1bC4zHf2NiKJApVxGEOougJYODwce6Wd9IUx4agOP2YAsK+SjOTRakckLM5z6+BFSlLg9s87e7kb8LtvOeo1mA2abkeXpDULLsZ0ByEK+xLH3HyCdXGNtNoTJZmTf6X68Tc77uklW58MoioIgClgdJgRRJLqZZPz6AkOH2rn2nRFESVN3fRQqWF1mLA4zmUR+ZxnFfIl0IkcmnuPic7cIr8QQRYGWviAPPLUPT+O7x5G9F3se6OPoE3sZvTiNvB3tZPdYkbQS/Yc76TnQDkAsnOLWuUkWJzcxWfUYzXpKxTKlQgWdQUtrb4DmLj+Xnr+9y21TLlZ4/SvXcPpsBNrq4obJaqB7fztNfUG0Og1mS70TpVKqcOO1cW6dm3nHbf1u147Ta+PMM4c5eGaAWkXG6jL/tQWl74e12RDP//FrFDJ1VwoCHH50D0ef3IfVZcHisiLXQrvmEUQBs8PMldcnae3y43D91SKz/jaRtBpOPn0AT6ODhdFVyqUK/mY3vjYvc5MbrC1FaWr34mtyEd1MMje2Rng7lqmh0UnXUBOegINsMn+/AwDIRDOUUjlOPza86/lSvlwfH1fumwVBFPBu9xZBXayYvLW08zifK5NJF8jl6qJloVjhzFP7mRlZAqXu8qIGFocRg1FPuVwhtLBFLpXH4bXia/XuOidCy1t8+zOvkU3mUVBYW9iiqbcRV4tvx02XSeR5/VsjnH3mIBsrMYr5EqtzdXFsdT7M5lI9Puz0+/diMhuYuFwviTeYdDR2NfDEz53i1qvj5Laj8LQ6CU+Tm/Ebc1SNWmLRDMO9fqYWo8wtRwn4bFi3RapSscL+wx1YTVr27WvmxnduEQ9naBtsonuwibmJdZbmttAbtVSLVawOIxZJ4Ev/v29jthpw2I1kknmCvUHWtz931ufDHH10GEGESrlGpVwlFcuiKAqde5opFcvodBq0LjM1WcHf7MZiN2G2mYiHkjS0emg+3kVSo3BzKcTG5Dx2SctQ0MfJY21kMmXujm8SC6dRFGhsdePy24msxpC0GrwOE1sWI8VcGXejc8dhloxl6BoMsrG4RSFfQm/QoTNIiKKIVi8xcKjjPd1bgiDQMdRMa18jhVwJvVGLVvfufTgqKioqKioq3xtVRFFRUVFRUVFRUVH5W8bb5MbbdC+Gp/tQB+e/dqMe8aLI3HhlHFHUoMgKk9cXOHh2iOhmiuWZTbwtXh76iRMY9Fo6h5o48b69OxFY/qCLBz90hMWxVYr5ehyXJGmQdBKHHxume387JrMerV6LxqCnisDiTIjLL4+yMh3isY8fRdJJbK7GCba4uP3qGPMTG8RW42xVqrgbnBSrNUrFCkIkTWRiE+eQn0SmwHIouUtEMdmMDBzr5upLY7vumO8/3MmF5+/QMRiktT+IRq9l5M1J5CuwFcpw7LEh+rYFiEqxgq/FQ6Uqs7IQoVyq4nAYcXitTN1epv9oD9M359FIGnoONmF3WwkvR3fta40kolQVvvNn5ynm6oP4sqywNLFGIVvgQ//o0V29BN8LRVFIxrLUqjUe+5mTCIJAZvuueQTYc7KX1v56xFOtWuPCd+5SLtfvLM9nShjNOkw6Cb2phs1pomu4iXQiv0tAeYtaTWZ9PkygzUu5XGH69grj1xdRUJC0Ei2dfgYOtmF1mLA4LO+6zcH2d+46sTq+f2EilS6wFc+iyDIWix6/x74zeP5eVCtVrj5/656AAqDAjZdHaelrRDIZCPYFCa/EiKzFkWUFUSNw7Ml9RGM5yuUa4fX4X0tEyaXz9W4YvRZ3g/1dy9+jm4l6z43VgMtvJ7yRJBZJIwgCXr8db8BOIpzi1qujZOI5ioUSN14dZc+pAWSNSD5bQiOJPPj0AaYmN5iYXCeXK+HzWollikQSGY6f6sP4HufZd7tKAAItbvr2tTA1snLftKFD7TQ03RMAZVmmWqm7mOLxLBuriR2nlCwrpJN5llaiNPUEWJve3Jlvz4luFLnGy5+7xNztJVBAFAX2PTTI0ffvR6fToigKI69P7PT8FLIlUokcqcuznG33YTTpd/qcatW6YBJocZNN5DANNuIJOtFa9Pi7/ZjMei4+d5NAu5f3/eIZUrEsRosBu9eKp9HJ+nyYxg4/siwjywquZhepaI4XPvM6eVlBkkRa23ycPt7NlWsLiAK0d/jp6vEzM7ZOZjPBjVdGkUsV8pkCE1dmiaxE6TrcxeZqHL1Ri6SXcBm0vPH5C9RqMulkfsfBks+WaO0NsDS1gSwrJOM5Wnobmb+7wtp8GK1Owmw1sudED+e+cnXn88bqNPPAM4dx+m1YnGb0Zj3OA828cG2KQr6E1qClJ+Bh88oCX/3GbTSSiM1i4uipATYtOhLJPPFImkqlhsVp5sBgkI2pNfae7CHY1cDdK/UOqUK+SCICwXY3hx7u5+brkxSyRaoVieaeAGc/fJiO/sb3uix20EgaLN8jxk1FRUVFRUXl+0MVUVRUVFRUVFRUVFR+wDi9NnLZIoV8mWqlWndWlKooikKtKqM3aLE5TPTsbSUWzVLIl6mUa2QzxV0dIgAHHxlCEAWuvXiX6Eacxk4/+88MMnC0m3ymyOjoKqlYFleDDY1O4tjD/Vx+/jannz7AS1+7ST5dxOYyszm9Trlao5QroxEESqUqoZUoTT0NlIoVFAXW5yMcPtxGIlMgkyve/8YkibM/dYL5u8vkkgVa+htRBA3pXAV/i4dsKs/MzSUaWjwUCxVe/uIVxq/O8cnf+hB9B9rwNLl5+RsjRDbrcUQCAhtLUYwWPY9+8ABf+H+/id1jxeG34w44GL00g8vv2LUJvQfbSUbTOwLK2wmvxNhc2qKt/3v3WpSLFebHVgmvxSlXZRKxLFaniZMfOkK1VKZSquJucOzqdQmtxVmZj+BwmWkbCLI0sU4hV3cXaESB7n3t9B5o5/w3b73ret+K8Ll5bprVmRDpcJKF0VUknYZiMksqnuHU+/fR2tuA3WPBaNIjyzKCWI8FSkUz9Oxr+Z7v793IZAqMTKxyd3yNwnZEk0YU6GjzcnBvG82B9y5wT8ey9/WvQF2Miq7F8XY0sLaWZPDMID25EqlYBgSBuyOrlMtVGlrcVCq1d5w/GctSrcpYbIZdnSuyLDN+dZ7rr4ySTRbQSCLde1s58vgwDrd153W5TJEbr44xfm2eaqWGotQ7StDpiGxH0WkkkYPHO1m8NkchU0TUCITXEhRyJa48P8KjP3OKfLaE1WXm8uU5Lpyf4tD+ViqRFCPX59FqNfQcbEdv1PPQ43voPtDG3XNTu96LKAoE3kHo0uokTj6xB7fPxt0r8+RzJcxWI3uPd9K3vxWNdM+5pTfoaO7yc/2NqV0CylvL79nTTCiUYnM9SXebm9RWhqEjnQwe7mDq2jxzI0tv238Kt14dI9Dho2tvG9lknpWp9Z3ppVJ1xx0T30wgy+AN2IlHMpRKFVKxLBaTxIVvjbDvsWFWYhnWp9ZIJ/NY7Ub2tfu49MId0qEkFqeZQqHC8Kl+kqkiroCD1ZkQoihgsJnwBN289BeXaNnunjFIGhYn1jFbjQx0N2CxGzl8uB2X387qXIQr56eobXeUxCNpUBSSW2mQZSRJg8NlRjLoKGcLlLe7VoC6kGIzEttMcGComZWZEAaTnq31BAajjoc/eoxsKl/vn3JbeOlPzxELpzCa9VSKFRLRDDdfGcXiteMMOClbdbx8Zw6dWY/RbsTts8FWjrmxVQBqVZlMLs/I1Ske+uAxvvntuxQyRYb3NtPX3UA2ksLc7ifY28jhhwboOdBGMpphfS7C3NgKmVSBlk4fHp+NcrmK3Wtj6GgXA4d+uNF3KioqKioqP66oIoqKioqKioqKiorKDxizzUhTp5/ZOytIWgl/i4f1+TAg4g06yWeKtPQGsLotFIrVnfkCbZ77lmU06XngqQMMneihkCliMOtxeKyszoX4zucubOfo12npa8Rg1NHcHeDcC6OUChUEUaC1w8PouQl0FgOtvY0kQkkUBQRBQX5bf4K70UF2OyrqneKgElsZbl+eR1YUtEYDt68ukk3nOfnEXs5/4ybRzSTZZB5ZUfA2Otlzopu5u6vceG2c3v2tLC9skcuV0UgatDqJ9cUIxXwZURC4YNbz+M+d4vk/foPIagy3387BhwYpFSqElrfQ6rUMHO1i/5kBJq/Nveu+L+bvF1e+m8hqlO/88ZuMnJ+ikCtjsBroO9HHq8/dobHFzVM/cZRDp/rum6+QLaHICololu6DHbga7MyOLNcHtQ+20zLUAghYnWZymWL9Lnlpd6SZN+giGkqyPB1i5uoMq7PhnWnhlRgHzw7SNdREW2+A3v2tPP+nFwivxlGApg4vH/iFBwl2vLMT5XuRyRX5zutjLK/Fdz1vNOoo5MqcvzDNYG8j3V1+TG8TMd6OVieh1WupVe/fzzqjDpvDhNGkY3kuwtLkxn2OnPXFLb77zNpYiXHn2gLzU5vUqjJ2p4l9xzrp39uC3qBlfnSVN75yfUdIqFVlpm4uUiqWefJnT+3Exo28OcGdi/UINEVRWF+OMXZrmcMPD2IwaikWKtSqMmvzEUavzdHQ6CSfLVHYFuQURSGXrrszLD47L/7lNQb7G7nz6hjxt3WZXH95jEKmSEdfgP0PDZKOZliaXAelvg9OfvAQZqeZyVtLFIsV7E4zja1uDCY9JouBg6f76D/YTqlQrpekm995X/fvb+P8S2P3dfVIkobe/W0srcbJpvJ07Gsj2OQkuC34zd9ZfsflbcxH6Nrbhs6gRW/S7QiRoiggy0o9lq5UY34qXL/GdRJun41sMk8EmWPPHubNq7MUsiUkvYSv2U1zo5M3vnyFxFaGri4fW+tJsqk8+ewIj/zMaZr6m5mf2MBkMWAPOtEbdJx55jCSWUcuU2T88hylUoXRq3N0HOnh6rlplsbWefYTJ2kI2Heu52K53vWztR5HkRWq5Sot3X4Onh1ifT2Fz2Xc9V5rNRlJJ+Hy2/E0OHAHHNSqMiaLjnQii9FqoFyqIGk1CAgktjLUqjIWu4myoYIgCKQTOVLRDJ52H2OZJDVZQanW8Da56OtvYv6rI/dWKNT/t7oao5TMMdTqweowsnZzmdDtVQ6e7iGyniQWztDWG8AXdGG1mzj3jZukolkAUqKA3WXBaJHYWouT7y+843FUUVFRUVFR+cGjiigqKioqKioqKioqP2AEQWDPAz0sT29SLlZweG1o9RK5VJ5jT+4jmy6gCALh1Ti5dJFcpoDVbkJvqA8smq2G+5Zpd1mwu+oRT5VShcvfubNLQAFYmdpAqxVp7vIjSRMoei2VcoVqRUajEckmc7j9dvQGXd19AjsRTg6PBVGrQdmOSGpwWflu9EbdfcXYrT0BRt6cRKMR0ZvqA8KJrTRbGwmaUnksDhMTNxZYX46yNBMi2OklHk4ze3eFakXGaNITbPcgCSDoJA48PMj1F0epVWUK2SLP/pPH2FiIsLWeILwU4c6bE1jfJQpKI4k4vbZ3nPYWlXKVc1+5xsztZdLJPKl4DsIpalWZlsEWlue3+PP/9houn42O3sCueY1mPYJQL3wOrSdx+52c+Vgj+VyRZCxHtVrj+T89Ty5doFqusr4Qxt/kxu6uH7em7gZcPivrc2EcDsM7lobfvTDNvtP9yDWZ669O4A44sDjMgILOoOXKS6MEWj00tLjvm/d7MTW7uUtAEQSB5gYHywtb3Lg0j9NuIrEWZ/L6PJ09AQYP1OPidu0Dq5HOfW289qUrVKs19EYtFqsRd8BBsNtfdyYc6eTrf3rhHSPNOnsD5JLZncebqzGe+8IVCvl7LoJUIs+b3xklnchz/Gw/Y5fn7hMSAJYmNwivRgl2+ElGM4xduSeuFXIlkvH6ekavzHLqmcNsFpJvvXMyqQIOl6XeJfI2JEmDzWVmZjaE3W7CbtKR3NrdQQMwM7LMwsQGHU/t532ffJjISpRivoSrwcHGSpw//H++SSaRr8dF2YwEO7ycfeYg3oADAJNZf9++/W4a2zwcfWSQW+enmRtfp1Ss0NTuZc/xLuYXIqyvJshlCty9ucRX/9cb9O1t4fCp7vu6iN5Cb9Ru/6lj76l+zn31GlA/r6vVGrIMWpN+57hVSlVCq3Ge+MghquUyL1+ZI58t7UyTqzJ6QSCVyCEIkM2WKBbrn0n5dIFapcbqWoKTzxxmfT5E/942brw2yupcmHKlRtdgM/tO9nD11XH0Bh2Vat2hVCpVuP76JI9+5DCtPQ0sz4So1WRKsoK32Y1WEtnzQC/xbIm1tfoxrYkigVYvm8t1l5TFZiRTqTL8QC81WWHoWBdt3Q0IgkI+XeDiN64T377uh451ceSxYVamNzHbjdQqNVbmwogaEYNZh9ZmwJSAB4/2sbCVxOqxIOm1mN8WnaXRiJQKZRRZoaTIrM2EsDvN5NIFalWZXKYeEScIEAml2YxkSCbzaF1WGpwWCvEMqWiW5Nv6jiSdOnyjoqKioqLyw0L9V1hFRUVFRUVFRUXl74Bgp58P/tJDzIwssTYfoXtfC30H22nta2R+fJ0bb0wS3UwSC6foHm6mpbuBV750maZOP4987Cg257t3YkRDKUKr9w/AAyyMrbPnwX6cXiuJrTTFfIn1lRgDR7uZuDbHhe+M8MRHjzF5a5nVuRA6o47Bw358zS4WF0O4DjThVCRaGnbHOlXKFcqlKk6fjcR2NFKlXMXmMDF6fhp/s4tSoUIymiHQ4iEWTrI0uc7Q8W7y2RJyVaZaqaHTacmlC+h0EnaHmb69TSyOrjE5tkoplWPoSBcnntpHPp1n7+kTpKJZXvnzCzv9DQC+FjeBNg+bS7v7UvafGcDX/N7iQmwzQXQzgcFqoFiooJE09YHNtTi9J3oByKYL3L4yT3tPw67eDX+Ti6Z2H5lkDq0ksDK5RrVSo6W3EZNZRzlfYmlqA4Du/a009zSwOLGOwaxn36k+tJLAV/6/FwivxigWygwe7mB+fI1M4t57q5Sq1Ko1xq8toCgKgiBgMN3r3qiUqyxNb/yVRZRcvsTIdvTQWzT5HVw8N0MqXaCzxU0llOS1V8cwm/TodRoeeHwPz/6Dh7Bti3flcoVrb8ywEc3RdaiTsQtTZCMZbD47J54+hN1dF7CGD7cxNbJEbDO5S0hp6/bT3uUlvHpPyBm9sbRLQHk7d64t0NUf2DWw/HYUWSGfqcfOFXIlyqV7rq7idkQdsC023tuORDJP76FOEmtRdDppRxgzWgzoLQZMRi0lFKp2HbV0HptZhyBpSCTzO4txeK1U8yVe+YtLVIplmnsCdOxpIbwa5zP/zzfJZQro9BK9w80IskxuK8XN18c586FDGIzvLZ68hSAIbMXziCYDB870IwgC0a0MN64vElpPYjTrKeVL5DMFUvEsV1+fIBpKcPBIO7WqjEa65/mRtBpa+uoRWvlMAV+rh4OPDjN5ZZbiVoa9x7tpG25ldjqEIAiUyxUUWcEfdKIAGpuJUrm6a/tq1RqKrGAw6pBrMnJNoSbX3UpGqwFFUdjaSBILpTj15B6uv3iHYlWmXKmh0YjMj66AAIFWDz0HOxifqbuyBCARzVKryXzkHz7CV/7by0S3BQ+TRc+J9+2jobOBuVcndrYlupVl7yPDuMZXWRxbJdDs5uEPHyGTKRJei9M1GOT5z7xG22ATr3/pCkaLgZa+RnLZIkuTG7QPNqHRSdvdVgorc2EGj3YRXYsxcnmG5UgCo0nHoUf3kBNFEtkCfce7mRldplysIggClXINrU6DbNHh8duYG13F2+RCEASWpkP0729GMOh47pu3MJjrQnk8kmJjcYvegUaaW9xEtoVVSauhsd37fZ0nKioqKioqKn/7qCKKioqKioqKioqKyt8RgTYvgbb7B8K6h5sxmnTotBoUWa5n48/XBxDX5sLM3F7h0EMD771w5a0iYSPVSo1curD9tILFbqSp04fTZyUVy1IqlHE2ujn4kI6RC9Ncf32c4eNdnPnoEVaWIiwtREjPbXD86YOs5Ys8fWoIk2F3aXapUGF1YYvDZwdZnw8zd3eVYqGEL+jC3+xiazOJw2VBkRXCa3F8TU4UWUFRFI6cHcTmNKMzaCnmSuQzRbLJPIdOdvPGV69TKdUHIWPhFBeeu8Xppw8wcLiDrr2tXH/57i4BxWw3oZE06A06OoebiYWSOL02+g530DbY9K5l41Dv1thaT1DMV8hnigwe6aRUrHDrjUk0kohWJ6HRiNRqMsuzYbLpIlb7vZggSavh5PuG+fZnz/PaV+/1nixPb3D44UEK2nvrjqzG0WhEuoZbaB9oxGLT8/LnLgD1iLZ4OM0bf3mFMx89zu0L0zvzGUx6PEEnM3d3Cx5vJx55Z1EhuZUhk6gXe7sDjl37Ip0pkkrfiwcyGrSE1hOk0gW0Wg2mWo2L206OmiyjKBouvjhKc7uXsx89BsDCZIibl2aB+kD5kacPIwoC2XyZ869N0djhx+Gu97gM7W/BZJTI58rIsoLJrKeUK7K5EOHoY8MApBI55qfulaLff7wUtiJpfEEXmUTuvumiRsTqrLuSzDYDBpP+HePczDYjb/exFPJlfO0+Ak1ONudD2F0WDFYD+84MEQ4lae308cYXL1Oryhw9009kOVaP0fPbSSbzGC0G9h5q59W/uEgg6EKn0zA/usLcnWVMLiu5TH0/Dx1q48JXrlHIFtHptVidJgw6iTMfOfqu7/m78fusnH9xdOexpNVQqdSolKuIGpFKuYr2bZFx85Ob9A81ceyp/czeWiSbyOEOODny5D4C7T6mbsxz+blbpGNZTFYDBx8ZZn0hwsiFac5/7Somu4lDJzqZuL1CS6cPUavh8otjDD0xiM5Qd7IUciVkuR73VRQFuve2srEUwaCVSMdFLFYj/hY31W2XjywrJMMp8vkSyVyJxg4fiVCKUqXG8sQ6H//1p5iYjVCryggC6PVaqpUqhVyJrr2tfPLffZjV6U2KhTKeRid6s45UsoDDbmR+un7+aHUS1UoVZ4uPX/mpB2jt9GFxmEknsmwuRfnfv/kFKsUyuVQeURTIpfKszWzS2OlneWqT1FaaPaf6uPriHVwBB4/97EkkSeL5Pz2Pzm7YOW/Of+MGH/hHjxAGUlaJ93/iDNdfukM0kqK5w0f/6T5WUznSkTS1ao3QcpSmTj+CCBa3leefu01r772ieJvLQiFbYnpiA43UjMNpppQvceaZw/iCru/7PFFRUVFRUVH520UVUbap1WpUKu98x5PK31+0Wi0azTvb21VUVFRUVFRU/i7ZXI7uCCcAgihgdZgQBIGpW4vsPdmDVvvOX9/dfjvde1vZXIywMrWJ0aqnpTtANl3A5jLT3htg5PI8GkmDzWEmny2yFUlhthh43y88RNtggPVKiUK+grXFxb4OD4JVT0Yj8PTRYXpa7gk/iqIQ30xSKpUxmHRsLMcw2U0ceXSYaChJPJbF7DCTSebR6qXtu98VRFGkY08Qh8fK4bODONwWBva1cvvKHFqthkCrm/k7K9QqNTSSiM1pRqnJ2ANOFiY3eOijR5G0EpuL90rM9UYdWoOWVz5/Ca1eovtAG1aHBZPNSGtvEJ1O+577fOzSLK9/+Rpzd1cIrcTIZYs0tHrYd7qPWCjJ/HQIvVFXd8wYtO8YIVUtVUmEEjR3+igX6wPZZque5cl19pzs3fXaWk0msZXGvG4kvHDvWOuNOuxuC5lEllQ0jd6ko7QdzfbQR4/Q1tvA1M2ldxQOAFy+3VFrlXKVO+enuPHKGKVCGUmrYeBoF0ce30MhVyYeSVNUZMqlKjp9/Zxy2c28caN+J3/Qb2f25sJ96xFEuHV+ihNP7kOr1zJ6Y3HXOsOh3dFua0tRHG9Fl3X5uf7qGKVC/TdXGsilCxTzZWKbSV743AXaBoJIWpHye9TYlAoVBo93sTS5fl/0Vt/BdnxN9YFmm9PC/tO9XH7hLgAGow5REJAVhX0ne4lFd+/LfKHCh3/uAarFCtl0npW5CKNX57C7Ldx8ZRSTUU9VkSlVarT2N7I2G0IS6tdo93ATyxNriJK4S8CYu71M63ALAIEWNzPX5ihk606ZcrlCtVLj4jduMHi8G+/2AHkyliGdzKPVSngDjl1RXLIsQ6WG2WIgly0iigJ6g45YtO5WqFaq9Aw0srkQ2fXewqEUzZ1ePv5rT1HIlrA4TGj1WlZnN3nlzy9Q2+5B0pv0fPuP3iCTyJFMFVEUhWQsSzKS5tAT+7j2+iRagw5BUZBrMoIg1K9BvYRWp6Wh1YPGrGf40WF056cILYSxO800d/gZOtXH0mwYSdJQrdavcaNZz1YyR6wmY29w0GjUkcvWhZX1bXeSxaynmC5QEeDai3e48ZLIwYcHOPz4HkIrUS48d5u5u/UeoqbuRjraPZRrCplUnkDAzrGHB+joDaAoCoVcEZPFSClbIBFKYbIaUGRlx6GjNWiJrMWButir0UqYHBYqZRmzzcQbX70OgEYQEAQBRVGQtBo2pzYJmzUsVip4/A4O/vRxlGKVydlNXhtd5PhgG9FSBahbnEr5MgMH2pidC+MOONAb7n1OSZKGQJsXu8dKMlPkwWcP0truxRPY7QRUUVFRUVFR+bvlx15EURSFUChEMpn8YW+Kyg8Jh8NBQ0PDe96lqKKioqKioqLyg+bthe6+ZheVSj1CBkWhYyBIciuLt9Gxa55aTSYRy5JN5lgYX2Xs0iy17R6BmVtLnHz6AMef2IPLa+PhD+zj6396kUyqgKTV4PI7kCSRzqEmegYaccUyxLIFaq0iWq0Gn9NCs89JtVAiFkpidZmpFKtce+E2E1dmqVVlAr2NLG2lARuhlRjh1TiCCHuPdqLTakhupXH57ZRLVToGgzz2kw/Qs68Fq6PuFuje00QqV0Rv1WPUirz2xcvUajIGvQ6dViS6kcQdcLKxHGPi5hINLR48jU5WtyOyXA0Ozn2tXjCuN+sRBJFsKs/45VnaBpro2tPyrvs7nchy5cW7yIqM0W7C4a2QzxUJL0fZf2YAZ7OHK2/O4PBYae7y0d7TsMuF8hapWAZREHb6ad6ikC1RyN6vBhhMOoL9AW69PEoomkYUBExGHQ6PFY0k4m92Y3KYEEWRAw8NsPeBHvQGPYOHO1iZCd1fKq7V0PpdXS3zd1e4+K17zphqpcbIG5OkUwUi0SyFXBlvk5PYRhKtUYvLZ6sP7m7HXwmCQKVUoVqpPzboTJi2XUNb60nmxtfxBp07HSOCAP4GO7VimWqlitFmIpEskIzfEyp8QRfv+7lTXH1pjI2lCNlkHgHY+0A3i2Nr1GoyMyNLtO9vYykfe8f+FACn20JbXyNP/OxJbr0xwdZ6Ap1By/DxLoaO9yCK9yKr9pzoQSNpuH1+ChSFlm4fzb1BZI2GSr7uZjJZDDjsRmrVGq9/cwRvwEF7b4ADp3po6fKTzxRYubNMc5uHtbU4CxPr9O9vpbWvkdhmApPHxt4Hennhc+fxNzgQ7q0eWVEo5upih8NtYfri5L2JSt0lJssyyUgKi8PEyKU5Rq/OUypWEAQItns5/sgQge2otlpVJrIa57H3DRNajREPpdDpJdpaHExMhWgMOjFpRObmd4sotZqMyWrEYDbsxEYBLE+u7wgoAAaznpWpdURJg8FiRJZBI0ApX0anEenf38bs2Cr9B9sJ+O1oJA2g4HTacAccOzFzq7EswWPd9JzooS3oJhPNMHl7Ga1Wwh90UpUVuve2sTi6jM9uqothokBNEvC1eXZ6TXQ6DSZJw8r0Jg88NsSlb9/G5jSzNhfimX94lu987jx3L80iV2XSiRzXXxnn2BN7yWaLHHywn5WZMKvTG4iKwvi1ecKrcUwWPS1dfkSNSC5doGOvlUq5iiKDTq+lmC8jaSXah1pYnNqgkCuBAgvj65itBhKRNNV8BbvFSLZYolKqUi2UGehvJ5upH+tYLEs0XyBZLiOIAnZF3BbFjZSLVbxBBx2DQZbemKpfe9+FKApYbEawGRF0kiqgqKioqKio/AjwYy+ivCWg+Hw+TCaTOpD+Y4SiKOTzeSKR+o+MQCDwPeZQUVFRUVFRUfnB4d2+g76hxc34rRU231Yyns2WqNZknvzJ43ga7AAszUe4c2OR9ZUYellmYmQZZ8CJJNbjdQxGHbH1BDqDjq3NJCPnpmhuc1MsVMmk8jjcFvaf6CKbyPGl//oS1UoNSauhfSCIs82LKBa5dvM2UzcWqBQr+Fs8BLt8TFyZpVqpCzX5eJamVjfzk5uszIXJJHLIsoK/0cGTP3uSjaUtivkSJouR40/uobWn/n2rXK5y9/Yy168tkM0UiW+lMekknAEXdqeF+EaCTDxHS3cD8a00Wr1EOpkntJage18b45dmKBcrVKs1SoX6QKXb79i1P+OhJLyHiJLaylDIFqmWq6QTOaweCx02I4VciWgkzWY0v1P87HBbGDzQ9o6/FYxvG5R+O2abEZPVQOpt7hFHg52CScfLtxdpbHAQujyzM81q1tPot/PAB/bT0t+EVqdB+zYnTftAkNNP7+fqy2PbnR5gdZo5/YH9BFo8u9Y9fnV21+NqtUZkPcHSXIijTx2su1FCKQa6A5y/MotGo6El4EQUBWRZYWU1RlN3A6HVGA67iVq+zMpaHI2koXtfK1denQBRoLHDy9zEJo1BB1e/eYNYOImigFYrcepDh7E7jGwubaE36nD57TR3NdDQ4iEWSjJ2ZY71+TBrs/ccOQgCS6OruNv9bIV3u1oAGpqcWKwGJm8uoigKDz57GK1OQqeXMNtM971eb9Rx4MF+eve3ks+WEDUCk6Pr3Lled9CYLHp0GoE7l2dxe21oJJHFyQ1e/OIV9h5uJ70Rp+dwB4E2D1q9FpPNwMzEBpMjy+gNWk4/c5CKyUge8Acc2CyG+9bv8tdj1Ir5Eja3hUQ4jSDUo8dMZgNmqxGjxcC1N6a4c3luZ15FgbWFLb79F5f50C+cwu2zo9VJtHb7mb+7zJ1zU4gaEVmRqZRrnHpyH0vLMRZnQ8iyvEtMagg6aer037d/3nI7vcVbQq4A2JwmNhajRMMp5JrM6lyYuzeWOPXUPgJBO729jQxFUhSLFbTvUHieSBdIiQInHw7gf6CHvgNtZFJ5DAYdCAKT1+do7WkkdX6KjoFGNKJIQ7uPtsPtTI5toJM0mHQS+USWo2f6GLs4TblQIVpIkk3mmBlZZuzK/M75rWyLbjdeG+PkBw6SiGUJtLioVWq88fUb5FJ5wqtxcpki+WwRb7OLlckN5m4vceTxfdx8dQydQUupWMHb7MLus7EyH6ZariIr4PBYMRi0FLPFevybRUdpq4rTZKSprYHvPHebSqW+/3x+Oyce6uNWNMNTj+5Fs5jCYjMi6TT0DDVz4sk9NHY2cOXGEtWqfN++ezu12juLiSoqKioqKip/t/xYiyi1Wm1HQHG7/2pFjCp/PzAa63cTRiIRfD6fGu2loqKioqKi8kMj2O5l4HAHy7PhXQKKVifh9FhJRLOMXVvgzNP7mZve5IWv36JWlbE7TazeXqJcqrEVTuNwm/AFnWST+frA58UZFmfD5DJFBAEQ6oPc8VCCqRuLLI6vIWk1WJ0m/M0eJq7Pc/H52zS1erh7cYaGVg92t4XV2U0uPXeTE0/tZ2OuPvCdjKTY8+AAuUweb8COqBEo58vM3F3hf1yc4fGffoDYVob+w53odBKbS1u4/HZGRpa5fLE+0C9JGjSiSDqVp30wyPlv3aKxwU5qS6BarVGryuw52YnebmQrkuLAsS6e/pVHGL04TalYxuax4PI5sGx3YbzF28vX3wm9UYeoEXf+y6YKmCwGBI2IwWxAn6mg2E00tXl44iOHaO3yveNyAm1emrobWJsNAfVy7Xy2hMGip2O4BZvXzuLEKjqDjqxJy2o8jcNjRbCZGDzaxcS1ORQF8sUKnt5G3G1eTJb7hRmNRmTviR7a+4JEQ0lEUcAXdGGy7n6tLMvkUve6TtKJLImtDBvLMUSNSGwjSTpdwOayYBFFmptchMNJkq1uOtq9zExvkoxladvbQqDNi1Susr5Qj1Dzt3ho7PQTi9QdKBsLWzS2uJi8OMnaQoRypQoKuAMOkuEkL3/uPJJWQtJp6D/UwaGHB7E6zRhMOmZvL1Mp7S4mB6hVqnT1NZCI53bEOoDmdg+BgJ2vf+YNKtuF5pJWw6Ez/Rw43feex9psM+2ILCf9DvqGm4ltpcmlC7z69Zv4Ao6d14a3HR6JcJqH3reHyEqMfDrP0uQGrgYHDz42RC5bRKPRYLeb0bkstHb6aLIZuPHK6K712lwWeg+2kStUuHlumv0PD/HSn55HlmVsTjO+Rgd9RzrRmgyMXb8/Pg0gnymyOLWJ21cXTz1+G187P4UgCkiSSGyzHv/2zc+8zrEPHACxfv4WskU0mrrT7MjDg1gc5vuW3dDq4dzXrpGNZ+tRfx4rRosRBMhnSxTyJSSthiqg0WrQSCJzd5Zp7zxIc5uHx3VDvPjqGDVZQavV4LEaEGsyskZkK13g2KFOGhsciKJA89tEnK21OAu3FtEatDz+0ye4+PWbFPIlVsbXqBTLHH9yPw3PHiaynuDzn3qO8aUIbf2N6PQ6FEVmaz3O2lyIWrWGIAoosoIgCgiigFyr/93hMnPn/BQT1xco5st4AnaGj3VTyJcYOTfN0bMDiJKG2HqCTDLLEz9/Gnejk2KuzNLUBtM3l8in69FrTq+F8FqcaqVKpVgFWcbnsiIiYPDZGF/e2hFQACLhFK985y4f+uAh5FCKvKJw5EwvSk3B7rHQ3h9Eq5Nweyz3ReB9Nw7H/eKgioqKioqKyt89P9YiylsdKCaT+sXkx5m3jn+lUlFFFBUVFRUVFZUfGnqjjsNnB7l7fQmT1UCtKmNzmbG7LRhMegBm7q4yfLyTS29M7cTwVKsyxrcNuse3siiVGqlIfXCumCty7eVRAu1e3A0OUOr9FQ6PhdFLs4gi9Tv1UwUufP06tZrC/jN9XP72bSqVKusLYbR6CZ1OopArkYrWB1xr1Rpmu5H5sVVuvTaBqBEpFctYnWbyqTyVchVZljnwQA8Ld1e4/sooKGBxmHC0eNBuF2JDfeC/Uq4Sy5c49PAgyxNr6M0GFEmktbsBwWbiG1+7RUunl1iywIHD7TzxiQcp5ApUK/KOgPEWRouBpq6G99zfnqCTrj3NzIws42lwEFmPk88WMZq0HDjdS1s8j0YjMHS4g57Bpnddjs6g5aGPHuXai3e5fX6KzeUY7UNN9B3uZGE2TCFTZN+pfswBG998cxyHtx7fE8kUsA8EeWSgiUqhjNaiJ1GpMb0Sxeu1v+v6bC4zNtf9A+JvIYoiHcPN3Hx1nHy2yNpcGL1JT7VSo7M/SHgjQTSUor1fIrISZ7jVh89jJZXK090bYHZmE0WB6fkIZ546gJLM0zaQRKvXojXp2QqnEbczq9KJPF39AcauzlEt3xM8Bg62c+FbIzR3N9DU6adahtFLs1TLVc5+/BhanYRWJ72jiKLVSbT3BWjva2RzLUG1UsPuMiNXKrzw+Su74sxKhTKvf+0GeqOO4WOdu9wX74XHb8Pjt3Hu+Tvo9ffcPsV8meT2dVMqlDGYdTz/mVcBAbPDxPSNeSw2I+GVKJlEjuWxlXpsXIubg2eH0Jv0jF6YolQs09wV4MDDgwTafZjtJmxOE+GNJB//tfezMbtJtVShsTtA/7FuktHMrlit72Z1PsKh032Uy1VCazFkUSSfK5JO5KlVa3jMeoKtHgRFYWujHm/W1OnH3+Ti7Pv2EFuPkYlnCHb4sW6LjbIsk4ik8AQcrM9uojPouPn6BAceHmTuzjLz4+vIVRmDUcepnzzEViiNx2NBkjQYbGZMViP9PQZ0OomxiXXSS2GufOEiuXQBr9/O+37mNAf2tiCK97u3tjbiyDUZt9/Bd/74HKV8iVKhjCzLjJ+bQpAVfG0+7F47g8e7KWRLXPrObTKJHFqdxPCxbhpaPRjNeoqFMgh1B1WtVEUUBRrbvFx54Q5b63HymSIoCqHlGKlolqOPDaM361lf2iLY00hzbxABBbPNxHc+e47W/iZESURnkKhWqwwe7cJsN3HhuREQBB54cg/+oBOjxcAen41rt5bJh1LodVoq1RqiKGA16Qn6nURXkzz2yAArkxuUS2Vaextp7Q/uiKRDe1sIh8be9bg7nCaamtUyeRUVFRUVlR8FfqxFlLdQI7x+vFGPv4qKioqKisqPCqJGg8VhomOwqZ7n813fU6rVGluhFPGtDJlUnlQsS7lYYd+BFqrX55EkiUq5SjqZR5I0eAIOarKCokBkJY7VYUa3XWIsaSUyqRxde5qRazIGiwGbx4LJYsTtd2BzmfE1uXAGXRisRkxGLfFIimwyj9GkQ2fU0tQbZOLmIqKkqReUCwKhlTgWmxGn10ZDk4sX/vwiFocJ57Z4sDwf4ebVec58+AhrkQxQ76UQZYVSqcbsbIiDRzoZ6A+QjGYoZApotBoeeqiXjViOsdFVNjeTPP3MARxOMw995ChXX7jD/N0VarJMY7uX4+8/gKvB8d77WhTp3t9KeDnKwtgKOq0Gd6OTgWPdLM+FKRUqdA+30NH7vSNfZQUw6Bk6NUDbgSI3zs1y4dMvYbYb6RtuwuI04dYKeJwWrJJEMV1E1Ajo9DqiuSIFWYZ03T1yZ2KNvX1BrO8SE/b90H+og/m7q0TW4siygiAImKwG2oaaufbG1PYAehqDSUd4OYbJrOfpjx+hVK3S4LbwwtdvkdxKsTYfYWspRngjgdNloaPbj+VtsVmiCJJeornDT3g1RrUm09YTILS0hSzLlMpVctkiVnt9npmRZfae7MXX7GbwaCfXXxm/b9s7h5vxNNQjsLxvc4i8+IW3CSiKQiqeJbKeoJQv8+JfXCa6EWfP8R68we+/PyK77TR4C7kq73SxmG1GVmc2KRcraPVa8ukCwyf7mLw2t/2cRHip7l569QuX+MAvn8XhsXLymUNY7WZ8Le4dUadcrDI/tg4CFEQRZ5MHUSOSTOS4fWmOvSd73nM79XottarM7esLRDcSRFej1LYFDr3dyFY4hc1uoq3Lj6TTIisyQ0e6iK3F+cPf/BInn9xDpVghHc8yfLyH4VO9VEpVRl4Zxey08NhPnwJJQ3g9Qb5Y48zHTuC/No+sKOgMOjbWEjjcFmwuC6JG3BHMBEGgq92HksrzxS9dwGXQ4bMaMVuN3HnpDu3dDbT0Be97P2/Ff2WTOVKxTF2wMukwmA2IWg0bCxG2YnmS8Rxt3X5uvDaOzWUhly5QKVcZOT9FY6eX3n1t3HhzgnymQLVSrfc0tXmIR1IUc2VqFRmNJFIqVBCFesyh3qhjYz7M5PUFGlrdlPJlNubD2FwWBo50sjy9ST5XZO/JXh788BFuvjHFxefv4PTbsbvMXH15jPb+IN37W5ndzLA8FUbUCDSY6i42FBBqMm67mUq5Rk0jcfZjx97xuHb3BghtJBkfXbtvmsGo5ezjQxi3BXQVFRUVFRWVHy6qiKKioqKioqKioqLyI4LZZiDQ7GJxOnSfgALgDTiQFYVoKEl4NQ7bY8rTEyH2nuxl4toCtUqNigBNXT4Gj3bV78SmLsCUi5UdEaWYL+FvdtMx0MytN8Y59/UbWKwGJI3IzVfu8tgnHmR0apMbb0xhMOnxNTrpP9xFR6ubpbvLGO1mvv25C/QeaCcaSlIt19AbdTi9VrbWEwhiPY4rHc+i0WpweqFcrJDPFklE0qzNhCgbdLh9FhpsemZDCShVOHSglc6eBl783HmiqzGSW2kURaF9oIknf+lhVuIFYtEM01ObHD3ehavBwROfOE08lKJWq+H02XZ1ibwbs3eW+eJ/eY5CtkjncAsmsx6tQUtzdwNtWglfk5NAsxu9cXcsWKVcZXM9TiZdRNSIeH02rrwyjsmi5/blOUYuz+8culyqwMzoOhvLcZ74icMUVpNcu7HIW1qApNVw5MFetBY96Xy9hD5bKJPOFv9GIoo74OSDv/ww575xk8WJNTyNTio1hdefu0NuW6yJ61PUqjUaWj20dTfQ3OxGoxHp7vAj5UssL24hShq8dhNaAUxGHWabcdd6yuUasXienoPtFHJFBEHA4bUye2cFnV7CoNfWXRsWPZJOS0VWmJ/aRKPXMnyih1Qsy+ydlXqfhQBNnX6OPjb8jjc5JSL3Yo/SyTxrc5EdUSUZzbC5Emd94SLP/NJD2Jzv7tR5Ox6/jbm36Tg6kxb9di+GxWYkFa2vU2fQko5l0ekkwitRDCY9epMdRamSzxZZnw3x5leu7TiiDCY9D33sKD372wFIRTNYHSYi63UHRiFXRGeon1frCxGOPjKI2Wogl9kt6rxFx0CQzY0kF755C0GR0Uga8pkiuXQBjaTB3ehA0WioCQJjF6awOs109gR49YuXGTjUztUX7pDY7jYZvTjN8KVpHvmpB5BlhUwsgyjAejjL0nQIjUbE5rOzsZ4ktBanlK87RJo7fZitRhweC+6Ge04pWZYZOz/J2uQGtUoNSSvh9NvxBJ1szIffUUQJtPuwOs0UCuX6NdTkIhnNks8W2ViKYrKb6BsKAgJXX7xLKpolk8jhDbooF8sYLQbCazEe+egxIusxlqY3yWeKNHd7OPuxYyyOr5FJZqlVq9hd9SguWQFRI1CryWwsbqHRahAEYacXJr0daZZJZLE4Ldw+P02g04/ZbmT/QwNEQyn0FiOHzg4h12RSiTzxXAW7x0ImnqOYqV+/Or1EsNO/IxSVS5V3Pf8MBi2nHuqnqcXNxNgasWgGSaOhd6CR7p4GfA3v7khTUVFRUVFR+btFFVH+ntPW1sav/uqv8qu/+qs/7E1RUVFRUVFRUVH5HoiiyNCRTlbmItRqu+N9BFHgwMkeisUSFpOOhFaivN0NEY9mKJUq7Ht4AK0o4nSZMRkk8pkCDreFzsEgCxPriNK9uKNsqsDeU71EN5Nc/NYtRI2A0aRjay2Ov8PHi1+6QvO+diqlCiarkUK+zPR0iMYOL/sfHWbqxiIDR7owWQ3YXRYq5Rq9B9uwOs3UKjV0eg2ZRB6DSY9GEinmS6zOhkGroVSsEFqJYvDbMVUqXDk3iaJApVIlvBLDKCgUkjmSW/VBbEmrYX0hwuz1eYJ721grVBgbXWF4bzMmk55CtkgqXo9E0hm0ODzvLaIUc0XOfeUaK1MbdZfOahyr04zVZcHuslIolpm7vUj7QBPBTh+lfJm528vEt9KY7CYqlRo1RcHptTFyPovLY6VWlbn08jiyrCCKAkaLAVmWKZerFAtlpu+skS6VeFsaFdVKjUuvTPDwB/cTr9S2i+y/t0talmWKhQoajYje8M7v1eG10djpY3MlRjKew+a2Ui7eG9DVGbSk41nMFgMHTnaj0Yg75+CBU30Y9RITV+Zoa3WztRbH7rYisHvbuoeaiETSmBtcDJ/sZfTSLIloho6BIKGVGCszmyiKQq1aQ6uTaOkPsroc5fbNJR794AEe/YnjDB/vJpPK1wUaWWF2ZIlqpUpDm5em7gC67bgtf7OLyEYCRVGIh1O7Yr08AQfFXIl8psjaXIiBw53fcx8CtPU2cPPCNJVtZ4UkaWho95KL5+geCCDKNXRaCWeDfVffidFioFapX3tyTWZ9Pkz3gfad6cV8ide/dBWNpGFpaoObr0+STuQJdvpYnguTimVx+e24/Xb0Bi16o54HHh/m1a/dvO+67+hrpLXbz60rs0zenEcQRQ4/voeR18aJhZKUSxW8jU4C/U1MT29idZh44H37WBhbQ9JpqBQrbCxGdorXi4Uyc+Nr+K/O4Wl2E12NoTXoSMVyABgselLxHL37WpmfWN/ZjkqpSigRRZJEmjrv9QOtTG0QWY0hSRIodaE0EU4h6aR3PZctdhMPfOgwc7eXWOoJsDi+Vne41KCQKeJpciNXZVLxLEuTG1RKFRQgHk7ibnDgaXRSLFTJ5ko88PQhHpBlalUZQRSYvL6AVi8hajSUixVsLgMWu4lcuoCiKNhcFiqlKt6Ag0qp7ih6i1wqj1ytYbboaWzzEmjzcvnlcbbWE8g1mWK+hKgROfboEE6rgawosrmaoLHTh0D92jFa9DsCCoDR+N7dTAaDlv7BID29AUqlCqJGxPAu17SKioqKiorKDw9VRPkR5gMf+ACFQoFXXnnlvmmXL1/mxIkT3Lx5kwMHDvwQtk5FRUVFRUVFReUHQVtvA49+5DDXXpsgvlWPu7K7Lew93M6d18eYujFPaCNF97424tkSm2tJAHKZIiNXF2nu8dHS6eXyt26xvhBBkkQGj/dw+un9KAgYzXoUWaFcqtLc3UA5V2LPiR62NmPEVuO4GhyEQykQ4ESHl3SpSiFXxuG10tPm5pXPXUArCvWB5r0tCBqRD//jRygVKkzdWWH0xhJGs55DD/ZiMuvoHAjganAQDaUoFSsYtRokrQaH14rVZebCX17BYNIjaTXodBK+RiebcyHS2+9doxF3eusSW2kcsQwajUQ+X6FUrBJdjXPlxTt194cgUC1XGTrWzdDx7vsGcaObSSZvLBAPp7j52jhavRZPoxNfi4d4OEUmkSMWTqKIIq9+9TpancTDzxxi5NwkngY76/NhtjaSDB3vpqHNy9f/4GW8TS42VuOcffYIw4fauXNtAVlWyGcKmG1GatUatZrM4kyIjj1NQHRne+SaTLlUZeT8DPqgjXimSFuHF9u7RPgoisLSbJiJkWU2V2JotRK9e5vpGWrCZNZRLVUx2Y07MVJtfUFG3pyinC9ze2yC008MsbYSZ3M5hs1hpqHJQXOHF7Nl9/pMViMHzw6z78EBarUaLQNNXHppjPJ2h4kgQMdAIz37Wnn+azcp5MsYHDYe/MkHEAUBu0XHZ377y4BCqVhBkkQq5Sqdg02UKjXK5Sovf+MWH/mFUwS3C8enby7w8p9doFa9161y8JFhjr9/PxpJQ9eeZiZuLlIqlCnmygiigN6gQ6MV6RpqYmlqs36ORDPf97Xma3Ty6IcP89o3blHcdiQ0NLuRmuzM3ZhDkWVCS1F0Bi2HHh0mshoj0OFDo9GQS+URRAGtTsIVcFCt1HYt293o5Mu//zJavURkNcbSdIipm4uc+uAB8pki4dU4tUqN7j3NjF6dw2Qz8sRPHmVxYpO1xQhGk57+A210DgYxmvWktjIoCpSyJc6/cIeuwSb6jnahkUT0Jj23R1cJtvp45GPHSSbz1GoyZquJyGoUQRBQuCc6SZKGlcl1znzsOHK1RqVUxu42k0rk8Da5KRUrRNYTnHxyLyMXpslnioiiSFOHj+FjXZTeJsYtjq3S2NXAy39xkcK2k8bf6qFSqWL3WN9xv8uyzOLYKqOXZuk/1k0qlmVlagN30Enf4U6m766yuRKne18LDo+VxLYbTWfQgUZkZT6CpNWwuRLnzefv0r+3mT1HO4iuJXB6behNOgLtHg49PEg6kaNWrrAwvobRYqSlpwGLzUAhW3cDOX02EpE05UIZq8uM2W7CaDVy4OEBpkaWEUSBQrb+vgRRxGg2cPfqPM/+gzNYG7WsLkbJpgo0v01Yegur3Ujw++w00UgiJkmN7lJRUVFRUflRRRVRfoT55Cc/ybPPPsvy8jKtra27pv3RH/0R+/btUwUUFRUVFRUVFZW/ZwiCQM+eZlq6fMTCaRQFtFqRb/z3lyhkipTyZURkLj93k+NPHSSmu+dIATh4rJvZm/MktzKYrEYESURr0OJv95FJF9iYjZBN5uk70MrMtTmuvzzKysQa3fvb6NvbxujVearxLN6gi0Qiz/z4OqJGpLfDyzf/12tIOgmbw4zVoGXqxiKnnznE5nyY89+8BYJA194W9p7q45v/61WQFZRqDY1GxNvipr2/keXpTZqa3Vg9NiqFEuVyFUmrobHVQ0Ozi3K+jMdnxeaycO2VUXKpAgr1gV+b00IxX0awatHrJKqlCiNvTrI+G2JmZIlaVaapuwG9UYfDa6W5+16fSTyc4vw3blAqVqhVa7gDDnLpPDavjTe/dh2AQr5ENJTE7rXR3hfAbDXynT85h91rIxZOE1mLI+kkVqc2sDrMVCs1ttbimC0G7lyaob0/iMVuJJsqoCgg1xS0eg2KrKDTa6m+TSCQazK5TJFqpUZkI0lfh5vV9QQenY6p2yscfKD7vqL08VvLvPHc7Z3eDigxdXuZ1YlV4usxqCk0tPsYPNGNr9mDJ+DgfT93ikvfuc3c6Aq3z02y53g3Z96/h3goRSqaZnVqnVpl3zueixpJg0bSsOdoJ03tXsLr9aJ3p8dCQ7ObarWGy20hHstSLJTZLJTRaERSoSpnP3aUyesLhJajWJ1mBo50kkpksATrg8rlcpWV+Qhev518usDFb93cJaAA3HptjPahJoKdDTR3+nn0o0e59to4Op2GarnG5nKUzoFGFkeXaO4JsrEc2ynt/n7pGgjiCzhYX4pSzJfRiAqvfu4cnkC9W8ViN5GKZlmZ2uTAwwMcPDvEua9eo5gr0dDupaHVg6/Fy/pcGEmrwdPoRKuTSEQzFAslSqUKhXwZT8BOdDPFrdcn2XOql7mxdRLxLLIo8ue//woC0LWnmff/1HFOv38PGknacQcBmLd7ZWRZBkVgbmwNQRSRazKHHxlCkDQ0NDvZ3EiSyxRp7PAxeWORti4vq9MbyNu71t/sJhXN4A44+PLvv0TXQJCh4110yHDl9Snauv1kYxm0Sg2zVc/DHzqI1qDD12AnspFgcyVKIpyiUqyAUo8mmx9d5cQHDjF/d4m2/ia2VqNoRJHb5ybxt3pw+h279nlkJcaNl0bRGnW88PlLeBudnHjmMNVyldWFKGsLEVxeK517muk71M6l5+/UC9tdZkKrccw2I/2H2kmmCrh8NmbH1sgkcph0dSeIZNRicdu5fn6GXLqAxW7iwQ8fxd/oZH1pi+79bZjtJvLpAuVShZ4D7WzMh3H5HTT3BDj9zCHKpQqjl2ew2M0MHO1kcylKPlukWpNpbPKQ2MoSaPMQbHKRTOXvv3Y0Iicf7MNkVoURFRUVFRWVvw+oIsqPME899RQ+n4/Pfvaz/Lt/9+92ns/n83zxi1/kP/7H/8hXvvIVfvu3f5u5uTkCgQD/9J/+U37t137tHZe3tLREe3s7IyMj7Nu3D4BkMonT6eT111/nzJkzvPHGGzz00EO88MIL/Kt/9a+Ympri+PHjfOELX+DmzZv8i3/xL1hfX+f9738///t//29MpvqXeUVR+NSnPsX/+B//g83NTXp6evit3/otPvKRj/zA95OKioqKioqKyt9HDCY9wXYvAGMXp3fu8jaa9YRWYjQ0Otic3aB3Tzvzs2F8ATt7DrVj1Wso5csY7Eb0ZgOudi+rmSLf+r0XsDrNtHX46Oz0EluL8cqfn8flt6GRNNx6dYxAu4/eg23Ekzn2PdjPlcvzCIKAxWIgth6jXKoiK5BNF7AYJLxBF7O35llfjJKKZwGYvD5POVfE2+hiYXyNzr4A+UyBudvLPPjhBnzNbg4+1E9NoyERyyBXZdwNdsxmHee+cg1JFHEHHCQiSQ4/sofL3xmhVCjj8tsRtBKOBgebySL79reSiWWYu7vM/OgqiqzQ0OahWqly8Vs3CXb4dokoS9MbLI6vMXVjAY2kYeBIB+mYjpuvjiNKIpViFUkrgSgye3eVMx86RLUqk8sUtgWXArWagsNmIhFJkU3m0Bm0FLNFLE4ztWqNmTvL7DnUwaVXJ7fXqqA36gi0uLG7zChvEwnK5eqOe8Fk0VMq12gKOLEIGq69PkVLhw//20rSk/EsNy/O4G+wUUjnEUQRs9NMMZXnpT+/iMtrQauTuH1xhvPP3eLEM0fwBBzsO9HNh375YbwBJ6GVKOViha2VGLKiUClX6d3fzuZylPmxNWxOM83dDVgc93eKSBoBoVpFKVfQCPV+CYNWxwMP9fOdb9zaeS+1moykl7j9+jhGi4GDDw1QKpaZurVIsN1H5W2Ojeh2z0kqliETy963znKxwtzIMkuTG1gcZtwNdnw+K3PX54iHUrQPBinnSoxdnGVxbI3THz5K07az5d3IZYosTm8yc3eVUqFCS7efrsEg/ftbd641cdv5BGC0GDFa6l0wB88OI9dkOoZbqNVkjCY9JruJL//XF5B0GjwNdi596wZOv4PIegLJoMPsMNe7iPQSwXYP2VQBb6B+LlfKNSZHlrfPFFic3OClr1zHZDlFe0/Dru1u620g2O7lzoWZeiybUBdcnV4bFreV4sQmwSYXyxMbAFRl6DvQhsVmwOI0IwAmi4FcKk+5UsPhdxDeWCayGudqZpSf/PWnkDQaPv9fnmN9LkylVEEjiZz+0GF6D3Rw4evXaRsM0tbdwPOfu0AmliERSfH0PzjD1PV5optJ3vfzD3L5uRtUy1WKuTKrM5tUKxU+/E/fh/Ft4lY8nKKYL6Iz6TCa9SxOrDE/ugoCBNr9aDQi5VIFo0nPyLlJHnj/PubvLpNJF9HpJYaOdSGZDcyPrRFeiYGiYDTpCQw2koiluXN9mWqlhq/JhaARqZSqfONPLnL6qb3Mj61x6FQvF749QiaeI5PIYXWZOf2BA2hEeOynTvD5//ebdO5vZ2M+gqKAJ+jE1+Qik8zjcFvIZQqkE1kMRi1D/Q2IFiNb8RylYgVBEGhu9bDvUCsd3+NcVFFRUVFRUfk/B1VE+RFGkiR+7ud+js9+9rP89m//9k4cwV/+5V9SLpc5fvw4R44c4Xd+53f4+Mc/zqVLl/jH//gf43a7+fmf//m/0bp/53d+h9///d/HZDLxsY99jI997GPo9Xo+//nPk81m+dCHPsR//a//ld/4jd8A4Dd/8zf56le/yn//7/+d7u5uzp07x8/8zM/g9Xp58MEH/6a7QkVFRUVFRUXlx5piobzzd4NZjztgZ2s9AQp09/jxea1USmX2HGjj4vO3Ca3FMTvM4DJz7fYKhVyJalWmWCizshSlVqxQXg6jNehQZKhWqji8NlLRDI2tHh796VPMLkbJZotoNCJavbRTUK/ICtVKFZPFgDfo4Mq3RzBvF3mLooDHbye2EefUhw4TaPUACrVSGYNZj0YjsOdkDyuzETLxLHa3hRNnB3F5rbz6xSvUSlVkUSCbzNHSH2RpaoPTHzpEciuL3W/H5LSQk8HtsWAVYeTNCW68NErvoXZ8TS7m7ixTq9ZoG2giv12g/hYb8xGWpzbQmfQUciXmx9c59vgexq8toNPrsDktoBGJbiYBhcRWGk/QiSAIiBoRoSbvOCUq5Sp6o5ZqpVYvqBZFcvkSBoMOg1FHY6sbWZZxeu24G+wYzXr6hoOMX1/E77YSTWR3RSL17G1BtOgIWk2EFmMArC/H8Aed1Goy0Y0Ea8tR5HyRV758G3m7N6NnXyvZeAYE2FyNY3eaURQFl8+GVlQIL0d5M5GjocVDKlcitpWmkMqzNr2B3qjj4MODJCNppm8t7myLy2/jyZ89hafxXgzR2lyIF/70HLntO+5FUeDok/s5eHaIjp4Gnv7oEcZuL7M4G6ZWk/E0u2np9DE/vk48lKzPoxHpPdLF5tY9scRoqvdF6IxaJJ1E9W2OqlKhzMrMJk09jYTX4ji9VkLLW1SKFUKLETKJHBvzIYJdDbT1B1mdDeH22fA23hOevptcusArX7/J8kx457mtzSSj1xZ4308cpaXLj8H8zk4WrV7CaDXi8t9f9v3wR48xenGaVz9/gVK+jMVuwt3oJLqZpFyskE7kUGoyepOO4eNdNHb4iETmqVTq55HL68fhtlAtV3F5rMyOrtLc4an3jLxFuUbPgXZqFZnRK7OUi1VaexvoOdzBwuIWe490sO9wO8FGJ5l0AZvTzPCxDiJrCQLtXiavzlFI57G5rTR2NbA2Fya1lSafyCJqRG68PEpsI45WAH/QSblUQVAU7rw2BtUanoCD269N0D7cRKDVjUGnwe23YbKYCHYHMNmNrE6tk0vk658PdhNWp4WNuTAb82E6995LVtAbteSSeYr5MsMnurn07dvbXUIiJqsBfVJH74E2YqEkK9MhirkyH/jkg2yuJilXaqyvJ0lOhaiVq3T1NVDOFlibXcdhEmnsCjA7vlHvgylXCa3GgbqTbfLWMsfPDvCNz55DFAQa27y0DzWRTxUYvTLHh375DC/92Xkq5SqprTSBVi+R9TjJSBp/sweDSUc6Ue+OCbR6yMRzlAtlnv3lA+itBvK5EhpJxO22opF2u8hUVFRUVFRU/s9GFVF+xPnFX/xFPvWpT+04RKAe5fXss8/y6U9/mrNnz/Jbv/VbAPT09DAxMcGnPvWpv7GI8ru/+7s88MADQD1W7F//63/N/Pw8HR0dAHzkIx/h9ddf5zd+4zfI5XJ8+tOf5rXXXuP48eMAdHR0cOHCBf7n//yfqoiioqKioqKiovI3xNvkAgFQ6nefewNODEYd7qCbXLqIy2ul70AbLd0NjJyfIp3I0by/jVffmMTltxELp4D6vOVyFaNBy/xSFIfVQHIrTSaRpVquYbIYMFgMjG9usbGeQKeXqFZqJGNZ+g63MzOyhCAIWG31O/MNJh1Gix5FAZPZgM1lIpfMkQinWJ0OceO1MbxBF2eeOUghW0IjaYhuJKmWq2wub7E4uc6DH9iPUqttd5qAq8GOw28nlshjMulpHW5Du5HA4rRQFgRsDhNdjQ7OfeUadq8Vh9+GXK3x3Gdew2Q1ItdkQotbOL1WCtki4dUYcq1GKpYhGk5T2hakkrEsgdkQiiAQ7PQjagRmR9d29rnepGd1LsLeUz2EVxNoDdq6mJQt4vTbURSolCr4m92UZCgXqxw61YvRrKOYK+JpcBBodWN1mDjxcD9CTebCt26R3Ezh9tsxeKzIikJHXyMD/UHWZ8OEorGd9VfKFeKRFJe+c4eliXXaB5t44XPn0WolDCY9glAXJpbG17C4rSRjWTx+O4NHOrj96hhrU+uUKzJNfY0c+8BBcpkSxXSBhbsrlAplJK3Epedu0TbQhM1rJZOoCyTxcJpbb0zy6E+eQBAEKqUKF795c0dAAZBlhSvPj9DYXi9/b27z0NTqJpWod3FYrAYOHunk1S9f4c6lGWwuKz2HOklmS7vO65aOeo+Eu8HJ4LFu7pyb3JmWiKRx+GzI2wXyBrOe2ZFlTFYDGkmD0WJAZ9CSCCc5cKYfnUGDTv/eP2/nJjZ2CShvUS5WuPjiKP4mF42dPrxNLrbW4rteM3Si9x0FFIDufW1kYhm8jU4UBYw2Ax3DrZz75giZeBalJmO2G3ngAweYHlnm6390juWZMA6PhSd/8jhTd1d47as3qFVrGC0GWrr8WExaeve34W5wUCqUGbs0zfzVWYLdAVzNLor5CmurMa5emOaxZ49w/MFeGls8tHbdc7BsrMa58fokV18ew2wzsv94J2985RpzY+uYrPVzSBBFFAWyyTxXnr+DRiNQK5UpZ4sko/UelnKxgkaSqNVkdDot1164zeZivdtndWaTUx88hFyt8twfvkIqVhf19GYDtUqVTDxLcit1b1+XKxTzJVwBB6szm7j8doaOdnHn4jQGq4F0PMPBh/rpP9hBtVpj+Hg3lVKFhdE1NrdyJOI5REEgl8rTO9zE/M15Qsv1bTGZtNx6Y5ITTx1kdSWBVq9FZ9CibMffJbYy5DNFalUZg9UACuTSRSqVGmaHmZnbq2g0IqIgEFmOsPfMEK98+TqgUMyXCLR52dqoL9do1mG26DnyyBC+7d4Tu8P0nuefioqKioqKyv+5/FBFlHPnzvGpT32Kmzdvsrm5yde+9jWeeeaZnemKovDv//2/5w//8A9JJBIcPXqUP/iDP2BwcPCHt9F/x/T19XHixAn+6I/+iIceeoj5+XnOnz/PSy+9xL/8l/+SD37wg7te/8ADD/B7v/d71Gq1nQLOvw579uzZ+bvf78dkMu0IKG89d+3aNQAmJiYoFos8+uiju5ZRLpfZv3//X3sbVFRUVFRUVFRU6gQ7/Rx4eIhbr42BUh88b+zw8/5PnsHX7EEj3fve19DmpbHTz+ziVv0Jpe4cAAFBFJBlmc1IiqauBlanN3BZjWTiWVCUekxNLEtT0EksnN5xwNRqMtlylaauBrbW41gtBoq5MrWqTPfeVlYXtqiUq2hEgWgsi9lmRLMdoxMPpRi7Oo/VbmDPA73Mjq/X+wssRvaf6kWjEanJCp5GJwaLgVA4zdxchEqpglYnkSmUKQgiRq2GIwc76Bts5Ov//RUUpR6V1TXczOtfuoIi1+OpFFmhVpNZGF3ljS9fZWZkEYfXSqUqkMsU0Ugi23oNqa0MFrsJBfA1ualUZdbmwgiigNlmJJctsffMINO3FlmfD+FpcuN0WTj6xB6+8ZnXcTc4kIw65EqN5k4f+0/1UiqUOXLmWXRGPQaTnoYmF/H1ON/6ny8ztLcZw8keivkyiiigNekpFCrMjqzcd8wtNiOvf+U66wsRAAqZArVyjVq5hiAKGIw64uEU/hYP2XQBg0FHS2+Aq8/dIh3PEuwOkEplGL0wTXNnA85mD1O3lhBEAZ3ZQLUmk07kmLw+z9mfOLEjogDMj61yLJ7D5rYQD6cIrWzdt32KohBeidG0HZkmCAIO170YsIY2L09+4gzmBjfJeJbNyO7C96H9bTS2uHceH358LwaznrFLM5RLFVr6gniaXGxsn8eV7VL7fLaIw22hXCgjSRokqe4E0um0u9wz302lUmVjKUpjm3v76NfdLvFIvXdoazNFZD1Bc6ePx3/2NCNvjLMwuoqk0zB0vJfhB3reddlQ7yxx+u6JLHqjlr6Dbdy9OMPDHzmEIsP8rQV8AScGm4mN5Rh6g45v/PGbHH10mEq5ik4vIcsyawsRzn1rhGw8y5mPHiUeTpHLFNmY3mRrOUrbnlZcbgs2UwNyuwenScvLX77BROsSD35gP56G+nYYDVp0GoGhA22IWhFRo6GQK6IoCul4jmCHb8dhVr8OFHRGPcV8GVlW0Ook5JqMpNVgshnoP9zBjVfHMNtNuBsdFDJFCtkS0yNL2GwGgl0NhJejNLb7iW0mSEXT5DIFVqZDmB0WnH4b5755iwvfuFEvqX+gj5uv3MXusfHh/+tRSvkykdU4NoeRiZsL3Lk8xzOffJB0IofVacHdAX/5mTfx+W2US1XMJh2R1brwqDNoqZSqCILA3QtT7HloiEpNwemxkopnUWQFq8NELlPAvB0ttrWZwGCqd5YU82VMVj0WpwWIoMiwfHeZJ37iGGPXFzGY9TjcFvYe76b/QCtWhxmn14pWp96XqqKioqKi8uPAD/Vf/Fwux969e/mFX/gFPvzhD983/T//5//Mpz/9aT772c/S09PD7/7u7/Loo48yPT2N1Wr9IWzxD4dPfvKT/JN/8k/4gz/4A/74j/+Y1tZWzp49i6IoOxFfb6EoyrsshZ1iyre/plKpvONrtVrtzt8FQdj1+K3nZLkeI/DWn9/+9rcJBoO7XqfXq0V6KioqKioqKip/UyStxPH376e1r5HoRgKdQUdTdwMOr+2+1+rNeo49uY8/+dPzQL0DwuW3U8yXqVZqaHUS4c0Up453sT4fwmDRUylVUYCTTx9g/Mocw6f6OX66lxe/cYtstYYiK4zdXePkQ/085LOS3EhQqci0DDYhDTYR37pMMV8mFc1QrdTYf3aIyHq8XoYty4RXYpz59fczMrWMI+Dg0IFm1m8tE91IMr+eINjhI5vKky3XyGbrd4oLooi/1cPtkWXyZZnstQUSsSxNTQ7SsfqA/MZciEC7r/59VBCQazI6vRab20IhWySxfQe8yWEhHs1hNOvQ6iQMZj1yTSEWSnL2Y8dYmtkkk8yh1Uo0djZw8qn96CwGHJsp5ua3kBwWDj22l0Crh+nJdTa2snz8197P6tQGkZUYDW1eho51MXd3DavLzN1LcxQLFcx2A03tXpRKFX+nH0kUuPXiCJGVGCanGVfQQ/eR7vuOodVhwmjU7QgoUI9RM9uM5NIFSvkyeoOWyFqcAw/2MX9nuR4rBqTjWVwBJ9WqjKLUy+Fnbi3wQJtnZzlVuYZOJ1Grymg08k6fyVuYbUbi0Qyba3Hkag25qiBqdv/uABC/R2SRy2vl5KNDTNxZZuruKuVyFbvDzJ5D7fQON6F72wC02Wbk2Pv2s+dUH+VShZf//NKOgAL1QXJJq0GWZWxuK6V8iVpNplqqYrGbaOlrRG/SkUvld0rYFUWhkC+xOLnJ1O1l8vky89MhkvF6nJrHb2foQBuZRI58prjT1+JudPLIT50km8yj0YoY3yXi6+00dvqxuswIokhDmxezw8z86Aof+MUHufTtWxSyZTZXoszeWcHb5OLkE8NM31khnymxOLFOW18DkfUkpXwZq9/I7QvTeP02lsbXsLmtZFN5eg60M3l9nuWxFYqF+u84s8NE74EOtNo060tRXvnqDZ75+ZNodRJTN+ZIbibIZ0vM3F6ipSfA4z9xgm995g1qNRlBALPVQFNPAI1O4sRT+9mYj9DS20g6keX26+NY7GYsDhM6g45cJkNiK0OlKpOMZXF6rZjsJnKpHLHVKMFOP0MP9DB9c5FyoYJWL3HsffvZ2oiz9vkLtO9pYXV6k9RWhuh6nP0P9tPY1YDDY2Ps/BSyrFAuVQktbfHEzz9I+0CQbLpAaivDxsIWbUPNDO5vJbn9OaPRCGgkDaKi4G2wE17ewtvsIbGVxuW3sRVKY3UYiW0mKZcq2HsDaDQiJouBZCyL2Waoi4p6CRSwOsw0+K3MjSwh12RS0Qy3Xx1l8EQPj/7sKXxBF06vded3tYqKioqKisqPDz9UEeXJJ5/kySeffMdpiqLwe7/3e/zbf/tvefbZZwH4kz/5E/x+P5///Of5lV/5lb/LTf2h8rGPfYx/9s/+GZ///Of5kz/5E37pl34JQRAYGBjgwoULu1576dIlenp63tGF4vXWi0k3Nzd3HCK3b9/+G2/fwMAAer2elZUVNbpLRUVFRUVFReUHhKSVaOkL0tIXfM/XeXx25mZDeIIuHNXq9g0xEutLWwiCgKQV0eqMXL21xPGPHser0+BvcmO0GIisJ1me2iDY3UCjWc8nPnmafLGKXCwDCtWyTCaRJZUps7kWw9Psplgo8sTPP0gmmmF9IYJcU4isx5ErMr4mFzqjFgwSNzY2ee3GDGabEbvLQrvXib/VSWZ8jYmbC7zvkw/xzc9dQGfUodUrBNu97HlokHSmSCKaoeY0IxcqrK7E8AZdLE9t1O9Qd+XpGGwmncii1UmYbUYqlRr5bBGDUbez7zbXYnzwVx7m9S9fJ7KWAAFaewOkc2V6D3dh1GnIpnJ4WzzcuTjL1moMV8BBV3eAuZkQ5XKNciZPa6sHvUnP5tQaxVSetl4/8Y0kf/npb/PM//U4X//jcyAIhFZi6Axa9AYtnQON+AN2Fm4tsLkQQVEUcoksCAKbM0YsAQ+5bUeAyaLnoffvpZQvggDeRieiICDpJE4/fYCJGwssT20iywoajUBoNcbH/sVTLI6voREUgt2B7dixEpJGwOG2YbIZsHps7HlokNhqjI35MOVyFYvDVBeeDFrcDQ40kojepCebq/DtP79MTZax2I1o9FrS0TTWt8UVSTqJ4HYcF0AxXyK8EqNSruDyO3airzx+G6cfG+bA0S7KlSpmiwG9YfcNWm/HZDVishrp2NPMxtI9ESUZzbD/oQGmbyxQKlWpKgI1WeDUh4/h8NkYvTDFnTcmMNtNHDw7DDqJ1bkw1UqNdDxHQ6ubl745gkYjImk1GEw6tjZTvPqtEU4/MQyKgt1Zf3/lYgVFUbA4TBQLJRYm1sgm8phsRhrbvZgs94sqVqeF0x85zitfuMgX/svzyIrCnlN9JLbSJMJpqlV5p/MlHc0gKjImixGtXiKbytPU1UBkLYFWp0FEoVqukUnmmby+wMMfPYrTa8NsNSJIAhuLW2yuJXD6bBx5ch+rCxE8fismsx5Jq2FlLkw5W+Db/+s1xi7NoNFKDJ/sRZEVQitRnvjEKZYn1rE4TLga7PQdaOe5z7xOMVdAFEUiK1G8QScPfuQYTp8Ng0XP61+9Se+BNjwBB6G1ONWKTGQtgdVuItjuwbO3haWxNQ49to/hUwOsTG6gN+tIxrIYzQYyiRyprQyFXJHm3gClYoVKqcrY5TlcPhtmuxFBFDHbjbj9Dl787JukElna+4PorUZCy1tM313l2GPDHHuoj4svj6HT63A32NFpNWQSWRQFDAYtg0e7kPRaEpE02ViWpg4vWp3EnqMdGE06VufDVCpVzFYjlVKFZCSNVifROdiETqfh9IePMH1zgWq5ijvg5MFnj9C3v+2+GxhVVFRUVFRUfnz4kfWeLi4uEgqFeOyxx3ae0+v1PPjgg1y6dOldRZRSqUSpdC9rN51O/8C39QeNxWLh4x//OP/m3/wbUqnUTt/Jr/3ar3H48GH+w3/4D3z84x/n8uXL/P7v/z7/7b/9t3dcjtFo5NixY/yn//SfaGtrIxqN8pu/+Zt/4+2zWq38+q//Ov/8n/9zZFnm5MmTpNNpLl26hMVi4ROf+MTfeB0qKioqKioqKirfH/5mF163FZfTxPLydseGUKapw0chWySfKaDVa2ls81IGFibWWZnapFwsk0nkaO0L4m/1Mnp5Dn+zi2I6T3g1TqlUwWDU0Xu4k3Qyx+kn9xJaiXL5xVFqNZnWngYGD7QxenGKtdkQAlBFIZMvcuDBfiRBxBNwUsgVKeRLhEpFbi+s8tDjg8x8e5T1UIqBR4axmfUgQLFUY3p8g+mJDRRFQVEUNBoNia0sT39oP5dfvItSk7l9fpL2oSZuvjKG02cnFc8gIHDmI0fJZ4o0dviJrsdxOk0sz0fpP9HLAPVItEQ8x8VXJtHpNPz0P34YgDe/dIV0Ik8+VyIVzxJZjvLYTx4nly4wO7aOp8FOeGqdV/78AuViBUEU8Da66DrQzsTVOfRGHdlUAavDVI8XUxQmbizS+rEjzI+v4XRbKOZKVMs17E4z5WyBg8c7SaYKlIoVctkir3xzBLfXSv/hTs597TrhlRiCKGCyGvA3uQi2eYiGUjQ0u3n8Z0/RvbcFjVZibnwFh9/O9K16d43FaSYUShMcauGbXx8hn8qh14gceGyYO69P4Ak46T/ShVytMXl5mmKuRPNAEw2dAWTq7vVsqkCgO0CpUKaQK2M067DYTZz60GF8zXV3y+bKFrdemyCbKlDIFYhtJtl7qo9Ahx9f0InTY2VzMcLMyBK5TIG2/iBN3XXxKbwaw2I30tZfP+/eou9QB1vrcWZuLaMoCvlMkZaeBpp6Gpm8sYDDZyfY7cdo0vG1//EKzZ1+9AYt8UiKz/7Hr/Hwx08giAIvfPYNSvkyZruRow8PsRVOsTIbJtDdgLW3gbvXl7hxYZaf+KUHkbQarr08yuTNBWo1hYFD7czdXSUeufeb0hNw8OjHj+MN7i6wz2UKvPrFy9x4ZRyNTkISqDswpjewOEwUciVEjQiKgk4vkY1laGx1s7G0RXtfI/4mFzpRweaykIplSYbTmKx6tDqJ5elNDp0d5PaFKfY9OEjLQIbhB4tUqwoj52eIrMYRBYVCtojFbuKn/tkTXPzGdULLW5gdJtKxLDdevsve0/2Mja6w73Q/wZ4GQosRxi9H0Uoiya0UZpuJlu4GAu1eysUye0/14fDauPziHXr2t9Ex3Mrs3VUkrUStWkYQBNyNTkILEZbH11gcX2VxfI1SvszgiR7GL89hdZrRm3QYzAYMJj2+ZjcLY/XuofahZiauzIEgbL/vDN5GF3fPTaI367HZjExcncPmsrDv0WEEQSS8FmPu9hJDx7tobPGy0eljYy6M2WakpTuA3WPlyV84Q65YYebWEnaXmfb+Rnx+O4lIklpey9GzQyxNbXDnwgz5bJGGZhdDRzt59YuXae8P8hP//Eke/uhxyqUyTr8dm9PyA/msVlFRUVFRUfk/hx9ZESUUCgH17o234/f7WV5e/v+z999Bltz3fff77nByzpNz3J3Z2ZyxwCIDBEgCpACKMilawY9M6dHDq7r2dXjKpVCWHlvP9ZWvfWVblmQFSiZIkBRBZGCBBTbn2dmZ2Z2cZ07O+XS4f5wVJBYl288jQbTEftVW7Z6emdM9c3p+tae//f1+/tKv+7Vf+zV+6Zd+6WM9th+En/zJn+R3fud3ePzxx+nq6gJg//79fP3rX+df/It/wa/8yq/Q2trKL//yL/83Q+V/93d/l5/4iZ/g4MGDDA8P86//9b/+nkLV/12/8iu/Qjgc5td+7ddYXl7G6/Wyf/9+/tk/+2d/5ec2GAwGg8FgMPyPM5tlDjwwREMS2di8hKbq2G1m4utJRFHEajej6xrx9QSnTxyn4ndgliXq1QZ94504/Q4uvHkHs0Vm6twcoqCj6TqBiJdCrszdK4s8/eOnWFuI8eF3b9GcFKtTyJRYmduhY6CF9bvb6JJAsVqnc6gVi9dOaTOHySxjcXmbgeUNBYvLyvVEkie/eBylonH5jSlEUaClw0/I52BmaoN69f74WUFAFBViO1nef/cup184yvKddbYXotjdDj7z808yfXEeT9DJwN4e+sY7Wbu7zTsvXUIURfY8tJt3v3MVd8BFsN1HpVT/06fl2MOjvP/NK/j8TqbOz2G2mgl3+HF7bbh9Tt5/+Srtw23cvrjAsUdGOfcn16lWmiO1KqUaO2sJBg/0El1NoKk6a/e2mheGfQ48fn8znLum0KgriJKIIAj427w4vXYkWcJiNTH7wRyN+p+N1TKZJS6/Pke92OxI0bVmIWFnLcnjnzvGZ44OEOkMYL0/amrv8QG8QQeirlMqVCnlK6TSRfacGqWEyNZylO6hViqFCpN3tjj9wlGGd7ejNhRe+U/vgq7j9NiZvjDP5kKUw88cILqVBWBnM0O4v5WhsXbau4P4W70474/MysTzvP/N60xemKecr+LxO+gcjPD6H5zn2JMTXM5WaO3yc/Xt25QLVWSzTLlQ4b2XLiKKIpb73UI33pvh6S89SM+uDgAcLhuPvniMsaOD5NNFbE4ry/MxZq6v4GkLIAigCiLX3r5DtVwnkyzQ0uEnlyo2w8PrDd57+QrpaI5wh59Stsw3//1bnPrMYVLxHIGwG5fdxMOP7yKeKuH2OXjna5c+GiFmd1m58vY087fX6BpqQVU01IZKKV/m3Ks3+dRPPoQkSxTzZbaWE2RiWa68OQkISPfHnCk1FZe/OVpOliV0VUM2y4BAoM2PKop09Ydo6/Lz/tcvU8iU0DSNUJuPx188zMB4J2dfvsKlV2+y58FRqsUaF16dZGMpgTvg5PhTEzTqDfadHKKYKyPJAnaHhZ3VONOX5qlV6rj9Ttr6IsQ3U2wtRnny7z1AS2+Q62emyaWK+Fs8rMxs4g97cAcciLKIzWTF5rASX09SzJfQkNhYjbK2lGTPyWGy8TyiKOAJOPEFnBQTOSSzRFtfGJPFTDFTQpJEWvpCJDbSVIpVOkeclIoV3vjD8wiCwMTJIRIbKZ760gNMnZujVKgyfKCP5dtr+MJuNhaa1wMsVjMWqwl/wMW3fvssLp+DndUk0fUUJouJz/zMw7T2homuJIh0BQi0elmZ3STQ6qUl7CC6VmH6g2nCHQG8QTdlUcDuddC/ux3L/XFyuVSByXP30FSdldlNyvkKvbv+2x1/BoPBYDAYfrj8T1tE+VN/UebHf6uN9p/+03/KL/zCL3z0OJ/P09nZ+bEd39+UY8eO/YV5J5/5zGf+wjyZP7W6uvo9j0dHR7l06dL3bPvzz/vQQw99336+9KUvfV9h5hd/8Rf5xV/8xY8eC4LAz//8z/PzP//z/53vxGAwGAwGg8HwcXO57Tz48C40AS5fXaKUq1DKlprdIfXmxfzh/jBv/+cz7Dk+zK7jQ1x5Z5oz375OpCvI+lyUgw8OszK1ji/oJLGVQVc1zFYz5UKFxFaa6atLyCaJRl1F15rh85oO2XiOBz9ziIqiUGkoJBI5Jm8sEWrx0uhwk0kUKRUqON02rDYT+WyZaWuMNtFCrVJHlETa2/1MXl76ngKKIArN3ghdZ+bWOhMHewn3RBja38fW4g7TV5ao1xW6hlqxO6y4PA4mz881g+Y1FU3VsDmtzWyY+8WKerXB/hMDnHnpEv6QG6XSQJIl6tU628txnB472VSR2Gaa0SMDhDr9lHJlzBZT86K6qiOKAoqisb0YZeTECNO31hDE5vuVYq6CKGXxhT1YHBbCHQFcfgctPSFMFhP5VJFwV5D1+R0CASfRaI77DSDYLCaS21mgWQSrleuIooDL6yC2nuKRF45+FIoNzewTq83C9maWBz93gng8TypRYObOJul4AR1IxXK4vA5kqxl72MPuY4N8/f98Ff/9QPRcuoQgCuSSBRrlP+vuB8hlSmytpzn2xJ6PttWqDd782iXefukymtrMYIlvpVmY3uD0pw8S20jR2hvipX/7Bt6gi3K+giiJ5Np93L22ROdgK6H2Zhh8o6Zw5c3btPdHMFma475kk0zHQAsAyWiWu7euAM1weWiOPhMEsDutVMs16nXlftaFjc2FKKrSzG40myXWtjPous7K1DpPfPYQb/7uWW6Xa4wdHWToYB/RxW10VcXltVPIlvEEnNz84C61cp2dlSSFbAlREECAxGaa0f29mGwm3vvWdcrFGn3DEXLpEqIo4HDbMJllCpkio4f6uP3BLJVChbbeEMmdLIqi4GvxkMuUOfHUBDfOTGOyyNicVjRVRdfB43Pwxn/5gEwsx66jA2wvxZi7uYrZasbjsyNLAu+/fIVHXzzGy7/5DtVyndaeED0DIURAVTSq5TrVShpvyE1rT4jkVoZMPMvi5Apjx4dR6ioWqxmH08P0hTky8TxOvx1PwEW5VMPf4cekNn/O+VSRXKqApmuMnRymsz9MqVAms5UhHcshCALhTj+aqtE30UV0PUmoM0h8PYVsljn8+B7eeukyB06P0j3QwsbCDsVciaU7a4weGqCtL4zL52Dj7hZrc9tIcrPYWMiXsTotZJMFTGYJTVGRTRIIAoVClUtv30HQNEr5Kg1NJ7qaYP7mCk//5MMEWn28/42raJpGNl6gf7wLb9DF6KFezr0yydTF+e85xwUg0PJnmUsGg8FgMBgMf+p/2iJKS0vzP8vRaJTW1taPtsfj8e/rTvnzLBaLEWZuMBgMBoPBYPihZzbLnD69i67uIO++eZtCojmSqLXNR4vPzvS7d9hajNE72sG9myv0jzcvfDbqKo1ag3CHH6fbSnIrg8kkUcyWqZazdI+2US3VUBUNb9BF50AESZZwuK1IskQpmWNhao357SQ6zUDwWl3B7nUQqzbQNA1NbQbBJ2J5Iq0eZjZitPV00tsfZub2OnaHmWQsR7jFw9GjfThsJhAForECK4sxSg2VjZUEN96+TfV+ceHAqRHGDnSzPLnC1vw2NpeF/vEuMvE8tUoNl8+Bx+dAUVR0VSXY4qFcqKI3FPKpEk6XFVPQiaKoSJKIpmpoukYuUWoWixSV5E6e9lYPiAIms0y9WsdsNaMqNdxBF1anDaWhommgqSqiqJNLFfEGXSCInH7hGBtzW9TLddbubeP02PG3+Hj5N9/FH/Fw6MkJtjazAKiq2gxu1yHU7keWRcwWE2arCUXRaNSV7ymiADQaCgJQqSl88N49SoUKACaLjKbpoOkU0kX6drezuZ4mnylRLVU/+npJ+rPAbKWuYndasDss1OsK+UwZh8v2Pftbmd1k8tw9LFYTmk7zddV1FEVl9voKRx/dhdkiUy5Um3kegoDL1ywClQs18unSR0UUgMXb61x5a4rEdppQR4Du0XY6+5vv/VRFQ1P/7GYvt8+BySTja/Uxd2sNk1nGbDXhcFkxWc0ggM1uQRRFlHozG0jXdboGW7j66k1KhQoDE11Y7SZe/9338YfdCJLEyMF+/B3+ZrdUTaFcrH7ULZTczjSLharG+sIOV87OsbEYw2SWaO3y09IdYmclTilfxe1rdhndu77M6ReOcufCPMntLIcfH2dwby/+Vg/94928+Qcf4PM7MO9uJ5MooNYVdE1n6vw9HG4btXKN1t4wV9+aolKsUsgU7/8sNCJdQTbmt+9/3zb2HB3gzoV7WF02RLOExWamXm1gs5vZWY3T2hshlypy58I8akPl8JN7WZrZpH2oFVESWJ3ZJBXLUcpVcAecSBYLb37zBiefnqC1L0DPUAs2pwV/xE06msMZdGFx2VERsFpMbCzGqJZroMORJyfo2tVJ+0ALHf1hEtsZxo8OMPXhXa6/cZtquYYoChx8bA8bCzvc/OAuX/ynn8LmsqHUVeoNFZvdjICA1W6hVmug6Do2k4QoCZgsMiaLzM5ygrEjfazMbOH2OagUK/jCHuJrcUaODHD0E3u59f4slWKVaqVG38RuBsa6iK2nWZnZIJcuoWs6NqeFYIsHt9eBxW5cTzAYDAaDwfC9/qctovT29tLS0sI777zzUQh6vV7ngw8+4F/9q3/1Az46g8FgMBgMBoPhf35ms8zoSBtKpkRIlppZI7kyV169RXyzmZcimSSyiQJTF+aYODbI8P5eouv9dA9GmPLYqZfrVEs1rA4LvpCbbCxPa0+Ie1Mb9Iz0cOHNKRr3L1JbHWYe+/R+Vhe2qdzvZJBMEroOLSNt3Lq1gNVhweZs5p54/A5KpRpWmwlF12nz2sn3hlAUjV3jHYQtIu//3vvkkwWcXgennjvEvokuzrw9Q6OuUMxVqJbrmEwS01dXSGylGR1rJ9jqR6krFBI5ghEP3rAHXRKxe2yUC1XsTivFbJlGrYHaUBElAV3T8YbdaJqG2WJCNsno9wsDo4f6SERz1KoNKnUNQZbwt/tRaw10TcMXdjNxejeaKJHYzmJzmLE7rYTafJSLVdw+B7VqneEj/Zz+7BFuvT9N12g7mq6zNNPMh0jtZFmf3STcE0YUweuz0zPayuZCHEmWcHr+rIDR3hfG4bZ93+vtDTixWJtdMpVSDe1+J4YgNkdM/WnHRrVUQ/FpiJJI71gXd87fA8DmsGC1mRHNEv3jHcxPrnH7vXncfheD+3sZHO/4nv2tzG6RiuaJb6XR9WaHuz/swSyLrN7d4oFnJli/u02gxYOmqgiiQKPWwBtwoqkaovhnEwZSOxk2F2NYHBbmbqwgSSLHn93P0acmCLX52FqKEfA7kC0Sokkmny7z9svX2LWvi0hXkMRWmq2VJFabmf2nd+Fv9bI0vYnNaUZTdcxWmUqpTqDNy40zdxBFgY7+CBe/ewOr3UIpV8Yb9nLnwj1OffYIjVqdQIuHfLqIySyxOb+DpulY7RaqlTozVxZxeazkks2w9MXpTUaPDpCJ5+5fsK9js1vYc3KYK2/fQdV1Ap0B1hdjzFxb4bmfeQR/xI2qaGRTRRq1Bj272nF57dRKzaJfJlHA7rRic1pRVY1GTaFSqmJ32rA6LCxNrdGzq53xY4NcPzNDMVtEVXWmLi9y6tOHufTqDSRZpJgtE2zzsev4EO984yq7Dw8gW01893fO4vI7WJhcw+W1M3iwn8KZabxhN92jHVx/f5bBsXbOvznFj/zEKV7+d28R30jR0umnd28vN9+e4eCJASx2M1tLMVRFw2SWMMkSgiiytZLg3OuTHH1sjIc/fYCv/8Yb1Ct1Bvf34Gn1o2sagqax+9gQy7NbNBoqfXu6ScdybK/EAfC3enjg+cNcfHu6OZrQZaPVIqPpIAkC9UodTdEYOzbIxPEhauUqd87PsbkQJdwZxO60cuCRMSRZZPhAPxvLCXLpIsP7erh7dYlKqYau6ZjMMqIk4vTaaesLYTAYDAaDwfDn/UCLKMVikcXFxY8er6ysMDk5id/vp6uri6985Sv86q/+KoODgwwODvKrv/qr2O12Pv/5z/8Aj9pgMBgMBoPBYPjbpa03xOU3JikXml0HNkfzTuuhvT0kNtNAc5zS3WtLHH9qD3cu3KOczhNs9XLv+jJmi4l6tUG1VOPIkxO4Aw6OPj7Ou9++zv5Hd4NFRmmo2M0yd66v8PgXHqD80nmyySItPUG69/VwfSuGbJKQZQlBEKgUqwTcdsZ624lE3OwbaGfy/XscGGvH5bUhtrs599Il8qkikixRK9V4548u8MBnDzO+txOX19bMGakpKA2VsE2m0VDp2t3Be390nuR2Btkks70YA0Hg5GcOc+zhUS68M8PuQ30U8xWq1TrhDj+P/Mhhlu+sM3ttmUdeOMbslUXiGyl0TefA6VECHUFKuQoHTvSzs5lm4tFxctsZkpspIp1+2gbbMLvtzF9f4enPH0NAZ/H2BtG1BG29IU4+uQen387Y0UFUVWVtPkohXWp2SzgsoOuUi1UsZompM7eJb6Zxh110DrQwfLiHtZU4iggumxm7o3lhXhTF73udvQEXe44NsLGSYO++TkqlOovzUSqlOk6v/aMxY426ittra17kPzXC9lKM1E4GSRY5+OhubC473/r3byFKIj27OthZT3Lr3Sl27e/+aF+lfJnttRS5dBGbw0q5WEUQBRLbaYKtXoItXjRFZXMxisVqRjZL5NPNvBJf2IMki7j9zcDuerXByswmYyeG2ZjfAZrj4S589ybt/RHOfP0yjZpKKpZjZz3F8N5uJFvzHJ69tU7f7i4OPzlBtVSjVKhh9rmYub2FqjdzaRxuG9lUHo/ficvvoF5TCLX7iK+nsFjNWO3mZhfO/ZrO5twOrUNtjB8dJLWTJZvIo6k6JquJWrXBocfHmbuxTNdox/3uG7h5bp5Hnz/I3tO7EQCrw0yjoWF326hX6pTLdXLJItDMWzHZLFx5dxp/i5dsqojFamJ1dou12U1URWVgoou+8S5wWfEEnGSTBVI7GQDK+Spmq0ywzY/La8dkVahX6oBAsNXL+kKUC29MMnZsmFC7j2K2RL2h8sYfX0RVNNr6Irz39Uu4Ay5K+QoWq5m1+R22VhOcfPYA185Mk8/XiW42x7GFWj1cfH0Sh8NC3+52ukY7uHB2rjlCraGy5+QIZouJ6Eocb9jD3gdHuXnuHpIk0berndkbqxx5bBxV0Rg7Pc7F92aZmpnBE3Lji3ixLCVJp8u89vJ1unqDPPsPHyO1lUYQBfKZEvFolsG9XVjdNsqFCrl0iXK+gt1p5fHPHaNWqXPn8iLr89uc+a8XcXrt+IIurrx5G1XVGD3UT3wzQ6AjT7lYJZ8uMTDeySMvHOXCqzcp5ZvrYrDFw6nnDxlB8gaDwWAwGL7PD7SIcv36dU6fPv3R4z/NMvnxH/9xfu/3fo9//I//MZVKhS9/+ctkMhmOHDnC22+/jcvl+kEdssFgMBgMBoPB8LdOoMXLJ/7+g9x4b4b1+SgtPUFaekKIssjczdWPPq9zqIVsIo8/6OT8KzfQdHjw+UNkE3lkk0yo3c/ARCfjxwapX1qkZaKLi7fXURoqqqYhChAJusFupf/pCdI7WVSLzI3NOB6Pk5qi0VBU6rUG+wc7sCSLbL47y2ZDZcXj4MQnDzB3Z5N6oUY5kaWULSGJAr6wm91HB9HvB9yHOvxIVjOHHhohkyiSSRVw+53YLCbmr69w+Y3bSLKEJDfvLA+0ekmsJ3EFXfzozz3GG9+4xvpiDEkSiW5kqOTLjB8aYObcPT585QZjh/s5+tQEfbvbsTpsfPDNq0xdmGvmTvSGQA2yHSvwiS89SDpRIBXLMdzi4666iFKpc+vsLIqiIug6uUSeN796nsNPTXD00Wb3hckkEe683y2TLrLnSB9Or4Nz376KI+giL2jsrCfYyRQ5cmKE/olONleSBDt9nHh0nJbuAJVSFavd8n15kS6XhdjdDZYvzlMo1tjzwAhlJFKpZhaNKAhYbGbG93ZhtZmx2vw893NPsL0Uo1atc/mN29w6e5PtlThKQ2VtdovTP3IEQRC4dXaWwb09WGxmoutp6pU6siyhqhqtPUF6hloQgHSiQOdQhMkP7rGzEmf/6V3Uaw3yqWYRYWMxyt/7p58iuhJDVXW8IRdPfPEB1IbGxnz0o++ltSfElbenUBoqsizhcFqIdPhZn9+hY7gNs9WESZZo7woQ20gSXU0QaPMRtgZYW4jxwBNjyIJGOVPi4Old2D12Fu9sMrS3m2wihyfoYmdZolau0z7Ygs1po1apI1vkZoh8rsSLP/8E7798hR1LinCXn2Cbj9vn57CYZfKpAg6XlVqlTqVYZWMlQf/udsrFKm3dIWq1BroEvogHbSdLudwMet//8G6++3sfIonNTor23iCyJLA63fxdCnUEqJbrXPzuTR753HFunJlm/0OjvPXV8x9l5jRqCsE2H06vg9R8lI7BFgLtPoJtPu7eWEEUBS68MUlrT5De4XZW57ZRFQ2r3UIuVcBsMWGxmaiUaljt5mZ3Uq5CLlmglKvg9DqwOa2szGxy9KkJ3vgvH9DZEyTU5sPusdNoKEiyhKqonHvlBm29IUaOD5NLF3n32zfw+R0UCiX6xrvYWUsR38pw9JMH+dYfnKdSqGJ32ZCtFtYWY8R3cowd6GZ4MMK9K4u8+wfnCEZclPNlDjw6jibCiU8fIva7H7I6t4PFIhMIe+gebqWYLWN3W/GHXUxfnEepKxSzZWxOK+jNLBSHx07PeBebKwmgOWIQYORALx0DEZI7GSRJJNwRwGIzf3wLscFgMBgMhr+1fqBFlL8oxPzPEwTh+wLMDQaDwWAwGAwGw/91bb1hWv5+kFyqiCAILEyt8d43rmC2mJBMEsN7uzn+9F5unrmDJ+hGUzXyqRIXv3sTd8CBx+8kvZOhtSeEIEtcvrnK8loSm9PSzKoQoFFtkK/U+OOvXuTZzx3m9u1Vjgx30sjFqW3HOLCrnZpd4ta9TSzZCju3N4iuJNi1t4eW7iCXXr1FW3+YSKuHjK5hscgE2/30jnVx/rXb6LpGx0ALdreN0YN9pDZSIAh09YVJRLP4e4JsL8cQRAFN09AbOpVijUqxhsksceyZA1z+4B6Duztwu61sriRQGwpOr4PbV1eYODmMcGkBT8DVzJ/IV5m7tszm/DblYpV6pdkxUSvXePhzJ5ojhhoqP/Kzj2JzWjj82Dh3ryyi683ClSBAYjuLkilz48w0x58Yp3eknZFD/bz8/32TrcVmwUBtqBz7xD7cASeryRzlSh0ARde5M73KJ3/+ScwjEbLFKn/8/iQnVuLEp7do74uw+0g/HfdzQ8r5Cm/+7lk25ncIt3hobKa5c+4eh5+cYGWugMVhweq2sedAD8OjbR+dGw6PncH9vcxeXWRpeoNsIod2PxtG13UuvHKDJ77wAPlUkXKhgsVmplat4/TYef5nHqacr7Bxb4ub796mnK/SN9bJyJ5DzF9ZJNTuJ7aW4MhTewl3BZFEEW/QxcFHx7DaLdx6f4bLb9xi9soSkixx8pP7ESSJfLJAsN1HKV/lzsV5FEXD6rDgDjjx+J1EV+PsPTqE22vl4neukU0UqVbq3LuxQmYnw5OfPcCFV26yencTi8XE8N5OBB1Ov3iMXfu7WZ3ZRGkoNOoKke4giqJRqzboHmnj5DMHcPodeAIuNhZ2MJklWjp9gM7Zb10DIBDx0Lerg4XZ63QNtzC8r5fVhRhXP7hH73AbhXKdN75+Gcls4tnPHMDpspJJFQm3+Xj19z+kmK/idFowmSTMZpmb78/S1hchny6S2Erj9DYD3i02M5lYDgR4+LNHSO5kKeUrRLoChDr85LMlzBaZbDxPLlFgZyPN6RePUStVqVcbuAIunF47awvb+EMuPAEnNocFs92ML+KhspKg2YLTLMYJAgRbvdRqzcwdl9eO3WnD6XMQ6vATavfjDHkIRjwkdnIUchV8IRdbywlW56IoDQV0ECUn4fYAse0MkknCZJaYurnWPCafA4fXweZqEkFo5ptksmUKiTxLU+vY3VbMNguyLLI+u8kTX3qIRrFGz3Abuw/2oisaZruZ7aUYl96aItTmZffhflbvrDezXGSJarlOS1cAp8eBJInEtzOgQ6TDT0tn4KNz3+mx4/TYP/4F2GAwGAwGw99q/9NmohgMBoPBYDAYDIa/XqIo4gu5Adj3wAgtHX521lIIAoQ7AwRavNx8bxpVUQm2+xFEsVlMSJdR6iqtfWF0AS5fmCeeLIAgIJtkMN1/fgFks8zORprt9TRHO1p4+6sXkE0SlXKd1cUonX1hHn94F3e+O0l6O0tnfwSbzcKZly5jtpiIbaawXFth76lhesc6CXUG+eA7NxFFAUmSEUQBpaHynd85y+nPHGbu+jIWu5mOvhBrs5v0DrcCIJskxPtB6dVyDZvLzlf/w3tc+3Ae2SzRO9TC0EQXU+fnETyQ2M5Qq3fij7jYWIxx6JHdzFyc5+Zbt2nUFVq6QzQUtXk3v9VEKOzi2S+epG+0DbfXwbd/5wMmz90lEHDSP95JJp5rdh08NEohU8LtdXDv6iJ3Ly9SrdQpZktUyjV0DZSGgiA0g+FFWcTmsCCJAjaHBUESqdYUYoUS26txsvECYlVhyGRh7tYqy7ObPPPjp+gcbGHt3ibzk6ukdnIIIvgjXkx2kbXpDcYP9CCaZR7+xARje7txOK3fd37E1lJk4jmsdiuFbBlda2aqVCt1SoUKbr8Du8tGpVRjezXJ9XP3UBWVzi4/K9MbtHYGKRdrVItV3v3j8zz5xZPEVpPUqg0kWSLY6qNcrtE33kWkM8DMpQUSmynaeiP3w71t2BxWzn/nGvHNNME2H/lMiUc+d4LbFxdI7mSJbqToGGhBlCRaOv0s3VgitZkhly5gtVtoafdSSJeo5svENpKIgoCiqJRyFVLbGW6emSYdzSKKAvseGecL//vzvPtH59ic3yHQ5uehzxzm4GNjlLIV3vz9s9RrDfaeHOHlf/cWVqcFb8CJDgRavYR6Qjz+o0eRZIlMNEvAZ6W7N0C+UOONr13m0IMjXDu/wJvfmeQzXzpJuqSgrKeoVRV0XW928dQbyGYT5UIVXddJbmUw20wodZVqqUa1XKNzuI18Ms/S7TUqhSqSWWL6Yoonv/AAawtROgZasDnNKIpKo9bg5rk5NEBTderVBpEOH5/40oOszmyS2EzT3h9hYzFKJlmkYzCCrur3CwnNzBFdFGlU6pQLVUYP9TI3vcGjLx4jX6iytpOFdJVQf4SRQ31kE3l2Hx3iyttTaJqOUm/+7qHD4N4uzn53khNPTWCymslmyrj9TrLxPA6/C4vdTCDiQTbL9A9GuHvhXjOYHgF06OoPUSvXWLq5gsVlZfLcHP5WH4FWL0pdYXMpTiFTAl1HUTWcXjuJjTRmazPTSG2olEs1LHYLlWyz2PPgp/Z/1IliMBgMBoPB8D/KKKIYDAaDwWAwGAw/hGSTTOdQG51Dbd+zfXh/H7fP3WX/6d2899IlEAQkk4Q35KZabtBQ4f13pu9nm4Cu6eg072B3uO3ksmVcXgdasUZidgub1US9oWJzmKlVFTZX4pyU9mCWRXRNY2hXJ++9dAkd4aMLy2azzO1zczz79x9k6tICogAWh4VIVxBBFNhaSSAIArlUkWyqhGxOcejR3UQXYxQyZfrGu+4HXasIgo5sNWPx2Nn4cJFgq4fWNi/VaoMP353loSfHmb+5gt1lZfnuNl6HmY7BCG999RwdgxGq5TrlUpXyzAatvWGi62kkUzOsW9M0yvkK187MMHnuHt6gi3wyz60P7gJw4PQurrx2k0alztq9bWxOK49+/iT5bAld1+ke7aBeraNpGioC9YaK22ZG0gFVxyyKjEz0YnZZMOdLlDMVxvtaMNdVfO1uLGaZxE6WqYvztPeHiW2mqZaaXSwWqxk0DRlw2U08/en99O5qwxtw/6XnhMkioyoaDpcVWZZQFLV5rpib2ydOjWKxmTn32iR3ri4BAv27Orj2xk0qpRr1mg2n24baUGntDnHzzAxzN1YIdQa49eE9/C0eDj0+wXvfuYHL72BzMcr1d6fJxHLUqvVm4UsUGdzbTTFXweawUCnVuHHmDp6ID4fbRiDsRpQEhg/0YZIEJs/OUMqX0RSNQqaEUldoH2xh/d4WTreNWqmG2SLTqCsI6Eydv0e4M0AuUWB1ZoN7lxfY/+g4feM91Cp13vv6ZYYP9hNfixNbS+ALexDQ+eI//xQLt9ZoNFRsHjuyzcJbX7/KY88f5MJ3rpOO5/AG3WzO73Dw9C5Cj+2iVKzx7AsHefuVSYqlOqsLMYZ6A+i6jsNlw+m2kk0qzN5YoX+ii+2lGKqqUS3WkUQRQWgGyA/u6+H6u3foGGplezlOvdog2OajVldo7w1RKZYZOzyAw2Pnylt38Ld40U0SiqpRKVUZHu/gra+ep1FV0DSNfPo6bQMtrNzdZnFqg8E9XYwd66dzqI2L70xTrytUizUGxzvp6ItgtpuYX0wwe2OFel0h0OoltpNDlCQOHu2jmMhz5PFxNhZixLfS9Ay10t4b4s61FY4+NobHa8fjd9DdG6St08/mSgKz3YpslbFYzfd/x2o0VI1wh5/4ZoZysUqjrpLczuBv8dIdcaM0VOLrze4VHR1d1zFbTTjcNuZvrTK4t5eNuSiSSUKUJPwRD317Otlzchi7y0ZbXwiHy/YxrKYGg8FgMBj+rjOKKAaDwWAwGAwGg+EjY8cHia4nyCULPPDcIW5/eA9BEhmY6EZFYGstwU48j91jwxd0sbkcp1FXkE0ysknCZJHRVA2trpCO5bBXNdxOK1U0LGYZQRQpZkrsPzpEYmabaqmGroMoNS+MipKIKAvEV9KsLkQJdgQYPtRPraZQyJapVxsEWrzEN9PomobT27xob7Wa6RyMsDS9Sbg3TKQnRDZRoK0vjGCW2d7Jsf9oH6u311m6Mo/VZmbvkQFWF6J0DbeSS5fwBV2UM0U2F6JoqkZqO8vu44NMn58DQaBWrmG2yshmE4qmsTKzRaVYY/LiEtVSDTniJrbRDMS22s3IkkAmmiUVzWF1WOje3UEuU8LpsX0UjC1JIoIgsLq0w8OfP8G1NydZmFrHZJLpGWpFFkWK01EayRzPTPTzyu+/TzpZYD7kxWu38tjfe4BcqsD1d++g1FUGJrpZndlAVVS2l2Lo6IwcHmBteh2Hw4wsSdjdtr8wmL6lO0hbb5hMLEfXSBvZRJ5SvsLhJ/YwdKCPtv4Q87fXuPnhXSRJor03hN1hplFXEUWRcrFG5P7IJ5vTwsZiM6tDUVQsVhPpaI7EehKTzcLGYoyNue3mqCqaRR+z1cTmQpThA70MH+hlazFKuCPA4tQ6p54/wtmvX2T6g5n7HQdJTj13mECbj1yqiMVmQhBEVEWlkC4xfKCP2euryGYZr9+JySRRylfoGm6lmClTzFVQago7qwkWJ9dQNZ1qqYbJIjN/YwmlodI62Mbc5DrJS0tY7RYOPDjM+NEBShWFt75+lUDEQ3o7RUdfGLvdRKVY4cADw5z/znVEWSKbLRPp8HHo+AjZTIlKsYpglrFaTYi6zvp8lEK2jNNto+OBYaxWE/H1NIIIokni0GN7KBYqDO7toaU7xOU3b6EDrb1hwp0B6g2Va2/fJrqSINIZwOG24g+5qBYruPxO2rqD2MY6SW+lsdnMxNaSiKJAPl2kXKjy2I+dJBnL4fTYmTgxSLDVT1tfmO21FNVqHVkUSG2nSBUbXD47i9vnbJ47gsDA7g42VhJcu7zEyVND3Hh3Go/PwUOfOczoRBeJrQy+sJtauYbNInP+m1dZvLuFbJIZOzGM2W0nuZFk9c46kiThn2hH3NXOh69O0tbpp1qqEd9IYnVY8ASdVItV2vvDbC3FyacKCKJIo9rAF3IzfKCXC69Nko7lePhHj5GLF1DqCqc+fZCxE8ME23x/w6uowWAwGAyGv2uMIorBYDAYDAaDwWD4iDfk5hM/8RDr97aZurjAgcf3UMhVmLu9TqVYo3tXG3a7mWKuQj5TJtjqRZIFggE3ZrNEOltmbmoDySShqRqZnRxmqxmX34HTZsbptCJkqlTRGdrbgyiLiKJwP4vBh6aq2JxWvEEXoXY/yVietaU4pXwVq81ErVInny3R1h2ktStIbC1Jz0grssXE+Ilh5m9vcOmtO7T1hdh1oA/JJNHaHyZXqHHr7SlW7m0DkE+XSHz7Gsc/sR+TScbhttHWHWA5V0JRVTx+J9vLMVo6A4Q6A6zNbmJ3WQl3+jn85F7u3lzDbpGJS2l0NKwOK7lEnmQ0S1tvGE/AxdZCtHnXvKYzcrCfZLzA9Deu8OQXH6BSrLEys4k36KJcqhHoC2L1OQgMtNC9pxtZh+V7O5z51nVae4LsOb2Lb/7GG9gcFjRVo1FT8PV5ee9rFykXa7T3hVib2+HgI7tx+Rx88PIlTGaJh148jqaovPwbr9PSGcDhdTDx4Chmq5ldRwfoHetCEATW7m1z5qULtPeFkCSRjfltukbaGNzfi9lqRpJEvv7/fh1vq5+715YJdQYItvow2y3sPjrAyp0NRLGZb9HaHSS6lqRarLK9FEOURPwRLy63lc3FKLtPjpKO50lFs99z7skmCdkskdrJsOvIIIVMiWKuwq5jA2R20jSqDdr7I1SrDZbvbpGLv8NjP/4Q63M7zeKbAEod7E4zIwd7WbqzTilfweW20agpiJLI6JFBZi4tUKs2KBcqH40ns9ibgeJOj51KoYqn1ceZP7mFLgiIJhOqBudem0ITRMwWmXDISe+DI2zObXLtzVtkEnmOPb2PV37rDACt/RHUhoYgiNw6O8MzP/kIK1473UMtVFIFLr85CYCqaKTjeWpVhf2PTjB8aIBsPEet1qBUqtHV4efCa5McfWIPg/v7aCg6m4sx7lxZwmRqdlss3lzD7rAyf2OZ409PUC7WEEWBWqVOa1+EncVtsvEcNqe1WbTUNHKpAkt31lmei1IuVMklctTKdU5++hCD452IksDL//ZNDj6xhzf+4/vNwHVBoJgrU8qV6bSZ6OwLkY7l2VzPcPSxPQRCLvRajWwsSzGZ48Jrtxia6Obst66iazrtQ23EtjLcvbrI4Fg7l1+7iabqqKpGOprhxf/1CWZvrJJOFujoCrC1EOXIExOU8mW2l+IceHQcs7WZhWJxmlEVlYMP76JaaWCxmigXalx84w4PPX+IkX3d7Ht4N56A629s7TQYDAaDwfB3l1FEMRgMBoPBYDAYDN/D5rAS6Q6hnl/g3OtT3/Ox2GqS8Sf28Np3buCPuBjsC1OvNFhfTKDrOj1DEUIPjNBQFNq6g8TXkoiSQCaex2IzE2lxY7Gbuf7eDPsfHKV3pJVCukQ+VaRRa6ADtVKN1p4gFruZ7dU4bb0hFu9sIptlJJOMpmlEuoN0DEZQGwot3UEK6QKzt9bp3dPJQ88fYntxm7tXFrDYLbz3rau88LOPI6gqHT1BsukSpfsXmm+fu8dzP/sYxx4bo5zKEYy4CLZ0kk8V8IVdXH17iqF9PTz8oydo7Q1jtlvIpEsoNYVkqoAki7T3hrmbXsFqt6JrEN/K4vDY8bd6ycZyCAI4g26uf3gPgLnJNQ4+MUE5V2JtdovDj++he183y4tRbl9fwu+yk1xPo6kaNoeZXLKA224lk8hTzpvwhpy4PXYqhQpzN5bZdXQQi82MxW7mytt3+MSXTiHKIrJJRhThrd+/hNVmZmsphtVhZe3eFp/8mce4/PokhUyZ/r3dnP3GJaqFGrVqHVVVOfJ0M+g+fD8bZ+nmCoIg4PI5MFlNJLfSWKwmBEFgz8kRiukiuWSx2VUQ8bC5FGN7OY7SaI4Ey6eLtPdHaO0Nows6vpCLcFeQ5HaGRrX5umuaTqDFizfkRqkr+CMequU6uw8P8M4fnadarlOKFxCaGegoHo3YWoKTzx1i8dYK5fsh50c/sY9X/8uHTBwfolFXuPDdm7T2BHnkcyc4+61r1Eo1+sbaae0NsTS5hqJomHUQJRFv0Elrf4Szr0+BJKDWVXRdJ5NoFgyvv3+XsUN9XHtvhkwih8tuppApAiBJErVKc5yaAKiaRrlUw2ozY5bg6Kkh1u9tE2zz8fzPPEIxU0bVNDQENEVjYymGIIKv1Qe6TnwjyeZiHIBcIk/fRCe3z8+zvRxHU1XsLjt9Yx1EuoMoDQW70wa6zs7iDtVynXqtQXIzyfjxYZanN1HqzSwWSZIQJQGH10G9rtLSE2L0cD+1YpV3/+gcia0M3SNtPPjpg8SSBYLtfvKZIrlkAYvdTLDV13x9PHY8rW68IRc+j4WXf/27tPSGkE0SsfUUhx8ba3ZDqTpKQyW9naa9J0T3aBsXv3sTp8tKIV/F67FjNpv443/7Js/+L4+RT+Xxuqwce3IvW8vbbC5EqVXqzN9YpmMgwuFHduNv9TF3c5Vr79xBMkuceHoCTdOxOy2090dYu7vF8acmPva10mAwGAwGww8Ho4jyt9hv/uZv8uu//uvs7Oywe/dufuM3foMHHnjgB31YBoPBYDAYDIa/A/uQTVEAAQAASURBVFxeO8E2L3anlXKx+tH2arlOwGnB7bFx+PAAV87OUchVPvr41kYKj9/Jcy8cJi42u1Hmb2/g9zroG23j8ONjxDZS9I62M315kY2FKCc/fYDFyXWuvDFJtVzjwKNjjBzo451vXycVzTN+dIDe3R0s3NlA0zS6h9vwhFysLe4wd3uNybMzuHwODj21l3wD1lcTvP+t6+i6TrjVS2dviK2FHcq5MjtrSbwhFy1tXuLRHJqq0tbhJ9zu48a7d1i4scKOx87OagKzWeKRF47y7n+9QDlfplqqEukOsXRvh/0PjPD+1y8zdmwQs8OCKImEuwKYrTLVUo3l2S0ef+EIiY0UXSNtxNaTuP0uPCEXtXKdu9eW6B5pJ9IfweK0kIsXkSWBQraMXRfoHWmjqztAIVVqXrgOOGnv8JPNlAgHXAyMtnPn3D3MZhlNVamWqgQiHsqFKusLUZZnNvEEnGSjWURBAKGZgyOZJKw2E1tLMa68McmVt25z9Km9mG3NsG2330nP7i4mz99jZzXJ0L4e2vvDdOzqYHk5zvJqEpPDRq1Rolio4vY72VpPc/jp/aj1Bt0jrVTzVVq6g839/ikdsvE8uw73I9ltLM5uEWz1kdxKo9wfBxfpahbGjjy5l+WpdVp6Qhx/dj/JaAGn10612sBsab6FVZVmx1Kw3cfafJSDj46j1hUANheiZHfSbC7GKBWq9O/tYWMpTrjaoFFrUC3XSMcL+JJFHnjuEJfemiLQ4qGl08/uo4OIZpm56yskttIoDQWbw0r7QAuJrQzekAtBbAafZ5NFXF1+0EEQBESp+f0KAmiahtfvpJCr4HBZsVpMmCSBrCjw3ksXySYLOFw2zDYTT/zYSd5+6TJ7jw+ST+SZu7JIx1Aru48NsL2UwOW1IwhQzlWwOy3U6wqCKFAp1zj3yi2OPbWHroEWHB4rU+fuUa82KBer9/+u0TGQZ3BvD1fenkKSRBRFYWRfP8t3d8iliuw9PkhyM8Orv/0e9WqdYJufOxfmmL26xE/+yxeZvLZKKppFaSgoeQVVUTl87Ajnv3mZVDSHbJYZGG7h0GNjXHjtFr27O8nEc1TLdarlGoqiIpkkSrkyVouMzWZG0HUCQRduj516QyWXLoIO6ViWM69O8uAjuxgZ78TlcyKbJATBTHI7gyxLHH/2IP17mh1U87dW2VyKkU0UCLR4OPToGEtT6xx+dAy7kX9iMBgMBoPhr4lRRPlb6qWXXuIrX/kKv/mbv8mJEyf4T//pP/HUU08xOztLV1fXD/rwDAaDwWAwGAx/y0myxOB4Jw88s5ez37n50R32ALVcmRdfOMKZt2fIpZsh6YIAgtQczeV2mJk8P8dDT4zTu6uNvadGyadLRDfTfP0/nmVwvAMRsFoktua3+a3z99h9dIDHfuw4vrCX6WvLvP2NKzQ0yCQLTN9YoaLp2OxmLFYT01Pr7BppY/ryHKlkkXDEg9lq4tXfP8eTP3Ga+dlNVFVDUzXK5TqVbIme4RYqhQoms0w6mkNtqDgcZiKdfixWmVKuzLV37qDrkEsVsTrMmEwyO2tJXvh/fIKZywvcODPNwUfHefhTB1id3eDY0xPUGxqZ9RQPP3eQudvrPPGFB5h8f5ZqpU46keehF49RSBXJ3x9/ltzKfFQAuHNpnq7RdkI9Ya6+eZuDz+xFNkn07Wonv57ktf98G13X8Uc85DNFeodaiW6kqWYrbC9GKRdrWBwW1ud2sLusdAy04A26cHnt9O/pIrWdweG2YbbKFNLFZneRorI0FaV7rAurw0K1WGNpah1/iweL3Uygzc+Zl6+iKiqIAtVKg9uXFpm/uwNmE/GdLPuPD3LhjdtsrKawOq34Ay5iW1mOPznOvpNDvPkHH7Jwa5VHPn+Cm+/N0NITJNzhxxt0Ua81WJ5co2eknanz99j38Bi3z86iKmrz4vwTe2npi9DSE8bf4sHhsnH13WlGDg/ywbeuUi3XmwWKoJNAZ5BqXWX93jYbs5toms7+07uwWM1YbRa8ITeX35qifaCFbLLIpbfucPDxiWYOjSRw5PE9lIpVOkbasFhkIt0h6nWVWx/cJZ8p0TncCqJEuVSlWlfwt3opZUu4/A5sDgvVUh1nwEXfni5ia0lyqQJt/WFyySImq5lyo4qm6VisJq68M83uI/3MXFmgUqzROdBKz+52+nZ3sD6/w2OfPcQ3//1baIpGrdpgdWaTQrLAxAMjvP6H57DYzKSiOcaODnDwkd1cfP02NRq4fXZauoKk43mC7V4qpRqZZIHkdhYEMJllzn7rKl/6F59h9Oggy9ObVMp1tteSrNzdZnh/N3aXlXf++ML9fWSpVRv07+mmUVdYvLHCwGCY8QM97GymuXhmhn2Hejn3zcsfdZdZrSaWJteoFKu09oaQJAFJlpj88C7Hnpzg3vVl0HUEQaBeVahW6ticVrKZEuVilWqpht1lw+13kM1WCLR6Gd7XzdrsJntOjmCyWcnFc7j8TtwBF+996xqiSWLfqREiXX6mzs+TSxVQVY3oaoKRA72MHRv6ga2dBoPBYDAY/u4xiih/DRr1BttLMXKJPJ6Qm7b+CCaz6WPd57/5N/+Gn/zJn+SnfuqnAPiN3/gN3nrrLf7Df/gP/Nqv/drHum+DwWAwGAwGww+H7pE2JJNEuMPH9moKXdPwhd3IJpHl+RilZAG/10a50hzHZLeZCUXceL12YmtJivkKF9+cYmctRalQxRNwkksVuXpmlgef3Us+U0ZRVILtPrKJAv0T3SwvxPF3BTFnSnj9LjaX4miCgNUkIulQL9XZNd5NKZokGPYQDLkp5srUGypenx2bWSQUcVFo81Iu13HYzSSSeZZmNhk7PsSVd+4AkE8V6Rnv4rEXT3DzyhIOs4yi6qiahmyW8Hpc7KzE2VlNEGrzsXhrFYvVTMdABEkWee7LT6LqKm9+9SJKQ2Vhco29x4coFsvI5j0UsyVqlQZ1FZYW4jz9Y8e5c2kB2WLCZDdTris0VJXN9SR1p4lMq4OSovKlrzxNcjHO9T+5DoA/7EYySVx5c5Iv/vPncXodrM9ts7MS59RzB/ng29cpFyo4vQ5mriwCAkMHeolvZhje142AzuLkKu39LaSiWVxeO6EOP4VMGbvLhtlqwu6ysTEf5dgn9nH3xhq1aoNKuYZskqnWFdLpEpl7UR5+4QjRrQyTl5c4eHoUq81CpVhlZF83IxPddPSHEEWRSGcQVdGYvjDHg585zMzFe9x4Z4papY4oy+w5NUomlmH28gLt/RFGjw7g8jkQgTsX5lic3qJSrGKxmekeCrM0tc6ekyP4Ih5uvHuHhqLSPtRKz+4uvvrrrzbHyPWFiK4myMQu8cTfewBBBLNFRjY13/I2ag0Abp2fp1KscfSJMW5/OMvOcgK7y4LNYeX8d2/SP9HN6vQGQwd6mL65hqbpSHJzTJfLayfS7sPldfDAJ/eTjucwO6xIdiv+Nj+1ap2jT+1laynO1nqKQqZMW2+Ig4/v4dv/8QybyzHGj/QTbPWRy1dRVY25KwukohkWri4QCLspF2s0ag00XWfm8jx9451kojn8LV40TePG+7Mcf2Yfj754lHKxiqqoRDcy6EoDt89JNlloZqJIzeyWWqWOoqjcu77M/ofH6NrVweZilE5zC52DEWJrKRJbaRLbaVxeBzaHlXBXgHy6SGIrjd1jI52uUC5WOfXcQU5/cj/UGhRzFTRVA8BqlsjVFdbvbvO5f/QsmXgOb8CJ1WGhpS9EqL0ZFO8Lu6nXFfKpAk/+/Yf42m+8SVtfiHCbD1XV6Bzt4OrFRXoHwrhcFrpH2lif26aUr7B0d4eOgQjlUrOY++Gf3KClM0hbT5hIR4DYeopysYLT4yDc6UcUxb/JpdJgMBgMBsPfcUYR5a+oUW9w4dtXmXx/GnRAgL2nxzjx3OGPrZBSr9e5ceMG/+Sf/JPv2f74449z8eLFj2WfBoPBYDAYDIYfTh39ETr6I6iKypV3prn+3gw2h4Vgd4jYcgJBFHC4bcgmCa1YxdLuw+lxYN9tpVhukE6VcQddFPNVGnUFs9VMKV/m7q01dh8dZGN+h+3lGHtPjzF9N8q5t6fRVA1dh/1H+zn8+Dg3LsxTLTeoVOqYLTKnHtvNtbtrxFcTKIpKoMXHznoSr9dBYiWOy+0gvpLA3+LBbjeDrrN+b5tTnzqE1WUjE8/hcFk5/sx+XvnGNQ6f3kVuI8nGQhRd07HazBQzZZw+J9VSjUZdweq0cPSpvdx8b5qBfT3kMkVESeT0Zw6jaiq1Qh2z3cRv/vOXiW6mcHvs6JqGbDFx8OFRqvUGw4f6uXNlEVQViyjgtNl44Nl9SLJERlF4e3aVE/v6aG/z0t4foVysks+WKKeK2Oxmbr03Q7g3wpGn9nHtzDRr81H6xjuJriTYXo7jDjg5/sx+zn7nBvVynZ21JM/9L4/wpV/6Ed76Lx9gspgYOTyAJ+zhyhuTlAoVHG4bJpNMuDtIsN1P/JWbVMt1XB4H3oiHTKKAoEOkw4euaWiahqoJ7GznaOkJYg+6ifQE6RqMfHTO9I510ru7k2q5SilbIrGRRjbL+Fu87KwkmLu2yOiBx3C4rKiKyvytVewOC4VUkd3Hh7DfnwLm9Nr44JtXcXkdJHay3JjawtwRwmUxkamrRN+8jaZpSJJEqVzDZJZp1Brcu75EpDtENpGna6SVarmBJIlomo7JJGFv8TCwu4P3vnYRVdXQNQ2TxYQoCpitZrr2dFGqagiT6zSqDcz3s180TWdgootMsUYumScVzaHrcPIzR4mtxlEbKprFjK+/FVdnCF3TKZZqVApV2ntDlItVrA4LSqKEzWHh9tkZZFGge6SNuWtLNOoK3aMdZJN5dE2jXtUo58uoqkagzUukO4DVbaNcquG2mjn36iQWq4nTnznMvod342vx4PQ4ECURh8tKLlVE1zXcfhfVaoOr79zB3+ZleXqTVDSLN+hiZXaT/rFOQu1+auU6Lr8DgO3lOCaLCV/Iw/JCHE3VeP/lqzz9Ew9SzJSQZQnBbsbvtVPKltBUnba+ILHVOO99/Qo2pwW334nZauKFrzzF1mKMXKZIqM1Po95A0XS+8E8+yeKNZVbvbmJzWsmlCowd6GH/sQHOfO0ShUypOVbPauaJHztBJpFH13QAirkysY0kTk8nkizR1hf+G18bDQaDwWAw/PAwiih/RdtLsT8roADoMPn+NH17uuka7fhY9plMJlFVlUgk8j3bI5EI0Wj0Y9mnwWAwGAwGg+GHWzZZYPJcMxi9UVdA02jtCdKoN8cwubx2ZJNEIVUg1O5HUTSK+QqaDg1Fo70vTCaewxd2UylWyWfKOH0OUtEsu44PkawoJFfXycRzCAhIJonFu9scOjXC0UfHuHtzFUXROPnoLs6/co1Im5/FqQ2UhkK1WKNrVzsbczuUSzUKuTJHntrL9TN3sDstRDoDDO7v5fqZaRZvLBPq8DOwt4erFxaItPuJr6WoZIqoioYgQKlYpVxq5lqMnRgi0hNCUTUmz83hDzqplhv863/4u3iCbkLtPsLtPsJtXlp6glTLNdxuG8mtNJIs4fLZEWSZxbkY1Xqdhz51gGKqgKpoqKrKzTN3eLw7yP7RLt65cI+XVq/yD588QC5VRG2omGUJwW7BZJHxBJyUsiWS0RyzVxaxOW1YnRae/uIDbMzvYLGbufzuDOlYHlEU0HWd+ck14tsZDj17gPR2lrGjA/zH/9cfUcyWkc0ymqJSyJTYfXyI8eND3PhwDl0QiW1lSNzZwOqwUirVEAR45IWjFOsaoixic1oREKiU62TSpe85V+xOC0///VO8943LTJ2dYXBfL6IsotQVRg8PMn9zmXQsS2wtQSlTItIXpl5V0DSd9oEW4ts5oJkzojQ0PCEXmiRRzlfYypYpF2u0dviRaw10XUepK6TiBYZ3tbG1GL3fSVOnUW/wqZ96mJf+3VuYLCZkk4TVYWH34T6y8RyqqtGoNyjlKmiajtkiY/fYcXWH+fDMNEceH6dRaVDMN3NNAm1eUtkyO1eWOXGsl4uvT6EoKgcfGWNxKc7Oepq2nhCpaBZV09h/uBePWeD8Ny/jcNs48MAIwTYfl87cY3Sig3yygD/kwmSV2f/IGOv3tsgm8niDbsqFCjaHBUESef5nH0Myy8hmmVQ0iyRL2HwOXviFp9i4t03/RCeZbIX8tRV2HR+mkCmRTeSxu6zYHFb2PTLG5IUFOgYjDE90M31xEVEQURWVlu4QsY00Bx4YYer8PRCgUmp2srj9Dhqqjstjo7W7+btezVfo393B1sw6qc00tVIV5/0iqjfkYnMxSqPewKzIuP0OLr56E5vTiiYIqIrOG394gf0PjeKqNZia2SS+nUFFIJspUyvX2ffQLuavL1Er13F67JhtFmSTyN2rS+w6OkBqJ/vReaZpOgaDwWAwGAx/E4wiyl9RLpH/swLKn9Ihl8x/7PsW/nxQI9yfRS38JZ9tMBgMBoPBYDD831cp1VAaKgCqqiFoKhKQzTY7MiqSgCSLeMMeAERR+ChYXGloqIqGJ+xGEAS6d7VRKVRR6goun4O+A3188O4M+VQRi80MCMhmicHd7Zx7dwa320aoww8IxNYSrM5F8YU89O/tZvXOBrVqHa2ucOr5w9y+NE+lUCPSH+HwU/uw2EzsOTnMhdduUciUEASBSHeYzt1dvPXqJK2dQZxWifkbK5x4dh9X35qidn88mdVh4fDjE/z2v3gJARGnz87Bp/Yxc2URX8hNfDtLKV9hc2GHob09yCaJSIeP2StLHH50jFqlGaydyVdxhdz4ymXe+6/nKeXKAAjAwy8cY+7iPB1jnTxxcJCVeIaZeIbhQ31cfm0Sq92M1lCbgeayTKjFSzbZLFRlkgXqaYWNxRhTF+cJdwTQFJVIh59KuYZaVxElEQGwO22spDbIJAscemKCK29MoirNcUz9ezqpVurcODtLqN3PB69M4nBZGN7bjarpLM1u0dEf4dq5BQYmOqnWVURRIBxxo9YVSqkid64s4Qu72FrYIb6eQlNUdpYTHHxiL6/+9vtkE83Cjtlion0ggsVh5aEfOcbMhTmK6SJdox2MnxpFvT8iCiCfLrHv9C5W56Ioywmiy3G6RttZLyfY2Uhx7Hg/K3e3AJAlEZPVRHt/hJOfPEC13MDmsuLwWPix/+czLM9sUM5XCXcFcLqsRFfjaIpKpVijUqphtZvJpYoc6AqwuZlGlCQuvn8Xq9VE33gHmVKNjRur5FJFLHYLrk/vY+/p3dSqDURRZM++bg4/MMytC/O0dPjZe6iHa6/eYGthp5mzkygQXUnwD37tcyDoKA0Fs0Vm/MQwuVSBYraEzWFjz8lRZq8tUc5X2HV4gP6xTs68fIVasUatWqd/optbFxaw2EyEe8M88OlDvP/qLRrlOqqqcfzhUfae3oXFIqPcL3BuriSpVWt0D7Vy9e3bmM0SqxtJWIdQhx9JEimWqjz02cNsLcVJbmXon+jG4rAiySIet435q0u4fHb2HOlncXab3SdGuPXWJOt3N7E5rIyfHKZ9oIXpi3N0j7RTLlRIbWexOaxoqsb87Q1A58iTe0htp+kZbuH1//weFoeFtv4whbRCfDXOhT8pceqzxxBFgVCbj1KhQilXIbGdwWyWPjo3LDYTwVbvx7fgGQwGg8FgMPw5RhHlr8gTcjff/fz5QooAnqD7Y9tnMBhEkqTv6zqJx+Pf151iMBgMBoPBYDD8dWgGlJuoVxvomo7SUDl4rI8zr03RqCsIgoAginhDbkRR4NjpUdbmdj76el2HakWhmCuj1BVOPbOX4X1d6JrOrSvLVEv1Zi7Kni7W5rYp55sjmKxmmY2lOLGNNADDvX5kWeLejRXcPjsPfvYIxXyFnqEWLp2ZJrGVaYbHx3IszW5hNst4eyM0zGZe+MrTaKrK+ddvk0jk2b2/lytn73HgSC+J7QyqpnH8kweoV+qIoki9plAu1zj41H5sDgvx7RyLszusTK4yeqSfjaUY1VKV9r4wc7dWmTg5zL6Tw/QOt5LYSmMyS+QyDeamNzn4iXHadrVzon6Q5TtrhDr8tHQFufzmFJVCBdFmYTVdpFZXECYcjD69l97xbhZvreIPu/GG3VicNi6/N0t0LcWJJ/eSS+aJriWa4eRz26zNR6mWm5kR3pALp8+OIApE11PENlJ0DrWRTxWYvrTA0af3YbKYkGSR1FaGpal1ZIsZ3Szxws89yu0PZlm+s4HL5+TJFw6Dxczbf3KL1p4AZpeN1hYPF9+cIp8p0drhY2c5zubcNsmtNEqtTqg9wPjRARZvr1IpVBCE5k1g9VqD5HaGYq7M5mKUsQdHaeuLUC3XuPbuDD27Oxg91Mfd68vUqw08kVaqk+t0eG088swElaqCWmtQrasogsjB07u5d20Jq8VEPlEg1Blg4c4GLT1h9FKVarHOt37zHRweG1a7mfROCqWh0T/eidVhIR1v3vymqTpmq4nhg/1c/+ol1IaKySwjWUxkkkXiG2kinQG6BiK0hZzc+2CWYiyP3etgZnKNwd3tLF5doJIpc+ixMaqZAtuLUTqH2th9dIBKsYokS80CxVgnTo+Vo0/v5frbdyjlyzi9dpweO3fO3+PUZ4+gazrdI+380a9/l1q1QTlbppivsHxng8e/8ADX3p+llCqQ3UnjNomsbSZo7Qvjdpi5dmmBG+/PIorQPdzK0L4eDj0yRj5VYPKDu7QPtHLi6b3ENlOYLCaOPbGHtbktXvmt93jmp08zfLifb//2WYYnurj25m3KhQo6UMqXufDKDXY/MMorr0/yk//b4wzt7yWxkSLQ4WPy7AyVYg1d1+keaUUXBKYvLpDYzhLbSFHMllic2uDwI7tRVQ2by0q91qCYKZOO5nB67FjtFvLpIjaXFeF+IaVSrH004g+a59GRx/fgC31877kNBoPBYDAY/jyjiPJX1NYfYe/pse/LRGnt//iKGWazmQMHDvDOO+/w3HPPfbT9nXfe4VOf+tTHtl+DwWAwGAwGww8vX8jNxIkhrp2ZASC6nqa9L8RzXzjO1maGVLKIL+xmeKKLXXu7sVpllu5uM3aol+lrK3/2RLpOz0grjYZKo66QSxcoFauIooAsi5RyZYJtPmqVBm2dfj54bQrE5l1L5WINd8hNtVzH7LWxvZpE0CEdzdIz0srG3A6CIOINOMmVaoiySOdwC6vrKfYdG2D+5hKBjiCZZJFARwCLVUapK1RrCt6gi2KmzI2zdykXqmiaTvdwK52FKjubzfFMuqozdqSfzFYKu9PCscf30GgoON02WnvDJLcznH/lBul4ntROFpNF5sf+8bNkCnViM1v4fHYkUWBwopvtxSiL6RK7DvWxsRRDkkWqVYXodhqPz0ki4iOxmWXw2DAetxWvz0FD1fD4nZTyVZaW4lSqdfafHmNhdpvW/hZiWxn8YTdmiwwCHHlsnPhWGlEUcXnsTF2Y48Qz+9DuB42bzDJWhwXZLDN6qA/JLBLqCPCtf/cWSqM5ok3TNM595wanPnuY46dHaAm78LZ6OPudW2SSBdxuG3abianz91i8vYbJbCLc5mVzKYov5GJrKU64O0i92qBeqWN32UAQ2Li3g6IDgshrv/M+7qCbVCzPvevL9I138MgLx7j6zjSqoiKL8ObvncUbdpNJlhg7OczycpJiokD/7jbGDvdjtUgodZXl6XVEBO5eXeLRzx3lw29do1quUS3XAJAkkeFDfVw/M83xTx7k3rUlNuZ38IbcTDwwgmySae8NkclW0EwSGjrFukqwL4QkQm+7h9d/9yztHT40VaNcqHHqR45SrSukYjkq+QoLN1c48MhuXvjKU2wsRHnnj85jspjwBFx0jrTSu7ePD16/zcEjvZQKFZSGSjZRQLaYaOkOUciUCLR4WbqzQSaex+VzUMw3C1GapjN3fRl/yM3QeBcffO0i8XgBEbBaZH7/l7/Jkaf3IckimqqxenebfKbEj/2jZ3nz9z8kGy+QieUBgUCrF0GAa+ic/uwRZEFAV1VURae9P4LWUKgUq837BXWdSFcQySRx79oiQ2Md7GykcHjsvPXVc/SOdrD/4TGWp9bxt3rJxvPUK3VOffogVrcdSRYRZZF6pcHVMzMceWw3oiThcJtw+5rZKQgCoc4AuWwJt7eZzeL02OkZbUOWJVw+B56Ai8GJLnpG2/4GVz+DwWAwGAw/7Iwiyl+RyWzixHOH6dvTTS6ZxxN009of+dhC5f/UL/zCL/CFL3yBgwcPcuzYMX7rt36L9fV1fuZnfuZj3a/BYDAYDAaD4YfXvlMjmMwyk+fnKBeqZBJ5Dj08xtNfOIGmNe8QtzstiGIzb8HttaM2VB777EGyySKapmG1m9lYTiAIArHtDP1jXVTvbbO6nMQdcJGJ52nUFcLtXgrpMl6/g1y2jCiICKLA+laWvQ+OMnNpnkDYQ61co2dXO5vrGU48c4CLr91EtpiQaio2p5Vdx0eQ7RZ8HjvvnZvn0JM2Bvd0YhKhYyjC0mCE5bkYDzx/mLtXF9lZjoMAo4f66NrVQbWikE0VyCWLgM7m/A6+sBtJltD1OqVsGVEQSG2lufj6JP6IB7ffgdvvQJQkZq+vkEgWKdYb7D05SHQtwZ1z9xBFgUZdQZREvvDPnyOWrxDdXkRTNWZvr/H4UxPMLtzk+ndv0DvUgi/oYuRAL4+9eJR0ssB2LI/TaUGvNrh1aYFIp5/PfeUpZi7OUynVCEQ8FNNFIp0BBElicy1N355udtZSHHtmP3evLFFrqMR2sphtFjprGsV0BpfHwc5aAl0DTdWQLTKHHx1j7dYq0a0086ZmNkvX7k7QIbWdpb3Tx9ZSDHRo1BqoqoZsllFVlZFDfdSrzW3rc9uYTDKJzRR2j426opLezuD0OKjXVewuKyazzOZ8lEatwfDBXkqZIncuzFHOVyikiwxMdHPvygKf/KmHeeur50kubHLw9C5uf3AXURJYvdfsfpp4cJRatUE6lv2ec7iZg6Lg9DnZWIiiITB0oI96rYE37Ea0SOw7Ncw3XrpCI6dQrzYQAEEUOHywl8mzs7i9NsyySK2hotTqVNIFhg70ohUrzW6QjgCCrrFweZF3X7qEJImI5Tr5dBGTRaZjqI1wu5/F6U18LV4EUUAUm+d3o9ZgZXqLjqE2SvkK9bqCruuIsoh+f9RZLlWkc7SdRq1BqVABwGI1oasauVSRtdkt3EEXpWzpfmGyQi5VZGBfDyuzm5TyFdB1ktsZBAF6d3eQz5aIDLSSz5exOMw8/WMnWJleZ+76MoIo4HTbsbmsxHay+IIu9j0wwtpynGqxyhM//RgmQSfS7sfpc/D1//NVRKmZm5PYStMx1NYMrq80UOoqDpeV9YUYJz99gNsf3AUB6jUFh9vGIz96nNuXFj8aBQjgCTh54vMn6Nv98WSOGgwGg8FgMPz3GEWUvwYms+ljC5H/y7z44oukUil++Zd/mZ2dHcbGxnj99dfp7u7+Gz0Og8FgMBgMBsMPD6vdwsGHdzNyoI9qqYrdZcPusv6FnyvJEice2cVb37rJzlYWSRJBgGS8gGyS6OgJsLOSwO6ycvyRMba2zuOPeNheidNIFZEliXu3Vtl7tJ+L782i6xomk8zmSpKWDh8PvniM4V1tlApVVu7uMHV1hbHDvXzxf3+ebLKA1WHG2+pndSnO7M1V2tu8PPjZwwTCHr72G6+T3MoQaPFw6Im9pFo9vPqtm3T0Bjn0if1YrSZmbq1TU1RMms7YwV62V5O0dgaY+nCGUq7CF//5p7nw+m2e+sJJzn37Oq29QVp7Q4Q7/Ny9ttQck/T0BN/+7Q9o6Q5hMclU8mWiO1la+yMUUwUURSPQ6uXerRUUvwtV0ZBMEk6HhfhijJW72zhcVnxhNzaHhcvvz1LTwdcVJK02WNsqcmqsk0OnhvGH3bz6u2fRlWZeyfyNZbxBF/17urH53Fw/exelofLgJ/cyerAfq89FOV+hXlMolmpcv7BAsMXHsrhJIOIhuZPDZJHxRdzUilWmLszRPdKGzWmlXq5x/a3bHP/kQRKbKWRZQjbJzcISOgggyxJ2p5W7VxdZv7uFbJY5+vQ+GnUVpa4Q6gjQUDSUap1CvoIoS5gtMhabCZNFJpMoYPfYWbq5TL3WAEFAVXUSW2n8YTfz15bQ6w1kk8TKnQ3iG0mCbT5ESUDXYeHmCqc+fYj2gRYWbq1issgM7e1BVVTUhsqDnzpAsCPAxe/eoJivEmrzceP9WVxtXlr2dNHXE2JmegNRFNBUHTQdi0kikynS0x1kez5KLlVg/yNjTH04y92rCzg8du5OreFp8XL4kTG2luPomo6qayAJyCaZfLrI6tQaD3zqAKt3d1icXEUUBRyuZnbI1kqCT/7UaVKbKawOK06PnWy6hC/oIh3LIQjQ3h9G01R0TcXtsaPWC1jNIiaThGyWycSzBLtCZOM5ygUFURTJJgvMTG1y4rkjXHrlOoVMEXQdQRIZ3NtDulAnsZ1BajQ484fncbmt9Iy08+Bzh5i7uUI2U2ZjZotIux+zw8Z7b0wR284SCrtJp4q43DZ8EQ9v/+E5fC1e7C4ryWiOUqbM1Ll7HP/UQbaW4wTbvPjCHuxOK1a7iad+4jTby3FMZglJlrl3Y5UX/9fHSWxnSe1kcXntdA23Eu4I/A2vdgaDwWAwGAx/xiii/C325S9/mS9/+cs/6MMwGAwGg8FgMPyQcXpsOD22/+7nBSMePvljR9lcSbC5mkQQoKMnhC/gZOXuFqVsCUXRMMsSz3/xAc6+NYUv7EGpK6gauH0Opi4t8Pgn97F0L8racoxGXcNmt+Bv9bO8lmLtXgyby0pkIML0nU0+eP8eAFabiU/++Ek2NzM4vHaS2xlMqs4f/tq3MVvMaJpOKprj7rUlRo8OEoq4yWVK3Lq2isUq89CT41gEnfe/cRm9odI1GGForA2LSUCSJTYXY4wfH0QUBYJtHsKdAa6/c4epD+9hsppAgPlbazhcNkQRfC4bsg7b22msVjM9/S3UqnWy2TKJW2vsfnQcydQMzt6zp5uFG80slFDEjdZQSccLCILArfNzDJ8ScWvQYjOxMb2OVq2zcHWRPUcHmL2+TD5VRNV0LE4r18/d49hTe1EVFUGEm+fmGTs2SCxWwOO1kYkXiG6msdisKA2FekPD7Xcim2USW1k6B1qYuTDXfM29dtKxPC6fnUKqSDFTpF6uI5skatUGJouJcrGCJIr0j3fy9lfPMbSvl0h3iHQ0x/nv3OCpLz1IsMOPxe8kIIjImsbc5Dq6Xkc2OT8qWphtZnQddE0j0uFnZzWJomrUqw0KmRLt/RHsbhttfSEWb65SLdXIp4oEIl4i3QGC7QHW5rbpGmzB6bZjssi89fsfUMqVMZll4msJDj05QT5dZHs5TrXSwB12s3R1mXGThMUhcer0KBtrKTKZElarTNdwC7nlNnKxHIqi4PDacbpsFLMlfC1eKnWVfKGKSpZ8PIcgi3gCTtBBVVQ0VSMTy1HrD7N8ex3ZYqJ3tJXVmS3yqQIgEOkO0tYb4g//5Xd47MdPMbCnm6mLcwiyhCfoQlM0WnrCCLJAIOzhndkL6DrkRYFcIk9bTxBvxEd0K43S0NC0ZqHO4rBgc9qYubnKsWf38/YfnsPmtPDQZw7hbvVx47XbhL1Wzn77Gla7GUkSSO6k2VzYpmdPD7O3b+B0WalVG3Tu6uDa5WVURSOTLuFwWamU60Q3MwiiQGw9hSRLOL0O6tU6NocVm8NCpDOAdH9kXzFXYu3uFiefPUB0NYmqqPTu7mBgoouW7hCtPeGPcwkzGAwGg8Fg+L/EKKIYDAaDwWAwGAyGj43LbWN0oovRia7v2R5p97Hv5BCqomNzmFEUDbfLzO3rq6w4LeSSeYb3dLC9sMPVNyaZODlCZ6cPQRQpFioEIm7uTq4TjWWx5y3EdrLoOpjMMoIoYLGYyKdKbC3HcHsdePZ2kVqNYbGakU0yPUMtqKpOtVrnzuUFnnrxOKlkkXKximSSEDWVV37vA1RFpbXDz62zs9z64C5HP7Gfq+9O86l/8DDLd7e5+NYdJEkg3BlgeyWBqqiY9GZXhmSWKRcquANOlm6v0Td8BKvVjNJQKBQqlAtVCsUK7X0RypU6f5qcPTzaxq2NFFZZJLoSR5IkKtU6dreNzuFWtGyJm+/cZmsx2ixMdYfY//Buvvvb7/PQjxzl6ltT+CMe4tEcsiyh61Cr1LE6LXhDbkq5ElqxzO0bCwgI7DkxTCxeIJepUK2r2L0OUtsZwm1efEEXdocFf6uXdKwZ/p2OZpHNEqqqkY7nmb2xytCeLuZvrxGIeAi0ecknC/gjXnRNx+62YbFbqJZrJLfTdO/qJLuTY+72Oq0dXp7+0oOgaWwsRlmf22HkUB9Ov4u1u1sM7u9l9vIikQ5/M4zcacVkkjBbTazcWaecLxPpChBbS6CqKi29IVRF48NvXWPPqRFyyQKlfIW+3R2YzSZsbT58YQ+lYo3v/tYZTnzqEKtz0WbAvN1MIVdm7sYKgw/tIp0pcGBPO6IOFqeFRKbE0PFhbnz3BuHO4P2Cj8qRp/aRSeaZndnCYjfTqDYwW030jXVx9+oysgiVUjOTxeG20TPawfTFOTpHOgi0+Rk7NkQ2UUDVdLpH21i9t41slohvpTG77Tzz04+QS+QJtHho7Q0Ti+YQNI30Vpr2gRa2l2Jomk692kDXdPY+OMprf3AOX8iFzWmlc6gFWRJpbXHhsMsEOgJ8+f/4UXKpIvVGg1uXF7GaRO5eXcTusuL02NBUnbWFOKGIm/7dHQiyiDvgIluocfn8AvWagsVmQlFUsukSXo+NWKxAoM3P9nIcTdUxmSXMFhOaptOoqyS3M0S6Ajz8/CFWptZwe+wk1hOMHe4FQSCfKrKzGEXXdYQ/N87LYDAYDAaD4QfNKKIYDAaDwWAwGAyGHwiL1fzRv0VRYHt2E6emcvxoLxaLidhakseeP8TFN6eaWQ7FGuhw/MlxBAna+kNsrCZQG0pzbJKigiBgd1jo7AmxthzDZJJxWmVe/+2zDE10kksWMZllFEVpjs9y2hCkCuvLCVZWkyS20rS2+1jPl6hVGwCkE3mUukq9rlCv1NF1OPut6wzt66aQLpJJ5Onsi9A51MLGfJRysUaw1Usg7KZarlMr16mW6ixNbbBrops715aoVRt4Ii50QWfocD/vf3AXn8/BqQdGsZtNdA+0sHl3i3q1gWzWKZdqlIs1nvjccd759hVquTIAAgLpeJ7MTpbe0XZqhQq7DvczdW2Jek1h1/5e0vECDreNSHeQ/tFWzr18ldW5bWwuG6V8hQvfuc7pF4+haGk211IcOjVMW1eATDyHzW3j5HOHmLm8ADRzUvLpEu0DEVw+J5Issjq3w97jgzz64lF2VhL4wm7UcgVv2E02WaBarmFzWPCF3QiCwNLMJtlkkXwix8rUGja7mT3HB/H4HTz/c09QUTRCrR52VuJoiDz0I0d5+4/OoSkaFquJB188xsVXbzT301Dp3t3B1lIU2SQTbPdz+Y1JukbbKdYU4gsxTKJI9PVJTjx/kOnzc2wsxvCF3eh6M4ujWqrij3gYmuhmQFUREBjsDTOXLfHG739ArdLMdOnoC/PsTz/CvscmqGeKRNr9pDZSXHlzklCHn8c/c5jF26tszO3gctvZmI9y6lMHuPTaTQQERFnkgU8dJLGTQRRFWnsC1MoNzn/nOt6Qm4OP7aGtJ4SmaAwf6MVht2CymqlVG4hmGavTxtpClMROjo4uH3euLjF+dJDhfb3EN1N4Q25sTiueoAtfxIuqquw+2Ec6muEb/5/XqFXq9I514HLbcZgESoUyoc4QN66u4XJZECUBt89BMpqjXms0s41UjYW5KDs7RQ6NdvDKt8+CpiNb5PvZRwqKomKymkglChx/ZJy7VxZRFY3EZppQu5/jz+6nocIDn9xP10CYzYUoFruZnpE2Nue2v2dN2PvQLkRR/JtZgAwGg8FgMBj+BxlFFIPBYDAYDAaDwfADpWkaUxfmkEwyV9+9zvKdDVp6Qjz03CEquSKHHxyirmiYzCYsNjPf+g/vgCDwU//yRXYWY6wtx2nv9JNKFJoh4DoMjndy5eI8oYADvVhhazXO0L4uJFkkHcvR1hcmm8gjSSLdYx2sryapFKs4XDaqxSp6rowoCoiS2Ay5bv5B1zQ8QTeZeB5F0VAUlXK+QqlQoZAt07OrnWq5hj/splyocOpTB1hfjBFs97O+FOf4k+OMHRkgnypQQadjVwelap0Xu4L4PQ7e+9olch0BRHQiXUG2lmLUGyroMLSvG0VRkQWRqta8W99mNVHOV7h7fZldRwfJxLIoqk6oxUfXcCsjB/s4883rhDsDCALoqkqjVkdpqKiKiiSJlPIVbrx7h2B/K/4Wb/Pn4HOwsZFh++oKQ3s6sXscbK+mcLis+CNeDj22h4ai8fmfe4zoVhpJEukb78QZdOOwy1hkieU7GwjomMwyO2tJsskCL3zlad762kUsVjONcg2Hy0q1XOfG2Xs8/w8foV6tY5ElLr96HZvLjiQ7kWwWfuKXXmD93hblQpVqrcGJTx7k3T8+T6VYY2Vmg89+5RMUsmV0XadjpJ2qolEtVCkVqjid1ub4sUId0STjb/Fgd1gQgFK+Qmonx6lPH2D5+hJbqwk8QRersxsM7ulGFEQaDRVJFIhvpFi9scKNqU0O7OnkO//pDGaTRDFbIhPPszG3zaEn95JN5JmfWgdBxBt28+l/8CjVcpVAm5/lO+tcfvUWslmic7iNi6/d5LHPn6ShqOysJrj+3ixuvwOXx8bCzWXa+iJMX11k4uQI3/2P76CqGq29YTwOE7KqMn1hjmK+QtdQK7GNJG6fE4vDQqgryMhEJ3/y/3uLXLKAzWVDqassT20wemiAreUYuqbT3hfE6jCzvhTnwPFhrr091cygAXRdx+awoGg6awsxRvZ2YTbLVCt1QEAQBWqVOiaTxMbCDrJJJtwb5rP/25Ms39nEZJIY2NfD8t0tJEni2JMT5FMF9hwfonesk+2lKDtLUVRFA6BjqJXRI4M/qGXIYDAYDAaD4S9lFFEMBoPBYDAYDAbDD9TOWpKl6Q0uvXaL1E6WvvFOfH4nL/36d3H7HQTafHQOtZLcyaBq4I942FlNMn9xjsHeIC0RN8uzW7SFnbT3R+gaagFJ5LEnx7nw7WtsLcYQBIG5W6sceXIvZ1++0sz/qDYYG+vA1hZk7uICka4A+VSRZCLP8eMDJN6aJBzwYreasMgi5UKVtv4Ic3e2QIByrtIMUHfZUBUVXQdRFPEE3fTv72Py6gqhNh+94x2sz0UZP7iXYrrAjTOz6JpG90gb5nKDxUuLWF0W7mxl8fiddA9EiK7GGT3Sx8iBPnKFCpqms7meolapYzGJmIMutIaCWlew2cz4Ix6SsSwjh/px+51U81WmL81z671p9p8cZGcjS7lUwxtwgqZhtpioVeqoitrMNKnUmxfI6yoDuzt4++tXKGRLyCaZudsbtHb5efCFo9idVroGI1x57Rbzk2vYXTbGjg8S3UghW8ykYllaWtwkVhP4Ix7unJ/D5rTS2hOkrS9MbDON2WImlyyQTxVx+hy094XYfbCP+Eqcexfn2HVsiJbOIJtLMVA1WvrCVEs1cqkiiqKR3M6gqTqPfP4BBAF6dncSaPWwMLVOsVAlna9iMstUSjUEUaBeVwh1+FFUlXSmREvYTSlXZuRwP7lMiUhPkPhagsR6CpNZxua2Ieo6xUyBZ144ytZGku2NFOVMidvn7tE22oHVLFHOl2lYTLgCLmx2M9VSFV3T2f/YBOe/ewurVWb28jx/7598kjd//0NyiVvkYnlkWSTU7qdWqWN3WqmUa6zc22Z7OYGqaiAIWCwyn/nyY/z+L32Dg4+Ms7m4Q6NSo1FTKCYL1Mo1vGEXTq+D2StLJDZSRNeSfP4fPcuHL19hJ1ZA1lVyqeJH56fNbUVRNBJbKQI9EWQBdhaj7D3Sz/Zygo7RDjYXo+TSZXRdwxt0cfjpfSzMxwHYWI7RPdjCwswmZquJbLKAJInYbSaKmRJmk8zKnXV6http6W1h5soS73/7Bu6Ak0/91GmOPrEH+X7uD0BLd5D2gRbS0Sw2h4XWvghWu+UHtQwZDAaDwWAw/KWMIorBYDAYDAaDwWD4gUrtZIitpyjmKnQMttDWFeC9r11CEJvjlirFKh+8fIWnf+I0Z799jUOPT5DayeJv9fJff/01fC1u2vsjWGxmtqfXufD1izz/s48T3cqwMruFw2VF0zRyySLb6ykOPr6HzsEWCrkSssXEyk4eUWwGxkuyhCAK+Nt9HD29m9vvz5BpKOi6zlNfeoiaolIpVmnUGrgDDmauFpBkkdnryxx/ai+ioOPwubh8boFGXWV1McbkxQV+5KcfIhvLkornCXb4cXlseAJOpi/Ok43l6PS0cvzR3Zz5rxfZnF6nXKjQOdRG394epm+skkkXOfX4GI1KlWwshySJ2Kwm8pXmqLDukXYadQVP0MU7f3AOEXAFXCzdXufetRWe+elHEM0mWrr9qA2V9t4AO+tpVEVDlAT693Rj99g5MNZOvdqgUq5jspg+eo121tOk4gX2nxzk3T86z8KtVTRVJxvLsbUY5dmffhhVEukdauHqqzfYWY7hC7k59sx+NFWjfTDC1kqCa2em6R/rpHi/a0RpKIwfHuC9ly7gC3tQGwq5VIFwZ4DhgwOszm7g9juoV+qktzO09IU5952bzewZs4wv4uH5n32MC6/dItDuJ1dXkWWJQrpEIORErTUoZssMPzBM50g76Xgen9fOocf3kIoXqCsaj714hA+/foVwu4+GLFLTdfYe7OPM1y6yMrVObDtL7+4O9hwZZHl2m77BViy6SrDdT6PaIJ8p0TfW0ewCsVuIdAY5/tQE1968jdkkEV1JMHKgj3qpSiaew+62Y3HauPzKdUaODPDhK7do6wujNFSk+3kvSl3hzsUF+vd0E2j3ceuP71IpVQGBUIcPURIxW8w0qgonntmPIAqUi1Vmri7hCTp4/NQoic0ULq8dVdVQNZ1auU64zUujrjI/s83Gwg6f/7n/P3v3HSbXYRb6/3vOmd7rzvZetE29rbpkFXfHdmLHzjV2CE5CQkLqj5sAeW4CCTcQgi8EcIBAgCS202zHTZYsyerSSqu22l1t73V67+f8/lhb95oA9yY4GMj5PI+e1c5557SZM8/svPO+735aOkuYGKknEkmREbVsf89mADLZAv1XZ9HqREwWA/0XJ3nwI3tYnAuTTuVAAafbglwoIooCqzfWceSpM2y5cw0j16bZ//BW6joqkWWZzq7GtyRQAARBoLTGS2mN99/x1UalUqlUKpXqZ6cmUVQqlUqlUqlUKtU7ShREIv4YiVgavUnP0nQQo8VAOpEmGU1hsRvR6jWEl6IU8wXMNgMCkEnnqWktY3p4geBchEKhiN6ow+62kkpmSafzlNd5WZoJvVExYkApyvR3j+Lw2ug+1EtoIcqOd2+k+o7VXLs8idNno7rWjUkSGJkNsn5fJ3JRxuayMN43RW1HDVaHCZPVwNJ0CI1WQi7KZNN5CoUiW+9YzWDvHBqdhmg4SSaZZe3OFkJzIY4/10OxKGO26HGV2HGVWElGEmh0GiKBBD2vHULSSET9ceo6KhntncLmtnDL3WvJ5/LEg3FG++dYuaeD5GKExakADStraFpfTzSQYOjSGFVNpYTnI3grXaQiKUoqXSAK3Lg0itbrYGh0kfV3rKX3eB9Wix53iQ2Xz07XgZVE/FHigRglVR4a2isYH5h9c9Y9Wr2WyvoSlEKRdDL7xlyZIgigM2gZuTzBqgOrCM2FQJbRajVMDc4zNTiPIArYXRbqV1XjLnOQy+axe6xo9RrKaj0MXBxFb9IjCGB1mkklMsyOLrJiYyOCKJLPFjBYDFS3lnP8RxfJprI3EzCb1q5k6NIEdo+VykYf8b4Zbn1oC7lUlhPPdhMLxGnb1MTU2CLHfnKRWx7ZgaQROfPKVdbv7WCkb5ryOi86s45EOIVG1LBqcxMnnu0mk8xi99pQFIXR3ilKK90ceN9WrlwYp7GhBLkogyiw/e41LE0HuXDoGk6fg1Qqw+77N9F1YBVHf3CGqcE5ul+7TlNnFflsnpYyF1M3ZomHk2h0WjKpLHqTnmJRRm/W46vxkkplmZsK0LSqerkyxagjk8pS1VxGsVDk+A/PY7QY8JS7WJoJ0XXHGiS9lunBeVwlVnoOX2XLHWtpWl3N4nSITCqHRiORiKVwV3mYvjhBPleg+9QQ0cUIdr2E22UGQeBy9zhanQar1UAqlkIpyngqXVBiIRFNsXFbM+FggnAwQT6dxeWyUF5hZ6h7BKvTTDEvk4ynOfjdU2y/ax317ZXY3dZ39DVGpVKpVCqV6t9CTaKoVCqVSqVSqVSqd5TerMdkM5FN50nG0thsDkRJpJAvotFp0Og06Aw65KJCaV0JYX8UvUFLaCFCSbUHs93EQPcouexy0mTVznamxxYZG1xg894OtALEoym0Gonx/mn+23+/mxs9E9R1VOGr8mA06li9qoJynxWDxYhULPLq3x1lbmQRl9eGAsyOLlBa48XutvDQp25jon+OsD+Gu9SB1WlEo9dR0VTKjavTTIwFCC/FMFkMrNpYR2m5k6M/OEcqnkFnWJ7rEg3Gmbwxy/a71jB2dZKKRh/X4xlcpXYqGn0YzDoaV1Yxdm0Ci8vCyLUpAvNhtt61DoNFj82ix1FiZ3pwjvG+Ga6dvEEuk6empRyDSY/dbSUWTbGwGCWbzqM3aOms8NDXO0MuV+SW9+0gF0mQTmQBgXQsxdjVSUavTmL3WDE7rey8czXD1+cQJQGzdTmRlYqlkfNFHB4roiSi0UhEQgkmBmbZePcGCvkiADa3hdBidHkOi6yg1Wsoygr1rRUYrAZefeocSlGmot5HcC6Ew23F5jKTTecQRRFZlsll8pQ3lNC0tpZCvogM5PMFNDoNxXyR2rYK9AYdr33vNHse7OL7f3yNeDyDIomUlTu49X3bKMgKx1/oYez6NCAw3T9DeVMZKAqJSIrq+hJe+fZJVm1t4uSlC/iq3egFAUEQcJc5MVmNmK0GLDYjhWweq9UAyQwnX+yhqrmM6Ruz5LJ5ek8PYXWY0GhEsoksh79zivf993vYsH8VF18fQKORWJwOQlFmZmQBk81ASZUbSRJxlzlIxlLsvm8DmWSWkD9GTYMPnUmHqMgMX5umfWsLl4/1UdNWwaUjfctJl4KMyWqgkC9y5fV+3v2J23G6zTz7jUMgCIxdm+K+3ziAfXSJ8evTGMx6Nt65lqs9EySjKXzVHoLzEWKlNrpfuEhJrZftd6zn+uVJktEU+UyesloviXCS6Rtz3Pf4LirqvCzMhFi9roqeYwNE/TJT18a5/FIQg1lPdXMZLp8NURRRigpVjT5WblHnnKhUKpVKpfrPTU2iqFQqlUqlUqlUqndUNJSkvrOKvu4RIktxOjc1cKN7DLPdhN1lIbQQQavXUFLjxuq2MDU0T0mlE7koMzO6RD5bYPMdaynk8izOhDn09Fne+8lbaV5dy/zoAsPDCxiMWryVbrbcu4mfPH0eWQGH28rK/StpaKtg+PIEDqeJvmO9yEUZAYH1+1Zy5sVLpOJpBAS0eg0737OZNrcVvV5icWwRh8+O1mJgcXqJnjOjiJIAWg2CRqK6wUt4MUZ0MYrOoEXSiBQLRQr5Itn08nD38FIck9VANpUll8lRLMjojFqigQTFvEwuU0AQBXRGHVaHmWPfP8vm21Zz8tkLVDSU0LSmlvI6LyIgSSKVjaWMX5++uS2DJGJxmaluq2BpPkIunSedyTM0FWb1ykpGXrhIXUs5p5+7QN+5YQQB4qE4rjIXOr2GTbeuIjAXQavTMjmySEWVk1wmRywQp1iU0Rm0JGJp2ruamRpdRKvXgyih0yt4yh2EFmMoRZmWDQ0gCHjK7UwMLrJ6azMAde0VmC06pofmmJsIIEoCRqMeg1mPw2ul++BVjn//HHUdVWw4sJJd92+k52g/ZruRhs4qLh3po7q1gr5zI/hnQ4haDTXNpcwMztN/dpgd967HatSxfnc7Pa/3szQVpHFl9XKiqcSGXJS59bHt5FJZbnnvFgZ7xhAUcLqsmOwmIoE4VbUlFApFZobmOfrMGaZuzNO5fQXTo0vc9aG9nH72AuUNPor5ItFgHK1u+bFOxTP4F8MohSJyoUgylqak3IUkCWi1GhCgvqOSWCJLaYWTi4evEY9llmelmPWs2tZCe1cj108N4i13sGFfJ1anGQC9UUdZrZdIMMHiVJBsJs/FY30ko2l2vHsT/WeHyWfzLEwGGB3107CxmWw6x5WeSRRFoHpFBYVcgemRBWQFuu5Yw/TwIhpBpqzCwcBSjFg4ydJsmKrmUu7/0G4kReZvv/B9BEHg3b+xnxvnh1mYDCDLyvLsmXSe8voSKupLaOysRKORKK/1otWpHzuoVCqVSqX6z019N6NSqVQqlUqlUqneMYV8kb7Lk8gFmfd87FYmB2YJLUS49zcO0Hf6BuHFGK0bG1mzp4N8QUYRonTdvhqDWU8um8fiNHPsR91cOXmDYkGmmC+y4971KIrA0KUxLr9+A4fXSlWDl0wRvvunh6jrqCKdzrEwH2XyBxexOwdYu6WByEQQQacl44+xelc7PUd6yaayiKIIKEhaiakbc5TW+bjwWi/5TJ7a1XWcOdyHzWUmmciSjKXYcEsH02N+3F4bUzfm8JbYKBYUdAYtmWSWbDqHRqdBq9OgN+tYnFjCYNLjrnBhMOmIBRPIRQX/TJB1ezvxzwQRUYj6Y+TSefwzIcprvcwML1BS4aaQyXHx0DVESUSSRHzVbkZ7p5mbCiK/kehYs7sdu1bLzFQIk9VALpPn0vlRtt6xlrFrEwxeGEFn0KLRSMhFBY0kYLLqOfg3R0klc0g6DRv3d1Ja42bnfRuZH1sElmfWzE8F6dzRxk++exZJEtlxWyfBySVSsTQ1K8ro6GrBXekkGojjLndz4oWr5LN5TFYj6Wye1V2NXDzSSyaRxWI3kQgnWXdLBxF/HFEQWL2rjZ4jvVw/M0h7VzNrdrYwNxHAPxuivqMSb5Wbl/7uOFqdBpvTzMi1KSSNRC6bJ53MMnhpnLaNjVTU+2hZW0upz47nXesQBInZqSC93eN4yhys2liHxqClcXUtV08OMNo7hdFsQBQFlqaDbHvXevrOj1HIF7h+6gbr9q1ifGCOdCpDYC5883wIokA2m0cBsok8xYKM2WpEFAUK+QIt6+qpXV1H++4YMrDjnvW8/K2jFN8473qHCZ1ew9TgHGaHCXelB41eS217BSXlLiYHZshmCmQzeaaHF1AU0Ok0oMDFY/3Ut1UgaiQWhuYx2Uy4q7xcONpHIpJCo9WgM2pJxtJ4y51o9Vp81W6e+crz3PexA0xPhlhaiNDUWYnJYsBiM5BPZvB4zJz88QUsNhMmi56B86PUtJTT0FnNeP8MGo2GurYKZkcWSG9qRKfXUlLpwlftfsdeW1QqlUqlUqneLmoSRaVSqVQqlUqlUr1jFBRQFOLRFIPRFBabkYa1Lsw2A3Vr6/AEEkRDCV77wXmQFVZtX8G2d63n5HM9RIMJzrx0ie13riWdyr7R3shIZYMXq9NMIZ2lqbMSrU5DZUsFr714FZPViKSRiMUyZFI5dAYt81NBws0l6EQRo8VIeZULo8WwvIOCQDFfwGDSU9lYin8uhCLLKPnlOSnXL4yRz+TIZZdnegAosoyv0kU8kiS8FGPFqmoGLo7jKrFhc8kkIkncpXYEUaCywcfI5QnCwSS73tPF/PgS104PUVrloqKpFEmnYbB7FKfXhkYjodFKFHPLH9SX1nhZmglS1VxKIV+gsb0WjV5DeWMpDatqGeubJpstYDAbCMxHqG4q5dY7VyEZdUzPRYmFEmxYV0vEH0eQlme7CDoN6Xia8sZSTv74Ip4KJ5FAHF+Nh/OvXMXltdJ7sp94OEUymqKysZS7P3gL4+N+CvkCiiLx8lPnqFtRzp0fOYDdYSIWShIIJJiL5ih7Y76JIApEQgmqWsp46e9Psv7AauRcAblYxOIwEw3EmZvw465wcuyZMxSLMgDTg3O4Ey7ufP8uTj5/geFLE3gqXNS1VpBKZpFEEXfp8tyVbDqHXJSRiwrDl8fZ89AW6tqqOPH8RYKLMSKhBOXVHjbtXMHxg9cIB2I0t5ezNBdi3d6VdB+8svxcCcXp2NJMPlvAYjMwO7JAXFagWMRk1rFuTwcv//0JUBQEAfLZAmabEWepg2Q8TUVDCal4Bk+ZA61Rh9Zt4+ArvVjNeqprXGhFEbNVj1KwEoskMRh0pBNp5EKRfCbPwMVRtHotQ5cnuOdDexEkiUwyQSKWQavXgqLQdeca/EsxKhpLSaVy7Lh7PRWNpcRDcXyldoavLbco0xl1pOJp0sksVocZi81IdCHMpjtWMzK8SN+FMfLZAr1nRxFFAa1Ow5571hBairFiQwOKLDM/4UeWoefIdWrbKqmsKyEWTtL96lWMViP5bB53qYPd929Cb9S9My8sKpVKpVKpVG8j8Z3egf8KMpk8kxMBBm/MMTkRIJPJ/0K3VygU+J3f+R3q6uowGo3U19fzpS99CVmWb8YoisL/+B//g/LycoxGI7t27aKvr+8t68lms3zsYx/D4/FgNpu5++67mZmZeUtMOBzmkUcewW63Y7fbeeSRR4hEIm+JmZqa4q677sJsNuPxePj4xz9OLpd7S0xvby87d+7EaDRSUVHBl770JZQ3p1S+4fjx46xbtw6DwUB9fT1PPvnkTx37j370I9ra2tDr9bS1tfHss8/+PKdQpVKpVCqVSvUfhFarobGj8ubviVia+ekQI/1zOMtcuMqc2JwWmlfXct9H9/Hu39jHiR9fXJ4vgcJdv7aH3jNDnHvlKolIEo1W5NAz57n0+gD+mTB6g4Z0MkNRENFqJZKxFPF4mog/Tj6bRxQFdHoNFq1E3/Hr/OSvj3Di2YsMXhzFU+HEV+2hsrmU8gYfS7NhkpEUJquRbC6Ps8xJyB8nncySiqWwOc0IgsDU8AINjV6cHhuSJDE/FaShs4pELEUhX6C8wYev2s2ue9ZhthjYed8GyppK8YdSBOJ5tr57Mx27O3FVeQguxQABvWl5joosy1S1lJHPFnD6bCSiKRxeGy0bGqhfWcO5ly7zzB+/yGtPnWbw8gSechc6k46Z/hkO/t3r9LxyhdM/OEe504jDaSKXySHni5TVl6DRaijki5TWlTDRP4tGryEZz5CKZ4gFEmg0Igf/7jjZbJGlmRAmm5H5qQCnf3KR6hoP++9dx6pN9ey4fRUtnZVcPTHAX/7W9xA0IiaXhaZGH8GJRUqr3ARmQmQTGbQ6iUwyy/mD17h84gaRUIrhK5NMjyyiFIuM906TyxQwmPUYTHoSsTRhf5zzr1whtBCleW0dtSvKyWZyZJIZlmaCLE4GUIoKzhI7ngonq3esYMuda1m/p4Of/M0x/PMR5IKMBhi7NsnV169z5/3rMFNEKBQREJgcmqd5czM7H+hi0x1rSSWyXDlxg1QsTTadRynKLIwvceP8CDJwy4Nd+KrcmO0mqleUcdv7d/HCd8/QsLae9q0tuMtdVK6ooGJVLQPX5/A6DDTUuvBPBJgemiObzLE45ScRThKYDRGYDbM4FaC02kMhX0RRFMJLMSL+OIVCka471uIqddDR1cTOd29iajzAaO8MkWCSho5KCrkC2WyBtq4W3B4zTe0VGMx60okMGr0Gk9VIIpbiPY/vYqJ3Br3NRCySppAtoCjKzX+yLHPy5WsIksSlo730nRumrqOKmhXlmO0mktEUM6NLRPxxXD4HHZub6Nzawrs/up/SGs8786KiUqlUKpVK9TZTK1H+DRRFYXR4kbNnhgn44zdv93itdG1poqHJh/Dm19HeRl/96ld58skn+fu//3va29u5ePEi73//+7Hb7fzmb/4mAH/4h3/I17/+db797W/T3NzM7//+77Nv3z4GBwexWq0AfOITn+CFF17g6aefxu128+lPf5o777yTnp4eJEkC4OGHH2ZmZoaDBw8C8MEPfpBHHnmEF154AYBiscgdd9yB1+vl1KlTBINBHn30URRF4c/+7M8AiMVi7Nu3j927d3PhwgWGhoZ47LHHMJvNfPrTnwZgfHyc22+/nccff5zvfOc7nD59mo985CN4vV7uv/9+AM6ePcuDDz7I7/3e73Hvvffy7LPP8sADD3Dq1Ck2bdr0tp9nlUqlUqlUKtW/j+bOKgYuT5KKZ/73jQrMz4S59YFNlFU6EUQBs83ExMAsS7MhADKpHLlsgfKmMna9ezN950c48ZPLy7NAzHpsbguzo0vUtlciCJBJZrE6LcQiaVAUFFkhly3QuaaGE89dwGDQoNFqSKVyLE0H6dzSwvy4n0wyS7Eok8vkKKvzkU1nKWTzmCwG9G+06FIU0GglNu/vJB5KcvbVa+y4cw3NnRWEFqKUVDi45b4N6A1alEKewEyI8GyQYsaCfz6MrdTDmddv4PBYuXB+nGQiw7pN9RQRcJQ7yeeKgEDrhgZmJ/34A1FSmSxltSXE4xm8VR5e/ccTCKKAViehFGUWp4IMXx6nprkMvUmHXbCQTmVBEjn7Qg8PffYuYksxWjY2YrAauHj4GktTQXR6DYgSnnIni1NB9EYd+VweSSMSDWQw24yAwsJkgPK6EkLzEVAUdBIYBZmFoeUvZlU2lbL1nrU4vTaO/dUROjc2MnFtGqvLvFx9FEqSS2YpZAtkkllKqlyMXZtEkRXK6ktw+hyMXpmkUCggCAL5fBGL00AxV6BQlKlsKmVxws/EwBzr961kanAeq9NM1B/j0rF+3v2JA2TTBeYmAoz0TpFKZOjY2MD8TICrJwYp5Iv4Kl2MXp3EW+ZgaWKJmeEFtt61lrA/TjiQZH4yiEEnMTOySD6bR+u1odVJy1UpTjOXj/fTddda+i5MsPOBLnQGLalEjsWZEG3r6pkaXcJV5qRoNLIQyrBldS1XDl1jLpXl2FQAm8vCrY/tpGltLUOXxjHZDCiigCCC3WVBa9BgshlBgfbNjYxenaD/7AiechdWj40CcOTHPdg8luWYdbUc+/55KhtKiIUSjFybYt97u1i9roa6ljKCgTixUBKH08yqrgYiCxFsHit9PZM4vDYUWP4bVlAQBIFiQSaRTaPV68hnCsSCSeZHF/HVelm3u53QUhRJIyFJIjaXhdt+ZTsNHVX/zq8gKpVKpVKpVL9YahLl32B0eJGXX7xys7T8TQF/nJdfvMJtd66iqbnsbd/u2bNnueeee7jjjjsAqK2t5amnnuLixYvAcnLniSee4Ld/+7e57777APj7v/97fD4f3/ve9/jQhz5ENBrlW9/6Fv/4j//I3r17AfjOd75DVVUVr732GgcOHGBgYICDBw9y7ty5m0mKv/7rv6arq4vBwUFaWlo4dOgQ/f39TE9PU15eDsAf//Ef89hjj/HlL38Zm83Gd7/7XTKZDN/+9rfR6/V0dHQwNDTE17/+dT71qU8hCAJPPvkk1dXVPPHEEwC0trZy8eJFvva1r91MojzxxBPs27ePz33ucwB87nOf4/jx4zzxxBM89dRTb/t5VqlUKpVKpVL9+/CWObjrfVu4em6E0f45isUivgoXq7c00dBW/sZMkmXZ9FsrnnPZPAsTAURBoP/C2Bsf/BaZ6J9lz70biIUTyIUiBr0GQRBwlNiYm40gSAKiJIICOp2AAKTfSJZIWon58QALk0HuevwWul+9SiKc5Lb37yIRSRELJbE6LfQcvsaKLSu4em6Uzk31OBwm+s4PI4kSW/a0YTBoWbOlidefvcDl4zeQbpGwmTScf+kS8XCSQn452bP7vVvRaZY/NA+GkmQzBUwWPaePDLB2SyMr11azOLnE9vs2cOKFi0wNLwAQT2V577s38Q9/+SrbtrYiSiKZVA6b00LxjZZi6/d0MHhxlEKuQNPqWvR2I5dODJLP5MmEEixNB6htr8BT4WLbuzYgIKA1aElE0zz7l4coFmQkjYTJZiQZz9Cyto4bF0aQNBKCICBKAmW1bmbHFrFaDPT0zZCKp0EQyKSyHHh0F9fPDJFKZNHpNVw7OUCxqLD59jUgimQzeVZtbyEwHyayFMdkNRBeiiFJIpUNpcTXxYmFEoiiiMVhQNJrEBFo6KjiH7/yHA/9f/fwgz97FY1OYusdaxAkkarmUm55bxfJSJLesyOYbQYkjcTs6BJXTwxyy3u7uHSkH1EUkIsyhVwBRZYJL8XwVLrJpvOYzDoW52Pkc3matrWQCMdJRtMkoimMFgPr9zURjyTxVrkJLcaYGZnHU2pj4MIYc1MhioUioiiy76EupsYCjA3Os357C4e/c5JsIoOoyMhFmYg/xtkXL7Jp3yp2P7iZ8WtTpNM5Orc2ozfomB5eIJfJ09BRRcemRg5/5yR60/IcnaYNTcTCSfY9vIVMJk91vZdjPziH3qglGV+eeRKcj/D8Xx3hjkd3YDTqSM8FWRpeYCyaQsxkGb8xy6bb1tJ3bZaELkVptYf5CT8gIGpECtkCdo+VYrGIqFm+Dsf7ZvBWuVi3u42SGg/hxRg6o5bKBh++arX6RKVSqVQq1X89ahLl55TJ5Dl7ZvinEihvKhZlzp8Zoarag8GgfVu3vW3bNp588kmGhoZobm7m6tWrnDp16mYCYnx8nIWFBfbv33/zPnq9np07d3LmzBk+9KEP0dPTQz6ff0tMeXk5HR0dnDlzhgMHDnD27Fnsdvtbqjw2b96M3W7nzJkztLS0cPbsWTo6Om4mUAAOHDhANpulp6eH3bt3c/bsWXbu3Iler39LzOc+9zkmJiaoq6vj7Nmzb9mXN2O+9a1vkc/n0Wq1nD17lk9+8pM/FfPmcatUKpVKpVKp/vPyVbrYd/8GNu5OUizK2BwmtLqf/nPFWWJH0ogUC8vvw40mHXqzjmw6iySJy5UWb3SNvXJ6kPV7V7JiTQ2KAi1ra/EvRNFIAjmEmzMsREHE5jCxOBNCb9QhAslYGr1By/DVScKBBAaznuvnRrE5TTi8VlyldmpbK2jZ1IjDZ2dpfJFTJwYQxOXZE0szAXbft4HJ/mna1tWydlcrHp+Do989QTQQBwEEUSCfKzB4cZS1ezvYureNqcFZUMDstnKpe5yrF8bZemsH3//2UXQWPZ0raymr96HRa8gXZZayWe79wB6y/jhmhwlftQeD2YBGK1HTWs7RZ06TjmfI54tcOHIdo83A6h1tDJ4fYfjKBFdPDeIodXD1xACtGxqZGF6gosaDfzaEs8ROYDaM2WpAo9ciSBK+Gg8T16eIhxJkUznMthQbmsppWlfHt7/8HKGlGNnM8qyZhtW1XD8zSDSYoKzcgbPEhkavIRdNc/LZblrW1rB6Vwc6k46mVVUMXZogk8yyZucKmtbUE/FHaVxbRzQYJ5vOkcrkSSRz7LpzNQtTQZrW1HLpeD+CJLBx30pef/YCGo1E122rePqPXiCdXG5Ftjwjpprx3ikyqSyDPWPUrCjDPxdBEgUMZj0Ojw2NdnnmzED3CO/6yD5OPN/DtdNDRPxx7v/1W5ALBUKLMURBYOjKBAsTfoxmPQiwcmsLZ16+gs1lQVFkJElEZ9By6fV+Ora3UsgVqW/2cebH59AZtFTXumlcXYuiKIiSREVjKQf//nUqG0tpWV/P/FQAvVFH84ZGylsq8FW6+P7XXiAVS1PfXkmxUKRxZTUvP9NN+PochXwROV9gaS5CTVMp6XiK2bElsukcgiCQjGd49R9P0baxAQWFxlXVTA7NMzu8SGprioaOSq6dG6G6uYyKhhLyuQKKrKDVajBa9ESXYpRUuin4litwttyxjrL6EnT6t/dvXZVKpVKpVKr/iNQkys9pcT7ylhZe/xy/P87ifISaOu/buu3f+q3fIhqNsmLFCiRJolgs8uUvf5mHHnoIgIWF5W+m+Xy+t9zP5/MxOTl5M0an0+F0On8q5s37LywsUFJS8lPbLykpeUvMP92O0+lEp9O9Jaa2tvantvPmsrq6un92PT6fj0KhQCAQoKys7F+MeXM7KpVKpVKpVKr/3ARBwOG2/Ksx3gona3e2ceHIdQBymTylVW6MZj1j12eQZS2FQhGtVoPTa2P0yiTbbl/NmZeuUFfrIhZKIDnNpFM5CvkitdUuVu9cQTIQuTmcHkVBq9fQtK6O4EKUpdkQckHGXWrn7sd388yfvEhwNkIhV6BQKFLXXsnU1TR2jxVFUbDZTRTzBU4/38MtD22h58h1mtfWYtRryeUKbyRQRJSCjCIrRP1RSmtK+OFfHGZhNgyAwajlvo8eIBnLEAvEWLmqlisXRzlzvI9ivoAsQ9vqWjw5hReePc+2nSuwV3rwTwaQ/QnWbGtmvG8GUZLwVLpZmPCjlQQKqRz5VBa9SUshX0Rn0LE0F6VxczOj40FykgaN00qty0JtexXFYpFivkhRAXuJnXw6y873bKb74BUK+SI77tvI9MgSks1MJJgAwGwzsnZHG90Hr6LIRRxeO7FQguB8hH3v28aLf3UEOVegcW09izNBSms8DHSP4vLZ0eu1jF2bJpfO076lmevnRui6ex2JSApZVnD47MxPB9FoJdzlLroPX2fLHWs4/+pVsukc625fzcnnLmC2Gskks4iSyGDPOGarEb1Rh8GsJxFJUdbgI/1G+7i6tkp6Tw1gNOnQaEVy2QL5fIFMpsDWu9eRyxQ49MNuVm1qpP/sIBqtxOJUEJ1Ow6pd7Vw4foPmlTUUCzJWp5lCQUar15BKZFmYDtGSylLfVk5elkEUcJbYqG2r5NiPuinkigCEl6I8+Nm7ufZ6H3Oji1jcNnQ2C91H+qlvLef8oeuU1XnpPzdCMV/EV+Oh+8QgiWiSfGa51ZrdZUGn0zA1vEBVfQlyIYIiKwgiN2ec9HeP0nXbKvq7R7C6rWRSWS68eo0Nd6xhbsJPNpXD6jIhCAJyoUgqkcFo0pEKxzHbTQjAtnetp6a14hf0KqBSqVQqlUr1H4+aRPk5ZbL/b8Pj/1/jfhbPPPMM3/nOd/je975He3s7V65c4ROf+ATl5eU8+uijN+P+6TwWRVH+rzNa/mnMPxf/dsS8OVT+7Yj5RcydUalUKpVKpVL9xyQIAuv3duAqtTPYM04qkWX/ezcTiyQZvjJJYC6Mq8SG1WEiHoyx5Y419F8Yo+f1PlZubaGjoxyz24Zk1GNwmBif8PM33zzK+nV11Egi2UgSOZOnurkUjV5Hf/cYJZUuBKBlXT3+uTA6vZ7yeh+uUhurdrZSLMgkoyn0WpF0PM38aAyzw4SnwonLZ6djawurdrQQmo+SSWXx1XgJzoWRC8stnVZub2X02gS5bB4UECUBm83EiWdO0ba5he+91M2K1XV0rqphatJPNJFmxbp62je18Pd/dQwBOHr4Ojt3rMBkMdB3ehhHqQOHx0IkmGT4ygROn53ATAitXgOKwprdHZx84RKb7lhDX/88wniAWChBJpVjaT5KNJzkgffvYOzyOOM35mlfX0f38RukYhl8VU4q2mupay0jFojSsauN868PoDdq8c9FsNhNxIJxUok0ZpsRq9OMIsukYilymQJr93aSz+QwWY0MdI9y5fV+Iv4YKFBS5aZz+wpGrk6xZlc7i5NLjPZOMTO2hFavpaGtEneVh/IaN3ZFQaOVlhNCBXl5Lo1GIpvKoTdoMZh1pFM5nF4rA90jrNu7ksBchPrOKhxeK6G5MB1dTUQXI7x+dgiz3QiyzJqdbUwPLzJ0ZZLR69NIGg2iJHD2UC+b93dS3VSKfyaIKImce32A8FIcnV6Do8TGzEQAq8MEgoCsKDhcZspqPAQSWS6cHaV1fT01TSVk41m6DqxCkWW0Jh3D16Y5/qML7Ht4C93HBrhxfZbQYhRREhm6MsFdj+3A6W7AW+HCV+WhvLmMF797hkwySzQYRxQF5qeCGK1G8qEEuVyBwpvVWlYDclFGEAVSsQxWj4XOLc0YbQYGzo8iSgKXDl+ldU0Dpw9fJ5vJUd1Uuty+bXUNtTVuZm7MYDDpWbmzlfau5nf0NUClUqlUKpXq35uaRPk5GQy6tzXuZ/HZz36W//7f/zvvfe97Aejs7GRycpI/+IM/4NFHH6W0tBRYrvIoK/vfM1mWlpZuVnKUlpaSy+UIh8NvqUZZWlpiy5YtN2MWFxd/avt+v/8t6zl//vxblofDYfL5/Fti/mm1yNLSEsD/NUaj0eB2u//VmH9anaJSqVQqlUql+q9Np9eyYl09K9bV37xtZmie2x/ZujxM/eoUKDKdt63GU+bEPx9GUeDy8QHcpXbaN5sx2w0cevUa2VwBg0HH0PAiHq+FhvYqHE4LU5dGuXZyCEeJDRQwWA20bqjn2A/Oo9Vr8JQ7ad3YSF/PODVNpegMWuZGFsikchRyBfL5IulkFkVRaNlQTzycQlZg+7s28txfHsJd4UKr1aAz6WjbsoKX/uYIqUQOURKQJBERheBsFKPNQCKZ5fyJAdwlVu791d0MTC2hc5k5+foAhfxyJYMoCpw4NcSa1dW8/wv3Mnp5nPHeSVLxLKu2r2BxJkQ+L+NwmzHZTbz0DyfxVbsZHfUzPx2irNZDYD6KIstYHGaMFj3PP3WONetqaFlZTfdrfUiSiN6oIxpKEQsmmZ3ws/2OVfR2jzF4fY7N+1Zx6B9O4PRYWZgMIIoipTVe5seXyKZypJMZBOCOX9uDr8bDwLkRdHotclHGbDWSzeQJL8VQijIWp5ngXIhCXmZ23M8b369ifipAbWsFR5/r4faHuqhoKiUVz1AoFLG5LGh0GkSNSD5XxF3mIDMdIpvKIUoizhIbVqeJtbvaiCxFKavx0HtqkOoVZbzrYwfIpnIIAtStrubqiSFEUaRYlFGUAqIsIkkiF17rIxbPsuVAJ8N906zfvgJFUdDpJFrX15HNFuk+2ofNaSZfkBEFgaKi4Ct3EI9nKCuvwioqPPPtE8sVT7rlwfE779/E4JVJRq7P0HtuBIfHRlldCWabEa1WolAosrQQpX51HYlYhpOvXicWzdC6rh6NVuTc4ev0XRxnxx2ruHFhlGJued6O3qBlzc5Wrp8bxlPhIufKEQ/EySSzWB0mPvpHD/MP//N5EuEUyWiG2+5fj8VlQWvQYXOZaWiroLrRRzycQG/SYXfb3oErXqVSqVQqleqdpSZRfk6+Ujser/Vfbenl9VopLbO/7dtOpVJvGa4JIEkSsrz8TaO6ujpKS0s5fPgwa9asASCXy3H8+HG++tWvArBu3Tq0Wi2HDx/mgQceAGB+fp7r16/zh3/4hwB0dXURjUbp7u5m48aNAJw/f55oNHoz0dLV1cWXv/xl5ufnbyZsDh06hF6vZ926dTdjPv/5z5PL5dDpdDdjysvLb7b56urq4oUXXnjLMR06dIj169ej1Wpvxhw+fPgtc1EOHTp0c19UKpVKpVKpVL+8Smo95I70komlaFtXiyIrRPwxSqrcmO1mJGn5/fPSTAjT8CILV2eZHprD4rSQjKYAhfnRBSLBBPl8gX23rSYRTjEzvICr1M7aXW1o9Bq23LWW+XE/o30zjP3DSbRaiXgwTtOqGqZvzGF1WdDbjSQyeZo2NjDgj1KhlVjoncEgCTSvq+NXv/QAo1cnMZgNFGWFdCaHu8yB0WEmGUtjMunIpbLYHD5sLgsIAiATi6S43jPG9VCUjrZq3C4ze3a0LLdz0muZmAlR4rHx4reOEZgN07q6mnQsw8VD19h2zwaC82E0Oi2pZBYUhfpVNQyNBXD57OSzBaoaS5CLMg1tFaRTeXpOD6IgkElm0Rm0ZNM5wtNBNBoJnUFLJpmlrNZLWaOP1NF+entn2PGeLgw6iUI6h6TTEFyMkkxkMZn1CKKAu9zJ0mQAvVHD5WN9BBfCFAsy2Ux+eZ3pHIszIcx2E7lsnmJRZsOeDgwmHflcHr1Bj91tQRRFfvyt49z3+G6igTh2j5V4OEkum8do1iNIIuFAgoaOKgLzYSobS0EUuOP9u+g+1ItOp2F6cA6H10o2mWVg3E8mlSM4HyGRyLFiXR0DlyZAUdDoJLQ6LaXVLqobS8lm8mQTGcwOM9de72d2dJF0KkdpuYNsMkvXvk6Grk5S1Gu49aEtXDw5yMJ0CIvTTHt7BcefOoXFYSKfLyLLCnJB5vLR63hqSxA1y+3X4vEMcraAqJGYXohSKMhs2t/BwR9exD8bpry+hLmJAJNDC9hdZnbetYYzB69x4fUbbN7XQW2Tj/mxBWZHFrly4gbZTA6TucDGAytJhBJcPTHAFQXqOqt49PP3cuXkAMgQmPKTDMVYu7udzbevxWQ1AGC0GN6pS1ulUqlUKpXqHacmUX5OBoOWri1NvPzilX92uLwkiWze0oT+FzBo76677uLLX/4y1dXVtLe3c/nyZb7+9a/zq7/6q8Byi4NPfOITfOUrX6GpqYmmpia+8pWvYDKZePjhhwGw2+184AMf4NOf/jRutxuXy8VnPvMZOjs72bt3LwCtra3ceuutPP7443zzm98E4IMf/CB33nknLS0tAOzfv5+2tjYeeeQR/uiP/ohQKMRnPvMZHn/8cWy25W8pPfzww3zxi1/kscce4/Of/zzDw8N85Stf4Qtf+MLNVlwf/vCH+cY3vsGnPvUpHn/8cc6ePcu3vvUtnnrqqZvH/Zu/+Zvs2LGDr371q9xzzz08//zzvPbaa5w6deptP8cqlUqlUqlUqv9cdDotux7YQv/ZQQYvjqEzaNlx/0ZkRaD76HW23b2O0y9eIpvKYXRZiMzOUtFYyuJ0EL1RSyaVw1ftIZ3KYbIauHRxjO0HVrJyazN2txVJLzHRN4dGEgguRpgcmieXzlFe62Vu1M+qLc3c9/EDXL04TiZXoKOjimAqy8JijKGRJQrZPKvrfPzgiZfZfu8mmjY0Ln/YL4r0XZrEVeVl/vQNRFFg5NoCOo3IxtvWcP7ly9x970a6j15HY9BithnR57JUljuZOTXC6VODKIAAdHQ1YTfqSESS1LaUkoqn0Rq06Ew6Fqf93PrIDhw+G3/+/z2NRivRtLYOWZQQgVyhiIyC1ajjxrkhjBYDW7c24fRauT44hygIJKJpNBqJXDaPRiMCCiO903RuaaJYkEnEMlw4P44gwJYdzShAMpZmxYZ6SiqcKEWZFRsaWJwO8uo/nMJT7mTs+jRltV7mxxZRZA2CIOCrdqPRSpisRlZubeHYD84RCyURBFBk2Hr3Wm59uIuX/vE0h3/YTefGeu7/6H5OPX+RcCDObe/fxZEfnMNs0rM0E6S2tYINB1ZSzBdwlNhoWFVDNp7i/MErLM0EKaly461wI4oCnnW1TA/P43Bb2HRLGydeuEJwIcqOu9cwdX2GhD9GMprk4iuX2HbfJpLxNMlEFkkS8S/GsDvN6HQS2961Hm+pg6e+cZhkLE3b2lpKSu0UEhmMRh1RUUSvE1BkBVmWCS5EWLW7nTwioWACSRIxmXT45yPkc0UMVgPhUIq5iQCSRiQVz2C2mYgG48RCSabH/DhLbPhnw8xOBqhu9NLYWc3SVAij1UBZrZf69grCC2EuHOolk1re54HzI+SzBbR6DddODrL5ttU0rqyhfXPTzQSKSqVSqVQq1S87QXlz8MR/UbFYDLvdTjQavfmh/psymQzj4+PU1dVhMPzsbxAVRWFkeIHzZ0bw/x8VKV6vlc1bmmho8v1C5nXE43F+93d/l2effZalpSXKy8t56KGH+MIXvnCz0kNRFL74xS/yzW9+k3A4zKZNm/jzP/9zOjo6bq4nk8nw2c9+lu9973uk02luueUW/uIv/oKqqqqbMaFQiI9//OP85Cc/AeDuu+/mG9/4Bg6H42bM1NQUH/nIRzh69ChGo5GHH36Yr33ta+j1+psxvb29fPSjH6W7uxun08mHP/zhtyRRAI4fP84nP/lJ+vr6KC8v57d+67f48Ic//JZj/+EPf8jv/M7vMDY2RkNDA1/+8pe57777/k3n89/6PFCpVCqVSqVS/ceSz+YRRAGNdrkS4gffOIQgCLhKbAQXI4gOC6eO38DqNJPPFZAkEUEQ0Oo1FPNFAnNh8rkC2zfVk49naF1Xy8nnL5KKp1mYDCCIAptuX8PZV66hN2pxeqxsv3c9w6EE02NL5PMFlvxxTFYjzhIbs6OL6PQavGVOao06Lh28RjFfZMPeTrRagZXbW5EVhWImx9UT/eRzRTwVLnrPDDE3vEDz+nr8gRi5dI47PrKPK8EwpYrE9VevMzvhv3ncoijwvo8c4OzLlwnNh0nF0svV6gq4y53c8+t7KaRz5GUFh8fG0nSQ5/76KMWigsGoZdvtqxkdmGW8bxa9SU82k+eeD+winZc59eJVCvkiLp8VFIhHU8SCCeraK9i4u42engkWZ0LIMqTiaYwmHfc9soVsOMGlo9eJLEYxWQ1E/HF23LeR+bElfHUlnHuph2w6h8Nro5AtYHGYuOWhrQz0jLNhXzvnXr7K+VevoShgsRsxO0zojHpaNzUhGXUYLQb8C1FmJgOUlTuxWvVozHoaGn3Mji5g1Gm4cX6E3hMDiBqRd/3GAbQ2C4szIYqpDMOXx8mlcsTDCTq6mimt9TJ+Yw5HmYvRvlmaV1Zh91iILUbo7x4lm87hKbWjyAqJaJLWrhUMXplElhVAQaORUGSF7e/ZzPDlSQr5AuU1bsauTDByZZJtd61lsHsYjVbD7OgixcLyrBJXiZ3feOJX+Os/epnAXARREnF5rMy98fi+92P7efn73RQyecpq3OiNOkRJYrR/lny2gCiJdO1tZeLGPLc/1EXPK5cIzIVYs6edhckg9e1VLE0HOPOTHkRJIB5KImlEtHotFY2lbLlrLZlEllU7VlDe4KO0xvsOXb0qlUqlUqlU/z7+tbzBP6UmUd6GD88zmTyL8xEy2TwGvZbScscvpAJF9YuhJlFUKpVKpVKp/msbH5jl2I+7SYRTjN+Yp3pdPTeGF1icCaHRahAFMFn0iJLI3Ojy7D6jSc/Dj25htm+aaydvEF6MUtdeSdO6ehLRNFq9lqqmMo4/fwFJEtn/63v5/rMXiYcTRP0xDGYD6WQGg0mPzWEiMB9B0og8+NBWzn73NIVsgU37O0EpUtFYTmgxgsNl4eyLPZhtRiweG6lIkuFLE8iyzM73bmFuIUzP2SHu/PAtXDs2QGDCTyaaIRpOAmA26nnw1/dy5OkzhBejFAtFFAXkQpGObS3k8zIru5qYGJihfXMTixN+goE4vedGMRo0LE342fPQFo7++AJGi4FUIktdk4/bPrCbV54+T11jCTOD8xQKRapaylicjeCrchONJOjY2MiZk0NMDi3PMDTZjJRWuWhv8ND9ymXi4SQ6gxaNVoPNaWbDgZWcevEyrevrWZwIEJgLsWJDPVUt5Tz7l69hshnZcvtqpobmCcyGqWuvJBlZHnxv99qxem2UNpZy8PvnCfoTpJNZKhtKaFhbx8kTg2xdXYmUz3PhJxcp5PLkMnmKBRmH18beX9lJPJUjnSmyalMdN84MojPo8Fa60Oi1pBJZjr1wmcXpIFqdhn0PbubMcxcI++NotCK+ShfJWBqjUcv6fSt57flLy4PtZRlRFKlu8nHfr+/lB395FEFR8JXauHDoGpIk4qty09RZxaUjvdg91uWZNgJsvmMtoVCC1bva+f6TR/HPhnF5rYQWY2za14HNbeHahXFa28qZ6Jsmk8ywYkMDokHHUO8shUKRW9+9AbNZx6VDV+g52oecl9GZdNS2V9LQWYXZZuLpP34RlOW5Qtl0Dp1BS2VzGRv2r1yuQllV845dpyqVSqVSqVT/nn6WJIrazuttYDBoqalTv6mjUqlUKpVKpVL9R1TXWkHJxw6wOB1icnAOfzDJpcsTuErsiJKAXq9hrHcab6ULRVFQFIXm1jKmrk0h54ssTQXZdPsqFERe/vZJtHoNqXiGhtXV3Pb+XUzMR7jcO8P8YhSNRqKsuZxCKks+W6CQLyJqJGxWA40NPpKJDOv3dnD+pat4K930nBzk5GuD6E16NmxvxFvlIR5OcuPcMHqTntW72wjOhckksvRfGiefzZMIxDEYtcRSGfQGDeU1HuRsASUvY7abWb2zleM/7CZfzAPgq/XiKXdx5uWrtG1soGVtHT2HrnL99BCuMgerNjeQjKdZHF8iNB/B5rKQiKapqvMSXowSDyZYv6WRH37jEIqiYLEbsTlNrN3SiKjVUCq7aF1djbvUTi5bwGg14F+MohUELr96BUEQsLksZFNZIktRsqksCxMBNh9Yyckfd+OtclPZXEp1ezXh+TDb716H3qRDZ9BRUV/CinV1vPjXR0lE0xTyBTRaDZvvXIOuys1tj2xjpH+OfL7Ioj/O6ZNDSJKE0aQjH0gjF4vIBRmtToPeJKLRafDPBJAMBq6dHyUcjLNpexM9R/o4/uJl8pk87/rofny1JfhqvIQWImQzeRAERElAEEWymTwarYTDayOXK2K2GAgH4iiAVifSsr6BoeuzGE06ZFlm7NokxUIRQYDF6SCbDqykc1sLCxMBBE2R2o4qLp0aYmZsCaPdwt2PbmN+JozRoGN6dIG+yxNU1nppay/n/MuXMVv0KAr0nxvG6rbiKnXhX4ySzuZRikXmx/0kwik0Ool8rsBQzxiXj/bxyW+8H5fPwcKEH0VWsHsslNZ4sTgtbNjXqSZQVCqVSqVSqf4FahJFpVKpVCqVSqVS/Zdntpmobzdhtpv43v86SFWVm6sXx5E0EiXlDhQFBHG51axWK9HaXsHL3zjI3oe3suuBTVidVl77/nlc5Q40muUPp3UWE9/9s0Nsum010VSWXCaPolOYnw1jsRpwltoRUOja2Mj8jTl6Tw4ydmGMNWvqeO+nb+eF75xmangRQRCIBRNY7KvofvkywdkwcqGIKIkMX5nkvo/sY7BnDF0yR4nNjJKXKanzMt43g91qRC+IiEaFyFKERDRJcDHB/l/ZQWghAkAsmKD78HXyuQL1beW89DfHCM6FKRRkCrkik71T7Lh/Ew6nFYNNz/xMCIfTjCAXqW4qxeG1crZ7hO13rcFd5sTuNnPmhUs8++evUt7gY/XONo48e5HzRwbIZnJo9Vr23LuOuiYf+UyO8FIUh9t6s+1UPJQAFAbPD9O2uZHRa9O0b2oiE09x8tluNtzSwY3uETKpLBVNpcwMFtAbdWTSOQRRoCjLTA7M0bm7k0Ayx7FjNxAkEeGNx06rFSmt8zIbipFOZFDk5eYLgihQ1VRGsSAT88cwmvSkE1mSiTx950fQaCU237qKYqHI0JVJfJUu7F4r0XCa2x/dwezIApFgnMhSjGJ+OVlU3VLK6LUp7HYDMiKd21oYmwii02uxuS1E/TF0Bh0arUQhLyNpYGkmRO+5ETq3NjM9GeTs4esU80UkrUQ8nubwDy7QuLqaseEFCsksoaUY9S1lLIzMQ7HI/NgSoiSiM+gQFIXqtkpcPjunjw1gc1to29HG6LVJ5IKMzqAlnylgthkZ75vijl/dxZFnzmIw6fCUO7E4zKze1UbDyup35sJUqVQqlUql+k9ATaKoVCqVSqVSqVSqXxq+Shf7H9hM97F+AEaHF8ll8pjtRgq5Ir5KF51t5cz1z2D3WMkksoxdn8FgNRGPJIlHktjdFhrW1LK4ECWyGCOfzGC1GFBkhVw2j96gI5st4C5x4tZq6Dl4lfmpIAajDqPZwOzIAsH5CCXlThZnwm+03JLfaB+lw2QzIssykiiiN+roOdZPZXM540OL2LUaGqtLmBya4+53baL/3DCDPWOU1fm47bFdzE4F8FQ6ud49SiqWxj8dfGPmiJX1t3QQD6fo75nAW2rH5bORiqUIzoU492IPklZD4+oaVm5soL97lHQ6x8Y71yMXFepbK7h66gYoCi988zVESaSk0oXJauDFb5+gfXMjNreZpekcmWSWV757hvf8+i10bGkhGUsTnAsjF2XymTwarQYUGLo0wfr9q8gXZMb6ppkZnKNtYwNnXrxELJzAYDbQ3tXMlWPXERDQShJGgw5PpYtENElg2s/kUpLaei/+pThyQSabyqK1GpganKeuoxpftYfgfASdQYvDayMRT9Ne4yEcSjHYP4/D5yDkj6IzaOnc0sxAzzgOn4Odt61kYdKP3WWioa2S6+eGiMxHMJh17L53PQDJVI7vPvEqK7ua0GgkmtfWcL1/gbHBBbQGDe96bxenftJD8/p6ZscWEUQBQRSwOk3k8wUWpkJM9M8BIGlEGjqrGB9cRAAEBeLRNE1tFZhsRsrKnZzvGSGfySNpl+euZNJZFib97Cmzc/bcKIuzYRBFRnN5VmxsYuD8CJJGolgoUlLppqTKi8Gk467H9yxX5hh1VDT4qG6tWH5MVCqVSqVSqVT/LPWdkkqlUqlUKpVKpfql0r6hHleJldLTw6xaU4MiCMyOL2EyainxWBk4P8xo7wx77tvAy393nMrmckTtcqUDQD5bQNJpiYUSKCjkcgWsRh2SJFIsyoiSCAIszUdp7qzi2lQQuSiTSmSorHARnvQj6jNYnGZESSCbzuOucJKMZ5idCaHTa6ioKUEUheVB9zNBmtbWUd9eSUVrJd/7m+Ns29PGy3/3Orsf6EJv0hNcjPL6j7rZ+/BWtAYt82NLjF2dwu6xUlLlpnpFOWt2tbIw7kcUBTLpHN5SO9FAHEEQEEWR8kYfs6OLrN7dSSYnY7QbGbmxgNFmovtIH7FAjMrGUgDkokxwIYKrzIFclBm5OsnqvSuJh1NkUlmKBZmjz/Xw4S/cQ1GWOfb0WYJzIeweG+tv6eD8wcsAjPVOUVbj5eSPzpOKpdGb9CSiqeV9EiAZSeLyOZgemqes1ks0lCAeimO2mahs8HHizBm23NLBwecukYqlEQQBq83I0nSI8wev8p737+b6qRvMDM2hNWrp3N6G1qijoslO5WwUg2m5mkMQBSx2M55yBwtjC8yOLbEwHaKixs2ZZy9w669s58a5EeRikdnhBTbeuppwMIHLZ6f37DD5bJ6hq5OsvKUTt89OJpOn79o0a3a0YLbouffX93PtRB+ZdJ6ha1O877fu4diPLwAgCNC4shp7mYupC+PYnWY0OolULM3A1Um0GpHt+zu4/IqGtChgMOqQZQVBXH4cBFFgbjpMNpVldnSB2rZK1u5bydzIAolIkrbNTex6zya0Oi1Gi4HGVTX4qj3vxKWnUqlUKpVK9Z+SmkRRqVQqlUqlUqlUv3TKarwUijJHvt+N3qBFCcW5cGGMRCRFXXsFW+9YRU1bJSabkZGrE+y4byMj1ik0Og2ZdI5MOgeKgl6vo1BUuHpikC1bmjjXPYpGpyERSWK2GpALMoqiIEoiZouB+FIMX7WbUDBFIpbB7bMT8ifIFmR0puVZF9lMgWy2gKQVifpjWD1WPFUeZqfDxEJJ3D4Hh39ymU17OvHPhrhy4gaKrGA067lxYYT2rmaMRi277tuAp8KFxqAluBDlyLM9VNWX0LKmhny+yMLoIggCWqOWms5qzh/pw+mxEAnFuXphnLK6EiZvzOJ0msikcqSTyy3LBBFQQJJEUvEMsqyQSmTIZ/NodRoEoCjLFAsyS/MxBIOOuz90C0MXx4iFElw4eAW724rdY0UQQKfXkIqnsbutGEw67G4L+WwBRVFYmPBT01bBRP8MmWSWRCiBpNXQuXUFwekgVqOWs0f6WLWmilxBZmY6DApUN/mIzoU4f7gXX6WL1q2tKChYHSamhxaYGffTsKaO9s1NnHr+AmW1JWQzOcprvBz/cTdWh4n61nJigTganYazL12mvauJS0evU8gVCM5HGL42xfoDq3j9uR50Bi2BuTAWq5GF2TCKotC2uhq7y8zI1SkiwTir967C5bVy7dIEaRmqOqopqfMttzhLZpgd9+N0WzAatFTUeGhoT2Iw63E6jCj5Is1r6jj1/EUKynLlCoJAdVMp0TdmxSiKQjadZ2Z0kb23reQDX3oPFqeZ2rZKfNUeBEH4v10WKpVKpVKpVKp/hppEUalUKpVKpVKpVL+Uqup93HL/Bq6dHUZsraS6tYJ4MIHZbkKjlcikshSKMnVtlcRCcdq7mug52odclAEwWo2s393GxfOjRMNJbC4T61bXMjSxhKSREERh+ScCRqMOp0m/nBSxGREF0Bt1xBNZHD47gUCCggI2h4lIII7FZmB+MkBoIUrzunpef+Ey8+NLmG1G9jywiYnBOUxuK3PXJiir9ZJNZUEAURQBgWw6h7vcybULYwxcGENRFBxeG+lklv3v3sTxH55Ha9AiZXOs3tXOYO80hVyBmTE/u99jx1vlJhKIk0lmyWXzxKMpSqo9WF0WdHotIKAoUHwjSdSxqZnZyQD53PLcE5NJR31rBRSLxEMpkoUC2UyObCrLyh2t+KcDODxWKpvLCC5GaVxTy+zIIrFQklQig8GoR5ZlkrEUp567yF2P7yE4F6aiqRRXqQNvtZtkPM2mzQ30XJriyrkRUBQ6NzVSLMoYbEYcbgtzQ/MMX56gdWMj1c0+Tj3fw/z0cms1OZfD5TSy5daVzI37yecKRAMJjCYdsWACpSgjigJarUg8nEDSiGTTeYxmPblMDq1eQi4UkYvL58DuthD0x9AZtAhAba2bH//VUdxeG9FggomBORwuC3c/vouXXrxGU2MJZ48OsH5rI1Iuh5BMUd1STsPKaiQROtfWMDXhp/v4IP0WPT6HgTt+bTej12dIJzKUN5YSDScJ+JeriWRZQaPVoMgKFpuBqioX1Ssq3ni8VCqVSqVSqVQ/LzWJolKpVCqVSqVSqX5pVTWVUtFQQjKWRpJEzr5ylf4LYyiygkavoWVdPeO904z0TlPfUcmB920ll81T215FJJXj4tlhEvE0oigweGWKTV4bezY3M78UxWDW4bIZaWwsITIfJR5MkM8WyOfy6LQSpdVuhq7P4K0tIZvOc+X8OBv3raSQzJAMJ9Abdex690Ymx/zkMnkqa70kIwkmrk7y0OO7CQTjpOJppgfn0Og05DJ5DjyynWKuwKYDK1mci9LfPXrzWHPZPP7ZCNNTIbQuO3ffvZ7B7hGunhkiEUlishgoqfYgSyKaNwbAi5JILlsABPzzUZZmgmy+fQ2nX7xENpXDbDNQVuvFXupgcOAGuXSOXfesJRGIkfSHOffCJbbcs5be4/2s2tXK4pifC69eJZvJEZgLEwnE2XT7GprX1XPs6bMMXRpn5/2b6T11A7PNQMQfp6yuhMBsiJ6jfbhK7fSeGWbrPes5+oPz7H7PJvbd2o6gX8O1SxMIgoS31E42mqJjcyPVDT7Gr0+zensL10/dQC4WcXutACiFIq/8zVG23rue/nMjtG1qpqzWSzqRRRAFUoksnjI74cUMkkZCFEU0GhFRFPCUu7h+bgS704zdY6VYKLJ+XyeDIwF8ZXa23dLOhcNXiQaSJONZKus8REJJYrE0h54+y657N9J9eoiHfn0PR//xBPFQAp1ew9iVcWYG59j/+F5eePoc3koX0VCCoizT3F7BleP9eMvspPNFertHWbOngwvnRhBEAY1WgygJ1DSVsmpTIy6v7R26qlQqlUqlUqn+a1GTKCqVSqVSqVQqleqXmiiKWB1mAFZta7lZATIzvMjaXW14ypycev4iMyOLSBqJ2vYKek/dQLGa0FuM1HdWAwJ6k454LEu0d4ZMLEN9pYPXf3SB1dtXMBjJkE3nMJr1iJJEY2clOq1IY0cVOWDfnStJheKYtVC7vRmTxciP/voo544PosgKXpeJ6aE5tFoNgdkwl472UtdWSSaZI5vOIRdl7vrQLZjtJubH/UhakeBiFINJT6EgY7EZMFgNSBqJRDTDwJUpIuEUdY0ltG1qJB5KUtNWTh6JUDCJJInojDryuQJ9PRNsunUVQz1jTA7OozPq2Xr3OrwVLjQ6DSaHmb/96kvoDTrWbGui93g/iqJQ1VhCOJTk8HdPs/X21YxcmeL6yQHMViOKABa7iUJB5viPumnZ2EjdyhrW7l1uUfauj+5non+GyqZSul+5wqWjfeTSOcKLUawuM1a3hfd/8X5C/jjzEwGSoQSbd7ezMBUiEUnhn/LTe3YEk8XA5ts6cfjsDF2eIJ3IAlBW7WZmaB5JEhEFEZvTzED3MA9+5k4qGkqYHl4ARUGQRFylDqqaShm/PoVclGnd1EgsmqRpdQ2FfBGPz8q6PR0IBh1GpxWbw0R4KczSdIjKei+gkMvksTtMAMyO+pkamMOk1xBbDGM0aYkHZaKBOMVCkeoV5Yz3TmKyGfFPBwGYG1vihckA67e3sGp9HcGlKMlMnuuXp0gmcugMWhQU9AY973pfl5pAUalUKpVKpXobqUkUlUqlUqlUKpVKpXqDp9zJuz64h9nRReLhJDaXmeoVZaAopFNZ5ib89J0fw2jUYlFkWtprOPLSFRLRNJ4KJ4Ki0LamhtV723n6j14ktBjj9R9foOv2NZTWeDAYNIz3T7M0GWRh6gabbl9NQ0cV3/+Tl8ll8sTCSQTxLA9/6nY8XivJSBqNJLI4sQSyggKU1XmZGV2gmMlxy4ObGbk2RfvmZgYvjXHk6bPoDVrW7V+JoihYnGaMViOJRIZUqoDDY8DhtiBKIvlsntNHB6hvLUc0mzhxdAhFgT13r6G80YfTZ+fcwaskY2l6L47xvo/tZ+jyOOlEFq1Bx3DvNNdODlK9opyHProPRQBRUUgshjFbjSzOhJBEkakJPzq9hm13reXSa73ksjHKakvQ6CQGL02gKFDaWMbk6UEEBPa8ZzNavY7r50a4eKSPtTvbSKdyRJZibL59NZ5KD7JGw4tPdxOYC+MosfHg4zsZ7B5lemQRu9eGpJHYc/8GLr7eTy6VQ5Ak1uzuYOTqJALLg+QBvDUe0okMoYUIocUoJ5+9wJ73bGS8f47JgRkkSaK+s5qGVTW0bGgguBBhcnAem8XAbb+6m/HBeTwVHs6/1ofda2Okfw63z876Pa2UVrkZvj5DMV9AAbQ6DWargco6L/UtPuKRJNHZIPPjftKpLDq9lqpGH+FIivBCBLPNwOjVSewuC6W1XlAUFhaiBF7t5cA9a+k+cQNnqQOrx0okEKe20cf+e9ayeVfbO3oNqVQqlUqlUv1XoyZR/o2SmRyT/jATS2HS2TxGvZbaEie1JU5Met07vXsqlUqlUqlUKpXqZ2Sxm2hZW/eW2/QGHUe/f450IksqkcFb4aS5s4pCvsCv/vpuMnmZdCqLVoDuFy+xaNGy74GNiBqJbKZATWs5/pkQ33/iFYqFIpWNPuxuK9Ut5fzkr44wO+5HlmUcbgtajcQz/+sVHvrsXVw4doOKajcaSaSgkbA4jDi8VqZuzJEIxMll86zcsYKRK1M4S+zc9Wu7ARi8PMGKzc1cOT/O3HRweai4IJCZyVG+EKV5ZRVLc2Eqaz1Mjy2xan0tvk21GM16vCVmDv5okFWbGujoaiK0GKWlvZxkLEkqmeXa6WHyuQKVTT40Og2j16aYHpxn533roVAkn84xNhVEq9eQjKTIZfP0nx+h67bVGGwmitk8iALB+Sh6gw5fjYfFqQAr1tYzP7HIa989hSwoNK6sRpQkzh68grfKzaY71+GucJGIJpmbCLCio5wbKNxyzxp+/I1XKa3xYnMYmbw+hd6kY2Z4ga23rcLksvL6oT4qqjwUr04R9sepqnHhKnOwdk8Hw5fGWZoOUl5fgkYrMdA9wvzYEjVtlThL7PSfHcJRYqPvyiQ2pwWbx0rrxgYOPXOOmZFFalZUYLYZkTQi3nInwcUonjIHngoXQ71Ty63ANCJajUQikqKirgS5UCS0EMPpsS8n34B4ME4+myedzFJS7eXq1SlEQSAZT5OIpSjmi0gaifJGH9FgAlIZ9j3YRTiVp6rSRUdnBXaH5R24YlQqlUqlUqn+axPf6R34z2x4LsDfHbnAD8/0cnFkhr7pRS6OzPDDM7387WsXGJ4L/MK2feLECe666y7Ky8sRBIHnnnvup2IGBga4++67sdvtWK1WNm/ezNTU1M3l2WyWj33sY3g8HsxmM3fffTczMzNvWUc4HOaRRx7Bbrdjt9t55JFHiEQib4mZmprirrvuwmw24/F4+PjHP04ul3tLTG9vLzt37sRoNFJRUcGXvvQlFEV5S8zx48dZt24dBoOB+vp6nnzyyZ86ph/96Ee0tbWh1+tpa2vj2Wef/amYv/iLv6Curg6DwcC6des4efLk/+10qlQqlUqlUqlU/6qGjioe/swd3PGrO7n9sZ3UtFcxObhANJjg+T87yPe+8H1+/D+fZ7Zvmr3v3UJgNszcyCKB6QALY/NcO9FHSYWT5jU11K4oJ58tsGJjPRM35lmYWMJk1qM3aNFqJfzzEZLRNDNjfu794G5qWysor/PSddsq1u9uZ/DCKEouTyFXQKPTMnJlkpnheV78m6M89bUXuXi4l037OpElkY7NDRQLCnJRppgvIssKcxNLbNnfQUNbBfFois3bmxk40c/Vo9cZOD3I839+iNvuW4PTZWTzzmYe+vAutt2+itnRJZo6q+nsaqSjq4ma1gp2v3cLt7xvK3c9vod0JIXTZ0fSakgls4iSRDqZRRAESirdDF6eYNOBlUSDCRLRFMloCgTo3NEKKATnQ/SdHiKTzJCMZ7hxaQK5KHP7B/awdv8qDGY9P/zTg5x49iLnX7nK0afPsGZjHdlYktqWMm50D3HyxxfIpbOUVLqpbS5jbjJEz9lRxm7M0987w+rb1rDjgS667t3Emv2rmRmeJ5vKYnOa8U+HKKly07F1BZtuW0NwNsRQzyi3PbaL0loPtz+8hep6L94SG/MjiyxMBrG7LYxcnWRudJHQXJhiJotWKxJZihONpmlaWU2xUESr01DIFyirdrNuTxsXzoxz/JVehgbmMVhNjPbNIel1CILAqq0tFLQaYuEUxaJMIV8ABRTAZDVgsRrIptK4KpwYbUY2bm5kw6YGNYGiUqlUKpVK9QuiVqL8nIbnAvzgzDUKRfmfXR5KpPnBmWu8Z8tKmso9b/v2k8kkq1at4v3vfz/333//Ty0fHR1l27ZtfOADH+CLX/widrudgYEBDAbDzZhPfOITvPDCCzz99NO43W4+/elPc+edd9LT04MkSQA8/PDDzMzMcPDgQQA++MEP8sgjj/DCCy8AUCwWueOOO/B6vZw6dYpgMMijjz6Koij82Z/9GQCxWIx9+/axe/duLly4wNDQEI899hhms5lPf/rTAIyPj3P77bfz+OOP853vfIfTp0/zkY98BK/Xe/P4zp49y4MPPsjv/d7vce+99/Lss8/ywAMPcOrUKTZt2gTAM888wyc+8Qn+4i/+gq1bt/LNb36T2267jf7+fqqrq9/2x0GlUqlUKpVK9cvDbDPR0F7Fy71nyaZytGys5/j3zyNIEuWNpVgcJqwuK69+5yRRfxy5KFNa6+X2X93N0kyQ5755mMaVteiNOmZGF3H6HAz3z+MpczE7toivys3STAhRFDFa9aRTeRZmZzGZ9Gy9e3k4+9LYAvHFKMWizC0f2M3ItQkyiRwD3aMIgoAoioxcm6JudS3TiwkcLgsHHtqMSa9FUIoYzXqKisKpQ73c9sBGfJUuIhML2BwmUvE0c5MBGjoqOf/iJSxOM54yB4VMntmJJdbf0smL3zxMRaMPX52PVE5GRERntZDIyzR0tWC36CibDDI6MEs+V8Bo1pPN5Onc1sLF13rxVLjZeu9Gyms9ZFJZFqeCHH7qLHves4nj3z+LIAqY7EZS6TyJaJpzr/ay3Wommchw4+wgRVmmUJDRGbRkMzmWpvxU1Ho49fzFmy26pgfnCcyGuf83b+PUkQFsPjuuUjvBhQgXQkn0Bi1DQ4ts3dVCWbWbpUk/NpcFp8/BzNgil4/20ba5ib2/shO9XsuxH5xjYdxP66YGjjx1GovdxC3v247DY2Hs+jQajYTdY2V+3E8mlaWs3ofVYcS/EMVX4eBdH9xDLBBDZ9STzxc4daiPcHB5kHwgkKCk1MbeR7YjSSJ1rWVYfQ6e+dZJTFYDxXyBbCpHsVDEYNRR2VxOS3sF225ppaqlHIvd9A5fFSqVSqVSqVT/9alJlJ9DMpPj1cuD/2IC5U2Fosyhy4NUuG1ve2uv2267jdtuu+1fXP7bv/3b3H777fzhH/7hzdvq6+tv/j8ajfKtb32Lf/zHf2Tv3r0AfOc736GqqorXXnuNAwcOMDAwwMGDBzl37tzNJMVf//Vf09XVxeDgIC0tLRw6dIj+/n6mp6cpLy8H4I//+I957LHH+PKXv4zNZuO73/0umUyGb3/72+j1ejo6OhgaGuLrX/86n/rUpxAEgSeffJLq6mqeeOIJAFpbW7l48SJf+9rXbiZRnnjiCfbt28fnPvc5AD73uc9x/PhxnnjiCZ566ikAvv71r/OBD3yAX/u1X7t5n1dffZW//Mu/5A/+4A/ejlOvUqlUKpVKpfolVtdWwfbbV3P+tT4y8SzZTA5RErF7rNR1VNF98AqRQIKm1TUkIklmRxYYuz5Fz7F+8pkC4aUY7lI7u+/fyNJchEQkyZpbOihkc+j1GkRRRJIUNu5fxdiNeSgUGAvECc2HuPXhLSyMzpOMpPFUupBlGZvTyqUj5wEoFmQURUEUBGxOC/UWI/lMnvIyG2PXJrly4gayrCAXCmy5ZyNhf5xkKMHI1SmW5sLwRqV4bWs5hWyBubElJq5Pozdq6djSwtzIAoqiMHh5koFLE2y5cy0Tk1EmhxcxmvUAbL+1k7V7O0ArsTQVoLaljFymwNVTg8hFCC3FCC7FEAx6HA4jF472UcgWEUURrU6DxW7C6jSzND+PoigUCkUQQKuRiAbimGxGtDqJgl6DTq9Fo9cydm16+cFRQJQEECCdyCAXZBAEFEHEajeh12uZn/STSWZpXVWDfyrAzMAM2ViKQr6IzqDDU+kmtBDBZDWSSWQZvjSOfybI7OgiNW3lGN5ICi2MLVDTXMbEwCyyrKAzaBEE0Ook7A4jmUSamD9KeC5IPJxiZniefDbPgf+2jYXZCK4SK27PcuXI3GSI6TE/iqwQCCVpba9g/dZGXn/5GmX1PjLJLFqthEanweWxsP++9dS3lP37P/lVKpVKpVKpfkmp7bx+DpP+MKFE+v8pNphIM7kU/gXv0VvJssxLL71Ec3MzBw4coKSkhE2bNr2l5VdPTw/5fJ79+/ffvK28vJyOjg7OnDkDLFd+2O32mwkUgM2bN2O3298S09HRcTOBAnDgwAGy2Sw9PT03Y3bu3Iler39LzNzcHBMTEzdj/s99eTPm4sWL5PP5fzXmzX3J5XL09PT8VMz+/ftvxqhUKpVKpVKpVP8WkiSyZmszD3zkFsrrPFQ2lFDfVoG33InJrEcuyhiMOkILEeZGF1m1s41iQaGq0cdtv7KdVDzDS3/7Oq/8/XFsDiPrd7YyMrhEw4YmOne0sXZvB7f96i4WlhJotSKKLKM36hi5NsXCZAC92cDidJCX//Z18m+058qkshjNeiSNiMNro3NrM4lQnFM/Ps/FQ1d59k9fYfL6NHWtFWRTWQxmA9dODGAyarC6zFg9VuxOExV1Xho6KimpdHP2xUvMji6QTWWJ+uMc/LvX0Rt11LZXEQunsLksvP5cD+VlNnR6DfFIkmQshd1uZH4mzOTQAuFYhht9swhaCW+Fi+Y1NTSsrmHl7nZmJwMMXp1i//u2sX5vO7lcjs5tKyir9aAoCiaLfnnuSmMpi3MRZEVGo5MQAEWWyaZzaPUaCtkCZocRg0mPVq9B0kiggMVpJpnKsvPutSQiCeLhJKGlKAaTDrPViMdt4uRzF0kncwQXoiQiaRYnA9jcFiSdhomBGdxldhweKzUrKtj/yDasDjOrd7SRz+YJzIXxldtZs2MFVa3lFBUBo9PKmj0dbLl9NaG5MPlMjkwqh86gxeGxotVriYUS5LM5irk8w5cniIeTLEz4CS9E8c+GiIZTpBIZipEEGzc3UFbhpLK+BJfHwobNDey/cxV1zaXv9GWgUqlUKpVK9UtFrUT5OUz8jEmRiaUwrVW+X9De/LSlpSUSiQT/83/+T37/93+fr371qxw8eJD77ruPY8eOsXPnThYWFtDpdDidzrfc1+fzsbCwAMDCwgIlJSU/tf6SkpK3xPh8bz02p9OJTqd7S0xtbe1PbefNZXV1df/senw+H4VCgUAgQFlZ2b8Y8+Z2AoEAxWLxX41RqVQqlUqlUqneDi6vjZY1tfSdGbo56+/Nn1qdRDqRZstd6zj7ylVMVgPNa+t46usvU8wXcJU6SCeyhBZjXDo3RtAfp//SBGXVLlxeG+d/cJGaJh/JUIKlqQA2p4XSGi9Gk46rx/oZvjIBAgTnwlgcFsrqSyip9uCqcBNYitPUVs7lI724vLblllmzQRLhBHXtldgcJnLpHGazHqmoUFHjxu5Yw5HvnGJuNoyiQPOaWoqyTDFTQEHBbNYjywpj16cpqyvB5jKTSmZRFAWNTkMmlUMuyuy5ay3XTg8y0jfHwx/bx8Vj/UwPzzMxssiaPR1Y7Eae+9vjJGNTtHVWkktmOPfqNeweK7e/fyeB6SDFfIGlmSCVDSVYnRYa1tVz8mAvJRVO1u7uYHF8kUQ8gwIgKzSuriaXyhMPp4j64ySiKYwWA5JOg7fSw5HDfbSua+D8kV4yiQyCKJBN50jHUssVJDoNoigAMlqdxPzoIt4KF3a3FUVRGO2d4vLRPrQ6La5SO2tv6WDn/ZvIpLLoDVqa1tRinQ4jF2UsTjN954f5/qUJtu3v4JZ713Px1BD+2RDeChfFooxWq6GkwkVsKUJlUymhxQiiJN5c5nBZCS1ESIaTHHh0BxSLTPTN4PTZqWoqo2ltHYIgvFNPe5VKpVKpVKpfSmoS5eeQzuZ/pvjUzxj/byXLy23G7rnnHj75yU8CsHr1as6cOcOTTz7Jzp07/8X7Koryljfl/9wb9Lcj5s0/MN+OmH962/9LjEqlUqlUKpVK9W9VVu9j8+1rOP/KZWRZQaOVsDrNSBoJSSPSe3aYVDyNw2tD0ogkoykKuQJmmwlniY2xwXnmx5fwVLiw2I3kcwVqV5RRzBeZuDGL2axDFEUKuQJbb+2kmM0TXopgc1vIpnPkswV6Tw/y4Kfv5CffOcPVF6/i8FjRawQWZyOUVDoRBAGzzUg6nmHo0gQmq4HAXBjljQHzla2VLPoTrDywmumBmeW2UVoJSaclm84higL5vIzRsjxbMZ3MUN9eic1rw2gxUJRECvkCGq2GyhoPJ567RKFQ5Jm/PMItD2zE3VDC6I0FXnn5Kl07V+Ard5J1ZJHzRQKzIXw1Xho7q5kb9/Psnx7EV+OmrN7HljvXMjsVZGp4idVdjVidJiprPXh8NvwzIQSNyKrtrYQDMSKBKKl4hng4ga/WS76osOHAKqamguRzRS6eGWbFyhrOvXYdp8/Gqk0NVJY7KK92Y7YaCADxUBJJI1FWX4KrzMXg5QkuHe+neU0dH/36r9DzWi+nnr9ASbWbhYkAG25dicZo4ORLV/DPhknH09g9NixOMzq9lrNH+nnvh3fjq9jE8PUZtEYdNfVu2tbXkcnkmBcUwv4YCAKSRkTSSpTVePFV2MmGYrRubKDr1lUIgsCWO9e9s090lUqlUqlUql9yahLl52DUa3+meNPPGP9v5fF40Gg0tLW1veX21tZWTp06BUBpaSm5XI5wOPyWapSlpSW2bNlyM2ZxcfGn1u/3+29We5SWlnL+/Pm3LA+Hw+Tz+bfE/NNKkKWlJYD/a4xGo8Htdv+rMW+uw+PxIEnSvxqjUqlUKpVKpVK9XSRJZP3eDqqaSvHPhNAZtazY0MD3/+RlrG4L18+NIkkiFrsRRVYQRQFBFEjF0wiiQDyaJhqIg6IgaSWcPjv93aM43Fa69nVgd5kIzoRIx1Icf+o07/n0ncyMLJDL5PFWuEmEEzhLHZx69TpIEvWdVctJGrsJBJifDFBe48Fc62WwZwyjRU86lcNk1tO6oZ6hK9MUEBEkDYdevIqrxIooFLGli9i8drLJDLlUFqPFQOWqaqqaSnGVOTj7kx6GLo7iq/Xiqi5h1cZ6RK3E0kwYQQRRFMhlCrz6vXM0tJXT2lqORqfBU+bAbtahRWBmcA6tvoKKxlKWFmKcff0G5R01uF0mTvzwHKlYmr2PbGd6ZB6TScP80DwjPWOYrAYe+PitGC06Zkb9HHn6LLlsnvL6Ejp3tiHLMis2NvLqK9dpaSsnNBfG7rNjdZnxVjjoXFPL2cO96DY1UMwXmZ8IYHVbyaRzdGxtoWltHVOD8zjcVqZuzHMp0o/WoGPzPRvYfPc6rr3ex8ptrRx/tpvyFcuzVMpqvciygqgRmR9bQpRETFYTYzfm6T8xQGgxzG2P7qTvzCBXX+5h/+P78M9G0Gg1oAAKuEoduErtuOxGlqJJVu9oVb8IplKpVCqVSvUfhJpE+TnUlji5ODLzM8X/e9LpdGzYsIHBwcG33D40NERNTQ0A69atQ6vVcvjwYR544AEA5ufnuX79+s1h9F1dXUSjUbq7u9m4cSMA58+fJxqN3ky0dHV18eUvf5n5+XnKypaHGx46dAi9Xs+6detuxnz+858nl8uh0+luxpSXl99s89XV1cULL7zwlv09dOgQ69evR6vV3ow5fPjwzeqaN2Pe3BedTse6des4fPgw9957782Yw4cPc8899/xbTqlKpVKpVCqVSvXPEkWRsroSyur+dxtcSSMxO7LA6LVp5KLMSO8U9Z1VWBwmcpk8JpsRURSxOI2YrEY0Og1mm4mZkUUcHisT/bPoDFr2vmstZ5+/gM1pprS+BFmWUWQFjVaDIMCV4ze49+O3cuq1AcLBBMV8EQWFzvV1FApFCrkigYUobo+F2tZKVu1opefIdVbvbMNd7WGkd4ahq1M0bmpEFAVC/jjZdB6bxYDFaycUTlJa7aGYyRELJSir83LmJxfRm3RUrqhgejzA3EyErXevpXJFFYlInG0HOjn+4lW8ZXYKRZnh67P090xgcZi499d2Y3ZaKHEaWZwOEVuMkRlaJDAbwmjWEw6nyBdl/tvv3Es8keW1l6+Sy8vk80Vq1tRSVukin8hw6WgvGq1EMpVDEJYrz0euTTFydQpZVsgXFQRZIRJMotdpEOUibaurqavzcOj753G6LEzemGP9vpXMjS4wP7bIuz95B4sTAX74pwdJRFLoTTru+MBupqbDHDvUx/B4kGwyS0NrORavlfZNjYyO+MlnC0QDcRRZwVXmQKvXkklmSMXSJGMlSBoRT5mTpcklFif8mB0mRi4MsXZzHUsLMWKxDGarkZJSKxaznkQwzq77N9K4qvodfFarVCqVSqVSqf5PahLl51DjdeKyGP+fhsu7LUZqfgFJlEQiwcjIyM3fx8fHuXLlCi6Xi+rqaj772c/y4IMPsmPHDnbv3s3Bgwd54YUXeP311wGw2+184AMf4NOf/jRutxuXy8VnPvMZOjs72bt3L7BcuXLrrbfy+OOP881vfhOAD37wg9x55520tLQAy0Pb29raeOSRR/ijP/ojQqEQn/nMZ3j88cex2WwAPPzww3zxi1/kscce4/Of/zzDw8N85Stf4Qtf+MLNb1d9+MMf5hvf+Aaf+tSnePzxxzl79izf+ta3eOqpp24e42/+5m+yY8cOvvrVr3LPPffw/PPP89prr92srgH41Kc+xSOPPML69evp6urir/7qr5iamuLDH/7w2/4YqFQqlUqlUqlU/5yWdXUIokBHVxODPeOU1/nI54tUt1YwM7SAt8JJPJKkoaGU2o5KUGB6cA4RECURuVDE5rATmAlS21pBJBinc2sLmWSGzm0rGLo0DgIIosDcmB+jzYhGr0WWFZLxNOeO9bP9XRu50T2M3qClpMxBaYOP6fEgnrpScgj0nR/FbDNhcprRaDWYbUZikSSiJDI4ME9Leznb7q3EZdWxNBXEZDHgnwkycnkCURRoWt+ARiNQ2lCKpCj0HesltBilvL6ERz+xj6WFGANXp6hbUY5ep8HptVLI5HC5LSSDCQwGiZWbm5iZCiIIEA8nyWcLFAsyWY2O5793HFepHaPNhCgKTEyGiCZz1JbbsNqMmOwmXIqAvyFARaMPq9PMjcsTmC1GVu1sZWRoEaPdhN1pJBnPcuH4DRKBOKGlGHaXBbPdyPTYEhF/nPatLQQXosyMzCMXZfK5AmaHiYPfO03H9jasbyRdJI3IzMgCI30zNNW76ehq5OBT50iEU1Q0lDA7vEBFo49CrkA6nsHqMOEqseHw2hi5PE6xICMgULOikuM/Pk+xINO8ro61m2vp3NqMKEqY7UYsdvM7/RRWqVQqlUqlUv0f1CTKz8Fs0HFgTQs/OHONQlH+F+O0ksj+NS2Y9Lq3fR8uXrzI7t27b/7+qU99CoBHH32Ub3/729x77708+eST/MEf/AEf//jHaWlp4Uc/+hHbtm27eZ8/+ZM/QaPR8MADD5BOp7nlllv49re/jSRJN2O++93v8vGPf5z9+/cDcPfdd/ONb3zj5nJJknjppZf4yEc+wtatWzEajTz88MN87Wtfuxljt9s5fPgwH/3oR1m/fj1Op5NPfepTN/cZoK6ujpdffplPfvKT/Pmf/znl5eX86Z/+Kffff//NmC1btvD000/zO7/zO/zu7/4uDQ0NPPPMM2zatOlmzIMPPkgwGORLX/oS8/PzdHR08PLLL9+swFGpVCqVSqVSqX7RNFoNbRsbsTnNHPreaWYnAvRfnaZ1ZTUdm5sY7Z0mFk5gsehx+2yMXpshFkqgKAq5bJ6qljI61tVy9vlufFUedt2/GbPDhN6op6zOS01rOcG5MHaPDXuJndGxXkKLMcx2E6GFCGabiZ5zozz8oT0EZkPEA3GGL01S0165nEyIpHC4zKQTIbasqyMRTb1R5QKSRkQURcZGlkgmc5hzaW5cHKe+pZTW1TXk0nkMZj2JUIK6xhKqW6t4/YfnqGgqwz8bYn58ieBsiK3vWk/5HSuZmQhwrXuMwjWZjrU1HPyrw1TUeRnsnUZv0LHt3o0MXZvGbNET8cdYt6edY69co7K5jFQiy8JEgHyugCgKWBxmOlZuJR9JcOrZC0haDdUtZQyeHyYWjLNmdzuldSUc+vYxcpkCe967hbQocv3SFFv2tDIzukgslCCdzFJa5UZCJjAXobGziotHrmN3mlAU0GgkzA4z89Mh9DoNEX8MARAQQBBYmA7SurISh91ELr1cDZOKpymt+//bu/PwKus7///Psy9ZTvblZE+AkBDWRCCggAsIiohtlY4tauvYUq2Uoh2r7XQ6nVat2jqt1qUz87MzTitOS3FFBBVBZA+EfScQsu97zn5+f/DltBGKoChLX4/rynVxPp/3fZ/3fed95Up4n8/9SaG7rReDAYaW5VM2ZSg7jSHe+I/3MJqMxKe4iI6PIi03mWv+YSKhUAhvr5e96w8ybOwg0gfrEcQiIiIiFyJD+MTu3Zeorq4uXC4XnZ2dkZURJ3g8HqqqqsjLy8Nut5/1uQ/UtbB86z5aT7EiJTHawbTRhQx2J33i3OXz8WnrQERERETkb+nv9VB3uInqIy34PH4OVB6hubYDb08/h3bWcMeDN1J3pJm1b2/H5w2QkZtESrqLzoYO0nOSyBuexfuLN7J740GcMQ6+ePc03l+8AavdTP6wLAxmE/0BWPnaFrKGpNPR0kN/j4cv3jmJDa9vpraqBZvdgtNppa/Xw6QvjWfZog2YLUam3DCawWNy2bq5mvaufvZsOwYGju/TYYCyCYNp2XWE7tZuHDYz+cMyqdp2hLqqJtz5qaTnp9De2InfF6Sn10cgECQ21kHtoQYmf3E8Hy7dSmJaHCVThlF3rJ363cfoau3GlRCF0WyivaWH6DgnmSNy2be9hqGjsgkYoK2jH4fVRNWuGkyW4x/wMhoN5A1Jw9jvYVCRm2UvvM9VcyawYekW+ns8x/chibZjNBkYOn4I696sJD45luJJRSTkpmIKh2mtb+fDZTvw+wJYrWYGlWQS9PrJGZLKtpW7iHbZ6e/10dfdT9hkoqO1h2lfvYIPVuzEYrUQCATx9HiwOa3kDXWTl+nCaLOy5u2deD1+Mgen4/P4MJuNjCkvYN/avRSNH0zTsVb6uj0MKc0nOSOe/ZsPE/AHB9TJF+6dTlah+/MvUBEREZG/U6frG3yUVqJ8CoPdSWQkxnK0qZ0jTe30ef047RZyk+PJSYn/TFagiIiIiIjIxcMRZadgeDb5JVnUHm2htbaNXRuraGtoJ6MglT0Vh0lJiSExzkYoZKPpYB2739+JxWbG2+/FPSSN9LwUqvfVMWhkLmvf3ELBqGzSspOJT4nFlRRD2GAkbDKxd+sRXInR5Bel01bbRs3hZgD6e72EgkFi46OwmIxc9YXLMNvMpKS76Onox2kzkz4sg8IiN811HRiM4HQ5OXqokVFTitj4+haiYhxsW7Wb0VcOw2g2EQ6H6W7vxWq3Egx68Xn8RLsc1FU1EQ6FMRoNePt8NNW0Ur3zGIPK8tm7ehfBQIiezj5SMhMIBoO0NXYyLM6B3xfAZjPTUNOOPdpOf68HgHDo+Gf+AsEQWblJrH9lE6lpLuJTXHS3ddPb2Yc9ykY4FKa9uYvE9HgCXj9xSTGYLEbiE6Ko3nKIfZXVFF+Wx5SZI9m8eh/dnf0EAkG6Wrvoao+i6LICmo61YMCIwWCgz+PHbDHjiHUQFeukp7MPo8lAMPD/nkQQhrySLNrq2/ny3VeDyciBbUfx9fnwtPfw2tNvkZqZSDAQZO/mKlIy40nLTuTQ9uqTGigJaS6SMxM+v6IUERERkbOiJsqn5LRZKcpKpShLS69FREREROTUDAYDmbnJXHHDGKoPNGK3W/D0efng9S3cdt91DBmVx/L//QCfx4fVbiGnKIMJM0sJhcLEp7kYPCafrMFp7Fy3D19/gD88+ir2KDt2p5WCkdlcf9fV5OQlcXBXLcPG5rPzgz2YzSY8fV4AersD9HZ7qDncRFZJFsv+sJYv3zOVdSt20lrXwYTrRlK9v4G+Ph9eb4BwKMSYKwqx2kwMGzeIuoN1AOxet5+pX51EV3sv3e3dWKxmdq0/SHdrN/GJUYSDIQwGA2abmVAoTCgU5tiBOkZfVYLNYaW7vRejyUY4DITB7rTg94cIB0N0dfUTG+/E7w0QDkNMQjSePi9miwmTyURMjJ24OAcmk5FQKISv34fRaCAUCGEwGrBYTISDIfzeAHanlaJxg1jx+zXEJMXS1+1h5/pDJKbGMv6qYla/vZO+Ph8hg5G4tDjcmfH0dfexa88BjCYjiVlJlE4dwZa1B7DaLdh8Vrpau7E5rQT9QdKz4vH7gvh8AQJeHy01baz+39UEA0HC4TBGo5FEdwIGk4lQIEjp1JE013cw9rpRbH9/N20NnQDEJsVw5ZwJ2KO0Il5ERETkQqUmioiIiIiIyOckqyCVrz80i/Vv7+CD17fgiLLz/utbuPHOK/nWyGzamzoxYKCtuYv/efgVfB4/837+D2xcvoO+Xi8jJhSy5DfLAQM+j4+E1Fi2vLcbR7SDUChM7tB0gj4/KVmJGIzH3zP0/1ZzGI0G4tPiaGvsxNvjw+6w0tHSzfAJg3h70Xp8/X4S0104ou3093hZ+9Y2brpzMh3VzZReMwJ3QRp+b4BdGw/SVN1CfVUTZVOHUzR2EHVVzXj7fdidVsbPHMPRPXVYrCasNgvOaDtHDzQwYtJQ1r2+FbPZiCPKSlxiNIWXFbBnRw0AZouJ0vIC1r2/F6vVRG84jNliwuawYbUaMRugr8eD0Xh8RUhsUix+XxCTxYwBSMlOovFIM/GpLmoPN1J7sAGL1YzfH8JgMoDRQGdHH/3d/cz46gRiYx1U7TzGlnd3825LFzd94yqyC920NXSQMyyT91fsoru9D5vTSnpuMiaTkb6efuISYxk1fhD+3n783iApmQm8/6cNTL65nJaaVjx9XvKGZ+P3BzAA0782BWesg5aGDqwOO1/67vU0VbcQDkNKViLOGMf5KEUREREROUPaE0V7YfzdUx2IiIiIyOctHA6zddUedqzdz9ZVe/D2e0nNSKDhSDMGoxFvvw9PnxeTycjISUPJH5HDK8+/y6QbxvDqcysIh8NExTjo7/GQNSQdW5SVonGDaa5tJ6MwgyN76wj2e6lcvZdgMIQBGDWlmK5ePwUlmbTWt1M0IoOqQ814+vxs/WA/BsPxvMwWM+FgCKPZyKjywfQ0tZKWk8z2D/biiLbjjLFjMBg4tO0oMQnRfGH+tdQebCYYDBIbF8XaNyro7erH5rDS2dbLZdNHsXHVPgpKMskdlEJbTSvtLT0MnziEkNlMU207BoOBnPwkTISpr22nr8+HLcrO3r0NtDV3M3laCRvfquDyqcOpWLGD0VcOo7erj+7WHo7uriEmPopgKERqVhLtzV34vH6sdht9fT6aGjoxmoz093gxGA2k5SSRVphJZnYC9YcaqK1qpqulm6g4J2OvKuK9//0Ab5+Pa26fzL69DXR39RObEE1Pey+p2UlcMXUYva2drH5lM9V76vnivdM4WHmUgD+APcpOdKyD6v31hMNhyq8bRWdLF0lZyRitFqJinVz31Ynnu/xERERE/u5pTxQREREREZELmMFgIKcwnbVvVOD3+IiKdtDT0Ut9VTODx+RRc6gRA2AwGji6t56+Xh8jJw7BEW1j0Mgc+rr78fX7iE+JpaOlm7y0LFyJMaRmJxIMQVN9B+68ZG6Ydw1tDR2EDUaOHWmhobqVYaW5RNuMxCfGYHHa+ODNHZGcLDYLBoOBkNFAqjue1oZ23BkJ7N14kMumjWDDW5V4ejw4o+1MmTOB9LxUjuyuIz03me4uDwEMDBqVR3dLF66kaIaUDaK6qoXsQakkp8aSV5yJ0WKiqbWPZX/aTGp2IqFAiLyh6ZgN8Prz7+CIceILhggEofyGMRyoasXhMOO0mqnaVcNl146g6WgLcSkusosyKBo7CJPZiNFswmK34Pf4CBsMNB5toaW+k8IxefR192O2mmmobSc5M5GEjATeXrSeotE5JLnjaKltw9/UidlqJTkjAU+fl2O7aygqziC1IA2r3Yozxo7FaKBmXx1RLge1BxqJiXdS8d4u0nOTObK3Dk9VC67EKFrrOzAYoK/bQ15JLseOtuDt7yKvWB/aEhEREbnYqIkiIiIiIiJyHiSmx3Pz/Bm8+8f1bF+zj9wiNz5vgGMH6nHnp9BS14EzxkZ0XBQ1hxoxm00YzSaOHWzAYjERCoaJS44Fg4Fx00fSXNfOh69vITrOwVWzy3hvySYOVh4lPi0evy+A2WTkC1+7nHVLNuJKjOL9xRvIGupmRHkBjbXt9Pd66evuxx5lJ9bloK2xgxETBmOzGji6p46ya4Yz447J9HZ58AXD7Ks8SsWa/dicNox2G4OK3VTvq2XY+MEEAwF2rN7L7x9ewtgZo7jh9okc3d/EO3/ehNVho7A0j2FjDdQcbKK4LJeYaCtv/sd7BH1Bejt6ccbYMZsM7P1gN1/+/mz2bjxIV3MXuUPSeeXZFdjsVsLhMBabmbTsZPy+AK6U43ufFIzMob6mnWGleTRUNbFh9W4AgsEQxeMHUzw2n//55duEQmH2VB7l6hvHcHRvHYnp8Xj6vFx27QisditGswm/x8+BtfvIHOrmSGsPRoORnCI3JiNc+9WJ+P1BNi7fQXJmAsPLh9Ba307AHyQxPY6ya0YQDMORQ02RzeTzhrrPZ8mJiIiIyCegJoqIiIiIiMh5kpqdxPSvTcFdnIGno48ju2uJT44lFAqTkZ+M0WRk9JQilv7PB3hCPnZvPszkL41n74YDHN1diz3KRsHoXELhMKv+vJFgIERukZsdq3aRlZ1AX7+fzrZesnKTGTlxMB0N7RjCYULhMEf31LF302H+4cEbGVSSQeOxNgDCoTB93f14+33YbGai4px89cEb6evx4vcG8AVCrFyyGaPZiNFoxBlrYtWSzezbmkRigpMt7+wiLimGYeMHk5DuwpUcx8ZV+9m2Zj9hwvR3e9i1/iCJ7jjGTxlK/b5aOqJsxKe48PX7aGvspK2+HZvTRsAXoPFwA5Uf7GXCrFK8PV5MZhN9PR4MBgOu5FjqatpITI4hLSeZd19ex6TZZRSW5dNwtIXag404Yh14+33EJsXQ3d7D0d11BIMhwqEQFoedcBiS3PEA5BdnUL2nlmNVLTRWtzCyfAiF5YUs/+MGWho6SXIn8O6rWxgxYTDRdhNb3tnBtNsmsXPdASZcP4bisQX093rZVXGE6kONhIJ/eXq2OzeJnKHp56PMRERERORTUBPlUwiHw9T3dnOgrYX97a30BXw4zVaGxCcyOCGJ9KgYDAbD+U5TREREREQuYAkJ0YwqL+Tw4Sam/MNEtq3cxdE9tcQmxZKam0Rnex+tDR0kpsXR0+Xhw7d3MKQkgylfHEd9VRNRMXa2vr+H1vpOolwODAYDR3Yco6W+jYS0OBxRdnYebcTX1cPls8ooGJ2HI8pOTHwsR/fWsv6NrZTNGE1LbRu1h5oIBkIkpru4+guXcXRfLUNGjOG1Z5dTd6iRa2+fzMrXthKfGofP68NgMNJc04bBaKCppp3iMbm01bWzf0sVKVmJ7F63j8HlRRw93Ex/rwerzXJ87xUDNFW3smvLUUaPz8OVEM3R3bU0HG0mFAwRCoaJspowWYwQNuDp9dHd0U/xZflsff/4yhKb04rPH8LvCxDEQO7wLCb1+1j1p3UUTS6hv72X3h4vfn8AwtBU30lHWy+F4wYTnxxLX48Hv9dPV0sX1XvrGD1pKPFJMezs6CPW5aDTbCQhzcX69/fQcKyNxPR4mmtaCQZCbF29j8uuLiYhI4FVSzYx4brRpGYlkjn4eJMkIS2O/duqObD9GCazkaLSPAaVZBLjcp7PUhMRERGRT0BNlE+ow9PPymOH2dpYhz8UGjB3uLONd6sPMSbVzZXZ+bhsjvOUpYiIiIiIXAzi4pyMGZNLbbyTAxVVJGcl09PZR+XqfcQkxTDj9kkc21fPwd112B1WUnJScCXHULWjms6WLmw2C5n5ydij7FTtOobFbsFiNdNS04bZasZoNHBkTw0Fo/L44NXNBAMhJt10Gd0dfTii7axfto3Bw7PIL8og4A/Q3tTJurcqGTwym/VvVWIymXBE2QkEwxhMRhqONpNVmI7fG6CtwUOUy4nRaMDrCeDr9xPwB/D0eUnPT6XmSAs2h/X45u/+43879fd4CIeh+kADU79YRl9nH0Muy2fPxoP4QwEs1uMfRiu9ejhtje24EqNxxDhY8cpW0galsW/zYULtfTijbRiB0qnD2bruEIauborLh7Jj3UHyh6TR09FDlCuKrrZewuEw3n4fzhgH0TE2bA4L6VkJhMMhJl0/Cle8g2AgRGZeEv/3izcwW82kDXFzqPIomYPTCIfCBANBjGYjoUCI3VuOMHpsPtve38moKUUUjS2IfD8T0+IoT4tj7NXDMBgNGI3G81FWIiIiInIOqInyCXR4+vnjvp0c7mz7mzH+UIgN9TW09Pdxc2GJGikiIiIiIvKxMvJSuP72Sbz9+w/ZX9mD2WLG4bBSVFbAhFmldLf1YrNbiYq20lrdgisphr5uD/HpcbQ0dNDf7SE+OYZQIEQ4DEaTEb/XT5TLSXxKHD0dPQT8QTy9XjYt387460fT1tBBTHIsxw400N7UhclsxGA0YLKYsDtt1B+ox+awEAiECAVDmEwmYhOjjz/KK9pOdJyTno4+rHYrDqeVxmMtpGYnY3faSEhz0bqlhsZjDSRnJdHW0IHP68fmtGI0GknLTabpWCs7Vu9h1JRivvLQTezbdBAwkJ6fTFtDB3aHFXd2AlGxDprq2skuSOGKm8bSVt+O1W7BPSidzav2EpsYTcnwTIJhA0azmZDRQNaQdJpr2rBHWfH2+UjJSiJsNNHb2cuwsYPIzk2gbn8drqRososz2bpmD4c3H2LQiGwsVjPdnR5sThtdLd1ExUURDIQwGA1YbGYsVguxqS6GXV5IwfBsrHbrSd9Pk9n0+ReRiIiIiJxT+jjMWQqHw6w8dvi0DZS/dqijjZXVhwmHwx8ffIYeeeQRLrvsMmJiYkhJSWH27Nns27fvb8Z/85vfxGAw8O///u8Dxr1eL/feey9JSUlERUUxa9YsampqBsS0t7czd+5cXC4XLpeLuXPn0tHRMSCmurqaG264gaioKJKSkpg/fz4+n29AzI4dO5g8eTIOh4OMjAx+8pOfnHRPVq1aRWlpKXa7nfz8fJ577rmTrmXx4sUUFxdjs9koLi5myZIlJ8U888wz5OXlYbfbKS0t5YMPPvib90ZERERE5EKTPyyT2x+6ke/88it891dzue/Xt3PlFy5j1PjBDB2ZzfaVO3jnxQ+oXL2X4ZOHkj8ql5ScZJLcCfh9AfJKsiObm7vzU0nPTyElK5GSCUMAmDhzDJO/MJZol5PVf95EX4+XZHc8Cakuejp6aa5ppe5Q4/FVLGYjDqcFR4yDzMFpdDS2k1+SSX+Ph2AgSGN1C86Y4x8Ys9jMGAkTDITo7eknPi2OGJeT5PRYDEYjrXVtpGYnkZgWR1JGAlEuJ72dvRzdXcPh7dV0NncR5XJQMDIHkwkaj7SQNcRNX3c/Pl8QW7SD8VOHM3hULuYoO44kF2GbncoNh4lJjKG7tZeGI634PH5a69o5uLOWoROGMnb6KIaMyuWar1zOiKtK8Hh8jCzLJTs7jo76Do7uqWP/lir2VlRht1nx+wI017bh6fVid1gIBoOEQmHCoTAp2UmYrWZsUTY8Hj9hs5ExU4aRnJl4PktGRERERD5DaqKcpfrebrY21p3VMVsa62jo7T5nOaxatYp77rmH9evXs2LFCgKBANOmTaO3t/ek2FdeeYUNGzbgdrtPmluwYAFLlixh0aJFrFmzhp6eHmbOnEkwGIzE3HrrrVRWVrJs2TKWLVtGZWUlc+fOjcwHg0Guv/56ent7WbNmDYsWLWLx4sXcd999kZiuri6mTp2K2+1m06ZNPPXUUzzxxBP88pe/jMRUVVVx3XXXccUVV7B161Yeeugh5s+fz+LFiyMx69atY86cOcydO5dt27Yxd+5cbrnlFjZs2BCJefnll1mwYAE/+MEP2Lp1K1dccQUzZsygurr6k99wEREREZHPmTPaTu7QDIaOySMpPS4yfrDyCL0dfcDxD3h1t/dTV9PGH59aTkdLN6OmFBMIBJg17xqiXE4MJgM2p42yqcMJ+vwcqjjCxrcqWf96BY4oG5fPGkPNwQba6tsZPjaPKTddRnpuCu7cFC6/YTQ2m5nicYPpaukm4A9Q8e4ucvKTSc1Kor/HS3+PF2+/j8zB6Uy7ZSx71u9n8hfHUnb1cLa8t4v3/7SRtIwEQqEgAV8Ab7+XpuoW2hs6aG/soHhkNoe2HaH0mhJSc5JpqW1j17r9NNa0sX9LFW+98D7RqQlUrD/E8kXrWPPWdl5/cQ37d9QQGx9FY00bjmg7TcfaKChyU72vhu6WLqx2M12tPRzZ10hzWz+dvT7WrdjJyj9toK+li91r9lCxYgeV7+/CGeugta6dqm1HMVqM5A3LJC45ln6Pn2R3HH5vAKfLiT3GTnJWIvkjcrA5baRmJjD8sgIm3zRWe2GKiIiIXMIM4XO5ROIC1NXVhcvlorOzk9jY2AFzHo+HqqqqyKqFM7Gq+jDLjhw46zym5w1mclb+WR93Jpqbm0lJSWHVqlVMmjQpMl5bW8u4ceN4++23uf7661mwYAELFiwAoLOzk+TkZF588UXmzJkDQF1dHVlZWSxdupRrr72WPXv2UFxczPr16xk3bhwA69evp7y8nL1791JYWMhbb73FzJkzOXbsWKRRs2jRIu644w6ampqIjY3l2Wef5cEHH6SxsRGbzQbAo48+ylNPPUVNTQ0Gg4EHHniA1157jT179kTynzdvHtu2bWPdunUAzJkzh66uLt56662/3Nfp04mPj+ell14CYNy4cYwZM4Znn302ElNUVMTs2bN55JFHTnn/PkkdiIiIiIicD3/+zdsc23v8Q10ms4nBEwr50/PvUjImj/6uPmx2C1aribqD9bjzUoiOi6JixQ5MZiMx8VFsX70HR7QDZ6yDYCDImKtLiE2KpbW+g9iEKDIGpXJ0Ty1NNW00HWshIz8Vs9WM2WqmvamLnWv3Uzx+EFfdUs7md3fh9wcxGAwkp7lwxlghDLs3HiR3WDahUJjmo8201bczZHwhG1buoau9j5jEaMwmI6WTCzmwbh8xCVHkDctiz4YDVO08RlxyLCMmDeXdP6yl/KaxbFi1j4IROfR19WO2WamvacNkNpFf5MYRZaO3q4/+rj6yMlysfWUj8alxjL2+lHf+XEF0fBQtdW1kF7rx9HooKcvDagzT3tTFlnd30tvZx5Sbx3N4Vw1Gs4nr/vFKOuo7ePcPa3BG27HGOhl02SD27ajDbDdz5GATFquZ7Lwkrr6plMk3jiHKYTvPVSEiIiIiZ+t0fYOP0kqUs7S/vfWTHdf2yY47E52dnQAkJCRExkKhEHPnzuV73/sew4YNO+mYiooK/H4/06ZNi4y53W5KSkpYu3YtcHzlh8vlijRQAMaPH4/L5RoQU1JSMmCly7XXXovX66WioiISM3ny5EgD5URMXV0dR44cicT8dS4nYjZv3ozf7z9tzIlcfD4fFRUVJ8VMmzYtEiMiIiIicjFLzvjL7/yJ7ng2fbCf8VNHsPatbaxbsYs1y3YQCEFzbTstte0EfH6ajrWQPTSD/VuqSM5MxBljx2Izk5gez7o3t5BRkELFiu2sXryBre/tYs2STTgcFlwJ0Wx9fzcV7+ygqbqFkVcWM/Url+Pp9bH+za1k5SfjtJmwmuBwZRV71x+kpbaNoztrsNrMVKzcRcHIHDqau1n6/AqS4myMGZ9PWXkBX7prMu6MOKZ+5XJGXF7E1vd2YTQaMRgM1B1qZP0bW5k4q4zmhi78/X48fT7aGjtpqm4mISkaZ5SVmsON5A1Np+yKIQwZksI7L36AzxPAGW1nxe9WMqY8n/JpJRSV5uLOTmTGl8eDz8f+iiq2rdpDKBgiOi6KpMwEouKc5BS52bZ6D0tfXE1yXir5Y/KYeEMpSRkJ5BW5SctOJLsghbGThzJl5igKh2epgSIiIiLyd0BNlLPUF/B9fNApj/Of40yOC4fDLFy4kMsvv5ySkpLI+M9//nPMZjPz588/5XENDQ1YrVbi4+MHjKemptLQ0BCJSUlJOenYlJSUATGpqakD5uPj47FaraeNOfH642ICgQAtLS2njTlxjpaWFoLB4GljREREREQuZoNH52JzHt/A3OK00VDTRsgAl88cRWpWImaLiZ0VVXzh3ukYLSYsDhtJ7gSiXA6C/iDd7b14+nzYHFZCoRBWm4UDW6oIB0NEuZzYo2wEA0FWL95I/aEmgv4gvZ19lM8cg6/HizPaTlK6i8bqFtLykomOi8Lb5yU2KZbujl4q399DxuB0erv6GTIqF1eqizFXlxAV52TPuv1U76om6PHyXw++RGdLN9X7G9jwViX1Vc3UHmzEEW3HaDLS1dZDfGosHe29pBek0t7QQWdLN53N3XQ0duDt7gd/gOgoM/7efrIL07FHH9/PJOAP0tXWQ8jnZ8MrG6jbcZQ9q3ey+v/W0nSslZoDDSSmx2GPtpExKI2D245ht1mIS4mlp62XzpZedqw7wJb397D6tQq6O/sxWc0cOdRMbGI0TbXtpOcmkV+ccZ6rQUREREQ+D+bzncDFxmm2fsLjLOc4k+O+/e1vs337dtasWRMZq6io4Fe/+hVbtmw562fzhsPhAcec6vhzEXPiKXLnIuajY2cSIyIiIiJyMUrLSebGedewe8NBPN4A8Ukx+AMh9m07RtbgVLIL0yAMOzceJoSRlvpOxs0cw94NB4hPjaPxSBOZhW5a6tqJT3Ex/rox7Fy7j77ufvz+AIRCXHlLOTs/3Ie334cjysb0Oyaz48N9uPNS2bPhAAnpcVw2bQRVO2torW9nw9JKQqEQcPx37tScJKJcDta+uZVQMEh/Vz95w7Ow2a10t/Xw7ktriY5z0tXWS2K6i5oD9fR29QFhkjITMFtMtDd24vcFGFSSRWNdJ03HWgmHwkS5HISCIYKGIJ4+L1arhf17asgpymDBb+6kZl894VCY/OHZVO+rpf5wEwF/AMKQlptM5uA0dq7ZR3Sck4KROWQMSaf2cBNDRuXw/p824OnzYTKbMAFRsQ6iou3Ep7nYsWI30S4nXl+QkRMHM7ysQH9jiIiIiPydUBPlLA2JT+RwZ9vZH5eQeM5zuffee3nttddYvXo1mZmZkfEPPviApqYmsrOzI2PBYJD77ruPf//3f+fIkSOkpaXh8/lob28fsBqlqamJCRMmAJCWlkZjY+NJ79vc3BxZ7ZGWljZgY3eA9vZ2/H7/gJiPrgRpamoC+NgYs9lMYmLiaWNOnCMpKQmTyXTaGBERERGRi116Xirpecd/v7XGx7K78iijJxex9s0t9Pf4CAaCxCVGUzwmh+Hlg6l8dyfp+SmMnlzMwe1Haa1rJ+APkFOUQYI7jsSMeDqauwj4AgRDsK/iMO6CVCbeeBmHdlSzevFGIIzNaSMu1UVLfQdWu5X6Q42EQyHKpo5gy3s7CfgCpOYmM2rKMJb9fjVtde1sXrGT4RMG09fZT83eehzRxzdnD4VCmK1mNq/YSWFZARuXVRIKhqg90IArKYbi8iEMGpWL1x+i/e2dxMRHYXPaIBymt7MPZ4yDzPxkuls6iE2I4b3/20B/Vz9DSvMpu6aE915eS097L1aHhVAwBMCgkTls/2Av078+hZyiDLa+v4ejB5poqG4hJTuJvm4PBqOBMGFcCdEYjEYKywrYsvEIgTD093goHp7FtC+UYnN8Nh+SExEREZELjx7ndZYGJyRhMZ7dbbMYjQyJTzpnOYTDYb797W/z5z//mffee4+8vLwB83PnzmX79u1UVlZGvtxuN9/73vd4++23ASgtLcVisbBixYrIcfX19ezcuTPSRCkvL6ezs5ONGzdGYjZs2EBnZ+eAmJ07d1JfXx+JWb58OTabjdLS0kjM6tWr8fl8A2Lcbje5ubmRmL/O5URMWVkZFovltDEncrFarZSWlp4Us2LFikiMiIiIiMilZMRluUTHOAiaTFz5pfEMGZWNOzeJuOQYRkweSldHLzUH61nxv2s4vKeGzrZeJt08npl3XY09xsbOD/dROCafgD+II8ZB07FW9m46jCPaTlNNCxmDUpnxtcmUTRtBjCuKcdNHceWXxpNTnEl6fiqHd1Szf2sVY64u4fIvjGXQqByaa1ppb+giFA7T1dqNz+vHGePE6XLiiLbR1+1h9JUlxCREYzAZcETbKB4/GKPRgM/jx2qzMGLSUN75/RqWPr+c+FgbNqcVT3c/wUCQcCiMKymGsZML6WztYeX/raehqonO1m6O7avlvT+t55aFM8kqTCch1UVKTiJX3zqR3u5+At4APe29bHh7BxuWbSMhJYbh5YPp7Ohl+MRCnDF2BpVkkZKVQP6ILLJHZTN0dDaXTSpk6uxSvvT1y0lJj//4b4yIiIiIXDIM4RPPTLpEdXV14XK56OzsJDY2dsCcx+OhqqqKvLw87Hb7GZ0vHA7zysHdbKyvOeMcxqVncuOg4nO23Pvuu+/mD3/4A6+++iqFhYWRcZfLhcPhOOUxubm5LFiwgAULFkTGvvWtb/HGG2/wu9/9joSEBO6//35aW1upqKjAZDIBMGPGDOrq6nj++ecB+MY3vkFOTg6vv/46cHyFy6hRo0hNTeXxxx+nra2NO+64g9mzZ/PUU08Bxze+Lyws5KqrruKhhx7iwIED3HHHHfzoRz/ivvvuA6CqqoqSkhK++c1vctddd7Fu3TrmzZvHSy+9xBe/+EUA1q5dy6RJk/jZz37GjTfeyKuvvsoPf/hD1qxZw7hx4wB4+eWXmTt3Ls899xzl5eX89re/5T/+4z/YtWsXOTk5p7w3n6QOREREREQuFE11HezcepT9u2qIjrHjinOSOSiFupYOPEfbWbt4Pb1d/ZgsJhLT4zlUeRS700pUnJOGIy2k56Uw6Ytj2bR8O36vn4T0OPKHZ9PR3EV/t4dNK3ZgtZnxeQMYjAYmzjq+EiM61skrv3mbzpZu4PjjrzIGpVIwKo+Vf1pPX7cHwmCxWxg2fhAz//EqGqtbOLq3lujYKLav3k18WhwV7+xkWPlgCkbm0NvRh8liJOgP0d/bj8Npo6u1m+LLi8BspuFoK3a7mfwiN3s27GfX+oO0N3QSDoXp7erHlRSDzWmjaFwBIy8fSnd7D1abmcZjrbTUtmM2m4hPi+OV594lFArxrce+QmNdG8FAiJyhbgKBAP2+ABannbDFwtG6DgDMZiNXXzOMYdoHRUREROSScLq+wUfpcV5nyWAwcGVWPi19fWf0WK9BcQlcmZ1/Tp+X++yzzwIwZcqUAeMvvPACd9xxxxmf58knn8RsNnPLLbfQ39/P1Vdfze9+97tIAwXg97//PfPnz2fatGkAzJo1i6effjoybzKZePPNN7n77ruZOHEiDoeDW2+9lSeeeCIS43K5WLFiBffccw9lZWXEx8ezcOFCFi5cGInJy8tj6dKlfPe73+U3v/kNbrebX//615EGCsCECRNYtGgRP/zhD/nnf/5nCgoKePnllyMNFIA5c+bQ2trKT37yE+rr6ykpKWHp0qV/s4EiIiIiInKxS3HHcZU7jrKJg/D7gjijbTT19rH10DFCHb2UXDGUmn31bFq+nbScZFrq2rA5rBhNRqLjnDTXtnKw8gg9nX1YHVZsThsHth4hu9DNmlcq6GrtwWIz4+3zYrVb2b3+IGOnjcAfCPIPD8xi26o91B5oYNiEIQwdO4jfLHwRZ6wDV2IMAV+Q2IQoMvJTWft6BW317ZRNHcn//NufCYXCZBdlkpwRz4EtR+ho6qKns4/LbyzjwJYDNB5twRljJz4llpceXkJKZgIllxfiiHXR1dxJf7eXjqYu/L7A8f1SYh3EpcRiMBgIB0O89/KHlEwYQl8X1B9qpOlYK7kl2VSs3I3NYWHw6FyaGtrp7ejFEWXnw2XbGD61hEM+P57OfsqGZpKUFE1BQQr5+cmkawWKiIiIyN8lrUT5hCsQOr39rKw+zJbGOvyh0EnzFqORMalurszOx2U79eoQuTBoJYqIiIiIXGo8fj9vrd7BhlcquKwoF29PPytfXkdKdiK71+4nFIZwKERqdhJWp5X03GSccVHkD8vC6bLT1+UhFAiy6Bdv4u3z4en1YDAYMJlNFJblU3pNCQ1VTaRkJVIwMpeqPTU4HFa6O3rpaOpi9ZJN2O1WHDF2nLEOvjR/Bi//8k1aatuYMHMMu9btx+8NYLGaGVKaRxgwW8zEJkaTnpfMU9/5bxwxdowGAxabGWeUnbHXjSJ/RA6vP7scm9NGYWk+h3fVsHXlLkwWM44oGzEJ0XS1dnPtbVdQ8XYlDVXNTL55PM64KNLzUnln0VqajrVRMDKHIWPy6O3up2rHUVxJsWSPzCWhOJ3dR5qx2yzccfN4YqLs2kBeRERE5BKklSifA5fNwY2DihmXnsX+9hb2t7XSF/DjNFsYkpDIkPgk0qJi9Au3iIiIiIh87uwWC7kFqdQXZ9Lm8WDo85E/Ohez0YjX48dgNBAV66Sppo2kzATyhmfz4RtbaalrJyE5lj0bDjD9jsmEAkGCgSAmk5FwGOJTXTRWt9DV0sPu9Qc5uPUIezdXcflNl9HR0oXVZsFoMjLjjsm0N3WRmB6HuyCVYCiMwQhZhenYnVY6mjrp6/bgSozh0LajHNlTS25RJjFJ0Rzb38DN372epf/1Hn5fgL7ufuJTXCS643nn9x/Q3+053ii5fTIJ6fF0tHQTCoYIh8LUHmwge6gbR5SNuoONYIAt7+6kdNpIFv3iDW781jRqDzeRnJmIM9rG7g376Wrr5co5E2g3mTjS2AlAbLQdh82iv+dERERERE2UT8NgMJAeHUt6dCyTs/LPdzoiIiIiIiIRg9OSOFaaRf+xTvrruxlbkkl7XQcttYUc2VWL0WIkIzMNi92KKzUOZ4ydrEGpvPL0cuxOK4e2HaFs2kg2vb0Nb78Pm8NKbEI0rfXthEJBGqqacCXF0NLYRUxiNOn5aRzecZSc4gzi4qNJzkxk96aDvPt/G5j/y6+QNSiNjW9vx5UQjdFoJBwOY3NY6OvpJ7MglbHTR9Lf58WVFENyZgJfXDCD/m4PsQnRtDd18sHiDRzdXUN/t4e4VBdt9e0s++9VXDZtFF0dvfS09ZBb5MY9KJXVf1qP1WGNbETfUtdGYqqLQCDAoNE5vPwf79He2ElOfirTvjKRfU1dEGOlxxvAZjUzsjgDi0V/LouIiIiImigiIiIiIiKXpGi7jcklg9hkP8aHLV34A15yMhO49s4raavtoOZQI9FxUeQWZ7Dy1QpGTCmm8UA9US4Hfq+f6j11DL9iKOOmj6LmYD193f3YomxMv20S696oIBwO01rXTlJmIuFgCEwGvP0BXn3uXcwWM/3d/RgMBm64cwp9vV6ajrVSfv1o2ps7mfmNq6neW8vejYeIinUy5qphRMU5CAUDfLh4A6FwmEGj8vD7/eQNy+TY3lr6uvrx9HqJT3MxdvooVr68jituGsvONftore/AaDIy5upitq3cid/jJxQI4u33M/qqfHZvOozFZsFkMbP1UAvDrxmBzWomMyeRvnCIjq4O/F0BYqLspCRGk5+dfL6/fSIiIiJygVATRURERERE5BLlctqZMiyfQ3saaOrswdjXg6fdA+EwQZOZNct3snPrUcZcXsi6ZdsZUZpFalYiJosJe5SNfZsPE+1yMv22SYQBq9XMa8+tIBwKY3da8Xn89LT3YLWY6W7sYPY3r2TM5EI6W7tpre8gKtaBI9pG89EWGqqaObTtKPZoO0f31FFYmseVcyaQkBpL47E2mo600NrYzqxvTcXT7+PIrhpyh2VS+d4uPP1err51In5vgMM7jnGw8gh+X4CtK3cxZEwuo64aRvXeWsKhMEkZiYTDYUxmE4NG59F4rBW/1487PxWv2cSRY610dvUTE2snMc1F0GmhzxDEEDSQm5HINVcMxRWjfS1FRERE5Dg1UURERERERC5hZpOJ8SPy+K+l64mx2IlJdOC0WvH0+Bh7TQmePi/OGDtjJg8lJsZKb1c/XW3dZBW66e/2ULWrhuS8VPzeAIZQkK62bgwGI/YoGyaLCbPJhMEIrvgonrv/RQAc0XZGTSnG0+dj3+ZDxCe7GHf9KNa9vpW+nn4S0+LYu+EgI64o4oV//j8Gjcqlt6ObjuZuNr+9nX94YDYmi4n//vEfSclOorG6haodx7DaLQwak8f+zYdoa+zE7rRyxezL2LBsK8217SS74wn4AzhiHEy7bQqrFm+gqbaNvOHZTPjSeDbsrqe5pYuc7GQKB6eSMTiFigP1DC5I5fLRBRTkJGGzWs7zd0xERERELiRqooiIiIiIiFzicjISccfHUt3bS3p/kOojzeSkJTLqiiF8uGw7+3bVEp8YDWYLX/zudaz479UE/EEyB6cz+prhvLd4M6WTCtmwdAtXfbmcjqZuAoEgmYPSSEiLA8KsfHkdjUdbsDmtdLX28OZ/vsfEGy/DZreSXeSms6Wbr/5gNg1HWmipa8Xv8VOzv55gIIh7UArvvXQQv9eP2WKmrb6dYDBIMBiiv7sfR7SdxupW3PkpxMZH4+n1kJgex2XTRrJv8yEGjcylek89Df4WDAYDmdEOag43kZKXSsGYfBypcWzaXUfIYGDMmFyGlmRiiLVQUd1AZnY811xWSEpCzPn+NomIiIjIBUhNlE8hFA7R5Gmjuq+BRk8rnqAXu8lGqj2RbGcaKfYEjAbj+U5TRERERET+zsXFOpgzvZQn//g+u8K9lI5JItmayI6tR8gZnMaqNyo5sP0Yw8pySc1I4AsLr8diMbNu2TbsMU5Kxg/C6rASDsP21Xtx5yWTmJHAykUf4oi2kVuSTWtdG+n5KTQfa8UafXw1h91hpWzqcKr31lG9r57l/7sGR5QNo8mI2WykaNxgJn1xLB++uhmz2UTAF8BqN2OPttHR1EVKViIGo4H0tDiO7q3H6/HT3tRJ4WWDMBgNHNx2hP5eL4FgiK/92y1s/2AvWUPdxKUl4IyxU3u0BXu0nbhUF6kFqdhdDjq9Xlp7+3A5bcyaNJzstHicdut5/g6JiIiIyIVKTZRPqL6/ma3teznSV08oHBowd7Svns3tu8l1pjM6fijpDm1KKCIiIiIi51eeO5Gv3TSOjfuq2X+oGmuik44uL709fq760lhaGzoI+YOYrWYsDhtdrT2k5yTz7qK12KJsDJlVSnpuElEuJy31HcSluoh2ObFYzbiSYjBbzHQ2dZKWm4zVbsHb7yMYDNLe2IXPF2D76j04Yuy0NXRgtpqYedfVdLd303ikCbPZBKEw0XFRJGclYI92YGjtpa6qGQhjtVtIcsfhjLbjiLbh6fUcf5SY1Yyx34fJZKT6cDNGp4P0wRlsXLUXPwaa6zsoHJGFJdpOUn4ipaPysFn0Z7CIiIiInDn99vgJHOmtY0XDOrwh/9+MCYVDHO6tpba/ialp5eRGuT/HDEVERERERE42MiuDXkOQnpgAfkMfJdn5dO7vpafHx56qZtLc8eRlJVBb3Yqnu4+EpGjcBSnExEXR1tjB8ElFrH9jM0nuRKp21jL961PYtHw7VpuV/h4PBgMYjQaajrURHe9g2MShmEwG6o80MfWrV9DV1k3AH2TQiBw2r9hG3aFGxs0YTd7wbI7tqyfa5SQ6MYat7+/CEe1g3IyRHNhyhIAvgKfPR9m0kfR39dLf68XT56WpupX0/BTSCtLYt6OGsVNLePfVCq64bhT+YIi4pGh6e/qwJkQxZKhbDRQREREROWuGcDgcPt9JfJa6urpwuVx0dnYSGxs7YM7j8VBVVUVeXh52u/2Mzlff38wbdatP20D5KJvRwkz3JK1IuUB9kjoQEREREblYhcNh9ra2UFFfQ0N3I5YeI5mWRKL6jNTVtPPh2v2MKMli3OgcDlQeobOxgz3rDxIIBDGZTQwty6PosgKOHWwkJSsJu9XEujcqyB2WSd2hRppq2nBE2ykeP5i+Hg9J7njWvbmFYeVDsFhNJGcksv6NLdQdaqCno5ec4iyKJwxh78ZDJKbHY7KYqNpVg98XYNSUImLinNQfaqK7s4+45Fhi4qPobu3ig1c2kZGfxtgZo4hKcmF1WPH6AiSmxdHZ78VkM2MwhHBkxDIsO524KMf5vvUiIiIicoE4Xd/go7Rhx1kIhUNsbd97Vg0UAG/Iz9b2vSc99uuTevbZZxkxYgSxsbHExsZSXl7OW2+9BYDf7+eBBx5g+PDhREVF4Xa7ue2226irqxuYk9fLvffeS1JSElFRUcyaNYuampoBMe3t7cydOxeXy4XL5WLu3Ll0dHQMiKmuruaGG24gKiqKpKQk5s+fj8/nGxCzY8cOJk+ejMPhICMjg5/85Cd8tHe3atUqSktLsdvt5Ofn89xzz5103YsXL6a4uBibzUZxcTFLliw5KeaZZ56JNENKS0v54IMPzvi+ioiIiIj8PTAYDBQlJXNryShuGTaeScOLcWe78EQb6CbA+Mvyae/oZU3FIWLS48gfk8/U2ydz3demcP2dV1I0bgiBMGAwsPatrRzdX8+WVXtYvmgtsSmxmG0W2po6+cNjr7Nv82E2rdhBel4KbfUd7PxgH0/c9Tz1VU1kDXFz7R1T8PsDNBxupmrnMfZuPMi293dx1S3jue7rkzFbzLTUtWMwGfH2eVm1eAOePi+J2cnc9qObueFb00jKTcHutFLX2E1tr4c/rdrJyopDtMUE6MgwMb4wVw0UEREREfnE1EQ5C02eNo701n+iY4/01dPsaT8neWRmZvLoo4+yefNmNm/ezFVXXcWNN97Irl276OvrY8uWLfzzP/8zW7Zs4c9//jP79+9n1qxZA86xYMEClixZwqJFi1izZg09PT3MnDmTYDAYibn11luprKxk2bJlLFu2jMrKSubOnRuZDwaDXH/99fT29rJmzRoWLVrE4sWLue+++yIxXV1dTJ06FbfbzaZNm3jqqad44okn+OUvfxmJqaqq4rrrruOKK65g69atPPTQQ8yfP5/FixdHYtatW8ecOXOYO3cu27ZtY+7cudxyyy1s2LAhEvPyyy+zYMECfvCDH7B161auuOIKZsyYQXV19Tm57yIiIiIilxKjwUC2K47R6ZmUFwxievkwLhtXgC/WyuXThhGXGceOhna8YVj+5g6Wvradxb9fT3tHH54+H0nueC6fOYbkzATMFjO5RZl88OdNbFu9h8M7jmFzWOjv8bB5+XYsdjNJ7niaa9pwxjioPdBAxbs7WfLrZaRmJ+Hp99Lb1U9Hcyctte3s3nCAQ5VH2fBWJdFx0exYu4/2pi6m3DyerMIMXInRJGcl4g1BTJyTzn4/oVgr6w/V0BP0k1IUT58jxDV5gzAZ9WeviIiIiHxyepzXWTzGaWPrTja27fzEuYxNKGFsYsknPv50EhISePzxx7nzzjtPmtu0aRNjx47l6NGjZGdn09nZSXJyMi+++CJz5swBoK6ujqysLJYuXcq1117Lnj17KC4uZv369YwbNw6A9evXU15ezt69eyksLOStt95i5syZHDt2DLf7+J4vixYt4o477qCpqYnY2FieffZZHnzwQRobG7HZbAA8+uijPPXUU9TU1GAwGHjggQd47bXX2LNnTyTnefPmsW3bNtatWwfAnDlz6Orqiqy4AZg+fTrx8fG89NJLAIwbN44xY8bw7LPPRmKKioqYPXs2jzzyyN+8d3qcl4iIiIjIX+zYW8MHmw7i7feRnZZAssuJv9vHljX7aTjWdrwp4fNy5awxEApACJa+8D7xyS52rt1Lf7cXk8WIOz8Vvz9A3cFGpn71clpr26jeU4vBYKC7vYeAL4gj2sYt99/Ahrcqaa5tIyk9nsFj8ti1fj/X3Ho5Xo+fre/vxp2XTGdbL4TDtLb0MfGG0cSlxhGfEsX+qhZ2HmqiI+DFbwxRNiaXoSMzGJ2RQez/+xtEREREROSv6XFen5FGT+t5Pf5UgsEgixYtore3l/Ly8lPGdHZ2YjAYiIuLA6CiogK/38+0adMiMW63m5KSEtauXQscX/nhcrkiDRSA8ePH43K5BsSUlJREGigA1157LV6vl4qKikjM5MmTIw2UEzF1dXUcOXIkEvPXuZyI2bx5M36//7QxJ3Lx+XxUVFScFDNt2rRIjIiIiIiIfLzhQzOZ95XJlE8uJDYlmsaefsKxFjIHJzNr7kTKrizk2n8o5+j+emLiY7A6rMz+1jSyCtMYcflQYhKiyCnKxNvvx2gwYLFZMFvMOGMdtNZ3EAqFKSzNJz0/heSsRNoaO7E6bBSW5dPT1c87f/iQgDdI07E29mw+wpQvjiNraAbp+akkuBOZcfvlDBmdi6/XQ8BgwpLoZNTYXGbOGMm37riSOdPLmJyfrwaKiIiIiJwT5vOdwMXEE/R+quO9Id/HB52hHTt2UF5ejsfjITo6miVLllBcXHxSnMfj4fvf/z633nprpKPW0NCA1WolPj5+QGxqaioNDQ2RmJSUlJPOl5KSMiAmNTV1wHx8fDxWq3VATG5u7knvc2IuLy/vlOdJTU0lEAjQ0tJCenr634w58T4tLS0Eg8HTxoiIiIiIyJkxGg0Mz0vnbd8BWvwBEm0WskdmYvQEyRiSSsgTIDU3ie3rD5E/zE13Wy/uwekMKx+CyWKm4WgzPZ19hMNhZt89jbqqRuxOG+78FFzJMWAwEJsYQ0xCFGm5KRzYUkVjZy8Wq5nU7CR6u/rwevy4kmPo6Oijs7mbossKaKxpJSM/hYO76/AbYNXOfZQV53BdWSHRTq0qFxEREZFzT02Us2A3fbpPMtmM1nOUCRQWFlJZWUlHRweLFy/m9ttvZ9WqVQMaKX6/ny9/+cuEQiGeeeaZjz1nOBzGYDBEXv/1v89lzIknyJ2LmI+OnUmMiIiIiIh8vCirlRlDh7C9voEttfUU2lz4/CGSohw09XuJG5RKXGIs9UeaSUxzYTGZqD3aytiZYzAbDfR29NFwtIUD249ic9hwxDqYfudVtNS04unxEJMYQ2xSLIFAkMLLCti2ei82h5WoOCcz77oKV2oczU1dNFY1kZgex4GdxxgxYTCH9jXQZg7RZAoyY2Qxl5fkYTGbzvftEhEREZFLlJooZyHVnsjRvk+2sfyJ488Vq9XKoEGDACgrK2PTpk386le/4vnnnweON1BuueUWqqqqeO+99wY81y0tLQ2fz0d7e/uA1ShNTU1MmDAhEtPY2HjS+zY3N0dWe6SlpQ3Y2B2gvb0dv98/IOajK0GampoAPjbGbDaTmJh42pgT50hKSsJkMp02RkREREREzo7DYmFcdhYj0tOobe+gtbuPlsYeuoMB0lwufDYL2XYLe7cexQhUvLcbo9HAkBGZHNtTy+QvlNHe3EXDkWaiYhz0dvWTnJPCurcqCR1pxWwxM2pSIbnDc0jLS8XmsBCX4sIXCNLd7cFhNTFi/CCiE6Lp6/PjTI4mKtqENRjiCncig7NOXj0vIiIiInIuXRR7ojzzzDORTb9LS0v54IMPzkse2c40jIZPdsuMBiM5zvRznNFfhMNhvN7jjxs70UA5cOAA77zzTqQRcUJpaSkWi4UVK1ZExurr69m5c2ekiVJeXk5nZycbN26MxGzYsIHOzs4BMTt37qS+/i+NpeXLl2Oz2SgtLY3ErF69Gp/PNyDG7XZHHvNVXl4+IJcTMWVlZVgsltPGnMjFarVSWlp6UsyKFSsiMSIiIiIi8sk4LBYGpSQzriCHYUPdDMpPpr6rl53HmnAmOhk5voAJ1w1n+q3jsViMdLX2YrZa+P9++io5JZlcNaecmIQorrhpDJn5idx8z9V8+7E5lEwYTHNtB54+L80N7cS74/EHg3TUtRP2+klyu4hOjOLwvkZ6zbCjphmPJ8CwvFQ1UERERETkc3HBr0R5+eWXWbBgAc888wwTJ07k+eefZ8aMGezevZvs7OzPNZcUewK5znQO99ae9bG5znSS7fEfH3gGHnroIWbMmEFWVhbd3d0sWrSI999/n2XLlhEIBPjSl77Eli1beOONNwgGg5HVGQkJCVitVlwuF3feeSf33XcfiYmJJCQkcP/99zN8+HCuueYaAIqKipg+fTp33XVXZHXLN77xDWbOnElhYSFwfNP24uJi5s6dy+OPP05bWxv3338/d911V2Tly6233sq//uu/cscdd/DQQw9x4MABHn74YX70ox9FHrM1b948nn76aRYuXMhdd93FunXr+K//+i9eeumlyDV/5zvfYdKkSfz85z/nxhtv5NVXX+Wdd95hzZo1kZiFCxcyd+5cysrKKC8v57e//S3V1dXMmzfvnNx3ERERERGBrIR4shLiac/sp7Grm/52LwaDgUN76kjLiuOK60ex4Z2d5BWlU3u4gfde3kDB8Eymf3k8f/jJn1nz5+Or2SfeNJYb753Bod31bHh7GwXDswn7AvS29pKWl4zT5SDgC+Hxh4kZnkxVewfZ7njK8rOIj3Kc57sgIiIiIn8vDOETm09coMaNG8eYMWN49tlnI2NFRUXMnj2bRx555GOP7+rqwuVy0dnZOeCRVnB80/WqqqrIKpczUd/fzBt1q/GG/Gd8DTajhZnuSaQ7ks/4mNO58847effdd6mvr8flcjFixAgeeOABpk6dypEjR8jLyzvlcStXrmTKlCnA8Wv/3ve+xx/+8Af6+/u5+uqreeaZZ8jKyorEt7W1MX/+fF577TUAZs2axdNPP01cXFwkprq6mrvvvpv33nsPh8PBrbfeyhNPPIHN9pf9Y3bs2ME999zDxo0biY+PZ968eQOaKACrVq3iu9/9Lrt27cLtdvPAAw+c1Pz405/+xA9/+EMOHz5MQUEBP/vZz/jCF74wIOaZZ57hscceo76+npKSEp588kkmTZp02vv5SepARERERESOa+3q4eDRFvz9fqr21JMSayc1KYa+nn4IGzi6u5ZBY3KoWLqFxb98Y8CxX1hwPdfNm0bFu7uIjrGRmpuMM8aOPwTd3R66nGEOdHWRlxzPMHcq2Ylx5+ciRUREROSScrq+wUdd0E0Un8+H0+nkj3/8IzfddFNk/Dvf+Q6VlZWsWrXqpGO8Xm/ksVZw/GZkZWWdsyYKwJHeOlY0rDujRorNaGFaWjk5Ue4zPr98vtREERERERH59Po8PmqbOmhr7aa/rZ/EKAst1a04nTbSC9L46Zd/SdWO6gHH5AzL5AcvzaeuqgVntB1Xiovmll5cqbEkpMdis5iJttswGS+KJ1GLiIiIyEXibJooF/Rvoi0tLQSDwZM2Bk9NTT1pA/ETHnnkEVwuV+Trr1dWnCu5UW5muieRH5XxN/dIMRqM5EdlMNM9SQ0UERERERG55DntVgZnp1A2Mo/EnAQ6zQbiMpOIcsdjj7WQmHHy442TsxKxRkWRkBaH32Cis9dH1uAUhg7NIMUVg8vpUANFRERERM6rC35PFGDAY5/g+CbqHx074cEHH2ThwoWR1ydWopxr6Y5kUu2JNHvaOdpXT6OnFW/Ih81oJdWeSM7/2wPlk25ELyIiIiIicjEyGY0MH+Smpb2H2qZOenw+TF1ept42hZ2r9+Dp8wFgd1qZ+tXJ1DV24XDZIdpCbmYK8THO83wFIiIiIiJ/cUE3UZKSkjCZTCetOmlqajppdcoJNpttwH4cnyWjwUiqI5FUR+Ln8n4iIiIiIiIXi6T4aJLio4HjH4RzJcXw/f/9Dvs2HwJg8Jg8ci/LpDNkJjcljmiHHq0rIiIiIheeC7qJYrVaKS0tZcWKFQP2RFmxYgU33njjecxMREREREREzpTBYMCdHId79lgmzh47YO7cPzdAREREROTcuaCbKAALFy5k7ty5lJWVUV5ezm9/+1uqq6uZN2/e+U5NREREREREREREREQuYRd8E2XOnDm0trbyk5/8hPr6ekpKSli6dCk5OTnnOzUAPIEG+vxH8AU7CIW9GA02rKY4nJZc7Oa0852eiIiIiIiIiIiIiIh8Qhd8EwXg7rvv5u677z7faQzQ4ztEl3cnnd6dhPGdNG/AistWQqythGhrwXnIUEREREREREREREREPo2LoolyIQmHg7R7KmjsXUaY4N+Ow0eHdwud3m2kRk0n3l6KwWD6HDMVEREREREREREREZFPw3i+E7jYtHsqaOhdetoGyl8LE6ShdyntnorPODMRERERERERERERETmX1EQ5Cz2+QzT2LgPCZ3lkmMbeZfT4Dn0WaYmIiIiIiIiIiIiIyGdATZSz0OXdecYrUD4qTJAu765znNFxjzzyCAaDgQULFvzl/cJhfvzjH+N2u3E4HEyZMoVduwa+v9fr5d577yUpKYmoqChmzZpFTU3NgJj29nbmzp2Ly+XC5XIxd+5cOjo6BsRUV1dzww03EBUVRVJSEvPnz8fnG7hPzI4dO5g8eTIOh4OMjAx+8pOfEA4PbEatWrWK0tJS7HY7+fn5PPfccydd6+LFiykuLsZms1FcXMySJUtOinnmmWfIy8vDbrdTWlrKBx98cCa3UURERERERERERERkADVRzpAn0ECnd+enOkendweeQOM5yui4TZs28dvf/pYRI0YMGH/sscf45S9/ydNPP82mTZtIS0tj6tSpdHd3R2IWLFjAkiVLWLRoEWvWrKGnp4eZM2cSDP6lUXTrrbdSWVnJsmXLWLZsGZWVlcydOzcyHwwGuf766+nt7WXNmjUsWrSIxYsXc99990Viurq6mDp1Km63m02bNvHUU0/xxBNP8Mtf/jISU1VVxXXXXccVV1zB1q1beeihh5g/fz6LFy+OxKxbt445c+Ywd+5ctm3bxty5c7nlllvYsGFDJObll19mwYIF/OAHP2Dr1q1cccUVzJgxg+rq6nNzw0VERERERERERETk74Yh/NHlAJeYrq4uXC4XnZ2dxMbGDpjzeDxUVVVFVi2cTlv/Bhp6l37qfNKiriPBMe5Tnwegp6eHMWPG8Mwzz/DTn/6UUaNG8e///u+Ew2HcbjcLFizggQceAI6vOklNTeXnP/853/zmN+ns7CQ5OZkXX3yROXPmAFBXV0dWVhZLly7l2muvZc+ePRQXF7N+/XrGjTue8/r16ykvL2fv3r0UFhby1ltvMXPmTI4dO4bb7QZg0aJF3HHHHTQ1NREbG8uzzz7Lgw8+SGNjIzabDYBHH32Up556ipqaGgwGAw888ACvvfYae/bsiVzfvHnz2LZtG+vWrQNgzpw5dHV18dZbb0Vipk+fTnx8PC+99BIA48aNY8yYMTz77LORmKKiImbPns0jjzxyyvt4NnUgIiIiIiIiIiIiIhe30/UNPkorUc6QL9h+js7TcU7OA3DPPfdw/fXXc8011wwYr6qqoqGhgWnTpkXGbDYbkydPZu3atQBUVFTg9/sHxLjdbkpKSiIx69atw+VyRRooAOPHj8flcg2IKSkpiTRQAK699lq8Xi8VFRWRmMmTJ0caKCdi6urqOHLkSCTmr3M5EbN582b8fv9pY07k4vP5qKioOClm2rRpkRgRERERERERERERkTOlJsoZCoW95+g8nnNynkWLFrFly5ZTrq5oaGgAIDU1dcB4ampqZK6hoQGr1Up8fPxpY1JSUk46f0pKyoCYj75PfHw8Vqv1tDEnXn9cTCAQoKWl5bQxJ87R0tJCMBg8bYyIiIiIiIiIiIiIyJkyn+8ELhZGg+3jg87oPJ/+cVHHjh3jO9/5DsuXLz/t46cMBsOA1+Fw+KSxj/pozKniz0XMiafInYuYj459kusWEREREREREREREfkorUQ5Q1ZT/McHndF54j71OSoqKmhqaqK0tBSz2YzZbGbVqlX8+te/xmw2n7TK44SmpqbIXFpaGj6fj/b29tPGNDY2nvT+zc3NA2I++j7t7e34/f7TxjQ1NQF8bIzZbCYxMfG0MSfOkZSUhMlkOm2MiIiIiIiIiIiIiMiZUhPlDDktORiwfqpzGLDitOR+6lyuvvpqduzYQWVlZeSrrKyMr3zlK1RWVpKfn09aWhorVqyIHOPz+Vi1ahUTJkwAoLS0FIvFMiCmvr6enTt3RmLKy8vp7Oxk48aNkZgNGzbQ2dk5IGbnzp3U19dHYpYvX47NZqO0tDQSs3r1anw+34AYt9tNbm5uJOavczkRU1ZWhsViOW3MiVysViulpaUnxaxYsSISIyIiIiIiIiIiIiJypvQ4rzNkN6fhspXQ4d3yic/hsg3Hbv70KyJiYmIoKSkZMBYVFUViYmJkfMGCBTz88MMMHjyYwYMH8/DDD+N0Orn11luP5+Jyceedd3LfffeRmJhIQkIC999/P8OHD49sVF9UVMT06dO56667eP755wH4xje+wcyZMyksLASOb9peXFzM3Llzefzxx2lra+P+++/nrrvuIjY2FoBbb72Vf/3Xf+WOO+7goYce4sCBAzz88MP86Ec/ijxma968eTz99NMsXLiQu+66i3Xr1vFf//VfvPTSS5Fr/M53vsOkSZP4+c9/zo033sirr77KO++8w5o1ayIxCxcuZO7cuZSVlVFeXs5vf/tbqqurmTdv3qe+7yIiIiIiIiIiIiLy90VNlLMQayuh07uNMMGzPtaAiVjbsM8gq1P7p3/6J/r7+7n77rtpb29n3LhxLF++nJiYmEjMk08+idls5pZbbqG/v5+rr76a3/3ud5hMpkjM73//e+bPn8+0adMAmDVrFk8//XRk3mQy8eabb3L33XczceJEHA4Ht956K0888UQkxuVysWLFCu655x7KysqIj49n4cKFLFy4MBKTl5fH0qVL+e53v8tvfvMb3G43v/71r/niF78YiZkwYQKLFi3ihz/8If/8z/9MQUEBL7/8MuPGjYvEzJkzh9bWVn7yk59QX19PSUkJS5cuJScn59zeYBERERERERERERG55BnCJ3bvvkR1dXXhcrno7OyMrIw4wePxUFVVRV5e3mk3aP9rbf0baehdCpzNbTOQFnU9CY7LzuIY+bx8kjoQERERERERERERkYvT6foGH6WVKGcp3n58n4/G3mVntCLFgInUqOnE28d81qmJiIiIiIiIiIiIiMg5pCbKWTIYTCQ4xmI1JdLl3UWndwdhfCfHYcVlG06sbRjR1oLzkKmIiIiIiIiIiIiIiHwaaqJ8QtHWAqKtBSQ4xtHnP4Iv2EEo7MFosGM1xeG05J6TTeRFREREREREREREROT8UBPlU7KbU9UsERERERERERERERG5BBnPdwIXgnD4bDaJl0uNvv8iIiIiIiIiIiIicip/100Ui8UCQF9f33nORM6nE9//E/UgIiIiIiIiIiIiIgJ/54/zMplMxMXF0dTUBIDT6cRgMJznrOTzEg6H6evro6mpibi4OEwm0/lOSUREREREREREREQuIH/XTRSAtLQ0gEgjRf7+xMXFRepAREREREREREREROSEv/smisFgID09nZSUFPx+//lORz5nFotFK1BERERERERERERE5JT+7psoJ5hMJv1nuoiIiIiIiIiIiIiIRPxdbywvIiIiIiIiIiIiIiLyt6iJIiIiIiIiIiIiIiIicgpqooiIiIiIiIiIiIiIiJzCJb8nSjgcBqCrq+s8ZyIiIiIiIiIiIiIiIufbiX7Bif7B6VzyTZTu7m4AsrKyznMmIiIiIiIiIiIiIiJyoeju7sblcp02xhA+k1bLRSwUClFXV0dMTAwGg+F8pyN/Z7q6usjKyuLYsWPExsae73REPjHVslwKVMdyKVAdy6VAdSyXAtWxXApUx3IpUB3LJxUOh+nu7sbtdmM0nn7Xk0t+JYrRaCQzM/N8pyF/52JjY/WDXC4JqmW5FKiO5VKgOpZLgepYLgWqY7kUqI7lUqA6lk/i41agnKCN5UVERERERERERERERE5BTRQREREREREREREREZFTUBNF5DNks9n4l3/5F2w22/lOReRTUS3LpUB1LJcC1bFcClTHcilQHculQHUslwLVsXweLvmN5UVERERERERERERERD4JrUQRERERERERERERERE5BTVRRERERERERERERERETkFNFBERERERERERERERkVNQE0VEREREREREREREROQU1EQR+Yx5vV5GjRqFwWCgsrJywFx1dTU33HADUVFRJCUlMX/+fHw+3/lJVOQUZs2aRXZ2Nna7nfT0dObOnUtdXd2AGNWxXOiOHDnCnXfeSV5eHg6Hg4KCAv7lX/7lpDpVLcuF7mc/+xkTJkzA6XQSFxd3yhjVsVzonnnmGfLy8rDb7ZSWlvLBBx+c75RETmv16tXccMMNuN1uDAYDr7zyyoD5cDjMj3/8Y9xuNw6HgylTprBr167zk6zIKTzyyCNcdtllxMTEkJKSwuzZs9m3b9+AGNWxXAyeffZZRowYQWxsLLGxsZSXl/PWW29F5lXH8llSE0XkM/ZP//RPuN3uk8aDwSDXX389vb29rFmzhkWLFrF48WLuu+++85ClyKldeeWV/N///R/79u1j8eLFHDp0iC996UuRedWxXAz27t1LKBTi+eefZ9euXTz55JM899xzPPTQQ5EY1bJcDHw+HzfffDPf+ta3TjmvOpYL3csvv8yCBQv4wQ9+wNatW7niiiuYMWMG1dXV5zs1kb+pt7eXkSNH8vTTT59y/rHHHuOXv/wlTz/9NJs2bSItLY2pU6fS3d39OWcqcmqrVq3innvuYf369axYsYJAIMC0adPo7e2NxKiO5WKQmZnJo48+yubNm9m8eTNXXXUVN954Y6RRojqWz1RYRD4zS5cuDQ8dOjS8a9euMBDeunXrgDmj0Riura2NjL300kthm80W7uzsPA/Ziny8V199NWwwGMI+ny8cDquO5eL12GOPhfPy8iKvVctyMXnhhRfCLpfrpHHVsVzoxo4dG543b96AsaFDh4a///3vn6eMRM4OEF6yZEnkdSgUCqelpYUfffTRyJjH4wm7XK7wc889dx4yFPl4TU1NYSC8atWqcDisOpaLW3x8fPg///M/VcfymdNKFJHPSGNjI3fddRcvvvgiTqfzpPl169ZRUlIyYJXKtddei9frpaKi4vNMVeSMtLW18fvf/54JEyZgsVgA1bFcvDo7O0lISIi8Vi3LpUB1LBcyn89HRUUF06ZNGzA+bdo01q5de56yEvl0qqqqaGhoGFDXNpuNyZMnq67lgtXZ2QkQ+V1YdSwXo2AwyKJFi+jt7aW8vFx1LJ85NVFEPgPhcJg77riDefPmUVZWdsqYhoYGUlNTB4zFx8djtVppaGj4PNIUOSMPPPAAUVFRJCYmUl1dzauvvhqZUx3LxejQoUM89dRTzJs3LzKmWpZLgepYLmQtLS0Eg8GTajQ1NVX1KRetE7WrupaLRTgcZuHChVx++eWUlJQAqmO5uOzYsYPo6GhsNhvz5s1jyZIlFBcXq47lM6cmishZ+PGPf4zBYDjt1+bNm3nqqafo6uriwQcfPO35DAbDSWPhcPiU4yLnypnW8Qnf+9732Lp1K8uXL8dkMnHbbbcRDocj86pjOV/OtpYB6urqmD59OjfffDP/+I//OGBOtSznwyep49NRHcuF7qO1qPqUS4HqWi4W3/72t9m+fTsvvfTSSXOqY7kYFBYWUllZyfr16/nWt77F7bffzu7duyPzqmP5rJjPdwIiF5Nvf/vbfPnLXz5tTG5uLj/96U9Zv349NpttwFxZWRlf+cpX+O///m/S0tLYsGHDgPn29nb8fv9JnXORc+lM6/iEpKQkkpKSGDJkCEVFRWRlZbF+/XrKy8tVx3JenW0t19XVceWVV1JeXs5vf/vbAXGqZTlfzraOT0d1LBeypKQkTCbTSZ8GbWpqUn3KRSstLQ04/kn+9PT0yLjqWi5E9957L6+99hqrV68mMzMzMq46louJ1Wpl0KBBwPH/Y9u0aRO/+tWveOCBBwDVsXx21EQROQsn/jP54/z617/mpz/9aeR1XV0d1157LS+//DLjxo0DoLy8nJ/97GfU19dHfsAvX74cm81GaWnpZ3MBIpx5HZ/KiRUoXq8XUB3L+XU2tVxbW8uVV15JaWkpL7zwAkbjwMW4qmU5Xz7Nz+SPUh3LhcxqtVJaWsqKFSu46aabIuMrVqzgxhtvPI+ZiXxyeXl5pKWlsWLFCkaPHg0c3/9n1apV/PznPz/P2YkcFw6Huffee1myZAnvv/8+eXl5A+ZVx3IxC4fDeL1e1bF85tREEfkMZGdnD3gdHR0NQEFBQeQTH9OmTaO4uJi5c+fy+OOP09bWxv33389dd91FbGzs556zyEdt3LiRjRs3cvnllxMfH8/hw4f50Y9+REFBAeXl5YDqWC4OdXV1TJkyhezsbJ544gmam5sjcyc+eadalotBdXU1bW1tVFdXEwwGqaysBGDQoEFER0erjuWCt3DhQubOnUtZWVlkVWB1dfWAPapELjQ9PT0cPHgw8rqqqorKykoSEhLIzs5mwYIFPPzwwwwePJjBgwfz8MMP43Q6ufXWW89j1iJ/cc899/CHP/yBV199lZiYmMiKQJfLhcPhwGAwqI7lovDQQw8xY8YMsrKy6O7uZtGiRbz//vssW7ZMdSyfvbCIfOaqqqrCQHjr1q0Dxo8ePRq+/vrrww6HI5yQkBD+9re/HfZ4POcnSZGP2L59e/jKK68MJyQkhG02Wzg3Nzc8b968cE1NzYA41bFc6F544YUwcMqvv6Zalgvd7bfffso6XrlyZSRGdSwXut/85jfhnJycsNVqDY8ZMya8atWq852SyGmtXLnylD97b7/99nA4HA6HQqHwv/zLv4TT0tLCNpstPGnSpPCOHTvOb9Iif+Vv/R78wgsvRGJUx3Ix+PrXvx75HSI5OTl89dVXh5cvXx6ZVx3LZ8kQDv/V7sAiIiIiIiIiIiIiIiICgPHjQ0RERERERERERERERP7+qIkiIiIiIiIiIiIiIiJyCmqiiIiIiIiIiIiIiIiInIKaKCIiIiIiIiIiIiIiIqegJoqIiIiIiIiIiIiIiMgpqIkiIiIiIiIiIiIiIiJyCmqiiIiIiIiIiIiIiIiInIKaKCIiIiIiIiIiIiIiIqegJoqIiIiIiMhZ+N3vfkdcXNz5TkNERERERD4HaqKIiIiIiMinduzYMe68807cbjdWq5WcnBy+853v0Nraer5TO2srV67kuuuuIzExEafTSXFxMffddx+1tbXnOzUREREREfmcqYkiIiIiIiKfyuHDhykrK2P//v289NJLHDx4kOeee453332X8vJy2trazneKZ+z555/nmmuuIS0tjcWLF7N7926ee+45Ojs7+cUvfnG+0xMRERERkc+ZmigiIiIiIvKp3HPPPVitVpYvX87kyZPJzs5mxowZvPPOO9TW1vKDH/wgEpubm8u//du/ceuttxIdHY3b7eapp54acL7Ozk6+8Y1vkJKSQmxsLFdddRXbtm2LzP/4xz9m1KhRvPjii+Tm5uJyufjyl79Md3f3p7qOmpoa5s+fz/z58/n//r//jylTppCbm8ukSZP4z//8T370ox/9zWOfffZZCgoKsFqtFBYW8uKLLw6Y//GPf0x2djY2mw232838+fMjcz6fj3/6p38iIyODqKgoxo0bx/vvv/+prkVERERERM4NNVFEREREROQTa2tr4+233+buu+/G4XAMmEtLS+MrX/kKL7/8MuFwODL++OOPM2LECLZs2cKDDz7Id7/7XVasWAFAOBzm+uuvp6GhgaVLl1JRUcGYMWO4+uqrB6xoOXToEK+88gpvvPEGb7zxBqtWreLRRx/9VNfyxz/+MdLQOJW/tQ/KkiVL+M53vsN9993Hzp07+eY3v8nXvvY1Vq5cCcCf/vQnnnzySZ5//nkOHDjAK6+8wvDhwyPHf+1rX+PDDz9k0aJFbN++nZtvvpnp06dz4MCBT3U9IiIiIiLy6ZnPdwIiIiIiInLxOnDgAOFwmKKiolPOFxUV0d7eTnNzMykpKQBMnDiR73//+wAMGTKEDz/8kCeffJKpU6eycuVKduzYQVNTEzabDYAnnniCV155hT/96U984xvfACAUCvG73/2OmJgYAObOncu7777Lz372s091LbGxsaSnp5/VcU888QR33HEHd999NwALFy5k/fr1PPHEE1x55ZVUV1eTlpbGNddcg8ViITs7m7FjxwLHm0EvvfQSNTU1uN1uAO6//36WLVvGCy+8wMMPP/yJr0dERERERD49rUQREREREZHPzIkVKAaDITJWXl4+IKa8vJw9e/YAUFFRQU9PD4mJiURHR0e+qqqqOHToUOSY3NzcSAMFID09naampr+Zx1+fa968eX8z17/O80zt2bOHiRMnDhibOHFi5Jpuvvlm+vv7yc/P56677mLJkiUEAgEAtmzZQjgcZsiQIQNyXLVq1YDrFRERERGR80M9TtWUAAAD4klEQVQrUURERERE5BMbNGgQBoOB3bt3M3v27JPm9+7dS3x8PElJSac9z4nmRSgUIj09/ZR7gvz147QsFstJx4dCob95/srKysi/Y2NjTxkzZMgQOjs7qa+vP+vVKB9tvvx1QyYrK4t9+/axYsUK3nnnHe6++24ef/xxVq1aRSgUwmQyUVFRgclkGnCO6Ojos8pBRERERETOPa1EERERERGRTywxMZGpU6fyzDPP0N/fP2CuoaGB3//+98yZM2dAk2H9+vUD4tavX8/QoUMBGDNmDA0NDZjNZgYNGjTg6+MaMafz1+c58Vixj/rSl76E1WrlscceO+V8R0fHKceLiopYs2bNgLG1a9cOeMSZw+Fg1qxZ/PrXv+b9999n3bp17Nixg9GjRxMMBmlqajrpetPS0j7ZxYqIiIiIyDmjlSgiIiIiIvKpPP3000yYMIFrr72Wn/70p+Tl5bFr1y6+973vkZGRcdI+JR9++CGPPfYYs2fPZsWKFfzxj3/kzTffBOCaa66hvLyc2bNn8/Of/5zCwkLq6upYunQps2fPpqys7DO7jqysLJ588km+/e1v09XVxW233UZubi41NTX8z//8D9HR0fziF7846bjvfe973HLLLYwZM4arr76a119/nT//+c+88847APzud78jGAwybtw4nE4nL774Ig6Hg5ycHBITE/nKV77Cbbfdxi9+8QtGjx5NS0sL7733HsOHD+e66677zK5XREREREQ+nlaiiIiIiIjIpzJ48GA2b95MQUEBc+bMoaCggG984xtceeWVrFu3joSEhAHx9913HxUVFYwePZp/+7d/4xe/+AXXXnstcPyxWEuXLmXSpEl8/etfZ8iQIXz5y1/myJEjpKamfubXcvfdd7N8+XJqa2u56aabGDp0KP/4j/9IbGws999//ymPmT17Nr/61a94/PHHGTZsGM8//zwvvPACU6ZMAY4/huw//uM/mDhxIiNGjODdd9/l9ddfJzExEYAXXniB2267jfvuu4/CwkJmzZrFhg0byMrK+syvV0RERERETs8QPrHTo4iIiIiIyGcsNzeXBQsWsGDBgvOdioiIiIiIyMfSShQREREREREREREREZFTUBNFRERERERERERERETkFPQ4LxERERERERERERERkVPQShQREREREREREREREZFTUBNFRERERERERERERETkFNREEREREREREREREREROQU1UURERERERERERERERE5BTRQREREREREREREREZFTUBNFRERERERERERERETkFNREEREREREREREREREROQU1UURERERERERERERERE7h/wdvHZX1AH8NhgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the effect of price differences on volume\n",
     "plt.figure(figsize = (20,10))\n",
@@ -726,11 +1165,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 129,
    "metadata": {
     "id": "B8zeVZt7C2fT"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the effect of price differences on adjusted close\n",
     "plt.figure(figsize = (20,10))\n",
@@ -750,11 +1200,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 132,
    "metadata": {
     "id": "EpjnJ3bJC2jq"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the relationship between 'Open - Close' and 'Adj Close'\n",
     "plt.figure(figsize = (20,10))\n",
@@ -772,11 +1233,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 135,
    "metadata": {
     "id": "omOa3x81C2uS"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1250x1250 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Create a pair plot of stock features\n",
     "subset = df[['Open', 'Close', 'High', 'Low', 'Volume']]\n",
@@ -794,11 +1266,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 138,
    "metadata": {
     "id": "gNjDWptzC2zP"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Create a 3D scatter plot of 'Open', 'Close', and 'Volume'\n",
     "from mpl_toolkits.mplot3d import Axes3D\n",
@@ -822,11 +1305,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 141,
    "metadata": {
     "id": "Soovqm1IC24P"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Calculate the correlation matrix\n",
     "correlation_matrix = df.corr(numeric_only = True)\n",
@@ -857,7 +1351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -874,9 +1368,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 148,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the closing price over time\n",
     "plt.plot(df['Date'], df['Close'])\n",
@@ -895,9 +1400,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 153,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'candlestick_chart.html'"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Plot the candlestick chart\n",
     "fig = go.Figure(data=[go.Candlestick(x=df['Date'],\n",
@@ -905,7 +1421,7 @@
     "                high=df['High'],\n",
     "                low=df['Low'],\n",
     "                close=df['Close'])])\n",
-    "plot(fig, filename='candlestick_chart')"
+    "plot(fig, filename='candlestick_chart.html')"
    ]
   },
   {
@@ -926,9 +1442,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 157,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the daily volume\n",
     "plt.bar(df['Date'], df['Volume'])\n",
@@ -940,9 +1467,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 159,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the scatter plot of closing price vs volume\n",
     "sns.scatterplot(x=df['Close'], y=df['Volume'])\n",
@@ -954,9 +1492,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 161,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the histogram of closing prices\n",
     "plt.hist(df['Close'], bins=50)\n",
@@ -975,9 +1524,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 164,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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l7ty5vPDCCyQSiV4vb3ZFbW1tyB+LxQBIJpO9LkMIwXnnnce9997LX/7yFyZOnFhU3wac70dDQ0PBZ1hXV4dhGP73BxyhYdmyZb6Ad//995NOp3utS9PTs3l11dfXh9IZhlGQtytuv/124vE4Rx55JM3NzTQ3N7PzzjszZswYZsyY4S8HnnHGGcRiMX9n3CeffMLs2bNDy3He92avvfYq+N48+OCDBYOjkpKSkFI4gG3bHHHEETzyyCNcddVVvPjii7z11lu88cYboWdvamry9cPyyQ9btWqVnza/XW+88YbfrsrKSl555RV23XVXrr32WnbccUcaGxv5+c9/3qslfEX/oXZjKTY7kUiEn//85/z+979nzpw5PaYfNmwYQ4YM4YMPPuhTPYlEguuvv56LLrqIp59+ekObC8CoUaO46KKLuPzyy/n444/Zcccdu0zrdRIrVqwoiFu+fDmaplFdXe3bvemt4uLf//53fvazn3HQQQfx/PPPs+uuu/a6/WPHjmXPPffsMV1vhChvFmrp0qWMHDmyaJohQ4YA8Mc//rHLnULFOpkghx56KI8//jivvPIKtm3zta99jfLychobG5k5cyYvvPACBxxwgN+BDzTnnnsu1113HX/5y1/45S9/2WW62tpa3nzzTaSUofe9evVqTNP03x3A1KlTaWxs5M4772Tq1KnceeedTJ48mR122GGTtNn7rq5atYrhw4f74aZphoSurvj88899HbRRo0YVTfPcc89x1FFHUV1dzfHHH8/dd9/NjTfeyJ133kk8HvcVvCH3vfnnP//pz8R0R7Hv65w5c/jggw+YMWMG55xzjh/u6al5eH+DQYV3j5UrV4b8Q4YMQQjBf//736Lft2DYpEmTeOCBB5BS8uGHHzJjxgx+8YtfkEgk+PGPf9zjMyn6BzWzo+hXinX4kJtKbmxs7LGMpUuXsnbtWurq6vpc//nnn8/222/Pj3/8415tG29ra6O9vb1oXH6bu5oB2HbbbRk+fDj33XcfUko/vKOjg4cfftjfoZVIJDjooIP4xz/+UXSXSD41NTW88MILbL/99hx88MH+SHVzc8QRR6DrOn/+85+7TLPffvtRVVXFJ5984s/U5V/BpYNiHHbYYaxatYo//OEPTJkyxbdXc+ihh/Loo48ye/bsXi1hxWKxPs3SbCjDhw/nRz/6Eccee2yok83n0EMPpb29ncceeywUfvfdd/vxHrquc9ZZZ/HYY4/x3//+l7fffrvL5bEN4cADDwQosHn1z3/+E9M0e8zvKSH/v//3/3jppZdC1zPPPEMkEgkpbp933nksX76cZ555hnvvvZdvfOMbIeXyqVOnYhgG8+bN6/J70xOeAJQvlPz1r38N+UtLS9lzzz157LHHQpsB2tvbeeqpp0JpjznmGKSULFu2rGibJk2aVLQdu+yyC7///e+pqqri3Xff7bHtiv5Dzewo+pWpU6cyYsQIjj32WLbbbjts2+b999/nt7/9LWVlZXz/+98PpU8mk34nblkWCxYs8Je6Lr/88j7Xr+s6N910E9/4xjcACnRU8pk7dy5Tp07lm9/8JgcddBDDhg2jqamJp59+mttuu42vfe1r/rLJuHHjSCQS/P3vf2f77benrKyMxsZGGhsbmT59OmeccQbHHHMMF198Mel0mt/85jc0Nzfz61//2q/vd7/7Hfvvvz+TJ0/mxz/+MePHj2fVqlU88cQT/PWvfy0wSFdeXu7vIDn88MN54oknOPjgg/v8XjaGMWPGcO211/I///M/JJNJTjvtNCorK/nkk09Yu3YtN9xwA2VlZfzxj3/knHPOYf369Zx88snU1dWxZs0aPvjgA9asWdOtsARwyCGHIITg+eefD9k8Ouyww3xhojfCjjfSfvDBBxk7dizxeLxo57QpCH62XXH22Wdzyy23cM4557Bw4UImTZrEq6++yk033cRRRx1V8Eznn38+N998M6effjqJRKLoDq8NZccdd+S0007jt7/9Lbquc8ghh/Dxxx/z29/+lsrKym7NQ5imyd13383222/fpUHDY489lieeeII1a9YwdOhQjjjiCEaMGMEll1zCypUrC3aUjRkzhl/84hf85Cc/Yf78+Rx55JFUV1ezatUq3nrrLUpLS0PfhWJst912jBs3jh//+MdIKampqeHJJ5/0lwKD/OIXv+Doo49m6tSpfP/738eyLH7zm99QVlYWWmbdb7/9uOiiizjvvPN4++23OfDAAyktLWXFihW8+uqrTJo0ie985zs89dRT3HrrrZxwwgmMHTsWKSWPPPIIzc3NHH744d22W9HPDJRmtOKrwYMPPihPP/10OWHCBFlWViYjkYgcNWqUPOuss+Qnn3wSSpu/G0vTNNnY2Ci//vWvy5dffjmUtqfdWPnsu+++EuhxN1ZTU5O88cYb5SGHHCKHDx8uo9GoLC0tlbvuuqu88cYbZWdnZyj9/fffL7fbbjsZiUQKdsQ89thjcvLkyTIej8vS0lJ56KGHytdee62gzk8++USecsopsra2VkajUTlq1Ch57rnnylQq1eWzptNpedJJJ8l4PC6ffvrpLp/H2yX0j3/8o9vn7u7dFdvhJKWUd999t9xrr71kPB6XZWVlcrfddivYmfbKK6/Io48+WtbU1MhIJCKHDx8ujz766B7b47HbbrtJIPTeli1b5u+Ss227x7YuXLhQHnHEEbK8vFwCcvTo0VLKrt/NggULiu6yy6fY51KMYrvB1q1bJ7/97W/LYcOGScMw5OjRo+U111zjf+b5eN/frnbA5X/3umqb98wvvfSSH5ZKpeQPf/hDWVdXJ+PxuJwyZYqcNWuWrKyslD/4wQ+6fK7HHntMAvIPf/hDl2meffbZgl1M1157rQTkyJEjQ7sV88s++OCDZUVFhYzFYnL06NHy5JNPli+88IKf5pxzzpGlpaVF83/yySfy8MMPl+Xl5bK6ulqecsopcvHixQXvSUopH330UTlp0iT/b+/Xv/61vOyyy2R1dXVBuXfccYecPHmyLC0tlYlEQo4bN06effbZ8u2335ZSSvnZZ5/J0047TY4bN04mEglZWVkp9957bzljxowu35Fi8yCkDMyzKxQKheIrz+uvv85+++3H3//+d04//fSBbs5mJZvNsuuuuzJ8+HCef/75gW6OYhOhlrEUCoXiK8zMmTOZNWsWe+yxB4lEgg8++IBf//rXTJgwIWTkcGvlggsu4PDDD2fYsGGsXLmSv/zlL3z66af87//+70A3TbEJUcKOQqFQfIWpqKjg+eef5w9/+ANtbW0MGTKEr3/96/zqV7/qky2nLZW2tjauvPJK1qxZQyQSYffdd+eZZ57ZaPtNisGFWsZSKBQKhUKxVaO2nisUCoVCodiqUcKOQqFQKBSKrRol7CgUCoVCodiqUQrKOGepLF++nPLy8j6fOaRQKBQKhWJgkFLS1tZGY2Njt0YwlbCDc15RV2f8KBQKhUKhGNwsWbKk28OJlbADvkn+JUuWFJygq1AoFAqFYnDS2trKyJEjC47WyUcJO+QOjquoqFDCjkKhUCgUWxg9qaAoBWWFQqFQKBRbNUrYUSgUCoVCsVWjhB2FQqFQKBRbNUpnR6FQKBSKAJZlkc1mB7oZCiASiaDr+kaXo4QdhUKhUChwbLasXLmS5ubmgW6KIkBVVRUNDQ0bZQdPCTsKhUKhUIAv6NTV1VFSUqKMzA4wUko6OztZvXo1AMOGDdvgspSwo1AoFIqvPJZl+YJObW3tQDdH4ZJIJABYvXo1dXV1G7ykpRSUFQqFQvGVx9PRKSkpGeCWKPLxPpON0aNSwo5CoVAoFC5q6WrwsSk+EyXsKBQKhUKh2KpRwo5CoVAoFF8BhBA89thjA92MAUEJOwqFQqFQbAWsXLmSSy+9lLFjxxKLxRg5ciTHHnssL7744kA3bcBRu7EUio2gI23y/pJm9hs/ZKCbolAovsIsXLiQ/fbbj6qqKqZPn87OO+9MNpvlueee47vf/S6fffbZQDdxQFEzOwrFBrJ4XScn/fl1zrtzNu8ubhro5igUiq8wl1xyCUII3nrrLU4++WQmTpzIjjvuyA9/+EPeeOONonk++ugjDjnkEBKJBLW1tVx00UW0t7f78S+//DJ77703paWlVFVVsd9++7Fo0SI//sknn2SPPfYgHo8zduxYbrjhBkzT7Pdn3RDUzI5CsQH894s1fO++92hJZhlSFkNKOdBNUigUmxgpJcmsNSB1JyJ6r3chrV+/nmeffZZf/vKXlJaWFsRXVVUVhHV2dnLkkUcyZcoUZs+ezerVq7nwwgv53ve+x4wZMzBNkxNOOIFvfetb3H///WQyGd566y2/Tc899xxnnnkm//d//8cBBxzAvHnzuOiiiwD4+c9/vuEP3k8oYUeh6ANSSv7ff+fz6399hi1hl5FV/PXMPWiojA900xQKxSYmmbXY4brnBqTuT34xlZJo77roL7/8Eikl2223Xa/L//vf/04ymeTuu+/2BaQ//elPHHvssdx8881EIhFaWlo45phjGDduHADbb7+9n/+Xv/wlP/7xjznnnHMAGDt2LP/zP//DVVddpYQdhWJLJpmxuPrhD3nig+UATNtzBL84fifikY0/pE6hUCg2FG9muS/2aD799FN22WWX0EzQfvvth23bzJ07lwMPPJBzzz2XqVOncvjhh3PYYYcxbdo0/8iGd955h9mzZ/PLX/7Sz29ZFqlUis7OzkFnnFEJOwpFL1iyvpOL73mHT1a0YmiC647dgbOmjFYGyBSKrZhEROeTX0wdsLp7y4QJExBC8Omnn3LCCSf0Ko+UssvfLy/8zjvv5LLLLuPZZ5/lwQcf5Kc//SkzZ85kypQp2LbNDTfcwIknnliQPx4ffDPdSthRKHrgtS/X8r373qWpM8uQsii3nL47k8eqs3MUiq0dIUSvl5IGkpqaGqZOncott9zCZZddVqC309zcXKC3s8MOO3DXXXfR0dHhp3/ttdfQNI2JEyf66XbbbTd22203rrnmGvbZZx/uu+8+pkyZwu67787cuXMZP358vz/fpkDtxlIoukBKyd/+O5+zbn+Tps4sO4+o5Inv7a8EHYVCMei49dZbsSyLvffem4cffpgvvviCTz/9lP/7v/9jn332KUh/xhlnEI/HOeecc5gzZw4vvfQSl156KWeddRb19fUsWLCAa665hlmzZrFo0SKef/55Pv/8c19v57rrruPuu+/m+uuv5+OPP+bTTz/1Z38GI4NfZFUoBoBU1uLHD3/IY+87+jkn7j6cm74xSennKBSKQck222zDu+++yy9/+UuuuOIKVqxYwdChQ9ljjz3485//XJC+pKSE5557ju9///vstddelJSUcNJJJ/G73/3Oj//ss8+46667WLduHcOGDeN73/seF198MQBTp07lqaee4he/+AXTp08nEomw3XbbceGFF27W5+4tQqo9s7S2tlJZWUlLSwsVFRUD3RzFALOsOcnF97zNnGWt6JrgJ0dtz3n7jVH6OQrFVkwqlWLBggVss802g1Ln5KtMd59Nb/vvAV3GGjPG6UDyr+9+97uAs4xw/fXX09jYSCKR4Gtf+xoff/xxqIx0Os2ll17KkCFDKC0t5bjjjmPp0qUD8TiKrYA35q/juD++ypxlrdSURrnngr05f/9tlKCjUCgUWzADKuzMnj2bFStW+NfMmTMBOOWUUwCYPn06v/vd7/jTn/7E7NmzaWho4PDDD6etrc0v4/LLL+fRRx/lgQce4NVXX6W9vZ1jjjkGyxoYQ1CKLRMpJTNeW8AZf3uTdR0Zdmys4Inv7ce+49QxEAqFQrGlM6A6O0OHDg35f/3rXzNu3DgOOuggpJT84Q9/4Cc/+Ym/te2uu+6ivr6e++67j4svvpiWlhZuv/127rnnHg477DAA7r33XkaOHMkLL7zA1KkDs2VQsWWRylr85NE5PPyuMyN4wq6N/OrEnUlElX6OQqFQbA0Mmt1YmUyGe++9l/PPPx8hBAsWLGDlypUcccQRfppYLMZBBx3E66+/DjhGjbLZbChNY2MjO+20k5+mGOl0mtbW1tCl+GqyZH0n0/46i4ffXYom4KdHb8/vT91VCToKhUKxFTFohJ3HHnuM5uZmzj33XMA5qh6gvr4+lK6+vt6PW7lyJdFolOrq6i7TFONXv/oVlZWV/jVy5MhN+CSKLYV/f7aKY/74Kh8ubaGqJMLd50/mwgPGKv0chUKh2MoYNMLO7bffzte//nUaGxtD4fkdT3dWH3ub5pprrqGlpcW/lixZsuENV2xxmJbN9Gc/4/wZb9OSzLLLyCqevuwA9p+g9HMUCoVia2RQ2NlZtGgRL7zwAo888ogf1tDQADizN95ZHACrV6/2Z3saGhrIZDI0NTWFZndWr17Nvvvu22V9sViMWCy2qR9DsQWwui3FZfe/xxvz1wNwzj6jufbo7YkZatlKoVAotlYGxczOnXfeSV1dHUcffbQfts0229DQ0ODv0AJHr+eVV17xBZk99tiDSCQSSrNixQrmzJnTrbCj+Gry5vx1HPN/r/LG/PWURnX+eNpu3HD8TkrQUSgUiq2cAZ/ZsW2bO++8k3POOQfDyDVHCMHll1/OTTfdxIQJE5gwYQI33XQTJSUlnH766QBUVlZywQUXcMUVV1BbW0tNTQ1XXnklkyZN8ndnKRRSSv76n/n85rm5WLZkYn0Zt56xB+Pryga6aQqFQqHYDAy4sPPCCy+wePFizj///IK4q666imQyySWXXEJTUxOTJ0/m+eefp7y83E/z+9//HsMwmDZtGslkkkMPPZQZM2ag62q0roCWZJYrHvqAFz5dBcA3dhvOL7+x0xZxuJ9CoVAoNg0Dvox1xBFHIKUMnbLqIYTg+uuvZ8WKFaRSKV555RV22mmnUJp4PM4f//hH1q1bR2dnJ08++aTaXaUAYM6yFo7543954dNVRHWNm74xid9N20UJOgqFYqvi3HPPRQjBt7/97YK4Sy65BCGEv9M5yOuvv46u6xx55JF9qid4TZkyJZRmsJ5qMODCjkKxqZFSct+biznxz6+zZH2SEdUJHv7Ovpw+eZTaVq5QKLZKRo4cyQMPPEAymfTDUqkU999/P6NGjSqa54477uDSSy/l1VdfZfHixb2q58gjjwydfPDMM8+E4gfrqQZqiKvYqujMmPz00Tk88t4yAA7bvo7fnrIrlSWRAW6ZQqFQ9B+777478+fP55FHHuGMM84A4JFHHmHkyJGMHTu2IH1HRwcPPfQQs2fPZuXKlcyYMYPrrruux3pisZi/WzqfwXyqgZrZUWw1zFvTzgm3vMYj7y1DE/Djr2/HbWftqQQdhUKxYUgJmY6BuaTsc3PPO+887rzzTt9/xx13FNWHBXjwwQfZdttt2XbbbTnzzDO58847kb2o8+WXX6auro6JEyfyrW99i9WrV/txG3qqweZAzewotgqe/nAFV/3zAzoyFkPKYvzp9N2YMrZ2oJulUCi2ZLKdcFNjz+n6g2uXQ7S0T1nOOussrrnmGhYuXIgQgtdee40HHniAl19+uSDt7bffzplnngk4S1Pt7e28+OKL3e5k/vrXv84pp5zC6NGjWbBgAT/72c845JBDeOedd4jFYht8qsHmQAk7ii0ay5ZMf+4z/vrKfAAmb1PDH0/bjbqK+AC3TKFQKDYvQ4YM4eijj+auu+5CSsnRRx/NkCGFluHnzp3LW2+95RvyNQyDU089lTvuuKNbYefUU0/13TvttBN77rkno0eP5umnn/YP7C5Gb04+6G+UsKPYYmnuzHDp/e/x3y/WAnDxgWP50dRtMXS1OqtQKDYBkRJnhmWg6t4Azj//fL73ve8BcMsttxRNc/vtt2OaJsOHD/fDpJREIpGCEwm6Y9iwYYwePZovvvgC2PBTDTYHSthRbJF8trKVi+5+h8XrO0lEdKafvDPH7jJA080KhWLrRIg+LyUNNEceeSSZTAagqEKwaZrcfffd/Pa3vw3p1gCcdNJJ/P3vf/eFpZ5Yt24dS5Ys8Y90Cp5qMG3aNCB3qsH06dM35rE2GiXsKLY4nv5wBVf+4wOSWYsR1QluO2tPdmisGOhmKRQKxYCj6zqffvqp787nqaeeoqmpiQsuuIDKyspQ3Mknn8ztt99eVNhpb2/n+uuv56STTmLYsGEsXLiQa6+9liFDhvCNb3wDGNynGqj5fsUWg2VLbn72M75737sksxb7jx/Ck9/bXwk6CoVCEaCiooKKiuK/i7fffjuHHXZYgaADzszO+++/z7vvvlsQp+s6H330EccffzwTJ07knHPOYeLEicyaNavgVIMTTjiBadOmsd9++1FSUsKTTz454KcaCNmbvWZbOa2trVRWVtLS0tLlF0QxsLR0Zrn0gff4z+drALjowLFcpfRzFArFJiKVSrFgwQK22WYb4nG1wWEw0d1n09v+Wy1jKQY9c1e2cdE9b7NoXSfxiMbNJ+3M8bsO7zmjQqFQKBQoYUcxyHnmI0c/pzPj6Of89aw92LGxcPpVoVAoFIquUMKOYlBi2ZLfPj+XW1+eB8B+42v502m7U10aHeCWKRSDB9uWrO/MUFsaHXA7JgrFYEYJO4pBR0tnlu8/+B4vz3X0c751wDZcfeR2Sj9H8ZWnpTPLe0uaeG9xM+8ubuL9Jc20pUx2H1XFL47fiZ2Gq1lPhaIYSthRDCo+X9XGRXe/zUKln6P4imPZks9XtfmCzXuLm5i3pqNo2ncXN3Psn17ljMmjuPKIbakqUTOgXzWklNhSYloS05ZEdEHUGNgdUIMJJewMEixb0pbK0pLM0po0aUlm/Str2VSVRKhMRKgqiVJdEqEqEaU8bqBpW/7U9eq2FO8uauLthU3c99ZiOjMWw6sc/Rw1UlV8VVjXnub9JZ5g08wHS5rpyFgF6cbUlrDbqGp2H1XFbqOqqSqJMP3ZuTzxwXLufWOxc07ckdtx6p4jt4rfh68yUkos2xFeTMt27xLTtn2hJmvbWJYka8uCgzwTEZ2KhNN3xAztK73UqYSdzcSDsxdz9cMf9Xs95XGD2tIo1aVRakqi1Lju6pKoH14RNzB0gRACTQg0AZoQCPfuhYlAnB+vFaYXBNJodFumLeGL1W28vbDJEXAWNbF4fWfoGfYdV8ufTt+dGqWfo+gjs+at45aXvqQ9bSLBPzla5pxIZM4tnTjHnesonHBZNI2fqovwgryB/kd20R7LhrXt6YLnKY3q7DKyit1HVbObK9wU+7v4v9N247S9R/HzJ+bw+ap2rnnkIx54azG/OH4ndhlZ1f1LUwwapJQks5Y/6M2YNoFvXK/QhUDXBVnTJpm1SGYtVrWmiBkaFYkIFfEIJVH9Kyf4KDs79L+dnaaODLv9z8xNXu7WSnnMIGJoIYFLEBDAtDwBLCRcBQW0LgS2QBm58ovk1+hGIOyuPjdMC6cPPUMXaXoss9tnKPLM3T5D796RlyYk1HYh+PZYZiD9pv6xveeNRdzwxMeY9pb7kza+rozdRjpCze6jq5hQV47eh9mZrGVz96xF/GHm57SlTYSAb+41kh9N3U4NHnpgoOzsSClJZix/Jj9j2QVpdE1gaBqGLjA0QUTXnDBdENE0DNdtaJo/m2daNq0pk9Zklra0GRLmI7pGRTxCRcKgNGagDXLBZ1PY2VHCDpvHqOBfXpnHr//1me+PGhoVcYPyeITyuOFcsQi6Lpy1VxtsKbFlbi3Wlk6YlJDMWjR1ZmjqyNDUme2XNisU/YkoEOhyAltYoOpegLJsybLm5Ea3RxNOp+KVr3vCnCbQhfCFu5w7kMZ3h4XxomUWlOOkGVldQlVJtLhgrRUKtd0JvuvaM9z87Gd0BpbBDt2ujjOmjHI6xPx3qnUj1AbKT2YsPlzazDuLmljVmman4RXsPqqaHRoriBl68c+pyGxv/jMMhlmGzSnsSCnpDAg42YCAowlBedygMhGhJOrMwm+sMOKpSbQmTdpSWaxAt69rwhV8IpTHBqdqhDIquAXQ0pnl6oc/5PlPVlJVEvGl6fKYc/e+ZFFDo5jY6U1hWpZkRWuKJes7WbSuk5bklingjKktYUJ9OZoAyy4U5OyAoCfdMMuNJ5DOsnPxXlorkF+6aS0pfbcTl1eXzK2Lb8ETAlsk3mfmdMcD//JtCbYlB0Vb+oMXP1vNi5+t3qRlzpq/DliwScscKIaX61x/cB2ZRCvCSA1YO2wpfSFoc2DZ0hk4dzqHh1YmIgwpiwHOgAScmW/PFZS7RMAhcGaMBoPgWgwl7PQzT3y4nGc/XglAc2eW5q/4LMzCdZ0sXNfZc0KFQqFQ9Iqf/eASnvjn/Zx85rn87Fe/D8X98toreOieOzju5NP4n9/fGop7/+03Oe+ko5hywMH8+d5/AnQpaGWzWf70mxt59d8zWbp4EeXlFUw+4CC+/+OfU9fgnHpeEY8wrNzgyiuv5P777yeZTHLooYdy6623MmLECL+spqYmLrvsMp544gkAjjvuOP74xz9SVVW1KV9LCCXs9DNHTxrGo+8u5d3FzZu8bF0TjKopYVRNCSNrEpRGi3ycRYRsUSSwmDAucEbepiXJWrZ75dwZ03Gbtk3WlKQtm9WtKVa0bNioaHhVgnF1ZYwfWkZ5PPwsxdtXfATR1bP0Kl0vRyW9bc9GtaWX5RWj2HP0vt7uy0ubFs2dzg9iczJLS2eW5mTG+ZHszNKaMnvXyH4iamgMq4wzaXgl+4yrxdBC49LCDBvwDrpP18uwTfz59tSeYktbu4ysAin5YGlLt/UNLY+x15hq9hxdQ2NVeBlBSmcQ855r92d1W6GidZCoobGTu+xlebuJbEnWcnYcFew+snPhWde9NczCxgwdIcib0Xfn8n3l9WBMMCCsKI+AhsYRPPfEI1z1818RTyRAQiqV5F9PPMyw4SMoxmMP/p3TzruIR+6/hxXLljBs+Eh3diZXtldPqiPF3Dkf8p3Lr2LbHXaiqbmZ6ddfw/fPP537n3kJAEMTXH755Tz55JM88MAD1NbWcsUVV3DMMcfwzjvv+IeBnn766SxdupRnn30WgIsuuoizzjqLJ598ckNeZa9QOjsM7EGgti1pzzhKZK1Jk9ZU1nGnnO3nZTGdUTWljK4tob4i3idlxcFAxrRZvL6DL1d3MG9NO/NWtzNvTTtfrm4vuq3WY2h5jPFDyxhXV8q4oWWMrytj3NAyhlXGB+006ZaElJL2tJkTWlyBxfM7YRk3PCzQpLKFCpR9oTxmUFkSoco1oVBZEqEq4ZlWCIdVJCK0p01WtqRY2eII0ne81vtlk5rSKEfu1MAxOw9j8ja1W9zfT3/wzqImfv7EHOYsay2Iixkau46sYq8xNew5pprdR1dTEY/0uY5kxmLO8hbeX9zMe0uaeH9xM8s3cBC0ufCWseoaRyCMTafMrWuCyniEypJIvykDn3vuuTQ3NzN//nyuvvpqzjjjDADuu+8+fv3rXzN27FiqqqqYMWOGn6ejo4Nhw4Yxe/Zsfv7zn7PDDjtw3XXX9VhXR9pkTVua1lSWOe+/yxnHHsp/3/2EXbcfj5nqoK6ujnvuuYdTTz0VgOXLlzNy5EieeeYZpk6dyqeffsoOO+zAG2+8weTJkwF444032Gefffjss8/YdtttC+pUOjtbAZqnHBaPQPVAt2bTEzU0xteVM76uPBQupWRVa5ovXeHHE4DmrWlnVWuaNW3O5egE5CiJ6owbWsa4oaW+ADS+rozRtaVEjc1gYVlKSDZB20poXwltq5z7bmdB6ZD+r38DaU+bPPreMv759hKWNiVpTmaxNmJ4rAmoKok6QoormFSVRKkMCi1FhJmKRITIBlrCTmUtfvzwh77/zCmj+NkxO9CSzLKqJc3K1hQrW5KsbE2xtCnJf79Yy/qODPe9uZj73lzM0PIYR+3UwNE7N7Ln6OpBqYi5OdhjdDWPf3d/7n9rMQ+9vYSGirgv3OzYWLlJ/o4SUZ29xtSw15gaP2xVa4r3XOHnsxVt2FKGFLt1T5HZUwgPKod74QHlb11zLuEqe+uuErfuliW8NK4StJc+lz9cVlRY1IpWGivjROMJBM7vVNp2hDTh/hvSWQnqtIjcXJ1w/xHgK7FDlpTZezWGhJHo88DuvPPO48477/SFnTvuuIPzzz+fl19+uSDtgw8+yLbbbsu2227LmWeeyaWXXsrPfvazonVKKWlLOUJORyY3cysznQgh2HnccMriEf79+rtks1mOOOIIP01jYyM77bQTr7/+OlOnTmXWrFlUVlb6gg7AlClTqKys5PXXXy8q7GwKlLCjGBCEEDRUxmmojLP/hLCQ0JbKMm9NB/NWt/NlYDZo0bpOOjMWHy1r4aNl4Wl3b0lvXJHZoMpEL0amtgUda8MCjH93r/ZVzmVlCvOP2mdQCjtfrm7jnlmLePjdZbSnC5eXoobmG6nMCS05A5bB2RYvvLIkQll08+7aWNWa4qK73+aDpS3omuD643bkrCmjAagr16krjzOJsAFK07J5fd46nvpwOc/OWcmatjR3zVrEXbMW0VAR56hJwzhml2HsNrLqKzdbqGuCM6eM5kz3HW4O6iviHLlTA0fu1LDZ6uwLzuxBB+WJKHF3Nqsz28n+9+03IO158/Q3KYmU9CnPWWedxTXXXMPChQsRQvDaa6/xwAMPFBV2br/9ds4880wAjjzySNrb23nxxRc57LDD/DRSSpqTWda0pUllnZl4IQTVJRHKDcn5N13P6aef7s+orFy5kmg0SnV1eOReX1/PypUr/TR1dXUF7amrq/PT9AdK2FEMOsrjEXYdWcWuecbQspbNonWdoVkgTyhqT5ssWNvBgrUdvPApgKSUFLWilfElSbavSDO+NMWoeAf1ejs1tJDINiE61kLHGkfQkV0vqxWQqIayBiivd+6x8p7zbCZMy+aFT1dx96xFvD4vNzM2dkgpZ04Zzb7ja33hJR4Z/ObkP1jSzEX3vM2q1jRVJRFuPWN39h3Xs2Bp6BoHThzKgROHcuMJk3jty7U8+eFyZn68ipWtznLYHa8tYHhVgqN3HsYxOw9j0vDKXgs+UkpSWZsmb7mvM0NzMkvatBybKJrA0J27ZxMlaCsl363ruVmILQVDE9S6O3cUA8+QIUM4+uijueuuu5BScvTRRzNkSOHfyty5c3nrrbd45JFHADAMg1NPPZU77riDww47DNvdobWmLe3b/dGEoLYs6uzUsi1OOeUUbNvm1ltvLSg/Hyll6O+qq9mj/hx0KGFHMfiREjIdRJJNjLfWMz6+nqlD10PpeqhvRnauI9W6lmTLauz2NRjJdZRkm4jizsBYQJN7dYONoNOopiM6hFR8KNnEUMzSeqzSerTyBrSKBqJVjUSrhlFWUkppTB9Uh5OuaUvzwFuLue+txb6SuCbg0O3rOXuf0ew3bsgWt3Tz+PvL+NE/PyRj2kyoK+Nv5+zJ6NrSPpcTNTQO3q6Og7erI5W1+M/na3jqwxW88OkqljUnue0/87ntP/MZXVvC0ZOGsfOISlqSWZrcHZSe/pIv2CQd+1YZc+P0l7YGDpw4lN+cvDP1FZvPEN/mImEkePP0Nwes7g3h/PPP53vf+x4At9xyS9E0t99+O6ZpMnx47txBKSWRSISPFy5HRMswbee7bWgaQ8oca/yGrpHNZpk2bRoLFizg3//+d0hPpqGhgUwmQ1NTU2h2Z/Xq1ey7775+mlWrVhW0ac2aNdTX12/QM/cGJewo+h8pwUxDuhXSbZBqce6+v9V1tzpxySbobILkeuhc79yLLR25CCDhXgVVR0rIxGroMKpZTyUrrTKWpkuZ35lgtV3BOipYJytYKytZRwUW3c10pID57uVQETdorErQWJVgWGXcdccZVplgeFWC+op4v+oSSSl5d3ETd89axDMfrSBrOXo4NaVRTt1rJGdMHsWI6r5NhQ8UGdPm7UXreXnuGp54fzkrW3MKrTsNr+D+b02hfAOUZfOJR3SO2LGBI3ZsIJmxeHnuap76cAUvfraKRes6ufXleX0qz9CEo79UEqG6JELM0DEDO4ycu+P3dx+55xrl7zqyt7BtRqYt+c/na5j6h/9w0zcmcdSkYQPdpE2KEKLPS0kDzZFHHkkm4/xeTp061bcvlrVs1rWn6UxnuPOuu/jxDTcxZf+vhSyOX3HxOdx7772cdu5FRHWNIeUxakqi/iDJE3S++OILXnrpJWpra0N177HHHkQiEWbOnMm0adMAWLFiBXPmzGH69OkA7LPPPrS0tPDWW2+x9957A/Dmm2/S0tLiC0T9gRJ2tjSkBCsLtgl2Fiwz57ZN1++5s44uip0N5DEDcd253bxWNq/sXtRrZnLCiyfM2JvAvpAWgZIaSNS492rn8sNqoXSoew2B0iGIaCkxIAbUAOPdorKWzeL1ncxf00FzZ4aOtElHxnLuaZP2tEVnxqTd9XdmLN/dkbH8EX1ryqR1ZRufrWwr2mQhYGhZjGFVCYa7QlBjVYLGyjg1pVFKY4Z76ZRGjV6fWZPMWDz+/jLunrWIT1bkdtXsOrKKs/cZzVGThg3qJaq2VJalTUneWdTEvW8s6vL9ecxZ1srZd7zFkTs2MHXHBsYM6fvsTjESUZ2vTxrG1ycNoyNt8uJnq3nmwxWsaktR7SpgBwWZnNvRZ6oujVL6FTxnyOPL1e1c/uB7zFnWyiV/f5eTdh/B9cftsEmEUkXXOIJz7mDQtGmTNi1WtCQxLcmzr76NaUs+X91B1rJpT5uIlMmy5iT/fvZpWpqbOebk0ymvcPTcdE0QNTSOOf4Ennzo71z9w8upSERCO8dM0+Tkk0/m3Xff5amnnsKyLF/Hpqamhmg0SmVlJRdccAFXXHEFtbW11NTUcOWVVzJp0iRfF2j77bfnyCOP5Fvf+hZ//etfAWfr+THHHNNvysmghJ3NR7oNlr3jzmIEZjVCsxvBcNedamWrsugaLYd4haPjEnPvIX9FTpDxhBjPHS3rvSGSHojomrurq2yD8mctm460ydr2NMuaU6xoTrK8OcnylhTLm5OsaEmxrDlJxrRZ3ZZmdVuaD5b0XK4QUBLRKY0ZlMUMSlwhyBOKymKObZJn56z07dnEDI3jdmnk7H3GMGnE4DklPpW1eH9JM5+taGVJU5KlTY71754EmyA7DKsgEdV5Z5FzEvh7i5v51b8+Y7uGcl/Zddv68k0ibJTGDI7bpZHjdmnc6LK+KoyvK+OR7+zH/774OX9+eR4Pv7uUNxes4/en7hraiaUIHwLrObwQyyZwknngRPO8k8672kHZmTZJZ23WeDaOIgl0CB1D4fH4g/ey34FfY1TDUCK6RsRwdMUkcPJJJ3PL7/8/3nvvXXbedbeQvZ/FCxf6RgB33XXXUJn3PvIMe+6zP7aUXHjl9TSnLE465RQyqRSHHnooM2bM8G3sAPz973/nsssu83dtHXfccfzpT3/q6yvtE8rODpvBzk66HX41vOd0Xxmcjkn6HZS7qTPPHxZs8sN68neXpqv4PtTTizRSCPdoC3ckFnCb/rEV7tEV4P74uXkD9finaefFGXruFGPHfkzv21b8eXr7DoqnMW1JhzsD1pZyZsbsXjyHR21ZnNrSKLGITszQQzZx0pZkfUeGdR2ODSBb5spKRDRqy+MMKYtSHovkvkeb5LvSnYXATfdd6W0eiXTem9tROpdznh7S+c5J93vUXVrnDiCdz8hN43WNUuKGu2khlM87tT1Y7oqWJJ+saHfyA4mIzh6jq53nkE7ZwbzOd0MipciLc9oU9Js27lIfNHdmWOV26lI67ybhzq6Z7tIhwNDyOKUxI1Af7vEwgXqk8OONkgrG7zGVEcOHYRi5eYD8DjJ0ij1F3AFDfL3vXAv/TnqieDrRYyFdR3U9YMjPoyHRhI2GRMdGc785q2UVZkAVoDIR2SAdu3yUnZ0tBc2AiuHQumygWzJIcP50RL6cvZWJ3QLQ3avHhMF7b5FAp3sNAgyg0r0A6KuqUjfPEgOGuVdBuTbQ4l5bOXki7aBiHLB/sEeRwMJ+qiy/5woeaeb9wfXxbyMlRrJA7ke1bCYuN+ItD4YPaIDaUEqKL6QzsNeFGFRK60rY2RxE4vDDTzZNWZ6AIPPGDvn+3qTZgDxp02Jte5o1rWnfgNuq1hSr3fuq1hRr24JGuHDHhXQRJouGCySNVXH2HF3DHqOrqC2NBg4GtcE/1DN30KeUtn9AqOfGy2PbLFmf5JPlzXy6si1UTz6imzZ19Rz5ediAPL1rU1d5AmlF8XK6bmvP5bORz1H4Xja8TfnvtlfP0cU76U37uyu/57b2/t32JU+xv63evtvQ5yI2wbtFomGjBy4DKxcmvHDLjXNmAgwsDCwiwiSKSSR4CYsY2VBYVPTBNIRiQEhU1rFzWdVAN6MoStgZYJo7M9z2n/msa8+ETv/OmDZPf7RioJu3AUTda+NZ2QzvNqfhg8JtihtOmXttxWxlM2RbGho2MTLEyDqXcO9emO93wwL+KKYvJGgiJzwEhQktdJfoorv4PLcoFi+L5ylSf/Ey1Reuv/DHtkDxpa5C8T1/CdxZhhTO0mDgsntwW2jYUsNGw0K497A/SH1FnPqywTOTk48SdgaYl+au7vNWV4Xiq0rIPL8fJkJhQkh0K028QLgI+8t1k6jIEvcFjQxxskTz05MlGvAH46OBvF66CGoGIogtRaCTdC6haVjuXI8pDEwMMugkTY0MBlkMTHQyMueOx+NILYIpnDmhrIhgo5MVBpaIkJU6nbZGp6nTbkJ7VnPz62QDZWa9OSYZ7txroiWcJasQdh26HcH7RvUkWBi6IKrr/nezcE4s55d4KkzF470yQzG9WJIKCkWeGm6+0n6xYrKWTTK78d9XXRNUlQzuHXhK2Blgjp7UyKcr2njgrcUDflp0bymPG0R1DV0TRNy7ZyXWCzP03Jk23rk0nlu45940dWb5YEnzQD/ORnPxQWPZfVQ1Ufe0YO+8HS3wrJoW9LthQrCyJcWLn63mhU9X5XZSBDh0uzqm7tTAPmNriRpa+AfL7/jdzr6IrnG+IBBMFzp1O09PVkrcLfgmbakslg0T6srcNti+qQJhW2BbCOmaLZAWwouTphNnWyG/k881XSCtQDlmIM5CSNf0gZmETCdkOiDb4biznr/TjWvPubOdEBkksw2aAUYcjFjv7noEhA6annfXegyXQmNVu8mXazr5fE0n89amyNhgydyIXAqd2vIE9VUl1FeVMqyqlMbqMhqqSohHo3nla0Xq63u4JgRfrmrj+w+8z6eumYRT9xzJdcfuQGXM4PNVbdz7xiIeeXcZ7ZncDsNjd2nkzCmj2WVEJfPWtPPqF2uZs7yVtlSW9rRJe8qkzb17JiE2hmG2TgaDtIwgKLQKHTU04oYespvlKDtL3+04CsOCQoiUXhonprAM201PICx3GrqvnhTIt6mJR3TK3R2ghp47Y8w7f2xzsyn2UQ34bqxly5Zx9dVX869//YtkMsnEiRO5/fbb2WOPPQDnIW+44QZuu+02mpqamDx5Mrfccgs77rijX0Y6nebKK6/k/vvvJ5lMcuihh3LrrbcyYkTxY+3zGchTzz06M85BjXe9vpDPV7UPSBsUgxOBTYwsCdIkyJAQaeJkiZOmXM9QKjKUijRlJCkVKcpEkhLSlJKkTKQokUlKSVFKkgQp4mSIuLMQUbIYX/GZiE4ZI0kMESuFSCmmnsA0Eph6CZae8P2WUYKll2C5btNIYBuO3zZKsY0EVqQE2yhB6nHQo6AbvoArcGUThC8U+3dygnHuDo4UKl19NXL6aW6vt7otzX++WMNrX65lVWuhsAyOkLr/hCHs2FjJNkNKiRk5oTxqaAwpi4Xr7bJ9hbMFfSFtWvzu+c+57b/zkRJG15ZQXx7nrYXr/TTbDCnljMmjOHDiUD5e3sKrX6zjtS/XhgxM9kREF5TFDMriBmWxiNtp6+iaRns6S2vSpM27p3I7+xKG4LdT62hsqEdLDJ7jXwYSgSAe0UhEdUqiBomoTtzQNrvAs27dOlavXs3EiRNDW9ih9/33gAo7TU1N7Lbbbhx88MF85zvfoa6ujnnz5jFmzBjGjRsHwM0338wvf/lLZsyYwcSJE7nxxhv5z3/+w9y5cykvd76Q3/nOd3jyySeZMWMGtbW1XHHFFaxfv5533nmn4MUUYzAIOx5SSmbNW8fpfytuovyACUMoiepkLUevJ2PZZC2bjOncTUtSVRKhrjxOXUWMoWUxhpTHnG28nj6QZfPxslZmL1rP/DUdm6rlRPyJaTtwlzm3sPz1/uC6v+HGu2NPdGG7So/OtkZBcIuj9NNpgTzBrZCeLkGvyhB5ZdB9GZ5+gyac50qQoYQUpSLlChgpSkWSUtLExCYwpKjYIrECSzem+62z8i4bDUuG451vH/7SirM9W4T8Ib0LGUy3YRRT2u4mMcG5Qg3pOgPzhH6awIxi4ERwzyBnn+otgo3AxMDWDIRmgB5FMyJIzcAWBpZ72cL9hRFOWj9MRLCFQdISdJgaHVnBiFFjmThuHFXVtehGxNnK77ZUktsLlwvL6cR4T+TPpnqCI/4rcnzu+xGBOxAy4CdELl9IyAwIoyJQR/C9i8D7T5s2rUnTP/ohSDyiUxGPENGd1LaUpEybdNYiZdpdWvMWQhA3NOIR3RWEDCL9dGyOlJLOzk5Wr15NVVUVw4YVWujeIoSdH//4x7z22mv897//LRovpaSxsZHLL7+cq6++GnBmcerr67n55pu5+OKLaWlpYejQodxzzz2ceuqpACxfvpyRI0fyzDPPMHXq1B7bMZiEnSDLmpPcO2shD781n1SygxgZKgyTo7er5oRJtYyt0p0p/mwqcHcvK5OzauxZQQ75nWWH/8xdQUcy5eyKwMJwd0L4OyXcMN8diHPCnDhDqDOCtkbSRMhgkCFKRjo6FCY6pivCOvoPTked+9HP4dvUkWH9h5ytnXx/F/kD6YuFhfN63XFYsPaUc4M7hbydQUHB2xFkwzuINDet546onUFbJRLBygmn0zz6687M3AaW4dyhcA9boQJxvm5QOL53aYvFhevMj/f+LsPpwmWKgr+3YJgXrgmoq4hhaP13LE5VVRUNDQ1FZ5S2CGFnhx12YOrUqSxdupRXXnmF4cOHc8kll/Ctb30LgPnz5zNu3DjeffdddtttNz/f8ccfT1VVFXfddRf//ve/OfTQQ1m/fn3o4LFddtmFE044gRtuuKGg3nQ6TTqdm/JtbW1l5MiR/SvstC6HxbMcC8KxMig4byXwMUjp6CDcdWz/tEWhUCgUXWLpCbLx2t5pByty7Pt92P2sTVpkJBLpdoVmizAqOH/+fP785z/zwx/+kGuvvZa33nqLyy67jFgsxtlnn+2fu5F/Emp9fT2LFi0CYOXKlUSj0ZCg46Xx8ufzq1/9qqgQ1G+0rYTfbb/56lMoFArFBqNbSfSOpQPdjC2PL5+Gfb810K0oyoAKO7Zts+eee3LTTTcBsNtuu/Hxxx/z5z//mbPPPttPlz91JaXsUUGquzTXXHMNP/zhD32/N7PTb8QqIF4Fqeb+q0Ph7P4QgalUIXJbGrr9vjhKoDn3htZfWJ8EbPe0a9s/G8dV/CRnMh8Cuzl6iUT4E9CFW1mDjyzQhARfr0D6yq6bsr6u0vUmT/Fycp9G9y0V6JpjsRUhHOsfAT0HgfNus+6ZQ72tL7hzzt+F4h6ZAN4J5oC0iz6fs0vR2ZloaKG9b0gga4Pp6tzlq0doAndXo0ZEE6HPsrvvaldPJ3EUnL2T11NZq1caMxv7+fWdDf9b7Gv7nCqEswArnL+NnC5MsXcr3R1QgTZKZ6lTSAtDOjqLBtZX0/bQft+Hr1070K3okgEVdoYNG8YOO+wQCtt+++15+OGHAWhoaACc2ZugYtLq1av92Z6GhgYymQxNTU2h2Z3Vq1d3eVx8LBYjFivcWtgvNC2CP+0FVvGdEoq+IwELMIXw71nAFJAVkoyAjBBkBGSFRgpBu4jRIQySROgQEZKaTlIYpIRBSuikhU5a08iikdXAEtK5cO/CzoUJiUnObwqbs1u3Y4RV7ioW5rbYe52vENCasljeksz7Uc5fk3fdvo6Ls92zujRCPOJs+Td0DdN2FNTT7pWxbFJZ59DAwnIL6/DCKhIRastiDC2LMqQs5p8xFTf0gLQksKSkM2OxriPLpytambO8hc6M3WW5PdWb7/bQNUFtaRxL2qxpzwbSOlSXRplYX86EunLGDS0lHnF/woRz5lNb2mRFS5qVLSlWtCRZ0ZpmVWsay7YL9IcK3rn/Tgy2GVpO3NCIGgbxiEbM0IhGdGKuYmbUCLsNTbCiJc3nq9r4bGUri9enQnVEdY0J9eWMqilhwboOPl/V4X9WEohoGhPqy9ihsZKdhlfSUBkPKJ4Wfl+cz8OmPZ11ziNLWbSmsrSmTNpSpuNOumHJLG1py7E23tX3rcjnIoGakijrOzM9fm6h+CLvV2gaVQnDObsq69goOmCbCvYfW4ltZshmUljZDGYmjZlNs7a5jVXNrbR3Jn3ryr6VZWG6+lMWBjYx3Ua4pgoMkQsP362c3pUIhEtH2ywizYAxxQ0UVAZoxctGIy1ipInSKaN02FFSREgSIyWjJImRJEpSxkgRJeW6kzhxaSIkZQwLjSwGy6KjSSTKiJeUUVJaSkVJKftPHMqxuzT2myLy5mBAhZ399tuPuXPnhsI+//xzRo8eDcA222xDQ0MDM2fO9HV2MpkMr7zyCjfffDMAe+yxB5FIhJkzZzJt2jQAVqxYwZw5c5g+ffpmfJoumPdvsNLYQLsmMBGYQmAKXDdYhP1O5y2wXLeNe4iedwVnDQLhCOdHxwuDwMF+wptFyMvn/oEW5BOB+sjtPMiFh8PC5QqyIiyMhJ8N/x2En93NF0ibnz8rnHQbjw1k3Gvj2Sv5AnumeiHQbuhfXMq9uqNXB3HlYQLN7tVD0eXuNRo4EqC/bIh5z1ms/AywxL3yEECFe20bjOjre7GA4ivg3VIJbA8cD8WNiK91Lyj+PVjnXh/1XFfw8+gVG/pZmWwqg+hOWZD7LBa7V3f09u9ly+2DNxoNm4RMkiBJlROwUdxnHsK1TRdCk4l36Nwj7y/ndzM/59sHjePkPUYQj/T1h2bgGVBh5wc/+AH77rsvN910E9OmTeOtt97itttu47bbbgOckfHll1/OTTfdxIQJE5gwYQI33XQTJSUlnH766QBUVlZywQUXcMUVV1BbW0tNTQ1XXnklkyZN4rDDDhvIx3PY9XRSrcvZd8kDqI3IWy/nN9SRCCwLdSWOdRnex8Fkd9t2+1x3H8PDKYS3N9aZiXCXA3pbvj/jE7Ad4y0neXZeAPcMNDedLDSlFnx/bnOcJSjcbbx5jeju2Ww7Z8DNbZW/FObhz8l087n1tHBagMyPz+2byc3z5e7eoiTg74Xrjg35nLsqs6/Djb5+7zf9d9VBAlaRgZwX56cLpfHic5+KF09BmsJ0xdME0on88GJ5RKhOKMwjN8kgEOBLyvlxQWgLcPNc5+qKn035GdO2nbaJ2rFpGXCjgk899RTXXHMNX3zxBdtssw0//OEP/d1YgG9U8K9//WvIqOBOO+3kp0mlUvzoRz/ivvvuCxkV7K0eTn9vPe/MdjLlvin0l7VLhUKhUCgGmvGZDI8uC0yLnvUojDukX+vcIraeDxY2h52drJ1lfXI9hmZgaAaa0JBS0vL5f6l89IyC9H0ft3evnNif4V01qetyimfoc719TC/73M6+tmfTPNeman9XdJd+U302m+674ixrWt4SJt4SaHA5NBeevwzquxHuEmjeUiq5pVF/yTSQvlh5BUu0anuyYgtHlxJdgoHEkBLDCwPH78bpeXdDgu7eS22bK9Y302Dl2aD69qvQMKnf2r5FbD3/KhHRItSX5rbQ/+TRj3ji/eW0pWEn8T88FftpL0v6ysumgwanIwbL7/gcHSQrTxfJCty9eK+ztNwO1wp2nnl+q4s6zLyyuiqz0J+bvg/qbtkErcI6ulienpcdCPOm4G1y0/merlY4jRcn8tK5+UUwTS7Mq8/+iggRwu1cDLdz0aREw1G9ENJbonL8mnSX+JB+vOcXbrxGII0f76aR5MoOxOfKD9Tltk0Lxbv5irQvXL/ssr1eWwAMN7/TaTr1ex2tRq5j1QLvRy+4Ox1ul3cvjfd+3bj8Bdfg0qD3XsPhhXkUveCx7zgCzwCjhJ0BQErJy3PX0JZ2NPY+lmO41zyUM40XN1kdNhR0kk7n2HWn6fmzeYrB+aPq/BFwcNRrFVFWznWm4XXyYp0igXS+cnVex+iXLUTYHwgLK3QXdtrBdXdPMCn2PooKMO47+Kp0xoONoHBgSIh4o9Eio82IlH6YEcqXc0dwOsJc/pw7kp+vaFnhEbEh3Xx5ZekyV1+wrC1P1VOh6AMrP4JX/wD7Xz6gzVDLWPTvMlZzqpkfvPwD3l719iYtV6HIJ3+GwOvA9YAA4I2S82cCIDwTEJwtyJ8N8PJBeAZA4HTcIuD3ZgW8PKFZAcIzB+G6crMFep7w4c0KQO9G2b1R/t6UYmuv2rSJ0jjpNs1PeG+V5Dfn823az6XnB+z1O99E36m+Pl93g7CuBq/ewMzCGex5O3ytImFVts2P1jcxxOqH43+uXQHR/JMDNh61jDVIeH7R80rQUWwWpKuLklUzTgqFYgNp0TT+smpNn/N16o4hBGdAJIgaGpr3U3T8Lf0i6PQFJez0M1PHTOXFxS/y+vLXQ+ECga7pWLaldmkpFAqFYlDwWkmCSduM2mTldSz8Dje27sLpm6zEDUMtYzGwp54//uXj/PS13ionKxQKhUKx5WClhiGW/ZAPr5+Krm36Wefe9t9fYbuTA4CUYKbBzLj3NPvV7cmI0uED3TKFQqFQKDY5erSZn59c2S+CTl9QMztsppkdKeGeE2D+y90m+/HQWp4uK+2fNigUCoVCMQC8dcZbJIzEJi9XzewMRrI9HW4E+yWTm6EhCoVCoVBsHkZHDu0XQacvKAXlzYUQcN6/oGkBZJO8vfp9Lvng92SlhS1tbKWkrFAoFIpBipVqxOocDSJgMS3gdvbjO3ercxuyTXvjWZGaAyRPtkhEB86qlBJ2NieaBrXjkFLyg5e/Q9LeNCduKxQKhULRn+jx5ejx5b1KG6n4EGlHMVv2AOCpS/cfUEEHlLAzIAghuHbytVz1n6s2S33SP1TJNdMmPXd+WLFwzxSXCMeF0ueHa4E4/Pq8k5lzpuolmvDM4+cu3Xfbjin50GW7d8s1nmehS9vJk2fqXpNhU/dBQ2BdnTNV9P0BthSY6JjoWGju3fFruoFmREjEYpTEY6AbIHTXmrPAkjaWtLClhSltbGm5YbYfZyKxpXTu2FhSYiFzdz/eLghXc4IKhWKwYXWOAWCXEZXsNLxyYBuDUlAGBnbreQgpwcpCthPMlHPPpsBMQjYZdltZsE3nkrbvti2TT1es59k5y7BllsbKKOOHxvC7RmkipQXSwnbdTvdpI6WJLR23c7ewbRsLCyltbGx3yc3GwnbzSmzp+C1sbOl1wF6Hbbu+3CGT+Uc/5B8fAXlHSxSkz4V76fw8QiA1A6npSKEjNQNb00HLuZ1wHSm0wrt72e7xGM6xG5KstElmsiRNk7SZJWOaZGwTKW0QtjudayOERNMkCOcdKrZOHIFaoCHQhXDsZgkNTWjoaAgh0IWOpunuob8arSmbzoxEoiGEQX1FCVUlMdJZSXvKpjVp0pGxQboiutQwdJ2akhi1ZVFqS2PEDB3hHqsivP9E7g6Ew4JpA2FZS9KazNKaMmlNmrQkTda0p91RQHDA494LBkhdhTvu8ACrr2W5J1TJnNtrlyY0hNDQhUDDcWtCQxMCgUbWlnSm7cBzBAdtIvduA2WCoDIRJWvivv/CvCAYVVPqvj3n8/Xqbek0WdWWDuWrKY0wZexQ9h1Xy5SxtVQlYqH3L4Tg81Vt3PvGEv710UoylgQJjVUJTtljJI1VJZRGdRLRCKVRg9KYQannjkdIRPQudzfZtjMwsiXuPeeWNlh+mMS2c2mkBCuQV0pnUOWlkTKXV7ppvPQyz21L6fph99FV1JXHu/xb2ljUqed9YECEned/Cq//sU9Z5kUMThjR2E8NUii2XryDJb27N/PnHD6JO5uIe0BkLtw7NNI7QDJ4nIZHd5OEAukK88IX0oUQaJqGhubo8gm3RCGwEVg2mLYka+N0On5+ga5pRA2dqKFj6M5cqS0dU/+2FKHOxi5yl3bO7yH9tjpPEtEFEV0jYmhEdI2o7twF+B2caUss28a0nc7QdN2WexX2Kr2bSg22RQJRwxFsPKPgUroH8FpOG3pH13U7n4lECpm7C+kM0YJhobsdSht0bw5+PPIozmjYNy808Iyii2/npgoPxkmJzHZiptqwU+3EZAoyHdCwM2x/bF4Z/YM6LmIwIyV89HCfsy031MelUGwI3jlAg+8oDW+uMkBv9shKwOwmPl8i21As9+oOT3pUbBZuWfgUp/3nL4PmlQsgUizixP8HO0/bzK3pGtV7DgRCwLf/C7P/BnoUIgkw4uG7ZjjpAkOkA5A80L6EeZ0r0dAwhOZPpb/25XreX9yMkIIpY2s5eEK9Ey+cdAJBeBIvzy2LhLvpC7RCQqPCwvSEYoPxNtiWs+RmZZ3lNCsLdjYXbptgWUiZBct0w7NOOunlNUE66ZCmG+/mtS2kV07+RW4prRjdjct60vHZ0DFdsXzeSezFDvMz3XsKg5QWJaVFSYsoaS1C1rtrEbIiQlYzsDTn2ExvBGoXG7mS89vkLn8aQ3Na5S5s+vpCtrQDy4rOJy2lDPvdJ3L0nmTInx9HfllF8uZKyE+nUGx5GFKi+wf3OofdejONwcN7c+Fw/dp1g0bQ6ZZhuw50C0KoZSwGkc7OBpC1bH70jw947H1HS/5nx+zABftv48dbtsUDcx/gXwv+5XQO3nortuu2kLbl3h09HCktbNvR08FVqJXSS2/7l43rR7rTy7muyNOlsZGu7kuu05aDbnStUGw96Ag0wECElul06SjxG9JGk7bfwXodq7dkFzzdXsjACfV54d5fsX+KvcxtZQjmgdxmAUJleeXIgjxC5k69l1JguRsCLOncnY0Ogc0MMreJIeq6I+7GhYi0iSCJYBN1n9sTMozAO/CFirzn646eTlLfVL90vWtL93gDg4wwyGhxMlqMrBYjo8Ww9Bim7txtPYZlxLCNOLYeQxpRpBGHSBQiTvxHK5PMXtpBp9RIE6G6PMHhOzSwY2M5B4zYn2Flwzb+oXuJ0tnpA4NW2LFtVyE5qKScdJWXk6SS7dz+0ifMW76WUi3DtF1qmVQXhUw7ZDoh08HryRVcnJ030E+iUCgUiq8IL097mdpE7WapS+nsbKn897fw4i96lTQOfBcg6gZ87F4BdtQEY4Y1sDBadFV1qyc4QivcM1IYJnCXjPBmoTZTQxUKhWIroN60KP//tnfVDixAwpkPw/jDBrRdStjZjNjSZnHrYseuim06W7al5Vy2e1/6KnYi7uoF5pZ+PAVL271bwlkaMt04GzBFcLlIuGng4M4ksjOZt+Xb3cHhdubuxml/B6gN/nbx4BZv77KF8NPlh+X8bpnB8kS4fumG2UXCpNumcHnhLeyh+glvUy/W/mCYoxvj1aekGkWYgqWXwJJLUEgO2nAKxofS+bu4ZFEBW+sqv8xPm5dfklefDOUtrEsWaVO4rXqevSoR2MEmKLRflW/fSgTjfbWvQBr3PQbT+La38vIE1cY0pL+slrPV1dPgxdFHLPbOQ3uNpCwIL8iT9xkVrw+3PlkQHqov9P5l8bLyPnsIfA4y8E4C76zwHXb93oLLhgWfIZuYB8+Ca5aCpiwob/VIKTn7X2fzwZoPek7cUNf/DVIoFD7BjgVyArmZ30Nt+m5AoegVDy1bwfaZ7EA3Y8M49n8HVNABJexsVhJGyUA3QaFQFMFbrvzKKzAqBi0LI5EtR9j5wSdQOXygWxFCCTubgZSZ4uuPfJ21ybUD3RSFQqFQbIFcVTeEzXPA0Ibz3aYs3/7adYNO0AFlCmqz0JppVYKOQqFQKLZq3t7+ANj19IFuRlHUzM5mIJMuQyy9hqTxSS5QFigDENIHkIVqa+UyyfeNRxgiWgtsUoRtYRQqVAZrcuxWaFjoJETGV6pz4gsVKPNaVkR5ThZP47n7sDYw2Hc/ydDlKIF7SuIWYIqc0rgT5+h9WPTgL5a/K7+bz1NEzwaU1n2/yCm2Zwn7TaWMrRhgtIBCcPgID9feT8DtHeOhBZRvdfd3ygvXXYVcPaB86x0P4oQHjgeBkE0g797dL3JYmdkLk6H0XSktAz3+vobKydMfC/7GdtW2/DzB9nX5O16gAB28F68vmCe706lEG7bz0yWMBCdNOInBihJ2NgOaEBj2ULJN+2xUOU3A9ezfx1w2CAuEs5laeG7hbLB2/Lkwged2D7gM+t2yhB9vuWnCfqfcXB25su1AWYFyQm30/tBkQEoK+oM2c3Nh/h9nKE13Zbj1FKSnS7/YTGffbE2IwC4PCO/EgcCOkLwdOl6Yt9skt2sk9+PvlVt0R5IkZFDPkLnOzzG4Fz4vy0trkOsU/TOxAv6g4T2/zFBZuQ43V78XL0Jl5MoWoXaGBAD/4EoNTTr7cYTt7rGRrmV0NGw0LDScv0bN3cUY7kqLGcArJvbmzvPy/rJy7ggWJaQoIU2JSFFKirjYQvRIBiG2FHQQp5MY7TJBiihJYqRkhCQx0kRJEfX9KaKskxWkiZDFICMNMkTIort3L8zw/VkM0tK558J0in/6vWfPeDX/PDz/jK7BixJ2NgN15TG2rS9n1vx1fchlEx9+H5GKOf3WLsXWjZQCpDuWlU7nCDpIzQmX7ljZ9+sBfzCtO06WAiHz7OCGTrfWfH94gzahKbvCMSN5ccW74OL0/gdb9mnacAPTFhtC96bconL0pmmvQBIlSwyTCCZRTGIiS5QsUUw/znNHRTg85odl0UOfkAAS7rUhLS58aEvqfgedRffvttR9Ac72BTBHsAuG2TI/jRbK54fLYF6toFyrWH6ZqzO/DeE6NLIYmFIng4HpPkPB38SGvLWi32HPsEe+0Nl1uUWtroUGxKY7ALUCA2Iz4LZI1cS5eOZdZO0sO9TswCW7XkJJZPBuwlHCzmZgRUuqj4IOCKNFCTqKjUII6fxoef4BbIti4Em7V9+J0EX3uBmQOAvF3Z16OrjRgNhAN6IfWJSCRc4pRcxeOZuEUcq3Jl1MRBeIQbhUro6LYPMcF/Hvz1Zx35uLKY0ZlLmX744XD3tr9Us8MPcuInoEQzP8KyIi6JqeCxPOPZWBlqRFU4fF+g6LdW0WbSnbHdXrudF6l3T1VejLV6QPafu8LLTx7Sj6J9indmyKNndRRtHGbf53tPHt6L82920pUWIEZjOimESEScSf0XAuw11i1fwFIYkmcmN6LT8uFI4/tteENxdQmDb/lXb5l9aXiZ8un7p4mOXOOGSlgenOmmTcu4k7CyG9GZXwDIuJ7uczhYZEBpauvSXq3JK48JewvdkBb1k7EB9a9v7Kd0NbPB3zfoCdqWdkTYKnLzuAivjmEZDVcRGDjEO2q+eQ7er7lOcbVUfxjYlHFYRLKVmyPsmHy5r5aFkLHy1t4aNlLbSlio9+RtYk2GFYBdsPq2CHYRXs0FjB8KoEUsLa9jT3vrmYv7wyj4xpF+TdflgFUkqaO7M0JzOksoVpNoSymMGomhJG15bQUBkHwLalc6K27V1g2TaWdOJM28aynROwc2mcqzmZYWlTks6MtUnapxgMSOJkqKKdStFBJR1UCcddQQdVblil6KCcTspEknI6KRdJynAuQ9iB0pz5gawQZH3lcEfhOxtw+1a2yVnphpw176AlcscqePfWxO1AGOQWHUw0TDSyrr6NKXRMNCypYQonzkJz25kLtxBkMEgTIY1BGoMUBlnNIKPpdNjC0eEQgoxbjinAFrKIzl1Ql87zZ0Gk8nTycsJJ8J0qNgxpG86ysTT8JeR8vzNANYq4c2l0EcG2NWy76/ThPIYb130dG2NHOZmxGYxTKErYGeRIKVnWnOSjpS18uKyFOcta+HBpCy3JQqXAqK4xsaEsJNhsN6yCykRxCVsI+N597/HWwvVd1v/pitZN9ixB2tMmn6xo5ZN+Kn+wUFUSYWhZjFhEQxcCTRMYmkATAl0LXME41x90a5rgkxWtfLCkudv6Dt52KHtvU0tEd+r4YnUbL89dw4qWVJHUkprSCIdvX8/+E2spjepomqsgq4Mm3EtzlVaFBZhIbNrTGZo6U8RjkkQUIgZkzCyz5q/mpc9XsGh9OwKLYVVRRlXpjKywaEhkqYll0GUSM9NBJt1OS3srLR2tJFMdmNkkusj6lxAWUkh/F1rWvTcLWBsQTrwdbf5dRB1NE1Hl7mbL7Xjb8giKTBuOTvdzuhuLox+W0/MKziRr6OiagWVpWLZwO9acnpinbJ3r9D0F7KA/mCdw8IGnwC0DKuq+UrcTLrsIL0yj5cL9+FyY9MOdcsYPLWOXEdXUlsWpK08wpDSOacPKljQPzl7C4nXuouEmEiQm1pcxbmgZdeUxhpbHqCuPM7Qi5vtrS2PoWq7crGWzui3NypYUq1pTrGhJsbIlyZxlrbyzqImMteHfqbryGMOrE4yoLmFEdYIR1QmGVzn+kTUJYsbAWksuhlrGYnCdet6RNnn1y7Uh4WZ9R6Zo2m2GlLLryCp2G1XFbiOr2W5YORG9b6aTrn30I554fzmaoMfONxiv5XXGhp7rwL04Xcvv3EHXNOfei85dF6Bpzo4pTXOXAzR3p5e/48tZPpD+9Hhghxc2EousbZG2smQti6x3t03nsiyydhbTctK1pdK0pTO0p9N0ZrN0ZDKksmZguj039Z7bsRYYGWMX3WUmsNE0m4gBuiYxdNz34b33nGABEiklEmc2K5kxSZkWGdMif3eZABDOPWIIIrpw8gfKsKXt+203rO9LTVsn3hJwRMstFWtCQwiBhnMXCD/Md+PoJXjpNOH83XnpQKALLZy2WDmeG43OjIUtBaXRCFEjnFdKwdKmJF+u7qAjZRHek0ZIUJC+UnogLOCXrrCgYTC8spQR1eWMqikjEYnQ0mmTMKIcNLGBte0mc1d08PHydj5e2kFHWpITZpzyyqIxdhlZg67pvP7lWky7d9+rvbepIZ21+HRFW5ed7gEThnDY9vUkIjrLmpMsbUqyrLnTvSc3++zBGZNHcch2dYyscTr4kmh4rqC5M8Pf31zM3bMWsqrVEXTiEY0Tdx/B+fttw/i6MjrSJh8ta+GVz9fwwier+GJ1+yZrn64JakujriAUIx7RmbuyjflrO/pcVkNF3BFgXEHGE2qGVyVorEoQjwweYaa3/bcSdhhcws60v8xi9pJFJEbehZ5YOqBtUSgGAuHtAJPOLhyn8w501FInEYlSEY8RNyJEtAgR3SCTFbSlbJo7bFJZAiNqLZDfucf0CCXRKDFDI2roxAzNdTv3hooEI2tKiEd0UlmTzoxd9J7M2iQzFsmslbtnLSxbIgRENI2IrmHoGoYmiOgaEd0ZHFg2LGvqxOrhF3hIWYz68hh1FXHqymNUJqJuGY6AlTFtklmL1mSWFS1J1rZnsFyhI5m1aE+ZdKQt7KI/9d3PMNSURmmoiFMWN8iYkqbODM2dmaIzy70przdpymIGmnCeK2s5S9vdskmNc/W+rNKoQXVplOrSCIYQfLS8BdP9MMvjBntvU8OY2jLWdaRZuj7JkqYkq1tTRYcZQ8vjjKxOMLImQVNHltfmrfU/w9E1pewzrgZd02hLZWlLmbSlTFpTpuvP0J4xCUl//mDLmRUUef5cuOe32Wl4OWOHlhCPCBC2f1C1bbt3aWNKsyD8m9t9k8NHH75hr3sToISdPjCYhJ3/feEL7v3sTlLlTw5oOxQKhUKh6A1PnvAkYyrHDEjdSkF5C+W7B4/j+D0v4+r/LuST9R91mU7gTHH767/uWrKUwr3ITXH7U92eOxBeLAyK20uReWlC5QTDHLeuCSzLCQvZTymSNmfLRfOn2YM2XkJhPYy+NGwi/i4ciwgmhnB342BhkCUqLD+Nfwkn3tu9Y2DSGzWPjRktWDJny8NGYAnXLXM2QLy4oD0Pi6Btj2JpHEVWx55IOI0MpQ9cwnM7yrimvytHx/LMAOYbYfTDnDfhfWsQkqghiBrOjIah496Fv3wnpWRpUydZ2/bzlkQ1hICsbZO1zNzyZMggZXD5MGz0Uu3qUSg2L5MbJjO8fPCdhZWPEnY2M28tWM8dry6gLZ2lI23RmXGmmJNZi460SdrfEXVGv7fF0AQViQgVcYPyeISKhEFFPEJFPEJ53KAiEaEkqtOSzLKmLe1c7WlWtyVZ25bClBZ4W1C9zk8ErBlTzEKxs623oTLurAdXxRheHWd4hc6IUpuaaBbd7MTOtGNnOiDTgZ3twM50ILOdyEyn4892ulcSmelEZpPYZhLMJNI2fY2W/J0z+TtiJM5umvBOmpxGjNcNZ7UoWT2GqUXJiihZLRK+iwhZYZARBlkiZFx3xjOOJh3hw5RgSYmJjSUlNjY2Fra0kNLxSc+P7V5OnMR2lYQdd7jTDwsC4alrm3zr030RCjx10L5iA75atLP32bmCxMM/Qr4dGB20gTLtsgUg8wcMBf6AG1y9nbywLvNpBfHBQY9AY2h5jLjhWOG1pXD0w6TAtsGyIWtJTEuStZzve0U8Sm1pjOqSKIamowkNTdPQcJSWW5ImLZ0WTR0mLUnLVXh29ZKkhqHpDC2LU19RQn1FnIaKEuJGxClHaOia7utO6W75UgpmzWvi1S/WYdmCuK5z1KThTN5mCBJBxoSsCWlTkjUh47ozWUkqC+msTcqUNHdafLK8jZbOLGE9qe4Gf8F3XTx98XIcd0TXqYhHXJMkET5e3tplOWNqy9hv/FAOmDCEPUZXEzMMdOG8A13o/vv4qjOgy1jXX389N9xwQyisvr6elStXAs7I74YbbuC2226jqamJyZMnc8stt7Djjjv66dPpNFdeeSX3338/yWSSQw89lFtvvZURI0b0uh2bcxnr+w+8x+PvL+8yXostp2T0bQi92O4ZhUKxsUhvR03+zKE/e5hnXTqQNmRxuiCfN8vq7RTSQ/5cXcFdPoG71wnKQAdJrtMPCyrdPeCm1GGh2zrH1ZVSVx53NzhoCPA3KWiacHf05e66puX8brwuBMLbmCDAsiXLW1IsXtfJkqbOLs1JlMUibDO0lJFVCTKWTWfG8q/PV7blXscmeM7eoAtBSdQgHtGIRTTihk4sohN39cLiEUd3K5O1SWZNV8/LDul7pbJWN+31xNXcIFLX4YL9xzC8Kh7YhGD7GxPCmxOcwRQSZ5Al7fAmhkAaTywIluG588OlN2iThWV75f1gjx8wtnLsRr3frthilrF23HFHXnjhBd+v6zkt7+nTp/O73/2OGTNmMHHiRG688UYOP/xw5s6dS3l5OQCXX345Tz75JA888AC1tbVcccUVHHPMMbzzzjuhsgYLPz16B6aMrSVj2pi2xLIdJbxoai01bZ9zV+qfLJBK0FEo+gtnVsu1G+OFDVxztmiWAcs6+6lwDaiFeBfRJvCFBV/kG6fXIN7YT23qARNod69QoElgmjOAZ5zaPWWhq2ftjrs/34BMm5nZK2fz2jdfQ9cGrk8ecGHHMAwaGhoKwqWU/OEPf+AnP/kJJ554IgB33XUX9fX13HfffVx88cW0tLRw++23c88993DYYYcBcO+99zJy5EheeOEFpk6dulmfpTcMLTU4LfEWdK7LGb5Y+znM/hsAE2JRzmgsfB/doQUONPRsJPsn/LoHDAZPDvYOMgy6w6cFewcnBuIDZYvA6cHBE4vzD27sDb0ddW36dL1sYS+TDdxzbNp0vSurdy9FisDyIc4SomtXF8tdOrSEqx/k3nMG+EKb6wPLkLnnyR1QmatLBuLsvDDIGQb0Jj/8+rx6Csrx2iMCZeSWPxUKRc+cvcPZAyrowCAQdr744gsaGxuJxWJMnjyZm266ibFjx7JgwQJWrlzJEUcc4aeNxWIcdNBBvP7661x88cW88847ZLPZUJrGxkZ22mknXn/99S6FnXQ6TTqdOyWmtXUzGrZ7+gp4584uo3dOZ/howeLN155NTKEl2aDuSzgs2BES6OwgZ7k2qDtj+x1UoLz8dCEdne4t3uZ3hH4HKnIm3Ao6PhHufP38gWcKd67BDlQUlJnrvAvzhp9PuIIBWO5zWt67KBbuCxaBfJ6AEYwPvHeriNtLk19nYZnOe8sXZmwlEAxqDCn9U991CYY7wMm/506Od/zgm/VzBjcyt8iW0/iR/in1ufTuJWUovZ8mEB68NJnTOOqqnFyYly6cx3nWwmfyBocGBN6F4/cGjV4ZXvt8PTb3nfhn0+cN+LwBpPDSyEBZbrpgGs8fSiODzywD6QNly8J3M6BcswxiZQPdihADKuxMnjyZu+++m4kTJ7Jq1SpuvPFG9t13Xz7++GNfb6e+PnzEQn19PYsWLQJg5cqVRKNRqqurC9J4+Yvxq1/9qkBXaLMxap9uhZ3NzfSaKu6pHNjt9gqFYtPhddBGoOP2/Eags9fo9fwmEsgKAJHXkxYvwVkodIRiy5258wRm0w93hGILNUu2JXLN2vWc3lbEKGLj7hBJbP4G9cCACjtf//rXffekSZPYZ599GDduHHfddRdTpkwBKDg9VUrZ44mqPaW55ppr+OEPf+j7W1tbGTly5IY8Qt/Z5VTn2hCkhHkvwupPQeig6SC03N0P8+6iSJju2v938sx6+3+gc8WmfUaFQjFg2O6sWlYJEIp+5JntD+b0o+4d6Gb0mgFfxgpSWlrKpEmT+OKLLzjhhBMAZ/Zm2LBhfprVq1f7sz0NDQ1kMhmamppCszurV69m33337bKeWCxGLBbrn4foTz58EB69eJMW+XcheLqsBIkITZFCbhoVmZu29aaOg9OlxfR1ctPJOZ0eKJzm9dL1xCbfYEJ4uctb6grqcASXyIJpcvoj4aW44BZ2b9nNX4ILPENwWQ8/Xfd6KMElufy2e/5gOQTyFNVDydtun7+EF1xK854t5A+UI/PD80fzOGdaOSP5QJwXVmQGwBJb6llWisFC/hIXhH+Xii2XdRnnL9MVxtFFvCYDdebnDSw70UWclzc4AxfMJwJ5tbw8ud/l3I/relnJO/bEvFTg7BJ0w2Rea7uKs6O8Ovcgxvzn6YL3ftSkBm45ffceJyU2N4NK2Emn03z66acccMABbLPNNjQ0NDBz5kx22203ADKZDK+88go333wzAHvssQeRSISZM2cybdo0AFasWMGcOXOYPn36gD1HX0lbaa585UpeXvJyz4m3GdXfzVEoFIotHhlQKg8h/H++YmSJ8PFGl2KlhpFcch7YxZeqnvloJe1pk/L44DKUNaDCzpVXXsmxxx7LqFGjWL16NTfeeCOtra2cc845CCG4/PLLuemmm5gwYQITJkzgpptuoqSkhNNPPx2AyspKLrjgAq644gpqa2upqanhyiuvZNKkSf7urEGLbUOmjU+av+TU584d6NYoFAqFQtEjenwFP639ERe0tHWd6NfApGkQLQEjDnoUxh0C4w7ebO3MZ0CFnaVLl3Laaaexdu1ahg4dypQpU3jjjTcYPXo0AFdddRXJZJJLLrnENyr4/PPP+zZ2AH7/+99jGAbTpk3zjQrOmDFjcNnYsS349//Aq78viJpTXgZDagagUQqFQqFQ9I09kym+0daLk9Q/eijsf/3/4OzHYezX+qVdPaEOAmUzWFBe/j7cdlBB8GLD4JKGoSyKDK7pPoVCoVBs3YzNZDGkDOgS5u5B3UFw9H9+tWYtO2S6Ou2+l/zgE6jctOdobTEWlL8S1O8Ee5xXsOX8T9WVStBRKBQKxWZnfrRvfc8l9XW8tGRZ99pOB/4IDvnpRrWrv1AzO2zes7E8rn/9eh7+4uHNUpdCoVAoFJuL+pJ6Hjr2IWri/a+i0dv+Wx2FOgBkrSyPfPHIQDdDoVAoFIpNzqrOVby3+r2BbkYItYw1AET0CM+c+Ax3f3I3mplGrJyDZiYRZhbNziKsLFYmjTQz6HYWw85g2FleKYnzyZZoH0ihUCgUA86YijGAY6zX/y9gDyfkDsQLwmm8/4J+538n7NBRh3LQiEI91YFECTsDxIjyEVw7+VqYdQt89ELPGYC9UinOH1bfc0KFQqFQKAIc19bOLxf8x/fPO/FpVpduT2sqS2syS2vKpDWZpS1lBsKytKdNxtSWMnmbGvbeppYJdWVo2pZnp0jp7DAwOjs+Hevg7dsh3Yan+75gbQczP1kFgGcWK2geK2g5Mz+ObuLy84XTFcbRTVx3bfAGByKk39+LfIE46e8HCFsatrFdS8ESW0gs3527274fbGG71oxzcf6BlUL6fhunLMdCsVOj7daua+6oJbBrwUJiS+cKHeApZHhXQ8jice5Zbf+3QhZYRfbKEcIdafl1OxaO/XYE8mVsm5RtYyMRwgbh2C62RdhqsXdoZ1aEzycyyVk09qwdq/OKFIOZ7iwk+2kClokJpM1Pp7uW3nXp3d1DT3EsIetuOX64a1Ved60X67gHgnrhXjk4Z5UF3cEytYIy+16n7lpPDoV79bhtSUjJnql0SLn4isy3edg+sM/vvaokwl5jath7TA17b1PDjo0VGPrAacT0tv9Wwg6bV9jpzHZy24e3sbJzJVJK50Ji2TambZO1nHvQ3Zk2Wbiug6zlGuQXwe6UgNuzGR7cNOjZLM/5RUF4oYCDyDv3W9hOJ4rtxtndhH3lv1IKRQiBQBc6uqY7d9etCQ1DGGiahi50DM1AF264ZuTihYau6RjCCOcLhHtlBOsxhI4mdAxNR0NzwzV0YaAH6nDyeW3Q/TDDLU8TmnMKuG2iW1l020Q3s+hW2vGbGTQrjcimwEyBmQYzCdmUczdTkE0hzJTvzqVJgpXOvaxe/nz0RhTfVGmcdD03bFMOD3rV9m6alMaghVJaZCktssx1l9FGgi/EGD6NTqIsZlAWNyiLRSiLRyiL6ZTHPL9OWcwgamh8ubqVD5c28/GKZtKm6f7eO/1CIiKYWF/GdsNK2bahjGO3m0JtyeabNFDCTh/YnMLOT179CU/Me6Jf61AoFAqFYqCYZN7C707em4bKeL/XpezsDFKmjpmqhJ0i6NKZjtXdaVxd5t/duMC9LxOnvZXoe5OuV2mKDMvyD/W0RO6AUe+w0OABpN5ho1Ygn59WLTEpFIpBiJ2p4fV5zXy4tJmGyoaBbo6PmtlhYHR21raneeT/ruSizN09ppXAB7EorZoWOCk61xHaeSdKeydum27n6J0i7elr2AFdDQunE/Xj88LyT7+G8AnY+ZcXH7bGGTwlOxjueOz8tHlp8hflwqd4hxblQmWE84XLKdSTEX54/knn/qnlriASFD68epSOi+KrQjFdGVy37sbpOEssmquXEjytO+gXCDedV7YIhfs7fvxTt0HzRxJuSunnCJ8Y7palhcp1S/PrFP5SkAj+G9L7Ce5QIqctEPrXReZSB1peEBfOJ7opUwTaInxdI283lCZz70oLvbP8OIhKmygmMSziZIlhEsMkjklcmsTJOm6yxGWWBFkSwkS4ekDfy1zKiSedg6ZpaGgIobv3wt+9ykSEfcbWbhZFZrWM1QcGVEHZyoK0Qbrdrn/Phf1/7/2Juz5/YPO2S6FQbN04mvCuRwT83gBCFA3vHV2kLTblKSxHB0RYrg6gohjt865AZoYOdDN8EhGd7YeV8829RzGqpgRdE2hCoGsCXQg0DT9ME4La0ijVpdFN3g61jLWloPdssntUzYTN0BCFQvGVIrDzMDC1EI5WDBq0yHqsQSTsJLMW7y5u5t3Fzb1KH9U1Hv7OvkwaUdm/DesCJewMNqSEznXutR461zEtmWLauAucsOR66GwKuNc7d7lhI6KUjLCeclpkGU2yjCbKaKOcNq2MNWYJzZTRIstolmU0U+rey0gTRROwXUMFe4yuZs8x1ew+qpoR1Ymi05q9akvW4oMlzby1YD1vLVzPO4ua6MxYoTQlUZ3dR1VTXxH3d6xlLenew27TkmQCbi/etm1n6crbNu7+3ktspAzo5Ehnu3gwzLKL7YQLI0SuzGDas6aM5vQpo2isTFASNUJb0QHaUlm+XN3GvDUdfLm6jc9Xd/DlqjZWtqa6qEnSWJlgfH0p4+vKGT+0lAn15YwdWkoionf7rnMlSNrTJn/7z3zumrWQjCXRBJyy53C+d/AEqkuiobRdlVEQ1ocJ467S9lSflJKsnSVrZcnYGTJWxr8Hw9NWms5MmhfnLufluSuQmKCZIEyEsECY6JpFIgbRiEXWytKRSYNw0glhEo1ISmOSaEQihEnWzvr1mbbZ62dVbAKku9FaaiA1pNRBuhutfb+72TuYJuCvKolTGY9i2pAxJRlTks5KklnLLT9vhisvjMCyGqF4/HSySBgy3w/uQpv/bLIgvcBKjcBOjt6kr3FTMrImgaFpWLbEsiUdGZPmzvChoWVxg4rEwIkcahmLAV7GyufhC+Gjf2y26uzAqrjjcH0i94cuEdhSYrnyVFgPJqwzg/CmMDVnClMTCG9F3peBhJ827A7HScC0IesKLRnL26ofbFuuLcG7h5Rdpyn2HBRJm59fCEFE1zB0LXSP6BopU7K+I03KdHIZukZdRZzqkmhACCzyzN3EmRLasiatGYsW06Qta9NqWaRcwcumUMHZMDTiEY1oxCAa0YlFNCIRHSFyCtK+LSL32VKWxbKWFM1JE4lE0wRDKuLO1LMQvm5VWK9J+uXlynTCbCl9ZWuvniySLJKMlGSEJCttx48kg01GykAamwy2nz6LHUjjhG/JOPorru6FEK5f87tU6SqESVu6K9oBDZagPktAz0UTgqpElLiho7l+5y/Qq8O5hAjfNYSzFV1oRISO4bo9vx+GRkQz3Djd9es5v5vWT695bp32lM2iNUnmre5k2foU0hZorvAhpPMmdGHQUB6nsbqE4VUJhleX0FidoCLuzYAX/1vpSFvMX9vBF6udgcKXazqA4n/zXf0OUOT3rDf5CaSVsqv8gTR9+K1BCKoSEapLY1SXRKgpi1FdEqO6LEpNacxdGnLiIrpe8G5Sps1nK9t45Ys1PPnBilDdAkkJaUpFii/sEayl+IzLkLIoe46uYa9tHNs62w8r79Kuzpq2NHe+toB73lhEW8oZBDRUxLlg/204bfIoymKbXthROjt9YDAJO9l/fIvIxw8NaBsUOSRgAhkhSAsRuufcufi0cAz2FXNn3LSF+YvlIxSndl/1jCElEfeKSohKSQTP74R57oiURMAND4TJXFgELxw/PhdH2B/MJyUGuR2DjpJszuBcYByvUAwa9k//L0tl75fJzttvDCfuNoIh5VGSGYvbX13AP95ZSsZ0hkVjh5by7YPGccKuw4ka/Wd0UOnsbIF8+553ePbjE4hxFPkWhnPyei48P4xA2p7zF8+XK7vresEbQITD/HGP8PaESTyjhUK4y1HCCoS7cwrCnQvww2wQbhsCxqv8vVNeOhGuA2EhvUszw343TArb9ZtuWDCN7YfZgXyDrWcSUiCk5oyKXT+4I/3g6B98v3CnzMO7W8jF5ZUT3gmTFxfyu6F5u2u83E7aXFm61NCkQJPCd+fuzkhfd926+4y6dPLpfppcWiMY5r0f/+59V4Pj2cK/i2J/KyaOleyUX17Xf089/a0UC+tN/uJ/z12UJzby77nHZ+z+Oeihju7eTc9txDdUWixfb9sY3I/ZuzaG/fRQR1fPpmFTItKUk6REpBmstMu+2cS587WF3PnawoLwXUdW8Z2vjePw7esH1bESStgZRKxpd/4Q0nSlse506IisIzxoWYQwQWR93QJEFuHrI2Rd3YSs73bSmKG8wk/jhPu6DK7QkbvLgMVkf+HCEUy+IrsopK2DNJDSAGmAHXF1BCJ+mCEiICMInHCkgfDvhhNGwB30E3HDvTw6wgsjgpARBBrBiZ7gz0kxfSkvaE1bmrS5aT6nIWVRKhMRv3AZaEe4bcHuULKqNU1LMryW399EdMGk4bkpeu8dhd+be6f4i+3q2ToyJp+vaiOV7Z/vf3nc8JcD8tl5RCV15TG/dZ6u2LLmJJ+uaO2X9gwmIrpgSFnMvaIMKYuFZhCKfabF/m66/JuREk2axOwUMauDmNVB3G4nbrUTtzqIWe1uuOu3HX/U6iBmtgfiO9DYtN+PjFZCRneurF7quI0SsnoJazNR5qyTdMg4HSRoJ06HTLDXtiMpK68m66bL6KVkI2XYmtPffCvvHbWlTNa2pVnTnmZte5q1bRnWtqcx3eXz0qhOeTzih31t26F8+6BxTN6mZoP1NvsTJexsBmxpc/fHd/O3OX8jbaZJW2mKKl+WQPn2m799WyMCQUyPEdWjRPUoMT3mXxE94rujWtRPF7wH3YaIsGhdlg8Wt/HBktaQwDCiuoS9tqlmz9E1IWXe/D/2sIWNrjvVHtP2puwuf2iMgrQATZ1ZljZ1sqwpxbLmpHM1JbsQjJz8TRKaOuDkPUYwoqYEKaGpI8Pi9Z0sWtfJovWd2Hb+d1yAAL2ki+blt02G4+IRjQn1ZYyvK2fc0DKWrk/yxvx1LFzXWVCSRHD8ro2cOXm0c7ZZfk15pzuHa+rb+0ZUF7S7qTPLwrUdLFrXyYJ1HSxa28nipk6yZnGtgUREd5Rj88rukAIt5rgrEwYn7jaCY3ZppCxW+Fnm2lkNNPrxUkrWdmT4clU7n69q48s17Xyxqp017ZmibXEy9dRZdR8fwSJBmhLSxEWGEtIkSBPFxMBCQxLVLKriGjVxneqEoNSA9W0pVre0I20LAwsdG0PY6J4bG11aVMY1hpVFqC/XGZLQMbDBshBZE2wLbBOkibBctx24S9N3C9tyzX9Yrt8EO+ums8MCcY9vIH/WCbJAVvN8Ona0DCtSjmk4AkpSKyUpSuighHbipLVSZKwMLVaBHi9Dj5djJMqJl1QSK60gXlZBSWklZfEIJVGDhCZI4HwnlzZ18teX5vH4+8uwpSO0HDNpGJcdMpHRQ0oD7Sr4wcnz9v7vQSCwbUenMh7RB6WQ46F0duh/nZ1P1n3CqU+dusnLVSgUCoVisHDxzhfzvd2+t1nrVDo7g4gJVRM4YvQRPL/o+W7TaULLzShoPcw2aLmRXf4skee3pWRNa5rFTZ2sbk35qXRN0FARY3hVgpqyKFpAGs+XfQvKDsR3Va+TDla2JFm4rpOOtDMNLwQMq4zTUBmnPG6ERtzBclOmTUtnhpZklpZklo60WVCXJqAiEXGueISKuO7vEOjxGfJn1QLeZNZkdZszbZvK5ra9R3RBbWmM2rIo5XEjkLXr91OM8Dva8HZubFndtbP7zxwypsXK1pT/uRYrwSNmaMQiGjFDd9yGFv7cN6CdEknGtGlLmXSkTd8cgKELqkoi2FLSmbZImWGzBRFdUBLViUX0kK6LM7R19TGEN5ItjO+qnT29++4+N3D+Tk3bxrId8wjO9l3bLdcz7RfWLdE1529AF85dc/WSJBLPGKmX38kq3Ti3QbL4xv7gTjtL5LRcrEE8YlcMHlozg3f5VM3sMLh2Y20Kvlzdzj/eXsLD7y5jbXtYIe64XRr50dRtGVnT5VrCJkVKyUtzV/Pnl+cxe2FTKG7skFJ2Gl7JTsMr2Gl4JTs2Vjp6IHl0pE0+WNrMOwubeGdxE+8uaqK1iB7DxPoytmuowNA37If5y9XtfLi0xfcnIjqH71DPCbs1csCEoUS62G75VaWlM8vLn6+mPG4wrDLBsMo4lYkIQkrnZGsr7Z5s7Z1wnee3gnEpMDPO3c66uui2cyFB2rQlM3yxqpXPV7XS3JFGINGQJAzBDsPK2Xl4BYanSyZtkpksy5o6Wbq+gzWtSUcHQ3ibf6V7lIHtChM2mitIaO5m+lwaJ50nXEQ1iaELIhpENImhCQwhiWiOEGIInHZoEhuJFNI5vkXY/lb7rLd93rbJygxZK0PWtjDtjLP04e7Oy7o780xy7qwQZF2/GfITypPNy2PmpyGXXx130v9Etai/rG5oBhEt4vjd8IgWcS49QlSL+nfvBHtNaP5p9LYEyxJYNpi2wLQgbhg0VJQ45gQCaT23hsbcVR3M/Hg1a9uzlMUi7NRYzW4jq5lQV0FEN3Jp8/O6fq/sYnFxI87oitGb/b2qred9YEsSdlJZiyXrO1nTnmZ9R4amjgzrO7Isaerkn+8s7XU5wyrj7NhYwQ7DKtihsZIdGys2yiBgb3h74XpmvL6Qdxc1sbyluKG80bUljgDUWMkkVxCqKgkrbNu25Ms17byzqMm/Fqzt2CRt1DXBAROGcMKuwzl8h3pK+8EuRFGkxD8iJNDBd+2n9+mtbFio6EkIMVNguYJHt4JKGttMYpoZslba6bCtNFlphjtWt9MN2vXxDjPt6hwy251V8M55swmkDfhtcG35iEBaZz7E80tyZ8J5ZUpy58HJvLzeuXBO27sXPAqEkECerd5cQNDQXsjonjsXlRcfNM6nC42SqEFpNEI8YiA8e0Bu5+nY/+nCLRybPJ7AENEjvqCgE2F9h8XK5izLmrK0JCVIb1OBY3wwqkVIRKIkIhFKot4VpTQSpSwWpTQWpSwapTwWpTwepSIec65EjLKoU48mNHRNxxAGuuZ0/J5bF3ruLgaHHst7i5u46ZlPCwacHrWlUY7YsYGjJw1jytiaLu3oDEaUsNMHBpuwY9uSZc1JFqzt8K/5azuYv6adZc1JNuQTq4gb1JbF6Eg7yzTFKInq7NhYwY6u8LPT8ErG15X1y4zG2vY0Hy9vZc6yFj5a0sxny9exqrmdCBYRV4kxIiwMTEZWGGxXl2B8bYyKqCQqbCLCSRd106UzKZava6O1o5OosIgK272bTplu+gie4qOJLk00O4smTTRpEhU2Q0s0osJVWrSyziyD5SotWhmw7Z6FC98ve4gPnInm4nTMbNDo3OtozaKj++7KoMvOPCisZIVbdqBOcxD8kG8xuCYDnEtHSM3tiHWk1JHuLj8pDWz3kjgdtY5BVI8Q1SPEjChx90pEopREYkT1CJ1paE9JWpOSlk6b5g7bLztXj4FjSTgc7lgcDgokmt/m7gSavlBXHuOoScM4dpdh7DayerNsS160roOmziyViQgVcYOKROQrNUO7eF0n05/7jKc+XAFAPKJx4f5jufCAbfhgaQvPfLiC5z5ZGbJ2XFMaZeqO9Rw1aRhTxtYO+velhJ0+MBDCjpSSps4sC9a2M3+NI8ysWrmC3Vc8QHsyibSdqXTdn16Xvj+mQ2lUoy2ZCUyxy0D63NS87k/VB6bj/Sl6x+3l1bCL1pefPujWhZ0TTrDQsQATTbMdlxCY7t1yO1LL7YQtt/P00nmdaDBP0B3siE3XbYrcqe6ehkNwRB88rdybCYD8WQU3rwjMFhT1B6wP54WF/bl2yFBbRCiN6QsSYSFkS15OEFKgCR0NHcferu5fwjWnFzjXGs++LwhsG3ebtde5OtaEpRREdZ2h5XHHMrDI2Rp2ZgQ01zqwhhBartzgjIA7axBML/wycn4vjy50DM3AELlLE4YfFvHitEgujRZxZxsc8wOGcPwaGuksdKRt2lMmrSmT9rRJW8qkLZ2lNWnSlrJoS2dpS5mhI1K6+nHOt8LbXXh+GZWJCDWlUWc3T1SnJOLeo7p7j1AS0/zw0phBIqKTiOiORXRXj0kI3HeL63bq1hyPa/vHuetC0FgVR9e89MLP58x8FHmeLv8OugjfJOn7sezN+Hfd3JnhT//+krtmLSRrSYSAk3YfwRVHTGRYZSKUNmvZzJq3jmc+WsFzH6+kKSD4VJdEOGKHBo7aeRj7jhucgo8SdvrA5hZ2spbNCbe8xsfLw8pcL0V/wDbaqo0qWwL3VJTz16qKkCChFAy/2gStC0ckvmXhiMS1Jpznd60EG0XzFJYRlRJDSrr6Kezp27fx8d38jPXwC9f/bdvwvD0hevj5HtRt39j4bh692+9DL8ruif58bz22rcfv84Y/e36ct6TsWF1zBpWWF7b9sdjbH4tpm9jSxsbGtm1M6fql7cdZ0mKP+j3Yq2Gvnp6uz6jdWIOYrGUXXUp6yt6HS7XHNqrseZEIv6mt3qgyFFsf3ixZcqAbolAotg7WzYJXZ/Upy9+O+BuTh03upwZ1jxJ2BoCSqMF/rzqY9R0ZbCl5ae4afvPYmzxm7cfj1r7+DhDh3+1QWLElqBhZYmSJZjI0tr7D8orlG9w+XTrn+hiu25BguKN4HS/OCdcD94h087r5Iu49eBZQT9OIGzvNuDHl95h3I2fH+rVtGxnfE909e09lW+AqAzvLjjaiIMzyR405xeFgmOmFqZ1DCsUWy0Ds1vJQws4AEY/oNFY5a6dnjFzPWfFv9ZCjD6xzry0cWeTyw0VAPwf8TtEOdZzFw+y8jtWflsXpTG2cs5FsV1/I0+8Jd9R5Yd101FZeR20X6bxzdbnxrjuXP5fHO0XIfx8i/z2JvPdU5P0F9IeKv2tRtGzy0njx+WU4eZVA8lVGuAMfgTPo8dSenQNR3TjXreXFFXfnH6jq2hzKixPglu3UoQXceH637vD5ZUXUr6UXJ8PhgTLCeWVhWYE0XZWF355wWZr/fM4gUwc0b0BJ7rBZTUqMQJwG/rKy5g5ONXKDT0Pm3le4LOnGFa9HD77T7j9+n3dGncce5/+hl6n7DyXsDAI0Ixbyr9M0rh1ayzvxmN+pbvXbWBWKzYAWmHn0OhLd70hycTr5nUyus9EDnYDmzoBqgY5d5M1m5nd8Xoeo5cUhCzvCXJ6uO1Otuw65SAdKN2XnhwfTal5eGQzLvct8YaS/9ZG8tg2mNP1RT3AQEdw84flt158OhovcgCRooqGrjRN4ZQXCvDx+HLmyg4Oub7S3s12m6/PuMut7bxKlP1HCzgCSylrc+dpCPl2RZunQp1jW1Mnq9gzRoc8SLfnPQDdPodjq8GbpsmrwoFBsEu6rLGenL7+BZpVgo2FJDQuNvccOYfdxw/jagYcMdBMBJewMKJc/8D7PfrwyL1Qj07w3keq3EHpxw3t9wTfsJd2xl2tDQ/r+XJiTzhvfgT/GCB0KKArvMj+sWN7gmJKCuNw9sC5SrK5iZQafpei4aFPQi3J61X9uxvZsqrq62/bS2yJ8V35ZotDVy/pEl54eg3PxQmLZUPzwhL6wcflzp3WHl1W6JS9BOnTyujd75JiTEAH9PuevzB2rC3e8LnJjfiECcwgisGjqWqeWRdzSrVMK3xVYAnWmgoLmGRRbD2NKd+SGS6+goaKEqK4R1bXNYkOpryhhZwA5cOLQIsIOyOwQ4stvojzuHJ3gWQcI/pwGZ3jzz6Qq5g6Vn3fWUZ/K7CI9vUpfWG+4vcXbRR/KK6y/53bR5+fu/jkUWz7CPWFbw3ZP6TbRMTG0NLrIoOsZhMigiQyalkUTGYTIIrQMQpgIkQUtC5rrFiZSM5HCBGFhayZSWNiahRSW8wXyjrooEC4IxwXc0ndDqSF94SKoaxW0SO3ph31VEBIMKTCkQJcCXWpoUqCF7hrCNfioucYevQt0hGt0Udj/f3tnHidXVSX+732v9u5Op7uTdKeTEAKENWFJUGSRZYAAgo7jKCDIojgDskgEBGGcUUYlggNuwzKoA4pKlGH/iUgQCGJYEyAJS9hCFpJOZ+21tvfe/f3xXr16VV29JOnq6u6c7+fz+q7v3nOrquuduss5OUOMIdDeoqdjogmBDmE5YbI6iqUjZHWUrBMhS5gMETI6jOPvdoGcgcagVWmK44F0n/V20tjjQNljXBVPXXVsWdoeCkTZqSBnHbYbZx22W6XFEMpIgWJUBqWtsK/+629Xv4PdXlE7uahrh8PB0Ta2drAdG0c7aBwsbXs2O2xsx8FxbLTVhZ3pRGc60dkunGwnZLpwsl3Y2S4y2U6y2S7SVhfZbDcZK0nGSZGx02TsNGknQ0ZnSaO9C9IqF+KGhiKlFBnlhmnPVlWha9HhRqkZ1p0nt+nXvQImIXVhOr/b3c/JzySTs9Ccm4ENgw6DjqCJoHUETRSIoHQIxwmRsQwyWRPHMd26jmtd2nX94KbxLE7jhP0ydAjtleFvS9459hxf5buxmVTnHixxtOb59zdz/5K1pApm1Vw+OX0cn5o50Xc505tJu97+P1JZh/XbkqxrS7G+Lcm6bSnWbUuStnr2tT1EQwbNY+O+U+bm2jgTxkQJGYY/rvZUli2dGbZ0Z9jSlcF2NN/59P471W+lEWVHEMpI0C9O7z+oB+/hZDs2aTtNyk6RtrzQTrtXIJ2yUmTsTL68j7pZO+sbBus1dHovdxwbW1temFdqHO1gD9qy3gAwCTz7du4hGHU0Ua2JaNegYlRD2MEzsKhco4uOO6MQ1hDBJK7cWQTDBsMxUHZupsFEa9deuW+3XCvXhYRn/9zxZhIyOkyGsB+Gwgmqq6qJROJEYlVEo1XEE1XE49VUJaqorh7DmOoaaqvi1MUjJCLhAgeOQSvTvfmlGk6s2dLN8x9s5m/vbuLJNzeQzJZWP/ebOIbDptUTMhRdGddadVfaoitt05F2453elRmA8vD+xi7e39jFw6/1btJj94YEn5s1mX86ZFKBo2WtNcmsTWfKoiNt0ZnK9x2Mb2hPsW6bq9isb0uxpSszoNekJhaiuTbOxLExJtbGaa6NMXFsPpxYGyMWHhylbyQjyo4glAnLsfIKRR+KRsoKKBkDVEpKtZu20li6pzf4kY6hC4/N5o/fKgylvNB1SBFSJlEjREyFiBhhomaEmBklGop5V4JYOEE0XOWmzRgxr6wzZfJ+a5q3WzK805ImbUewnQi2jmDrKLbjzkCgQ3R4CmpdIkxTbZyGMVGaauM0jYlhGvCHV9awZsvATDjWxsOMTYQZm4gwNh6mLhEmHjFZ35ZizZZu1mxJkrF7fyDvZiY4cFwtB06uZeakscycXEv1UDmwLQOOo8nYDumsw8bONP9v6ToeWPIRq7d0D7iNt9a389b69v4rDiL1VRFe/nALT69odZUpT7npSls4O6jTx8MmE8e6sy8Ti5SYXDiS3+uhRNxFMPwcgQ53LMcqeEhn7AwODlprbG2jtfbNh/fI0w4a7f/yz+WXyuvv/uA10LydvV9rjaWtQqUkmyRtp0hbKVI5hcXJYOmdm27eWcIaYiiiQFRDTLthFE3U0cS0Jqodoo5DzNFEtE3MtohqNx3Vbp1QwLZG8Mh2zlaK0W+5d0w7nMAMJTAiVZjhBEa0GiNcjRlJYESqMSLVmNEajEgNZqQaIzYGI1KDitVApBqiNRCpcuPhBBg756cnYzm8tNJ9OD39disfbOoqKB9fE3Wn+8e40/1NtTEm1sZoHJMP+/rFvKUrw/8+t5I7Fr6PFXjanX3Ybpx/xO40VEepjYcxB7iZsytt8ca6dpau3cbStW0s+6iNlUUygzuDuMe4Kg6a7Co+B04eywHNY/r9dR9UMtK2TTrr+Gk3tAvTlk3Gckhbjh+mszbpgnt6v7c7bbNmazdZe+cfQbnZjUTUdBVg5fnkMlyFOBg3lEJrTchUhAzDL4uFXN9grr+wnJ+wUMBnmEnIMFi7tdt3zLy+LcW7Gzp7nWEKYiiojobcK+aGVdEQNbEQVZEQ42uieSWmNk7z2Bi18fCwm10bbow431jz5s3juuuu4/LLL+cnP/kJ4E7/XX/99dx5551s3bqVww47jFtvvZUDDjjAvy+dTnPVVVdx7733kkwmOf7447ntttuYPHnygPseDcqO5VikrBQpO0XSSvoP4qSV9PNTllfmKSpJK+nn5+r0t6QxWmcPyk1Ya1+BCF4x7bhhyTLtl0WC6WCdPsp22mVfpNpTMqp7xkvm1QTSVRCpyZcNQDnRWrOxI80Hm7pY6V0fbOziw81dhAzFnuOr2XN8FXuMr2bP8dXsMb7K3w8xUFrbUzy9opWn3m7luXc30RVwuhkyFB+fVs8/7DuB4/adwB7jqgblQdOWzPLrRR/yq+dW0pZ07ZFMqY9z8bF78c+zJgdOYu1A291Zlq9r4/W121i2to2la9v4aFvPGSXTUOw1vppwSPVQUNzQHhSlo9ycOnMie06oJmIqlq5toytjUZeIEDYN0paroKW98bjKlxtPZQN5loPtaBrHRLnm5H35p0Mm7dT7bDualZs6eWNdO7ajeyg0uXg8bIriUgZGlLLz8ssvc/rppzNmzBiOO+44X9m58cYb+cEPfsDdd9/N3nvvzfe//32effZZVqxYQU1NDQBf+9rXePTRR7n77rtpaGjgyiuvZMuWLSxevBjTHNg65XBSdt7Y9Ab/8sS/0JHtqKgco4WgFdfcckjOqqiZ2zbpxRX5GYlSVlvddlwLozmFIh5UPDCIKpOYESZmhIkaYaLKJKpMTMMEZYIyUMp0H/xeGsPL90KU2Ufcu88w3XaCbRW0k4uH/D6UX6dEO7k+jDCEYxCKoVT+IVyw94jS8cJocK9SYf2utEVLW4qW9rS/P6GlLcX6bSlSll3YUMG3U0/TBvVVESbVxZnk/SKeVJdgUl2CcVURlFI4Gt5r7WDxqm0sWb2V91sLZ0LGJiLM3q2OQ3ev4+Apdb7yNKBx9THGUiSzDv/v9XXcv2QtbUn3B8O46ihnHDqFk2c0EQ2ZvbezHTJs7crwTmsnb6/v4J0N7by1vrOX/R+9PHi1OxMSCRmEDYNo2CRsKCJh002HFMmsw9buLNu6i43J9d5mafLLgZPrEuzWkGBKXYLdGqrYrS7OpLo4tTH3vdzYkeKpFRtZ8MYGFq/eWrRRv5R5jD7GG7j3Y7vX8x+n7c+eE6p7/5zvzOd/IO2IErTDjBhlp7Ozk1mzZnHbbbfx/e9/n4MPPpif/OQnaK1pbm5m7ty5XHPNNYA7i9PY2MiNN97IhRdeSFtbG+PHj+eee+7hjDPOAGDdunVMmTKFxx57jJNOOmlAMgwnZecLj36Bt7e8XVEZBEEQhF2Lv53xN8bGxlZajO1mxHg9v+SSSzj11FM54YQT+P73v+/nr1y5kpaWFubMmePnRaNRjjnmGBYtWsSFF17I4sWLyWazBXWam5uZMWMGixYt6lXZSafTpNN5r+Pt7UO7ka1XtObKgy/lX566tEdRRJlEVYi4MtwZBJR/AkRnbcg4hG3PWadyCCmIhRRhQ2M5Do7tnphRKuhU1PGdi/am8erArw9d+odRr3EGUKcgrop+nZlhMEJoIwRmCFQIbYbACOXz/bhZmO/V92c9KDo+XXCUupf8Xgz8DMa9A2qjt6OqQyh7XzJoDdnA3o2M5e71yFgO2R4bagvbCZmKiGkQDnmhaRA2FWHT3UMxEBksx+2n8NJFfWsMpYiFDWJhk3jY7LGa1t/r0Ft5r6/rAF7jXFnackhmLHIrSIaCaNggZLizK4Zyf/9v93s7QBmK6zjaff2ckq95IE8FDrorP8urkyvcPhmEytJtdTOWsZUWo2xUVNmZP38+S5Ys4eWXX+5R1tLiGttrbGwsyG9sbGTVqlV+nUgkQl1dXY86uftLMW/ePK6//vqdFX/nef0P8OC/FmR9AlhWGWkqQzjh7vUouMaAGfG+RXPfpEU2RIrTWuXda/cod/LVUNiOxtJg2Q62BsvWGIZBJGQQDZmYhgpMK6v++x5wmtLlRh/3D1rfBU+kAdfXKDozNlu6MmzpyrKlK83mriybuzJs7c5iO5qg49HcPQDRsElDdZSGqigN1RE/Xl8dJRYyBij79o/TcjRbujO0tqeJRULs3pAg5C9pD/A9Ket7UJjOOppF72/ikdfX09qRLng9NYqwqairilKXiFBfFaW+KuLGq/PpsYmIaydlB2VZuy3J719czXPvuR6EDUMxpS7BuJoY46qjjKuJMt6Lj6+J0VAdIRIqfE0dNGknS9rOknIypJ0sKTvj5WVIORmy2sLR2jU9gPbME2i2pdK81dLOipY2PmpLgnIPCGilaRwTYfdxCaaOixOLKNcOE24bds42U86UQSDPKYpbvkmEvF0ny6vTlsywoaObrOO66o1HDMbETZTSZJwMSW+fY6UVNUMZRM2of8VCMSJmhJgZK8iPhgJ1TK9OKNbrvfvW70tDvKGiYys3O6XsvPfee7z//vscffTRxONxtNYDXntcs2YNl19+OU888QSxWKzXesXtDaSP/upce+21XHHFFX66vb2dKVOmDEjunWFLV4YHlqwlGjaJhgw++cp8Jpa912FOttu9OjcMSXcK90Nf8SnNEYICarxranFhgd2aXuj0rqF5ewH3vZ3gXSOBMHCMdxHppVLKu7YMrE0NWOAaRDTyhhHd0PDz83mKRqU4bYIi5eWnlSKdVazapljR5tUNlOXbNEgrRWYwXATEvSvAKmBV7nNUTsL5aBJI9uOtR3kHAuLeQYGYd+AgpjVxP+2Wxb38fDoXd0q0kdsH6JZFdKldSDvwQ8semN2eXvnYV+HUm3eujQqyQ9/5mzdv5owzzuCpp55CKcW7777LHnvswVe/+lXGjh3LzTf3/4IsXryY1tZWZs+e7efZts2zzz7Lf//3f7NixQrAnb2ZODGvErS2tvqzPU1NTWQyGbZu3Vowu9Pa2soRRxzRa9/RaJRoNNpreTnI2g5H3fgU3YHTH1WczXnmeKIqQ5eO0UmCTh2nm2huMhiAvFepXJijMN1bvf7v771+fqrazUuETXd63atjeD98FW6YzronHvqTgV7yg/V7a2P7x73j9+fGvT2vWXF+JGS4R1nDBvGIQTwcIhE2iIYN9/Uj//rhKeq58QeXCJT3qcjlF6Rzfarca6fAz9dkHU06a7mnUrIBo2opy19i6DEGVfiaVUVCjImZVEfD1MRMqqMhEhED5clACRldNwjF72tf70GuO+3X6WHSuWxpepbr3LKj47pd0NrzKuX6hbIDaQfQ2sHGXapxAFu7cwGO0m4drQvKs1qTVpoUngVnL0wpTRqHFNCtHZLarZNEk9aalCJ/j4JM7jLy8Uq6hQhp7Zk6cA0u5gwvhrR2DwrovL2knO0kE++HiOrFtIHOe5oPeqg3iuv6aa+u30cwHSinb5MKkRLKSEw7vSghQ0Vvn90ydrnhjTI2Xn52SNn5xje+QSgUYvXq1ey3335+/hlnnME3vvGNASk7xx9/PMuWFS7YfPnLX2bfffflmmuuYY899qCpqYkFCxZwyCGHAJDJZFi4cCE33ngjALNnzyYcDrNgwQJOP/10ANavX8/y5cu56aabdmRoZSNkKI7bdwJ/Wrrez+sizm32P+5Qe5GQAdr78vW+UIdkq/nwtpc/PLFwf5WPdLJAT7MuHhqw876gPB9RGFnfT1RxiMqiCkLL8y3l+EqbGzqgcoqnpwQrNw8CjisVQN6xZdCZJbl70PkrkNY4PerpYJ1BcIY6+Az8UaudcMClQthN67DnViGQ9lwvBPPioSi1sSrqEgkaEtWMr6piXFU1iXCMiOEuhUTMKEqHWbM5yzvrU7zd2jUgo4oTa2N8bPd6TpnRRFNtzLeJY3iKWjDt/7Dy9jEFy3L57o8HN8S/T6EcCyPbhcq0Y2Y6UJlOjEwHRqYDle5AZTpQ6XZUNolysuBYrNrYxptrNmFbGULY7FYbYuK4GBFlg22BkwXbu3Jxx3JnUPy4V+YMQ3Md0Vp320BsTH77QHA7Qaw2H4/XwfQ5/bc5jNkhZeeJJ57gL3/5Sw9bNtOnT/f30/RHTU0NM2bMKMirqqqioaHBz587dy433HAD06dPZ/r06dxwww0kEgnOOussAGpra7ngggu48soraWhooL6+nquuuoqZM2dywgkn7MjQyoZSilvPmsWtZ/Us01pjOe7GwKylSduuzYus5RrgylgOVdEQ08ZV9duPa3zPVYK0F0Jh2vG+03NKUq4sd69rTM9Ntyct/rRsHfe9spbWjnRBX4fsNpbPzZrMrN3GkrEczvrFi75xraYxMWJhdw9MLGISC7kbRHMbRWMhLx7JxQNl3n2GwpO5UCZdND4dGHdxXjJjsy3pHpFtT2bZlszQ5qXbklk6Ur1/CSUi7uxFTSxEdSzMmIDdjKB9F0drOlMWGzvTbO50fcls7kpXyG5J7sGv/bgyLOhF4ciF8YhDJGyjyaJVBq3ckEAa7wrGURlPcbFcJaVCI+4rPZTonFNGzydUPq5AG/k5uFy5Njw/T0WKhu8LKlcWSAcUkZ51eio06JzxhR0jA7QBq3uUpL1rx1nfluKR19fxyOul3TCEsKgiRY1KUkM31SSpVklqvLCabmpUkmqSfliclyBJQm2/nLt7l79UW86ltJzZBzPsmoHw4166VNwM55WU2JgSYW0+nVNqItWFS1u7EDuk7HR1dZFIJHrkb9q0aVCXh66++mqSySQXX3yxb1TwiSee8G3sAPz4xz8mFApx+umn+0YF77777gHb2BkOKKX8kyjumn24v1v6bMtUYA7WBGsd7N88hm+etC+rNndx/5KPeGDJWtZuTfLq6m28unobu9UnfFPuNdEQD1x8BNMba/ppeGCkLZv2pEV7ylVW2lMW7cksXSmL9qRFWzLbo8xNu/dkLBs8T9M579Pu7IMFysKMu2n3YR0IDYusstimLLbpLKRsVCYLyl2EUMohbOK+byEwDQAHXa2h2qHBX+jIzR74Cx3ekojr8NJybDSO9wvVQSlvicqLozR41qRzzjELZywcf8aDnVQ2Brqi398nS6GIhqLEzXhgw2Ruc2TM2zAZJWJGiRpuXtSMEjYiRMwYYSPstWK4l3KVA0PlPtVuPrhuInKLfAamt4jm3Ude4ShIa1DK9FbY3Lraq6NRGL6nacPTnExvUdD1hK0BtCuX9srRrk0fher9R4T/Y6JQac8p84V1c/cXthWs6zg9+8mlc21YtsODr35UYMEZXCODn5rpbg/w+9GuVemW9tSAfTOZ2MRJEydDTKVJePG4ShMnTY2RZUoNxMgQ0ykiOk3UC2M6RZQ00YJ4hhhpEjpJFd3EBvypHBhJHaGTOB06TidxOr2wgzgdOkGKKFlMbAyyOuT5ujewcOPKDHHZCfvROLamSAnxToCWVFi8U6N+POzVDeXv3Ulr4EL/7JCdnVNPPZVZs2bxve99j5qaGpYuXcrUqVM588wzcRyH//u//yuHrGVjONnZGQk4jualD7dw/+K1PLZsfYEVWoCbPn8gp8xoIhaBrkyKzV3dbOnuZkt3F9tS3WxLJmlPeVc6SUcmRVc6RVc2RXc2TdJKk7Jy1pqzvvKRV0iyKGX3kpctUGyUIetuAGEjTCznB8o7iZELcyc5YqFY7/lmjGgoWtiGF/fLvHjYGHwT9/6+opH4q9S2INsFVhoc213S0LYb146bdmwvzyKTzdKVytCVTNGdypC1LJRjeXWL78+HKph2bDKZDC+v3MTWziQmDmOiik9MG8t+jQlMHHCcwrZK9WGlIdMF2aR3daOz3ehMN2STGM7gKiO90tupzeKll4KZDjetI9U4kTHoSBWOEckri8Wzw05+BrmUAqm165aiJrbjP0aFwaesRgXffPNNjj32WGbPns1TTz3FZz7zGd544w22bNnC3//+d/bcc8+dEn6oGS7KztqOtfzo5R+RdtJ5n1Fa571H537hO3m/UQXepfvwB5UrK2hLF/me8mYPdmVCKkTEjBA1oyXDsBFGYeBoheMoN7TB8WceAt6jMTANs8CrtFIGJibKS5vKxHYU3RmHrrRNV0rTlXboSFl0pBxSWXoug2jPDnTRUkl1NMrYuHscuS4Ro74qRkNVlAnV1YyrduNj42FU4NRMsbXfUhZetXZ9Jrk2bDRZyyZja7KWJus4vo0dy3br5ZZe/bSVvzdjuT6SspaX9uzyZGxNxnLLs45DJqvdPgLl0ZDBtIagu4hq9hhXRW0i3HMcxfNPPZK5NdIsZJMoKw1Wyr0ySZQXb+/sYE3rFlo3b0NbKWJkiOqMPwsR0RnCOk3YyRB2UoR0mpCTJmSnMO00hpPGLNqv0XOTfhG697KdTRcv8vWoX/Q0UL3Ee6YVRBIQikM4DuEEKpyPE44XpkNeOlIVqJ9wrXeHExBJoHKKTKQGQuGi3vp+v4uV4v7KhZFL2S0ot7S0cPvtt7N48WIcx2HWrFlccsklBSenRgrDRdmZ839zWN+1vv+KgiAIQtnoy5XDDinWfaV3UjHL2BlsPfQz2JfPupyvzvzqkPdbTNktKDc1NQ0Pw3yjiHP3P5cbX76x0mLsEArlncqIEDEiGCrsLp2EosTMqH+6IkcPa7LF6T508P7q7kzbxeU73db2ytpH333J6WiN5VnJdrTGdjSO47hHn7XGcbQ/axc8+u2mc/GAvexej9wH6geOzfvl+bPjJff2FM9q5Op5CwnuPVoXjL10S4EWior727Bc6lUNjjY/t+lJq/JlxXWDbemisGRfqsezsFfpKm3AbldmIBa8+7h5l2CkuTXaoZmdu+66i+rqar7whS8U5N933310d3dz3nnnDZqAQ8FwmdkZKXRnLE7/n+dZ/lE70ydUc//FRzBG1rEHTm4PR7rT3Q+RyYXBeB9lpe6z+j/mO9pxUNiYZHSILCZZTHdjqXbj2lticcIJupwI67sVbVaYJFGy3u8+QynG10SZWBujqTZOU22MiBm0TAwF6koF8/Nf3QHFK1BF5yxQK1d+ndvM7adz5d5Fb+WGG/fvV97Kqpt2UGS0TRqbjHZIK4O0aZLxjA66IaS1dm0AaZu0nfavrJ0lZafI2BkydqagLJjO2BkyToa05aWdDNZwPNI9jDCVWfAjtDju/iCNETEihM2wf6AgWMdPe/GoGWVMZAxHTTrKdXBcYco6s/PDH/6QO+64o0f+hAkT+Nd//dcRp+wIA8dxNFf84XWWf9ROfVWEX533sdGj6Gjt2sjIJt29G7lNmVYSsikvLI73UjfbXaSkBBWT4WB0R+UfpKNkn5br880irIoegDkFwKHgtPTBUPobsNu7dnJF2TMC4F4KbG+myFauYpbLD8ZdZwXKy8/HvTOA3r2eYqfcPmxcxcb2+1J+v3Yg7nh1sgEFJOPHVR9x+iy3htn+FwVEMYkog4gyCCkDE8O1ueOd4MuFpnJP6ZnKK1eGv8fOzO2xC4SGMv39d4ZhYqgQhhHCNNzQvcIYphuaRhhlmJjB+wL79dzTs/kwZIS8k4l5xSOnhAQVlWIFJGJECBkh2YvUBzuk7KxatYpp06b1yJ86dSqrV/e0xiCMHv7riRU8/kYLEVPxP2cfzG61JmS6i051BE+MWEWnPqx8nYJ07iRIIF0qb3v7sNP9KCVFyswoefD3j3c2eThhhAqP6/r2RHrLL0xbymRTyuGjLouPurOs77bowvDdG1iGxlIOtndZhsZRDqGIoqYqRHXCxIwo0tomo21S2iGDTcqxSePQbVt02RYpx8bx2rLwLCNTaB3Z3kUXoZSGKBDRrvXkiNZEtEPUcTwryrm84jgly6PadW5cGKdkO7kwRP/mEYYUZYAZhVDEDc3IgD/Tfdczt7ONSF6GkCdHQRiUMVw0qzjy2SFlZ8KECSxdupTdd9+9IP/111+noWF0OxPbWf60dD33vPCha+NDBSx8KnwLoDlroa4dkZ6WQkuGePcZYOAQdVJEdcq1beGkOGTLn/hE6x93Wv6rgatzrsx+s9PNCSOZoOGzoN0QM4RlmGSMMCnTJGOYpAyTtGGSNgw3VIbvmymtDHeZA01au4pFWjulQ2zSOksaTUrbJB2HlO2QwSGjNI4Cqr1re8l6V7/j9sJB0mZcdwfeIpL29kFplc8j5yrB/T/PmQh0z/7huULw4iofN3G/E0xcy0QmEFKKCBBBEdGKKIooEMaLa6/My3fj+PlRL+3e6yo1+bp4iobyFWkHXDclGZtsJonOpjCspGtnhyxRNZAXfISjHfeH1Ehdav7yn2Fq7+6XRgo7pOyceeaZfP3rX6empoajjz4agIULF3L55Zdz5plnDqqAo40HlqzlhQ+2oHCopYsEaRIqRZyMa5BLpYh6cTc/TcIz0JUoiseL4gkvvkt8gQwRGs/XEMp3dujHe0zru9P92RJT/bnlBx1YovBMAuKo/JKHxl2SCJgO9Jcl/GURpQqWSHrW9/rxy/NLJbnynPey3PIJBOoElkry9fPy5stLj8dWNrZy6FVz0IHB7Cz5HdI9CAVmBKJaE3WK0r1d21EvTN6/ksL1y+QqIjqgxAR8QZH3B5W7RjMGJX17DrOpF6FPOkbHCeEd2qCcyWQ455xzuO+++wiFXH3JcRzOPfdc7rjjDiKR3lz3Dk+GcoNy1+uPUPXgOWXtYziTe87ZlN7H4O4/cPODexqC9bMD2muQr1NcFtyL0FcbGQbJk/MujusU0l1iiDleOBjKRh91c8sZgtAXvlvagCVuvB8L5DZo5zZlB/e5BTZyU9xGro7yVFl/czd+uSraPN7zPq+OUuDYrlFJJ+uGdsZdqs8tRWvvJKN2/Lib73jL8js5BRkdk28r2G6u7VJL/x/7FzjpBndZrMyU3c4OwDvvvMPrr79OPB5n5syZTJ06dUebqihDehrr92fAO49v920O+A/x3LR/7uGcDjykUwUnIJS/VFBcN5eXLdrM2JciEtxoGdxAGdw0WWqTZUH9Eb4O7O49KL2/IKI1EXK/+PEfvGGtXW/PuMsR7lebLliiMAJ5uTq5GYHcORkDjQoueQRmD+itTmAWId+X9ttUOrext2d5KdlUYLaiL9ljgdel8uc1BEEYcs59GPY4tuzdDImyM1oYUmWncyMs+HfoWE+ybRNvb7bYpDT/PiVJlzwVhpxwQFHJzRSUUl6iRRsfe1qa7f3fqFi9UwMs65Hup8/tabfPPvuwotuzn76/PrZrrLr3sp7tBu0E9dNuX+30Mdb+292x97y43+17r3p/z4vT/b2PA22nR7v9vefbMba+Xu8e7fb1ed+Oz2zPdgcuU4+xD9JY+/6Mbsd7vh3t9ntvH983xXUdFElDkVRu2N14AKl9TiEWqeas/c6iKty/E+sdZdCVnSuuuILvfe97VFVVccUVV/RZ95Zbbtk+aStMpezs/MPNz/DBxi7MqndI7Pa/Q9avIAiCIAwFRzYfyR0n9jRVM1gMup2dV199lWzW3XC4ZMmSXs/zyzn/gfOtk/flX+9ZjN21F8mPziBe+w6xUIxEOEYiHPUtERuEMYh4oeubqXe099cN25NZNnWl2dyZLSgHeqruvbQVpC4RpqEqSk08dzak97r5w7e999OvleAie7QZS9OVztKVselKW6Qtxy/rT/aBldHP6zLwsh7/CdvRbthUVMdCVEVChE3F2q1JsnYvY1X5YLf6BJFQ8POhC2N9/LYpfq17k88BbMf1d2XZDlnHwbY1WcdNF3vY7v010942BR0YRmFcFcmU3zGhi+oW9qOUxlAwJhomETXJb71y9zMUvjeFbWk/XviKuGldor9Ani5+DUu9ppqulEXKsgPj62NsSlMTDRExVUE7wXt7yON3GxxLb7JrDO1uR1fetnSlHb8s/74Up/OyuvdoDGwMbXt+w21M7e6+MyntzmBgn7j+6/ZoN/AcsrVyvZjjnghMKYMU3p4+A7IKskqTVRrH2OUXOgadObvPqbQIgCxjAbuOBeUHlqzlij++DrhKSzRkkszaJLOus8WdoS4RZmpDFU1jYhgGtLanWbctybq24WBArzLUV0XY0jUwr9CT6+Ls21TD3o017NNUg6M1C1ds5Jl3NrKtu+eppupoiOP2ncBx+4xnn6YaFDnzBfhKqBvP7XtUftotU35Z7l78uqXL/cdHUV6wrteTW6eoPNe+5Wg2d2aYUBMlZKrC/ovqpy3H/4wmMzaprHvl0tGwybSGKprHxgiZI+Bsk52FdId7ZToh3YFOtbNx8yZWrd/A+g0b2bx5E1aqgzAWYVzFIaQcaiMQD2kihiaqbCKGQ0Q5hJVDWLl1cvVNHExtY2jLV0CUnQ3YoMrFB2aB2N17F9izR+GhgtyJwcK8EnFVuI8vd6+tDJxwHDsUxwnHsEMxL4ySCUVJm2HSZoiUGXJNGXh7DjdlLFq6M2zsTrM1ncY2HBzl4CgLDBtUFqUsMLKgLFS/P/DKi9YGOCG0DoMOgRP241qb+M59dX7DtKlMtHadDmsdcADsOQjWAcfA1dEIe4yvQWnDcy7sOhjuSDnYDuScCOuiNsBgt7oqdm+oYdq4aqY11FCbiPYwhmgq10BiLBQjEUoQD8XzVzhOxIgM+YRH2fbsWJZFLBbjtddeY8aMGTst6HBgV1B2srbD/v/xOFl7l9dthRFIKWXIV+wKykorYrkKfl4gHryXgnvdiRqlHRIqTRXdJEhSpZNuXKeoottNq24SOkmCbqJ0E1HdmCpJiBSmSmGqNBhptLJ98wQ27qEAC7C80PbyLaWwFFiegpA7hWjj5gfvswNhVuXrW/59ub7ctK+QqNIHC3yFxFdgRt9svdYqoGyEQIfRjhcG851wUZ6nmDgllBWnKNRh7/5cPAQD3K4/rjpaSmgMpTFw/d3Ztk1Hyv0xZeB4qpH2Dg+417iqCATyMpZNd9r98ZSbx3MVUveYguPnuXfYXl5jTZxENEQ8YhIPm8QjJomISSLi5iXCbjoeCZGImHxy+jj2GL8jxq62n7K5iwiFQkydOhXbHnovq6OSje/A7YcP+BdW7yjXoqYyA6HhhsrV4J8L22RDBo7OfYjdy/ZDFYh75drwfskZ3sS0mz8Aafqgv02tA9/4uT33lrPt/vrd0Xb773dn2+6jrJ9fweUac19ljre0pBWBGYOgPSHt5yllkyBJTKWIqTQhZRU8zK3ACcbcKcaUd4Ixnzb8vHSg3lalWF9UN1eueygHYe8ami//SmAEThsWntoLGE0M5Jkl8nJxU2tMNCE0pndYIKYdYtohrh3PbIHjWVJ28mYNejFFUKo8TP//OztFfsJkxxjo4yDWT3nxY1oN4J5SDNTgZo4nvPCs+2Dv4bGMtUOmKL797W9z7bXX8tvf/pb6+vrBlmnXYvn9g6DoAGivHavnBxz3q7YR8v+EgoBn4VYVPsiLH+6pEnnpnBLg5ScD5gxyMxbFyxrFMwqlZhms3H3kzRX4eTs0wxDxrqHHDDxscyf9cvGwdyQ/5J3yC2ntPeQpKPPraNdQYTB0lYGiNrx+Q0X3mYH6JtpXOBTuPYbOG0J0jR/qgEKie+QV1hWEXtiwbGQrOz/72c947733aG5uZurUqVRVFR4rW7JkyaAIt0tw+MWwbTWsXuR6w7Yz7nq6nbsy7LRRKMEnq6JkzDhZI4ZlRMkaMS8eI2vm8uJYZtTNM2JYpltumzH3MZFbGgkun4C/1rKlK8O7rZ1s7izcr1P8Lo6Jh9l7QjWT6hIYhtumozW2sslik9EWWWWR1RZZ8nkd2TQdmQydVoYuK0O3naXbzpJysihTY4Y1RshBmRpMB224exiy2iKrs2Sw3LaxyQyKGePhgQo87H13Cd6DP2c/KP9gd90qRJRBFJOoESKqQsSMoHfnmBsaEWJGmJgKuTMJjkPUsYjZFnE7S8zOErUybtxKE7VSxLJJwtkUKtsN2STKEavmQ4oy865MjEDcDLg4MUJ9+J4K5vfloyrE1pTm/c1J3tuY4oNN3aRtndvmDUBjbYLpjTVMb6xl6rhqb2+ZotCgoBf3DRCWKjfozFis3ZZm9ZYka7YkWbU1xeqtSdK2t01c5yyj5xayCrad93iZ4mGD3eoTTK2PMrUuzpS6GFPqotTHQ+4Ma2/GBHtcJcprmmDP44fwTe+bHVJ2PvvZz6KUQvY2DwKxWvin24esO9vR/P7FVfzoLytoT7kzSl+YPZlrTtm3cJ1Y64CzzmDo+OltXSleX72ZpWu2sGzNFj5obfMeOA6mt0jWVBNmxsRqZkysYr+mKibWRFB22vUKnk0GwmQfeSmcbBfpbBI7m8K2unGsFHY2mTdcWLQZMjdrULgPIRcmsUm592qwHYXjuLpmzjO04+2XKNy7AI4RxjHD2GbEC0M4RjA0capDRGtMJhgmtjJoy1psSWfYlsnSYWWxlcYyHLSyeErZ2B22l6exBrqB0mDgkxYD1GdCgan+oIXjmHbcMJBfUO4ULheEgr/6fQUkqGzk84KzDEbxzAMGhhEmlPMibUYxIzUY0WrMSDVmdAxmpArDjGCGYqiSTg7D7htb8Nnq67PXns/LdLmf+6FEmRCpgnAcQjEIJ9x4OOGOx/CWpZWZfzD6eW5+2ta8t6mbd1qTJC2HsGliaYOUnd+nEdyjkduXEQ6ZjIlHGZOIUZuIUJuIEYuECIXcKxwyCfvxEJGwGxp9ymTSloE3Wrp4fV0Xr67tZEOXjUWIrHdmKx6LcfDU8cyaNoHD9prApPoaPtyW5Xcvr+OPS9bRlnI/wJGQwacPbObcw6dy0JSxQ/q2aK3pyths7kyzqTPDhvYUL3ywmWff2ciHm7sL6jZURfjk9HEcvfd4Pjl9PONrSuy/6QfLdvhgUxdvrW/n7ZYO3vbC9W027jpUDKjz68fCBvs01rDfxDFMrouz/KN2nv9gM23J0kr2x3ev58Jj9uDYfSZg7iJW4rdrg3J3dzff/OY3eeihh8hmsxx//PH8/Oc/Z9y4ceWUsezsChuUARav2sp/PLycN9a1A7D/xDF877MzmD21rp87e6K1ZuE7G/mfhR/w/qZ2Wjs6qYk5dFpJlMqCkUEZWfc0hJHJh0YWpdwwErKorQKtMu6FGzqBMHdpNbBTTaOJYuXDVzpKLIvESigqxQpJzN/vECgP1Bf3Cn1QrITk4uGEp5TECxWTcBzCsZ55oXhRnVzcq2uGt0usjlSW5R+188CStdy3eO2A7omFDU6ZMZE9xlWxW0OCqQ1VTK1PMDYRLutJGst2SFkOb65r58m3NvDkWxv4YGPXgO698Jg9+JdP7lF64+4OkrEctnRl2NSZZnNXhs2daTZ3ZjxTHW56fVuK1Vu66c4MTOk9fI8G9mmqQWuN5WgcrbFsje24actxSqa7MzbLPmobtLENFKXglBlNHL5HA0fvPZ6pDeUz/lcuynIa65vf/Ca33XYbZ599NvF4nN///vcce+yx3HfffYMidKUYzcqO5Vh81NbGfy1Yxv9bthplZKmK2Zzx8SaOnF5L2kmSslL+lbTz6aSV7JHXnu5mQ2c7HenugDIzGHuOdoyeRzHdY5u+QwRdIt8/1umWKZS/OdLd/+Ae4Q3jENEOUSziZInrLFWkqSLjHfEN7HWgcHYiuGTSU8FweiopRTMk4mZh6LDMOFkzQTZUhWUmyIYSZM0qL0yQMauwjIh7pkUZ3kkVA0flZkfy8Vy5hSKZhc6MQ2dG05V1yDgK0wxhmCZhM4QZMgmZYUIhk1AohGmG/PtzJ2Lcy8RR0JWBFa1dvLkpS0pH6SZKioj3SdxxEhGTWbvVUZsIu2aI0L7LJUd7G8JdI01uPpDK2qxvS/HRtuROm60QhgemoXjw4iM4cPLYSouyXZRF2dlzzz35wQ9+4Hs2f+mllzjyyCNJpVKY5sj9aq60smM7mjVbunmvtZP3Nna6YWsnqzZ3UV8V4YDmWvZvHsMBzWPYf+IYGrxfNy+3vMw9b95Dd7abpJ2kLdXF6q1t2DrtKiJGFqVGzqk5pSHiKMJaEXUUUU9RiHuzEgnHIeIpIhHtEA6sz/Rmyry3OCXyezNzXxgv/e8ykD5z7fcux06MISCW+7B1ra3YOhd3T9JZmNjaLHEaL3/cFPAUxP7k7YmJu4wZwvKVRhPbtQND4RX24z0flqXGPKD3YUDvYd9j6PV9GEDb9JJfejyBBndCbksbZAmRIUxGh714iIwOkSGSTxMiS5i0Dntpr64Ok/biubI0YbI67LZJqPCz0etIA+W6VHlv9/WR3287O9NG6XtLjlPvxBj6lWt7P0E7JlfIMDAMN+wKzlQF2pj/r59gYm3cayHQhhetjdYyJjK8JgTKouxEIhFWrlzJpEmT/Lx4PM4777zDlClTdk7iCjJUyk4qa/PBxi5foXm/tZP3N3bywaau7fp11DgmygHNtWyO38UHqb+VTV5BEARByBE2wtx76r3sU79PpUXxKYudHdu2iUQKd0SGQiEsq3LLGMOdt9a3c/MT7/DOhg7WbO0usCavcJihPuQQ0sSMDFEyRMkSUxniyqIxAdlUN8pJu/m58mSG6PtZMmaadxMxDGUFfjFbhFTgl3NgZqc3c+u9xnv7MRHQ+IsN1RfH6SVf9/LDqzDeU4CByGph+jMbFia2cucZLJXLD2ErA1vl4t4siDIDBvFdeymuYS0TR7nl7pKFiaNCJG3Y2GXnDOPnZ05Q7Faf8Db+eWb1Ve7HkfKnCHInugp+v/kJ7ddXStOZtujO2Git2ZYs4fqjxKtSFTVpHBMjGjL7eOV63tudtVm1udReCo1pKKY2uC4plJ+7fe9+LqYGcG/edUNx3dJt957vxh0NGdsmbWnSWYuutNXTvUWvm8RLz/iU7H8AbWx3virMzy9y5eMhQxNSmpByML3QstyDBQYOhsrN5+miMB9XKrd1WQfe4/6l3t7/affUUN6OV3CztC6K68D9PeNuaBoK0zAwjNz/r/t+O1q7BxEC8dxyXK5e4feYKpBb90gH35AS34AKXGvNuT2Ko+Mgj9aKdKqGk3/8Ejr7XkFZJGQQDRlEQybRkEEsbBALm/m8sMGXDpvKCfs3Vkj67VR2tNacf/75RKP5TWKpVIqLLrqo4Pj5Aw88MHgSjnAefm0dT761oWTZ3eGbOMZc2vvNGfIWt3qju48yYcA4uqcCl08XP9oGULcd+nok9tXm9tTt+TVaVDfw0duhcZSqu7n3PvsaR88+d/z1KSjT5X+de/S5g69lf6/PQF/LwXqd3f5McpZ9i+uGsYiTJq7SJEiTIEVkEJbGNa6NupThGm5Mq7ytppRnqDFTZFXaUUX2mig8KRm022SpvHXowrJAexTacMq5xMjFraK2g6c+LS9067n35MZQ2rBkedFO2LPcHEY7ES8MgxPxLTm7YaT/esF87dV3wrifkdLjylgOGcuhow9riM+s2MhfrzyGPYfIsnIx26XsnHfeeT3yvvSlLw2aMKORrx2zJ3uMrwo4rPTQmknLp8FHfSg7wpBh7LDzT6HiDO1zZdiTBdcQpJEzCGmQUa67iaB7iZy7iWyJvJyrCkvl8pU3Q1pTop1gvcL7swElIGeUMqfUOKPQDUUxocCew9xhhLiTP5gQ8w4rxP20V0c7ftwvD7QTbOMdezfShEjpMElv03pSR0gSJe3HI24+UVJeWYoI3TpKNzHXyYl2wyTuZvzBZvbUOibXxQe93YEijkCpwAZlKw1dm6B7M3Rvgq7N6K6NfLBqFVs2rmdT6zrqVQcNtFOv2hlLVz8P40KysXE40TFE2z4gHR7LttA4WqljjVXLe8lqWnUdG3Qdm3QtmSJ9N7hpcvf6BDMnjWFKfZzJYxM0jokG/AYFvTRDcALYclwfLOmsTdoLM5ZD2rLJ2A6ZQDpr2aQth7TlkLFsXl+zjWTWLpBHoQkbYCp8z98KqIooPr57PUfu1cBe46tcObR2ZcmFuTzPi3NhmnzaL3NK3pu2bN5uaWf5R22sWN9O1rZzi13+axbGIkaWqMq4obfsGFVuPEaGxjiMjThEVRZDe3aLvEtpd2LfsS1S6ay/nGDgLk1ETVBB79Tam+zXebNhufpCaTR5i9G5WYOev9iDjip7WnwunGXIzwg4Xpu+rSaCsxLB+4tmDHJtBmYMes5CuGW+VeuAIpNWimTAuvVI82Vl6ODDv/BEYs4qtIFruylE0E5T3nhkzvp0KbtO+bK8NemcFWjfrpNmwG0GZQi2mTMPEQ+MY/sMCYwskjoSOOigeFdP4vLMpazDNUVz8JSx/Oq8Q/0DNeWibI5ARyNDouxoDfPPghWPlad9YIuuZpOuxcFgX2NN2foRhHKggYyClDJIeg/wnKuKpDIK04bh5bsP/mTOj5Wh6A4oBEnPrUW+ztAvMVQKFZgViHiKQ87NREGo824pwp69pWKXE4X35N1QhHXeXYXpGZTMtRkmYCvKm43I+7nKz2KEkMm50UJqv88RO+OuIe2zbI5AhR3EsWDtK2Xtol51Uq86y9qHsGuicX0TJksoEK7CYfjKRE4pSXrKR04pKVZQSikxQ7204f+S13lbSXm7SUWzBLqnTaWQzm2ry5f7Mwf+rES+/ZAudGUxsPZzZRQoClFPkQlat87NLsScIXB2KQhFxI66tNIi9IooO0NBugNSbfDF+Tx47/+wcpuNhUFcZfxNf4nABkA3niLhbQwcp9orPQJhAFjQw3N2bpnCoqfbioLlkFJLF7mlD3puxCy1KbOUewx/o2ZuiaWXjZq5uOXJ3x1YJskpI0O5PJLb6xAvsUch7jgFexyCdfL5bp247rkPIqcg9L7dUhCGH6elv89yvUelxfD55PRxfOPEvTlo8tgR4XJClrEo8zLWq7+Dhy8e3DaFARHcn1HKe3cyWGYUevLO5wXuC8xIpHPLJgHFxtpFlkdMT4mIO4WbLosVjViRwpJXOvL3xn2FxdvvsAvsdRCEHeFP9sdZrRv9XYJ5Mxn4aZfA8Xxduk7wCD+9tNNX/RwaxdhEmC8fuTsaRU00hNIOpLZB9xZIbvWuLXDY1+CgM4P2NQYFWcYaLiz/v51u4qlEnMsbxw+CMIKw89hK0akUncbgn9gQBKE0H0t+yBjng4K8vtSGnVEpVB9zIMXttgPXvwFNts1FW9uo7u3ehy6C5kNgwr47IdmOI8pOmckmxvHb2hreD4dduwy+/Qd3aSEYzyjcPQzevoekUnQbBl3yUBEEQdileTkeq7QI/fJQdRXf27SF3NlX93wpaKU4eNKRNDXsWTHZRNkpM2emx/FOfV2lxRAEQRCEstJmmny9t1WI7Ar+ktxIc3Xz0ArlIVMGZaaGgystgiAIgiBUnESoMtaTQWZ2ys7dZ53Bw68dxbfnL6JWdRLBIoxFBIsQGTLVqzBUlrBWxByHzxovMlMV2sjp23tQfgV1TTjENyeMK9dQBEEQhF0UOzmZ7LaP46THYWcawa7q/6YAe46vYkykpkzS9U9FlZ3bb7+d22+/nQ8//BCAAw44gP/4j//glFNOAVxfXNdffz133nknW7du5bDDDuPWW2/lgAMO8NtIp9NcddVV3HvvvSSTSY4//nhuu+02Jk+eXIkh9WDBmxu4fP5rQIIOnehZoWM64boXMGPrCI9dzDIAJg6pjIIgCILQF2Z8LWZ8bUGenZyEZ1MftIFGYbXNcn11WTXY3dMAxT0XfIJP7NHgOWmtDBVVdiZPnswPf/hD9tprLwB+/etf84//+I+8+uqrHHDAAdx0003ccsst3H333ey99958//vf58QTT2TFihXU1Lga4ty5c3n00UeZP38+DQ0NXHnllZx22mksXrwY0zQrOTwAHn19XUE6XPd3Yk2PVkgaQRAEQRgczPhHPfJCiQ975H170SwWTv/1EEjUO8POzk59fT0/+tGP+MpXvkJzczNz587lmmuuAdxZnMbGRm688UYuvPBC2traGD9+PPfccw9nnHEGAOvWrWPKlCk89thjnHTSSQPqs5x2djrTFv/y61d4/gPXVXR8t18Qqnp/UPsQBEEQhOHKhPB+/PWsP5al7RFnZ8e2be677z66uro4/PDDWblyJS0tLcyZM8evE41GOeaYY1i0aBEXXnghixcvJpvNFtRpbm5mxowZLFq0aMDKTjmJh02u/dS+ZG3N2q3dPLXiWha2PES31QU4KOUdPlfeIT3loMykn6fQkKvjx7V3Xz5NoB3lteO3q4IHAfHu1z3yCOYp/LjaDiekgiAIO4Mq4eiz2PFm0KVGKTcbQWefOSefOWeixa48DK+fXL7y+lQl2jU8GYyifOXlB50j58zweYs83tiK65Sor/Nlhl+u/fZ7XDpvTtAokZ8lREpHSBMhrSNkCJPSUSwjQihaRTSWIFFVQ3VVDaFoAhVKoCIJCCUwInGMUBwVimCGopihGGY4TiQcJ2SGiIXCJCJhULCtK8vcP7zK2q1JJtfF+ckZhzC2yjUPuvuY3Xf04zBoVFzZWbZsGYcffjipVIrq6moefPBB9t9/fxYtWgRAY2NjQf3GxkZWrVoFQEtLC5FIhLq6uh51Wlpaeu0znU6TTqf9dHt7+dwx3PDYW/zquZUoHH4e/jk/NV/0yzqV4vdjakgZrql+nTPlr/MuAdwrb9bfyeVrz4WAztvp8euowD1eXJP37BzMD3p8znlfdusW2gCyKbQNNNI8KwvCaCD3YA767TJLPPBDuM4/ozofRp1cHKLaKQj9OoH6Ye9h6T94i9L+w1rnbfAW5FPoFdykUGHp7Rtke39b7cg30fbes2Pfdv0NRPl2aByvg9x3sva/i5Vfrsl/T2tgStaiybZ3SDKf5M7dXsyz4GpcbcCdRYVn3w/TTxjcDreDiis7++yzD6+99hrbtm3j/vvv57zzzmPhwoV+uSp6qGqte+QV01+defPmcf311++c4APk0Kl1PPL6OsyOdZwWUHQAvjKxkbeikSGRQxCEkU/ux8au4ppE6JvfrGvhkHSm0mIMiE3/77uM+8YurOxEIhF/g/Khhx7Kyy+/zE9/+lN/n05LSwsTJ+ZPJ7W2tvqzPU1NTWQyGbZu3Vowu9Pa2soRRxzRa5/XXnstV1xxhZ9ub29nypQpgzquHKekHuMY60punFzHUYlJfr4G2ofBBmpBEARhZHJucxPVjgPk55EUBZsTCsIgxXmBTQ75PG8Wyca1gtwXE7Kar21OUaMt9s2kmWiniaj8zNP5rWfwy7YUTbWVsQQ97IwKaq1Jp9NMmzaNpqYmFixY4JdlMhkWLlzoKzKzZ88mHA4X1Fm/fj3Lly/vU9mJRqOMGTOm4CoX2aV/4CsTJ/BATTVtpulfougIgiAIO0unYdDpuRXqKop3e1eyxJUqutLelTGUf2U9B8f9KToArWHF9U1xrppYw2lTx7EhXHjP72r+m8bqyj33Kjqzc91113HKKacwZcoUOjo6mD9/Ps888wyPP/44Sinmzp3LDTfcwPTp05k+fTo33HADiUSCs846C4Da2louuOACrrzyShoaGqivr+eqq65i5syZnHBC5abLgnx3z4N4Y9X6SoshCIIgCENGwptxylE75QAwKqdyVFTZ2bBhA+eccw7r16+ntraWAw88kMcff5wTTzwRgKuvvppkMsnFF1/sGxV84oknfBs7AD/+8Y8JhUKcfvrpvlHBu+++e1jY2AE4YrdjeWTV45UWQxAEQRCGDH9f2XmPwrSjKysMw9DOTiUop52dHF0dbcRun4XZvQmAW8fWckddbVn6EgRBEIRK8vuPWpiZycARX4c53ytbPyPOzs5oZsWrf+PH9z3JHZFNJJXikeoqqh2HKzdvLTga7sbhnjFj6DCH3XYqQRAEQejBNzdvJeTbLtIclMqwdzYLs8+HY79VafEAmdkByjyz88wP4Zl5fvKLzY0sj0YHtw9BEARBGGY0WRa/XN/KVMuifeKRjPmXR8EY3C0mA31+y/RBmWlVeS/kP6mrFUVHEARB2CVoCYU4bUozC+Mxxqz/O3RtqpgsouyUmfv1ceye+i3TExfxq7GyR0cQBEHYtfjfsWNYfvIfoaax/8plQpSdMnPeEVM57dD1xBofq7QogiAIgjDk/MsGk9veH0cqu5PuLXYCUXbKTCIS4sq2+ZUWQxAEQRCGnK9v2UbaquexZS0s+6itYnLIaawyc8+b93BTaGOlxRAEQRCEIedn9WOhvoNx6vvs3/xUxeSQmR1BEARBEMpKWneSddIV619mdsrMOfufw4bOzfz6rV9VWhRBEARBKDsz0mn+ZVs74Dom3TruYxx31m8YGxtbMZlE2RkCrvr4XK4yauH/zQVgSTTKec2V25UuCIIgCOVieTTKNyaMo8rR2KEwYxMZDuhupS5WVzGZZBlriNCTZvnxg9Nprt+4mY8nU+yTzlRQKkEQBEEYfByl6DANurXNuq51fHfRdysqjyg7Q0Aqa/Nfr4X4P9t1hmYAn+vs4tBUihXRSGWFEwRBEIQyc9msyyravyxjlZmWrha+8H/foDO1nr2mrOePNKHQKOANsaYsCIIg7AKs7Vhb0f5F2Skz3130XbaxHGLwNjKLIwiCIOx6fO+F73HqHqdSFa6qSP+yjFVmzt7v7EqLIAiCIAgV5d8/8e8VU3RAZnbKzicnf5Jlp9wHtx0GwI31Y/lt7SB7VhcEQRCEYUKTZbF/OsN3Nm2h3nHgzHthn09VVCZRdoaA1up6vvGx01i6aWmf9SKOJowmrN0rBIS0JqQhhBdqjZnLR2Nq/LqmFxoatHLbfDIRJ2XIBJ4gCIIwNLSEQrSEQjTYNv+xeSu8eg/sK8rOqGf+2/P7VXQAMoYigxoCiQRBEAShvOzb2URqxsnETvlBpUURZWcoOH2f01mwagEftn9YaVEEQRAEYacIa83FK6fyoTUdBwMLAwcDWxvYmLzo7MsG6rka6Jq4P1+uaqi0yKLslJtVmzp5ZP5v+XxXMw+O2cb7iW2VFkkQBEEQdpisUnTXL+OzyZe5LHMprbrBNaiiFa6DiG4MuplUW8s/HTKp0uICoLTWutJCVJr29nZqa2tpa2tjzJjB2zystea/vncF33T+l3fDYT43eeKgtS0IgiAIw50vVe3FNWMPgpqJcOgFYA7uHMtAn98ys1NGlFIctO90eBP2ymb5901b+O2YGkw0hgYTMNEsF+OCgiAIwigk/NESWP6Um2g6EKYeXhE5RNkpM3PSCwBoMwxurh9Lt5yMEgRBEHYR7ho7hr2yWT7T2QXj96mYHPLkLTeHX8JLsSifnDpZFB1BEARhl2OV08hzR9wFifqKySAzO+VmrxNo+afb4IXrKy2JIAiCIJSNy6aexpemfgptp9FWGp1oINR8EPFQvNKiyQZlKN8G5SBvbn6TM/7fGWVpWxAEQRAqwZHdSSJac2A6w3lt7YSLK5x6C3zsgrL1P9Dnt6yrlJktXRkeW7aeDz4ay5X7/YrdqvartEiCIAiCMCj8PRHn6aoEP60fy6xpu3HH2CKFY+xulRGsCFnGKjNX/PE1nlmx0UtpJuz1Pj1VX0EQBEEY+bwSiwHt+YzffR6AZ/b5D4794pWVEQpRdsqK1jqg6MB4tnF/y4f8qH4stlJoKLgWJSq/rikIgiAI20PMcbhmy1bijmZOV3fJOpk3/4TWV6BUZVwiibJTRmxHM6U+zrc7fsBJ5itupgU/a91Usn63Uvw1EQcFGoWDqwThhc/HYzxeXTUUoguCIAijiFtbWjk6mSprH/+ZPYclOJg4rj05L95JnN/Zx/NK2qImVpmlDdmgTHk3KKctm+j3t/+43SbD4MaGOlaH8/rom2J8UBAEQRhmXLdpC1/s6PTTX858k6edQwrq3PT5Azn90CmD3rdYUB4mREMmf22+kOPX/c+A7/lpXS2/HFtbRqkEQRAEYXC4YVw9n+/o9Lej3hX5EfzHVhhGtuWGjySjmM3tXdtVv2MYfUAEQRAEoS9ua2ntee5mmD3HZBmL8tvZyWbStC59EjO1BSPbiUq38c7Sn7E4GsLUYKAJaddPlun5zGoJmQAYGpTrTxZw9+44KGwFthc6uO4onpD9PKMGpTUhIKQ1phcqwPLfe7CUwq7QZj9BEIQgIa0xve+rb4X255hP/jt1U2eWfUOyLGMNF176BZk/f5OrJk4odPjZ2FA5mYRhj1aKLJAVZUYQhBGApRSW9331H87bvPLrT3JU8qdsDDXywrXHU18Vqah8w2ueabRhZ+Hxb7E8GhHP5oIgCMIuwTWbtxLV8PfY5bwTOpP6H42HV+6qqEwys1NOzDB86QFm/OHLfGWTxV9rQhhaMZYkYQ0vxWOVllAQBEEQtpvZyRTHJJNA3kRKLp5W8KvaGqZmLY7vTrrbMJJbKiBlnooqO/PmzeOBBx7g7bffJh6Pc8QRR3DjjTeyzz55N/Baa66//nruvPNOtm7dymGHHcatt97KAQcc4NdJp9NcddVV3HvvvSSTSY4//nhuu+02Jk+eXIlhFbLHMczb72F++8Jq8OwLHmUs45eRecyZ0kxLSPRNQRAEYWSxOB5j8QB+sF815VOcd9C/QsOeQyBV71R0GWvhwoVccsklvPDCCyxYsADLspgzZw5dXfnTSzfddBO33HIL//3f/83LL79MU1MTJ554Ih0dHX6duXPn8uCDDzJ//nyee+45Ojs7Oe2007BtuxLD6sG/fWp/vnrUNE47cCInHzCBrslHMi11D+99eB3aruw6piAIgiCUixnxCRVXdGCYncbauHEjEyZMYOHChRx99NForWlubmbu3Llcc801gDuL09jYyI033siFF15IW1sb48eP55577uGMM1yv4uvWrWPKlCk89thjnHTSSf32W87TWAvXLOTSpy4d1DYFQRAEYaSx8IyF1Me238huX4xIr+dtbW0A1Ne7L8bKlStpaWlhzpw5fp1oNMoxxxzDokWLAFi8eDHZbLagTnNzMzNmzPDrFJNOp2lvby+4ykVHtqP/SoIgCIIwytnQtaFifQ+bDSNaa6644gqOOuooZsyYAUBLSwsAjY2NBXUbGxtZtWqVXycSiVBXV9ejTu7+YubNm8f1118/2EMoyWl7nMahjYfSlm6jq6MdFnyH2g0v+nZzXDs6rtbZEjL5ysTGPloTBEEQhJHHH077A/s17Fex/oeNsnPppZeydOlSnnvuuR5lxUaJtNb9Girqq861117LFVdc4afb29uZMmXwfXbkaKpqoqmqCV2nObnjW6zo6AA0f49+nUlqs19vimVx5/oN/HFMje8JnRIhuHZYinfA58LidUkNaFWqriqR11t/Re1595aqW5CvittWPerlDCM6KBzPYF7OcKLGNZ7oBOoU59ve6yEIQnkwtMYAzFwYyCvI94yk5uImGsMzlKo8o6mK3A88XfCjz/DyyZUF6vr3eLsucj8Qc0sTyus/f793T8Aoa295fnt+f9pvI5hXIEuJcQTbyI0hN16j6PXKleXiyn+N8q9lLq5KvN6G99oW9xlk+78R+9/RogOGbG2lsINhwOCprRQW8LBzOMut/fifc69i/4a6PtsuN8NC2bnssst45JFHePbZZwtOUDU1NQHu7M3EiRP9/NbWVn+2p6mpiUwmw9atWwtmd1pbWzniiCNK9heNRolWwO6NUoqDptSyYkMHoPhc+npejBXu5zk8lebwVNqzlOxdnrVk9wPmfeCC+d6H78Hqau6sE59a24sKfIGWCnPx3Jekn+6jzZIKZ05BVL1/rZS+z4sHFDqlC798i2UsNYac9W1fcRQFURggjnK/fyz5zIxaTujq5pbWTTugJPXOp/kr8FfeeG0CTLl8EFvefiqq7Gitueyyy3jwwQd55plnmDZtWkH5tGnTaGpqYsGCBRxyiOtBNZPJsHDhQm688UYAZs+eTTgcZsGCBZx++ukArF+/nuXLl3PTTTcN7YB64ddv/Jr/euW//HSNN5PXDcxkt8oIJfjklIh+f9co/0/FKZ7Z8xlGMgqCMHJ4sipBRkG0DEeWYmPGD36j20lFlZ1LLrmE3//+9zz88MPU1NT4e2xqa2uJx+MopZg7dy433HAD06dPZ/r06dxwww0kEgnOOussv+4FF1zAlVdeSUNDA/X19Vx11VXMnDmTE044oZLDA+Cp1U8VKDqCIAiCMBw5dPcy/fj+cB5Xj7c5Z/9zytP+AKiosnP77bcDcOyxxxbk33XXXZx//vkAXH311SSTSS6++GLfqOATTzxBTU2NX//HP/4xoVCI008/3TcqePfdd2Oa5lANpVdufuXmSosgCIIgCBXlruV38aX9vlR2x6C9Mazs7FSKctrZeWPTG1z21GUkMx102qlBbVsQBEEQhhNTs1nsTAMf6ibqqyLsO7GasGnyrY99i91rdx/0/gb6/BZlh/IqOz6OzdN/+QZfb326PO0LgiAIwjDhkq3bWNp6Pid8/kI+N6t8rptE2dkOhkTZ8Xhp/Uu8uWkZ6uVfkW1bw0/rx5a1P0EQBEGoBEoDymRcbDx3zLmVvev2HvQ+Bvr8HhZHz0czdrKNjr/dgbIzoDX7oNm/s4Xq1W8A0GxZXDNhXIWlFARBEITBxbXPZrMx1cJNL97EL0/+ZcVkEWWnzKy5+Vh2tz7otfxTXd3cnc7wVlQcggqCIAijk39+7UX0HAdlVMZLlSg7ZebdumPYfWPvyg7AZVu3cXHThCGSSBAEQRhpRByNQvsGHh12znL8l7e1M6er27eFn7PUHNaasIaQ1oQJxLUmxI471Hx17JyKKToge3aA8u/ZueWJFfzsqfcA2Fet5r/Cd2DisJ+x2q+jgedjMTaEzLxpc503aR5MB02fG555duhpDv3leIxfjBWLyoIgCLsi/VlaD7r+CJF/noQKQtc1RS7P1PCv29o4Jjmw08Xrzn+J5t33KcPoXGTPzjCipuENavb7DgAfAV/0S8R6siAIglAeymVp/dKmCcxMpal1HCwFFoovtXdwfHeyR93mquFh0V2UnSHgsbX3VFoEQRAEQRg0lsUK/Uu+Eo/x5zUfMdmyAWg366ietB/Gu09A/TQww5UQ00eUnSHg5mNv5tvPfZuklURrxYb2NG1Jy9uq7mq9CjhQrcRWWjYrC4IgCCOKo7uTNHmKDsAYeyusXuReiXo4+KwKSid7doChs7Pzh5dXc839y0qWfdJYyj2RHwLQrRTLoxFCGkJoQtpdJw1766XFW7wG6kG7t/zXYlGWRiMYQEjn1mc1Jnj9aT/f9GTKyVE8Sap7mRoNeu8eaJnuZfZT9+i17/YpUZZru1Q7uT4G1E6JNnvk9yqT6rWO7i2/19ekv3b6Hk/OE3vOM7qtcnEvXylsP533nK4B25sqD26azMXdeirQtptn5/JUvk3bK8vJoP37vfJAn0EZbBRage3FM8bwmDYXhF0ZQ7t7go7pTvKf0/6ZMaeUxzG37NkZZixb29ZD0YmSYSydxFSGbbqaD5wm9jBaSGjNx1PpIZHrhViUfx/fMCR9CYIgCLsGuVNjf61KcEjqI86rsDyi7AwRu9UnOGxaPS+u3ALAPmo1f4l+q8JSwRTLqrQIgiAMASGtiXhXOHD6JoSbdssgot2ZXf/EJ/lTPcX0NofW19zajtxTsn4fixLb28eOzAUOtC0NBTObwZlPd/ZSFcyiurObqihdONtZkA7k5e6DwllRp2im1vFmQ/MzsPmZ29xMrT1IDjunJpo4ec4tg9LWziDKzhBRmwjzhwsPz2e0LIM7drw9jTttn1UKywvdy4ujsFQgv6BO/p6UUsQdh2QF7R8IgjBwlKeohLS7vOwub7t5ppcXCthHMXN1veXnsJcXLnFPLuyrHRN3aVvpgLkLLw9deMzZl7m/Me1k+UDoSzkaTDmGZCw7UMdd8nUVG4u8gmN7y8pu3A0tTyHLlTkKXowdztrQJBxto5RDtZpKvT4ShUHupa2Kmlx7yn7sPq5qEEY5uIiyU2aeXv00X3/666ULp8nRc0EQtg8d+PEiCEPHm97lshWYMnElp037HI7W2I6Dox1e3LCQhR9ZjKsOYxoKRzvUxer4xMRPoCr4mZUNypRvg3LWyTLrnlmD1p4gCIIgjES+OvOrXD7r8kFvd6DPb1m7KCNhI8z3jvxev/VMrYk5DjW2029dQRAEQRhplMPj+fYgMzsM3dHzApbeBw98tUe2Bm5oqGP+mJqhkUMQBEEYFpRy7wDuRnJwTX0E3T4QqJtzA3HhtjYOSacxPZcQppfvuh3SBW6GDC+ed0ukvTLPnURgX5bppRXwgPVJHnKOJIT22nEwcTBwXJcSXnyxszcPf/uLjKsuNEA4mMjR8+HO9BPQkz6G+uhlAJZGI5zd3FRhoQRBEIYPxZuxc5usc2HOv1Pe31NgszT5B3nuyj2wzZzjyyK7ZcETS9CzLGcbKp9WhWUU2qyiRB2nyMZXrp1iW2GOCrZbaJNLq6I+A33dVTuG/y2QsWc7wfstz4bV9py++nz7cn67+W8DWxrKHgdMHXDb5UKUnUoRr2PrWY/xsR88ie1o6uJ/BRZUWipBEIRhg2zGHp7835gaohr2ymYKFMqcmQLfeTWg/vh5xpz4fT4xbY5sUK40FVnG8ni7pZ3/WfgBD766llDNGxiRVkARnfCXIZVDEARBEMrFmfucyb994t8GvV1Zxhoh7Ds+wYZ1q2hmG6HO8YSpI4xNh3kQ7Q2vV1o8QRAEQdhpptdNr2j/ouxUkkw33DCRL1YluG6/cZWWRhAEQRAGjUMmHMIdJ9xBIpyotChy9LyyaEDxp+rhZ21SEARBEHaGV1tfZcXWFZUWA5CZncqhNSgDrniTH917Br/YtpoOQ+X90WhIGoqHaqorLakgCIIgbDdXf+xqDhp/UKXFAGSDMjD0G5Ttl37J+gX/zimTZelKEARBGL3sUbsHvzrpV4yLl+d5JxaUhzF/fvFlrhsnMzaCIAjC6OaDtg/4xE/+m8WrtlRUDlF2hpikleRXzRlejcUqLYogCIIgDCoRRxN3HMJ2CMeqIrP1MKz2g7h70aqKyiV7doaQFVtW8PlHP19pMQRBEAShLLS1nI7VNpsZk8Zg2Zqs7TBpeoLrPrVvReUSZWcIqaT1SEEQBEEoN7+Y1cZxtavBCLmXGYaq8VBTPv9YA0E2KDO0G5QzdoakleyR/9629zj/8fPL2rcgCIIgDJTjurqJa+373sr5DdNKlfC7lc/P1ftCRyfHd3vPu0MvgNNuGXQZxYLycEJr6N4CaCJAJOjP1pvtmV0zjWVfWOhnr7/3EiaueaxsIl0xYRwLqipv6EkQBEEYnjy9k8+IvyfiPLx2HXtkLZh44CBJtWOIsjMU/PZz8P5TJYtsoNMwsPG0YQW2pwx9FDJxUDi4HnAd6JHG81ib06TtgGZto9D+feAo5ccnZa0yDlgQBEHY1Tlo/EFM/tJiMCOVFkWUnaGgK21RykZyUik+vvuUIZdHEARBEMrJiVNP5OZjbh42e1Xl6PkQcPyGyzk+/SNed/YoyN/lN0sJgiAIo5IXPlrCvS99yAsfbK60KIDM7AwJlx4/nY2P/p6DjA8K8hNas2TlatpMA6VdzdNAY+i+tVAN/C0RZ1U4VHqZK7is5aX9TWNK+UtmjhcvXv7K1csvf+WXz+xAXHtpHZAh16cO1M3HvbTqZZktILszTH4NCIIwMjG0DrjfCcQBpUGhve9cN22i/XIjWK5z97nlpvf93Fu54ZcXfp+7/QT6CMipvPrF8kG+rdw3oipqh0CZDoQFce/7vcZxGG/ZBf0ptNdf4QVwv3U0S/TeXkM5iQI1dPEd3kMARUfnvly37E0AbvznmZzxsd36fL/KjSg7Q8CXPjEV9v0uzi8XYXSuLygLA+NsZ7va+82YGn7UUDeIEgqCIIwucnsUAf8giADf27iZz3Z2DajubOdVjs98aaf73GtC5T0GVFTZefbZZ/nRj37E4sWLWb9+PQ8++CCf/exn/XKtNddffz133nknW7du5bDDDuPWW2/lgAMO8Ouk02muuuoq7r33XpLJJMcffzy33XYbkydPrsCI+mDsbvz9pP9Hw32fY39j5yxJ7pvJDJJQgiAIwq5E3Bn4j+s9jfV8GDtru9p/r/kz7HX+/0BkeJ32raiy09XVxUEHHcSXv/xl/vmf/7lH+U033cQtt9zC3Xffzd577833v/99TjzxRFasWEFNTQ0Ac+fO5dFHH2X+/Pk0NDRw5ZVXctppp7F48WJM0xzqIfXJEdGVmMYq7q+u4rvjGyotjiAIgrCLcVXjeK4qaw+vcc7Np3Bb29UAzNptLHd9+ePUxsNl7bU/ho1RQaVUwcyO1prm5mbmzp3LNddcA7izOI2Njdx4441ceOGFtLW1MX78eO655x7OOOMMANatW8eUKVN47LHHOOmkkwbU95AZFbQtWPQzvvXGnfypwm+8IAiCIJSD8R0T+WDt5X76ji/N5uQZTWXpa8QbFVy5ciUtLS3MmTPHz4tGoxxzzDEsWrSICy+8kMWLF5PNZgvqNDc3M2PGDBYtWtSrspNOp0mn0366vb29fAPxePnDLfzno2+Sys5Gcxuprc+BkSS/scvbaqaD29Dc0MTmhtD/Bk0R9thghi7cyObfrTUrohF+Mba2fIMTBEEQRh0Jx2G3jEaj0NpAY6CUica9ukmwSY1DY6C1QmvArqWt7RTqEmFsR/O5WZM5dp/xlR7K8FV2WlpaAGhsbCzIb2xsZNWqVX6dSCRCXV1djzq5+0sxb948rr/++kGWuG8Wr9rKso/aAjmzB3xvFrglPJ796h7EUO45KNM756SUexbK8M5EuScE8vkKzZ/HDovJO0EQBGEEcfCkI/mfOXdWWoxBYdgqOzmKDRJprfs1UtRfnWuvvZYrrrjCT7e3tzNlSnmN+x0/U3Prh9/a4fvbgBcGTxxBEARB6JN/3bq10iIMGsNW2Wlqctf3WlpamDhxop/f2trqz/Y0NTWRyWTYunVrwexOa2srRxxxRK9tR6NRotGh88A678V5/P7t3w9Zf4IgCIKws5yfeht+PXNAdWcnU/yqpZUex4L2OgE+fxfEyutkuz+GrQXladOm0dTUxIIFC/y8TCbDwoULfUVm9uzZhMPhgjrr169n+fLlfSo7Q83z65+vtAiCIAiCUDYWx2N0GiVWVN57Ej54ZsjlKaaiMzudnZ289957fnrlypW89tpr1NfXs9tuuzF37lxuuOEGpk+fzvTp07nhhhtIJBKcdZZ77r+2tpYLLriAK6+8koaGBurr67nqqquYOXMmJ5xwQqWG1YP5p87nTyv/5C+vqc5NqFd/g0p3opTC0ZDMuPaLx1hb2WCa/Kx+bKXFFgRBEHYxPtPR6c/O5Kzqg2ukMWeZOXdVOZqxju3e19lFrVNif+gh58D0E8stdr9UVNl55ZVXOO644/x0bh/Neeedx913383VV19NMpnk4osv9o0KPvHEE76NHYAf//jHhEIhTj/9dN+o4N133z2sbOwkHIcvpALGux/pfe/OupDJSVMmDY1ggiAIghDgkZrts3Z884aNzOlOFuT9n300V2UvAuClfzieCeHYoMm3owwbOzuVpKx2dhwbbtkPOjf4WU8m4nyjsfJH8QRBEAShnMyaMJv/Pv7n1ERq+q+8Awz0+T1s9+yMGpTBhtqDC7LuHVOeN10QBEEQhhNLWhfz0pqFlRZDlJ1yY2v45Kov87HUbTxnH0C7TnBdazdHd6WZYNmVFk8QBEEQysY/dHVzFJX3kzVsj56PFkxDccvpB3HZfX/lwt0iGJFxlRZJEARBEMrKpAyctwne6zyJFeGDOLDC8oiyMwRMGL+W6r1uqrQYgiAIglAW9ugYy0HbxjE5FcfEwcTmt/YxvKGn8dVamdkZ9diOzUVPXlRpMQRBEAShbHxQs40/bl5KNKBVnB96gq7L3qSqNl45wTxkz06ZMQ2Taw+7ttJiCIIgCELZ+NetbUSLznZvNRtIjJ1QGYGKkKPnlPnoeV90boT/2qsg67VohHOam4ZOBkEQBEHYCWalUszp6kbjWlDOGR0EmP3FBzlg4sfK1vdAn9+yjFUhtNY8+n6Wdw5ZwEkf3sjMrU8CsHcmy37pDG9FIxWWUBAEQRD6Z0ksxpJYL4YDn/gKv//U75k5fmA+tsqFKDtDxF/f2sAFv36lIG8cbbwS+1pBXkJr/riuZbvaXhaJcNYkmQ0SBEEQhh9NVZV/PomyM0Q8+VYrk1UrCyPfwFSFK4dLolEubRyPrfJTf3n/Iwqt6OGTRKsSDtcEQRAEYQjYM5MhEniUaaCLKKv1BKaOqyYWNrli9hUcOenIiskYRJSdISBrO5x3xFRYfFcPRUcDX28cR4cpe8UFQRCEkcH7kVJbLTSKDaz23CNd9ORFdKz4LjgxfvOVj3P03pVzkyTKTpnJWA6H3fAkR6UW8vPIrwrKthoGR0+dXCHJBEEQBKF8ZDYfCY67l+e1NdtE2RnNKAXVYcWl1kM9yrbJbI4gCIIwxBzf1c1PWjeVrf3L4j+kdezBTJgYY/z0KAdOruUfD24uW38DQZSdMhPWFs+aF6KMzT3KpmUtHlvzEcuiUd/gkdIaA1DFlwbD29GjwK+Dl1+qvhsvLDO8fCjsh+01QFDGLUPltIUgbZeifG/mSH1NtHy+h4zyvh7l/WyvD5k8Xl2F0iW+g6H3fHLf0UXfz17DyntV/O95Cr+/i8t7+77P5+fLxzgOn+/oLNvr8v5nHuLns44rW/s7iig7ZSRrO5zwo6d5IJWhoZf/uSmWzRSre2gFEwRBEHaeNJzWJd/fOTbsdTp7HnRUpcUoiSg7ZcSyNavaLD7G7dTSiePNwTgoL943N4Xv5NPmC0MiqyAIgiDsDI3v/RG+98eSZWtOvospn/jcEEuUR5SdMhKPmLz5nyfx5rp2cifFW5Mf0ZbZ6tVwD5JrtBtV8HZLG799YRUAj5qNNJgxKDqSnr+zkNxRdchPw5eq11s7fhg41t5nPfDnWEtZzgz2WypeKGPh1Fdv/QWXF4J9Ft9DL/f0bLuXsZZ4zXP1i2UrlKmwj1LT6KXaL9VHcft9fQY0qsfnpPg1L+6DfuUoHqfqvaz43n7qFpT18jno7b7i17v49aIo3vvr18/nvOi+UveW7LeXz0O/fZf4nx1Q333012/fJV6bUv9D+bKe3y8UlBelt8NERrXj8PiadQOu3z+VW9SrpGEQNcBhB//3Kf7OKREv/H4p/T831nGI9eKUYd27r4myM5pJdK7h0Pd/C2ge7lzJt7e90v89U93weeB5hodfEUEQhHLSaRgcJadTRzw3rwrRYJnYGDgY2NogstfRzPridyoqlyg75STdAT87GID5NdX8YFx9ZeURBEEQhDJygLGKSYZdmLnyDXhtbzj0K5URClF2yku4CvY4Dj54muXi60oQBGGXJhRY4ulttSmXryheVh2axbEvtbVzwbb2/AlhCk+E5STKnfYK1oloTbi3hsftXQ5xB4x4PWdovJ472uGZNc/Qle1ic2ea7//pbUBh4vDT8G1Az2OK32isnAEmQRAEYdfkgbXrmZ7NDl6D33gTaicNXnsBBvr8FmWH8ik7tmPzxT99kbe2vDVobQqCIAjCSKNcns8H+vwWE75lJG2nRdERBEEQdnle3/h6RfuXPTtlJBFO8Mzpz/Ds2mdRSqFQfvh/i9fS+v5SLgs95C9brQuZ/Kmqindkf48gCIIwSvjmod/krP3OqqgMouyUmYZ4A/80/Z8K8t7f2MmbSx7m3sgz7GV08044zD9PnlghCQVBEAShfMzZfQ6GquxCkig7Q8ztz7zPY395jJdj38YG7hg7hlvrxlZaLEEQBEEYNOp1jKmN+/Hdw79LU1VTpcWRDcpQvg3KLV0tfOr+T5PVqUFrUxAEQRBGEhPC+/HwP/+G6mhs0NuWDcrDgAdXPC6KjiAIgrBL05p9i1+/VNkNyqLslJGPWgbTz4sgCIIgjEyOdV6uaP+i7JSRie+9U2kRBEEQBKHi/ObDRyravyg7ZWRCTSV93wqCIAjC8KBq3CEV7V+UnTJyZ1dbpUUQBEEQhIpTT7qi/YuyU0YubjieqONUWgxBEARBGHJijsOhyRQ3tG5in7FfrKgsYmenjBwwdRKvPLEWgDZDcX9NNQp4KxLhz9VVlRVOEARBEAaRjydTnN3ewXHdSYo3caysrewPf7GzQxm9nmvNpiWP0LX0IZzOTbQns2hAa42jFRqNRqE1ON670Jm2sBwIvik68LHRRWkX1WuZDpT1bEtR+OYXlRV9MvJtFbZZOq/3usX0HE/f+aXaK1UzN7riUZaqr4pG01v9vtoqXebJUELAUvXxZe69/g7LQH5kPdvv+75cjYG8DqXaKE5rVI/2+5JnR/rr6/PTH6Vep4F8UQbHVYq+JAq+NqXy+7qnWIa+7tvRV6X39rZfhh1Fed+ZQ8HO9rPz9+8c63UDacI72UrfhE2Dg6eMxTQC3/Na+/Hcs636oH9kj4+fUhYZBvr8lpmdcqIU42b/I+Nm/2OlJREEQRCEXZZRs2fntttuY9q0acRiMWbPns3f/va3SoskCIIgCMIwYFQoO3/4wx+YO3cu//Zv/8arr77KJz/5SU455RRWr15dadEEQRAEQagwo2LPzmGHHcasWbO4/fbb/bz99tuPz372s8ybN6/f+8u2Z0cQBEEQhLKxy/jGymQyLF68mDlz5hTkz5kzh0WLFpW8J51O097eXnAJgiAIgjA6GfHKzqZNm7Btm8bGxoL8xsZGWlpaSt4zb948amtr/WvKlClDIaogCIIgCBVgxCs7OVTR2V6tdY+8HNdeey1tbW3+tWbNmqEQURAEQRCECjDij56PGzcO0zR7zOK0trb2mO3JEY1GiUajQyGeIAiCIAgVZsTP7EQiEWbPns2CBQsK8hcsWMARRxxRIakEQRAEQRgujPiZHYArrriCc845h0MPPZTDDz+cO++8k9WrV3PRRRdVWjRBEARBECrMqFB2zjjjDDZv3sx//ud/sn79embMmMFjjz3G1KlTKy2aIAiCIAgVZlTY2dlZxM6OIAiCIIw8dhk7O4IgCIIgCH0hyo4gCIIgCKMaUXYEQRAEQRjVjIoNyjtLbtuSuI0QBEEQhJFD7rnd3/ZjUXaAjo4OAHEbIQiCIAgjkI6ODmpra3stl9NYgOM4rFu3jpqaml5dTOwI7e3tTJkyhTVr1uySp7xk/DL+XXX8u/LYQcYv4x+68Wut6ejooLm5GcPofWeOzOwAhmEwefLksrU/ZsyYXfIDn0PGL+PfVce/K48dZPwy/qEZf18zOjlkg7IgCIIgCKMaUXYEQRAEQRjViLJTRqLRKN/5znd2WQ/rMn4Z/646/l157CDjl/EPv/HLBmVBEARBEEY1MrMjCIIgCMKoRpQdQRAEQRBGNaLsCIIgCIIwqhFlRxAEQRCEUY0oO2XktttuY9q0acRiMWbPns3f/va3Sou03Tz77LN8+tOfprm5GaUUDz30UEG51prvfve7NDc3E4/HOfbYY3njjTcK6qTTaS677DLGjRtHVVUVn/nMZ1i7dm1Bna1bt3LOOedQW1tLbW0t55xzDtu2bSvz6Ppm3rx5fOxjH6OmpoYJEybw2c9+lhUrVhTUGc3jv/322znwwAN9w2CHH344f/7zn/3y0Tz2YubNm4dSirlz5/p5o3383/3ud1FKFVxNTU1++Wgf/0cffcSXvvQlGhoaSCQSHHzwwSxevNgvH83j33333Xu890opLrnkEmCEjl0LZWH+/Pk6HA7rX/ziF/rNN9/Ul19+ua6qqtKrVq2qtGjbxWOPPab/7d/+Td9///0a0A8++GBB+Q9/+ENdU1Oj77//fr1s2TJ9xhln6IkTJ+r29na/zkUXXaQnTZqkFyxYoJcsWaKPO+44fdBBB2nLsvw6J598sp4xY4ZetGiRXrRokZ4xY4Y+7bTThmqYJTnppJP0XXfdpZcvX65fe+01feqpp+rddttNd3Z2+nVG8/gfeeQR/ac//UmvWLFCr1ixQl933XU6HA7r5cuXa61H99iDvPTSS3r33XfXBx54oL788sv9/NE+/u985zv6gAMO0OvXr/ev1tZWv3w0j3/Lli166tSp+vzzz9cvvviiXrlypX7yySf1e++959cZzeNvbW0teN8XLFigAf30009rrUfm2EXZKRMf//jH9UUXXVSQt+++++pvfetbFZJo5ylWdhzH0U1NTfqHP/yhn5dKpXRtba2+4447tNZab9u2TYfDYT1//ny/zkcffaQNw9CPP/641lrrN998UwP6hRde8Os8//zzGtBvv/12mUc1cFpbWzWgFy5cqLXe9cavtdZ1dXX6l7/85S4z9o6ODj19+nS9YMECfcwxx/jKzq4w/u985zv6oIMOKlk22sd/zTXX6KOOOqrX8tE+/mIuv/xyveeee2rHcUbs2GUZqwxkMhkWL17MnDlzCvLnzJnDokWLKiTV4LNy5UpaWloKxhmNRjnmmGP8cS5evJhsNltQp7m5mRkzZvh1nn/+eWpraznssMP8Op/4xCeora0dVq9XW1sbAPX19cCuNX7btpk/fz5dXV0cfvjhu8zYL7nkEk499VROOOGEgvxdZfzvvvsuzc3NTJs2jTPPPJMPPvgAGP3jf+SRRzj00EP5whe+wIQJEzjkkEP4xS9+4ZeP9vEHyWQy/Pa3v+UrX/kKSqkRO3ZRdsrApk2bsG2bxsbGgvzGxkZaWloqJNXgkxtLX+NsaWkhEolQV1fXZ50JEyb0aH/ChAnD5vXSWnPFFVdw1FFHMWPGDGDXGP+yZcuorq4mGo1y0UUX8eCDD7L//vvvEmOfP38+S5YsYd68eT3KdoXxH3bYYfzmN7/hL3/5C7/4xS9oaWnhiCOOYPPmzaN+/B988AG3334706dP5y9/+QsXXXQRX//61/nNb34D7Brvf46HHnqIbdu2cf755wMjd+zi9byMKKUK0lrrHnmjgR0ZZ3GdUvWH0+t16aWXsnTpUp577rkeZaN5/Pvssw+vvfYa27Zt4/777+e8885j4cKFfvloHfuaNWu4/PLLeeKJJ4jFYr3WG63jBzjllFP8+MyZMzn88MPZc889+fWvf80nPvEJYPSO33EcDj30UG644QYADjnkEN544w1uv/12zj33XL/eaB1/kF/96leccsopNDc3F+SPtLHLzE4ZGDduHKZp9tBOW1tbe2jDI5ncyYy+xtnU1EQmk2Hr1q191tmwYUOP9jdu3DgsXq/LLruMRx55hKeffprJkyf7+bvC+CORCHvttReHHnoo8+bN46CDDuKnP/3pqB/74sWLaW1tZfbs2YRCIUKhEAsXLuRnP/sZoVDIl220jr8UVVVVzJw5k3fffXfUv/8TJ05k//33L8jbb7/9WL16NbBr/O8DrFq1iieffJKvfvWrft5IHbsoO2UgEokwe/ZsFixYUJC/YMECjjjiiApJNfhMmzaNpqamgnFmMhkWLlzoj3P27NmEw+GCOuvXr2f58uV+ncMPP5y2tjZeeuklv86LL75IW1tbRV8vrTWXXnopDzzwAE899RTTpk0rKB/t4y+F1pp0Oj3qx3788cezbNkyXnvtNf869NBDOfvss3nttdfYY489RvX4S5FOp3nrrbeYOHHiqH//jzzyyB5mJt555x2mTp0K7Dr/+3fddRcTJkzg1FNP9fNG7NgHfcuzoLXOHz3/1a9+pd988009d+5cXVVVpT/88MNKi7ZddHR06FdffVW/+uqrGtC33HKLfvXVV/0j9D/84Q91bW2tfuCBB/SyZcv0F7/4xZJHECdPnqyffPJJvWTJEv0P//APJY8gHnjggfr555/Xzz//vJ45c2bFj19+7Wtf07W1tfqZZ54pOIbZ3d3t1xnN47/22mv1s88+q1euXKmXLl2qr7vuOm0Yhn7iiSe01qN77KUInsbSevSP/8orr9TPPPOM/uCDD/QLL7ygTzvtNF1TU+N/h43m8b/00ks6FArpH/zgB/rdd9/Vv/vd73QikdC//e1v/Tqjefxaa23btt5tt930Nddc06NsJI5dlJ0ycuutt+qpU6fqSCSiZ82a5R9ZHkk8/fTTGuhxnXfeeVpr9wjmd77zHd3U1KSj0ag++uij9bJlywraSCaT+tJLL9X19fU6Ho/r0047Ta9evbqgzubNm/XZZ5+ta2pqdE1NjT777LP11q1bh2iUpSk1bkDfddddfp3RPP6vfOUr/ud3/Pjx+vjjj/cVHa1H99hLUazsjPbx52ynhMNh3dzcrD/3uc/pN954wy8f7eN/9NFH9YwZM3Q0GtX77ruvvvPOOwvKR/v4//KXv2hAr1ixokfZSBy70lrrwZ8vEgRBEARBGB7Inh1BEARBEEY1ouwIgiAIgjCqEWVHEARBEIRRjSg7giAIgiCMakTZEQRBEARhVCPKjiAIgiAIoxpRdgRBEARBGNWIsiMIgiAIwqhGlB1BEEYE559/PkoplFKEw2EaGxs58cQT+d///V8cxxlwO3fffTdjx44tn6CCIAw7RNkRBGHEcPLJJ7N+/Xo+/PBD/vznP3Pcccdx+eWXc9ppp2FZVqXFEwRhmCLKjiAII4ZoNEpTUxOTJk1i1qxZXHfddTz88MP8+c9/5u677wbglltuYebMmVRVVTFlyhQuvvhiOjs7AXjmmWf48pe/TFtbmz9L9N3vfhdwPTdfffXVTJo0iaqqKg477DCeeeaZygxUEIRBRZQdQRBGNP/wD//AQQcdxAMPPACAYRj87Gc/Y/ny5fz617/mqaee4uqrrwbgiCOO4Cc/+Qljxoxh/fr1rF+/nquuugqAL3/5y/z9739n/vz5LF26lC984QucfPLJvPvuuxUbmyAIg4M4AhUEYURw/vnns23bNh566KEeZWeeeSZLly7lzTff7FF233338bWvfY1NmzYB7p6duXPnsm3bNr/O+++/z/Tp01m7di3Nzc1+/gknnMDHP/5xbrjhhkEfjyAIQ0eo0gIIgiDsLFprlFIAPP3009xwww28+eabtLe3Y1kWqVSKrq4uqqqqSt6/ZMkStNbsvffeBfnpdJqGhoayyy8IQnkRZUcQhBHPW2+9xbRp01i1ahWf+tSnuOiii/je975HfX09zz33HBdccAHZbLbX+x3HwTRNFi9ejGmaBWXV1dXlFl8QhDIjyo4gCCOap556imXLlvGNb3yDV155BcuyuPnmmzEMd0viH//4x4L6qtQDAQAAAYpJREFUkUgE27YL8g455BBs26a1tZVPfvKTQya7IAhDgyg7giCMGNLpNC0tLdi2zYYNG3j88ceZN28ep512Gueeey7Lli3Dsix+/vOf8+lPf5q///3v3HHHHQVt7L777nR2dvLXv/6Vgw46iEQiwd57783ZZ5/Nueeey80338whhxzCpk2beOqpp5g5cyaf+tSnKjRiQRAGAzmNJQjCiOHxxx9n4sSJ7L777px88sk8/fTT/OxnP+Phhx/GNE0OPvhgbrnlFm688UZmzJjB7373O+bNm1fQxhFHHMFFF13EGWecwfjx47npppsAuOuuuzj33HO58sor2WefffjMZz7Diy++yJQpUyoxVEEQBhE5jSUIgiAIwqhGZnYEQRAEQRjViLIjCIIgCMKoRpQdQRAEQRBGNaLsCIIgCIIwqhFlRxAEQRCEUY0oO4IgCIIgjGpE2REEQRAEYVQjyo4gCIIgCKMaUXYEQRAEQRjViLIjCIIgCMKoRpQdQRAEQRBGNaLsCIIgCIIwqvn/ODcFBItj7xAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the moving average chart\n",
     "df['MA_50'] = df['Close'].rolling(window=50).mean()\n",
@@ -1002,13 +1562,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 167,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot the Bollinger Bands chart\n",
-    "df['MA_20'] = df['Close'].rolling(window=20).mean()\n",
-    "df['Upper_BB'] = df['MA_20'] + 2*df['Close'].rolling(window=20).std()\n",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the Bollinger Bands chart\n",
+    "df['MA_20'] = df['Close'].rolling(window=20).mean()\n",
+    "df['Upper_BB'] = df['MA_20'] + 2*df['Close'].rolling(window=20).std()\n",
     "df['Lower_BB'] = df['MA_20'] - 2*df['Close'].rolling(window=20).std()\n",
     "\n",
     "plt.plot(df['Date'], df['Close'], label='Close')\n",
@@ -1030,9 +1601,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 170,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the RSI chart\n",
     "delta = df['Close'].diff(1)\n",
@@ -1063,9 +1645,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 173,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the heatmap\n",
     "corr_matrix = df[['Open', 'High', 'Low', 'Close', 'Volume']].corr()\n",
@@ -1095,7 +1688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 177,
    "metadata": {
     "id": "yi9UnG0BXtGG"
    },
@@ -1107,7 +1700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 179,
    "metadata": {
     "id": "_QwN3imwXZGZ"
    },
@@ -1119,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": null,
    "metadata": {
     "id": "WYCTE93pXhyC"
    },
@@ -1161,7 +1754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 185,
    "metadata": {
     "id": "CFGQWjNTX4Mf"
    },
@@ -1174,7 +1767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 187,
    "metadata": {
     "id": "LoX_CWbbX-tN"
    },
@@ -1195,9 +1788,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 190,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "of how many past days you want to see graph:  3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the SMA graph\n",
     "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
@@ -1233,7 +1844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 193,
    "metadata": {
     "id": "lTkIdld1YaxN"
    },
@@ -1255,7 +1866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1274,7 +1885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 198,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1283,7 +1894,25 @@
     "id": "dqR2l5r-YngD",
     "outputId": "07d7a94a-4fd6-4f87-cf37-54b6980fa5cb"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "of how many past days you want to see graph:  3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the RSI graph\n",
     "\n",
@@ -1320,7 +1949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 200,
    "metadata": {
     "id": "4j9soKAXXvZX"
    },
@@ -1347,7 +1976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 203,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1356,7 +1985,25 @@
     "id": "BV8z1ge1Ysl3",
     "outputId": "9ddf6a13-ee48-45ea-f6f4-e361d1decc11"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "of how many past days you want to see graph:  5\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plot the MACD\n",
     "fig, ax = plt.subplots(2, 1, figsize=(10, 8))\n",
@@ -1393,7 +2040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 205,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1402,7 +2049,456 @@
     "id": "I7pi-rfLZBGf",
     "outputId": "549af121-3ef6-476f-e359-2e3ce2cbb166"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>MA_50</th>\n",
+       "      <th>MA_200</th>\n",
+       "      <th>MA_20</th>\n",
+       "      <th>Upper_BB</th>\n",
+       "      <th>Lower_BB</th>\n",
+       "      <th>SMA_10</th>\n",
+       "      <th>SMA_50</th>\n",
+       "      <th>RSI</th>\n",
+       "      <th>EMA_12</th>\n",
+       "      <th>EMA_26</th>\n",
+       "      <th>MACD</th>\n",
+       "      <th>Signal_Line</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Open</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999812</td>\n",
+       "      <td>0.999793</td>\n",
+       "      <td>0.999613</td>\n",
+       "      <td>-0.246129</td>\n",
+       "      <td>0.985126</td>\n",
+       "      <td>0.936083</td>\n",
+       "      <td>0.994502</td>\n",
+       "      <td>0.993450</td>\n",
+       "      <td>0.992257</td>\n",
+       "      <td>0.998788</td>\n",
+       "      <td>0.992463</td>\n",
+       "      <td>0.060667</td>\n",
+       "      <td>0.998913</td>\n",
+       "      <td>0.997290</td>\n",
+       "      <td>0.201107</td>\n",
+       "      <td>0.211920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>High</th>\n",
+       "      <td>0.999812</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999717</td>\n",
+       "      <td>0.999829</td>\n",
+       "      <td>-0.243362</td>\n",
+       "      <td>0.985031</td>\n",
+       "      <td>0.935987</td>\n",
+       "      <td>0.994398</td>\n",
+       "      <td>0.993568</td>\n",
+       "      <td>0.991903</td>\n",
+       "      <td>0.998675</td>\n",
+       "      <td>0.992366</td>\n",
+       "      <td>0.063160</td>\n",
+       "      <td>0.998821</td>\n",
+       "      <td>0.997184</td>\n",
+       "      <td>0.201486</td>\n",
+       "      <td>0.211589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Low</th>\n",
+       "      <td>0.999793</td>\n",
+       "      <td>0.999717</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999819</td>\n",
+       "      <td>-0.247681</td>\n",
+       "      <td>0.984818</td>\n",
+       "      <td>0.936005</td>\n",
+       "      <td>0.994254</td>\n",
+       "      <td>0.992972</td>\n",
+       "      <td>0.992266</td>\n",
+       "      <td>0.998567</td>\n",
+       "      <td>0.992135</td>\n",
+       "      <td>0.064062</td>\n",
+       "      <td>0.998719</td>\n",
+       "      <td>0.997029</td>\n",
+       "      <td>0.202962</td>\n",
+       "      <td>0.212715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Close</th>\n",
+       "      <td>0.999613</td>\n",
+       "      <td>0.999829</td>\n",
+       "      <td>0.999819</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.244964</td>\n",
+       "      <td>0.984837</td>\n",
+       "      <td>0.935956</td>\n",
+       "      <td>0.994232</td>\n",
+       "      <td>0.993180</td>\n",
+       "      <td>0.991986</td>\n",
+       "      <td>0.998513</td>\n",
+       "      <td>0.992144</td>\n",
+       "      <td>0.065853</td>\n",
+       "      <td>0.998676</td>\n",
+       "      <td>0.996998</td>\n",
+       "      <td>0.202614</td>\n",
+       "      <td>0.212016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Volume</th>\n",
+       "      <td>-0.246129</td>\n",
+       "      <td>-0.243362</td>\n",
+       "      <td>-0.247681</td>\n",
+       "      <td>-0.244964</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.241808</td>\n",
+       "      <td>-0.156613</td>\n",
+       "      <td>-0.247522</td>\n",
+       "      <td>-0.241989</td>\n",
+       "      <td>-0.252852</td>\n",
+       "      <td>-0.247841</td>\n",
+       "      <td>-0.247594</td>\n",
+       "      <td>0.051904</td>\n",
+       "      <td>-0.247778</td>\n",
+       "      <td>-0.248238</td>\n",
+       "      <td>-0.025686</td>\n",
+       "      <td>-0.041019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MA_50</th>\n",
+       "      <td>0.985126</td>\n",
+       "      <td>0.985031</td>\n",
+       "      <td>0.984818</td>\n",
+       "      <td>0.984837</td>\n",
+       "      <td>-0.241808</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.954341</td>\n",
+       "      <td>0.992381</td>\n",
+       "      <td>0.991173</td>\n",
+       "      <td>0.990316</td>\n",
+       "      <td>0.987473</td>\n",
+       "      <td>0.992357</td>\n",
+       "      <td>-0.008551</td>\n",
+       "      <td>0.988224</td>\n",
+       "      <td>0.990678</td>\n",
+       "      <td>0.085060</td>\n",
+       "      <td>0.103564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MA_200</th>\n",
+       "      <td>0.936083</td>\n",
+       "      <td>0.935987</td>\n",
+       "      <td>0.936005</td>\n",
+       "      <td>0.935956</td>\n",
+       "      <td>-0.156613</td>\n",
+       "      <td>0.954341</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.943263</td>\n",
+       "      <td>0.942357</td>\n",
+       "      <td>0.941031</td>\n",
+       "      <td>0.938292</td>\n",
+       "      <td>0.947045</td>\n",
+       "      <td>-0.005434</td>\n",
+       "      <td>0.939204</td>\n",
+       "      <td>0.942774</td>\n",
+       "      <td>0.046120</td>\n",
+       "      <td>0.050529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MA_20</th>\n",
+       "      <td>0.994502</td>\n",
+       "      <td>0.994398</td>\n",
+       "      <td>0.994254</td>\n",
+       "      <td>0.994232</td>\n",
+       "      <td>-0.247522</td>\n",
+       "      <td>0.992381</td>\n",
+       "      <td>0.943263</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.998546</td>\n",
+       "      <td>0.998185</td>\n",
+       "      <td>0.996501</td>\n",
+       "      <td>0.993994</td>\n",
+       "      <td>0.010021</td>\n",
+       "      <td>0.996732</td>\n",
+       "      <td>0.996960</td>\n",
+       "      <td>0.148834</td>\n",
+       "      <td>0.176405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Upper_BB</th>\n",
+       "      <td>0.993450</td>\n",
+       "      <td>0.993568</td>\n",
+       "      <td>0.992972</td>\n",
+       "      <td>0.993180</td>\n",
+       "      <td>-0.241989</td>\n",
+       "      <td>0.991173</td>\n",
+       "      <td>0.942357</td>\n",
+       "      <td>0.998546</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.993487</td>\n",
+       "      <td>0.995246</td>\n",
+       "      <td>0.992805</td>\n",
+       "      <td>0.013123</td>\n",
+       "      <td>0.995469</td>\n",
+       "      <td>0.995654</td>\n",
+       "      <td>0.149849</td>\n",
+       "      <td>0.175100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lower_BB</th>\n",
+       "      <td>0.992257</td>\n",
+       "      <td>0.991903</td>\n",
+       "      <td>0.992266</td>\n",
+       "      <td>0.991986</td>\n",
+       "      <td>-0.252852</td>\n",
+       "      <td>0.990316</td>\n",
+       "      <td>0.941031</td>\n",
+       "      <td>0.998185</td>\n",
+       "      <td>0.993487</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.994474</td>\n",
+       "      <td>0.991904</td>\n",
+       "      <td>0.006521</td>\n",
+       "      <td>0.994714</td>\n",
+       "      <td>0.994990</td>\n",
+       "      <td>0.147187</td>\n",
+       "      <td>0.177256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SMA_10</th>\n",
+       "      <td>0.998788</td>\n",
+       "      <td>0.998675</td>\n",
+       "      <td>0.998567</td>\n",
+       "      <td>0.998513</td>\n",
+       "      <td>-0.247841</td>\n",
+       "      <td>0.987473</td>\n",
+       "      <td>0.938292</td>\n",
+       "      <td>0.996501</td>\n",
+       "      <td>0.995246</td>\n",
+       "      <td>0.994474</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.994842</td>\n",
+       "      <td>0.036431</td>\n",
+       "      <td>0.999915</td>\n",
+       "      <td>0.999055</td>\n",
+       "      <td>0.179858</td>\n",
+       "      <td>0.201548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SMA_50</th>\n",
+       "      <td>0.992463</td>\n",
+       "      <td>0.992366</td>\n",
+       "      <td>0.992135</td>\n",
+       "      <td>0.992144</td>\n",
+       "      <td>-0.247594</td>\n",
+       "      <td>0.992357</td>\n",
+       "      <td>0.947045</td>\n",
+       "      <td>0.993994</td>\n",
+       "      <td>0.992805</td>\n",
+       "      <td>0.991904</td>\n",
+       "      <td>0.994842</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.009370</td>\n",
+       "      <td>0.995598</td>\n",
+       "      <td>0.998145</td>\n",
+       "      <td>0.083592</td>\n",
+       "      <td>0.101745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RSI</th>\n",
+       "      <td>0.060667</td>\n",
+       "      <td>0.063160</td>\n",
+       "      <td>0.064062</td>\n",
+       "      <td>0.065853</td>\n",
+       "      <td>0.051904</td>\n",
+       "      <td>-0.008551</td>\n",
+       "      <td>-0.005434</td>\n",
+       "      <td>0.010021</td>\n",
+       "      <td>0.013123</td>\n",
+       "      <td>0.006521</td>\n",
+       "      <td>0.036431</td>\n",
+       "      <td>-0.009370</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.033634</td>\n",
+       "      <td>0.013165</td>\n",
+       "      <td>0.579522</td>\n",
+       "      <td>0.425595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EMA_12</th>\n",
+       "      <td>0.998913</td>\n",
+       "      <td>0.998821</td>\n",
+       "      <td>0.998719</td>\n",
+       "      <td>0.998676</td>\n",
+       "      <td>-0.247778</td>\n",
+       "      <td>0.988224</td>\n",
+       "      <td>0.939204</td>\n",
+       "      <td>0.996732</td>\n",
+       "      <td>0.995469</td>\n",
+       "      <td>0.994714</td>\n",
+       "      <td>0.999915</td>\n",
+       "      <td>0.995598</td>\n",
+       "      <td>0.033634</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.999384</td>\n",
+       "      <td>0.173035</td>\n",
+       "      <td>0.193561</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EMA_26</th>\n",
+       "      <td>0.997290</td>\n",
+       "      <td>0.997184</td>\n",
+       "      <td>0.997029</td>\n",
+       "      <td>0.996998</td>\n",
+       "      <td>-0.248238</td>\n",
+       "      <td>0.990678</td>\n",
+       "      <td>0.942774</td>\n",
+       "      <td>0.996960</td>\n",
+       "      <td>0.995654</td>\n",
+       "      <td>0.994990</td>\n",
+       "      <td>0.999055</td>\n",
+       "      <td>0.998145</td>\n",
+       "      <td>0.013165</td>\n",
+       "      <td>0.999384</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.138353</td>\n",
+       "      <td>0.160681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MACD</th>\n",
+       "      <td>0.201107</td>\n",
+       "      <td>0.201486</td>\n",
+       "      <td>0.202962</td>\n",
+       "      <td>0.202614</td>\n",
+       "      <td>-0.025686</td>\n",
+       "      <td>0.085060</td>\n",
+       "      <td>0.046120</td>\n",
+       "      <td>0.148834</td>\n",
+       "      <td>0.149849</td>\n",
+       "      <td>0.147187</td>\n",
+       "      <td>0.179858</td>\n",
+       "      <td>0.083592</td>\n",
+       "      <td>0.579522</td>\n",
+       "      <td>0.173035</td>\n",
+       "      <td>0.138353</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.952623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Signal_Line</th>\n",
+       "      <td>0.211920</td>\n",
+       "      <td>0.211589</td>\n",
+       "      <td>0.212715</td>\n",
+       "      <td>0.212016</td>\n",
+       "      <td>-0.041019</td>\n",
+       "      <td>0.103564</td>\n",
+       "      <td>0.050529</td>\n",
+       "      <td>0.176405</td>\n",
+       "      <td>0.175100</td>\n",
+       "      <td>0.177256</td>\n",
+       "      <td>0.201548</td>\n",
+       "      <td>0.101745</td>\n",
+       "      <td>0.425595</td>\n",
+       "      <td>0.193561</td>\n",
+       "      <td>0.160681</td>\n",
+       "      <td>0.952623</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Open      High       Low     Close    Volume     MA_50  \\\n",
+       "Open         1.000000  0.999812  0.999793  0.999613 -0.246129  0.985126   \n",
+       "High         0.999812  1.000000  0.999717  0.999829 -0.243362  0.985031   \n",
+       "Low          0.999793  0.999717  1.000000  0.999819 -0.247681  0.984818   \n",
+       "Close        0.999613  0.999829  0.999819  1.000000 -0.244964  0.984837   \n",
+       "Volume      -0.246129 -0.243362 -0.247681 -0.244964  1.000000 -0.241808   \n",
+       "MA_50        0.985126  0.985031  0.984818  0.984837 -0.241808  1.000000   \n",
+       "MA_200       0.936083  0.935987  0.936005  0.935956 -0.156613  0.954341   \n",
+       "MA_20        0.994502  0.994398  0.994254  0.994232 -0.247522  0.992381   \n",
+       "Upper_BB     0.993450  0.993568  0.992972  0.993180 -0.241989  0.991173   \n",
+       "Lower_BB     0.992257  0.991903  0.992266  0.991986 -0.252852  0.990316   \n",
+       "SMA_10       0.998788  0.998675  0.998567  0.998513 -0.247841  0.987473   \n",
+       "SMA_50       0.992463  0.992366  0.992135  0.992144 -0.247594  0.992357   \n",
+       "RSI          0.060667  0.063160  0.064062  0.065853  0.051904 -0.008551   \n",
+       "EMA_12       0.998913  0.998821  0.998719  0.998676 -0.247778  0.988224   \n",
+       "EMA_26       0.997290  0.997184  0.997029  0.996998 -0.248238  0.990678   \n",
+       "MACD         0.201107  0.201486  0.202962  0.202614 -0.025686  0.085060   \n",
+       "Signal_Line  0.211920  0.211589  0.212715  0.212016 -0.041019  0.103564   \n",
+       "\n",
+       "               MA_200     MA_20  Upper_BB  Lower_BB    SMA_10    SMA_50  \\\n",
+       "Open         0.936083  0.994502  0.993450  0.992257  0.998788  0.992463   \n",
+       "High         0.935987  0.994398  0.993568  0.991903  0.998675  0.992366   \n",
+       "Low          0.936005  0.994254  0.992972  0.992266  0.998567  0.992135   \n",
+       "Close        0.935956  0.994232  0.993180  0.991986  0.998513  0.992144   \n",
+       "Volume      -0.156613 -0.247522 -0.241989 -0.252852 -0.247841 -0.247594   \n",
+       "MA_50        0.954341  0.992381  0.991173  0.990316  0.987473  0.992357   \n",
+       "MA_200       1.000000  0.943263  0.942357  0.941031  0.938292  0.947045   \n",
+       "MA_20        0.943263  1.000000  0.998546  0.998185  0.996501  0.993994   \n",
+       "Upper_BB     0.942357  0.998546  1.000000  0.993487  0.995246  0.992805   \n",
+       "Lower_BB     0.941031  0.998185  0.993487  1.000000  0.994474  0.991904   \n",
+       "SMA_10       0.938292  0.996501  0.995246  0.994474  1.000000  0.994842   \n",
+       "SMA_50       0.947045  0.993994  0.992805  0.991904  0.994842  1.000000   \n",
+       "RSI         -0.005434  0.010021  0.013123  0.006521  0.036431 -0.009370   \n",
+       "EMA_12       0.939204  0.996732  0.995469  0.994714  0.999915  0.995598   \n",
+       "EMA_26       0.942774  0.996960  0.995654  0.994990  0.999055  0.998145   \n",
+       "MACD         0.046120  0.148834  0.149849  0.147187  0.179858  0.083592   \n",
+       "Signal_Line  0.050529  0.176405  0.175100  0.177256  0.201548  0.101745   \n",
+       "\n",
+       "                  RSI    EMA_12    EMA_26      MACD  Signal_Line  \n",
+       "Open         0.060667  0.998913  0.997290  0.201107     0.211920  \n",
+       "High         0.063160  0.998821  0.997184  0.201486     0.211589  \n",
+       "Low          0.064062  0.998719  0.997029  0.202962     0.212715  \n",
+       "Close        0.065853  0.998676  0.996998  0.202614     0.212016  \n",
+       "Volume       0.051904 -0.247778 -0.248238 -0.025686    -0.041019  \n",
+       "MA_50       -0.008551  0.988224  0.990678  0.085060     0.103564  \n",
+       "MA_200      -0.005434  0.939204  0.942774  0.046120     0.050529  \n",
+       "MA_20        0.010021  0.996732  0.996960  0.148834     0.176405  \n",
+       "Upper_BB     0.013123  0.995469  0.995654  0.149849     0.175100  \n",
+       "Lower_BB     0.006521  0.994714  0.994990  0.147187     0.177256  \n",
+       "SMA_10       0.036431  0.999915  0.999055  0.179858     0.201548  \n",
+       "SMA_50      -0.009370  0.995598  0.998145  0.083592     0.101745  \n",
+       "RSI          1.000000  0.033634  0.013165  0.579522     0.425595  \n",
+       "EMA_12       0.033634  1.000000  0.999384  0.173035     0.193561  \n",
+       "EMA_26       0.013165  0.999384  1.000000  0.138353     0.160681  \n",
+       "MACD         0.579522  0.173035  0.138353  1.000000     0.952623  \n",
+       "Signal_Line  0.425595  0.193561  0.160681  0.952623     1.000000  "
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "corr = df.corr()\n",
     "corr"
@@ -1410,7 +2506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 207,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1419,7 +2515,18 @@
     "id": "1OGAC3uxZCxG",
     "outputId": "9d7ad372-d66d-4c2a-b72a-717179294401"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10,8))\n",
     "sns.heatmap(corr, annot=True, linewidths=0.5)\n",
@@ -1428,7 +2535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": null,
    "metadata": {
     "id": "GvIj_HpsZJ26"
    },
@@ -1446,7 +2553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 211,
    "metadata": {
     "id": "FV2-jJCSZQaA"
    },
@@ -1459,7 +2566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 213,
    "metadata": {
     "id": "MStjwvxqZV1e"
    },
@@ -1471,7 +2578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 215,
    "metadata": {
     "id": "adMP-Zn1ZW7s"
    },
@@ -1485,7 +2592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 217,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1493,7 +2600,18 @@
     "id": "7nfzuX5hZZtj",
     "outputId": "6e0f44fa-598b-482f-db74-449179691b30"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((5609, 4), (1403, 4))"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "X_train.shape, X_test.shape"
    ]
@@ -1518,7 +2636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 221,
    "metadata": {
     "id": "9AJm7PhjZZ9p"
    },
@@ -1536,7 +2654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 223,
    "metadata": {
     "id": "lYpraDj7Zwqr"
    },
@@ -1555,7 +2673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 225,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1563,7 +2681,24 @@
     "id": "xOAnEaKoZxuY",
     "outputId": "202a1add-cae6-4637-8106-735d3f958007"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 1.5855428625582284\n",
+      "MAE: 0.9162461522010917\n",
+      "MAPE: 0.006286719404596185\n",
+      "Accuracy: 0.827512473271561\n",
+      "Precision: 0.8384279475982532\n",
+      "Confusion Matrix:\n",
+      " [[585 111]\n",
+      " [131 576]]\n",
+      "Recall: 0.8147100424328148\n",
+      "F1 Score: 0.8263988522238164\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse1)\n",
@@ -1594,7 +2729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 229,
    "metadata": {
     "id": "XxOnG95gZ0NR"
    },
@@ -1608,7 +2743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 231,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1617,45 +2752,911 @@
     "id": "heZ6MwXaalFT",
     "outputId": "732d3ac8-014f-47fb-f1af-e3f30afbb037"
    },
-   "outputs": [],
-   "source": [
-    "# Train the model\n",
-    "GV_SVR.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Make predictions on the test set\n",
-    "pred2 = GV_SVR.predict(X_test)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "984H1ej2apPP"
-   },
-   "outputs": [],
-   "source": [
-    "GV_SVR.best_params_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Without Grid Search"
-   ]
-  },
-  {
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning:\n",
+      "\n",
+      "One or more of the test scores are non-finite: [nan nan nan nan nan nan]\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1], &#x27;epsilon&#x27;: [0.01, 0.1, 0.5],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;sigmoid&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
+       "             param_grid={&#x27;C&#x27;: [0.1, 1], &#x27;epsilon&#x27;: [0.01, 0.1, 0.5],\n",
+       "                         &#x27;kernel&#x27;: [&#x27;sigmoid&#x27;]},\n",
+       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: SVR</label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=0.1, epsilon=0.01, kernel=&#x27;sigmoid&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVR(C=0.1, epsilon=0.01, kernel=&#x27;sigmoid&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(estimator=SVR(), n_jobs=-1,\n",
+       "             param_grid={'C': [0.1, 1], 'epsilon': [0.01, 0.1, 0.5],\n",
+       "                         'kernel': ['sigmoid']},\n",
+       "             scoring='accuracy')"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "GV_SVR.fit(X_train, y_train)"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 233,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# Make predictions on the test set\n",
+    "pred2 = GV_SVR.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {
+    "id": "984H1ej2apPP"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C': 0.1, 'epsilon': 0.01, 'kernel': 'sigmoid'}"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "GV_SVR.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Without Grid Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVR.html\">?<span>Documentation for SVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVR()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "SVR()"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#fitting without grid search\n",
     "model2.fit(X_train, y_train)"
@@ -1663,7 +3664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 240,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1673,7 +3674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 242,
    "metadata": {
     "id": "ErnLBNW6apM4"
    },
@@ -1692,7 +3693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 244,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1700,7 +3701,24 @@
     "id": "82Gd1kSwapKR",
     "outputId": "1dc4a6a9-f0ad-4b96-eeaa-9682f4912767"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 156.84695243787496\n",
+      "MAE: 126.89278487685152\n",
+      "MAPE: 2.6555431984901294\n",
+      "Accuracy: 0.9992872416250891\n",
+      "Precision: 1.0\n",
+      "Confusion Matrix:\n",
+      " [[714   0]\n",
+      " [  1 688]]\n",
+      "Recall: 0.9985486211901307\n",
+      "F1 Score: 0.9992737835875091\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse2)\n",
@@ -1724,7 +3742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 247,
    "metadata": {
     "id": "YwerJPNxapGt"
    },
@@ -1737,7 +3755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 249,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1746,7 +3764,425 @@
     "id": "fspXTQOXapBl",
     "outputId": "16e51cc3-390d-4dad-8a04-68b48104ac0b"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 249,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model\n",
     "model3.fit(X_train, y_train)"
@@ -1754,7 +4190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 251,
    "metadata": {
     "id": "Hu0iAgE9ao-_"
    },
@@ -1766,7 +4202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 253,
    "metadata": {
     "id": "WTu2YAoQao6Q"
    },
@@ -1785,7 +4221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 255,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1793,7 +4229,24 @@
     "id": "9_7z9JcRao4C",
     "outputId": "9b6dbe5a-de58-4519-d12b-afd1783b5024"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.029204610750147\n",
+      "MAE: 1.1867529135400767\n",
+      "MAPE: 0.007926835753080005\n",
+      "Accuracy: 0.8717034925160371\n",
+      "Precision: 0.8688760806916427\n",
+      "Confusion Matrix:\n",
+      " [[620  91]\n",
+      " [ 89 603]]\n",
+      "Recall: 0.8713872832369942\n",
+      "F1 Score: 0.8701298701298701\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse3)\n",
@@ -1817,7 +4270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 258,
    "metadata": {
     "id": "D75bIlqqao1t"
    },
@@ -1828,7 +4281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 260,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1837,7 +4290,425 @@
     "id": "uUHqBGsTa9TV",
     "outputId": "4694a027-32b2-4c7b-c9cc-ee84e6c4b425"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "GradientBoostingRegressor()"
+      ]
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model\n",
     "model4.fit(X_train, y_train)"
@@ -1845,7 +4716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 262,
    "metadata": {
     "id": "T7XyO3qIa9QU"
    },
@@ -1857,7 +4728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 264,
    "metadata": {
     "id": "j2DCsz23a9M3"
    },
@@ -1876,7 +4747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 266,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1884,7 +4755,24 @@
     "id": "tl4jTkC9a9Je",
     "outputId": "4d2f1ee3-2886-4176-e9dc-2062b3890377"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.6474866528787193\n",
+      "MAE: 1.6833712037473636\n",
+      "MAPE: 0.011965660284805309\n",
+      "Accuracy: 0.9059158945117605\n",
+      "Precision: 0.9058823529411765\n",
+      "Confusion Matrix:\n",
+      " [[655  64]\n",
+      " [ 68 616]]\n",
+      "Recall: 0.9005847953216374\n",
+      "F1 Score: 0.9032258064516129\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse4)\n",
@@ -1908,7 +4796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 269,
    "metadata": {
     "id": "rmgm3VADa9Gb"
    },
@@ -1921,7 +4809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 271,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1930,7 +4818,455 @@
     "id": "txadd-U3a9AP",
     "outputId": "0a5d9230-a87f-486b-cda4-197face55486"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model\n",
     "model5.fit(X_train, y_train)"
@@ -1938,7 +5274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 273,
    "metadata": {
     "id": "xYWVMZJ5a89t"
    },
@@ -1950,7 +5286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 275,
    "metadata": {
     "id": "qeDxXt9pa87W"
    },
@@ -1969,7 +5305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 277,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -1977,7 +5313,24 @@
     "id": "TdY1rRy_a83-",
     "outputId": "d43b90b2-d098-402f-fbf7-46130f5310e3"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.2456035075665794\n",
+      "MAE: 1.410556742694623\n",
+      "MAPE: 0.010246789191938107\n",
+      "Accuracy: 0.8959372772630079\n",
+      "Precision: 0.9013698630136986\n",
+      "Confusion Matrix:\n",
+      " [[599  72]\n",
+      " [ 74 658]]\n",
+      "Recall: 0.8989071038251366\n",
+      "F1 Score: 0.9001367989056087\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse5)\n",
@@ -2001,7 +5354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 280,
    "metadata": {
     "id": "Xze9G-tUa802"
    },
@@ -2014,7 +5367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 282,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2023,7 +5376,425 @@
     "id": "DEmXpAV8a8wC",
     "outputId": "5cadcb29-13ce-45f0-cfe1-f9e21a24866b"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;AdaBoostRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.AdaBoostRegressor.html\">?<span>Documentation for AdaBoostRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>AdaBoostRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "AdaBoostRegressor()"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model\n",
     "model6.fit(X_train, y_train)"
@@ -2031,7 +5802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 284,
    "metadata": {
     "id": "KvI77shea8tk"
    },
@@ -2043,7 +5814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 286,
    "metadata": {
     "id": "catCNPwla8rL"
    },
@@ -2062,7 +5833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 288,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2070,7 +5841,24 @@
     "id": "wpUqeumZa8oZ",
     "outputId": "db2f5904-a96b-46d9-fbc3-ec519d5b7c7a"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 8.722169035954618\n",
+      "MAE: 7.095684834597975\n",
+      "MAPE: 0.17762022210893855\n",
+      "Accuracy: 0.9786172487526729\n",
+      "Precision: 0.9716981132075472\n",
+      "Confusion Matrix:\n",
+      " [[858  15]\n",
+      " [ 15 515]]\n",
+      "Recall: 0.9716981132075472\n",
+      "F1 Score: 0.9716981132075472\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse6)\n",
@@ -2094,7 +5882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 291,
    "metadata": {
     "id": "qCvEszOKbVGV"
    },
@@ -2107,7 +5895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 293,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2116,7 +5904,425 @@
     "id": "5lYl0STGbWw8",
     "outputId": "9aabd48a-73aa-4737-e243-ae4eb5899765"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeRegressor()"
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model\n",
     "model7.fit(X_train, y_train)"
@@ -2124,7 +6330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 295,
    "metadata": {
     "id": "Wu34WtF-bXy0"
    },
@@ -2136,7 +6342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 297,
    "metadata": {
     "id": "H0NdEFHXbYuI"
    },
@@ -2155,7 +6361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 299,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2163,7 +6369,24 @@
     "id": "Wq6aws7ZbZft",
     "outputId": "3f0b27ba-3257-458b-b5d3-abba03fa5b47"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.607949173764777\n",
+      "MAE: 1.482377351389879\n",
+      "MAPE: 0.009969005736653144\n",
+      "Accuracy: 0.8752672843905915\n",
+      "Precision: 0.8674521354933726\n",
+      "Confusion Matrix:\n",
+      " [[639  90]\n",
+      " [ 85 589]]\n",
+      "Recall: 0.8738872403560831\n",
+      "F1 Score: 0.8706577974870657\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse7)\n",
@@ -2187,7 +6410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 302,
    "metadata": {
     "id": "5kz2DGKMbaem"
    },
@@ -2201,7 +6424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 304,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2210,7 +6433,425 @@
     "id": "PFdqC4oXbeg9",
     "outputId": "b06c9cd8-c45c-4a8b-bec3-17fe23c6dc6d"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsRegressor()"
+      ]
+     },
+     "execution_count": 304,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model\n",
     "model8.fit(X_train, y_train)"
@@ -2218,7 +6859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 306,
    "metadata": {
     "id": "_Rv2V1W8bfhp"
    },
@@ -2230,7 +6871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 308,
    "metadata": {
     "id": "K_QgGMDybgj8"
    },
@@ -2249,7 +6890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 310,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2257,7 +6898,24 @@
     "id": "PKv6I2oCbha3",
     "outputId": "ef250676-44d3-47ff-f7bd-ade1697c4789"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 145.2817848019368\n",
+      "MAE: 108.28649615205543\n",
+      "MAPE: 1.7063415745133514\n",
+      "Accuracy: 0.9935851746258019\n",
+      "Precision: 0.9904458598726115\n",
+      "Confusion Matrix:\n",
+      " [[772   6]\n",
+      " [  3 622]]\n",
+      "Recall: 0.9952\n",
+      "F1 Score: 0.9928172386272945\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse8)\n",
@@ -2272,9 +6930,427 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 312,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-9 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=150)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_neighbors=150)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsRegressor(n_neighbors=150)"
+      ]
+     },
+     "execution_count": 312,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Grid Search parameters\n",
     "GV_KNN.fit(X_train, y_train)\n",
@@ -2284,9 +7360,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 314,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "results = GV_KNN.cv_results_\n",
@@ -2306,9 +7393,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 317,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.model_selection import cross_val_score\n",
     "\n",
@@ -2349,7 +7447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 320,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2357,7 +7455,18 @@
     "id": "oj1tv3-Sbm4M",
     "outputId": "0d547cbc-d317-400a-a304-1bc283287926"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning:\n",
+      "\n",
+      "Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Create an ANN model\n",
     "model9 = Sequential()\n",
@@ -2368,7 +7477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 322,
    "metadata": {
     "id": "Llqm4lw3bnxu"
    },
@@ -2380,7 +7489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 324,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2388,7 +7497,18 @@
     "id": "8gzrlwUTbpG7",
     "outputId": "c3b2372d-3881-4568-efd3-59ffe8bce6f3"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1e3b1fdbe60>"
+      ]
+     },
+     "execution_count": 324,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model\n",
     "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)"
@@ -2396,7 +7516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 326,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2404,7 +7524,15 @@
     "id": "cI6QW1utbp-J",
     "outputId": "1c8b27b2-3380-40a1-ac68-17a68092f650"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n"
+     ]
+    }
+   ],
    "source": [
     "# Make predictions on the test set\n",
     "pred9 = model9.predict(X_test_scaled).flatten()"
@@ -2412,7 +7540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 252,
+   "execution_count": 328,
    "metadata": {
     "id": "-qDeEJUgbrUq"
    },
@@ -2431,7 +7559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 330,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2439,7 +7567,24 @@
     "id": "dEhvx1BTbvXO",
     "outputId": "af73599a-ae1e-4d56-9288-8193e4aaad2b"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 2.7093653508543825\n",
+      "MAE: 1.6660259540370086\n",
+      "MAPE: 0.010817771201279731\n",
+      "Accuracy: 0.8880969351389879\n",
+      "Precision: 0.9202453987730062\n",
+      "Confusion Matrix:\n",
+      " [[496  65]\n",
+      " [ 92 750]]\n",
+      "Recall: 0.8907363420427553\n",
+      "F1 Score: 0.9052504526252263\n"
+     ]
+    }
+   ],
    "source": [
     "# Print the evaluation metrics\n",
     "print(\"RMSE:\", rmse9)\n",
@@ -2463,7 +7608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 333,
    "metadata": {
     "id": "3QajxGzXbxM4"
    },
@@ -2482,7 +7627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 335,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -2490,7 +7635,18 @@
     "id": "gNRiL_B0bxJ3",
     "outputId": "045b5c85-f8ef-4fc4-bf38-1c253d886c82"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\Anaconda\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning:\n",
+      "\n",
+      "Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Create an LSTM model\n",
     "model = Sequential()\n",
@@ -2500,7 +7656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 337,
    "metadata": {
     "id": "DsndIHIQbxHV"
    },
@@ -2512,11 +7668,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 339,
    "metadata": {
     "id": "SzhA1fh9bxEz"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x1e3b1f32f60>"
+      ]
+     },
+     "execution_count": 339,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Train the model\n",
     "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
@@ -2524,11 +7691,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 341,
    "metadata": {
     "id": "AQXh8a46bxCx"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step\n"
+     ]
+    }
+   ],
    "source": [
     "# Make predictions on the test set\n",
     "y_pred = model.predict(X_test_reshaped).flatten()"
@@ -2536,7 +7711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 343,
    "metadata": {
     "id": "iIXfZ2eubxAD"
    },
@@ -2555,11 +7730,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 345,
    "metadata": {
     "id": "9Gjh33nNbw-O"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 14.360781467875562\n",
+      "MAE: 10.393914758397726\n",
+      "MAPE: 0.18937449614271654\n",
+      "Accuracy: 0.9899569583931134\n",
+      "Precision: 0.993581514762516\n",
+      "Recall: 0.9885057471264368\n",
+      "F1 Score: 0.9910371318822023\n",
+      "Confusion Matrix:\n",
+      " [[606   5]\n",
+      " [  9 774]]\n"
+     ]
+    }
+   ],
    "source": [
     "# Print evaluation metrics\n",
     "print(\"RMSE:\", rmse10)\n",
@@ -2606,11 +7798,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 351,
    "metadata": {
     "id": "-hFs2mnDbwGQ"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -2637,11 +7840,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 354,
    "metadata": {
     "id": "1tBYk4qmcEMk"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -2668,11 +7882,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 357,
    "metadata": {
     "id": "BS0oljCZcFhM"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -2699,11 +7924,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 360,
    "metadata": {
     "id": "9woaoeNVcGx5"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -2730,11 +7966,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 363,
    "metadata": {
     "id": "bbRSmd3tcIIX"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -2761,11 +8008,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 366,
    "metadata": {
     "id": "tDNk7iuWcJpS"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -2789,7 +8047,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
diff --git a/stock_market(complete).ipynb b/stock_market(complete).ipynb
index d3fe033..31f67c0 100644
--- a/stock_market(complete).ipynb
+++ b/stock_market(complete).ipynb
@@ -2,11 +2,23 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 83,
    "metadata": {
     "id": "Rqr4Dq5vWXmV"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tensorflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[83], line 27\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtree\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DecisionTreeRegressor\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mneighbors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KNeighborsRegressor\n\u001b[1;32m---> 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dense, LSTM\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
+     ]
+    }
+   ],
    "source": [
     "# importing modules\n",
     "import numpy as np\n",
@@ -18,21 +30,29 @@
     "from sklearn.impute import SimpleImputer\n",
     "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
     "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
+    "from sklearn.metrics import (\n",
+    "    mean_squared_error, \n",
+    "    mean_absolute_error, \n",
+    "    mean_absolute_percentage_error, \n",
+    "    accuracy_score, \n",
+    "    precision_score, \n",
+    "    confusion_matrix, \n",
+    "    recall_score, \n",
+    "    f1_score\n",
+    ")\n",
     "\n",
     "from sklearn.model_selection import GridSearchCV\n",
-    "\n",
     "from sklearn.svm import SVR\n",
     "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
     "from sklearn.tree import DecisionTreeRegressor\n",
     "from sklearn.neighbors import KNeighborsRegressor\n",
     "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense,LSTM\n"
+    "from tensorflow.keras.layers import Dense, LSTM\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 85,
    "metadata": {
     "id": "BigUM4vtWZdc"
    },
@@ -42,15 +62,6 @@
     "df = pd.read_csv(\"SBIN.csv\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "RqOe6KPNWs8Q"
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -62,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 88,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -173,7 +184,7 @@
        "4  76613039.0  "
       ]
      },
-     "execution_count": 75,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -185,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 90,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -222,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 92,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -360,7 +371,7 @@
        "max    4.469483e+08  "
       ]
      },
-     "execution_count": 79,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -372,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 94,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -395,7 +406,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,6 +416,240 @@
     "df.isnull().sum()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Documentation: Handling Missing Data in Stock Price DataFrame\r\n",
+    "\r\n",
+    "In this section, we handle missing values (NaN) in the stock price DataFrame by using forward fill (`ffill`). This method ensures that missing values are filled by propagating the previous non-null value.\r\n",
+    "\r\n",
+    "#### Code Explanation:\r\n",
+    "\r\n",
+    "1. **Filling NaN using `ffill()`**:\r\n",
+    "   - We use the `ffill()` method, which fills missing values by propagating the last valid observation forward.\r\n",
+    "   - This is particularly useful in time-series data like stock prices.\r\n",
+    "\r\n",
+    "2. **Checking for Remaining NaN Values**:\r\n",
+    "   - After handling the missing values, we use `isnull().sum()` to verify that there are no remaining NaN values in the DataFrame.\r\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date         0\n",
+       "Open         0\n",
+       "High         0\n",
+       "Low          0\n",
+       "Close        0\n",
+       "Adj Close    0\n",
+       "Volume       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Using forward fill for time-series continuity\n",
+    "df['Open'] = df['Open'].ffill()\n",
+    "df['High'] = df['High'].ffill()\n",
+    "df['Low'] = df['Low'].ffill()\n",
+    "df['Close'] = df['Close'].ffill()\n",
+    "df['Adj Close'] = df['Adj Close'].ffill()\n",
+    "df['Volume'] = df['Volume'].ffill()\n",
+    "\n",
+    "# Checking for remaining NaN values\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Documentation: Handling Outliers in Stock Price Data\r\n",
+    "\r\n",
+    "In this section, we handle outliers in the stock price data using the Interquartile Range (IQR) method. Outliers are defined as values that fall outside 1.5 times the IQR below Q1 or above Q3. \r\n",
+    "\r\n",
+    "#### Code Explanation:\r\n",
+    "\r\n",
+    "1. **Detecting Outliers**:\r\n",
+    "   - We first calculate the IQR for each column (`Open`, `High`, `Low`, `Close`, `Adj Close`, and `Volume`) to identify potential outliers.\r\n",
+    "   \r\n",
+    "2. **Handling Outliers**:\r\n",
+    "   - We replace values identified as outliers by capping them at the 1st percentile (lower bound) or the 99th percentile (upper bound) to reduce the impact of extreme values.\r\n",
+    "\r\n",
+    "#### Python Code Implementation:\r\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Adj Close</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7074.000000</td>\n",
+       "      <td>7.074000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>180.566028</td>\n",
+       "      <td>182.967081</td>\n",
+       "      <td>177.861698</td>\n",
+       "      <td>180.320342</td>\n",
+       "      <td>165.509789</td>\n",
+       "      <td>2.709130e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>154.378208</td>\n",
+       "      <td>155.940446</td>\n",
+       "      <td>152.529681</td>\n",
+       "      <td>154.199712</td>\n",
+       "      <td>151.382248</td>\n",
+       "      <td>1.988562e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>13.478195</td>\n",
+       "      <td>13.935802</td>\n",
+       "      <td>13.214009</td>\n",
+       "      <td>13.346102</td>\n",
+       "      <td>9.531410</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>28.494329</td>\n",
+       "      <td>28.871737</td>\n",
+       "      <td>28.054413</td>\n",
+       "      <td>28.489611</td>\n",
+       "      <td>19.865707</td>\n",
+       "      <td>1.299895e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>173.100006</td>\n",
+       "      <td>176.500000</td>\n",
+       "      <td>170.290001</td>\n",
+       "      <td>172.937500</td>\n",
+       "      <td>152.463203</td>\n",
+       "      <td>2.064730e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>265.245011</td>\n",
+       "      <td>268.892494</td>\n",
+       "      <td>261.237503</td>\n",
+       "      <td>265.082496</td>\n",
+       "      <td>245.745831</td>\n",
+       "      <td>3.651406e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>620.371033</td>\n",
+       "      <td>628.923630</td>\n",
+       "      <td>611.012137</td>\n",
+       "      <td>619.971824</td>\n",
+       "      <td>584.566015</td>\n",
+       "      <td>7.178671e+07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Open         High          Low        Close    Adj Close  \\\n",
+       "count  7074.000000  7074.000000  7074.000000  7074.000000  7074.000000   \n",
+       "mean    180.566028   182.967081   177.861698   180.320342   165.509789   \n",
+       "std     154.378208   155.940446   152.529681   154.199712   151.382248   \n",
+       "min      13.478195    13.935802    13.214009    13.346102     9.531410   \n",
+       "25%      28.494329    28.871737    28.054413    28.489611    19.865707   \n",
+       "50%     173.100006   176.500000   170.290001   172.937500   152.463203   \n",
+       "75%     265.245011   268.892494   261.237503   265.082496   245.745831   \n",
+       "max     620.371033   628.923630   611.012137   619.971824   584.566015   \n",
+       "\n",
+       "             Volume  \n",
+       "count  7.074000e+03  \n",
+       "mean   2.709130e+07  \n",
+       "std    1.988562e+07  \n",
+       "min    0.000000e+00  \n",
+       "25%    1.299895e+07  \n",
+       "50%    2.064730e+07  \n",
+       "75%    3.651406e+07  \n",
+       "max    7.178671e+07  "
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Detecting and handling outliers using the Interquartile Range (IQR) method\n",
+    "\n",
+    "def handle_outliers(df, column):\n",
+    "    Q1 = df[column].quantile(0.25)\n",
+    "    Q3 = df[column].quantile(0.75)\n",
+    "    IQR = Q3 - Q1\n",
+    "    lower_bound = Q1 - 1.5 * IQR\n",
+    "    upper_bound = Q3 + 1.5 * IQR\n",
+    "    \n",
+    "    # Cap the outliers at the lower and upper bounds\n",
+    "    df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else x)\n",
+    "    df[column] = df[column].apply(lambda x: upper_bound if x > upper_bound else x)\n",
+    "\n",
+    "# Applying the function to the relevant columns\n",
+    "columns_to_check = ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']\n",
+    "for col in columns_to_check:\n",
+    "    handle_outliers(df, col)\n",
+    "\n",
+    "# Checking for remaining outliers by printing descriptive statistics\n",
+    "df.describe()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -416,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 17,
    "metadata": {
     "id": "HpS7l8oSCf6y"
    },
@@ -437,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 19,
    "metadata": {
     "id": "oVEjj8ZMCnDx"
    },
@@ -465,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {

From 227078b7f71514aa71d67be198030b82e5cd0375 Mon Sep 17 00:00:00 2001
From: RAMESWAR <rameswarbisoyi@gmail.com>
Date: Sun, 20 Oct 2024 18:56:00 +0530
Subject: [PATCH 36/76] Performed EDA: Handled missing values with forward fill
 (ffill) and capped outliers using IQR method for stock price data.

---
 .ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb b/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
index 618d424..3c08fd7 100644
--- a/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
+++ b/.ipynb_checkpoints/stock_market(complete)-checkpoint.ipynb
@@ -458,7 +458,7 @@
     }
    ],
    "source": [
-    "# Using forward fill for time-series continuity\n",
+    "# Using forward fill for time-series continuity in stock prediction\n",
     "df['Open'] = df['Open'].ffill()\n",
     "df['High'] = df['High'].ffill()\n",
     "df['Low'] = df['Low'].ffill()\n",
@@ -467,7 +467,9 @@
     "df['Volume'] = df['Volume'].ffill()\n",
     "\n",
     "# Checking for remaining NaN values\n",
-    "df.isnull().sum()"
+    "df.isnull().sum()\n",
+    "\n",
+    "\n"
    ]
   },
   {

From c92b1f9757cb1c5bd3c5fad8b86b0047e0dff6a1 Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Sun, 20 Oct 2024 20:37:23 +0530
Subject: [PATCH 37/76] Update pr-checker.yaml

---
 .github/workflows/pr-checker.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pr-checker.yaml b/.github/workflows/pr-checker.yaml
index dbab3ef..2d3e74c 100644
--- a/.github/workflows/pr-checker.yaml
+++ b/.github/workflows/pr-checker.yaml
@@ -43,7 +43,7 @@ if not re.search(r'Fixes #[0-9]+', pr_description):
 
 # Check if the PR title starts with FIX, FEAT, or DOC
 if not re.match(r'^(FIX|FEAT|DOC)', pr_title):
-    print('The PR title should start with FIX, FEAT, or DOC.')
+    print('The PR title should start with Fixes, Closes, or FIX.')
     sys.exit(1)
 
 print('PR description and title are valid.')

From 8809ace66bc994d8421b975276fb496dc71400dc Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Sun, 20 Oct 2024 20:40:54 +0530
Subject: [PATCH 38/76] Update pr-checker.yaml

---
 .github/workflows/pr-checker.yaml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/pr-checker.yaml b/.github/workflows/pr-checker.yaml
index 2d3e74c..71901d3 100644
--- a/.github/workflows/pr-checker.yaml
+++ b/.github/workflows/pr-checker.yaml
@@ -42,7 +42,8 @@ if not re.search(r'Fixes #[0-9]+', pr_description):
     sys.exit(1)
 
 # Check if the PR title starts with FIX, FEAT, or DOC
-if not re.match(r'^(FIX|FEAT|DOC)', pr_title):
+if not re.match(r'^(Fixes|Close|Closes|Closed|Fix|Fixed|Resolve|Resolves
+Resolved)', pr_title):
     print('The PR title should start with Fixes, Closes, or FIX.')
     sys.exit(1)
 

From de5940fe44e7c1b7fac095e6fbcd9b2143b6e4af Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Sun, 20 Oct 2024 20:46:30 +0530
Subject: [PATCH 39/76] Update pr-checker.yaml

---
 .github/workflows/pr-checker.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pr-checker.yaml b/.github/workflows/pr-checker.yaml
index 71901d3..52f6dda 100644
--- a/.github/workflows/pr-checker.yaml
+++ b/.github/workflows/pr-checker.yaml
@@ -38,13 +38,13 @@ if not pr_description:
 
 # Check if the PR description contains 'Fixes #<issue-number>'
 if not re.search(r'Fixes #[0-9]+', pr_description):
-    print('The PR description should include Fixes #<issue-number>.')
+    print('The PR description should include Fixes , Close, Closes, Closed , Fix , Fixed , Resolve , Resolves #<issue-number>.')
     sys.exit(1)
 
 # Check if the PR title starts with FIX, FEAT, or DOC
 if not re.match(r'^(Fixes|Close|Closes|Closed|Fix|Fixed|Resolve|Resolves
 Resolved)', pr_title):
-    print('The PR title should start with Fixes, Closes, or FIX.')
+    print('The PR title should start with Fixes , Close, Closes, Closed , Fix , Fixed , Resolve , Resolves')
     sys.exit(1)
 
 print('PR description and title are valid.')

From ac4e037bf1212ced1c71e68ac4fed291fdf444e4 Mon Sep 17 00:00:00 2001
From: sapnilmodak <sapnilmodak@gmail.com>
Date: Sun, 20 Oct 2024 21:41:43 +0530
Subject: [PATCH 40/76] Stock recommendation added

---
 Stock_Recommendation.ipynb | 49144 +++++++++++++++++++++++++++++++++++
 nifty_500.csv              |   502 +
 2 files changed, 49646 insertions(+)
 create mode 100644 Stock_Recommendation.ipynb
 create mode 100644 nifty_500.csv

diff --git a/Stock_Recommendation.ipynb b/Stock_Recommendation.ipynb
new file mode 100644
index 0000000..89b6bdd
--- /dev/null
+++ b/Stock_Recommendation.ipynb
@@ -0,0 +1,49144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to\n",
+      "[nltk_data]     C:\\Users\\sapni\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     C:\\Users\\sapni\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     C:\\Users\\sapni\\AppData\\Roaming\\nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "\n",
+    "\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('wordnet')\n",
+    "\n",
+    "lemmatizer = WordNetLemmatizer()\n",
+    "stop_words = set(stopwords.words('english'))\n",
+    "\n",
+    "def preprocess(text):\n",
+    "    tokens = word_tokenize(text.lower())\n",
+    "    tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalpha() and word not in stop_words]\n",
+    "    return ' '.join(tokens)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company Name</th>\n",
+       "      <th>Symbol</th>\n",
+       "      <th>Industry</th>\n",
+       "      <th>Series</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Previous Close</th>\n",
+       "      <th>Last Traded Price</th>\n",
+       "      <th>Change</th>\n",
+       "      <th>Percentage Change</th>\n",
+       "      <th>Share Volume</th>\n",
+       "      <th>Value (Indian Rupee)</th>\n",
+       "      <th>52 Week High</th>\n",
+       "      <th>52 Week Low</th>\n",
+       "      <th>365 Day Percentage Change</th>\n",
+       "      <th>30 Day Percentage Change</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3M India Ltd.</td>\n",
+       "      <td>3MINDIA</td>\n",
+       "      <td>Diversified</td>\n",
+       "      <td>EQ</td>\n",
+       "      <td>21950.00</td>\n",
+       "      <td>21999.00</td>\n",
+       "      <td>21126.05</td>\n",
+       "      <td>21854.05</td>\n",
+       "      <td>21575.00</td>\n",
+       "      <td>-279.05</td>\n",
+       "      <td>-1.28</td>\n",
+       "      <td>4159</td>\n",
+       "      <td>8.965357e+07</td>\n",
+       "      <td>27800.00</td>\n",
+       "      <td>17273.00</td>\n",
+       "      <td>-10.18</td>\n",
+       "      <td>8.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Aarti Drugs Ltd.</td>\n",
+       "      <td>AARTIDRUGS</td>\n",
+       "      <td>Healthcare</td>\n",
+       "      <td>EQ</td>\n",
+       "      <td>400.50</td>\n",
+       "      <td>401.80</td>\n",
+       "      <td>394.10</td>\n",
+       "      <td>403.85</td>\n",
+       "      <td>400.00</td>\n",
+       "      <td>-3.85</td>\n",
+       "      <td>-0.95</td>\n",
+       "      <td>31782</td>\n",
+       "      <td>1.262731e+07</td>\n",
+       "      <td>749.75</td>\n",
+       "      <td>378.00</td>\n",
+       "      <td>-42.92</td>\n",
+       "      <td>-5.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aavas Financiers Ltd.</td>\n",
+       "      <td>AAVAS</td>\n",
+       "      <td>Financial Services</td>\n",
+       "      <td>EQ</td>\n",
+       "      <td>1997.10</td>\n",
+       "      <td>2004.05</td>\n",
+       "      <td>1894.50</td>\n",
+       "      <td>2015.45</td>\n",
+       "      <td>1943.15</td>\n",
+       "      <td>-72.30</td>\n",
+       "      <td>-3.59</td>\n",
+       "      <td>150704</td>\n",
+       "      <td>2.907728e+08</td>\n",
+       "      <td>3340.00</td>\n",
+       "      <td>1815.00</td>\n",
+       "      <td>-25.69</td>\n",
+       "      <td>-9.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ABB India Ltd.</td>\n",
+       "      <td>ABB</td>\n",
+       "      <td>Capital Goods</td>\n",
+       "      <td>EQ</td>\n",
+       "      <td>2260.35</td>\n",
+       "      <td>2311.50</td>\n",
+       "      <td>2260.35</td>\n",
+       "      <td>2300.90</td>\n",
+       "      <td>2280.00</td>\n",
+       "      <td>-20.90</td>\n",
+       "      <td>-0.91</td>\n",
+       "      <td>97053</td>\n",
+       "      <td>2.210935e+08</td>\n",
+       "      <td>2487.85</td>\n",
+       "      <td>1618.05</td>\n",
+       "      <td>27.25</td>\n",
+       "      <td>-5.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Abbott India Ltd.</td>\n",
+       "      <td>ABBOTINDIA</td>\n",
+       "      <td>Healthcare</td>\n",
+       "      <td>EQ</td>\n",
+       "      <td>18700.40</td>\n",
+       "      <td>19200.00</td>\n",
+       "      <td>18605.00</td>\n",
+       "      <td>18760.40</td>\n",
+       "      <td>19199.80</td>\n",
+       "      <td>439.40</td>\n",
+       "      <td>2.34</td>\n",
+       "      <td>12396</td>\n",
+       "      <td>2.346770e+08</td>\n",
+       "      <td>23934.45</td>\n",
+       "      <td>15514.00</td>\n",
+       "      <td>11.61</td>\n",
+       "      <td>5.84</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Company Name      Symbol            Industry Series      Open  \\\n",
+       "0          3M India Ltd.     3MINDIA         Diversified     EQ  21950.00   \n",
+       "1       Aarti Drugs Ltd.  AARTIDRUGS          Healthcare     EQ    400.50   \n",
+       "2  Aavas Financiers Ltd.       AAVAS  Financial Services     EQ   1997.10   \n",
+       "3         ABB India Ltd.         ABB       Capital Goods     EQ   2260.35   \n",
+       "4      Abbott India Ltd.  ABBOTINDIA          Healthcare     EQ  18700.40   \n",
+       "\n",
+       "       High       Low  Previous Close  Last Traded Price   Change  \\\n",
+       "0  21999.00  21126.05        21854.05           21575.00  -279.05   \n",
+       "1    401.80    394.10          403.85             400.00    -3.85   \n",
+       "2   2004.05   1894.50         2015.45            1943.15   -72.30   \n",
+       "3   2311.50   2260.35         2300.90            2280.00   -20.90   \n",
+       "4  19200.00  18605.00        18760.40           19199.80   439.40   \n",
+       "\n",
+       "  Percentage Change  Share Volume  Value (Indian Rupee)  52 Week High  \\\n",
+       "0             -1.28          4159          8.965357e+07      27800.00   \n",
+       "1             -0.95         31782          1.262731e+07        749.75   \n",
+       "2             -3.59        150704          2.907728e+08       3340.00   \n",
+       "3             -0.91         97053          2.210935e+08       2487.85   \n",
+       "4              2.34         12396          2.346770e+08      23934.45   \n",
+       "\n",
+       "   52 Week Low 365 Day Percentage Change 30 Day Percentage Change  \n",
+       "0     17273.00                    -10.18                     8.22  \n",
+       "1       378.00                    -42.92                    -5.10  \n",
+       "2      1815.00                    -25.69                    -9.39  \n",
+       "3      1618.05                     27.25                    -5.21  \n",
+       "4     15514.00                     11.61                     5.84  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('nifty_500.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company Name</th>\n",
+       "      <th>Industry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3M India Ltd.</td>\n",
+       "      <td>Diversified</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Aarti Drugs Ltd.</td>\n",
+       "      <td>Healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aavas Financiers Ltd.</td>\n",
+       "      <td>Financial Services</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ABB India Ltd.</td>\n",
+       "      <td>Capital Goods</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Abbott India Ltd.</td>\n",
+       "      <td>Healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>Zensar Technolgies Ltd.</td>\n",
+       "      <td>Information Technology</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>ZF Commercial Vehicle Control Systems India Ltd.</td>\n",
+       "      <td>Automobile and Auto Components</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>Zomato Ltd.</td>\n",
+       "      <td>Consumer Services</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>Zydus Lifesciences Ltd.</td>\n",
+       "      <td>Healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>Zydus Wellness Ltd.</td>\n",
+       "      <td>Fast Moving Consumer Goods</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>501 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         Company Name  \\\n",
+       "0                                       3M India Ltd.   \n",
+       "1                                    Aarti Drugs Ltd.   \n",
+       "2                               Aavas Financiers Ltd.   \n",
+       "3                                      ABB India Ltd.   \n",
+       "4                                   Abbott India Ltd.   \n",
+       "..                                                ...   \n",
+       "496                           Zensar Technolgies Ltd.   \n",
+       "497  ZF Commercial Vehicle Control Systems India Ltd.   \n",
+       "498                                       Zomato Ltd.   \n",
+       "499                           Zydus Lifesciences Ltd.   \n",
+       "500                               Zydus Wellness Ltd.   \n",
+       "\n",
+       "                           Industry  \n",
+       "0                       Diversified  \n",
+       "1                        Healthcare  \n",
+       "2                Financial Services  \n",
+       "3                     Capital Goods  \n",
+       "4                        Healthcare  \n",
+       "..                              ...  \n",
+       "496          Information Technology  \n",
+       "497  Automobile and Auto Components  \n",
+       "498               Consumer Services  \n",
+       "499                      Healthcare  \n",
+       "500      Fast Moving Consumer Goods  \n",
+       "\n",
+       "[501 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df = df[['Company Name', 'Industry']]\n",
+    "filtered_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sapni\\anaconda3\\lib\\site-packages\\sentence_transformers\\cross_encoder\\CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  from tqdm.autonotebook import tqdm, trange\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\sapni\\anaconda3\\lib\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sentence_transformers import SentenceTransformer\n",
+    "\n",
+    "# Load a pre-trained sentence transformer model\n",
+    "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "electric vehicle renewable energy\n"
+     ]
+    }
+   ],
+   "source": [
+    "customer_interest = \"electric vehicles, renewable energy\"\n",
+    "customer_interest_preprocessed = preprocess(customer_interest)\n",
+    "print(customer_interest_preprocessed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company Name</th>\n",
+       "      <th>Industry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3M India Ltd.</td>\n",
+       "      <td>Diversified</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Aarti Drugs Ltd.</td>\n",
+       "      <td>Healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aavas Financiers Ltd.</td>\n",
+       "      <td>Financial Services</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ABB India Ltd.</td>\n",
+       "      <td>Capital Goods</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Abbott India Ltd.</td>\n",
+       "      <td>Healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>Zensar Technolgies Ltd.</td>\n",
+       "      <td>Information Technology</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>ZF Commercial Vehicle Control Systems India Ltd.</td>\n",
+       "      <td>Automobile and Auto Components</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>Zomato Ltd.</td>\n",
+       "      <td>Consumer Services</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>Zydus Lifesciences Ltd.</td>\n",
+       "      <td>Healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>Zydus Wellness Ltd.</td>\n",
+       "      <td>Fast Moving Consumer Goods</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>501 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         Company Name  \\\n",
+       "0                                       3M India Ltd.   \n",
+       "1                                    Aarti Drugs Ltd.   \n",
+       "2                               Aavas Financiers Ltd.   \n",
+       "3                                      ABB India Ltd.   \n",
+       "4                                   Abbott India Ltd.   \n",
+       "..                                                ...   \n",
+       "496                           Zensar Technolgies Ltd.   \n",
+       "497  ZF Commercial Vehicle Control Systems India Ltd.   \n",
+       "498                                       Zomato Ltd.   \n",
+       "499                           Zydus Lifesciences Ltd.   \n",
+       "500                               Zydus Wellness Ltd.   \n",
+       "\n",
+       "                           Industry  \n",
+       "0                       Diversified  \n",
+       "1                        Healthcare  \n",
+       "2                Financial Services  \n",
+       "3                     Capital Goods  \n",
+       "4                        Healthcare  \n",
+       "..                              ...  \n",
+       "496          Information Technology  \n",
+       "497  Automobile and Auto Components  \n",
+       "498               Consumer Services  \n",
+       "499                      Healthcare  \n",
+       "500      Fast Moving Consumer Goods  \n",
+       "\n",
+       "[501 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sapni\\AppData\\Local\\Temp\\ipykernel_25592\\477415791.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  filtered_df['Industry'] = filtered_df['Industry'].apply(preprocess)\n"
+     ]
+    }
+   ],
+   "source": [
+    "filtered_df['Industry'] = filtered_df['Industry'].apply(preprocess)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Company Name</th>\n",
+       "      <th>Industry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3M India Ltd.</td>\n",
+       "      <td>diversified</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Aarti Drugs Ltd.</td>\n",
+       "      <td>healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aavas Financiers Ltd.</td>\n",
+       "      <td>financial service</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ABB India Ltd.</td>\n",
+       "      <td>capital good</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Abbott India Ltd.</td>\n",
+       "      <td>healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>Zensar Technolgies Ltd.</td>\n",
+       "      <td>information technology</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>ZF Commercial Vehicle Control Systems India Ltd.</td>\n",
+       "      <td>automobile auto component</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>Zomato Ltd.</td>\n",
+       "      <td>consumer service</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>Zydus Lifesciences Ltd.</td>\n",
+       "      <td>healthcare</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>Zydus Wellness Ltd.</td>\n",
+       "      <td>fast moving consumer good</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>501 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         Company Name  \\\n",
+       "0                                       3M India Ltd.   \n",
+       "1                                    Aarti Drugs Ltd.   \n",
+       "2                               Aavas Financiers Ltd.   \n",
+       "3                                      ABB India Ltd.   \n",
+       "4                                   Abbott India Ltd.   \n",
+       "..                                                ...   \n",
+       "496                           Zensar Technolgies Ltd.   \n",
+       "497  ZF Commercial Vehicle Control Systems India Ltd.   \n",
+       "498                                       Zomato Ltd.   \n",
+       "499                           Zydus Lifesciences Ltd.   \n",
+       "500                               Zydus Wellness Ltd.   \n",
+       "\n",
+       "                      Industry  \n",
+       "0                  diversified  \n",
+       "1                   healthcare  \n",
+       "2            financial service  \n",
+       "3                 capital good  \n",
+       "4                   healthcare  \n",
+       "..                         ...  \n",
+       "496     information technology  \n",
+       "497  automobile auto component  \n",
+       "498           consumer service  \n",
+       "499                 healthcare  \n",
+       "500  fast moving consumer good  \n",
+       "\n",
+       "[501 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stock_embeddings = model.encode(filtered_df['Industry'].tolist())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "customer_interest = input(\"Enter your interest: \")\n",
+    "customer_interest_preprocessed = preprocess(customer_interest)\n",
+    "customer_embedding = model.encode([customer_interest_preprocessed])[0]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stock Embeddings:\n",
+      " [[-0.0023276  -0.00333788 -0.01594339 ... -0.08072303 -0.03491797\n",
+      "   0.00427012]\n",
+      " [-0.03235555  0.08877651 -0.00343593 ... -0.04697321  0.08099543\n",
+      "   0.00860554]\n",
+      " [-0.06748401  0.00503902 -0.07492065 ... -0.10049688  0.03232934\n",
+      "  -0.01964198]\n",
+      " ...\n",
+      " [-0.12713586  0.01321788 -0.04178813 ... -0.00761455  0.05024449\n",
+      "  -0.01091575]\n",
+      " [-0.03235552  0.08877648 -0.00343592 ... -0.04697322  0.08099545\n",
+      "   0.00860555]\n",
+      " [-0.04195232 -0.06478065  0.02186874 ... -0.02244484  0.0067175\n",
+      "   0.06623445]]\n",
+      "\n",
+      "Customer Embedding:\n",
+      " [ 3.14664170e-02 -3.78815085e-02 -3.73417661e-02 -3.17229889e-02\n",
+      " -4.22344357e-02  1.27025442e-02 -2.39155442e-02 -6.93003461e-02\n",
+      " -3.21524963e-02  7.96454027e-02  7.77523369e-02 -4.75082286e-02\n",
+      " -1.95211302e-02 -1.25046214e-02  5.08183353e-02 -2.46512946e-02\n",
+      " -1.49427941e-02 -2.74640676e-02  4.25236784e-02 -7.36578787e-03\n",
+      " -8.90878867e-03  1.84313732e-03 -4.04891334e-02  2.95713730e-02\n",
+      " -5.97193651e-02  1.87571477e-02 -5.67692332e-03 -8.21459852e-03\n",
+      " -2.31289323e-02 -2.73498092e-02  2.54001822e-02  1.30668739e-02\n",
+      "  9.44364443e-02 -8.01635440e-03  3.55471559e-02  1.28795162e-01\n",
+      "  2.49239113e-02 -9.53501761e-02  1.58603266e-02 -3.89398336e-02\n",
+      " -1.34050464e-02 -9.93881673e-02 -2.93484535e-02 -7.17466623e-02\n",
+      "  3.43848653e-02 -1.67269222e-02  2.82975789e-02 -3.10652656e-03\n",
+      "  2.12582219e-02 -7.64291584e-02  4.05744687e-02 -2.67640091e-02\n",
+      "  2.98122708e-02 -9.98979155e-03 -5.23542874e-02 -1.77336130e-02\n",
+      " -1.46305542e-02 -6.34800317e-03 -4.22016680e-02 -5.87929562e-02\n",
+      " -6.84524998e-02 -7.19780475e-03 -4.78288569e-02 -5.24268188e-02\n",
+      "  5.48108257e-02 -9.64927487e-03  1.59333013e-02 -7.21244812e-02\n",
+      " -1.53982667e-02 -7.07860738e-02  4.63903956e-02 -8.87077451e-02\n",
+      "  4.21138760e-03 -4.27451804e-02 -3.15879434e-02 -3.48816104e-02\n",
+      "  1.73769016e-02  1.20111547e-01  1.23920493e-01 -1.45363241e-01\n",
+      "  7.32854903e-02  5.91961406e-02 -4.84295599e-02  6.06471710e-02\n",
+      " -6.54613897e-02  1.24825360e-02 -7.38825463e-03 -6.54625967e-02\n",
+      "  2.87184883e-02  1.88502856e-03 -8.01817980e-03 -3.96523885e-02\n",
+      "  7.24581480e-02  1.22633632e-02 -5.44842100e-03  1.16946828e-02\n",
+      " -4.74422099e-03 -4.22262959e-02 -1.53517053e-02  9.96556208e-02\n",
+      " -4.84725051e-02  3.07871047e-02 -3.94323021e-02 -3.33751626e-02\n",
+      " -7.20618889e-02 -7.62435235e-03  9.71943699e-03  2.18201359e-03\n",
+      "  5.96895888e-02  1.00962352e-02 -7.43536279e-03  1.28121506e-02\n",
+      " -3.44882496e-02 -4.63309400e-02  3.64253037e-02 -5.68599366e-02\n",
+      " -4.72689755e-02 -1.85630340e-02  2.15455409e-04 -5.69709577e-02\n",
+      " -2.88812886e-03  1.34512419e-02 -6.94445893e-02 -4.80364412e-02\n",
+      "  1.98717043e-02 -6.87178075e-02  4.69160378e-02 -3.48460129e-33\n",
+      "  2.74309292e-02  4.61018123e-02  7.90321007e-02 -1.77468895e-03\n",
+      "  2.93568131e-02  1.36835873e-02 -3.85059714e-02 -4.15082574e-02\n",
+      "  4.04559672e-02  1.43633969e-02  6.32987246e-02  5.11144586e-02\n",
+      "  4.15699705e-02  1.00233350e-02  1.26954451e-01 -1.22519739e-01\n",
+      " -1.47484569e-02  8.62846524e-02  7.75998011e-02  4.46178764e-02\n",
+      " -1.41220577e-02  5.68904029e-03 -1.14176879e-02 -2.26635188e-02\n",
+      "  1.14526093e-01 -4.34458293e-02  1.74916312e-02 -1.74409226e-02\n",
+      "  2.41210591e-02  5.08027188e-02  7.86003917e-02  1.14285775e-01\n",
+      " -3.16002294e-02 -3.04858722e-02 -1.72049068e-02  3.13026309e-02\n",
+      " -3.78056103e-03 -2.68692020e-02  8.44113063e-03 -3.65200266e-02\n",
+      " -9.06182155e-02 -5.41768707e-02  4.64576595e-02  5.30998260e-02\n",
+      " -8.45028013e-02  7.69533683e-03  4.33015600e-02  5.77162132e-02\n",
+      "  1.57650374e-02  3.23144123e-02  2.93194950e-02  5.57424016e-02\n",
+      " -7.00408267e-03 -5.76511910e-03  9.61290859e-03 -2.98861135e-02\n",
+      "  3.35550979e-02 -5.78683540e-02 -2.72784918e-03 -5.11349514e-02\n",
+      "  6.91856742e-02  3.93171422e-02 -9.10947323e-02  1.25231802e-01\n",
+      " -7.74377361e-02 -3.65904607e-02  2.45778412e-02 -3.15613784e-02\n",
+      "  1.32436842e-01 -4.68552709e-02 -3.28906663e-02  1.51393292e-02\n",
+      " -4.87497970e-02 -2.50118654e-02 -6.71052039e-02 -2.61116866e-02\n",
+      " -4.84636873e-02  5.14720157e-02  2.30410714e-02  3.72129828e-02\n",
+      " -8.30372125e-02  1.39289838e-03  8.76997970e-03 -6.48937235e-03\n",
+      "  1.66297719e-01  7.46646076e-02  4.32111137e-02  7.86511973e-03\n",
+      "  6.49285689e-02  1.44748529e-02 -1.45551831e-01  1.85465429e-03\n",
+      "  3.69937643e-02  1.24513529e-01  6.94522038e-02  8.31680528e-34\n",
+      "  2.02758648e-02 -1.38148433e-02  8.69328249e-03 -3.77335213e-02\n",
+      "  4.28531915e-02  4.57742624e-02  3.20917927e-03 -7.67260492e-02\n",
+      "  2.34734826e-02  7.90196955e-02 -8.84439349e-02 -2.00472027e-02\n",
+      "  3.51752974e-02  4.71815728e-02  3.01404763e-02 -9.60588306e-02\n",
+      " -1.09162629e-02  2.62741130e-02  3.75837716e-03  4.07930352e-02\n",
+      "  1.16740474e-02 -4.42948379e-02  5.25881089e-02 -2.52405480e-02\n",
+      " -4.48426185e-03  1.36668356e-02 -6.41934201e-02 -4.62563448e-02\n",
+      " -3.71463336e-02 -5.36741503e-02  2.67249644e-02 -4.97082695e-02\n",
+      " -5.00039123e-02  1.82751343e-02 -8.67619663e-02 -2.51742024e-02\n",
+      "  1.15126157e-02 -3.29780951e-02 -3.40419933e-02 -3.42883095e-02\n",
+      " -1.14182731e-04 -4.34406884e-02  2.27891263e-02  1.50547381e-02\n",
+      " -3.02064866e-02 -5.12561463e-02 -3.14933769e-02  3.78211439e-02\n",
+      " -7.33443052e-02 -1.62566863e-02 -6.89671859e-02  8.55399668e-02\n",
+      " -1.29097393e-02  2.51021376e-03  2.23773625e-02  3.67263928e-02\n",
+      " -1.82489175e-02 -3.63597204e-03 -3.53036486e-02 -9.63828806e-03\n",
+      "  6.90663457e-02  1.02930024e-01 -5.99898584e-02  1.27931476e-01\n",
+      "  7.77645856e-02 -5.71305789e-02  4.69792672e-02 -4.12638411e-02\n",
+      "  2.58938950e-02 -3.08347158e-02 -3.00339106e-02 -5.49983121e-02\n",
+      "  3.68826687e-02 -1.24603510e-02  4.68766242e-02 -1.11738769e-02\n",
+      " -6.12557940e-02 -8.41406267e-03  5.76944388e-02  3.37682106e-02\n",
+      "  8.08468238e-02 -1.47831952e-02 -5.12435287e-02 -1.43610025e-02\n",
+      " -1.72178708e-02 -1.24791898e-02  1.05243884e-01 -8.38583037e-02\n",
+      " -8.27235915e-03  5.26939481e-02 -6.68532029e-02 -7.83333704e-02\n",
+      "  1.74317770e-02  6.10077605e-02  8.48254338e-02 -1.45727634e-08\n",
+      " -5.60169155e-03  3.13913748e-02 -1.43741500e-02 -3.30618732e-02\n",
+      "  4.62461524e-02 -9.12346542e-02 -9.48227867e-02 -6.03580996e-02\n",
+      " -3.57683040e-02 -1.11233955e-02  8.02720189e-02  3.13891210e-02\n",
+      " -1.37412539e-02  3.30802463e-02 -5.60475104e-02 -3.96210775e-02\n",
+      "  1.10384878e-02  2.29135510e-02 -4.95473966e-02  4.28589480e-03\n",
+      "  8.42921063e-03  4.52581532e-02  3.36574055e-02 -1.93255581e-02\n",
+      "  5.11594769e-03  4.19118740e-02  3.12094409e-02  6.94123842e-03\n",
+      " -1.22232875e-02  8.34257528e-02  3.83116268e-02  7.67853260e-02\n",
+      " -2.68143434e-02 -2.39269249e-02 -5.86269535e-02 -7.08768563e-03\n",
+      "  1.90878119e-02  5.11739142e-02  5.80927432e-02 -8.05346519e-02\n",
+      " -1.35047799e-02  4.48757410e-02  9.85127836e-02  4.68116690e-04\n",
+      "  3.26827727e-02  2.04949491e-02 -1.01781540e-01  9.10897565e-04\n",
+      "  3.98706049e-02 -1.28018588e-01 -5.66593781e-02  4.23376933e-02\n",
+      "  3.15444754e-03  5.05354777e-02  2.17250474e-02 -1.54494355e-02\n",
+      "  1.74987838e-02 -2.67964997e-03 -8.95080194e-02  3.47001143e-02\n",
+      "  8.55300725e-02 -6.96479157e-02  6.53972477e-02  2.12662946e-02]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Stock Embeddings:\\n\", stock_embeddings)\n",
+    "print(\"\\nCustomer Embedding:\\n\", customer_embedding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stock Descriptions and their Embeddings:\n",
+      "3M India Ltd.: [-2.32759910e-03 -3.33788223e-03 -1.59433894e-02  1.83002464e-02\n",
+      "  2.54555140e-02 -6.96712919e-03  1.24405146e-01  3.74317504e-02\n",
+      " -2.26003341e-02 -9.38108191e-03  7.38121569e-03 -4.46281619e-02\n",
+      " -2.51477528e-02 -1.05396081e-02  2.64941957e-02  1.29251014e-02\n",
+      "  3.01193818e-02  3.20924260e-02 -1.29294917e-01 -3.89382280e-02\n",
+      " -3.69867086e-02 -2.97117177e-02 -3.49156410e-02  3.20979692e-02\n",
+      "  3.16793076e-03  4.70022783e-02  1.51405949e-03  2.02399190e-03\n",
+      "  1.24467090e-02 -1.14337705e-01  9.39006805e-02  6.62291795e-02\n",
+      " -3.18582100e-03 -1.25789819e-02 -1.66373197e-02  7.99866766e-02\n",
+      " -6.03048168e-02 -3.98007371e-02  6.03630543e-02 -1.10751539e-02\n",
+      " -5.29428981e-02 -1.89303700e-02 -6.13464564e-02  2.01346353e-02\n",
+      " -1.87562741e-02 -4.52948138e-02 -4.28285114e-02 -3.13006416e-02\n",
+      "  1.79695729e-02 -3.92590351e-02 -3.56187150e-02 -3.76130901e-02\n",
+      " -8.49955603e-02 -4.19510081e-02  3.51959802e-02  3.60498913e-02\n",
+      " -7.18339980e-02 -2.24302448e-02  1.53797527e-03 -3.25705633e-02\n",
+      "  1.16625883e-01 -1.56557350e-03 -1.50631862e-02  4.98869419e-02\n",
+      "  6.02208711e-02 -6.45501465e-02  1.42840948e-02  9.79392007e-02\n",
+      "  6.35929080e-03 -6.68662190e-02  7.92451724e-02  1.86227690e-02\n",
+      " -2.77360138e-02 -6.96697785e-03  3.61399236e-03  1.09763201e-02\n",
+      "  4.71825190e-02 -6.70278594e-02  2.55656540e-02  7.39489198e-02\n",
+      " -5.79650365e-02  1.87688340e-02 -4.82925773e-02 -6.90813884e-02\n",
+      "  5.70813119e-02 -4.77391332e-02 -7.10094050e-02 -5.70729226e-02\n",
+      " -6.40136469e-03 -1.28354542e-02 -3.24331261e-02 -5.67168277e-03\n",
+      "  1.05156161e-01  1.56914629e-02 -1.06199302e-01  6.76377618e-04\n",
+      " -1.80345029e-02 -4.28995304e-02  5.03131449e-02  2.28882283e-01\n",
+      "  4.54692692e-02  7.09451810e-02  2.00039451e-03  2.12981347e-02\n",
+      " -5.56720458e-02 -8.82062167e-02 -3.70013937e-02  5.97805716e-02\n",
+      "  3.62342447e-02  4.82548866e-03 -5.02875112e-02  6.78890198e-02\n",
+      " -1.98884085e-02  5.65072596e-02 -6.55352697e-02  2.89025810e-02\n",
+      "  3.51873506e-03  8.86420161e-03  6.80674538e-02  1.10256903e-01\n",
+      " -2.06525438e-02  2.55258400e-02 -1.55882034e-02  3.69046293e-02\n",
+      " -1.98447686e-02  2.75109261e-02  1.57346558e-02 -4.45385883e-33\n",
+      " -1.64646264e-02 -2.99713369e-02  4.49895151e-02  6.96964264e-02\n",
+      " -3.36403139e-02  9.40562022e-05  1.17363296e-02 -5.71031123e-02\n",
+      " -1.40197337e-01 -5.69247408e-03  1.26271313e-02  1.68062210e-01\n",
+      " -4.48756963e-02  6.64636493e-02  2.01800633e-02 -6.30298257e-02\n",
+      " -7.46646971e-02  1.18307933e-01  6.62541538e-02 -1.15115628e-01\n",
+      " -2.59245317e-02  7.45442510e-02  1.76481605e-02 -9.41452943e-03\n",
+      " -1.90864084e-03 -1.13179877e-01  6.68639829e-03 -2.87526678e-02\n",
+      "  4.83043678e-02  4.32433896e-02 -6.69934135e-03  4.25498709e-02\n",
+      " -1.66484695e-02 -1.94241032e-02 -3.15628424e-02  5.31170964e-02\n",
+      " -6.68852404e-02 -8.03998038e-02  9.29130893e-03 -2.64395773e-02\n",
+      "  3.54514830e-02  1.76029897e-03  2.89407633e-02 -5.48042299e-04\n",
+      " -3.56975524e-03  7.67783746e-02  2.76548713e-02  8.63143206e-02\n",
+      " -5.50709032e-02 -3.54674645e-03  1.55914086e-03 -2.90392041e-02\n",
+      " -5.41966269e-03  6.61830127e-04  1.28434421e-02 -6.11222051e-02\n",
+      " -2.46005096e-02  5.66547737e-02  4.76841535e-03  2.37913653e-02\n",
+      "  2.99174734e-03  2.38334723e-02 -9.74291712e-02  1.71731692e-02\n",
+      " -1.20883398e-02  6.56297654e-02  6.18584566e-02  2.49796566e-02\n",
+      "  1.56092104e-02  1.00496896e-01 -2.61889212e-02  4.40136455e-02\n",
+      "  6.84746057e-02 -1.45444721e-02 -1.53595675e-02 -4.19073030e-02\n",
+      "  2.06142925e-02  7.47264624e-02  5.53372921e-03 -4.55914736e-02\n",
+      " -2.65808497e-02  1.04356736e-01 -3.87409143e-02  8.54308084e-02\n",
+      " -5.23209162e-02  1.96815021e-02  3.85511480e-02  7.21851783e-03\n",
+      "  3.04099806e-02 -1.38766076e-02 -2.13565063e-02 -2.50088219e-02\n",
+      "  7.04771653e-02 -7.19688088e-02 -3.12348995e-02  3.09036914e-33\n",
+      " -4.99026105e-02 -4.92914431e-02 -4.45572985e-03  5.98860867e-02\n",
+      "  3.33882160e-02  1.15142937e-03 -1.56639572e-02 -4.49012518e-02\n",
+      " -9.83680412e-02 -3.03461030e-02 -2.84866858e-02  2.34157015e-02\n",
+      "  9.74282995e-02 -1.78795233e-02 -1.06869936e-01  6.45851670e-03\n",
+      "  5.76061159e-02  4.05999795e-02  7.97678716e-03  1.02042779e-02\n",
+      "  4.40389663e-02 -1.65717099e-02 -2.58148778e-02  2.45852191e-02\n",
+      "  2.80654542e-02  3.47544327e-02 -1.17063053e-01  3.27713937e-02\n",
+      "  7.70159904e-03 -2.23936364e-02  2.38337535e-02 -6.43201843e-02\n",
+      " -6.65061772e-02 -4.37313281e-02 -2.67606489e-02  3.66581529e-02\n",
+      " -3.07512768e-02 -3.05431560e-02  3.01755555e-02  1.25844717e-01\n",
+      " -4.91082110e-02 -4.42270525e-02 -4.62905783e-03  6.55150339e-02\n",
+      "  4.46607247e-02 -4.71847542e-02 -5.15438430e-03 -6.44685924e-02\n",
+      "  7.23418891e-02  8.14077351e-03 -1.44985048e-02  2.33565588e-02\n",
+      "  2.18508951e-02 -1.93452761e-02 -5.27785309e-02 -1.65917631e-02\n",
+      " -2.14482192e-02  4.02824134e-02 -4.52037267e-02 -3.84387076e-02\n",
+      "  2.14753393e-02  2.03893352e-02 -6.82437271e-02  1.29291296e-01\n",
+      " -1.01819849e-02 -5.06817773e-02 -3.19466479e-02 -1.64574310e-01\n",
+      " -1.04043573e-01 -5.38696647e-02  4.17750105e-02 -7.68206716e-02\n",
+      " -1.50548900e-02 -5.12630232e-02  6.18488993e-03 -5.11264913e-02\n",
+      "  5.94733842e-03 -1.07750995e-02 -1.87183358e-02  9.46810171e-02\n",
+      " -7.74797723e-02 -3.42557766e-02  5.36597334e-02  8.67206510e-03\n",
+      " -1.81261112e-03  6.90565482e-02 -3.65183763e-02 -3.44872265e-03\n",
+      "  2.32912390e-03 -2.68334392e-02 -8.09416622e-02 -4.69287746e-02\n",
+      " -1.90885179e-02  1.23290643e-02  1.13014970e-02 -1.14592629e-08\n",
+      "  7.11335465e-02  3.33103128e-02  7.79330404e-03  9.29388851e-02\n",
+      "  3.44540738e-02 -8.56203493e-03 -6.27465844e-02  1.61846261e-02\n",
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+      "Aditya Birla Fashion and Retail Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      "Affle (India) Ltd.: [-3.85812297e-02  6.10318147e-02 -2.31247488e-02 -3.22841741e-02\n",
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+      "Alkyl Amines Chemicals Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "Alok Industries Ltd.: [-9.64162573e-02  6.90967515e-02 -1.66647322e-02  4.47280891e-02\n",
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+      "Apollo Tyres Ltd.: [-5.15867323e-02  9.61385481e-03 -2.78992136e-03 -2.43441798e-02\n",
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+      "Aptus Value Housing Finance India Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "  5.75537607e-02  6.14175200e-03 -5.98475114e-02  1.44835347e-02\n",
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+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "Asahi India Glass Ltd.: [-5.15867211e-02  9.61384457e-03 -2.78991903e-03 -2.43441928e-02\n",
+      " -5.72687853e-03  7.73921013e-02  6.80568665e-02  8.52750391e-02\n",
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+      "Astral Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "  5.20288646e-02 -2.72753071e-02  8.94622728e-02  1.44767771e-02]\n",
+      "AstraZenca Pharma India Ltd.: [-3.23555171e-02  8.87764841e-02 -3.43591906e-03  1.08567076e-02\n",
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+      "Atul Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "Avanti Feeds Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "  3.85916717e-02 -2.24448442e-02  6.71749795e-03  6.62344471e-02]\n",
+      "Axis Bank Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Balaji Amines Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "  1.18093111e-01 -5.92013784e-02  1.25763372e-01 -1.41946170e-02]\n",
+      "Balkrishna Industries Ltd.: [-5.15867211e-02  9.61384457e-03 -2.78991903e-03 -2.43441928e-02\n",
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+      "Balrampur Chini Mills Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "  3.85916717e-02 -2.24448442e-02  6.71749795e-03  6.62344471e-02]\n",
+      "Bandhan Bank Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      " -1.87233668e-02 -1.69751104e-02  2.15576608e-02  9.46953744e-02\n",
+      "  3.63857038e-02 -2.70375013e-02 -2.59825401e-03  3.28301229e-02\n",
+      " -8.22136775e-02 -1.50117781e-02 -3.84484380e-02  1.21809384e-02\n",
+      " -4.60985303e-02 -3.61101441e-02  8.05378780e-02  4.19188514e-02\n",
+      "  2.48036589e-02  1.47437453e-01 -9.57677662e-02 -3.21226083e-02\n",
+      " -6.71315789e-02 -2.84012146e-02 -6.90367538e-03 -3.09999901e-33\n",
+      " -8.49685296e-02  3.09389271e-03  9.21666771e-02 -4.34447601e-02\n",
+      "  1.22442255e-02  3.68888639e-02 -1.22230034e-02  2.05156766e-02\n",
+      " -7.43388310e-02  1.04906507e-01 -1.59085914e-02  8.18398148e-02\n",
+      " -1.24132831e-03 -7.12995157e-02  1.23707056e-02  3.22200661e-03\n",
+      " -5.09940386e-02  2.33332310e-02  1.41722234e-02 -1.65840331e-02\n",
+      " -1.95089132e-02  5.89064322e-02  5.30074462e-02 -3.06126755e-02\n",
+      "  1.15061946e-01 -4.11616452e-02 -6.06578887e-02 -2.60953023e-03\n",
+      "  1.11202694e-01  2.24721152e-02  3.28111164e-02  3.02952100e-02\n",
+      "  1.63336471e-02 -2.17415188e-02  8.63373186e-03  1.05685517e-02\n",
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+      " -5.47524840e-02  2.12684087e-02  1.61400065e-02  1.00404620e-01\n",
+      " -4.33133468e-02  9.37208086e-02  5.44147938e-02  6.80338219e-02\n",
+      "  1.98733024e-02  4.28674594e-02 -5.87543510e-02 -1.43116033e-02\n",
+      " -5.94924316e-02  5.50571177e-03  5.25777135e-03 -1.93778519e-02\n",
+      "  7.12580904e-02 -2.78725512e-02 -7.84434676e-02 -1.11414619e-01\n",
+      "  4.51268535e-03 -6.22626841e-02 -9.49203894e-02 -1.04746604e-02\n",
+      " -4.61553484e-02  2.78756507e-02 -6.44110050e-03  3.58653180e-02\n",
+      "  4.66831736e-02  8.44673812e-03 -5.91061115e-02  2.47220322e-02\n",
+      "  1.24228820e-01  6.95521608e-02  1.04136392e-02  1.53003540e-02\n",
+      "  4.21527326e-02 -1.29196495e-02  4.99776565e-03  4.63007428e-02\n",
+      " -1.21367248e-02 -3.71289365e-02  1.02042099e-02  9.49959531e-02\n",
+      "  1.19429842e-01  9.20799598e-02 -1.13122817e-03 -3.92101593e-02\n",
+      "  6.45325705e-02 -4.80205566e-02 -1.19421966e-01 -5.92616247e-03\n",
+      "  5.02024554e-02  1.10228352e-01  2.04713345e-02  1.10097526e-33\n",
+      " -5.70912138e-02 -5.60722984e-02  9.51438025e-03  5.33662103e-02\n",
+      " -2.99604516e-02  1.42369336e-02 -2.69682787e-04 -3.97430733e-02\n",
+      "  2.59364136e-02  1.15682386e-01 -2.19538640e-02  1.46582294e-02\n",
+      "  1.35957841e-02  1.12169180e-02 -2.93238740e-02 -3.60185690e-02\n",
+      " -2.20576227e-02 -2.31763534e-02  9.63147264e-03 -1.54953133e-02\n",
+      " -1.73137765e-02  6.67176396e-02  1.01144202e-01  3.85425873e-02\n",
+      "  7.45612837e-04  5.02424911e-02 -4.50820588e-02 -3.60520072e-02\n",
+      " -5.31075969e-02  2.00294494e-03 -1.56001300e-02  2.54387353e-02\n",
+      "  2.34996174e-02  2.34714653e-02 -1.09591268e-01 -2.39514560e-02\n",
+      "  2.57666465e-02  2.29664575e-02 -2.01982725e-02  3.04362774e-02\n",
+      "  1.80224758e-02 -2.72389352e-02  2.17333119e-02  5.00385696e-03\n",
+      "  3.16333361e-02 -7.04337955e-02 -4.08428386e-02  6.11861842e-03\n",
+      "  4.67761792e-02 -3.80361937e-02 -1.30412206e-01 -3.40778306e-02\n",
+      "  9.99209005e-03  4.52099629e-02 -5.23773022e-02  7.76050314e-02\n",
+      "  2.33950187e-02 -3.14359479e-02 -5.94295636e-02  1.71926953e-02\n",
+      "  1.59134828e-02  5.89880869e-02  3.74533050e-02  8.88321549e-02\n",
+      "  1.52008962e-02 -4.88559864e-02 -1.79699138e-02 -2.71071400e-02\n",
+      " -2.60509215e-02 -2.93554235e-02  3.67353410e-02 -4.62744795e-02\n",
+      "  3.21477721e-03  4.32825834e-02 -1.24537637e-02  2.04074234e-02\n",
+      " -7.84306154e-02 -1.00847520e-01 -2.20632460e-02  2.06698366e-02\n",
+      "  2.47497912e-02 -1.71160586e-02 -4.12131101e-02  6.91024438e-02\n",
+      " -8.75407532e-02 -6.30359352e-02  1.14089474e-01 -5.44028096e-02\n",
+      "  2.47743316e-02 -4.64105979e-02 -3.86144742e-02  3.68286506e-03\n",
+      " -1.91598013e-02 -2.60107056e-03 -7.02821910e-02 -1.26411548e-08\n",
+      " -4.76241820e-02  4.16124053e-02 -2.64470764e-02 -2.67952736e-02\n",
+      "  9.05413460e-03 -8.46313909e-02 -2.10898332e-02  1.56228188e-02\n",
+      " -1.15304813e-02  8.54013674e-03  4.81116921e-02  1.43051809e-02\n",
+      " -3.28882709e-02 -3.08988318e-02 -1.29428906e-02 -6.32665455e-02\n",
+      "  1.73102170e-02 -3.90352271e-02 -2.63434811e-03  4.01208512e-02\n",
+      "  7.21684769e-02  9.85997170e-03  4.63772789e-02  2.07026792e-03\n",
+      " -3.45242284e-02 -3.55410315e-02  3.91834788e-02  1.21130645e-01\n",
+      "  5.75537607e-02  6.14175200e-03 -5.98475114e-02  1.44835347e-02\n",
+      "  5.66149093e-02 -2.74031945e-02 -9.77561325e-02  7.47879501e-03\n",
+      "  4.30650376e-02 -4.85842600e-02 -4.67660800e-02  3.54495011e-02\n",
+      " -1.59194730e-02  1.54233128e-02 -1.23488279e-02  1.01204030e-03\n",
+      "  4.84212115e-02  2.95556076e-02 -3.25922444e-02  4.14495915e-02\n",
+      "  9.55884233e-02 -1.06032133e-01  1.43242609e-02 -8.30611214e-03\n",
+      "  5.54339737e-02  3.58873680e-02  9.96736623e-03 -3.64325829e-02\n",
+      " -7.36321975e-03  3.76238744e-03 -7.30165541e-02  3.36904116e-02\n",
+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "Bank of Baroda: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
+      " -7.34059215e-02 -1.66739728e-02  1.01092011e-01  1.52869674e-03\n",
+      "  7.41633102e-02 -2.24150252e-02 -1.72783025e-02 -1.99839361e-02\n",
+      " -2.85700578e-02 -2.20315512e-02 -5.35634421e-02 -1.34030312e-01\n",
+      "  6.96685491e-03 -2.35260818e-02 -4.42431085e-02  2.70755813e-02\n",
+      " -4.17872779e-02 -7.22487085e-03 -7.05016404e-02  9.35958326e-03\n",
+      "  3.18855681e-02  2.26669349e-02 -3.15466151e-02  1.88774727e-02\n",
+      " -4.92369570e-02 -9.53472629e-02  2.28959452e-02 -9.20847151e-03\n",
+      "  5.41803204e-02 -2.79574618e-02  7.13210925e-02  7.49085546e-02\n",
+      "  6.42826781e-02  6.00483418e-02  6.32355511e-02 -2.14099437e-02\n",
+      " -5.91793209e-02 -8.34407881e-02  1.68331377e-02 -5.95049113e-02\n",
+      "  2.81869657e-02 -3.86524014e-02  3.48484106e-02  4.75979559e-02\n",
+      "  3.41861285e-02  6.94228783e-02 -4.30157483e-02 -1.54380184e-02\n",
+      "  1.02773905e-02  2.99895331e-02  4.88830507e-02  3.19402292e-02\n",
+      "  8.47197101e-02 -8.24522413e-03 -8.88420865e-02 -5.75427487e-02\n",
+      " -1.06175169e-02  6.75682968e-04  4.91183391e-03  4.81875911e-02\n",
+      "  1.01896144e-01  8.30458030e-02 -4.60584648e-03  4.44551557e-02\n",
+      " -5.02469242e-02 -1.36948809e-01 -5.71422698e-03 -9.00605470e-02\n",
+      " -8.00312757e-02  4.88258712e-02  3.62499319e-02  5.68610290e-03\n",
+      "  6.23985082e-02  3.84147093e-02  4.64469865e-02 -3.95821594e-02\n",
+      "  6.55861050e-02 -6.49959147e-02 -3.49017121e-02  8.47571790e-02\n",
+      " -2.67272536e-02 -7.13121053e-03 -4.77144346e-02 -1.80950277e-02\n",
+      "  1.84503198e-02 -8.92676972e-03  5.12143672e-02 -5.47849294e-03\n",
+      "  5.65982684e-02 -4.55755629e-02 -7.56006166e-02 -2.66146958e-02\n",
+      " -5.72668482e-03 -7.37986192e-02 -3.24654654e-02  1.37050822e-01\n",
+      "  7.35773146e-02  3.34469974e-02  3.98678854e-02  4.86416444e-02\n",
+      " -1.87233668e-02 -1.69751104e-02  2.15576608e-02  9.46953744e-02\n",
+      "  3.63857038e-02 -2.70375013e-02 -2.59825401e-03  3.28301229e-02\n",
+      " -8.22136775e-02 -1.50117781e-02 -3.84484380e-02  1.21809384e-02\n",
+      " -4.60985303e-02 -3.61101441e-02  8.05378780e-02  4.19188514e-02\n",
+      "  2.48036589e-02  1.47437453e-01 -9.57677662e-02 -3.21226083e-02\n",
+      " -6.71315789e-02 -2.84012146e-02 -6.90367538e-03 -3.09999901e-33\n",
+      " -8.49685296e-02  3.09389271e-03  9.21666771e-02 -4.34447601e-02\n",
+      "  1.22442255e-02  3.68888639e-02 -1.22230034e-02  2.05156766e-02\n",
+      " -7.43388310e-02  1.04906507e-01 -1.59085914e-02  8.18398148e-02\n",
+      " -1.24132831e-03 -7.12995157e-02  1.23707056e-02  3.22200661e-03\n",
+      " -5.09940386e-02  2.33332310e-02  1.41722234e-02 -1.65840331e-02\n",
+      " -1.95089132e-02  5.89064322e-02  5.30074462e-02 -3.06126755e-02\n",
+      "  1.15061946e-01 -4.11616452e-02 -6.06578887e-02 -2.60953023e-03\n",
+      "  1.11202694e-01  2.24721152e-02  3.28111164e-02  3.02952100e-02\n",
+      "  1.63336471e-02 -2.17415188e-02  8.63373186e-03  1.05685517e-02\n",
+      " -3.95311825e-02 -7.36117586e-02 -1.81138236e-02 -5.37877046e-02\n",
+      " -5.47524840e-02  2.12684087e-02  1.61400065e-02  1.00404620e-01\n",
+      " -4.33133468e-02  9.37208086e-02  5.44147938e-02  6.80338219e-02\n",
+      "  1.98733024e-02  4.28674594e-02 -5.87543510e-02 -1.43116033e-02\n",
+      " -5.94924316e-02  5.50571177e-03  5.25777135e-03 -1.93778519e-02\n",
+      "  7.12580904e-02 -2.78725512e-02 -7.84434676e-02 -1.11414619e-01\n",
+      "  4.51268535e-03 -6.22626841e-02 -9.49203894e-02 -1.04746604e-02\n",
+      " -4.61553484e-02  2.78756507e-02 -6.44110050e-03  3.58653180e-02\n",
+      "  4.66831736e-02  8.44673812e-03 -5.91061115e-02  2.47220322e-02\n",
+      "  1.24228820e-01  6.95521608e-02  1.04136392e-02  1.53003540e-02\n",
+      "  4.21527326e-02 -1.29196495e-02  4.99776565e-03  4.63007428e-02\n",
+      " -1.21367248e-02 -3.71289365e-02  1.02042099e-02  9.49959531e-02\n",
+      "  1.19429842e-01  9.20799598e-02 -1.13122817e-03 -3.92101593e-02\n",
+      "  6.45325705e-02 -4.80205566e-02 -1.19421966e-01 -5.92616247e-03\n",
+      "  5.02024554e-02  1.10228352e-01  2.04713345e-02  1.10097526e-33\n",
+      " -5.70912138e-02 -5.60722984e-02  9.51438025e-03  5.33662103e-02\n",
+      " -2.99604516e-02  1.42369336e-02 -2.69682787e-04 -3.97430733e-02\n",
+      "  2.59364136e-02  1.15682386e-01 -2.19538640e-02  1.46582294e-02\n",
+      "  1.35957841e-02  1.12169180e-02 -2.93238740e-02 -3.60185690e-02\n",
+      " -2.20576227e-02 -2.31763534e-02  9.63147264e-03 -1.54953133e-02\n",
+      " -1.73137765e-02  6.67176396e-02  1.01144202e-01  3.85425873e-02\n",
+      "  7.45612837e-04  5.02424911e-02 -4.50820588e-02 -3.60520072e-02\n",
+      " -5.31075969e-02  2.00294494e-03 -1.56001300e-02  2.54387353e-02\n",
+      "  2.34996174e-02  2.34714653e-02 -1.09591268e-01 -2.39514560e-02\n",
+      "  2.57666465e-02  2.29664575e-02 -2.01982725e-02  3.04362774e-02\n",
+      "  1.80224758e-02 -2.72389352e-02  2.17333119e-02  5.00385696e-03\n",
+      "  3.16333361e-02 -7.04337955e-02 -4.08428386e-02  6.11861842e-03\n",
+      "  4.67761792e-02 -3.80361937e-02 -1.30412206e-01 -3.40778306e-02\n",
+      "  9.99209005e-03  4.52099629e-02 -5.23773022e-02  7.76050314e-02\n",
+      "  2.33950187e-02 -3.14359479e-02 -5.94295636e-02  1.71926953e-02\n",
+      "  1.59134828e-02  5.89880869e-02  3.74533050e-02  8.88321549e-02\n",
+      "  1.52008962e-02 -4.88559864e-02 -1.79699138e-02 -2.71071400e-02\n",
+      " -2.60509215e-02 -2.93554235e-02  3.67353410e-02 -4.62744795e-02\n",
+      "  3.21477721e-03  4.32825834e-02 -1.24537637e-02  2.04074234e-02\n",
+      " -7.84306154e-02 -1.00847520e-01 -2.20632460e-02  2.06698366e-02\n",
+      "  2.47497912e-02 -1.71160586e-02 -4.12131101e-02  6.91024438e-02\n",
+      " -8.75407532e-02 -6.30359352e-02  1.14089474e-01 -5.44028096e-02\n",
+      "  2.47743316e-02 -4.64105979e-02 -3.86144742e-02  3.68286506e-03\n",
+      " -1.91598013e-02 -2.60107056e-03 -7.02821910e-02 -1.26411548e-08\n",
+      " -4.76241820e-02  4.16124053e-02 -2.64470764e-02 -2.67952736e-02\n",
+      "  9.05413460e-03 -8.46313909e-02 -2.10898332e-02  1.56228188e-02\n",
+      " -1.15304813e-02  8.54013674e-03  4.81116921e-02  1.43051809e-02\n",
+      " -3.28882709e-02 -3.08988318e-02 -1.29428906e-02 -6.32665455e-02\n",
+      "  1.73102170e-02 -3.90352271e-02 -2.63434811e-03  4.01208512e-02\n",
+      "  7.21684769e-02  9.85997170e-03  4.63772789e-02  2.07026792e-03\n",
+      " -3.45242284e-02 -3.55410315e-02  3.91834788e-02  1.21130645e-01\n",
+      "  5.75537607e-02  6.14175200e-03 -5.98475114e-02  1.44835347e-02\n",
+      "  5.66149093e-02 -2.74031945e-02 -9.77561325e-02  7.47879501e-03\n",
+      "  4.30650376e-02 -4.85842600e-02 -4.67660800e-02  3.54495011e-02\n",
+      " -1.59194730e-02  1.54233128e-02 -1.23488279e-02  1.01204030e-03\n",
+      "  4.84212115e-02  2.95556076e-02 -3.25922444e-02  4.14495915e-02\n",
+      "  9.55884233e-02 -1.06032133e-01  1.43242609e-02 -8.30611214e-03\n",
+      "  5.54339737e-02  3.58873680e-02  9.96736623e-03 -3.64325829e-02\n",
+      " -7.36321975e-03  3.76238744e-03 -7.30165541e-02  3.36904116e-02\n",
+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "Bank of India: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
+      " -7.34059215e-02 -1.66739728e-02  1.01092011e-01  1.52869674e-03\n",
+      "  7.41633102e-02 -2.24150252e-02 -1.72783025e-02 -1.99839361e-02\n",
+      " -2.85700578e-02 -2.20315512e-02 -5.35634421e-02 -1.34030312e-01\n",
+      "  6.96685491e-03 -2.35260818e-02 -4.42431085e-02  2.70755813e-02\n",
+      " -4.17872779e-02 -7.22487085e-03 -7.05016404e-02  9.35958326e-03\n",
+      "  3.18855681e-02  2.26669349e-02 -3.15466151e-02  1.88774727e-02\n",
+      " -4.92369570e-02 -9.53472629e-02  2.28959452e-02 -9.20847151e-03\n",
+      "  5.41803204e-02 -2.79574618e-02  7.13210925e-02  7.49085546e-02\n",
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+      "Bharat Electronics Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "  1.53644662e-02  1.00373393e-02 -5.79135679e-02 -2.22017765e-02\n",
+      "  5.20288646e-02 -2.72753071e-02  8.94622728e-02  1.44767771e-02]\n",
+      "Berger Paints India Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
+      "  1.81541499e-02  1.65187474e-02  8.32148734e-03  4.23818789e-02\n",
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+      "  3.14017721e-02  9.47139710e-02 -1.38023440e-02  3.50176431e-02\n",
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+      "  1.40744429e-02  1.17705446e-02  5.18257217e-03  1.64701827e-02\n",
+      "  1.04847543e-01 -9.03233960e-02  1.54254243e-01  1.15292318e-01]\n",
+      "Bharat Forge Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
+      " -3.31258029e-02  2.87598353e-02  6.94174021e-02 -1.55117903e-02\n",
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+      "  1.53644662e-02  1.00373393e-02 -5.79135679e-02 -2.22017765e-02\n",
+      "  5.20288646e-02 -2.72753071e-02  8.94622728e-02  1.44767771e-02]\n",
+      "Bharti Airtel Ltd.: [-4.66608182e-02 -1.55256661e-02 -2.75107380e-02 -1.13982193e-01\n",
+      " -1.06568620e-01 -4.68478911e-02  1.24976903e-01  7.55328313e-02\n",
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+      "  3.74587514e-02 -4.89598662e-02 -2.52309833e-02 -6.80055916e-02\n",
+      "  3.52255926e-02 -2.43787356e-02 -3.00374236e-02 -3.88206169e-02\n",
+      " -2.01861691e-02  1.05799116e-01  1.01877466e-01  8.42225738e-03\n",
+      "  1.54546779e-02 -4.06029373e-02 -7.28311017e-03  4.50336374e-02\n",
+      "  7.57834986e-02  3.48625407e-02  1.69014186e-02  7.82289207e-02\n",
+      "  1.37456814e-02  7.32251769e-03  6.79691359e-02  2.10309345e-02\n",
+      "  4.21724208e-02  8.93608015e-03 -2.19948459e-02 -1.70143787e-03\n",
+      " -1.79402791e-02  3.99090443e-03 -9.90569219e-02  3.77345607e-02\n",
+      "  1.25132445e-02 -2.07464024e-02  6.10805564e-02 -1.26587888e-02\n",
+      "  6.17787521e-03 -3.54906693e-02 -1.05783772e-02  6.04554117e-02\n",
+      "  1.34562448e-01 -2.58161165e-02  6.47965893e-02 -1.60024986e-02\n",
+      " -3.91299650e-02 -1.00170434e-01 -3.88166565e-03  5.32665849e-02\n",
+      " -2.14759842e-03 -4.81507778e-02  1.47423493e-02 -9.25607458e-02\n",
+      "  6.34607524e-02 -4.40604351e-02 -6.71741441e-02  6.73868209e-02\n",
+      " -1.51760271e-02  7.25797415e-02  1.95078701e-02  1.38409159e-04]\n",
+      "Bharat Heavy Electricals Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
+      " -3.31258029e-02  2.87598353e-02  6.94174021e-02 -1.55117903e-02\n",
+      " -7.68500194e-02  3.57285701e-02  3.30422334e-02  1.74193848e-02\n",
+      " -4.65507526e-03 -3.75167914e-02 -1.09876168e-03 -5.66148460e-02\n",
+      "  2.71939859e-02  4.30904739e-02 -6.03955612e-02 -1.89458905e-03\n",
+      " -1.07684053e-01 -2.56799404e-02 -4.94121632e-04 -2.71421988e-02\n",
+      "  2.40264107e-02  1.56711712e-02  3.84374312e-03 -3.77227440e-02\n",
+      "  1.85761619e-02 -7.55665749e-02  9.48907528e-03 -6.37890771e-02\n",
+      "  6.18806332e-02 -6.52241632e-02 -1.81017201e-02  5.01318835e-02\n",
+      " -1.77988695e-05  9.64897871e-03  3.08954716e-02 -2.60623861e-02\n",
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+      "Borosil Renewables Ltd.: [ 2.46381462e-02 -2.18525864e-02  2.99072079e-02 -1.04453489e-02\n",
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+      "  5.20288981e-02 -2.72753406e-02  8.94622803e-02  1.44768255e-02]\n",
+      "Bosch Ltd.: [-5.15867211e-02  9.61384457e-03 -2.78991903e-03 -2.43441928e-02\n",
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+      " -4.64398824e-02  6.34204149e-02  8.36712047e-02 -2.24977471e-02]\n",
+      "Bharat Petroleum Corporation Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
+      "  8.70224833e-02  4.26123515e-02  9.30845886e-02  3.62931238e-03\n",
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+      "Britannia Industries Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "Birlasoft Ltd.: [-3.85812111e-02  6.10318258e-02 -2.31248103e-02 -3.22842002e-02\n",
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+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "Century Plyboards (India) Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "Clean Science and Technology Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "  1.18093111e-01 -5.92013784e-02  1.25763372e-01 -1.41946170e-02]\n",
+      "Coal India Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
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+      "Colgate Palmolive (India) Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "CSB Bank Ltd.: [-6.74839616e-02  5.03903395e-03 -7.49206170e-02  2.28400938e-02\n",
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+      "City Union Bank Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "Cummins India Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "  5.64854294e-02 -7.40710795e-02  7.42400810e-02  1.01314252e-02]\n",
+      "Dabur India Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "Dalmia Bharat Ltd.: [-7.29177594e-02  4.10846360e-02  1.53632332e-02  4.46023345e-02\n",
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+      "DCM Shriram Ltd.: [-2.32759910e-03 -3.33788223e-03 -1.59433894e-02  1.83002464e-02\n",
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+      "Dixon Technologies (India) Ltd.: [-7.71643817e-02 -3.33326956e-04  1.01158125e-02  2.20314581e-02\n",
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+      "  4.87573147e-02  2.50528976e-02 -2.07229033e-02  2.92262863e-02]\n",
+      "Avenue Supermarts Ltd.: [-1.27135858e-01  1.32179102e-02 -4.17881347e-02 -6.45975769e-02\n",
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+      "  9.86876637e-02 -7.61453528e-03  5.02444915e-02 -1.09157600e-02]\n",
+      "Dr. Reddy's Laboratories Ltd.: [-3.23555507e-02  8.87765065e-02 -3.43593163e-03  1.08566936e-02\n",
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+      " -4.64398824e-02  6.34204149e-02  8.36712047e-02 -2.24977471e-02]\n",
+      "E.I.D. Parry (India) Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "  4.29333420e-03  2.51996499e-02  4.96561266e-02  2.73171756e-02\n",
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+      "  3.85916717e-02 -2.24448442e-02  6.71749795e-03  6.62344471e-02]\n",
+      "EIH Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      "Emami Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "Exide Industries Ltd.: [-5.15867211e-02  9.61384457e-03 -2.78991903e-03 -2.43441928e-02\n",
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+      "Fertilisers and Chemicals Travancore Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "  9.56889391e-02 -4.69732061e-02  8.09954330e-02  8.60553514e-03]\n",
+      "Federal Bank Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Galaxy Surfactants Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "Great Eastern Shipping Co. Ltd.: [-1.24083109e-01  2.02381201e-02 -7.62994541e-03 -4.88203913e-02\n",
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+      "GMM Pfaudler Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "GMR Infrastructure Ltd.: [-1.24083132e-01  2.02380549e-02 -7.62996683e-03 -4.88204174e-02\n",
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+      "Gujarat Narmada Valley Fertilizers and Chemicals Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "Go Fashion (India) Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      "Godrej Agrovet Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "Gujarat Pipavav Port Ltd.: [-1.24083132e-01  2.02380549e-02 -7.62996683e-03 -4.88204174e-02\n",
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+      "Granules India Ltd.: [-3.23555171e-02  8.87764841e-02 -3.43591906e-03  1.08567076e-02\n",
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+      "Grasim Industries Ltd.: [-7.29177594e-02  4.10846360e-02  1.53632332e-02  4.46023345e-02\n",
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+      "  5.20288646e-02 -2.72753071e-02  8.94622728e-02  1.44767771e-02]\n",
+      "G R Infraprojects Ltd.: [-5.91711886e-02  5.09898998e-02 -3.55952815e-03  3.27332392e-02\n",
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+      "  3.84598933e-02  2.19692197e-02  9.55206603e-02  6.50335476e-02]\n",
+      "Gujarat State Fertilizers & Chemicals Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
+      "  1.96524933e-02  4.04251702e-02  1.45659164e-01  1.14778295e-01\n",
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+      "  7.11467257e-03 -5.09114377e-02 -1.50287524e-03  2.67157275e-02\n",
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+      "  3.51646990e-02  6.18754923e-02  3.86029668e-02 -3.61294784e-02\n",
+      " -5.04803769e-02  7.50240963e-03  9.26274108e-04  5.62416911e-02\n",
+      " -5.53895766e-03  3.22167575e-02  4.43729870e-02  1.81321707e-03\n",
+      " -4.19663489e-02 -4.07846682e-02 -6.01987019e-02 -2.95594707e-02\n",
+      " -5.07385992e-02  3.58443372e-02 -2.47695502e-02  1.79982353e-02\n",
+      "  8.91129300e-03 -2.31274050e-02  4.40114774e-02  4.56267521e-02\n",
+      "  2.79025473e-02  3.33823338e-02  4.52452805e-03  1.38519499e-02\n",
+      " -3.08096763e-02 -6.23992644e-03  5.47887161e-02 -6.56372458e-02\n",
+      "  5.61623424e-02 -8.74374658e-02 -5.05879708e-02 -4.19108476e-03\n",
+      " -4.69712429e-02  6.21743165e-02 -3.05779558e-02  1.65579282e-02\n",
+      "  5.02341166e-02 -5.15387878e-02  2.36214604e-02  4.69543487e-02\n",
+      "  1.62465870e-02 -2.45488249e-02  1.11379055e-02 -3.40469740e-02\n",
+      " -5.48923016e-02  5.20713208e-03 -5.91151696e-03  3.20686400e-02\n",
+      " -7.61842728e-03  1.30477874e-02 -6.63001314e-02 -3.78954485e-02\n",
+      "  1.18093111e-01 -5.92013784e-02  1.25763372e-01 -1.41946170e-02]\n",
+      "Gujarat State Petronet Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
+      "  8.70224833e-02  4.26123515e-02  9.30845886e-02  3.62931238e-03\n",
+      " -5.47133982e-02 -9.96530503e-02 -2.09179260e-02  5.53032756e-03\n",
+      " -7.52215162e-02 -2.35815402e-02 -4.26835492e-02 -2.87619531e-02\n",
+      "  1.01589561e-01 -6.65992349e-02  8.03106744e-03 -6.61153942e-02\n",
+      "  9.43806842e-02  1.64883230e-02  7.47972354e-02  4.65554446e-02\n",
+      " -8.81454814e-03  9.93454233e-02 -6.60015550e-03 -1.55321667e-02\n",
+      "  9.73864868e-02  3.38635370e-02  2.54910607e-02  1.92538928e-02\n",
+      " -1.68140978e-02  3.27502228e-02 -2.00417470e-02  3.29703726e-02\n",
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+      "  1.18093111e-01 -5.92013784e-02  1.25763372e-01 -1.41946170e-02]\n",
+      "Gujarat Gas Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
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+      " -1.37542868e-02  4.29236852e-02  9.61407125e-02 -1.29464902e-02]\n",
+      "Hindustan Aeronautics Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "Hatsun Agro Product Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "  3.85916717e-02 -2.24448442e-02  6.71749795e-03  6.62344471e-02]\n",
+      "Havells India Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "  5.20288646e-02 -2.72753071e-02  8.94622728e-02  1.44767771e-02]\n",
+      "Hemisphere Properties India Ltd.: [-7.89830163e-02 -2.06320938e-02 -7.58103058e-02  2.75196936e-02\n",
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+      "  4.18342724e-02  6.10448699e-03  3.50802951e-02  1.11384690e-02\n",
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+      " -6.17281049e-02  1.55829499e-02  6.25955760e-02  3.72404559e-03\n",
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+      "  6.82374761e-02 -1.97994728e-02 -4.88818660e-02  4.06518430e-02\n",
+      "  9.56889242e-02 -4.69732173e-02  8.09954479e-02  8.60554539e-03]\n",
+      "Hindalco Industries Ltd.: [-8.54082480e-02  7.21397176e-02 -2.08139271e-02  7.19632208e-02\n",
+      " -4.06680812e-07 -5.49386851e-02  1.09949410e-01 -2.13119294e-03\n",
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+      "  4.47645690e-03 -3.93106230e-02 -2.63823476e-03 -3.21210660e-02\n",
+      "  4.12422158e-02  2.11369782e-03 -1.09004378e-02 -5.83827086e-02\n",
+      "  7.80699998e-02  2.56532268e-03  1.03506021e-01  2.08582878e-02\n",
+      " -2.73716897e-02 -5.12010232e-02  3.14127728e-02  9.79194185e-04\n",
+      " -3.55245247e-02  1.90016143e-02 -2.68820450e-02  4.64876741e-03\n",
+      "  1.21604083e-02  4.10571247e-02  1.72643401e-02 -3.37485522e-02\n",
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+      "  3.48278694e-02 -9.82571840e-02  4.32403013e-03 -6.79854304e-04\n",
+      "  1.14075154e-01  1.83878485e-02  2.91329157e-02  1.29294649e-01\n",
+      "  2.35078596e-02 -4.89729270e-02 -7.04030171e-02 -1.93983316e-02\n",
+      "  7.97320828e-02 -7.59844109e-02  7.68048735e-03  1.89556349e-02]\n",
+      "Hindustan Copper Ltd.: [-8.54082480e-02  7.21397176e-02 -2.08139271e-02  7.19632208e-02\n",
+      " -4.06680812e-07 -5.49386851e-02  1.09949410e-01 -2.13119294e-03\n",
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+      "  7.97320828e-02 -7.59844109e-02  7.68048735e-03  1.89556349e-02]\n",
+      "Hindustan Petroleum Corporation Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
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+      " -1.37542868e-02  4.29236852e-02  9.61407125e-02 -1.29464902e-02]\n",
+      "Hindustan Unilever Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "Home First Finance Company India Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Honeywell Automation India Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "Housing & Urban Development Corporation Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Indo Count Industries Ltd.: [-9.64162573e-02  6.90967515e-02 -1.66647322e-02  4.47280891e-02\n",
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+      "IFB Industries Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "  1.04847543e-01 -9.03233960e-02  1.54254243e-01  1.15292318e-01]\n",
+      "Indraprastha Gas Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
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+      "Indian Hotels Co. Ltd.: [-1.27135843e-01  1.32178953e-02 -4.17881198e-02 -6.45975620e-02\n",
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+      "  9.86876935e-02 -7.61454878e-03  5.02445027e-02 -1.09157283e-02]\n",
+      "India Cements Ltd.: [-7.29177594e-02  4.10846360e-02  1.53632332e-02  4.46023345e-02\n",
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+      "Indiamart Intermesh Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      "  9.86876562e-02 -7.61454739e-03  5.02444878e-02 -1.09157525e-02]\n",
+      "Indian Bank: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
+      " -7.34059215e-02 -1.66739728e-02  1.01092011e-01  1.52869674e-03\n",
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+      "  4.05681767e-02 -1.18184499e-02  7.73379160e-03 -5.10588698e-02]\n",
+      "Indigo Paints Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "  1.04847543e-01 -9.03233960e-02  1.54254243e-01  1.15292318e-01]\n",
+      "Indoco Remedies Ltd.: [-3.23555507e-02  8.87765065e-02 -3.43593163e-03  1.08566936e-02\n",
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+      " -3.85239348e-02 -1.07011497e-01 -9.64392275e-02 -1.98261123e-02\n",
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+      " -6.51068464e-02  2.33125277e-02  7.07373470e-02  3.86740752e-02\n",
+      " -2.75178719e-02 -3.47020966e-03 -3.42134796e-02 -3.99260111e-02\n",
+      "  4.19766568e-02  5.27611077e-02  1.46560550e-01 -8.72509647e-03\n",
+      "  8.78036208e-03 -1.08211422e-02  1.49930427e-02 -1.54977860e-02\n",
+      "  7.29676038e-02 -2.21988950e-02 -7.83034135e-03  1.64755322e-02\n",
+      "  3.05245277e-02 -4.07592766e-03  7.05926269e-02  6.76124096e-02\n",
+      "  5.33701479e-03  3.96137610e-02 -1.51483249e-02  1.30753964e-03\n",
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+      "  4.12250012e-02 -4.61657122e-02  6.81541935e-02 -9.20418929e-03\n",
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+      "  2.33622808e-02  9.70079622e-04 -8.02751817e-03 -2.07081232e-02\n",
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+      "  4.23087068e-02  4.59270626e-02  3.12849395e-02 -2.37021428e-02\n",
+      "  1.00297749e-01  1.88582795e-04  6.30759215e-03  6.43197298e-02\n",
+      "  5.64854257e-02 -7.40710497e-02  7.42401108e-02  1.01314029e-02]\n",
+      "Indian Overseas Bank: [-6.74839914e-02  5.03898738e-03 -7.49205723e-02  2.28400771e-02\n",
+      " -7.34059066e-02 -1.66739896e-02  1.01092003e-01  1.52870291e-03\n",
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+      "  6.45325631e-02 -4.80205826e-02 -1.19422004e-01 -5.92617784e-03\n",
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+      "  2.34996248e-02  2.34714355e-02 -1.09591268e-01 -2.39514336e-02\n",
+      "  2.57666335e-02  2.29663961e-02 -2.01982688e-02  3.04362793e-02\n",
+      "  1.80224814e-02 -2.72389501e-02  2.17333138e-02  5.00383647e-03\n",
+      "  3.16333286e-02 -7.04337955e-02 -4.08428460e-02  6.11865195e-03\n",
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+      "  2.33949013e-02 -3.14359777e-02 -5.94295375e-02  1.71927400e-02\n",
+      "  1.59135088e-02  5.89880720e-02  3.74533422e-02  8.88321847e-02\n",
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+      "  3.21478746e-03  4.32826020e-02 -1.24537656e-02  2.04074103e-02\n",
+      " -7.84306005e-02 -1.00847498e-01 -2.20632423e-02  2.06698626e-02\n",
+      "  2.47497745e-02 -1.71160493e-02 -4.12130803e-02  6.91024140e-02\n",
+      " -8.75407606e-02 -6.30359128e-02  1.14089526e-01 -5.44027798e-02\n",
+      "  2.47742962e-02 -4.64106165e-02 -3.86144929e-02  3.68279545e-03\n",
+      " -1.91597864e-02 -2.60109641e-03 -7.02821389e-02 -1.26411548e-08\n",
+      " -4.76241410e-02  4.16124053e-02 -2.64470577e-02 -2.67952662e-02\n",
+      "  9.05415229e-03 -8.46313760e-02 -2.10898016e-02  1.56227956e-02\n",
+      " -1.15304710e-02  8.54011253e-03  4.81116734e-02  1.43052088e-02\n",
+      " -3.28882299e-02 -3.08988430e-02 -1.29428282e-02 -6.32665157e-02\n",
+      "  1.73102189e-02 -3.90352309e-02 -2.63434951e-03  4.01208214e-02\n",
+      "  7.21684918e-02  9.85996332e-03  4.63772863e-02  2.07022065e-03\n",
+      " -3.45242433e-02 -3.55409868e-02  3.91834378e-02  1.21130601e-01\n",
+      "  5.75537421e-02  6.14175061e-03 -5.98474964e-02  1.44835450e-02\n",
+      "  5.66149317e-02 -2.74031945e-02 -9.77560878e-02  7.47882389e-03\n",
+      "  4.30650413e-02 -4.85842638e-02 -4.67660762e-02  3.54494900e-02\n",
+      " -1.59194656e-02  1.54233426e-02 -1.23488298e-02  1.01203832e-03\n",
+      "  4.84211706e-02  2.95555573e-02 -3.25922109e-02  4.14496101e-02\n",
+      "  9.55883786e-02 -1.06032073e-01  1.43242478e-02 -8.30610562e-03\n",
+      "  5.54339737e-02  3.58873755e-02  9.96735226e-03 -3.64325829e-02\n",
+      " -7.36322999e-03  3.76237603e-03 -7.30165541e-02  3.36904339e-02\n",
+      "  4.34937440e-02 -1.00496806e-01  3.23293544e-02 -1.96419992e-02]\n",
+      "Indian Oil Corporation Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
+      "  8.70224833e-02  4.26123515e-02  9.30845886e-02  3.62931238e-03\n",
+      " -5.47133982e-02 -9.96530503e-02 -2.09179260e-02  5.53032756e-03\n",
+      " -7.52215162e-02 -2.35815402e-02 -4.26835492e-02 -2.87619531e-02\n",
+      "  1.01589561e-01 -6.65992349e-02  8.03106744e-03 -6.61153942e-02\n",
+      "  9.43806842e-02  1.64883230e-02  7.47972354e-02  4.65554446e-02\n",
+      " -8.81454814e-03  9.93454233e-02 -6.60015550e-03 -1.55321667e-02\n",
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+      "Indian Railway Finance Corporation Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Jamna Auto Industries Ltd.: [-5.15867211e-02  9.61384457e-03 -2.78991903e-03 -2.43441928e-02\n",
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+      "  9.56889242e-02 -4.69732173e-02  8.09954479e-02  8.60554539e-03]\n",
+      "Jindal Steel & Power Ltd.: [-8.54082480e-02  7.21397176e-02 -2.08139271e-02  7.19632208e-02\n",
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+      "Justdial Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      "Kalyan Jewellers India Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "  1.04847543e-01 -9.03233960e-02  1.54254243e-01  1.15292318e-01]\n",
+      "Kansai Nerolac Paints Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "LIC Housing Finance Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Macrotech Developers Ltd.: [-7.89830163e-02 -2.06320938e-02 -7.58103058e-02  2.75196936e-02\n",
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+      "Lux Industries Ltd.: [-9.64162722e-02  6.90967590e-02 -1.66647360e-02  4.47280817e-02\n",
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+      "Mahindra & Mahindra Financial Services Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "Bank of Maharashtra: [-6.74839914e-02  5.03901066e-03 -7.49206021e-02  2.28400957e-02\n",
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+      "Mahindra Logistics Ltd.: [-1.24083132e-01  2.02380549e-02 -7.62996683e-03 -4.88204174e-02\n",
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+      "Mastek Ltd.: [-3.85812297e-02  6.10318147e-02 -2.31247488e-02 -3.22841741e-02\n",
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+      " -4.70869243e-02 -4.66481559e-02 -5.96125908e-02 -3.12274657e-02\n",
+      "  3.14511657e-02  4.49946821e-02 -5.98557740e-02  5.35196625e-02\n",
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+      " -1.00747095e-02  5.75814322e-02 -2.98222266e-02  2.13121623e-02\n",
+      "  4.86960411e-02  8.88834000e-02  3.59251164e-02 -6.83013489e-03\n",
+      " -8.09224602e-03  3.52949351e-02  7.14871585e-02  5.82813062e-02\n",
+      " -2.92901658e-02  3.65484841e-02  6.60205036e-02  5.52527793e-02\n",
+      "  2.50047771e-03 -9.40450579e-02  2.65789423e-02  5.70457764e-02\n",
+      "  7.37377480e-02 -2.71158237e-02  3.90133001e-02  3.71872680e-03\n",
+      "  1.55548763e-03  5.51950075e-02 -1.01234587e-02 -8.63470435e-02\n",
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+      "  5.97366765e-02 -8.26660991e-02  3.82084697e-02 -3.11993435e-03\n",
+      "  3.40640396e-02 -3.55937257e-02  3.08737587e-02 -2.34663486e-02\n",
+      "  1.53645100e-02  1.00373356e-02 -5.79135977e-02 -2.22017169e-02\n",
+      "  5.20288981e-02 -2.72753853e-02  8.94622952e-02  1.44767771e-02]\n",
+      "United Spirits Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
+      "  3.91230322e-02 -4.17834119e-04  2.32371464e-02 -2.98845991e-02\n",
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+      "  1.96520295e-02  1.75466295e-02 -3.61373574e-02 -2.18616566e-03\n",
+      "  8.11307319e-03 -1.12621360e-01 -5.25486320e-02  2.53123827e-02\n",
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+      "  3.18472758e-02  8.71470012e-03  1.08878920e-03  6.77157119e-02\n",
+      "  3.96043882e-02 -7.12699839e-04  2.40281112e-02  3.63050550e-02\n",
+      "  4.02212515e-02  6.04619645e-02  6.55885087e-03 -3.49235372e-03\n",
+      "  2.48958748e-02  6.74311025e-03 -7.16477409e-02 -8.01346172e-03\n",
+      "  9.63222794e-03  1.57958746e-01  7.74511322e-02  1.45721538e-02\n",
+      "  4.29333420e-03  2.51996499e-02  4.96561266e-02  2.73171756e-02\n",
+      "  6.69671670e-02 -1.18501652e-02 -4.99147736e-02  3.06649227e-02\n",
+      "  2.05701999e-02 -5.64133981e-03  6.07472882e-02  2.25201752e-02\n",
+      " -2.37061772e-02  2.92219035e-02 -2.39416435e-02 -4.20965068e-02\n",
+      " -5.74703980e-03  3.54500264e-02 -9.70290378e-02 -1.83146622e-03\n",
+      " -9.87919979e-03 -4.83104214e-02 -8.23866948e-02 -2.26372536e-02\n",
+      "  1.26401074e-02  7.76717514e-02  2.24403571e-02 -1.29538421e-02\n",
+      "  1.95268281e-02  3.46522033e-02  1.20828124e-02 -4.80264686e-02\n",
+      "  8.03388562e-03 -5.38482554e-02 -1.70898438e-02  3.68946791e-02\n",
+      "  3.85916717e-02 -2.24448442e-02  6.71749795e-03  6.62344471e-02]\n",
+      "Multi Commodity Exchange of India Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
+      " -7.34059215e-02 -1.66739728e-02  1.01092011e-01  1.52869674e-03\n",
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+      "Minda Industries Ltd.: [-5.15867211e-02  9.61384457e-03 -2.78991903e-03 -2.43441928e-02\n",
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+      "MTAR Technologies Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "  9.56889614e-02 -4.69732322e-02  8.09954703e-02  8.60551093e-03]\n",
+      "National Aluminium Co. Ltd.: [-8.54082480e-02  7.21397176e-02 -2.08139271e-02  7.19632208e-02\n",
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+      "  7.97320828e-02 -7.59844109e-02  7.68048735e-03  1.89556349e-02]\n",
+      "Info Edge (India) Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      "NCC Ltd.: [-5.91711886e-02  5.09898998e-02 -3.55952815e-03  3.27332392e-02\n",
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+      "NESCO Ltd.: [-1.24083132e-01  2.02380549e-02 -7.62996683e-03 -4.88204174e-02\n",
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+      "  4.05681767e-02 -1.18184499e-02  7.73379160e-03 -5.10588698e-02]\n",
+      "Nestle India Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "Narayana Hrudayalaya Ltd.: [-3.23555171e-02  8.87764841e-02 -3.43591906e-03  1.08567076e-02\n",
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+      "  9.56889242e-02 -4.69732173e-02  8.09954479e-02  8.60554539e-03]\n",
+      "NHPC Ltd.: [-5.48450388e-02  1.57037973e-01 -1.95232090e-02  9.12069231e-02\n",
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+      " -1.64632760e-02 -1.18666850e-02 -5.22172078e-02 -8.82186368e-02\n",
+      "  3.00037209e-02  4.67471108e-02 -2.37851776e-02 -9.66880377e-03\n",
+      "  2.96388171e-03 -4.19828575e-03 -4.25693356e-02  9.07909572e-02\n",
+      " -8.17338824e-02  6.61700517e-02 -2.60155811e-03  9.66179278e-03\n",
+      "  6.84861168e-02  7.74055161e-03 -1.48610743e-02  1.00559089e-02\n",
+      " -5.41111417e-02  3.20558585e-02 -1.00825086e-01  3.35033387e-02\n",
+      " -5.44887222e-02 -2.76068542e-02 -5.38989604e-02 -5.05668335e-02\n",
+      " -5.54661192e-02 -2.18163840e-02 -3.38006988e-02 -9.62922052e-02\n",
+      " -1.92560367e-02  1.26971910e-02  4.84701097e-02  2.12938283e-02\n",
+      "  6.88613206e-02 -4.26223464e-02 -3.21765877e-02 -3.07641167e-04\n",
+      " -5.01470529e-02 -1.33071663e-02 -2.99499650e-02 -1.21164465e-08\n",
+      "  4.07562964e-03  4.77178348e-03  4.30320427e-02 -3.07702199e-02\n",
+      "  7.18034133e-02 -6.23956881e-03  6.02718210e-03  1.12238470e-02\n",
+      "  6.42999262e-03  4.58754636e-02  1.09793022e-01 -1.12162232e-02\n",
+      "  9.96327996e-02  5.75421266e-02  6.12221248e-02  3.22996057e-03\n",
+      "  1.50828122e-03  3.80712338e-02 -5.06020226e-02 -2.69476697e-03\n",
+      " -7.94165730e-02 -2.43245885e-02  1.08114900e-02  2.84932200e-02\n",
+      "  9.40695312e-03  2.70398296e-02 -2.73434329e-03  9.71252285e-03\n",
+      " -1.29965795e-02  1.53193856e-02 -1.38811795e-02  4.83232699e-02\n",
+      " -8.42666700e-02 -3.24286297e-02 -8.81163627e-02 -1.09680342e-02\n",
+      "  5.83330356e-03 -1.85781885e-02  3.00557278e-02  9.53526050e-03\n",
+      "  2.15306785e-02 -4.98513840e-02  2.50572115e-02  1.18771940e-02\n",
+      " -1.38178393e-01 -1.76792350e-02 -3.91530618e-02 -6.31099641e-02\n",
+      " -1.92221683e-02  9.72186308e-03  1.39304278e-02  6.51673451e-02\n",
+      "  2.54352037e-02 -3.46043939e-03  6.26007542e-02  6.83955103e-02\n",
+      " -3.19796391e-02  7.40314573e-02 -9.38996673e-02  2.53431927e-02\n",
+      "  1.16565406e-01  3.29949893e-02  4.42770161e-02 -2.87038963e-02]\n",
+      "The New India Assurance Company Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
+      " -7.34059215e-02 -1.66739728e-02  1.01092011e-01  1.52869674e-03\n",
+      "  7.41633102e-02 -2.24150252e-02 -1.72783025e-02 -1.99839361e-02\n",
+      " -2.85700578e-02 -2.20315512e-02 -5.35634421e-02 -1.34030312e-01\n",
+      "  6.96685491e-03 -2.35260818e-02 -4.42431085e-02  2.70755813e-02\n",
+      " -4.17872779e-02 -7.22487085e-03 -7.05016404e-02  9.35958326e-03\n",
+      "  3.18855681e-02  2.26669349e-02 -3.15466151e-02  1.88774727e-02\n",
+      " -4.92369570e-02 -9.53472629e-02  2.28959452e-02 -9.20847151e-03\n",
+      "  5.41803204e-02 -2.79574618e-02  7.13210925e-02  7.49085546e-02\n",
+      "  6.42826781e-02  6.00483418e-02  6.32355511e-02 -2.14099437e-02\n",
+      " -5.91793209e-02 -8.34407881e-02  1.68331377e-02 -5.95049113e-02\n",
+      "  2.81869657e-02 -3.86524014e-02  3.48484106e-02  4.75979559e-02\n",
+      "  3.41861285e-02  6.94228783e-02 -4.30157483e-02 -1.54380184e-02\n",
+      "  1.02773905e-02  2.99895331e-02  4.88830507e-02  3.19402292e-02\n",
+      "  8.47197101e-02 -8.24522413e-03 -8.88420865e-02 -5.75427487e-02\n",
+      " -1.06175169e-02  6.75682968e-04  4.91183391e-03  4.81875911e-02\n",
+      "  1.01896144e-01  8.30458030e-02 -4.60584648e-03  4.44551557e-02\n",
+      " -5.02469242e-02 -1.36948809e-01 -5.71422698e-03 -9.00605470e-02\n",
+      " -8.00312757e-02  4.88258712e-02  3.62499319e-02  5.68610290e-03\n",
+      "  6.23985082e-02  3.84147093e-02  4.64469865e-02 -3.95821594e-02\n",
+      "  6.55861050e-02 -6.49959147e-02 -3.49017121e-02  8.47571790e-02\n",
+      " -2.67272536e-02 -7.13121053e-03 -4.77144346e-02 -1.80950277e-02\n",
+      "  1.84503198e-02 -8.92676972e-03  5.12143672e-02 -5.47849294e-03\n",
+      "  5.65982684e-02 -4.55755629e-02 -7.56006166e-02 -2.66146958e-02\n",
+      " -5.72668482e-03 -7.37986192e-02 -3.24654654e-02  1.37050822e-01\n",
+      "  7.35773146e-02  3.34469974e-02  3.98678854e-02  4.86416444e-02\n",
+      " -1.87233668e-02 -1.69751104e-02  2.15576608e-02  9.46953744e-02\n",
+      "  3.63857038e-02 -2.70375013e-02 -2.59825401e-03  3.28301229e-02\n",
+      " -8.22136775e-02 -1.50117781e-02 -3.84484380e-02  1.21809384e-02\n",
+      " -4.60985303e-02 -3.61101441e-02  8.05378780e-02  4.19188514e-02\n",
+      "  2.48036589e-02  1.47437453e-01 -9.57677662e-02 -3.21226083e-02\n",
+      " -6.71315789e-02 -2.84012146e-02 -6.90367538e-03 -3.09999901e-33\n",
+      " -8.49685296e-02  3.09389271e-03  9.21666771e-02 -4.34447601e-02\n",
+      "  1.22442255e-02  3.68888639e-02 -1.22230034e-02  2.05156766e-02\n",
+      " -7.43388310e-02  1.04906507e-01 -1.59085914e-02  8.18398148e-02\n",
+      " -1.24132831e-03 -7.12995157e-02  1.23707056e-02  3.22200661e-03\n",
+      " -5.09940386e-02  2.33332310e-02  1.41722234e-02 -1.65840331e-02\n",
+      " -1.95089132e-02  5.89064322e-02  5.30074462e-02 -3.06126755e-02\n",
+      "  1.15061946e-01 -4.11616452e-02 -6.06578887e-02 -2.60953023e-03\n",
+      "  1.11202694e-01  2.24721152e-02  3.28111164e-02  3.02952100e-02\n",
+      "  1.63336471e-02 -2.17415188e-02  8.63373186e-03  1.05685517e-02\n",
+      " -3.95311825e-02 -7.36117586e-02 -1.81138236e-02 -5.37877046e-02\n",
+      " -5.47524840e-02  2.12684087e-02  1.61400065e-02  1.00404620e-01\n",
+      " -4.33133468e-02  9.37208086e-02  5.44147938e-02  6.80338219e-02\n",
+      "  1.98733024e-02  4.28674594e-02 -5.87543510e-02 -1.43116033e-02\n",
+      " -5.94924316e-02  5.50571177e-03  5.25777135e-03 -1.93778519e-02\n",
+      "  7.12580904e-02 -2.78725512e-02 -7.84434676e-02 -1.11414619e-01\n",
+      "  4.51268535e-03 -6.22626841e-02 -9.49203894e-02 -1.04746604e-02\n",
+      " -4.61553484e-02  2.78756507e-02 -6.44110050e-03  3.58653180e-02\n",
+      "  4.66831736e-02  8.44673812e-03 -5.91061115e-02  2.47220322e-02\n",
+      "  1.24228820e-01  6.95521608e-02  1.04136392e-02  1.53003540e-02\n",
+      "  4.21527326e-02 -1.29196495e-02  4.99776565e-03  4.63007428e-02\n",
+      " -1.21367248e-02 -3.71289365e-02  1.02042099e-02  9.49959531e-02\n",
+      "  1.19429842e-01  9.20799598e-02 -1.13122817e-03 -3.92101593e-02\n",
+      "  6.45325705e-02 -4.80205566e-02 -1.19421966e-01 -5.92616247e-03\n",
+      "  5.02024554e-02  1.10228352e-01  2.04713345e-02  1.10097526e-33\n",
+      " -5.70912138e-02 -5.60722984e-02  9.51438025e-03  5.33662103e-02\n",
+      " -2.99604516e-02  1.42369336e-02 -2.69682787e-04 -3.97430733e-02\n",
+      "  2.59364136e-02  1.15682386e-01 -2.19538640e-02  1.46582294e-02\n",
+      "  1.35957841e-02  1.12169180e-02 -2.93238740e-02 -3.60185690e-02\n",
+      " -2.20576227e-02 -2.31763534e-02  9.63147264e-03 -1.54953133e-02\n",
+      " -1.73137765e-02  6.67176396e-02  1.01144202e-01  3.85425873e-02\n",
+      "  7.45612837e-04  5.02424911e-02 -4.50820588e-02 -3.60520072e-02\n",
+      " -5.31075969e-02  2.00294494e-03 -1.56001300e-02  2.54387353e-02\n",
+      "  2.34996174e-02  2.34714653e-02 -1.09591268e-01 -2.39514560e-02\n",
+      "  2.57666465e-02  2.29664575e-02 -2.01982725e-02  3.04362774e-02\n",
+      "  1.80224758e-02 -2.72389352e-02  2.17333119e-02  5.00385696e-03\n",
+      "  3.16333361e-02 -7.04337955e-02 -4.08428386e-02  6.11861842e-03\n",
+      "  4.67761792e-02 -3.80361937e-02 -1.30412206e-01 -3.40778306e-02\n",
+      "  9.99209005e-03  4.52099629e-02 -5.23773022e-02  7.76050314e-02\n",
+      "  2.33950187e-02 -3.14359479e-02 -5.94295636e-02  1.71926953e-02\n",
+      "  1.59134828e-02  5.89880869e-02  3.74533050e-02  8.88321549e-02\n",
+      "  1.52008962e-02 -4.88559864e-02 -1.79699138e-02 -2.71071400e-02\n",
+      " -2.60509215e-02 -2.93554235e-02  3.67353410e-02 -4.62744795e-02\n",
+      "  3.21477721e-03  4.32825834e-02 -1.24537637e-02  2.04074234e-02\n",
+      " -7.84306154e-02 -1.00847520e-01 -2.20632460e-02  2.06698366e-02\n",
+      "  2.47497912e-02 -1.71160586e-02 -4.12131101e-02  6.91024438e-02\n",
+      " -8.75407532e-02 -6.30359352e-02  1.14089474e-01 -5.44028096e-02\n",
+      "  2.47743316e-02 -4.64105979e-02 -3.86144742e-02  3.68286506e-03\n",
+      " -1.91598013e-02 -2.60107056e-03 -7.02821910e-02 -1.26411548e-08\n",
+      " -4.76241820e-02  4.16124053e-02 -2.64470764e-02 -2.67952736e-02\n",
+      "  9.05413460e-03 -8.46313909e-02 -2.10898332e-02  1.56228188e-02\n",
+      " -1.15304813e-02  8.54013674e-03  4.81116921e-02  1.43051809e-02\n",
+      " -3.28882709e-02 -3.08988318e-02 -1.29428906e-02 -6.32665455e-02\n",
+      "  1.73102170e-02 -3.90352271e-02 -2.63434811e-03  4.01208512e-02\n",
+      "  7.21684769e-02  9.85997170e-03  4.63772789e-02  2.07026792e-03\n",
+      " -3.45242284e-02 -3.55410315e-02  3.91834788e-02  1.21130645e-01\n",
+      "  5.75537607e-02  6.14175200e-03 -5.98475114e-02  1.44835347e-02\n",
+      "  5.66149093e-02 -2.74031945e-02 -9.77561325e-02  7.47879501e-03\n",
+      "  4.30650376e-02 -4.85842600e-02 -4.67660800e-02  3.54495011e-02\n",
+      " -1.59194730e-02  1.54233128e-02 -1.23488279e-02  1.01204030e-03\n",
+      "  4.84212115e-02  2.95556076e-02 -3.25922444e-02  4.14495915e-02\n",
+      "  9.55884233e-02 -1.06032133e-01  1.43242609e-02 -8.30611214e-03\n",
+      "  5.54339737e-02  3.58873680e-02  9.96736623e-03 -3.64325829e-02\n",
+      " -7.36321975e-03  3.76238744e-03 -7.30165541e-02  3.36904116e-02\n",
+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "NLC India Ltd.: [-5.48450388e-02  1.57037973e-01 -1.95232090e-02  9.12069231e-02\n",
+      " -1.52640697e-02  2.12074281e-03  9.64464024e-02 -3.56362052e-02\n",
+      "  6.55648410e-02  2.42379643e-02 -4.87037189e-03 -1.32874390e-02\n",
+      "  9.74986330e-03 -1.84444245e-02 -1.13606509e-02  7.05087259e-02\n",
+      " -1.94735676e-02  3.16210277e-03 -1.35073781e-01  4.99160029e-02\n",
+      "  7.16021210e-02  5.85673116e-02  3.87186999e-03  3.67179923e-02\n",
+      "  5.63702406e-03  4.47808951e-02 -4.32951674e-02  7.10248598e-04\n",
+      "  3.07370778e-02 -9.30750221e-02 -2.45008916e-02 -3.06267533e-02\n",
+      " -1.09252112e-03  7.11406907e-03 -6.39539510e-02  6.73627853e-02\n",
+      "  8.94637555e-02  7.10065737e-02  2.09593587e-02 -2.13197339e-02\n",
+      " -2.89006829e-02 -1.21078573e-01  4.49899063e-02  2.80513521e-03\n",
+      " -7.26106018e-02 -1.45850442e-02  2.85122767e-02  2.27766968e-02\n",
+      "  9.79264639e-03  2.21978407e-02 -1.69379879e-02 -7.08036944e-02\n",
+      " -7.82256350e-02  2.17790697e-02  5.05620427e-02 -1.63319651e-02\n",
+      "  5.42980470e-02  1.43881468e-02  3.05299722e-02  1.29690012e-02\n",
+      "  2.65204720e-02 -8.92582312e-02 -6.41938597e-02 -5.68557670e-03\n",
+      "  9.67522040e-02 -1.80494785e-02  6.95240200e-02  4.23563644e-03\n",
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+      "  3.27589214e-02 -5.25754020e-02  4.41665947e-02 -5.80146052e-02\n",
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+      "  4.74366546e-02  2.80368282e-03 -9.31843743e-02  1.06934115e-01\n",
+      "  4.89378460e-02  9.52549558e-03  3.46231908e-02  6.82196813e-03\n",
+      " -3.42763700e-02  1.58513952e-02 -6.98815510e-02  2.25187894e-02\n",
+      "  6.96379840e-02  3.12734023e-02  2.81166192e-03 -5.80634661e-02\n",
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+      "  6.88680634e-02  2.47112755e-02  1.16427019e-01 -2.00740173e-02\n",
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+      "  3.80702056e-02  5.25505804e-02  2.53648013e-02  6.08534291e-02\n",
+      "  1.66572761e-02 -2.11252347e-02 -3.31229046e-02 -1.35716349e-02\n",
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+      "  7.42026046e-02 -9.75012258e-02  5.89637756e-02  1.41679030e-03\n",
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+      "  1.44631974e-02 -3.73050086e-02  9.33163688e-02 -4.60611321e-02\n",
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+      "  4.17952324e-05  4.53009494e-02 -3.08764097e-03 -2.36461610e-02\n",
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+      "Oberoi Realty Ltd.: [-7.89830163e-02 -2.06320938e-02 -7.58103058e-02  2.75196936e-02\n",
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+      "  5.64854331e-02 -7.40710795e-02  7.42401034e-02  1.01314187e-02]\n",
+      "Oil India Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
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+      " -1.37542868e-02  4.29236852e-02  9.61407125e-02 -1.29464902e-02]\n",
+      "Oil & Natural Gas Corporation Ltd.: [-8.67648423e-02 -1.13507053e-02 -4.59416620e-02  2.03539450e-02\n",
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+      "Power Finance Corporation Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "  9.56889242e-02 -4.69732173e-02  8.09954479e-02  8.60554539e-03]\n",
+      "Phoenix Mills Ltd.: [-7.89830163e-02 -2.06320938e-02 -7.58103058e-02  2.75196936e-02\n",
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+      "  4.87573147e-02  2.50528976e-02 -2.07229033e-02  2.92262863e-02]\n",
+      "Pidilite Industries Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "PI Industries Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "Punjab National Bank: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Power Grid Corporation of India Ltd.: [-5.48450388e-02  1.57037973e-01 -1.95232090e-02  9.12069231e-02\n",
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+      "Hitachi Energy India Ltd.: [ 2.46381462e-02 -2.18525864e-02  2.99072079e-02 -1.04453489e-02\n",
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+      "Prestige Estates Projects Ltd.: [-7.89830163e-02 -2.06320938e-02 -7.58103058e-02  2.75196936e-02\n",
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+      "PVR Ltd.: [ 3.85597572e-02 -2.48620175e-02 -6.33045360e-02 -1.87215880e-02\n",
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+      "  3.31665352e-02 -9.31467488e-02  3.98504622e-02 -6.17916435e-02\n",
+      "  1.12080939e-01  4.76427143e-03 -3.47305462e-02  3.31065729e-02\n",
+      " -6.86231628e-02  3.13030928e-03 -4.74340320e-02 -4.01677266e-02\n",
+      "  4.31794021e-03 -1.03967056e-01  3.12075000e-02 -6.32209256e-02\n",
+      "  1.36328936e-02  5.62180392e-02  2.70789079e-02  3.27605195e-02\n",
+      " -5.80120785e-03  6.30680621e-02 -3.31020635e-03 -1.97106991e-02\n",
+      "  8.94454867e-02 -5.89653179e-02 -3.74651514e-02  2.19064467e-02\n",
+      "  2.92452723e-02 -9.71667916e-02 -4.18981723e-03 -9.31541398e-02\n",
+      "  1.42218351e-01  4.93098795e-02  9.91012901e-02  9.79371183e-03\n",
+      "  5.19338902e-03 -2.30199937e-02 -9.37222615e-02  3.15020569e-02\n",
+      "  8.28062296e-02  9.69584100e-03 -3.93477604e-02 -9.45642777e-03\n",
+      "  1.07738562e-02  2.31609400e-02  4.58136853e-03  4.71265651e-02\n",
+      "  2.83096116e-02 -6.67941198e-02  5.18452115e-02 -2.42890753e-02\n",
+      " -3.49901915e-02 -2.06040032e-02  1.68712977e-02  1.26204477e-03\n",
+      " -8.12087506e-02 -7.32488930e-02 -3.53110768e-02  6.23357343e-03\n",
+      " -9.38818902e-02 -1.21671027e-02 -4.42806594e-02  3.47672254e-02\n",
+      " -3.25381616e-03 -3.59301828e-02 -1.77236851e-02  5.69034442e-02\n",
+      "  6.03887020e-03 -5.41788414e-02 -4.11484856e-03 -2.60561109e-02\n",
+      "  4.42460589e-02  6.53927848e-02  2.71487492e-03  6.39784709e-02\n",
+      " -1.13749921e-01  1.60978418e-02 -4.07338627e-02 -1.33395899e-08\n",
+      "  9.66213923e-03  4.00061160e-02 -2.08837129e-02  2.34596282e-02\n",
+      "  7.77055025e-02 -3.09200864e-02  5.11660315e-02  9.77574382e-03\n",
+      "  1.92794781e-02  4.14462462e-02  2.64971014e-02 -2.57562604e-02\n",
+      "  9.47153941e-03  2.86374111e-02 -1.29220271e-02  1.17635978e-02\n",
+      " -6.40865555e-03  5.04307039e-02 -7.53374845e-02  2.43263692e-02\n",
+      " -1.08608203e-02  5.23127168e-02  1.10182613e-01  1.48393512e-02\n",
+      " -5.64829484e-02  2.17382275e-02 -1.24553414e-02 -2.50650421e-02\n",
+      "  3.14017721e-02  9.47139710e-02 -1.38023440e-02  3.50176431e-02\n",
+      "  3.17430496e-02 -8.19012672e-02 -2.17181966e-02 -7.25853071e-02\n",
+      "  4.73166965e-02  1.18325872e-03 -6.47311881e-02 -1.92115214e-02\n",
+      " -4.96120527e-02 -2.49055792e-02 -2.34676264e-02  3.60804535e-02\n",
+      "  1.11536920e-01 -2.10288237e-03 -2.46287715e-02  3.04278359e-02\n",
+      " -5.31787947e-02  8.13050941e-02 -3.79242450e-02 -7.28732906e-03\n",
+      "  1.65241305e-02  2.00348347e-02 -7.81217068e-02 -4.13013808e-02\n",
+      "  1.40744429e-02  1.17705446e-02  5.18257217e-03  1.64701827e-02\n",
+      "  1.04847543e-01 -9.03233960e-02  1.54254243e-01  1.15292318e-01]\n",
+      "Rallis India Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
+      "  1.96524933e-02  4.04251702e-02  1.45659164e-01  1.14778295e-01\n",
+      "  2.23184191e-02  9.90610197e-03 -2.04917369e-03 -8.94042850e-02\n",
+      " -1.21727958e-02 -1.92619429e-03 -3.61507460e-02  1.64732486e-02\n",
+      "  1.07986340e-03 -3.33747678e-02 -4.00832668e-02  8.70431215e-03\n",
+      " -3.07971593e-02 -4.11584089e-03  2.15127207e-02  5.59391566e-02\n",
+      " -9.07457918e-02  1.36992231e-01 -2.93681547e-02  3.17149088e-02\n",
+      "  1.77342389e-02 -9.44148600e-02  5.20355925e-02  1.34843752e-01\n",
+      "  7.90072754e-02 -4.30030636e-02  4.27719764e-02  1.44560486e-02\n",
+      " -7.62957186e-02 -1.03890067e-02  4.18954995e-03  2.78973263e-02\n",
+      " -4.12653126e-02 -7.91344568e-02 -2.95327278e-03 -1.84065737e-02\n",
+      "  5.02883419e-02 -3.85249816e-02  6.28650412e-02 -6.00700751e-02\n",
+      " -1.26916785e-02  5.10789491e-02  6.23284327e-03  2.17614435e-02\n",
+      " -6.81083575e-02  4.88058850e-02  1.91847850e-02 -7.24712759e-02\n",
+      "  2.10554451e-02  2.36040242e-02 -6.78651780e-02 -2.24266797e-02\n",
+      "  8.99144784e-02 -1.06938006e-02 -6.75727203e-02 -1.47686235e-03\n",
+      "  1.06798679e-01  1.90694612e-02  3.29494551e-02 -2.17033247e-03\n",
+      "  8.83191973e-02 -7.61841312e-02  5.02167679e-02 -1.97436493e-02\n",
+      "  5.59572503e-02  2.17828322e-02  7.63821602e-02 -6.89529756e-04\n",
+      "  3.85131799e-02 -6.21362552e-02  9.02832150e-02 -3.12325489e-02\n",
+      "  3.81626077e-02  1.17191184e-05 -5.98369688e-02  9.66283493e-03\n",
+      "  4.83758822e-02  3.40884551e-02  1.52732832e-02 -5.82153723e-03\n",
+      " -3.33373509e-02 -1.99678596e-02 -6.22651130e-02  4.65609580e-02\n",
+      "  6.68445751e-02 -1.93773452e-02 -1.28608614e-01  7.16678873e-02\n",
+      " -2.56277882e-02  5.03722113e-03  1.58575177e-02  1.84774071e-01\n",
+      "  2.19843369e-02  1.28412247e-01 -1.68607324e-01 -1.88009515e-02\n",
+      " -1.46218082e-02 -5.16444407e-02 -8.87590833e-03  7.04610422e-02\n",
+      "  1.11552207e-02  4.64937128e-02 -6.04240149e-02 -4.92369058e-03\n",
+      " -6.84176479e-03  3.66837084e-02 -7.76976161e-03  5.92845753e-02\n",
+      "  5.14250472e-02 -9.98085015e-04 -1.63703896e-02 -3.31506096e-02\n",
+      " -2.02238187e-02 -2.35775970e-02 -6.85685202e-02  3.55787762e-02\n",
+      " -3.14029269e-02 -6.93987161e-02  7.25974794e-03 -5.54664430e-33\n",
+      "  1.81340834e-03 -4.44501918e-03  7.53323361e-02  1.00986194e-02\n",
+      " -4.91875149e-02 -2.51609390e-03 -4.48482297e-02 -1.15528092e-01\n",
+      " -4.83582616e-02  5.05737253e-02 -2.40918137e-02 -6.23568241e-03\n",
+      " -8.51330161e-02  4.10608053e-02  9.81391370e-02 -1.53882115e-03\n",
+      " -7.50888586e-02 -2.21367572e-02  7.17162117e-02 -5.85370734e-02\n",
+      " -1.01201184e-01  1.21817745e-01 -3.92921157e-02  3.41176093e-02\n",
+      " -2.25663017e-02  6.11866266e-02 -6.52661920e-02  5.05935121e-03\n",
+      " -3.20160240e-02 -2.18114387e-02  4.81838658e-02  3.77141871e-02\n",
+      " -1.34932240e-02  7.70428590e-03 -1.68428328e-02 -5.21663204e-02\n",
+      " -9.34058651e-02 -2.75732130e-02 -1.77663096e-04 -7.16588199e-02\n",
+      " -2.30775271e-02  2.34818961e-02  4.17547040e-02  4.22628373e-02\n",
+      "  2.01548301e-02  1.81640424e-02 -4.70870249e-02  1.61830205e-02\n",
+      "  8.03889427e-03  2.10950226e-02 -4.34927307e-02 -1.50457267e-02\n",
+      "  5.21832705e-02 -2.66981386e-02 -2.64056846e-02  2.58957520e-02\n",
+      " -3.02209333e-03 -4.16998565e-02  1.34887947e-02  6.91012591e-02\n",
+      "  2.45822202e-02  1.53581992e-01 -4.15896252e-02  4.93612513e-02\n",
+      "  2.75986847e-02 -3.63455117e-02 -4.55682725e-03  1.12983352e-02\n",
+      "  9.21461731e-02 -1.13241309e-02 -9.60000232e-02  1.59059856e-02\n",
+      "  6.94607794e-02  2.54095383e-02 -2.54185721e-02  2.87570637e-02\n",
+      " -1.03647076e-02 -7.52542764e-02 -2.64152344e-02  7.92765897e-03\n",
+      " -3.02512888e-02 -5.34264892e-02 -6.00828417e-02  1.43329632e-02\n",
+      " -1.22721508e-01 -8.57322197e-03 -3.50733697e-02 -2.92459503e-02\n",
+      "  6.45917803e-02  5.10411784e-02 -8.47780257e-02 -9.91419107e-02\n",
+      "  8.34667161e-02  2.97595956e-03 -4.76544164e-02  3.42680370e-33\n",
+      " -9.37354937e-03 -7.14136064e-02 -1.48748159e-02  9.70377773e-02\n",
+      "  1.21461079e-02 -1.57848112e-02 -5.01609966e-02 -6.50879294e-02\n",
+      "  1.94703005e-02  5.34455068e-02 -7.31661776e-03 -4.53671440e-02\n",
+      "  6.10591285e-02 -2.37928685e-02 -6.42780513e-02  4.54034023e-02\n",
+      "  1.89227480e-02  5.28606400e-02  5.20263538e-02  2.80872378e-02\n",
+      " -2.65854280e-02  3.94806005e-02 -7.85273463e-02  1.95816476e-02\n",
+      "  1.53575717e-02  6.17567487e-02  4.91269790e-02 -6.60195276e-02\n",
+      "  1.51563417e-02  3.21262330e-02  1.94941554e-02  1.41739063e-02\n",
+      " -2.50978768e-03 -2.11043917e-02  2.65740566e-02 -9.17184725e-03\n",
+      "  4.59026136e-02 -2.30979882e-02 -4.04246300e-02 -7.03151617e-03\n",
+      " -1.21452129e-02 -1.41687680e-03  1.64284278e-02  1.52070522e-01\n",
+      "  3.80124077e-02  3.99557985e-02 -5.67238927e-02 -6.39074575e-03\n",
+      "  2.07507238e-02  2.04600710e-02  2.42910944e-02  7.83564709e-03\n",
+      " -3.25063132e-02 -7.27827922e-02  7.29429126e-02  2.37630848e-02\n",
+      " -7.82035664e-02 -5.86452596e-02 -3.62306125e-02 -9.98733286e-03\n",
+      "  6.19958304e-02 -1.95465758e-02 -2.84744352e-02 -2.45618019e-02\n",
+      " -9.08462629e-02  3.34633812e-02 -4.83564772e-02  7.92677602e-05\n",
+      "  3.15794758e-02 -4.20492515e-02  1.54928192e-01  7.36223832e-02\n",
+      "  2.17161532e-02 -6.34528929e-04 -4.27062847e-02 -8.13182816e-02\n",
+      " -4.49407659e-02  2.09528990e-02 -5.10383323e-02  6.85009547e-03\n",
+      " -2.54680663e-02  2.03149319e-02 -3.73893715e-02 -1.75727680e-02\n",
+      " -4.16043550e-02 -4.25748155e-02 -1.05988188e-02  5.26984185e-02\n",
+      "  7.11467257e-03 -5.09114377e-02 -1.50287524e-03  2.67157275e-02\n",
+      " -7.80262519e-03  6.37736842e-02 -4.26553413e-02 -1.22750876e-08\n",
+      "  2.79750377e-02 -4.35140580e-02  4.53696027e-02 -5.63813113e-02\n",
+      "  3.51646990e-02  6.18754923e-02  3.86029668e-02 -3.61294784e-02\n",
+      " -5.04803769e-02  7.50240963e-03  9.26274108e-04  5.62416911e-02\n",
+      " -5.53895766e-03  3.22167575e-02  4.43729870e-02  1.81321707e-03\n",
+      " -4.19663489e-02 -4.07846682e-02 -6.01987019e-02 -2.95594707e-02\n",
+      " -5.07385992e-02  3.58443372e-02 -2.47695502e-02  1.79982353e-02\n",
+      "  8.91129300e-03 -2.31274050e-02  4.40114774e-02  4.56267521e-02\n",
+      "  2.79025473e-02  3.33823338e-02  4.52452805e-03  1.38519499e-02\n",
+      " -3.08096763e-02 -6.23992644e-03  5.47887161e-02 -6.56372458e-02\n",
+      "  5.61623424e-02 -8.74374658e-02 -5.05879708e-02 -4.19108476e-03\n",
+      " -4.69712429e-02  6.21743165e-02 -3.05779558e-02  1.65579282e-02\n",
+      "  5.02341166e-02 -5.15387878e-02  2.36214604e-02  4.69543487e-02\n",
+      "  1.62465870e-02 -2.45488249e-02  1.11379055e-02 -3.40469740e-02\n",
+      " -5.48923016e-02  5.20713208e-03 -5.91151696e-03  3.20686400e-02\n",
+      " -7.61842728e-03  1.30477874e-02 -6.63001314e-02 -3.78954485e-02\n",
+      "  1.18093111e-01 -5.92013784e-02  1.25763372e-01 -1.41946170e-02]\n",
+      "The Ramco Cements Ltd.: [-7.29177594e-02  4.10846360e-02  1.53632332e-02  4.46023345e-02\n",
+      " -3.79272327e-02 -6.93855211e-02  1.09778337e-01  9.53358598e-03\n",
+      " -7.87316412e-02  6.45030066e-02 -7.26805851e-02 -5.85789680e-02\n",
+      "  7.52867432e-03  4.77965698e-02 -2.59788744e-02 -2.97568850e-02\n",
+      "  5.75735094e-03 -4.15442802e-04  4.98548197e-03  1.40401898e-02\n",
+      "  1.39848525e-02  5.13270199e-02  1.54880174e-02 -7.27072079e-03\n",
+      "  4.89170291e-02  9.32239443e-02 -1.35607962e-02  4.16364521e-02\n",
+      "  1.64053738e-01 -7.87914619e-02 -5.18548582e-03  2.65484676e-02\n",
+      "  2.13531833e-02  2.52596708e-03  2.56580226e-02  5.06366417e-02\n",
+      "  4.91896225e-03  3.07592261e-03 -2.29462329e-02  2.29551401e-02\n",
+      " -1.17365956e-01 -3.00336219e-02  4.69695143e-02 -1.77967567e-02\n",
+      "  4.18308042e-02  3.72755043e-02  5.57434335e-02 -8.39152187e-02\n",
+      " -2.01751068e-02 -2.83041876e-02 -6.93832571e-03 -1.94092318e-02\n",
+      "  8.66317889e-04 -1.86796319e-02 -8.94263852e-03  8.14397857e-02\n",
+      "  3.96024995e-03 -7.68851722e-03  1.32559203e-02 -2.99680363e-02\n",
+      "  9.68297198e-02 -3.32265883e-03 -4.71320786e-02 -1.69878062e-02\n",
+      "  9.42474455e-02  3.05231307e-02 -7.21337944e-02  3.26202177e-02\n",
+      " -2.43838946e-03 -5.68008330e-03  8.96351039e-02 -4.05485779e-02\n",
+      " -3.22913821e-03  5.70665710e-02  4.83116955e-02 -1.33160222e-02\n",
+      "  3.74108963e-02 -3.13409790e-03  2.36045886e-02 -1.40646175e-02\n",
+      " -2.87028272e-02  4.15524356e-02 -9.71001573e-03 -8.82914755e-03\n",
+      " -2.52291914e-02  4.06176560e-02  7.18911514e-02  7.99774304e-02\n",
+      " -3.20907421e-02 -3.33416611e-02  3.93013135e-02  4.85642143e-02\n",
+      " -1.11265197e-01  3.74102853e-02 -2.13868730e-02  1.95384938e-02\n",
+      " -6.93437904e-02  2.18201953e-04  2.58696433e-02  1.16658881e-01\n",
+      "  3.91603960e-03  1.95219293e-02  3.21991108e-02 -2.22334918e-03\n",
+      " -9.76835415e-02 -9.02459472e-02 -6.47679642e-02  4.30860408e-02\n",
+      " -1.69644617e-02  1.09695774e-02 -4.71972600e-02 -3.79491299e-02\n",
+      " -9.92718339e-02 -4.91025038e-02 -7.64191523e-02 -3.95451672e-02\n",
+      " -2.24995310e-03  1.37957875e-02 -7.66719971e-03  5.79475686e-02\n",
+      "  1.01103313e-01  1.14519931e-02 -4.35994193e-02 -3.33244819e-03\n",
+      " -7.15946406e-02 -1.55774102e-01 -2.06390563e-02 -3.18466913e-33\n",
+      "  3.83646674e-02 -4.00796086e-02 -3.52748297e-02  6.64700121e-02\n",
+      "  5.78087419e-02  6.58044731e-03  8.33998714e-03  3.62803787e-02\n",
+      "  4.93328236e-02  3.44043933e-02  2.22013127e-02  6.04068348e-03\n",
+      " -6.40206113e-02  5.25248758e-02  1.24502458e-01 -9.85963568e-02\n",
+      " -1.77915357e-02 -3.81156206e-02 -8.02085102e-02  5.20016700e-02\n",
+      " -9.95328203e-02  3.99510755e-04  4.62550297e-02  5.04952669e-02\n",
+      "  1.13943383e-01 -5.14502376e-02  1.01189934e-01 -2.96361726e-02\n",
+      " -1.60211667e-01  4.62930184e-03  4.70433477e-03  5.64933522e-03\n",
+      "  5.45999445e-02  5.47873788e-02 -6.29060939e-02  4.09415513e-02\n",
+      " -5.42361997e-02 -5.49740903e-02  1.49755981e-02  2.39433115e-03\n",
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+      "Restaurant Brands Asia Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      "Relaxo Footwears Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "Rail Vikas Nigam Ltd.: [-5.91711886e-02  5.09898998e-02 -3.55952815e-03  3.27332392e-02\n",
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+      "Sapphire Foods India Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      "  4.34937626e-02 -1.00496881e-01  3.23293433e-02 -1.96419824e-02]\n",
+      "SBI Life Insurance Company Ltd.: [-6.74839914e-02  5.03901066e-03 -7.49206021e-02  2.28400957e-02\n",
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+      "State Bank of India: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Shipping Corporation of India Ltd.: [-1.24083154e-01  2.02380326e-02 -7.62995845e-03 -4.88204248e-02\n",
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+      "Sheela Foam Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "Shriram City Union Finance Ltd.: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "Siemens Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "  1.16565406e-01  3.29949893e-02  4.42770161e-02 -2.87038963e-02]\n",
+      "SKF India Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "  5.20288646e-02 -2.72753071e-02  8.94622728e-02  1.44767771e-02]\n",
+      "Sobha Ltd.: [-7.89830163e-02 -2.06320938e-02 -7.58103058e-02  2.75196936e-02\n",
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+      "Solar Industries India Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "SRF Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "Sumitomo Chemical India Ltd.: [ 1.21723162e-02 -1.08713554e-02 -1.99898817e-02  5.79892062e-02\n",
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+      "Sundram Fasteners Ltd.: [-5.15867211e-02  9.61384457e-03 -2.78991903e-03 -2.43441928e-02\n",
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+      "Sterling and Wilson Renewable Energy Ltd.: [-5.91712594e-02  5.09899370e-02 -3.55948578e-03  3.27332653e-02\n",
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+      "  3.84599231e-02  2.19692346e-02  9.55206975e-02  6.50335252e-02]\n",
+      "Symphony Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "Tata Power Co. Ltd.: [-5.48450388e-02  1.57037973e-01 -1.95232090e-02  9.12069231e-02\n",
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+      "  1.16565406e-01  3.29949893e-02  4.42770161e-02 -2.87038963e-02]\n",
+      "Tata Steel Ltd.: [-8.54082480e-02  7.21397176e-02 -2.08139271e-02  7.19632208e-02\n",
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+      "  4.47645690e-03 -3.93106230e-02 -2.63823476e-03 -3.21210660e-02\n",
+      "  4.12422158e-02  2.11369782e-03 -1.09004378e-02 -5.83827086e-02\n",
+      "  7.80699998e-02  2.56532268e-03  1.03506021e-01  2.08582878e-02\n",
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+      " -3.55245247e-02  1.90016143e-02 -2.68820450e-02  4.64876741e-03\n",
+      "  1.21604083e-02  4.10571247e-02  1.72643401e-02 -3.37485522e-02\n",
+      "  2.60592885e-02 -2.74525862e-02 -5.62610861e-04 -9.70786065e-02\n",
+      " -1.41732693e-01  4.99550253e-02  2.37820540e-02 -4.75868350e-03\n",
+      " -3.14087234e-02  2.70113274e-02 -4.57031988e-02  6.40651770e-03\n",
+      "  3.48278694e-02 -9.82571840e-02  4.32403013e-03 -6.79854304e-04\n",
+      "  1.14075154e-01  1.83878485e-02  2.91329157e-02  1.29294649e-01\n",
+      "  2.35078596e-02 -4.89729270e-02 -7.04030171e-02 -1.93983316e-02\n",
+      "  7.97320828e-02 -7.59844109e-02  7.68048735e-03  1.89556349e-02]\n",
+      "Tata Steel Long Products Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
+      " -3.31258029e-02  2.87598353e-02  6.94174021e-02 -1.55117903e-02\n",
+      " -7.68500194e-02  3.57285701e-02  3.30422334e-02  1.74193848e-02\n",
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+      "Timken India Ltd.: [ 2.46381797e-02 -2.18525901e-02  2.99071502e-02 -1.04453033e-02\n",
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+      "  1.04847513e-01 -9.03233588e-02  1.54254228e-01  1.15292296e-01]\n",
+      "Torrent Pharmaceuticals Ltd.: [-3.23555171e-02  8.87764841e-02 -3.43591906e-03  1.08567076e-02\n",
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+      "Triveni Engineering & Industries Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "UCO Bank: [-6.74840137e-02  5.03902463e-03 -7.49206468e-02  2.28400789e-02\n",
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+      "UltraTech Cement Ltd.: [-7.29177594e-02  4.10846360e-02  1.53632332e-02  4.46023345e-02\n",
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+      "Vedanta Ltd.: [-8.54082480e-02  7.21397176e-02 -2.08139271e-02  7.19632208e-02\n",
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+      "  7.97320828e-02 -7.59844109e-02  7.68048735e-03  1.89556349e-02]\n",
+      "Venky's (India) Ltd.: [-4.19523194e-02 -6.47806525e-02  2.18687411e-02 -2.37952862e-02\n",
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+      "Vardhman Textiles Ltd.: [-9.64162573e-02  6.90967515e-02 -1.66647322e-02  4.47280891e-02\n",
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+      "Whirlpool of India Ltd.: [-7.71643817e-02 -3.33318749e-04  1.01158116e-02  2.20314469e-02\n",
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+      "ZF Commercial Vehicle Control Systems India Ltd.: [-5.15867211e-02  9.61384457e-03 -2.78991903e-03 -2.43441928e-02\n",
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+      "Zomato Ltd.: [-1.27135858e-01  1.32178823e-02 -4.17881310e-02 -6.45975620e-02\n",
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+      " -5.02248034e-02 -3.06014530e-02  1.54899303e-02  2.25743279e-02\n",
+      " -7.76153011e-03  5.74035663e-03  2.14360785e-02 -1.18210102e-02\n",
+      " -3.46778184e-02  3.36872302e-02  3.50645669e-02 -1.13666621e-33\n",
+      " -1.38889909e-01  1.61070079e-02  3.78616750e-02 -8.53946507e-02\n",
+      " -6.22987486e-02 -5.90138137e-03 -4.00576256e-02  3.03657935e-03\n",
+      "  3.57751139e-02 -1.79465655e-02 -1.34972930e-02 -2.76990719e-02\n",
+      " -5.10045514e-02  1.09993301e-01  2.52689607e-02 -1.25467941e-01\n",
+      " -3.43660861e-02 -1.61850061e-02  9.60320458e-02  5.38618362e-04\n",
+      "  1.60255544e-02  5.05558040e-04  1.50848972e-02 -3.46151628e-02\n",
+      "  3.30427997e-02 -4.86387536e-02 -5.60231023e-02 -3.39170831e-04\n",
+      "  7.14766234e-02  3.21187563e-02 -5.94063336e-03  3.04528177e-02\n",
+      " -5.70922792e-02 -7.70216733e-02 -4.70327251e-02 -4.74993978e-03\n",
+      " -7.67055601e-02 -6.40858561e-02  2.70777941e-02 -2.33279727e-02\n",
+      " -5.76546900e-02  5.53144440e-02 -5.07902503e-02  2.79295426e-02\n",
+      " -7.25087803e-03  1.06836155e-01  3.52930352e-02 -8.29963759e-03\n",
+      " -6.83458000e-02  6.03325516e-02 -4.69122157e-02  3.37887858e-03\n",
+      " -5.95927015e-02 -5.75835258e-03 -2.53365207e-02 -1.75462206e-04\n",
+      "  5.67293763e-02 -2.08465438e-02 -1.43371643e-02 -4.41675400e-03\n",
+      " -3.64958793e-02  4.64801453e-02 -2.25395653e-02 -2.45838873e-02\n",
+      "  1.21860032e-03  3.54316644e-02  4.35418217e-03 -5.43301692e-03\n",
+      " -1.49388919e-02  2.53087021e-02  5.50108217e-02 -1.61987115e-02\n",
+      "  1.85262896e-02 -7.03425705e-02  6.31755367e-02 -1.32140443e-02\n",
+      " -9.74476188e-02  4.96701039e-02 -2.80644484e-02 -7.32449889e-02\n",
+      "  1.08140334e-02  4.47634421e-02  4.78407694e-03 -1.99706899e-03\n",
+      "  1.13587745e-01  3.55156958e-02 -1.73052531e-02 -7.69868493e-02\n",
+      "  3.84565964e-02  5.05486690e-02 -4.19223728e-03  4.05462869e-02\n",
+      "  3.96019109e-02  7.48211294e-02  1.64110493e-02  4.94331328e-34\n",
+      "  4.65126336e-03 -3.04703135e-03  5.74055053e-02  1.49116680e-01\n",
+      " -3.69283892e-02 -5.45512736e-02 -8.03579986e-02  6.18904037e-03\n",
+      "  8.33517499e-03  8.42046514e-02 -1.46176875e-01  3.48921027e-03\n",
+      "  9.30754617e-02  1.35464119e-02  8.26925337e-02 -1.26509788e-02\n",
+      "  1.26559168e-01 -2.13346481e-02  2.91678067e-02 -3.62831540e-02\n",
+      "  4.26232405e-02  3.42257358e-02 -6.09845109e-02  6.06343523e-02\n",
+      " -2.96642296e-02  4.44921255e-02 -7.22386390e-02  5.94313964e-02\n",
+      " -3.59613299e-02 -9.81355980e-02 -6.40813485e-02 -1.69149414e-02\n",
+      "  4.73213457e-02 -3.84501042e-03 -1.55453128e-03  6.92314729e-02\n",
+      " -4.08097133e-02  1.00003397e-02 -8.86739418e-03  7.62793645e-02\n",
+      "  7.92214721e-02 -6.09861240e-02 -2.31058486e-02 -5.00661470e-02\n",
+      " -4.39821333e-02  6.79104775e-03  4.08012886e-03 -3.69424149e-02\n",
+      "  1.62465731e-03  8.85059237e-02  6.15928918e-02  6.40431568e-02\n",
+      " -3.65431644e-02  1.59414846e-03 -1.69308931e-02  9.00977328e-02\n",
+      "  6.66400865e-02 -1.18475175e-02 -8.51497694e-04 -2.96685733e-02\n",
+      "  1.86491385e-02  1.35950781e-02  1.72020681e-02  2.71538682e-02\n",
+      "  7.67747164e-02 -2.67101936e-02  1.14700414e-01 -1.00439064e-01\n",
+      "  3.11968960e-02 -4.65152934e-02  3.92768234e-02  4.19787318e-03\n",
+      " -5.70761487e-02  1.33486120e-02 -6.50266036e-02 -2.82209795e-02\n",
+      "  1.03249997e-02  3.09994724e-02 -1.87489875e-02  2.51580980e-02\n",
+      " -5.97541966e-02  2.94076689e-02  1.43752368e-02  2.02032551e-02\n",
+      " -3.47373225e-02 -6.27343729e-02  9.39113554e-03 -1.21272817e-01\n",
+      "  3.47814523e-02  2.32638773e-02  1.49381431e-02  2.17519514e-02\n",
+      "  8.55728704e-03 -5.49990125e-02 -1.50043871e-02 -1.41426293e-08\n",
+      " -1.51578698e-03 -6.13876097e-02  4.47901487e-02  7.99873769e-02\n",
+      "  3.18472758e-02  8.71470012e-03  1.08878920e-03  6.77157119e-02\n",
+      "  3.96043882e-02 -7.12699839e-04  2.40281112e-02  3.63050550e-02\n",
+      "  4.02212515e-02  6.04619645e-02  6.55885087e-03 -3.49235372e-03\n",
+      "  2.48958748e-02  6.74311025e-03 -7.16477409e-02 -8.01346172e-03\n",
+      "  9.63222794e-03  1.57958746e-01  7.74511322e-02  1.45721538e-02\n",
+      "  4.29333420e-03  2.51996499e-02  4.96561266e-02  2.73171756e-02\n",
+      "  6.69671670e-02 -1.18501652e-02 -4.99147736e-02  3.06649227e-02\n",
+      "  2.05701999e-02 -5.64133981e-03  6.07472882e-02  2.25201752e-02\n",
+      " -2.37061772e-02  2.92219035e-02 -2.39416435e-02 -4.20965068e-02\n",
+      " -5.74703980e-03  3.54500264e-02 -9.70290378e-02 -1.83146622e-03\n",
+      " -9.87919979e-03 -4.83104214e-02 -8.23866948e-02 -2.26372536e-02\n",
+      "  1.26401074e-02  7.76717514e-02  2.24403571e-02 -1.29538421e-02\n",
+      "  1.95268281e-02  3.46522033e-02  1.20828124e-02 -4.80264686e-02\n",
+      "  8.03388562e-03 -5.38482554e-02 -1.70898438e-02  3.68946791e-02\n",
+      "  3.85916717e-02 -2.24448442e-02  6.71749795e-03  6.62344471e-02]\n",
+      "\n",
+      "Customer Interest Embedding:\n",
+      "[ 3.14664170e-02 -3.78815085e-02 -3.73417661e-02 -3.17229889e-02\n",
+      " -4.22344357e-02  1.27025442e-02 -2.39155442e-02 -6.93003461e-02\n",
+      " -3.21524963e-02  7.96454027e-02  7.77523369e-02 -4.75082286e-02\n",
+      " -1.95211302e-02 -1.25046214e-02  5.08183353e-02 -2.46512946e-02\n",
+      " -1.49427941e-02 -2.74640676e-02  4.25236784e-02 -7.36578787e-03\n",
+      " -8.90878867e-03  1.84313732e-03 -4.04891334e-02  2.95713730e-02\n",
+      " -5.97193651e-02  1.87571477e-02 -5.67692332e-03 -8.21459852e-03\n",
+      " -2.31289323e-02 -2.73498092e-02  2.54001822e-02  1.30668739e-02\n",
+      "  9.44364443e-02 -8.01635440e-03  3.55471559e-02  1.28795162e-01\n",
+      "  2.49239113e-02 -9.53501761e-02  1.58603266e-02 -3.89398336e-02\n",
+      " -1.34050464e-02 -9.93881673e-02 -2.93484535e-02 -7.17466623e-02\n",
+      "  3.43848653e-02 -1.67269222e-02  2.82975789e-02 -3.10652656e-03\n",
+      "  2.12582219e-02 -7.64291584e-02  4.05744687e-02 -2.67640091e-02\n",
+      "  2.98122708e-02 -9.98979155e-03 -5.23542874e-02 -1.77336130e-02\n",
+      " -1.46305542e-02 -6.34800317e-03 -4.22016680e-02 -5.87929562e-02\n",
+      " -6.84524998e-02 -7.19780475e-03 -4.78288569e-02 -5.24268188e-02\n",
+      "  5.48108257e-02 -9.64927487e-03  1.59333013e-02 -7.21244812e-02\n",
+      " -1.53982667e-02 -7.07860738e-02  4.63903956e-02 -8.87077451e-02\n",
+      "  4.21138760e-03 -4.27451804e-02 -3.15879434e-02 -3.48816104e-02\n",
+      "  1.73769016e-02  1.20111547e-01  1.23920493e-01 -1.45363241e-01\n",
+      "  7.32854903e-02  5.91961406e-02 -4.84295599e-02  6.06471710e-02\n",
+      " -6.54613897e-02  1.24825360e-02 -7.38825463e-03 -6.54625967e-02\n",
+      "  2.87184883e-02  1.88502856e-03 -8.01817980e-03 -3.96523885e-02\n",
+      "  7.24581480e-02  1.22633632e-02 -5.44842100e-03  1.16946828e-02\n",
+      " -4.74422099e-03 -4.22262959e-02 -1.53517053e-02  9.96556208e-02\n",
+      " -4.84725051e-02  3.07871047e-02 -3.94323021e-02 -3.33751626e-02\n",
+      " -7.20618889e-02 -7.62435235e-03  9.71943699e-03  2.18201359e-03\n",
+      "  5.96895888e-02  1.00962352e-02 -7.43536279e-03  1.28121506e-02\n",
+      " -3.44882496e-02 -4.63309400e-02  3.64253037e-02 -5.68599366e-02\n",
+      " -4.72689755e-02 -1.85630340e-02  2.15455409e-04 -5.69709577e-02\n",
+      " -2.88812886e-03  1.34512419e-02 -6.94445893e-02 -4.80364412e-02\n",
+      "  1.98717043e-02 -6.87178075e-02  4.69160378e-02 -3.48460129e-33\n",
+      "  2.74309292e-02  4.61018123e-02  7.90321007e-02 -1.77468895e-03\n",
+      "  2.93568131e-02  1.36835873e-02 -3.85059714e-02 -4.15082574e-02\n",
+      "  4.04559672e-02  1.43633969e-02  6.32987246e-02  5.11144586e-02\n",
+      "  4.15699705e-02  1.00233350e-02  1.26954451e-01 -1.22519739e-01\n",
+      " -1.47484569e-02  8.62846524e-02  7.75998011e-02  4.46178764e-02\n",
+      " -1.41220577e-02  5.68904029e-03 -1.14176879e-02 -2.26635188e-02\n",
+      "  1.14526093e-01 -4.34458293e-02  1.74916312e-02 -1.74409226e-02\n",
+      "  2.41210591e-02  5.08027188e-02  7.86003917e-02  1.14285775e-01\n",
+      " -3.16002294e-02 -3.04858722e-02 -1.72049068e-02  3.13026309e-02\n",
+      " -3.78056103e-03 -2.68692020e-02  8.44113063e-03 -3.65200266e-02\n",
+      " -9.06182155e-02 -5.41768707e-02  4.64576595e-02  5.30998260e-02\n",
+      " -8.45028013e-02  7.69533683e-03  4.33015600e-02  5.77162132e-02\n",
+      "  1.57650374e-02  3.23144123e-02  2.93194950e-02  5.57424016e-02\n",
+      " -7.00408267e-03 -5.76511910e-03  9.61290859e-03 -2.98861135e-02\n",
+      "  3.35550979e-02 -5.78683540e-02 -2.72784918e-03 -5.11349514e-02\n",
+      "  6.91856742e-02  3.93171422e-02 -9.10947323e-02  1.25231802e-01\n",
+      " -7.74377361e-02 -3.65904607e-02  2.45778412e-02 -3.15613784e-02\n",
+      "  1.32436842e-01 -4.68552709e-02 -3.28906663e-02  1.51393292e-02\n",
+      " -4.87497970e-02 -2.50118654e-02 -6.71052039e-02 -2.61116866e-02\n",
+      " -4.84636873e-02  5.14720157e-02  2.30410714e-02  3.72129828e-02\n",
+      " -8.30372125e-02  1.39289838e-03  8.76997970e-03 -6.48937235e-03\n",
+      "  1.66297719e-01  7.46646076e-02  4.32111137e-02  7.86511973e-03\n",
+      "  6.49285689e-02  1.44748529e-02 -1.45551831e-01  1.85465429e-03\n",
+      "  3.69937643e-02  1.24513529e-01  6.94522038e-02  8.31680528e-34\n",
+      "  2.02758648e-02 -1.38148433e-02  8.69328249e-03 -3.77335213e-02\n",
+      "  4.28531915e-02  4.57742624e-02  3.20917927e-03 -7.67260492e-02\n",
+      "  2.34734826e-02  7.90196955e-02 -8.84439349e-02 -2.00472027e-02\n",
+      "  3.51752974e-02  4.71815728e-02  3.01404763e-02 -9.60588306e-02\n",
+      " -1.09162629e-02  2.62741130e-02  3.75837716e-03  4.07930352e-02\n",
+      "  1.16740474e-02 -4.42948379e-02  5.25881089e-02 -2.52405480e-02\n",
+      " -4.48426185e-03  1.36668356e-02 -6.41934201e-02 -4.62563448e-02\n",
+      " -3.71463336e-02 -5.36741503e-02  2.67249644e-02 -4.97082695e-02\n",
+      " -5.00039123e-02  1.82751343e-02 -8.67619663e-02 -2.51742024e-02\n",
+      "  1.15126157e-02 -3.29780951e-02 -3.40419933e-02 -3.42883095e-02\n",
+      " -1.14182731e-04 -4.34406884e-02  2.27891263e-02  1.50547381e-02\n",
+      " -3.02064866e-02 -5.12561463e-02 -3.14933769e-02  3.78211439e-02\n",
+      " -7.33443052e-02 -1.62566863e-02 -6.89671859e-02  8.55399668e-02\n",
+      " -1.29097393e-02  2.51021376e-03  2.23773625e-02  3.67263928e-02\n",
+      " -1.82489175e-02 -3.63597204e-03 -3.53036486e-02 -9.63828806e-03\n",
+      "  6.90663457e-02  1.02930024e-01 -5.99898584e-02  1.27931476e-01\n",
+      "  7.77645856e-02 -5.71305789e-02  4.69792672e-02 -4.12638411e-02\n",
+      "  2.58938950e-02 -3.08347158e-02 -3.00339106e-02 -5.49983121e-02\n",
+      "  3.68826687e-02 -1.24603510e-02  4.68766242e-02 -1.11738769e-02\n",
+      " -6.12557940e-02 -8.41406267e-03  5.76944388e-02  3.37682106e-02\n",
+      "  8.08468238e-02 -1.47831952e-02 -5.12435287e-02 -1.43610025e-02\n",
+      " -1.72178708e-02 -1.24791898e-02  1.05243884e-01 -8.38583037e-02\n",
+      " -8.27235915e-03  5.26939481e-02 -6.68532029e-02 -7.83333704e-02\n",
+      "  1.74317770e-02  6.10077605e-02  8.48254338e-02 -1.45727634e-08\n",
+      " -5.60169155e-03  3.13913748e-02 -1.43741500e-02 -3.30618732e-02\n",
+      "  4.62461524e-02 -9.12346542e-02 -9.48227867e-02 -6.03580996e-02\n",
+      " -3.57683040e-02 -1.11233955e-02  8.02720189e-02  3.13891210e-02\n",
+      " -1.37412539e-02  3.30802463e-02 -5.60475104e-02 -3.96210775e-02\n",
+      "  1.10384878e-02  2.29135510e-02 -4.95473966e-02  4.28589480e-03\n",
+      "  8.42921063e-03  4.52581532e-02  3.36574055e-02 -1.93255581e-02\n",
+      "  5.11594769e-03  4.19118740e-02  3.12094409e-02  6.94123842e-03\n",
+      " -1.22232875e-02  8.34257528e-02  3.83116268e-02  7.67853260e-02\n",
+      " -2.68143434e-02 -2.39269249e-02 -5.86269535e-02 -7.08768563e-03\n",
+      "  1.90878119e-02  5.11739142e-02  5.80927432e-02 -8.05346519e-02\n",
+      " -1.35047799e-02  4.48757410e-02  9.85127836e-02  4.68116690e-04\n",
+      "  3.26827727e-02  2.04949491e-02 -1.01781540e-01  9.10897565e-04\n",
+      "  3.98706049e-02 -1.28018588e-01 -5.66593781e-02  4.23376933e-02\n",
+      "  3.15444754e-03  5.05354777e-02  2.17250474e-02 -1.54494355e-02\n",
+      "  1.74987838e-02 -2.67964997e-03 -8.95080194e-02  3.47001143e-02\n",
+      "  8.55300725e-02 -6.96479157e-02  6.53972477e-02  2.12662946e-02]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Stock Descriptions and their Embeddings:\")\n",
+    "for name, embedding in zip(filtered_df['Company Name'], stock_embeddings):\n",
+    "    print(f\"{name}: {embedding}\")\n",
+    "\n",
+    "print(\"\\nCustomer Interest Embedding:\")\n",
+    "print(customer_embedding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recommended stocks for you based on your interest:\n",
+      "                                     Company Name\n",
+      "209  ICICI Prudential Life Insurance Company Ltd.\n",
+      "233                          Indian Overseas Bank\n",
+      "40                     AU Small Finance Bank Ltd.\n"
+     ]
+    }
+   ],
+   "source": [
+    "similarities = cosine_similarity([customer_embedding], stock_embeddings)\n",
+    "\n",
+    "# Get the indices of the top similar stocks\n",
+    "top_indices = np.argsort(similarities[0])[-3:][::-1]  # Top 3 recommendations\n",
+    "\n",
+    "# Display the recommended stocks\n",
+    "recommended_stocks = filtered_df.iloc[top_indices]\n",
+    "print(\"Recommended stocks for you based on your interest:\")\n",
+    "print(recommended_stocks[['Company Name']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
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+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/nifty_500.csv b/nifty_500.csv
new file mode 100644
index 0000000..1897f18
--- /dev/null
+++ b/nifty_500.csv
@@ -0,0 +1,502 @@
+Company Name,Symbol,Industry,Series,Open,High,Low,Previous Close,Last Traded Price,Change,Percentage Change,Share Volume,Value (Indian Rupee),52 Week High,52 Week Low,365 Day Percentage Change,30 Day Percentage Change
+3M India Ltd.,3MINDIA,Diversified,EQ,21950.0,21999.0,21126.05,21854.05,21575.0,-279.05,-1.28,4159,89653566.68,27800.0,17273.0,-10.18,8.22
+Aarti Drugs Ltd.,AARTIDRUGS,Healthcare,EQ,400.5,401.8,394.1,403.85,400.0,-3.85,-0.95,31782,12627306.42,749.75,378.0,-42.92,-5.10
+Aavas Financiers Ltd.,AAVAS,Financial Services,EQ,1997.1,2004.05,1894.5,2015.45,1943.15,-72.30,-3.59,150704,290772818.72,3340.0,1815.0,-25.69,-9.39
+ABB India Ltd.,ABB,Capital Goods,EQ,2260.35,2311.5,2260.35,2300.9,2280.0,-20.90,-0.91,97053,221093527.71,2487.85,1618.05,27.25,-5.21
+Abbott India Ltd.,ABBOTINDIA,Healthcare,EQ,18700.4,19200.0,18605.0,18760.4,19199.8,439.40,2.34,12396,234676981.32,23934.45,15514.0,11.61,5.84
+Aditya Birla Capital Ltd.,ABCAPITAL,Financial Services,EQ,89.0,90.6,88.35,89.4,90.25,0.85,0.95,2401323,215182554.03,139.2,85.6,-23.75,-11.57
+Aditya Birla Fashion and Retail Ltd.,ABFRL,Consumer Services,EQ,235.6,243.5,235.6,237.05,242.2,5.15,2.17,1014277,243548193.24,322.0,189.35,9.87,-12.62
+Aditya Birla Sun Life AMC Ltd.,ABSLAMC,Financial Services,EQ,400.05,406.7,400.0,400.05,405.9,5.85,1.46,34489,13901826.12,722.9,375.0,-,-5.09
+ACC Ltd.,ACC,Construction Materials,EQ,2122.95,2174.0,2122.95,2121.95,2171.7,49.75,2.34,284645,613620612.3,2589.0,1900.0,5.32,-3.48
+Adani Enterprises Ltd.,ADANIENT,Metals & Mining,EQ,2189.9,2242.0,2138.25,2190.9,2229.25,38.35,1.75,2141743,4695578770.63,2420.95,1332.75,45.33,1.08
+Adani Green Energy Ltd.,ADANIGREEN,Power,EQ,1928.0,1978.9,1902.0,1929.6,1963.25,33.65,1.74,1206875,2344656406.25,3050.0,874.8,-,2.15
+Adani Ports and Special Economic Zone Ltd.,ADANIPORTS,Services,EQ,670.0,680.0,656.0,672.05,678.3,6.25,0.93,3151179,2105491760.64,924.65,651.95,-4.50,-9.16
+Adani Transmission Ltd.,ADANITRANS,Power,EQ,2500.0,2583.7,2226.3,2473.65,2452.0,-21.65,-0.88,741152,1847854989.44,3000.0,863.0,-,20.37
+Advanced Enzyme Tech Ltd.,ADVENZYMES,Fast Moving Consumer Goods,EQ,266.2,268.7,258.4,266.2,264.0,-2.20,-0.83,40076,10549606.24,435.95,258.4,-35.78,-7.02
+Aegis Logistics Ltd.,AEGISCHEM,Oil Gas & Consumable Fuels,EQ,208.7,212.6,205.15,209.0,211.45,2.45,1.17,247355,51731824.7,386.0,167.25,-39.22,-3.95
+Affle (India) Ltd.,AFFLE,Information Technology,EQ,1060.0,1060.3,1004.65,1060.3,1010.0,-50.30,-4.74,241660,246983769.8,1511.0,763.02,-,0.78
+AIA Engineering Ltd.,AIAENG,Capital Goods,EQ,2233.8,2242.45,2170.2,2223.8,2210.0,-13.80,-0.62,42620,94240491.6,2350.0,1475.05,3.91,-2.37
+Ajanta Pharmaceuticals Ltd.,AJANTPHARM,Healthcare,EQ,1233.0,1275.05,1229.35,1235.6,1265.9,30.30,2.45,109740,137293519.2,2435.0,1186.0,-42.50,-27.87
+Alkem Laboratories Ltd.,ALKEM,Healthcare,EQ,2995.0,3035.0,2970.95,2998.55,3011.25,12.70,0.42,21837,65524757.31,4070.0,2828.0,-6.28,-4.78
+Alkyl Amines Chemicals Ltd.,ALKYLAMINE,Chemicals,EQ,2525.0,2609.9,2506.2,2517.65,2552.85,35.20,1.40,26377,67248425.27,4749.0,2506.2,-30.00,-7.77
+Allcargo Logistics Ltd.,ALLCARGO,Services,EQ,259.0,271.5,256.0,262.95,269.0,6.05,2.30,502907,133391052.68,412.0,152.5,64.50,-17.76
+Alok Industries Ltd.,ALOKINDS,Textiles,BE,22.3,22.3,21.5,22.0,21.55,-0.45,-2.05,1040692,22645457.92,35.8,18.85,-,-
+Amara Raja Batteries Ltd.,AMARAJABAT,Automobile and Auto Components,EQ,459.1,471.7,456.05,458.7,469.5,10.80,2.35,438273,202727558.88,781.6,438.05,-38.43,-8.69
+Amber Enterprises India Ltd.,AMBER,Consumer Durables,EQ,2251.0,2348.2,2251.0,2246.65,2325.0,78.35,3.49,85424,197744600.64,4025.95,2040.0,-23.22,-14.67
+Ambuja Cements Ltd.,AMBUJACEM,Construction Materials,EQ,363.95,368.6,362.1,363.0,367.5,4.50,1.24,2763265,1011078663.5,442.5,274.0,6.56,-1.73
+Angel One Ltd.,ANGELONE,Financial Services,EQ,1202.0,1228.0,1185.0,1206.2,1216.05,9.85,0.82,414524,499671374.84,2022.0,839.05,-,-18.25
+Anupam Rasayan India Ltd.,ANURAS,Chemicals,EQ,610.0,626.2,604.6,610.1,620.7,10.60,1.74,37228,22843845.36,1107.55,546.75,-19.72,-11.28
+APL Apollo Tubes Ltd.,APLAPOLLO,Capital Goods,EQ,843.9,855.0,836.0,849.95,845.1,-4.85,-0.57,165325,139906281.25,1114.55,718.08,-47.83,-9.31
+Alembic Pharmaceuticals Ltd.,APLLTD,Healthcare,EQ,727.75,731.6,717.0,728.25,729.9,1.65,0.23,63552,46195948.8,1016.7,678.3,-26.01,-0.51
+Apollo Hospitals Enterprise Ltd.,APOLLOHOSP,Healthcare,EQ,3680.0,3745.0,3635.2,3683.5,3732.0,48.50,1.32,316894,1169161399.36,5935.4,3361.55,1.76,-7.43
+Apollo Tyres Ltd.,APOLLOTYRE,Automobile and Auto Components,EQ,185.7,190.0,184.3,185.1,188.9,3.80,2.05,1276512,238682213.76,250.0,165.25,-17.73,-15.23
+Aptus Value Housing Finance India Ltd.,APTUS,Financial Services,EQ,276.5,294.7,274.3,281.8,293.65,11.85,4.21,449163,128519009.19,394.9,220.1,-,-1.38
+Asahi India Glass Ltd.,ASAHIINDIA,Automobile and Auto Components,EQ,536.8,549.0,530.25,539.6,548.0,8.40,1.56,261012,140842075.2,612.9,324.65,60.43,20.85
+Ashok Leyland Ltd.,ASHOKLEY,Capital Goods,EQ,148.0,149.0,144.65,147.8,146.4,-1.40,-0.95,19976245,2920926543.9,153.5,93.2,20.55,7.49
+Asian Paints Ltd.,ASIANPAINT,Consumer Durables,EQ,2704.9,2780.0,2685.0,2695.2,2769.65,74.45,2.76,1477191,4055317680.39,3590.0,2560.0,-9.94,-5.75
+Aster DM Healthcare Ltd.,ASTERDM,Healthcare,EQ,179.55,179.55,174.3,178.0,176.0,-2.00,-1.12,92393,16259320.14,237.0,148.4,15.55,-10.84
+Astral Ltd.,ASTRAL,Capital Goods,EQ,1651.0,1664.95,1639.9,1655.5,1657.0,1.50,0.09,125312,207061789.44,2524.95,1581.55,-16.79,-4.30
+AstraZenca Pharma India Ltd.,ASTRAZEN,Healthcare,EQ,2700.0,2727.0,2664.0,2705.2,2724.0,18.80,0.69,4613,12434526.02,3675.0,2445.7,-25.09,-10.05
+Adani Total Gas Ltd.,ATGL,Oil Gas & Consumable Fuels,EQ,2392.55,2442.95,2370.0,2392.9,2389.0,-3.90,-0.16,436503,1045280639.01,2740.0,774.95,-,-0.21
+Atul Ltd.,ATUL,Chemicals,EQ,7988.55,8070.0,7891.0,8030.8,8012.0,-18.80,-0.23,10672,85048369.6,10969.0,7750.0,-10.55,-1.40
+AU Small Finance Bank Ltd.,AUBANK,Financial Services,EQ,586.0,587.3,563.3,591.7,580.0,-11.70,-1.98,2741511,1574011125.54,732.98,462.5,-42.88,-52.72
+Aurobindo Pharma Ltd.,AUROPHARMA,Healthcare,EQ,512.25,524.0,506.5,513.1,522.75,9.65,1.88,831959,429032936.71,1013.0,503.45,-46.84,-3.33
+Avanti Feeds Ltd.,AVANTIFEED,Fast Moving Consumer Goods,EQ,432.85,436.5,425.0,432.85,436.0,3.15,0.73,142890,61885659.0,674.85,384.2,-20.70,0.87
+Axis Bank Ltd.,AXISBANK,Financial Services,EQ,633.0,646.6,631.8,636.8,644.0,7.20,1.13,6549359,4191262292.05,866.9,618.25,-14.90,-7.06
+Bajaj Auto Ltd.,BAJAJ-AUTO,Automobile and Auto Components,EQ,3667.95,3678.7,3601.1,3706.6,3621.7,-84.90,-2.29,756589,2746788798.61,4250.0,3027.05,-10.34,-4.08
+Bajaj Electricals Ltd,BAJAJELEC,Consumer Durables,EQ,1021.0,1048.0,1000.2,1023.95,1042.0,18.05,1.76,326410,335030488.1,1588.95,858.55,-0.29,3.41
+Bajaj Finserv Ltd.,BAJAJFINSV,Financial Services,EQ,10920.0,11343.0,10727.2,10931.75,11315.0,383.25,3.51,408983,4572364502.72,19325.0,10727.2,-9.72,-15.48
+Bajaj Holdings & Investment Ltd.,BAJAJHLDNG,Financial Services,EQ,4534.9,4655.0,4511.0,4585.2,4570.0,-15.20,-0.33,59937,275246886.99,6598.0,3586.0,26.86,-10.33
+Bajaj Finance Ltd.,BAJFINANCE,Financial Services,EQ,5364.0,5633.95,5335.05,5400.5,5617.95,217.45,4.03,1221890,6717694623.1,8050.0,5220.0,-10.23,-11.21
+Balaji Amines Ltd.,BALAMINES,Chemicals,EQ,2860.0,2897.0,2831.7,2867.3,2889.0,21.70,0.76,21727,62488155.62,5223.55,2651.0,7.24,-10.79
+Balkrishna Industries Ltd.,BALKRISIND,Automobile and Auto Components,EQ,2125.0,2179.0,2109.5,2149.15,2178.85,29.70,1.38,142057,304220747.78,2723.8,1690.55,-4.02,-8.11
+Balrampur Chini Mills Ltd.,BALRAMCHIN,Fast Moving Consumer Goods,EQ,360.0,362.5,354.65,359.45,360.7,1.25,0.35,1214196,435192130.32,525.9,297.6,1.05,-11.10
+Bandhan Bank Ltd.,BANDHANBNK,Financial Services,EQ,262.5,266.0,259.05,263.5,265.0,1.50,0.57,5414612,1423501494.8,349.55,229.55,-20.21,-19.16
+Bank of Baroda,BANKBARODA,Financial Services,EQ,97.6,97.8,95.6,97.4,97.35,-0.05,-0.05,32287305,3114433440.3,122.7,72.5,13.39,-2.84
+Bank of India,BANKINDIA,Financial Services,EQ,44.2,45.0,43.9,44.35,44.9,0.55,1.24,1053018,46711878.48,80.75,40.4,-43.07,-7.51
+BASF India Ltd.,BASF,Chemicals,EQ,2727.0,2759.95,2626.95,2728.55,2720.0,-8.55,-0.31,19400,52420740.0,3930.0,2351.0,4.97,6.81
+Bata India Ltd.,BATAINDIA,Consumer Durables,EQ,1665.0,1706.95,1650.0,1668.7,1705.0,36.30,2.18,122583,206081636.28,2262.0,1550.0,4.51,-11.11
+Bayer Cropscience Ltd.,BAYERCROP,Chemicals,EQ,5032.15,5135.0,4982.0,5007.15,5105.0,97.85,1.95,5806,29271761.84,6130.0,4218.05,-13.50,-7.24
+Bombay Burmah Trading Corporation Ltd.,BBTC,Fast Moving Consumer Goods,EQ,891.0,924.0,891.0,903.25,924.0,20.75,2.30,20192,18467603.2,1353.15,839.9,-31.75,-9.45
+Brightcom Group Ltd.,BCG,Information Technology,EQ,32.9,32.9,32.9,31.35,32.9,1.55,4.94,821102,27014255.8,122.88,10.73,40.27,-51.17
+Bharat Dynamics Ltd.,BDL,Capital Goods,EQ,677.0,679.95,660.05,681.25,668.0,-13.25,-1.94,693286,462442560.58,904.9,356.85,90.32,-13.07
+Bharat Electronics Ltd.,BEL,Capital Goods,EQ,233.0,235.5,227.25,234.1,229.7,-4.40,-1.88,5108014,1175354021.4,259.5,162.35,31.52,-0.28
+Berger Paints India Ltd.,BERGEPAINT,Consumer Durables,EQ,570.45,583.95,563.75,568.3,583.0,14.70,2.59,1316578,757664307.44,872.95,543.6,-29.41,-7.58
+Bharat Forge Ltd.,BHARATFORG,Capital Goods,EQ,645.15,649.35,635.0,651.8,643.0,-8.80,-1.35,650298,418492774.92,847.95,595.0,-14.45,-7.53
+Bharti Airtel Ltd.,BHARTIARTL,Telecommunication,EQ,684.85,685.8,666.0,684.95,673.0,-11.95,-1.74,4488771,3024129910.41,781.8,514.85,30.31,-2.18
+Bharat Heavy Electricals Ltd.,BHEL,Capital Goods,EQ,44.8,45.8,44.4,44.95,45.65,0.70,1.56,11083459,499420662.54,80.35,41.4,-31.43,-12.21
+Biocon Ltd.,BIOCON,Healthcare,EQ,308.75,312.0,305.0,308.4,311.3,2.90,0.94,1136834,351054339.2,414.4,305.0,-23.75,-7.69
+Birla Corporation Ltd.,BIRLACORPN,Construction Materials,EQ,874.9,877.1,826.0,870.95,839.9,-31.05,-3.57,120235,101789748.65,1650.0,823.0,-28.19,-10.63
+Blue Dart Express Ltd.,BLUEDART,Services,EQ,7850.0,7877.0,7730.1,7802.15,7765.55,-36.60,-0.47,16566,129401664.48,7880.0,5306.5,34.59,4.17
+Blue Star Ltd.,BLUESTARCO,Consumer Durables,EQ,892.1,903.75,885.35,903.75,890.9,-12.85,-1.42,42446,38077882.14,1225.0,758.0,10.75,-11.78
+Borosil Renewables Ltd.,BORORENEW,Capital Goods,EQ,603.0,614.8,595.2,601.7,607.7,6.00,1.00,469179,284435076.96,833.35,261.2,-,-8.39
+Bosch Ltd.,BOSCHLTD,Automobile and Auto Components,EQ,15273.0,15731.2,15201.55,15229.6,15650.0,420.40,2.76,71619,1114657346.49,19250.0,12932.45,1.36,4.12
+Bharat Petroleum Corporation Ltd.,BPCL,Oil Gas & Consumable Fuels,EQ,308.5,319.25,305.7,308.4,318.5,10.10,3.27,4416695,1381939698.55,503.0,293.35,-34.12,-5.46
+Brigade Enterprises Ltd.,BRIGADE,Realty,EQ,439.0,447.45,436.05,444.4,443.5,-0.90,-0.20,140806,62332000.08,554.7,268.7,58.88,-4.36
+Britannia Industries Ltd.,BRITANNIA,Fast Moving Consumer Goods,EQ,3412.0,3600.0,3412.0,3466.4,3575.0,108.60,3.13,292487,1029595188.18,4153.0,3050.0,-5.02,-4.97
+BSE Ltd.,BSE,Financial Services,EQ,594.95,604.95,586.45,596.9,600.15,3.25,0.54,778640,463088353.6,1046.67,296.33,-33.71,-21.03
+Birlasoft Ltd.,BSOFT,Information Technology,EQ,353.7,357.1,343.55,353.25,352.1,-1.15,-0.33,1752456,612360700.08,585.85,316.45,-11.60,-4.68
+Computer Age Management Services Ltd.,CAMS,Financial Services,EQ,2258.8,2258.8,2207.0,2257.75,2230.0,-27.75,-1.23,57998,129291461.52,4067.4,2037.15,-19.15,-2.92
+Canara Bank,CANBK,Financial Services,EQ,181.65,185.7,178.45,181.25,185.5,4.25,2.34,5768579,1048958405.36,272.8,142.1,19.36,-11.59
+Can Fin Homes Ltd.,CANFINHOME,Financial Services,EQ,427.85,443.4,424.7,426.35,441.45,15.10,3.54,663285,290260148.85,722.0,406.65,-18.57,-11.86
+Caplin Point Laboratories Ltd.,CAPLIPOINT,Healthcare,EQ,704.0,714.15,704.0,710.4,711.25,0.85,0.12,26432,18764605.44,1034.8,626.0,6.49,-4.28
+Carborundum Universal Ltd.,CARBORUNIV,Capital Goods,EQ,715.0,721.55,698.35,716.9,710.0,-6.90,-0.96,117136,82768297.6,1035.0,590.55,18.25,-0.50
+Castrol India Ltd.,CASTROLIND,Oil Gas & Consumable Fuels,EQ,103.3,104.15,102.75,103.3,104.0,0.70,0.68,228227,23644317.2,150.0,99.05,-27.81,-4.97
+CCL Products (I) Ltd.,CCL,Fast Moving Consumer Goods,EQ,380.0,385.15,375.25,383.45,382.3,-1.15,-0.30,132875,50844618.75,514.95,310.0,6.03,8.29
+Central Depository Services (India) Ltd.,CDSL,Financial Services,EQ,1111.4,1117.7,1096.0,1114.4,1105.0,-9.40,-0.84,208635,230664769.65,1734.4,974.0,13.88,-11.38
+Ceat Ltd.,CEATLTD,Automobile and Auto Components,EQ,933.0,949.0,917.75,929.2,935.55,6.35,0.68,29484,27602036.28,1477.75,890.0,-31.66,-8.36
+Central Bank of India,CENTRALBK,Financial Services,EQ,16.9,17.0,16.25,16.9,16.8,-0.10,-0.59,891053,14978600.93,29.65,16.25,-39.86,-10.58
+Century Plyboards (India) Ltd.,CENTURYPLY,Consumer Durables,EQ,526.0,526.35,511.05,525.7,512.1,-13.60,-2.59,35943,18514598.73,749.0,341.8,29.95,-8.41
+Century Textile & Industries Ltd.,CENTURYTEX,Forest Materials,EQ,808.0,817.95,797.0,805.7,806.5,0.80,0.10,156605,126723199.95,1024.0,607.7,30.97,0.98
+Cera Sanitaryware Ltd,CERA,Consumer Durables,EQ,4141.7,4160.0,4085.9,4132.75,4111.25,-21.50,-0.52,2801,11566421.39,6450.0,3515.0,-7.85,-1.61
+CESC Ltd.,CESC,Power,EQ,70.95,71.9,70.5,71.2,70.75,-0.45,-0.63,565200,40174416.0,102.45,7.23,-90.75,-8.72
+Capri Global Capital Ltd.,CGCL,Financial Services,EQ,685.55,689.15,681.05,687.4,681.15,-6.25,-0.91,16875,11555156.25,805.0,436.85,29.13,2.38
+CG Power and Industrial Solutions Ltd.,CGPOWER,Capital Goods,EQ,192.0,196.25,188.6,191.65,189.65,-2.00,-1.04,928649,178430618.86,219.65,71.7,-,5.33
+Chalet Hotels Ltd.,CHALET,Consumer Services,EQ,314.8,331.8,310.05,314.65,327.15,12.50,3.97,866335,280848480.3,332.7,159.05,77.57,5.92
+Chambal Fertilizers & Chemicals Ltd.,CHAMBLFERT,Chemicals,EQ,267.7,272.4,260.8,266.15,271.3,5.15,1.93,1548368,412593620.96,516.0,260.8,-13.19,-26.76
+Chemplast Sanmar Ltd.,CHEMPLASTS,Chemicals,EQ,469.8,479.0,460.8,470.15,476.25,6.10,1.30,98313,46324102.47,826.0,382.7,-,-2.52
+Cholamandalam Investment and Finance Company Ltd.,CHOLAFIN,Financial Services,EQ,616.2,643.15,616.05,619.5,637.95,18.45,2.98,1694794,1067737167.94,768.0,469.25,20.70,-8.84
+Cholamandalam Financial Holdings Ltd.,CHOLAHLDNG,Financial Services,EQ,590.25,610.5,586.9,593.2,602.05,8.85,1.49,17853,10648243.32,762.3,564.05,-10.42,-4.58
+Cipla Ltd.,CIPLA,Healthcare,EQ,911.2,956.05,911.2,917.2,948.0,30.80,3.36,1793834,1677593556.8,1083.0,850.0,-5.63,-7.63
+Clean Science and Technology Ltd.,CLEAN,Chemicals,EQ,1486.95,1490.0,1456.0,1490.05,1458.0,-32.05,-2.15,77405,113280669.4,2705.0,1422.1,-,-14.79
+Coal India Ltd.,COALINDIA,Oil Gas & Consumable Fuels,EQ,185.0,186.15,176.6,185.6,183.2,-2.40,-1.29,17248729,3132369186.4,209.0,132.75,26.56,-3.78
+Cochin Shipyard Ltd.,COCHINSHIP,Capital Goods,EQ,310.9,313.0,307.15,311.7,312.85,1.15,0.37,34795,10809066.75,402.6,280.75,-19.08,-3.53
+Coforge Ltd.,COFORGE,Information Technology,EQ,3538.0,3538.0,3419.9,3539.3,3495.0,-44.30,-1.25,536217,1852924654.35,6135.0,3218.1,-14.87,-9.52
+Colgate Palmolive (India) Ltd.,COLPAL,Fast Moving Consumer Goods,EQ,1488.8,1520.0,1484.35,1486.85,1515.1,28.25,1.90,437561,661491592.97,1823.4,1375.6,-11.80,-8.63
+Container Corporation of India Ltd.,CONCOR,Services,EQ,596.4,605.0,585.05,594.3,603.15,8.85,1.49,1065738,634167396.9,754.4,554.0,-14.90,-8.63
+Coromandel International Ltd.,COROMANDEL,Chemicals,EQ,953.0,969.5,932.75,953.15,968.05,14.90,1.56,366177,349230328.44,983.95,709.35,5.80,0.87
+CreditAccess Grameen Ltd.,CREDITACC,Financial Services,EQ,1012.95,1025.0,995.15,1015.8,1021.9,6.10,0.60,159386,160874665.24,1154.0,494.7,35.44,-5.20
+CRISIL Ltd.,CRISIL,Financial Services,EQ,3281.2,3398.0,3264.1,3286.7,3269.7,-17.00,-0.52,42577,142348109.87,3863.55,2425.2,29.43,-3.52
+Crompton Greaves Consumer Electricals Ltd.,CROMPTON,Consumer Durables,EQ,339.95,351.1,337.1,340.15,349.8,9.65,2.84,517467,179317839.51,512.8,312.0,-21.73,-5.89
+CSB Bank Ltd.,CSBBANK,Financial Services,EQ,194.5,195.0,190.0,193.1,191.6,-1.50,-0.78,198621,38345770.26,374.0,178.0,-44.38,2.20
+City Union Bank Ltd.,CUB,Financial Services,EQ,133.0,137.0,130.95,133.05,134.55,1.50,1.13,1461281,195154077.55,183.95,109.0,-20.14,-5.03
+Cummins India Ltd.,CUMMINSIND,Capital Goods,EQ,1016.0,1030.2,1014.0,1024.2,1030.0,5.80,0.57,149758,153313254.92,1162.0,821.6,13.86,-0.17
+Cyient Ltd.,CYIENT,Information Technology,EQ,760.45,766.35,749.1,756.65,756.85,0.20,0.03,196910,149269594.6,1292.0,720.0,-11.98,-3.41
+Dabur India Ltd.,DABUR,Fast Moving Consumer Goods,EQ,492.05,512.7,492.05,495.95,511.3,15.35,3.10,1704654,861071875.02,658.95,482.25,-12.80,-4.44
+Dalmia Bharat Ltd.,DALBHARAT,Construction Materials,EQ,1280.0,1337.8,1272.5,1283.65,1326.0,42.35,3.30,197336,259368571.6,2548.4,1212.5,-32.12,-5.79
+Dilip Buildcon Ltd.,DBL,Construction,EQ,190.0,190.75,187.45,190.4,189.75,-0.65,-0.34,231621,43871333.61,749.8,187.45,-66.80,-16.47
+DCM Shriram Ltd.,DCMSHRIRAM,Diversified,EQ,941.5,954.35,936.1,944.45,947.8,3.35,0.35,26354,24980693.06,1263.85,855.0,3.56,-4.80
+Deepak Nitrite Ltd.,DEEPAKNTR,Chemicals,EQ,1741.0,1755.95,1681.15,1736.4,1726.2,-10.20,-0.59,894640,1526175322.4,3020.0,1681.15,-3.99,-13.05
+Delta Corp Ltd.,DELTACORP,Consumer Services,EQ,167.05,174.55,166.0,166.45,172.45,6.00,3.60,10179569,1744370943.84,339.7,162.1,-6.09,-23.79
+Devyani International Ltd.,DEVYANI,Consumer Services,EQ,156.35,159.7,152.7,155.35,158.0,2.65,1.71,1367282,214950403.22,198.9,107.55,-,-8.89
+Dhani Services Ltd.,DHANI,Financial Services,EQ,37.6,37.6,34.2,35.85,35.3,-0.55,-1.53,20638577,751037817.03,242.9,28.5,-79.85,-28.44
+Divi's Laboratories Ltd.,DIVISLAB,Healthcare,EQ,3590.35,3649.6,3581.05,3630.4,3647.0,16.60,0.46,304397,1099975087.14,5425.1,3365.55,-17.65,1.09
+Dixon Technologies (India) Ltd.,DIXON,Consumer Durables,EQ,3575.0,3606.75,3517.0,3575.0,3594.0,19.00,0.53,157931,563530973.51,6243.6,3180.55,-19.14,-7.35
+DLF Ltd.,DLF,Realty,EQ,310.4,324.4,309.45,312.7,323.0,10.30,3.29,5557896,1764076190.4,449.7,279.45,11.24,-9.51
+Avenue Supermarts Ltd.,DMART,Consumer Services,EQ,3394.45,3406.65,3331.1,3406.1,3395.0,-11.10,-0.33,311228,1049656889.64,5900.0,3186.0,1.85,-14.26
+Dr. Reddy's Laboratories Ltd.,DRREDDY,Healthcare,EQ,4385.0,4395.0,4290.05,4393.8,4385.0,-8.80,-0.20,304799,1327369165.1,5614.6,3654.0,-18.98,0.55
+Easy Trip Planners Ltd.,EASEMYTRIP,Consumer Services,EQ,392.2,402.9,390.4,391.4,400.95,9.55,2.44,1105653,441277168.83,717.65,261.0,-0.66,2.78
+eClerx Services Ltd.,ECLERX,Services,EQ,1920.25,1920.25,1888.8,1920.25,1905.2,-15.05,-0.78,12475,23805543.5,2970.0,1755.65,-,-5.53
+Edelweiss Financial Services Ltd.,EDELWEISS,Financial Services,EQ,52.05,52.5,50.85,51.55,51.85,0.30,0.58,613698,31666816.8,100.9,49.4,-28.75,-5.76
+Eicher Motors Ltd.,EICHERMOT,Automobile and Auto Components,EQ,2775.0,2809.8,2750.4,2794.35,2789.0,-5.35,-0.19,367512,1018802065.92,2994.0,2159.55,4.61,0.49
+E.I.D. Parry (India) Ltd.,EIDPARRY,Fast Moving Consumer Goods,EQ,525.5,532.55,513.0,530.6,529.0,-1.60,-0.30,228073,119282179.0,576.0,377.3,24.42,-4.51
+EIH Ltd.,EIHOTEL,Consumer Services,EQ,124.4,127.95,124.2,124.9,126.9,2.00,1.60,162116,20512537.48,171.75,100.0,11.57,-7.58
+Elgi Equipments Ltd.,ELGIEQUIP,Capital Goods,EQ,351.5,362.4,344.35,358.5,347.0,-11.50,-3.21,204302,71922476.08,422.3,191.3,73.27,4.11
+Emami Ltd.,EMAMILTD,Fast Moving Consumer Goods,EQ,417.2,429.6,415.35,420.25,424.95,4.70,1.12,102892,43498621.92,621.8,393.4,-25.00,0.94
+Endurance Technologies Ltd.,ENDURANCE,Automobile and Auto Components,EQ,1418.8,1419.0,1396.85,1411.7,1410.0,-1.70,-0.12,140419,197643955.07,1989.0,1047.2,-13.14,7.94
+Engineers India Ltd.,ENGINERSIN,Construction,EQ,57.0,58.95,56.6,57.0,58.8,1.80,3.16,751759,43752373.8,82.5,56.0,-28.39,-8.51
+EPL Ltd.,EPL,Capital Goods,EQ,150.1,159.95,149.3,149.15,156.5,7.35,4.93,220028,34181349.8,291.0,147.15,-46.73,-9.19
+Equitas Holdings Ltd.,EQUITAS,Financial Services,EQ,82.4,83.0,81.5,82.0,82.55,0.55,0.67,198014,16282691.22,145.0,75.0,-12.77,-12.11
+Equitas Small Finance Bank Ltd.,EQUITASBNK,Financial Services,EQ,39.4,39.5,38.8,39.45,39.5,0.05,0.13,160741,6302654.61,76.8,37.45,-33.08,-2.95
+Eris Lifesciences Ltd.,ERIS,Healthcare,EQ,670.55,670.55,648.45,667.2,651.0,-16.20,-2.43,8326,5464104.02,863.0,600.3,-5.70,-0.09
+Escorts Kubota Ltd.,ESCORTS,Capital Goods,EQ,1449.95,1471.7,1424.55,1444.8,1467.2,22.40,1.55,489734,708072109.22,1934.0,1128.0,18.42,-11.36
+Exide Industries Ltd.,EXIDEIND,Automobile and Auto Components,EQ,137.5,141.95,136.8,137.8,141.95,4.15,3.01,854228,119053756.36,204.9,130.25,-24.43,-5.91
+Fertilisers and Chemicals Travancore Ltd.,FACT,Chemicals,EQ,96.4,96.4,93.4,96.4,95.0,-1.40,-1.45,227181,21543574.23,159.9,82.35,-30.17,-22.51
+FDC Ltd.,FDC,Healthcare,EQ,239.8,255.0,233.45,238.65,248.0,9.35,3.92,40209,9696400.35,388.7,225.0,-32.58,-3.01
+Federal Bank Ltd.,FEDERALBNK,Financial Services,EQ,90.55,94.35,89.3,90.2,94.05,3.85,4.27,16073373,1482447191.79,107.65,77.5,3.56,1.52
+Finolex Cables Ltd.,FINCABLES,Capital Goods,EQ,374.0,381.95,371.35,376.3,376.3,-,-,49522,18673755.76,608.6,343.5,-27.61,1.51
+Fine Organic Industries Ltd.,FINEORG,Chemicals,EQ,4804.45,4898.35,4780.0,4797.9,4840.0,42.10,0.88,25788,124989020.52,6139.9,2734.55,65.76,-1.46
+Finolex Industries Ltd.,FINPIPE,Capital Goods,EQ,135.8,137.5,134.1,136.2,137.2,1.00,0.73,118082,16022546.58,244.5,125.0,-25.96,-13.25
+Gujarat Fluorochemicals Ltd.,FLUOROCHEM,Chemicals,EQ,2800.25,2807.4,2696.95,2774.95,2755.0,-19.95,-0.72,44957,124253505.31,3198.9,1034.1,138.90,-3.89
+Fortis Healthcare Ltd.,FORTIS,Healthcare,EQ,236.65,238.0,232.3,240.0,235.5,-4.50,-1.88,536057,125769693.34,313.45,219.75,-1.40,0.82
+Firstsource Solutions Ltd.,FSL,Information Technology,EQ,102.5,104.3,101.35,102.9,103.7,0.80,0.78,1265235,130116767.4,242.85,93.0,-44.96,-7.34
+Gujarat Ambuja Exports Ltd.,GAEL,Fast Moving Consumer Goods,EQ,280.0,282.6,268.05,282.7,273.15,-9.55,-3.38,245672,67483641.68,394.15,150.05,62.85,-15.99
+GAIL (India) Ltd.,GAIL,Oil Gas & Consumable Fuels,EQ,134.1,136.4,131.0,135.2,132.6,-2.60,-1.92,10077624,1336595271.12,173.5,125.2,-9.66,-8.15
+Galaxy Surfactants Ltd.,GALAXYSURF,Chemicals,EQ,2934.6,2934.6,2838.0,2920.05,2849.95,-70.10,-2.40,18915,53998352.85,3585.65,2585.0,-3.40,1.68
+Garware Technical Fibres Ltd.,GARFIBRES,Textiles,EQ,3093.75,3093.75,2972.35,3078.35,3000.5,-77.85,-2.53,4359,13258639.53,4030.0,2619.8,-9.01,3.57
+Great Eastern Shipping Co. Ltd.,GESHIP,Services,EQ,395.75,398.9,385.0,395.6,390.25,-5.35,-1.35,120133,46909533.84,445.0,265.0,1.74,-0.39
+General Insurance Corporation of India,GICRE,Financial Services,EQ,114.1,116.4,114.1,115.2,115.95,0.75,0.65,90066,10390013.76,211.5,105.0,-44.75,-3.60
+Gland Pharma Ltd.,GLAND,Healthcare,EQ,2710.0,2710.0,2637.4,2701.7,2641.0,-60.70,-2.25,129469,344517009.0,4350.0,2495.15,-21.10,-5.57
+Glaxosmithkline Pharmaceuticals Ltd.,GLAXO,Healthcare,EQ,1505.0,1527.0,1499.0,1499.6,1499.0,-0.60,-0.04,54631,82318537.11,1918.75,1450.0,-0.17,-0.80
+Glenmark Pharmaceuticals Ltd.,GLENMARK,Healthcare,EQ,385.2,395.0,382.0,386.35,395.0,8.65,2.24,398227,155165168.28,690.95,348.5,-40.79,-1.83
+Glenmark Life Sciences Ltd.,GLS,Healthcare,EQ,441.0,444.5,440.0,444.3,441.8,-2.50,-0.56,15000,6619500.0,799.0,410.0,-,4.96
+GMM Pfaudler Ltd.,GMMPFAUDLR,Capital Goods,EQ,4355.0,4372.0,4300.05,4345.6,4330.0,-15.60,-0.36,14737,64042138.79,5295.0,3770.0,-7.22,3.63
+GMR Infrastructure Ltd.,GMRINFRA,Services,EQ,34.0,34.75,33.6,34.1,34.5,0.40,1.17,4338629,148337725.51,49.15,27.35,7.23,-10.26
+Gujarat Narmada Valley Fertilizers and Chemicals Ltd.,GNFC,Chemicals,EQ,566.0,582.0,563.25,571.8,580.5,8.70,1.52,1199375,687769600.0,912.0,314.1,51.59,-14.11
+Go Fashion (India) Ltd.,GOCOLORS,Consumer Services,EQ,990.0,999.5,980.0,993.45,988.5,-4.95,-0.50,6325,6258208.0,1339.9,847.3,-,0.84
+Godfrey Phillips India Ltd.,GODFRYPHLP,Fast Moving Consumer Goods,EQ,1089.0,1089.0,1042.65,1059.15,1061.35,2.20,0.21,12787,13588233.42,1408.75,933.0,7.91,-8.16
+Godrej Agrovet Ltd.,GODREJAGRO,Fast Moving Consumer Goods,EQ,503.85,507.65,500.6,506.95,502.0,-4.95,-0.98,22224,11190895.2,746.8,441.0,-20.06,-5.71
+Godrej Consumer Products Ltd.,GODREJCP,Fast Moving Consumer Goods,EQ,757.05,785.0,750.55,757.55,782.95,25.40,3.35,743399,571740736.91,1138.0,660.05,-12.95,-1.23
+Godrej Industries Ltd.,GODREJIND,Fast Moving Consumer Goods,EQ,433.85,435.65,424.0,433.85,426.0,-7.85,-1.81,59044,25182856.44,662.0,405.0,-24.07,-9.29
+Godrej Properties Ltd.,GODREJPROP,Realty,EQ,1167.0,1233.45,1167.0,1182.95,1226.0,43.05,3.64,776473,937738677.37,2598.0,1129.55,-15.37,-15.47
+Gujarat Pipavav Port Ltd.,GPPL,Services,EQ,75.95,78.5,74.5,77.1,78.25,1.15,1.49,679600,51744744.0,119.0,70.3,-31.62,-3.99
+Granules India Ltd.,GRANULES,Healthcare,EQ,272.1,279.75,268.9,272.25,278.2,5.95,2.19,879548,242684884.16,404.8,226.95,-18.14,0.85
+Graphite India Ltd.,GRAPHITE,Capital Goods,EQ,391.0,393.1,383.2,393.05,388.75,-4.30,-1.09,176151,68404717.83,754.0,350.0,-38.33,-11.49
+Grasim Industries Ltd.,GRASIM,Construction Materials,EQ,1320.0,1350.0,1305.3,1320.75,1345.7,24.95,1.89,516506,686565600.5,1929.8,1276.6,-11.88,-7.55
+Grindwell Norton Ltd.,GRINDWELL,Capital Goods,EQ,1676.6,1703.05,1645.15,1683.05,1680.0,-3.05,-0.18,15338,25625196.6,2018.35,1161.0,35.52,-1.81
+G R Infraprojects Ltd.,GRINFRA,Construction,EQ,1105.55,1105.55,1080.3,1100.55,1093.0,-7.55,-0.69,6544,7140616.48,2267.65,1078.85,-,-22.37
+Gujarat State Fertilizers & Chemicals Ltd.,GSFC,Chemicals,EQ,131.3,131.8,127.1,132.0,130.6,-1.40,-1.06,1507206,194535078.42,198.8,98.1,14.09,-20.82
+Gujarat State Petronet Ltd.,GSPL,Oil Gas & Consumable Fuels,EQ,220.4,221.95,215.55,217.9,217.95,0.05,0.02,882222,192994884.72,379.2,209.45,-34.64,-14.38
+Gujarat Alkalies & Chemicals Ltd.,GUJALKALI,Chemicals,EQ,683.3,692.95,664.4,689.35,689.0,-0.35,-0.05,215330,146672029.5,1044.7,401.0,71.35,-14.21
+Gujarat Gas Ltd.,GUJGASLTD,Oil Gas & Consumable Fuels,EQ,419.0,425.95,416.7,418.85,423.0,4.15,0.99,1078048,455033280.32,786.0,403.55,-36.61,-25.11
+Hindustan Aeronautics Ltd.,HAL,Capital Goods,EQ,1763.0,1779.7,1727.4,1769.8,1737.7,-32.10,-1.81,430150,750263328.5,1972.55,1010.0,73.20,-3.73
+Happiest Minds Technologies Ltd.,HAPPSTMNDS,Information Technology,EQ,826.4,826.4,813.0,828.95,816.8,-12.15,-1.47,227373,186004756.38,1580.0,785.6,-17.44,-8.41
+Hathway Cable & Datacom Ltd.,HATHWAY,Media Entertainment & Publication,EQ,16.3,16.45,16.1,16.25,16.35,0.10,0.62,670321,10886013.04,28.7,14.35,-37.26,-10.22
+Hatsun Agro Product Ltd.,HATSUN,Fast Moving Consumer Goods,EQ,874.7,886.25,869.75,881.15,875.0,-6.15,-0.70,88204,77170561.64,1525.95,837.6,-1.40,-4.76
+Havells India Ltd.,HAVELLS,Consumer Durables,EQ,1099.85,1120.0,1092.25,1098.2,1111.65,13.45,1.22,1848135,2049858935.25,1504.45,978.0,12.04,-8.62
+HCL Technologies Ltd.,HCLTECH,Information Technology,EQ,964.5,981.45,964.2,973.25,979.05,5.80,0.60,2827579,2746681964.81,1377.75,944.05,-1.04,-6.49
+Housing Development Finance Corporation Ltd.,HDFC,Financial Services,EQ,2155.0,2224.05,2133.1,2170.85,2205.3,34.45,1.59,2776869,6047243158.68,3021.1,2026.0,-12.30,-5.89
+HDFC Asset Management Company Ltd.,HDFCAMC,Financial Services,EQ,1782.0,1839.0,1779.7,1792.35,1833.3,40.95,2.28,447884,814118746.8,3365.0,1690.0,-38.56,-5.51
+HDFC Bank Ltd.,HDFCBANK,Financial Services,EQ,1343.95,1360.0,1330.05,1348.0,1354.95,6.95,0.52,5736226,7722508977.02,1725.0,1271.6,-10.01,-2.95
+HDFC Life Insurance Company Ltd.,HDFCLIFE,Financial Services,EQ,549.95,567.3,540.55,550.0,567.0,17.00,3.09,2052291,1143741774.3,775.65,497.05,-19.86,-8.17
+H.E.G. Ltd.,HEG,Capital Goods,EQ,995.8,998.95,963.3,997.85,984.9,-12.95,-1.30,69104,67787568.8,2629.0,890.8,-54.45,-11.08
+Hemisphere Properties India Ltd.,HEMIPROP,Realty,EQ,105.85,106.0,102.75,105.3,105.0,-0.30,-0.28,462015,48266707.05,165.9,95.0,-23.67,2.43
+Hero MotoCorp Ltd.,HEROMOTOCO,Automobile and Auto Components,EQ,2715.0,2770.0,2683.35,2719.7,2758.8,39.10,1.44,393346,1075824910.76,2968.95,2146.85,-6.30,-1.95
+HFCL Ltd.,HFCL,Telecommunication,EQ,55.15,55.7,54.25,55.15,54.95,-0.20,-0.36,2275417,124943147.47,101.35,51.55,-18.30,-12.39
+Hinduja Global Solutions Ltd.,HGS,Services,EQ,1075.0,1080.55,1050.05,1067.1,1074.1,7.00,0.66,180181,193514394.0,3920.0,846.0,-,11.13
+Hikal Ltd.,HIKAL,Healthcare,EQ,239.85,243.6,234.15,239.0,238.95,-0.05,-0.02,386423,92262355.48,742.0,212.0,-51.31,-27.67
+Hindalco Industries Ltd.,HINDALCO,Metals & Mining,EQ,335.0,342.9,330.25,338.65,341.8,3.15,0.93,11278576,3788248106.88,636.0,308.95,-8.98,-19.86
+Hindustan Copper Ltd.,HINDCOPPER,Metals & Mining,EQ,86.1,88.75,85.65,87.1,88.3,1.20,1.38,2962596,258130989.48,159.4,81.2,-39.56,-17.13
+Hindustan Petroleum Corporation Ltd.,HINDPETRO,Oil Gas & Consumable Fuels,EQ,218.0,229.0,216.2,217.2,227.5,10.30,4.74,8196598,1840464114.92,354.8,206.5,-25.92,-4.65
+Hindustan Unilever Ltd.,HINDUNILVR,Fast Moving Consumer Goods,EQ,2226.0,2293.0,2203.6,2230.6,2291.0,60.40,2.71,1235612,2788393244.28,2859.3,1901.55,-9.74,-5.21
+Hindustan Zinc Ltd.,HINDZINC,Metals & Mining,EQ,246.0,249.0,245.35,249.35,247.75,-1.60,-0.64,310474,76659135.34,408.6,245.1,-26.51,-17.15
+HLE Glascoat Ltd.,HLEGLAS,Capital Goods,EQ,3320.0,3447.9,3252.4,3295.2,3410.0,114.80,3.48,13768,46522898.08,7550.0,2950.0,-5.48,-4.13
+Home First Finance Company India Ltd.,HOMEFIRST,Financial Services,EQ,746.95,748.0,726.05,743.7,733.95,-9.75,-1.31,37017,27411828.84,921.3,525.05,37.40,-4.41
+Honeywell Automation India Ltd.,HONAUT,Capital Goods,EQ,34069.8,34069.8,33133.65,33787.7,33244.05,-543.65,-1.61,1860,62036431.2,47275.95,30185.35,-19.23,4.96
+Housing & Urban Development Corporation Ltd.,HUDCO,Financial Services,EQ,34.8,35.0,34.6,34.8,34.85,0.05,0.14,544695,18949939.05,56.15,30.6,-34.40,-3.20
+Indiabulls Real Estate Ltd.,IBREALEST,Realty,EQ,61.2,62.3,60.15,60.95,61.7,0.75,1.23,4909229,299462969.0,195.9,60.15,-48.26,-18.90
+Indiabulls Housing Finance Ltd.,IBULHSGFIN,Financial Services,EQ,94.5,96.4,93.1,94.75,96.2,1.45,1.53,12436219,1174725246.74,297.8,90.75,-63.70,-19.70
+ICICI Bank Ltd.,ICICIBANK,Financial Services,EQ,703.45,706.75,694.1,707.2,706.0,-1.20,-0.17,9884899,6924865994.45,867.0,629.8,12.09,-6.06
+ICICI Lombard General Insurance Company Ltd.,ICICIGI,Financial Services,EQ,1120.0,1150.0,1105.85,1120.95,1144.0,23.05,2.06,228217,258033551.05,1675.0,1071.0,-28.47,-11.45
+ICICI Prudential Life Insurance Company Ltd.,ICICIPRULI,Financial Services,EQ,489.0,502.45,484.2,489.0,501.75,12.75,2.61,757837,374288115.93,724.3,430.0,-20.19,-6.25
+Indo Count Industries Ltd.,ICIL,Textiles,EQ,131.25,138.0,130.4,131.05,136.5,5.45,4.16,387980,52474295.0,315.0,119.55,-22.98,-12.05
+IDBI Bank Ltd.,IDBI,Financial Services,EQ,30.9,31.0,30.6,30.8,30.85,0.05,0.16,3091913,95076324.75,65.25,30.5,-18.73,-15.96
+Vodafone Idea Ltd.,IDEA,Telecommunication,EQ,8.35,8.6,8.2,8.4,8.5,0.10,1.19,65435996,547699286.52,16.8,4.55,-15.58,-12.50
+IDFC Ltd.,IDFC,Financial Services,EQ,49.35,51.4,48.65,49.15,50.8,1.65,3.36,5859079,293422676.32,70.2,41.65,-8.81,-0.61
+IDFC First Bank Ltd.,IDFCFIRSTB,Financial Services,EQ,31.35,32.45,30.6,31.45,32.4,0.95,3.02,38981951,1227541636.99,56.25,28.95,-41.97,-13.36
+Indian Energy Exchange Ltd.,IEX,Financial Services,EQ,158.0,161.15,157.0,158.75,159.9,1.15,0.72,9279366,1476625511.58,318.67,124.45,-57.68,-18.44
+IFB Industries Ltd.,IFBIND,Consumer Durables,EQ,903.0,903.0,885.8,899.6,894.9,-4.70,-0.52,6449,5766953.76,1365.85,800.0,-11.98,3.77
+Indraprastha Gas Ltd.,IGL,Oil Gas & Consumable Fuels,EQ,357.15,361.9,352.6,355.9,359.95,4.05,1.14,1103024,394121505.44,602.05,321.0,-36.19,-5.90
+IIFL Finance Ltd.,IIFL,Financial Services,EQ,323.5,335.0,317.15,323.75,333.5,9.75,3.01,526247,172893189.38,396.0,242.9,-,0.76
+IIFL Wealth Management Ltd.,IIFLWAM,Financial Services,EQ,1488.5,1550.0,1458.95,1481.05,1543.0,61.95,4.18,191551,288908711.26,1908.3,1122.35,27.20,-5.45
+Indian Hotels Co. Ltd.,INDHOTEL,Consumer Services,EQ,222.1,228.1,220.4,225.15,226.3,1.15,0.51,3902317,873104405.58,268.95,129.17,59.17,-4.19
+India Cements Ltd.,INDIACEM,Construction Materials,EQ,156.5,161.8,154.65,156.5,160.55,4.05,2.59,2206090,350724188.2,259.95,145.45,-18.47,-7.45
+Indiamart Intermesh Ltd.,INDIAMART,Consumer Services,EQ,3822.5,3877.4,3791.05,3809.2,3818.0,8.80,0.23,98110,375615116.1,9710.7,3785.1,-45.52,-16.36
+Indian Bank,INDIANB,Financial Services,EQ,148.35,150.75,146.65,149.85,149.65,-0.20,-0.13,746419,110776043.79,194.95,115.1,3.56,-11.57
+InterGlobe Aviation Ltd.,INDIGO,Services,EQ,1610.55,1667.0,1591.6,1606.2,1651.8,45.60,2.84,692564,1130735391.52,2380.0,1511.75,-6.45,-12.64
+Indigo Paints Ltd.,INDIGOPNTS,Consumer Durables,EQ,1404.5,1405.0,1372.7,1395.65,1388.0,-7.65,-0.55,12205,16919913.55,2709.0,1353.6,-44.45,-12.79
+Indoco Remedies Ltd.,INDOCO,Healthcare,EQ,368.25,372.95,365.65,367.05,370.0,2.95,0.80,21554,7949977.36,530.25,322.7,-11.06,3.94
+IndusInd Bank Ltd.,INDUSINDBK,Financial Services,EQ,793.0,810.0,783.0,794.35,808.0,13.65,1.72,2796110,2241976920.2,1242.0,763.2,-21.84,-14.66
+Indus Towers Ltd.,INDUSTOWER,Telecommunication,EQ,210.0,210.9,204.5,209.1,208.35,-0.75,-0.36,2187045,453177594.45,332.8,181.2,-12.38,3.62
+Infibeam Avenues Ltd.,INFIBEAM,Information Technology,EQ,13.9,14.1,13.4,14.1,13.6,-0.50,-3.55,2029884,27667318.92,28.05,12.5,-72.81,-4.41
+Infosys Ltd.,INFY,Information Technology,EQ,1454.0,1485.0,1442.35,1461.9,1481.0,19.10,1.31,4651635,6800411271.9,1953.9,1367.15,-7.52,-2.77
+Inox Leisure Ltd.,INOXLEISUR,Media Entertainment & Publication,EQ,509.1,525.0,503.0,508.45,523.2,14.75,2.90,441730,226943204.8,563.6,291.0,64.71,0.98
+Intellect Design Arena Ltd.,INTELLECT,Information Technology,EQ,635.6,659.0,623.35,633.35,658.95,25.60,4.04,329488,211271000.48,986.65,562.65,-12.51,-5.89
+Indian Overseas Bank,IOB,Financial Services,EQ,16.45,16.8,16.1,16.45,16.6,0.15,0.91,718064,11840875.36,29.0,15.25,-41.35,-10.11
+Indian Oil Corporation Ltd.,IOC,Oil Gas & Consumable Fuels,EQ,75.0,75.15,72.85,74.25,74.7,0.45,0.61,22029311,1629728427.78,94.33,67.7,-31.19,-35.99
+Ipca Laboratories Ltd.,IPCALAB,Healthcare,EQ,890.25,914.25,890.25,897.5,912.0,14.50,1.62,154987,140412022.52,1383.1,831.05,-55.71,-0.57
+IRB Infrastructure Developers Ltd.,IRB,Construction,EQ,203.0,205.9,198.0,202.25,204.0,1.75,0.87,636428,128240242.0,345.85,148.05,25.31,-8.42
+IRCON International Ltd.,IRCON,Construction,EQ,35.95,36.2,35.8,36.05,36.15,0.10,0.28,190000,6838100.0,53.4,34.8,-26.13,-9.99
+Indian Railway Catering And Tourism Corporation Ltd.,IRCTC,Consumer Services,EQ,576.2,585.8,575.7,576.2,578.3,2.10,0.36,1928361,1117909438.92,1279.26,404.29,-71.67,-16.81
+Indian Railway Finance Corporation Ltd.,IRFC,Financial Services,EQ,19.7,20.05,19.6,19.65,20.0,0.35,1.78,2510750,49687742.5,26.4,19.3,-20.93,-7.31
+ICICI Securities Ltd.,ISEC,Financial Services,EQ,427.1,429.6,412.0,428.9,413.5,-15.40,-3.59,570584,237003476.08,896.05,408.4,-33.08,-4.05
+ITC Ltd.,ITC,Fast Moving Consumer Goods,EQ,273.5,285.0,271.2,273.5,284.75,11.25,4.11,35165665,9842517976.85,285.0,200.9,34.93,1.05
+ITI Ltd.,ITI,Telecommunication,EQ,100.65,103.0,97.15,100.65,98.8,-1.85,-1.84,1243276,124091377.56,143.8,81.0,-23.43,9.58
+Jamna Auto Industries Ltd.,JAMNAAUTO,Automobile and Auto Components,EQ,122.2,129.8,122.2,122.15,125.4,3.25,2.66,6199390,784966761.8,129.8,78.6,44.30,10.64
+J.B. Chemicals & Pharmaceuticals Ltd.,JBCHEPHARM,Healthcare,EQ,1578.0,1587.0,1558.9,1572.65,1563.0,-9.65,-0.61,15467,24327425.62,1985.0,1342.2,-5.56,-3.26
+Jindal Steel & Power Ltd.,JINDALSTEL,Metals & Mining,EQ,326.2,334.6,322.05,329.25,333.0,3.75,1.14,4320099,1419973340.31,577.8,304.2,-17.16,-12.86
+J.K. Cement Ltd.,JKCEMENT,Construction Materials,EQ,2106.2,2160.9,2100.0,2108.95,2149.0,40.05,1.90,128324,274755799.64,3838.0,2003.7,-26.04,-11.47
+JK Lakshmi Cement Ltd.,JKLAKSHMI,Construction Materials,EQ,416.05,423.45,412.0,420.25,416.95,-3.30,-0.79,92645,38667243.65,816.0,366.25,-27.22,-5.34
+JK Paper Ltd.,JKPAPER,Forest Materials,EQ,298.0,309.75,297.05,295.7,303.25,7.55,2.55,2270069,691690024.3,387.4,191.25,39.55,-12.06
+JM Financial Ltd.,JMFINANCIL,Financial Services,EQ,61.5,61.5,59.5,60.95,59.65,-1.30,-2.13,368055,22193716.5,117.7,57.1,-31.13,-7.02
+Jindal Stainless Ltd.,JSL,Metals & Mining,EQ,100.0,100.6,97.35,99.8,99.8,-,-,492895,48845894.5,224.4,95.05,-8.98,-10.89
+JSW Energy Ltd.,JSWENERGY,Power,EQ,204.3,207.75,199.0,207.95,205.3,-2.65,-1.27,662114,134740199.0,408.0,161.4,23.96,-24.83
+JSW Steel Ltd.,JSWSTEEL,Metals & Mining,EQ,556.55,580.5,556.55,564.5,577.5,13.00,2.30,3737790,2128073358.6,790.0,520.05,-17.46,2.40
+Jubilant Foodworks Ltd.,JUBLFOOD,Consumer Services,EQ,512.25,545.0,508.5,512.25,541.15,28.90,5.64,5437843,2894998856.34,918.0,451.2,-83.37,-7.02
+Jubilant Ingrevia Ltd.,JUBLINGREA,Chemicals,EQ,485.5,490.85,473.05,485.85,476.0,-9.85,-2.03,163007,78083613.14,838.75,401.2,-7.18,-4.00
+Jubilant Pharmova Ltd.,JUBLPHARMA,Healthcare,EQ,348.8,349.0,281.8,343.7,340.0,-3.70,-1.08,71858,23945959.92,767.0,281.8,-53.06,-10.55
+Justdial Ltd.,JUSTDIAL,Consumer Services,EQ,560.0,569.1,551.05,565.25,558.0,-7.25,-1.28,194406,108597135.66,1138.0,520.0,-41.18,-14.55
+Jyothy Labs Ltd.,JYOTHYLAB,Fast Moving Consumer Goods,EQ,148.35,152.5,147.5,149.65,150.0,2.85,1.94,771549,115686057.06,187.3,130.15,-3.33,0.44
+Kajaria Ceramics Ltd.,KAJARIACER,Consumer Durables,EQ,947.7,965.0,937.2,947.7,952.0,4.30,0.45,72125,68652902.5,1374.9,885.3,-3.29,-8.24
+Kalpataru Power Transmission Ltd.,KALPATPOWR,Construction,EQ,354.0,358.75,353.15,356.7,354.85,-1.85,-0.52,17142,6076667.58,496.0,331.8,-16.23,-1.07
+Kalyan Jewellers India Ltd.,KALYANKJIL,Consumer Durables,EQ,58.5,61.8,58.05,59.9,61.3,1.40,2.34,501257,30220784.53,83.6,55.05,-23.45,-0.42
+Kansai Nerolac Paints Ltd.,KANSAINER,Consumer Durables,EQ,372.35,375.0,368.3,370.5,370.05,-0.45,-0.12,269671,99896925.24,675.0,358.15,-35.59,-7.42
+Karur Vysya Bank Ltd.,KARURVYSYA,Financial Services,EQ,44.95,44.95,43.7,44.95,44.25,-0.70,-1.56,1017461,45022649.25,60.7,38.4,-13.06,-
+Kec International Ltd.,KEC,Capital Goods,EQ,393.15,393.15,384.1,391.25,386.1,-5.15,-1.32,149531,57938776.57,550.0,345.5,-13.59,-1.22
+KEI Industries Ltd.,KEI,Capital Goods,EQ,1152.55,1208.0,1152.5,1152.55,1200.0,47.45,4.12,340326,406628311.32,1377.35,687.5,66.21,-6.77
+Krishna Institute of Medical Sciences Ltd.,KIMS,Healthcare,EQ,1182.0,1233.45,1167.0,1184.55,1210.0,25.45,2.15,44951,54397452.65,1565.0,937.55,25.08,-9.68
+KNR Constructions Ltd.,KNRCON,Construction,EQ,230.5,236.15,228.75,233.05,235.7,2.65,1.14,58500,13689585.0,343.9,207.5,-2.31,-10.37
+Kotak Mahindra Bank Ltd.,KOTAKBANK,Financial Services,EQ,1645.0,1679.5,1636.0,1661.1,1678.0,16.90,1.02,2300514,3822234995.58,2253.0,1626.0,-2.62,-10.06
+KPIT Technologies Ltd.,KPITTECH,Information Technology,EQ,512.0,514.95,495.4,517.65,500.9,-16.75,-3.24,940281,471738977.7,801.0,245.2,99.33,-2.39
+K.P.R. Mill Ltd.,KPRMILL,Textiles,EQ,516.0,516.4,506.5,515.65,508.5,-7.15,-1.39,121702,62085058.28,769.0,304.0,-66.13,-17.94
+KRBL Ltd.,KRBL,Fast Moving Consumer Goods,EQ,217.0,218.8,215.3,217.85,216.7,-1.15,-0.53,62909,13657543.9,337.95,184.5,-7.49,-0.86
+L&T Finance Holdings Ltd.,L&TFH,Financial Services,EQ,67.0,68.95,66.3,67.6,68.8,1.70,2.53,3855009,260752808.76,95.95,58.5,-27.23,-10.82
+Dr. Lal Path Labs Ltd.,LALPATHLAB,Healthcare,EQ,2167.95,2248.0,2148.45,2160.4,2235.1,74.70,3.46,210696,464932328.4,4245.5,1805.55,-33.98,3.63
+Latent View Analytics Ltd.,LATENTVIEW,Information Technology,EQ,332.0,336.8,330.3,333.95,335.15,1.20,0.36,212311,70890642.9,754.9,305.25,-,-16.67
+Laurus Labs Ltd.,LAURUSLABS,Healthcare,EQ,464.0,467.1,459.5,464.7,466.8,2.10,0.45,443092,205311109.12,723.75,436.8,-32.52,-18.11
+Lakshmi Machine Works Ltd.,LAXMIMACH,Capital Goods,EQ,9050.0,9068.0,8876.0,8981.85,9068.0,86.15,0.96,1507,13561553.28,11790.0,6499.95,37.88,-3.93
+LIC Housing Finance Ltd.,LICHSGFIN,Financial Services,EQ,326.9,339.25,319.9,327.35,337.0,9.65,2.95,2366258,784012263.14,476.55,291.75,-30.35,-13.40
+Linde India Ltd.,LINDEINDIA,Chemicals,EQ,3150.0,3248.0,3111.0,3198.15,3224.0,25.85,0.81,40600,129405192.0,4190.0,1564.1,94.42,6.85
+Macrotech Developers Ltd.,LODHA,Realty,EQ,1060.05,1070.95,1045.0,1069.0,1068.7,-0.30,-0.03,39067,41512984.87,1539.0,645.0,56.37,10.41
+Larsen & Toubro Ltd.,LT,Construction,EQ,1560.95,1576.0,1534.6,1558.25,1571.8,13.55,0.87,1294305,2016255385.95,2078.55,1456.35,3.85,-5.82
+Larsen & Toubro Infotech Ltd.,LTI,Information Technology,EQ,3945.0,4015.95,3905.25,3975.45,4000.65,25.20,0.63,268702,1067066695.38,7588.8,3733.3,-2.35,-6.54
+L&T Technology Services Ltd.,LTTS,Information Technology,EQ,2995.0,3027.2,2937.0,3030.8,2984.0,-46.80,-1.54,364261,1087584995.53,5955.5,2801.0,4.40,-13.46
+Lupin Ltd.,LUPIN,Healthcare,EQ,613.1,630.55,608.7,610.95,627.3,16.35,2.68,908035,561083906.85,1191.55,583.0,-46.83,-1.24
+Lux Industries Ltd.,LUXIND,Textiles,EQ,1754.45,1765.0,1729.05,1754.45,1740.0,-14.45,-0.82,15428,26888535.52,4644.0,1684.85,-50.96,-12.56
+Laxmi Organic Industries Ltd.,LXCHEM,Chemicals,EQ,283.0,287.35,280.6,284.65,287.0,2.35,0.83,338168,96235849.44,628.0,219.6,29.18,-22.65
+Mahindra & Mahindra Ltd.,M&M,Automobile and Auto Components,EQ,1092.4,1113.35,1064.0,1093.15,1107.0,13.85,1.27,2839545,3101862167.1,1121.45,671.15,40.56,5.68
+Mahindra & Mahindra Financial Services Ltd.,M&MFIN,Financial Services,EQ,176.05,179.7,171.05,175.4,177.2,1.80,1.03,2173337,380899042.62,206.35,128.0,11.65,-1.63
+Bank of Maharashtra,MAHABANK,Financial Services,EQ,15.6,15.7,15.5,15.55,15.65,0.10,0.64,892261,13910348.99,26.3,15.0,-38.78,-11.90
+Mahindra CIE Automotive Ltd.,MAHINDCIE,Automobile and Auto Components,EQ,233.0,233.1,226.1,233.1,229.95,-3.15,-1.35,276850,63426335.0,312.0,163.95,-1.89,19.63
+Mahindra Logistics Ltd.,MAHLOG,Services,EQ,474.0,474.0,455.0,471.65,461.0,-10.65,-2.26,17972,8287068.92,821.0,391.1,-14.55,2.67
+Manappuram Finance Ltd.,MANAPPURAM,Financial Services,EQ,84.9,89.6,83.65,85.0,88.25,3.25,3.82,13581092,1194185419.56,224.5,81.5,-49.63,-11.04
+C.E. Info Systems Ltd.,MAPMYINDIA,Information Technology,EQ,1399.9,1421.25,1373.2,1411.9,1407.0,-4.90,-0.35,83012,116391955.32,1917.4,1128.8,-,6.27
+Marico Ltd.,MARICO,Fast Moving Consumer Goods,EQ,478.0,493.0,477.6,477.9,492.0,14.10,2.95,846365,411604226.8,607.7,455.65,-9.97,-10.45
+Maruti Suzuki India Ltd.,MARUTI,Automobile and Auto Components,EQ,8440.0,8450.0,8305.6,8470.75,8399.95,-70.80,-0.84,530569,4440634385.33,9050.0,6536.55,12.70,6.33
+Mastek Ltd.,MASTEK,Information Technology,EQ,2130.0,2133.05,2081.3,2145.0,2093.0,-52.00,-2.42,27911,58699624.1,3669.0,1850.7,-5.34,-19.45
+Max Healthcare Institute Ltd.,MAXHEALTH,Healthcare,EQ,364.6,367.05,357.0,366.95,360.05,-6.90,-1.88,326670,118052004.6,458.05,250.6,44.18,-3.52
+Mazagoan Dock Shipbuilders Ltd.,MAZDOCK,Capital Goods,EQ,246.7,249.95,241.55,246.25,249.1,2.85,1.16,68727,16861482.18,342.4,221.1,1.34,-11.02
+United Spirits Ltd.,MCDOWELL-N,Fast Moving Consumer Goods,EQ,760.5,791.5,754.05,759.75,787.1,27.35,3.60,2124003,1649755610.16,1019.95,629.1,14.83,-6.13
+Multi Commodity Exchange of India Ltd.,MCX,Financial Services,EQ,1305.95,1324.45,1275.5,1307.95,1282.95,-25.00,-1.91,433916,564494341.88,2135.0,1143.0,-14.04,-4.64
+Medplus Health Services Ltd.,MEDPLUS,Consumer Services,EQ,770.0,770.0,750.0,767.3,750.1,-17.20,-2.24,10669,8052107.68,1343.0,708.0,-,-11.22
+Metro Brands Ltd.,METROBRAND,Consumer Durables,EQ,600.0,631.25,600.0,598.75,617.5,18.75,3.13,582857,359389626.2,673.0,426.0,-,0.39
+Metropolis Healthcare Ltd.,METROPOLIS,Healthcare,EQ,1400.0,1439.95,1397.05,1406.45,1435.1,28.65,2.04,100318,142381337.4,3579.9,1378.05,-50.40,-12.98
+Max Financial Services Ltd.,MFSL,Financial Services,EQ,782.35,808.4,772.15,782.35,802.2,19.85,2.54,320516,254855092.24,1148.05,696.5,-26.22,-1.55
+Mahanagar Gas Ltd.,MGL,Oil Gas & Consumable Fuels,EQ,760.55,771.0,757.05,760.6,770.7,10.10,1.33,170132,130261565.8,1212.0,665.8,-33.08,0.56
+Mahindra Holidays & Resorts India Ltd.,MHRIL,Consumer Services,EQ,213.5,231.0,211.1,214.4,227.0,12.60,5.88,1395618,315423624.18,274.9,165.43,-14.26,-3.01
+Minda Industries Ltd.,MINDAIND,Automobile and Auto Components,EQ,925.0,947.0,900.0,923.15,943.7,20.55,2.23,178456,167143674.16,1257.4,629.15,44.15,0.09
+MindTree Ltd.,MINDTREE,Information Technology,EQ,2874.0,2905.9,2810.0,2887.05,2890.75,3.70,0.13,582730,1666776791.7,5060.0,2457.55,11.02,-5.27
+MMTC Ltd.,MMTC,Services,EQ,39.4,40.35,38.9,39.2,39.35,0.15,0.38,1476769,58435749.33,64.7,31.0,-30.74,-4.51
+MOIL Ltd.,MOIL,Metals & Mining,EQ,141.6,142.9,140.7,140.1,141.85,1.75,1.25,95406,13533341.1,201.9,137.3,-27.13,-13.01
+Motilal Oswal Financial Services Ltd.,MOTILALOFS,Financial Services,EQ,754.1,766.0,745.0,766.5,753.3,-10.20,-1.34,27524,20773739.0,1188.0,714.0,-2.22,-3.33
+MphasiS Ltd.,MPHASIS,Information Technology,EQ,2279.2,2299.45,2220.1,2294.05,2239.9,-54.15,-2.36,332856,746179938.0,3659.75,2076.05,7.49,-11.55
+MRF Ltd.,MRF,Automobile and Auto Components,EQ,70300.0,72500.0,70300.0,70800.9,71900.0,"1,099.10",1.55,5409,386007226.92,87550.0,63000.0,-11.59,-8.82
+Mangalore Refinery & Petrochemicals Ltd.,MRPL,Oil Gas & Consumable Fuels,EQ,88.05,91.25,81.55,90.6,81.55,-9.05,-9.99,26739501,2226330853.26,127.65,37.05,76.61,11.65
+MTAR Technologies Ltd.,MTARTECH,Capital Goods,EQ,1279.0,1279.0,1238.95,1271.25,1241.0,-30.25,-2.38,104163,130305829.74,2563.3,1010.95,12.78,-10.90
+Muthoot Finance Ltd.,MUTHOOTFIN,Financial Services,EQ,980.0,1048.3,977.1,976.3,1039.8,63.50,6.50,1982052,2016083832.84,1721.95,960.4,-34.11,-14.59
+Nippon Life India Asset Management Ltd.,NAM-INDIA,Financial Services,EQ,270.6,273.9,269.05,271.25,272.0,0.75,0.28,205261,55794045.02,476.45,259.0,-25.93,-4.49
+NATCO Pharma Ltd.,NATCOPHARM,Healthcare,EQ,652.0,653.0,642.7,649.8,647.0,-2.80,-0.43,48437,31447237.88,1189.0,607.75,-42.46,-5.96
+National Aluminium Co. Ltd.,NATIONALUM,Metals & Mining,EQ,68.0,68.9,67.0,68.8,68.65,-0.15,-0.22,16360489,1116276164.47,132.7,67.0,-12.19,-28.48
+Info Edge (India) Ltd.,NAUKRI,Consumer Services,EQ,3771.9,3774.0,3606.0,3758.8,3720.65,-38.15,-1.01,413841,1521109841.19,7465.4,3313.0,-23.54,-9.74
+Navin Fluorine International Ltd.,NAVINFLUOR,Chemicals,EQ,3665.0,3696.85,3557.95,3651.6,3675.0,23.40,0.64,112850,410425293.5,4329.0,3202.5,-2.60,-6.95
+Nazara Technologies Ltd.,NAZARA,Media Entertainment & Publication,EQ,670.0,676.9,638.3,671.4,649.9,-21.50,-3.20,302182,197974537.3,1678.0,475.05,-55.68,-44.79
+NBCC (India) Ltd.,NBCC,Construction,EQ,28.5,28.8,28.1,28.4,28.8,0.40,1.41,1801802,51189194.82,55.5,26.55,-47.36,-15.35
+NCC Ltd.,NCC,Construction,EQ,54.05,55.95,53.1,54.1,55.2,1.10,2.03,2281956,124389421.56,98.5,52.2,-37.71,-16.19
+NESCO Ltd.,NESCO,Services,EQ,550.0,555.5,545.6,550.85,553.6,2.75,0.50,12047,6631150.68,696.8,501.95,-9.09,1.71
+Nestle India Ltd.,NESTLEIND,Fast Moving Consumer Goods,EQ,17400.0,17829.4,17331.05,17470.0,17800.0,330.00,1.89,38547,681238432.71,20609.15,16000.0,-0.92,-1.25
+Network18 Media & Investments Ltd.,NETWORK18,Media Entertainment & Publication,EQ,68.2,68.75,66.5,69.0,67.45,-1.55,-2.25,1455769,98468215.16,117.5,46.0,37.86,-8.61
+Narayana Hrudayalaya Ltd.,NH,Healthcare,EQ,634.1,644.35,631.5,632.55,640.0,7.45,1.18,50400,32060448.0,777.0,468.3,28.50,-3.38
+NHPC Ltd.,NHPC,Power,EQ,30.65,31.35,30.6,30.75,30.95,0.20,0.65,6808415,211197033.3,37.6,25.3,17.37,-8.62
+The New India Assurance Company Ltd.,NIACL,Financial Services,EQ,79.8,80.6,78.15,79.8,78.9,-0.90,-1.13,289192,22805681.12,178.4,78.15,-52.95,-21.26
+NLC India Ltd.,NLCINDIA,Power,EQ,63.6,64.25,61.6,63.65,62.75,-0.90,-1.41,2402689,150240143.17,92.4,49.8,4.26,-15.64
+NMDC Ltd.,NMDC,Metals & Mining,EQ,107.15,110.0,106.35,108.2,109.85,1.65,1.52,5571985,602833057.15,186.9,101.55,-41.23,-14.20
+NOCIL Ltd.,NOCIL,Chemicals,EQ,253.75,254.5,244.35,253.2,247.7,-5.50,-2.17,373166,92500388.08,321.3,190.7,16.92,3.54
+NTPC Ltd.,NTPC,Power,EQ,142.0,143.2,139.75,142.9,141.1,-1.80,-1.26,9592229,1355381957.7,166.35,111.95,22.77,-8.40
+Nuvoco Vistas Corporation Ltd.,NUVOCO,Construction Materials,EQ,299.0,307.4,292.5,296.85,305.0,8.15,2.75,150042,44912071.86,577.9,260.25,-,-1.54
+FSN E-Commerce Ventures Ltd.,NYKAA,Consumer Services,EQ,1399.8,1420.0,1381.4,1406.95,1405.05,-1.90,-0.14,143804,201378807.48,2573.7,1207.5,-,-5.86
+Oberoi Realty Ltd.,OBEROIRLTY,Realty,EQ,734.35,751.0,731.35,737.65,740.25,2.60,0.35,563385,418724633.55,1051.9,605.5,17.14,-8.45
+Oracle Financial Services Software Ltd.,OFSS,Information Technology,EQ,3090.25,3123.9,3044.25,3098.3,3064.05,-34.25,-1.11,61691,188830598.81,5145.0,2950.0,-15.15,-5.94
+Oil India Ltd.,OIL,Oil Gas & Consumable Fuels,EQ,245.5,249.45,210.05,251.5,214.2,-37.30,-14.83,16490601,3699501428.34,306.0,153.1,59.99,5.47
+Oil & Natural Gas Corporation Ltd.,ONGC,Oil Gas & Consumable Fuels,EQ,148.95,151.15,130.0,151.55,131.4,-20.15,-13.30,125788305,17143688088.45,194.95,108.5,28.76,0.20
+Orient Electric Ltd.,ORIENTELEC,Consumer Durables,EQ,272.3,274.95,265.1,272.05,274.3,2.25,0.83,43089,11637477.12,408.0,244.0,-13.24,-2.79
+Page Industries Ltd.,PAGEIND,Textiles,EQ,40199.0,41599.9,40124.95,40166.5,41236.6,"1,070.10",2.66,13064,535251806.64,46737.7,29119.95,36.06,-11.12
+One 97 Communications Ltd.,PAYTM,Financial Services,EQ,672.9,675.7,652.0,675.7,657.7,-18.00,-2.66,3069693,2027839195.8,1955.0,510.05,-,3.48
+PCBL Ltd.,PCBL,Chemicals,EQ,104.0,106.4,103.05,104.75,104.0,-0.75,-0.72,547189,57274272.63,138.65,89.0,-,-0.76
+Piramal Enterprises Ltd.,PEL,Financial Services,EQ,1661.5,1682.9,1626.3,1655.7,1672.0,16.30,0.98,536946,887040161.46,3014.95,1560.1,-30.96,-12.63
+Persistent Systems Ltd.,PERSISTENT,Information Technology,EQ,3385.0,3422.25,3270.05,3401.8,3329.0,-72.80,-2.14,234003,777882132.72,4987.5,2725.05,15.75,-9.52
+Petronet LNG Ltd.,PETRONET,Oil Gas & Consumable Fuels,EQ,215.3,222.4,214.8,217.3,220.6,3.30,1.52,2021428,439842518.52,243.55,190.25,-3.76,-4.23
+Power Finance Corporation Ltd.,PFC,Financial Services,EQ,104.3,106.1,103.2,104.45,105.9,1.45,1.39,2843672,298670870.16,153.75,97.1,-18.91,-6.20
+Pfizer Ltd.,PFIZER,Healthcare,EQ,4105.0,4117.95,4077.5,4080.35,4103.0,22.65,0.56,4025,16489096.75,6175.0,4060.0,-28.05,-5.39
+Procter & Gamble Hygiene & Health Care Ltd.,PGHH,Fast Moving Consumer Goods,EQ,13330.0,13450.0,13224.6,13369.15,13450.0,80.85,0.60,23356,311314226.04,16448.7,12310.1,0.47,-3.25
+Procter & Gamble Health Ltd.,PGHL,Healthcare,EQ,4192.0,4194.25,4102.1,4190.85,4111.0,-79.85,-1.91,3956,16433184.44,5851.75,3901.05,-24.49,-1.82
+Phoenix Mills Ltd.,PHOENIXLTD,Realty,EQ,1183.7,1214.0,1162.8,1183.7,1173.05,-10.65,-0.90,172501,205640167.11,1244.95,793.4,45.21,1.44
+Pidilite Industries Ltd.,PIDILITIND,Chemicals,EQ,2099.95,2164.35,2082.8,2089.9,2158.0,68.10,3.26,501109,1067727979.57,2764.7,1988.55,-2.97,-6.93
+PI Industries Ltd.,PIIND,Chemicals,EQ,2559.5,2590.25,2511.95,2559.5,2576.1,16.60,0.65,79059,202265336.19,3534.9,2333.55,-12.06,-7.21
+Punjab National Bank,PNB,Financial Services,EQ,29.05,29.6,28.75,29.0,29.55,0.55,1.90,17767070,518087761.2,48.2,28.05,-31.44,-7.50
+PNB Housing Finance Ltd.,PNBHOUSING,Financial Services,EQ,329.55,329.55,324.65,330.3,327.55,-2.75,-0.83,59692,19533610.08,784.4,311.45,-,1.55
+PNC Infratech Ltd.,PNCINFRA,Construction,EQ,243.0,243.0,236.05,242.15,237.65,-4.50,-1.86,90779,21687103.1,395.9,219.0,-16.05,-1.16
+PB Fintech Ltd.,POLICYBZR,Financial Services,EQ,571.95,603.0,571.4,572.95,585.05,12.10,2.11,543062,322052057.86,1470.0,540.1,-,-13.67
+Polycab India Ltd.,POLYCAB,Capital Goods,EQ,2202.5,2216.8,2156.25,2201.2,2180.0,-21.20,-0.96,409500,894397140.0,2820.0,1762.0,11.45,-9.85
+Poly Medicure Ltd.,POLYMED,Healthcare,EQ,750.9,769.15,729.4,750.85,738.95,-11.90,-1.58,23654,17687988.12,1094.25,651.1,-26.14,0.14
+Polyplex Corporation Ltd.,POLYPLEX,Capital Goods,EQ,2204.0,2248.65,2167.95,2202.3,2236.0,33.70,1.53,146621,324750852.9,2870.2,1325.45,58.75,-6.98
+Poonawalla Fincorp Ltd.,POONAWALLA,Financial Services,EQ,227.5,232.0,224.1,227.7,230.45,2.75,1.21,2033688,465165456.24,343.8,140.75,-,-12.24
+Power Grid Corporation of India Ltd.,POWERGRID,Power,EQ,211.05,212.9,205.2,211.9,206.55,-5.35,-2.52,8350248,1735766051.76,248.35,167.15,-8.82,-9.02
+Hitachi Energy India Ltd.,POWERINDIA,Capital Goods,EQ,3168.3,3175.0,3092.0,3151.7,3100.0,-51.70,-1.64,17764,55873997.4,4042.6,1741.0,66.73,-10.72
+Praj Industries Ltd.,PRAJIND,Capital Goods,EQ,364.9,364.9,357.4,363.9,358.5,-5.40,-1.48,207864,74752051.68,448.0,289.05,-,6.17
+Prestige Estates Projects Ltd.,PRESTIGE,Realty,EQ,385.0,391.75,381.1,387.15,387.4,0.25,0.06,209983,81070236.64,554.9,284.0,33.71,-9.79
+Prince Pipes and Fittings Ltd.,PRINCEPIPE,Capital Goods,EQ,593.15,603.0,590.15,598.15,594.5,-3.65,-0.61,51385,30605933.7,897.0,549.3,-16.74,-4.99
+Privi Speciality Chemicals Ltd.,PRIVISCL,Chemicals,EQ,1075.75,1076.0,1040.0,1087.3,1072.0,-15.30,-1.41,16783,17740470.15,2249.0,1015.3,-2.00,-6.87
+Prism Johnson Ltd.,PRSMJOHNSN,Construction Materials,EQ,107.95,108.45,106.1,107.95,107.5,-0.45,-0.42,24056,2587222.8,160.0,96.65,-17.34,-
+PVR Ltd.,PVR,Media Entertainment & Publication,EQ,1860.85,1895.0,1828.9,1860.85,1888.0,27.15,1.46,358082,664456959.2,2003.8,1224.05,39.43,1.13
+Quess Corp Ltd.,QUESS,Services,EQ,613.5,613.5,592.1,613.65,598.5,-15.15,-2.47,76594,46016143.32,989.9,527.4,-24.98,-12.84
+Radico Khaitan Ltd,RADICO,Fast Moving Consumer Goods,EQ,872.0,898.9,860.25,872.5,871.85,-0.65,-0.07,257073,226039147.44,1294.0,723.2,14.94,8.47
+Railtel Corporation Of India Ltd.,RAILTEL,Telecommunication,EQ,91.15,91.65,90.7,91.1,91.05,-0.05,-0.05,162424,14801699.12,150.4,84.0,-35.57,-6.28
+Rain Industries Ltd,RAIN,Chemicals,EQ,145.5,149.55,144.1,146.5,148.75,2.25,1.54,946679,139067145.1,272.75,128.6,-24.74,-12.85
+Rajesh Exports Ltd.,RAJESHEXPO,Consumer Durables,EQ,622.0,630.85,615.0,625.4,621.0,-4.40,-0.70,128888,79999492.72,994.7,518.5,10.77,15.46
+Rallis India Ltd.,RALLIS,Chemicals,EQ,189.0,189.6,186.35,188.65,188.8,0.15,0.08,219820,41279997.8,341.85,182.5,-41.94,-5.77
+The Ramco Cements Ltd.,RAMCOCEM,Construction Materials,EQ,634.0,648.95,613.75,636.8,646.0,9.20,1.44,327788,209502422.32,1132.7,575.65,-37.92,-8.87
+Ratnamani Metals & Tubes Ltd.,RATNAMANI,Capital Goods,EQ,1634.05,1659.7,1553.55,1654.4,1580.0,-74.40,-4.50,107552,170150490.56,1913.7,1237.07,-19.33,-37.18
+Restaurant Brands Asia Ltd.,RBA,Consumer Services,EQ,112.0,113.05,108.85,111.0,109.7,-1.30,-1.17,978252,108732709.8,189.7,86.5,-,14.20
+RBL Bank Ltd.,RBLBANK,Financial Services,EQ,82.95,86.7,82.1,83.45,85.95,2.50,3.00,16258460,1376278639.0,226.4,74.15,-60.36,-25.69
+Rashtriya Chemicals & Fertilizers Ltd.,RCF,Chemicals,EQ,77.95,78.7,76.1,78.15,78.7,0.55,0.70,1110880,86082091.2,112.65,66.0,-6.52,-18.34
+REC Ltd.,RECLTD,Financial Services,EQ,122.8,126.0,120.1,123.75,125.75,2.00,1.62,19076925,2358671007.0,168.85,109.65,-16.69,3.86
+Redington (India) Ltd.,REDINGTON,Services,EQ,125.0,128.0,122.6,125.55,127.4,1.85,1.47,1081859,135340560.9,179.2,109.4,-53.05,-3.42
+Relaxo Footwears Ltd.,RELAXO,Consumer Durables,EQ,961.0,979.0,960.1,963.85,970.45,6.60,0.68,41820,40657822.2,1448.0,925.0,-16.33,-2.75
+Reliance Industries Ltd.,RELIANCE,Oil Gas & Consumable Fuels,EQ,2574.9,2592.0,2365.0,2595.65,2406.0,-189.65,-7.31,37841671,92119871798.85,2856.15,2016.25,22.98,-1.41
+Shree Renuka Sugars Ltd.,RENUKA,Fast Moving Consumer Goods,EQ,46.5,46.5,45.1,46.4,45.6,-0.80,-1.72,8262303,377752493.16,63.2,21.5,17.92,-1.07
+RHI MAGNESITA INDIA LTD.,RHIM,Capital Goods,EQ,502.5,513.95,497.65,501.5,501.95,0.45,0.09,165626,83506972.94,674.0,317.3,-,-6.55
+RITES Ltd.,RITES,Construction,EQ,234.0,235.15,232.25,234.85,234.95,0.10,0.04,29804,6981288.96,317.9,226.2,-14.96,-4.78
+Rossari Biotech Ltd.,ROSSARI,Chemicals,EQ,880.8,882.0,850.4,873.05,874.8,1.75,0.20,20002,17471947.02,1619.0,795.1,-26.84,0.05
+Route Mobile Ltd.,ROUTE,Telecommunication,EQ,1242.0,1290.0,1241.05,1242.85,1267.0,24.15,1.94,655866,830962546.02,2389.0,1052.0,-27.88,-10.73
+RattanIndia Enterprises Ltd.,RTNINDIA,Services,EQ,43.0,43.2,41.2,42.4,41.75,-0.65,-1.53,622861,26029361.19,71.0,30.5,-,-13.91
+Rail Vikas Nigam Ltd.,RVNL,Construction,EQ,30.1,30.25,29.8,30.0,30.0,-,-,763473,22896555.27,44.8,27.5,-10.98,-7.41
+Steel Authority of India Ltd.,SAIL,Metals & Mining,EQ,68.2,70.2,67.85,68.55,69.9,1.35,1.97,20563587,1415391693.21,145.9,63.6,-47.51,-9.74
+Sanofi India Ltd.,SANOFI,Healthcare,EQ,6415.0,6435.05,6333.0,6437.4,6376.85,-60.55,-0.94,11452,72904577.2,9285.0,6333.0,-16.10,-5.92
+Sapphire Foods India Ltd.,SAPPHIRE,Consumer Services,EQ,1084.0,1104.0,1070.45,1085.0,1086.0,1.00,0.09,145969,157913643.27,1540.0,909.0,-,2.09
+Saregama India Ltd,SAREGAMA,Media Entertainment & Publication,EQ,362.9,368.95,353.65,361.95,359.95,-2.00,-0.55,120158,43447931.22,550.59,295.3,-,-16.26
+SBI Cards and Payment Services Ltd.,SBICARD,Financial Services,EQ,767.8,777.55,763.75,768.25,771.85,3.60,0.47,1073345,829083878.35,1165.0,655.7,-20.83,-1.51
+SBI Life Insurance Company Ltd.,SBILIFE,Financial Services,EQ,1085.45,1105.35,1075.0,1081.6,1100.25,18.65,1.72,696341,760118872.19,1293.25,996.05,7.29,-7.88
+State Bank of India,SBIN,Financial Services,EQ,463.35,468.8,460.55,465.9,468.5,2.60,0.56,9405495,4374119504.7,549.0,401.25,11.14,-0.47
+Schaeffler India Ltd.,SCHAEFFLER,Automobile and Auto Components,EQ,2289.0,2289.0,2250.0,2290.5,2260.0,-30.50,-1.33,46062,104262718.86,9700.0,1598.0,-59.35,-0.44
+Shipping Corporation of India Ltd.,SCI,Services,EQ,97.25,98.0,95.75,97.65,96.85,-0.80,-0.82,367655,35618416.4,160.2,85.6,-11.79,-17.42
+Sequent Scientific Ltd.,SEQUENT,Healthcare,EQ,93.55,93.55,91.2,92.85,93.0,0.15,0.16,625299,57690085.74,328.7,82.0,-70.32,-15.78
+Sheela Foam Ltd.,SFL,Consumer Durables,EQ,2649.0,2679.0,2612.0,2660.45,2651.0,-9.45,-0.36,3071,8128230.67,4055.0,2185.6,19.27,-11.46
+Shilpa Medicare Ltd.,SHILPAMED,Healthcare,EQ,389.35,398.0,381.0,389.35,397.75,8.40,2.16,69762,27260896.74,670.4,380.0,-27.78,-10.37
+Shree Cement Ltd.,SHREECEM,Construction Materials,EQ,19050.0,19272.0,18898.85,19009.7,19233.05,223.35,1.17,17699,338754081.27,31469.95,17865.2,-30.89,-14.05
+Shriram City Union Finance Ltd.,SHRIRAMCIT,Financial Services,EQ,1660.55,1707.05,1657.25,1660.55,1705.9,45.35,2.73,35205,59115883.95,2602.0,1416.05,-3.06,-3.26
+Shyam Metalics and Energy Ltd.,SHYAMMETL,Metals & Mining,EQ,282.0,282.45,275.7,280.45,277.0,-3.45,-1.23,62789,17444667.87,461.15,273.1,-24.12,-10.36
+Siemens Ltd.,SIEMENS,Capital Goods,EQ,2381.0,2425.0,2366.3,2400.45,2410.0,9.55,0.40,208443,502199635.47,2576.85,1920.0,18.77,-1.38
+SIS Ltd.,SIS,Services,EQ,456.0,456.0,449.8,453.9,452.0,-1.90,-0.42,29489,13327553.55,560.0,420.0,5.56,-3.31
+SJVN Ltd.,SJVN,Power,EQ,26.8,27.55,26.7,26.9,27.4,0.50,1.86,834442,22588344.94,33.75,25.35,-3.58,-2.36
+SKF India Ltd.,SKFINDIA,Capital Goods,EQ,3620.0,3786.65,3578.05,3637.8,3720.0,82.20,2.26,68901,249967315.92,4239.95,2502.3,35.72,1.10
+Sobha Ltd.,SOBHA,Realty,EQ,569.0,577.9,551.3,577.05,555.0,-22.05,-3.82,338751,191191064.4,1044.95,462.0,22.50,4.99
+Solara Active Pharma Sciences Ltd.,SOLARA,Healthcare,EQ,350.0,354.55,341.0,352.3,346.0,-6.30,-1.79,56960,19701324.8,1827.0,320.0,-79.09,-15.14
+Solar Industries India Ltd.,SOLARINDS,Chemicals,EQ,2725.2,2769.95,2702.2,2745.3,2715.0,-30.30,-1.10,53477,145652096.28,3189.95,1550.0,73.21,4.78
+Sona BLW Precision Forgings Ltd.,SONACOMS,Automobile and Auto Components,EQ,551.1,570.0,547.0,553.8,569.0,15.20,2.74,352045,197391631.5,839.9,333.25,61.79,-0.65
+Sonata Software Ltd.,SONATSOFTW,Information Technology,EQ,669.45,671.95,655.0,669.45,655.0,-14.45,-2.16,57392,38285055.36,1024.9,610.1,-10.36,-2.29
+Sun Pharma Advanced Research Company Ltd.,SPARC,Healthcare,EQ,209.1,209.7,202.35,209.1,204.0,-5.10,-2.44,145955,29906179.5,349.0,170.45,-8.99,-5.94
+Spicejet Ltd.,SPICEJET,Services,EQ,37.9,39.5,37.7,38.15,39.05,0.90,2.36,2091895,81416553.4,87.3,37.15,-52.90,-19.60
+SRF Ltd.,SRF,Chemicals,EQ,2230.0,2239.75,2133.05,2252.2,2161.4,-90.80,-4.03,809031,1754035840.17,2773.35,1418.8,-69.04,-8.60
+Shriram Transport Finance Co. Ltd.,SRTRANSFIN,Financial Services,EQ,1266.2,1274.8,1238.5,1281.35,1264.0,-17.35,-1.35,1071657,1348476719.67,1696.4,1002.0,-4.57,8.94
+Strides Pharma Science Ltd.,STAR,Healthcare,EQ,325.05,340.0,325.05,330.7,337.0,6.30,1.91,632891,210695742.81,818.0,263.35,-56.45,-0.54
+Star Health and Allied Insurance Company Ltd.,STARHEALTH,Financial Services,EQ,490.0,490.0,469.05,488.7,475.9,-12.80,-2.62,1600279,758932315.75,940.0,469.05,-,-28.43
+Sterlite Technologies Ltd.,STLTECH,Telecommunication,EQ,148.5,150.1,145.05,148.45,147.5,-0.95,-0.64,374778,55276007.22,318.0,135.5,-43.85,-14.71
+Sudarshan Chemical Industries Ltd.,SUDARSCHEM,Chemicals,EQ,419.25,423.8,416.8,419.25,420.0,0.75,0.18,25363,10650684.59,794.0,402.0,-38.37,-8.27
+Sumitomo Chemical India Ltd.,SUMICHEM,Chemicals,EQ,424.15,425.95,420.95,424.15,422.75,-1.40,-0.33,84550,35780714.5,492.0,344.25,9.81,-8.49
+Sundaram Finance Ltd.,SUNDARMFIN,Financial Services,EQ,1735.25,1799.0,1735.25,1761.65,1798.5,36.85,2.09,6156,10914464.88,2729.0,1555.0,-34.14,8.31
+Sundram Fasteners Ltd.,SUNDRMFAST,Automobile and Auto Components,EQ,724.0,752.0,718.45,724.9,747.0,22.10,3.05,46481,34167253.48,993.7,673.05,-10.36,-7.08
+Sun Pharmaceutical Industries Ltd.,SUNPHARMA,Healthcare,EQ,829.9,836.95,825.0,830.6,830.8,0.20,0.02,2316527,1921188502.18,967.05,664.2,22.97,-3.49
+Sunteck Realty Ltd.,SUNTECK,Realty,EQ,498.3,514.0,491.55,496.55,511.0,14.45,2.91,1770328,895184056.48,589.9,292.05,68.87,9.73
+Sun TV Network Ltd.,SUNTV,Media Entertainment & Publication,EQ,413.8,425.7,411.85,412.55,423.5,10.95,2.65,2838077,1191935578.46,612.0,402.55,-22.12,-7.40
+Suprajit Engineering Ltd.,SUPRAJIT,Automobile and Auto Components,EQ,330.05,339.75,326.0,332.45,337.25,4.80,1.44,108993,36269600.61,474.9,272.05,17.81,-1.79
+Supreme Industries Ltd.,SUPREMEIND,Capital Goods,EQ,1760.05,1780.0,1752.45,1766.85,1761.0,-5.85,-0.33,10943,19356306.69,2693.9,1666.25,-18.37,-3.82
+Suven Pharmaceuticals Ltd.,SUVENPHAR,Healthcare,EQ,460.0,465.0,457.7,462.3,460.1,-2.20,-0.48,46758,21604066.32,631.75,425.7,-3.68,-7.53
+Suzlon Energy Ltd.,SUZLON,Capital Goods,EQ,6.75,6.95,6.7,6.85,6.8,-0.05,-0.73,36152847,244754774.19,13.1,5.7,-14.38,-19.88
+Sterling and Wilson Renewable Energy Ltd.,SWSOLAR,Construction,EQ,294.8,300.0,290.1,296.6,298.5,1.90,0.64,97483,28705819.01,509.1,248.1,9.02,-9.71
+Symphony Ltd.,SYMPHONY,Consumer Durables,EQ,876.95,879.5,855.05,876.7,865.5,-11.20,-1.28,180991,156613322.21,1215.0,828.0,-14.78,-12.71
+Syngene International Ltd.,SYNGENE,Healthcare,EQ,550.0,564.1,544.8,554.75,563.0,8.25,1.49,135870,75301871.4,700.0,510.05,-4.89,-3.10
+Tanla Platforms Ltd.,TANLA,Information Technology,EQ,998.0,1004.0,980.3,1004.65,1002.0,-2.65,-0.26,160015,159008505.65,2096.75,780.0,-,-24.38
+Tata Chemicals Ltd.,TATACHEM,Chemicals,EQ,791.0,804.9,773.35,793.35,800.0,6.65,0.84,770388,605756084.4,1158.0,719.3,9.61,-16.36
+Tata Coffee Ltd.,TATACOFFEE,Fast Moving Consumer Goods,EQ,193.9,199.0,192.25,193.9,198.25,4.35,2.24,568731,111198285.12,254.4,178.6,3.33,-6.58
+Tata Communications Ltd.,TATACOMM,Telecommunication,EQ,917.45,937.4,908.9,914.25,931.0,16.75,1.83,283046,261489216.64,1591.95,856.25,-28.97,-7.19
+Tata Consumer Products Ltd.,TATACONSUM,Fast Moving Consumer Goods,EQ,700.0,722.95,697.0,706.35,721.5,15.15,2.14,954981,679086989.1,889.0,650.2,-6.37,-7.03
+Tata Elxsi Ltd.,TATAELXSI,Information Technology,EQ,8170.55,8239.0,8022.0,8170.55,8155.15,-15.40,-0.19,270194,2196669114.18,9420.0,4033.1,89.82,-2.51
+Tata Investment Corporation Ltd.,TATAINVEST,Financial Services,EQ,1343.5,1343.5,1331.0,1342.15,1340.0,-2.15,-0.16,8592,11485270.08,1733.9,1118.0,18.73,-12.22
+Tata Motors Ltd.,TATAMOTORS,Automobile and Auto Components,EQ,410.65,414.6,402.3,411.8,413.7,1.90,0.46,14725989,5997895319.7,536.7,268.45,21.26,-7.16
+Tata Motors Ltd DVR,TATAMTRDVR,Automobile and Auto Components,EQ,197.7,198.65,192.0,197.8,198.25,0.45,0.23,1610634,314927266.02,298.65,129.15,27.45,-7.16
+Tata Power Co. Ltd.,TATAPOWER,Power,EQ,202.7,207.65,200.6,202.2,207.2,5.00,2.47,15807993,3224356332.21,298.05,118.4,65.60,-14.01
+Tata Steel Ltd.,TATASTEEL,Metals & Mining,EQ,866.95,875.0,859.1,867.05,871.2,4.15,0.48,5476189,4746979672.76,1534.5,827.0,-25.68,-17.87
+Tata Steel Long Products Ltd.,TATASTLLP,Capital Goods,EQ,575.0,582.95,564.7,575.6,581.0,5.40,0.94,10870,6187856.2,1173.0,534.0,-,-12.96
+TCI Express Ltd.,TCIEXP,Services,EQ,1580.0,1624.85,1580.0,1594.4,1590.25,-4.15,-0.26,31026,49884223.32,2572.0,1280.0,10.80,-1.50
+TCNS Clothing Co. Ltd.,TCNSBRANDS,Textiles,EQ,537.0,543.7,526.9,530.8,537.0,6.20,1.17,20185,10831069.15,932.0,494.1,-8.98,-10.98
+Tata Consultancy Services Ltd.,TCS,Information Technology,EQ,3260.0,3333.0,3252.05,3267.1,3331.0,63.90,1.96,1502879,4930344847.4,4043.0,3023.85,-2.35,-2.89
+Teamlease Services Ltd.,TEAMLEASE,Services,EQ,3252.0,3358.0,3203.6,3236.15,3300.0,63.85,1.97,17375,57512466.25,5550.0,2910.0,-10.54,-1.99
+Tech Mahindra Ltd.,TECHM,Information Technology,EQ,997.0,1014.0,989.3,1000.0,1011.25,11.25,1.13,1842102,1848880935.36,1838.0,943.7,-8.71,-15.27
+Thermax Ltd.,THERMAX,Capital Goods,EQ,2060.0,2079.95,2022.35,2097.15,2036.25,-60.90,-2.90,30977,63452667.26,2347.0,1286.95,41.28,5.16
+Thyrocare Technologies Ltd.,THYROCARE,Healthcare,EQ,622.9,627.0,613.2,621.05,617.8,-3.25,-0.52,33220,20545573.4,1374.9,613.2,-52.69,-5.85
+Tube Investments of India Ltd.,TIINDIA,Automobile and Auto Components,EQ,1803.7,1835.0,1751.0,1841.3,1758.0,-83.30,-4.52,88311,157356955.35,2025.0,1081.05,57.56,17.25
+Timken India Ltd.,TIMKEN,Capital Goods,EQ,2385.0,2484.75,2385.0,2377.3,2476.0,98.70,4.15,102059,249572016.83,2730.0,1450.0,61.79,4.26
+Titan Company Ltd.,TITAN,Consumer Durables,EQ,1895.0,1954.35,1825.05,1941.25,1944.75,3.50,0.18,4064801,7729950765.68,2768.0,1662.5,12.05,-12.40
+Torrent Pharmaceuticals Ltd.,TORNTPHARM,Healthcare,EQ,2850.0,2925.0,2838.05,2860.25,2920.0,59.75,2.09,157824,457984730.88,3298.5,2484.15,-1.43,0.98
+Torrent Power Ltd.,TORNTPOWER,Power,EQ,451.0,460.6,449.4,451.65,460.6,8.95,1.98,220456,100386844.16,606.55,415.25,-1.84,-0.96
+Trent Ltd.,TRENT,Consumer Services,EQ,1074.15,1088.1,1059.1,1074.15,1078.0,3.85,0.36,555368,595593304.24,1346.85,838.0,26.39,-4.44
+Trident Ltd.,TRIDENT,Textiles,EQ,38.65,38.8,37.65,38.7,38.2,-0.50,-1.29,2633568,100365276.48,70.35,16.3,136.70,-18.87
+Triveni Turbine Ltd.,TRITURBINE,Capital Goods,EQ,154.15,157.65,154.05,156.35,156.6,0.25,0.16,126459,19678284.99,231.7,107.1,26.80,-8.19
+Triveni Engineering & Industries Ltd.,TRIVENI,Fast Moving Consumer Goods,EQ,237.0,237.0,225.7,236.45,229.7,-6.75,-2.85,543017,124758155.75,374.5,159.45,22.77,-19.19
+TTK Prestige Ltd.,TTKPRESTIG,Consumer Durables,EQ,828.0,833.4,816.95,830.75,830.0,-0.75,-0.09,17948,14811048.56,1264.65,744.7,-91.14,-0.14
+Tata Teleservices (Maharashtra) Ltd.,TTML,Telecommunication,EQ,121.4,122.7,118.5,121.1,119.4,-1.70,-1.40,2252618,269660900.78,290.15,32.75,-,-3.43
+TV18 Broadcast Ltd.,TV18BRDCST,Media Entertainment & Publication,EQ,39.1,39.45,38.4,38.95,39.1,0.15,0.39,4948800,192508320.0,82.6,33.2,-9.21,-3.35
+TVS Motor Company Ltd.,TVSMOTOR,Automobile and Auto Components,EQ,837.5,886.3,827.3,841.95,836.05,-5.90,-0.70,3263584,2765887440.0,886.3,495.0,35.69,14.31
+United Breweries Ltd.,UBL,Fast Moving Consumer Goods,EQ,1453.8,1509.0,1440.0,1452.8,1501.95,49.15,3.38,174055,256786822.6,1785.8,1274.5,0.45,-5.81
+UCO Bank,UCOBANK,Financial Services,EQ,11.0,11.2,10.75,10.95,11.05,0.10,0.91,1157345,12707648.1,15.9,10.55,-27.96,-5.60
+UFLEX Ltd.,UFLEX,Capital Goods,EQ,580.6,583.0,568.05,580.6,579.0,-1.60,-0.28,59052,34016314.08,719.0,427.0,2.08,-5.34
+UltraTech Cement Ltd.,ULTRACEMCO,Construction Materials,EQ,5600.0,5699.75,5485.0,5607.3,5690.0,82.70,1.47,329610,1855160443.5,8269.0,5157.05,-17.25,-7.81
+Union Bank of India,UNIONBANK,Financial Services,EQ,34.4,34.65,34.0,34.25,34.55,0.30,0.88,2376699,81520775.7,54.8,32.7,-11.15,-9.51
+UPL Ltd.,UPL,Chemicals,EQ,630.0,646.0,621.95,632.4,642.85,10.45,1.65,3575548,2276015079.4,852.4,607.5,-20.24,-18.88
+UTI Asset Management Company Ltd.,UTIAMC,Financial Services,EQ,643.0,643.0,628.05,645.9,640.0,-5.90,-0.91,46115,29270573.95,1216.2,595.0,-24.34,-5.71
+Vaibhav Global Ltd.,VAIBHAVGBL,Consumer Services,EQ,301.0,306.95,287.9,299.9,303.0,3.10,1.03,131705,39561547.9,860.0,287.9,-62.43,-10.68
+Vakrangee Ltd.,VAKRANGEE,Information Technology,EQ,26.75,27.5,26.15,26.75,26.7,-0.05,-0.19,2363491,62963400.24,47.2,23.55,-35.93,-6.47
+Varroc Engineering Ltd.,VARROC,Automobile and Auto Components,EQ,300.5,307.45,295.1,299.7,305.1,5.40,1.80,134195,40414166.2,493.85,260.0,-18.37,-12.87
+Varun Beverages Ltd.,VBL,Fast Moving Consumer Goods,EQ,790.0,809.0,786.2,790.55,790.0,-0.55,-0.07,951574,759584429.76,809.0,472.67,8.35,-25.17
+Vedanta Ltd.,VEDL,Metals & Mining,EQ,221.1,223.6,206.0,223.0,213.7,-9.30,-4.17,26130465,5596884298.35,440.75,206.0,-15.16,-30.51
+Venky's (India) Ltd.,VENKEYS,Fast Moving Consumer Goods,EQ,1897.15,1910.0,1875.75,1896.6,1901.0,4.40,0.23,8251,15614192.4,3746.0,1772.95,-47.10,-6.86
+V-Guard Industries Ltd.,VGUARD,Consumer Durables,EQ,224.2,226.75,217.1,225.2,218.5,-6.70,-2.98,212688,47031697.44,274.8,181.0,-15.07,-3.88
+Vijaya Diagnostic Centre Ltd.,VIJAYA,Healthcare,EQ,333.2,361.0,331.55,333.2,351.25,18.05,5.42,203583,71673430.98,672.0,295.0,-,-8.26
+Vinati Organics Ltd.,VINATIORGA,Chemicals,EQ,1977.0,2024.9,1936.7,1982.9,1983.0,0.10,0.01,40500,80528580.0,2290.0,1674.2,8.36,-3.20
+V.I.P. Industries Ltd.,VIPIND,Consumer Durables,EQ,626.5,631.8,606.4,626.5,618.9,-7.60,-1.21,125113,77473722.99,774.6,374.0,56.41,8.04
+V-Mart Retail Ltd.,VMART,Consumer Services,EQ,2497.65,2500.0,2425.0,2485.2,2471.0,-14.20,-0.57,28139,69418913.0,4848.8,2406.85,-11.25,-24.14
+Voltas Ltd.,VOLTAS,Consumer Durables,EQ,949.8,969.65,938.1,972.4,959.0,-13.40,-1.38,1129398,1079952955.56,1356.9,922.55,-4.90,-4.48
+Vardhman Textiles Ltd.,VTL,Textiles,EQ,265.45,273.25,262.1,265.05,272.95,7.90,2.98,431463,115235138.04,2880.0,245.5,-80.62,-7.58
+Welspun Corp Ltd.,WELCORP,Capital Goods,EQ,217.0,221.55,213.1,218.85,214.65,-4.20,-1.92,880477,190852194.52,250.65,105.85,52.94,-3.29
+Welspun India Ltd.,WELSPUNIND,Textiles,EQ,69.6,70.4,68.15,69.45,69.9,0.45,0.65,1174496,81275123.2,170.7,62.2,-27.96,6.27
+Westlife Development Ltd.,WESTLIFE,Consumer Services,EQ,498.0,508.0,490.05,497.3,491.0,-6.30,-1.27,50304,25038816.0,629.0,402.3,-0.46,9.25
+Whirlpool of India Ltd.,WHIRLPOOL,Consumer Durables,EQ,1552.0,1579.8,1538.5,1560.85,1560.0,-0.85,-0.05,62441,97504743.55,2550.0,1365.1,-30.18,-4.90
+Wipro Ltd.,WIPRO,Information Technology,EQ,415.0,423.2,411.4,416.05,422.05,6.00,1.44,4453624,1855023468.48,739.85,402.05,-23.75,-12.97
+Wockhardt Ltd.,WOCKPHARMA,Healthcare,EQ,206.95,208.8,205.15,208.55,207.0,-1.55,-0.74,375081,77671773.48,592.0,205.15,-59.74,-19.34
+Yes Bank Ltd.,YESBANK,Financial Services,EQ,12.65,12.75,12.55,12.65,12.7,0.05,0.40,18633694,235902566.04,16.25,10.5,-6.64,-3.80
+Zee Entertainment Enterprises Ltd.,ZEEL,Media Entertainment & Publication,EQ,213.0,215.85,210.6,214.1,215.5,1.40,0.65,4131369,880807870.8,378.7,166.8,-0.30,-15.33
+Zensar Technolgies Ltd.,ZENSARTECH,Information Technology,EQ,273.15,273.55,268.4,272.1,270.0,-2.10,-0.77,186578,50467483.22,587.0,246.65,-12.61,-11.02
+ZF Commercial Vehicle Control Systems India Ltd.,ZFCVINDIA,Automobile and Auto Components,EQ,7748.0,7900.0,7525.3,7716.6,7680.0,-36.60,-0.47,12790,98846236.0,8780.05,6793.3,-,1.43
+Zomato Ltd.,ZOMATO,Consumer Services,EQ,54.15,56.7,52.55,53.85,56.0,2.15,3.99,112078641,6193465701.66,169.0,50.05,-,-27.86
+Zydus Lifesciences Ltd.,ZYDUSLIFE,Healthcare,EQ,356.9,364.05,354.3,357.0,364.0,7.00,1.96,862373,310014469.77,651.9,319.0,-,-5.64
+Zydus Wellness Ltd.,ZYDUSWELL,Fast Moving Consumer Goods,EQ,1635.0,1635.0,1605.0,1636.85,1627.0,-9.85,-0.60,10803,17495674.56,2476.85,1430.0,-22.08,6.25

From 0742df173f3aecbc342943ceffcca9c3a9bb870f Mon Sep 17 00:00:00 2001
From: Balbir Singh <bbhatia_be22@thapar.edu>
Date: Sun, 20 Oct 2024 22:06:23 +0530
Subject: [PATCH 41/76] Created a Web Page for predicting the stock price and
 also implemented concept of ensemble learning

---
 Stock_Price_Prediction(Updated).ipynb |  668 +--
 Updated_SBIN.csv                      | 7066 +++++++++++++++++++++++++
 main.py                               |   68 +
 prediction.pkl                        |  Bin 0 -> 360276 bytes
 requirements.txt                      |    4 +-
 static/css/home.css                   |   50 +
 static/css/stock.css                  |   59 +
 templates/home.html                   |   20 +
 templates/stock.html                  |   47 +
 9 files changed, 7452 insertions(+), 530 deletions(-)
 create mode 100644 Updated_SBIN.csv
 create mode 100644 main.py
 create mode 100644 prediction.pkl
 create mode 100644 static/css/home.css
 create mode 100644 static/css/stock.css
 create mode 100644 templates/home.html
 create mode 100644 templates/stock.html

diff --git a/Stock_Price_Prediction(Updated).ipynb b/Stock_Price_Prediction(Updated).ipynb
index c108568..540eace 100644
--- a/Stock_Price_Prediction(Updated).ipynb
+++ b/Stock_Price_Prediction(Updated).ipynb
@@ -38,15 +38,8 @@
       "outputs": [
         {
           "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7074,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7074,\n        \"samples\": [\n          \"11-08-2016\",\n          \"30-10-2007\",\n          \"17-01-2017\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.7732294451065,\n        \"min\": 13.478195,\n        \"max\": 703.650024,\n        \"num_unique_values\": 4758,\n        \"samples\": [\n          174.399994,\n          31.0324,\n          187.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.34507839355788,\n        \"min\": 13.935802,\n        \"max\": 728.349976,\n        \"num_unique_values\": 5403,\n        \"samples\": [\n          473.0,\n          495.450012,\n          78.321663\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.98051601861624,\n        \"min\": 13.214009,\n        \"max\": 694.200012,\n        \"num_unique_values\": 5488,\n        \"samples\": [\n          60.2957,\n          22.677523,\n          16.983376\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.63054905628158,\n        \"min\": 13.346102,\n        \"max\": 725.25,\n        \"num_unique_values\": 5975,\n        \"samples\": [\n          633.599976,\n          241.100006,\n          107.834999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.90324918554683,\n        \"min\": 9.53141,\n        \"max\": 725.25,\n        \"num_unique_values\": 6575,\n        \"samples\": [\n          12.345289,\n          223.836212,\n          16.758821\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34627439.399630256,\n        \"min\": 0.0,\n        \"max\": 446948261.0,\n        \"num_unique_values\": 6948,\n        \"samples\": [\n          29959130.0,\n          1648453.0,\n          14077470.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-              "type": "dataframe",
-              "variable_name": "df"
-            },
             "text/html": [
-              "\n",
-              "  <div id=\"df-287e60b3-d1a5-4500-8f98-5b9d7c4acf53\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -126,216 +119,7 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-287e60b3-d1a5-4500-8f98-5b9d7c4acf53')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-287e60b3-d1a5-4500-8f98-5b9d7c4acf53 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-287e60b3-d1a5-4500-8f98-5b9d7c4acf53');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-e66d5a74-d0fe-4999-9ec9-3f969fd226ee\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e66d5a74-d0fe-4999-9ec9-3f969fd226ee')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-e66d5a74-d0fe-4999-9ec9-3f969fd226ee button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
+              "</div>"
             ],
             "text/plain": [
               "         Date       Open       High        Low      Close  Adj Close  \\\n",
@@ -390,15 +174,8 @@
       "outputs": [
         {
           "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7074,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.7732294451065,\n        \"min\": 13.478195,\n        \"max\": 703.650024,\n        \"num_unique_values\": 4758,\n        \"samples\": [\n          174.399994,\n          31.0324,\n          187.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.34507839355788,\n        \"min\": 13.935802,\n        \"max\": 728.349976,\n        \"num_unique_values\": 5403,\n        \"samples\": [\n          473.0,\n          495.450012,\n          78.321663\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.98051601861624,\n        \"min\": 13.214009,\n        \"max\": 694.200012,\n        \"num_unique_values\": 5488,\n        \"samples\": [\n          60.2957,\n          22.677523,\n          16.983376\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.63054905628158,\n        \"min\": 13.346102,\n        \"max\": 725.25,\n        \"num_unique_values\": 5975,\n        \"samples\": [\n          633.599976,\n          241.100006,\n          107.834999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34627439.399630256,\n        \"min\": 0.0,\n        \"max\": 446948261.0,\n        \"num_unique_values\": 6948,\n        \"samples\": [\n          29959130.0,\n          1648453.0,\n          14077470.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
-              "type": "dataframe",
-              "variable_name": "df"
-            },
             "text/html": [
-              "\n",
-              "  <div id=\"df-20d26075-7ea5-45d5-bff5-1bc0edf2bd43\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -466,216 +243,7 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20d26075-7ea5-45d5-bff5-1bc0edf2bd43')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-20d26075-7ea5-45d5-bff5-1bc0edf2bd43 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-20d26075-7ea5-45d5-bff5-1bc0edf2bd43');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "<div id=\"df-e1f4b696-990c-40d9-8eb5-880395089282\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e1f4b696-990c-40d9-8eb5-880395089282')\"\n",
-              "            title=\"Suggest charts\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "     width=\"24px\">\n",
-              "    <g>\n",
-              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-              "    </g>\n",
-              "</svg>\n",
-              "  </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    30% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "  <script>\n",
-              "    async function quickchart(key) {\n",
-              "      const quickchartButtonEl =\n",
-              "        document.querySelector('#' + key + ' button');\n",
-              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "      try {\n",
-              "        const charts = await google.colab.kernel.invokeFunction(\n",
-              "            'suggestCharts', [key], {});\n",
-              "      } catch (error) {\n",
-              "        console.error('Error during call to suggestCharts:', error);\n",
-              "      }\n",
-              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "    }\n",
-              "    (() => {\n",
-              "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-e1f4b696-990c-40d9-8eb5-880395089282 button');\n",
-              "      quickchartButtonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "    })();\n",
-              "  </script>\n",
-              "</div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
+              "</div>"
             ],
             "text/plain": [
               "        Open       High        Low      Close      Volume\n",
@@ -697,7 +265,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -871,53 +439,6 @@
       "outputs": [
         {
           "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Close</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>5286</th>\n",
-              "      <td>257.350006</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3408</th>\n",
-              "      <td>129.464996</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5477</th>\n",
-              "      <td>279.350006</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6906</th>\n",
-              "      <td>588.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>530</th>\n",
-              "      <td>21.644367</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div><br><label><b>dtype:</b> float64</label>"
-            ],
             "text/plain": [
               "5286    257.350006\n",
               "3408    129.464996\n",
@@ -1386,9 +907,9 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "RMSE: 1.6881364643681482\n",
-            "MAE: 0.9433353485344729\n",
-            "MAPE: 0.006085435990853812\n",
+            "RMSE: 1.688136464368173\n",
+            "MAE: 0.9433353485344464\n",
+            "MAPE: 0.006085435990852853\n",
             "\n"
           ]
         }
@@ -2357,9 +1878,9 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "RMSE: 224.91534614035237\n",
-            "MAE: 162.96706374804316\n",
-            "MAPE: 0.7504263376635859\n",
+            "RMSE: 224.92098628418847\n",
+            "MAE: 162.97484777804317\n",
+            "MAPE: 0.750567780636277\n",
             "\n"
           ]
         },
@@ -2367,7 +1888,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
+            "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
             "  warnings.warn(\n"
           ]
         }
@@ -2860,7 +2381,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but GradientBoostingRegressor was fitted with feature names\n",
+            "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but GradientBoostingRegressor was fitted with feature names\n",
             "  warnings.warn(\n"
           ]
         }
@@ -3860,9 +3381,9 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "RMSE: 211.5202666508501\n",
-            "MAE: 150.08505165249466\n",
-            "MAPE: 0.710014475295362\n",
+            "RMSE: 211.78853786766155\n",
+            "MAE: 150.2478993232628\n",
+            "MAPE: 0.7064489351945415\n",
             "\n"
           ]
         },
@@ -3870,7 +3391,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but AdaBoostRegressor was fitted with feature names\n",
+            "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but AdaBoostRegressor was fitted with feature names\n",
             "  warnings.warn(\n"
           ]
         }
@@ -4363,7 +3884,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
+            "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
             "  warnings.warn(\n"
           ]
         }
@@ -4843,14 +4364,6 @@
         "outputId": "6c449192-697f-4bb9-9a8e-bc9aed955532"
       },
       "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
-            "  warnings.warn(\n"
-          ]
-        },
         {
           "name": "stdout",
           "output_type": "stream",
@@ -4860,6 +4373,14 @@
             "MAPE: 0.7365233640314862\n",
             "\n"
           ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
         }
       ],
       "source": [
@@ -4894,7 +4415,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
             "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
           ]
         }
@@ -4933,7 +4454,7 @@
         {
           "data": {
             "text/plain": [
-              "<keras.src.callbacks.history.History at 0x7f9afeafbcd0>"
+              "<keras.src.callbacks.history.History at 0x24a0fcf4c10>"
             ]
           },
           "execution_count": 40,
@@ -4961,10 +4482,10 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
-            "RMSE: 2.767324426981657\n",
-            "MAE: 1.7200117039677931\n",
-            "MAPE: 0.010909365212209457\n",
+            "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
+            "RMSE: 2.8762951713017757\n",
+            "MAE: 1.8845151984048805\n",
+            "MAPE: 0.015311824533529984\n",
             "\n"
           ]
         }
@@ -4988,7 +4509,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 51,
+      "execution_count": 42,
       "metadata": {
         "id": "keiZDN4w7UH0"
       },
@@ -5006,7 +4527,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 52,
+      "execution_count": 43,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -5019,7 +4540,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
             "  super().__init__(**kwargs)\n"
           ]
         }
@@ -5032,7 +4553,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 53,
+      "execution_count": 44,
       "metadata": {
         "id": "3UJtO3wC7WWe"
       },
@@ -5044,7 +4565,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 54,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -5052,18 +4573,7 @@
         "id": "ld9dofMD7YNO",
         "outputId": "2b3021f3-d88f-431c-a059-a9b6e065a29b"
       },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<keras.src.callbacks.history.History at 0x7f9ad5b81150>"
-            ]
-          },
-          "execution_count": 54,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "model10.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)"
       ]
@@ -5215,6 +4725,97 @@
         "plt.tight_layout()\n",
         "plt.show()\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pickle\n",
+        "with open('prediction.pkl','wb') as file:\n",
+        "    pickle.dump(model5, file)  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "New CSV file created with the desired columns!\n"
+          ]
+        }
+      ],
+      "source": [
+        "file_path = \"SBIN.csv\" \n",
+        "df = pd.read_csv(file_path)\n",
+        "\n",
+        "df = df.drop(columns=['Date', 'Adj Close'])\n",
+        "\n",
+        "new_file_path = \"Updated_SBIN.csv\"\n",
+        "df.to_csv(new_file_path, index=False)\n",
+        "\n",
+        "print(\"New CSV file created with the desired columns!\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "file_path = \"Updated_SBIN.csv\"  # Replace with your actual CSV file path\n",
+        "df = pd.read_csv(file_path)\n",
+        "\n",
+        "# Rearrange the columns so that 'Volume' comes before 'Close'\n",
+        "df = df[['Open', 'High', 'Low', 'Volume', 'Close']]\n",
+        "\n",
+        "# Save the updated dataframe to a new CSV file\n",
+        "new_file_path = \"Updated_SBIN.csv\"  # Replace with your desired output CSV file path\n",
+        "df.to_csv(new_file_path, index=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Original dataset shape: (7074, 5)\n",
+            "Cleaned dataset shape: (7065, 5)\n",
+            "Cleaned dataset saved to Updated_SBIN.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# Load the original dataset\n",
+        "data_file = \"Updated_SBIN.csv\"  # Path to your original CSV file\n",
+        "df = pd.read_csv(data_file)\n",
+        "\n",
+        "# Display the original dataset shape\n",
+        "print(\"Original dataset shape:\", df.shape)\n",
+        "\n",
+        "# Remove rows with any null values\n",
+        "df_cleaned = df.dropna()\n",
+        "\n",
+        "# Display the cleaned dataset shape\n",
+        "print(\"Cleaned dataset shape:\", df_cleaned.shape)\n",
+        "\n",
+        "# Save the cleaned dataset to a new CSV file\n",
+        "cleaned_data_file = \"Updated_SBIN.csv\"  # Path for the cleaned CSV file\n",
+        "df_cleaned.to_csv(cleaned_data_file, index=False)\n",
+        "\n",
+        "print(f\"Cleaned dataset saved to {cleaned_data_file}\")\n"
+      ]
     }
   ],
   "metadata": {
@@ -5226,7 +4827,16 @@
       "name": "python3"
     },
     "language_info": {
-      "name": "python"
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.6"
     }
   },
   "nbformat": 4,
diff --git a/Updated_SBIN.csv b/Updated_SBIN.csv
new file mode 100644
index 0000000..82042ad
--- /dev/null
+++ b/Updated_SBIN.csv
@@ -0,0 +1,7066 @@
+Open,High,Low,Volume,Close
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diff --git a/main.py b/main.py
new file mode 100644
index 0000000..69b3328
--- /dev/null
+++ b/main.py
@@ -0,0 +1,68 @@
+from flask import Flask, render_template, request
+import numpy as np
+import pickle
+import os
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+
+app = Flask(__name__)
+
+models_dir = "Stock-Price-Prediction/prediction.pkl"
+data_file = "Stock-Price-Prediction/Updated_SBIN.csv"
+
+df = pd.read_csv(data_file)
+print(df.columns)
+
+# Load the model
+try:
+    with open(models_dir, "rb") as stock_file:
+        stock_model = pickle.load(stock_file)
+except FileNotFoundError:
+    stock_model = None
+
+def save_data(inputs, close_prediction):
+    new_data = pd.DataFrame([inputs + [close_prediction]], columns=['Open', 'High', 'Low', 'Volume', 'Close'])
+    with open(data_file, mode='a', newline='') as file:
+        new_data.to_csv(file, header=False, index=False)
+
+def retrain_model():
+    X = df[['Open', 'High', 'Low', 'Volume']]
+    y = df['Close']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    stock_model_xgb = xgb.XGBRegressor(objective='reg:squarederror', random_state=42)
+    stock_model_xgb.fit(X_train, y_train)
+
+    with open(models_dir, "wb") as stock_file_xgb:
+        pickle.dump(stock_model_xgb, stock_file_xgb)
+
+@app.route('/')
+def home():
+    return render_template('home.html')
+
+@app.route('/predict_close', methods=['GET', 'POST'])
+def predict_close():
+    if request.method == 'POST':
+        try:
+            inputs = [
+                float(request.form.get('Open')),
+                float(request.form.get('High')),
+                float(request.form.get('Low')),
+                float(request.form.get('Volume'))
+            ]
+
+            close_prediction = stock_model.predict(np.array([inputs]))[0] if stock_model else None
+
+            save_data(inputs, close_prediction)
+            retrain_model()
+
+            return str(round(close_prediction, 2)) if close_prediction is not None else "Error: Stock model not loaded."
+        except Exception as e:
+            return f"An error occurred: {e}"
+    # If it's a GET request, render the stock.html page
+    return render_template('stock.html')
+
+if __name__ == "__main__":
+    app.run(debug=True)
diff --git a/prediction.pkl b/prediction.pkl
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HcmV?d00001

diff --git a/requirements.txt b/requirements.txt
index 30db39a..87e198d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,7 @@
+Flask==2.3.2
 pandas==2.1.1
 numpy==1.26.0
 scikit-learn==1.3.0
 statsmodels==0.14.0
-pickle-mixin==1.0.2
\ No newline at end of file
+pickle-mixin==1.0.2
+xgboost
\ No newline at end of file
diff --git a/static/css/home.css b/static/css/home.css
new file mode 100644
index 0000000..d8f0a59
--- /dev/null
+++ b/static/css/home.css
@@ -0,0 +1,50 @@
+body {
+    font-family: 'Arial', sans-serif;
+    background-color: #f0f8ff; /* Light blue background */
+    margin: 0;
+    padding: 20px;
+}
+
+.container {
+    max-width: 600px;
+    margin: auto;
+    background: white;
+    padding: 30px;
+    border-radius: 10px;
+    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
+    text-align: center;
+}
+
+h1 {
+    color: #003366; /* Dark blue for heading */
+    font-size: 2.5em;
+}
+
+p {
+    color: #555; /* Dark gray for paragraph */
+    font-size: 1.2em;
+}
+
+nav {
+    margin-top: 20px;
+}
+
+ul {
+    list-style-type: none;
+    padding: 0;
+}
+
+li {
+    display: inline;
+    margin: 0 15px;
+}
+
+a {
+    text-decoration: none;
+    color: #0066cc; /* Blue link color */
+    font-weight: bold;
+}
+
+a:hover {
+    color: #004999; /* Darker blue on hover */
+}
diff --git a/static/css/stock.css b/static/css/stock.css
new file mode 100644
index 0000000..7d22f7a
--- /dev/null
+++ b/static/css/stock.css
@@ -0,0 +1,59 @@
+body {
+    font-family: 'Verdana', sans-serif;
+    background-color: #f5f5f5; /* Light gray background */
+    margin: 0;
+    padding: 20px;
+}
+
+.container {
+    max-width: 600px;
+    margin: auto;
+    background: white;
+    padding: 30px;
+    border-radius: 10px;
+    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
+}
+
+h1 {
+    color: #ff4500; /* Orange-red for heading */
+    font-size: 2.5em;
+    text-align: center;
+}
+
+label {
+    margin-top: 10px;
+    display: block;
+    font-weight: bold;
+    color: #333; /* Dark gray for labels */
+}
+
+input[type="text"] {
+    width: 100%;
+    padding: 10px;
+    margin: 5px 0 15px 0;
+    border: 1px solid #ccc;
+    border-radius: 4px;
+}
+
+button {
+    background-color: #28a745; /* Green button */
+    color: white;
+    padding: 10px;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+    width: 100%; /* Full-width button */
+}
+
+button:hover {
+    background-color: #218838; /* Darker green on hover */
+}
+
+.result {
+    margin-top: 20px;
+    padding: 10px;
+    color: white;
+    background-color: green; /* Green background for success */
+    border-radius: 4px;
+    display: none; /* Initially hidden */
+}
diff --git a/templates/home.html b/templates/home.html
new file mode 100644
index 0000000..858ccb0
--- /dev/null
+++ b/templates/home.html
@@ -0,0 +1,20 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Stock Price Predictor</title>
+    <link rel="stylesheet" href="static/css/home.css"> <!-- Link to your CSS -->
+</head>
+<body>
+    <div class="container">
+        <h1>Welcome to Our Stock Price Predictor</h1>
+        <p>Use this tool to predict stock prices based on historical data.</p>
+        <nav>
+            <ul>
+                <li><a href="{{ url_for('predict_close') }}">Stock Price Prediction</a></li>
+            </ul>
+        </nav>
+    </div>
+</body>
+</html>
diff --git a/templates/stock.html b/templates/stock.html
new file mode 100644
index 0000000..0a0acb3
--- /dev/null
+++ b/templates/stock.html
@@ -0,0 +1,47 @@
+<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <link rel="stylesheet" href="/static/css/stock.css"> <!-- Link to your CSS -->
+    <title>Stock Price Prediction</title>
+</head>
+<body>
+    <div class="container">
+        <h1>Stock Price Prediction</h1>
+        <form id="stockForm" method="post" onsubmit="send_data(event)">
+            <label for="Open">Open</label>
+            <input type="number" step="0.01" id="Open" name="Open" required>
+            
+            <label for="High">High</label>
+            <input type="number" step="0.01" id="High" name="High" required>
+            
+            <label for="Low">Low</label>
+            <input type="number" step="0.01" id="Low" name="Low" required>
+            
+            <label for="Volume">Volume</label>
+            <input type="number" step="1" id="Volume" name="Volume" required>
+            
+            <button type="submit" class="btn">Predict Stock Price</button>
+        </form>
+        <div id="prediction" class="prediction-result"></div>
+    </div>
+
+    <script>
+        function send_data(event) {
+            event.preventDefault();  // Prevent the default form submission
+            var form = document.getElementById('stockForm');
+            var formData = new FormData(form);
+            var xhr = new XMLHttpRequest();
+            xhr.open('POST', '/predict_close', true);  // Ensure this matches your Flask route
+            document.getElementById("prediction").innerHTML = "Predicting Stock Price...";
+            xhr.onreadystatechange = function () {
+                if (xhr.readyState == XMLHttpRequest.DONE) {
+                    document.getElementById("prediction").innerHTML = "Predicted Stock Price: " + xhr.responseText;
+                }
+            }
+            xhr.send(formData);
+        }
+    </script>
+</body>
+</html>

From 6c975c8bc39a1d1710735d77cfea5bd196c1e91c Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Mon, 21 Oct 2024 08:16:03 +0530
Subject: [PATCH 42/76] Github actions for more connectivity!

---
 .github/workflows/comment_open_close.yml | 29 +++++++++++++++++
 .github/workflows/pr_merge_comment.yml   | 38 ++++++++++++++++++++++
 .github/workflows/pr_rise_checker.yml    | 40 ++++++++++++++++++++++++
 3 files changed, 107 insertions(+)
 create mode 100644 .github/workflows/comment_open_close.yml
 create mode 100644 .github/workflows/pr_merge_comment.yml
 create mode 100644 .github/workflows/pr_rise_checker.yml

diff --git a/.github/workflows/comment_open_close.yml b/.github/workflows/comment_open_close.yml
new file mode 100644
index 0000000..15e02b7
--- /dev/null
+++ b/.github/workflows/comment_open_close.yml
@@ -0,0 +1,29 @@
+name: Issue Auto Comment
+
+# Created by @smog-root
+
+on:
+  issues:
+    types: [opened, closed]
+
+jobs:
+  comment:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Add a comment when an issue is opened
+        if: github.event.action == 'opened'
+        uses: actions-ecosystem/action-create-comment@v1
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          issue_number: ${{ github.event.issue.number }}
+          body: "👋 Thanks for opening this issue! We appreciate your contribution. Please make sure you’ve provided all the necessary details and screenshots, and don't forget to follow our Guidelines and Code of Conduct. Happy coding! 🚀
+"
+
+      - name: Add a comment when an issue is closed
+        if: github.event.action == 'closed'
+        uses: actions-ecosystem/action-create-comment@v1
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          issue_number: ${{ github.event.issue.number }}
+          body: " ✅ This issue has been successfully closed. Thank you for your contribution and helping us improve the project! If you have any more ideas or run into other issues, feel free to open a new one. Happy coding! 🚀
+            name: Auto-comment bot for issues"
\ No newline at end of file
diff --git a/.github/workflows/pr_merge_comment.yml b/.github/workflows/pr_merge_comment.yml
new file mode 100644
index 0000000..42a8979
--- /dev/null
+++ b/.github/workflows/pr_merge_comment.yml
@@ -0,0 +1,38 @@
+name: Auto Comment on PR Merge
+
+# Created by @smog-root
+
+on:
+  pull_request:
+    types: [closed]
+
+permissions:
+  issues: write
+  pull-requests: write
+
+jobs:
+  comment:
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+    if: github.event.pull_request.merged == true
+
+    steps:
+    - name: Checkout Repository
+      uses: actions/checkout@v2
+
+    - name: Add Comment to Issue
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      run: |
+        COMMENT=$(cat <<EOF
+        {
+          "body": "🎉 Your chaotic pull request has been successfully merged! 🎉 Thank you for your valuable contribution to our chaos. Your chaos are greatly appreciated. Feel free to reach out if you have any more contributions or if there's anything chaotic we can assist you with. Keep up the chaotic work! 🚀"
+        }
+        EOF
+        )
+        curl -X POST \
+          -H "Authorization: Bearer $GITHUB_TOKEN" \
+          -H "Accept: application/vnd.github.v3+json" \
+          https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/comments \
+          -d "$COMMENT"
\ No newline at end of file
diff --git a/.github/workflows/pr_rise_checker.yml b/.github/workflows/pr_rise_checker.yml
new file mode 100644
index 0000000..104bd96
--- /dev/null
+++ b/.github/workflows/pr_rise_checker.yml
@@ -0,0 +1,40 @@
+name: Auto Comment on PR
+
+# Created by @smog-root
+
+
+on:
+  pull_request_target:
+    types: [opened]
+
+permissions:
+  issues: write
+  pull-requests: write
+
+jobs:
+  comment:
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+    steps:
+    - name: Add Comment to Pull Request
+      run: |
+        COMMENT=$(cat <<EOF
+        {
+          "body": "Thank you for submitting your chaotic pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our chaotic [CONTRIBUTING.md](https://github.com/vansh-codes/ChaosWeb/blob/main/CONTRIBUTING). If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊"
+        }
+        EOF
+        )
+        RESPONSE=$(curl -s -o response.json -w "%{http_code}" \
+          -X POST \
+          -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
+          -H "Accept: application/vnd.github.v3+json" \
+          https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/comments \
+          -d "$COMMENT")
+        cat response.json
+        if [ "$RESPONSE" -ne 201 ]; then
+          echo "Failed to add comment"
+          exit 1
+        fi
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
\ No newline at end of file

From 461bd39838d889c28a45f5d4e5665cf7957bee0f Mon Sep 17 00:00:00 2001
From: Balbir Singh <bbhatia_be22@thapar.edu>
Date: Mon, 21 Oct 2024 11:48:25 +0530
Subject: [PATCH 43/76] Updated contributing.md

---
 Contributing.md | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/Contributing.md b/Contributing.md
index c245b91..2697890 100644
--- a/Contributing.md
+++ b/Contributing.md
@@ -87,6 +87,39 @@ If you're introducing new algorithms or modifying existing ones, please update t
 ## 🧑‍🤝‍🧑 Community
 Join the discussion in the [Issues](https://github.com/your-repo/issues) section! Share your ideas, ask questions, and collaborate on exciting features with fellow contributors.
 
+## 🔄 Recent Updates
+
+### Changes in Flask Route for Stock Prediction:
+- The route `/predict_close` has been updated to handle both `GET` and `POST` requests.
+  - **GET** request: Renders the `stock.html` form when the user navigates to the prediction page.
+  - **POST** request: Processes the stock price prediction based on user input (Open, High, Low, and Volume).
+  
+Make sure to follow the updated code structure when adding new routes for similar tasks. The current structure is as follows:
+
+```python
+@app.route('/predict_close', methods=['GET', 'POST'])
+def predict_close():
+    if request.method == 'POST':
+        try:
+            inputs = [
+                float(request.form.get('Open')),
+                float(request.form.get('High')),
+                float(request.form.get('Low')),
+                float(request.form.get('Volume'))
+            ]
+
+            close_prediction = stock_model.predict(np.array([inputs]))[0] if stock_model else None
+
+            save_data(inputs, close_prediction)
+            retrain_model()
+
+            return str(round(close_prediction, 2)) if close_prediction is not None else "Error: Stock model not loaded."
+        except Exception as e:
+            return f"An error occurred: {e}"
+    # For GET request, render the stock.html page
+    return render_template('stock.html')
+
+
 ## 🎉 Thank You!
 Every contribution counts! Whether you’re fixing a bug, improving documentation, or building a new feature, we appreciate your efforts to make this project better.
 

From 193aadd11368adc6aa37ef3b07a3ec4bb044eb6d Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Mon, 21 Oct 2024 16:06:49 +0530
Subject: [PATCH 44/76] Update comment_open_close.yml

---
 .github/workflows/comment_open_close.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/comment_open_close.yml b/.github/workflows/comment_open_close.yml
index 15e02b7..fb088f2 100644
--- a/.github/workflows/comment_open_close.yml
+++ b/.github/workflows/comment_open_close.yml
@@ -25,5 +25,4 @@ jobs:
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           issue_number: ${{ github.event.issue.number }}
-          body: " ✅ This issue has been successfully closed. Thank you for your contribution and helping us improve the project! If you have any more ideas or run into other issues, feel free to open a new one. Happy coding! 🚀
-            name: Auto-comment bot for issues"
\ No newline at end of file
+          body: " ✅ This issue has been successfully closed. Thank you for your contribution and helping us improve the project! If you have any more ideas or run into other issues, feel free to open a new one. Happy coding! 🚀"

From 24022ed5d917e00137fd1bcf31a07efcfa9d31a8 Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Mon, 21 Oct 2024 16:07:55 +0530
Subject: [PATCH 45/76] Update pr_merge_comment.yml

---
 .github/workflows/pr_merge_comment.yml | 44 ++++++++++++--------------
 1 file changed, 20 insertions(+), 24 deletions(-)

diff --git a/.github/workflows/pr_merge_comment.yml b/.github/workflows/pr_merge_comment.yml
index 42a8979..d34db75 100644
--- a/.github/workflows/pr_merge_comment.yml
+++ b/.github/workflows/pr_merge_comment.yml
@@ -1,38 +1,34 @@
-name: Auto Comment on PR Merge
+name: Merge Thank You
 
 # Created by @smog-root
 
 on:
-  pull_request:
-    types: [closed]
+  pull_request_target:
+    types: [closed]  # Trigger when a PR is closed
 
 permissions:
   issues: write
   pull-requests: write
 
 jobs:
-  comment:
+  post_merge_message:
+    if: github.event.pull_request.merged == true  # Only run if the PR was merged
     runs-on: ubuntu-latest
-    permissions:
-      pull-requests: write
-    if: github.event.pull_request.merged == true
 
     steps:
-    - name: Checkout Repository
-      uses: actions/checkout@v2
+      - name: Post thank you message
+        uses: actions/github-script@v7
+        with:
+          github-token: ${{ secrets.GITHUB_TOKEN }}  # Ensure token is used
+          script: |
+            const prNumber = context.payload.pull_request.number;
+            const owner = context.repo.owner;
+            const repo = context.repo.repo;
 
-    - name: Add Comment to Issue
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      run: |
-        COMMENT=$(cat <<EOF
-        {
-          "body": "🎉 Your chaotic pull request has been successfully merged! 🎉 Thank you for your valuable contribution to our chaos. Your chaos are greatly appreciated. Feel free to reach out if you have any more contributions or if there's anything chaotic we can assist you with. Keep up the chaotic work! 🚀"
-        }
-        EOF
-        )
-        curl -X POST \
-          -H "Authorization: Bearer $GITHUB_TOKEN" \
-          -H "Accept: application/vnd.github.v3+json" \
-          https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.pull_request.number }}/comments \
-          -d "$COMMENT"
\ No newline at end of file
+            // Post a thank you message upon PR merge
+            await github.rest.issues.createComment({
+              owner: owner,
+              repo: repo,
+              issue_number: prNumber,
+              body: `🎉🎉 Thank you for your contribution! Your PR #${prNumber} has been merged! 🎉🎉`
+            });

From 96f46436c9f79e39d004f637c34033ee2710d839 Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Mon, 21 Oct 2024 16:08:37 +0530
Subject: [PATCH 46/76] Update pr_rise_checker.yml

---
 .github/workflows/pr_rise_checker.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/pr_rise_checker.yml b/.github/workflows/pr_rise_checker.yml
index 104bd96..3db26b4 100644
--- a/.github/workflows/pr_rise_checker.yml
+++ b/.github/workflows/pr_rise_checker.yml
@@ -2,7 +2,6 @@ name: Auto Comment on PR
 
 # Created by @smog-root
 
-
 on:
   pull_request_target:
     types: [opened]
@@ -21,7 +20,7 @@ jobs:
       run: |
         COMMENT=$(cat <<EOF
         {
-          "body": "Thank you for submitting your chaotic pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our chaotic [CONTRIBUTING.md](https://github.com/vansh-codes/ChaosWeb/blob/main/CONTRIBUTING). If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊"
+          "body": "Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our chaotic [CONTRIBUTING.md](https://github.com/vansh-codes/ChaosWeb/blob/main/CONTRIBUTING). If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊"
         }
         EOF
         )
@@ -37,4 +36,4 @@ jobs:
           exit 1
         fi
       env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
\ No newline at end of file
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

From 9f6d87a0a480e2e0af6828d60869f335b1516d1c Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Mon, 21 Oct 2024 16:08:54 +0530
Subject: [PATCH 47/76] Rename pr_merge_comment.yml to pr_merge_comment.yaml

---
 .github/workflows/{pr_merge_comment.yml => pr_merge_comment.yaml} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename .github/workflows/{pr_merge_comment.yml => pr_merge_comment.yaml} (100%)

diff --git a/.github/workflows/pr_merge_comment.yml b/.github/workflows/pr_merge_comment.yaml
similarity index 100%
rename from .github/workflows/pr_merge_comment.yml
rename to .github/workflows/pr_merge_comment.yaml

From 5542ff22ec822594a65b2784f55edb889097e05b Mon Sep 17 00:00:00 2001
From: smog-root <machha.shashank@gmail.com>
Date: Mon, 21 Oct 2024 16:15:09 +0530
Subject: [PATCH 48/76] file name type

---
 .github/workflows/{pr_rise_checker.yml => pr_raise_checker.yml} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename .github/workflows/{pr_rise_checker.yml => pr_raise_checker.yml} (100%)

diff --git a/.github/workflows/pr_rise_checker.yml b/.github/workflows/pr_raise_checker.yml
similarity index 100%
rename from .github/workflows/pr_rise_checker.yml
rename to .github/workflows/pr_raise_checker.yml

From 5e660b74a129ed1f4071133a474a5b9a398a2f10 Mon Sep 17 00:00:00 2001
From: sapnilmodak <sapnilmodak@gmail.com>
Date: Tue, 22 Oct 2024 11:49:35 +0530
Subject: [PATCH 49/76] df

---
 Stock_Recommendation.ipynb |  464 ++-
 sentiment_model.h5         |  Bin 0 -> 9895568 bytes
 stock_data.csv             | 6090 ++++++++++++++++++++++++++++++++++++
 stock_sentimental.ipynb    |  383 +++
 4 files changed, 6695 insertions(+), 242 deletions(-)
 create mode 100644 sentiment_model.h5
 create mode 100644 stock_data.csv
 create mode 100644 stock_sentimental.ipynb

diff --git a/Stock_Recommendation.ipynb b/Stock_Recommendation.ipynb
index 89b6bdd..fd3b8a0 100644
--- a/Stock_Recommendation.ipynb
+++ b/Stock_Recommendation.ipynb
@@ -2,22 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[nltk_data] Downloading package punkt to\n",
-      "[nltk_data]     C:\\Users\\sapni\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n",
-      "[nltk_data] Downloading package stopwords to\n",
-      "[nltk_data]     C:\\Users\\sapni\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Package stopwords is already up-to-date!\n",
-      "[nltk_data] Downloading package wordnet to\n",
-      "[nltk_data]     C:\\Users\\sapni\\AppData\\Roaming\\nltk_data...\n",
-      "[nltk_data]   Package wordnet is already up-to-date!\n"
+      "[nltk_data] Error loading punkt: <urlopen error [Errno 11001]\n",
+      "[nltk_data]     getaddrinfo failed>\n",
+      "[nltk_data] Error loading stopwords: <urlopen error [Errno 11001]\n",
+      "[nltk_data]     getaddrinfo failed>\n",
+      "[nltk_data] Error loading wordnet: <urlopen error [Errno 11001]\n",
+      "[nltk_data]     getaddrinfo failed>\n"
      ]
     }
    ],
@@ -44,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -232,7 +229,7 @@
        "4     15514.00                     11.61                     5.84  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -244,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -363,7 +360,7 @@
        "[501 rows x 2 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,26 +372,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\sapni\\anaconda3\\lib\\site-packages\\sentence_transformers\\cross_encoder\\CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
-      "  from tqdm.autonotebook import tqdm, trange\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From c:\\Users\\sapni\\anaconda3\\lib\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sentence_transformers import SentenceTransformer\n",
     "\n",
@@ -405,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -424,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -543,7 +523,7 @@
        "[501 rows x 2 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,14 +534,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sapni\\AppData\\Local\\Temp\\ipykernel_25592\\477415791.py:1: SettingWithCopyWarning: \n",
+      "C:\\Users\\sapni\\AppData\\Local\\Temp\\ipykernel_3976\\477415791.py:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -576,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -695,7 +675,7 @@
        "[501 rows x 2 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -706,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -715,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -726,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -749,102 +729,102 @@
       "   0.06623445]]\n",
       "\n",
       "Customer Embedding:\n",
-      " [ 3.14664170e-02 -3.78815085e-02 -3.73417661e-02 -3.17229889e-02\n",
-      " -4.22344357e-02  1.27025442e-02 -2.39155442e-02 -6.93003461e-02\n",
-      " -3.21524963e-02  7.96454027e-02  7.77523369e-02 -4.75082286e-02\n",
-      " -1.95211302e-02 -1.25046214e-02  5.08183353e-02 -2.46512946e-02\n",
-      " -1.49427941e-02 -2.74640676e-02  4.25236784e-02 -7.36578787e-03\n",
-      " -8.90878867e-03  1.84313732e-03 -4.04891334e-02  2.95713730e-02\n",
-      " -5.97193651e-02  1.87571477e-02 -5.67692332e-03 -8.21459852e-03\n",
-      " -2.31289323e-02 -2.73498092e-02  2.54001822e-02  1.30668739e-02\n",
-      "  9.44364443e-02 -8.01635440e-03  3.55471559e-02  1.28795162e-01\n",
-      "  2.49239113e-02 -9.53501761e-02  1.58603266e-02 -3.89398336e-02\n",
-      " -1.34050464e-02 -9.93881673e-02 -2.93484535e-02 -7.17466623e-02\n",
-      "  3.43848653e-02 -1.67269222e-02  2.82975789e-02 -3.10652656e-03\n",
-      "  2.12582219e-02 -7.64291584e-02  4.05744687e-02 -2.67640091e-02\n",
-      "  2.98122708e-02 -9.98979155e-03 -5.23542874e-02 -1.77336130e-02\n",
-      " -1.46305542e-02 -6.34800317e-03 -4.22016680e-02 -5.87929562e-02\n",
-      " -6.84524998e-02 -7.19780475e-03 -4.78288569e-02 -5.24268188e-02\n",
-      "  5.48108257e-02 -9.64927487e-03  1.59333013e-02 -7.21244812e-02\n",
-      " -1.53982667e-02 -7.07860738e-02  4.63903956e-02 -8.87077451e-02\n",
-      "  4.21138760e-03 -4.27451804e-02 -3.15879434e-02 -3.48816104e-02\n",
-      "  1.73769016e-02  1.20111547e-01  1.23920493e-01 -1.45363241e-01\n",
-      "  7.32854903e-02  5.91961406e-02 -4.84295599e-02  6.06471710e-02\n",
-      " -6.54613897e-02  1.24825360e-02 -7.38825463e-03 -6.54625967e-02\n",
-      "  2.87184883e-02  1.88502856e-03 -8.01817980e-03 -3.96523885e-02\n",
-      "  7.24581480e-02  1.22633632e-02 -5.44842100e-03  1.16946828e-02\n",
-      " -4.74422099e-03 -4.22262959e-02 -1.53517053e-02  9.96556208e-02\n",
-      " -4.84725051e-02  3.07871047e-02 -3.94323021e-02 -3.33751626e-02\n",
-      " -7.20618889e-02 -7.62435235e-03  9.71943699e-03  2.18201359e-03\n",
-      "  5.96895888e-02  1.00962352e-02 -7.43536279e-03  1.28121506e-02\n",
-      " -3.44882496e-02 -4.63309400e-02  3.64253037e-02 -5.68599366e-02\n",
-      " -4.72689755e-02 -1.85630340e-02  2.15455409e-04 -5.69709577e-02\n",
-      " -2.88812886e-03  1.34512419e-02 -6.94445893e-02 -4.80364412e-02\n",
-      "  1.98717043e-02 -6.87178075e-02  4.69160378e-02 -3.48460129e-33\n",
-      "  2.74309292e-02  4.61018123e-02  7.90321007e-02 -1.77468895e-03\n",
-      "  2.93568131e-02  1.36835873e-02 -3.85059714e-02 -4.15082574e-02\n",
-      "  4.04559672e-02  1.43633969e-02  6.32987246e-02  5.11144586e-02\n",
-      "  4.15699705e-02  1.00233350e-02  1.26954451e-01 -1.22519739e-01\n",
-      " -1.47484569e-02  8.62846524e-02  7.75998011e-02  4.46178764e-02\n",
-      " -1.41220577e-02  5.68904029e-03 -1.14176879e-02 -2.26635188e-02\n",
-      "  1.14526093e-01 -4.34458293e-02  1.74916312e-02 -1.74409226e-02\n",
-      "  2.41210591e-02  5.08027188e-02  7.86003917e-02  1.14285775e-01\n",
-      " -3.16002294e-02 -3.04858722e-02 -1.72049068e-02  3.13026309e-02\n",
-      " -3.78056103e-03 -2.68692020e-02  8.44113063e-03 -3.65200266e-02\n",
-      " -9.06182155e-02 -5.41768707e-02  4.64576595e-02  5.30998260e-02\n",
-      " -8.45028013e-02  7.69533683e-03  4.33015600e-02  5.77162132e-02\n",
-      "  1.57650374e-02  3.23144123e-02  2.93194950e-02  5.57424016e-02\n",
-      " -7.00408267e-03 -5.76511910e-03  9.61290859e-03 -2.98861135e-02\n",
-      "  3.35550979e-02 -5.78683540e-02 -2.72784918e-03 -5.11349514e-02\n",
-      "  6.91856742e-02  3.93171422e-02 -9.10947323e-02  1.25231802e-01\n",
-      " -7.74377361e-02 -3.65904607e-02  2.45778412e-02 -3.15613784e-02\n",
-      "  1.32436842e-01 -4.68552709e-02 -3.28906663e-02  1.51393292e-02\n",
-      " -4.87497970e-02 -2.50118654e-02 -6.71052039e-02 -2.61116866e-02\n",
-      " -4.84636873e-02  5.14720157e-02  2.30410714e-02  3.72129828e-02\n",
-      " -8.30372125e-02  1.39289838e-03  8.76997970e-03 -6.48937235e-03\n",
-      "  1.66297719e-01  7.46646076e-02  4.32111137e-02  7.86511973e-03\n",
-      "  6.49285689e-02  1.44748529e-02 -1.45551831e-01  1.85465429e-03\n",
-      "  3.69937643e-02  1.24513529e-01  6.94522038e-02  8.31680528e-34\n",
-      "  2.02758648e-02 -1.38148433e-02  8.69328249e-03 -3.77335213e-02\n",
-      "  4.28531915e-02  4.57742624e-02  3.20917927e-03 -7.67260492e-02\n",
-      "  2.34734826e-02  7.90196955e-02 -8.84439349e-02 -2.00472027e-02\n",
-      "  3.51752974e-02  4.71815728e-02  3.01404763e-02 -9.60588306e-02\n",
-      " -1.09162629e-02  2.62741130e-02  3.75837716e-03  4.07930352e-02\n",
-      "  1.16740474e-02 -4.42948379e-02  5.25881089e-02 -2.52405480e-02\n",
-      " -4.48426185e-03  1.36668356e-02 -6.41934201e-02 -4.62563448e-02\n",
-      " -3.71463336e-02 -5.36741503e-02  2.67249644e-02 -4.97082695e-02\n",
-      " -5.00039123e-02  1.82751343e-02 -8.67619663e-02 -2.51742024e-02\n",
-      "  1.15126157e-02 -3.29780951e-02 -3.40419933e-02 -3.42883095e-02\n",
-      " -1.14182731e-04 -4.34406884e-02  2.27891263e-02  1.50547381e-02\n",
-      " -3.02064866e-02 -5.12561463e-02 -3.14933769e-02  3.78211439e-02\n",
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      ]
     }
    ],
@@ -855,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -48961,102 +48941,102 @@
       "  3.85916717e-02 -2.24448442e-02  6.71749795e-03  6.62344471e-02]\n",
       "\n",
       "Customer Interest Embedding:\n",
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      ]
     }
    ],
@@ -49078,7 +49058,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -49086,10 +49066,10 @@
      "output_type": "stream",
      "text": [
       "Recommended stocks for you based on your interest:\n",
-      "                                     Company Name\n",
-      "209  ICICI Prudential Life Insurance Company Ltd.\n",
-      "233                          Indian Overseas Bank\n",
-      "40                     AU Small Finance Bank Ltd.\n"
+      "                             Company Name\n",
+      "251                       JSW Energy Ltd.\n",
+      "361  Power Grid Corporation of India Ltd.\n",
+      "90                              CESC Ltd.\n"
      ]
     }
    ],
diff --git a/sentiment_model.h5 b/sentiment_model.h5
new file mode 100644
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diff --git a/stock_data.csv b/stock_data.csv
new file mode 100644
index 0000000..2100b22
--- /dev/null
+++ b/stock_data.csv
@@ -0,0 +1,6090 @@
+Text,Sentiment
+"Kickers on my watchlist XIDE TIT SOQ PNK CPW BPZ AJ  trade method 1 or method 2, see prev posts",1
+user: AAP MOVIE. 55% return for the FEA/GEED indicator just 15 trades for the year.  AWESOME.  ,1
+user I'd be afraid to short AMZN - they are looking like a near-monopoly in eBooks and infrastructure-as-a-service,1
+MNTA Over 12.00  ,1
+OI  Over 21.37  ,1
+PGNX  Over 3.04  ,1
+AAP - user if so then the current downtrend will break. Otherwise just a short-term correction in med-term downtrend.,-1
+Monday's relative weakness. NYX WIN TIE TAP ICE INT BMC AON C CHK BIIB  ,-1
+GOOG - ower trend line channel test & volume support.   ,1
+AAP will watch tomorrow for ONG entry.,1
+i'm assuming FCX opens tomorrow above the 34.25 trigger buy. still very much like this setup.  ,1
+"It really worries me how everyone expects the market to rally now,usually exact opposite happens every time we shall see soon bac spx jpm",1
+AAP GAMCO's arry Haverty : Apple Is Extremely Cheap  Great Video !!!,1
+user Maykiljil posted that.  I agree that MSFT is going higher & possibly north of 30,1
+Momentum is coming back to ETFC Broke MA200 resistance on solid volume Friday. ong set-up  ,1
+HA Hitting 35.65 means resume targeting 42 level ..  ,1
+user gameplan shot for today but I liked  on trend break from May or c+h break. OC weekly trend break back to july 2011,1
+with FCX gapping well above ideal entry looking for a pull in to at least 35 on open for an entry,1
+"user great list again, particularly like FISV and SYK. All buy & hold types should check the free list out.  ",1
+ATHX upper trend line   ,1
+NG - nice PNF BY - breakout - need follow thru  ,1
+Won't believe AAP uptrend is back until it crosses above MA(50),-1
+X swing still on   ,1
+SWY - 30% of float short and breaking out - ouch,1
+BIOF wants 4.90's comin!!!,1
+VS inverted head and shoulder play out well. Wasn't able to catch the entry. Eyes on it  ,1
+" red, not ready for break out.",-1
+EI close to breaking out now.  My trigger is at 30.40.   ,1
+user BAC For a quick Trade to late..But for investing ~11.98 is a good entry point IMHO,1
+"CHDN - ong   49.02. Trailing Stop  56.66 from 6 prior Stops of 54.17, 49.88, 49.82, 45.47, 43.02 & 41.92 -  ",1
+AAP VOME today is impressive. At this rate and well probably get to 30M shares traded today.,1
+"user: been adding VXY long off the bottom today for trade, also got WPI near low",-1
+"I repeat, if global economy is going to get better this year go with C instead of BAC",1
+GOOG go ONG on close above 725.,1
+NKD looking like a good short. Failed to break price level resistance at 116 today.,-1
+GS like the price action. So far holding above 130. Still deciding whether to go ONG calls or stocks.,1
+SBX buy when it clears resistance at 54.5,1
+here is NEW up target for AAP and notice shakeout reading at -8.49 ! and we had buy dot Monday  ,1
+Too early to short into this move. Stock Market needs a few days to settle down. #Patience  COH BWD DT AAP PAY,-1
+AAP obbers hit Apple Store in Paris.  Prefer to take merchendise over cash.  Bullish.  ,1
+AXA Here is my tip of the day. Buy now! You do not even have to sign up for a newsletter or anything O If I'm wrong I will eat crow :),1
+X out the 1/2 due to the move today and the Kirby on the daily around 26.00 - still swinging 1/2  ,1
+"AAP 10 min short won now longs have fully added in, look for a pop here with market,shakeout reset  ",1
+NG stellar move - still long swing - full position - I believe user & user in it 2  ,1
+ATICE & TADE SETP:  KO AN CNX,1
+IDCC 50SMA acting as a good support. Short-term momentum indicators have switched to the upside.  ,1
+"NEW POST: BAC Is At 19 Month Highs, But Showing Signs of Stress  ",1
+c two daily  bull flags on top of each other=bullish still  ,1
+NG - traditional and AT point and figure - bullish BY signals multiyear high  ,1
+jpm closed>10sma on Monday(u know my rules)...large cup n handle....still bullish  ,1
+EOG ascending triangle - original target 127 extension EPS Feb 11th? 1/2 position swing  ,1
+"aapl daily , broke downtrend(from closing prices) now w v pattern back in play.......600 target  ",1
+PHM PulteGroup Option Bear bets 1.5 Million on 11% down move by April  ,-1
+BAC In a 11.97 for a trade,1
+Short Setups displaying relative weakness: AO COH PAY BWD DT SHD WTW P Prepare to short the stall. ES_F  ,-1
+"When AMZN monetize all electronics it makes with traffic to marketplace, AAP GOOG will wake up and say why didn't we buy EBAY",1
+"CVI starting with a clean book, making first buy of the yr - entry 49.54 stop 47.43  ",1
+SAM cheers to the temporary solution to our economic woes - entry 139.81 stop 131.92  ,1
+SK to the GAVE!,-1
+ovti ttm ending Oct 2012 Negative 151 million operational cash flow.. 273% decline vs yoy ttm ending oct 2011,-1
+AAP and GOOG should respond positively to good jobless claims numbers,1
+Analyst price targets and earnings targets coming down---EXACTY what you want to see prior AAP reporting.,1
+user Nice! Color me an INTC bull as well!,1
+"Heads p: AA Earnings (1/8/13) ess Than a Week Away, Tho The Season Doesn't EAY Begin Cranking 'Til 1/16 w/ GS JPM EBAY et al...",1
+"trade idea buy WO  market 26.64, target 36, cut below 23.50  ",1
+"Trade idea buy DC  market 58.50, target 67 , cut below 53  ",1
+GOOG looks all set to make new ATH. Now before or after E is the unknown.,1
+GOOG trade this one long verse 10sma  ,1
+"FNF - ong  19.43. Trailing Stop  21.04 from 6 prior Stops of 19.71, 19.52, 18.70, 18.65, 18.07 and 17.87 -  ",1
+"FOD should see a solid move up tom, system predict nice green candle.",1
+This is the year of fitness and health apps...the winners will be gynormous and the speculation will run high  nke,1
+It's time to invest in MSFT COMPQ   ,1
+"New Post: Shorts Will Make Dimes, ongs Will Make Dollars  AAP SPX QQQ P",1
+nbelieveable payment trend that VC's are chasing has easily played out in public markets.... EBAY V and Mastercard MA all time highs,1
+Some of the watch list stocks that triggered today OEX AGNX TV VBD XIDE PPC EXP  ONTY  BMN FS WK CHMT FBC  ISI AIG,1
+Nice Bull flag breakout here OC #technology #software  ,1
+My setup alerts went bonkers today..One of many that got triggered.Cup+Handle breakout in PFE   ,1
+The host with the most....congrats to the cloud today AX and user more all-time highs.  wow,1
+X Did .S. Steel Just Breakout?  MT too,1
+DNDN has broken through the clear 5.5 resistanc level on heavy volume & could be headed much higher  ,1
+EXPE looks good for higher prices  ,1
+apc weekly setting up long.  ,1
+I am going into this morning long 3 each AAP 560 call options & GOOG 725 calls Jan 4th expiration,1
+ES_F path higher unthreatened unless 1452.25 support fails. See resistance at 1466.00 SPX #stocks.  ,1
+Monitor the S line/price dynamic. Stocks that closed Dec.28 with the S line in new highs be4 price. MHO TSM IPGP EQIX BC BDC ASH,1
+DDD IDCC continue higher,1
+"Had hoped to pick up some OST today, but looks like I'm a day too late. Nice move up this morning.",1
+GPS wow that wa s a fast fast fade...,-1
+AMZN going above 260. Should go near 300 if market behaves for a few weeks.,1
+Huston we have a GEEN AAP,1
+P long 9.55,1
+HN breakout...but 3rd day up so look for pullback to enter long.  ,1
+NG - ascend triangle much like FS & X breakouts 127 exten then 161.8 targets - fibs subjective  ,1
+KCG ... things that make you go hmmmm?  ,1
+VNG - Huge  news from patent attorney Dan avicher  ,1
+NG nhod - check the weekly - target previous lows 2006 :),1
+NG nhod - what do you see? check the weekly - target previous lows 2006 :)   ,1
+AIG American International Group Option Traders bet on 4% down move by next Friday  ,-1
+P out balance +.32,1
+VNG buys vs. Sells?,1
+BAC In  12.00 since there is consolidation  this level. We could close = > 12.09 if market is stable in pm IMHO.,1
+VNG Parabolic moves are a comin',1
+New Sony pre-owned block tech patent unearthed SNE ... 50% of GME profits from pre owned ..last one out turn off the lights,-1
+"we liked VS, we love WYNN from 111 entry to new up target yesterday at 121. very nice !   ",1
+user: SPX 1464.90 Wave iv would be 20 points lower [1467.00 KEY] ES_F HOD 1460.00 (under neckline 1458.50 very bearish) ,-1
+"DDD consolidating the strong move from previous days between 55 and 60, next buy point above 60 on volume (holding).",1
+VNG - 12 mil shares shorts plus long buying = massive VNG short squeeze. Just when you thought story was dead avicher kills short thesis,1
+"Wow, not good for BKS T user ... sales for the Nook were down 13% over the holidays  ",-1
+AC from watch list - volume did pick up and is now 139% of full day average -from 200-day bounce   ,1
+"The new wightwatchers ads are more fun and light and with regular people more interesting, but I still dont trust them WTW (no position)",-1
+AMZN holding up well - next buy point when clears this upper trend line on heavy volume  ,1
+MA exactly what you want to see a follow thru day after breaking out to new highs on volume  ,1
+ZCS user There's your buying,1
+AAP SPY no more QE for 2013. Now bring the Geithner.,-1
+Over extended stock market + unexpected news from the FED minutes = Down ESD & a panicky ES_F NQ_F.,-1
+"SPY ncle Ben needed to spice up things a little. AAP people were bored, wake up now.",-1
+AAP might close at the lows of the day. Bears will be in control again below the descending trendline. Watch it.,-1
+FAN - seeking 200d. Im adding some synthetic shorts here and will add again at 200d.,-1
+"CWST low of day 4.50, near high of day 4.56, watchn for b/out, minimal risk",1
+All of the above: gme Gamestop short targets  spy,-1
+time to get out of the #cloud stocks VMW CM,-1
+BAC Keep Overniight mmy pos. will bin premarket Tmorrow too.,1
+Polk Expects 2013 .S. New Vehicle egistrations to each 15.3 Million - Polk -  F GM I think this is low,1
+Very bullish on market  also talked trends for SCTY OST ANGI FIO  PANW A JCP P YEP MNST GPN on show.,1
+BAC what happens next?  ,-1
+KCG someone bought 3.22k 4 Jan 18 Calls....,1
+Man I didnt know about Skippy when I made this...would have thrown in a jar H #Hormel  ,1
+"Big move in bonds today, 7 months high for 10 y rate, still going lower here ZN_F, could get truly ugly if good NFP number in AM SPY TT",1
+Market Wrap Video + Additions to Watch ist  including: BCEI BJI CEE ES SONS SQNM NX YEP,1
+Maybe when vrng asked the judge for a 7% royalty it became too risky for goog to deal with?  they want to settle,1
+BJI Over 35.53 if good volume  ,1
+CEE Over 35.12  ,1
+SQNM  Over 5.05  ,1
+NX On flag break  ,1
+3D Systems Brings Next-Gen Consumer 3D Printing to CES 2013  DDD,1
+AAP will top out in April when Aston Kutcher stars in 'jOBS'.  Kinda like how Facebook did when 'Social Network' came out. FB,-1
+"VNG w/ MACD turning up & above its signal line, VNG could post a nice rally from current levels  ",1
+PI 3Q 10Q operational cash flow for 9mos -20million. 269%decline vs. 9mos year ago.,-1
+bbby 3Q 10Q Operational Cash Flow 135 million. 53 increase vs. year ago Q3. 64% improvement q o q,1
+DI 6mos operational cash flow 267 million. increases 187 million vs.6mos year ago. 233% increase,1
+AM 3Q 10Q 9mos operational cash flow 32 million vs. neg 26 mil 9mos year ago.,1
+am 3Q operational cash flow neg 16 million.  vs. year ago 3Q of negative 68million.  52 million improvement,1
+"A quiet Carl Icahn = Bullish.  NFX shorts should be paranoid on every close as they will get acquired.  Think aapl, msft, amzn, fb",1
+I am an aapl bull but cannot break this trend line in OBV   ,-1
+user Phantom7 I've been calling +4 ever since the start of this thing. 3.75 should be a given~ KCG,1
+NX on watch list above small flag on volume of 26% of ave full day so good volume  ,1
+GOOG poised to clear this next resistance (buy / add area) watch action / volume closely here   ,1
+GDP on watch list slightly over line and 50-day EMA light volume of 5% ave full day   ,1
+"Took some profits off DDD, will add if breaks 60 on volume.",1
+CHK - what I'm looking at - small risk position calls only. Scalp - maybe a swing XE watch.   ,1
+"ONTY If the holders would just hold out or raise their ask here, this could really pop. Going to climb slowly regardless...",1
+EOG PXD OAS #energyswag,1
+AAP moving down with volume. Couldnt cope with the descending trend line. Trend is still down. Perhaps 460 is not impossible after all.,-1
+AAP 50EMA continue to work as AAP is making lower highs in the MACO sense   ,-1
+"Went short SPY yesterday, should have picked AAP instead. Still think market needs to move down a bit then resume move to the upside.",-1
+AAP people be realistic. Market undecided what to do with apple. Earnings will lift that cloud. If GOOD we might EAY see a reversal.,-1
+Will watch the close carefully then decide whether to ONG or SHOT AAP. Below 530 SHOT!!!,-1
+"JCP Keep an eye on this triangle. big short int, 34%. reports Feb 27. cup & handle. at DT. ong  ",1
+sold a little goog ...first time in a while,1
+Nice cup with handle breakout on MCA,1
+FAF new highs.,1
+SPY giving AAP the finger. We don't care about you anymore lol,-1
+The Smith&Corona doing ok. AMZN,1
+"VA - ong  43.07. Trailing Stop  58.98 from 11 Stops of 54.53, 52.45, 48.05, 47.32, 46.71, ... 41.32 & 39.57 -  ",1
+AMZN alert update: a last minute push tried to shake out puts. we're hanging on for now.  ,-1
+AAP volume higher today than monday. Keep that in mind.,1
+"HB.A - ong  73.67. Trailing Stop  71.92 from 3 prior Stops of 70.86, 68.64 and 62.41 -  ",1
+"The key is not to panic here.  AAP is in the domain of twitchy day traders on robots now, but long term should hold up on fundamentals",1
+NVDA 4 hour - love to see the vol explode.   ,1
+VNG - Take a hard look at GOOG FTC settlement over patents.  FTC making GOOG play nice with VNG?  Makes Sense  ,1
+some worried I had gone OCO with my 737.71 up call for GOOG ! now at door step of nearly 40 move  ,1
+VNG its crazy that anyone listens and trades off this tool.,1
+GOOG  is getting fueled by AAP's reversal.,1
+user why make a BBY offer now when he can shave 4 more off offer price? Bid went from 24-26 to just 20 in a matter of months.,-1
+NVDA - to answer your question entry (add) was the red candle at 10:50AM today. ove flags  ,1
+"AAP 950 lot bid in the Feb 580 call at 9.50, might push the stock up or create support. 26 delta option",1
+"WPI - was on our short candidate list 12/31 and we missed it, got started short today",-1
+MYE breaking out,1
+Short HT under the 200 SMA stop just over the high of the day. First target 53.95. Second target 53.21 gap fill.,-1
+QCOM introduces StreamBoost:  average home connects up to seven devices to the Internet,1
+NVDA love to see momo this next week like previous histogram bars noted.   ,-1
+HMA unusual option activity. Trader buys 11k of the Jan 9 Put. Short term bear play,-1
+"ADBE option trader buys 5,300 of the Feb 39C against OI of 741. Bet is on stock above 39 by Feb 16th",1
+"NVDA ooks bullish but it must stay above 13.05 on a closing basis (monthly), EPS say no?  ",-1
+SQNM from watch list over line on volume of 86% of ave full day  ,1
+HOTT Failed breakout?  ,-1
+ZN_F ticking up.. I'd be a seller of SDJPY here (*if I weren't already)...,-1
+"INO Wow, nice accumulation today, heavy...",1
+ZNGA Fat juicy Short back to lower levels as it closes Gap  2.72?  ,-1
+GDOT nice bounce from its 50SMA. ooks ready to test the recent highs  ,1
+HMA ooking for 10's; headed higher!  ,1
+"SPF ooking for 9's, headed higher with lower rates.  ",1
+"GS JPM XF HOD, they are hungry for stocks out there... SPY",1
+CHK weekly bullish engulfing   ,1
+GS looks good heading into next week.,1
+AAP all i know is ipad minis are out of stock everywhere. been going to 8 different stores and none of them have em in stock,1
+As goog explodes and aapl implodes MINTE 21 to 23 of user vid from a few years ago have Mark nailing the subject  ,1
+"KKD Krispy Kreme Doughnuts, Inc. at IC XChange Conference (ive) 01/17/13 at 9:15 a.m. ET",1
+Picked up FS on pullback due to downgrade. Held support to close week. First PT 37.30  ,1
+"user: I've been picking this one for a while, but I'm calling KKD as the next food company to make a DPZ like run in TWENTY13.",1
+"ZNGA Still shows red and dead on a enko view, PF Box size 0.25  ",-1
+"GS enko view of GS, PF Box size = 5 showing potential to break to 175  ",1
+"NVDA enko view showing red and dead, need 14 or more for a long & less than 13 for a short  ",-1
+"CSX enko view, PF Box size = 1,  VEY bullish above 21, coal stocks confirming move on rails.  ",1
+GOOG And on enko w/ a PF Box size of 50 it looks like a failed breakout  ,-1
+user At 155 for GS but very bullish on fins as rates are low = The FED Put!,1
+user ZNGA I use lagging indicators on lagging stocks ;),-1
+"GOOG, I like it with a close above 744.96",1
+FS b/o above bull flag on heavy vol. etest of support today on light vol. 41% short float  ,1
+MSFT gap close. Continues base. SI rising. Nice : pickup w/ stop below major support & 28.22 PT.  ,1
+Financials continue ripping. Easy money. C approaching 43 PT  ,1
+VS follow throughs. 10 DMA crossing above 200 DMA. Trading above all MA's. Great setup  ,1
+"user BAC Yeah ... they made the easy profits by slashing jobs, now they have to close win-win sales.",-1
+New buys today from watch list all did well  NX 20% SONS YEP  BCEI  GDP  AY and SQNM Total triggers this week 39 - a big week,1
+"SBX  #SI #EarthThis is the kind of stuff that makes us perk up (so sorry!).but, go on...bullish five waves to 58.    ",1
+"Can you say no brainier? ong SBAC stock, 60/80 Feb collar for protection...  ",1
+Anyone can say how much svu would go even if the deal goes through? 4? 5? Not sure if much upside left.,1
+NVDA the bounce continues and the stock has now cleared its 200SMA and the 13 level. ong   ,1
+AXTI Momentum picking up w/ MACD climbing/SI rising.OBV indicator made a new higher high this week  ,1
+GDOT OBV is moving upwards which means that big money is moving into the stock slowly. ong  ,1
+aapl daily (see notes)  ,1
+AMZN daily (see notes)  ,1
+C daily (see notes)   ,1
+"EGOV long 16.62 strong above all MA's, resting/basing, room to upper bolly to absorb 10/20 day atr  ",1
+AA Fibs   Friends   ,1
+TSA: Judge Dismisses Massachusetts State Automobile Dealers Association awsuit -  ,1
+BAC up trending doesn't appear to slow down as of yet. It's prudent to take some profit at time.  ,1
+"FE - looks like it fired a sell signal Friday, may trade this short on tick below Friday low - have to do more work on utilities later...",-1
+coh also got rejected by cloud but still in slight uptrend for past few. buy when it enters cloud  ,1
+yum looks good here out of cloud with stops of either 200 sma or cloud under it. target of 50 sma  ,1
+EQIX EXPE FB GS HOG MON PII QCOM QIH SODA Some ideas for next week.  Favorite setup is EQIX  ,1
+"EQIX No position, would like to see a breakout over 216.50. Prefer more consolidation though  ",1
+Time for a contraction in AAP.down to test 500 this week by Weds.long Jan11th 510/500 put spread  ,-1
+AAP Bounce Coming?   ,1
+FS en fuego!!   ,1
+AXA Over-Sold in Oscillating ange....indicators suggest buy price holds above prev close + increase in volume.,1
+A few I'm not holding but will check out. user: Bullish MACD cross: BBG COP ENI EOG JEC IG SFY XE XOP,1
+"JDS beautiful trend, 13.5 should bring out the dip buyers",1
+ko has trouble staying above 200 day sma past few weeks. could short here or higher stops above 38  ,-1
+mako a red day monday breaks support and could test old lows of 9-9.25  ,-1
+After-hour Most Advanced: OPT MAKO FOD GNMK JDS EGE AMN PX AAY DDD SV PM BKS F,1
+GOOG with simple Fibonacci projections on close over 739. 127.2% at 766 and 161.8% at 802  ,1
+Why I am buying AAP on Monday -  ,1
+user: AMZN over 263 has a Measured Move to 307  ,1
+user The INO pipeline and the partnerships with the ni's and MK--that pretty much sums it up...,1
+"AMZN the data company, not the retailer.  A new post by me.   ",1
+Trading environment favors long side. Don't over stay welcome. Too few quality leaders. GS AMH OCN EGN HOV AAP  ,-1
+AAP what an amazing run it has been since 2005. I don't think it's over.   ,1
+A Tale of Two Stocks  AAP GOOG SPY QQQ,1
+F Ford's Powerful Bullish Pattern Dictates Price Direction  ,1
+DT - Descending triangle. ower highs. Didn't participate well in the rally & it's overbought.   ,-1
+CVA Over 19.00  ,1
+MCK  Over 100.05 -   ,1
+VMED you know we prefer 3-4 day pullback to reverse so maybe Mon or Tuesday on flag break or at sup  ,1
+"Just kidding, more legible version. Currently long FS (tweeted on friday)  ",1
+Ford will perform well due to Hurricane Sandy's destruction. F Palladium will do well PA Banks are a buy picks are BAC C SAN GS,1
+GS is a buy on pullback to 133 or on bigger pullback to 130.,1
+CEG keep it on you watch list.,1
+I wish TV would die already like all the Newspapers told me  CBS ftw!,1
+I think AAP is heading back down to check in on 506 support again,-1
+AMZN but but but.....yap yap yap,1
+SNDK pattern looking more interesting. Can it successfully break into the gap this time around?  ,1
+"#AMZN at all-time highs pre-market , aapl still plunging.  should be an interesting trading day!",1
+See risk to ES_F 1442.00-1423.75 as bearish momentum divergences resolve SPX SPY #stocks.   ,-1
+user: ONXX strong price & vol expansion friday. possibly looking for higher prices  > another biotech with 2 levels,1
+ZNGA Momentum building to the upside.? On watch.,1
+ES from watch list also fast move today  volume of 77% ave full day as usually tiny volume  ,1
+TWI like this long 24% short...  ,1
+"Green Weekly Triangle on HEO,....pdating  ",-1
+EOG - 60 min pullback to breakout - watching 1/2 position    ,1
+KCG to 3.75?,1
+ZCS almost hit my stop loss...glad to see it heading back up,1
+AAP closed my short for some nice gains.,-1
+"V beautiful uptrend since June '12. Never once broke higher high, higher low trend  ",1
+The most awesome AAP trendline ever. Since 1986!!   ,-1
+"DDD - ong  27.95. Trailing Stop  42.35 from 7 prior Stops of 41.70, 39.03, 36.53, 32.35, 27.95, 25.44 and 22.93 -  ",1
+NX was largest watch list gainer new buy on Friday and biggest mover today so protect gains  ,1
+"Green Weekly Triangle on KTOS,....pdating  ",-1
+ak corrected me - PPHM  largest watch list gainer today - though it gapped up big on open   ,1
+AAP positive close above 530 today is bullish. I'll be looking for possible ONG set up.,1
+CEG possible ONG today if you want to play the JPMOGAN BIOtech conference.,1
+"FME: ong  10.25. Trailing Stop  13.75 from 8 prior Stops of 12.29, 11.50, 10.97, 10.63, 10.36, ... 9.64 & 9.03 -  ",1
+I showed this MA on Friday with the upside orange dot traget at 517+ hit that this morn now red dot  ,-1
+TO triggered above 34. First target 34.55-.90,1
+SXC - breaking above key downward channel level. ikely to move much higher from here.  ,1
+"must follow the young man user: AMZN the data company, not the retailer.  A new post by me.   ",1
+PG short back on,-1
+OVTI downgrade on Sept 7th 16.77 after Q1 looking good. next filing Q3 end of feb.  ,-1
+VS option trader sells 26k of the 47.25 - 52.25 call spreads in March. Big bet that stock will be below 47.25 by March expiry,-1
+"AA option traders bet on bad earnings selling 6,200 Jan 9C and buying 3,200 Jan 9P. Could be hedge on stock. Both trades against low OI",-1
+SKX Q3 operational cash flow negative 60million. 80million decline vs.Q3 2011,-1
+SKX 9 mos. 2012 operational cash flow negative 20 million. 95 million decline vs. 9mos 2011,-1
+"ed Weekly Triangle on SNSS,.....pdating  ",1
+BAC avrg 12.127 ,1
+First trade coming back from vacation. HOT short at 59.80. Stop at 60.30,-1
+"Green Weekly Triangle on SCC,....pdating  ",-1
+PODD breaking out to new 52wk highs from a decent base  ,1
+3 horsemen of high beta momo chase FS GMC NFX being slammed a bit here : advanced tell that the new year party is over for the mrkt ?,-1
+PNA looks like a solid set up,1
+Names to buy on pullbacks GS CEG FB F,1
+BA for a flush down,-1
+"Amazon Breaks 1,600 (adjusting for the splits)  via user AMZN",1
+"Financials XF have been driving this mkt higher, today they are weak-ish (except for JPM) for 1st time in while, little red flag 1...",-1
+on our ive Broadcast we got this long signal on PCN and covered discount buy-in ! needs push  ,1
+"AMZN VOME is a go, big boys grabbing some stocks. Higher prices to come.",1
+TWI at highs not seen since September  ,1
+"user won gold in our 2011 stock contest w V,  won silver in 2012 w TKC. Will his DDAIF call win in 2013?  ",1
+AMZN FB your pick for higher prices this week. Both breaking out with considerable volume.,1
+Eceryone wants a piece of PCN gorwth and magic...stakes are so high...innovation so fast and marketing stakes high  ,1
+"user scheplick It's easy to short AMZN - buy an iPad Mini, a tax prep book at Borders and some ackspace time.;-)",-1
+Might open a position on S today. Cup and handle can make this one fly to 7-7.5 quickly.,1
+"CS, KOS positive.... nice reversal in KOS",1
+AXP trying to make a move too. Getting interesting.,1
+MA a strong follow thru day after breaking out to new highs last Friday - confirmation is solid  ,1
+"V new highs, looking for higher prices from here  ",1
+CEG a strong breakout to new highs today on 2x the daily avg. volume. ooks good for more upside  ,1
+WETF cheap financial stock that is trying to b/o. Would like to see a pullback/consolidation first  ,1
+"NFX shorts should be paranoid on every close as a buyout will come 1 night from aapl, msft, goog, amzn, etc.  dominate webtv long nflx",1
+"As in the last 3 sessions, last hour buyers are back, more money need to be put to work... money flows... SPY TT ES_F",1
+"user The stock isn't telling us anything new, but the trend is saying a lot. This E is AAP's most anticipated in years.",1
+All of the above: rax setting up again  spy,1
+PCN - long entry  ,1
+"Green Weekly Triangle on AMPE,....Sell Short at 4.15  ",-1
+"Green Weekly & Monthly Triangle on FWS,....pdating to Monthly  ",-1
+"user are you saying ZAGG will up another 23%, how?",-1
+"Green Weekly & Monthly Triangle on PAB,....pdating  ",-1
+744.96 IS A great level for a close on GOOG,1
+"AAP short-term outlook. 3 possible scenarios, 2 of them are bullish  ",1
+Barclays upside case for CSOD is a year end price target of 46.  Based on 8x cy'14 revenues and 10% upside to estimates. Quite achievable!,1
+CTXS (conservative swing) once again bouncing nicely off support (w/volume)  MCP (aggressive swing) however strong probability.,1
+user: AMZN it's time for this bird to fly south!  ,-1
+"VVS Technicals are positive for a continued climb. SI, OBV and MACD all look good to me  ",1
+"AKX b/out 13.78, like the base action under spot & declining vol, ripe for pop  ",1
+"HAO b/out 7.20, price was rejected at 7.19 today, also high on 12/21  ",1
+"HWAY b/out 11.08, nice base under the spot  ",1
+yum down premarket through the cloud but has support at about 62.75   ,-1
+"Multi-day downswing in ES_F favored, see demand zone at 1442.00-1423.75 SPX SPY #stocks.   ",-1
+"MON - not making sales here - we will be buyers of weakness, looks like game on for a while...",1
+FIN 9mos Operational cash flow declines 15million vs. prior yr first 9mos.  198% decline,-1
+AYI Q1 Operational Cash Flow turns Negative,-1
+AYI 1st negative operational cash flow quarter since quarter Novemebr 2008,-1
+AYI 2013 Q1 operational Cash flow negative 14.5 million. decline of 41 million vs. Q1 2012.  152% decline,-1
+my 526.20 bid not hit in aapl so i am expecting at least a move to 540 today :),1
+"MON 2013 Q1 operational cash flow 1.569 billion,  a 459 million increase vs 2012 Q1.  41% increase",1
+fio nothing but air under stock until 17.70-17.80  ,-1
+"AAP daily fade in motion again, people that want to be out are still at work...",-1
+I shorted NFX here with a 1.00 stop.,-1
+CTIC got long,1
+Catch up user: Hot inks is totally off the hook this morning - so many great reads  AAP MACO,1
+IHS year end operational cash flow declined 27 million vs. 2011. an  8.19% decline,-1
+Watch XF JPM BAC OD closely...,-1
+unit cost at 1.60 this see a bounce CTIC,1
+with little whips on pops and drops don't go all in on one purchase avg in through out the day limit exposure and % of portfolio CTIC,1
+"SHD Get short, someone big must be exiting imo... Sitting on some 40 puts from this am!",-1
+MON - added,1
+"If you'd like to gag on the New American Socialism, enjoy this video courtesy AIG:  ",-1
+JNP - added,1
+X - added,1
+"user Mr. Florida, hope all is well.  You've trade SAVE in the past, so Im asking you.  Do you like it here?  ooking to fill gap?",1
+SHD ... why won't you die already? ampert doesn't know 2 sh*ts about running a retail. He bought Sears for the property... ,-1
+"WN: ong  19.79. Trailing Stop  24.39 from 6 prior Stops of 23.76, 20.42, 19.79, 18.09, 17.13 and 16.39 -  ",1
+"DECK: Short  66.41. Trailing Stop  44.16 down from 5 prior Stops of 51.65, 59.07, 66.41, 74.13 & 81.85 -  ",-1
+WFM - short back on,-1
+"nice base building here 1.50 -1.54 on CTIC should test hod's in the afternoon, setting up nicely for follow thru tomorrow. #tradeoftheweek",1
+up to 16 million shares on CTIC almost 10x avg daily vol. I don't think this is a one day story. Gap up to 2.05 plenty of rm to run imho,1
+SHD Eddie ampert is a 1 trick pony...,-1
+"et the angry, anecdotal comments begin: Why I still think BlackBerry & IM are doomed.  IMM AAP GOOG NOK T VZ",-1
+user I think AAP putting in a long term bottom.....of course I could be wrong but that is what it looks like :-),1
+i had a price alert go off for WWW while off the desk.,-1
+GG to be delisted soon typicall pump and dump same story over the last few months be careful one day trades over a mil next day ur stuck,-1
+TSO option trader buys 10k of the Feb 40P for 1.60. A 1.6 million bet stock will be below 40 by Feb exp. Could be hedge too. OI is 544,-1
+DDD SSY Stops honored.  So be it. Now.... must.not.become.bearshitter.,-1
+CYBX keeps consolidating near all-time highs  ,1
+DDD The sentiment on this stock can change in a blink of an eye.  Not for the faint of heart.  Still long term bullish.,1
+MCP bottom at 11.00? Strong esistance. nder these level...short breakout Chance.,-1
+AIG wins the awsuit then announces dividend reinstatement. :),1
+TIVO is a buyout coming? Sure seems strong of late?,1
+biggest mistake i made all day was not sticking to my gut on reversal in #SOAS #2 not sticking to my watchlist DK STP ANA KOG,1
+DDD looks an easy short,-1
+My instincts are spot on in 2013 and still think CTIC will hit 2.05 this wk but still want to stick to my morning watchlist & not chase,1
+AX looks good for higher prices  ,1
+CM holding up well   ,1
+"ANA with the conference tomorrow, I guess we will hit 10.05 after all...",1
+"NFX - hearing PJCO out cautious following comScore data: Following Weak December, Overall 4Q Traffic Again Turns Negative user",-1
+"se pullback 2 initiate quick trades, not invest, long. Keep stops tight & sell up.  AAP EGN OCN AMH DDD NSM",1
+"Piper Jaffray making negative comments on NFX and CST ed Box : comScore Q4 data weak for edbox, Netflix",-1
+Maybe a good time to buy TD the 44 level appears to be holding as a key support level,1
+"CSN option trader buys 1,500 of the Jan 11-16 call spread against low OI indicating entering a position for .50. Bet on data bef. Jan 19",1
+AA there goes the kids college fund!,1
+X option trader takes profits in Feb26P selling 8k against 8k OI and rolling position to Feb 25P buying 8K at 1.48,-1
+"Green Monthly Triangle on ACAD,....Net Profit  7,177.00 (11.49%)  ",1
+So far so good regarding our APO #earnings strategy. Far too soon to call it a win though.  ,1
+AA this is why I went all in..,1
+"Does AA hold the key for the SPX?  When AA is up on earnings, SPX up +1.90% a mo. later.  ",1
+"My call for the week AA, WFC, APO, MON",1
+AA i don't like the SPIN on operational cash flow,-1
+APO Q1 2013 operational cash flow 210 million. a 90million decline vs. 2012 Q1.  30% decline,-1
+"ed Weekly Triangle on AMS,....Net Profit  26,135.00 (38.40%)  ",-1
+CM AVG THO AMWD  coiling for breakouts to new highs. Set buy stops slightly above the most recent highs.,1
+"user jemberlin that is my number too, will be a good swing, but personally I hate AMD,",-1
+SPY SPX ES  Green Hedges Stars model: next stop 1410.56 SPX 1387.92 if lower.,-1
+SWHC looks like ban on large capacity magazines and more hurdles to purchase are on the table for the politicians.  Was on the Evening News,1
+aa Q4 Operational CF declined 209m vs. Q4'11. 18% DECINE. P Spin says it was higher than Q3 2012. note Q3 '12 was 263m less than Q3'11,-1
+HT closed > 200 sma (barely) for 3rd time since Oct- eps out of the way- watch for volume > 55  ,1
+user PWE and the crazy HAO is in Green Weekly Triangle are you in short or long,-1
+WT had a huge 39 seller today... check out the daily... hold over 40-42 loooks good for a swing,1
+AJ Over 18.00 on good volume   ,1
+CYBX broke out to all time highs accompained by a strong volume suggesting higher levels.  ,1
+ONTY looks poised for bounce from bottom. SI starting to turn up & MACD is crossing up its signal  ,1
+Still trading within an uptrend channel. Short-term uptrend still intact. I'm still bullish on NVDA  ,1
+VVS Vivus CEO eportedly Says Acquisition is 'Inevitable' talking to Deal eporter at the J.P. Morgan  ,1
+user: C     what is a runner     ,1
+"V is now one of my longest growth stock holdings, ever (19 months and counting)...",1
+fio bouncing some this morning (25% short interest) Still nothing but air till 17.90s  ,-1
+tol doji yesterday could mean another lower high. Wait for  close into cloud before going short   ,-1
+"AMWD setting up very nicely above 29.33.  In the very strong building and construction product group, along with APOG.",1
+"TAB I like this one, down for no real reason, new contracts picking up slack, 2013 is about 'small cell' tech, & Software-As-A-Service!!!",1
+FS - buy signal just triggered for us,1
+ANF on the radar for a move above 48.70-49,1
+DGI FTD.  Pivot range 26.98 to 28.33.,1
+HPQ Option traders buy 10k Aug 13P at .78. 780k bet stock will be below 13 by Aug expiration. Could be a stock hedge,-1
+"FAF new highs, still holding. Needs volume here.",1
+NS Perfect day for a reversal candle on the dailies.,-1
+VOC actually triggerd yesterday but closed back at line - today itstrying again - vol 16%   ,1
+added APP,1
+"WFM - added small  89, prefer the 90-91 level to lay short out",-1
+MAS from watch list broke out of flag today- now also at top line on volume of 11% of full day ave  ,1
+CMG - 5m descending triangle. 9% avg volume.  ,-1
+user: DDD broke out from the bear flag to the upside.... ots of shorts who do not understand momentum being caught STDY,1
+DGI still moving,1
+SSYS maybe too few shares outstanding to short.. but ....11 million in operational cash flow ttm and trading at 1.9 bil mkt cap,-1
+SSYS operational cash flow declined 42% ttm vs. ttm,-1
+"Covering 1/2 WAC short 47.15, looking lower but got to book some...",-1
+"VTX option trader buys 3,700 put spreads in April 48 and 42 strikes. Could be big bet on down move or stock hedge",-1
+FS 4 hour - nice bounce ichard.   ,1
+"GNC - nice inverse head and shoulders, as well as a nice pullback to support from the previous box-breakout",1
+"user I hate to be under 3, as average I end screw up, no good for me NN",-1
+DA nice run from entry; moving stop up to breakeven  ,1
+"ed Weekly Triangle on CTIC,....Net Profit  675.00 (3.88%),...gly waiting more than 3 months  ",-1
+CW Clearwire Corp arge trades in puts show bearish interest despite DISH bid  ,-1
+APO Strong push off support (w/volume),1
+from watch list HAO slipped over line when I was not watching - volume is 76% of full day ave  ,1
+"The most bearish thing I have seen in a week, not good for the overall market at all JPM SPY ES_F  ",-1
+"user in a bullish you sell always, no buy; in a bearish market you buy no sell NX",-1
+VOC from watch list added Tuesday has now run over the 50-day EMA vol increased to 86%   ,1
+ANA getting the breakout yest and holding up today. Presenting at JPM conf today at 6:30est  ,1
+"trade idea buy ESC  25.95, target 35.50, cut below 21.40   ",1
+"SWKS decision coming there soon...expecting a break...up...looking at SI, MACD & TIX  ",1
+Just a small dose of reality on CTIC :  ,-1
+"VS option trader closes out Feb 50C selling 12k against 22k OI. Opens position in Feb 55C selling 23k on hard bid vs. OI of 2,700",-1
+Six years ago apple introduced the iPhone to the world. Do you remember where you were? aapl #sixyearsold,1
+EW Went long on EW at 93.15. ooks like it is ready to make a move to 98 w/breakout of consolidation  ,1
+EBAY basing  ,1
+Short YHOO,-1
+CHK thanks JPM upgraded   ,1
+dec 6 BB&T CM initiated SSYS with a buy  68. wonder if they still have it a buy up 28% in 30 days?,-1
+user: SSYS  EACHED SI 80%  Crash is near,-1
+SSQ pulling back as suspected. uckily sold near 11.20 res (tweeted). Watching 10.80 to get bck in  ,1
+"user The day of the MA IPO I tripled my position at 42 (IPO priced at 39) never touched it since, probably my best stock move ever...",1
+AAP P&F is bullish again...   ,1
+"AAP IF support broken then I won't hesitate to sell as lots of downside. For now, : favors longs  ",1
+user: eaders EAD. BAC >On which side is the Question... Even AAP is Broken since few Week. Medium Outlook does not look Bright.,-1
+"Green Weekly Triangle on DEPO,....Scaling at 6.40  ",-1
+"Intel a tobacco stock?...interesting read and analogy...up to Intel I guess, but the dividend is tasty  intc",1
+"Green Weekly Triangle on FNFG,....Scaling at 8.22  ",-1
+"FAF - ong  16.92. Trailing Stop  22.31 from 8 prior Stops of 22.04, 16.92, 16.40 ... 15.17 and 15.10 -  ",1
+"SWY - inside day - held 50 sma, rsi holding above 40 , looks like counter-trend move",1
+"Despite weakness in AAP, SPY didn't fill 2day's gap - reversed a mere 0.13 away.  Every stock out there outperforming AAP these days",1
+IPX  Over 22.15  ,1
+MY   Over 28.25 or 28.50  ,1
+XXIA Over 18.00  ,1
+user SHOT YHOO TO 0!!!!!,-1
+"WPI - buying looks counter-trend, possible bear wedge, wont be happy if it closes 2-3 days above 50sma, will have to re-evaluate if it does",-1
+MON - and monthly - game on...  ,1
+"HA EPS estimates being slashed as squeezed stock crash lands, looking for low 5's  ",-1
+"TSO Was long now crack spreads say low 30's = Short, VO good short too!  ",-1
+"AAP No pain and no gain, will stay above 500 (mostly) but not higher than 580 and go as low as 470  ",-1
+GEOY Should be trading at: 41.09 as each share worth 1.425 shares of DGI (28.84) x 1.425 = 41.09:  ,1
+China is FIX ing itself STDY,1
+CHK Good Info user: user S&P over 1470 and this thing rips IMO,1
+"with a name like SkyPeople Fruit Juice, SP is going to be the next AAP. just wait and see.",1
+"WH consolidating at the highs, with gap support immediately under the price. -  from  ",1
+ MT BEAV --- all setting up for possible higher prices,1
+MGM Caught in a ange user   ,-1
+New post - I could have Peter ynch'd this stock  MD,1
+FSC goes ex-dividend tomorrow paying monthly 0.0958 per share with next payment on Jan. 31st with 10.60% yield,1
+user 3DTradingtrader I do have 3.1Mio to expend and race up any offer for DEPO,-1
+VOXX a user stock. Kudos to you my friend!,1
+"MCP Performing as expected, so many red flag raised on that one over last few months, so many here could say I told you so...",-1
+"SWY - *WSJ: Cerberus, Supervalu Strike Deal - Sources - may put a bid under SWY",1
+"SE SHOT CECO, SM, ACI, DMND, NS, GCY, BBG, EF, TM, EOC, XCO, VCCF, PQ, AXAS, PST, P, KCG, MNTA, NAV, PAC",-1
+HPQ in the 16.50 calls for .04!,1
+AAP daily pop then vomit in motion...,-1
+"MCP don't catch that knife, if you think it will survive define risk buy some January 2015 EAP option and forget about it till then...",-1
+"PX - ong  9.01. Moved the Trailing Stop to 13.30 from 3 Stops of 9.01, 7.78 & 6.55 -  ",1
+AAP ... I don't see any more than 1 up 3min candle in a row so far this am.,-1
+"AAP about to go red, amazing how many times that pattern can repeat over and over...",-1
+user u didn't miss it if it is still going lower :) MCP,-1
+GDOT making a new higher high. MACD bullish cross + OBV trending upwards.Time to fill this gap.   ,1
+Back to a full size position in ZNGA here,1
+FCE triggered Tues gapped Wed so today IMO do not have to think - you just take profits   ,1
+"Goodbye FAF, acting weak, I've never liked you anyway - NEXT",-1
+not likin MSFT neg dvrgnce+hammer today goin into quadruple bottom. Still have some longs w/ stop  ,-1
+WFC If they miss I will add,1
+BAC wait for it to setup again. ooks like it will retest 10.66 for sure.,-1
+"MCP you had a lot of red flags, but when the CEO was abruptly fired without a permanent replacement this should have got your attention...",-1
+AAP gonna bounce hard off 516,1
+GS is looking super strong.,1
+wait to you see what they do w/schools in 2013 user: The JMBA airport locations are generally packed. Good news user,1
+GS wants higher,1
+"AAP watch CS, it's a tell for AAP imo!",1
+BK - added to long,1
+user: long a few NFX 100 calls at .27 expire tomorrow out at .40 +48% no need to be greedy with them expiring tomorrow,1
+JNP - added to long,1
+CEE 5 min retest   ,1
+AAP - trimmed 1/2 of our short exposure 516 level,-1
+AAP lower highs and lower lows intraday...where is big near term support?,-1
+The cult of Apple is dying -  AAP,-1
+ZION...looks to be basing under breakout...could touch bottom of triangle though  ,1
+HPQ ocket since Noon!,1
+HPQ ook at that Volume too! Very Nice! 16.50 Calls Do it!,1
+"X - took long exposure down  25.95 - 26 area - still very bullish on T look, stock has a rip - n - dip rhythm, want dry powder 2 trade",1
+"user sweet money, I need another 1.8% to scaling in Sell Short again CSN",-1
+HPQ Man these call options are so Cheap!,1
+user: user SHOT YHOO TO 0!!!!! ONE DOWN 19 TO GO lololol,-1
+"user I lost 15% of my holdings when it first opened, but have recouped it all through AAP and now FB. Buying more of both",1
+TE - new short on board for utility sector,-1
+"user Tr8r_x nice face, check the next HBAN Green Weekly Triangle (Sell Short)",-1
+"Green Weekly Triangle on HBAN,....Sell Short at 6.69  ",-1
+"user yea, but CSN in just one in my whole portfolio, when you scale, you always can fix the cost and ending with profits",-1
+BAC Out 11.78 waiting for a pullback.. if we have one today.. ;-),1
+AAP moving above 525 pivot today is bullish for tomorrow.,1
+AAP getting my attention. Showing some strength. Might actually jump in.,1
+ES SPY SPX can we hold this breakout on the ES futures?,-1
+AAP Someone catch a leak of some news at 2:30 or what???,1
+WFM - not getting much lift from the rally - looking like a possible bear flag - adding to short,-1
+"ed Weekly Triangle on HEK,...Net Profit  4,539.00 (10.84%)  ",-1
+V interesting reversal today,-1
+"BAC 100% Cash -->I will never listen to the sirens say Keep r Trade Overnight, it will Gapp Never... I Promise ;-)",1
+HPQ is 17.50 too much to ask for tomorrow??? :),1
+SDJPY No breakout on the close. isk rally with equity traders in front of their skis into earnings.  ES SPY SPX,-1
+Market underperform day thanks to my DDD + SSYS positions.  Staying long 3D printing.  AAP kept me green.  More upside to come.,1
+"Green Weekly Triangle on HEK,...Sell Short at 3.96  ",-1
+"FS - Solar Market to ise 22 Percent in 2013, Deutsche Bank Predicts  ",1
+nice! - user: Huge volume in MS breakout today.,1
+The price action is saying that AAP is not going to die after earnings. It had multiple chances to break 500 but didn't.,1
+BN BISH!!! to announce a 15% increase in Holiday sales  ,1
+CS nice move above the 50 and 200 DMA. Nice base structure on a daily/weekly pattern.,1
+"OCZ -CES 2013, as Vegas, Nevada, January 8th through 11th (in progress)!",1
+user: T user: user: GAME ON!? #readyscoregoals. This company just gets it... #CanadianGrown ,1
+don't tell everyone ... user: That's a nice looking diverse list of 52-week highs: JNJ MMM GS OC F WAG DE,1
+Alarm bells should ring for DDD investors. ed flags are highlighted in this detailed and well researched article  ,-1
+AAP Marketing Chief Phil Schiller will  not resort to a cheaper iPhone to expand its market share ETES,-1
+"Squeeze squeeze slow grind higher continuing SPY 147.45 ES_F 1471 , WFC better deliver the goods tomorrow...",1
+HSA It always comes Back.....,1
+user HSA Good Day?,1
+YHOO Cramer kiss of death thanks bro. Can I get a BAC and JDS Cramer nudge? user kifmonster,-1
+MGM held this long term resistance: user: MGM Caught in a ange user   ,-1
+AON Continuation on good volume  ,1
+NFX  Over 28.20 or 28.40  ,1
+ZC Over 4.75 on good volume  ,1
+ES SPX SPY star on 4hour,-1
+A doji like candle close today on F may signify possible retracement between 100% and 61.8% Fib levels. Watching closely for more bull run.,1
+SWHC will continue to accumulate more mar 9 calls on any pull back below 8.10,1
+ISIS - with continued volume this can go much higher 13.22 to 14 is easy for this - it has some big green moves,1
+"user TSO Don't be fooled by one day moves esp. after a large drop, the trend and my stop says I'm still good.",-1
+SM - Decent base. ook poised to break out.,1
+user: NFX  -- I let my  wkly 100 CAS go from VEY green to ed.  Not good. But will hold them,1
+TSO user Attached shows Fib and old resistance stopping upward move.  Will stay below both.  ,-1
+CGI ove this trucking co. should be bought on any pullback!   ,1
+QCOM Best play on Samsung (Snapdragon) and anti AAP play.  Best tech. play too.  ,1
+QCOM Another view w/ blue sky above  ,1
+Student Debt: A Trillion Dollar Bubble -  SM NNI,-1
+COF is approaching key resistance level at 62.92 a break of which could accelerate the stock price  ,1
+AON has been in an uptrend since July and Thursday's break suggests a continuation of the upmove  ,1
+GDOT W/ Bullish MACD cross along bullish SI action it should only be a matter of time to take off  ,1
+"BAC failed to break below, uptrend continues",1
+"user: With the Financials (XF) already +4.6% YTD, Wells Fargo WFC might have to deliver the moon today #expectations",1
+One small step to send cable TV back to the 80s. Quality content exclusive on NFX. Can't wait for the return of AD.  ,1
+ong setup SM  ,1
+user: DDD 3D Systems Wins CNET Best of CES Award for CubeX 3D Printer,1
+COMPQ SPX AAP FB BAC GD SV 5 Characteristics of a successful trader  ,1
+"NKD and SINA are two breakout stocks on watch list for today.  Buy high, sell higher!",1
+msft if it takes out 26.29 it has a gap to fill from jan 2012 ~26  ,-1
+MS Morgan Stanley Hits Our 1st Bull Signal Target (Called Dec 11)  ,1
+JCP Cash-flow distress That doesn't sound very good... BS downgrade to sell,-1
+Market Wrap Video + Additions to Watch ist  including: AEG DI IPX NG MY ONTY QIH ST XXIA,1
+GOOG so far so good in premarket. If the averages help this could get above 745 today.,1
+HPQ where do you think that new Mutual Fund money is going???,1
+X adding a little,1
+DK moving out of his Darvas box - keep an eye on volume.,1
+GF Open new position at 35.52 - new uptrend starting  ,1
+BAC It is the effect of WFC... so it SHOD be over in half-hour... I'm moderately Bullish,-1
+user: NG long setup   ,1
+"Trend in outsourcing...workforce...is extremely strong, especially in the medical industry... ASGN has a lot of room to grow. -  Drogen",1
+AAP climb 525ish then took a small step backward - wedge forming. Breakout is imminent.,-1
+I hate to do it but reducing DDD position today. Just getting too big in portfolio. Still very bullish.,1
+AAP - took down short exposure by 2/3,-1
+Would be healthy and desirable for  to eventually put in a little handle/shakeout before climb over present 58.90 pivot STDY,1
+"ed Weekly Triangle on MH,...Net Profit  4,895.75 (7.22%)  ",-1
+"its time to put GOOG under the warming lamp and get some heat into it, just languishing for now  ",1
+"Green Weekly Triangle on MH,....Sell Short at 4.06  ",-1
+"GOOG averages trading up, and this is lagging now. Actually near the lows. Not what you want to see. Strong support at 736-737.",-1
+OC osing mkt share to JDS  T now and looking for *much* lower prices. Will miss earnings too!  ,-1
+WFC I think we go green at some point today,1
+user 3DTradingtrader if go over 6.60 I will sell more DEPO,-1
+"So much topping action here. Shorting KEY at 8.90, Shorting CAT here at 95 with a stop at 95.6. ike these trades A OT.",-1
+"MDSO - ong  27.51. Trailing Stop  37.00 from 7 prior Stops of 31.30, 30.17, 29.41, 27.86, 27.51, 25.15 and 22.79 -  ",1
+been talking about  low priuced stocks flying- FCE buy was Tuesday  - 30% gain way overbought now  ,1
+And there goes that 520 level on AAP.,-1
+WPI - whoopie - that's the stocks nickname,-1
+NFX is this developing a HGE cup&handle with pivot at 120?,1
+DNB Call volume spike ahead of earnings  ,1
+S have anyone noticed the HGE volume today? If this thing pushes through 6 today we might have a runner for next week.,1
+"MCP craters, management tells u biz sucks, all sorts of red flags, and some people call that bullish?  bottom? delusionnal.. #youondrugs?",-1
+"user IV having a down day today, but take a look at APO, 20 green days in a row since Dec 13th! Now, there's a rip and a tear!",1
+ST from watch list over lien a bit on volume of 51% of full day average   ,1
+"APO 20 green days in a row so far, but no new high yet today.",1
+Not looking for any sustained upward momentum until after next Friday's options expire. Too much interest in holding it down. AAP,1
+BAC I hate analyst.. They Scrap a beautiful day :(,1
+The big setup for early next week is GNC. It will probably run into earnings as market expects huge numbers. eports Feb 11 (long),1
+BAC at least it' is a resilient stock...,1
+CAT holding that 95 level beautifully today. Cup&Handle from September to January. A good market and this BEAKS OT.,1
+GD Trying to break d/t line after finding support at 200 sma. ikely needs GD to hold 158.5  ,1
+F 9.8 is a possibility before market close... so much power under the hood.,1
+"Green Weekly Triangle on KTOS,..pdating  ",-1
+DE Approaching next level to Short back lower - 61.8% Fib and old   ,-1
+F evel II easing a bit but still allowing the possibility of a late day run.,1
+AWK En Fuego part 2   ,1
+user welcome to the TK club,1
+GTAT healthy pullback on low volume after a nice run on heavy volume.Positive MACD divergence. ong  ,1
+TAB user alexcampbello This is what the street sees and hence lower she goes  ,-1
+sold 3/4 of AN +0.27 (+2.94%) - keeping 1/4 of position overnight,1
+AAP - didn't get the pricing we wanted but added to the short after the close,-1
+Market is looking strong. Think financials will catapult SPX towards 1500 next week. GS BAC,1
+AN breakout.,1
+HT bearish,-1
+"Green Weekly Triangle on DN,....pdating  ",-1
+Demand for breast implants at all-time highs - good or bad for economy? AGN  old wallstrip too  ,1
+"...offhand, that includes in growth: EN Y SPF MHO ITB AD TTM HTZ DOM WK EMN PO SCA WO FET BCEI FXI HDB EVE ...",1
+Wk #2 perf 13 Stocks for '13: DDD DNKN FT INVN KOS NKD MOV NTSP AX SCCO SCA SSYS V  ,1
+NKD stormed back in week #2 with a 4.29% gain after being the dog in week #1 #13for2013   ,1
+cstr found support at 26 ema.  nder the 50 sma tho look to short under 26 ema & add on cloud break  ,-1
+pnra missed this one yest on the short side but will short under 50 sma   ,-1
+NKD following FB lead. un to 122-124 before more resistance? user  ,1
+"Although sounds random, when AA is down on earnings (like this week) SPX down 0.62% next mo.  ",-1
+"O - short setup if signal fires, like this, it will be a 50sma reject and weekly & monthly time frames look like they are weakening",-1
+IMM didnt come out this XMAS with BB10 so they take over next XMAS - take share from AAP - they smart well timed,1
+user ShadowTrade1134 I think it's a fair analogy - VHS beat Beta because it was less expensive. AAP,-1
+ Bear flag?  from  ,-1
+PCN bull flag & pennant?  from  ,1
+"DNDN: nice vol expansion friday, and may be worth watching for a few bucks.  ",1
+So what I used to do at Borders is called showrooming...i hope this works for TGT...the nature of retail is changing  ,-1
+AAP Trend emains down and Giving Another Sell Signal...  ,-1
+"user: SV super cheap stock, that appears to be getting ready . to fill a  gap above current price!  >Nice name  2",1
+"user jmixer INO since Jan 7 it is a green weekly that is why I closed my long at 0.67, my broker doesn't has share to borrow me,....",-1
+user jmixer I will wait for 0.57 to re-charge again my weapon against INO,-1
+ddd nice st/pennant pattern here.  ook for a breakout   ,1
+IWD short 15 to 13.50,-1
+AAP - Korea Hot Stocks-G Display falls on talks of Apple order cut -  ,-1
+Sigh AAP will continue to keep QQQ down. I'd love to see AAP rebound on negative news.,1
+"AAP IMM: Apple reduces component orders for iPhone 5 on weak demand, WSJ says - Party is OVE!",-1
+AAP i love you but I'm,-1
+Song for AAP bulls  ,-1
+"Today could be the day where AAP breaks 500 and the rest of the market doesn't care, what a change... #divorce QQQ SPY",1
+AAP The only thing you should ask yourself; is today the day to buy those Feb 480 puts...,-1
+AAP - 25% of short covered - blows that system would not route to NYSEACA before 0700 EST when offerd below 499,-1
+AAP - 1/2 short covered,-1
+CS will play lower,-1
+well done to those short aapl friday.....gutsy. got the news you needed.,1
+AAP under 500 is a great long term buy.  Ignore the noise.,1
+Thank you AMZN for pricing Fire at 199 in Nov 2011. et's do same with a phone in 13 and the wireless service in 14. Customers WIN!,1
+JNP - acting well again - added some back,1
+"AAP - Bearish News at the Bottom, Or The Start of a New Downleg?  ",-1
+AMZN has momentum. Its a better buy than AAP or GOOG for now. Close above 270 might push it to 300 quickly.,1
+"Green Monthly Triangle on CHTP,.....Net Profit 11,664.75 (115.73%)  ",-1
+PCN wait for that close above 662-663 to get involved in this name. And watch for VOME to confirm uptrend continuation.,1
+"AAP sending a reminder to NOK,",-1
+CMG - Still a good low risk/reward short below this descending trend line. Core short is Bear Calls  ,-1
+COH close above 60 is extremely bullish. Watch it.,1
+HPQ heading back over 20 imo! Funds oading!,1
+BAC out of my swing trade for 8%.  Protecting profits.,-1
+I remain convinced you're either in Apple stock AAP for another five years... or you should have sold already. There is no middle ground.,1
+"I am long AAP (about break-even) and EAIEST I'd sell is 2014, if no dividend hike happens. PE is under 9 right now on FY14 EPS of 56.",1
+CSCO c'mon 21.,1
+aapl symmetrical triangle on 5 min looking for topside breakout  ,1
+AAP drops over 25% since Sep b/c of concerns of maybe a 10% bump in Android phone sales? #mathfail #oversell,1
+aapl in long per earlier tweet on topside breakout of ST,1
+AAP appears to be dragging down QQQ and SPY #stockaction,-1
+"AMZN SNTA ANA all ripping today, even X setting new all-time highs this am.",1
+"FOM is breaking above Friday's high, a strong continuation higher is likely. Next resistance 5.37  ",1
+VVS ong set up:   ,1
+ook how long DE has been fixing itself. Miraculously HPQ has done what DE can't. Not! QQQ XK,-1
+AAP Going to be interesting to see the pop and squeeze on Jan 23rd if we see a positive report.,1
+A week later these stocks are trending bullish: 5 Shale Plays for 2013 APC HES COP MO DS.B  ,1
+"user The rare earth business fundamentals are terrible right now and MCP has all sorts of issues on top of that, compelling ?!!!",-1
+user xraystocktrader Great dividend pay and continued upside. Eager to see the next acquisitions CVX,1
+user Krisanti that is why I had mentioned that AAP sent a reminder to NOK,-1
+drugs are back...lance is gone user: arge-cap Health is doing well. Both PFE and JNJ hit new 52-week highs this morning.,1
+"Marc Benioff Has Swagger, But What About That  Income Statement? -  CM",-1
+"Green Weekly Triangle on SHO,....pdating  ",-1
+user: ANF triggering at the 48.70 buy price. Nice 15 min candle. Need to build on this intra-day momentum,1
+HBI from watch list briefly went over line and puled back but volume is ok at 57% full day average  ,1
+XXIA 5-min found it helpful to watch the 9-period EMA pink - when using gap rule to protect profits  ,1
+AAP fills the upside gaps within an hr but takes an eternity to close the downside gaps. environment is clearly bearish,-1
+nice DE.,1
+GW  Higher volume on the way down as the news pumps the stock up doesn't sound very bullish to me.  ,-1
+Student Nobiano  made 14% roi on a BT trade. Great for you sir!,1
+user says you might double your money in closed end fund GSVC this year if Twitter pulls off a good IPO.  ,1
+bac fib levels  ,-1
+F is a buy to 11 imo. That's my price target for Feb options.,1
+Triple bottoms are rare.  This is the third time that AAP traded near 500 in last 2 months.  Still too many AAP fanboys.  AAP,-1
+here  at hedgestreettrading  we are very  on sons,1
+HPQ my 17.50 calls Are Happy Now!,1
+"HPQ Trading at a Multiple, oad the Freaking BOAT IMO!",1
+Awesomeness...Monday links: swapping risks & around the financial blogosphere.  via user AAP JO GA,1
+Going Private is the new Going Public. DE,-1
+user T3ive A looks very strong for a breakout play.,1
+CSCO MSH! MSH! MSH!! 21.00 c'mon mon..,1
+"etail drill-down raises questions - XY, XT, TH  -  SPX, SPY, M, GPS",-1
+F coiling so hard right now,1
+K: ong  53.69. Trailing Stop  52.16 from 2 prior Stops of 51.25 and 51.05 -  ,1
+"Si VFC alcanza los 153,98 , reconfirmaci?n de la tendencia que lleva la sesi?n, provechando el rally alcista de final de sesi?n....",1
+DE 5x vol many people gonna lose their butt if there is no buyout,-1
+I think way too many people bought these dips to 500 and won't know what to do with aapl at 420....no plan equals all our mess,1
+Jeff Bezos...having just a wonderful day....as usual....and he's thin.  Bastard.   amzn,1
+CSCO OO!! 21.,1
+AAP lets see if we say goodbye to 500 today.,-1
+I totally helped  beat revenues...i do and I do and I do ...but yoga bores me and western world will bore of it as well #doritos,-1
+Why can't they take AAP out of the index? It's holding everyone back! Slacker !!,-1
+"Green Weekly Triangle on SONC,....Sell Short at 10.96  ",-1
+user: DE 14.75 would fill a huge gap from 5/22/12 lol good luck with that,-1
+"ssys website investor relation page uses morningstar stats to quote mkt cap. MON Statistics are wrong. there r more shares out, way more",-1
+Shares of FOM are drying up as the accumulation continues. Daily technical indicators are bullish.  ,1
+"ABC 123 Clearing up and out of a nice base, #Drugs PJP SPX SPY  ",1
+"user an error on Morn data.its not updated. shares outstanding equals 41 million , not 21million. mkt cap 3.4 bil not 1.8 bil",-1
+user i stumbled upon a data discrepency for SSYS. On MON YHOO and ssys website it says 21m shares out.  really 41m shares out,-1
+SNTS Getting puts at the open. eally pleased that a decision came after hours. Allows for much better entries into positions.,-1
+SFY would start with a trade to the second line  - so starting over 33.30,1
+A typical set up - Over 26.11  ,1
+"INAP 717, riding 10MA, extended, based, bolly deviation1 & 10MA right under, room to upper bolly  ",1
+AAP needs to hold here 500 or could see lower prices. Earnings within couple wks  ,-1
+"HPQ oad every dip, everyone wants in!",1
+. user: AAP hits 491.50. Hedge fund margin (leverage) highest level since 2004. The question is how many hedge funds blew up today?,1
+Out of my aapl 490p at 8.50 for 47% 5K!,1
+GNC is perking up despite general market weakness. One of the better risk/reward long setups (long),1
+user: PI nice early action... on watch   ,1
+PO is perking up again. Very strong specialty chemicals' stock,1
+JPM wants to go higher. XF SPY,1
+DE outside bbands - I have to believe this pulls in a bit.   ,-1
+"BK - want to buy lower but hourly showing improvement, added to long",1
+AAP - took 1/2 of remaining short off,-1
+tol finding support at top of the cloud   ,1
+ZCS broke out last week & has held up well pulling back on low volume. Keep an eye for a possible breakout over .83,1
+"CSN,....sell short at 9.38 scaling",-1
+New Blog Post - 'Momentum Monday - AAP dip buyers missing the market mojo...  ,1
+"AMPE,...sell short at 4.48,  scaling",-1
+So to be sure. Everyone can short AAP and make money. Everyone is going to jump in when market crashes on Debt Ceiling. Perfect.,-1
+Great move by Guggenheim in hiring ex interim CEO of YHOO...    ,1
+AMZN has a pretty strong intraday support at 269.5. I'm still in until that support breaks.,1
+APKT back over 24 here is where it gets interesting   ,1
+AAP when are earnings?,1
+"Don't look now, but INT is clearing all-time highs. 10th time is the charm",1
+user:  erased the opening drive down. still down 4 so plenty of room...let it consolidate,1
+GOOG that's some nice and fast move off the lows.,1
+"Again, this mkt is FAT while AAP is down 2.5% cratering thru huge psychological level, tells me this tape is strong SPY #dontbeshort",1
+AMZN remember holding 269.5 is very important for break out continuation. Watch it. I'm long and will add on break of yesterdays high.,1
+user: HEO beautiful flag forming...7 and 10 eventually,1
+"DEPO sell short 6.43 scaling, next level 6.27",-1
+"AAP Study These easons Why Apple Failed, and Where It's Going  STDY",-1
+user: NVDA is a growing cash cow with a 20 P.T by EOY 2013-2014 XK AMD STX QCOM INTC #Technology  ,1
+PATH See no real company or new drug approval value lost!!! just shorters manipulation,1
+THC from watch list set up well yesterday and today above line - volume 46% of ave full day   ,1
+AJ from watch list triggered a few cents yesterday and continues today - volume 41%  ,1
+ODP - Breaking out from resistance  ,1
+aapl 484 going to level in to an aapl trade today for a daytrade to the long side....first quarter buy now.,1
+no reason for you to try to get cute and try aapl long.. stick with trend until it [rev] holds above 489 and 500,-1
+AN - Confirming nicely today.  Seeing a double bottom on the daily & now pushing above the 20 day moving average of 9.53.  Good action.,1
+TAB Mikel H. Williams helped the acquisition of Viasystems Group and Dennis F. Strigl use to be COO of Verizon Com & CEO Verizon Wirless!!,1
+"Flipping out the AAP trade for 6 points ....never got the meaty trade off.  still obviusly long, chipping away",1
+user AAP In what world did Apple expect to order components for 65 million iPhone 5 handsets in the seasonally soft March quarter?,1
+SWHC Obama/Biden to speak tomorrow on gun control legislation...,1
+"user a lots, I want to cover at 3.88, so I have to complete 25 to 35K of shares AMPE",-1
+CMG Not the most exciting stock lately however upward trend still in tact; consistently pushing above the 20 day average. 297.00 now key.,1
+eading Stocks eading & Stock Market ESIIENT on down days. Further indication ally to continue. ES_F GNC NSM EVE  DDD STI NF,1
+APO Being accumulated.  Bounce off critical support and above the 50 day (+20.00) tells me that this now (likely) continues higher.,1
+AAP  Just broke through a downward triangle coming off a massive head and shoulders.  This thing is toast.,-1
+AAP  The number one problem is that earnings expectations are way too high and the smart money knows this.  ,-1
+CHK squeezie   ,1
+VVS breaking out of a flag setup. MACD cross-up. ong  ,1
+CHK 15 min kelt/bbands fired earlier while at lunch out 1/2 the 17.50 this AM +100%   ,1
+"HPQ and here she comes back, if DE gets bought out HPQ will ocket!",1
+AAP dumping into the close still see this going sub 400 maybe at or after earnings report in a wk or two. GY TOWN,-1
+AAP Why do so many people believe that Apple should be commenting about stock decline b/4 EC? It's called the quiet period for a reason.,1
+DGI DDD both acting strong ending at hod with decent volume at the end.,1
+"ed Monthly Triangle on CYTX,....Net Profit  9,845.00 (11.01%)  ",-1
+"Green Weekly Triangle on CYTX,....Sell Short at 3.02  ",-1
+WMB might fill this gap and reverse.  ,1
+FS - inside day - we will add to long on break above & our stop lives a little below 30 area,1
+AAP  Probabilities rising for a test of the 2003 trendline in the ST. Mean reversion pts to 350.  ,-1
+"WFM - looks like a bear flag, we are short, will respect the flag and exit on a close above   ",-1
+BCEI Over 31.30  ,1
+DCTH Back over 1.52 - 1.55 is you like volatility and  low priced ones   ,1
+AMZN - still in its uptrend motion. Buy dips.  ,1
+NFX - next resistance level is at 110  ,1
+PCN - long setup  ,1
+"AAP If it breaks the 5 year support line, Apple would have to depend on iTV to take it the 52wk-h  ",-1
+VG is consolidating inside a large ascending triangle w/ TP of around 3. Breakout watch over 2.55  ,1
+"AAP trading close to point&figure trend line support. Next target is 470, at trend support.  ",-1
+BAC Yep it Will B a good day for Financial... As soon as we have volume (9.30am) it will ise P !,1
+CIE COX CMG getting smacked off the open,-1
+SSYS postpone Q4 release from Feb to approx March 4th.,-1
+"Check out list of top performers since AAP peak, from user - Dogs like FS DE? +80% in NFX? Please.  ",-1
+NEW POST: Positive Earnings and Cost Containment Continue to Propel MS  ,1
+GOOG and AMZN showing some early weakness.,-1
+If AMZN takes out yesterdays lows ONG better be careful.,-1
+if AAP reverses early gains QQQ will be in really big problems.,-1
+"OABC - ong  16.55. Trailing Stop  22.80 from 8 prior Stops of 21.16, 20.18, 19.00, 18.70, ... 16.74, 15.90 & 15.25  ",1
+"TWC on the way to potential breakout over 99 channel. Tgt 100, 102. Wht u think user   ",1
+NVDA breaking 30 min trend   ,1
+"AAP ong position in Apple after reaching 503,38 . Maximum of the day. Stop loss 500,4 .",1
+"CEG Too high, too fast.  This one is well due for a pull back here.  100.00 is key.",-1
+"AAP If Apple reaches 504,44 , change the Stop oss and put it in 501,60 . Also a chance to go long.",1
+ANN - 200 period - hammering. ooks good. 4 month pattern.   ,1
+BAC p p Step By Step... esistance 11.65-67,1
+Dip buyers alive and well everywhere... user: Dip buyers alive and well in JPM.,1
+NVT at 1.73 vs. OC at 1.73 I went w/ OC vol difference today but should roll over by end of wk. OC just my opinion could be wrong,1
+"Green Weekly Triangle on GPN,...pdating  ",-1
+BAC 11.75 -12.00 Today ???,1
+CNDO - we are in at 5.78.  ooking for 7% on this one.,1
+NVT has the most unusual increase in #socialvolume,1
+ong CF,1
+"FIO ioSCale announced today at Open Compute Summit for hyperscale & spinning disk replacement, another game changer",1
+user: Twitter followers! Are you bullish or bearish on AAP ? We want to know. Bullish and cautious! =^.^=,1
+NVDA - IHS daily oversold   ,1
+DECK Once again proving to be bullish (confirming) bouncing above the 20 day on increased volume.,1
+AAP Still long.  Bulls in charge today.,1
+user: NFX ising wedge. Normally bearish. Close below 94 confirms.   >  nice one,-1
+CSN did not catch the bottom but bought some on weird dip - hope that manipulation means good things to come,1
+GS a solid move higher into new highs today on on strong volume.   ,1
+NYNY moving right along. ,1
+Student Stephanie N said  HOY GACAMOE!!! In 30 SECONDS I made 24% OI on NFX.. :-D  user TradeSmart,-1
+"ed Weekly Triangle on CSN,....ong at 6.8815  ",1
+AAP  100 week support   ,-1
+WCG onged today swing...  ,1
+"AAP One of the most reliable patterns I've come across, surprising its an intraday pattern. study  ",1
+YHOO volume is BIG. Closing above 20 today will give enough fire to make a new 52 week high later this week.,1
+HPQ opens over 18 tomorrow!,1
+"user hey great call on HPQ, you think funds are buying now O!",1
+BAC IMHO think will gap up on E,1
+"HPQ I'm going to nail this Mofo! HPQ going to be in the Spotlight tonight, ooking for a eal Gapper tomorrow!",1
+SWHC bet PM will be mid to high 9's,1
+AAP We are long Call atio Back Spread 510/505,1
+"user HPQ that double top will be taken out by the close, watch and learn!",1
+G SWHC Market may be figuring out legislation on guns won't pass or be much at all. Shootings? Time to load up.,1
+BAC ike I told 11.75 in AfterMrkt = Very Good Setp for Tmorrow ! :),1
+"The GOOG daily chaos event is looking good green line is moving above white, just like dec9 move  ",1
+"Green Weekly Triangle on CYTX,....pdating  ",-1
+"HPQ Autonomy was being looked at as a total loss, now they could sell it if they so wish??? HPQ in a strong ptrend here! OAD!",1
+Apples are falling down? AAP   ,-1
+HPQ how many upgrades tomorrow assuming Autonomy is now worth something...,1
+HPQ very nice close imo!,1
+"FOD cover at 3.93,....net profit 1,571.00,...ugly triangle, no more with FOD",-1
+CSN My target remains at 9 before the data! More news...   ,1
+DVA really nice break through DT line on volume today  ,1
+SSYS exuberance is a stock generating 2million in operational cash flow for 9mos trading at 3.4 billion. or u could say a top,-1
+Big win for brokers... New Mini Options Bring Big Changes For Traders  via user SPY AAP GOOG AMZN GD,1
+"WMt buy signal was too easy, just the math baby, and good discount too !   ",1
+some thought my M call was too early but it screamed fashion makeover and WOW we look good now  ,1
+JPM - a whale of a trader,1
+Now watching BA,-1
+"CMG > keep these levels in mind: bounce to 284-288, followed by down leg to 238, a bounce to 254, and down leg to 212 / 202.",-1
+V Over 11.35  ,1
+Disney's new Infinity game is going to be huge. My take  DIS ATVI,1
+"BA  Now it will come back to haunt them for years, as they say,  you eap what you Sow ",-1
+"PGX no hype, spot 6.88/.90, b/out 6.91, no resist on daily til 200SMA 7.33ish, 50SMA sliding under  ",1
+DN first hint of trendline break to the upside ?  ,1
+"AAP Going on my confirmed theory selling came from those with inside info last 45 days, cat is now out of bag, no suckers left, bullish",1
+AAP Another Downgrade !  Broken,-1
+BAC Bank Of America Earnings Stronger Than Expected  ,1
+GOOG and AMZN calls are on my watch list for a day trade.  Taking a break from AAP,1
+AIG  Going up in the premarket so somebody knows something I don't know.,1
+BAC since the earnings did not seem to impact the price a break over 12 is in the cards.  Good longterm hold.,1
+ANY looking good for 22.50 calls :),1
+"user: GOOG put options at 8 year high, interesting",-1
+Trade Setup AKAM -  ,1
+Trade Setup AA -  ,1
+Trade Setup CF -  ,1
+OW PT hit and now at resistance  ,1
+SANM from watch list with volume of 9% ave full day - slightly over line   ,1
+"HPQ should Explode today, 18 coming imo!",1
+CEG Confirming the expectation below 98.50.  ikely to crack here.,-1
+You can't garden wall a browser AMZN AAP,1
+American Apparel APP presents at 11:05,1
+CSN Correction: Thermodox is a nanosoccer ball that's anti-vascular as well as anti-neoplastic!,1
+PI daily thx for the heads up user   ,1
+GME over 23.40 here looks good to go,1
+"WOW, just saw the price of next weeks (E 1/22) GOOG 720c at 20!",1
+raising stop on OW right under today's low around 35.90,1
+"WN: ong  19.79. Trailing Stop  26.64 from 7 prior Stops of 24.39, 23.76, 20.42, 19.79, 18.09, 17.13 and 16.39 -  ",1
+"When is Demark coming back to prop up AAP?  Yesterday a classic dead cat bounce.   Now a value stock, need go lower to find size buyers.",-1
+NG - liked it where stewie mentioned - off the 20MA. First pullbk2 same would B good 2nd entry  ,1
+IN SOME ANA,1
+Bookmark arry Page on why Moonshots matter ...must be why options markets exist :)  goog,1
+SWY - now drinking Safeway store brand (Columbian whole bean) coffee out of SBX cup,1
+Back in BK long. Nice reaction to earnings. This has the makings of a runaway/measuring type gap.,1
+KOS kept wishing it was  first cousin.  looking good here.   ,1
+"TIVO holding well above MA(50) 11.60 this morning, I believe it is a long setup",1
+SMB adar weak today FAST ADTN CAM   ,-1
+ssys adds over 100 million in market cap today,-1
+user: ANA looks like a coil setting up,1
+HOTT FNGN PIM FNF - moonshots all over,1
+"WMT - H/S, struggling at the 200dma as well, MM target about 10 handles to that gap fil below   ",-1
+IPX biotech on watchlist with vol of 42% full day ave moving toward gap and 200-day needs more vol  ,1
+SWY - wow doing a number on the bears this week - scaled out of a little today,1
+AAP 900 lot offered in the eg Jan 500 Put at 1.71. -28 delta put could push the stock up or create support,1
+WCG Everybody looking for something to buy...basement trade w/fundamentals...  ,1
+AMZN trying to break higher  ,1
+"user INTC Yep i had to follow that money, looks like something good is coming down the pipelines!",1
+BA SHOT 130  75.25,-1
+"Green Weekly & Monthly Triangle on ISIS,....Sell Short at 14.14  ",-1
+GS exploded in the last hour. Green bars since 1.,1
+"BAC has to turn and run with the SPY, watch for EOD eversal!",1
+FAF looks good. Pivot range 24.87 to 26.11  Volume +76% today. See if close and/or get over days highs at 25.60.,1
+"user AM is down because, who really buys cards after Christmas? ",-1
+"C Big Pop, BAC to Follow!",1
+"WPI - inside day cracks to downside, we are already short - no add here",-1
+ZNGA There are a lot of supports here SMAs. I just bot few shares at 2.54 Falling on low volume  ,1
+GEVO Watch for a break above 2.08 to test the recent highs at 2.43  ,1
+ssys Voyant Advisors negative report. sees high risk in earnings. An earnings risk 9 on a scale 1 to10,-1
+"ssys on Nov. 13 report Piper Jaffray put new price target in report at 76, Dougherty in December's report put tgt at 67",-1
+user: : BAC Consolidation in progress...    yep its consolidating  to 10>  Should go to Emotions Anonymous  Take it to hard !,1
+ZION Cup & Handle pattern. The stock would confirm the pattern by breaking up through the 22.8  ,1
+GEVO Broke out of a bull flag pattern to the upside today & the move was accompanied by high volume  ,1
+Added to holdings in AAP BID FB in AH. Also holding DDD. Down to 25% cash. ,1
+"BAC Hawken has a price target of 11.50, estimating the company will earn 95 cents in 2013,",1
+Nice one from user Marketshare or Margin....the AAP dilemma... ,1
+"DPZ nice intraday upward reversal adding to the bull flag, keep on watch for move over 46  ",1
+"TEX another nice bull flag, watch for the MACD to signal next breakout  ",1
+"WH also a strong day today, looks ready for the next leg up  ",1
+"GMC keeping a nice consolidation, should make a big move soon, MACD & SI turning up  ",1
+"AJ really strong up here, but caution overbought SI and declining MACD  ",1
+"CEN nice gap support, looks ready to head higher  ",1
+"WYNN nice bullish consolidation, could pop soon  ",1
+"VS similar pattern to WYNN, bull flag building, keep on watch  ",1
+A in a clear downtrend with no sign of relief  ,-1
+" also not very healthy, fell back below DT line after breaking it, SI weak, MACD turning down  ",-1
+"NFX hammer on the breakout back-test, watch tomorrow for a bounce  ",1
+INTC Anyone have any thoughts on how late algo trading and option OI will affect the stock price tomorrow? My guess 20.50 to 21.50 at close,1
+Need body parts? IHI SYK MDT ZMH user nice call BSX  ,1
+"AAP QIH The cheapest, most transparent China play is Apple. I avoid SOEs... management decisions based on guanxi and party interests.",1
+"user a36tran user as I said many times AAP big money is shifting towards this stock - that what I did 11,12,13,14",1
+OW Breaking out to a new 52 week high.  ,1
+G has been quiet lately; good to get in before the pop,1
+IF  Over 19.60  ,1
+"Intel earnings - capex & mobile matter not EPS.  I mean, is the PC biz decline news to you? INTC HPQ AAP DE AMH",1
+NEW Blog Post- Watchlist 01/17.  F KBH GPX SSQ SBX CSOD HD WT OCN EBAY TEX I CHK ITG S  ,1
+BAC user Hope for a consolidation at this level if it drop it will stop to 11 support. Nxt 4-5 day s to come back to12 IMHO.,1
+Hot osers FNS TSX INTC AMN JDS,-1
+"GEO nice pop this morning, moving stop up to breakeven  ",1
+long ACI 7.39,1
+CEE - to be clear - no chasing - entered Monday ong  ,1
+"ADBE continues to weaken, not following market. Short on break under 37.60. user  ",-1
+"V so far found support on the ceiling of the old channel, will look good if it holds above it  ",1
+"user: ADBE continues to weaken, not following market. Short on break under 37.60. user  ",-1
+"SHW - ong  131.42. Trailing Stop  140.12 from 4 Stops of 138.36, 125.06, 122.79 and 118.70 -  ",1
+"BAC fully engaged within a secondary reaction off of its 12.43 high on 1/07, and points to 10.80/50 target zone",-1
+BAC still see this getting back to 10.50 possibly lower when the overall market pulls back next month.,-1
+"user: G two weeks away from its earning, ready to rocket within two days",1
+AAP > the 497 area will likely fail.,-1
+WH gap filled,1
+Monday AAP said 500 pin - still confident of close to a  500 pin for - take a look at this crazy OI spike.  ,1
+STT strong bounce today continues to make new highs - been sharing this one for days now. Nice run   ,1
+CSN Another SA article for the ONGS...  ,1
+AAP -   percolating? Perhaps a push above 500 soon...,-1
+"SWY - corn on a run, bears getting the cob  SWY",1
+"AAP Wall St. most hated stock currently. Careful short, anything short of disaster will be viewed positively. Bit risky long. #MainEvent",1
+FIO: Insightful analysis on how Fusion-io is avoiding commoditization (via user):  ,1
+"MCP.. sorry kids but Nissan, Siemens = NOT getting in the mining business, but have some fun buying whatever somebody is trying to unload..",-1
+"CSN I know it's a crazy thought, but WHAT IF this goes green by EOD?",1
+SCHW - breaks out of flag,1
+GS  Ask is being pummeled...managers cannot miss this and afraid they are missing the train...,1
+WH nice rounded reversal pattern intraday since the gap fill,1
+"ES_F spikes off bottom. eading Stocks comatose but still leading. WAKE P!! OCN SBGI NSM FIVE GNC Makes you say, hmmmm?!?!",1
+AMAT reached very high overbought conditions. I will wait for a pullback to enter long  ,1
+"ACI Option trader buys 20k of the Feb 7P on a hard offer vs. OI of only 1,400. Earnings are Feb 5th BMO. Could be a hedge against long stk",-1
+ZION Stalking. Cup & Handle potential breakout w/ anemic volume on Handle. Breakout candidate  ,1
+VFC - we are short this one again - bulls need to hold the support it is in now or the break could be significant,-1
+BAC anyone think this might slush and fall below 11 today?,-1
+Big boy stock GE starting to build a very constructive right side of a current 14-week cup base.,1
+AAP This possibility exists   ,1
+"egarding , going into KSS WMT TGT TJX OSS you see so many copycat clothing items, reminds me of COX 70+ ----> Sub 1",-1
+"DNKN not seeing a lot of talk about this one, but put in another nice day in this steady uptrend  ",1
+"Green Weekly Triangle on ESSX,....pdating  ",-1
+"AppleSoft: No, It Was Not Different This Time -  AAP",-1
+"Green Weekly Triangle on HBAN,...pdating  ",-1
+ltra base? TEK  #ltratech Step it up! #Stepper  ,1
+in dec GE long in the IA's under 20.00 stock had plague now world loves it 22+ tells me we headed down now just as then knew we heading up,-1
+AN - eversal confirmed closing nicely above the 20 day of 9.47  Also confirming today: IMM CHK  Others I like:   ,1
+Wk #3 #13for2013 portfolio: DDD DNKN FT INVN KOS NKD MOV NTSP AX SCCO SCA SSYS V   ,1
+INVN reports next week & is 3rd with a 13.5% gain this year #13for2013 (watch cup w/ handle - wkly)  ,1
+"NTSP is the laggard in the #13for2013 portfolio, down 2.03% for the year (closing in on 50-d ma)   ",1
+ACHC I might be crazy but this stock is rising. ow volume however.Price matters first.,1
+Short term bearish on CYH as it now goes parabolic above the upper band of it's uptrend channel.  ,-1
+CST watch for inverse h+s. >20days2cover   ,1
+Watching HOV for a bounce to the upside. It appeared to touch 61.8% Fib at last Friday's close.  ,1
+BONT a gap up like this may cuz it to go little higher to test 14 but we retest 13.00 for sure,-1
+FIE - Decending Triangle w/ possible bear flag last week. Open gap at 36.38.   ,-1
+DGI toppy   ,-1
+BAC Bank Of America Will ikely Double Again Within 3 Years  These stocks are in my IA.,1
+SHD - Appearance of a 180 Bullish Cup and Handle. Someone has put on a SPD.  ,1
+"Tight consolidation above 9EMA is quite bullish, so I'm stalking SII for a potential long entry  ",1
+EW Into gap Over 95.00  ,1
+FOE   Over 4.60   ,1
+PAB has resistance from June to Sept but Over 6.21 to start a trade   ,1
+xray   Over 41.00  ,1
+CNK - potential head and shoulders leading into BOJ  bit.ly/XStZaG    ,-1
+ooking for this to catch up to other Nat. Gas producers like NG Bought Feb. 18 calls CHK   ,1
+SCSS usually likes to make a run to 30-32 when at these levels.  Bought Feb. 30 calls for swing  ,1
+Friday's high quality breakouts still within range of their pivot points: TV TMI OC WT MDZ FEIC HA SB OTEX,1
+AEO TECHNICA TADES WATCH IST tonight. These 20 stocks have the good characteristics with the market being strong.  ,1
+CGX TECHNICA TADES WATCH IST tonight. These 20 stocks have the good characteristics with the market being strong.  ,1
+CVT TECHNICA TADES WATCH IST tonight. These 20 stocks have the good characteristics with the market being strong.  ,1
+GDI TECHNICA TADES WATCH IST tonight. These 20 stocks have the good characteristics with the market being strong.  ,1
+SM TECHNICA TADES WATCH IST tonight. These 20 stocks have the good characteristics with the market being strong.  ,1
+"the only surprise is upside user: With more negative AAP news, it feels like a short term bottom is coming.  ",1
+BAC Bank Of America Is The Best Bank Stock On The Dow  ,1
+DECK this sucker moves both ways this time i like it long  ,1
+ZION when banks pull bank this becomes a teenager  ,-1
+iberty Media reports increased stake of 50.25% in SII Breakout watch over 3.19,1
+GOOG - has potential for slightest disappoint stock could gap 50 points down - did it - Q4 earnings of 10.58 per share,-1
+GOOG november repeat 760 to 660 - good put spec call here,-1
+"DDD seems over bought, everyone raising PT, ATH, etc.  eminds me of aapl at 700... At the same time I want to buy.",1
+Bullish QIH AAP IBM,1
+GOOG glasses will definitely give AAP a run for their money...  ,1
+GS you might get to start a position at 140 then add more at 138 if we get there.,1
+"Sorry guys, but Netflix bulls need to hit the pause button. NFX  ",-1
+NG from watch list sneaking over line on volume of 4% ave full day   ,1
+OPEN table over line form watch list - vol just 7% of ave full day   ,1
+Buying back the goog i sold in 730's on the stream a few weeks ago,1
+EN SHOT 250  41.70,-1
+"VVTV - We are in at 2.55 per share.  This looks like a good one, its moving up already - we will sell at a 7% gain #stocks #insidertrading",1
+CO pointed out this wknd - huge Outside Day - Bearish Engulf took out all of Jan's gains. Watch.  ,-1
+O - I am starting to see Blu everywhere. very small portion of business but holiday sales may have been BTE.,1
+ANF - SI overbought small position puts.  ,-1
+ooking to add to BJI shrt/puts today - EAT,-1
+INTC BY 500  21.10 STOP 19.00,1
+XF moving with VOME again today and at resistance. Close above 17.20 and I'm grabbing some GS JPM for swing trade.,1
+Further proof BBY is trying to tank it on purpose to go private on the cheap: The great gift card giveaway 2013.  ,-1
+KO made with sugar is sold at local COST. Consumers prefer that in CA.  user-Cola should make shift nationwide. I'm bullish SB_F SPY,1
+"VA - ong  43.07. Trailing Stop  60.12 from 12 Stops of 58.98, 54.53, 52.45, 48.05, 47.32, ... 41.32 & 39.57 -  ",1
+"I was looking at CS for a potential bounce play w/ AAP earnings, but imo AAP won't do 13 eps even if it does guidance will be key",-1
+D pays a nice dividend tomorrow and holding up well. Would like to see it close above 10 longer term looks good to 14 imho,1
+"DDD - ong  27.95. Trailing Stop  46.44 from 9 prior Stops of 45.23, 42.35, 41.70, 39.03, ... 25.44 and 22.93 -  ",1
+WFM looking really good here breaking this DT line on good volume so far  ,1
+user On any significant Vol over 11.30 in BAC it's a good Entry IMHO.,1
+"DDD strong winner, buy right and sit tight. Darvas would said hold em until they stop moving.",1
+"A nder Armour option trader buys 3,200 calls before earnings betting on 6% up move  ",1
+BAC run don't walk.,-1
+MCP I like the company but no...,-1
+KB Homes KBH price targets  spy,1
+DN buying this flag breakout; entry 6.49 stop 5.98  ,1
+SANW Piper puts 14 tgt on it today. They did recent offering. Would love to know what they said. I think will be a quiet yr pricewise,1
+PW making a new 52 week hight today on good volume,1
+KBH breaking out,1
+"In about 20 minutes, some yutz in his 70's  will leak GOOG earnings at some firm that should not exist deep in the bowels of wall street",1
+KBH Big move today. At resistance  ,1
+"When GOOG beats estimates, stock averages 2.7% gain. When it misses, shares fall 7.9%  via user",1
+CHK just ripping  ,1
+"I was big fan of AMZN but daily and weekly very heavy now, take your gains,selling calls wont be ok  ",-1
+"Kudos to Tim Cook 4 pushing E to Wed...emember MK weekend 2 yrs ago/day b4 E, when Steve Jobs went on  medical leave 4 last time? AAP",1
+"Most eading Stocks lagging ES_F, AMH FT, others displaying climactic action, DDD MTZ. Positive reaction 2 AAP EPS is a Bull Trap.",-1
+"Green Weekly & Monthly Triangle on ISIS,....pdating  ",-1
+BZH Beazer Homes SA option traders getting bullish before earnings buying over 8k calls  ,1
+"Green Weekly Triangle on AMPE,....pdating  ",-1
+"AN No serious resistance till 10.00, 10.17  Just getting started (likely)",1
+SNFCA moving stop tighter after seeing reaction to resistance  ,1
+SPY Dismal volume today. It appears everyone's waiting for AAP to release earnings before they take a position.,-1
+GOOG > I made a slight change by relocating wave 3 to last Friday's low.  ,-1
+"ed Weekly Triangle on FT,....pdating,..near to end the trend  ",1
+KEX closed 3.37 called the long at 3.08 - biggest winner of the day!!!1,1
+IBM apparently the numbers were good,1
+GOOG lower trendline adjusted  ,1
+CHK looks like an intermediate term bottom  ,1
+TXN Why down in AH on beat?,1
+"Green Weekly Triangle on DEPO,.....pdating   ",-1
+GOOG is the best. chrome on IOS it better that safari. goog innovation is far better.,1
+"user: OK, correction, WDC  15-16 year highs.",1
+goog short 738. Think they will sell into the open tomorrow. Would be lovely and same procedure than every quarter  :) Good Night Guys!,-1
+"GOOG thank you for the sake of my calls, will love to see you go higher in the morning!",1
+AAP - 3 scenarios to watch ahead of Apple earnings and the resulting price action  ,-1
+user KEX with volume continuing I see this going to 4 - pretty easy - long today right above 3.00,1
+"TSA some nice PVE here, but got rejected at 35.50 area again, keeping on watch for a push through  ",1
+IBM GOOG AAP FB COMPQ SPX 79.6% of S&P 500 Stocks Overbought   ,-1
+"WH held above previous day's lows continuing bounce from the gap fill, SI back above 50  ",1
+"GMC haven't missed the party yet, still consolidating, could go any day  ",1
+AAP Anyone have any suggestions for bullish positions assuming 5% increase after earning? 530 long calls seem to have limited upside.,1
+"VMW testing its recent uptrend here, not real impressive as of late, SI now in bear country  ",-1
+"aapl so aside from simply levering on common stock or writing puts, any other *risky* strategies to play earnings for maximum reward?",1
+SGMO monthly  ,1
+user I'd guess if there were other AAP issues with the magnitude of maps we'd know it by now.,1
+"user I'd buy an iPad if I weren't wedded to 64-bit gigaflops, gigabytes of AM and inux. AAP",1
+AEG Over 28.23  ,1
+homes are building - KBH may break over October levels - Over 17.20 or 17.26  ,1
+"FFIV another CSS falling through lower trend support today, seeing a trend here  ",-1
+Good stuff user: Google Earnings eview: Hopeful Signs in a Multi-Screen World  GOOG XK,1
+user I don't know about NOK and IMM but I suspect Mr. Ballmer won't make his numbers. MSFT,-1
+Positive GOOG earnings pushed NQ_F higher and it finally looks in sync with ES_F & YM_F  QQQ SPY DIA,1
+Bloomberg : Morgan Stanley's ist of Companies with Higher Odds of a Takeover Offer  FS is a TAKEOVE candidate !!!!,1
+FAO is deciding its direction #waitforit  ,1
+"GOOG much better positioned than AAP.  Does not rely on hardware sales, less competition, search monopoly, better vision.",1
+CSX CEO on CNBC now,1
+MCD Whats the news?,1
+"user: IBM up 3.7% in pre-mkt, GOOG up 5.2.  eaction to tech good, despite warts on both quarters",1
+well MCP top short from 11 to zero still in play same goes with VXY from 12 and TVIX from 6 all three will evaporate to nothing,-1
+COH defended the 50.50-50.60 area a few times.,-1
+im long ATHX 1.34 and D 10.02 - .26 dividend paid today now unit cost is 9.76,1
+ATHX b/o above 1.35 w/ buy stops to 1.50 range easy on this. Wait for first hr to shake out and volume to come in STAY ONG STAY STONG,1
+DECK short 38.10 bear flag working,-1
+XEC short 63.90,-1
+AMZN short 268.57,-1
+DECK out half short +.50 cents,-1
+INVN also over line - from watch list this a trade to top of red candle on the left   ,1
+"MIDD - ong  97.69. Trailing Stop  118.01 from 10 Stops of 115.61, 113.95, 110.38, 99.33, 95.82, ... 90.46 & 87.31  ",1
+user Another actionable alert with the KBH 18 Feb calls. Thanks,1
+IBM and GOOG adding to gains in regular trading hours. I like them both but I prefer GOOG above 745.,1
+AMZN out half short +1,-1
+"If GOOG holds up above 745 today on good volume, very bullish",1
+Did you like my IBM calls and GOOG calls from last night?,1
+VXY 10 by eod AAP ramp,-1
+XEC out half of short +.50cents,-1
+AMZN covered short +1 now long 267.61,1
+VXY Da bears wacked again? gap fill from yesterday. 10 on AAP 15% rev beat?,-1
+XEC out 1/4 of short +1,-1
+DDD I sold my position way to early at 45 having bought at 30....good trade but left quite a bit on table.  ooking for pullback,1
+user Do you have a strategy for setting stop loss on a winning stock?  What about setting at the 10day MA if stock is above FDX?,1
+BAC Indtitutional buy  11.30 large Block,1
+"Tues gave sell on AMZN because I was concerned for user, but he was out ,good thing!  ",-1
+"I get devices shtick, but still don't think DE deal helps MSFT at all in short- or medium-term.   via user",-1
+"Oh, and 1st comment on my user post from a guy with 45 target for MSFT ... BY END OF Q2!!! A tad aggressive?  ",-1
+"QSII getting close to the big gap above, SI a good indicator here  ",1
+GOOG Nice jump!  ,1
+OY O'eilly Automotive Inc. option traders making big downside bets selling 27k calls buying 17k puts  ,-1
+"AMD 2.72 is pretty much the pivot resistance here. If that breaks, we could see 2.83 quickly.",1
+here is the AMZN with the down target magenta at 260.43  ,-1
+OY Massive 80000 contract 8 way rolls bullish trade in OY into a call spread collar  ,1
+"user yes, it is working fine ISIS, might I will post a update today",-1
+GOOG clearing next buy area per video this morning  ,1
+"FCFS Q4 beats on top/bottom, guides full year below estimates. evs +24% YoY, net income +30% YoY  ",1
+"FCFS expects to open ~75 to 85 new stores in 2013, the majority of which will be in Mexico",1
+"Green Weekly & Monthly Triangle on ISIS,.....pdating  ",-1
+"SWY - clean up isle 6, 7, & 8, we have short sellers bleeding out everywhere",1
+user nobullshytrader I will post a update soon SNSS,1
+"ed Weekly Triangle on SNSS,....pdating, for complete study check    ",1
+"mentioned it was poised yesterday.  congrats big BE  - IBM p 5%, Near All-Time High  via user IBM",1
+bought a little aapl at 510 ... pressing my position a bit because GOOG to me is just prooof of an expanding mobile pie.,1
+FS just looking more and more like a head and shoulders pattern here,-1
+Why I BOGHT a little more AAP today pre-earnings (or how to break all your own rules because of a feeling)  ...,1
+1 of 2: the game the momos have to play now is what stock will pop next on earnings? price action today says NFX,1
+SWY Safeway Inc. bullishness in March Calls for earnings  ,1
+"NFX Q3 2012 operational cash flow  was 150,000 vs. Q3 2011 of 49 million. 99% decline q over q",-1
+"Most eading Stocks lagging ES_F, DDD SSYS AKS. Others displaying climactic action, AFSI AD SNTS. AAP at Bat! Tighten Stops.",-1
+MEA SI MACD and KDJ are saying it's still not time to here. Monitoring stricly for an entry point lower,-1
+VXY 9 here we come - AAP Blowout,-1
+VXY AAP Blowout Huge Dump AAP Miss #FED pump VXY Dump,-1
+"AAP Apple had 58 billion in current assets and 92 billion in long-term marketable securities,? mostly probably Treasury bills.",-1
+"AAP plus another 26 billion in off-balance-sheet liabilities, mostly contractual commitments to buy parts from suppliers.",-1
+AAP So Apple's true stock price is really about 430. This is the starting point for understanding the company's valuation,-1
+"user DDD exhibiting climax run characteristics: ate stage base, 30%+ in 2 weeks, extended over ma's, violating upper trendline, etc...",-1
+HOV - haven't looked since selling in November - Nice move in Dec preXMAS  ,1
+"BCM I work in this industry. don't just look at smartphone #s, they are everywhere in the network/comm/server hardware space",1
+"user Yeah, I have to think AAP is an underpriced value stock. ;-)",1
+"user I suppose I should tweak my guess again, eh? AAP",1
+"ed Weekly Triangle on CB,....pdating  ",1
+SWY nice volume today.,1
+MSI - glad I waited until 11:52AM b4 placing close stop (not hit). Now can breath much easier as it is up for the day. ,1
+AF no complaints as it confirms M b/o. ,1
+"IBM and GOOG earnings beat, and SPY barely budged. ooks like AAP needs stellar earnings to keep bulls raving. more than 14 per share",-1
+NFX murdering shorts. Wow.,1
+user  I think the same aapl. Iphone lost the wow factor.,-1
+"user: NFX shorts destroyed, now if AAP beats nicely the market is gonna have an orgasm",1
+AAP das + 2 % gef?llt mir nicht.,1
+AAP down we go,-1
+CANT WAIT FO AAP TO GO BACK TO 400,-1
+AAP Sell sell sell,-1
+AAP Wow my god,-1
+AAP wooooow really bad! I won't touch the apple-stock! Wild ride...,-1
+"AAP believers will never give up.  The competition has caught up, and AAP will have to lower prices or lose revenues.  425 coming soon.",-1
+AAP with 137 billion in cash that is crazy,-1
+VXY 10 on AAP conference call,-1
+AAP  user like the guest said aapl is lacking innovation,-1
+I don't care what anybody says. NFX may have a little earnings boost and I stopped out but the MACD divergence is there! Will short again.,-1
+user I'd buy MON. I'm waiting for a more serious pullback to get cheaper but not sure if that will happen. What do you think? 98/100?,1
+"Green Weekly Triangle on FNFG,....pdating, check blog:   ",-1
+"AAP breaks low from 1/15.  I'm just saying, even here, down from 700, I feel there is more stock that can be sold, then money to buy it.",-1
+AAP What does Al Gore know that we don't?,1
+Apple call: cash+securities 137.1B vs 121B - 16B increase. That's less 2.5B in divs and  2B in buybacks too. (but 92B offshore) AAP,1
+"user: .. AAP 600 is too far, highly unlikely crosses 557 ever again. Will C - More than likely now. 425 is next level 2 watch",-1
+"AAP From earlier,  Stay away from failed patterns",-1
+AAP next stop = 430,-1
+"SPY afterhours thanks to AAP fail, is coming in hard and fast. #stockaction",-1
+AAP I shorted after E and Win but now it's OVEEACTING Stock will be around 475-85 By EOD Tomorrow !,1
+Charles Sizemore on Straight Talk About Money - Sizemore Insights  MSFT DE,1
+"Instead of the classic M. Night Shyamalan twist, we got the recent M. Night Shyamalan disappointment AAP",-1
+user: AAP Hedge Fund Hotel margin call switchboard blow-up tomorrow,-1
+"AAP, anybody remember, how i said they've reached saturation point now? Where's all of the guys who laughed?",-1
+user: AAP 2012 GOD & Quarterly histograms ED  #FT71 / 460 done....still work to be done below shared Jan 15,-1
+AAP no volume support at these levels until 420ish. Broadening top looks complete- we'll see  ,-1
+NFX is the easiest long side swing trade right here.,1
+"user if you are reading my Scaling Plan Sequence, yes, my high level for p is 5.80 SNSS, but a few swing will show out remember that",1
+AAP 462.50!,1
+"GGC - Textbook. From the watchlist, volume showed a clear indication on 1/22. This one was easy.   ",1
+Talked about this possibility a few months ago AAP QQQ NDX   ,-1
+ACTG possible breakdown tomorrow...,-1
+H broke support today and may drop out of the channel tomorrow... if it does watch out.,-1
+HFC - 2 day performance +3.7%. Short-term traders looking to lock in some quick gains. A breakout from here would still be buyable.,1
+B bearish,-1
+VHC another one I may buy tomorrow.... looking nice.,1
+user not feeling HEOP    Good luck.,-1
+TSO is on a good upward run but a resistance level has formed at 45.44. Keep it on your radar.   ,1
+BCN Higher volume often indicates reversal points.Technicals/saying the stock is about to take off  ,1
+"BAC December 2012 was a solid channel up, with two bull flags. January 2013 is seeing a solid channel down form. Bear flag #2 in formation",-1
+HC if you go to their website it is a quasi DDD type play. It really hasn't give entry point post earnings. Excellent risk/reward. None,1
+CS Short Setup:  ,-1
+AAP I told you before the after market :),-1
+AAP The support is 430.. is the real value of the company.. to day,-1
+user Prop_Trader user --- you have the January affect of new inflows and new money wasn't going into AAP clearly,-1
+user: CS reports after the close; big growth expected but may not be enough; more than 50% of biz with AAP,-1
+AAP COMPQ Apple sinks to Nasdaq  ,-1
+HAO ong Setup (perhaps my best recent winner; long this name) (needs to clear 8.44 and .50):   ,1
+NEW POST: AAP Has Disappointing Quarter...Its Bear Phase esumes  ,-1
+AAP under 450 gets ugly I've been saying for a while AAP hits 400 in 2013,-1
+AAP i have been warning ppl to stay away from this stock for a long time. Its cheap I agree but stocks always can get cheaper,-1
+Went to VZ last night. Phones today are all competitive. ots of gd choices samsung android heck I may go back to flip phone AAP,-1
+user: FIO fugly,-1
+"SWY - running late today, had to run to Safeway and get some coffee (again) - I really should cut back, goal is get to 1 pot a day from 2",1
+NFX what a brutal short squeeze :-) +40%     I hope to see the same on BCN ZNGA VVS AMD GTAT and so on....,1
+The bigger picture looks even more frightening for AAP perma-bears. Check this from my friend user  ,-1
+user AAP back to 350 I would buy hand over fist until then it's still inflated valuation. Now longer a growth story it's new MSFT,-1
+NFX no stopping. Next stop 160+. If it goes to 130 in the near future then going all in.,1
+AAP 465 is resistance and heading to 435 in the near term.,-1
+user 415/425 by end of wk imho AAP,-1
+ATHX doesn't like 1.35 but I do staying long for a wk or two,1
+"Out of aapl long from 2009.  Still up 10%, but what a learning experience this stock has been.  study",-1
+EBAY early morning bounce holding support + 10-day MA  ,1
+WNC bounce from 20-day and about 8 cents from our line on volume of 12% ave full day   ,1
+ZNGA is about to take off ! Watch out Shorts ;),1
+user: AMZN< beauty,1
+"user: in AAP 420p at 8.78 stop if it breaks 453 again // out at 11.75.  Figure, I make the best trade of the year on aapl worst",-1
+"AAP Head and Shoulders pattern plays out, one for the textbooks  ",-1
+ANY still not done?,1
+OW a few cents away from PT 38.30. Dumping more of position here for great gains  ,1
+"VS breakout back-test right here, would be a good entry point for anyone interested in this name",1
+"FIO: Considerable selling pressure due to AAP link (AAP & FB account for 50%+ of revs). AAP guided capex to 10B, +2B Y/Y. (1/2)",1
+AAP ong for Trade,1
+AAP The buy is on 430-440..,-1
+AAP just remember knees AND toes come after head and shoulders,-1
+Keep OSTK on your watchlist - breaking out from a huge cup w handle pattern.,1
+BC from watch list triggered on Tuesday its move today 147% volume - above top B band - set stops   ,1
+True that user: I think market is telling AAP not to let go mobile to #Android the way they lost lap/desk top to #windows. GOOG MSFT,1
+VMW short 97.01,-1
+AMZN short 275.13,-1
+DECK love this price action and all the people that made this call yesterday on this stream. GGS for wife for life,1
+CEN breaking out,1
+"AXP coming off Catfish buy, extreme fractal buy and an orange dot buy ! and Chaos event PWmo2  ",1
+AJ entry 18.35 stop 17.22  ,1
+"ed Weekly Triangle on PBY,....pdating,...more inform on:    ",1
+"Si VFC cae bajo 145,53 , entrar cortos. Stop loss en 146,55 .",-1
+"ong in BBBY after beating 159,07 . Stop loss in 59,74 ",1
+user: You Are Here AAP   ,-1
+"ong-term bullish as anyone, but don't underestimate TSA's ability to print an ugly candle today. oves to sell off in the afternoon.",1
+MSFT Thinking of buying this one for swing trade. VOME is huge today and breaking out above resistace.,1
+"AAP 1,000 lot bid in the next weekly 470C at 5.30. 36 delta option, could create support with the stock or slight rally",1
+No 1 is talking abt Demark's botched AAP call. Media always parade the hot hand so masses B under the illusion that experts know something,-1
+It's a racket out there and the media is on it else they would have taken every expert who had been pumping AAP all along to task today.,-1
+BAC Consolidation in progress... 33% Done... ;-),1
+reports next Wed.  user: ATW: Barclays starts at Overweight,1
+MSFT nice breakout !!! earnings after close !!   ,1
+Buying NFX 140 puts for tomorrow. 1.70 avg,-1
+AAP close- would like to see Williams back under 80 line - SI back under 30 last time gain huge   ,1
+"My spot on y u wanted to avoid AAP pre-earnings w/ user yahoofinance. Starts at 2 min mark, posted yesterday.   ",-1
+"adio spot w/ user today.  Talking IWM leadership, why new highs on SPY are likely, and of course, AAP.  ",1
+FBHS - another #3WeeksTight. Tons of these out there right now  ,1
+"Since user blog, we've found many #3WeeksTight patterns: TMB, FBHS, EQIX. Find others with his screen  ",1
+GS 140-142 feels about right to enter swing position.,1
+VXY Added 2nd tranche short  11.33 dead cat bounce done BA bull,-1
+ACX Phase III statistically better than St of Care Morphine. 5Billion market. Conservative 10% of mkt at 1x sales = 10 stock. XV BBH,1
+AAP Added Some here !,1
+GOOG 15 min dot alert subscibers have made out HGE on these signal!! OMG that it serious good  ,1
+DAKT volume indicate coming explosion to the upside?  entry 11.72 stop 11.21  ,1
+user: H broke support today and may drop out of the channel tomorrow... if it does watch out. Posted 1/23. Next 5.25 then 5.00,-1
+X option trader appears to have put on 9k bearish put spreads in the Jul 20-13 puts against lower open interest in both strikes. Pos. hedge,-1
+MSFT Breaking out of solid consolidation and accumulation. I've outlined 3 areas of strength in Microsoft  ,1
+"EBAY ripping, nearing PT around 55.50ish",1
+I don't trade stocks but my system warns declining prices in AAP since sep 2012  ,-1
+H Closed below daily T.   ,-1
+"AAP downgraded at Jefferies to hold from buy, Jefferies said. 500 price target  ",1
+AAP Oppenheimer reduced price target to 600 from 800.  ,1
+AAP BC Capital lowered price target to 600 from 725.  ,1
+AAP  Hilliard yons give a buy rating in a note issued to investors on Thursday  ,1
+nice!  - user: GID Working on a very nice Bullish Base.,1
+AAP Netflix Is Overpriced but Apple Is On Sale  ,1
+"Watchist: VBD COX CSC COF All need confirmation, but if confirmed...!!",1
+"AAP in a 12 hour short sale ban at present. Wonders never cease, much like the sentiment shift on stream here ",1
+NFX is going to the moon. Easiest long side trade for the long term.,1
+Watch list stocks that triggered as new buys today included - WNC DGI NTI XAY IF HTA,1
+MCP I seem to be the only joker gaming a position here amongst all you speculators!,1
+AAP What the analysts are saying:  ,1
+"AAP - Should see a bounce between 425 and current price over next few weeks, but  ultimately heading down to 400  ",-1
+AAP down 60 and VXAP down 11. Good that VXAP is not tradable else many would have hedged their AAP shares by going long VXAP.,-1
+DIS bearish to downside to 51.50,-1
+BBBY - bearish back to 57 easy from 58.99 or any higher,-1
+IBT - volume lower than today and red brings this back to 21.50 from 23.06 or higher,-1
+TPX - easy short on next move higher to 50 - just like IMM wait for surge then 3 - 15 minute red bars with volume and short away,-1
+APO - eversal confirmed with increasing volume.   ,1
+VVTV bought this long for wealth position overnight from 1.80 to 1.90 during XMAS as it was same column as eBay on finviz selling now 2.70,-1
+VNG been sleeping for too long now predict about to wake up with big volume surge as catalyst higher levels 4.50 shot from here - 20MM day,1
+AMD bailed out at 2.70 dropped under 2.50 thought good but may rebuy in now as going to 3 with catalyst confirmation by end 2013 - 6.00,1
+"KBH is my trade for tomorrow as long my conditions are met I will SHOT at 18.24 ,18.21,18.17,18.14,18.09 18.04 and cover 17.20,17.10",-1
+APA Back over 83.55  ,1
+ANAD Over 2.54 or 2.70 - we like these two level set ups   ,1
+"THO will enter on confirmation tomorrow, looking great (no pos, yet)  ",1
+GTAT closed above its 50SMA. MACD continues to trend higher near to cross above 0.  ,1
+"AAP Why so hard to comprehend? Classic pump & dump asset bubble after Jobs died, one last pay out (parabolic 700). Back to normal stock",-1
+user brain fart in earlier post. Meant ZAGG not DECK. AAP buying DDD & distrib designs via iTunes #FTW B great 4 ADSK too ,1
+"KCG, i wonder who keeps painting the tape with these few hundred share lots in the AM? ast few times we rip during the day~",1
+AMN  Penny stock alert! This thing will be worth cents before the end of the year.,-1
+"MCP When I look at the volume v price, most of the transactions were around 7 this morning = line the sand for now",1
+OVI on watch list has volume of 10% of ave full day - over line a little  ,1
+ANAD on watch list over 1st line with volume of 17% ave full day   ,1
+FBN on watch list over 1st lien but under the 200-day EMA with volume of 19% ave full day   ,1
+WPI - whoopie changed its name to Actavis ACT - sounds like a bowel health yogurt or an OWS protester - we are still short,-1
+a 4-6 point pullback would be reasonable after a move like that. nflx,1
+TIBX getting some love today...gap fill around 25  ,1
+15/16 stocks on Watchlist higher- Hope ur listenin  F KBH GPX SSQ SBX CSOD HD CHK EBAY TEX I ITG ,1
+no follow thru on NKD out for a 60c profit  oh well,1
+CS looking to make a move over 29 looking gd. I don't trade into earnings but to do after :) down from 31 just last wk. & off b/c AAP,1
+user come over to the CS train left the station can jump on in at 29 stop after that moving onto to 29.21,1
+"MCP testing highs, stops getting hit~ ",1
+PCN any close above 680 is bullish today. I love the VOME. If the big boy want it I want it too.,1
+39.50 on HA is a big intraday level to play off today,1
+AAP Go baby go!!  425-440,-1
+"Nice deals of the day CHY 4.5 mil shares secondary priced O/N 25 now at HOD 27.19, MCP trainwreck also priced 37.5 mil shares 6 now 7.34",1
+AMZN is another free money trade for today. We will see where it lands today at the close.,1
+AAP trader keeps reloading selling in 250 lots in the Feb 445P. -49 delta option could create support or push stock up temp,1
+GTAT Holding 50SMA. ooks like the stock could try and take a trip to 3.62 if volume comes in.  ,1
+AAP doji being put in on 60 min after 7 down candles,1
+AAP should buy a gold miner at this point and just drill the earth with their cash hoard.  'Be evil' campaign...mix it up,1
+CS is looking gd at HOD only 11:30am next station we pull up too 30 then my end PT is 200day magnet to 31.84 still about 2pts away,1
+GMC looks good filling gap up to 50,1
+This market is rollling over folks Study 8/10 days green? With momo stocks leading this market lower AAP VXX,-1
+user: GMC looks like the free money trade for today. Headed to 49+ - so far so good,1
+ooking at FB and CST for longs next wk,1
+Took off my final DDD piece at 70.6 long from 35.2 +100%,1
+i dont have a tech friend in world that owns the stock ...thats why user: PCN No one Believes anymore :   ,1
+Why would you buy AAP when you can buy CS where the growth is... As AAP sells ipads and Iphones like crazy CS benefits,1
+user NKD the next NFX ???,1
+AAP By By 440!! next 425,-1
+user: CS still getting squeezed lol agreed for another 2 pts,1
+"gap filled, time for aapl to wake up.",1
+AAP I told you!!,-1
+Anybody else hearing about one of the large credit card payment processing firms being down (unable to process payments)? MA V AXP,-1
+AAP weekly SI finally back under 30 - 1st time since autumn 2008 by lower trend line- Williams 80  ,1
+ANAD looking to break out over 2.70,1
+My pal has me all jazzed about TSA which I just bought for first time ... user better be right. user nailed this and my axe on it,1
+AAP Bull Trap Here,-1
+FAVED... user: AAP It must be close to the bottom because I'm making money on the short side.,1
+SH Enfuego...maybe Icahn long  ,1
+AAP Ouch !!!!,-1
+GS got some 145C march options today. Will leave them probably till expiration.,1
+SWI was a beaut,1
+Next time you see a free money trade free tweet then ignore at your own risk ;) AMZN GMC,1
+AAP has a herculean task ahead at 465,-1
+Is it the doomsday for AAP?,-1
+AAP Hmmm ower on Volume.... Will see...,-1
+AAP Ouffffff,-1
+AAP we are long 445 and mostly thru call ratio back spreads 2 to 1 ratio 460/440,1
+user zeno123 AN of course looking at intra-day is irrelevant for swing trading. no sign revrs  ,-1
+some listened to my BIG signal on NVDA this morning and are shocked how fast that money was !  ,1
+AAP sharing a few thoughts Have a great weekend. POSTED VIDEO   ,-1
+VNG - taking an overnight position for few days 3.30 - looks prime - someone accumulating some here - for something,1
+"CEN hitting resistance near 84 again today, IMO, when it breaks it, will be a big run to new highs  ",1
+SCHW - new 52 week hi & looking to close on hi - like it - we will buy more,1
+"CSN Good luck to everyone holding long, cancer patients need this more than we do!",1
+AET DVA nice job shaking off the hate and closing with bullish doji candles for the day,1
+"AAP failed to get a candle close over the 50% retrace on the day. inverse abcd, imo   ",-1
+AAP is dead. No 2 ways about it.,-1
+"Netflix had a great year this week, up 71%. Wow. NFX",1
+Glad that the easy money trade is over. Every joker thought that making money in the market is synonym with getting long AAP,-1
+CAT ooking for miss.  Gap fill to 89,-1
+INVN nice follow through day with great volume again.,1
+Some breakouts today: AGCO CAM F GMC HA JEC KAC MCS MOS NKE OSK PG TEX TMO TOO,1
+Don't shoot the mailman Point & Figure target AAP NDX QQQ Sorry #APPE Heads  ,-1
+The phase irrational exuberance comes to mind when thinking about the absurd valuations of these companies AMZN CM AX NFX DDD NKD,-1
+ADNC Now that the gap is filled...  Other set ups I like: MCP CHK WCG APO   ,1
+coh - no  ,-1
+ZMZ - this looks like a big power move to 24 - doing more research on it now see short intetest,1
+SEM see this going down.,-1
+user: AAP next stop is 435 - low for today was 435. This stock may be dead but follows technicals to a tee,-1
+AF:  As noted - Clear buyable action on Tues. Close +3.9% for the first week. Support now near 51. MOE HEE  ,1
+CAM - Closes the week strong after a decent first day action +1.5% coupled with an increase in volume. MOE HEE  ,1
+QGC seems to be finding long term range between 18.50 - 8.50 - This should be more of a position trade where you can accumulate lower,1
+user I thought FIO is pure FB play (flash) what do u know that analyst don't FB maybe losing the youth but the kiddies OVE Instagram,1
+HP after 7 straight green days that little red bar is signal to switch tracks to red for some normal profit taking,-1
+CBOE the decreasing volume that slight red bar indicates after 19 straight up days it's time to switch to red bars and take some profits,-1
+HES decrease volume now and a red bar will send this back to 57 - 2 points to be made more if goes higher - patience and wait,-1
+HA this gets one red bar size will ruin from Jan to now in 2 days target 46 - 4 points or more to made if goes higher wait for first red,-1
+INVN - any panic surge higher start short entries and save  for red bar risky but timing top on next move is good scaling start for short,-1
+INVN keep close eye on this one for surge up from short bagholders - may take 2 days first red bar finish is top big short score to be made,-1
+YHOO esting here after a nice move since November.  Watching for a break of 20.85 after earnings  ,1
+GPN Inverted Head and Shoulders still in play !!   ,1
+"JPM broke out an important resistance at 46,87 with solid volume. The trend is in favour of Bulls  ",1
+GIS - this is what all the others will do - first red bar major short!!! target 40 test,-1
+DA volume decreasing looking for start of the red bar sequence,-1
+GPN could test the 5.50 quick and make major move higher above 6.00 very bullish,1
+DSCO many little bios did well last week - DSCO will have a huge day soon - it's in the history,1
+CYCC - have this one at 5.25 and 5.50 buying more 5.90 for b/out thru 6 - can go 7 easy - very small float 7 million and 18% short,1
+GMC that gap 30-35 puts the weakest floor on this stock wouldn't be surprised to see it spill over and BN!! everyone hot red coffee bars,-1
+"MCP either way, it's just 2 candles and back to 8. Next week will set the tone which way overall investor sentiment wants to take this",1
+ZQK first sign of decreasing volume and 3 (15) minute red bars with catalyst volume - short this back to 5.00 easy,-1
+Super shot - user: Please help me get this viral. I mean what a putt under pressure  GOOG,1
+SAM nothing holding up the mountain that green bar eft shoulder that red bar head (smallone - lol) we see red right bar start major short,-1
+"user I looks like it's getting ready for beast mode. ook at insider buying. Nice pick. I will be partaking, I think. Time2research.",1
+user ots of good setups posted here 3 weeks ago: EQIX EXPE FB GS HOG MON PII QCOM QIH SODA,1
+"AAP , want to buy more around 350",-1
+P see what happened from October to Dec that slide - well it going to happen again on this historic rise wait for the red then start shorts,-1
+VMI nice head and shoulders forming just need the left shoulder now and we see this go retest 140 to 138 range,-1
+user ABV INFN CBS - look great for shorts thanks,-1
+AAP weekly says that 350 is where there is some modicum of support. The game was over long time back.,-1
+Think GMC is the next mini NFX in the making. Earnings are around the corner. Needs watching how it behaves going into it?,1
+"What would I pay for Gmail.... 1,000 year, 2,000 ? I mean how do you value something so extraordinary ... goog",1
+VHC has formed a symmetrical triangle. Breakout point is 35.5. MACD is bullish and SI is above 60  ,1
+WNC consistent pattern of new highs and sell offs - next one looks like the selloff,-1
+AIA - decent pattern be patient for move above 21 will run to 23,1
+CTIC MYX - keeping close eye on both for volume surges up - don't miss the boat will go fast,1
+OS time start uptrend to fill upside gap break above 7.50 yields least .50 cent and test to 7.50 = .50 more from here = 1.00 - like it!,1
+OS stock very close to magic number 7 and holding see stabilization over 7.00 it will retest 7.50 break point - really like this one,1
+KBH tried short on Friday 18.25 tested 18 then came back 18.25 then 18.55 when comes back 18.33 I'll short attempt #2 break 18 target 16.50,-1
+user That IFE is going to turn like that TPX just did - if TPX continues red - similar pattern,-1
+CST looks kind of interesting. Needs more volume. A lot more.   ,1
+YHOO setting up for it's new 2013 range 22- 30 - move starts this week,1
+NKD nov and dec had disease now its the cure - not a believer - shorting,-1
+HOT - Fundamentals off. More of technical play. More here ->  ,1
+HFC - Great group. ooks good. More here ->  ,1
+STZ - Strong breakout then drifted a bit lower. Good action on Friday. More here ->  ,1
+WFC - Nothing. Still watching for a buy signal. More here ->  ,1
+WAC - Down to near the 50 DMA. Great group.  More here ->  ,1
+Has anyone shorted ONP?,-1
+CM should be a good SWING trade on move above 175 with VOME.,1
+ecent breakouts in DNKN GMC SBX appear to be the start of a group move. Still forming big cup bases. JVA also on watch.,1
+INVN huge accumulation with no selling since July low. Starting to form right side of deep long consolidation pattern. On watch.,1
+N setting up beautifully   ,1
+AO ong Setup (ising 3-methods Candle Pattern):   ,1
+CAT worth eyeing today - could see breaking back above 100+ soon   ,1
+"JOSB just hitting its 50/50 risk profile on quant macro view, 29.50-33.50 marks model base with oscillation zone, still room below ",-1
+JOSB outlier risk profile closer to 24 so will be interesting to see what happens as folks exit options they've written ,-1
+SPX COMPQ IND CAT AAP Wall Street futures were unchanged with the results of Caterpillar and the threat of Fitch  ,1
+"If u believe AAP was dropping clues pre-earnings, then user says you should be watching FB now  ",1
+"user: CAT Sorry, this e/r is NOT good and I'm not saying just because I have puts.  Nice wide guidance kitty cat.",-1
+GGC from watch list with a gap open and volume already 35% of ave full day   ,1
+DDD NICE buying opp on dip back - re-entry i had been waiting for,1
+AN 20% short float. may be a runner today  ,1
+"BAC rolling over, could see 11.02 again. Bulls have to wait for a better setup here",-1
+Take Profits & Tighten stops. SA indicating major correction looms.  OCN NSM EGN BID GMC FB NKD,-1
+Added DT on break of 41.31,1
+keep an eye on SBX for possible intraday bottom at support on the daily. ike stock if it can hold  ,1
+eading Stocks lagging ES_F much worse then usual for this rally. DDD SSYS SODA QIH Y PX KOS FT INVN  ,-1
+AAP we are back baby...largest company in the world.  dousing myself in oil to celebrate and booking fridays daytrade,1
+DDD - setting up an awesome looking bear flag....  ,-1
+"MIDD: ong  97.69. Trailing Stop  122.65 from 11 Stops of 118.01, 115.61, 113.95, 110.38, 99.33 ... 90.46 & 87.31  ",1
+Closed all my ANY calls today... looking to re-enter on a pull back. ,-1
+"Screw GS utilization lag on BP, I think it's a solid buy! ook at that div, and MP's are hot right now. Oil still needs to be moved ",1
+DDD bear flag worked very well there...broke and went to 60.55....quick money....,-1
+MA broke YHOD earlier & is going higher.,1
+WH we are at the 38.2% retrace here  ,1
+VXY We are Calling the AAP bottom in...,-1
+ZNGA on the move!,1
+CEN is setting up near 52-week highs  ,1
+"In fact I'm going short 100 shares of ANY up here, with a public stop at 25.11, open downside target working off of call option profits ",-1
+xrx golden crossing imminent 50sma and 200sma,1
+AAP ow risk with a tight stop here however would like to see this one get some follow through (today) to better confirm.,1
+AET also looks like it has an upwards reversal in the making here,1
+user FTC decision on FHTM will have ripples throughout the whole industry dumb dumb! HF NS SNA,-1
+"VHC also getting an upwards reversal intraday, looks good if it can break HoD from the morning",1
+been holding this ZNGA long since like dec.. 2.75-2.70 is big resistance.. if it holds abv itll go to 3,1
+NKD what a hot piece of bullflag!  ,1
+GS daily. See if you can get a tweezer top into the ABCD reversal here for a short next few days  ,-1
+GPN close to magic 5.40 may popski to new levels trending trade,1
+AAP We will close this gap before the rally IMHO...,-1
+"it started ugly for NFX today, even though you saw higher at open,   ",-1
+HB H& Block makes bets on 11%+ up move by April  ,1
+AEZS new hod bought more 2.78 2.79 2.80 - target 3.50++ now - holding it overnight,1
+"HF NS SNA are in deep trouble here, Fortune sold the exact same BS supplements, they held them up in the press conference!",-1
+PCN while I didnt promise to fill short target in a day here we are !  shouldnt be a shock at all.  ,-1
+MT (really) ugly today; downside volume with nobody making doji accumulation attempts. 65 min  ,-1
+Added short TM to portfolio,-1
+Definitely a major top/high in today on 3d printing.  back to show me period on this group.have an exit plan on rallies methinks DDD SSYS,-1
+"user EPS is a South African payment system company... not really the same as KCG, not even technically... DONT BY!",-1
+VNG may go green today and test high 3.29 long today's buy at 3.18,1
+PETM ooks like water coming out of a garden hose...  ,-1
+ANY weird action... seems like it's artificially stuck at 25... ,-1
+YHOO  can it break this 10y channel and 21-22  resistance? mid term bearish until proven otherwise  ,-1
+FX was the other big morning gapper,1
+SH going hard ya baby !!!,1
+good end of day volume coming into DECK and MAKO,1
+watch CM ss on VMW,-1
+seller stepped down to 89.25 in vmw.,-1
+IMN good earnings and 23% short.  looking for a move up here.,1
+AAP DownTrend if After Market = Bullish Sentiment For Tomorrow !,-1
+My pal user has been long and strong YHOO from 15 my axe on the stock.  Took much abuse.,1
+user: time to get out of the #cloud stocks VMW CM  ~~> valuations finally coming to earth...thankyou...,-1
+AMZN- Bearish set up ahead of earnings. TCT members I will be writing a note this evening re: trade  ,-1
+NKD 130 is very close and may try to get close any more upside from here start scaling into a short target 120,-1
+INVN CEE - wait for slowing volume and a red bar - and these 2 will move down identical - easy ,-1
+MOV this may be the best of the best - volume slowing - red bars 2 of them - mkt drop 100 points this = cliff dive for 5 or more points,-1
+AMZN has the new look of what AAP had when it start the plumment,-1
+GOOG here is the leader of the pack for the ride down gap fill to 710 coming any higher is a joke,-1
+The gap fill at 53.47 for DDD is certain. This is where ride the profits thesis gets tested.,-1
+Nice action here #all time high CM #technology IGV FDN SKYY MTK YT #salesforce.com  ,1
+"Sorry AAP, the #BlackBerryZ10 Is Hotter Than the #iPhone  IMM",1
+New Blog Post - 'What Could go WONG...And What if Apple is Still eading the Market?'  aapl spy qqq,-1
+"watch for a weekly close Friday above resistance, then go PIG OT! MCD DIA DJIA #FATTY #MMM  ",1
+ASGN over 24.19  ,1
+AAP range is 500 to 350 - can go towards to lower end,-1
+There is profit in social gaming...ask my pal user founder of user  (how is that user an advisor .... ) EA,1
+DDD this is why trend matters.   ,-1
+For those who did not have a chance to buy G any pull back is your buying opportunity  ,1
+ZNGA broke out of a consolidation range to the upside. I don't see anything until the 3-3.06 area  ,1
+GEVO Pullback on very low volume. All dips should be bought with a stop 1.75.Watch for next leg up  ,1
+DDD still falling in pre-market.,-1
+"Time to all in short AX AMZN NTAP IBM. #cloud-is-dead, iPads now coming in 128Gb version AAP eally Tim? That's all you've got? ",-1
+NGT #FED up to bat... JPM HSBC headed into Comex & ME for todays bashing,-1
+AAP we knew these 440 calls purchased yesterday were the right move. AAP NEEDED to save their stock with a release like this temporarily,1
+"user Sorry but only innovative thing AAP has done over last 2yrs is change size, shape, memory size, crappy map, & snippy siri.",-1
+BTO AMZN Feb 16 210 puts for 1. olling the dice!,-1
+like that consol above 455 for aapl. stop is below 454,1
+mrge flag break. like this look here added a few 2.88  ,1
+DVN Breaking out from this saucer base. like toward 58  ,1
+YHOO  Yahoo is a iberal hangout. History shows us that those who love big government always lose out in the end.,-1
+"yhoo  short term buyers speculating on a NFX type move up are fleeing here. ental traders shaking out here, long term investors shud buy",1
+YHOO short interest ratio is skewed because there are less shares outstanding since buyback imo,1
+ADJPY - sitting here with my SPY puts and ENOC. Only positions.  ,-1
+"user: If you bought my stock of the year HES, you are now up 26% YTD - sell at least 1/2 ",1
+GS my trade against the 8SMA from yesterday is paying today. Volume is above average too. Will trim some.,1
+KEX trying a short 8.47 filled and more at 8.57 with a .10 stop,-1
+"ed Weekly Triangle on PBY,....Net Profit  19,140.00 (16.48%)  ",-1
+yhoo Will Brokerages come out with pgrades/Downgrades this week? Will they pgrade or Downgrade the stock? could make + or- 3 point diff,1
+"Green Weekly Triangle on PBY,....Open Sell Short at 10.95  ",-1
+DDD wow - nice swing if you caught the bottom pre market,1
+"Green Weekly Triangle on KTOS,...Net Profit 1,236.00 (1.22%) gly Profit  ",1
+"AMZN 1,200 lot bid in the Feb weekly 255P. 27 delta, could push market down or create resistance in the stock. Will probably bid up IV",-1
+AAP ready...460 c 5.05,1
+DECK pulling back for possible good entry long calls I like DECK130216C40 at 1.95 run into earnings  ,1
+Procter PG breaks out - Today up 1.37% - It's one of my biggest positons with over 4% portfolio share. How big is Procter for you?,1
+"Green Weekly Triangle on AMPE,....Net Profit  22,293.00 (18.42%)  ",1
+CSN 413K worth sold in block trades so far today... The Celsion roller coaster is making people sick on here...  ,1
+GNC (power generators) is perking up again,1
+CSN Buying more!,1
+"MDSO - ong  27.51. Trailing Stop  37.67 from 9 prior Stops of 37.11, 37.00, 31.30 ... 27.51, 25.15 and 22.79 -  ",1
+kbh breakout coming? Next stop 23?,1
+GS ippin' good today,1
+DE continues to make new highs since breaking out on 1/2  ,1
+GOOG holding up well,1
+GS is a no brainer trade to 149+ pretty soon.,1
+GS Goldman breaking out of EBV-2. Good sign for GS and the market.  ,1
+GS is breaking out of bull flag. ow volume but technicals look good  ,1
+"TES strong run up to resistance, moving stop up again  ",1
+ong positions: ANA FB HES IMM SHD WCX,1
+"CEN chomping at the bit here to clear 84 at the 78.6% retrace, earnings next Tuesday 2/5  ",1
+GS all of all remaining calls. Will revisit later. ooks good though.,1
+AAP Dow Theory = Volume must confirmed the trend (When the volume doesn't support the trend)  ,-1
+AMZN how low will it go?,-1
+GOOG deciding what strike to buy into the close for calls. AAP has been flagging since yesterday.,1
+M - the breakout updated   ,1
+GPO on watch list at the line with volume of 75% of average full day   ,1
+VXY GS just keeps buying ES_F futures contracts with #FED funny money - why fight it?,-1
+in GEVO.,1
+"user good, because that is what I did today, it is in a green weekly triangle since Jan 23 SNSS",-1
+like is said....don't fight the jungle. AMZN,1
+"Didn't plan on holding these MSFT Apr. calls overnight again, but when do things ever go as planned?",1
+AMZN a fool's errand,1
+"Tops- :  HPQ (-3,17%) &  CSCO (-1,38%)",-1
+Jeff Bezo's the wizard...it's all about Gross Margins and theirs are getting less gross...AMAZON reads Wall Street's minds AMZN,-1
+"Boy if aapl had beat I would be having a hell of a January :) ... ahhhh, the markets !",1
+user: Solar Stock Sector Bottoming... TAN TS FS STP  ,1
+Market loves AMZN for showing its middle finger to Dumb Media,1
+CHK  Confirming the expectation.  Other set ups that I like:  IMM COST AAP etc.  ,1
+"ed Weekly Triangle on SNSS,....Net Profit  16,644.00 (9.17%)  ",-1
+AMZN has spent 15 yrs not caring about qtr to qtr. Others can't turned on a dime and say we do that from now on.,1
+"user: IMM I love to short, They are just revealing a phone (that everyone has seen it does not make you younger) think ip5 aapl",-1
+PCN up 8 points since this short call on ST - 700 so close - magic number will break it hard down same way did up - 10 more pts to go,-1
+GOOG patience still here - mkt goes red big so does this,-1
+user: XOM - target 89.50 - mkt goes red big tomorrow so does this - lead the pack down,-1
+"If you've been playing poker for half an hour and you still don't know who the patsy is, you're the patsy.? Buffett on AMZN shorties",1
+"Market Wrap Video + Additions to Watch ist  including: FS, JCP, , NAVB, VMC",1
+"AMZN, Hunting elephants,,,a plan, patience, execution, patience, and more patience...  ",-1
+"Speaking of vehicles, this going on my bucket list DE HOG #Harley #Deere #love their new venture!  ",1
+MCS triggered last week and is pulling back on lighter volume so watch for reversal by support   ,1
+"Man AMZN is so great, even the Fed is hosted on an ec2 instance.",1
+BAC See some negative price action here tomorrow most likely.,-1
+From Nasdaq site : NVDA NVIDIA Building iPhone and iPad Competitors   ,1
+Bought NH to open . ooks like a new trend,1
+FS from watch list just over line with volume of 8% of full day average   ,1
+SD thx user - daily   ,1
+"been talking CSOD, keep your eye on it today",1
+"From user: Hess has two proposed plans for unlocking value, and either looks promising for investors. HES  ",1
+NGT Dumping pop on #FED JPM and CB intervention in  GD SV GDX HI GOX GDXJ,-1
+"AA BA HD Top + Dow Jones: Alcoa (+0,84%) & Boeing (+0,54%)",1
+IMM lol AAP-tlye presentation - get creative guys,-1
+MOT CBI on top watchlist waiting for vol MOT above .50 and CBI over 1.68 see what happens,1
+stopped out of CS at 28.50 from 28.71 earlier sorry for the delay,-1
+"Green Weekly Triangle on GTE,...Net Profit  4,333.00 (3.14%)  ",1
+KEX Option Traders continue bullish trading in the stock buying a net 589K deltas in the first hour of trading  ,1
+like to see more volume but I'm leaning toward CBI off the BPAX profit taking moving into CBI see what happens buystop at 1.67,1
+"iding FIE down, lots to go",-1
+My hot money canary DDD and SSYS reversing lower...,-1
+VXY AAP bottom looks in - Add FAS here #FED-Short more VXY acct vacuum cleaner - GDP is OD Sandy news - We have relief funds in now,-1
+"NS should start to slide here just like HF, that MM that was shut down in KY is having ramifications imo, longs getting nervous...",-1
+"BT Cortos en Peabody Energy si cae bajo 25,40. Stop loss 25,76.",-1
+"user: BT Short in Peabody Energy if goes bellow 25,40. Stop loss 25,76.",-1
+"user: BT Cortos en Peabody Energy si cae bajo 25,40. Stop loss 25,76.",-1
+MGAM on watchlist (no position)  ,1
+NGT GDX GDXJ Short GOD & Miners into FOMC - JPM notes,-1
+"Same story, different day...eaders lagging, aggards eading as ES_F fighting off bears. AKS QIH Y EN KOS   ",-1
+DEPO bouncing from the 50% Fibo line & breaking out 6.70 resistance line all indicators BISH  ,1
+HEB ripping up 23% now in that one .25 to .305 still holding,1
+Seems AAP is losing strength to go higher.,-1
+AAP Added to my short,-1
+ from watch list on volume of 50% of ave full day a bit over line  ,1
+AAP DownTend seem confirmed after broken the triangle...,-1
+You have to wonder where Fidelity Contrafund added more where they reduced their AAP. It sure looks like GOOG,1
+FIO im in ONG for 24 - 28  untill March 2013,1
+NGT Nice bounce off gap fill... good to reshort into JPM move,-1
+CF if we open green here tomorrow we should see test to 40 looking to be setting up again.,1
+GOOG I expect a new 52 week highs in the coming weeks.,1
+Two trades I'm eying GOOG and GD.,1
+"COMPQ on a mission to 52 Week Highs...With,...or Without... AAP. Juuuuust aaaanooootherrrrrr 1.35%. You can do it!!! ES_F",1
+AAP GOOG FB IMM - IM dies to reinvent BlackBerry Z10   ,1
+bgcp nice range break in the works here.   ,1
+GOOG is hitting its head on the up trend line it broke I like 755 puts GOOG130201P755 at 2.60  ,-1
+FIO huge Mar calls Volume  ONG,1
+"AAP - fired sell signal on hourly, targets 435 area - just shorted it",-1
+user: AAP Ok... this action makes no sense. >>>> Yes it make Sense : ook  Daily Volume since 3 days ...,-1
+AAP HOW DAE THEY ONY 38% Profit Margin on 50 BIION/yr!! and a huge bank balance..  Insanity waiting for FB100,1
+AAP - stock is down 17% from the start of the year.  If you are an institution do you want this on your books at the end of the month?,-1
+user: AAP stop kidding yourself . there is no support around here,-1
+"AAP if AAP closes below 455.01, i plan to swing a few puts into the next couple days...",-1
+user: AAP Gap down for sure tommorow,-1
+"Oh QCOM, you are a thing of beauty!",1
+"FIO 15.35 I'm in, I'll ride this one a while, overdone IMHO",1
+FIO crushed 20%,-1
+BYD hopefully VS beat will help this stuck stock move on up.,1
+CAT 100 roll and poss bearish bat  ,1
+AAP Sinking in after-hour.. Get eady to gap Down  Tomorrow Morning !,-1
+yhoo Blackrock increases position SC 13 G filed today. 70 million shares . 5.94%,1
+"Green Weekly Triangle on PBY,....pdating  ",-1
+user: Buffett  isn't interested in AAP at this valuation? too much profit and dead capital sitting in a bank overseas ;),1
+user Just an opportunity to buy IMHO BAC was 11.70 monday in pre-market,1
+AVNW Over 3.78 then 3.89  ,1
+Has anyone shorted KEX? What price?,-1
+GV Over 2.80  ,1
+F Over 7.88  ,1
+San Diego was rocking today....QCOM and BOFI ...Banking and chips. Not the safe bet in 2008...,1
+KEY has been holding at support 9.07 but facing resistance at 9.50. On watch.  ,1
+FIO ong! & Add! Q2 2013 earnings of 0.13 per share on Jan-30-2013. This beat the 0.08 consensus of the 19 analysts covering the company.,1
+"FIO Turner Investments and Calamos Advisors purchases in the current quarter at 7.2M shares, 9.32% of it. Blackrock doubled position at 8%",1
+A Q4 2012 operational cash flow 35million down from 141mil in Q42011 ..75% decline Q4 vs. Q4....,-1
+A Q4 net income 50 million and operational cash flow 35 million. Q4 2012 is OPS 70% of EPS...in Q4 2011 OPS was 440% higher than EPS,-1
+A year end accounts payable increased 43% from 100million to 143million,-1
+FIO azard Capital upgraded Fusion-io to Buy from Neutral with a 23 price,1
+A few names with a Dark Cloud Cover candle pattern: MHO Y SAFT FXS,-1
+"MCP still mining for asteroids, short update   ",-1
+A few names with a Bearish Engulfing candle pattern: AEX AFG ASFI ATHN BECN TMH GW FO MHK IBN CS AOS BT AIXG JBHT NHI,-1
+"user KiwiS hello friend, did you see CSN pre-market today, untouchable situation!",-1
+More names with a Bearish Engulfing candle pattern:IBT BECN AYI HCG MO SG MP EG BMS CEN WIE INE,-1
+Trade Ideas for Today  MS ONXX NVE BGCP  ,1
+VXY GS JPM HFT PPT Window Dressing programs online,-1
+Published ZNGA estimate on Estimize: +0.02 EPS and +6.09M evs compared to Wall Street's consensus.  ,1
+AAP 600 lot bid in the weekly 460C at 2.73. 44 delta could temporarily push stock up or create support,1
+CAT Bullish MACD crossover.,1
+I am calling on CSN for a ed MONTHY Triangle open ong position at 1.65,1
+JVA 7.25 TZOO 21.50 IDCC 43.35 CF 37 BT 25 looking good here,1
+"the man who got me in A at 47 user: A  Nice bounce off of support, after a 25% decline!   ",1
+AAP break of 453.63 should see retest of 435 lows and lower fairly soon thereafter.,-1
+"NFX Netflix, Inc. (NFX) Increased Debt Causes Concern [EPOT]  ",-1
+"VIX rising, eaders lagging & reversing on vol, laggards leading, speculative names climaxing. Don't ignore warnings. DDD SODA Y FB",-1
+CBMX B/O smart money coming in this afternoon looking for a push to 200day this low float could 6 by the end of wk little resistance there,1
+user: Apple AAP Is it Time to econsider a Stock Split?  10:1 would solve this problem FOEVE!,1
+Four Bearish Canaries In the Bullish Coal Mine  AAP TIP KOS SPY,-1
+AAP --  mac world maybe TC will anounce the i-time-machine... boom we are all rich again!,1
+"K: ong  53.69. Trailing Stop  53.24 from 3 prior Stops of 52.16, 51.25 and 51.05 -  ",1
+fnsr great intraday look..,1
+PCN Just trying to keep it simple and potentially capture the primary trend.  ,1
+PM 1H. hold 2/3 til close monday PM (21 bars) and 3PM on wednesday (34 bars) for the balance.  ,1
+"user: AN AutoNation Stoploss in 47.46, after beating 47.90",1
+VS is breaking out !!  ,1
+i'd short AAP right here if I wanted to put more positions on. (455.51),-1
+APO - Short setup off the 50DMA in a weak group. Downside 15.50 initially.  ,-1
+KSS has been on a nice uptrend since bottoming out in early January.  Picked up some for my roth around 43.50. Slow mover but 50 possible.,1
+"TIVO BY!  Price action? Check - breaking out above 12.95. Volume?  Check, 2x avg.",1
+AT Declining growth & margins. Head & Shoulders setup. PE contraction Downside Price Target 25.  ,-1
+AMZN ooks like the stock has topped out for now. Today is another distribution day.  ,-1
+A Did you Know.... YHOO is down 1.5% for month of January,1
+The second best Steve...Steve Wynn on tap today  - after the bell - and netsuite (enterprise)  N WYNN,1
+Gap backfill in FIE?,-1
+NFX Keep in mind that 500 lot offered in the 150P at 7.75. Stock was at 163.70 when he offered now at 167 ook for him if stock goes dwn,1
+ASGN from the watch list is at the line on volume of 81% of ave full day   ,1
+"OCZ pleasantly surprised that FIO results did not affect this one more.  Shook me out of 1/2 my position, but still bullish",1
+user Not yet sir... The jury is still out on ANA. CSN is plain old fraud!!!,1
+AMZN I am riding the remaining positions with stop  above 290. ooks like the chickens are coming home to roost.,-1
+yhoo Business Insider post about yhoo out this afternoon. would be beautiful if aapl met with Marissa,1
+GV of new buys this was the largest % gainer today - also was a fav as it was a pullback reversal  ,1
+user Tried to buy a ange over and got frustrated. Very ong EBAY,1
+WYNN aises Dividend from 0.50 to 1/Share >> CC was quite bullish on all fronts & analysts seen enthused,1
+TPX my move is in play - motion to 35 then 33 and will cover,-1
+CEE this is about to tip over the edge and go over the falls target 39,-1
+"user ots of ppl r making the move to Samsung, I was going to EWY as AAP started reducing ipad screen size & calling them new models.",-1
+"Watching A DMND for low entry points. A replaced CSCO with the State of California, C and CS contracts. May see some pops later on",1
+"user Class action against DE may be the reason. Search: Mohan, et al. v Dell Inc Case for details.",-1
+"IACI short setup, bear flag up into neckline from last week  ",-1
+"Green Weekly Triangle on SHO,....pdating and Scaling Down  ",-1
+Market Wrap Video + Additions to Watch ist  including: BYD INFN JNP SE TGH HS,1
+Video Analysis of our 16% 3 day trade by shorting AMZN  ,-1
+What are you thinking on a GOOG entry? A close above 765 or 776 ?,1
+zzzactly user: Watch as China has grown to become Apple's second-biggest market:  AAP (long),1
+ESPN 30 and 30 is magical.  Serious family entertainment.  The future of content and TV and web is so bright mixed well DIS,1
+wow TV is underrated and underloved....looking at stock prices I am sick that I missed this hate trade in 2009 .. CBS DIS,1
+INFN  Over 7.20  ,1
+The AAP drop will stop only when Wall Street and sellers stop fearing the margin drop...take a look  ,1
+High quality stocks producing pocket pivot points on Thursday: ABT GPN ASGN OCN MTH OY NGVC WEC TY WYNN VS PFMT TTS STDY,1
+TM - ahhh! stupid prelim results! short but wanted to add puts before earnings...,-1
+user: AAP 40 minutes to get it over 460 pre-market !  ot of fools will B toast !,-1
+"ES,SPY  You wont here this on CNBC,,,real time assessment of jobs number mkt reactions...  ",-1
+N from watch list with a big gap up this morning   ,1
+MS from watch list at the line with volume of 4% ave full day   ,1
+ESI from watch list at the lower of 2 lines on volume of 11% of full day average  ,1
+F from watch list at the line with volume of 4% of full day average  ,1
+AAP PMI Manufacturing Index ---> In Few minutes...,-1
+AAP If PMI Manufacturing Index eslts are Bad = Below 450 ???,-1
+AAP no longer trending finally ppl get the hint. The growth and innovation story is over... PT 375 fair value,-1
+user EMN Eastman Stoploss in 72.31 after beating 72.98,1
+JCC and VNG at 3 right in my wheel house,1
+user: AAP How come the spy goes up daily and AAPl down //  No More Growth !!!,-1
+GOOG will make new highs soon. Surprisingly option premiums are very cheap.,1
+GME really nice pop today after that intraday reversal yesterday afternoon,1
+AAP Nothings goes Straight Down or Straight High.. It's the Main Trend AND Volume you have to look for...,-1
+"user: AAP people think growth slowing  is not true bec this quarter was only 13 weeks,?// ook Year Over Year for Same Quarter.",-1
+GOOG only 10 points away from new hold time highs. This market leader continues to lead  ,1
+user EMN Eastman Stoploss in 72.91 after beating 73.56. Eastman Stoploss en 72.91 tras batir 73.56,1
+DPZ looks ready to break out for the next leg up,1
+WFM building out a nice bull flag here  ,1
+user: AAP rookies are selling today  Mest loose OTof  being Stubborn !,-1
+user: the right thing to do was to stop out of AAP and flip short// Ther is OTHES Stock Better.. nless  are AEADY Short,-1
+AAP Cracked !!!,-1
+AIG looking for entry here.,1
+Nailed GOOG today.,1
+"user: AAP lets go 425,  (sorry longs, im long also)// r iht 425 annd IF consolidate for 3-5 Week After Bullish",-1
+"Switched my ATHX gains on ASTM. ASTM average TP is 4,07. 1% chance of bankrupcty  ONG",1
+user EMN Eastman Stoploss in 73.46 after beating 73.85. Eastman Stoploss en 73.46 tras batir 73.85,1
+KEX the break of the 8.49 secondary price was your obvious signal...,-1
+EAP nice upgrade and cross over 50 ma,1
+OVTI moving out of long base to the upside  ,1
+BAC 11.85 in sight... ;-),1
+"ed Weekly Triangle on CB,......Net Profit  1,797.00 (3.44%)  ",-1
+"Green Weekly Triangle on CB,....Open Sell Short at 3.38  ",-1
+SBX Solid move today continuing uptrend and holding above support to close the week  ,1
+DGI poised to break 30's.,1
+AMZN delivering awesomeness every single day. Prime ocks. I am not biased ;-),1
+QQQ with the Qs breaking above it basing pattern today I expect GOOG to hit 800 withing the next 2 weeks!!!!,1
+AAP don't touch it unless we get a close above 465 with volume (Earnings gap).,-1
+VP Drummond of GOOG sells 190 shares. He can be an outstanding trader based on his timing.,1
+user: AAP back to 450 i guess for a pin,-1
+ACTG was a top pick for earnings  starting to breakout. ook for 27.50 soon.  ,1
+AAP aw of Attraction & Newton Sem to Win Again,-1
+eality will hit NKD. eiterated as Sell and worth just over 80 per share. PT based on discounted cash flow analysis  ,-1
+"QCOM looking good here for a long, yesterday's close as stop",1
+AAP I agree 450 pin Afternoon !,-1
+GOOG lookin good,1
+"GOOG with need a bit more time to break 775, we might get it by EOD though.",1
+"DNDN waking up, almost GO TIME here!",1
+NFX as I said before...it feel like another distribution day.....  ,-1
+guess what happens to those chasing SYNM...,-1
+COST Confirming beautifully here.  52 week high magnet.  Others I like today:  IMM MNX WCG WT  ,1
+AJ starting to really move; moving stop up again  ,1
+AAP To much peaks on either side (up or Down) #Broken Good 4 Trade but not for investment IMHO,-1
+"AAP Almost every long will want to goo out for the Week-End, Nobody wnat to be caught 350-400 for months...",-1
+VSN pulling out of the station,1
+user: AAP 450 pin??  Yep !! 450.00 Pin,-1
+AAP emember last Friday in the last 5 minutes APP Collapsing !,-1
+long some MENT I like the Cup and Handle pattern. Earnings aren't till late Feb.  ,1
+"Big Block Trade Alert: IG, 2/1/13, 11:26 AM, 504260 shares SOD  6.50",-1
+user: AMPE bouncing after 8-day sell off.  Estimates of how high?,1
+AAP Will Pin  450 or bellow..,-1
+CA like the volume in this up move; entry 22.64 stop 21.55  ,1
+Scaling into VT Mar 25s calls right now. ight for now. ooks Good. Watch for volume next week.   ,1
+SWHC Swing Trade Quick Pick: Smith & Wesson |   ,1
+AAP Closed my short for 1K Will short ater again till gap will be Filled -HAve a Nice Week-End to all G ! :-),-1
+SCM Scaling my short,-1
+FIO this action in this super strong tape = Not bullish...,-1
+3 calls for 2day. SPY and GOOG makes new highs and GS goes above 149+. All came to fruition. Market has been kind 2 me.,1
+"user: NBS strong financials, more contracts for Progenitor in pipeline, approval for AM-001 expected, this is the sacred cow of 2013 ",1
+"BIG With a strong SI & an improving OBV, I have a feeling that it will break higher shortly.   ",1
+"PEIX strong volume w indicators moving up.Once 0.398 breaks, resist to watch 0.418 followed by 0.43  ",1
+"TQNT had a great day on Friday. TQNT not only had momentum, it had also strong volume. ong setup.  ",1
+Currently long: ANA FB HES BBY WCX VSN,1
+C its about eckitt Benckiser (no ticker here?): met equal dist.tgt bearish weekly candle. down.  ,-1
+"AAP is having a hard time at the moment with the 8dsma line, will continue to watch this one.  ",-1
+"#13for2013 INVN still the leading gainer - shed 5% this week, profit taking in overbought market   ",1
+California Water Service Group CWT aises Annual Dividend for 46th Consecutive Year | P/E 18.17 | Yield: 3.18,1
+First ever  purchase..splurged on a new mat for Bikram (I hear frond green is hot w/ the ladies -->  via user,1
+Great analysis of PNA  ,1
+COH Monthly h+s getting closer to snapping neckline  ,-1
+"user: QCOM, Friday's lows provided a nice back-test of B/O level  ",1
+AAP has come so far that the gap fill at 425 fits the Eiffel tower analogy. Need 2 search Eiffel on my site if  don't know what it is?,-1
+"CEG daily rejected at Fibextension of the bat, with short ter h+s. Wait to setup user   ",-1
+user: Commented on: Zynga: eady For eal-Money Games And Improving On The Cost Side  ZNGA // #TENDEVESA,1
+System Flags Buys: AT BAC BDN GFI IPG JCI M S TS VVS,1
+WMT breaking out of channel + All MAs are lining up in upward direction.   ,1
+"GTXI long 5.16, will take early assuming no gap, SPY green & detect above avg vol  ",1
+"If you have OII, short. They have over 1B in liabilities, not cash.",-1
+"AMZN SI and MACD, suggesting further downside to near-term support in the region of 260 ( 50ema )  ",-1
+ATI is in Breakout mode for Monday. It cleared its 200EMA on high volume + Bullish MACD cross. ong  ,1
+"A GOOD TIME TO STEP BACK AND CONSIDE THE BIG PICTE, ES,YM,TF,SPY,S,C,EC,JY, ",-1
+Anyone follow CST? earnings Feb 7th AMC looks interesting here off 50day under 50/share may get a bid into the report on #WATCHIST,1
+GAE long 1.82 1.83 1.84 target 2.00+,1
+SEV nicely green on red market,1
+JNP from watch list near first line   ,1
+FFIV looking like it wants to breakout today,1
+Good eye... user: FDX broke resistance. ooks to continue its bullish trend  ,1
+PEIX long .45,1
+PEIX long more going to .50+,1
+GAE buying more 1.83,1
+SCM Going to tank real soon IMO,-1
+"GME GameStop's Tony Bartel Announces Shift Toward Mobile (Audio). ong after beating 25,24. Stoploss 25,11.  ",1
+M Patent Could pset  AAP. ong in M after beating 7.97. Stoploss 7.86  ,1
+user: M Patent Could pset  AAP. ong in M after beating 7.97. Stoploss 7.86  ,1
+bot SYNM .52 bot PEIX .435,1
+"NFX asym tri formed up here, waiting for resolution to make a trade  ",1
+"NN average TP is 1,5 Could easily come back to 0,8. HODING ONG",1
+CEE that was short at 43.83 target 43 lower,-1
+NVAX interesting on that level. Now just over an P trend line from June. Could be a good entry point with a close stop loss,1
+ZCS bounced on 50 day,1
+ZCS feel bad for those stop-losses that got taken out,1
+AAP No kidding Terranova!,-1
+Marijuana stocks smoking MJNS CBIS HEMP the new trend. Next hot sector Biofuels PEIX SYNM BIOF money in focus,1
+"GOOG just like we said 7hours ago, it is national GOOG puts week. Easy +50% option scalps. NFX puts here we go..weeee!",-1
+"Despite market spook by media and the WSJ, going AAP calls this week.",1
+"PCN working off some overbought conditions but rising shakeout today despite market overall,  ",1
+CEE shorting more 43.76 avg me to short at 43.73,-1
+ANTH ZCS p 4 times average volume today. ots of people have taken interest here.,1
+added to ebay in trading account,1
+COH head and shoulders update.   ,-1
+"3 tech stocks with staying power - IBM, Intel INTC & Qualcomm QCOM. And QCOM lets you keep AAP exposure to boot!  ",1
+NFX nibbled a few earlier and holding over night. Sharing a few thoughts POSTED VIDEO  ,1
+"NFX new HoD, nice",1
+holding ANA BBY FB GAE HES VSN WCX,1
+After Hours Most Advanced: CC EDMC BKS HAYN PIM EZPW MC DGIT PAB JNY MM GFF THO CACI PMI IF STZ CODE NB,1
+CI - getting long building position here for move thru 7.00 - stock like a rock today - target 7.00++,1
+CI - you don't see a spike like that one huge green bar without some sort of continuation - somebody knows someting,1
+Market Wrap Video + Additions to Watch ist  including: CAH CMI HA KAC NFX,1
+AMZN Has Post-Earnings eversal  ,-1
+YM fails to break above. Downtrend continuous.,-1
+K is closing lower slowly with greater-than-average volume. Be Aware,-1
+HA  Over 41.25,1
+"PCN, Short interest very low <5%, BEAISH POINT AND FIGE TIANGE, GAP lower confirms, 625inplay  ",-1
+and HA -   ,1
+DK Over 34.75 for a continuation  ,1
+"SPY, ES, We trade what we see,,,turn of CNBC noise or nonsense,,trust ur technicals...  ",-1
+user edon PEIX has a history of management dropping the ball on runs in the stock price. Hopefully they stay silent this time around,1
+"WAG should be a good stock winner, because it is turning into a horrible company for its employees!",-1
+my weekly blog post is a collection of yhoo notes newsgrade.blogspot dot com,1
+GS  out weekly C1501.04,1
+Way to go! DDD. Seems to have found support from MA(50). p trend continues guys.,1
+BAC 11.75 to 11.80 today IMHO,1
+AJ stopped out +6% for a nice gain  ,1
+"AMWD still working well, play of the week so far",1
+"DEXO has this inverse h+s, bounced off fib, -longer term W bottom..",1
+TES moving stop up again; been a huge winner  ,1
+ong EN with stop arnd 39.40- entry 40.10  ,1
+NFX BOOM,1
+NFX OI loading up in OTM Calls,1
+"PNA trying to break the DT line again and already at average daily volume, earnings tonight  ",1
+"G SWHC both breaking up out of similar bull flag patterns, volume light though, so keep that in mind",1
+SWHC breaking out of this bull flag  ,1
+"G same story, moving in tandem here  ",1
+GME new HoD,1
+Watchlist for bounces MHP 47.45 COH 49.15 AMZN 265 4TA 98 A OOK IKE GD ONGS FOM HEE,1
+QCOM is really getting interesting. Might see new highs really soon.,1
+"PI making an effort, watch TIP too here",1
+"ed Monthly Triangle on CSN,......pdating  ",1
+CEN price action looking good going into earnings tonight,1
+MCP take-over chatter... (I know don't laugh...),-1
+"ZNGA If you are bearish here, you don't know poker.  This is incredible.",1
+CSC moving stop up again,1
+GOOG this market leader continues to hold up well  ,1
+CW long here 3.22,1
+BAC Very Strong   Making  since 1 week on both sides. But Today Bull !,1
+NFX failed attempt at a new high possible hesitation at the top I like the NFX130208P170 at 3.60  ,-1
+"user for AMPE, the Gap for business is 28%, so a low on 3.69 x 14% is 4.20, and the MA-20 is also 4.20, so that is my target",1
+BAC going above resistance with some really HEAVY volume today. Should be in everyone's radar.,1
+CBK looking for the breakout; entry 6.51 stop 6.19  ,1
+NFX looking good so far on hourly. ooking for it to pull in to the 20 ema hrly at 170 maybe more  ,-1
+AAP perhaps is time to start considering a ONG position on this name again. Finally some commitment.,1
+AAP Short 451.50 Will go down as soon Mrk will ollover,-1
+sold my amzn stocks... better be flat ^^,-1
+"user Market only seemed to care about beating expectations, which were inflated it seems SII",1
+AAP Begin to ollover I,-1
+"I think it's obvious that Valero VO has more room to run, but I certainly like HFC a lot more. Compare their P/E and revenue growth.",1
+Are we seeing a minor pullback for NFX or the beginning of a major correction? This investor isn't about to test the water. Still bearish,-1
+GDP from watch list 20 crossed above 50-day and near line with volume of 27% ave   ,1
+"NW - ong  19.75. Trailing Stop  21.06 from 5 prior Stops of 20.26, 19.93, 19.75, 18.95 and 18.15:  ",1
+"user CSN, yesterday I bough at 25K1.2375 and 25K1.1550, I will continue scaling up/down",1
+"BAC Out  12.82 Not being Greedy, Will wait for a pullback or another day to reenter.",1
+ZGNX Here it goes again.,1
+MHP MCO SPX SPY Will S&P and Moody's once again threaten sovereign downgrade in response to mortgage fraud action?   ,-1
+TMB suggested by a follower - poised to breakout to new highs. Buy area 64.00 on volume  ,1
+MHP MCO SPX SPY #CNBC  reports that Justice is seeking 5B+ from S&P for mortgage ratings in civil case/no criminal charges to date.,-1
+AAP BAC two trades I'm looking to get involved at the close.,1
+long FS  30 could be late though watch ur stops,1
+ooks like those MCP 8 calls for friday are paying well! ,1
+NVDA Breaking a short-term downtrend line w/ SI rising and MACD trying to cross above 0  ,1
+user: AAP Sold because the CNBC dump is coming,-1
+VMW next trip to 79 its not going to be denied,1
+"BI: QIK wins on functionality, loses on cost/service. 2 Q's of deferred rev declines + back-end loaded estimates = tough bar for 4Q (7/7)",-1
+ESX making a move to break into the gap.,1
+"ed Weekly Triangle on KWK,.....Scaling p  ",1
+V - On watch for tomorrow.   ,1
+ZNGA up almost 5% in after hours.,1
+"TIP coiling like a rattlesnake, long, looks higher   ",1
+EXAS Pretty set up   ,1
+ZNGA When a company beats by 1cents you have to seriously doubt if that is due to accounting tricks,-1
+No Porn for me only ZNGA Pokeher,1
+"MHP Kids, don't do steroids   ",-1
+"thats good below the equator? user: ZNGA yoy sales growth over last six Q's: 80%, 59%, 32%, 19%, 3%, 0%",-1
+ooks like the intermediate bullish trend has been broken in SNTA - been safe money for months now   ,-1
+"CS weekly (tied heavily to AAP but, absent that catalyst, this pattern persists):  ",-1
+AMN  Yep like I said. Penny stock in the making.,-1
+AMN  Not only will this become a penny stock but this company will continue to dilute its shares like EC Silver did in the past.,-1
+EV Amazed how an 18% gainer gets no ST recognition. Guess that goes along with only having 15 followers. #undertheradar,1
+IBM Short Setup - 196.25 target:  ,-1
+DNDN A subtle buy signal occurred today. (the set up)    ,1
+Put FS at the top of your radar?today after Citigroup initiated?at Buy and target price of 41,1
+AAP #BOKEN STOCK,-1
+user: DOV weekly.  Going higher  - bot into close eod yest unit cost 4.15 see we get a gapper today,1
+GMC CST report after the bell expecting a #SFY type of quarter out of both. NOT TADING INTO EANINGS WI ONY TADE THE EACTION,1
+AAP  possible bounce above 462  ,1
+VT  breakout point 25+  ,1
+ANAD  b/o point 2.72+  ,1
+DOV PT on this btwn 5.30 and 6.00 two areas of resistance. Valuation incredibly cheap see if volume comes in again today,1
+Back in BAC ong ! :),1
+"NN something is going to happen. Two days ago we saw the hugest volume of the year. Oversold, insiders buying, high TP. ong",1
+AAP Going under 435 IMHO !,-1
+"So Iger says he likes NFX, go bid 250 lol",1
+NFX trying to break out of bull flag. New 52-week highs  ,1
+DPS breaking out this am. looks good. earnings feb 13th BMO,1
+PAN Caught an upgrade with 26% short interest..Breaking out here:  ,1
+last minute add-on to watchlist: DPS  ,1
+AAP Follow the downtrend.,-1
+AAP Short,-1
+AAP Drop like a ock !,-1
+AAP heading for the gap,1
+AAP The Dump will come faster than the pump IMO !,-1
+AAP Dump in Progress...,-1
+FS squeezing,1
+DA back in playing the breakout to all time highs; entry  14.30 stop 13.41  ,1
+AAP Bullish signal here.  Want to see a close (preferably) above 462.60 to confirm.  ,1
+looks like someone got tired of BJI going down today,-1
+"Jon Stewart sums up everything dangerous, dumb, & hysterical about the Post Office  circa 2011 cc user FDX",1
+"me too.  yay user: So AAP didn't gap up this morning toward ST upside target (480), but I will take a sudden reversal.",1
+"Watching closely (long) ... AAP lifting higher on buyback chatter, details on the Stream  ",1
+Nice all-time high attack by Tesla today (long ) tsla cc user,1
+AAP Free-falling Now to 457,-1
+AAP  reloaded and ready to keep going up + 4%  today? I would be good with 3 ;),1
+CEE short higher and will short more here 44.35,-1
+Short term targets for ZNGA? 3.25?,1
+BAC and AAP my two trades from yesterday are holding up really well. Both trading with HIGH volume in a weak tape.,1
+BAC will add more if we close above 12 today. AAP needs to take out 465 to attract more buyers.,1
+AN breaking hourly trendline,1
+"TWX - ong  38.59. Trailing Stop  44.71 from 4 prior Stops of 42.61, 41.06, 37.08 and 35.57 -  ",1
+FS daily few like the long call on this one but after chaos and mini sell we got oversold PWmo2!  ,1
+BAC ot of buying on last minute !!!,1
+DNDN Beast mode today,1
+"DNDN TS nicel day for both today, lets see what EOD brings",1
+AAP thank god for the dividend tomorrow or I thinks we would be back lower than where we started IMHO,1
+user: AAP continues to have no volume / confirmation to take higher - watching closely to reshort this one,-1
+BAC Added Some on the pullback !,1
+AAP sharing a few insight about volume is key POSTED VIDEO  ,-1
+AMZN short working nicely  watch for a break and run of 262,-1
+AAP is Sinking....,-1
+I need alka seltzer to sponsor our aapl stream with all the pop and drops it gives people ...people are just way too tied to it,1
+FS is in a big rising channel w its top at 39 & bottom at 28.Momentum indicators turning up again  ,1
+AFFY break down the support line  18.41  ,-1
+NGT #ECB and #BOE and #FED JPM HSBC shortfest GC_F GD SV tomorrow morning Test weekly OWS.,-1
+Netflix should buy the SPS. Netflix disc traffic is probably the only profitable and useful thing the postal service does. NFX,1
+AAP Volume is ABSENT... Soon the numbers will be negatives...,-1
+AAP Drop !,-1
+JOY anticipate the bear flag break  ,-1
+FS intraday break !!,1
+DE higher bid offering coming? look at all the recent action on the April 14 calls per Najarian? PPS above 13.50 here???,1
+PCP Earnings today.  Stock up 5%,1
+OC  more weakness here and it might hit a dime under 34.,-1
+"Never got in SFY, but they are taking the right steps by focusing on distributing digital content and not the physical. Congrats to owners",1
+BAC ooking good for Tomorrow we could go over 12.20 imo ! Healthy pullback today with volume.,1
+AAP 500 lot bid in the  Feb 440P at 3.00 could create resistance or a temporary sell off,-1
+AAP Covered to not Pay dividend will short again Tomorrow !,-1
+user: AAP Cover your Shorts unless you want to pay the dividend. >>>Done and will reshort tomorrow !,-1
+SEV next stop 8,1
+Tomorrow the stock Should Gap down of Average 10.60 which is the dividend of AAP,-1
+solid day of trading. Mrkt may be flat SPY DIA but there was still lots of profit to be made and was. Nothing not on watchlist NFX DVA,1
+user: user AAP still holding your short from 451.50? No it cost me 300 overnight to keep will short again in premarket.,-1
+GMC headed to 37 Bombs AWAY....,-1
+GMC Weekly dead cat retrace done on NO volume - Bagholders will bail en masse,-1
+SWHC yes! close over 9.,1
+GMC ICE is at 37... Short away.,-1
+GMC 37 No Brainer - FEE CASH - Sell MOTIME - SOTP,-1
+TBI  Another staffing co. breaks out   ,1
+GNC  generators running hot   ,1
+Market Wrap Video + Additions to Watch ist  including: AMSC KBH MHK TW TTMI,1
+Prudential liquidates CM stake... 1st ones off ship get lifeboats  ,-1
+"Key levels, confirming the expectation on: BBY DNDN BAS GD These and other set ups...  DIA QQQ SPX T",1
+P poised. Needs over 11.90.  ,1
+"In it's own secular #bull market COO #The Cooper Companies, Inc. Bull Flag Daily included #dogs  ",1
+"M Consolidating here, heavy volume inverted hammer candle, we should see a nice green bar up tom, unless all buyers exhausted from today.",1
+ABT Bullish /T Setup - Breaking from a 14 year base:  ,1
+" ES,EC,JNK,DAX  Is the S&P diverging from other key mkts? The canaries?  ",-1
+P has pulled back to its trend line and looks to be setting up for another upside move. es 11.90  ,1
+Only a rise towards 480 can confirm the new bottom of AAP  ,1
+user: BBY upgrade2 outperf vs mktperform  WFC; raise PT from 11-13 to 19-20/  More royalties on surging BB10 sales,1
+yhoo is like a PE VC w a biz.. holds big position in yahoo japan which is up 32.5% YTD (helps balance sheet).. also a position in alibaba.,1
+APP catalyst: 1. efi 2. Deals with WMT TGT  A NKE (possible) 3. 100s new stores globally,1
+F headed for 12,1
+KSS ong Setup (Solid SSS this morning should trigger the gap):  ,1
+I estimate WMT will buy 1/2 bil of Made in SA clothing per year for next 10 yrs. APP is only massive scale plant left in SA,1
+user: Sour Apple. Einhorn not happy with AAP. This should be fun. POXY FIGHT!,1
+GMC 37 next target on Bailing VOME in next week.,-1
+MS Morgan Stanley Is Nearing Our Medium Term Target  ,1
+BBY - The new AAP as Apple bleeds on the No Jobs report. Target 55,1
+You know you are doing something right when you are the comparison benchmark. AMZN,1
+user: BAC 11.97 no longer a problem!,1
+C  breakout watch above 43.4  ,1
+GEO  looks good above 33.10  ,1
+AAP looking like a nice short off 460,-1
+user: EXC = undervalued here.  Where is the yield support!?,1
+"MCP Breaking out of it's base, above the 8.04 mark. Next key level, 8.12. MACD, Slow Stochastic, SI now showing some very good strength.",1
+"SCM Very very slow, some holders should start selling off real soon IMO",-1
+GOOG(long if beats 778.71. Stop loss 773.91) & YHOO(long iafter beating 20.27. Stoploss 20.16) fit well the advertidsing deal,1
+AMZN nibbled a few short   ,-1
+C_F SO Scratching my Head how OI goes P in a shale OI GT and refiner VO profits are P 20X. Do we feel Screwed?,1
+AAP On next pop if I'm out of other trades I will short for a quick trade.,-1
+AMZN breaking below 50-day MA,-1
+"user: Einhorn sounds like a trapped long to me, didn't care about preferred stock last year AAP",-1
+Price is what you pay. Value is what you get.? Those words never have been so true as in the case of NOV -  ,1
+O orillard option trader buys 5k Puts betting on a 15% down move. A bad earnings omen?   ,-1
+Scaling some AMZN puts 02/15 puts bot at 1.2 at 2.50,-1
+APP added 1.49 risk of bankruptcy abating. Not out of the woods yet. More work remains. Adding if thesis benchmark met. American Apparel,1
+ACX No negatives here.  Just an algo bringing it down.  Here's a chance to get stock cheap. BBH XV,1
+BBY Headed back to 5 on over saturated Cell Phone Market - That GOOG will own as next mobile Microsoft OS.,-1
+For once the market is making fresh lows and AAP is hold firm.,1
+BYD moving up on big down day,1
+"CST Coinstar option trader buys 5,700 Feb call spreads betting on good earnings  ",1
+CEE goes back above 44.00 all over that short again - I think I can make 10 points in a week on this one,-1
+"AAP Still looks like its in trouble to me, no sign of a low, so remaining bearish #elliottwave  ",-1
+MED to 21? Volume picking up..,-1
+SETPS some small some big a variety to watch - TZYM .56 JAG .75 C 1.23 AV 1.29 IDIX 4.85 MCP 7.75 MO 34.37 KO 38.75 HF 36.75,1
+OMX another bricks and mortar dinosaur falling to online retailing> Details here  ,-1
+SWHC now we need that volume,1
+CEE short entry here 44.36,-1
+DGIT long for 10 crack at 9.97,1
+"Green Weekly Triangle on CB,....pdating  ",-1
+CDX long 7.72,1
+SWY - made some sales 20.30 - 20.60,1
+Diving in... (considering schwab on pullbacks) Thursday links: spin-off successes   via user DE SCHW,1
+DECK about to give some major loving?,1
+user StockTiger Currently long HA  ,1
+JET entry 8.94 stop 8.2  ,1
+DOV  Nov eport  Feb early release earnings next month has made major strides  CAB TA,1
+GOOG it's acting really good despite the choppy tape.,1
+ZAGG Coming off the bottom with the Grandma pattern.  ,1
+BAC really wants to get BACK to retest its old HIGHS.,1
+MTX attacking multi year highs - entry 14.94 stop 14.00  ,1
+GNTX Swing Trade Quick Pick: Gentex Corp (GNTX) |   ,1
+AAP need 465 to be taken & no looking back if this is for real,1
+AAP Volume Fade away... like as usual !,-1
+"user: That was the hardest,  10 rally in AAP history, like an old man climbing stairs. .~ He still got it up!",1
+AAP Short  468.44 on NO NEWS and no sustain in volume !,-1
+"Green Weekly Triangle on ESSX,....updating  ",-1
+Don't get me wrong buying into earnings was a mistake  what I read over 2billion in evenues nothing to scuff  CST,1
+"S. Jobs was Keeping Money to innovate and buy New Tech, since AAP no longer innovate why keep  !",-1
+Schwing! bingo NKD can I please sell my Feb 8 115 Call that I bot earlier for 10.50 NOW!!! #GamblingAddict,1
+In order to grow u need to SPEND MONEY aka INVEST in the future. At 10x earnings CST is a steal and shorts will cover over time BuyTheDips,1
+my NKD estimate was based on linear extrapolation. Beating my estimates means they are actually accelerating growth,1
+user also CST is better NFX b/c streaming movies is 4 suckers. The movie clarity is awful. The movies skip b/c int connections.,1
+user just saying ED BOX is not the future why? CST considering the growth of the company over the yrs. and valuation is cheap,1
+user That's what happened last time CST missed. Opened down big the following day then a massive short squeeze and closed +5%.,1
+I guess I'm old fashioned I own over 500 DVDs & love Blue ay. ntil streaming can improve clarity and no skipping I'm stickin to DVDs CST,1
+MSG - beautiful to see all these highs  ,1
+user ProfitsBoy CST sticking2 buying over 250million in stk there unit cost is 49.90. Also cash flow 200mil. & Costs go down,1
+user: This CST report is goofy Acquistion costs were factor citing lower rev aka same store sales from dispensers acquired NC Corp,1
+"ed Weekly Triangle on FNFG,......pdating  ",1
+user: AAP typical pump dump today will talk about later tonight via video,-1
+Absolutely Delicious CPB #Campbell's Soup SPX SPY #Fortune 500  ,1
+APP Compare the squared areas. Aren't they look similar?  ,1
+CECO breaks above its downtrend / triangle pattern.   ,1
+"user: Fascinating email chain between user and Einhorn debating the merits of AAP preferreds,  ",1
+CST bot 51.40 I will hv to be patient IMO CST moving in right direction. Don't underestimate loss of Sat Mail too. 250mil buyback 10 p/e,1
+time to return to Apple.  aapl record 23 billion operational cash flow quarter!   ,1
+"TWI added to Small Cap 600; 24% short int; cheap valuation, though growth via acquisitions E 2/25  ",1
+BBY Pulls an AMZN  ,1
+AAP Beautiful Bull Trap !,-1
+James D. White: Jamba: A Juicy Turnaround on Vimeo  1hr video from 2010 JMBA .. this is great stuff,1
+AAP No Volume + No Conviction = Bearish,-1
+added again to CST 48.84,1
+lnkd pulled a nflx topping out so far at 146. day is still young so watch this pull back,1
+user mention the lower same store sales in CST well I guess him & Mellisa ee Didn't Factor in the NV Acquisitions. Do ur DD ppl,1
+TW from watch list needs a volume boost - now at 2% - but watch as close to top   ,1
+TTMI on watch list as a continuation trade and it is   ,1
+OSG BIO MTS DYNT WYY all off watchlist DOV next to 5 this baby is in its prime.,1
+AAP Volume Fade fast !,-1
+CST nice intraday base here could see a pop here after 10:30 short covering on into the wknd. Valuation cheap. Biz model on track its a GO,1
+CST as we close in on hod this one should get perky MY GESS WE COSE OVE 50day maybe even up on the day #TIMESTAMP goin out on a limb,1
+INT #1 on SMB adar weak today,-1
+CST mentioned this level building base here over 48 next stop 49 repeats over that. ADDE P intraday to over 50day watch 50.13 HIT TODAY,1
+AAP Confirming nicely here above the 20 day of 473.12   ,1
+"DNDN breaking out 6.74, CMGO looking VEY bullish",1
+Mining & related stocks showing weak relative strength versus ESD & ES_F last few weeks. FCX NEM A GG GD SV CF MCP ACI,-1
+DNDN Confirming nicely (continuation) above 6.57 Also like AAP (breaking out above the 20 day)Others I like today:   ,1
+AAP Europe now closed now lets BAST OFF!,1
+user: AAP Highlighted Major esistance Areas To Watch . .   ,-1
+NFX: Time for a Major Correction?  ,-1
+MGM - already at average volume for the day and trying to get separation from resistance  ,1
+"ANGI ripping, lots to go",1
+user: AAP  what is the cash pile per share worth ? anyone know ? ~ 140/ share is CASH (give or take),1
+user: AAP recent trip to Thailand...even the taxi drivers have iphones ! ~~ wait till they get into China!,1
+user: Great rotation..  Grasso confirming what I have been saying here for a while; Money coming out of AMZN and buying AAP,1
+"MDSO - ong  27.51. Trailing Stop  38.58 from 10 prior Stops of 37.67, 37.11, 37.00 ... 27.51, 25.15 and 22.79 -  ",1
+FB I do wonder if the same shorts in FB today were in NKD last year at 80. They seem to be making the same argument. QQQ XK,1
+HG is breaking out ahead of an expected earnings beat  ,1
+AAP Who really want a broken stock ? nless you have kept over E & Being Trapped !,-1
+OPK low of the day was 6.08. arge candle wick growing downward. MACD divergence downward. Stochastic downward divergence. SI downward.,-1
+NFX is planking,1
+Where I stand on SBX...  ,1
+Jeff eeves and Charles Sizemore Discuss inkedIn Earnings on The Slant   NKD FB AMZN MWW,-1
+Added more shares of ACTV,1
+"MHP McGraw-Hill Companies is having a huge sell off because of the rating agency, Fitch, has cut their ratings.  ",-1
+DNDN Good place to lock in (some) profit at  7.20  p 20% since alert at 6.00 from Feb. 4th   ,1
+DECK  lets squeeze!!   40% short!,1
+"Brandon !!  ACTV should break into the 6s very soon... nice breakout , just needs a little more volume !!",1
+"user About G, it is in a sidelines mode, that is no a good trend, I am seeing 2.22",-1
+VNG I swear never seen such terrible reporting on one stock ever in my life > The Cuban news is not negative lol,1
+AAP See   Flash Crash  3.58pm ;-),-1
+user: AAP...IMHO...will be sell the news..sorry if  I offend anyone...G warriors,-1
+AAP Selling begin First Wave of Dumping.,-1
+AAP Flash Dump Begin ????,-1
+GOOG IGV #Google #Software firing on all 3 cylinders compq NDX  ,1
+"closed my DDD Feb 60P/60C/65C bull / here for +8, roll to StO Feb 70 puts  4, I'll take the shares, the premium, or some of both",1
+SCHW - adding to long,1
+PO Swing Trade Quick Pick: Polyone Corp PO |  ,1
+"STO Mar 425 put  +1.98, STO Mar 465 put  +10.75,  BTO Mar 475 call  -17.65, BTO Apr 480 call  -21.65. AAP will fill gap to 500. =^.^=",1
+AAP Dump in progress by Fund Managers 25% Done.....,-1
+Stop on AAP is 475.7 hard stop risking .30 to make 3-4 pts,1
+AAP Flash Dump in progress 35% Done Be patient or press Escspe !,-1
+AAP Dump by  Einhorn in progress 40% Done Be patient....,-1
+"STO March 465 puts in AAP  credit of 10.70. If put to me, I buy shares  net cost 435.25. If not, I keep 1,070. Win/Win. =^.^=",1
+solid day with MGM AAP SBX leading the way for me. Out 1/2 MGM and 1/2 AAP (added a little after selling 2/3 in AM),1
+"user we should see again 1.35, and after that my exit 1.43 CSN",1
+Damodaran on AAP: getting a pretty good business for the price and worth about 600  ,1
+user: GOOG Eric Schmidt to sell roughly 42 percent of stake  ,-1
+"ed Weekly Triangle on HEK,....Scaling p  ",1
+"Green Weekly & Monthly Triangle on ISIS,....Scaling Down  ",-1
+DT appears 2 b hammering out a bottom here at its 50 DMA. Solid EPS/sales growth+ low P/E ratio = possible earnings winner. Earnings 2/27,1
+I just legged into GOOG  780! Why? Sergey & arry aren't selling any of their shares...and Eric's keeping a few. :-) ,1
+Yes I saw a great opportunity when it dropped to 17 as you can tell from my twits user: user are you still long CHK ?,1
+Brutal declines on SD will create a buying opportunity soon. user: user  like SD or XCO for a trade or T hold?,1
+Buying op in this range IMO user: user You still long in COP Are you a buyer today,1
+"54 Short Ideas to prepare for the next correction,  BID COX SV GD FXE PB WTW HF FCX CF MNST #Prepare",-1
+PEIX gets volume Monday it could go thru tops and set new highs over .50+,1
+Acting well above 26.19 flat base trigger b/o from 1/9. ooking for a retest of 50day SMA at 26 PFE  ,1
+One of Buffet's favorite banks. Cup with handle trigger of 105.02 MTB  ,1
+"MCBC long 4.36, healthy pullback into sweet zone, between upper bolly & 20SMA, hammer today a plus  ",1
+"PXC long 10.87, like to base a few more days but ready if it pops  ",1
+AA - really liking the scale in entry here - limited downside nice upside,1
+A little more of the write up on ZNGA is available at  ,1
+STX - high call vol. 12.71% short Check WDC as well. STX better hard drives. always rumor buyout  ,1
+DNDN breaking swing High.Volume looks good.   ,1
+Week 6 - #13for2013 DDD DNKN FT INVN KOS NKD MOV NTSP AX SCCO SCA SSYS V  ,1
+#13for2013 - 8 of the 13 stocks are up double digits & one is up 8% w 9% dividend NKD INVN DDD FT MOV SCA KOS DNKN SCCO,1
+Market Wrap Video DJIA SPX TX + Additions to Watch ist  including: AGNC ANY FS HP OST SQNM,1
+MBS The long awaited break of its 200-day EMA is just one step away. Watching for 5.68 breakout.  ,1
+PEIX Bullish volume pattern. ike it on a close over .43  ,1
+YHOO ong play. Watching over 20.88 for movements.   ,1
+FS  Over 158.00 or 158.63  ,1
+SQNM a Continuation - wil take it over the top Bollinger band  ,1
+MSFT Waiting for break out of bull flag+horizontal channel   ,1
+"Commentary on AGNC. DDD. ZNGA, HX, AGNC, BSX, VT  ",1
+"Going long msft tomorrow, I think 30 is written all over this one ex div is also coming up.",1
+"Morgan Stanley Initiates Coverage on HCA at Overweight,announces 47.00 PT.Breakout watch over 39.6  ",1
+"Feeling good this morning about AO after Friday... Technicals still holding up, and fundamentals are coming around!",1
+"ed Weekly Triangle on HEK Closed at 4.30,....Net Profit  15,137.50 (5.30%)  ",-1
+"ed Weekly Triangle on HEK Closed at 4.30,....Net Profit  15,137.50 (5.30%)   ",-1
+"ADBE SHOT! will see around 17 - 20 very soon, 39 is to much for adbe, thanks and have a good day.",-1
+"Today is the very important DAY 3,we haven't closed green for 3 days in a row since Sept.Very bullish short term if this happens AAP",1
+DDD and SODA look interesting but will wait until after earnings release,1
+AAP Next big  (little) thing... iwatch  ,1
+MSI SHOT! will see the price around 32 - 34 very soon 1-2 months. Becefull. Have a nice day,-1
+From Yesterday- 'NEW Blog Post: Watchlist for the Week'  IWM SPY AAP SBX AXP CSX X ,1
+CSX getting the Barron's bounce. At resistance. Wouldn't pull trigger till break of recent highs  ,1
+ed Monthly Triangle on CSN closed at 1.46,1
+ADM holding on nicely,1
+AMZN back to shorting a few - clearing 50 day ma.  ,-1
+DNKN clearing back about this resistance area - worth eyeing  ,1
+"GS continues to trend higher, a solid run since Dec.  ",1
+BAC holding up well - could see much higher prices by year end.  ,1
+A couple biotech stocks that are setting up well in various time frames: JAZZ PCYC,1
+"AAP resistance near 500-bearish below 555, targets at 420 and 350  ",-1
+AAP bounced back to the trendline. Should it break above? I still short here,-1
+ZNGA suddenly jumped to 3.61,1
+BKS remember that one? - low volatility pointed out couple weeks ago. Forgot to check. Next. :)  ,1
+"annoyed with BBG steadily going up, i want to grab more",1
+OVTI maybe today is the day for a breakout over 16.  ,1
+I think its a great level to long TIVO after its pullback from 13.5 to 13.0,1
+ZNGA continuing rally,1
+GMX -10% ouch,-1
+SHD Sure is wound tight here:  ,1
+SB WFC Broke trend line Ho hum CA,1
+doesn't seem to be much talk on financials today. GS XF new highs ,1
+WMT want more march calls,1
+keep an eye on IDA presentation bout postive drug results at a conf 3pm in NYC today on watch for volume to come in,1
+CST about to go ed to GEEN watch break of 50day by wks end time to rock and roll baby,1
+"DPZ a five bagger in pizza, no carbs, if you own domino's stock since 2010...' the markets are magical '",1
+FS Nice Breakout w volume. Next resistance lies at 33.38  ,1
+"Green Weekly & Monthly Triangle on ESSX,.....Net Profit  18,664.75 (6.81%)  ",1
+AAP is having a great day! I think we've seen the bottom.,1
+Currently holding ANA DVAX ENDP FB GAE NKD WCX,1
+NKD a strong follow thru day on volume - exactly what you want to see for confirmation  ,1
+we have a joke abt how often it seems JEF is wrong when they make a reco or PT change the day b4 a co reports: raised PT on KOS...#uhoh,1
+TEK long 1.65,1
+"Green Weekly Triangle on MNK,....Open ong position at 3.17,...Opening both ong and Short   ",1
+"Green Weekly Triangle on MNK,....Open Short position at 3.17,...Opening both ong and Short   ",1
+ZNGA BYD MGM These ready to sell yet on the NJ online gambling bill that will fail?,-1
+NVDA Wants to breakout.12.69 is short-term resistance.   ,1
+"EXP watch this consolidation, a volatility squeeze seems eminent. eady for next move up   ",1
+GMX -13%,-1
+PCS - shhhhh,1
+P is breaking out.  ,1
+is this a potential h&s on  with neckline 65ish?  ,-1
+EAP abouve 6 again - lining up to break out,1
+hmmm user: TSA user coming after NYT writer. Elon says vehicle log shows he took long detour.  WOW,1
+AAP Crack nder 480 !!!,-1
+AAP Sell Off !!!  Somebody Know Something and Dump ?,-1
+M Settling in with 27.40.   ,1
+"Green Weekly Triangle on PBY,....Scaling p  ",-1
+"AN doing what it needed to today, bouncing at support  ",1
+"GMC still sitting above key support, may test further but no damage done longer-term  ",1
+ast time AAP Dropped was when Tim Cook this was when Tim Cook spoke.... Hummmm !!!!,-1
+GMX -18% any news ?,-1
+MCO out the last 1/2 from this morning +1.00  30 min opening range,1
+"AAP setting up for a sell the news tomorrow on Cook speech.  He is no Jobs, market will be disappointed.  Iwatch?  Gimme a break.",-1
+"user: AAP setting up for a sell the news tomorrow on Cook speech.  He is no Jobs, market will be disappointed. Iwatch? Gimme a break",-1
+user: AAP is WEAK.. wants lower. Tmrw will be nasty folks. Sideline if ya long.. Bears taking over.,-1
+AAP Made in SA = ess Profit for Shareholder and Share will drop on anticipation...,-1
+user: AAP If TC announces bringing cash back to S>> Made in SA = ess Profit for Shareholders & Share will drop on anticipation...,-1
+GOOG can break above the old 52 week high of 786.67 tomorrow. It gave us another green candle on the daily and weekly! What a beast!,1
+"INVN - Do I believe? Well, 111 tweets since 2012 says I do (tweets w/ InvenSense mentioned)  ",1
+esrx gap fill idea.. 56 looks big  ,1
+" , ES,SPY, Ground Hog Week, distribution at highs..  ",-1
+OMEX Odyssey's ecord Breaking ecovery to be Featured on Discovery Channel SIVE SH Premieres February 24 - 8 pmET  ,1
+EVC - so close to magic 2.00++++ - ready for a big volume day with great earnings due 3/7 Target 3.00,1
+", AMZN,,,Headed to lower weekly channel support at approx. 233??  ",-1
+OCN Over 41.50  ,1
+Momentum Monday Post - thick and deep but dont chase ....NKD NFX TSA  ,1
+FOS  bullish engulfing.,1
+OVTI If it clears the 16.03 resist the move may be powerful.Breakout candidate! nder accumulation  ,1
+expect NFX to trade higher on Dish founders positive comments about NFX.  Basically calls them game changer.  ,1
+FOS worth eyeing - earnings coming out so could move either way  ,1
+"Watch SZYM, nice base, working a bull flag, could get legs to the 200 day ma around 10.30 on b/o  ",1
+NX will be watching this week for higher prices   ,1
+user: SHD  could be getting ready for a move to the 200 day   ,1
+"NFX Held the 10 day ma like a champ yesterday, lets see if it can squeeze some more shorts   ",1
+T user INVN ..lots of bullish technical discussions tonight. Fundamentals have always suggested a higher price.,1
+V can only appreciate!!!!!,1
+INVN last one...watch for the breakout...good luck today   ,1
+AMZN looking to add to short position on any bounce  ,-1
+"NIS sold stocks at 2.24, now we are at 2,15. We will come back P soon. Patience my friends. I'm ONG",1
+"MEA sell stocks at 1,3 we wait for it lower",-1
+MITK moving again,1
+COX continues the pattern - tension building as it creeps up,1
+FS obama speech tonight moving it on up in channel - squeeze continues,1
+GAE 2 & 3 calls waking up,1
+BAC  Out  12.06 waiting for a pullback to comeback 3K profit   :),1
+XXIA on watch list with 21% ave full day volume close to line  ,1
+JOE successful test of that 22-23 support I highlighted last week   ,1
+AAP Cover my Short B4 Conference !,-1
+EC nice set up,1
+YGE opening range is in. Feet up. FS JASO X and BD similar trades.   ,1
+AAP back to yday opening range high - support bounce   ,1
+Cook should inquire as to how many indictments Goldman has faced every time he is asked for numbers...walk off the stage  gs aapl,1
+OPXA taking some here long small stop,1
+"Green Weekly Triangle on HEO,.....Open Today Scaling  ",-1
+"Green Weekly Triangle on HEO,.....Second Scaling  ",-1
+"NW - ong  19.75. Trailing Stop  21.08 from 6 prior Stops of 21.06, 20.26, 19.93, 19.75, 18.95 and 18.15:  ",1
+"Green Weekly Triangle on HEO,.....Third Scaling and Closed  ",-1
+AXP new multi-yr highs,1
+P breaking out after expanding from Shark Pattern yesterday  ,1
+"Green Weekly Triangle on HEO,....Net Profit  30,990.00 (3.08%)  ",-1
+FS - daily   ,1
+NIHD  Gap and go? (likely)  Also liking the action today on:  WT CM  Others:   Also bullish:  DIA QQQ SPX T,1
+"AAP like I said yesterday, APPE is a broken stocks and TC is not the right CEO for the company. Still SHOT.",-1
+AAP when I got lot of hate mails yesterday after saying SHOT apple I realized my short was definitely going to work today.,-1
+"GOOG has been trading like a leader, ride it until it stops.",1
+"AAP enjoying some really nice gains right now from my SHOT. Next stop 465, if that does not hold I expect APPE to make a new 52OW soon.",-1
+"Based on historical research about GS upgrades/downgrades, K is a short right now after being upgraded from Sell to Neutral. Sell signal.",-1
+ike it or Not AAP will fill the gap B4  will see 500.00 IMO,-1
+NEW POST: AMZN Is Still in the Grasp of Corrective Forces  ,-1
+CTIC long - next one to pop out base 1.40 1.41 1.42 1.43 - get it!! - GAE hot too at new hod,1
+long AAP 470.10 holla,1
+Price Match is a reputation destroyer since you can match price anytime with gr8 technology. Price Match = Friction. AMZN,1
+"ed Monthly Triangle on CSN,....Scaling",1
+AAP hit resistance yesterday and continues to roll over - holding short currently  ,-1
+AAP 480 weekly uptrend support becoming new resistance and bear flag in the making   ,-1
+NFX SI is sloping lower/MACD is about to cross down.Negative Divergence on Daily.Distribution ?  ,-1
+"user: OCN trying to go, looking for follow-through I think the follow through is coming. This wants more",1
+AMZN clearing this rising trend line / support off 5 minute looking to add to short position   ,-1
+PHM ready to go. I'll buy some even up here too along with user,1
+AMZN sharing a few trading insights POSTED VIDEO  ,-1
+SPW new HOD,1
+VXY Added some AAP Mar Bull Straddle for EOD squeeze,-1
+DISH Break out watch over 38.10  ,1
+SPW SunPower Corporation Option bulls make bet on 50% up move by June  ,1
+"BAC huge spike on AG 17 CAs just now. 10,000 contracts.",1
+"JBHT short right here, maybe write some MA70 calls if buyers show up before close today",-1
+BAC breaking out I suggest you ONG some if we hold above 12.20 at the close.,1
+"SWI currently a loser for us but maintaining higher lows, would like to see more volume on upside  ",1
+BAC huge VOME spike in the last 10 min.,1
+OCN still looks good but need that volume to come back to truly power on from here  ,1
+BAC now that's what I call VOME!!!!,1
+"DHI: ong  14.39. Trailing Stop  20.50 from 11 Stops of 19.05, 17.90, 16.93, 15.93, 15.65, ... 13.39 and 12.39 -  ",1
+"V still taking a breather, still above first support, good place to add  ",1
+"JPM very efficient trend, love seeing these   ",1
+ZNGA Merrill upgraded this on 2/5 - just a retrace to base of breakout say 2.60,-1
+BAC shorts better make a decision today... #OBAMA will give another push for continuation tomorrow.,1
+"NKD sure to be mutual fd PM's putting in limit orders and never getting filled, more to come  ",1
+just know your gut will betray you quite often in the trade em thing. even on CEG.,1
+DVAX Options Trade for Heplisav FDA Approval Decision  ,1
+TES moving stop up again and protecting profits on big gains  ,1
+"OABC - ong  16.55. Trailing Stop  23.59 from 10 prior Stops of 23.08, 22.80, 21.16, 20.18, ... 16.74, 15.90 & 15.25  ",1
+"PX - ong  11.40 - Trailing Stop  19.13 from 9 prior Stops of 17.31, 16.69, 14.17, 13.14, ... 10.40 and 9.40 -  ",1
+Innovative Drugs May Win .S. Approval After Early Trials  good news for #biotech DVAX GAE GOVX,1
+Took two position overnight BAC and PHM,1
+CSOD Conf Call: CEO: feeling good about our position and good momentum coming out of last year,1
+VTS there we go!  A great end of day pop in the last 30 mins on 90k volume! ooking for a little bit more tomorrow -  ,1
+AX ptrend was exhausted and started to form a downward channel. Downward divergence in MACD. Strong support at 60.04. Great SHOT!,-1
+BWD Wants to fill the gap which will be complete at 83.10. Higher highs and MACD and SI showing bullish signals.,1
+For the past 8 years GOOG booked an avg return of -6.5% during Jan to Mar option expiration. It has posted positive returns for 2/8 years,-1
+For the past 8 years GOOG has posted a Best return: 10% Worst return: -28% during the Jan to Mar option expiration time frame,-1
+"AAP, More like crapple as one student said today. Bearish with a move lower tomorrow. It's failing exactly where I thought it would.",-1
+BAC Fiscal Cliff talk by President Tonight = Ouch !!!,-1
+#vmware VMW where? here? there? best wishes dealing with this complex H&S  ,-1
+AAP Mac manufactured un SA said Obama = ower Profit Margin !,-1
+user: AAP How will smaller margins and higher wages and unions help apple?,-1
+#hammer + #wedge worked out from last week Y HOV TO PHM ITB XHB etc  ,1
+user  AAP will make computers in S. The 1st line of tablets will be called the Pinto: They explodes if swiped from behind!,-1
+BAC Obama is slowing the rally... Ouch !,-1
+DHI  Back over 24.30 - high today was 24.66  ,1
+CMCSA Will gap open higher; No idea where 2 enter. Would love 2 get in at 40.28 but may not get it.  ,1
+PCYC Fresh long should be made only when the stock closes above 72 with an up candle.  ,1
+EBIX we're in a short term upward trend with the MACD positive/rising and w/ CMF rising nicely too  ,1
+"ANTH look interesting at this priced after OBIMED ADVISOS C bought 1,762,00 stocks on 01/25/2013",1
+DE #ONG if hold above 95.5. be aware of resist 97.5,1
+JPM increases target to 205 on NFX,1
+"WF EPS beats by .08 revenues up 42.8M - p 6% premarket, should have a nice run today after #POTS remarks last night!",1
+PPHM FIED 2.44 SOD 2.49 WI BY BACK AGAIN I WI TAKE .05 WHEN THEY IT QICK WE TADE THIS 10 TIMES TODAY FO NICKES GOT .50 EASY WAY,1
+COMPQ SPX IND BAC C Futures traded with rises pending macro data   ,1
+CM Continuation (likely)   ,1
+SPW the dip that refreshes 12.63 gartley target on 6 month daily 3.90A 9.12B 7.41C 12.63D 1:1 14.62 1:1.382,1
+"ong ZGNX user: 2/13 Watchlist: ACW, ACHN, GGS, IBCP, MENT, NAT, STP,ZGNX.  ",1
+PO (specialty chemicals) is slowly approaching new all-time highs,1
+AMZN short stopped out.  rough to walk into a gap up,-1
+this could be the last good chance to short AMZN,-1
+"O delivered 4Q 2012 adjusted earnings of 0.79 per share, which exceeded 4Q 2011 earnings by 8.2%. ong if beats 41.52. Stop loss 41.11",1
+CSN Scaling Down at 1.3445000,1
+For the past 10 years F has booked an avg return of -4.4% from Jan to Mar option expiration. F has posted negative returns for 2/10 years,-1
+"Exact reason why ETFs are safer than individual stocks, News Kills a stock - Trade KO Not CF  ",1
+user O Stop loss in 41.54 after beating 41.74,1
+user: user O Stop loss in 41.54 after beating 41.74,1
+OCN running IGHT NOW,1
+P approaching resistance area around 12.47/.50  ,1
+"FDO spread buying Jul 57.50C 5,400 times at 2.90 selling the Jul 52.50P 5,400 times at 2.20. Both against much lower open interest",1
+The ise of Google Play: App Annie Index January 2013   h/t user GOOG AAP,1
+V going into full breakdown mode,-1
+Majority of eading Stocks regularly lag NQ_F & ES_F on strong up days.  AMH QCOM KS GS SSYS KBH FT GOOG,-1
+"user 5 stocks i've been buying at 50 with hopes for 70, SBX KOS PANW EBAY HAIN - AM I diversified? Will i see 70 this year?",1
+"Buying V on any and all pullbacks.  Doubled since IPO, expecting another double sooner than later",1
+un-p Trade Into DVAX FDA Approval Decision  ,1
+AAP and 20EMA   ,-1
+CEE should crack 45 not letting go above 45.15 with full cover then will reshort again - CEE on the red train down now,-1
+"FFIV - short, anybody thinking of buying this should be watching EZCH  - EZCH a supplier to comm./switch co's",-1
+IDCC .. Take a look at how IDCC performs after touching lower Bband previously!!!  ,1
+"MDSO - ong  27.51. Trailing Stop  41.40 from 11 prior Stops of 38.58, 37.67, 37.11, ... 27.51, 25.15 and 22.79 -  ",1
+NFX short via 2/22 Put 4.65,-1
+user Where do you see it by the end of next week? NFX,-1
+CIMT is a player in 3-D imaging CAD software designs that has explosive growth paying SDIV in the industry DDD SSYS ONVO PCP DASTY,1
+"With the mess that Twitter andThe Grammy's made of Hashtags #sting (are you kidding me), excited for GOOG to just step in and print  off #",1
+"TTM revs, retained earnings, FCF last 5yr. Yoga growin lifestyle trend. Can  stay on top of it?  ",1
+"Deere & Co DE predicts slowdown in .S. farming, but boom abroad",1
+Why would anyone buy AX's overpriced services when there are many more high-quality alternatives that essentially cost half the price?,-1
+"SCTY cools off, but SPW continues upward. Our take:  also, look for our updated solar market outlook soon",1
+"CSCO get on the train to San Jose now, 23.80 before next Qtr earnings",1
+pep trying to break thru the 9 ema on the 5 min after triple doji bottom,1
+AAP here is some news none of us have ever heard..  ,1
+NFX tight flag 10minute,1
+BAC New OD !,-1
+BAC New ow of Day 12.20.Broken --> 12.18,-1
+BAC Oups OD is 12.16 now,-1
+BAC Back run down to 11.75 area to consolidate.. Sell Off &  Profit Take !,-1
+BAC ot of Big Sellers on 2 New OD again 12.12 Expect a big sell-of   EOD,-1
+ES_F Vivid bearish volume histogram divergence in XHB home sector-( TO EN PHM )  ,-1
+BAC It's will be healty to consolidate few day around 11.75 before going to 13.00 !!!,-1
+BAC Big Boys are selling on 2 lot off Big Stake for sale !,-1
+user: BAC All the banks are reversing now!,-1
+BAC ook  evel2 lot of selling occur from funds !,-1
+out 1/3 of VMW short more. carrying remainder overnight also short full position FB and BID,-1
+AAP GS says higher div likely .. more value investors? Why no stock split instead?  ,1
+"ed Monthly Triangle on CSN,.......pdating  ",1
+user still holding those puts? :) MCP,-1
+Swing Trade Quick Pick: The J.M. Smucker Co. SJM |   ,1
+user 45000 Did you check my update about CSN?,1
+BBY Best Buy Founder Schulze Considers Alternatives to Buyout Plan Feb 13 at 15:48 Profile hits: NONE Disclosure Short BBY,-1
+"CEG ooking to Add to, soon.",1
+znga looking to continue to add as well.,1
+PPHM is going down like a Ship!,-1
+"YB holders cashing in after big run since November, GS underwriting 25 million shares secondary",1
+CSCO Never mind Crisco - Call her KY for the longs.... 16 target,-1
+"NN uptrend still intact. Had resistance at 0,4. It will attack that again in the next days. First gap to close is 0,5 then 0,8. TP 1,5",1
+Our 2013 solar outlook:  SPW SCTY FS YGE TS STP,1
+user CalMustang That's why we own 0.05 puts... doesn't get any cheaper than that on the risk side MCP,-1
+Omega Advisors initiated a 3 million share stake in FB  also sold his entire stake in Apple AAP   ,-1
+Take a look at how IDCC jumped up quickly after touching the lower Bollinger Band. Happening again.  ,1
+"AMZN - no matter how I look at it, I still can't figure out why it's valued at 269.47 >   ",-1
+Market Wrap Video + Additions to Watch ist  including: AGO AS CHK CVA GA NFX GP - by My Valentine,1
+BAC Don't forget OPEX is Friday (3rd Friday of month) So we will see below 12.00 for OPEX IMHO!,-1
+After Hours Most Advanced: SKX AWC IO BT THO NFX   ANGI EDMC JCOM BCO VCK Z SGEN EQIX MBFI,1
+INVN CEE - started it's red bar down shorting more and CEE hasnt made it move yet - patience,-1
+user You're Wrong I was long BAC till Yesterday. I think you are falling in love with Stock instead of making  ;-),-1
+BK buys HNZ is HSY next? Warren must like chocolate bars with a coke KO,1
+"AN rally on MS upgrade might be enough to keep us long, price needs to reclaim 10wk  ",1
+COMPQ SPx AAP C CSCO GM - Wall Street: Futures in red before jobs data   ,-1
+Side note: Nelson Peltz (Trian) actually designed the new HNZ ketchup packets. Wendy's is one Peltz's fav investment.,1
+BAC Euro-zone GDP plunge stokes rate-cut expectations  ,-1
+DE looking for a bounce,1
+"FIO Hedge Funds Are Buying These 4 Oversold, Highly Shorted Stocks With Troubling Sales Trends  ONG",1
+the last two times I saw Warren Buffett he told me to 'Catch p' ....i just thought I was slow! .... ahhhhh hnz,1
+COH 52w low...,-1
+"eal estate on fire this morning wit GY and Z, Z only up 4 since the open #NewMomoStocks",1
+"AFFY oversold.  etter is precaution, not a death sentence.",1
+HTZ Great relative strength today in red tape.  This wants 19+!,1
+"SYNT full year 2012 revs +13%, EPS +51% vs 2011",1
+EAP being sat on at 6.58 by large sell blocks - maybe some people need out of shorts?,1
+AFFY unbelievable round trip!,1
+"SWI still acting sluggishly but not invalidating yet, hanging in  ",1
+"SWY - long late night checkout line was funny last night - all men with last minute Valentines flowers, cards & candy  - did you get yours?",1
+MCP so much for my 7 puts... at least I have common stock! SC here we come!!! ,1
+GMC upside volume low but confirmed strong support beneath  ,1
+"OCN closing off its highs recently, but volume remains on upside, no damage done yet  ",1
+news on NS?,-1
+NS aggressive put buying in Feb options,-1
+"GS continues higher - a great ex. of buying right and sitting tight, allowing your winners to run  ",1
+NS breaks below the triangle,-1
+The Harbor - ValuePlays | ValuePlays  HHC XOM FB M DDS GGP,1
+user Me too but NFX has been kicking my A!,-1
+"NFX daily had the buy, its in extreme overbought so up should slow down but orange EH is power.  ",1
+SWKS ong Setup (Triggered):   ,1
+user: SWKS ong Setup (Triggered):   ,1
+SFY - Now that's a pretty Short Stroke forming.,1
+SFY - Now that's a pretty Short Stroke forming.  ,1
+INVN is a tough one to figure out & trade short term. I'm in long term so I brush off the intraday volatility,1
+"ed Weekly Triangle on AMPE,....pdating  ",1
+user Nice JVA Super ed,1
+"Sold partial SKX It is in a bear market you make most of your money, you just don't know it then aising ",1
+AAP weak... no volume..,-1
+ACX Fidelity now owns 2.981%. XV BBH,1
+WFM fibs say here  or 81.67  ,1
+"BAC altho vol during formation not ideal, still a saucer nonetheless & possible C-n-H. Trend clear  ",1
+CT declining 22 % after a dividend cut of 26 %.,-1
+HNZ do you guys think it will fill the gap?,-1
+VXY IVAN buy SPW after a minor pullback - 14.70 target,1
+AAP 65mins Bear Flags  ,-1
+SEV needs a little bump to get it moving up again.,1
+CST - 150% avg daily volume. ooks good for higher. Next resistance is gapfill.   ,1
+OVTI eclipsed yesterday's highs...16.50 initial tgt  ,1
+HA from morning follow up - best new mover from watch list today with volume of 145% ave full day   ,1
+ES_F Hedges rotating into Junk stocks like Solars and squeezing them FS SPW - Sign of End of QE Inflation ally?,-1
+Halliburton having a monster day. The Evil Empire wins again. HA,1
+W looking nice,1
+ADM 50ma crossing above 200ma,1
+"AN ooks like that pullback entry worked, now lets see the action.",1
+SPW seeing a bearish divergence forming.  on watch for a flush to short.,-1
+VVS at the highs  ,1
+Solar stocks are ripping. SPW up 20% and FS up 7% and Trending  ,1
+call vol has tempted me in! :) ONNN,1
+GPN is breaking out !! Tiger Global aised Stake in Groupon from 1.3M Shares to 65M Shares  ,1
+"AN technicals turning positive. SI rising, MACD crossover. Cleared 50EMA.  ",1
+SPW short 12.33,-1
+HF entry 18.34 stop 17.55  ,1
+PPHM  Going down to 0.69 ouch...,-1
+"GPN indeed has broken out of 9 mo base with min next upside target of 6.12/16 if hurdled and sustained, 6.50/55 with 7.50 med term outlier",1
+BAC Sell-Off of the day begin !,-1
+2013 ooks Bright for .S. New Vehicle Sales - Blog Posts by Tom ibby | . . Polk F GM TM HMC,1
+"user I agree it's accumulation, but on SHOT term I'm bearish and on long term I'm bullish on BAC",-1
+GEVO still holding for breakout !!,1
+SPW covered 12.12 not trusting it for a swing.  will watch for another short tomorrow,-1
+agree user: hedge funds sold AAP in Q4. We'll see if it was a smart move or they dumped the stock in a panic. I say the latter,1
+PPHM 3 million shares stuck in a squeeze. Disinfo agents already appearing! user  ,1
+VHC looks ready to go...really tight range last few days  ,1
+WOW! WFM. Bearish past 86.72,-1
+TSA this is why electic cars will go mainstream soon  ,1
+NVDA rebounded strongly today on pretty heavy volume and might be on the way up again.  ,1
+GEVO The volume is still large so it could potentially break out again at any moment.  ,1
+user check out CNN reporters finishing up successful TSA drive from DC to Boston.  Prob have cameras in car  ,1
+Selling ICE Short check out my video analysis  ,-1
+"WT Bullish signal (initial confirmation) pushing above the 200 day average, for the 1st time since July of 2011.  ",1
+"ES,TF,YM,SPY,IWM,DIA, Hurricane FAGS are P!   Another warning lite...  ",-1
+Told you on SPW  I still target 16 and higher  ,1
+ADS breakout   ,1
+HNZ Another American Institution sold to foreign interests as SA is gutted. Buffett has 0 management power over 3G.  ,-1
+GD SV NEM ABX AY - The Price of Gold plunges to six-month low    ,-1
+COMPQ SPX IND AIG KFT AEP  Futures traded in red with an eye on the G-20   ,-1
+"T user DDD & SSYS working on left side of new bases. Wait patiently for them to form & better price action, as entry is everything.",1
+Hot Stock today - QIK OPXA MAKO DAA,1
+Market Wrap Video + Additions to Watch ist  including: AEG CAM DMND DC FT SFY ZOT,1
+GD SV NEM ABX AY P DZZ - The Price of Gold plunges to six-month low  ,-1
+PreMarket Most Advanced: QIK GTAT IEP MEKO WTSA QGEN TIBX ATM ZNGA JB,1
+today could be the beginning for a MSFT run! (no pos),1
+going to full position in M,1
+M currently above resistance this morning  ,1
+"54 Short Ideas to prepare for the next correction,  BID SV GD FXE PB WTW FCX CF MNST Starting to tighten.",-1
+GEVO Good breakout on high volume.  Possibly a nice long trade for a 30-40 cents profit,1
+SWY - added to long,1
+anad long 2.61 flag breakout.  ,1
+user: NTI buying 8.80 -8.83 - BYING MOE 8.87 -8.90 - magic 9 test,1
+ATVI short with a tight stop.,-1
+SHW keep eye on this one - poised to breakout to new highs  ,1
+PCYC strong winning trade since Wednesday up 10+ since (holding)  ,1
+Equities esearch Morning post at my home page SSYS DDD,-1
+SHD - Coiling snake,1
+user I was early...but right on! thanks for lookin out ! ssys,-1
+user I want to frame this post u sent me....you may have been only one to read it lol ssys,-1
+TIP - p fill the gap,1
+"EXAS nice pattern, maybe trying to break out of this handle or wedge   ",1
+GOOG stopped out of half --,1
+FS alert update: I suggest selling at least half again booking profits over 36% thanks again FS,1
+DDD - Breaks 50 day.... Stops at 50,-1
+SSYS - 65 or 60... pick your winner...,-1
+It really wants it. user: ZGNX...C'MON 1.60 + PINT  ,1
+BAC 11.95 could be the pin today !,-1
+ZNGA 3.22 better be holding!!!,1
+user: Why is BAC falling? Montlu OPEX and Pullback of OVerall Market,-1
+"IPHI ....that would be called a spike, lol, in at .49",1
+N found buyers near its rising 50dma and it is looking to resume its uptrend  ,1
+I was out today so missed it but CAY from watch list did well on vol of 197% of ave full day   ,1
+GME big volume coming in,1
+FT another watch list added yesterday that triggered today has volume of 55% of full day average   ,1
+QCO made like 3 points scalping this today!!!!!!!!!!!!!!!,1
+user: BAC accidentally bought the dip ->Now Close r Eyes Till end of March ;-) Or buy more on EOD Should Close 11.95 12.00 Opex,-1
+Nice share.... user: AMZN intraday short explaination.  ,-1
+SWY pretty lame selling SWY because WMT sold horse meat in Europe. TH SPY,1
+SBX breaking down from ascending channel. 54.57 gap close  ,-1
+QCO Questcor ises On nusually High Volume (QCO),1
+AAP note +4 on 30 min opening range this morning short. Still moving lower.   ,-1
+SBX gap close...that was fast,-1
+"WMT Wal-Mart on report of slow February sales: Often see internal communications that are not entirely accurate. -2,8 %at this time.",-1
+DIS staying pretty strong on the board through that market dump,1
+APO good volume bounce; entry 22.19 stop 20.50  ,1
+DV Pulling back to breakout point. Needs to hold the 2 area. Strong Bullish trend still intact.  ,1
+What??? No! How is this possible? ;) user: Payroll taxes eating WMT lunch.,-1
+I have a feeling the last 10 minutes of today's close are going to be interesting for NX.,1
+VXY Short again - GS just downgraded volatility into June,-1
+HF - looks like icahn will be losing 100 times of his schmuck insurance that he waged war with ackman or his total NFX profit so far,-1
+Canadian MTY.CA much better choice than CMG #valueinvesting,1
+"WMT Scared over an e-mail? Ha, in on this for the bounce back.",1
+"ed Daily Triangle on SONC,.....Open ong at 10.99",1
+CF  #cliff the Head & Shoulder still thinks it's right!  ,-1
+QCO  5 Stocks ising on nusual Volume,1
+SGY broke down its 50EMA & trendline support on increased volume and selling pressure.  ,-1
+"DDD - still an 11% gain for year but shedding profits, look for a base to build (could be months)  ",1
+MITK - been waiting for pullback since 4.00 not happening now looks to go thru 5.00 great company,1
+MITK perfect example of cup and handle breakout from 4 to 5 - EVC has this pattern now,1
+"CWST long 4.67, Ascending triangle, 4.65 touched several times,4.66 high. 200SMA 4.80'sh - magnet  ",1
+"BIOF looking very strong, I would like to see a pullback to re-enter.  ",1
+"ZOOM long .76, looking for continuation/momo  ",1
+BCD price is holding above recent breakout area. Technicals look good.  ,1
+Keep an eye on this bull flag on VZ Over 45 would be buy trigger.   ,1
+Google will open its own stores by the end of 2013 GOOG #bull,1
+ltra ever dry will boost TEK in the next few months....,1
+DVAX buzz around this stock now - will be in play Tuesday with news limited downside big upside,1
+AAP - Series of lower highs. ooks like it wants to retest the recent lows.   ,-1
+EA if price doesn't hold above 9SMA then 16.71 is the next support.   ,1
+FFIV - Worked off overbought levels quickly after testing the top side of this long term base.  Gap fill at 99.05. Ascending triangle.,1
+V - Tested the 50ema & working out of this bull flag. Expecting a test of recent highs.   ,1
+PIP breakout at 1.48 and then again at 1.70   ,1
+"SANW read thru eps and cc. Nothing fancy, just steady execution of a plan. The best is yet to come. Some patience required",1
+SANW to get a management team like this for such a small operation is unusual. Grewal is building a monster in a smart manner.,1
+SANW btw I am not a buyer here. I already own a lot. I will add on pullback. I think there is a point in the future where this rockets,1
+"MTG Major resistance at 2.80 which is Fib level and 20 day MA. if you are long, good luck.  ",-1
+DVAX tempted to increase position on Tue,1
+"Shorts Tues, most I stalk have 1 - 4 days inside base or bounce after red kicker ACCO BT DMD EY GMX GO HAO NPSP OEH OZM TEO",-1
+EBAY Nice consolidation since break out after earnings. Waiting for break out above 57.27..   ,1
+HPQ NVDA QCOM umored HP tablet could use Tegra 4 chip and Android as its OS  ,1
+SWY :  bought long-term calls.,1
+"AAP broke 10 & 20 day MA's on Friday,acts dreky, support levels seem to be in 10 chunks to 430   ",-1
+CJES C&J Energy: ook For The pward Trend to continue  ,1
+SPW Interesting analysis and speculation of share price.  ,1
+"SSYS short/medium term trend is down. Next major support is at 64.72, its 200-day EMA.  ",-1
+NKD target 150 - 12 points to be made on downside start short scaling here,-1
+PETM - ooks weak. Next support level is 63.40ish.   ,-1
+I don't like the technicals on OMX but the fundamentals look like they could turn around with a few good quarters.,-1
+"NX setting up beautifully, and much is still short  ",1
+"user arge % owners on Morningstar rival 1990's mania...CSCO, AO, etc. AAP range bound as funds diversify away w/o growth. JMHO",-1
+possible new shorts MCD XT DDD DE EA FFIV QCOM TA,-1
+"AA - China's buy into AA joint venture trumps weak aluminum demand and pricing  SPX, DJIA",1
+AEP Over 45.20  ,1
+WYN  Over 60.32  ,1
+"JNP FX ong, getting in tom.",1
+GOOG this market leader is heading to next Century mark 800 could see resistance  ,1
+GD SV ABX NEM FCX - Distrusts Soros sells gold and much of their holdings -  ,-1
+ABT - Abbott breaking out yday. Cup w/ tea and crumpets? handle Jan.  ,1
+FDX no position - watch list on this flag  ,1
+" Holding horz support, BB squeeze, rising vol. watching to clear 200 sma 68.33  ",1
+INFI from watch list gapped to over line today on vol so far of 16% full day average   ,1
+COH worse and worse,-1
+BAC ong if beats 12.15. Stop loss 12.01,1
+"CT nice move this morning, gap filling in action!",1
+SPW 1;1.382 14.62 1:1.50 15.24 1:1:618 15.86 Poss Daily Gartley moves,1
+GOOG crosses above 800 wow !!  ,1
+GS continues to make new highs -   ,1
+China Shanghai Composite Breaks Key Supprt FXI BID NTES SINA SOH YOK YZC XME JOY CAT X  ,1
+QIK holding below 26.50 makes sense on the short side,-1
+GAE Xi Pharmaceuticals [XII] Announces pcoming Presentations  ,1
+NFX Has that I wanna be a 200 stock feel,1
+People are way too damn happy about NFX. I wouldn't touch it with a 10 foot stick right now. Needs a serious pullback.,-1
+GOOG sold 1/2 of long position   ,1
+GAE something going on long 1.84 -1.85 1.86,1
+user: BAC Stop loss in 12.09 after beating 12.20,1
+AAP in time will retest 1/25 low 435 area (holding short)  ,-1
+So if AAP and WMT crack... it won't bode will for the XT... ,-1
+MGE buying long 2.68 2.69,1
+FNS long a bit,1
+AMD Chooo Choo pop to bankruptcy?,-1
+WFM Watch 87.11. If breached get out of the way,-1
+GIS Stop loss in 45.04 after beating 45.27,1
+user AKAM on the radar as a potential short vs 39.30  ,-1
+user: user AKAM on the radar as a potential short vs 39.30  ,-1
+GAE 1.90 now may buy more 1.93 - gut shot to 2.00,1
+user in on EV since 14.95 it has had a huge run and isn't really showing signs of stopping yet. Don't want to be greedy...thoughts?,1
+"The first target for NEM below 43 is 40, but there is room for a bigger move to 28 for this value trap as long as it holds below 43",-1
+Whoopsie: Costco CEO: We Shouldn't Have sed Tiffany to Describe ings [EXCSIVE]  COST TIF ,1
+EAT lod  ,-1
+GS with Fibs & next res pts at 160.69 and 166.06  ,1
+NIS over SMA30 and SMA50 now. Et's go. ONG,1
+short FCX needs to retest 30 level,-1
+GAE 1.95 now - 2 is a lock in now!!!!!!!!!!!,1
+"After the failed breakout last week, JAZZ is setting up again. eports Feb 26 AMC  ",1
+"BCD holding up well. BollingerBand Break, OBV Confirms, SI confirms, ADX tryin to reflect uptrend  ",1
+"user I agree, GOOG is severly overbought in this overinflated market.. expect a short position from me soon...",-1
+AAP Expect to break upside;Triggers:Einhorn+Shareholder meeting+iWatch+Good base around 450 area.  ,1
+Good news AAP rated #1 brand.. call your broker and have them raise it's value!  ,1
+egional Banks Have The Best Trade Setups Out There BAC FME MTB NPBC  ,1
+SPW ooking for 50% retracement of move from 8 to todays high 13.66,-1
+SPW I love these bankrupt solar companies on momo pumps FS TS CSIQ,-1
+ACX Today's P reads very positive.Would like to see the stock back at highs by month-end. More results coming. XV BBH,1
+WT -Time to break out of the inverse H&S... Target 50,1
+CHE (home healthcare svs) at new all-time highs on heave volume  ,1
+user: GOOG Stop loss in 803.79 after beating 805.00,1
+BAC Will go to 12 imho,-1
+GOOG Stop loss in 804.41 after beating 805.50,1
+Just picked up a couple MA13 PTS on ODP good idea? ol.,-1
+BAC Vol. Fall on EOD.. Possibly  Small Sell-Off !,-1
+BAC Out of my short for  Should go around 12.00 to consolidate.,-1
+QCO  Is this running up into the close? Wonder if that is a Positive indicator. I still have not bought enough. Please come down a little.,1
+"ed Weekly Triangle on SONC,.....Scaling p  ",1
+QCO new hod nice - holding overnight to 34,1
+"ed Weekly Triangle on DVAX,...Open ong at 2.95  ",1
+Two straight dojis for CM at 175 - a big level.,-1
+"TSCO nice setup here, maybe sideways a few more days then a close above 105 to seal the deal.",1
+GAE closed right on the money at 2.00 - should see 2.20 tomorrow,1
+"user So, What That Mean? for DVAX",1
+"ed Monthly Triangle on CSN,.....Scaling P  ",1
+"user of OSV: Fundamentally, it is extremely cheap by being priced for zero growth. Coming from someone who doesn't like AAP/products",1
+ADM #Food #Archer Daniels Midland #breakout!  ,1
+"ed Weekly Triangle on FNFG,......Scaling p  ",1
+SMA nice,1
+#ed obin GB boing!  ,1
+en fuego! FMX EWW #Mexico! #international KO  ,1
+GEVO uptrending from double bottom; 50 DMA crossing 200 DMA. Asc T/ = 20 DMA. Stop under 2.25  ,1
+Market Notes Video  - today's News buys included  INFI NTS BMN ANAD SGI DC   AEP,1
+ZNGA looks good long here w a stop 3.09.. still long some but mostly sold [not adding],1
+"Trade idea buy GGS  4.60 , target 5.47, cut below 4.20  ",1
+NOV National Oilwell Varco: Excellent Opportunity To Invest In ndervalued Company  ,1
+AfterHours Most Advanced: NTSP MBS SINA MDX OPE STA CTT AIA BEAV DMD ZB PI GDOT AWI,1
+GOOG  I only use Google. Heck I use Google for everything. I don't own the stock but I am still bullish on it.,1
+I believe there is a sort of manipulation going on with the XT when you compare the price moves against the underlying holdings like WMT,-1
+For the past 10 years  has booked an avg return: 6.6% from Jan to Mar option expiration. Posting positive returns for 8 of past 10 years,1
+For the past 10 years  has posted a Best return: 21% Worst return: -8% during the Jan to Mar option expiration time frame,1
+For the past 10 years VFC has booked an avg return: 7.3% from Jan to Mar option expiration. Posting positive returns 10/10 years (100%),1
+Both GI and  have increased in price for 8 of the past 10 years in the Jan to Mar option expiration time frame.,1
+VFC has increased in price for 10 of the past 10 years (100% of the time) from Jan to Mar option expiration:  ,1
+The dip last week to 15.55  might have been an amazing opportunity for investors to buy OVTI  ,1
+MSFT closed above its 200EMA.Short-term uptrend is intact and signals further bullishness  ,1
+GD ABX NEM DZZ AY  - Deutsche Bank: Today is an important day for investors in gold  ,-1
+ODP and OMX two failing companies joining to fail as ONE!,-1
+"QCO Screener- over 500k vol-over 2% Divy-Peg under 1-Positive Inst. Trans-25% growth Q/Q-short interest over 20%, There's only one QCO",1
+user  IFE is good?;),-1
+user EBAY is on the radar for a move out of this new range above 52.26  ,1
+Pre-Market osers: HN GMN BHP BB SODA GD IO AGO A GDX,-1
+NEOG has positive fundamental trends & a push through 48 of a cup&handle. pside mid-50+  ,1
+"SPW pauses, SCTY continues its run, up > 20% in 1 month:  ",1
+short CAT,-1
+ODP it's on!,1
+ISG this technology is still in its infancy...the demand is there.  Hospitals/surgeons want it.  Will only get better over time imo.,1
+GAE 2.11+ selling from daytrade & swing trades played from 1.85 to break 2.00 - it sure did thanks GAE,1
+VXY ZB_F reversal - #FED distributing cash to banks GS JPM MS BAC,-1
+Fund trxfr will be good to go by tomorrow - will be joining DVAX bulls by adding more calls,1
+NSPH Our software initiated a purchase at 2.03/share.  We are looking for a quick 7-8% #swingtrade on this #stock.  ,1
+AAP Apple VIX up today.   ,1
+"CM 30 min O - ema just below. Short thx user - no position yet,  ",-1
+JVA moving up nicely,1
+hum below 72.6 was strong can get all the way back to 71 and 70.,-1
+"Green Daily Triangle on MH,....Open Sell Short at 3.93  ",-1
+GMX penny again soon ?,-1
+MON How quickly investors forget the massive beat in earnings reported in Jan. And the big uptick in 2013 guidance. Short minded. SPY XB,1
+AON eaches New 52-Week High. ong if beats 59.13. Stop loss 58.79  ,1
+SWI still down small but put in context this still looks good  ,1
+SPY triggered at 10:30 AM sorry _ got tied up with AAP   ,-1
+"OCN down small from entry, low volume decline into support  ",1
+BAC ally will resume after Fed minutes digested IMHO.,1
+"GOOG notice volume still coming in on up days, looks solid  ",1
+"CTGX more like u/c than a winner but nice recovery, uptrend intact.  ",1
+HTZ reasonably tight consolidation so far of last weeks advance  ,1
+AEE who says utilities are boring  ,1
+BAC Based on Tech Analysis we have reach bottom for Today. So it should rise up for the rest of the day (minus reaction for Fed Minutes),1
+"EN Take your profits and run. Nice run up,but it's over. Wal-mart sales down so who's got money for a new house. SE",-1
+AON Stop oss in 58.96 after beating 59.30,1
+ast week was interesting because tiny amount of good news and Huge pop on AAP everybody wants it when it takes off,1
+WEN ho hum lots of love today. CA,1
+"ed Weekly Triangle on FNFG,...Net Profit  21,240.00 (2.71%)  ",-1
+VXY #FED POMOs flowing from ZB_F to GS JPM MS BAC - free cash to squeeze derivatives to pop markets to 1546 ES_F target,-1
+VXY Buy SPW 15.75 target near term solars new squeeze zone. VXY to 0.,-1
+Valentine & Winthorpe are gonna pick up some DVAX starting tomorrow,1
+"Green Weekly Triangle on  FNFG,....Open Sell Short at 8.22  ",-1
+"Green Weekly Triangle on FNFG,....Open Sell Short at 8.22",-1
+"Green Weekly Triangle on FNFG,....Open Sell Short at 8.22  ",-1
+DNKN  I love Dunkin Donuts right here!,1
+NVDA not working so far still holding though.,1
+GOOG Glass is coool  ,1
+WEN in 5.65,1
+GS Conviction Buy Stocks With the Most pside: AAP  ,1
+Glade I'm fully hedged and then some with MCP 7 puts for this friday. I think I'll just settle and be done with this trade,-1
+AAP 5 min - w/ O overlaid   ,-1
+user reporting that NVDA Tegra 4i should have a solid GP and that the integrated modem should be perform well by launch. Great!,1
+"but user thinks the name T4i is a red flag. not sure how that'd impact value proposition. Given source, sounds net bullish NVDA",1
+SPW ...almost time for my daily short,-1
+Bot EC at 2.65,1
+"(given general negative bias when it comes to NVDA, one has to read between the lines with user )",1
+EC looks poised to breakout these next few days as attention appears to be returning to the sector  ,1
+BAC Small Did on FOMC Minutes... Now we will resume to close Green IMO !,1
+AAP 15 min - I'm in otus position trying to emulate user,-1
+EC Breakout Imminent.   ,1
+user - CM thx for the party invite. Along for the ride.,-1
+PHI Our software auto sold this one so we are cutting our losses on this one at -5% on the trade.  ,-1
+eading stocks continue to breakdown even though major indexes one day off multi year highs. TO EXP SODA KOS Y KBH AD OAS FB,-1
+ODP Has dropped below 4 on high vol. #fallingknife,-1
+"ed Daily Triangle on HEO,.....  pdating ong and Short Position  ",1
+AAP and CM - if you're following the strategy you should take some profits b4 close.  ,-1
+Trouble for homebuilders in recent data...  XHB TO PHM DHI,-1
+COX short looking good but we'll see after the bell.,-1
+i wonder what metric Elon Musk will try to get analysts to focus on as it matures and grows as a public company ...#notacarcompany tsla,1
+"CS - You can say whatever you like about the 6mo, but thats a 9mo long head-n-shldrs folks on 1yr  ",-1
+"Damn near impossible to get me CS for anything but a scalp long-side til we see 21.50, at least-18 possible.",-1
+WMT expect that miss from late last week to materialize tomorrow morning... ong 67.50 puts ,-1
+user Meanwhile our MA9s are decaying... SWHC,1
+"PAY Barron's Blog: Verifone Plunges 25%: Warns FYQ1, Q2 To Miss by a Mile - Disclosure Short PAY",-1
+"AAP Short Setup: A catalyst could counter (whoa, remember AAP catalysts?) but 425 tgt, 435 resis  ",-1
+WMT DJ Wal-Mart de Mexico 4Q Earnings Slightly Below Expectations ,-1
+"WMT added some more calls today, might be good , gapped down before earnings the last 2 times, then rose over the next two weeks",1
+"So glad I sold pay around 31,I was tempted to buy back in, now won't touch it till all dusts settle,it's in Norman's land",-1
+AAP (65mims) bear flags at work (i watch the volume to confirm those)  ,-1
+Video- ICE Short Part II.   ,-1
+that's all folks CF #Cliffs Natural #h&S says much lower #beep beep!  ,-1
+" SPY, ES, Define risk always, keep sizes proportionate, risk only what u can afford to lose..  ",-1
+JACK GB BK Burger stocks are on fire WEN needs to catch up.Declining employment means rising burger index.,1
+NVDA ZTE to ship some of the first Tegra 4 phones by mid-2013  ,1
+The #SEC should launch an #investigation into #Citron This company eleased inaccurate information on DDD They only short sell to profit,1
+PAY VeriFone price target lowered to 15 from 27 at Deutsche Bank - 1 of 4 Massive downgrades - Disclosure: Short PAY,-1
+WMT I'm still short....,-1
+WMT how can you not look at these numbers and not see business slowing!,-1
+HSNI has missed the expectations: 4Q2012 EPS diluted is 1 vs 1.04 consensus vs 1.06 whisper,-1
+WMT if i had enough cash on hand i'd be shorting this right about now,-1
+IBM to double investment in mobile. Analysis of IBM:  & broader mobile movement:  ,1
+"PSTI in a consolidation mode, close to triangle breakout & Bollinger bands volatility squeeze   ",1
+Yesterday's earnings from CNK suggest a 25 handle before 30  ,-1
+CAT Gap fill area approaching. Will be filled in next few days.   ,-1
+Estimates to move lower for SAFM after EPS and a trend line break likely today with support at 47  ,-1
+"WMT BOOM:Feb sales started slower than planned,due . . .to delay in income tax refunds, Bill Simon,CEO WMT.S #IT'SWOSETHANYOTHINK ",-1
+"GDX GC_F GD NGT Squeeze JPM and the Comex shorts - been getting away with murder, TX",1
+"IBM trading below the box (also50EMA.. if the bottom of the box resistance holds, further downside.  ",-1
+EC looks like another day of heavy volume/accumulation. Could get interesting.,1
+"ed Daily Triangle on HEO,....Cover Short Position,...Net Profit  76,560.00 (7.46%)  ",1
+AAP 30 min - have to walk away. All out.   ,1
+Call me crazy but i am buying AAP here 0_o,1
+I also bought BAC and VOD. I am catching knives here.,1
+"FIO No onger A High Flier, But Is The ong-Term Story Intact?  ",1
+CBMX long 4.90 -4.95 play thru 5.00 again,1
+user: CBMX long 4.90 -4.95 play thru 5.00 again - selling half 5.20 raise stop on rest,1
+EA nice move (long),1
+SWHC added more march 9's and 10's here.   I'm done.,1
+DVAX buy'em,1
+WMT opening range 30 min. JPM comments negative. No position  ,-1
+user: F Foot ocker increases quarterly dividend 11% to 0.20 per share. ong if beats 34.86. Stop loss 34.64,1
+"BAC when she loses the 50 day, the whole market goes imo...",-1
+"Is it just me, or does that look like a double top on HD?",-1
+ATHX MACD cross down,-1
+CS - To those that doubted me; today is you're official break/trigger of H&S pattern - lower prices coming up.,-1
+DVAX Valentine & Winthorpe buying more here,1
+"ed Weekly Triangle on SD,....pdating  ",1
+AAP 200? user could buy it w/ his V with 137 cash in the bank!  give me some of her  kool-aide #mediawhore,1
+FOS short working great up 4 since posting yesterday!,-1
+SWY Option Bears buy puts betting 500k on over 10% down move  ,-1
+FOS took 40% of short off here,-1
+"Green Monthly Triangle on ISIS,....Scaling Down  ",-1
+CBMX that is unbelievable!!!!!!!  but I am happy with my sell I am not good at calling the exact top,1
+out of FOS short for now as it tests its 50DMA awesome trade up 4 2 days,-1
+"QCO is one of my favorite value stocks. Zero debt, great margins, low P/E and PEG, and soaring income. I am long!",1
+"Green Weekly & Monthly Triangle on HBAN,....pdating  ",-1
+PK watching for a decent spot to get in,1
+EC Big picture.Bullish B/O of a symmetrical Triangle confirmed on Expanding Volume.Buying dips  ,1
+BAC Thank you to the greedy guy who bought my shares  11.99 yesterday when it hit my stop. I'm sure they're now an investor.,-1
+"ed Weekly Triangle on SHO,....pdating  ",1
+"BAC Breaking Down, SPY is Next!",-1
+"FAZ about to Explode, Get eady, BAC just broke under the 50 day!",-1
+MBS Nice action above the breakout  ,1
+just in case u haven't watching JCP closely. look for the volume spike.  ,1
+"ed Weekly Triangle on DVAX,....Scaling P  ",1
+"V just look at Visa, what a champ, textbook bounce at support and now outperforming again  ",1
+MHK > I meant to say TD Buy Setup nderway?,-1
+GMC another one that's holding up well in this selloff  ,1
+"Green Weekly Triangle on FNFG,....Scaling Down  ",-1
+ODP is hanging on for dear life...,-1
+KKD let put in a cup and handle before earnings,1
+"user iGreed AAP Einhorn should hold weekly conferences, would help this stock! looks like he helped the whole mkt >It work Nsdq is p",1
+AAP tips for TC.. The other tim did it TMI raises div buys back stock  ,1
+BAC 11.52 small resistance...,1
+HPQ user option guest on bloomberg tv just said buy march 18 calls too but also hedge by selling feb 17.50 calls other guests T,-1
+Our software stopped us out of NSPH today for a 5% loss on the trade.  Still hanging on tight with HA -  ,-1
+PCYC volume is everything! POSTED VIDEO with sound   ,1
+"Green Weekly Triangle on CYTX,....Net Profit  31,761.00 (11.09%)  ",1
+HPQ bought a few March 19 lotto calls before the close... Just in case!,1
+Ops Misstype ! user Better to buyback BAC  12.62-65 on high volume IMO,1
+ties into user 's #fashology theme... GOOG + Warby Parker.  ,1
+user: BAC what's the play short term ?>> Will depend on economic data + StressTest -  ong term?>> 13-14.00,1
+WEN need to take out 5.60 for next leg up. ADX MACD +VE  ,1
+AAP we can have the iToilet for all i care just get the stock back to 480.,1
+MCD bear flag on daily.. needs to hold below 94-93...,-1
+"trade idea Buy GOOG  795, target 980, cut below 740  ",1
+XCO is a gift no matter how its looked at oversold undervalued IMO prices of NG correlated with possible correction : VXX,1
+DDD SSYS XONE and nice entry to dip toe into CIMT 9 beat the street in everyway Much potential  : NGT FCX CO nice entry IMO : VXX,1
+user AMZN holding above the 50dma in a diamond pattern. ooks good for higher prices...imo  ,1
+BAC Fed's Bullard: Fed Policy to Stay 'Easy' for 'ong Time'  ,1
+BY of the day PAY ...market over react yesterday!  with a 5.2 Forward P/E it's just a gift!,1
+Pre-Market Winners: HPQ APO AIG COG ETFC FAS GDI EDC TS COG AIG ONXX TNA DB,1
+Pre-Market osers: NFX AX ANF VXX ISG O JWN,-1
+shorts WG STK SNSS NPSP MM NDC HAO,-1
+ZNGA was approved for Nevada gambling license which will help drive revenue,1
+AAP after yesterdays prediction of 200/share I was thinking maybe a split or finally they will double the dividend?,1
+looking for JCP to finish ystdy's move into 22.50....#shortterm,1
+user Why is BAC struggling to get up and moving today? > IMO We fill the gap & go p after !,1
+"JCP  The next BBY (IMM) GMC, NFX?  Action would certainly indicate such.   ",1
+"Green Weekly & Monthly Triangle on ISIS,....Net Profit  75,852.90 (4.93%)  ",1
+DVAX rocket time hold on  long 2.99,1
+adding to short on WYNN,-1
+"Green Weekly & Monthly Triangle on ISIS,....Net Profit  75,852.90 (4.93%)   ",1
+SAVE again edges up; our analysis + a look at budget carriers:  YAAY,1
+"ed Daily Triangle on DEPO,....Open ong and Short at 6.66  ",-1
+"PAY is on the run, short covering in action, now waiting for small down to enter...I'm always late!",1
+"PK still in swing shares, still at least 17milion shares feeling pain and still short",1
+"GOOG reminds me so much of AAP in Sept  bouncing around the all time high.. no new products on the horizon, time to sell, buy back later",-1
+SFY - Attempting a short stroke breakout or forming a 3 week tight pattern. Study  ,1
+"ed Daily Triangle on CYTX,....Open ong and Short at 3.38  ",1
+MD a steady gainer since mid January. One of our alternative Bakken picks. North Dakota piece here:  CP IET,1
+GOOG is now doomed user is calling it COO! SE SE SE,-1
+"user: Bulls are trying to keep this market in the green, but you can tell its a struggle. Nasdaq now up just 8 pts. AAP fading",-1
+WBMD trade for move thru 20.00 long scaling here 19.75 - 19.80,1
+CF The trend is over. Time to step aside and wait for the next one.  ,-1
+"EON in 2.78, stop 2.74",1
+NFX failure today  182.50-183 doesn't bode well for next week. top of my list to short Mon. w/ close below 180,-1
+"Green Daily Triangle on MNK,....Net Profit  9,587.50 (5.32%)  ",1
+Watching GS closely on today's trend-line. Has been leading SPY up AND down.,-1
+"ed Daily Triangle on HEO,.....pdating  ",1
+"out of AN, WBMD, and BOX, I like BOX as having the best chance of a continued run-up tomorrow",1
+AAP over 451 could start an ankle grabbing session 0_o,1
+V and MA FYI: When they tagged the 50d's yesterday they also touched off multi-year trendlines. Good a spot as any to be long (holding).,1
+Obligatory monthly post of MA's 2010 uptrend. February 2013 update: Still going.  ,1
+V Price sitting just above 2011trendline as we speak.  ,1
+user I follow up closely my scaling plan and I am in every level without any dough CSN,1
+I'm heavily betting into SYX for Monday's earnings.   ,1
+ this is so bullish for ZNGA [online gambling legalize in nev],1
+EC pulling back to its rising 50SMA on low volume. Golden cross on daily. Should react here   ,1
+AAP 450 is going to be hard with all the open interest for this week exp.,1
+user: GOOG is rolling over and AAP is rolling up...rotation time. Always bothered that GOOG does 10x less vol? fast exit,-1
+EV buying on this pullback of this strong stock; entry 39.74 stop 38.02,1
+"GOOG no traction,no vol,  3rd day lower, last option day of the month... flush?",-1
+user:Glass potential to make everyone a spy or a creep GOOG ~ appeals to hipsters & pervs & if voice cmd is as bad as siri oh-no,-1
+NFX Doesn't anyone else find it delightfully amusing their hit series is titled House of Cards?,-1
+emember when Microsoft was one of the world's most valuable companies and the Gov't conspired to take them down? Apple knows now too. AAP,-1
+short WYNN. I still think we've seen at least a short term top.,-1
+TA nice move since shorting at 88.60  will carry overnight.,-1
+GPN Groupon Bear bets on over 20% down move by March 1st buying 5K Puts  ,-1
+"IP sneaky breakout seconds to go before close, waited all day for this one",1
+CHK Consistently pushing above the 20.00 mark & the 50 day now arching up nicely above the 200  ,1
+Some #insurance stocks at all time highs! AON AS TV WB Weeeeee SPX SPY  ,1
+ANA & AMN bounced off same double bottom - may see test back to 9.00 now on ANA once into the 8.75+ range,1
+CBMX - history repeated another HGE DAY - now look at BGMD and TEK they have same EXACT pattern - CBMX float 1mill - TEK 2.4,1
+DVAX witnessed a record number of call and put contracts during the busy trading session.,1
+OPK broke out from its sideways consolidation with a 5% rally and a close over resistance at 7.  ,1
+IDCC Breakout on either side on closing basis may give good direction. 50SMA acting as support  ,1
+YHOO Sitting just under highs. It will interesting to see if it can break 21.5 next week   ,1
+NFX If it breaks down below 178.23 w volume it may retreat to support at 160. Short-term bearish  ,-1
+"ZCS Got an alert this morning, bid  0.89, ask is 1.46",1
+HPQ awesome breakout. Buy on dips  ,1
+"NTSP was the big winner in the #13for2013 this week, up 25% due to buy-out.  ",1
+"DNKN digesting gains with selling pressure, pullback to the 50-d ma would be healthy #13for2013  ",1
+"FT - the stock is extended, take some profits & look for new base to form #13for2013  ",1
+"NKD well above the moving averages, look for som eprofit taking & new base formation #13for2013  ",1
+"SSYS currently at 200-d ma, look for min 8 week base to form (would be healthy) #13for2013  ",1
+S from the action last week looks like S and CW may be heating up again for big run #3,1
+"Short list monday, AZK CENX  DAKT EMK  IO MVG QTY SNSS STK TAS VOCS",-1
+"DAKT short 9.88 / 100sma break, ideal flat/trendless/red spy, will trade relative weakness  ",-1
+eading stocks analysis updated  Failing breakouts & high vol 50DMA breaks confirm correction. AAP FB DDD ES_F,-1
+CTIC looking to scale back into a long Monday hoping for sub 1.30 stock,1
+DAA nice double bottom formed scale buy here 1.00 bullish,1
+"CBK long 6.22, wants back top of range 6.60's ?, hammer off 50sma & 01/31 low, light vol inside day  ",1
+CHK ove this consolidation after the big move up.Strong support at 20.Watch out for a move over 21  ,1
+XX - Cheap name but ready to clear this bull flag if volume continues.   ,1
+NKE bull penant?  ,1
+END added to our list of BEST STOCK PICKS - Trade Alerts will be sent to members.  ,1
+ICE is added to our BEST STOCK PICKS as a short trade. We took profits on our short last Thursday  ,-1
+BBG Short 14.86 or wait for 14.79 break 02/04 low  ,-1
+HS Over 48.20 at first for a small trade to second line  ,1
+VXX VXY may be in play SPY QQQ DIA may pull back Watch NG  GAZ AAP GD SV and some accumulation on GDX and NGT,1
+JCP aaaand there goes that SG &A increase . . . #OSCAS2013,-1
+OMEX - was on TV tonight show called Silver ush - huge potential check this out -  ,1
+NKT - this gets to 9.60 will go thru 10.00 on the radar watch!!!!!,1
+"NIS over 2,60 will run hard. ONG  ",1
+iking the user email I just got about DECK very useful cc user,1
+"AIM looks interesting for a trade, will look to open 1/2 pos",1
+Pre-Market Winners: SI FAS DB TOT HTZ ASM EX TNA E NKD,1
+Pre-Market osers: DDD DDS DEO DST PSO VXX FXY GSK IO NGG,-1
+DDD split today and stock is down 12+% in  33.25 its cheap with growth expectations at this point IMO nice to dip your toe : On VXX SPY,1
+OMEX at 3.50 now and may b/out!!!,1
+"CHK 6,600 Weekly 20.50P bought vs. OI of 148",-1
+OMEX sold some 3.52 buying back 3.43,1
+DDD dipping in long here - oversold on split adn earnings confusion,1
+AAP Next resistance trendline and then 463.. go aapl go..   ,1
+"M back above ascending triangle resistance after breaking down last week, bear trap  ",1
+"GD SV P ABX FCX AY NGT - Gold ebounds per euro progress, attention to Fed report  ",1
+user: DDD split today and stock is down 12+% in  33.25 its cheap with growth expectations at this point IMO nice to dip your toe,1
+BAC I told last week for peoples that are not in the trade..Next Entry 11.65 On HIGH Volume IMO !,1
+NFX Anyone noticing the weak candles/support in the last hour?,-1
+DDD ... safe from Italian b/s :) ,1
+ridiculous move in nflx.  can't believe it's still going too.  i'm obviously short. x-(,-1
+DDD come on 40! I got some calls at 0.15 avg I would OVE to have pay ITM :),1
+so far it looks like WYNN failed at 50ma,-1
+"ed Daily Triangle on DVAX,....Net Profit  900.00 (3.78%)  ",-1
+"bought a little FTNT. After all these hacker attacks, I needed some Cyber Security in my portfolio",1
+"HIG Fast Money Crew is just not too bright, buy HIG cause its down 1% on no news O! Genius! Tomorrow it might be down 3%...",-1
+"ed Daily Triangle on DEPO,.....Scaling Short Position pdate  ",1
+VZ making new highs again,1
+DDD needs to clear 35.80 and get up to 38 or else we're gonna get short term chop!,1
+PA 1.42 has pulled back quite a bit but due to shortages of PA and PPT this has a great long term outlook along with SWC,1
+shorts on GMN SINA TA working well. Covering CVE.to for a small loss here,-1
+AAP Apple's stock is 36% lower than its September high.  ,-1
+"covered TA short for a nice profit .  Careful about tomorrow, BErnanke and consumer sonfidence numbers.  Pare down positions. SPY",-1
+Is this the last stance of SPW before the pump and dump collapses to 11.63?,-1
+"user: DDD Motley Fool apologizes for wrong math, not accounting for stock split. Guidance much higher than being reported by most.",1
+DG Dollar General Option Trader buys 10K Calls betting 1.85 million on up move by April  ,1
+still looking for nflx to drop back below 183.,-1
+news on APO  ?,-1
+"There's a reason why ZNGA is one of, if not the largest, lobbyist for online gambling",1
+user: Cash is Now 33% of Apple's Market Cap AAP  ,1
+VZ playing out nicely from last week's video.  SPY,1
+GOOG update alert: Almost 100% today! Now harm in selling half and let the other half ride with a trail stop,-1
+BAC next stop 10.50,-1
+"ed Monthly Triangle on KWK,....Scaling Down  ",1
+CVM announced that its Taiwanese partner has added two additional clinical centers in Phase III cancer clinical trial for Multikin,1
+AXN becoming again interesting near multiple bottom. We made lot of  some days ago here  ,1
+ANTH CPX monitoring stricly,1
+AAP It may be wise to hold off on buying #AAP till they fix this second security bug.  They better patch it fast!,-1
+SPS Subtle (bullish) close.  1 Year (weekly)     ,1
+"WMT next levels of resistance 71.70, 72.60, then nuthin' but air (note to self: fills gaps nicely)  ",1
+"XCO looking good, user may be onto something here, big sellers of June 6 puts bit of a floor  ",1
+AAP GOOG IMO stock splits has wealth effect ExBuy100 shares WMT in1970 worth 14+Million receiving 300k+yearly SDIV Ex KO GIS MCD Etc,1
+"BAX holding T trendline, nice r/r from here  ",1
+Market Wrap Video + Additions to Watch ist  including: AVP EOC INFI SEE SWY TAT,1
+Everyday I have a great Hangouts experience and business use case... goog,1
+"  PAT ONE ES, SPY update  ",-1
+" ES, S  PAT TWO, update  ",-1
+" ES SPY, S Part three final update..hedge and define risk ...  ",-1
+NVDA NVIDIA shows off Phoenix reference phone at MWC 2013  ,1
+Watch for SGY to break its downward trend line to the upside for a potential move above 23.50  ,1
+"user: user really believe this Open will be a fade sold BAC 11.14> Hard to say, should stay above 11.10 IMO",1
+GD SV NGT ABX FCX NEM - Goldman Sachs cuts forecast for gold prices in 2013 and 2014,-1
+HD bullish after it?s results. ong if beats 67.24. Stop loss 66.64  ,1
+user: HD bullish after it?s results. ong if beats 67.24. Stop loss 66.64  ,1
+"SWI still holding up well, yet to emerge from consolidation  ",1
+"JPM 4 of last 8 days saw distribution, may yet test 50dma, but little damage done longer-term  ",1
+Would play it wait and see before adding more SPW:  but adding more SCTY on dips:  ,1
+SWY on watch list close to line with volume of 16% of full day average   ,1
+V might still get a flush to 152 but longer-term this is solid  ,1
+APP Eric Beder at Brean Capital launches on APP: An nsinkable Brand: Initiating Coverage of APP with a Buy ating & 2 PT ,1
+AEE quiet refuge in the recent storm  ,1
+DOE looking pretty ugly,-1
+AXTI Starting to look strong again. 3x avg volume. First upside target 3.6  ,1
+"AAP I am still hoping for a big Split  shareholders meeting, bring in the value investors chase away the hedgies",1
+user: APO : SHOT setup posted last week. Would look to book gains if u played this one  Study,-1
+AAP new strategy.. I just purchased Jan 2015's should be able to scalp the premium back .. over time,1
+"MS making the turn back up here, 23 calls are CHEAP!",1
+"Short Ideas,  digesting recent gains. BID CF APO MCP FCX NEM INTC CS IAG AKS AAP & more.",-1
+Short Ideas digesting recent gains. BID CF APO MCP FCX NEM INTC CS AKS AAP Further downside remains.  ,-1
+"ed Daily & Weekly Triangle on DEPO,...Cover Short Net Profit  24,338.00 (3.31%)  ",1
+AAP never imagined aapl in dumps for so long BBY NOK,-1
+PPHM  ike I said Jump off the Ship My feet are Wet...,-1
+"F, GM, slammed by consumer reports Auto  TM, tops again",-1
+user: PPHM Damn. Sorry folks. Pharms are getting taking to the shed today.,-1
+user: PPHM why the big drop,-1
+user: PPHM next year right next year lol that's what they said for the last 15 years. ock them up put ES in jail and be done with,-1
+user: Cancer trial sabotage who's going to jail first. Who worked with saboteurs a FBI will figure it out. PPHM  DNDN jail time soon,-1
+NBS A Steal  this level.,1
+Beaten down large cap technology stocks leading intra-day short squeeze. INTC BID AAP  ,-1
+long AAP 439.5   ,1
+ZCS  My PPS is a dollar. There is no reason stopping this from going to 5 dollars or more. IMHO....,1
+"V been pounding the table on this one, outperforming SPY, MA, needs follow thru to seal the deal  ",1
+HN trying to hold on to the bottom of this channel  ,1
+AAP MO is to add it to the Dow --- 4 for or 6 for 1 split  ,1
+"AAP don't know if it is the tail that wagged the dog (3M shares!) but the whole mrkt up & holding  (except FB, BBY)",1
+AAP that little pop is just a taste of what the hedge funds will do when they all pile back in.  BEWAE shorts. coming soon.,1
+AAP what is the value in 450 share? other than keeping it a plaything of the ultra rich (and those of us who like to play in their pool?),1
+"CVX  i think there's a good point to enter one second trade as sell. Since has testing one broken support, now resistance.",-1
+XCO 6.52 has technical support 6 min downside with nice upside and largest shareholder Wilbur oss NG prices down Caution if oss sells,1
+AAP are rumours news? #Idontthinkso we know there are not many sellers below 437,1
+"SII Sold 1/4 position at 2.09 today..wanted to lighten up on stocks going into the uncertainty this week, its still my largest holding",1
+AAP OK all the bears out of the pool!,1
+AW eod trade catch the shorts long here 2.18+,1
+AMN BY AY FCX ABX GOO and GDX GDXJ GDX GGGG GT are near lows with nice entry points on GD SV exposure some pay SDIV,1
+user: ZNGA 3 reports that NJ has signed the Online Gaming Bill...    ,1
+SVM has come down approx50% SV prices may swing Straddle options H SVC SI miners to gain exposure of the SV DSV to HDGE,1
+ZNGA Zynga closes Timonium video game studio in broader consolidation Feb 26 at 17:38 - Ticking up in the post =  ong: ZNGA,1
+PZZA sold off big on high volume  the close.Then they announce a restatement.Market rigged??NAHHH!  ,-1
+"MON SI declining, downward trend, 98 support weakening, 8 & 20 DMA curling down. 96 is next.  ",-1
+AVY Over 92.66  ,1
+EV over  40.72  ,1
+EXH  Over 24.90  ,1
+STZ Over 45.00  ,1
+AAP don't be surprise if #apple go much lower #done and I mean much lower and done,-1
+NFX   175.39 sell short,-1
+AAP Apple Support Further Below by user Below  ,-1
+NIS time to run higher. Breaked the descending line  ,1
+Trade Ideas  TIF PSN O F-short  ,1
+APA perfected DeMark sequential 9 buy today..started a long yesterday.  ,1
+GMC watching 30 O flag   ,1
+HBAN Huntington Bank aunches Spartans Debit Card for Michigan State niversity Fans. ong if beats 6.92. Stop loss 6.86.,1
+Transports moving pretty strongly this morning JBHT KS NP AGT,1
+BAC Home SAles Out VEY GOOD !!!! :)  ,1
+AAP I would prefer if it hit the bottom early then started the long climb up...,1
+AAP (65mins) Evening star reversal with potential downtrend resistance   ,-1
+"overall market up,  AAP going down.. GOOG is about to pop 800 again",1
+"Southern Comfort is tasty! OOPS, I mean SCCO and JJC   ",1
+"user iSwing I like AN, FS here.. different sectors but looking good",1
+NKD just delivered the knockout punch to the shorts still fighting this beast  ,1
+DDD let's see if we can break through 36.50 and hold today - former resistance level,1
+"AAP innovation is dead? What about the best selling computer, iPad mini, released on 10/23/2012.  iWatch? iTV?",1
+bought some C today.,1
+"AAP has only historically released a significant new product every 2-3 yrs. iPod 11/10/01, iPhone 6/29/07, iPad 4/3/10, iPad mini 10/23/12",1
+CAT being a major thorn in my side today. Me being short that is.,-1
+user: TJX stop loss in 43.91 after beating 44.27,1
+X nited States Steel Corp. option traders bet 670K on a 4.5% up move by May Expiration  ,1
+V is looking better for a swing trade long as it bases at highs  ,1
+CF 30 min O triggered earlier.   ,1
+"AAP in some 480 calls into Timmy Cooks investor meeting, for .42...",1
+PCN further bookings acceleration could drive multiple expansion going forward  ,1
+user znga wanna see the 10 day ema cross the 200 SMA for confirmation & entry for a trade,1
+Equity Alpha: QCOM long watch. New chips and Internet Everywhere appealing. B/O  65.70 Tgt 67.50   ,1
+watching CME today for possible breakout,1
+VBD may be about move back up  ,1
+"SPW First Solar Showing Signs of Fatigue.  SunPower Yes, First Solar No.  ",1
+FIO reversal,1
+I like H here.,1
+TJX stop loss in 44.04 after beating 44.43,1
+AAP Massive volume there,1
+EA is breaking out.  ,1
+AAP this right here is why I hate stops,-1
+TSO - I see this one mentioned on the Matrix a lot today. I see why. Nice flag and 20 ema bounce  ,1
+"user: New Jersey, Nevada egalize Online Gambling, Good News For Zynga  ZNGA",1
+TXN from watch list back up to line on volume of 45% of full day  average  ,1
+GEVO Gevo: An ndervalued Spec Play With arge Short Interest And Trading Below Cash Value  ,1
+"user:  Somebody explain to me why, AAP has user on its board? Best snake oil salesman in the world went from 2M-500M 10 yrs",1
+user: CAPES  on Bloomberg says permanent holder of AAP -  What am I missing? they bought 10 and return 100% on div yr/yr,1
+user Bi-furcated market rules the day . . .my friend MS WMT TGT DT PP ,-1
+AAP has AWAYS ignored what Wall Street wanted. Nobody complained when the stock was going up.,1
+MPC Marathon Petroleum Option traders bet 2.4 million on a 21% up move by July for a 8 million dollar payday  ,1
+AAP  sure likes 444,1
+AAP seems pressured to fill the gap in the 430-431 area,-1
+user: TJX stop loss in 44.34 after beating 44.59,1
+ove the price action so far considering the tensions out there and volatility... GOOG and NKD strutting (long),1
+AEG 30 min O and 1 overlaid 20 ema cruising higher   ,1
+I want to sell my NKD so i am just chatting about it instead and reverse jinxing myself ...it just feels like 200 is in it,1
+HBAN Huntington Bank stop loss in 6.96 ater beating 7.03,1
+VZ not even flinching .... the new utility of choice!!!!!,1
+"While the world was waiting for Apple to remake the television industry, Netflix beat them to the punch. AAP NFX",1
+user: AAP will finish green as I predicted .... I agree,1
+SPW 60 min  ,1
+CEE - user out did himself overbought 60 now   ,1
+user: GOOG and AAP are really showing off... They both look like male peacocks here. AAP with 10X volume 2X market cap,1
+CEE 30 min O updated - nice trigger early on 11:15AM   ,1
+user: AAP Did I miss something? Is there an actual reason for this movement? downdraft,1
+"Waiting for the big rotation, when profit takers get out of Vapour~ware and back into AAP  and it's DIV, high EPS and low P/E",1
+QCO picked up some for long term position,1
+user.. buy on a dip! is -35% dip enough? AAP,1
+"AAP Get ready, I see a big move coming off 440 Triple Bottom Here!",1
+FS - nice trading peeps. Take some off trail some. Hammer is fully formed now.   ,1
+AAP Gonzo!,1
+"AAP when the market  hates ow P/E, High EPS, NO Debt and HGE profits.. something is wrong IMHO",1
+ANA Bought 8 mar calls on top of my long position,1
+JET sold +9.22%; taking profits here  ,1
+user: user BAC did u buy any?  Yep Full position Now !,1
+"Pretty soon, it'll only take one share purchased on the offer of BK.A, PCN, or APP to move markets up 2%. #NoMoreVolume",1
+Wow INVN took a beating today...,-1
+chear up AAP holics allot of bulls just got butchered at GOOG,1
+"GPN needs interests rates to rise before having any hope of making money. Earning the float is their business model, folks.",-1
+Soon GPN will need to start offering Groupons for their stock.,-1
+user: AAP Earnings hope - flushed out.  GS conference hope - flushed out.  Annual meeting hope - flushed out.= time to buy IMHO,1
+JCP Our CEO is tinkering with new idea at home about how to bring customer in. Be patient till you are wiped out.,-1
+"user: SHD can operate with out a retail store. JCP cannot. Big difference Sears is even worse, they need a retail store even more",-1
+"AAP interesting how ppl blame the c/o for  heggies and Dark_pools do with stock prices, once public there is very little anybody can do",1
+"G full year 2012 revs +50%, EPS +72% vs 2011.",1
+IF DDD XONE SSYS PB and 3D printing are not the next big thing then someone tell me what it is?,1
+Great article on an AAP  10:1 Split that I have been pitching all year!  ,1
+owered my price target for GPN to -1,-1
+AAP #4 is not used in S. Korea because it means DEATH.  so 444 is kinda like 666 in korean,-1
+Split adjusted ...Kansas City ailroad was 25 cents in 1984 and just 10 in 2009. Today an all-time high of 104. Short AAP GOOG hmmm!,1
+TIF looking good.Still need to break 67. 74 is next resistance level.  ,1
+all my positions did great today except AAP :o/,-1
+NYSE A/D remains strong NYX NYA NYC SPX DJIA  ,1
+"Quietly grinding higher, tons of products JAH #JADEN Corp where they come from #WOW!  ",1
+looking for FCX to hold support above 30  ,1
+FOM A break above the neckline would give a measured move target of nearly 5.85.  ,1
+CZO had nice up move looking to into SQQQ TQQQ SPX SPXS SPX SDOW PO to straddle options or swing trade OI prices up GAS up,1
+"JCP J. C. Penney tgt to 22 from 25 at Citigroup, tgt to 15 from 18 at JPMorgan following earnings - Short JCP",-1
+"user: Anyone like V? ooks bullish here, needs a little volume and we have lift off.  ",1
+SWHC G liking the SWHC PM but volume still very low but nice at 9.59,1
+DDD up 3% premarket... I wonder who just bought those shares off me at 37.50.... ,1
+AM from watch list gapped open has volume of 4% average full day   ,1
+"NFX over 186, looking for a move to 190",1
+AMZN alert update: It went hard against us but is playing out now. I would consider adding on or getting in here.,-1
+WEN Nice eady to sprint to new high CA,1
+The SPW that we've come to love returns:  now up significantly over FS since we covered last summer,1
+"Nice to see NKD giving the bears/shorts their day in the sun, -0.1%",1
+ssys if fundamentals matter... headed under 40. income is 10% sales. 40mil net inc. puts this at PE of 60 with stock at 60. doubt it,-1
+INFI from watch list moving up toward 42 line though volume so far is light today  ,1
+AMI taking some off here,1
+AMI taking more off 820,1
+CBMX happened so fast from 3.80 to 3.98 couldnt get ot out quick enough without missing it now back to 3.73 may do another run watching it,1
+DDD possible move to 38ish,1
+DF looks bullish. ong if beats 16.59. Stop loss 16.49.,1
+long YHOO 21.42. daily setup looks too great to pass up  ,1
+AAP most likely has a nice batch of new products to hatch  ,1
+EXH from watch list just over line on volume of 13% of average full day   ,1
+FFCH from watch list just a bit over lien with volume of 13% of average full day   ,1
+JCP Time for user to pound on balance sheet and cash issue,-1
+"ANIK trying to head higher, move to 11.90ish poss",1
+"Some good, strong follow through early on today on BBY  DNDN   Others that are poised (set up) to move:  ",1
+"even more breakouts for YHOO , highest levels since 2008.  (typed from home office)",1
+DDD just picked a half position here,1
+NG NG_F  Natural gas weekly supplies lower than at any point last yr.  -12.1% below this week last yr.   XCO ECA,1
+PCN possible short to gap close if it cant hold 685,-1
+"PCN covered more 682.90ish, leaving last piece for gap close 685 stop",-1
+Picked up SPY 151 puts  .25 each. Following the ESD and GS as tells here.,-1
+"PCN final tgt 680.30 or so, 684.30 stop",-1
+the ticker has actually stopped on AAP,1
+JCP Piper out with 16 target I think it's should be 6,-1
+WEN Ho hum new 52 week high CA SPY DIA,1
+user: there comes NKD with some oomph (technical term),1
+DNKN starting to move,1
+MAKO on watch list moving up toward line at 12.54 with volume of 54% of ave full day   ,1
+user catching its breath.  watching DNKN?,1
+CMG creeping up on 200 MA and long term down trend.  ,1
+SNTS approaching 52wk high,1
+C perfect follow through day...  ,1
+MA - Been struggling with vol. resistance all year. Watching closing for a long position.   ,1
+PK new highs,1
+PK peeling some off here 4.16,1
+ZB taking some off here,1
+BAC New High Of Day :),1
+FCFS easy look at last 4 Q's income data:  ,1
+MTG may not be done,1
+user: Anyone know what options strikes einhorn has AAP? He must be getting crushed news was he is in around 525,1
+DDD worked with 3d design software today. click on a layered part of object  in 3d graphic and 3d printer creates it. Amazing stuff.,1
+YHOO trying to break HOD,1
+HD Busted HOD,1
+AAP no buyers and no sellers less than 1M shares in the last  2 hours,1
+DN will try long over 3.11,1
+"long some CSOD, anticipating breakout, and like the space",1
+"DN MTG GNW Window dressing here, would not be surprised to see a reversal tomorrow.  SPY DIA QQQ",-1
+If you're invested in or trading HF or JCP. Step aside & let these two elephants do battle. You'll just be collateral damage.,-1
+Bought some ZNGA  3.47. iking that it broke the 200 SMA yesterday & online gambling legalization  ,1
+"AFFX strong, no pos",1
+"yup user: BAC JPM  easoning is nothing more than innuendos, speculation, and well crap. Mayo should be ashamed",1
+AAP look at this channel hugging 444 for last 2 days outside 1 big pop and drop  ,1
+"AAP > Told you yesterday it's not a buy yet. When I say it's a buy, I'll be backing the truck myself.",-1
+"JCP 1x2 ratio spread goes off in May. Buying 30,000 16P for 1.57 and selling 60,000 12P for .45 against lower OI. Opening position",-1
+"JCP think this one rolls over here towards the EOD, this one does have the chance of bankruptcy, cash balances lower, avail. C lines lower",-1
+Bought 1/2 position in SSYS  62.79 going into earnings Monday  ,1
+"Somehow this reminds me of TGT, JCP #Ackman at least did not buy call options on JCP, still in big trouble, fund redemptions coming imho",-1
+"#Groupon & #JCPenney taking the hits on the mkt today, down about 24% & 18%.  GPN  JCP",-1
+G beats. Our take on investing in guns:  SWHC,1
+ISG someone got a margin call on this move...Craziness in the market towards the close SPY DIA QQQ could not break through Mon.'s high,-1
+user: user: AAP 1.5 M trades at close l - lot of sellers  broad sell off but if no buyers = bigger drop look  isrg,1
+wow ISG being probed literally lol by regulators,-1
+user: GS a text book trendline break intraday -   ,-1
+"Having worked for Eric efkofsky myself, I wouldn't count him out. The dude can work magic in tough situations. GPN",1
+OVTI down 8% in AH,-1
+Seems like a good time to rehash this post about working for Eric efkofsky...  GPN,1
+"GPN Mason getting the boot is probably the best news for that company since it came public, doesn't solve the problems with the biz tho...",-1
+"  Elephant hunting ISG, PCN, AMZN, A point and figure review, high risk , define loss w opt  ",-1
+"China's natural gas drive may cut oil demand by a tenth, euters :   NG NG_F XCO CHK ECA SD COG KWK P SO",1
+  ISG An update to our Feb 20th video review..if it closes below 495 much lower to come soon  ,-1
+Market Wrap Video + Additions to Watch ist  including: AIZ CBMX CDX MAN MDSO NDAQ TWO,1
+HD there is a nice entry at 67 which I will look out for. Not a stock to short. No position for now.,1
+user: Building a position in ZNGA. ooking to add on pullbacks. Can't ignore the volume and the story  ,1
+AAP Apple showing stong support in this area. However 400 is next critical price.,1
+user hell yes :) YHOO,1
+"If I can get a nice 1-2 day move down in SWY, I am gonna go pig wild on it. Traded in and out 3 times already, but big  still waiting..",1
+SCCO double top? 50 SMA turning down.  ,-1
+"NFX Mar 205C (Mar 8th expiry), TOS shows it firing at the bid (1.50 x 1.53), but sorry folks, BOT!  ",1
+"NVDA Nvidia Tegra 4 Benchmarked, Crushes Competition.  ",1
+user: GEVO the beginning of a new uptrend:  ,1
+Today's game HF for the Icahn team and maybe a little bit of GPN in memory of Andrew !,1
+TEK sleeping giant one of these day soon booom target 2.5-+,1
+NFX gets exclusive streaming rights to The Hunger Games,1
+CES watching for a move over 3.50,1
+"SD this is the ammo to help fire Tom Ward, was needed imho... unfortunately once again at the shareholders expense. NG SO",1
+INFI from watch list gapped over line - vol just 5% of ave full day - watching  ,1
+CES trying to stay above 3.50 needs followthrough here or the pattern may fail,1
+AMTD looking for a bounce off the 50sma,1
+SQNM trying to bounce needs better volume,1
+"CES took some off, 3.45 stop",1
+CDX from watch list over line on volume of 15% ave full day   ,1
+"GMC really like how this one continues to hold up so well, volume on the up days  ",1
+"Nice day for the kicked out of the Nasdaq boys.  NFX, BBY, GMC all green.",1
+AAP breaking support on heavy volume - sharing a few insights VIDEO   ,-1
+"Notable S in OPEN, but general market heavy",1
+AAP Breaking important level here.   ,-1
+CES long 3.50-3.52 target 3.58 -3.60,1
+"user ooks green to me, plus the 3% i got out of it buying at 35.25 today DDD",1
+Get the Elephant guns...I'm going hunting for AAP I'm not shooting yet...But I'm close.,1
+BAC MarketMaket will make lot of  from Short Seller Today till March 7 IMO,1
+NSPH peeled more off 2.19,1
+KEX made awesome move thru 7.00 to 7.09 made nice score was A IN!! - made nice profit all those shares sold buying back 6.95 and less,1
+AAP Self-serving tweets re AAP's continuous slide w/TC as CEO. Spending for &D's on hyperdrive & 100+ talented people work w/Jony Ives.,1
+HHC sold 30m of residential lots in Summerlin in Vegas in 2012.... For Jan/Feb 2013? 31M,1
+NSPH trying to move into the gap,1
+AAP user More on the Fed after this. New 1 yr low today for Apple  431.88. Could 310.50 be next?,-1
+"Some math HHC sold 206 condo units in 29 hours in Hawaii in Dec. Avg 1100 sqft, avg 1,500 sqft/condo (1.65M /unit) (own 25% of project)",1
+Adding short BWS to portfolio. I think next years estimates are too high.,-1
+BAC Big Volume Today = Big Boy Involved = I'm IN too ! ! = ,1
+BAC Today on Weekly OPEX should  pin around 11.35-11.50 & next week going higher as  we approach March 7th StressTest IMO.,1
+AAP is oversold here,1
+Equity Alpha: APH looks awesome  break of 71.25. Great Jan earnings and good consolidation. Tgt 75  ,1
+NKD kisses 170 hello... I am a golden God,1
+YHOO Bullish Breakout confirmed on Expanding Volume  ,1
+"VMC MACD, Slow STO turning up from oversold...EMC buying w/ 89 mil float. J-O bottom.  ",1
+NSPH looks like a possible move to 2.35 ... no pos here,1
+user you so funny AAP bulls,-1
+YEP nice...may go along w/ OPEN.  ,1
+On way up: I love AAP! I love AAP! I love AAP! I On way down: I HATE Apple! I HATE Apple! I HATE Apple! #TradingWithoutAPlan,-1
+YHOO is probably done for a couple months ... sideways to down is just reality from here.,-1
+GMC is breaking out. Bullish MACD crossover.  ,1
+AAP New 1 yr low today for Apple  431.28. Could 310.50 be next?,-1
+What is JCP real estate worth if no-one is buying big box space in today's market?,-1
+"Come fly with me, let?s fly, let?s fly away ! al",1
+VS Swing Trade Quick Pick: as Vegas Sands (VS) |  ,-1
+AAP loading up on Jan 2015's 440 strikes  62. 7 options for the price of 100 shares where will it be in 2015?,1
+QCOM vs QQQ = elative strength  ,1
+AFFY GMX GOOG NVA THD MACK 5 Stocks Poised for Breakouts - My story via user  ,1
+"ooking to short on GDX production company, was looking at H SA IAG... Anyone have a short thesis on a name?  Wld greatly appreciate it",-1
+nwinding of the Worlds Most Widely Held Position is never pretty. STDY this AAP move and identify the next Hedge Fund Hotel.,-1
+"user: user H and IAG are on my shorts list. SA I dont know about>> TY! was looking at Cash flow for H, burning abt 30mm/Q",-1
+VXY Another ong BPOP Snort... 9s next week on VXY to new AT highs in indices,1
+"AAP Smile of Friday EOD : The ast 6 Times Tim Cook Has Talked, Apple's Stock Has Dropped  ",-1
+AAP we close below 435 today.... thats no good (great for bears),-1
+I wonder if AFFY might go bankrupt. Or change their management. I mean 85% drop in the stock price and now a lawsuit.,-1
+AAP Bearish MACD Crossover,-1
+AAP bad news keeps coming. They say when it rains it pours. Puts paying dividends. 420 damnit,-1
+Holding AAP Mar 8 420 puts into the weekend. Every intraday rally got sold into. ooks like poop.   ,-1
+"Gap filled, doubt it holds AAP NDX COMPQ SPX #Toaster #Apple  ",-1
+AAP holding all of my current positions over the weekend   ,-1
+#Ackman you should be really going after PI. Classic example of MM firm. worked for them a year or so. Momo is in favor. #yourWelcome,-1
+VS weekly. Sorry suckers. bearish bat under resistance.   ,-1
+"Some profit taking today, but Mar 12.5C OI is pointing higher for GDP. 26M float, 24% short, love it over 13.85",1
+user: GEVO  Thanks for sharing !! Great article !!,1
+Whisper expectations for SWHC  ,1
+"Isn't AAP getting unjustly punished for having too much cash, good problem to have, no? I still love my Iphone! happily trapped in ecosys!",1
+"AAP    Capitalizing on the bigger picture moves, with the higher time frame weekly MACD  ",-1
+PPG - Seems like a pretty low risk/reward short opp.  Stop above volume resistance.1st tgt gap fill  ,-1
+It's not the first time that AFFY has problems with their drugs.,-1
+AMZN short idea  ,-1
+CAT short idea  ,-1
+CME long idea  ,1
+CSOD long idea  ,1
+WYNN short idea  ,-1
+SM breaking upper channel resistance line to make fresh 52 week highs on high volume. ooking for continuation.,1
+DECK Bullish Breakout on Expanding Volume.A move towards 48 can be expected as long as 45.2 holds  ,1
+MYGN sell stop under Friday's low or limit to short near 26  ,-1
+eview Of Open Positions - Stocks -  AEE AN ASGN GMC GOOG HTZ NKD PO SWI V,1
+"Stalking. ising channel on FI. If a pullback happens to 25.8, must be bought with a 25.45 stop  ",1
+Stalking. XX watching for a break of 8.23  ,1
+BSX Weekly: 10 & 200 meeting  ,1
+"  SPY,IYT, FDX, PS A closer examination of transports and market best to have seen 2 prev vid  ",-1
+"EV anything financial, over 41.20   ",1
+"OVI  volume returns again, buy once it gets through top of that downtrend line    ",1
+Tough loss...but MSG still looks good #knicks  ,1
+BAC daily triple bottom? ;) Bullish unless loses support (then short it)  ,1
+BAC 60 in bullish harmonics  ,1
+BAC 60 min bullish harmonic w/Fibs   ,1
+user: user I went long on MSFT for this week. I'm still bearish on MSFT but we're in bull market. I'll close out this week,-1
+H great risk vs reward with stop under Fib 2 at 4.50  ,1
+"ong Nike NKE, ange esources C,  h/t Nike user",1
+"53 Short Ideas,  led by miners, FCX NEM IAG CF PAAS, BID, & AAP eyeing 400. Many extended, but trade able.",-1
+Chromebooks...the future...  goog aapl,1
+SHD When was the last time you saw Kmart and Sears full of people?,-1
+KO #AXP WFC IBM - Warren Buffett launches into another giant hunting  ,1
+"Automatic cuts starts today, european crisis goes worse and worst but SSYS DDD XONE are going to pop! Anti austerity hype stocks!",1
+"BYD horrible quarter, pretty much declines across the board.  Borgata was expected, but other locations saw declining revs & EBITDA",-1
+user: AAP  down .6% premarket . Anyone know where the bottom is ZEO? if it does not stop before that I 'm buying it !,1
+opening momentum watchlist: KEX CHTP THD DCTH ZIOP ZNGA HES VMW XONE ACAD SPT,1
+AAP hmm. I'm not saying a word. Fan boys/girls keep your hate to yourself.,-1
+"Get divi approval from Fed (likely), it takes off user: Post from last night on Bank of America BAC -  ",1
+"PPO: 2) GM estimates 20% Y/Y unit growth for volt (36K). great headline, but PPO built capacity for 60K+. this is bad for margins. (2/4)",-1
+"PPO: actual #s for volt & leaf tell a different story than mgmt's. 3) feb leaf sales down M/M again, now to 500 units. (3/4)",-1
+DCTH strong start,1
+TA swing from Friday :) and watchlist  ,1
+user: GOOG gonna take out 2x AAP shortly now all it has to do i split the stock 10X for an accurate penis measurement,1
+Today's Watchlist for SHOT Stocks: MTG; ITMN; ANAC; OWW; DMND,-1
+CSOD Blue Sky Breakout today:  ,1
+user: AAP please tell me how I could explain my wife I lose 51K on the best cie in the world Tx ~share when you figure it out,1
+"NKD I am a golden God, do not fight me  ",1
+GOOG 1000 PT coming soon!,1
+STEM starting its climb back up,1
+ISIS another leg higher,1
+"Watch PCYC , looks higher",1
+"PO continues to recover well, but really needs some volume on the upside to sustain it  ",1
+HN over 18 again doing above avg volume   ,1
+"DCTH better volume now, 1st tgt 1.75",1
+"AAP has 939M shares, GOOG has 329M shares MSFT has 8.38B shares so close to = share price puts Goog around 285, AAP 425, MSFT 271",1
+"SZYM like the start, needs a move over 8.90 on vol",1
+"GDX 3 yr low here, think this move is forecasting the long-term top on gold (GD) H IAG getting hit hard today #bubblebursting",-1
+AAP trading 263/ share plus CASH,1
+ODP buy 4.15 -4.17,1
+AAP another leg down to 422ish,-1
+DK from watch list just over lien with volume of 27% of full day average  ,1
+CSOD DVA not letting up on the gas this morning,1
+Bull of the Day: ed obin Gourmet Burgers (GB)  GB MCD WEN,1
+CSOD #beastmode,1
+BYD gearing up for another leg higher,1
+Hi all! Catch me on user today at 2:45 p ET breaking down JCP and an update on Terry vs. Martha M MSO APP JCP TGT ,-1
+Z Zillow from watch list over line as a continuation with volume of 40% of average full day   ,1
+AAP 422 nxt tgt,-1
+AAP closed 1/2 of my positions and riding other half    ,-1
+user: Fund mgrs are the sellers right now. Have to clear AAP. In downtrend early seller gets  worm. ~should have started in OCT!,1
+Best behaved in today's market action? PFE,1
+AAP Just pulled the trigger on Jan 2015 430 65 sold thursday 430's  for 4.5! in the ONG game now,1
+PFE is the street asleep? I want some commentary on this move!,1
+AMZN GOOG and CM move higher as the NQ_F turns green,1
+"AAP dips below 400B, Buffett tells Cook to ignore cash complaints, buy back stock  AAP",1
+BYD possible bounce zone,1
+Equity Alpha: ike IBM on break of trendline resistance at 203.50 Should retest earnings high  207  ,1
+Equity Alpha: Watch for PEP to start to take off over 76. ooking for 79.50 in couple weeks.   ,1
+"user: AAP GOOG etail - dont get stuck holding google bag like you did aapl, google a sucker high here 1/10 the vol=  fast fall",-1
+FFIV looking for  a quick move to support at 86 especially if SPY lead the way down,-1
+".user, Cook is fine for AAP. People were skeptical about Chambers at CSCO, too. Markets are not always prescient.",1
+user: tour de AAP gap fill  ast gap of hope filled,-1
+user: AAP 15min. one green 15M candle all day and it was the lowest vol of the day  ,-1
+user: AAP Apple will call the bottom with a big buy followed by announcements. you are pretending there are adults in charge  AAP,1
+"Green Daily Triangle on MH,....Scaling Down  ",-1
+"BYD starting to work, 6.88 stop",1
+user: SPY HG_F C_F FCX These breakdowns suggesting a SOFTE global economy?  Pull SPX with it?  ,-1
+"BYD taking some off here, 6.90 stop",1
+"PCN looking for a move to 710-711, has to hold 705",1
+"ISIS, peeling some off here 16.19",1
+"For those not following MBI, a conservative Fair value in 18 ...25 is realistic ...when they settle w/BAC (soon) it'll get there FAST",1
+AXN taking half off here,1
+user: AAP I am sure we all have our equivalent Mercedes size loss somewhere in our career i'm down 1 or 2 till it comes back!,1
+THD finally,1
+"BBY I'm in some 18 calls, short squeeze is starting...",1
+ISIS looking for 16.30-35 next tgt,1
+"VX all time high, and on good volume, just what you want to see for a sustained move  ",1
+AAP long 423 and under buying,1
+"This is AAP's 9th consecutive day under the 2003 trendline. When a multi-year trend breaks, be short or be long gone.",-1
+ssys statement of cash flow still a secret. no mention of operational cash on CC. no analyst asked about Cash flow either in Q&A. oh well,-1
+DCTH finally ... taking more off 1.79,1
+SWFT watching for a move over 13.95,1
+"like the look of MCHP here, giving us a nice entry, probably write something overnight",1
+"HES broke intraday bollinger band top, now c&h & holding above resistance- watch for intraday b/o  ",1
+Take a look at how IDCC behave when it breaks above or have a Bullish MACD crossover.   ,1
+user:400 delta between AAP and GOOG today is possible As soon as GOOG does a 1:3 split will it be comparable + > 1/10 vol?,1
+"ed Weekly Triangle on JB,....Scaling p  ",1
+WFC BAC C Your dark horses for the week. Will come from behind to  to take a lead. CA,1
+user: AAP traders are attracted to round numbers like 400.  sure but this stock is traded 75% by computers,1
+"user: AAP, simplest answer is  the right one (Occam razor). why apple keep going to south? no buyers until turn around of some sort",1
+just bought AAP cheaper than it has been in years..+ a 2.65% div!,1
+Massive sell off AAP. GEAT,-1
+AMZN flagging today. Might buy before the close and hold overnight if it closes above 273.,1
+"user: AAP After google hit bottom a few yrs back, it rallied for a year. What's your time frame? 10 X 420 Jan 2015 !",1
+MS Morgan Stanley Option Bull bets 1.6 Million on an up move by July  ,1
+"user: GOOG almost 2x AAP now, gonna hit it tomorrow, prob 824/412. please check your facts AAP is 2.85x more than GOOG +10X vol",1
+BAC Solid Buying on EOD !!!!,1
+GEO took profits here +18.6%  ,1
+HD breaking and holding above 70 for new all-time highs  ,1
+M breaking out on heavy volume and bounced off nicely at Fri's close  ,1
+380 AAP could do a leveraged buy out of itself with cash on hand and low 8 earnings finance.. if there was a bank big enough,1
+user: AAP I sure hope their P dept monitors this sentiment after IPO company don't care and APP never did,1
+user Just waiting for the numbers. No insights into them APP,1
+SNTS kills it,1
+APP goal EBITDA margin of 15%. The Dov dude is focused on 20%. 800 in sales at 15% to 20% that is lots of bacon,1
+"NFX - We have no current plans for a BBY app - No app for AMZN Kindle, Evernote, Hootsuite, Whatsapp, IDG.TO Kobo",-1
+"ES_F Interesting parallel between apple AAP bubble,and the fed has your back bubble: AAP peaked with 4th phone.Fed peaked with 4th QE",-1
+user: DIS Eyes on Disney... knocking 56 door again.  ,1
+ANTH today was a great entry point. Huge insiders and institutional bought here. Just recapitalized. High Target Price. ONG,1
+Three Dividend-Paying Guru Stocks - Sizemore Insights  ADM CHK JNJ HNZ WFC DVA BBY,1
+user: AAP Motley fool says apple gets to 1k b4 GOOG on CNBC since AAP has 2.85x more stock it is already over 1200 IMHO,1
+E Competition egulators to fine MSFT before end of Mar for breaking 2009 promise to offer rival browsers in Win OS - 2nd failed to comply,-1
+AAP holding weekly puts.,-1
+"ed Daily Triangle on TPM,...... Open ong at 6.40  ",1
+"user: AAP Pigs Do Fly!! user slags TC for not listening to shareholders, not buying NFX twitter,or  1Bper wk stock!",1
+AAP to many people think the ip5s will be bigger/smaller or what ever but what if it was the watch? think dick tracy ? micro sized,1
+NEW Blog Post: Followup to Yesterday's Watchlist- ots of Movers  DVA CSOD HES DXJ HD AXP CSX BAC OIH BBY,1
+AAP wouldn't be surprised to see retest of 435 level as new resistance sometimes this week   ,-1
+VNG when it gets ugly like this is always time to buy 2.90 to 3.00 will start #1 entry buy tomorrow,1
+AAP when it gets ugly like this is always time to buy 410 to 420 will start #1 entry buy tomorrow #2 at 400 to 410 or 421,1
+ISIS we open anywhere here or under 16.75 we see 17.25 - easiest .50 cent trade ever,1
+EVC Bought more today for the 4th time - going much higher - all the major funds in media plays will buy this one - target easy 3.00+,1
+user: bet it all on AAP right now. ather bet on AAP 52 week low than all the vapour-ware 52 or A time highs.. poof,1
+Set ups I like for tomorrow:  ZNGA BOX FB VMW GDOT W Others set ups I like:   ,1
+ong idea in MCHP for tomorrow  ,1
+user: AAP Who is gonna trade in their watch for an iWatch? what if your watch was actually an iphone?  ,1
+Market Wrap Video + Additions to Watch ist  including: CYH DGIT EWBC  NGS PKG,1
+"BAC 14,000  11,45 after-hour ... Somebody Know something ?",1
+"For those that believe GM still leads mrkt tops, recent action is not positive DIA SPY DJI SPX  ",-1
+"More like 100. John is conservative user: APP Aims to Open 60-70 New Stores Over Next 3-5 Years  next step, re-fi",1
+OXGN this stock has majort support at 4.00 range and should rebound hard back to 4.30+,1
+"user: AAP  What point does this get delisted from the NAS 100? ? not while it is in the top 100 duh!  currently #1  nasdaq, #2 world",1
+  On Jan 24th we posted this video on aapl....no change...be careful...   ,-1
+"trade idea Buy JIVE  16.80 , target 20 , cut below 15.70  ",1
+AAP Fib 50% retracement from 2006 low of 6.36 to 705.07 would be 355.72  ,-1
+CYN on flag break or Over 43.00  ,1
+ Over 64.05 may need to rest first  ,1
+PKG  Over 42.46  ,1
+... cloudy big gap bottom? VMW  #hammer    ,1
+#ContrAlert Don't Panic: Wall Street Is Going Crazy For Student oans -- But It's Not a #Bubble -  SM,-1
+ZNGA entire body is about 200 day moving average today for the first time!!!!!!!!!!!!,1
+AAP still flying off the shelves..  ,1
+GDI breakout on huge volume ! ooks ready to test the highs.  ,1
+SWFT Flagging for a move higher. Watch for a break over 14.30 with volume  ,1
+Watching AD for more upside. Breakout at 1.76.   ,1
+ZNGA Wants to breakout. 3.76 is short-term resistance.  ,1
+"AAP could buy back 100m shares for 40B, saving 1B/yr div payout! 35% discount on share price +10% value increase to shareholders win-win",1
+user: VZ 6.2 million of 9 million phones sold were AAP strongest period of sales since 2011 FIE THE CEO!,1
+"From the look of it ill be averaging down aapl around 380, there is no bottom around here.",-1
+AAP Is Still Cool  ,1
+AAP will be buying on all dips today - an APP trade a day keeps the doctor away trade range 422 to 428 today,1
+NKD no rest for this stock as it soars into new highs   ,1
+CEG exactly what you want to see a stock do after basing / consolidating. New Highs   ,1
+HD - patience always pays off. Broke out to new highs   ,1
+OY worth eyeing - keeping on your watch list  ,1
+CPW is flirting with new 10yr highs in pre-market,1
+Watchlist for today TTNP SNTA IMM FM OEX DCTH,1
+VHC  breakout watch 36.7+  ,1
+"ANTH +13% in PreM. HODING ONG. Average TP 2,6. Huge institutional and insiders bought in previuos week.",1
+E  ready to break key resistanec at 65  ,1
+"SWHC still holding all my shares bought at 7.69. Have June10 Calls that are playing well too. Sold my MA9s yesterday, too soon.",1
+AN peeling some off,1
+AN taking more off,1
+I will short ASNA under 18.35.,-1
+ACX watch it over 5.35.,1
+"V nice clean move above 160, close near here would be very bullish  ",1
+user: GOOG at 830 feels a OT like AAP at 705. .feels very overextended here. CATION are you afraid of that OW 800k Vol?,-1
+ACAD trying to move over 6.50,1
+NDAQ triggered pennies yesterday and continues a bit today with volume of 16% of full day   ,1
+Whisper Expectations for F  ,1
+if AAP were as dead as some predict I would hope that the premium on puts/calls would be lower,1
+really having some anger boil over for missing NFX short on the gap today. not gonna chase.,-1
+long a little TP,1
+BAC Here is the entry for the long IMO ot of Volume + over 11.65 !!!,1
+AAP in some lotto 500 calls for next week!,1
+"#13for2013 SCA & NKD are champs, both up 50%+ in 2013  ",1
+HES the latest target of increasing energy-focused activist investing. Investors can cash in. OXY  ,1
+STEM continuing move up - possiby getting some bounce from ANTH upgrade,1
+AN moving higher from consolidation of earnings pop  ,1
+"NS oad puts, HF is about to get blown up imo!",-1
+"NKD mild divergence creeping in, could arguably ease back to mid 160's, but long-term rock solid  ",1
+JOE holding that 21-21.50 area for now but longer-term trend endangered  ,1
+"NAN like the daily and intraday setup, will try over 18.80",1
+user: AAP you know its a extremely bullish day in the market when apple is up 2% = 10 BIION!,1
+AXP - Boom-sha-ka-laka via user  ,1
+"CTGX rallied smartly from first support, strong breakout today, surprised so few follow this one  ",1
+"PO still some work to do, needs volume on any move higher from here  ",1
+AAP not falling getting ready for the next leg up?,1
+"Other longs are S down small, and FNP, NKE, C, MCHP in last two days",1
+"WFC Ho hum  Giddy up CA eady to sprint to new high, BAC needs to clear 11.64 resistance",1
+AC tightening stop to 3.30 now,1
+AAP goes higher on European close.. IMHO,1
+CEE wants 51.82. Then I think it's time is up imo.,-1
+ZOT Sold at 10.99 the shares I bought Mar 1 for 8.8,1
+"AAP hats off to user who dropped the last blood yesterday, should have split down!",1
+"HES another leg up- only position in the red yesterday, now well in the green",1
+CEE hit fib level 51.80.,-1
+PFE sticking with!,1
+AN taking some off here,1
+I need to liste to the little devil on my shoulder more often > In 1000 MTG  5.17 lord help me lol,1
+Oh CEE....,-1
+user: user  'old technology' are more expensive .Market not for AAP ~THINK more nano/phone/watch dick tracy not rolex,1
+MBI is stuck at 12.70ish. I say this is bullshit.,-1
+"CMG While traveling through Kalamazoo, MI on Friday at 6:00, the place was empty. This is the growth hopes for the company. eshorted  325",-1
+AAP iphone watch like this..  ,1
+"user: AAP wonder how many jumped on the short side with that 52 wk low,  hardly ever smart to go short on lows or long on highs",1
+"CMG is not necessarily Kalamazoo, but towns like Kalamazoo. They will not buy a 7+ burrito, they don't see the value in it like I do.",-1
+"AAP ppl buy Android cause it is cheap, but a phone that you wear like a watch cool  Game changer!  Think big! all the parts are there",1
+Helped my brother buy BGS & AAP yesterday. F last week. Better cash in a free dinner while I'm hot.,1
+"lots of good ideas are to earl AAP didn't invent tablet just introduced it when right,  goog glass=smartphone/glasses perv device",1
+"user: AX looks good for higher prices, gapped above base and held, updated 30 min view:  ",1
+"user: AAP Wow, when was the last time we were up 3% doug Kass umour, Einhorn umour let it HOD on no rumour and then :D",1
+Quietly IBM has been on a nice run. Value lies in its transformative nature. Our take from fall:  ,1
+AN stop to 775 on remainder of position,1
+AAP  finally --- now we can run !!,1
+WFC SB is culprit for weakness. Bowe puts 44.75 target,1
+user: AAP This rally won't last IMO great I will sell you some AP5 430puts for 14? money where mouth is?,1
+user: Suddenly everybody is bullish on AAP. You should hear people on AAP yesterday last 6 months!,1
+user calling for rotation out of everything back to AAP everybody back on the AAP CAT!,1
+Small weekly put position in NFX here,-1
+The easons I journal...and Pivotal Moments in the Market  (new blog post ) DIA SPY AAP NKD,1
+"NFX daily make green dot buy yesterday 192 up target, not overbought or oversold still positive.  ",1
+MTG Cant wait to pull the trigger on that one!!!!,-1
+"AN still flagging, will add to my position over 7.86",1
+AAP traps works better with a QICK SNAP!. dead bulls and bears IMHO,1
+SHOT MSFT  28.29,-1
+AAP I imagine the bears jumping up and down on that 433.. got it down 0.25 in 30 minutes,1
+"user: AAP nothing like firing of CEO to make investors panic other than stock price, it has been best co profit and sales ever",1
+CEE mother faacker.,-1
+"user: AAP Price is volume, volume is key  15M+ action I will stand on the other side of your shorts",1
+user: Is GOOG ripe for shorting yet?  looking for that 10%-15% drop? it happens in a second with the goog (low Vol),1
+TAB looks like its breaking out a falling wedge  ,1
+"Equity Alpha: user F looks like its bottomed and ready to go over 13. If breaks, look for 14+   ",1
+ENOC - need a catalyst? thx szaman not paying attention to it.   ,1
+"Equity Alpha: INFA is our fav short opp. No growth this year, high valuation. Short under 34.50   ",-1
+MTG don't be a bagholder!!!!,-1
+"user:tools - macbooks/phones etc - i do not imagine much time for toys AAP ipod- toy, iphone= itouchw/phone, Ipad=big itouch",1
+BAC IMO 12.50 is a question of day from Now !,1
+"AAP once you have the smartphone-mini you can ad as many APPS you want , because you wear it, not stuffed in pocket.. think SII v/comand",1
+user: user AAP  GOOG Glass is not a rumor.Gglass is a s/phone/glasses *perv alert* & women hate  ,1
+I will short ASNA once it fails 19.07. Gonna be worth the wait.,-1
+"user: why curse GOOG by raising target to 1,k? (currentlyAAP is trading at 1200 FYI)    stock price = dick size (or lack of)",1
+AAP & GOOG it is all Skynet trading right now lockstep 3 Pm it will get exciting again,1
+"KKD Yummy, yummy, doughnuts. ong at 14.25",1
+VZ ooks like an 11 day winning streak,1
+"AAP probably small fadethen a pop finish (I think 435-437).. everything is green today,  tomorrow.. who knows",1
+"As long as it doesn't cross the 21.58 Fibonacci evel, the short is valid. INTC",-1
+user: AAP Treadmill stock. Dare I place 440 call? lots of people betting on 450 next friday,1
+CPSS - yup I see it :) nice stocastic cycle  ,1
+"eading stocks topped early in the day, even as ES_F pushed higher. elative weakness on a strong up day. SODA SPW QCOM FB AAP BID",-1
+JMBA from watch list has volume of 103% of ave full day - slightly over line   ,1
+user: AAP Covered 1/2 +7.27 (434.97 - 427.70). ? good in and out you might get to re buy in 30 minutes,1
+does everybody know that AAP  & TC can do VEY little in regards to a stock price? you should be praying to SKYNET and GS,1
+SHOT MTG  5.18,-1
+Bot AAP puts as this is a dead cat bounce. Free fall will continue into Friday.,-1
+Come on MTG do the Harlem Shake.,-1
+"user: JCP if ya think this won't bounce on that news, see GPN>> Agree, but still a dying business on both accounts",-1
+CIE Seems geady to go South  ,-1
+AAP strong if they keep buying into the close..,1
+"Watch SOH here under 46.73, could be a short imo.",-1
+"DN MTG, anyone thinking of shorting these names?  These 2, specifically, DN seems to have a history of huge spikes up and then collapses",-1
+COST 5 min   ,1
+"SK Check the intraday, tell me this thing isn't ready to blow up.  Almost 30k shares traded in 1 minute EOD :)",1
+user: AAP The days you wished that you had bought in. I spent some money averaging down to 420 yesterday yeehaw!,1
+DNKN slightly above resistance  ,1
+"user: AAP, if everyone thinks its going up tomorrow, its going down.. ~ you speak as if humans were in charge.. not SKYNET",1
+NYSE After-hour Most Advanced: HCI EX EPZ POE PQ EDG SXC GTI EGY,1
+SWHC call starting - sunshine and bullets.  Ha! lol,1
+MTG MGIC Investment commences offering of 135M shares of common stock - Trading Thru Post market highs at 5.5 on dilutive news ong MTG,1
+"SHD added to short position on move up, Sears headed lower part II now available:  ",-1
+"PAY - DOWNGADE.... lemme just check something real quick... actually, PGADE! - anyone else notice a 12 gap here?",1
+user BrokeInvestor634 I may have been wrong about that pullback. SII,1
+Market Wrap Video + Additions to Watch ist  including: AO GMC IMC IQNT JEC SOX,1
+TWX Wait for re-entry point: 54-53.,1
+AAP  from  Still nothing bullish here,-1
+AAP How an Internet-trained Apple analyst lost tens of millions of other peoples' money  ,-1
+big freakin HT to myself as CEE was the ONY stock mentioned in  my Dec12 video as a breakout stock for 2013 :),1
+seen to outperform peers this week: NF CEG PCYC N MWIV GW VMI TM NKD GOOG MPC ADS ATHN CEN BIIB MIDD MSI,1
+TAB has just broke a Falling Wedge Pattern. Short-term sentiment has turn bullish.   ,1
+NVDA MACD indicating that the current short term price trend is bullish.Breakout watch over 12.85  ,1
+AAP sharing where and why I added to short position yesterday VIDEO  ,-1
+BAC Around 10AM On Factory Data I expect a Slight Pullback that could be a entry point B4 the run over 12.50,1
+will AAP go down?,-1
+"BAC finally bought BofA, don't know why.  Just one of those hunches.",1
+AAP  -  Apple may introduce the next iPhone and iPad in April  ,1
+NVDA Will watch to see if it can break 12.85,1
+AAP nice to rally without a rumour,1
+AMTD TD Ameritrade two massive blocks of upside calls over past week.  ,1
+KEX strong start,1
+BAC in next weeks 13 calls at .02...,1
+ENOC good start like it over 17,1
+CNQ nice start but needs better vol,1
+"user: user Fail to see how you made money. AAP Buy low sell high FYI or sell high, buy back lower.. same everywhere ;)",1
+VSAT interesting here. Above .20 could signal attempt at recent highs near  50.,1
+"user: ast year, cash announce was March19, third Monday. Expected March18 this year? AAP bottom was called today 2009",1
+DDD also following through nicely from yesterday to the downside,-1
+BAC Stressesult Tomorrow 4.40pm ET & After we will have the Dividend as a follow through catalyst !!!,1
+OSI peeling some off here 9.1,1
+APA Insiders staring to buy and at a macro bottom IMO...patience.   ,1
+Joining the party. user: NKE is my only position not ripping today.  But it looks ready.  ,1
+PAY Awesome action (with volume) and breaking through the 20.95 mark. (key pivot point),1
+ENOC trying to get over 17,1
+HS watch for a possible bounce around 45.65. pic.twitter.com/adwJrD75NE,1
+BAC next week's 12 calls getting a lot of Play!,1
+AAP breaking down. Still holding puts.,-1
+ME opened a position at 4.64 -  ,1
+ISG Decent amount of Vol. behind this bounce. One day does not make a trend. Holding Short,-1
+"SAVE continues run, all-time high within sight. Our budget airline analysis from fall:  ",1
+ENOC taking more off here,1
+DDD thinking about adding here.,1
+WAG nice short under 48.24  ,-1
+WAG nioce short in .02 cents.,-1
+Short WAG  40.23,-1
+"ENOC 17.60ish my final tgt, 1715 stop",1
+"ENOC here comes the next push, getting close to my final destination 1760 or so",1
+"ZOT good entry here, next fib level is 10.39, and current support is 10.08.",1
+Watch 19.50 for EXP ahead of an expected earnings beat next week  ,1
+"CZO going to 24.30, volume is good.",1
+With some luck BT 22 is current target.,1
+"VVS working, peeling some off here",1
+DST <<<<< SHOT ME GATA - SHOT JPM,-1
+"user: AAP if TC did his job and managed the stock  not his job to mng stock.. mng Co yes, stock  has very little to do with Co.",1
+"FOD nice pop, no pos",1
+"ES_F in danger of stalling, that's 5 d-days in the last 2 - 3 weeks for the indexes. Not exactly bullish behavior. NKD SCA AAP BID",-1
+AAP 20 minutes till Europe closes we will see if buyers :) over there or sellers :(,1
+user: BEBE long setup on the radar  ,1
+VSN looking pretty,1
+user Do you think the ZOT support level means anything now after the recent news?,1
+SHOT CAT  89.90,-1
+QCO close to 30.33,1
+QCO on launch pad in .03 cents.,1
+"SWI SolarWinds, this is Houston, you have cleared the tower, proceed to space  ",1
+AAP has anybody ever noticed that the decay matches the slow climb up on the day before expiry?,1
+"FOD continuing to act nicely, 2.90-3.00 potential target zone",1
+"CAT Bingo, it is Bingo everywhere today.",1
+SWHC Just bought back the shares I sold yesterday at 9.58,1
+KEX took some off,1
+I think NX can be shorted under 24.70. For a full dollar roll.  ,-1
+WOW ME is on fire!  I love the 1 day #swing #trade.  We are out at 5.12 for a very nice 10% GAIN.   ,1
+Watch NX under 24.70,-1
+user: AAP cnbc dump comin up :) First time for everything O,1
+WFC 5k march 36 7 k april 37 call buyer,1
+FCX if it can go over 32.83 it will go gangnam style.,1
+AEE for a short in .06.,-1
+TEC www.short-term-stocktrading.com Mar6 decent trade in TEC today:  ,1
+user: Nice B/O for BMY today. Not a lot of volume though. First pullback to confirm.  ,1
+"GOOG keep in mind there are about 1,000 contracts offered at 20 in the Apr 850C. Could temporarily create resistance or a sell off",-1
+MTG short 5.42,-1
+"Very important day for JPM, it will interesting to see if it can break 50.11  ",1
+Equity Alpha: BAC brekaout over 11.90 resistance. Tgt  12.50. Best bank in 2013. user   ,1
+"Equity Alpha: TWTC looks like great short under 24.90. Weak fundies, overvalued, weak technicals.   ",-1
+user BAC we are seeing...,1
+"VVS took a little more off 1094, 1080 stop now",1
+"CAT daily,got overbought 4 days ago and yellow owns EH not oversold either.   ",-1
+"AEE crosses below 34, and I am shorting it.",-1
+SHOT AEE  33.99.,-1
+"ed Weekly Triangle on KWK,....pdating  ",1
+AAP interesting how SKYnet can keep a 400B stock in a 0.0025% price flux channel for an hour with 1M+ trades?,1
+user: What time are the stress test results being released tomorrow? BAC GS C JPM WFC > 4.30pm ET,1
+NVDA nice accumulation taking place here... ong and strong !  ,1
+BAC FEDEA ESEVE BEIGE BOOK IS OT --> Everything looks good !!!  ,1
+"Green Daily Triangle on MH,....pdating  ",-1
+BAC Yep Over the 11.90 esistance... :)   NHD,1
+BAC Big Boys come In Buying arge Block !!!!,1
+Can the AAP iPad escue a Struggling American Education System?  ,1
+BAC +12 by close even Better!,1
+MBI breaking out  ,1
+"OKE Oct 50C bought 15,000x at .80 vs. OI of 22. Suspicious out of the money big trade, probably opening",1
+BAC C COF giddy up lotto call,1
+"BAC now XF is pushing too, Watchout 5% day is Possible!",1
+AEG Parabolic ....  ,1
+JPM back over 50 roll,1
+"SPS buying some here, small position.  Trades at 8x free cash flow and below 10X PE  Mgmt. has bought back over 3B in stock last 3 yrs",1
+"SZYM decent setup on multiple timeframes, volume not that great",1
+BAC Cleared 12....,1
+Interactive Intelligence back near all-time highs. ININ  ,1
+user: user: AAP can jump 50 in nano seconds after E if they beat Sure but that is Skynet trading with itself,1
+XCO ~310k purchase just moments ago,1
+STT a solid breakout of this tight basing channel   ,1
+CM continues to move up nicely  ,1
+"AAP Down.. DE up hahhaaha, TC should have bought Dell with Cash like MSFT did with AAP in the 90's",1
+Intermolecular is looking promising. Pivot above 10. IMI  ,1
+user: BAC once upon a time this stock was 17.xx>> History repeats itself -Mickey Mouse,1
+AAP looks like maybe a fade then pump to close? oversold on 2min/ 5min,1
+adding more weight to MTG sorry folks u had ur fun. Hammer down time. Holding over night Have plenty of cushion from 5.89 short,-1
+"BAC volume in 12 calls today is Huge in all months, something big is coming imo...",1
+"user: With egal eserves ow, Bank of America Faces a Big awsuit  >> = Big BS ! BAC",1
+MTG holding small short pos from 5.72 for swing,-1
+"GOOG to Face Off with FINA, Exchanges in Audit Trail Bidding  ",1
+AAP wow up almost 4 on Skynet's After hours inside trading,1
+MGM ooks like its getting ready to fill the gap,-1
+Time Warner Announces Plan to Spin Off Time Inc.  TWX TWC,-1
+SWHC and QE both killed earnings and still were punished...hopefully its a matter of patience since I am holding,1
+AAP this is the best graph shows mode.. we are now in despair heading towards mean (500 ish IMHO)  ,1
+"user:  I still say AAP  1k before GOOG don't show your ignorance, right now AAP has x 2.85 shares or 1211 equal basis",1
+Penney's and Macy's and Martha  . .  . Oh my! : JCP M MSO APP KO ,-1
+GD SV as mentioned a few days ago GOD stocks oversold IMO GDX GDXJ GDX JAG .50 GP AY ABX FCX The royalties TX SW GD,1
+"SK 6.75 printed AH, this baby's gonna pop tomorrow. Share buy back, euro expansion, fast growing company here :)",1
+user: user: user:  I still say AAP  1k before GOOG ignorance is bliss my friend and an education is expensive ;),1
+SK - 28 mil share buy back - estimated EPS 1.01 vs. 1.00 last year.  ,1
+Time for some shorty pants? CSX #Trains #esistance  ,-1
+JAG used to be 8 and trades on several exchanges that had a buyout a few years ago GD SV IMO we'll see M&A in GDX GDXJ space:on TC,1
+SK Closes  6.75 AH,1
+AT 3s a charm and this can be a trade and long term hold with reinvesting SDIV EX is another area that may see M&A DK SO ED N TE,1
+Market Wrap Video + Additions to Watch ist  including: AAP BSFT END FS JCI PQ TTWO WGO,1
+Investors into GD SV PA PTM stocks in GDX GDXJ GT GDX ING GGGG SWC PG PA and GOO AY H NEM AEM ABX pay SDIV,1
+IG HA SB IEP is Ichan Ent showing interest in the space SO OO OIH CO shows support 90 trading with NG SSN TAT WTI DOW,1
+GOOG Ice Cream Sandwich and Jelly Bean Are More Popular Than Gingerbread  ,1
+"SK Great day today. Breakout on huge volume. ots of shorts, congrats to members who joined in!  ",1
+GEVO 2.25 Target Short un Price Action.,1
+"I invested in MSFT, but I have more than a few words for Steve Ballmer",1
+BSFT ran into the gap today and pulled back - maybe a trade in to it again   ,1
+END  ran into space with large red candle on left and may try it again soon for a trade  ,1
+GDXJ bounced pretty good so gold stocks may move up some - like VGZ GOD NG and silver PAAS  ,1
+"Trade idea SE the breakdown in NEM  40 , target 31.50 , cut above 43.50  ",-1
+TAB finally starting to break out. MACD crossover today/should support higher prices in short term  ,1
+AXTI An ndervalued Stock.P/E of 15x with a PEG of just 0.65x.No debt and about 46 million of cash  ,1
+VHC still looking interesting.Trading within an ascending triangle pattern w resistance at 36.25  ,1
+NFX The hourly shows a Head & Shoulder formation forming.  ,-1
+user: uncapping 1/2 of NVDA 13/14 CS for 4c in case that 12.5k buy of 13's knows something Nvidia Acquisition umors esurface,1
+"The A/D line moves up, it means NVDA is being accumulated ( bought ).  ",1
+"user: AX working nicely, forms another consolidation above 56.75  ",1
+Post: How ong Can You Hold Your Breath?  SHD DE,1
+MTG between yesterdays session and today's PM almost the entire float has been traded lol,-1
+BAC right back to 12 I'm eady!,1
+VBD blast off!!!,1
+GMC from watch list gapped over line with volume of 10% average full day   ,1
+"Slowdown in PETM services troubling, would look to other pet companies to play pet humanization trend:  PETS WOOF",-1
+NKE looking good here for another run at those highs  ,1
+MCP possible grind to 6.70-75,1
+"ASTM like it over 1.32, needs better vol",1
+Today's Watchlist for SHOT Stocks: SA; SB; IG; PB; CA,-1
+I bought some JCP. I love knife catching,1
+ZNGA like it over 3.56,1
+user: AAP Barclays price target for Apple  is now 530. says decline is b/c it might make only 30% profit (40/shr!),1
+MTG still holding short from 5.70's I think we will see low 4's by tomorrow,-1
+"MTG if it can hold this 5.10 level, it is a good long imo.,",1
+"GS nice bullish start, no position here",1
+personally I would like to see AAP  42/share before 1000 IMHO (after a 1:10 split of course) then we would see 100 very quickly IMHO,1
+"user: AAP is really dead... you should go chase the fast money over at AMZN, NFX or FB has a big announcement today",1
+INFN at 7,1
+ooking at a 10am revesal on APP. will go back to 2.00 level soon.,1
+"Green Daily Triangle on TC,.....Open ong at 3.37",1
+"Green Daily Triangle on GTE,.....Open ong at 5.93",1
+ZNGA Put/CAll ratio 0.18,1
+Starter position in SHD 47.21 eady to hold by breadth for 10 to 20 years,1
+"AAP We break and close below this support, 360 is the target and im calling it THE bottom   ",-1
+"user: user So why the stock is falling? Can you explain? The fast money worried AAP will not grow as fast, so they are out",1
+APA peeling some off here,1
+user: user: AAP 360 is the target  If no growth maybe but it is +50B cash = bottom 410 if NO GOWTH (and a div!),1
+NFX Breaking down the neckline w heavy selling volume accompanied by bearish technical indications  ,-1
+fonr shares are up 50% since equities research upgrade and up 200% since stockdiagnsotics upgrade,1
+AAP approaching 420 low,-1
+"INFN continuing to look strong, looking for a move to 7.15",1
+NFX such a monster gap to fill not sure how anyone could fill comfortable sitting long here...,-1
+MCP finally,1
+BAC Here we come 12.25....,1
+GE has been lagging. 1 of my favorite longs. Above 24 I think it runs. DIA,1
+INFN taking some off here,1
+"3 stocks owned by Paul Tudor Jones I'm watching for long entries PKI, MV, MGN h/t user",1
+"MCP taking some off here 6.50, looking for 6.75",1
+user Market esearch Portfolio's ook at BA flying into the wind now~~ More to come!!!! BA,1
+NFX looking to short on a weak bounce to green arrows then fade if we see momo turn South   ,-1
+"GPS big time move today, breakout out of upper BB. ST Pullback/Consolidation won't be surprising  ",1
+"SBX, slow and steady wins the race.  p > 20% since we covered the third place concept:  ",1
+SK - 2 Stocks to Watch ight Now  SK FB,1
+NFX short moved targets for reversal up - not in yet S1 and MA  ,-1
+BA sold 1/3 earlier off my post from the other evening. Starter in GMED. like the setup and the recent eps. Acts right and I will add,1
+"OST m, my position hurt so bad today. butt, it just slide back to last wednesday. maintain 65.00 target.",1
+AMZN nice intraday flag,1
+user: AAP looks like 419-421 is bottom for this week nice to put that in the rear view mirror!,1
+"DCTH nice flag, like it over 1.92",1
+G movin.. but I wont scream victory! until it really pops above 2.55 or so...patience...,1
+JDS watch it for a possible short.,-1
+"TIP is getting trippy, boss.",1
+"PETM possible leg down now, looking to cover some 61.75",-1
+AAP now we need a strong scared 1M+ buyer,1
+"PETM short hit my 2nd tgt, took more off, final tgt 61.45-61.5",-1
+isten if NP crosses 136.82 it will go.,1
+BAC Next Breakout Based on Tech Analysis should be Around 1.00pm Not Before!,1
+I am long a little PAYX waiting for a breakout. Earnings March 25.   ,1
+BAC Here comes another push higher! JPM calls being bought today too? BAC more bang for the Buck!,1
+"AAP it is funny listen to you ppl who play one side of the mrkt and expect to make , when you play against ppl who make  both ways",1
+AAP still holding puts. Will test 420 again.,-1
+"Admittedly more a commentary on digital media than just Yahoo, but here why YHOO is set to crash and burn from here.  ",-1
+AAP trying to bounce here,1
+BAC Oupss !!!Sooner than expected.. that's what's alerts are for...,1
+"AAP looking to take some off around 429, should retest 430 on this move",1
+DAA on launch pad over 1.11,1
+EBAY SHOT 53.12,-1
+VXY VIX is Done till Jobs eport - Try CAS on FAS BAC GS C XIV,-1
+user:  ask if you can SE MAY 2013 515 CA for 3. If AAP goes above 515 you get 515 +3 per share if not you keep the 3!,1
+user: AAP breakout is imminent.  Although trading on pathetic vol now.. no -sellers -GOOD,1
+Can someone wake me when NKE gets to 65?  Thanks.,1
+SHD added to short position on uptick!   ,-1
+STO VS AP 50 puts for 1.40.,1
+  FDX double toppoing  BEAISH MONTHY GATEY pattern....time to exit ..,-1
+ PCN doulble top at key fib retracement weekly....time to exit ...,-1
+COX possible breakout play   ,1
+  The rodeo clown sent BK screaming into the SI weekly red zone...time to peel away before it turns...,-1
+ONG DAA  1.12 1/2 position.,1
+user: ETFC nice movement  ,1
+"KWK this one is heavily oversold here, think with the other players rallying massively this one is due for a big pop.  P XCO ECA NG",1
+"Shorting COG against XCO SD...  COG so far above the 50dma, think it pulls back to the 50dma here, way too overextended imho",-1
+AMTD nice breakout ...This was a stock I alerted to my subscribers last night   ,1
+" Why is FDX important, gave the MAKET OPEATOS, cover, give the DOW THEOISTS their story, while we sell out...gonna be one helluva fak",-1
+user: user: AAP as long as TC  silent this goes down TC is waiting for another check so he can load up user ;),1
+SHD  Sears looks good on break out this morning. SHD  ,1
+AAP does nobody watch volume? today: buys=high volume sells= low volume,1
+"user: AAP Divi announce came on third Monday of March 2012, the 19th. Will it come on March 18th this year?  ",1
+"AAP a bull bought 2k Apr 425C for up 16.75 each, laying out nearly 3.3 million in premium.",1
+SK Whoa!  Somebody has some confidence in SK :) TO DA MOOOOONNNN!!!!,1
+COG rolling over here...  think it will lose most of this move today.,-1
+"CTXS could rally to 95, my thesis here:  ",1
+user: PCS broke through more daily candle resistance!,1
+BAC Volume is Strong and Bullish Will be higher Today than Yesterday -- Already 141M !!!! ,1
+HBAN breakout today  ,1
+Don't know squat on SPT but P flack just sent unsolicited email asking for plug & his first graf touts 80% gain in shares.- ZICH on biz.,-1
+user - never buy on day 3 of more than 10% move. Good advice for the conservative.  XCO,1
+MAKO Nice Cup and Handle pattern...Stock worth 20 IMO...  ,1
+"for the record, I do not like these highs in SPX, ES, NQ, IND. the air makes me nervous up here. seeing more SHOT pre-signals. ",-1
+SFD here it comes.,-1
+SD one of the cheaper stocks in the entire complex.  If we get rid of the corrupt CEO I think this is easily a 9-10 stock. SO NG CHK,1
+ASTX perfect follow through day...   ,1
+"ed Monthly Triangle on CSN,...Scaling P  ",1
+E: NG NG_F I think that if we have a hot summer we could easily see 5 plus NG.  ig count still down in the 400's  best plays XCO ECA,1
+Talking headsCNBC talking the GOOG~ AAP rotation suggest max target 840 unless strong move on AAP then it will be like a teetertotter,1
+BAC Tomorrow we fly right thru 12.50!,1
+user:  AAP still here? Do you you spend the whole day about Apple? yep that how I keep an eye on my Money! and take yours ;),1
+BAC gonna go BOOM!,1
+"GMC clean break, on good volume, and closing near highs, nicely done  ",1
+AAP I can't remember another time I was so happy on a 10 move .. it has been so dark here,1
+AAP if any bulls bought next weeks 450's this morning 1 you are now +100% !!,1
+"ed Daily Triangle on JVA,...Open ong at 7.35",1
+CSN shoing a real nice green vol bar  - we trade heavy tommorrow 1.15 then Monday 1.20+,1
+"SK 100k block trade, I'm with that guy.",1
+SK G SKHEADS,1
+MTG closed at the low. still short from 5.70's,-1
+user: AAP always good to look at all timeframes.   I was looking  2-5-10 and they all said something different O plan around that,1
+BAC Happy to have sell  1 cent of the HOD !!  Congrat to Me ! estaurant Tonight ! Happy for Every ong !,1
+AAP will find last break out here before heading lower.    ,-1
+"user: AAP music service is delayed? maybe buying P? waste of money, nothing proprietary it is just an App, no value IMHO",1
+Funny C has 8% cash and wants stock buyback but TC silent about AAP's 25% Cash,1
+"ed Weekly Triangle on JVA,....pdating  ",1
+"user: user no, P lol Vapour~ware. one strong breeze and it is gone IMHO",1
+"KWK Note the heavy volume here, levered play on NG & NG, basically a loaded call option, if they don't go under cld be a 10 stock",1
+user:cash  AAP TC is hanging on to the cash overseas in case SA implodes or tries to take it as tax for all Obama's pipedreams,1
+user:  < Opens door for AAP deal with China Mobile  Sad they are building 4G and not TE,1
+Tcamitrader yep! EOG  from  ,1
+OI SO XOM CPE IG -How does the price of oil Venezuela without Chavez's new?  ,1
+"user: nonstop bullish remarks on the way down, non stop bearish on the way up AAP Probably paper traders w/ real money O",1
+GPS has the stores and loyalty to show growing sales!,1
+EVC traded some heavy volume today looking for the next move to 2.50 now,1
+COH reversed down from the 200-week sma with volume closing 40% above average. S line in new lows well ahead of price.,-1
+DDD IMO The 3D Sector will pay off SH andT CIMT ONVO DASTY PCP SSYS ADSK and NANO TEC will be Next gen of AAP and GOOG stock,1
+AFFY - volume follow thru will send this right back over 4.00 can go to 5.00 tomorrow - will catch the full run - going to be fun,1
+CHK Over 21.00  ,1
+GEO Over 35.60 but it will need good volume   ,1
+DN  Watch for rebound - maybe by line  ,1
+SKX Over 22.50 - about   ,1
+CSPI buying in new nano cap folio.,1
+CWBC  buying in new nano cap folio.,1
+SCTY day trading 16.49 with tight stops easing the TAN PW to clients and creating energy off homes IMO 10 years they will rival DK,1
+EVI buying in new nano cap folio.,1
+GEVO broke out of its 50-day SMA and surged considerably higher amidst impressive buying pressure  ,1
+"Schmucks think if you're bearish a stock, you MST be short it (& vice versa). Pros do their deep research & study history. zagg",-1
+NVDA Third Avenue purchased 2.15 million shares of NVIDIA giving the holding a 1.15% weighting in their portfolio.  ,1
+SK the long awaited short squeeze will not happen today!,-1
+Why These 4 Biotechs Could Be Next In ine To See Their Share Prices ise  AMBS.OB CHTP MACK PPHM,1
+"All you Bears, don't forget that AAP is trading for 290/ Share (net Cash) and pays a 2.5% Div, Contrarian bets can work, So trade strong",1
+"user:  AAP do you think your pep talk can influence the market? I am not the one who lost 51k, but your insight is insightful thx",1
+BAC 13 is a coming!,1
+AXTI eleased news. Watching if this can go into 3s today  ,1
+NVDA ets see if it breaks over 13. Medium set-up on watch.Volume needed; ADX starting to point up  ,1
+"NVDA From VW to olls-oyce, New Cars at Geneva Auto Show Feature a Full Tank of NVIDIA  ",1
+BAC oad this mini Pullback!,1
+MAKO this could get going today...  ,1
+"MTG looking for a move to 5.20ish, low stop",1
+"BAC gap Filled, back up we go!",1
+"BVSN good start, like it over 9.5",1
+Today's Watchlist for ONG Stocks: MDP; AVAV; AT; KA; BS,1
+22.50 held as resistance for PI,-1
+"Short GOOG 830, hedge of ong pos",-1
+BAC oad oad oad the Boat!,1
+would like to see some long red candles on aapl 2day.  still think it takes out 420 very soon.,-1
+AAP HGE Volume (compared to recently) One big Bull coming back will take through the resistance,1
+i think we'll see aapl at sub 430s by the end of the day.  not saying it pins 430 but i think it revisits it for sure.,-1
+CDX taking some off,1
+Security software stock FIE is almost back to its all-time highs after a 30% pullback. FTNT from the same group is also setting up,1
+SHOT P  13.95.,-1
+AAP a classic move to 435 testing on no cause. Webinar followers I've added to short position  ,-1
+"11.59 is next fib level in P, but if we can get a dollar roll, I will be happy.",-1
+user fonr is a strong buy based on my fundamental work,1
+AAP 435 retest and now probably 420,-1
+user: BAC - ooking to get in... Which price I should buy? Wait till we head to 12.15 on volume it will be god IMO.,1
+BAC We can see another rally to 12.40 till 2pm but after --> Opex Power  will come.  Next Week Dividends + Buyback.,1
+MNKD squeezy,1
+Zillow has almost doubled this year....so if you are raising money for charity might be smart to hit up user :)  Z,1
+Cover 1/2 P here  12.62 and set last 1/2 stop to 14.,-1
+"Yesterday's 2-day long play SFD is now up 11%, but can be held for an extended play if it holds 25.15",1
+CIEN for a short under 17.11,-1
+"HM wants higher, looks good on multiple timeframes, but not a fast mover",1
+notable S this morning user: INVN ikely in new iphone 5s  ,1
+"PCN mini bear flag intraday, staying with the short as long as it stays under 718",-1
+"ed Monthly Triangle on CSN,....starting closing for profit",-1
+AAP looks like everyone has gone home vol<100k,1
+TSA having a quiet day after early session dip:  ,1
+"NKD 3 down days in last 4, going to wear off the overbought while giving back nothing, amazing  ",1
+NE not bad at all.,1
+FIO is going to break P the down channel. Could run hardly. If you are out better monitor that possible break,1
+CMG bounced off the 10 SMA.   ,1
+"CTGX this is what what I mean by a 'high, tight' consolidation following high-volume run, v bullish  ",1
+PO upside volume tailed off a little and some divergence intraday so may pause again around here  ,1
+V still frustrating me but given its history I will be extraordinarily patient with a winner  ,1
+HTZ some divergence creeping in intraday (not shown) so may tap on brakes here but long-term solid  ,1
+"ASGN made clear at outset where invalidation was and resolve got tested immediately, now in black  ",1
+"GMX took some off 3.75, 3.90 next tgt",1
+user: AAP having a lot of 'drip' ticks today.  A long tail signals support.,1
+user: AAP looks ready to move soon everyone is playing the euro close.. 20 minutes then they will bring the cash..,1
+AF short (AP 52.50 puts) working on the highs this AM :) Stochastic crossover  ,-1
+JCOM easy look at last 3 years annual income data:  ,1
+"BAC there goes JPM, BAC will Follow!",1
+GMX taking more off here 3.89,1
+"SII all out, think I'm done for a while, see ya at 2.90 or so",-1
+CIEN going short in .05 cents.,-1
+user Tell that to BID AAP APO NEM CF and more.,-1
+"ed Monthly Triangle on CSN,....Net Profit  70,825.00 (6.42%)  ",-1
+GMX close to the fence.,-1
+"user: BAC Covered all my shorts right here>>Was a good move, maybe next week After Dividend Will Short !",1
+user FIE has had a great run since BO. Suggestions for trailing stop? Targets?,1
+"QEP, volitality expansion just getting started. onger consolidation=larger move higher over time NG SO",1
+not normal - it looks like a set up for major one way or another - G AAP,1
+"user: user u call it rare, i call it unlikely ;-) only for AAP, happens with 90% of all stocks",1
+user How about those gains and volume surge in this little guy EDS,1
+Swing Trade Quick Pick: Alexion Pharmaceuticals AXN   ,-1
+"BVSN on the fence, if it goes under, it will be a nice short. with a 1 dollar roll possibility. Target 9.30.",-1
+BVSN cracking in my book.,-1
+AFFY 30 minute shift - Still long partials from yesterday 2.90 level,1
+"CAT looks to be bottoming, but it also has not moved much off its lows with recent strength. If it breaks 89 to downside, its good short",-1
+GOOG looks heavy but volume is drying. eal selling pressure comes with above average volume.,-1
+user: Surprising how quiet GMC has gotten on the stream. Been a fabulous performer and still has 25% of the float short.,1
+AF 30 min second shot at the 100MA - 20/50 crossing   ,-1
+"user: from the Sunday watchlist, trying to B/O here. CYH  ",1
+AXN rejected by the 50dma yesterday,-1
+"I didn't see any1 else buying with me 1 lower earlier #timestamped EBAY Trend still intact, this is a market leadr tking a break before 60",1
+"Taking profits on CMG from 321, not time to a hero over the weekend as this thing fades, I'll take my 1 and revisit, still like this one",1
+"COST is another nice one but I'm staying away for now since that special divvy, small pos in T account",1
+SSYS ooks like a buy to me here at 70 going into next week.,1
+AAP Bullish technical divergence: momentum indicators are above their late- January lows while AAP hit a 14-month low of  419  this week,1
+AF - engulfing about to take out tuesday candle   ,-1
+AAP Bullish technical divergence: momentum indicators are above their late- January lows while AAP hit a 14-month low of 419 this week,1
+ook at that intraday! Over 70 today?    ,1
+AF 5 min taking some off +50%  ,-1
+"Buyable when it reclaims 50d, easy move back to 14 F",1
+"COX hasn't moved much since this AM, but catching my eye. ight vol pullback, above res + 200-day  ",1
+"GS the current fib level support is  152.05. It bounce off of it today, not sure it will have the same luck next time it re-test.",-1
+"aughing all the way to the bank, Top 5 T account PEP This thing riding the upper BB on weekly chrt! Monster 10% move. ove me a bull mkt.",1
+JPM bounced off it's fib support level today too.,-1
+MCP looking better again. Tradeble.,1
+"user: AAP why short a stock down like apple is???  Kind of late and risky? very late, but some ppl like the high premiums",1
+FOD long 2.87,1
+AAP  anybody want a tip? I hear Bluestar airlines likes next weeks calls on ddd do your research,1
+BVSN 11.07 ai ai ai.,-1
+AF - Taking out the entire week. Gap then 20MA on daily. Shooting star weekly.  ,-1
+"user: what's taking so long for AAP to get sub 430?! 137B Cash, 2.5% div, FYI as soon as those go away it will drop",1
+"GMX I am not endorsing this trade, but from 3.98 it was a nice short. If SPY suddenly breaks lod we will easily see GMX  3.31.",-1
+"SD If you think you have not gotta a fair shake from Tom Ward and the Board, let the SEC know about it, #corruption  ",1
+Watch DECK over 49.20.,1
+DDD  here is that piece about the 3d printed skull cap  ,1
+AF 60 min ok really this is the last one. Note 100MA and 50% fibs just below. Sitting on S2 now.   ,-1
+GMX just like magic.,-1
+"I have no position in GMX, I just pointed out that under 3.98 it would be a nice short. With a 3.15 target.",-1
+SW TX GD are royalty companies in GD SV and a bullish sentiment in C COPX SCCO FCX as byproducts NINI JJN JJC JJP JJM,1
+EBAY tested the high today n it failed unless Hmary in25min.,-1
+user: Mr AAP is going to have to stop hanging out by the pool if he is to make 435 by close. All she needs is one fat finger buyer,1
+GMX just .20 cents until target.,-1
+"Back in on the short side of COG, hedging my other nat gas plays XCO, COG trading at 10x+ sales 6.37 book. CHK 1.09X, 1.08X respectively",-1
+GMX BINGO!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!,-1
+"KWK 5MM shares traded so far, this is getting interesting here... shorts want out obviously... #traders #action",1
+ook for a breakout V MA   ,1
+AAP holding my current positions over the weekend    ,-1
+"user: user Compelling enough for me to join you 64.71>>TY! Filled short at 64.7615, glad to see you're in as well ;0) COG",-1
+user: AAP finishing wave 3 of decline   I think an error in your calc is incl Einhorn's Pump&Dump,1
+"Back into that frothy zone here on SPY, raising cash & making a few selective hedges here on my NG stocks, short COG against XCO KWK SD",-1
+INO Some perspective:   ,1
+"emember how ZAGG sued a bunch of critics for eg defamation? Theyve gone further now, responses to discovery are a total joke.Short it to 0",-1
+"P pays record labels 0.12 per 100 songs, AAP offered .06 for their planned service. (Spotify pays .35) via NYPOST  ",1
+PAN went hard in the paint this week. TSA on a comback too. I put my allowance money there.,1
+"Historically with the amount of options, AAP 's ow day is Friday, and high day is Tuesday FYI, the non sell off today missed the pin430",1
+"I would be worried if I was short  trend is reversing, stock back above 50d on weekly and daily, macd curling up, get ready for squeeze",1
+"if EBAY breaks todays low next week, I will be fully hedging my T position",1
+MTW long good company plus some WI love for made in WI,1
+AGCO been adding on pull backs. Great company not only large machinery but also serves hobby farms I expect a lot of growth couple of years,1
+ZCS doji under trendline   ,-1
+BAC accumulated more May 12 calls today.  ooking for a move to the upside leading into Thursday.,1
+TXN raises 1Q13 earnings & revs to upper end of prev f/cast range - improving demand for chips - Expects 1Q13 EPS of 0.28-0.32,1
+KEY and HBAN have been flirting major trend breaks and did this week. ong both but sold some KEY pre mkt Fri. Both should work higher,1
+AMZN flagging.A clearance of this flag will open the door for more gains to test the all-time highs  ,1
+GOOG nibbled on buying goog into the close 830 GS 153,1
+"NKD  - great start to 2013, up 54%, 2nd best performer in #13for2013 portfolio  ",1
+"DDD watch for break of the DT line and STO signal, MACD also appears to be turning around  ",1
+TSCO nice 5 week base/consolidation.  ooking for a breakout of 105.50 on good volume.  ,1
+ Cup and Handle setup.  Can nibble some above 180 and then 182.50  ,1
+PCN Still buyable here off the breakout last week of the descending trend line.  Stop set at 713.  ,1
+FT Still buyable here with the rising channel as a stop (69)  ,1
+STT nice breakout last week.  Buyable here and add on pullbacks to 57.50.  Stop set at 57.31  ,1
+NKE still in this 6 week consolidation base. Breakout over 55.60 + 55.90 w/ volume as buying pts  ,1
+GEVO stock looks bad going to retest 1.80 base and go lower - beware,-1
+MO ong time shareholder but is this a double top? There has been some major distribution in the past few days.  ,-1
+AAP short term bottom is in,1
+PPHM long idea / entry from 1.42,1
+ONG Setups: AN ANIK NFX SSYS ZB MDX KS VMW and more ...  ,1
+SHOT Setups: AX CT WFM BID ICE QCOM WYNN AF DNB  ,-1
+"eview Of Open Positions - Stocks -  featuring SWI, JPM, GMC, TWX, ASGN, FNP, KOS, CAF, JOE, S",1
+ACM trying to be a playa.,1
+"F - potential short on bounce OHead resistance at Kirby. Stochastic OB, SI and huge volume exit.  ",-1
+AI on watch list short - no position   ,-1
+NKE potential short for many reasons. Check F post as well. Triple top OB rounding TOP.  ,-1
+"ASTX looking very strong, I would like to see a pullback staying above 4 to re-enter   ",1
+DA Broke out of bull flag. I'd look for confirmation for a leg higher   ,1
+HOT watching for S to SPX. Note green line another SCT setup. My favorite hotelier stock.    ,1
+"Blow-off top coming in the S&P between 1565-1575.  Vix is low, buy puts in the SPY April and May 154,153,152,151 strikes 1565-75  SPY",-1
+YHOO consolidating in bull flag. Technicals are positive for a continued climb. Watch over 23.09    ,1
+"CF Break that Descending Trend-ine & break 26.20 on volume, we will see a trend reversal here.   ",1
+V above 162.87 is a buy for me...  ,1
+ NEW Blog Post: Technical Analysis of Barron's BBBY BCM GPW GNW YV P SBX SSW CP CSX KMP KS NSC MP,1
+ECAP: orders in for tomorrow (calls across various strikes and months) include ABMD AMCX DTV EMC EXP FIN GDD IG THD TOX !!,1
+My reasons to avoid Monster Beverage Shares MNST KO PEP  ,-1
+AAP powerball is up to 183 million. Maybe ill just load up on appl stock.... Odds are the same?,-1
+"P lots to like,  trendline support, macd cross, decent volume.. target 63 stop 57.25  ",1
+GOOG setting up for another push higher. Still within a larger 3rd wave...with (v) of 3 to come...  ,1
+CM is bull flagging.  ,1
+"PVFC long 3.80/.81, range narrowing, higher lows  ",1
+VTS long 3.41  ,1
+"WIFI long 6.07, from the bottom, volume higher each of last 3 days following hammer on 03/05  ",1
+COX Crocs: Growing Brand At A Fair Price. It may be a good target for one of the larger apparel holding companies  ,1
+AAP clearing 435 will be very bullish for stock.,1
+NFX needs to get over 186 to be considered short term bullish again. I'm bearish,-1
+My portfolio: FIO ASTM NIS NN ANTH other interesting stocks: AAP ATHX MNKD MEA  ,1
+ENZN double bottom. Interesting,1
+"HCA entering long, here's the daily, like the volume on that bounce from support  ",1
+user: GOOG Doing some well deserved rest after the recent gap up  ,1
+mchp bull flag   ,1
+"BVSN over Fri highs, watching for entry",1
+BAC sitting tight on BAC calls!,1
+DDD should test 30 again.. weak price action!,-1
+ZNGA 3.9 nxt tgt,1
+Watch NP here under 138.93 could be a nice short. And There goes CIEN under 17.11.,-1
+CHTP from watch list hanging at line so far with volume of 7% of average full day   ,1
+"G 2.55 is 1st target, above it rip tits.",1
+MNK watchn thru hod 328,1
+Today's Watchlist for ONG Stocks: VET; QDT; AT; DCIX; BS,1
+DCTH Another target acheived  ,1
+BAC A rally to a new stage !,1
+"BAC nice little rocket, Friday was a headfake, get ready, she is going to 15 imo...",1
+"BVSN great intraday flag, 12 here we come ... needs to stay above 11 now",1
+POW may make a run to .25,1
+"CHTP like this flag here, expecting a move to 2.10 shortly",1
+ong CMG 321 again,1
+"CBMX wow nice move, missed it, no pos here",1
+BAC is going to play catch up to the Breakouts we have seen in C JPM and WFC It's Obvious IMO!,1
+BVSN took more off 11.80ish,1
+DMND It's all about 4:00pm #HopingForSomethingBig  ,1
+NP tap tap,-1
+SHOT BVSN here  11.50,-1
+There goes NP.,-1
+"CHTP trying the 2 level again, 1.95 stop here",1
+et's do this BVSN,-1
+ATHN wants the hundred-dolla-roll.,1
+GMC recovering from morning weakness opened pos 52.97,1
+CSX ooks bullish. ong if beats 23.34. Stop loss in 23.19.,1
+PK taking some off here,1
+QCO and it went again.,1
+CHTP is the next bingo stock imo. over 2.04.,1
+Notice that I am saying this way in advance.  I am just making sure everyone is on the same page. CHTP,1
+"Ok NP goes under 138.93, I will short it.",-1
+ONG CNE at 13.91,-1
+"CBMX nice flag here, could have another leg to 4.90-5",1
+New stock in my adar just now MTI  ,1
+Stop in NP is 139.50.,-1
+user: ong in NG after beating 25.16. Stop loss in 25.09.,1
+ETFC - The bigger the base...?   ,1
+SHOT CNE  13.7,-1
+user: F popping. p over +1.5% this morning. FB putting in a good early run. p almost +2%.,1
+looks like target is 44.07 HS,-1
+"MDX nice setup multiple timeframes, needs better vol",1
+ZNGA up 7.76%,1
+"STI 5 min 1 - and 30 min opening range, added calls.  ",1
+AAP back in  long (hope this time works out better then the past 300)- GOOG too :),1
+user: ATVI ATVI. long overdue short  ,-1
+STI 30 min opening range busta move. 2 first target. Calls  ,1
+"Buy signal at 70 in ate November 2012 was right on the money, bounced perfectly off the trend line MEI",1
+Added to PPHM,1
+STI daily - into the Kirby.   ,1
+"Nice breakout here TGT , ong 2000 shares in T account",1
+NP this is it,-1
+"Both MA and V should be on everyone's watch list, should show direction some time this week, break range and resolve its current pattern!",1
+ZIOP doing great PACB did great MNKD did the best - cant forget VVS making MoJo  too all called out today,1
+AAP 500 by Christmas or Co. news which ever comes first,1
+AMZN scale under 71 +4 looking for under 70 for next scale  ,-1
+Covered 1/3  45.90 DKS.,-1
+BVSN wow that was the perfect short from 11.50.,-1
+owered stop in DKS to fib level 45.90.,-1
+"Covered DKS, looks like a long if it can stay above 45.90.",1
+AAP Daylight savings has screwed up the European close.. still 1/2 hour to go,1
+Hey AAP Traders.... It's a bull market. Plenty of WAY better opportunities out there. Here's a good place to look:  ,1
+GS at the 50MA and lower trendline I'd like it long. Too many traders like it short right now. ;)   ,-1
+G good volume but still fading..patience...patience...,1
+"BAC is not going to lose 12, not sure why these bozo's are holding her back here, XF is off to the races!",1
+MAKO breaks out beautifully,1
+EXX is tempting to breakout above 14.50 following an eps beat and with positive trends. #breakout  ,1
+ONG BVSN  11.16 stop  11.,1
+AAP breaks under trend,-1
+5 small caps being shorted heavily - JCP DECK TSA HF ITT.  - All have their own unique problems.,-1
+"SV GD PA NGT FCX NEM AY - Gold prices traded steady, focus on dollar  ",-1
+AAP broke out of triangle and moving up,1
+Watch CAI under 4,-1
+"MAKo under 13, could sell fast and maybe hold 12.54 fib level.",-1
+aise BVSN to b/e  11.17.,1
+Some of My Market Thoughts and Swing-Trade Watch-ist Too | STDY HS W HOV SharePlanner  ,1
+"Selling 1/2 BVSN here  11.34 from 11.17, stop  1/2  11.17 b/e even.",1
+BAC  Bank Of America: Buy Now Before Dividend Increases = Thursday 4.30pm  ,1
++1 user: QCO  New MS Study results... enjoy,1
+"ed Daily Triangle on TPM,....Scaling p  ",1
+NFG breaking out right here. ong,1
+MON long setup  104. Earnings and rev growth will help build into Apr4 earnings. Tgt #1 106.50   ,1
+added to my holdings in V,1
+BVSN added here,1
+NKD giddy-up.,1
+AF watching SPX highs and relative strength weakness. AF at MA  ,-1
+A reminder that HIBB reports on Friday.  See DKS today.,-1
+aise 2nd 1/2 of BVSN to 11.34.,1
+"BVSN still acting ok, would like to see a pop thru 11.50 soon. 11.15 stop",1
+ZNGA nice breakout accompanied with good volume. Next resistance lies at 4.46 (2)  ,1
+Watch CAt under 91.,-1
+CF watch for a drop under 24.,-1
+WNC The bull roars in WNC   ,1
+AAP WEHAAAAAAAAAAA,1
+Out 10% 435 user: I have no idea whats going on so I'm taking profits in the shares I added today... Out another 10% 433.50 AAP,1
+AAP there was that 1M print I was looking for!,1
+"F really looking good here, great call option action too!",1
+GME breaking out of the handle,1
+Should I liquidate 1/4 or 1/3 of ZNGA I am holding?,1
+"AAP working, taking some off here",1
+F a close over 13.21 on volume (likely) would be,1
+"KWK heavily volume again today. Heaviest volume all year was Fri. This one is way oversold, too many shorts, don't let them cover NG SPY",1
+Oh lord DKS.,-1
+"SD one of the cheapest stocks in the market. Valued at a fraction of NAV,most NAV estimates are north of 10, even after disc. to Miss ime",1
+user: SD Dont 4 get to (VOTE). Time 4 change.Your (VOTE) counts.>> Don't forget to Vote Green ;)  #traders #proxyfight,1
+Big EOD otation that we have been waiting for? GOOG -->AAP ?,1
+CAI 4.94 will be the bitch slap.and tank.,-1
+first true bullish engulfing candle on AAP since Dec 17,1
+DKS round 2 tomorrow imo.,-1
+"holding 4 positions overnight: AAP and GD calls, CHK GD shares",1
+"PAY - Closing strong, well above the 20.00 mark.  Continuation, likely.",1
+FOM Any close above 5 will favor the upside. First target would be 5.17-5.19.,1
+user: AAP Not loving that close.  ong weekly 450 calls. 25000 of us praying for the 450 pin close,1
+"user: AAP Broke 435 and held above 437 which should lead higher  Since the slide began, the high days have been Tue, lows Fri",1
+AF daily kumo - ote: chikou circles (S/) and recent at kumo bottom. Flat kijun.    ,-1
+NTI turned away by 50ema  ,-1
+PAY ncertainty is never a good thing.,-1
+PKT long 13.51  ,1
+BYD is on the up (: Holding my 500 shares in at 6.46,1
+"NPSP long 8.31, chilling few days on lower volume, lets c if it wants the spot  ",1
+SHOTs if red/weakness is in fashion tomorrow ANGO BDBD CBEY CIMT DCIX DGIT FDM VET,-1
+user: AAP Going to drop hard when rumor is flushed out. No rumour hit a buy Target=50dma <430  ,1
+"Marriott International signed a partnership with IKEA, how I think it will be a genius move  MA",1
+user wouldn't consider it until >27 minimum CF,-1
+SC stericycle  working on an daily breakout! SPX SPY NDX  QQQ  ,1
+"ove PXP over 48.10 for a long swing trade, August call buyers last week. MACD went positive along with #VOSTOPBY last couple of sessions",1
+Short PN  see  ,-1
+NKD Over 179.35  ,1
+PAY p 7.6% AH  22.01 No real resistance until 23.40 (20 day avg.)   ,1
+CAT looks like it created a little bottom here. Can really pick it up this week since its OPEX. Def watching this one.,1
+user another good read. keep em coming and thanks. AAP,-1
+ove GNW!! 9-13 Calls are making me feel better about myself for not dumping AAP sooner!,1
+DCTH hitting new high,1
+watch for move on F M KO MO GD TJX BCD,1
+AAP will test 430 today,-1
+My new stocks just entered in my radar KIO TGT VM MTI OPXA  ,1
+"COMPQ DJIA SPX COST YM SCA - Wall Street retreats candles. The Dow Jones dismisses the 14,400 points  ",-1
+user: user AAP didn't rally 10 in 10 minutes just because it hit a buy target sorry Oh why did it then?,1
+ONG MNKD  3.55.,1
+EBAY breaking lower since sharing with those on Webinar last week. Hold on to your winners  ,-1
+G look for shorts to start unwinding their large positions,1
+GAE Jumped over 1st Target and hit 2nd Target took some profits upped stop  ,1
+G 2.66 is current support Fib evel fyi.,1
+GM looks like a break of the 3 month trend line here at 28.40,1
+IMM from watch list gapped a bit over line to 20-day MA with good volume of 15% ave full day   ,1
+nice opening strength for AMZN today.  our long trade working nicely.,1
+EN wants higher,1
+"Medical appliances stocks have been on fire lately. BSX from the same group, is setting up  ",1
+Short 828.30 GOOG 350 shares,-1
+Our signals called BBY and highlighted OCZ this week (up 24% today) - and next I think A is going to fly.  et's see!,1
+AAP hope you bought the dip,1
+"I'm not short, but if I was going to put a trade on, it would be a short from 530 back to the 50d MA",-1
+"user: AQ, GOO. Trading at key support levels, bullish reversal patterns complete.  ",1
+"Out the rest at 52.30, still long 3k shares in the T account user: Out another 25% at 52.40, +1.40 EBAY",1
+"SAT like it over 2.75, needs vol",1
+AAP stream is for sharing ideas not attacks! earn from each other not direct attacks. OK to have fun but there's a limit to it. Bearish!,-1
+Covering half +2.00 user: Short 828.30 GOOG 350 shares,-1
+BIOS taking some off here,1
+"  We explain in the Jan 24th aapl video how not to be gaffed by the operators,...fish hooks...aapl might be great co , but broken sto",-1
+AAP BTO 430 Puts,-1
+"user: my year tgt is 270 next year 60 That is funny because unless AAP raises their div, their cash balance will be 200B 2014",1
+MOS doing great for me last few days.. is it time to sell?,1
+"OCZ trying to find a bottom here, like it over 2.20",1
+OPXA today +12% after my call,1
+Stopped on rest of GOOG +1.25,-1
+ADBE Expecting a correction anytime now  ,-1
+ANAD on 2.14 aunch Pad.,1
+IFE is a short if it can't hold this 62.30 level.,-1
+GS waiting patiently for the rising 50 MA and trendline. Missed user short yday.  ,-1
+CIEN still a good short if it goes under 17.11 and stays there.,-1
+There goes IFE short....,-1
+MEA When will it will go P ?? mmmmm,1
+OCZ long with a tight stop.  lets see if it has the momo to go,1
+BAC Overall Market is digesting yan Budget !  ,1
+Back in AAPl,1
+ANAD above fib level.,1
+Holding up nicely in a weak tape. Someone is buying today. I think AXTI might see 3.2 to 3.4 this month IMO the buyback plan worth 6M,1
+"APA good short and risk reward. As long as it stays under 75.50, it will be great.",-1
+user: Want to get more short exposure. I like SNDK at long term resistance. Will take 2% position.  ,-1
+G going to 2.68.,-1
+VHC 4 hour ascending triangle evident  ,1
+AFFY little downside huge upside.  still holding 1/2 from thursday at cost basis of 1.50 from trimming into thursday's rally,1
+CS started a long here...In the basement..  ,1
+"SWHC Gabby's husband after testifying against assault weapons  went out and bought a A15, look it up, I  am glad he has that right",1
+"FCX Puts, will go down in history as one of the Dumbest Acquisitions of all time!, evering up at the wrong time IMO! Oil Correction Coming",-1
+GD consolidate1575 with move up Straddle with stops GD DGD The same on SV with SV DSV : on H A GOO AG AY SSI PAAS,1
+ebix on watch for short. bear flagging after earnings beat down.   ,-1
+user: user GOOG how do you know GOOG is going to 700 ? Can you share that reasoning with us? he is a George Castanza,-1
+BBY Good push back above the 50 day after some consolidation.  I also like: COST BOX MNX Others:   ,1
+AAP here come the Europeans.. maybe they will save us?,1
+SHOT CAT  89.85,-1
+"SSYS under 69, shorted here, 6940 stop",-1
+ANAD Went. slowly but went from 2.15 fib level.,1
+Picked up (1/3) AXP  avg of 65.52 //been really strong and stayed above  65 on the early move today,1
+SCHW -core long holding up well - possible falling wedge?  not sure but adding some back 17.65 area  ,1
+"Ok AAP  Bears turn off your user they are aboutto explain why AAP is going to break out soon, it will have technicals so you ignore!",1
+ANAD :o),1
+CIEN under 17.11 it is a short.,-1
+I like FS long here.,1
+"our AAP buddy user Great Cashen quote ~ After 7 in row in the Dow and 10% above M/A, bulls may pause at oasis to fill-up canteens",1
+Come on CAT do the Harlem Shake.,-1
+EBAY Green... hope you were buying at 51 with me this morning.,1
+"TITN when someone eventually looks at their operational cash flow , or i lack of ,for over 3 years in any one qtr. They'll call it TITANIC",-1
+Playing I from 53.81-54.04 is a fun way to pay for lunch every day.,1
+Kate Mitchell: The Advantage of inkedIn  NKD I remain long CEO Jeff Weiner,1
+"user CM needs to drop under this 181.44 fib level, then you are in biz.",-1
+"SSYS covered some 68.65, 68.4 next tgt",-1
+"SSYS hit my 2nd tgt, covering more here, looking for 68ish for the rest, stop to entry",-1
+CM dollar roll.,-1
+ONVO CIMT DDD SSYS XONE PCP PB IMO is a great ong Term plays into 3D sector and to find nice entry points on pullback Eye on VXX,1
+timestamped... user: This is where GS is heading-- to the line  ,-1
+AAP quietly show me consistent 120k 4every 2min print will send me back to cash or a spike +200k on a single bar.,-1
+"YHOO buying on the dips,  ",1
+ANF looks ready to break downnnn. Short under 48. short,-1
+"Disney cancels The Clone Wars, plans tv shows of its own exploring Star Wars characters and storylines DIS TWX  ",1
+OPXA Any close above 2.23 would be great . Could explode higher.   ,1
+VZ is breaking out  ,1
+OI Buyer scooping up CA,1
+"COX still holding its 200EMA it in this bearish environment, which is a Bullish sign. ong setup  ",1
+MOV finally getting attacked by selling! I'm short 39 calls...,-1
+EBAY ebaynow was dead on arrival and the company itself is leveraged a lot.,-1
+AX  next stop 42.90!!!,-1
+My holdings AAP   ,-1
+AAP take your profits sunshine and repeat! :),-1
+ZIOP nice close - TEK eod heat up this stock is next to soar,1
+PG got some of this looks good in favor with many like it target .20++++,1
+AFFY back to a full position size with avg cost of 2.38,1
+"AAP all round slow day, more concerned about be down on the high day, makes being higher Friday tough, but do-able?",1
+user: AAP lol pin at 450 O indeed,1
+AAP last minute vol is not bad.. i think it gives us a AM fade gap. see what happens in the AM.,-1
+BAC Better Volume Today than Yesterday !,1
+user: ZNGA woow look at this... Should we consider this as a bearish engulfing?  ,-1
+ebay paypal revenue is increasing by providing credit. You want to hold a credit provider or a payment processor?,-1
+"look at those last 4 candles on JPM, this thing is coiled so tight it's ready to run again  ",1
+"Missed the close (technical difficulties). I remain short the SPY, closed out my GOOG short for a profit. ong some VXX. Higher b4 lower.",-1
+AAP going to 399,-1
+S Buy stop at 5.97. All the technical indicators look good.  ,1
+ANF weekly kumo. Note Volume and MACD trying to turn down   ,-1
+"GOOG 1st target on short is around 822, needs to stay below 825",-1
+AAP 425 has been the BY target for the last few times it hovered there (only once bellow that 419),1
+NFX more off here,1
+NFX INSANE,1
+"user: AAP Does TC look like a B**CH? Then why did you try to short him like one?  Funny, but he sort of does.. IMHO",1
+"NFX took one more piece off here, my final tgt 194ish",1
+"user: user What's your take on AAP today?  huge Vol, strong buys = institutional investors IMHO",1
+NFX taking out the high of it's ight Shoulder. Why you don't anticipate.,-1
+ZNGA  Which two Casino Corporate Jets landed this morning at SFO from McCarren? Which two aw Firms  Jets landed at SFO from Dulles?,1
+"ETN looks solid right now to continue higher over 63.50. Building CCI and MACD. Tgt 65, 68 user  ",1
+"user: user  look  AAP, it is a fish rolling on the beach and dying -->You should sell it Short ? IMHO #doubleFY&die",1
+"NFX taking some off here, my 2nd trade",1
+user: AAP lets try the HOD again or lets try something new.. like a new HOW?,1
+"user: AAP up 3.91 Goog down 3.98  really guys? GOOG going to drop hard, AAP going to pop, almost 2:1 stock price diff",1
+"user: who wants to buy NFX for 200 a share? Why should AAP and FB? NFX's big problem is no cash, AAP could fix that bigtime",1
+user:  who wants to buy NFX for 200 a share?  AAP should buy DIS.. BOOM!,1
+JPM is a short under 50.,-1
+I wonder how much an AAP move up would break the bear's backs? We know the bulls will hold to 400,1
+MNKD moving from red to green,1
+NFX 193.40 stop now on rest of position,1
+user: AAP user  take some of that left over cash and buy 6X 450 Jan 2015's 60.,1
+"I really feel like the AAP long trade is high probability right here. Just use defined risk, set a stop you are comfortable with.",1
+"Green Weekly Triangle on JB,...Open ong at 6.79  ",1
+AAP looks like they got a pope now blast off,1
+"user:  Are they morons? They wait until it goes higher? Same reason ppl not buy AAP  these prices, co strong lots of profit?",1
+AAP ahhh my favorite short for weeks now -   Building cause for lower prices  ,-1
+5/5 today in my other account (I don't usually post these trades) AAP AMTD NKD SKS TA  ,1
+user: AAP anyone making bets on samesung ? # no ecosystem they have 80 phones talk about canibalization,1
+AAP TC makes after hours announcement about  split/ Div inc outshines  Samsung's  Big day .. Oh And ITV/ Iwatch combo&  time machine,1
+AAP - there is no heavy volume to take this higher. Most likely it will continue to break support  ,-1
+GES There's only one reason why a company would move their Earnings date,-1
+AAP closed the day with a Doji,1
+GME like that finish!,1
+Market Wrap Video + Additions to Watch ist  including: DXPE FT HOT INFI NVE TSN,1
+SHOT Setups: CF VBD TH CAT PSX DDS TV MSG OC WD  ,-1
+"AAP If it goes to around 433, I'll go short.",-1
+"user look at GOOG, every single switch=PAYDAY. Just need discipline to stick with it.  ",1
+".user clearly, if (but not until) the 20MA cracks and GOOG goes VSS, it is a short straight to the 50MA (I'll use OTM puts)",-1
+".user BINGO.Can't consider it a buy (personally) until >470 AND VSB. ntil then, a waste of time trying to game it. AAP",-1
+".user check out AAP monthly, you would have caught the trade of a lifetime. 130 to 600+ via common stock. Currently a SE.",-1
+"Google Closes The Book On eader, Announces July 1 Sunset  GOOG Personally quite disappointed to see this service go!",-1
+VNG closed back over the 3 mark and the volume was good. Next levels to watch 3.16 then 3.30  ,1
+AAP Samsung Galaxy S4 Promises More Pressure on IPhone  ,-1
+"looks like the typical sell the news 4 Samsung, waiting 4 user 2 show the lines for S4 O, maybe the big rotation has begun back to AAP",1
+more bad AAP news they might ONY SE 43B not projected 43.5B SE SE SE!,1
+BBY <<< Is this device the next AAP - ave reviews in corporate echelons on PIVATE network. More Security vs Open Public Nets.,1
+"Watch SPPI if it can hold above 7.86. It could go, but needs to stay above it.",1
+AAP now  400% (glass ceiling  434.5) rejecting both 434 and now 432.5.,-1
+JPM breaking higher from the tight consolidation we mentioned   ,1
+GOOG Seems there is more downside pressure to come. More picks at   ,-1
+user: GOOG NFX AMZN down - AAP up WTH The great rotation back? to AAP ?,1
+AAP starting to look better,1
+"added to CAT, betting she doesn't quite hit the 200ma",1
+ZNGA in at 3.71 1/2pos size will scale in rest if it starts to run over 3.80,1
+SHOT NFX  188.90 stop  190.,-1
+MGM up 3.06%,1
+AAP sub 430 flew clicks away.. thx IBIG.. for those negative notes.. really appreciate it.,-1
+MGM up 6.45%,1
+MGM up 8.38%,1
+NFX taking some off 190ish,1
+"DDS still at 80, getting weaker it seems, possible short to 79.70",-1
+SHD JCP both now claim hidden re value Vornado VNO can't buy them all.Garbage stay away   ,-1
+NFX moving stop 189.80 now,1
+haha user just reported that AAP has more CASH than Samsung's Martket cap O,1
+user: AAP fablets ??? - who would've thought - still sounds stupid #Apple #Android that giant one has almost sold 10m in 6 months lol,1
+Does help clear up my desk clutter just a little  AAP,1
+"AAP continues to show no signs of breaking out of downtrend. Buy  break of 50MA, short  425.   ",1
+JCP is anyone else listening to this? eal smart use pension plan as collateral to take on more debt with no core improvements.,-1
+JCP = 2'nd biggest hustle on wall street. 2'nd only to SHD.,-1
+Posted GOOG topping pattern(compared to AAP) yesterday.   Those April puts are paying nice today.  ,-1
+"MCP continues to print lower highs and lowers lows, down 1.34%. We should see a test of the Feb. lows soon.",-1
+"Short ideas,  setting up & following through despite the strength in the indexes. BID CF APO WT AAP  ES_F",-1
+"I am so sold on technicals for AAP swing long here, had to set my stop real tight to make sure I had myself under control",1
+"user: user might be some reaction to s4 unveiling. AAP ya YAWN, plastic, cheap 1 of 80 models they offer.. no lightsabre",1
+AAP hi i'm calling from my new Samsung S4  ,1
+AAP all the bears - all the downgrades - all the samsung BS-  I'm going long the 440 calls - overnight too ! #beermoney #runmonkeyrun,1
+JPM MS = Drop -- BAC = Peak BAC,1
+people slag AAP for cannibalization but samsung has 80 phones and no cult or itunes and IT is the new cool? #Idontthinkso,1
+ONG Setups: IBT ENOC AXN GNC GWE CTX EN APA CMG GOD VG  ,1
+SHOT Setups: TH PSX DDS EW  ,-1
+user: wasn't it AAP that isn't innovating? and canibalizing their own product.. but they don't make 80 models like samsung,1
+BBY made a nice cup now goes to the right a bit and little lower - patience as with AAP it drops below 400 this goes to 20 - BET YA,1
+user: AAP No matter what stock does tomorrow I'm not selling Tie a knot and hang in there!,1
+notable 52wk highs [20 > /sh < 50] AFCE AK ACO AG AMSF APOG CBS ENB FGI HOMB OMAB SAIA WMB  ,1
+AAP so according to this article Apple has some work to do. Definitely sounds like they are a step ahead.  ,-1
+user: AAP cool comparisson between Galaxy S4 and Note 2  whatever the rules BTW Here is hoping for more action today !,1
+"user: AAP, #NYTimes, #WSJ, #EndGadget, #Gizmodo give #Galaxy S4 thumbs down who da thunk samsung down 2.5% on release today",1
+user: AAP eally needs to manage expectations for its next product release... no time machines... rumour .. i-lightsabre!,1
+"AAP Gap bottom 437.72, gap top 441.34 and now firmly above resistance cloud.  ",1
+"user: AAP there are still better plays. lots of Vapour-ware out there.. AMZN, BBY, NFX, one small wind.. and they are gone!",1
+HOT stocks on watch for day trades: GPN ZGNX G,1
+Traders fretting over every move in AAP now remind me of those in 2001 fretting over YHOO EBAY MSFT DE. Just doesn't matter anymore.,-1
+"user: AAP  GAXY S4: SMAT SCO - tracks users eyes, I WANT IT!   wait for the creeper apps ;) #watchingyou",1
+user: getting excited about AAP on the 100th bounce attempt is a bit silly....,-1
+"AAP tagging 440, could have another 3-5 points of mileage today",1
+user: AAP stats: open above 37.5 about 56% probability of 42.5 hitting <- looking probable as long as 40 holds as support,1
+"DDS on short radar again, will try if it cant hold 80",-1
+AAP taking more off here around 441.9,1
+user: AAP looks great - pinching AND flattening out the 200 SMA on 30min,1
+Bought MGM at 12.99,1
+AAP show me the money  ,-1
+AAP taking some off here 440.90ish,1
+AAP Nice 10M volume first hour.. good luck bears,1
+VIX in single digits and SPX at new ATH is coming. Hopefully AAP will cut the ribbon for SPX at new ATH. ong time coming.,1
+MGM breakout seems to be holding with support above 13,1
+user: AAP maybe just sit around 440 til opex looks like yesterdays channel,1
+B from watch list at line on volume of 48% of average full day - hope it picks up a bit   ,1
+"AAP setting up for a move higher again, watching for an expansion bar",1
+AAP 640 lot offered in the Apr 450C at 11.45. Could push stock down temporarily or create resistance,-1
+"AAP all buyers, no sellers into the tube we go... death by decay",1
+AAP time to go batman,1
+my advice to apple: Bruce wayne father: Why do we fall down bruce? So we can learn to pick ourselves up again. Get some legs AAP,1
+"AAP let's not freak out yet! It needs to take the 50-day MA. ntil then, this move means nothing.   ",1
+user: wooow looks like AAP broke the resistance   two nice spinning tops earlier this week..,1
+user: AAP my premarket short has not worked but the 482 short shares r keeping me way above aqua death by decay,1
+AAP the BIG DT break on deck here!  ,1
+"NOV breaking the DT, this time volume looking much more legit  ",1
+AAP SWITCHED TO #VOSTOPBY TODAY!,1
+"PKI been stalking this one for a while, may be giving us a low-risk entry Monday  ",1
+"ES_F JPM JP Morgan funded Hitler. But, they were smart enough to not use so much leverage. Prepping 10x derivative bets on N Korea yet?",-1
+user: AAP  hod   WAT  CAN I SAY.... A THE BEAS>>OTA THE POO!,1
+user: AKAM insiders buying heavily  these levels   ,1
+"NQ_F Whatever GOOG arry page's illness is,it has to be neurological.There's no such thing as monotone voice laryngitis. I asked Dr drew",-1
+GOOG may roll over here,-1
+GOOG 815 1st tgt,-1
+"user: user: AAP worried about Apple's breakout volume?it's average, should we be worried? Skynet reaching for the pin 359",1
+SHOT JPM  49.92 stop  50.03.,-1
+AAP ruhh roooow....,-1
+AAP p Nasdaq Down.. Nice change,1
+"user: AAP lets c some volatility i can play  close, aint happening I think it is going to be another PFFT!  close",1
+"AAP 20 million Shares, NICE!!!",1
+"user: ong AAP, stop at 438. Weekend play. ? I like that Dude...",1
+AAP Did we just break a downtrend? Yes. Did we hold it into close? Yes. Did we bypass the monthly op ex pin? Yes. Are headed higher? Yes.,1
+user: AAP Bears are finally getting trapped and feeling pain. Its time to cover and ride this baby back up to 500-550. ?~~ I like it,1
+"user Also, Bill Miller plugged AAP today & Samsung disappointed last night. Divy & buyback announcements due by mid-week. user",1
+"AAP Close above 20 MA, confirmation of 3/5 reversal day on high volume, rising A/D line, cup/handle breakout in momentum, Bill Gross likes",1
+BID just like ZNGA in Dec at 2.00 got destroyed BID the smart money will start buying now here 80 - 85 range and sell it back over 100,1
+JPM saw a headline about fraud with a bank - how could that be they so honest - stock hits 48 before it ever sees 50.50 again bet anyone,-1
+GOOG stock will never see 820 again wouldn't be suprised if every insider sold here above 800 - wouldn't you - oh my thats alot ,-1
+GOOG pulling back in very light volume. Note the weekly.,1
+GOOG - not looking good here. Near the 20 day and T; watch for a break of them  ,-1
+user: 5 Tech Stocks That Typically ally After #SXSW  via user  NFX STX PCN AMZN EXPE,1
+"eview Of Open Positions - Stocks stks.co/r5am featuring KOS, V, S, CTGX, PO, AEE, NKE, C",1
+"JCP J.C. Penney Company Bullish Option Trade precedes huge up move in the stock buying 11,500 calls 5 days ago  ",1
+JCP Gets Fresh:  user ,-1
+"CAT really moving well here, come on 92!",1
+JCP good short under 16.75.,-1
+JCp still cracking.,-1
+oh JCP....,-1
+DTK CAY OC PFE MK TX BAC BIB from STEM JAZZ ONCY KEX XOMA etc and IMO a great pick SH and ong Term ONVO has potential,1
+"GOOG similar to other leaders like JPM, held first support, testing underside of MA's  ",1
+"Dollar General Corporation Option Bull Buys 10,100 Calls betting on a 10.4% up move by April. Earnings Spec? DG  ",1
+S this tug-o-war will end soon with the winners ending far from where their heels were first dug in  ,1
+Oh CAT...,-1
+S wants to break 6,1
+S is approaching the breakout price of 6,1
+"S rare to hold something that long when 'not working', but was never invalidated either, rationale and long-term trend remained intact",1
+S key resistance broken..  ,1
+CW & S = something brewing here,1
+Nice triangle b-out here decent risk reward w/ stop inside apex S AAP #Sprint SPX NDX  VZ T  ,1
+CAT grabbed some otto 82.50 puts! Could be a breakdown! .02 - .04!,-1
+CAT Breakdown! I'm short CF and FCX too! Going Down!,-1
+"CAT Failed at the 200 day MA, watch for the Trap Door to open imo!",-1
+"CAT added more 82.50 lotto Puts, Trap Door Coming!",-1
+"CAT Caterpillar Machine etail Sales Drop Accelerates, ed by Asia  ",-1
+Watch CAT under 87. Could flush all the way down to 86.40.,-1
+CAT FED going to buy Tractors too??? I don't think so! Should be down 4-5% today imo!,-1
+CAT ooking for 5% down Today!,-1
+"user CAT Exactly, someone has one too many clients in the wrong sector! Dumbasses!",-1
+CAT DE JOY down they go. PT,-1
+CAT following DE Freefall!,-1
+CAT DE in Free fall,-1
+"Another day, another new all-time high for Burger King - BKW. WEN is still sleeping.",1
+CAT 82 looks very possible imo....  ,-1
+"ES_F Bernanke sees spring slump? Will he blame 25% SPY crash on Italy?Are they hooking plows to vespa scooters,instead of CAT tractors?",-1
+"CAT Trap door will open soon imo, now German GDP Weakening, say goodbye to any growth!",-1
+CAT Once she loses 86.50 ookout Below!!! Straight to 80 imo...,-1
+"CAT Pure Stupidity imo... Germany and China Slowing, lets buy some CAT O! ignorance is bliss!",-1
+JCP in annual report says fixing performance could take longer than initially expected & any strategy could be expensive - Cuts 43K jobs,-1
+CIMT already up A gift IMO has much legs operates in 35 countries OC NGT GDX GDXJ as SV GD rise have a new floor1600 GAZ CO,1
+"And just like that, when no one is watching WEN too off",1
+Confirmed  intraday Short signal today after earnings: MOV OC OSS,-1
+AIG #MApattern  ,-1
+VTX finally broke above resistance after a period of consolidation. I still like this stock   ,1
+If VHC breaks the support level at 21.68 then it could bring 21 area. No reasons to buy yet.  ,-1
+AAP breaked SMA50. Clear ptrend here. In my radar from 419  ,1
+"Is AAP Alive? Well, yes ... yes, it is.  ",1
+Starter long BAC after hours on Friday  12.56,1
+AAP - It's time to invest in Apple shares?    ,1
+AAP Now it is time to fill the gap to the up above  ~502.  ,1
+ blog post  ,-1
+XCO  6.50 Wilbur oss has a stake in NG GA GAZ has done well taking 1/2 or All Maybe resistance 4 possible swing trade DGAZ BIOF,1
+Short term portfolio consists of 3 shorts CPB FTE and YCS. ong 1 stock: MS,-1
+"Adding COH Coach to short term portfolio, extremely oversold and good fundamentals. Superior sales growth, margins, OE. elatively low P/E",1
+YHOO 5 min - looking for a nice smooth 20/50MA 1 setup Monday for a ong. Scalp and 2.15% short.  ,1
+Surprise! GOOG chairman Eric Schmidt uses.. a BBY  ,1
+V - angebound in the consol. pattern. Volume coming in. Top of channel is vol res. Could explode.   ,1
+FCX - ooks like it wants to test recent lows. Stop is over the descending trend line.   ,-1
+cool stuff. Don't rock the boat tho! (see ISG) :) user Heart epair Breakthroughs eplace Surgeon's Knife  EW,1
+Bought my very first NKE golf apparel yesterday...golf is now a serious fashion biz.  crazy and young and colorful  ,1
+EMN - Increasing selling volume. Next support is the ascending trend line.   ,-1
+"Two New ongs For Monday: Cerner CEN, and Packaging Corp PKG -  ",1
+SHOT Setups: GOOG OVTI AN DOX WN CTXS OC PANW  ,-1
+DNDN Daily broke support last week at 5.1 and the trend is down. Momentum indicators are oversold  ,-1
+"#Bullish ZAGG T user Buy An invisibleSHIED, Get An invisibleSHIED Free [No Discount Code Needed] - zagg.to/dHxJYy",-1
+"Just finished The Week Ahead Commentary and posted to the members area. Foucs Watch Stocks Short FO,WPC,NKD,SQI,TTS,SSTK,OGE",-1
+CPB sitting at a 52 week high. I am short but bracketed with 43.70 being my stop and 41.78 being my trigger. Not looking good right now.,-1
+"user JT if you had missed AAP on Friday, what would you do tomorrow? buy if HOD from Friday is broken?",1
+DE anybody really expect a bid from BX especially much over the current price? Still sounds like a bunch of noise.,-1
+"Trade idea short  after the trend break  62 , target 45, cut above 71  ",-1
+M one of the ten new weekly 2faced longs purchased at friday's close.,1
+PGTI  one of the ten new weekly 2faced longs purchased at friday's close.,1
+opening momo watchlist: ATS DN KOG ZAZA ANA TSX SPT HAO MH GAE,1
+ACAD looking for a move over 7.95,1
+VHC could fall apart,-1
+Today's Watchlist for ONG Stocks: TIBX; HN; SNFCA; ECT; BBY; NIHD  #stocks #trading,1
+V - watching this channel closely.  Already at 11% vol. Slightly above average.   ,1
+M ong M -  looking for Fridays highs - Hourly buy setting up,1
+"ong V for trade at 161.20, hope you have been watching this channel.   ",1
+BAC Short Again Seem Weak a bit,-1
+"V here comes the breakout attempt, long time in the making let's see what it's got  ",1
+ACAD taking more off here,1
+GV nice continuation   ,1
+VS breaking out today:  ,1
+FAN just keeps on giving,-1
+"GS pennant signals a consolidation, then a breakout through resistance  ",1
+I will short FCX under 32.83.,-1
+AY 4.95ish next tgt,1
+Stocks Crossing Above their 50 day MA: CAM OW XOM AIG MHP PX CCI DOW AT ABT ADI HOT MO PP SB  ,1
+OWE STOP IN APO to 18.75.,-1
+Stock Crossing Above their 200 Day MA: AI NH  ,1
+"OWE STOP IN APO to 18.56, that is a guaranteed .50 cents gain on the second 1/2.",-1
+OWE STOP IN APO to 18.45.,-1
+"I am short 4,750 ATB and outright short 5,000  38.30 AIG , failed at trend line, stop at 38.40  ",-1
+I like APC short.,-1
+GPN looking like a short.,-1
+SHOT FCX  32.81.,-1
+CAT tap tap.,-1
+Hope you got short! AIG   ,-1
+"GT crosses 200MA - P/E 19.11, No Div  ",-1
+Out 3/4 of short VHC,-1
+TJX breaking resistance today  ,1
+user: AAP Is there such a thing as a long squeeze? if you have a tight covered call,1
+short OC starting to work towards 38,-1
+GPN trying to move higher,1
+OC coming to 29.50 or wishful thinking?? 0.8% Div - Buys Tekelec - 3rd major buy this yr  ,-1
+GE dividend gets better...3.20% now.  Buyer in the hole.  DIA,1
+Citi reiterates sell on BBY & TP of 6 - Shockingly low support - Fewer than 5% stores sold out - Most T employees not even trained,-1
+I will short FDX under 96.85.  ,-1
+"Strongest stock on my watchlist today is ECHO. ogistics, ftw.",1
+IBM Has 3D Printing on its adar as a Transforming Technology  ,1
+EXC seems that the road to the South is clear... More picks at   ,-1
+EDS up 11.61% Solid again.,1
+"Added long FTI  53. Stock is strong in weak tap, has  strong short-term news with new contracts.   ",1
+Notable S in oil & gas equipment & svs stocks: OII FTI TTI,1
+short MS small,-1
+A ot of New Additions to the Short Watch-ist | EBAY AIA BBVA at the top of the list  ,-1
+"HCA continued relative strength today, moving to fresh highs  ",1
+AKAM is on the radar as a potential short below 34  ,-1
+GOOG trying some short here around 809,-1
+"If one is day trading like we usually are, then target is something like 96.35 to 95.85. FDX",-1
+'Expensive' and 'Cheap' I dive a little deeper for you (new blog post)  nke aapl spy,1
+GOOG will look to cover little around 808,-1
+"Watch AMTd, once it crosses under 20.50, it will be a home wrecker.",-1
+user any thoughts on short entrance on GS,-1
+I WI SHOT WFC under 37.,-1
+CIEN still not done going down.,-1
+GOOG short next tgt 806-806.50,-1
+SHOT WFC  36.99.,-1
+OWE STOP IN FDX to 96.50.,-1
+user BrennanBasnicki was looking 145.50 but missed unfortunately GS,-1
+"GOOG short, moving slower than I expected, covered a little more here around 807.50, 809.20 stop now",-1
+VNG getting small position here 2.70 -2.73,1
+"APC, I passed on that short, and it did do a 1 dollar roll.",-1
+SHOT ATI  31.10,-1
+"STXS target is 2.38, that sucker will give almost everything back.",-1
+AAP gap fill and go? hope so!,1
+COVEED FDX  96.50 from 96.85.,-1
+Which stocks on your watchlist are holding up today? Focus on those. I'm watching: ECHO BCC WSM ABBV ANGI among others.,1
+AMTD Come on give it up.,-1
+"Healthcare stock BKS pulled back on a bad rating, but it's still technically sound  ",1
+AAP let's see if it can bounce to 465+ now... cautiously bullish,1
+"ANA Get long Arena here's why, seekingalpha.com/article/1298901  ",1
+we are loving the upside movement on our CAI position today.  great way to start the week!,1
+BAC aising My Stop a little...,1
+AAP moving great :),1
+eaders acting well. V new highs.  continues highers despite the market.,1
+GOOG having a hard time with 810,-1
+"AAP ove it or hate it...gotta' admit, it's rarely boring! ;-)",1
+WMT 5 min   ,1
+"FYI 4/20 165C's -Avg px 0.93 user: ong V for trade at 161.20, hope you have been watching this channel.   ",1
+"Alright. I think the 8th time's the charm...C'mon, guys...P & OVE 466.85 this time. AAP =^.^=",1
+Did I just see a 5.39 on GPN transaction?  Would be fine by me if it traded down to that this week.,-1
+BAC Begin ong position  12.37,1
+Goldman drops EA from conviction-buy to buy  ,-1
+"Green Daily Triangle on TC,....pdating ong and Short Position  ",-1
+"CA continuing to work well. Glad I bought a decent size block, probably cutting out of this one soon. lol",-1
+GOOG 809ish stop on rest of the short position,-1
+BAC Full Position (ong) for A quick swing trade,1
+"DNDN, turning green bull hammer close",1
+"I will swing WFC, ATI, and FCX Short.",-1
+AAP really looked like a B trap fri- to open.. Trade Strong,1
+"BAC No selling Target, Will sellwillit stop to move up. Will Just raise my Stop Step by Step till it will be triggered !",1
+user: TJX TJX. Fresh All Time High & plenty of support nearby.  ,1
+Keep going up GPN.  Open up an even better put buying spot.,-1
+HZNP looks to be setting up for a breakout on volume  ,1
+"MTG those trapped in here should actually be thankful the company diluted on everyone, otherwise it'd be back down to 2 lol",-1
+NG signs deal AMP ETF inches forward with holdings of companies in NG SO DIG WTI OI OI CO,1
+BAC Keep Overnight for Swing !,1
+"AAP Someone please mention a rumor and your analysis.. if your hoping for a stock to perform, your not doing your homework. #study",1
+SWFT Trend well in tact here. Now confirming the move. Other set ups I like:   ,1
+MS ready to crack. All technicals read bearish. Holding long Apr 22 puts.  ,-1
+Shorting Delta Airlines in the morning. Flying way too high for its performance - pun intended!  DA,-1
+"Eyeing ADBE as a possible short set-up, would like to see a little more downside volume first.",-1
+PHM Nice ooking Cup & Handle with a pivot of 21.6 when it breaks this resistance on volume go long  ,1
+V Cleared this consolidation channel today. Setting up for higher prices.  ,1
+GW Corning Set To Emerge From 18-Month Base  ,1
+GD SV FCX AY ABX NEM - Gold price falls below  1.600 lower demand for shelter  ,-1
+"ANA strong start, like it over 8.70",1
+IDTI like it over 7.41-42,1
+"ACAD trying to go higher, needs a followthru here",1
+AN get back down here!!! so silly.,-1
+Today's Watchlist for ONG Stocks: JASO; ZIOP; VHC; SNFCA; TS  #stocks #trading,1
+AAP kumo - :)~ earjet no kumo twist  ,-1
+Oil & gas equipment and svs stocks continue to show S: FTI TTI OII,1
+"SAVE flying high the last few weeks, time for lower altitudes More picks at   ",-1
+VS needs over 56....  ,1
+BAC Just let digest the news with a pullback... after we should Slowly rally...,1
+EDS ock on again. P 13.39%,1
+"OWW through 5.70, possible move to 5.90-6",1
+ASTX popping out of downtrend/mini wedge   ,1
+FEIC is approaching new all-time highs,1
+"THX posted the other day, may be getting ready   ",1
+EVC user missyaubaby  I'm short from ytd and today.  ,-1
+M 5 min flag.   ,1
+"I'm now out, but congrats to those who took this trade. Honestly not a hard decision to make. V   ",1
+"I like F and I like INTC, but I just sold them both to triple my PGH in one of my retirement accounts.",1
+"GPN nice intraday flag here, trying over 6.10",1
+"HCA still a picture of perfect health, up six days straight, nice trend  ",1
+"BYD already made a big move but flagging well here, may be worth a shot long over 8.16",1
+What's larger: Cash on the sidelines waiting for pullback to buy stocks? or Cash on sidelines waiting for an AAP product line refresh?,1
+SHD now trading under 200D Moving-Average    ,-1
+bought a little MT Domestic Drones are inevitable!,1
+AIG show us something!!!   ,-1
+eally considering adding some of those water utility names. WT AWK MSEX YOW SJW AW CWT Might pick 3 or so. ike the divi's,1
+"PO covering some here, possible pullback to 24ish",-1
+"NTS another one that looks very strong right now. Buy over 55.25, tgt 56 then 58. user  ",1
+Added bear call spread in STJ at Apr20 43/45 level for potential 12% gain.,-1
+"DNDN grow wings you pig, 50 million shorts going to start covering soon enough",1
+"XCO SD Staying patient here, not adding anything until market pulls back.  Will let my positions run if the market continues to take off",1
+user DNDN has always been a name to buy when things look the ugliest,1
+OAS market cap is in the sweet spot for a takeover bid,1
+BAC Add some on the pullback if my 12.22 order is fill,1
+OAS heading back to new 52 week highs,1
+AAP broke trend,-1
+ooking for SPX ATH soon. That's my thought if I don't use my brain. Has worked like this most  times then why mess by using my brain AAP,1
+OWE STOP in FMD to 5.26 20ma.,-1
+"NFX Almost time to short this puppy?  These multiples are ridiculous, it is basically an online Blockbuster, that is trying to make films",-1
+It's rare for AMZN to not rally in a holiday shortened week. Think last Thanksgiving was an exception.,1
+EDS Solid day agin for this one. I like jumpy stocks.,1
+AAP going back to OD,-1
+CAT green to red soon.,-1
+"DNDN, nice 5 minute green volume bars, lows in",1
+BA continuing it's run   ,1
+an epic ms collapse would be ideal and appreciated this afternoon.,-1
+BAC Big Block on 2,1
+GOOG still under ma10...just a few more days,-1
+"BWD, stepping in short. I do not see the necessary volume to take this past 86.5. Set tight stops  ",-1
+ easy look at last 3 yrs annual income data:  ,1
+NAN Spring loaded (ascending triangle)  SI strong here.  20.00 now key.  Others I like: AX DDD (MACD) VMW  ,1
+Watch NKD here. Selling volume has been picking up and it's now below its 20 day moving average. Candle still needs to close.,-1
+"NFX 10days ago gave you 10 short then orange dot buy called for 10 up ,DONE ! easy as streaming  ",1
+Smart traders taking your money! GPN,-1
+well no complaints at this point regarding the 185.72 bid in NFX :),1
+"AMZN daily,I have added arrows to buy signals each day target still 263+ no doubt will get there.   ",1
+"BYD triggered, 8.10 stop, 8.25 1st tgt",1
+markets at the highs of the day. spy  ms still chopping around.  ridiculous.,-1
+KCG back in at 3.8 :) ,1
+"BYD next tgt 8.35, not sure if it has the mojo, 815 stop",1
+"BAC Big Boy, Buy Big Block under... on 2 ;-)",1
+NFX uggh! the one put (May 2013) I had might get wiped out - to exit of double down?,-1
+"ANAC is toast, no volume, free fall from here.  Nice run while it lasted, time to close some gaps.",-1
+MSFT - A Spanish group sues Microsoft to the European nion,-1
+AAP I bought  431.79 on March 14th. The spx was  1563 that day and it's now  1563. I'm +6.88% and you guys are bearish? come on man!,1
+"Those were pretty looking breakouts on MA and V.  I was skeptical last week, but I'm on board now. et's see where they land.",1
+NVDA remains trapped between the 50SMA and the 200SMA. Slow sto trying to recover & cross over.  ,1
+ANAC come on up to me so I can short you more,-1
+"AMZN - Bearish Divergence, H&S, and 10EMA looking to possibly cross 21EMA. Might have to wait for SPY to top first",-1
+"AMZN  Bearish Divergence, H&S, and 10EMA looking to possibly cross 21EMA. Might have to wait for SPY to top first",-1
+MA  MAJO BEAISH DIVEGENCE(NOW CONFIMED ON MACD-AS WE AS SI). DECEASING VOME.,-1
+8 Notable 52wk HIGHS By Desc % Price Delta [EOD20130326] KS CBI OII APC FT BEN AW YN  ,1
+AMK a bear flag to keep an eye on for anyone interested in a short  ,-1
+"Shorting GM and TSA once again.  TSA At the high end short term over bought, channel continuation or right shoulder falling H&S...",-1
+V total animal #VISA AXP MA who needs cash? #deleverage deleveraging! XF  ,1
+G same pattern as HIMX developed at 4.00 will start buying a position for the next move up - this one is not done by any means,1
+AIG - on watch for tomorrow.   ,1
+PFE break out.. Pay attention to rising wedge.  ,-1
+BID -puts were active yesterday going out to Jan  ,-1
+AAP 458 PM good start for the bear :),-1
+FAN worse and worse in premarket trading even if it's on low volume,-1
+MNK long above 3.50,1
+ooks like Cliffs is falling off a cliff right now. Geronimo!!!! CF,-1
+MSFT - The news of the new Microsoft Windows Blue  ,-1
+GID like it over the high,1
+AN is at least gonna sink to 7.60.,-1
+A close above 197 on NFX and it gets into the next orbit that can take it to around 225 eventually.,1
+"if AAP runs up before earnings it will drop on disapointment or even a small miss, if we stay below 490 it will POP",1
+"GEVO nice start on ok volume, looking for entry",1
+"GID working, 46.50 1st tgt",1
+Short CF and MMM,-1
+"OAS, perfect opportunity to the buy the dip on this name. NG higher",1
+"BBY trying some short here, high of day stop",-1
+"GID reached my 1st tgt, 46.75 nxt",1
+"OAS wants to climb, this dip is a gift",1
+"CHTP nice bounce off 2, looking for followthrough here",1
+"GID still acting well, 46.20 stop on rest",1
+AAP below 557 has led to downside follow thru. i have 452 as my 1st support,-1
+"FIO barely red, this stock due for one of it's famous rips higher, keep an eye, I bought some yesterday",1
+i'm amazed that ms is holding up in this tape.  thought for sure once it hit below 22 this morning it would tank.  wrong again.,-1
+DV time to move South. More picks at   ,-1
+emember: ASTM need to raise money soon. At this point things are getting difficult. everse Split here too?,-1
+"GID taking more off on this pop here, just dont like the volume, leaving last piece for a move to 47ish",1
+Failing on a 30-minute AAP #Apple NDX QQQ fake-out on daily  ,-1
+"GOOG short, 804.20 stop on rest",-1
+SCHW - this 17.20 level is a 50% pullback from Feb. low - we added a pretty decent chunk here - looks scary - buy fear,1
+"MNKD flagging nicely, like it over 3.39",1
+GOOG tightening stop on rest - 803.2,-1
+"GEVO 1st tgt 2.30, 220 stop",1
+"ES_F ule of thumb is that former momo names need 3 years to reset from top to the next bottom, former popped junk like NFX best short",-1
+AMK a nice short below 3.99  ,-1
+JCOM - new all-time highs after consolidating its earnings gap for a month or so  ,1
+KS looks ugly to me. A close below 106 and its headed below 100.,-1
+"MNX  Bullish pattern, confirming: (break out mode) Also like here:  NAN SCA GDOT VMW PAY  ",1
+AAP 10 SMA about cross above 50 SMA; hasn't been above since October.  ,1
+"EPS 1st tgt 7.45-50 if it can bounce here, possible extension to 8 later",1
+JCC long overnight 1.78 -1.80,1
+"The pattern in DNDN reminds of HGSI, everyone threw in the towel & then they received a nice takeover bid with huge premium out of nowhere",1
+"FIO, someone needs to dig up the Wizard of Woz & put him on CNBC again. He always gets the stock running. Bought stock yesterday",1
+"EPS hit my 1st tgt, 7.65-70 next",1
+SHOT AO  38.99,-1
+"C should pop of this level, stop under lows if wrong",1
+"EPS still think there is a better than 50% chance it tags 8, but moving stop on rest to 7.35",1
+"NG NG_F finally taking off here, KWK XCO SD all these stocks are lagging the move in Natural Gas here, think these have a 10% up move",1
+"NG Increasing natural gas prices not exactly great for Cheniere, even if they are just a toll gate.  owers the arbitrage potential...",-1
+BAC WFC in the hole finally.,-1
+AAP this thing going for 450,-1
+MCP new 52-week low here.  eminds me of coal stocks ACI AN CF WT BT JCC,-1
+"GOOG 801ish 1st tgt, 803.20 stop",-1
+GTAT - breakout entry above 3.03  ,1
+HG is hovering near all-time highs  ,1
+NFX weekly 190 puts in play,-1
+Darn it we missed BSFT short.,-1
+EPS made a quick 10 min trade 7.52-7.73.  looks like might of jumped out too early might pass 8.00,1
+GS whoop that was it the 38.25 fib line.,-1
+GNW expecting this consolidation flag to resolve soon:  ,1
+"CF Shifting on the 15 minute intraday, suspect to trade OWE - careful shorting lows, backs and fills. Disclosure: Short CF",-1
+M breakout gap held - 60 min. ong 1/2 position post EPS. Back in J calls w/ profit preEPS  ,1
+CF trying to bounce here 191.50ish 1st tgt,1
+CF taking some off here close to 19150,1
+"AMZN long I have posted for days hits target today for nice 11+  gain, just sharing what it simple  ",1
+"VTX ..working a flag just above downtrend line breakout, looks higher   ",1
+AAP has made no attempt to fight this sell off,-1
+ONG CF here  18.30.,1
+"CF still in a flag here, not sure how much mojo it has, but hoping for a move to 192 or so from here",1
+"CF finally, looking for 141.80-90 next tgt",1
+GEVO taking half off 2.29,1
+"GS I think the odds on play is probably for higher prices, in due time.  ",1
+On a technical basis NVDA is ready to break its 200SMA. Some technicals are turning up here.  ,1
+BAC Thursday will Close Around 12.50 IMO (Opex),1
+"HTZ great move to new highs out of that triangle, very strong  ",1
+"PKG three back to back strong days, good volume too, very nice move  ",1
+added to my AAP long,1
+"TGT, searching for shorts, took a small put pos. for a quick trade, just to scratch that itch.  ",-1
+HOS Out of the triangle!  from  ,1
+"AMK broke untested support line of flag (can be adj), but holding prior retracement low (no pstn)  ",-1
+HEK Cup and handle formation just dying to pop!,1
+When we drop expecting a 8-10% correction   SPX ES_F SPY QQQ IWM NDX AAP,-1
+user: When we drop expecting a 8-10% correction   SPX ES_F SPY QQQ IWM NDX AAP,-1
+"CX is a good short here at 87.30.  P/E too high, hitting upper trendline  ",-1
+otten aapl   spy,-1
+CO had this on the list for break of 7.00 and risk was too much so no buy man I missed a good one - low volume deterrent caused the miss,1
+JCP 38.24% short float,1
+XONE beat the expectations tomorrow will be a 3D day DDD SSYS,1
+ONG Setups: CF SPT DDD CZ AMI APA AXN ANIK CVT  ,1
+SHOT Setups: AAP GOOG BBY  ,-1
+"EXPE not looking too good after breaking the uptrend, watch your fib retracements on the way down  ",-1
+"CEN just giving everybody more buying opp IMHO, magnet rip to 100 on deck  ",1
+IBM cant wait for test of 205 then 200 it;s coming who u fooling,-1
+"ock star Direct TV DTV continues to power higher, now up 14.3% since March 11 in the long term portfolio!   ",1
+Should I short PXD at the open for a BearishBuck?,-1
+user Time and time again revenues slow down for hot stocks like lulu and they bend over to hide the real cause.,-1
+Thinking NAV short. osing money and borrowing more.,-1
+CM insider sells 1M worth of stock every week  ,-1
+"This been good to us since Feb DECK now waking up again, your on #DECK #DECKES  ",1
+NKD looking distressed. Anticipating a gap down in the coming days. Closed off of it's today & up on declining up volume again.,-1
+ thinking out loud. 50 mva sub 200 mva- done. Bottoming tails at 61.60 providing obvious stop. Catalyst higher? 29% short interest.,1
+of some of the most watched biotechs AMGN looks to have the most room to go higher. CEG VX AGN BIIB look very close to stall speed.,1
+COST looking to go higher.,1
+MWIV looking to go higher.,1
+oh GID poised to go higher. almost forgot this one.,1
+Watching these a little more closely (again) after today's action:  BID DECK COST Others:  MNX VMW NAN TXH  ,1
+"no position for me, my boss took his 1st in position in a long time (short BBY) so maybe contrarian thesis here i am short GME tomorrow",-1
+Could this false breakout on BAC take us down to 11 ?   ,-1
+NVDA has started to bounce & broke out from a falling wedge closing above resistance 12.6 level  ,1
+TSO Keep this small Cup with Handle on your watch list. Breakout watch over 60 on volume.  ,1
+ADM Volume is picking up again. Next buy when clears 33.13 on volume.  ,1
+GPN Stock price finally broke out one of the most important resistances in the short term at 6.17   ,1
+"user benrab no position ever, no payment ever, just trying to explain the fraud that is likely zagg. Deny it at you're own risk.",-1
+"Trying to finish a post on zagg that, if it doesn't move you to sell everything, proves you are in complete denial and/or over your head.",-1
+MOS long  ,1
+"OC long, todays ultra small range, inside & following yest hammer, has my attention...lets watch  ",1
+"VITC long, higher lows with highs stuck at 20 & 50SMA zone, c if it clears & prints a .30 candle  ",1
+"My SHOTS, various strats, AXDX CMCO DGIT D ECYT EPIQ GDD GMAN ICGE IDIX MXW OME PETS ECN SCMP SGI SIMO SNFCA",-1
+"HZNP first step 3,5",1
+o-Hi Setups: MNKD DVAX ANAD ATM AZK BEE DANG GEVO HN MTO PG PPC SVM SAT  ,1
+New ong entry - IBM Big Blue Flashes Green -  ,1
+GOOG will add to this short position when breaks 3/18 lows on volume (winning trade)  ,-1
+It's fair to say trendfollowers who where short PX at the close yesterday will be having a very uncomfortable day today.,1
+Will short 300 shares of PXD at the open for a #BearishBuck.,-1
+DN looking for a move over 10.60,1
+"Hit stops, out of AAP longs from yesterday. Falling knife??",-1
+"AAP there it goes, peeling some here",1
+HAO like it on a pop over 6 w vol,1
+"ZNGA Short Week, elease of eal Money Gaming in K on April 1st after expiration of FB Contract. Should be a nice gap up next week. 4.00",1
+PX taking some off here 15.95,1
+GOOG AAP short positions for days paying off nicely 30+ gians VIDEO   ,-1
+DN trying to push through 10.60,1
+"SPW Still climbing after yesterday's gains.  I love buying panic, cheap shares.",1
+SGN time to book some profits. More selections at   ,-1
+AMK breaking below yest's lows & below prior infliction pt low. Illiquid stock  ,-1
+"MTG.. got a little long yesterday, like the orderly pullback/consol. after the big rip,target 6  ",1
+"SWI good follow-through to yesterday's bounce from support, ideally would like volume on upside now  ",1
+EAT is hungry for all-time highs today.,1
+keep an eye on CAI.   we are long from 3.95 and it is setting up well for earnings on 4/2.,1
+"HTZ another break to new highs with follow-thru, nothing bearish in any of these, follow the trend  ",1
+"S continues to work well, big breakout, retest, and follow-through, and all on good volume  ",1
+"DECK all on deck as the shorts scramble to cover - pop, pop boom!! uckly they don't wear ggs in Cyprus",1
+HEK still waiting for the breakout above 4. 10ema is support down at 4.2.,1
+SEV from yest post/entry out 100% +.20,1
+JB IDTI lookn for a trade,1
+good stuff for TSA here  ,1
+OC XX also watchn for a long,1
+user: CEN nice orderly channel. setting up for higher prices IMO. volume confirmation of course.  ,1
+CSOD  Expands Presence in Public Sector to Drive Continued Momentum  ,1
+OWEED STOP IN FCX to 33.12.,-1
+TSO on the move. Breakout watch over 60 on volume.,1
+VNG getting more 2.80 thats bottom,1
+"GOOG puts worked today, and so did CEG calls    (V options +100% this week)",1
+NN looking quite bearish at the moment.,-1
+NG bearish action today looking like a top as nat gas jumps.,-1
+SCHW - we added more to the long today,1
+"SGMO current 1st target is 10.03, and second is 10.32. Darn it.",1
+AIG in 38.50 w Put  target around 37.90,-1
+GOOG  ?Qu? esperas para caer de una vez?,-1
+"GOOG  Dont you see what is AAP doing? Fall Fall, damn it....",-1
+AAP big time drop today on a day when market is up SPY,-1
+"GOOG in the 795 puts for .55, AAP is Crashing!",-1
+GOOG Slipping!,-1
+"GOOG  The leaders move first and then the index follow, and not the other way around...",-1
+GOOG New OD!,-1
+GOOG and AAP  they want out for April earnings?,-1
+AAP Support at 442ish and then gap fill below.   ,-1
+VVS pop with volume,1
+user: AAP trying to bottom soon.  It may take 1-2 wks with a target low of 430-440.  ,1
+Watch APC under 88.,-1
+APC weeeee,-1
+"GOOG both FB and AAP down almost 2%, trap door coming for the GOOG!",-1
+"DE putting in a bearish engulfing so far, probably needs to test the 200 day ma   ",-1
+"user: GOOG next stop 750 area ?~~First has a date with the 50 SMA - EMA, if it goes below that then you?ll be right",-1
+MA daily had red dot but will confirm it with cross of PWmo2 white.  ,-1
+ we all know what happens to broken momo plays right?,-1
+AAP trying short here,-1
+bought some SAAS on breakout with volume,1
+NVDA looks super coiled and nice,1
+VNG Just bought this on a whim at 2.83 this AM... Wow. What a pick. ZNGA same thing will happen... hold on longs.,1
+any news here? EBAY,1
+VNG was so nice so quick almost too quick but not fast enough nailed 3.10+ sells still holding some looks great -everyone will love it now,1
+"Green Weekly Triangle on JB,.....Scaling p on ong Position  ",1
+"I will short CAT under 86.65., I also like CAM short.",-1
+VNG took some offt table from 2.75 longs today at 3.20 - still holding big part overnight too,1
+APP on a tear. Great short-squeeze play.,1
+AAP please fasten yor seatbelts and enjoy the ride....,-1
+AAP... you're going to have a tradable bounce on the heels of this post-triangle breakdown.,1
+"sky rockets in flight, afternoon delight EBAY",1
+"BBY It was interesting to hear on CNBC that there are big companies interested in this company, including Microsoft, Amazon,.. AMZN MSFT",1
+"user I'm an analyst, after/while I analyze zagg, I know for a fact it is a dog with fleas. I know much more than you, trust me.",-1
+AFFY iftoff Short squeeze?,1
+EBAY Management should be fired on that comment alone. What were you waiting for knuckleheads,1
+"AFFY 30 just two months ago, intrinsic value in the IP and 400M in back tax losses.",1
+DDD shows support at 30 good buy along with XONE PCP PB DASTY ONVO CIMT which mentioned yesterday was nice entry 3D imaging=Future,1
+AAP China intensifies attacks on Apple   ,-1
+"AFFY looking for a double like PX here, 75% shrs shorted just like PX.",1
+On the other hand u have AMZN who is takg the baseball bat approach to building the moat They have no fear doing that Invest Jeff Invest!,1
+ONVO I think we're all waiting for a little dip DDD was a nice entry yesterday OC Apr 20 call options a few days ago at 32 strike,1
+Why are more people not talking about this China News??? Big Deal Imo! AAP China intensifies attacks on Apple  ,-1
+BBY Downgraded by GS... SE,-1
+AEG and DECK if anyone can find shares. Good Shorts imo.,-1
+AAP (daily) Three Black Crows and 50 (ema) teaming up**  ,-1
+Milwaukee's CAT to lay off 300 employees in So. Mke branch  ,-1
+user: user: AAP (daily) Three Black Crows and 50 (ema) teaming up**  ,-1
+AAP Full Stoch analysis yesterday   ,-1
+"S hmmm, I dunno, I think this has some upside, what do you think?  ",1
+"CXW this stock is so underowned and played, nice breakout setting up here  ",1
+AFFY un to DOBE continues,1
+Major game consoles now stream 6 month old games at heavily reduced price.There won't be any need for physical used games anymore. GME to 0,-1
+VMI GOOG punch below bottom BB. both are ridiculously oversold imo.,1
+BAC is like watching a plant grow ;-) But my DO.to position is up 2.75% today !!!   :),1
+AAP China news will Catch Fire this weekend just watch!,-1
+VNG going to more hod's this is great so long under 3.00 thanks for following,1
+DECK just like magic.,-1
+BAC OPEX in Work !!! ,1
+oh APC....,-1
+Full Analysis Blog Post from yesterday Watch That 50! (EMA) - AAP   ,-1
+VNG I bought at 2.78 then went to bathroom just like any normal day came back and at 3.27 - what a poop lol,1
+"CSN,....Descending Triangle Formation, please check any adding on position open, Akstergi .......  ",-1
+"user check my post about CSN, it is for you",-1
+"ast wave of gaming consoles was released Nov '05 - Nov '06, look at GME price that year and the year after",1
+"GM, DA and TSA shorts all did well today in an up market.  Tell's me these were good choices to short.",-1
+MS long very close to my stop of 21.50. Not looking good on this one. ooking back I broke my long-side rule of solid earnings delivery.,-1
+Winners today in my long term portfolio: SP XV MGK BO COH IBM EWH  ,1
+"V looking extended here, could pull back 15 - 20 dollars.  Strong company though, too dangerous for me to short me thinks.",-1
+AAP ..final shakeout before earnings?  long and using tight stop of 437 or its toast    ,1
+"user NOV went up nicely this week, no doubt.  Not the only story in the market...no reason to be here...move on.  Pick another stock.",-1
+NVDA confirmed my previous analyse & broke decisively above its 200SMA. Bullish setup  ,1
+AD Next buy point would be on the day it blows through 1.95 on heavy volume.  ,1
+ASTX Daily technical indicators are bullish. Breakout watch over 4.58   ,1
+VNG allied hard Friday w volume.If it clears back over 3.43 50SMA may have some good upside  ,1
+ooks ready to make new highs. 9&20 SMAs are both rising w S trading above them. Breakout over 6.22  ,1
+With strong cash position/margins improving/ 0 debt & new devices being launched BBY is definitely a takeover candidate CSCO AMZN INTC,1
+"My Fox Business appearance yesterday.  Still bullish NFX, GMC, CST.   ",1
+KWK This deal gives Quicksilver ample liquidity for expanded production going into 2H 2013 & early 2014.This is a 4-5 stock trading ~ 2,1
+VNG let this V run up next week to the mid 4's or higher. I will sell & jump back in when falls the next few weeks. Been in this too,1
+Identified this topping pattern wks ago(  )  Here is the AAP / GOOG  update  ,-1
+Next week a nice pull back to 438 on AAP and 787 on GOOG could really set the QQQ up for it's run over 70.,1
+SBX As long as the 50EMA holds on a closing basis at 55.89 I remain bullish.   ,1
+MSFT broke out of a major resistance Thursday and may be headed higher from here.  ,1
+INTC ooks ready to move higher from here. Buy point is at 22.02 (200EMA). Bullish setup.  ,1
+QCOM could test the recent highs if it breaks resistance at 67.2 on strong volume  ,1
+STZ Nice Bullish flag. Buy point is as the stock breaks over 47.82  ,1
+PM best setup if market can hold 100 roll  ,1
+"VOCS example on 5min, using COSE below 20ema NOT low for your stop, not traded but good for +.15  ",1
+Food Sector as always doing extremely well: PZZA DPZ JACK MCD CAKE,1
+"Judging from past 3yrs, expecting 10-k to be snuck in tonight or on Monday for JOSB. The suspense is killing me.",-1
+How much did ZAGG pay Matt Barkley for this BS interview/endorsement????  via user,-1
+"AMZN to buy book focused social network Goodreads (16M users, 23M reviews, 30K book clubs) for est 150M",1
+JCP actually looks decent with stop under recent low and possible retest of resist line  ,1
+Sizemore on Fox: Cashing in on the Echo Boomers - Sizemore Insights  ABT MJN BBBY KMB,1
+"Groupon evisited: New Mission, New eporting Issues ? Grumpy Old Accountants GPN  ",-1
+"ANIK long 14.59 / .61, nice consolidation period, waiting for its day, past resist 15.27, 15.52  ",1
+ - any day now. When it gets below 61.60.   ,-1
+JPM - not looking pretty here.   ,-1
+ Weekly.Barely hanging onto double support area.Possible break this week after last week's rest  ,-1
+"All things gambling looking pretty good here with March Madness, MGM VS WYNN",1
+IBM - above 215.90 there are some clear blue skies.... Also big part of DJIA making news highs.  ,1
+"JOSB 50% off on almost everything. Funny, they cited increased promotions as a big reason why gross margins were down...",-1
+BCM Attempt #4!! hopefully it will make it this time!!CNBC/Analysts or some catalyst will help!!  ,1
+ADSK daily lined watching this one over 41.60 might continue   ,1
+BODY breakout on weekly over 20 and resistance trend line h/t user  ,1
+DE considering buying some put options...,-1
+Happy Easter! SHOT Setups: CS ISG CF AN NKD OW MOH MTZ BI  ,-1
+CAT may try to jump on the boat with some Put options here as well...,-1
+JCC this will turn and be the darling agian up very big - I am OADING up down here - mark my words 1.75 - will re share at 2.15++ target,1
+"If AAP goes to over 451, I will go short. Still weak stock.",-1
+MMM looks ready to break out. ooking to go long on minor weakness (105.50).  ,1
+AMZN - Closed over volume support inside of the descending channel and tested 50% retrace.  ,1
+BBT coiled up after finding support at 50/200 sma's after stress test results.  eports on 4/18  ,1
+"JCP In oscillating range, oscillators say over-sold, bounced on volume at pivot point, contracted B-Bands, i see a test of 16.50.",1
+"U.S. crude prices are on track to hit a new 18-year low Friday, putting the energy market on course for another bru… https://t.co/SYdekYg2aD",-1
+Megabanks enjoy certain advantages in this weeks markets which the oil patch definitely doesn’t. https://t.co/jui3VoKr7Y,1
+"Heard on the Street: U.S. stock bulls should take a careful look at China, where the post-coronavirus rebound has b… https://t.co/Ct9zPe4ZGG",-1
+China’s burgeoning market for bonds backed by mortgages and car loans is facing its first major test https://t.co/ywHrzgBm6r,-1
+Selling your home to a computer was supposed to be the future. But that was before the coronavirus pandemic. https://t.co/3EK1wJSvFs,-1
+A surge of Saudi crude is expected to worsen a U.S. supply glut as drillers and refiners cut output and storage spa… https://t.co/44Ndex9Qpz,1
+"RT @JChengWSJ: Coronavirus is making U.S.-China decoupling more likely, U.S. companies in China say, disrupting supply chains and straining…",-1
+RT @kosakunarioka: India’s RBI Takes Additional Steps to Support Economy https://t.co/XKnJ8zW5yz via @WSJ,1
+RT @joannechiuhk: Markets power ahead even as statistics showed China’s economy contracted 6.8% from a year earlier in the first quarter ht…,1
+"RT @JChengWSJ: @QiLiyan @TByGraceZhu Update: China's official first-quarter GDP was -6.8% from last year, the first decline since records b…",-1
+"China’s restrictions strand face masks, coronavirus test kits and other critical products bound for the U.S., State… https://t.co/2jAdokQBJj",1
+Shoemakers churning out masks. Auto giants manufacturing ventilators. This is how the private sector goes to battle… https://t.co/0OBG94I1yC,-1
+"The Daily Shot: U.S. Crude Oil Trading Below $20, Near 18-Year Lows https://t.co/UBEtSOlk18",-1
+"Another 5.2 million Americans filed for unemployment benefits last week, bringing the total seeking aid in a month… https://t.co/Aw9VhuWFUk",-1
+Google will be “recalibrating” its focus and pace of investments in areas like data centers and machines—the compon… https://t.co/rl7UUQfOhg,1
+Another week of very high jobless claims means that April’s U.S. unemployment report could touch levels not seen in… https://t.co/VGcKC94O9c,-1
+"Abbott Laboratories has what it takes to ride out the coronavirus crisis, not just because it happens to make three… https://t.co/buR8yptRsU",1
+"The White House has a low opinion of the World Health Organization, but restricting alcohol sales is an idea that d… https://t.co/gstcKFnLN9",-1
+"France, Belgium, Greece, Austria and Spain are among countries whose regulators have barred bets on stocks sliding… https://t.co/POWpZjLAaN",-1
+Debate over when to reopen the economy has left the impression that economists are at odds against epidemiologists—… https://t.co/a0nEveBVqW,-1
+"RT @brian_mcgill: Where the $250 billion small business loans went, per 1,000 small businesses
+https://t.co/jNuBmekelQ https://t.co/fk8gYOd…",1
+The forces fueling 2020’s oil bust https://t.co/28JP5GfWLR,-1
+"Global oil demand will drop by 6.8 million barrels a day in 2020, with the sharpest contraction coming in April ami… https://t.co/uqvTCl0bqm",-1
+Canadian bankers expect a bond boom https://t.co/YR90HrnovU,1
+RT @Birdyword: South Korean election results could revive efforts to wrestle control of major companies from the families that have control…,1
+RT @bysarahkrouse: Story on Verizon's purchase of conferencing platform BlueJeans here: https://t.co/CfZo9K1Zoa,1
+Heard on the Street: Digital business models and health care are places to hide in a pandemic-induced recession. Eu… https://t.co/ijgQm4OFZS,1
+"Officials expect another surge as applications processed for newly-eligible gig-economy workers, self-employed https://t.co/FZZRxqmYl4",1
+RT @AmrithRamkumar: A big disconnect—Stocks have rebounded even with investors hoarding gold and large swaths of the economy still frozen:…,-1
+"Heard on the Street: Investors are betting on IAC’s remodel, but just how quickly can the whole thing be fixed? https://t.co/COJsGVy9pH",1
+"RT @lizrhoffman: Morgan Stanley profits down 30%, the least bad of big banks. But warns it might not meet those financial targets that its…",-1
+"Morgan Stanley’s results fell just shy of projections by analysts, who had revised their estimates downward as the… https://t.co/vGv8FsK57t",-1
+"Stock prices suggest a short recession with a swift rebound in corporate profits, while gains in havens signal worr… https://t.co/yN9DOmq8J0",1
+Online banking experienced delays as the federal government began depositing stimulus payments https://t.co/LLzl7NyfdL,1
+The first major national election of the coronavirus era offers new avenues to reform Korea Inc. https://t.co/OkGnUj3AY2,1
+Investors are anticipating new-home construction data and a Fed survey on manufacturing conditions https://t.co/KeFDcsHLTG,1
+BlackRock’s first-quarter profit fell 23% https://t.co/YLdaUS5Rbx,-1
+"RT @Birdyword: South Korea's Moon Jae-in and Japan's Shinzo Abe aren't exactly good mates. But with a parliamentary majority, Moon now has…",-1
+RT @newley: Indian startup Oyo Hotels &amp; Homes is planning to move some employees off its payroll and onto the rolls of its biggest investor…,-1
+RT @kosakunarioka: Philippines Central Bank Cuts Key Rates as Covid-19 Disrupts Economy https://t.co/loqglU7hZC via @WSJ,1
+"RT @joannechiuhk: Major banks declined Thursday, after a big drop overnight in U.S. Treasury yields https://t.co/9ExvSxmsdw via @WSJ",-1
+"RT @greg_ip: Epidemiologists and economists aren't at odds. One measures the benefit of social distancing, the other costs. Their results s…",-1
+RT @DaveCBenoit: The banking system is not built for everyone in the whole country wondering if they got a government check on the same day…,-1
+Health insurance stocks should hold up fairly well during the Covid-19 crisis. Other parts of the health system won… https://t.co/GqkLCYg3Ai,-1
+Indian startup Oyo Hotels &amp; Homes is planning to move some employees off its payroll and onto the rolls of its bigg… https://t.co/vsCd711RHe,-1
+Executives at firms like Goldman Sachs and JPMorgan had to balance the opportunity for trading profits with the saf… https://t.co/WSU305BtPw,1
+Wall Street by the charts: the dispersion in GDP forecasts has hit a record amid economic uncertainty https://t.co/ROGJ6Vl4d7,-1
+Apple’s new iPhone SE offers a cheaper entry into the ecosystem that the company needs to grow. https://t.co/1uB2tpY4ng,1
+"Many of the millions of restaurant, hotel and store workers who lost jobs because of coronavirus shutdowns are ente… https://t.co/eXaAYcplfq",-1
+"Heard on the Street: Wall Street got a big lift from capital-markets businesses in the first quarter, helping cushi… https://t.co/w8lQO17EPr",1
+"Retail sales fell 8.7% in March, the biggest month-over-month decline since the records began in 1992.https://t.co/IMsh5t8Ksh",-1
+"Historically bad U.S. retail and industrial data for March mean April will be worse, digging a deep hole for the ec… https://t.co/1ymzmELEyR",-1
+RT @djtgallagher: A cheap iPhone still has its place for Apple - especially now. For @WSJheard - https://t.co/zvZQDMlPNW,1
+"Heard on the Street: Bailout is probably the best aid package that airline investors could hope for, but it doesn’t… https://t.co/SSXOdZWbr0",1
+Saudi Arabia Markets Dollar Bond to Plug Spending Gap https://t.co/Qcidt8s2lq,1
+"Occidental Petroleum, which has sought to conserve cash as oil prices plunge, opts to pay Buffett’s dividend in sha… https://t.co/PlhH0dWEoR",1
+"RT @ychernova: U.S. startups raised $34.2B in Q1, up 10% over Q4, per Pitchbook. But the situation changed. ""There will be a marked drop-of…",-1
+RT @jonsindreu: The U.S. airline aid packageis probably the best that investors could hope for. But the fact that carriers will already be…,1
+RT @AmrithRamkumar: Just a staggering increase in oil inventories recently. @sarahtoy17 explains why the trend could push some prices below…,-1
+Heard on the Street: The earnings hit for S&amp;P 500 companies will likely be much bigger than they are guessing https://t.co/Wesotl6Rwc,-1
+RT @PaulPage: The 8.7% drop in U.S. retail sales in March was the largest on record.  https://t.co/y03aCdeqAG via @WSJ,-1
+"Citigroup increased its loan loss provision, money for loans it now thinks can go bad, by $4.92 billion, https://t.co/s7oVQHaFq7",-1
+Dollar rallies as concerns about the economic impact of the coronavirus and the lack of a global policy response fu… https://t.co/urSIlVFS74,-1
+"RT @lizrhoffman: “We were on a good roll through January and February,” Goldman CFO Stephen Scherr tells me. “Then came March.” 
+
+Updated w…",-1
+RT @sarahtoy17: The coronavirus pandemic is turning oil markets upside down. https://t.co/SKuxRyyP3Y,-1
+RT @jdlahart: Don’t Trust the ‘E’ in the Stock Market’s P/E Ratio - my latest https://t.co/HxtMOwshmo,-1
+"RT @aosipovich: Scoop! Wall Street ""task force"" with BLK, Citadel Securities, MS and other firms eyes changes to US circuit-breaker rules a…",1
+Heard on the Street: Can’t get your hands on a Nintendo console to play Animal Crossing: New Horizons? You could al… https://t.co/l24tlufi8A,1
+"Paradoxically, the Fed’s success holds the key for further rallies in the price of gold https://t.co/lNHQOmqIac",1
+"RT @lizrhoffman: Goldman's profit down 46% on a wave of expected loan defaults, though its old Wall Street businesses held up well. https:/…",-1
+"RT @josephttwallace: Glutted Oil Markets’ Next Worry: Subzero Prices -- great article by @sarahtoy17
+ https://t.co/8v5PfdJlDX via @WSJ",-1
+"RT @WSJheard: Can’t get your hands on a Nintendo console to play the new version of ""Animal Crossing""? You could always buy Nintendo stock…",1
+"Heard on the Street: Luxury, liquor and sunglasses brands in airports will suffer from falling traveler numbers https://t.co/bW38B0LTjZ",-1
+"In areas where tanker-ship storage for oil isn’t readily available, producers could need to go to extremes to get r… https://t.co/secHR2Pscn",-1
+Emerging markets have rushed to tap a flood of easy money from investors in recent years. That is likely to put bon… https://t.co/x5XbcAmKth,1
+"The bank, the second largest in the U.S. by assets, reported revenue of $22.77 billion, down from $23 billion a yea… https://t.co/Vsgar9u1uV",-1
+Global oil demand is expected to fall by a record 9.3 million barrels a day this year as government-implemented loc… https://t.co/tC7XZi5Fcp,-1
+Global stocks fell as investors await key data on U.S. retail sales and manufacturing. https://t.co/UAw0HdXFKI,-1
+"Glencore, Teck, First Quantum and other miners, find some mines under siege by protesters accusing them of bringing… https://t.co/cS7tPLPgc6",-1
+"RT @Birdyword: Ironically, the surge in the price of gold is largely because the market expects the Fed's recent measures to be successful.…",1
+RT @CGrantWSJ: $JNJ CFO Joe Wolk tells me he wasn't 100% sure until last week that the company would be giving 2020 guidance today. Their a…,1
+"RT @joannechiuhk: The economy has almost certainly entered a recession affecting most of the world, with a severity unmatched by anything e…",-1
+RT @Trefor1: Renault is shutting its main China business. And it probably won't be the only struggling foreign auto maker to wave the white…,-1
+"RT @joannechiuhk: The banks for years rode consumer spending and borrowing to big profits. Now, they are preparing to struggle alongside th…",-1
+"For many small investors, their inability to cash out shares in nontraded REITs couldn’t come at a worse time. https://t.co/D1JJFO554f",-1
+KKR waves a flag of caution over damage to its business and investments from the pandemic  https://t.co/nJBHczoJLQ,-1
+Saudi Crown Prince Mohammed bin Salman’s Public Investment Fund is pressing forward with buyout of U.K. Premier Lea… https://t.co/gaL1jay0hG,1
+"Chinese businesses are bouncing back, but the pandemic has depressed economic activity and consumer demand in key W… https://t.co/6N34UNNHPF",1
+Clearlake Capital has raised more than $7 billion for new flagship fund  https://t.co/9vgKDP5ufP,1
+Lockdowns and travel curbs are delaying the work of compliance monitors who prefer to see first-hand if companies a… https://t.co/VYoWx2PhCR,1
+The Daily Shot: Freight Recession Deepens https://t.co/wMIRh7rXRa,-1
+Coronavirus is forcing companies to either postpone their annual meetings or take them virtual—a first for many exe… https://t.co/RwutyBuDxh,-1
+Biotechnology-focused venture capitalists are finding ways to continue forming new drug startups despite restrictio… https://t.co/DTBYJvl8z3,1
+"Mall and shopping-center owners are compiling a blacklist of large, usually financially stable tenants that haven’t… https://t.co/qgBnOqRKdg",-1
+"The economy has almost certainly entered a recession affecting most of the world, with a severity unmatched by anyt… https://t.co/57j70HPwXG",-1
+Distinguishing wants and needs in labeling which products are “essential” is tricky https://t.co/4nkl7acGsD,-1
+Sales of Listerine mouthwash have been surprisingly strong during the lockdowns. https://t.co/Fu3q2YAa0F,1
+Community lenders are having a tough time getting a piece of the $350 billion set aside under the SBA’s Paycheck Pr… https://t.co/ayl6imT2D7,-1
+"The banks for years rode consumer spending and borrowing to big profits. Now, they are preparing to struggle alongs… https://t.co/sJ5Ykau6Id",-1
+"While bank shares stumbled, the rest of the stock market took off today. Dow up 2.4%, Nasdaq up 4%: https://t.co/crzdvYix6F",-1
+"‘Amid coronavirus-related upheaval to the car industry, we believe Tesla increasingly has a leg up vs. other automa… https://t.co/EshF0EYkwx",1
+Heard on the Street: Johnson &amp; Johnson announced a dividend increase Tuesday—a bold statement in this economic envi… https://t.co/x29S85yW4l,1
+JPMorgan Chase and Wells Fargo have set aside billions of dollars to cope with the coronavirus fallout. That may be… https://t.co/4Qjo91tGth,1
+"A half-trillion-euro plan to cushion Europe from the coronavirus didn’t include common debt issuance, a negative fo… https://t.co/wEXnM8S7A6",-1
+"The mining industry, a bellwether for the globalized economy, has been hit by protests at mines world-wide… https://t.co/T3hnmdDpBA",-1
+"“Since humans started using oil, we have never seen anything like this.” The world’s thirst for oil is suddenly no… https://t.co/fMsg0XTP7b",-1
+"Heard on the Street: Norwegian Air could succeed in its last-ditch effort to survive the coronavirus crisis, but wi… https://t.co/mURLpUzK46",1
+Oil prices are down about 65% this year.  https://t.co/uB1V7H67xQ,-1
+The fund will provide financing to companies hit by the novel coronavirus pandemic. https://t.co/ED60IoCCDv,1
+"The Fed temporarily lifted restrictions on Wells Fargo’s growth, allowing the troubled bank to make more loans duri… https://t.co/9wEccwNQ28",1
+Heard on the Street: The plunging value of the portfolio companies in SoftBank’s $100 billion Vision Fund accounts… https://t.co/j82EgEAjzR,1
+Heard on the Street: Food deliverers such as Grubhub are dependent on the health of ailing restaurants https://t.co/BD1k2lqC8N,-1
+Heard on the Street: Don’t be fooled by improving Chinese exports https://t.co/0q5v6bc5Wl,1
+"JPMorgan Chase reported earnings, making it the first major U.S. bank to show how the spreading coronavirus has aff… https://t.co/FctuUBKRLJ",-1
+RT @miriamgottfried: Scoop: General Atlantic is teaming up with veteran credit investor Tripp Smith to raise a fund that will provide finan…,1
+Stock markets around the world rose on optimism that economic activity may improve resume in the near future https://t.co/iSORRoQWtL,1
+RT @joannechiuhk: SoftBank Expects Nearly $17 Billion Loss on Tech-Focused Vision Fund  https://t.co/6UDu1Ovy30 via @WSJ,-1
+"RT @NickTimiraos: Businesses, not governments, have a huge say in how economies reopen after the pandemic recedes https://t.co/wfRNRsokN4",1
+There are arguments for American banks both to suspend and to keep their dividend payouts. But the degree of credit… https://t.co/a30vacnv70,1
+"RT @PatFitzgerald23: The XFL was in the middle of a promising reboot season before canceling its games because of coronavirus, now Vince Mc…",-1
+"Citing improved money-market conditions, the bank said it would soon ease back on operations designed to add short-… https://t.co/GACHU03nOM",1
+Market research firms IDC and Gartner both reported a large and surprising drop in first-quarter PC sales on Monday https://t.co/3G9gnZn3IW,-1
+"The 30-year Treasury yield settled at 1.392%, its highest close since March 26. https://t.co/K6eY3hJOOs",1
+"RT @djtgallagher: WFH was going to be a very short-lived boost for PC sales, but even that didn't happen. Now,  a major recession ahead wil…",-1
+Beating the sports betting slump https://t.co/Mt4W89Clvd,-1
+"Increasingly desperate small businesses are seeking help wherever they can find it, filing claims totaling potentia… https://t.co/RcPVd0wfgV",1
+‘The Mooch’ is facing one of his biggest tests as SkyBridge struggles with the poor performance of its hedge-fund m… https://t.co/FffRTBcKg7,-1
+"GE is exposed in many ways to the economic downdraft of Covid-19, and Fitch’s downgrade is probably too mild and ov… https://t.co/mJFvX3wJCI",-1
+"With thousands of flights grounded, U.S. airlines are burning through cash. Selling miles to their credit-card part… https://t.co/i9dDmyJCDF",-1
+"Despite the market’s recent bounce off its late March lows, many investors who rely on technical analysis remain sk… https://t.co/FW863PWKbb",1
+Heard on the Street: Microsoft uses both intimidation and imitation against Zoom https://t.co/8R24awIr5H,-1
+Match Group could be a relatively stable suitor for jaded investors looking to get back in the game https://t.co/WgX4JCl6iW,1
+The loss will help push SoftBank into some of the worst results in the company’s 39-year history https://t.co/XbYyDRNm1W,1
+Oil prices waver amid skepticism that global supply cuts will be enough to offset plunging fuel demand https://t.co/3mUbz3m42d,-1
+How Coronavirus Spread Through Corporate America https://t.co/hyy2tYTAGN,-1
+Coronavirus cases rise in the U.S. as officials weigh when to restart the economy  https://t.co/swFM9pg2eH,-1
+A sharp drop in fuel consumption caused by the coronavirus pandemic and exacerbated by a feud between the world’s l… https://t.co/19EeJFLdB6,-1
+What the cuts in oil output can do is prevent an even worse collapse in spot prices and surge in inventories—Heard… https://t.co/FIMMkYUWB5,-1
+"Buyout volume, already sluggish before the virus, has come to a virtual standstill, and any hope of selling investm… https://t.co/ptKObwMktW",-1
+"Blackstone is investing $2 billion in Alnylam, giving the Massachusetts biotech company the cash it needs to bring… https://t.co/godFFsB1mr",1
+"U.S. stock futures edge lower amid thin trading volumes, while oil falters as a multinational agreement to cut crud… https://t.co/tdXIfNJWRY",1
+Streetwise: Scars from the coronavirus pandemic will persist for a generation of investors and future managers https://t.co/168sGkB8qH,-1
+“We haven’t really seen markets reflect the full extent of the damage that coronavirus is having to corporate profi… https://t.co/jTM2Mpi1jr,-1
+The Fed’s new programs to support the economy through the coronavirus crisis illustrate the sweeping influence the… https://t.co/z0KHVlteeE,1
+“We haven’t really seen markets reflect the full extent of the damage that coronavirus is having to corporate profi… https://t.co/d7h2syLa7G,-1
+One of the youngest CEOs in the U.S. oil patch wants Texas to mandate lower production as the coronavirus hits crud… https://t.co/57b5b5D2z7,-1
+"Heard on the Street: There is enough food to go around. But there is a shortage of transport capacity, agricultural… https://t.co/XEpYBXoIPb",1
+"Heard on the Street: Funeral providers would seem to have a reliable revenue stream in the Covid-19 pandemic, but t… https://t.co/5Ob6x5azoE",-1
+The largest U.S. banks begin to report earnings this week. Don’t expect much. https://t.co/42MSkGu6h4,-1
+Millions of Americans are entering old age saddled with mortgages. The burden will likely get heavier as the corona… https://t.co/RDr8qfeRMv,-1
+Denny’s Franchisee Dawn Lafreeda has had to close most of her restaurants and furlough staff; ‘It’s heartbreaking’ https://t.co/oqTkpajAav,-1
+The Federal Reserve previously made up doomsday scenarios to gauge whether banks could withstand a crisis https://t.co/L7rPbeid1S,1
+Here are seven major companies whose stocks moved on the week’s news https://t.co/5muUunVRzT,-1
+"Even as a loan program for small businesses weakened by the coronavirus pandemic expands, the first applicants stil… https://t.co/loiAEnfegp",-1
+"Months into the coronavirus outbreak, investors are still making returns off World Bank “pandemic bonds” and poor c… https://t.co/bYP89HWJEx",1
+"The cannabis sector must stay short of cash, but not too short, for property company IIP to maintain its edge durin… https://t.co/BQKvuhBJIX",1
+The new federal loans and a program available for bonds issued by companies that recently lost their investment-gra… https://t.co/buu9r5cr58,1
+"While the first quarter was horrible for almost any stock fund, some environmental, social and governance-oriented… https://t.co/vyJYbIYAOy",1
+"Lazard and Evercore will help Boeing analyze government aid and potential funding from the private market, people f… https://t.co/V3qPRWUfgJ",1
+Bond-Market Volatility Flashes Green Light for Stocks https://t.co/OiIxTmqCUu,1
+"The role of oil-world peacemaker is an unusual one for Mr. Trump, who has been a longstanding critic of cartel-base… https://t.co/y4aHnPJneF",1
+Floating-rate funds have taken on water lately. @jasonzweigwsj explains why these funds have taken such a big hit i… https://t.co/LW6e1tbklK,-1
+Mexico’s president said the country will reduce its crude oil production as part of an agreement with OPEC and othe… https://t.co/iI05phgxdG,1
+The big decline in oil prices has hit all three of the men’s power bases at a moment when the coronavirus threatens… https://t.co/zggVnn9sht,-1
+"The latest stock rally reflects optimism about the end of the economic paralysis, but investors have little perspec… https://t.co/6UTWZhiT6U",1
+"RT @GunjanJS: NEW: Bridgewater’s giant options bet helps limit losses 
+
+Trade that would pay out if the stock market declined before late M…",1
+Answers to questions from Wall Street Journal readers about how the temporary suspension of required minimum distri… https://t.co/bXF5nAtUbB,1
+"Is this the key to help restart an economy in the pandemic? Unlike many insurers world-wide, Chinese insurers are p… https://t.co/ZMenP8OCVy",1
+Fed Chairman Jerome Powell says central bank ‘watching carefully’ as firms brace for wave of missed mortgage paymen… https://t.co/huqWnS7D7t,-1
+Record-setting jobless claims and dire economic forecasts are giving fresh urgency to the debate over how rapidly c… https://t.co/JEB4TnPtdt,-1
+The pay boost for Blackrock CEO Larry Fink reflects a year where the world’s largest money manager took in record i… https://t.co/zuF7N9EUs5,1
+A bet that would pay out if the stock market declined before late March has made billions for the world’s largest h… https://t.co/GppXJn8Twr,1
+"As job losses ricochet across the U.S., Europe is conducting an unprecedented experiment in navigating the economic… https://t.co/5MhMo45HbG",-1
+Debt could hobble the post-coronavirus recovery. “Priority number one is going to be to pay down debt. We are not e… https://t.co/PutguQqbk8,-1
+Hedge funds are told they shouldn’t be taking aid if their management fees aren’t significantly impacted by the cor… https://t.co/A1vkeuP3KZ,-1
+LongTail Alpha funds capitalized on the markets’ reaction to the new coronavirus pandemic. https://t.co/hJaXfAmwhn,1
+"Even if the worst of the pandemic is over next month, job losses could be prolonged and their damaging effect even… https://t.co/VtoXsQO5D1",-1
+Heard on the Street: Central bank swap lines have prevented a global liquidity crisis. They could also pave the way… https://t.co/IfObCU0jcZ,1
+"The pandemic sends a flood of new subscribers to Disney+, offsetting some of the company’s other shutdown problems. https://t.co/mxbkBV225r",-1
+Morgan Stanley CEO James Gorman is the most senior Wall Street executive known to have been infected with Covid-19 https://t.co/NerWgqdgec,-1
+Prices for debt from companies including Ford jump on the Fed’s plans to buy new and existing bonds from so-called… https://t.co/lETbtyeyv2,1
+"Failure to complete the deal would be a blow to BP, which already has the highest debt levels among the major oil c… https://t.co/W9NDe5BuxE",-1
+U.K. foreign-exchange company paid about $2.3 million in bitcoin to cybercriminals months before its business began… https://t.co/ijJdf2DCJv,-1
+Russia and Saudis have agreed in principle to cut output https://t.co/1YbHOec5P5,-1
+"RT @djtgallagher: Disney can't pack people into theme parks, cruises and movie theaters for a while. Luckily, it's stream is now a cheap op…",-1
+"RT @jonsindreu: In my column today, I argue that central-bank swap lines should be used to finance Emerging Markets' balances of payments,…",1
+"RT @akaneotani: Something that seems unbelievable: despite another record jump in jobless claims, the U.S. stock market is on course for it…",-1
+RT @jdlahart: This Jobs Crisis Will Leave a Mark - my latest. I really did not want to learn how to spell “hysteresis” again. https://t.co/…,-1
+RT @kenbrown12: 2020 was supposed to be the year everything came together for Airbnb.The coronavirus has made that next to impossible. http…,-1
+Oil prices rise on expectations that major producers will agree to slash output https://t.co/mgUyo8yCuL,-1
+"RT @PaulJDavies: I mean, just jaw-dropping &gt;&gt; As job losses ricochet across the U.S., Europe is conducting an unprecedented experiment in n…",-1
+RT @JChengWSJ: @Kubota_Yoko Some U.S. companies are still plowing fresh investment dollars into China. Walmart said Wednesday it would inve…,1
+"RT @WSJheard: After two accounting scandals in less than a week, the Chinese growth story is becoming a tougher sell on Wall Street, says @…",-1
+RT @jasonzweigwsj: Latest round of emergency facilities expands the Federal Reserve’s footprint into credit markets it has previously avoid…,-1
+"Switzerland’s two largest banks, UBS and Credit Suisse, will pay 2019 dividends in two installments as banks across… https://t.co/sVFFlrvKi5",-1
+Heard on the Street: Consumers are craving some delivery options more than others amid the pandemic. https://t.co/7ysA9tMcq0,-1
+"Overheard: Disasters come and go. Human nature, unfortunately, stays the same. https://t.co/8gLiFoVa48",-1
+Heard on the Street: Germany’s biggest lender is a long way from profitability https://t.co/urm519LkSw,-1
+"When you pay with plastic, merchants have to pay up to banks. Soon, some small businesses could have to pay more. https://t.co/KcmN6jBj8j",-1
+Heard on the Street: Two accounting scandals are set to turn the spotlight back on corporate-governance problems of… https://t.co/q88bstB18d,1
+"Global stocks waver, while oil markets are buoyed by optimism that major crude producers including Russia may agree… https://t.co/PBDpkHgMcQ",1
+RT @PaulJDavies: Interesting timing&gt;&gt; Saudi Arabia’s sovereign-wealth fund has amassed stakes worth roughly $1 billion in four major Europe…,1
+2020 was supposed to be the year everything came together for Airbnb.The coronavirus has made that next to impossib… https://t.co/8bRrQvoRaz,-1
+Dow gains more than 770 points on hopes the economy will recover from coronavirus https://t.co/LoiMexyjZI,1
+President Trump gives a green light for Americans to make money in outer space. https://t.co/KWX3qAFbsA,1
+Trying to extend hundreds-of-billions of dollars of small-business credit through U.S. banks is like putting a roun… https://t.co/6CMGxbfnL5,-1
+"Wells Fargo has faced restrictions on its balance sheet growth for more than two years, the result of widespread co… https://t.co/QBnn2nDL2u",-1
+"Oil futures have rallied over the past week, lifted by hopes that the U.S. will join other producers in cutting pro… https://t.co/ZDIIL2DEbT",1
+Some retail chains fight for survival amid coronavirus https://t.co/x24GWiBmVb,1
+"U.S. statistical agencies are struggling to measure the economy during the coronavirus pandemic, with lockdowns and… https://t.co/iGobI7faYh",-1
+Investors in LSE-listed funds enjoyed solid returns for years but as the airline industry is pummeled by coronaviru… https://t.co/CjFvFYzxYw,1
+The coronavirus selloff has left fund managers nursing losses and local economies without a key source of financing https://t.co/xngaeSXiHH,-1
+Mainland Chinese investors have been buying Hong Kong-listed technology and bank stocks. The latter bet looks harde… https://t.co/Wo6JblQNvH,-1
+Italian government bond yields rose as Europe’s finance ministers failed to agree on a collective response to the e… https://t.co/Fi7sNJh76H,-1
+Mark Spitznagel’s Universa Investments made a crazy return offering a hedging strategy that serves as an insurance… https://t.co/nRi494kcxJ,1
+"Heard on the Street: For the next several months, consumer-price index data won’t reflect the drastic change in con… https://t.co/LFTtCQDgnb",-1
+New industry data provide the first hard look at how many Americans are struggling to make rent during the coronavi… https://t.co/jnD95fmOz6,-1
+Social media upstart Pinterest is showing that its ad business isn’t as disposable as some feared https://t.co/Ypeqi7tboL,-1
+"RT @Spencerjakab: Please ignore the “I saw the coronavirus coming, here’s what’s next” stuff and read my latest for ⁦@WSJheard⁩ instead htt…",1
+The Federal Reserve is limited in its efforts to revive the $4 trillion market for municipal securities. https://t.co/ymRweWcRXc,-1
+U.S. stock futures waver between gains and losses after a roller-coaster session on Wall Street… https://t.co/IkypxyNG6B,-1
+"RT @Birdyword: We're about to start getting inflation readings for March. The data will be volatile, and because it's based on a normal-era…",1
+Medical-product companies with female directors recall products with high severity issues 35% faster than those wit… https://t.co/598vPxQkdo,1
+"The coronavirus pandemic may, like the Great Depression, foster structural government policy change that outlasts t… https://t.co/nKY300dksw",-1
+"Many small businesses in the U.S. were vulnerable even before the coronavirus crisis hit, a New York Fed report sho… https://t.co/5Tx0aSTkWq",-1
+"U.S. economy is in a contraction, says Larry Kudlow, adding that it could reopen in four to eight weeks https://t.co/tgwRWgau7k",1
+"Riskier investments, including junk bonds and nongovernment-backed mortgages, are being left behind as markets atte… https://t.co/8uiVDNWl5b",-1
+"Office owners with long-term, stable leases hoped their buildings would become a haven for skittish investors when… https://t.co/t7FIR9436X",1
+While market strain triggered by the coronavirus pandemic has eased since the Federal Reserve’s historic interventi… https://t.co/AyvldKn33r,-1
+U.S. stocks are staging a remarkable two-day rally. But many analysts are calling the recent run a rally within a b… https://t.co/dR44js6oyO,1
+Investors hope Thursday’s OPEC+ meeting will signal an end to the Saudi-Russian oil-price war https://t.co/2dXum2tlfY,1
+SEC’s Clayton Says Companies Should Disclose Need for Bailout Funds https://t.co/R9S3scRwv4,-1
+"Qatar began marketing U.S. dollar-denominated bonds, the first Persian Gulf state to tap the debt markets since the… https://t.co/UknSZl5wZv",1
+"Indonesia sold $4.3 billion of bonds, including one it won’t pay off for 50 years https://t.co/YsELWjzMli",1
+"Business usually picks up in the spring, when warmer weather kicks off seasonal work. But the novel coronavirus cri… https://t.co/eJ7clEuR1l",-1
+"RT @BenEisen: A gap is forming between market haves and have nots. Riskier investments, including junk bonds and nongovernment-backed mortg…",-1
+"For years hospitals and large physician practices had a steady, predictable stream of sales and profits. The corona… https://t.co/PXcUx8bi7Y",1
+"RT @djtgallagher: Samsung - like Micron and Nvidia - is getting a nice WFH boost from data center demand. Pain may be coming later, @jackyc…",1
+Switzerland has lost its long-held position as the economy with the world’s lowest borrowing costs https://t.co/HScx73HvxY,-1
+While market strain triggered by the coronavirus pandemic has eased since the Federal Reserve’s historic interventi… https://t.co/H5iCiGtUtn,-1
+Heard on the Street: Social distancing has been good for Samsung so far. But its prospects depend on the global eco… https://t.co/yfEXvfksXR,1
+Heard on the Street: Fiat Chrysler-Peugeot is one of many big mergers now offering investors the prospect of huge b… https://t.co/X0cPiXHduu,1
+"RT @WSJheard: Social distancing has been good for Samsung so far, but that could change as the economy turns down, says @jackycwong
+https:/…",1
+RT @Spencerjakab: Some hot auto deals - not for drivers but brave investors - by Heard on the Street Europe editor ⁦@StephenWilmot⁩  https:…,1
+"RT @jdlahart: Spring usually brings a huge pickup in economic activity, but the novel coronavirus crisis is cutting that short. My latest h…",-1
+"Citi’s head of mergers and acquisitions for Europe, Middle East and Africa is self-isolating in the English country… https://t.co/v8CjkZE5q7",1
+"Riskier investments, including junk bonds and nongovernment-backed mortgages, are being left behind as markets atte… https://t.co/h67Ztr552b",-1
+Global markets extended gains amid early signs that lockdowns could be helping stem the coronavirus pandemic https://t.co/c7hNLeLevn,1
+RT @JamesGlynnWSJ: Glynn’s Take: RBA Not Sugarcoating Second-Quarter Slump #rba #ausbiz #ausecon  https://t.co/tIrw2j0hoF,-1
+"RT @WSJheard: Merger arbitrage has gone from being a game of small, fairly certain gains to one of potentially huge, highly speculative one…",1
+RT @R_Wall: Popular commercial-jet models have shed between 5% and 15% of their market value compared with the start of the year...they cou…,1
+"RT @JChengWSJ: In hindsight, Wall Street probably shouldn't have let Luckin Coffee's chairman have more or less unfettered access to half a…",-1
+RT @estherfung: Some Landlords Say Only 25% of Retail Tenants Paid April Rent https://t.co/1JuE0xG1AH via @WSJ,1
+"While the new $350 billion Paycheck Protection Program is aimed at businesses with 500 or fewer employees, big rest… https://t.co/F3O0m3u3J8",-1
+"With consumers worried about keeping their homes, a car dealer is spurring sales by making their mortgage payments. https://t.co/8OwvlffCHM",-1
+"Egg prices soar, forcing supermarkets and farmers to scramble  https://t.co/jvxxygK2ER",-1
+"RT @srussolillo: A beautiful story from @tepingchen: 
+
+Coronavirus forces Rosa Mendoza to shift from waitressing to feeding the needy. And…",1
+The development comes as the Chinese coffee chain’s share price has plunged after Luckin said much of its 2019 sale… https://t.co/aGtRWrRjk2,-1
+"A look at the short-term relief available to small businesses, so they can sustain their operations and keep their… https://t.co/fJEefW9EnW",1
+"Well-wishes: Mr. Hotze’s caps, hot-sellers online, read: ‘Make Oil $80/bbl Again.’ https://t.co/GyT54ta8Kx",-1
+The stricken Bank of Jinzhou will unload $21 billion of assets to the central bank for less than a third of their r… https://t.co/xGuCP9lBlH,1
+The SEC issued suspension orders for dental supplier No Borders Inc. and apparel maker Sandy Steele Unlimited Inc.… https://t.co/eb0ZFlNgjl,-1
+Prompts include showing investors the potential tax liability of a trade and the potential cost in terms of lost re… https://t.co/02HjAdaHgm,-1
+"Allstate said it would dispatch more than $600 million in shelter-in-place payback checks, https://t.co/OF3AyFHVkI",1
+The Dow has rallied 6% today as investors react to early signs that lockdowns in the U.S. and Europe may be helping… https://t.co/W7UUwREpdJ,-1
+"So many funds use ESG in their names now, regulators are looking hard at whether the products live up to their bill… https://t.co/KY2KEM9e1S",1
+One assumption to avoid: overestimating the duration of the market retrenchment https://t.co/xhEt9gDXki,1
+"Applications for participation in the Fed’s Commercial Paper Funding Facility must be submitted by Thursday, the Ne… https://t.co/j0cSiptJDj",1
+“You are playing with fire when you are investing in something that will have three times the volatility of the S&amp;P… https://t.co/nUjx4fHTcR,1
+Saudi Arabia and others in OPEC are working to convince oil producers there will be little space left to store thei… https://t.co/bJytfI2ytW,1
+The Fed and Treasury Department are planning to launch a program to buy loans that financial firms make through the… https://t.co/DUUAXs3yzq,1
+Berkshire Hathaway’s financing of Occidental Petroleum’s Anadarko deal could see it own a large chunk of the compan… https://t.co/lDO85FI6hz,1
+"James Dinsmore attributes Bancroft Fund’s unusually strong 2019 to falling rates, judicious use of leverage and a m… https://t.co/BBrBuvoWD9",-1
+"Not everyone panics in the stock market, but a professor has figured out what some do https://t.co/kKCDfT6Bko",1
+RT @PaulPage: The False Choice Between Lockdowns and the Economy:  Countries without lockdowns are in economic free-fall too https://t.co/U…,-1
+"RT @telisdemos: Luckin Coffee chairman had a half-billion dollar margin loan, global banks stand to lost $100m https://t.co/HdwRjLaCP8",-1
+A reason you don’t panic with your 401(k) when stocks plunge: The Dow is up 13% from its low https://t.co/DBPMUsCOF3,1
+Heard on the Street: Beijing has been more restrained with monetary policy in the coronavirus crisis than Washingto… https://t.co/DKPQAqjEki,1
+"After Lehman declared bankruptcy in 2008, a group of market-timing services recommended extensive short positions i… https://t.co/AhzlyhnZfg",-1
+Every target-date fund isn’t the same. That became clear in the market rout. https://t.co/XBaICwIj7Z,1
+"Congress has offered to delay the implementation of new loan-accounting rules for U.S. banks, but banks have good r… https://t.co/6mKibJZ6d0",-1
+The big banks’ decision to focus on existing customers could hurt smaller and minority-owned businesses that are le… https://t.co/abZxgFnH4z,-1
+What are professional money managers doing to limit the damage to portfolios? Here’s what a few had to say https://t.co/9xInrdyZJU,1
+Coronavirus pandemic has made videoconferencing upstart Zoom a household name https://t.co/2ufj74gwkL,1
+Car insurer American Family is handing out $200 million in refunds as Americans drive less and have fewer accidents… https://t.co/a0X6iJ3ppS,1
+JPMorgan CEO Writes in Shareholder Letter: Expecting ‘a Bad Recession’ https://t.co/Xli9jXZdz8,-1
+"Oil prices fell Monday, paring some of their recent rebound after a virtual summit for producers to discuss supply… https://t.co/2RHm8CWP5G",-1
+Inflows with a capital I: The S&amp;P 500 fund took in $11 billion in March https://t.co/kTk0dimVl4,-1
+"In this market, gaining 12.8% for the 12 months makes you the top stock-fund manager https://t.co/aVQ9SiucK7",1
+"RT @kenbrown12: Kept at home by the coronavirus, many Chinese fall behind on their debts. Consumer debt has risen a lot in the past few yea…",-1
+"RT @djtgallagher: Zoom is now a household name, which also puts its problems under a microscope. For @WSJheard - https://t.co/sQx0aPProo",-1
+"Bonds haven’t been much of a haven lately, so what now? Here’s what some advisers say bond investors should be doin… https://t.co/bePOby82D2",-1
+The gold-silver ratio has had a big run-up in recent weeks. Some traders see an opportunity. https://t.co/7Xu6leolLs,1
+"Most new bond sales are still coming from well-established companies with higher credit ratings, reflecting a conse… https://t.co/TXM1QAgeHD",1
+"RT @WSJheard: China has been more restrained with monetary policy in the coronavirus crisis than the U.S. or Europe, but that is probably a…",-1
+"Heard on the Street: Aircraft values have plummeted since the start of the Covid-19 crisis, but likely have much fu… https://t.co/CzcZD5rV0c",-1
+People who had the guts to invest in stocks during the panic in March 2009 were rewarded for years https://t.co/uwdyW3oWaK,1
+Heard on the Street: Countries without lockdowns are in a state of economic free fall too https://t.co/vOsllfe3G0,-1
+RT @jasonzweigwsj: Heard on the Street: Countries without lockdowns are in a state of economic free fall too https://t.co/Aram6sjZU1 via @W…,-1
+“Bond funds still make sense.” Negative rates won’t change that https://t.co/UjfPulPfYg,-1
+Global markets rise following fresh signals that lockdowns in the U.S. and Europe are helping slow the coronavirus… https://t.co/JZby0qx2Yd,1
+Are investors selling gold to cover margin calls? Some say that may explain a recent drop in gold prices https://t.co/dCL6G3hzfB,-1
+Many hedge-fund managers are hanging in during the market rout; but it’s not a good time to be in emerging markets https://t.co/EbJOdclnEv,-1
+"Saudi Arabia has delayed pricing its crude for May delivery, a cease-fire in a price war that has contributed to a… https://t.co/MXR4xmIlYK",-1
+RT @Birdyword: Lockdowns solve coordination problems: The alternative to enforcement could be millions independently trying to avoid infect…,1
+At least a quarter of the U.S. economy has gone offline due to the widespread shutdowns of business due to coronavi… https://t.co/PF3gG4Nqu1,-1
+"Many companies have suspended share-repurchase plans, removing a crucial pillar of support for the stock market dur… https://t.co/Ixi9QcssF9",-1
+"Wells Fargo curtailed its program for making large loans this week, one of the most pronounced signs yet of how the… https://t.co/y3nEawJpNy",1
+Investors want massive cuts to global oil supply as an initial measure to address the ballooning coronavirus crisis https://t.co/D3DmKTYKRM,-1
+"Kept at home by the coronavirus, many Chinese fall behind on their debts https://t.co/QMNpSF4abe",-1
+"RT @greg_ip: Virus-related shutdowns have reduced GDP by 29%, with 1/10th of that attributable to 3 counties: L.A., NY (Manhattan) and Cook…",-1
+"The Month Coronavirus Felled American Business, #longreads https://t.co/IVxakomknb via @WSJGraphics",-1
+"Supply-chain finance has become popular in recent years, but there are concerns the slowdown could expose weak spot… https://t.co/qwiBA9i4pj",-1
+"As the coronavirus pandemic intensifies, adherents of the Financial Independence, Retire Early movement are doublin… https://t.co/klzYRO889R",-1
+Movie theater demand should eventually recover—if the chains can survive https://t.co/kZhV0EkOtp,1
+"Coronavirus reminds investors that stocks have a risk premium for a reason, writes @jmackin2 https://t.co/b5TqyWVhIj",-1
+Outbreak: How coronavirus swept through JPMorgan’s trading floor https://t.co/Gs9igG8sA1,-1
+Here are seven major companies whose stocks moved on the week’s news https://t.co/Ttv5qTV3ow,1
+"U.S. banks will likely be allowed to keep paying dividends, even as the coronavirus pandemic threatens to create a… https://t.co/aHnWwiCClV",-1
+Outbreak: How coronavirus swept through JPMorgan’s trading floor  https://t.co/duMydNH3qy,-1
+The coronavirus pandemic has forced some difficult decisions for families and individuals. This series explores how… https://t.co/5AGI4pLUY6,-1
+Mortgage relief from the coronavirus crisis is off to a rocky start https://t.co/X7ls1xAE0l,1
+Investors on today's jobs number: ”I don’t think anybody’s surprised that it was a terrible month...We know it’s go… https://t.co/05YIheTICU,-1
+"If you fear you might lose your job, there are some proactive steps you can take to feel more secure about your fut… https://t.co/rhRCNZ6Inh",1
+What’s EFC on a college-aid letter? What about COA? Here’s what you need to know. https://t.co/SHFwDkX0e2,1
+"In a sign of receding tensions in international finance, the cost to borrow dollars against other currencies market… https://t.co/VAvMRmY3cR",-1
+American car-loan payment terms have never been so generous https://t.co/IkxqKz7B0L,1
+"Stunning as the drop in jobs in March was, it masked the true depth of the decline. And it is just the beginning. https://t.co/7pmmD1sLcy",-1
+Investors can survive a bear market the same way hikers survive an encounter with a bear: Remain calm and don’t mak… https://t.co/RLOCicZeGg,1
+Students and their families can request a financial-aid review if the economic fallout from the pandemic has hit th… https://t.co/PEKzZjwQ4U,1
+"Many merchants still ask customers to sign for card purchases, rankling shoppers who are nervous about the coronavi… https://t.co/jfSjiFtJHw",-1
+"Isopropyl-alcohol prices have more than tripled in the U.S. since March 10 and reached $3,160 a metric ton Tuesday,… https://t.co/O5EeZa1FVb",-1
+"RT @Spencerjakab: ""A lot of advice for surviving a falling stock market will sound familiar to backwoods hikers who’ve had grizzly encounte…",1
+The implosion of a Starbucks wannabe in China shows how expensive it can be to do reliable due diligence on faraway… https://t.co/lQQOc9vOCw,-1
+"RT @srussolillo: This is absolutely terrifying: Forecasting firm Oxford Economics projects that by May, the U.S. will have lost 27.9 millio…",-1
+"‘Recession blue-chips’ led the way in another turbulent week in markets, with Regeneron Pharmaceuticals, Gilead Sci… https://t.co/tmLGIg70nr",-1
+RT @miriamgottfried: SEC’s Clayton Signals Sympathy for Pleas of Private Equity-Backed Firms @davidamichaels https://t.co/JlOD0h6DP7,1
+The Federal Reserve has succeeded in flooding the market with cash. The challenge now is convincing everyone to use… https://t.co/he0xmItlGq,1
+"If your mortgage is federally backed, lenders are supposed to allow forbearance for those experiencing a financial… https://t.co/68EObPMqVO",1
+The March jobs report shows the start of a collapse that could shed all the U.S. jobs added by employers in the pas… https://t.co/pCJLsr1UJS,-1
+Is there a tax break hiding in your home office? https://t.co/xfCOlsTKYm,-1
+"Trying to get a break on mortgage payments? Servicers are supposed to help during the coronavirus pandemic, but jus… https://t.co/V8IXQuFxCa",1
+Property Bets Lose Curb Appeal in the Age of Coronavirus https://t.co/Tm7wZjfBJf,-1
+China’s ownership cap for foreign shareholders in securities and fund-management firms was scrapped as of April 1 https://t.co/tzGywGy2ys,-1
+"RT @WSJheard: The saga over Starbucks' Chinese challenger Luckin may give investors the caffeine crash they needed, says @jackycwong
+https:…",1
+Altria’s fraught investment in e-cigarette company Juul Labs will take even longer to redeem following a challenge… https://t.co/AN7j1yYOe9,-1
+"Stock markets around the world fell, as oil continued its rebound and fresh data showed the unprecedented impact th… https://t.co/VSLulC2WL1",-1
+"RT @JChengWSJ: For the second time in less than a month, China’s central bank will free up more cash for banks to lend, part of Beijing's e…",1
+RT @minzengwsj: The record unemployment claims reported Thursday are unlikely to show up in Friday’s key jobs report. Here is why.  https:/…,-1
+"RT @srussolillo: “A company wouldn’t admit to something this bad unless it has to"": Luckin, Starbucks' biggest rival in China, says employe…",-1
+The government raised the interest rate it will pay lenders under an emergency loan program to make it more appeali… https://t.co/LMvOPHTA6q,1
+"With a single tweet Thursday, President Trump accomplished what decades of wars, coups, terrorist attacks and OPEC… https://t.co/NySXLTP4uU",-1
+The U.S.’s national medical stockpile has sent out nearly half of its ventilators—an amount that pales in compariso… https://t.co/fexQEHpg4n,1
+American Century Investments’ new U.S. exchange-traded funds are aimed at reviving investors’ interest in stock-pic… https://t.co/4m5r9Be2Yz,1
+"Prizes for Powerball will drop significantly and rise more slowly after the next jackpot, making the coming days an… https://t.co/T1rUSuPUS8",-1
+This columnist got slammed like you did during the market’s coronavirus rout. But he didn’t give in https://t.co/gtwco9JLIC,-1
+"Japanese technology group SoftBank terminates $3 billion offer for shares in office-space provider WeWork, deprivin… https://t.co/HaZxjV1KU7",-1
+The leading bond-market investment benchmark’s outperformance against the S&amp;P 500 underscores the extent of the rec… https://t.co/qtSRIAg0pm,1
+"About 6% of the U.S. labor force has filed for jobless benefits in the last two weeks, up from 0.3% at the end of F… https://t.co/iDmK8birwA",-1
+Main crude-oil benchmarks just spiked more than 20% in the biggest one-day jump on record https://t.co/8LKMfJjiXu,1
+"RT @srussolillo: This is a really great chart and, perhaps, a hint of good news: How the market reacted 12 months after some of the biggest…",1
+Saudi Arabia signaled a reversal of its oil-war against Russia after U.S. President Trump sought help to rebalance… https://t.co/C99xvPvJaA,1
+Not even drugstore chains can tell investors what the future looks like. Investors of all sorts should take notice. https://t.co/lnIboF2atR,-1
+"JPMorgan Chase Chief Executive James Dimon returned to work this week, a month after undergoing emergency heart sur… https://t.co/N92vM0lYPh",1
+"U.S. corporate bonds suffer negative ratings moves, while analysts say more may be coming https://t.co/w35t7jJkwY",-1
+"Weekly jobless claims, which were once again far worse than expected, are the most important economic data series f… https://t.co/Rahxhfd0n8",-1
+RT @WSJheard: Heard on the Street's @jackycwong on Luckin back in January https://t.co/hD7FB2pln6 $LK,1
+"RT @AmrithRamkumar: Just unreal moves in oil right now after Trump touts supply cuts. Brent rose as much as 47%. Now up 30%:
+https://t.co/s…",-1
+"RT @kenbrown12: The stock market is gyrating wildly, but despite spending millions of dollars regulators can’t always get accurate and time…",-1
+Heard on the Street: Investors are right to be nervous about the insurance sector https://t.co/YnMR7Aorrt,-1
+RT @ScottMAustin: This is such a shocking jobless claims chart  https://t.co/2CoZDmWcob https://t.co/05wwSY5ms7,-1
+"Shares in big oil companies climb as President Trump says the Russia-Saudi price dispute could soon be resolved, an… https://t.co/AAg81QqaU7",1
+Claims for unemployment benefits surged again last week amid a drastic downshift in the labor market caused by the… https://t.co/QCnvOQU28Z,-1
+"For once, the odds aren’t in casinos’ favor https://t.co/tyA0Z3iZIf",-1
+RT @davidhodari: Oil jumps on hopes for an end to the price war between Saudi Arabia and Russia and the possibility of action from the Whit…,1
+"China is tiptoeing, not roaring, back. Hopes for a speedy recovery in the West once the epidemic peaks may also be… https://t.co/euKX0mMDJr",1
+"The stock market is gyrating wildly, but despite spending millions of dollars regulators can’t always get accurate… https://t.co/uF95YpKp1N",-1
+"The coronavirus pandemic is not only costing Trump properties over $1 million a day in lost revenue, but also compl… https://t.co/L7t8ljy70p",-1
+Hedge fund manager Chris Hansen started getting worried about the coronavirus in January. Now he’s up 36%. https://t.co/HO2K3h4RQm,-1
+A market-wide breakdown exposed vulnerability in the nearly $4 trillion municipal-bond market https://t.co/Wy3sjqt4Mo,-1
+Oil jumps on hopes for an end to the price war between Saudi Arabia and Russia and the possibility of action from t… https://t.co/x8Wn3hQew4,1
+U.S. stock futures recover ground while oil prices surge on hopes of an end to the Saudi Arabia-Russia price war https://t.co/NH2BwPhtsh,1
+"RT @nate_taplin: China is tiptoeing, not roaring, back. Hopes for a speedy recovery in the West once the epidemic peaks may also be misplac…",1
+"RT @PaulJDavies: U.S. stocks dropped &gt;4% yesterday, but Treasurys rose more normally as they did. Today so far European stocks are up a bit…",-1
+RT @kosakunarioka: SoftBank Group Abandons Tender Offer for Up to $3 Billion of WeWork Shares https://t.co/KdplPZNs8q via @WSJ,-1
+RT @lizrhoffman: The first 3 Wall Street banks have been hired by the federal government to sort through the corona-aid muck. They’re not w…,1
+RT @minzengwsj: WSJ: Boeing is expected to begin offering early retirement and buyout packages to its workforce as the plane maker comes to…,1
+"RT @NHendersonWSJ: The Fed is settling into its role as the world's central bank, again. Allowing foreign central banks to access the Fed’s…",1
+"RT @joannechiuhk: The Federal Trade Commission is suing tobacco company Altria to unwind its $12.8 billion investment in Juul, accusing the…",-1
+Check out today's Journal podcast on how corporate debt may be the next coronavirus financial crisis https://t.co/OmWrLIc6JN,-1
+"The surging popularity also has given rise to a new form of online harassment, known as Zoombombing, where online t… https://t.co/3ixDcO6kuG",-1
+The Federal Reserve on Wednesday said it was temporarily taking steps to ease an obscure capital requirement for la… https://t.co/VwBpv6hEvD,1
+"Whiting, one of top drillers in North Dakota’s Bakken region, is the first sizable fracker to succumb to the curren…",-1
+The Dow industrials just wrapped up their sixth worst quarter on record. The second quarter didn't start much bette… https://t.co/6sdYhPCjHi,-1
+A European fund imposes a levy on shareholders trying to exit the fund https://t.co/ipARaR7IO2,-1
+The federal government is sending out money. Here are all the details  https://t.co/ikrtCmd3FX https://t.co/ikrtCmd3FX,1
+"April will only be harder for manufacturers, with more states and localities stepping up efforts to halt the spread… https://t.co/750HMWQFmd",-1
+Exclusive: A top Occidental Petroleum executive has been forced out of the company as it deals with the repercussio… https://t.co/1YFkN3FoQg,-1
+One of the enduring changes from the pandemic could be a major reassessment of the complex global supply chains for… https://t.co/kWL9GcJQdS,-1
+"Sen. Kelly Loeffler and her husband, New York Stock Exchange Chairman Jeffrey Sprecher, purchased and sold about $1… https://t.co/ouqUJZPDFD",1
+British American Tobacco is an awkward fit among the pharmaceutical and biotech firms seeking a new coronavirus vac… https://t.co/JVwnfFRJeu,1
+"RT @paulwsj: When 40,000 Italian fans gathered for the biggest soccer match of their lives, none had heard of social distancing. Two weeks…",-1
+The de-facto OPEC leader lifted production above 12 million barrels a day. https://t.co/y7xEkGhnB3,1
+"RT @DrewFitzGerald: And then there were 3: AT&amp;T, Verizon and T-Mobile https://t.co/YbaAavirhx",1
+"Saudi Arabia is ramping up its oil output, boosting production capacity and hiring new tankers to fight its price w… https://t.co/OlZlB84kj8",1
+"Many companies are looking to suspend, delay or shrink their contributions to employees’ 401(k) accounts to conserv… https://t.co/9HMd1DpFvF",-1
+RT @AndrewScurria: 1st large oil-and-gas bankruptcy stemming from coronavirus/Saudi-Russia price war. Whiting Petroleum files for chapter 1…,-1
+"Markets have entered a calmer stretch, but investors warn prices for stocks and corporate debt could still fall muc… https://t.co/AJmgfLPtDj",1
+"RT @margotpatrick: It's a no to Coronabonds, says @jonsindreu - Proponents of coronabonds risk repeating the eurozone’s original sin: finan…",-1
+A public-health disaster of this magnitude always has more than one cause https://t.co/gU2nyqb4uC,-1
+"Heard on the Street: Allowing foreign central banks to access the Fed’s repo facilities is effectively cost-free, b… https://t.co/KDeHcUVL3C",-1
+Heard on the Street: Booming sales at supermarkets as well as e-commerce companies such as Amazon will be partially… https://t.co/gzMXZ98KKT,1
+"Investors are abandoning the market for loans in which the government doesn’t shoulder the risk, starving the lende… https://t.co/7T7mzIVhss",-1
+"For investors, the alternative to becoming an epidemiologist is to assume the worst and find investments that can r… https://t.co/Z5RaeEVzeP",-1
+"BlackRock, the world’s largest money manager, is among the foreign companies that have submitted applications to en… https://t.co/ZHasH2elqA",-1
+Global stocks fall after President Trump issues a new warning on the spread of coronavirus in the U.S. https://t.co/a7Igw0nEFQ,-1
+"RT @jonsindreu: Now is not the time for ""coronabonds:"" The ECB gives crisis-struck Southern Europe all the flexibility it needs to fight Co…",-1
+"RT @Birdyword: The US dollar is the world's unchallenged reserve currency, and the Fed is the world's de facto central bank. During the cur…",-1
+RT @TByGraceZhu: China scrambles to keep biggest graduating class in years occupied in coronavirus-hit economy  https://t.co/o4PbJnRG4A via…,1
+"RT @JChengWSJ: China's manufacturing rebound is real, but it's far from back to normal. ""The recovery path ahead of us is going to be slow…",1
+RT @JamesGlynnWSJ: Glynn’s Take: Scope Now for Optimism in Australia #auspol #ausbiz #ausecon   https://t.co/aiQFiSFGL1,1
+Operator of traffic cameras is in trouble as fewer people get tickets https://t.co/cWrhr5sy4z,-1
+"The coronavirus pandemic may or may not have saved HP from the clutches of Xerox. Either way, the PC and printer gi… https://t.co/j9VwY8VwpM",1
+Doctors and nurses are coming out of retirement to help with the coronavirus crisis even though many are in a vulne… https://t.co/OZT9vPBd11,1
+The senior housing industry was facing financial pressures from a supply glut even before the coronavirus pandemic… https://t.co/aurTQcP9jg,-1
+"Standard Chartered must pay £20.47 million for violating sanctions imposed by the EU on Russia, U.K. authorities sa… https://t.co/cLbqzZioYh",-1
+"Mortgage REITs have one of the riskiest business models in real estate: they issue high-yield loans, and finance th… https://t.co/JyXdfWSrT6",-1
+Coronavirus pandemic prompts Xerox to walk away from its more than $30 billion takeover bid for HP https://t.co/qEdB9KEo4J,-1
+The Dow falls 410 points to end a period marred by coronavirus https://t.co/v4PutP9tYc,-1
+Labor Secretary Eugene Scalia: Federal government will this week release funds from a coronavirus stimulus package… https://t.co/8lGOxvj5He,1
+Investors are showing more appetite for the junk-bond market https://t.co/RHRIXkxgTN,1
+"With the coronavirus pandemic bringing shelter-in-place orders, the real-estate industry has been compelled to find… https://t.co/80sxYZhiQL",-1
+RT @lizrhoffman: This is an absolutely wild chart. The Fed grew its balance sheet by almost *$1 trillion* in the last two weeks. Twice as f…,-1
+President Trump called for infrastructure investment to be a part of a fourth congressional coronavirus relief pack… https://t.co/E90MhctkTm,1
+Calm before the storm: the U.S. housing market was set for a strong year before the coronavirus pandemic https://t.co/2q3JRWc0dR,-1
+RT @ScottMAustin: Read this great story by @DanaMattioli and @SebasAHerrera about how Amazon didn't have a pandemic plan and is now straini…,-1
+Walmart to check worker temperatures amid coronavirus crisis  https://t.co/4uUurciDGy,1
+"With Saudi Arabia preparing to flood oil markets, countries including Iraq and Venezuela are cutting spending even… https://t.co/ssMonuktKI",-1
+"Overwhelming demand, mass absences and a restive workforce. Amazon struggles with its coronavirus response. https://t.co/IepzajuiU0",-1
+Wind and solar farms are attractive to investors looking for safety in volatile markets. But building new renewable… https://t.co/2fqFexxjpj,1
+"Government May Act Out Of Fear, Hold Back In COVID Fight: Rajiv Bajaj
+https://t.co/hJ0dxsJ0cR",-1
+"Sensex ends 986 points higher, Nifty reclaims 9,250 as markets extend gains to second day; financial stocks jump… https://t.co/l4iADS3BXL",1
+"RBI's New Measures To Support Financial Markets And Banks
+https://t.co/X904Zq4r3F",1
+"RBI Sees Inflation Falling Below 4% As COVID-19 Outbreak Threatens Demand
+https://t.co/UJC6biiehI",-1
+"RBI Cuts Key Rate To Boost Liquidity, Rs 50,000-Crore Push For Lenders
+https://t.co/HG1fgOgbwO",1
+"Oh Brother! Coronavirus Calls Split Family Fortunes On Wall Street
+https://t.co/9o7l9OrjmI",-1
+"Sensex Holds 500-Point Lead Led By Gains In Rate Sensitive Stocks
+https://t.co/VkKu5ZDlwx",1
+"90-day NPA norm not applicable on moratorium granted on existing loans by banks, says RBI Governor",-1
+"Liquidity adjustment facility reverse repo rate reduced by 25 basis points to 3.75%, says RBI Governor",-1
+"Auction of targeted long-term repo operations worth Rs 25,000 crore to be conducted today, RBI Governor announces",1
+"RBI to conduct Targeted longer-term refinancing operations (TLTRO) 2.0 for Rs 50,000 crore, says Governor Shaktikanta Das",1
+"India is expected to post sharp turnaround in 2021-22, says RBI Governor quoting IMF projection",1
+"Injected liquidity to the tune of 3.2% of GDP between February 6 and March 27, 2020, RBI says",1
+"India among handful of countries projected to register positive growth rate amid the Covid-19 crisis, says RBI Governor",-1
+RBI has been proactively monitoring coronavirus situation very closely: Governor Shaktikanta Das amid COVID-19 crisis,1
+Reserve Bank of India has been very proactive and monitoring the situation closely: Governor Shaktikanta Das,1
+"RBI Governor Shaktikanta Das starts Press Conference, his second press briefing since the COVID-19 outbreak",1
+"Sensex opens over 1,050 points higher, Nifty starts day above 9,300 tracking positive global cues #Sensex #Nifty #MarketUpdates",1
+"Gap-Up Opening Likely For Sensex, Nifty Today
+https://t.co/FTjaOdNnXV",1
+"Gold Demand Could Hit 3-Decade Low As COVID-19 Lockdown Hits Festivals, Weddings
+https://t.co/0d9fAlSZWt",-1
+"Sensex Ends 223 Points Higher As Markets Break 2-Day Fall
+https://t.co/M1eQAtRUVX",1
+"Sensex ends 223 points higher, Nifty at 8,993 as markets break 2-day fall; financial, energy, pharma stocks lead ga… https://t.co/oq4zRk8PkB",1
+"Merchandise Exports Shrink 35% In March, Steepest Decline Since 1991
+https://t.co/UR871jGaGz",-1
+"Government Announces Relief On Health, Auto Insurance Policies Due For Renewal During Lockdown
+https://t.co/qgGepVryGH",1
+"Sensex opens 284 points lower, Nifty starts day near 8,850 as coronavirus fears linger in global markets #Sensex #Nifty #MarketUpdates",-1
+"March Exports Shrink, Outlook Grim As Coronavirus Hits Demand
+https://t.co/jAFHsoxUfJ",-1
+"Growth May Slip Into Negative In Q1: Ex-RBI Governor C Rangarajan On Lockdown
+https://t.co/FYfJoMbgSm",-1
+"Wipro March Quarter Profit Down Over 5% At Rs 2,326 Crore
+https://t.co/yVOHG9z0pL",-1
+"Wipro profit at Rs 2,326 crore in January-March, drops over 5% from previous quarter",-1
+"Sensex, Nifty Extend Losses To Second Straight Day
+https://t.co/fvhJCJDiV5",-1
+"Sensex closes 310 points lower, Nifty at 8,925 as markets extend losses to second straight day #Sensex #Nifty #MarketUpdates",-1
+"Rupee Slides 17 Paise To Hit All-Time Low Of 76.44 Against US Dollar
+https://t.co/1qge6kMP0q",-1
+"Crude Oil Hits 18-Year Low Amid Coronavirus-Fuelled Demand Slump
+https://t.co/fvsloy6ewl",-1
+"Sensex Slumps Over 1,000 Points From Day's High; Banks Under Pressure
+https://t.co/klEnpzbE0I",-1
+"Sensex Slumps Over 700 Points From Day's High; Banks Under Pressure
+https://t.co/klEnpzbE0I",-1
+"Wholesale Inflation Eases To 1% in March
+https://t.co/mWg2yIUX38",-1
+"Wholesale inflation at 1% in March amid coronavirus crisis, as against 2.26% in previous month",-1
+"Sensex opens over 550 points higher, Nifty starts day near 9,200 tracking gains in global markets #Sensex #Nifty #MarketUpdates",1
+"Airline Revenues To Plunge 55% In 2020 On Coronavirus: Industry Body IATA
+https://t.co/CNzXsVOelC",-1
+"Crude Oil Price Rises Over 1% On Hopes For Stockpile Purchases
+https://t.co/wHp9AP22VE",1
+"Banks Issue Orders To Employees Over Branch Overcrowding: Report
+https://t.co/Q0sd3deYdG",1
+"India Inc Stands By Lockdown Extension Call; Seeks Stimulus Package To Rebuild Economy
+https://t.co/pPSa2aGqZ3",-1
+"India To Grow At 1.9% This Year, Global Growth To Shrink By 3%, Forecasts IMF
+https://t.co/urepSnT2VY",-1
+"Government-Run Gold Bond Scheme To Fetch 2.5% Interest. Important Dates, How To Invest And Other Details
+https://t.co/RsEcoxK54T",1
+"Amazon To Add 75,000 More Jobs Amid Coronavirus Pandemic
+https://t.co/nIWBVxFEmi",1
+"Retail Inflation Could Slow Further Going Ahead: Experts
+https://t.co/s2HZHX9U0q",1
+"India's 2019-20 Fuel Demand Growth Worst In Over Two Decades
+https://t.co/YfTp4gWwLS",-1
+"Consumer Inflation Eases To 5.91% In March, Lowest In 4 Months
+https://t.co/FCtgc2ux51",-1
+"Consumer Inflation Hits 4-Month Low Of 5.91% In March
+https://t.co/FCtgc2ux51",-1
+"Consumer inflation eases to 5.91% in March, lowest in 4 months",1
+"Operations At Three Ports Run By Adani Ports Disrupted: Report
+https://t.co/qTdcN7pTyb",-1
+"Coronavirus Crisis: Domestic Passenger Vehicle Sales Down 51% In March, Says Industry Body SIAM
+https://t.co/ftR5ijOVCu",-1
+"Sensex Falls Over 500 Points; Reliance Industries, HDFC Bank Top Drags
+https://t.co/Pj1zRQMFhK",-1
+"Crude Prices To Continue To Fall In Coming Weeks, Historic Deal Insufficient: Goldman Sachs
+https://t.co/tSM4X3B6WW",-1
+"China Central Bank Raises Stake In HDFC To Over 1%
+https://t.co/xSJqMprLWq",1
+"Rupee Edges Lower To 76.43 Against Dollar Amid Coronavirus Crisis
+https://t.co/7UCEt57hpb",-1
+"Sensex Falls Over 200 Points, Nifty Slides Below 9,050 Amid Volatile Trade
+https://t.co/ylhFKID7Mf",-1
+"Sensex falls over 200 points, Nifty below 9,050 in early trade tracking weakness in global markets amid coronavirus… https://t.co/di5z90Yfiz",-1
+"OPEC, Russia Approve Record Oil Supply Cut Amid Coronavirus Pandemic
+https://t.co/3NEkw9qGfB",-1
+"People's Bank Of China Acquires 1% Stake In HDFC Amid Markets Downturn
+https://t.co/9nwlOw70w1",1
+"World Bank Forecasts Worst Economic Slump In South Asia In 40 Years
+https://t.co/oOD4jxwWe3",-1
+"US Consumer Prices Record Largest Drop In Five Years
+https://t.co/SLzfbHMMFr",-1
+"Travel, Hospitality To Take Time To Recover Post Lockdown: Experts
+https://t.co/iLkyXEeSNb",-1
+"ICICI Bank To Deploy Mobile ATM Vans In Noida, Other Districts In UP
+https://t.co/BlDgnfeL6Y",1
+"US Jobless Claims Top 6 Million For Second Straight Week Amid Coronavirus
+https://t.co/ezbj324eet",-1
+"Saudi Arabia, Russia Outline Record Supply Cut As Demand Crashes On COVID-19
+https://t.co/V1HHFM0Pus",-1
+"Factory Output Grows 4.5% In February, Beats Analysts' Expectations
+https://t.co/8mNepboufG",1
+"World Faces Worst Economic Fallout Since Great Depression, Says IMF Chief
+https://t.co/NgvXN1nf3c",-1
+"Sensex Closes 1,266 Points Higher, Nifty Reclaims 9,100
+https://t.co/rQ8IvLd7C4",1
+"Cognizant Withdraws 2020 Forecast On Coronavirus Uncertainty
+https://t.co/okSuLypeuh",-1
+"Sensex ends 1,266 points higher, Nifty reclaims 9,100 as markets spike 4%; auto stocks jump #Sensex #Nifty #MarketUpdates",1
+"Coronavirus Has 'Drastically Altered' India Growth Outlook, Says RBI Amid Lockdown
+https://t.co/WYWkoIzUKf",-1
+"Sensex jumps nearly 1,100 points, Nifty crosses 9,050 amid gains across sectors; Cipla, Tata Motors, Bajaj Auto up… https://t.co/iRfGp6EKlr",1
+"COVID-19 To Impact Economic Activity Directly Due To Lockdowns: RBI
+https://t.co/o9a1NYqTiU",-1
+"Virus Crisis A Chance For India To Reform Its Economy, Say Some Experts
+https://t.co/hFXZUG1INt",1
+"Rupee Edges Higher To 76.11 Against Dollar
+https://t.co/qGDKsdasQq",-1
+"Consumer Inflation Likely Fell To Four-Month Low In March: Poll
+https://t.co/VEzKWZInDZ",-1
+"Oil Demand Slumps 70% In India, 3rd-Biggest Buyer, Amid Lockdown
+https://t.co/DG38aILF7m",-1
+"Tax Breaks, Incentives For COVID-19-Hit Small Businesses Likely Next Week
+https://t.co/7u1asxLK6g",1
+"Sensex Opens Over 650 Points Higher, Nifty Above 8,950
+https://t.co/5MdFuBM9LU",1
+"Sensex opens over 650 points higher, Nifty starts day above 8,950 tracking gains in global markets #Sensex #Nifty #MarketUpdates",1
+"Sensex, Nifty Likely To Open Higher
+https://t.co/shm8juDIOc",1
+"Rs 1 Lakh Crore Coronavirus Stimulus On Cards For Small Businesses: Report
+https://t.co/ODzhWRt2Mm",-1
+"All Pending Income Tax Refunds Up To Rs 5 Lakh To Be Released Immediately
+https://t.co/uZCLzpBCuf",1
+"OECD Says Leading Indicators Flag Biggest Monthly Drop On Record
+https://t.co/kmZKV9e0ry",-1
+"Rupee Sinks Below 76 To New All-Time Low Against Dollar 
+https://t.co/MKu4TJiimQ",-1
+"Sensex Ends 173 Points Lower, Nifty Settles At 8,749 As Coronavirus Fears Linger
+https://t.co/QGIFjWEwyH",-1
+"Sensex ends volatile session 173 points lower, Nifty settles at 8,749 as coronavirus fears persist #Sensex #Nifty #MarketUpdates",-1
+"SBI Cuts Interest Rates On Savings Deposits By 0.25%
+https://t.co/y47p5F5V3F",-1
+"Sensex Plunges Over 1,600 Points From Day's High, Nifty Tests 8,800
+https://t.co/tgPYPCSnUo",-1
+"Latest On The Spread Of The Coronavirus Around The World
+https://t.co/haDwbdVV1N",-1
+"Rupee Edges Lower To 75.85 Against Dollar
+https://t.co/0RsHsNXVLm",-1
+"Sensex, Nifty Turn Positive Amid Choppy Trade As Coronavirus Fears Linger
+https://t.co/HX0T49Fphj",1
+"Sensex Opens Over 350 Points Lower, Nifty Below 8,700 As Global Markets Lose Steam
+https://t.co/2A2bsDngZ1",-1
+"Sensex opens over 350 points lower, Nifty starts day below 8,700 tracking weakness in global markets amid coronavir… https://t.co/csmNyu74WS",-1
+"Small Businesses Struggle To Pay Wages Amid Coronavirus Lockdown
+https://t.co/dy4BoxDLZB",-1
+"SBI Slashes Lending Rates Across All Tenors By 0.35%, EMIs To Get Cheaper
+https://t.co/ao9oL2fCo5",1
+"Sensex ends 2,476 points higher, Nifty reclaims 8,750 as markets bounce back 9% after 2-day fall #Sensex #Nifty #MarketUpdates",1
+"Hindustan Unilever Market Value Tops Rs 5 Lakh Crore For First Time
+https://t.co/TzNyxgWcJM",1
+"State Governments Scramble For Funds As Coronavirus Takes Toll On Coffers
+https://t.co/kzTFrVxHci",1
+"World's Biggest Lockdown Brings Trucks To A Standstill In India
+https://t.co/gRHNn3Op4A",-1
+"Unemployment Rate Soars To 8.7%, Worst In 43 Months: Report
+https://t.co/YkS0KF3qYU",-1
+"Employment Rate Hits All-Time Low In March, Says Think-Tank CMIE
+https://t.co/YkS0KF3qYU",-1
+"Sensex, Nifty Open Higher Amid Gains In Global Markets
+https://t.co/A5y2mB4ZS9",1
+"Sensex opens over 1,300 points higher near 28,900, Nifty starts day firm above 8,400 tracking rally in global marke… https://t.co/143R02HJ5J",1
+"IMF Encouraged By Recovery In China, But Pandemic Could Resurge
+https://t.co/oC6fEINoG1",1
+"Markets Revise Trading Rules, Hours, Circuit Breakers As Volatility Surges: Key Facts
+https://t.co/jQNHn3pOA5",1
+"March Gold Imports Hit 6-1/2-Year Low On Record Price: Report
+https://t.co/icOxFWEfu1",-1
+"Gold Imports Slump 73% In March To Lowest In Over 6 Years: Report
+https://t.co/FK0QiSOT9D",-1
+"Mukesh Ambani's Net Worth Drops To $48 Billion In 2 Months Amid Coronavirus Crisis
+https://t.co/0TTjUMldfU",-1
+"India's Services Activity Hits 5-Month Low Amid Coronavirus Crisis: Survey
+https://t.co/Urv0Rj5eyv",-1
+"Financial Markets Shut For Mahavir Jayanti Holiday
+https://t.co/nMf0ueLc9e",-1
+"Air Deccan Ceases Operations, All Employees On ''Sabbatical Without Pay''
+https://t.co/4GVDLoOJ97",-1
+"FPIs Pull Out Record Rs 1.1 Lakh Crore In March Amidst Covid-19 Mayhem
+https://t.co/d10LRVDU6R",-1
+"Anxiety-Driven Panic Could Impact Global Food Supply Chain: United Nations Arm
+https://t.co/PqijTlLTU3",-1
+"Fitch Sees India GDP Growth In FY21 At 2%, Slowest Since Economic Reforms
+https://t.co/nH9RGCKb8s",-1
+"US Employment Falls 701,000 In March Amid Coronavirus
+https://t.co/GO1ZDIeJpi",-1
+"No Change In Basic Selling Price Of Petrol, Diesel: Indian Oil
+https://t.co/3YIpSKvodo",1
+"Gold Prices Subdued On Firm Dollar Ahead Of US Data Amid Coronavirus Crisis
+https://t.co/lCyGzdnX4u",-1
+"Sensex, Nifty Extend Losses To Second Day Amid Rising Coronavirus Cases
+https://t.co/1Lk0ZkMhdS",-1
+"Sensex ends 674 points lower, Nifty gives up 8,100 as markets extend losses to second day #Sensex #Nifty #MarketUpdates",-1
+"Sensex, Nifty Stage Mild Recovery; FMCG, Pharma Shares Gain, Banks Fall
+https://t.co/59GFTCCefW",1
+"India's Place In Global Markets Is At Stake In Virus Response
+https://t.co/FiA4QADjBO",-1
+"Rupee Slips By 48 Paise To 76.08 Against Dollar Amid Coronavirus Scare
+https://t.co/njXT0RFFwz",-1
+"Coronavirus-Induced Weakness In Rupee To Linger In Near Term: Poll
+https://t.co/Oc1IPIbfVh",-1
+"Sensex Falls Over 400 Points, Nifty Below 8,150 As Markets Give Up Early Gains
+https://t.co/zjBF4eSC3G",-1
+"Sensex falls over 400 points, Nifty drops below 8,150 as markets give up early gains; banking, auto stocks lead los… https://t.co/pjNiAyfyVl",-1
+"US Stock Markets End Over 2% Higher After Jump In Crude Oil Prices
+https://t.co/p1K4bvHIso",1
+"Gold Price Spikes As US Joblessness Data Lifts Safe-Haven Demand
+https://t.co/m8NswHy2Xs",1
+"British Airways In Talks With Union To Suspend Around 32,000 Staff: Report
+https://t.co/DJyN10xAmM",-1
+"Air India Suspends Contracts Of Around 200 Pilots Amid COVID-19: Report
+https://t.co/ruL87QaEfb",-1
+"Property Prices Face Steep Falls As Virus Freezes Market
+https://t.co/TLjQdihN1n",-1
+"Factory Activity Growth Hits 4-Month Low Amid Coronavirus Crisis: Survey
+https://t.co/zAVJljFHSw",-1
+"Coronavirus Impact: Hoteliers Stare At Worst Phase Since 2008 Financial Crisis
+https://t.co/Gr1qM9k4Rp",-1
+"Financial Markets To Remain Shut Today For Ram Navami
+https://t.co/TiF3O5veKe",-1
+"From Slowdown To Lockdown: Auto Makers See Steep Fall In March Sales
+https://t.co/aRCyZy3wL0",-1
+"Many Airlines At Brink Of Bankruptcy, Need Government Support: Industry Body
+https://t.co/ZJCcMFD3ei",-1
+"March GST Collection Falls To Rs 97,597 Crore Amid Coronavirus Outbreak
+https://t.co/Ml07LgjgGu",-1
+"Sensex closes 1,203 points lower, Nifty gives up 8,300 amid coronavirus crisis; IT, financial stocks worst hit #Sensex #Nifty #MarketUpdates",-1
+"Borrowers Unlikely To Get Loan Moratorium Benefit As Banks Will Charge Interest Later
+https://t.co/f8h7OXeKT3",1
+"Government Begins Fiscal Year Under Severe Strain Amid Biggest Lockdown
+https://t.co/jskjXFmAQQ",-1
+"Maruti Suzuki March Sales Plunge Nearly 50% Amid Coronavirus Outbreak
+https://t.co/05lZQly97H",-1
+"Coronavirus Lockdown May Hurt Government's Plan to Sell Record Bonds
+https://t.co/9z8pfzuNG9",-1
+"Oil's Unparalleled Demand Crash Likely To Be Gamechanger For Industry
+https://t.co/qa5tGhkWbU",-1
+"Sensex opens 37 points higher, Nifty at 8,584 as markets start day on a flat note amid weakness in global equities… https://t.co/p6N3W7Q7BN",1
+"Government To Borrow More Aggressively In April-September Than Expected
+https://t.co/cBv5yI8Djb",-1
+"Airlines Face Refunding $35 Billion Of Unused Tickets: Aviation Body
+https://t.co/pZw7IQkKva",-1
+"SpiceJet To Cut March Salary As Coronavirus Lockdown Hits Travel Demand
+https://t.co/byFY7IpXm8",-1
+"March Sees India's Biggest Monthly Foreign Investor Rout Ever
+https://t.co/qnpgKSvxt8",-1
+"Government To Borrow Rs 4.88 Lakh Crore In First Half Of Financial Year 2021
+https://t.co/hki09Epg5v",-1
+"Coronavirus Crisis: State-Run Banks Allow Borrowers 3-Month Delay In EMIs
+https://t.co/0FQ1oEOp0y",-1
+"Core Sector Growth Up 5.5% In February, Highest In 11 Months
+https://t.co/Zt5ULlq5F1",1
+"Nifty Suffers Worst March Since Inception As Coronavirus Rattle Investors
+https://t.co/xk8b9WUU97",-1
+"Sensex ends 1,028 points higher, Nifty settles at 8,598 as markets rise nearly 4% on last day of financial year 201… https://t.co/OfBlLb5Thm",1
+"Sensex rises over 1,000 points, Nifty near 8,600; oil &amp; gas, consumer goods, metal stocks lead gains #Sensex #Nifty #MarketUpdates",1
+"Government, RBI Try To Cushion An Economy In Slump Even Before COVID-19
+https://t.co/RqiEYHTYOV",-1
+"Rupee Edges Higher To 75.51 Against Dollar
+https://t.co/W88Y89rySY",-1
+"Sensex Rises Over 850 Points, Nifty Touches 8,500 In Early Trade
+https://t.co/02BN0859Cg",1
+"Sensex opens over 850 points higher near 29,300, Nifty starts day above 8,500 amid gains in global markets #Sensex #Nifty #MarketUpdates",1
+"Gap-Up Opening Likely For Sensex, Nifty Today
+https://t.co/Z1801WorBC",1
+"Government May Slash Borrowing From Market In April Amid Lockdown: Report
+https://t.co/3nN6Jqelh8",-1
+"Sensex Ends 1,375 Points Lower Amid Coronavirus Crisis
+https://t.co/J8tBkkoNTe",-1
+"Sensex ends 1,375 points lower, extends losses to second day amid coronavirus crisis; Nifty slides below 8,300 #Sensex #Nifty #MarketUpdates",-1
+"Sun Pharma Says Halol Unit Issues Not Likely To Impact Business, Shares Edge Higher
+https://t.co/AGSbRkFx8x",1
+"Coronavirus Crisis: IndiaNivesh Shuts Portfolio Management Business
+https://t.co/uH00oBT4oc",-1
+"Sensex Falls Over 1,100 Points, Nifty Slips Below 8,350 Amid Coronavirus Crisis
+https://t.co/aRUQxsZStO",-1
+"Sensex opens over 550 points lower, Nifty starts day below 8,400 amid weakness in global markets over coronavirus… https://t.co/DDOb3WKi5S",-1
+"Gap-Down Opening Likely For Sensex, Nifty Amid Coronavirus Crisis
+https://t.co/AiFWj2Q5q0",-1
+"India To Be Grow Fastest Among G20 Economies Amid Corona Impact: Report
+https://t.co/9dIIgHE3ub",1
+"Enough Stock Of Petrol, Diesel, LPG Available To Last Lockdown, Says Indian Oil
+https://t.co/JnKrw5vY4Q",1
+"State-Run Power Producers Asked To Ensure Supply Amid Coronavirus Lockdown
+https://t.co/x9Mc1e851m",1
+"World Already In Recession, Emerging Markets Need $2.5 Trillion: IMF's Kristalina Georgieva
+https://t.co/gSLoFWTiBc",-1
+"Grocery Tycoon Kishore Biyani's Battle To Keep His Company Afloat
+https://t.co/VDZbhnh1O7",-1
+"Vistara Calls For Mandatory Leave Without Pay For 3 Days For Senior Employees
+https://t.co/EWaWzu721b",-1
+"Domestic Flights Suspension Extended Till April 14 Amid Coronavirus
+https://t.co/a4V0v8gwvE",-1
+"Amazon, Flipkart Operations Being Disrupted Amid Lockdown: Report
+https://t.co/2lk47RpCzv",-1
+"Sensex, Nifty End Mixed Despite RBI's Steep Reo Rate Cut
+https://t.co/veT0PAZO5d",-1
+"Rupee Jumps To Return To 74 Levels Against Dollar After RBI Announcements 
+https://t.co/pCA3VolJsh",-1
+"Electricity Use Falls To Lowest In Five Months Due To Lockdown
+https://t.co/rKxydbrP2o",1
+"IndusInd Bank Crashes 27% From Day's High As Trading Volume Spikes
+https://t.co/holGm1K7mk",-1
+"RBI To Infuse Rs 3.74 Lakh Crore Liquidity Into Financial System
+https://t.co/EpztDDsxUN",1
+"SpiceJet Expects A Surge In Demand As The Virus Is Contained
+https://t.co/CPbPoDNBrt",-1
+"RBI Refrains From Giving Inflation Outlook; Says GDP Projections At Risk
+https://t.co/pBme2cosfc",-1
+"RBI Surpassed Market Expectations: Experts On Surprise 0.75% Rate Cut
+https://t.co/3gfU6trdg1",-1
+Indian banks are safe and there is no need to resort to panic withdrawals: RBI Governor Shaktikanta Das,1
+RBI has infused 2.7 lakh crore into the system since last February policy: RBI chief Shaktikanta Das,1
+Indian banking system safe and sound: Shaktikanta Das,1
+RBI will continue to be vigilant and take steps necessary to mitigate impact of coronavirus: Governor Shaktikanta Das,1
+"All banks, lending institutions may allow a three-month moratorium on all loans, says RBI chief Shaktikanta Das ami… https://t.co/FE1W5sbvm6",1
+Cash reserve ratio lowered to 3% from 4% for all banks for a period of one year: RBI Governor Shaktikanta Das,1
+RBI has reduced the Cash Reserve Ratio by 100 basis points.,-1
+RBI has cut the liquidity adjustment facility by 90 bps to 4%,1
+RBI has cut the reverse repo rate by 90 basis points to 4%,-1
+"RBI Cuts Rates To Tide Over ""Disruptive"" Impact of Coronavirus
+https://t.co/QVJkBTlV30",-1
+RBI cuts repo rate to 4.4% from 5.15% after unscheduled Monetary Policy Committee meet,-1
+"Moody's Cuts India GDP Growth Forecast To 2.5% In 2020
+https://t.co/y3c2BPb1k0",-1
+Moody's cuts India's economic growth forecast to 2.5% in 2020 from 5.3%: Press Trust of India,-1
+"Sensex Jumps Over 1,150 Points, Nifty Touches 9,000 Ahead Of RBI Announcement
+https://t.co/geY0uaRcKo",1
+"Sensex opens 801 points higher at 30,748, Nifty starts day above 8,900 amid gains in global markets #Sensex #Nifty #MarketUpdates",1
+"Sensex, Nifty Likely To Have A Positive Opening
+https://t.co/BGz6STkkhU",1
+"Global Oil Refiners To Deepen Output Cuts As Coronavirus Destroys Demand
+https://t.co/S4Q5pUQztl",-1
+"Yes Bank To Raise Up To Rs 5,000 Crore After Government-Led Rescue
+https://t.co/BsMRmN26hM",1
+"US Offers $58 Billion Aid To Airlines As Governments Seek To Avert Bankruptcies
+https://t.co/WkgaU0hnEx",1
+"Moody's Warns Of Downgrading Tata Motors
+https://t.co/UvsiMcmoBZ",-1
+"""Banks, ATMs To Remain Open"": Nirmala Sitharaman Denies Report Lenders Plan To Shut Most Branches
+https://t.co/Sj1Bakrw6N",1
+"Exchanges Cut Trading Hours For Commodity Trading Amid Coronavirus
+https://t.co/lfcE3TnndZ",-1
+"Sensex, Nifty Surge For Third Straight Day Led By Banking Shares
+https://t.co/jsKTQQdZiC",-1
+"Rs 1.7 Lakh-Crore Package For Migrant Workers, Poor: Finance Minister Nirmala Sitharaman
+https://t.co/uCczEZKYI7",-1
+"Employees can withdraw 75% of employee provident fund or three month salary whichever is lower, move will benefit 4… https://t.co/AXRxRyd0L2",1
+Government of India will pay the employee provident fund contribution of employer and employee for the next three f… https://t.co/i4aj37aG0N,1
+"Women under Ujjwala Yojana will be given free LPG cylinders for next three months, move expected to benefit 8.3 cro… https://t.co/EUesc5YPgo",1
+"MNREGA wages will be increased from Rs 182 to Rs 202, resulting in total rise of Rs 2,000; to benefit 5 crore famil… https://t.co/oG3CsUSo1m",1
+"Move will directly benefit 8.69 crore farmers, says Finance Minister",1
+"Sensex up 1,445 points, Nifty surges 385 points to trade above 8,700 ahead of Finance Minister Nirmala Sitharaman's press conference",1
+"Banks Plan To Shut Down Most Branches During Lockdown: Report
+https://t.co/HmMuH4mLx5",-1
+"When Will Coronavirus-Hit Global Markets Heal? 12 Charts To Monitor
+https://t.co/GbETRsgDa5",-1
+"Sensex Surges Over 1,550 Points, Nifty Touches 8,700; Financial, IT Stocks Jump
+https://t.co/2uzIGVXmRn",1
+"Sensex Opens Over 500 Points Higher, Nifty Starts Day Near 8,450
+https://t.co/2uzIGVXmRn",1
+"Sensex opens 538 points higher at 29,074, Nifty starts day above 8,450 amid gains in global markets #Sensex #Nifty #MarketUpdates",1
+"Sensex, Nifty Likely To Open Lower
+https://t.co/C84VwwxTug",-1
+"Aviation Industry Stares At Loss Of Up To $3.6 Billion In Q1 Due To Travel Ban
+https://t.co/puIlhqwcEn",-1
+"Flipkart Resumes Operations, Amazon In Talks With Government
+https://t.co/w0XXuZgSRJ",1
+"Coronavirus Crisis: GoAir Decides To Reduce Pay Across Board
+https://t.co/NF2zmMUHqd",-1
+"Sensex, Nifty Log Best Day Since 2009 On $2-Trillion US Stimulus Package
+https://t.co/CY1Dxf8CRG",1
+"Government May Unveil Rs 1.5 Lakh Crore Package To Tackle Coronavirus Downturn: Report
+https://t.co/mh4q6fVmJq",1
+"Sensex ends 1,862 points higher at 28,536, Nifty reclaims 8,300 as markets extend gains to second straight day #Sensex #Nifty #MarketUpdates",1
+"Sensex soars over 1,650 points, Nifty hits 8,250 amid broad-based buying; Axis Bank, ICICI Bank, Kotak Mahindra Bank jump 10%",-1
+"Sensex surges over 1,100 points, Nifty crosses 8,000 as markets rebound 4%; Reliance Industries, Maruti Suzuki up 9… https://t.co/XvYtNBO57J",1
+"Sensex up over 400 points, Nifty touches 7,900 as markets rise 1% amid volatile trade; auto stocks lead gains #Sensex #Nifty #MarketUpdates",1
+"Sensex, Nifty Recover Early Losses Amid Volatile Trade
+https://t.co/zmnh0k2Wpk",1
+"Sensex opens 174 points lower at 26,500, Nifty starts day below 7,750 as India begins 21-day lockdown to fight coro… https://t.co/rD3psz8Cz2",-1
+"Coronavirus Crisis: Global Stocks Surge As Investors Place Hopes On US Stimulus
+https://t.co/jnLmGJsPNh",1
+"Sensex, Nifty Likely To Open Lower After A Day's Breather
+https://t.co/XETJTnWtyQ",-1
+"Monitoring Stock Market Volatility, Assures Nirmala Sitharaman
+https://t.co/NRWKW4qut1",-1
+"Sensex Ends 693 Points Higher, Nifty Reclaims 7,800; Financial, IT Stocks Lead Gains
+https://t.co/CCbHhIVfLJ",1
+"Sensex closes 693 points higher, Nifty at 7,801 as markets rebound after worst day; financial, IT stocks lead gains… https://t.co/PuTysS82Zw",1
+"Government Raises Insolvency Threshold To Rs 1 Crore To Help Small Firms
+https://t.co/RE4sTuCoWt",1
+"May consider suspending Section 7, 9 and 10 of the Insolvency and Bankruptcy Code at a later stage, if the existing… https://t.co/dPsj9ZHHi5",-1
+For financial year 2019-20 if the independent directors of the company are not able to hold even one meeting then i… https://t.co/nMSyIHN8cn,-1
+"Sensex Surges 900 Points, Nifty Near 7,900 Amid Wild Swings Due To Volatility
+https://t.co/QTV3IuebKt",1
+"Gold Jumps Over 1% As US Central Bank Ramps Up Support Measures
+https://t.co/uc9oEel12q",1
+"Gap-Up Opening For Sensex, Nifty Amid Gains In Asian Markets
+https://t.co/nBuylWqckb",1
+"Sensex opens 1,075 points higher at 27,056, Nifty starts day above 7,800 amid gains in Asian markets #Sensex #Nifty #MarketUpdates",1
+"Sensex, Nifty Likely To Open Higher After Worst Day Ever
+https://t.co/3u7brRq2ek",1
+"Excise On Petrol, Diesel To Go Up By Rs 8/Litre To Fund Covid-19 Fight
+https://t.co/wN2oIm0nZj",-1
+"Gold Futures Cool Off After Rising Over 1% Amid Coronavirus Panic
+https://t.co/kAyrHZ1Zln",1
+"Sensex, Nifty Plunge 13% After A Day's Breather Amid Coronavirus Fears
+https://t.co/yK4bK5uWlK",-1
+"Sensex ends 3,935 points lower, Nifty settles at 7,610 as markets take 13% hit after a day's breather amid coronavi… https://t.co/DZP6sUya4V",-1
+"SoftBank To Raise $41 Billion To Expand Share Buyback, Cut Debt
+https://t.co/zOM9uhrFZz",1
+"Millions Of Jobs Threatened In Retail Sector As Coronavirus Hits Sales, Revenues
+https://t.co/bm7FbO87IX",-1
+"Sensex plummets over 3,500 points, Nifty cracks below 7,750 as markets extend losses; financial stocks worst hit… https://t.co/QgnEyeLXte",-1
+"Sensex, Nifty Resume Trading After 45-Minute Halt As Coronavirus Batters World Markets
+https://t.co/fgBVnHX2P6",-1
+"Sensex down 3,270 points at 26,646, Nifty near 7,800 as markets resume trading after 45-minute halt amid coronaviru… https://t.co/8CBbiXXFHn",-1
+"Rupee Drops Below 76 Mark Against Dollar Amid Coronavirus Crisis
+https://t.co/CKANJR47UG",-1
+"Trading Halted For 45 Minutes As Sensex, Nifty Plunge 10% Amid Coronavirus Lockdowns
+https://t.co/c3QaOG3Pfk",-1
+"Trading halted for 45 minutes as markets fall 10% on coronavirus jitters; Sensex down 2,992 points at 26,924, Nifty… https://t.co/uJVepH21Bu",-1
+"Sensex Plunges Over 2,700 Points, Nifty Hits 7,950 As Coronavirus-Led Lockdowns Hurt Global Markets: 10 Things To K… https://t.co/F2DQAD99A3",-1
+"Sensex opens 2,307 points lower at 27,609, Nifty at 7,946 as global stocks tumble amid more lockdowns on coronaviru… https://t.co/JZ9lOSqZ63",-1
+"Markets Likely To Open Sharply Lower
+https://t.co/T1yYBu91ud",-1
+"Coronavirus Crisis: Global Stocks Crumble As More Countries Shut For Business
+https://t.co/4sRc5bT90i",-1
+"Recovery Will Take A Long Time, Say Analysts After Markets Suffer 12% Weekly Loss
+https://t.co/kdqXjzBEIc",-1
+"Hero, Fiat Shut Manufacturing Plants In View Of Coronavirus
+https://t.co/0GkUqWaA0S",-1
+"Cabinet Approves Schemes To Boost Electronics Manufacturing
+https://t.co/nWF2YDRBvH",1
+"Market Regulator Sebi Tightens Norms To Limit Volatility On Coronavirus
+https://t.co/UcX5gayiBW",-1
+"As Coronavirus Derails Business, Banks Seek More Time For Bad Loan Classification
+https://t.co/UwwupXgNyx",-1
+"Equity, Commodity Markets To Remain Open: Maharashtra Health Minister
+https://t.co/ECcRy5RqMm",1
+"Sensex, Nifty Close Nearly 6% Higher After Four-Day Selloff
+https://t.co/GW2Oi28KOq",1
+"Sensex ends 1,628 points higher at 29,916, Nifty reclaims 8,700 as markets gain nearly 6% after four-day selloff… https://t.co/6d9nbjZo5N",1
+"Sensex, Nifty Take Breather From Coronavirus-Fueled Selloff
+https://t.co/CDJcBSP6JI",1
+"Sensex jumps over 750 points to cross 29,000 mark, Nifty touches 8,500; IT, energy stocks lead gains #Sensex #Nifty #MarketUpdates",1
+"Rupee Gains By 34 Paise To 74.78 Against Dollar
+https://t.co/xZqS8syQJI",-1
+"Coronavirus Crisis: Fitch Lowers 2020-21 India Growth Forecast To 5.1%
+https://t.co/EvQBt1DCZ8",-1
+"Sensex Rises Over 350 Points Amid Cautious Gains In Global Markets
+https://t.co/UeXRpkzLFX",1
+"Sensex Opens 173 Points Higher Amid Cautious Gains In Global Markets
+https://t.co/UeXRpkRn4x",1
+"Sensex opens 173 points higher at 28,461, Nifty at 8,284 as global markets post cautious gains amid coronavirus sca… https://t.co/qphiic53Gx",1
+"Wall Street Posts Cautious Gains After Steep Losses On Coronavirus
+https://t.co/orRiqCQjeM",1
+"Sensex, Nifty Likely To Open Higher Amid Cautious Gains In Global Markets
+https://t.co/GYBGZ7Nt46",1
+"Airline Industry Crisis Deepens As Coronavirus Pain Spreads
+https://t.co/KoSTQD85pH",-1
+"Sensex, Nifty Extend Losses To Fourth Day In A Row Amid Coronavirus Scare
+https://t.co/0ycaTIDnYQ",-1
+"Sensex ends 581 points lower, Nifty gives up 8,300 as markets extend losses to fourth day amid coronavirus scare… https://t.co/jlk5zc8pLS",-1
+"Gold Futures Rise To Hover Near Rs 39,800 Amid Coronavirus Fear
+https://t.co/cZtPvSzATZ",1
+"Foreigners Turn Net Sellers Of Asian Bonds In February On Coronavirus Fears
+https://t.co/aXHxvTUTb0",-1
+"Rupee Sheds 1% To Hit Record Low Of 74.99 Versus Dollar Amid Virus Fear: 10 Points
+https://t.co/tQu0q8AV1C",-1
+"Gap-Down Opening For Sensex, Nifty As Coronavirus Continues To Batter Global Markets
+https://t.co/yQ91f3XCNL",-1
+"Sensex opens 1,096 points lower at 27,773, Nifty at 8,063 as selloff in global markets continues on coronavirus sca… https://t.co/WlDRTfQF2R",-1
+"Sensex, Nifty Likely To Start Session Sharply Lower In Gap-Down Opening
+https://t.co/rrH5C0GMs5",-1
+"RBI To Buy Government Bonds In Open Markets To Boost Liquidity
+https://t.co/1Fly3Fg4D9",1
+"Banks Shelve Fundraising As Coronavirus Derails Markets: Report
+https://t.co/xZdFkil2D2",-1
+"Sensex, Nifty Crash 15% In Three Trading Sessions On Coronavirus Fears
+https://t.co/fp3yTuvQF6",-1
+"Gold Futures Drop Over 2% To Rs 39,354 Per 10 Grams
+https://t.co/n7sMKLg41N",-1
+"Yes Bank Stock Set To Close Higher For Fourth Straight Session
+https://t.co/g68PdWs9PN",1
+"Sensex, Nifty Turn Negative Amid Choppy Trade
+https://t.co/LixWSV5w6b",-1
+"Sensex Opens 390 Points Higher, Nifty Starts Day At 9,088 As Global Markets Rebound
+https://t.co/daKAxwn11K",1
+"Sensex opens 390 points higher at 30,969, Nifty at 9,088 as global markets rebound #Sensex #Nifty #MarketUpdates",1
+"US Shares Rebound 6% As Central Bank Boosts Liquidity To Fight Virus Effect
+https://t.co/YJBOHsEma7",1
+"Sensex, Nifty Likely To Open On Positive Note Today
+https://t.co/S9ZrtxeUpw",1
+"Global Stocks Shaky After Worst Crash Since 'Black Monday'
+https://t.co/IXnhwIGm9p",-1
+"Will Not Sell Any Share In Yes Bank For Next 3 Years, Says SBI Chairman
+https://t.co/X9JRhxWCrV",1
+"In past 3 days, more money came to Yes Bank compared to withdrawals as only one-third of customers withdrew Rs 50,0… https://t.co/sIaYn77tzE",1
+"Complete operational normalcy to be restored in Yes Bank from 6 pm on Wednesday, no worries about liquidity, says P… https://t.co/2w46VnhuNj",1
+"Sensex, Nifty Clock Cautious Gains As Coronavirus Pummels Global Markets
+https://t.co/nEVgalMijs",1
+"Amazon To Hire 1,00,000 Workers As Online Orders Surge On Virus Worries
+https://t.co/UsP4DVSWhD",1
+"Sensex Rises Over 400 Points, Nifty Touches 9,400 Amid Volatile Trade
+https://t.co/VowCyqThcx",1
+"Sensex Falls Over 400 Points, Nifty Near 9,000 Amid Volatile Trade
+https://t.co/VowCyqThcx",-1
+"Sensex opens 222 points higher, Nifty at 9,285; banking, metal, energy stocks lead gains #Sensex #Nifty #MarketUpdates",1
+"Crude Oil Price Jump By $1 A Barrel Amid Volatility
+https://t.co/V3wZIlLidr",1
+"Coronavirus Scare: Amazon To Hire 1,00,000 Workers As Online Orders Surge
+https://t.co/UsP4DVSWhD",1
+"Coronavirus Pandemic Can Affect Economic Activity In India: Shaktikanta Das
+https://t.co/IFj95m4fIj https://t.co/1fPlFmTSoY",-1
+Coronavirus has affected global financial markets: RBI Governor Shaktikanta Das on pandemic,-1
+"Sensex ends 2,713 points lower, Nifty settles at 9,197 as markets drop 8% amid coronavirus scare #Sensex #Nifty #MarketUpdates",-1
+"Wholesale Inflation At 2.26% In February
+https://t.co/3QxHfdjA7H",1
+Wholesale inflation eases to 2.26% in February from 3.1% in previous month,1
+"Weak Listing For SBI Cards; Shares Fall 12% At Debut
+https://t.co/2P4SEFqZI3",-1
+"Sensex Falls Over 1,950 Points, Nifty Below 9,400 Amid Coronavirus Fears
+https://t.co/RHwZ6W3bBX",-1
+"Sensex opens 1,000 points lower at 33,103, Nifty at 9,588 as coronavirus batters global markets #Sensex #Nifty #MarketUpdates",-1
+"Sensex, Nifty Likely To Open Sharply Lower Today; Yes Bank Shares In Focus
+https://t.co/IvUq5azsrK",-1
+"Wall Street Jumps After Worst Day Since 1987
+https://t.co/TAaRrfaAx5",-1
+"February Trade Deficit At $9.85 Billion
+https://t.co/kFpVciebDD",-1
+"Indian Stocks Rebound Sharply, But Suffer Worst Week Since 2009
+https://t.co/r5RNbKDRnL",-1
+"ICICI Bank To Invest Up To Rs 1,000 Crore In Crisis-Hit Yes Bank
+https://t.co/aLpDGELNcX",1
+"4% Hike In Dearness Allowance For 48 Lakh Central Government Employees
+https://t.co/mrz7hnaLCq",1
+"SBI to invest up to 49% equity in Yes Bank, other investors also being invited: Nirmala Sitharaman after Union Cabinet meeting",1
+"Sensex Closes 1,325 Points Higher After Hitting Lower Circuit
+https://t.co/KG6FWK7kDW",1
+"Crude Oil Market Set For Record Surplus Amid Coronavirus-Led Demand Slump: Goldman Sachs
+https://t.co/ZIy5kqur57",1
+"Sensex Recovers Over 5,000 Points From Day's Low As Trading Resumes After Halt
+https://t.co/gzqnxsqw3j",1
+"Sensex soars over 1,600 points to reclaim 34,400 mark, Nifty crosses 10,000 as markets bounce back #Sensex #Nifty #MarketUpdates",1
+"""Sebi, Stock Exchanges Have Robust Risk Management Framework"": Read Regulator's Full Statement
+https://t.co/MSawOL0lw8",1
+"Market Fall Significantly Lower Than Global Peers, Says Regulator Sebi
+https://t.co/MIiLONrbbV",-1
+"Sensex, Nifty Trim Losses As Markets Resume Trading After 45-Minute Halt
+https://t.co/R98oX7Bd5U",-1
+"Sensex rebounds 2,050 points from day’s low to touch 31,400, Nifty back above 9,000 as markets resume trading #Sensex #Nifty #MarketUpdates",1
+"Rupee Hits Record Low Of 74.50 Against Dollar Amid Coronavirus Scare
+https://t.co/e86E0M8yQV",-1
+"Trading Stops For 45 Minutes As Markets Slide 10% Amid Global Selloff
+https://t.co/I4qQQaYStv",-1
+"Sensex Opens 1,564 Points Lower, Nifty Starts Day At 9,108 
+https://t.co/I4qQQaYStv",-1
+"Sensex opens 1,564 points lower at 31,214, Nifty starts day at 9,108 amid record fall in global markets over corona… https://t.co/mHWIZJWa8x",-1
+"Crude Oil Falls For Third Straight Day, Set For Worst Week Since 1991
+https://t.co/nuI9IPLpUb",-1
+"Worst Day For US Markets Since 1987 As Coronavirus Triggers Recession Fear
+https://t.co/OnJPa6Pd3k",-1
+"Coronavirus Scare: Global Markets Suffer Record Meltdown As Global Virus Alarm Grows
+https://t.co/dm2QDiuJZz",-1
+"Sensex, Nifty Set For Another Day Of Mayhem Amid Global Selloff, Coronavirus Fear
+https://t.co/8mRLyuqWJu",-1
+"Wall Street Resumes Trading After 7% Slide Triggers Halt
+https://t.co/iVYBmkQag7",1
+"US Markets Crash Again As Dow Plunges 1,700 Points In Early Trade
+https://t.co/oLizZCixYO",-1
+"Tourist Industry Sees Crisis Looming From Coronavirus Visa Ban
+https://t.co/lcOLAgUiTO",-1
+"Industrial Output Rises 2% In January
+https://t.co/rmUQUI1HPd",1
+"Consumer Inflation Eases To 6.58% In February
+https://t.co/LOjPNFJ30r",1
+"World Economy Powering Down Daily Makes Recession More Likely
+https://t.co/1VOc7xbjQk",-1
+"Consumer Inflation At 6.58% In February
+https://t.co/LOjPNFJ30r",1
+"Consumer inflation eases to 6.58% in February, from 7.59% in previous month",1
+"Indian Stocks Crash To Worst Day Since 2008 As Pandemic Fuels Growth Fears
+https://t.co/IUUaVqggRI",-1
+"State Bank Of India To Buy Rs 7,250-Crore Yes Bank Shares
+https://t.co/ch0ofBg0oS",1
+"Rupee Drops By 82 Paise To 74.50 Against Dollar As Coronavirus Spooks Markets
+https://t.co/4Yx3iejiz1",-1
+"Nifty Slumps Below 10,000 As Markets Hit 30-Month Low Amid Global Selloff: 10 Things To Know
+https://t.co/3mVYe3Xrco",-1
+"Sensex, Nifty Likely To Open Sharply Lower Amid Global Selloff Over Pandemic Coronavirus Fears 
+https://t.co/yrNK4EB5eO",-1
+"Coronavirus: Global Stocks Plunge After Donald Trump Announces Ban On Travel From Europe
+https://t.co/GpNs8NZKTf",-1
+"Coronavirus Pandemic: Crude Oil Drops Nearly 4% As US Bans Travel From Europe
+https://t.co/p2qQXuJRa2",-1
+"Sensex, Nifty End Volatile Session On A Flat Note
+https://t.co/bCXcTD72dd",-1
+"Reliance Industries Surges 6%, Rebounds From Worst Fall In At Least 10 Years
+https://t.co/cWKhXtJTgx",1
+"Sensex, Nifty Struggle To Stay Positive Amid Volatile Trade
+https://t.co/0Jgj1Hwx50",-1
+"Sensex jumps over 300 points, Nifty touches 10,500 as markets recover early losses; Reliance Industries rises nearl… https://t.co/rY8iyzrRBW",1
+"Sensex Opens 166 Points Lower, Nifty Starts Day At 10,334; IT Stocks Worst Hit
+https://t.co/DG7okJmK3K",-1
+"Sensex opens 166 points lower at 35,469, Nifty starts day below 10,350; IT stocks worst hit #Sensex #Nifty #MarketUpdates",-1
+"Sensex, Nifty Likely To Open Lower As Markets Resume Trading After Holiday
+https://t.co/NwtpXVftK4",-1
+"Crude Oil Price Up Nearly 4%, Rises For Second Day Amid Hopes Of US Supply Cut
+https://t.co/zXLzL7a8Th",1
+"Tata Steel Europe To Cut 1,250 Jobs In Turnaround Push
+https://t.co/vmGe4aFWg1",-1
+"Foreign Investors Pull Out Rs 12,478 Crore From Stocks So Far In March, Set To Turn Net Sellers After 6 Months
+https://t.co/Q9MOBwE8lc",-1
+"Foreigners Sell $9-Billion Asian Equities So Far In March Amid Coronavirus Fears
+https://t.co/ABEBLoJ0FM",-1
+"Investors Lose Rs 6.84 Lakh Crore In Worst Day For Markets In Over Four Years
+https://t.co/FpKijonvXE",-1
+"In the broader market, the 30-share BSE Sensex ended 986.11 points or 3.22 per cent higher at 31,588.72.
+
+https://t.co/yFad5E3LmN",1
+"#CoronavirusPandemic 
+
+As bad as #China's economic dive is, the slump is even more perilous for the rest of #Asia.… https://t.co/PuRY8HSD1V",-1
+"#China #CoronavirusPandemic 
+
+The economy was forced into a paralysis in late January as the epidemic that first st… https://t.co/hTTmHwr18k",-1
+"Reliance Industries raises Rs 8,500 crore using cheaper NCD funds; plans to replace high-cost rupee debt
+
+https://t.co/nopSBtIZqD",1
+"Goldman Sachs tells investors to go defensive amid share market slump, pick large-caps to beat virus
+#sensex… https://t.co/QCVjebyKle",-1
+"#TCS #share price jumps 9% on no #layoffs, #dividend announcements; should you buy, sell or hold the stock https://t.co/ijDFJRDLOl",1
+"Industry body CII said #discoms are likely to suffer a net revenue loss of around Rs 30,000 crore
+
+https://t.co/8c6YJYJajL",-1
+"#Gold prices slip below Rs 46,000 as #investors book profits amid #coronavirus-led #recession fears https://t.co/fSylAJrUqv",-1
+Workers at Bajaj Auto have agreed to a 10% wage cut for the period between April 15 and till the lockdown is lifted. https://t.co/RgvrKPliNd,1
+"#Sharemarket LIVE: Sensex off day’s high, up 600 points, #Nifty tests 9,200, #TCS, private bank stocks lead
+https://t.co/3xgtLroKUI",1
+"#Sensex, #Nifty climb off day's highs, still up 2%; Key factors driving D-Street higher today https://t.co/jVQcousFp6",1
diff --git a/stock_sentimental.ipynb b/stock_sentimental.ipynb
new file mode 100644
index 0000000..ff8dd75
--- /dev/null
+++ b/stock_sentimental.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                Text  Sentiment\n",
+      "0  Kickers on my watchlist XIDE TIT SOQ PNK CPW B...          1\n",
+      "1  user: AAP MOVIE. 55% return for the FEA/GEED i...          1\n",
+      "2  user I'd be afraid to short AMZN - they are lo...          1\n",
+      "3                                  MNTA Over 12.00            1\n",
+      "4                                   OI  Over 21.37            1\n",
+      "Text         0\n",
+      "Sentiment    0\n",
+      "dtype: int64\n",
+      "(4632, 34) (4632,)\n",
+      "(1159, 34) (1159,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+    "\n",
+    "# Load dataset\n",
+    "data = pd.read_csv('Stock_data.csv')\n",
+    "\n",
+    "# Display first few rows of the dataset\n",
+    "print(data.head())\n",
+    "\n",
+    "# Check for missing values\n",
+    "print(data.isnull().sum())\n",
+    "\n",
+    "# Drop missing values if any\n",
+    "data = data.dropna()\n",
+    "\n",
+    "# Encode sentiment labels (assuming they are in a column named 'Sentiment')\n",
+    "le = LabelEncoder()\n",
+    "data['Sentiment'] = le.fit_transform(data['Sentiment'])\n",
+    "\n",
+    "# Split dataset into features and labels\n",
+    "X = data['Text']\n",
+    "y = data['Sentiment']\n",
+    "\n",
+    "# Split data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Tokenization\n",
+    "tokenizer = Tokenizer(num_words=5000)  # Consider top 5000 words\n",
+    "tokenizer.fit_on_texts(X_train)\n",
+    "\n",
+    "# Convert texts to sequences\n",
+    "X_train_seq = tokenizer.texts_to_sequences(X_train)\n",
+    "X_test_seq = tokenizer.texts_to_sequences(X_test)\n",
+    "\n",
+    "# Pad sequences to ensure uniform input size\n",
+    "max_length = max(len(x) for x in X_train_seq)\n",
+    "X_train_pad = pad_sequences(X_train_seq, maxlen=max_length, padding='post')\n",
+    "X_test_pad = pad_sequences(X_test_seq, maxlen=max_length, padding='post')\n",
+    "\n",
+    "# Display shapes of data\n",
+    "print(X_train_pad.shape, y_train.shape)\n",
+    "print(X_test_pad.shape, y_test.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sapni\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ ?                      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ ?                      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)         │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ ?                      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                   │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ ?                      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
+    "\n",
+    "# Define LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(Embedding(input_dim=5000, output_dim=128, input_length=max_length))  # Embedding layer\n",
+    "model.add(LSTM(128, return_sequences=True))  # LSTM layer\n",
+    "model.add(Dropout(0.5))  # Dropout to prevent overfitting\n",
+    "model.add(LSTM(64))  # Second LSTM layer\n",
+    "model.add(Dropout(0.5))  # Dropout\n",
+    "model.add(Dense(1, activation='sigmoid'))  # Output layer for binary classification\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
+    "\n",
+    "# Display model summary\n",
+    "model.summary()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 67ms/step - accuracy: 0.6284 - loss: 0.6606 - val_accuracy: 0.6268 - val_loss: 0.6536\n",
+      "Epoch 2/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 61ms/step - accuracy: 0.7146 - loss: 0.5622 - val_accuracy: 0.7745 - val_loss: 0.4920\n",
+      "Epoch 3/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 75ms/step - accuracy: 0.8898 - loss: 0.3168 - val_accuracy: 0.7756 - val_loss: 0.4849\n",
+      "Epoch 4/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - accuracy: 0.9278 - loss: 0.2154 - val_accuracy: 0.7433 - val_loss: 0.5643\n",
+      "Epoch 5/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - accuracy: 0.9486 - loss: 0.1725 - val_accuracy: 0.7594 - val_loss: 0.6393\n",
+      "Epoch 6/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 62ms/step - accuracy: 0.9671 - loss: 0.1213 - val_accuracy: 0.7530 - val_loss: 0.8493\n",
+      "Epoch 7/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 62ms/step - accuracy: 0.9738 - loss: 0.0973 - val_accuracy: 0.7357 - val_loss: 1.0902\n",
+      "Epoch 8/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 64ms/step - accuracy: 0.9737 - loss: 0.0950 - val_accuracy: 0.7357 - val_loss: 0.8958\n",
+      "Epoch 9/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 68ms/step - accuracy: 0.9826 - loss: 0.0744 - val_accuracy: 0.7443 - val_loss: 0.9316\n",
+      "Epoch 10/10\n",
+      "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 75ms/step - accuracy: 0.9851 - loss: 0.0689 - val_accuracy: 0.7303 - val_loss: 1.0088\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the model\n",
+    "history = model.fit(X_train_pad, y_train, epochs=10, batch_size=64, validation_split=0.2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m37/37\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.7407 - loss: 0.9643\n",
+      "Test Accuracy: 74.63%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the model\n",
+    "loss, accuracy = model.evaluate(X_test_pad, y_test)\n",
+    "print(f'Test Accuracy: {accuracy * 100:.2f}%')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step\n",
+      "Sentiment: Positive\n"
+     ]
+    }
+   ],
+   "source": [
+    "def predict_sentiment(text):\n",
+    "    sequence = tokenizer.texts_to_sequences([text])\n",
+    "    padded = pad_sequences(sequence, maxlen=max_length, padding='post')\n",
+    "    prediction = model.predict(padded)\n",
+    "    # Assuming binary classification: 0 for Negative, 1 for Positive\n",
+    "    if prediction[0] > 0.5:\n",
+    "        print(\"Sentiment: Positive\")\n",
+    "    else:\n",
+    "        print(\"Sentiment: Negative\")\n",
+    "\n",
+    "# Example usage\n",
+    "new_text = \"The stock market is performing bad today.\"\n",
+    "predict_sentiment(new_text)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    }
+   ],
+   "source": [
+    "model.save('sentiment_model.h5')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Text: \"The stock market is performing well today.\"\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+      "Sentiment: Positive\n",
+      "Text: \"The stock market is performing bad today.\"\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+      "Sentiment: Positive\n",
+      "Text: \"I'm very happy with the profits I've made.\"\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
+      "Sentiment: Positive\n",
+      "Text: \"I'm disappointed with the losses this quarter.\"\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "Sentiment: Negative\n",
+      "Text: \"It's a great time to invest in stocks!\"\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "Sentiment: Positive\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_texts = [\n",
+    "    \"The stock market is performing well today.\",\n",
+    "    \"The stock market is performing bad today.\",\n",
+    "    \"I'm very happy with the profits I've made.\",\n",
+    "    \"I'm disappointed with the losses this quarter.\",\n",
+    "    \"It's a great time to invest in stocks!\"\n",
+    "]\n",
+    "\n",
+    "for text in test_texts:\n",
+    "    print(f'Text: \"{text}\"')\n",
+    "    predict_sentiment(text)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 2aa1f24adb12f98ed92d325f33ab11f10ed8c9d1 Mon Sep 17 00:00:00 2001
From: Dipanita45 <132455672+Dipanita45@users.noreply.github.com>
Date: Tue, 22 Oct 2024 15:27:32 +0530
Subject: [PATCH 50/76] Create LICENSE

---
 LICENSE | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 LICENSE

diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..a03b1d0
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Rohit-Dubey
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file

From 5c9bb579e0f06f86df67b133ae62959ae601f3b5 Mon Sep 17 00:00:00 2001
From: sapnilmodak <sapnilmodak@gmail.com>
Date: Tue, 22 Oct 2024 15:52:11 +0530
Subject: [PATCH 51/76] minor fix

---
 stock_sentimental.ipynb | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/stock_sentimental.ipynb b/stock_sentimental.ipynb
index ff8dd75..746daff 100644
--- a/stock_sentimental.ipynb
+++ b/stock_sentimental.ipynb
@@ -258,15 +258,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step\n",
-      "Sentiment: Positive\n"
+     "ename": "NameError",
+     "evalue": "name 'tokenizer' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_10044\\3289756518.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;31m# Example usage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mnew_text\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"The stock market is  bad today.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mpredict_sentiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_text\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_10044\\3289756518.py\u001b[0m in \u001b[0;36mpredict_sentiment\u001b[1;34m(text)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict_sentiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0msequence\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtexts_to_sequences\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mpadded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpad_sequences\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxlen\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_length\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpadded\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;31m# Assuming binary classification: 0 for Negative, 1 for Positive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'tokenizer' is not defined"
      ]
     }
    ],
@@ -282,7 +286,7 @@
     "        print(\"Sentiment: Negative\")\n",
     "\n",
     "# Example usage\n",
-    "new_text = \"The stock market is performing bad today.\"\n",
+    "new_text = \"The stock market is  bad today.\"\n",
     "predict_sentiment(new_text)\n"
    ]
   },
@@ -300,7 +304,8 @@
     }
    ],
    "source": [
-    "model.save('sentiment_model.h5')\n"
+    "model.save('sentiment_model.h5')\n",
+    "\n"
    ]
   },
   {
@@ -337,6 +342,7 @@
     "    \"I'm very happy with the profits I've made.\",\n",
     "    \"I'm disappointed with the losses this quarter.\",\n",
     "    \"It's a great time to invest in stocks!\"\n",
+    "    \n",
     "]\n",
     "\n",
     "for text in test_texts:\n",

From 28a9555c141b13800588e880e8beaf8907a49ffb Mon Sep 17 00:00:00 2001
From: Mugundh <mugundhjb@gmail.com>
Date: Tue, 22 Oct 2024 15:52:31 +0530
Subject: [PATCH 52/76] Automated project structure updation using github
 workflows, python scripts and action bot

---
 .github/scripts/update_structure.py | 101 ++++++++++++++++++++++++++++
 .github/workflows/update-readme.yml |  39 +++++++++++
 PROJECT_STRUCTURE.md                |   4 ++
 README.md                           |   4 ++
 4 files changed, 148 insertions(+)
 create mode 100644 .github/scripts/update_structure.py
 create mode 100644 .github/workflows/update-readme.yml
 create mode 100644 PROJECT_STRUCTURE.md

diff --git a/.github/scripts/update_structure.py b/.github/scripts/update_structure.py
new file mode 100644
index 0000000..fdf8db3
--- /dev/null
+++ b/.github/scripts/update_structure.py
@@ -0,0 +1,101 @@
+import os
+import github
+from github import Github
+
+# Helper function to recursively build the repo structure and include file extensions
+def get_repo_structure(path='.', prefix=''):
+    structure = []
+    try:
+        items = sorted(os.listdir(path))
+    except FileNotFoundError:
+        print(f"Path not found: {path}")
+        return structure
+
+    for i, item in enumerate(items):
+        if item.startswith('.'):
+            continue  # Skip hidden files and directories
+        item_path = os.path.join(path, item)
+        is_last = i == len(items) - 1
+        current_prefix = '└── ' if is_last else '├── '
+
+        if os.path.isdir(item_path):
+            # Directory case
+            structure.append(f"{prefix}{current_prefix}{item}/")
+            next_prefix = prefix + ('    ' if is_last else '│   ')
+            structure.extend(get_repo_structure(item_path, next_prefix))
+        else:
+            # File case with extension
+            file_name, file_extension = os.path.splitext(item)
+            structure.append(f"{prefix}{current_prefix}{file_name}{file_extension}")
+    
+    return structure
+
+# Function to update the repo_structure.txt file
+def update_structure_file(structure):
+    try:
+        with open('repo_structure.txt', 'w') as f:
+            f.write('\n'.join(structure))
+        print("repo_structure.txt updated successfully.")
+    except IOError as e:
+        print(f"Error writing to repo_structure.txt: {e}")
+
+# Function to update the README.md with the new structure
+def update_README(structure):
+    try:
+        with open('PROJECT_STRUCTURE.md', 'r') as f:
+            content = f.read()
+    except FileNotFoundError:
+        print("PROJECT_STRUCTURE.md not found.")
+        return
+
+    start_marker = '<!-- START_STRUCTURE -->'
+    end_marker = '<!-- END_STRUCTURE -->'
+    
+    start_index = content.find(start_marker)
+    end_index = content.find(end_marker)
+
+    if start_index != -1 and end_index != -1:
+        new_content = (
+            content[:start_index + len(start_marker)] +
+            '\n```\n' + '\n'.join(structure) + '\n```\n' +
+            content[end_index:]
+        )
+        try:
+            with open('PROJECT_STRUCTURE.md', 'w') as f:
+                f.write(new_content)
+            print("PROJECT_STRUCTURE.md updated with new structure.")
+        except IOError as e:
+            print(f"Error writing to PROJECT_STRUCTURE.md: {e}")
+    else:
+        print("Markers not found in PROJECT_STRUCTURE.md. Structure not updated.")
+
+# Main function to compare and update repository structure
+def main():
+    gh_token = os.getenv('GH_TOKEN')
+    gh_repo = os.getenv('GITHUB_REPOSITORY')
+
+    if not gh_token or not gh_repo:
+        print("Environment variables GH_TOKEN and GITHUB_REPOSITORY must be set.")
+        return
+
+    g = Github(gh_token)
+    repo = g.get_repo(gh_repo)
+
+    current_structure = get_repo_structure()
+
+    try:
+        # Fetch the contents of repo_structure.txt from GitHub
+        contents = repo.get_contents("repo_structure.txt")
+        existing_structure = contents.decoded_content.decode().split('\n')
+    except github.GithubException:
+        existing_structure = None
+
+    if current_structure != existing_structure:
+        update_structure_file(current_structure)
+        update_README(current_structure)
+        print("Repository structure updated.")
+    else:
+        print("No changes in repository structure.")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/.github/workflows/update-readme.yml b/.github/workflows/update-readme.yml
new file mode 100644
index 0000000..6176e8e
--- /dev/null
+++ b/.github/workflows/update-readme.yml
@@ -0,0 +1,39 @@
+name: Update Repository structure
+
+on:
+  schedule:
+    - cron: '0 * * * *'  # Run every hour
+  workflow_dispatch:  # Allow manual triggering
+  push:
+    branches:
+      - main
+      - master
+
+jobs:
+  detect-and-update-structure:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.12
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install PyGithub
+
+      - name: Run update script
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: python .github/scripts/update_structure.py
+
+      - name: Commit and push if changed
+        run: |
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com" 
+          git config user.name "github-actions[bot]"
+          git add . 
+          git diff --quiet && git diff --staged --quiet || (git commit -m "Update repo structure" && git push)
\ No newline at end of file
diff --git a/PROJECT_STRUCTURE.md b/PROJECT_STRUCTURE.md
new file mode 100644
index 0000000..e2e0db4
--- /dev/null
+++ b/PROJECT_STRUCTURE.md
@@ -0,0 +1,4 @@
+## Project Structure ✨
+
+<!-- START_STRUCTURE -->
+<!-- END_STRUCTURE -->
\ No newline at end of file
diff --git a/README.md b/README.md
index 3afb6e2..96b1397 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,10 @@
 
 <img src="https://raw.githubusercontent.com/alo7lika/Stock-Price-Prediction/refs/heads/main/Images/212284100-561aa473-3905-4a80-b561-0d28506553ee.gif" width="900">
 
+## ✨ Project Structure
+
+Check the project structure here [Project Structure](PROJECT_STRUCTURE.md)
+
 ## 📚 Table of Contents
 
 1. [🌟 Overview](#-overview)

From a571a76fe997217267be661066f4e40d49aabd33 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 22 Oct 2024 10:22:59 +0000
Subject: [PATCH 53/76] Update repo structure

---
 PROJECT_STRUCTURE.md | 70 ++++++++++++++++++++++++++++++++++++++++++++
 repo_structure.txt   | 68 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 138 insertions(+)
 create mode 100644 repo_structure.txt

diff --git a/PROJECT_STRUCTURE.md b/PROJECT_STRUCTURE.md
index e2e0db4..cb4d67f 100644
--- a/PROJECT_STRUCTURE.md
+++ b/PROJECT_STRUCTURE.md
@@ -1,4 +1,74 @@
 ## Project Structure ✨
 
 <!-- START_STRUCTURE -->
+```
+├── ARIMA/
+│   ├── ARIMA_V2.ipynb
+│   ├── README.md
+│   └── hybrid.ipynb
+├── Contributing.md
+├── Data/
+│   ├── SBI Test data.csv
+│   ├── SBI Train data.csv
+│   └── SBIN.csv
+├── Financial Environment Segmentation/
+│   ├── Financial Environment Segmentation.ipynb
+│   ├── Financial Insights - Market Segmentation.png
+│   └── README.md
+├── Images/
+│   ├── 212284100-561aa473-3905-4a80-b561-0d28506553ee.gif
+│   ├── 329829127-e79eb6de-81b1-4ffb-b6ed-f018bb977e88.png
+│   ├── Image
+│   └── hacktober.png
+├── Intel_Optimized/
+│   ├── ARIMA_V2.ipynb
+│   ├── Stock_Price_Prediction_1.ipynb
+│   ├── Stock_prediction_Data_Analysis.ipynb
+│   ├── buy_sell_recommendation_system.ipynb
+│   ├── hybrid.ipynb
+│   ├── readme.md
+│   ├── reduced_redundancy_stock_price_prediction.ipynb
+│   └── requirements.txt
+├── InvestWise - Stock Prediction Model.png
+├── Market Trend Classification Model/
+│   ├── Market Trend Classification Model.ipynb
+│   ├── MarketTrend Analytics - Classification Model.png
+│   └── README.md
+├── Multi-Asset Portfolio Modeling/
+│   ├── Multi-Asset Portfolio Modeling.ipynb
+│   └── README.md
+├── PROJECT_STRUCTURE.md
+├── Python File/
+│   ├── Stock_Price_Prediction.ipynb
+│   ├── Stock_Price_Prediction_BACKUP_19716.ipynb
+│   ├── Stock_Price_Prediction_BACKUP_20502.ipynb
+│   ├── Stock_Price_Prediction_BASE_19716.ipynb
+│   ├── Stock_Price_Prediction_BASE_20502.ipynb
+│   ├── Stock_Price_Prediction_LOCAL_19716.ipynb
+│   ├── Stock_Price_Prediction_LOCAL_20502.ipynb
+│   ├── Stock_Price_Prediction_REMOTE_19716.ipynb
+│   └── Stock_Price_Prediction_REMOTE_20502.ipynb
+├── README.md
+├── SBIN.csv
+├── Stock_Price_Prediction(Updated).ipynb
+├── Stock_Price_Prediction.ipynb
+├── Stock_prediction_Data_Analysis.ipynb
+├── VWAP_included_stock_price_prediction.ipynb
+├── buy_sell_recommendation_system.ipynb
+├── candlestick_chart.html
+├── catboost_info/
+│   ├── catboost_training.json
+│   ├── learn/
+│   │   └── events.out.tfevents
+│   ├── learn_error.tsv
+│   └── time_left.tsv
+├── code-of-conduct.md
+├── images/
+│   ├── 085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg
+│   ├── 6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg
+│   └── f23e9194-72de-438d-bd69-744667680d3e.jpeg
+├── reduced_redundancy_stock_price_prediction.ipynb
+├── requirements.txt
+└── stock_market(complete).ipynb
+```
 <!-- END_STRUCTURE -->
\ No newline at end of file
diff --git a/repo_structure.txt b/repo_structure.txt
new file mode 100644
index 0000000..1fdd113
--- /dev/null
+++ b/repo_structure.txt
@@ -0,0 +1,68 @@
+├── ARIMA/
+│   ├── ARIMA_V2.ipynb
+│   ├── README.md
+│   └── hybrid.ipynb
+├── Contributing.md
+├── Data/
+│   ├── SBI Test data.csv
+│   ├── SBI Train data.csv
+│   └── SBIN.csv
+├── Financial Environment Segmentation/
+│   ├── Financial Environment Segmentation.ipynb
+│   ├── Financial Insights - Market Segmentation.png
+│   └── README.md
+├── Images/
+│   ├── 212284100-561aa473-3905-4a80-b561-0d28506553ee.gif
+│   ├── 329829127-e79eb6de-81b1-4ffb-b6ed-f018bb977e88.png
+│   ├── Image
+│   └── hacktober.png
+├── Intel_Optimized/
+│   ├── ARIMA_V2.ipynb
+│   ├── Stock_Price_Prediction_1.ipynb
+│   ├── Stock_prediction_Data_Analysis.ipynb
+│   ├── buy_sell_recommendation_system.ipynb
+│   ├── hybrid.ipynb
+│   ├── readme.md
+│   ├── reduced_redundancy_stock_price_prediction.ipynb
+│   └── requirements.txt
+├── InvestWise - Stock Prediction Model.png
+├── Market Trend Classification Model/
+│   ├── Market Trend Classification Model.ipynb
+│   ├── MarketTrend Analytics - Classification Model.png
+│   └── README.md
+├── Multi-Asset Portfolio Modeling/
+│   ├── Multi-Asset Portfolio Modeling.ipynb
+│   └── README.md
+├── PROJECT_STRUCTURE.md
+├── Python File/
+│   ├── Stock_Price_Prediction.ipynb
+│   ├── Stock_Price_Prediction_BACKUP_19716.ipynb
+│   ├── Stock_Price_Prediction_BACKUP_20502.ipynb
+│   ├── Stock_Price_Prediction_BASE_19716.ipynb
+│   ├── Stock_Price_Prediction_BASE_20502.ipynb
+│   ├── Stock_Price_Prediction_LOCAL_19716.ipynb
+│   ├── Stock_Price_Prediction_LOCAL_20502.ipynb
+│   ├── Stock_Price_Prediction_REMOTE_19716.ipynb
+│   └── Stock_Price_Prediction_REMOTE_20502.ipynb
+├── README.md
+├── SBIN.csv
+├── Stock_Price_Prediction(Updated).ipynb
+├── Stock_Price_Prediction.ipynb
+├── Stock_prediction_Data_Analysis.ipynb
+├── VWAP_included_stock_price_prediction.ipynb
+├── buy_sell_recommendation_system.ipynb
+├── candlestick_chart.html
+├── catboost_info/
+│   ├── catboost_training.json
+│   ├── learn/
+│   │   └── events.out.tfevents
+│   ├── learn_error.tsv
+│   └── time_left.tsv
+├── code-of-conduct.md
+├── images/
+│   ├── 085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg
+│   ├── 6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg
+│   └── f23e9194-72de-438d-bd69-744667680d3e.jpeg
+├── reduced_redundancy_stock_price_prediction.ipynb
+├── requirements.txt
+└── stock_market(complete).ipynb
\ No newline at end of file

From 9b75cea320fc030abb9c651ff1973a4a425beb04 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 22 Oct 2024 11:17:20 +0000
Subject: [PATCH 54/76] Update repo structure

---
 PROJECT_STRUCTURE.md | 1 +
 repo_structure.txt   | 1 +
 2 files changed, 2 insertions(+)

diff --git a/PROJECT_STRUCTURE.md b/PROJECT_STRUCTURE.md
index cb4d67f..db53de1 100644
--- a/PROJECT_STRUCTURE.md
+++ b/PROJECT_STRUCTURE.md
@@ -68,6 +68,7 @@
 │   ├── 6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg
 │   └── f23e9194-72de-438d-bd69-744667680d3e.jpeg
 ├── reduced_redundancy_stock_price_prediction.ipynb
+├── repo_structure.txt
 ├── requirements.txt
 └── stock_market(complete).ipynb
 ```
diff --git a/repo_structure.txt b/repo_structure.txt
index 1fdd113..51ea91e 100644
--- a/repo_structure.txt
+++ b/repo_structure.txt
@@ -64,5 +64,6 @@
 │   ├── 6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg
 │   └── f23e9194-72de-438d-bd69-744667680d3e.jpeg
 ├── reduced_redundancy_stock_price_prediction.ipynb
+├── repo_structure.txt
 ├── requirements.txt
 └── stock_market(complete).ipynb
\ No newline at end of file

From 2c71978fc8bbe4cdda28e0f74a197e6a40459b74 Mon Sep 17 00:00:00 2001
From: ygowthamr <ygowthamr@gmail.com>
Date: Tue, 22 Oct 2024 22:20:48 +0530
Subject: [PATCH 55/76] Added Issue labeler work flow

---
 .github/workflows/issue_labeler.yaml | 55 ++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 .github/workflows/issue_labeler.yaml

diff --git a/.github/workflows/issue_labeler.yaml b/.github/workflows/issue_labeler.yaml
new file mode 100644
index 0000000..273cad5
--- /dev/null
+++ b/.github/workflows/issue_labeler.yaml
@@ -0,0 +1,55 @@
+name: Issue Labeler
+
+on:
+  issues:
+    types: [opened, edited]
+
+jobs:
+  manage-labels:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v3
+
+      - name: Create or Update Labels
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          curl -X POST \
+            -H "Authorization: token $GITHUB_TOKEN" \
+            -H "Accept: application/vnd.github.v3+json" \
+            https://api.github.com/repos/${{ github.repository }}/labels \
+            -d '{
+              "name": "gssoc-extd",
+              "color": "0E8A16",
+              "description": "GSSOC extended contribution"
+            }'
+
+          curl -X POST \
+            -H "Authorization: token $GITHUB_TOKEN" \
+            -H "Accept: application/vnd.github.v3+json" \
+            https://api.github.com/repos/${{ github.repository }}/labels \
+            -d '{
+              "name": "hacktoberfest",
+              "color": "FF7518",
+              "description": "Hacktoberfest participation"
+            }'
+
+          curl -X POST \
+            -H "Authorization: token $GITHUB_TOKEN" \
+            -H "Accept: application/vnd.github.v3+json" \
+            https://api.github.com/repos/${{ github.repository }}/labels \
+            -d '{
+              "name": "level?",
+              "color": "5319E7",
+              "description": "Placeholder for difficulty level"
+            }'
+
+      - name: Add Labels to Issue
+        uses: actions-ecosystem/action-add-labels@v1
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          labels: |
+            gssoc-extd
+            hacktoberfest
+            level?
\ No newline at end of file

From bb18acf7713cc08c9b865090ddb0b9460a91233c Mon Sep 17 00:00:00 2001
From: gowtham <68599050+ygowthamr@users.noreply.github.com>
Date: Tue, 22 Oct 2024 22:31:43 +0530
Subject: [PATCH 56/76] Update issue_labeler.yaml

---
 .github/workflows/issue_labeler.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/issue_labeler.yaml b/.github/workflows/issue_labeler.yaml
index 273cad5..d42b037 100644
--- a/.github/workflows/issue_labeler.yaml
+++ b/.github/workflows/issue_labeler.yaml
@@ -30,7 +30,7 @@ jobs:
             -H "Accept: application/vnd.github.v3+json" \
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
-              "name": "hacktoberfest",
+              "name": "hacktoberfest-accepted",
               "color": "FF7518",
               "description": "Hacktoberfest participation"
             }'
@@ -52,4 +52,4 @@ jobs:
           labels: |
             gssoc-extd
             hacktoberfest
-            level?
\ No newline at end of file
+            level?

From 2829e9fa526eab65e909c2348fe55091b19baa6f Mon Sep 17 00:00:00 2001
From: Shristi Rawat <shristirawat68@gmail.com>
Date: Tue, 22 Oct 2024 23:05:37 +0530
Subject: [PATCH 57/76] set data index frequency and non-invertible MA features

---
 .../ARIMA_V2-checkpoint.ipynb                 | 350 ++++++++++++++++++
 ARIMA/ARIMA_V2.ipynb                          |  97 ++---
 2 files changed, 405 insertions(+), 42 deletions(-)
 create mode 100644 ARIMA/.ipynb_checkpoints/ARIMA_V2-checkpoint.ipynb

diff --git a/ARIMA/.ipynb_checkpoints/ARIMA_V2-checkpoint.ipynb b/ARIMA/.ipynb_checkpoints/ARIMA_V2-checkpoint.ipynb
new file mode 100644
index 0000000..ff47f3a
--- /dev/null
+++ b/ARIMA/.ipynb_checkpoints/ARIMA_V2-checkpoint.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Intial code\n",
+    "for reference purposes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pandas as pd\n",
+    "# import numpy as np\n",
+    "# from sklearn.metrics import mean_squared_error\n",
+    "# from sklearn.preprocessing import StandardScaler\n",
+    "# from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "# from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
+    "# import warnings\n",
+    "\n",
+    "# # Ignore convergence warnings\n",
+    "# warnings.simplefilter(\"ignore\")\n",
+    "\n",
+    "# # Load dataset with parsed dates\n",
+    "# data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# # Set the index to the Date column\n",
+    "# data.set_index('Date', inplace=True)\n",
+    "# # data = data.asfreq('D')\n",
+    "# # Feature Engineering: Add day of week and month\n",
+    "# data['day_of_week'] = data.index.dayofweek\n",
+    "# data['month'] = data.index.month\n",
+    "\n",
+    "# # Add lagged value of the Close price and moving averages\n",
+    "# data['lagged_close'] = data['Close'].shift(1)  \n",
+    "# data['moving_avg_3'] = data['Close'].rolling(window=3).mean()\n",
+    "# data['moving_avg_7'] = data['Close'].rolling(window=7).mean()  # New: 7-day moving average for long-term trend\n",
+    "\n",
+    "# # Add Volume as a feature (scaling might help)\n",
+    "# data['volume'] = data['Volume']\n",
+    "\n",
+    "# # Drop rows with NaN values\n",
+    "# data.dropna(inplace=True)\n",
+    "\n",
+    "# # Standardize the features (important for scaling)\n",
+    "# scaler = StandardScaler()\n",
+    "# exog_features = ['day_of_week', 'month', 'lagged_close', 'moving_avg_3', 'moving_avg_7', 'volume']\n",
+    "# data[exog_features] = scaler.fit_transform(data[exog_features])\n",
+    "\n",
+    "# # Split the data into training and testing sets\n",
+    "# train_size = int(len(data) * 0.8)\n",
+    "# train, test = data.iloc[:train_size], data.iloc[train_size:]\n",
+    "\n",
+    "# # Tune SARIMAX hyperparameters (ARIMA order (p, d, q))\n",
+    "# order = (2, 1, 2)  # Consider using AIC/BIC for finding optimal order\n",
+    "# seasonal_order = (1, 1, 1, 12)  # Adding seasonality with monthly frequency\n",
+    "\n",
+    "# # Fit the SARIMAX model\n",
+    "# try:\n",
+    "#     model = SARIMAX(train['Close'], \n",
+    "#                     exog=train[exog_features],\n",
+    "#                     order=order,\n",
+    "#                     seasonal_order=seasonal_order)\n",
+    "#     model_fit = model.fit(disp=False)\n",
+    "# except ConvergenceWarning as e:\n",
+    "#     print(f\"Convergence warning: {e}\")\n",
+    "# except Exception as e:\n",
+    "#     print(f\"Error: {e}\")\n",
+    "\n",
+    "# # Forecasting\n",
+    "# forecast = model_fit.forecast(steps=len(test), exog=test[exog_features])\n",
+    "\n",
+    "# # Calculate RMSE for forecast\n",
+    "# rmse_arimax = np.sqrt(mean_squared_error(test['Close'], forecast))\n",
+    "# print(f\"Improved ARIMAX Model RMSE: {rmse_arimax}\")\n",
+    "\n",
+    "# test_prices = [i for i in test['Close']]\n",
+    "# # Check residuals diagnostics (optional)\n",
+    "# residuals = test_prices - forecast\n",
+    "# print(\"Mean of residuals:\", residuals.mean())\n",
+    "# print(\"Standard deviation of residuals:\", residuals.std())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### immporting necessary libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: kaggle in d:\\anaconda\\lib\\site-packages (1.6.17)\n",
+      "Requirement already satisfied: six>=1.10 in d:\\anaconda\\lib\\site-packages (from kaggle) (1.16.0)\n",
+      "Requirement already satisfied: certifi>=2023.7.22 in d:\\anaconda\\lib\\site-packages (from kaggle) (2024.8.30)\n",
+      "Requirement already satisfied: python-dateutil in d:\\anaconda\\lib\\site-packages (from kaggle) (2.9.0.post0)\n",
+      "Requirement already satisfied: requests in d:\\anaconda\\lib\\site-packages (from kaggle) (2.32.2)\n",
+      "Requirement already satisfied: tqdm in d:\\anaconda\\lib\\site-packages (from kaggle) (4.66.4)\n",
+      "Requirement already satisfied: python-slugify in d:\\anaconda\\lib\\site-packages (from kaggle) (5.0.2)\n",
+      "Requirement already satisfied: urllib3 in d:\\anaconda\\lib\\site-packages (from kaggle) (2.2.2)\n",
+      "Requirement already satisfied: bleach in d:\\anaconda\\lib\\site-packages (from kaggle) (4.1.0)\n",
+      "Requirement already satisfied: packaging in d:\\anaconda\\lib\\site-packages (from bleach->kaggle) (23.2)\n",
+      "Requirement already satisfied: webencodings in d:\\anaconda\\lib\\site-packages (from bleach->kaggle) (0.5.1)\n",
+      "Requirement already satisfied: text-unidecode>=1.3 in d:\\anaconda\\lib\\site-packages (from python-slugify->kaggle) (1.3)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in d:\\anaconda\\lib\\site-packages (from requests->kaggle) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in d:\\anaconda\\lib\\site-packages (from requests->kaggle) (3.7)\n",
+      "Requirement already satisfied: colorama in c:\\users\\shristi\\appdata\\roaming\\python\\python312\\site-packages (from tqdm->kaggle) (0.4.6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install kaggle\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Udating features to dataset for proper time-series analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Ignore convergence warnings\n",
+    "warnings.simplefilter(\"ignore\", ConvergenceWarning)\n",
+    "\n",
+    "# Load training dataset with parsed dates\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# Set the index to the Date column\n",
+    "train_data.index = pd.DatetimeIndex(train_data.index).to_period('M')\n",
+    "\n",
+    "# Feature Engineering: Add day of week and month\n",
+    "train_data['day_of_week'] = train_data.index.dayofweek\n",
+    "train_data['month'] = train_data.index.month\n",
+    "\n",
+    "# Add lagged value of the Close price and moving averages\n",
+    "train_data['lagged_close'] = train_data['Close'].shift(1)\n",
+    "train_data['moving_avg_3'] = train_data['Close'].rolling(window=3).mean()\n",
+    "train_data['moving_avg_7'] = train_data['Close'].rolling(window=7).mean()\n",
+    "\n",
+    "# Add Volume as a feature (scaling might help)\n",
+    "train_data['volume'] = train_data['Volume']\n",
+    "\n",
+    "# Drop rows with NaN values after applying the rolling window and lagging\n",
+    "train_data.dropna(inplace=True)\n",
+    "\n",
+    "# Standardize the features\n",
+    "scaler = StandardScaler()\n",
+    "exog_features = ['day_of_week', 'month', 'lagged_close', 'moving_avg_3', 'moving_avg_7', 'volume']\n",
+    "train_data[exog_features] = scaler.fit_transform(train_data[exog_features])\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "train_size = int(len(train_data) * 0.8)\n",
+    "train, validation = train_data.iloc[:train_size], train_data.iloc[train_size:]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training and savinng model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model and scaler saved successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train the SARIMAX model\n",
+    "order = (2, 1, 2)\n",
+    "seasonal_order = (1, 1, 1, 12)\n",
+    "\n",
+    "model = SARIMAX(train['Close'], exog=train[exog_features], order=order, seasonal_order=seasonal_order,enforce_invertibility=False)\n",
+    "model_fit = model.fit(disp=False)\n",
+    "\n",
+    "#insert your folder name where you want the dataset to be downloaded instead of .kaggle\n",
+    "os.system('kaggle datasets download -d shristirwt/sarimax-model -p/.kaggle')\n",
+    "os.system('kaggle datasets download -d shristirwt/scaler-model -p/.kaggle')\n",
+    "\n",
+    "# Save the model to a file using pickle\n",
+    "with open(r'C:\\Users\\SHRISTI\\.kaggle\\sarimax_model.pkl', 'wb') as f:\n",
+    "    pickle.dump(model_fit, f)\n",
+    "\n",
+    "# Optionally save the scaler as well\n",
+    "with open(r'C:\\Users\\SHRISTI\\.kaggle\\scaler.pkl', 'wb') as f:\n",
+    "    pickle.dump(scaler, f)\n",
+    "\n",
+    "print(\"Model and scaler saved successfully.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading saved model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the model and scaler from the files\n",
+    "with open(r'C:\\Users\\SHRISTI\\.kaggle\\sarimax_model.pkl', 'rb') as f:\n",
+    "    loaded_model = pickle.load(f)\n",
+    "\n",
+    "with open(r'C:\\Users\\SHRISTI\\.kaggle\\scaler.pkl', 'rb') as f:\n",
+    "    loaded_scaler = pickle.load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading and processing Test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the test dataset\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv', parse_dates=['Date'], dayfirst=True)\n",
+    "\n",
+    "# Set the index to the Date column\n",
+    "test_data.set_index('Date', inplace=True)\n",
+    "\n",
+    "# Apply the same feature engineering on the test data\n",
+    "test_data['day_of_week'] = test_data.index.dayofweek\n",
+    "test_data['month'] = test_data.index.month\n",
+    "test_data['lagged_close'] = test_data['Close'].shift(1)\n",
+    "test_data['moving_avg_3'] = test_data['Close'].rolling(window=3).mean()\n",
+    "test_data['moving_avg_7'] = test_data['Close'].rolling(window=7).mean()\n",
+    "\n",
+    "# Add Volume as a feature\n",
+    "test_data['volume'] = test_data['Volume']\n",
+    "\n",
+    "# Drop rows with NaN values\n",
+    "test_data.dropna(inplace=True)\n",
+    "\n",
+    "# Standardize the features in the test dataset using the loaded scaler\n",
+    "test_data[exog_features] = loaded_scaler.transform(test_data[exog_features])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predicting share prices using model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Data RMSE: 4.883649507349637\n",
+      "Mean of residuals: 0.06489726947015648\n",
+      "Standard deviation of residuals: 4.8849520783077764\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Forecasting on the test data using the loaded model\n",
+    "forecast_test = loaded_model.forecast(steps=len(test_data), exog=test_data[exog_features])\n",
+    "\n",
+    "# Calculate RMSE for forecast\n",
+    "rmse_test = np.sqrt(mean_squared_error(test_data['Close'], forecast_test))\n",
+    "print(f\"Test Data RMSE: {rmse_test}\")\n",
+    "\n",
+    "# Check residuals diagnostics (optional)\n",
+    "test_prices = test_data['Close'].values\n",
+    "residuals_test = test_prices - forecast_test\n",
+    "print(\"Mean of residuals:\", residuals_test.mean())\n",
+    "print(\"Standard deviation of residuals:\", residuals_test.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ARIMA/ARIMA_V2.ipynb b/ARIMA/ARIMA_V2.ipynb
index bd5fe49..ff47f3a 100644
--- a/ARIMA/ARIMA_V2.ipynb
+++ b/ARIMA/ARIMA_V2.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,10 +95,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: kaggle in d:\\anaconda\\lib\\site-packages (1.6.17)\n",
+      "Requirement already satisfied: six>=1.10 in d:\\anaconda\\lib\\site-packages (from kaggle) (1.16.0)\n",
+      "Requirement already satisfied: certifi>=2023.7.22 in d:\\anaconda\\lib\\site-packages (from kaggle) (2024.8.30)\n",
+      "Requirement already satisfied: python-dateutil in d:\\anaconda\\lib\\site-packages (from kaggle) (2.9.0.post0)\n",
+      "Requirement already satisfied: requests in d:\\anaconda\\lib\\site-packages (from kaggle) (2.32.2)\n",
+      "Requirement already satisfied: tqdm in d:\\anaconda\\lib\\site-packages (from kaggle) (4.66.4)\n",
+      "Requirement already satisfied: python-slugify in d:\\anaconda\\lib\\site-packages (from kaggle) (5.0.2)\n",
+      "Requirement already satisfied: urllib3 in d:\\anaconda\\lib\\site-packages (from kaggle) (2.2.2)\n",
+      "Requirement already satisfied: bleach in d:\\anaconda\\lib\\site-packages (from kaggle) (4.1.0)\n",
+      "Requirement already satisfied: packaging in d:\\anaconda\\lib\\site-packages (from bleach->kaggle) (23.2)\n",
+      "Requirement already satisfied: webencodings in d:\\anaconda\\lib\\site-packages (from bleach->kaggle) (0.5.1)\n",
+      "Requirement already satisfied: text-unidecode>=1.3 in d:\\anaconda\\lib\\site-packages (from python-slugify->kaggle) (1.3)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in d:\\anaconda\\lib\\site-packages (from requests->kaggle) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in d:\\anaconda\\lib\\site-packages (from requests->kaggle) (3.7)\n",
+      "Requirement already satisfied: colorama in c:\\users\\shristi\\appdata\\roaming\\python\\python312\\site-packages (from tqdm->kaggle) (0.4.6)\n"
+     ]
+    }
+   ],
    "source": [
+    "!pip install kaggle\n",
+    "import os\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import pickle\n",
@@ -118,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -130,7 +154,7 @@
     "train_data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
     "\n",
     "# Set the index to the Date column\n",
-    "train_data.set_index('Date', inplace=True)\n",
+    "train_data.index = pd.DatetimeIndex(train_data.index).to_period('M')\n",
     "\n",
     "# Feature Engineering: Add day of week and month\n",
     "train_data['day_of_week'] = train_data.index.dayofweek\n",
@@ -166,21 +190,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
-      "  self._init_dates(dates, freq)\n",
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
-      "  self._init_dates(dates, freq)\n",
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
-      "  warn('Non-invertible starting MA parameters found.'\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -194,15 +206,19 @@
     "order = (2, 1, 2)\n",
     "seasonal_order = (1, 1, 1, 12)\n",
     "\n",
-    "model = SARIMAX(train['Close'], exog=train[exog_features], order=order, seasonal_order=seasonal_order)\n",
+    "model = SARIMAX(train['Close'], exog=train[exog_features], order=order, seasonal_order=seasonal_order,enforce_invertibility=False)\n",
     "model_fit = model.fit(disp=False)\n",
     "\n",
+    "#insert your folder name where you want the dataset to be downloaded instead of .kaggle\n",
+    "os.system('kaggle datasets download -d shristirwt/sarimax-model -p/.kaggle')\n",
+    "os.system('kaggle datasets download -d shristirwt/scaler-model -p/.kaggle')\n",
+    "\n",
     "# Save the model to a file using pickle\n",
-    "with open('sarimax_model.pkl', 'wb') as f:\n",
+    "with open(r'C:\\Users\\SHRISTI\\.kaggle\\sarimax_model.pkl', 'wb') as f:\n",
     "    pickle.dump(model_fit, f)\n",
     "\n",
     "# Optionally save the scaler as well\n",
-    "with open('scaler.pkl', 'wb') as f:\n",
+    "with open(r'C:\\Users\\SHRISTI\\.kaggle\\scaler.pkl', 'wb') as f:\n",
     "    pickle.dump(scaler, f)\n",
     "\n",
     "print(\"Model and scaler saved successfully.\")\n"
@@ -217,15 +233,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Load the model and scaler from the files\n",
-    "with open('sarimax_model.pkl', 'rb') as f:\n",
+    "with open(r'C:\\Users\\SHRISTI\\.kaggle\\sarimax_model.pkl', 'rb') as f:\n",
     "    loaded_model = pickle.load(f)\n",
     "\n",
-    "with open('scaler.pkl', 'rb') as f:\n",
+    "with open(r'C:\\Users\\SHRISTI\\.kaggle\\scaler.pkl', 'rb') as f:\n",
     "    loaded_scaler = pickle.load(f)\n"
    ]
   },
@@ -238,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,26 +290,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
-      "  return get_prediction_index(\n",
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
-      "  return get_prediction_index(\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Data RMSE: 4.673693537736142\n",
-      "Mean of residuals: 0.311316834051805\n",
-      "Standard deviation of residuals: 4.664969245987366\n"
+      "Test Data RMSE: 4.883649507349637\n",
+      "Mean of residuals: 0.06489726947015648\n",
+      "Standard deviation of residuals: 4.8849520783077764\n"
      ]
     }
    ],
@@ -311,11 +317,18 @@
     "print(\"Mean of residuals:\", residuals_test.mean())\n",
     "print(\"Standard deviation of residuals:\", residuals_test.std())"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -329,9 +342,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From f1e349cc0b410106c0b670fec6a928d732b475bd Mon Sep 17 00:00:00 2001
From: Mugundh J B <mugundhjb@gmail.com>
Date: Tue, 22 Oct 2024 23:38:53 +0530
Subject: [PATCH 58/76] Create dummy.txt

---
 dummy.txt | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 dummy.txt

diff --git a/dummy.txt b/dummy.txt
new file mode 100644
index 0000000..6d221f2
--- /dev/null
+++ b/dummy.txt
@@ -0,0 +1 @@
+Adding a dummy file to verify Automated project structure updation using github workflows, python scripts and action bot

From 182e9ca42c1440020e2e7fba1b492b057e6e7d82 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 22 Oct 2024 18:09:14 +0000
Subject: [PATCH 59/76] Update repo structure

---
 PROJECT_STRUCTURE.md | 1 +
 repo_structure.txt   | 1 +
 2 files changed, 2 insertions(+)

diff --git a/PROJECT_STRUCTURE.md b/PROJECT_STRUCTURE.md
index db53de1..b61dbd7 100644
--- a/PROJECT_STRUCTURE.md
+++ b/PROJECT_STRUCTURE.md
@@ -63,6 +63,7 @@
 │   ├── learn_error.tsv
 │   └── time_left.tsv
 ├── code-of-conduct.md
+├── dummy.txt
 ├── images/
 │   ├── 085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg
 │   ├── 6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg
diff --git a/repo_structure.txt b/repo_structure.txt
index 51ea91e..a6d0c44 100644
--- a/repo_structure.txt
+++ b/repo_structure.txt
@@ -59,6 +59,7 @@
 │   ├── learn_error.tsv
 │   └── time_left.tsv
 ├── code-of-conduct.md
+├── dummy.txt
 ├── images/
 │   ├── 085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg
 │   ├── 6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg

From e288f9f6093fbd20e3d3eb0fbd88e7150858e4de Mon Sep 17 00:00:00 2001
From: Mugundh J B <mugundhjb@gmail.com>
Date: Tue, 22 Oct 2024 23:39:49 +0530
Subject: [PATCH 60/76] Delete dummy.txt

---
 dummy.txt | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 dummy.txt

diff --git a/dummy.txt b/dummy.txt
deleted file mode 100644
index 6d221f2..0000000
--- a/dummy.txt
+++ /dev/null
@@ -1 +0,0 @@
-Adding a dummy file to verify Automated project structure updation using github workflows, python scripts and action bot

From d16503d12a9a0c3937911538255005a7b35ce861 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Tue, 22 Oct 2024 18:10:09 +0000
Subject: [PATCH 61/76] Update repo structure

---
 PROJECT_STRUCTURE.md | 1 -
 repo_structure.txt   | 1 -
 2 files changed, 2 deletions(-)

diff --git a/PROJECT_STRUCTURE.md b/PROJECT_STRUCTURE.md
index b61dbd7..db53de1 100644
--- a/PROJECT_STRUCTURE.md
+++ b/PROJECT_STRUCTURE.md
@@ -63,7 +63,6 @@
 │   ├── learn_error.tsv
 │   └── time_left.tsv
 ├── code-of-conduct.md
-├── dummy.txt
 ├── images/
 │   ├── 085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg
 │   ├── 6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg
diff --git a/repo_structure.txt b/repo_structure.txt
index a6d0c44..51ea91e 100644
--- a/repo_structure.txt
+++ b/repo_structure.txt
@@ -59,7 +59,6 @@
 │   ├── learn_error.tsv
 │   └── time_left.tsv
 ├── code-of-conduct.md
-├── dummy.txt
 ├── images/
 │   ├── 085ee2d1-3544-4bed-a558-5b0b801e806b.jpeg
 │   ├── 6c9ebb5b-a8ed-44de-8842-bf8f5c25990f.jpeg

From 284ef48c60f9a391f878d470f4769998be605261 Mon Sep 17 00:00:00 2001
From: gowtham <68599050+ygowthamr@users.noreply.github.com>
Date: Wed, 23 Oct 2024 06:20:31 +0530
Subject: [PATCH 62/76] Update issue_labeler.yaml

---
 .github/workflows/issue_labeler.yaml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/issue_labeler.yaml b/.github/workflows/issue_labeler.yaml
index d42b037..79b8ada 100644
--- a/.github/workflows/issue_labeler.yaml
+++ b/.github/workflows/issue_labeler.yaml
@@ -21,7 +21,7 @@ jobs:
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
               "name": "gssoc-extd",
-              "color": "0E8A16",
+              "color": "55a0ea",
               "description": "GSSOC extended contribution"
             }'
 
@@ -31,7 +31,7 @@ jobs:
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
               "name": "hacktoberfest-accepted",
-              "color": "FF7518",
+              "color": "263432",
               "description": "Hacktoberfest participation"
             }'
 

From f0190c3ffc0b83794cee9b395a7bef3642195819 Mon Sep 17 00:00:00 2001
From: gowtham <68599050+ygowthamr@users.noreply.github.com>
Date: Wed, 23 Oct 2024 11:27:30 +0530
Subject: [PATCH 63/76] Added all requested labels

---
 .github/workflows/issue_labeler.yaml | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/issue_labeler.yaml b/.github/workflows/issue_labeler.yaml
index 79b8ada..0622833 100644
--- a/.github/workflows/issue_labeler.yaml
+++ b/.github/workflows/issue_labeler.yaml
@@ -20,11 +20,21 @@ jobs:
             -H "Accept: application/vnd.github.v3+json" \
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
-              "name": "gssoc-extd",
+              "name": "gssoc-ext",
               "color": "55a0ea",
               "description": "GSSOC extended contribution"
             }'
 
+          curl -X POST \
+            -H "Authorization: token $GITHUB_TOKEN" \
+            -H "Accept: application/vnd.github.v3+json" \
+            https://api.github.com/repos/${{ github.repository }}/labels \
+            -d '{
+              "name": "hacktoberfest",
+              "color": "ff7518",
+              "description": "Hacktoberfest participation"
+            }'
+
           curl -X POST \
             -H "Authorization: token $GITHUB_TOKEN" \
             -H "Accept: application/vnd.github.v3+json" \
@@ -32,7 +42,7 @@ jobs:
             -d '{
               "name": "hacktoberfest-accepted",
               "color": "263432",
-              "description": "Hacktoberfest participation"
+              "description": "Hacktoberfest contribution accepted"
             }'
 
           curl -X POST \
@@ -50,6 +60,7 @@ jobs:
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           labels: |
-            gssoc-extd
+            gssoc-ext
             hacktoberfest
+            hacktoberfest-accepted
             level?

From 37499c1f4786f9343257bdc1fd8027f20e17e1c0 Mon Sep 17 00:00:00 2001
From: gowtham <68599050+ygowthamr@users.noreply.github.com>
Date: Wed, 23 Oct 2024 18:44:33 +0530
Subject: [PATCH 64/76] Updated all label color changes

---
 .github/workflows/issue_labeler.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/issue_labeler.yaml b/.github/workflows/issue_labeler.yaml
index 0622833..b2b0e24 100644
--- a/.github/workflows/issue_labeler.yaml
+++ b/.github/workflows/issue_labeler.yaml
@@ -21,7 +21,7 @@ jobs:
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
               "name": "gssoc-ext",
-              "color": "55a0ea",
+              "color": "0E4075",
               "description": "GSSOC extended contribution"
             }'
 
@@ -31,7 +31,7 @@ jobs:
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
               "name": "hacktoberfest",
-              "color": "ff7518",
+              "color": "006b75",
               "description": "Hacktoberfest participation"
             }'
 
@@ -41,7 +41,7 @@ jobs:
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
               "name": "hacktoberfest-accepted",
-              "color": "263432",
+              "color": "95D2AA",
               "description": "Hacktoberfest contribution accepted"
             }'
 

From 7eb18a7d4a5bf4c0a1aed0e36448420edf8ef2d7 Mon Sep 17 00:00:00 2001
From: gowtham <68599050+ygowthamr@users.noreply.github.com>
Date: Wed, 23 Oct 2024 18:50:10 +0530
Subject: [PATCH 65/76] Solved some errors

---
 .github/workflows/issue_labeler.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/issue_labeler.yaml b/.github/workflows/issue_labeler.yaml
index b2b0e24..db862ec 100644
--- a/.github/workflows/issue_labeler.yaml
+++ b/.github/workflows/issue_labeler.yaml
@@ -21,7 +21,7 @@ jobs:
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
               "name": "gssoc-ext",
-              "color": "0E4075",
+              "color": "0e4075",
               "description": "GSSOC extended contribution"
             }'
 
@@ -41,7 +41,7 @@ jobs:
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
               "name": "hacktoberfest-accepted",
-              "color": "95D2AA",
+              "color": "95d2aa",
               "description": "Hacktoberfest contribution accepted"
             }'
 
@@ -51,7 +51,7 @@ jobs:
             https://api.github.com/repos/${{ github.repository }}/labels \
             -d '{
               "name": "level?",
-              "color": "5319E7",
+              "color": "5319e7",
               "description": "Placeholder for difficulty level"
             }'
 

From d5c7c175be819acbc7385f5edb5faa8b68f4ef5a Mon Sep 17 00:00:00 2001
From: gowtham <68599050+ygowthamr@users.noreply.github.com>
Date: Wed, 23 Oct 2024 18:57:16 +0530
Subject: [PATCH 66/76] Solved potential issues


From a976ec638392cbdd2d25cca7d3d1267ad140f963 Mon Sep 17 00:00:00 2001
From: Saqib Javed <99673719+jvedsaqib@users.noreply.github.com>
Date: Thu, 24 Oct 2024 14:07:53 +0530
Subject: [PATCH 67/76] Create update_leaderboard.py

---
 .github/scripts/update_leaderboard.py | 66 +++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 .github/scripts/update_leaderboard.py

diff --git a/.github/scripts/update_leaderboard.py b/.github/scripts/update_leaderboard.py
new file mode 100644
index 0000000..90e9679
--- /dev/null
+++ b/.github/scripts/update_leaderboard.py
@@ -0,0 +1,66 @@
+import requests
+from collections import defaultdict
+
+# GitHub API URL for closed pull requests
+API_URL = "https://api.github.com/repos/rohitinu6/Stock-Price-Prediction/pulls?state=closed"
+
+GITHUB_TOKEN = None
+
+# Points mapping based on the label names
+points_map = {
+    "level1": 10,
+    "level2": 25,
+    "level3": 45
+}
+
+# Function to fetch closed pull requests
+def get_closed_prs():
+    headers = {}
+    
+    if GITHUB_TOKEN:
+        headers = {"Authorization": f"token {GITHUB_TOKEN}"}
+    
+    response = requests.get(API_URL, headers=headers)
+    
+    if response.status_code != 200:
+        raise Exception(f"Failed to fetch PRs: {response.status_code} {response.text}")
+    
+    return response.json()
+
+leaderboard = defaultdict(lambda: {"points": 0, "avatar_url": ""})
+
+prs = get_closed_prs()
+
+# Loop through each PR and calculate points based on the labels
+for pr in prs:
+    user = pr['user']['login']  # Get the username
+    avatar_url = pr['user']['avatar_url']  # Get the avatar URL
+    labels = pr['labels']  # Get the labels associated with the PR
+
+    # Loop through labels and add points based on the level
+    for label in labels:
+        label_name = label['name']
+        if label_name in points_map:
+            leaderboard[user]["points"] += points_map[label_name]
+            leaderboard[user]["avatar_url"] = avatar_url  # Store avatar URL
+
+# Generate the leaderboard in markdown format
+def generate_leaderboard_md(leaderboard):
+    sorted_leaderboard = sorted(leaderboard.items(), key=lambda x: x[1]["points"], reverse=True)
+    
+    md_content = "# Leaderboard\n\n"
+    md_content += "| Avatar | Username | Points |\n"
+    md_content += "|--------|----------|--------|\n"
+    
+    for user, data in sorted_leaderboard:
+        points = data["points"]
+        avatar_url = data["avatar_url"]
+        md_content += f'| <img src="{avatar_url}" width="50" height="50"> | {user} | {points} |\n'
+    
+    return md_content
+
+# Generate the leaderboard markdown and save it to a file
+leaderboard_md = generate_leaderboard_md(leaderboard)
+with open('leaderboard.md', 'w') as f:
+    f.write(leaderboard_md)
+

From 93d0738b71003f0e0ea5c326e65d4653a3b1f768 Mon Sep 17 00:00:00 2001
From: Saqib Javed <99673719+jvedsaqib@users.noreply.github.com>
Date: Thu, 24 Oct 2024 14:12:55 +0530
Subject: [PATCH 68/76] Create update_leaderboard.yml

---
 .github/workflows/update_leaderboard.yml | 35 ++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 .github/workflows/update_leaderboard.yml

diff --git a/.github/workflows/update_leaderboard.yml b/.github/workflows/update_leaderboard.yml
new file mode 100644
index 0000000..668929d
--- /dev/null
+++ b/.github/workflows/update_leaderboard.yml
@@ -0,0 +1,35 @@
+name: Update Leaderboard
+
+on:
+  pull_request:
+    types: [closed]
+
+jobs:
+  update-leaderboard:
+    runs-on: ubuntu-latest
+    
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+        
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.x'
+          
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install requests
+      
+      - name: Run leaderboard update script
+        run: |
+          python .github/scripts/update_structure.py
+
+      - name: Commit and push changes
+        run: |
+          git config --global user.name "github-actions[bot]"
+          git config --global user.email "github-actions[bot]@users.noreply.github.com"
+          git add leaderboard.md
+          git commit -m "Update leaderboard"
+          git push

From c5a1c8303b2845745c8009594505279382d6f2f4 Mon Sep 17 00:00:00 2001
From: Saqib Javed <99673719+jvedsaqib@users.noreply.github.com>
Date: Thu, 24 Oct 2024 14:38:34 +0530
Subject: [PATCH 69/76] Update update_leaderboard.py

---
 .github/scripts/update_leaderboard.py | 36 +++++++++++++++++----------
 1 file changed, 23 insertions(+), 13 deletions(-)

diff --git a/.github/scripts/update_leaderboard.py b/.github/scripts/update_leaderboard.py
index 90e9679..e2688a6 100644
--- a/.github/scripts/update_leaderboard.py
+++ b/.github/scripts/update_leaderboard.py
@@ -20,12 +20,24 @@ def get_closed_prs():
     if GITHUB_TOKEN:
         headers = {"Authorization": f"token {GITHUB_TOKEN}"}
     
-    response = requests.get(API_URL, headers=headers)
-    
-    if response.status_code != 200:
-        raise Exception(f"Failed to fetch PRs: {response.status_code} {response.text}")
-    
-    return response.json()
+    prs = []
+    page = 1
+    while True:
+        response = requests.get(f"{API_URL}&page={page}", headers=headers)
+        
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch PRs: {response.status_code} {response.text}")
+        
+        page_prs = response.json()
+        
+        # Break the loop if there are no more PRs
+        if not page_prs:
+            break
+            
+        prs.extend(page_prs)  # Add fetched PRs to the list
+        page += 1  # Increment page number for next request
+
+    return prs
 
 leaderboard = defaultdict(lambda: {"points": 0, "avatar_url": ""})
 
@@ -33,16 +45,15 @@ def get_closed_prs():
 
 # Loop through each PR and calculate points based on the labels
 for pr in prs:
-    user = pr['user']['login']  # Get the username
-    avatar_url = pr['user']['avatar_url']  # Get the avatar URL
-    labels = pr['labels']  # Get the labels associated with the PR
-
-    # Loop through labels and add points based on the level
+    user = pr['user']['login']  
+    avatar_url = pr['user']['avatar_url'] 
+    labels = pr['labels'] 
+    
     for label in labels:
         label_name = label['name']
         if label_name in points_map:
             leaderboard[user]["points"] += points_map[label_name]
-            leaderboard[user]["avatar_url"] = avatar_url  # Store avatar URL
+            leaderboard[user]["avatar_url"] = avatar_url  
 
 # Generate the leaderboard in markdown format
 def generate_leaderboard_md(leaderboard):
@@ -63,4 +74,3 @@ def generate_leaderboard_md(leaderboard):
 leaderboard_md = generate_leaderboard_md(leaderboard)
 with open('leaderboard.md', 'w') as f:
     f.write(leaderboard_md)
-

From 08ef0cd4a241ed0bc06c6dfe689c12469be663b1 Mon Sep 17 00:00:00 2001
From: Saqib Javed <99673719+jvedsaqib@users.noreply.github.com>
Date: Thu, 24 Oct 2024 14:55:12 +0530
Subject: [PATCH 70/76] Update update_leaderboard.yml

---
 .github/workflows/update_leaderboard.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/update_leaderboard.yml b/.github/workflows/update_leaderboard.yml
index 668929d..2e854bc 100644
--- a/.github/workflows/update_leaderboard.yml
+++ b/.github/workflows/update_leaderboard.yml
@@ -20,7 +20,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install requests
+          pip install requests PyGithub
       
       - name: Run leaderboard update script
         run: |
@@ -30,6 +30,6 @@ jobs:
         run: |
           git config --global user.name "github-actions[bot]"
           git config --global user.email "github-actions[bot]@users.noreply.github.com"
-          git add leaderboard.md
-          git commit -m "Update leaderboard"
-          git push
+          git add leaderboard.md || echo "No changes to commit"  # Prevent errors if there's nothing to commit
+          git commit -m "Update leaderboard" || echo "No changes to commit"
+          git push || echo "Nothing to push"

From 32b3c4d9f79f7cb2bce304adca2af7b26ba1c5cc Mon Sep 17 00:00:00 2001
From: "S.N.S.Bhavani Sankar" <s.n.s.bhavanisankar@gmail.com>
Date: Thu, 24 Oct 2024 15:10:17 +0530
Subject: [PATCH 71/76] Added Table to Display Repository Statistics in
 README.md

---
 README.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/README.md b/README.md
index f83cf88..6faa9e7 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,13 @@
 
 <img src="https://raw.githubusercontent.com/alo7lika/Stock-Price-Prediction/refs/heads/main/Images/212284100-561aa473-3905-4a80-b561-0d28506553ee.gif" width="900">
 
+## 📈 GitHub Repository Stats
+| 🌟 **Stars** | 🍴 **Forks** | 🐛 **Issues** | 🔔 **Open PRs** | 🔕 **Closed PRs** | 🛠️ **Languages** | ✅ **Contributors** |
+|--------------|--------------|---------------|-----------------|------------------|------------------|------------------|
+| ![GitHub stars](https://img.shields.io/github/stars/rohitinu6/Stock-Price-Prediction) | ![forks](https://img.shields.io/github/forks/rohitinu6/Stock-Price-Prediction) | ![issues](https://img.shields.io/github/issues/rohitinu6/Stock-Price-Prediction?color=32CD32) | ![pull requests](https://img.shields.io/github/issues-pr/rohitinu6/Stock-Price-Prediction?color=FFFF8F) | ![Closed PRs](https://img.shields.io/github/issues-pr-closed/rohitinu6/Stock-Price-Prediction?color=20B2AA) | ![Languages](https://img.shields.io/github/languages/count/rohitinu6/Stock-Price-Prediction?color=20B2AA) | ![Contributors](https://img.shields.io/github/contributors/rohitinu6/Stock-Price-Prediction?color=00FA9A) |
+
+<img src="https://raw.githubusercontent.com/alo7lika/Stock-Price-Prediction/refs/heads/main/Images/212284100-561aa473-3905-4a80-b561-0d28506553ee.gif" width="900">
+
 ### This project is now OFFICIALLY accepted for
 
 <div align="center">

From 1e76b35745febcf0fd074f17fcf0a919ad6778b4 Mon Sep 17 00:00:00 2001
From: Shristi Rawat <shristirawat68@gmail.com>
Date: Thu, 24 Oct 2024 22:15:26 +0530
Subject: [PATCH 72/76] Handling NaN Values in Time Series Data

---
 .../hybrid-checkpoint.ipynb                   | 817 ++++++++++++++++++
 ARIMA/hybrid.ipynb                            | 401 ++-------
 ARIMA/saved_model/arima_model.pkl             | Bin 0 -> 16001576 bytes
 ARIMA/saved_model/lstm_model.h5               | Bin 0 -> 406960 bytes
 ARIMA/saved_model/scaler.pkl                  | Bin 0 -> 719 bytes
 5 files changed, 912 insertions(+), 306 deletions(-)
 create mode 100644 ARIMA/.ipynb_checkpoints/hybrid-checkpoint.ipynb
 create mode 100644 ARIMA/saved_model/arima_model.pkl
 create mode 100644 ARIMA/saved_model/lstm_model.h5
 create mode 100644 ARIMA/saved_model/scaler.pkl

diff --git a/ARIMA/.ipynb_checkpoints/hybrid-checkpoint.ipynb b/ARIMA/.ipynb_checkpoints/hybrid-checkpoint.ipynb
new file mode 100644
index 0000000..e5870cb
--- /dev/null
+++ b/ARIMA/.ipynb_checkpoints/hybrid-checkpoint.ipynb
@@ -0,0 +1,817 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30240.523, Time=2.28 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30272.252, Time=0.19 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30243.723, Time=0.54 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30241.768, Time=0.77 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30270.982, Time=0.17 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30234.621, Time=2.66 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30239.276, Time=0.76 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30236.607, Time=4.12 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30236.577, Time=6.50 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30234.958, Time=3.86 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30240.618, Time=1.05 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30237.920, Time=5.87 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30233.414, Time=0.94 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30239.179, Time=1.16 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30237.952, Time=0.39 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30235.401, Time=1.76 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30235.372, Time=2.71 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30242.355, Time=0.28 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30233.748, Time=1.51 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30239.309, Time=0.48 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30236.712, Time=2.50 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 40.526 seconds\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 601ms/step\n",
+      "Hybrid model prediction for next day closing price: $244.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(data, pd.DataFrame):\n",
+    "        df = data.values\n",
+    "    else:\n",
+    "        df = np.array(data)\n",
+    "    \n",
+    "    df = df.reshape(-1, 1)\n",
+    "\n",
+    "    df = pd.DataFrame(df).ffill().values\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
+    "    \n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make hybrid prediction\n",
+    "    last_60_days = residuals_scaled[-60:]\n",
+    "    X_test = np.array([last_60_days])\n",
+    "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "\n",
+    "    lstm_prediction = lstm_model.predict(X_test)\n",
+    "    lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "    arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "    hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "\n",
+    "    return hybrid_prediction[0]\n",
+    "\n",
+    "# Example usage with custom data\n",
+    "# Assuming you have a CSV file named 'my_stock_data.csv' with a 'Close' column\n",
+    "custom_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "close_prices = custom_data['Close']\n",
+    "\n",
+    "prediction = hybrid_model(close_prices)\n",
+    "print(f\"Hybrid model prediction for next day closing price: ${prediction:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30240.523, Time=2.25 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30272.252, Time=0.22 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30243.723, Time=0.58 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30241.768, Time=0.82 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30270.982, Time=0.15 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30234.621, Time=2.72 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30239.276, Time=0.84 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30236.607, Time=4.21 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30236.577, Time=6.49 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30234.958, Time=4.04 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30240.618, Time=1.13 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30237.920, Time=6.01 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30233.414, Time=1.07 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30239.179, Time=1.18 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30237.952, Time=0.35 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30235.401, Time=1.82 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30235.372, Time=2.42 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30242.355, Time=0.29 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30233.748, Time=1.49 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30239.309, Time=0.54 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30236.712, Time=2.61 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
+      "Total fit time: 41.259 seconds\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 499ms/step\n",
+      "Hybrid model prediction for next day closing price: $245.04\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(data, pd.Series):\n",
+    "        df = data.values\n",
+    "    elif isinstance(data, pd.DataFrame):\n",
+    "        df = data.values\n",
+    "    else:\n",
+    "        df = np.array(data)\n",
+    "    \n",
+    "    df = df.reshape(-1, 1)\n",
+    "\n",
+    "    df = pd.DataFrame(df).ffill().values\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make hybrid prediction\n",
+    "    last_60_days = residuals_scaled[-60:]\n",
+    "    X_test = np.array([last_60_days])\n",
+    "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "\n",
+    "    lstm_prediction = lstm_model.predict(X_test)\n",
+    "    lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "    arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "    hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "\n",
+    "    return hybrid_prediction[0]\n",
+    "\n",
+    "# Example usage with custom data\n",
+    "custom_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "close_prices = custom_data['Close']\n",
+    "\n",
+    "prediction = hybrid_model(close_prices)\n",
+    "print(f\"Hybrid model prediction for next day closing price: ${prediction:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(train_data, test_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    if isinstance(test_data, pd.DataFrame):\n",
+    "        test_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "\n",
+    "    train_df = pd.DataFrame(train_df).ffill().values\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# You can also calculate percentage error\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def hybrid_model(train_data, test_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    elif isinstance(train_data, pd.DataFrame):\n",
+    "        train_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "    train_df = pd.DataFrame(train_df).ffill().values\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# You can also calculate percentage error\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential, load_model\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "import joblib\n",
+    "import os\n",
+    "\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "def create_hybrid_model(train_data, time_steps=60):\n",
+    "    # Ensure data is numpy array\n",
+    "    if isinstance(train_data, pd.Series):\n",
+    "        train_df = train_data.values\n",
+    "    elif isinstance(train_data, pd.DataFrame):\n",
+    "        train_df = train_data.values\n",
+    "    else:\n",
+    "        train_df = np.array(train_data)\n",
+    "    \n",
+    "    train_df = train_df.reshape(-1, 1)\n",
+    "    train_df = pd.DataFrame(train_df).ffill().values\n",
+    "\n",
+    "    # ARIMA model\n",
+    "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                            error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "    arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "    arima_results = arima_model.fit()\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM model\n",
+    "    lstm_model = Sequential([\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
+    "        LSTM(units=50),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "    lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=0)\n",
+    "\n",
+    "    return arima_results, lstm_model, scaler\n",
+    "\n",
+    "def save_model(arima_results, lstm_model, scaler, folder_path):\n",
+    "    if not os.path.exists(folder_path):\n",
+    "        os.makedirs(folder_path)\n",
+    "    \n",
+    "    # Save ARIMA model\n",
+    "    joblib.dump(arima_results, os.path.join(folder_path, 'arima_model.pkl'))\n",
+    "    \n",
+    "    # Save LSTM model\n",
+    "    lstm_model.save(os.path.join(folder_path, 'lstm_model.h5'))\n",
+    "    \n",
+    "    # Save scaler\n",
+    "    joblib.dump(scaler, os.path.join(folder_path, 'scaler.pkl'))\n",
+    "\n",
+    "def load_model(folder_path):\n",
+    "    # Load ARIMA model\n",
+    "    arima_results = joblib.load(os.path.join(folder_path, 'arima_model.pkl'))\n",
+    "    \n",
+    "    # Load LSTM model\n",
+    "    lstm_model = load_model(os.path.join(folder_path, 'lstm_model.h5'))\n",
+    "    \n",
+    "    # Load scaler\n",
+    "    scaler = joblib.load(os.path.join(folder_path, 'scaler.pkl'))\n",
+    "    \n",
+    "    return arima_results, lstm_model, scaler\n",
+    "\n",
+    "def make_predictions(arima_results, lstm_model, scaler, test_data, time_steps=60):\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(test_data[i:i+time_steps])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model\n",
+    "        arima_results = arima_results.append(test_data[i])\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Example usage\n",
+    "if __name__ == \"__main__\":\n",
+    "    # Load data\n",
+    "    train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "    test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "    train_close_prices = train_data['Close']\n",
+    "    test_close_prices = test_data['Close']\n",
+    "\n",
+    "    # Create and save the model\n",
+    "    arima_results, lstm_model, scaler = create_hybrid_model(train_close_prices)\n",
+    "    save_model(arima_results, lstm_model, scaler, 'saved_model')\n",
+    "\n",
+    "    # Later, load the model and make predictions\n",
+    "    loaded_arima, loaded_lstm, loaded_scaler = load_model('saved_model')\n",
+    "    predictions = make_predictions(loaded_arima, loaded_lstm, loaded_scaler, test_close_prices)\n",
+    "\n",
+    "    # Calculate accuracy metrics\n",
+    "    mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "    rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "    mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "\n",
+    "    print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "    print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "    print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "    # Plot results\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    plt.figure(figsize=(12,6))\n",
+    "    plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "    plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "    plt.title('Actual vs Predicted Stock Prices')\n",
+    "    plt.xlabel('Date')\n",
+    "    plt.ylabel('Price')\n",
+    "    plt.legend()\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from pmdarima import auto_arima\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from tensorflow.keras.models import Sequential, load_model\n",
+    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "import joblib  # For saving the ARIMA model\n",
+    "import os\n",
+    "\n",
+    "# Data preparation\n",
+    "def prepare_data(data, time_steps):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_steps):\n",
+    "        X.append(data[i:(i + time_steps), 0])\n",
+    "        y.append(data[i + time_steps, 0])\n",
+    "    return np.array(X), np.array(y)\n",
+    "\n",
+    "# Hybrid ARIMA-LSTM Model\n",
+    "def hybrid_model(train_data, test_data, time_steps=60, model_dir='./model'):\n",
+    "    # Ensure data is a numpy array\n",
+    "    train_df = np.array(train_data).reshape(-1, 1)\n",
+    "    train_df = pd.DataFrame(train_df).ffill().values\n",
+    "\n",
+    "    # Create a directory to save models if it doesn't exist\n",
+    "    if not os.path.exists(model_dir):\n",
+    "        os.makedirs(model_dir)\n",
+    "\n",
+    "    # ARIMA Model\n",
+    "    arima_model_path = os.path.join(model_dir, 'arima_model.pkl')\n",
+    "    if not os.path.exists(arima_model_path):\n",
+    "        model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
+    "                                d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
+    "                                error_action='ignore', suppress_warnings=True, stepwise=True)\n",
+    "\n",
+    "        arima_model = ARIMA(train_df, order=model_auto.order)\n",
+    "        arima_results = arima_model.fit()\n",
+    "        # Save ARIMA model\n",
+    "        joblib.dump(arima_results, arima_model_path)\n",
+    "    else:\n",
+    "        arima_results = joblib.load(arima_model_path)\n",
+    "\n",
+    "    # Get ARIMA residuals\n",
+    "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
+    "\n",
+    "    # Prepare data for LSTM\n",
+    "    scaler = MinMaxScaler()\n",
+    "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
+    "\n",
+    "    X, y = prepare_data(residuals_scaled, time_steps)\n",
+    "    X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "    # LSTM Model\n",
+    "    lstm_model_path = os.path.join(model_dir, 'lstm_model.keras')  # Updated file extension\n",
+    "    if not os.path.exists(lstm_model_path):\n",
+    "        lstm_model = Sequential([\n",
+    "            LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "            LSTM(units=50),\n",
+    "            Dense(units=1)\n",
+    "        ])\n",
+    "        lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "        # Early stopping and model checkpoint\n",
+    "        early_stopping = EarlyStopping(monitor='loss', patience=10, restore_best_weights=True)\n",
+    "        model_checkpoint = ModelCheckpoint(lstm_model_path, save_best_only=True, monitor='loss')\n",
+    "\n",
+    "        # Train LSTM model\n",
+    "        lstm_model.fit(X, y, epochs=50, batch_size=32, verbose=1, callbacks=[early_stopping, model_checkpoint])\n",
+    "\n",
+    "    else:\n",
+    "        lstm_model = load_model(lstm_model_path)\n",
+    "\n",
+    "    # Make predictions for test data\n",
+    "    predictions = []\n",
+    "    test_data = np.array(test_data).reshape(-1, 1)\n",
+    "    combined_data = np.vstack((train_df, test_data))\n",
+    "\n",
+    "    for i in range(len(test_data)):\n",
+    "        # ARIMA prediction\n",
+    "        arima_forecast = arima_results.forecast(steps=1)\n",
+    "\n",
+    "        # LSTM prediction\n",
+    "        last_60_days = scaler.transform(combined_data[-(time_steps+1):-1])\n",
+    "        X_test = np.array([last_60_days])\n",
+    "        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
+    "        lstm_prediction = lstm_model.predict(X_test)\n",
+    "        lstm_prediction = scaler.inverse_transform(lstm_prediction)\n",
+    "\n",
+    "        # Combine predictions\n",
+    "        hybrid_prediction = arima_forecast + lstm_prediction[0][0]\n",
+    "        predictions.append(hybrid_prediction[0])\n",
+    "\n",
+    "        # Update ARIMA model with test data\n",
+    "        arima_results = arima_model.append(test_data[i]).fit()\n",
+    "\n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "# Load and prepare data\n",
+    "train_data = pd.read_csv('../Data/SBI Train data.csv')\n",
+    "test_data = pd.read_csv('../Data/SBI Test data.csv')\n",
+    "\n",
+    "train_close_prices = train_data['Close']\n",
+    "test_close_prices = test_data['Close']\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = hybrid_model(train_close_prices, test_close_prices)\n",
+    "\n",
+    "# Calculate accuracy metrics\n",
+    "mae = mean_absolute_error(test_close_prices, predictions)\n",
+    "rmse = np.sqrt(mean_squared_error(test_close_prices, predictions))\n",
+    "\n",
+    "print(f\"Mean Absolute Error: ${mae:.2f}\")\n",
+    "print(f\"Root Mean Squared Error: ${rmse:.2f}\")\n",
+    "\n",
+    "# Calculate Mean Absolute Percentage Error (MAPE)\n",
+    "mape = np.mean(np.abs((test_close_prices - predictions) / test_close_prices)) * 100\n",
+    "print(f\"Mean Absolute Percentage Error: {mape:.2f}%\")\n",
+    "\n",
+    "# Plot actual vs predicted prices\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(test_data['Date'], test_close_prices, label='Actual Prices')\n",
+    "plt.plot(test_data['Date'], predictions, label='Predicted Prices')\n",
+    "plt.title('Actual vs Predicted Stock Prices')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ARIMA/hybrid.ipynb b/ARIMA/hybrid.ipynb
index 491551d..e5870cb 100644
--- a/ARIMA/hybrid.ipynb
+++ b/ARIMA/hybrid.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -10,42 +10,32 @@
      "output_type": "stream",
      "text": [
       "Performing stepwise search to minimize aic\n",
-      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.62 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.16 sec\n",
-      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.22 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.84 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.24 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.17 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.30 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.28 sec\n",
-      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.38 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.56 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.97 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.44 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.13 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.51 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30240.523, Time=2.28 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30272.252, Time=0.19 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30243.723, Time=0.54 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30241.768, Time=0.77 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30270.982, Time=0.17 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30234.621, Time=2.66 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30239.276, Time=0.76 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30236.607, Time=4.12 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30236.577, Time=6.50 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30234.958, Time=3.86 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30240.618, Time=1.05 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30237.920, Time=5.87 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30233.414, Time=0.94 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30239.179, Time=1.16 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30237.952, Time=0.39 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30235.401, Time=1.76 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30235.372, Time=2.71 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30242.355, Time=0.28 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30233.748, Time=1.51 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30239.309, Time=0.48 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30236.712, Time=2.50 sec\n",
       "\n",
       "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
-      "Total fit time: 11.362 seconds\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'numpy.ndarray' object has no attribute 'values'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[2], line 71\u001b[0m\n\u001b[0;32m     68\u001b[0m custom_data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../Data/SBI Train data.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     69\u001b[0m close_prices \u001b[38;5;241m=\u001b[39m custom_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m---> 71\u001b[0m prediction \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclose_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHybrid model prediction for next day closing price: $\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprediction\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "Cell \u001b[1;32mIn[2], line 34\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(data, time_steps)\u001b[0m\n\u001b[0;32m     31\u001b[0m arima_results \u001b[38;5;241m=\u001b[39m arima_model\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Get ARIMA residuals\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m arima_residuals \u001b[38;5;241m=\u001b[39m df \u001b[38;5;241m-\u001b[39m \u001b[43marima_results\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfittedvalues\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     36\u001b[0m \u001b[38;5;66;03m# Prepare data for LSTM\u001b[39;00m\n\u001b[0;32m     37\u001b[0m scaler \u001b[38;5;241m=\u001b[39m MinMaxScaler()\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'values'"
+      "Total fit time: 40.526 seconds\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 601ms/step\n",
+      "Hybrid model prediction for next day closing price: $244.92\n"
      ]
     }
    ],
@@ -56,7 +46,7 @@
     "from pmdarima import auto_arima\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
     "\n",
     "def prepare_data(data, time_steps):\n",
     "    X, y = [], []\n",
@@ -74,6 +64,8 @@
     "    \n",
     "    df = df.reshape(-1, 1)\n",
     "\n",
+    "    df = pd.DataFrame(df).ffill().values\n",
+    "\n",
     "    # ARIMA model\n",
     "    model_auto = auto_arima(df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
     "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
@@ -83,8 +75,9 @@
     "    arima_results = arima_model.fit()\n",
     "\n",
     "    # Get ARIMA residuals\n",
-    "    arima_residuals = df - arima_results.fittedvalues.values.reshape(-1, 1)\n",
-    "\n",
+    "    arima_residuals = df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
+    "    \n",
     "    # Prepare data for LSTM\n",
     "    scaler = MinMaxScaler()\n",
     "    residuals_scaled = scaler.fit_transform(arima_residuals)\n",
@@ -94,7 +87,8 @@
     "\n",
     "    # LSTM model\n",
     "    lstm_model = Sequential([\n",
-    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
     "        LSTM(units=50),\n",
     "        Dense(units=1)\n",
     "    ])\n",
@@ -126,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -134,46 +128,32 @@
      "output_type": "stream",
      "text": [
       "Performing stepwise search to minimize aic\n",
-      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=1.22 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.09 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.55 sec\n",
-      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.46 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.06 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=1.17 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.24 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.18 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.31 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.17 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.31 sec\n",
-      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.39 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.08 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.57 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.97 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.07 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.45 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.55 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30240.523, Time=2.25 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30272.252, Time=0.22 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30243.723, Time=0.58 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30241.768, Time=0.82 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30270.982, Time=0.15 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30234.621, Time=2.72 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30239.276, Time=0.84 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30236.607, Time=4.21 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30236.577, Time=6.49 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30234.958, Time=4.04 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30240.618, Time=1.13 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30237.920, Time=6.01 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30233.414, Time=1.07 sec\n",
+      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30239.179, Time=1.18 sec\n",
+      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30237.952, Time=0.35 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30235.401, Time=1.82 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30235.372, Time=2.42 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30242.355, Time=0.29 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30233.748, Time=1.49 sec\n",
+      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30239.309, Time=0.54 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30236.712, Time=2.61 sec\n",
       "\n",
       "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
-      "Total fit time: 13.125 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-      "  super().__init__(**kwargs)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n",
-      "Hybrid model prediction for next day closing price: $245.77\n"
+      "Total fit time: 41.259 seconds\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 499ms/step\n",
+      "Hybrid model prediction for next day closing price: $245.04\n"
      ]
     }
    ],
@@ -184,7 +164,7 @@
     "from pmdarima import auto_arima\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
     "\n",
     "def prepare_data(data, time_steps):\n",
     "    X, y = [], []\n",
@@ -204,6 +184,8 @@
     "    \n",
     "    df = df.reshape(-1, 1)\n",
     "\n",
+    "    df = pd.DataFrame(df).ffill().values\n",
+    "\n",
     "    # ARIMA model\n",
     "    model_auto = auto_arima(df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
     "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
@@ -214,6 +196,7 @@
     "\n",
     "    # Get ARIMA residuals\n",
     "    arima_residuals = df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
     "\n",
     "    # Prepare data for LSTM\n",
     "    scaler = MinMaxScaler()\n",
@@ -224,7 +207,8 @@
     "\n",
     "    # LSTM model\n",
     "    lstm_model = Sequential([\n",
-    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
     "        LSTM(units=50),\n",
     "        Dense(units=1)\n",
     "    ])\n",
@@ -255,68 +239,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Performing stepwise search to minimize aic\n",
-      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.62 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.15 sec\n",
-      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.21 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.03 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.85 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.21 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.25 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.96 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.21 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.32 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.29 sec\n",
-      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.43 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.58 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=1.01 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.44 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.15 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.52 sec\n",
-      "\n",
-      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
-      "Total fit time: 11.613 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-      "  super().__init__(**kwargs)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'ARIMA' object has no attribute 'append'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[4], line 88\u001b[0m\n\u001b[0;32m     85\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m---> 88\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m     91\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
-      "Cell \u001b[1;32mIn[4], line 76\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps)\u001b[0m\n\u001b[0;32m     73\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model\u001b[39;00m\n\u001b[1;32m---> 76\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'ARIMA' object has no attribute 'append'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -324,7 +249,7 @@
     "from pmdarima import auto_arima\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
     "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
     "\n",
     "def prepare_data(data, time_steps):\n",
@@ -338,13 +263,15 @@
     "    # Ensure data is numpy array\n",
     "    if isinstance(train_data, pd.Series):\n",
     "        train_df = train_data.values\n",
-    "    elif isinstance(train_data, pd.DataFrame):\n",
-    "        train_df = train_data.values\n",
+    "    if isinstance(test_data, pd.DataFrame):\n",
+    "        test_df = train_data.values\n",
     "    else:\n",
     "        train_df = np.array(train_data)\n",
     "    \n",
     "    train_df = train_df.reshape(-1, 1)\n",
     "\n",
+    "    train_df = pd.DataFrame(train_df).ffill().values\n",
+    "\n",
     "    # ARIMA model\n",
     "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
     "                            d=None, seasonal=False, start_P=0, D=0, trace=True,\n",
@@ -355,6 +282,7 @@
     "\n",
     "    # Get ARIMA residuals\n",
     "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
     "\n",
     "    # Prepare data for LSTM\n",
     "    scaler = MinMaxScaler()\n",
@@ -365,7 +293,8 @@
     "\n",
     "    # LSTM model\n",
     "    lstm_model = Sequential([\n",
-    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
     "        LSTM(units=50),\n",
     "        Dense(units=1)\n",
     "    ])\n",
@@ -435,68 +364,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Performing stepwise search to minimize aic\n",
-      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.63 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.05 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.17 sec\n",
-      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.24 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.93 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.23 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.30 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=1.94 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.22 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.30 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.15 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.31 sec\n",
-      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.42 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.10 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.61 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=1.00 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.05 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.45 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.54 sec\n",
-      "\n",
-      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
-      "Total fit time: 11.839 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-      "  super().__init__(**kwargs)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'ARIMA' object has no attribute 'append'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[5], line 88\u001b[0m\n\u001b[0;32m     85\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m---> 88\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m     91\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
-      "Cell \u001b[1;32mIn[5], line 76\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps)\u001b[0m\n\u001b[0;32m     73\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model\u001b[39;00m\n\u001b[1;32m---> 76\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'ARIMA' object has no attribute 'append'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -504,7 +374,7 @@
     "from pmdarima import auto_arima\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
     "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
     "\n",
     "def prepare_data(data, time_steps):\n",
@@ -524,6 +394,7 @@
     "        train_df = np.array(train_data)\n",
     "    \n",
     "    train_df = train_df.reshape(-1, 1)\n",
+    "    train_df = pd.DataFrame(train_df).ffill().values\n",
     "\n",
     "    # ARIMA model\n",
     "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
@@ -535,6 +406,7 @@
     "\n",
     "    # Get ARIMA residuals\n",
     "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
     "\n",
     "    # Prepare data for LSTM\n",
     "    scaler = MinMaxScaler()\n",
@@ -545,7 +417,8 @@
     "\n",
     "    # LSTM model\n",
     "    lstm_model = Sequential([\n",
-    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
     "        LSTM(units=50),\n",
     "        Dense(units=1)\n",
     "    ])\n",
@@ -615,64 +488,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Performing stepwise search to minimize aic\n",
-      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=30202.237, Time=0.59 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30233.097, Time=0.06 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30205.175, Time=0.16 sec\n",
-      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30203.304, Time=0.25 sec\n",
-      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30231.827, Time=0.04 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30196.421, Time=0.85 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=30201.003, Time=0.20 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30198.399, Time=1.28 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30198.353, Time=2.09 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30196.789, Time=1.29 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=30202.291, Time=0.33 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30199.886, Time=1.23 sec\n",
-      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30195.213, Time=0.30 sec\n",
-      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=30200.893, Time=0.38 sec\n",
-      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=30199.679, Time=0.09 sec\n",
-      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30197.192, Time=0.57 sec\n",
-      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30197.148, Time=0.98 sec\n",
-      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=30203.808, Time=0.06 sec\n",
-      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30195.578, Time=0.42 sec\n",
-      " ARIMA(3,1,0)(0,0,0)[0]             : AIC=30200.982, Time=0.14 sec\n",
-      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30198.679, Time=0.50 sec\n",
-      "\n",
-      "Best model:  ARIMA(2,1,1)(0,0,0)[0]          \n",
-      "Total fit time: 11.823 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
-      "  super().__init__(**kwargs)\n",
-      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
-     ]
-    },
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'saved_model\\\\lstm_model.h5\\\\arima_model.pkl'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[6], line 122\u001b[0m\n\u001b[0;32m    119\u001b[0m save_model(arima_results, lstm_model, scaler, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msaved_model\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    121\u001b[0m \u001b[38;5;66;03m# Later, load the model and make predictions\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m loaded_arima, loaded_lstm, loaded_scaler \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msaved_model\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    123\u001b[0m predictions \u001b[38;5;241m=\u001b[39m make_predictions(loaded_arima, loaded_lstm, loaded_scaler, test_close_prices)\n\u001b[0;32m    125\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n",
-      "Cell \u001b[1;32mIn[6], line 77\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(folder_path)\u001b[0m\n\u001b[0;32m     74\u001b[0m arima_results \u001b[38;5;241m=\u001b[39m joblib\u001b[38;5;241m.\u001b[39mload(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124marima_model.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m     76\u001b[0m \u001b[38;5;66;03m# Load LSTM model\u001b[39;00m\n\u001b[1;32m---> 77\u001b[0m lstm_model \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlstm_model.h5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     79\u001b[0m \u001b[38;5;66;03m# Load scaler\u001b[39;00m\n\u001b[0;32m     80\u001b[0m scaler \u001b[38;5;241m=\u001b[39m joblib\u001b[38;5;241m.\u001b[39mload(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscaler.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
-      "Cell \u001b[1;32mIn[6], line 74\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(folder_path)\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_model\u001b[39m(folder_path):\n\u001b[0;32m     73\u001b[0m     \u001b[38;5;66;03m# Load ARIMA model\u001b[39;00m\n\u001b[1;32m---> 74\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43mjoblib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43marima_model.pkl\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     76\u001b[0m     \u001b[38;5;66;03m# Load LSTM model\u001b[39;00m\n\u001b[0;32m     77\u001b[0m     lstm_model \u001b[38;5;241m=\u001b[39m load_model(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(folder_path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlstm_model.h5\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
-      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\joblib\\numpy_pickle.py:579\u001b[0m, in \u001b[0;36mload\u001b[1;34m(filename, mmap_mode)\u001b[0m\n\u001b[0;32m    577\u001b[0m         obj \u001b[38;5;241m=\u001b[39m _unpickle(fobj)\n\u001b[0;32m    578\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 579\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m    580\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m _read_fileobject(f, filename, mmap_mode) \u001b[38;5;28;01mas\u001b[39;00m fobj:\n\u001b[0;32m    581\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fobj, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    582\u001b[0m                 \u001b[38;5;66;03m# if the returned file object is a string, this means we\u001b[39;00m\n\u001b[0;32m    583\u001b[0m                 \u001b[38;5;66;03m# try to load a pickle file generated with an version of\u001b[39;00m\n\u001b[0;32m    584\u001b[0m                 \u001b[38;5;66;03m# Joblib so we load it with joblib compatibility function.\u001b[39;00m\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_model\\\\lstm_model.h5\\\\arima_model.pkl'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -680,7 +498,7 @@
     "from pmdarima import auto_arima\n",
     "from sklearn.preprocessing import MinMaxScaler\n",
     "from tensorflow.keras.models import Sequential, load_model\n",
-    "from tensorflow.keras.layers import Dense, LSTM\n",
+    "from tensorflow.keras.layers import Dense, LSTM, Input\n",
     "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
     "import joblib\n",
     "import os\n",
@@ -702,6 +520,7 @@
     "        train_df = np.array(train_data)\n",
     "    \n",
     "    train_df = train_df.reshape(-1, 1)\n",
+    "    train_df = pd.DataFrame(train_df).ffill().values\n",
     "\n",
     "    # ARIMA model\n",
     "    model_auto = auto_arima(train_df, start_p=1, start_q=1, max_p=3, max_q=3, m=1,\n",
@@ -713,6 +532,7 @@
     "\n",
     "    # Get ARIMA residuals\n",
     "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
     "\n",
     "    # Prepare data for LSTM\n",
     "    scaler = MinMaxScaler()\n",
@@ -723,7 +543,8 @@
     "\n",
     "    # LSTM model\n",
     "    lstm_model = Sequential([\n",
-    "        LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)),\n",
+    "        Input(shape=(X.shape[1], 1)),\n",
+    "        LSTM(units=50, return_sequences=True), \n",
     "        LSTM(units=50),\n",
     "        Dense(units=1)\n",
     "    ])\n",
@@ -824,43 +645,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000002DD2831BCE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x000002DD2831BCE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n"
-     ]
-    },
-    {
-     "ename": "UnboundLocalError",
-     "evalue": "cannot access local variable 'arima_model' where it is not associated with a value",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[9], line 107\u001b[0m\n\u001b[0;32m    104\u001b[0m test_close_prices \u001b[38;5;241m=\u001b[39m test_data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m    106\u001b[0m \u001b[38;5;66;03m# Make predictions\u001b[39;00m\n\u001b[1;32m--> 107\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_close_prices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_close_prices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    109\u001b[0m \u001b[38;5;66;03m# Calculate accuracy metrics\u001b[39;00m\n\u001b[0;32m    110\u001b[0m mae \u001b[38;5;241m=\u001b[39m mean_absolute_error(test_close_prices, predictions)\n",
-      "Cell \u001b[1;32mIn[9], line 95\u001b[0m, in \u001b[0;36mhybrid_model\u001b[1;34m(train_data, test_data, time_steps, model_dir)\u001b[0m\n\u001b[0;32m     92\u001b[0m     predictions\u001b[38;5;241m.\u001b[39mappend(hybrid_prediction[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;66;03m# Update ARIMA model with test data\u001b[39;00m\n\u001b[1;32m---> 95\u001b[0m     arima_results \u001b[38;5;241m=\u001b[39m \u001b[43marima_model\u001b[49m\u001b[38;5;241m.\u001b[39mappend(test_data[i])\u001b[38;5;241m.\u001b[39mfit()\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(predictions)\n",
-      "\u001b[1;31mUnboundLocalError\u001b[0m: cannot access local variable 'arima_model' where it is not associated with a value"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -886,6 +673,7 @@
     "def hybrid_model(train_data, test_data, time_steps=60, model_dir='./model'):\n",
     "    # Ensure data is a numpy array\n",
     "    train_df = np.array(train_data).reshape(-1, 1)\n",
+    "    train_df = pd.DataFrame(train_df).ffill().values\n",
     "\n",
     "    # Create a directory to save models if it doesn't exist\n",
     "    if not os.path.exists(model_dir):\n",
@@ -907,6 +695,7 @@
     "\n",
     "    # Get ARIMA residuals\n",
     "    arima_residuals = train_df - arima_results.fittedvalues.reshape(-1, 1)\n",
+    "    arima_residuals = np.nan_to_num(arima_residuals)\n",
     "\n",
     "    # Prepare data for LSTM\n",
     "    scaler = MinMaxScaler()\n",
@@ -1006,7 +795,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1020,9 +809,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/ARIMA/saved_model/arima_model.pkl b/ARIMA/saved_model/arima_model.pkl
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literal 0
HcmV?d00001

diff --git a/ARIMA/saved_model/scaler.pkl b/ARIMA/saved_model/scaler.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..6cff6ce09a67317aa79e6386f0e655a53b98fb7f
GIT binary patch
literal 719
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literal 0
HcmV?d00001


From c8d9100e630d7f1b3eb6c00a9fd0cb423314555f Mon Sep 17 00:00:00 2001
From: MCAHITK-MINOR-PROJECT <mcahitkminorproject@gmail.com>
Date: Fri, 25 Oct 2024 13:45:36 +0530
Subject: [PATCH 73/76] Update update_leaderboard.yml

---
 .github/workflows/update_leaderboard.yml | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/update_leaderboard.yml b/.github/workflows/update_leaderboard.yml
index 2e854bc..81398b6 100644
--- a/.github/workflows/update_leaderboard.yml
+++ b/.github/workflows/update_leaderboard.yml
@@ -16,15 +16,18 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: '3.x'
-          
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           pip install requests PyGithub
-      
+
       - name: Run leaderboard update script
         run: |
           python .github/scripts/update_structure.py
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}  # GitHub token
+          GITHUB_REPOSITORY: ${{ github.repository }}  # Repository info
 
       - name: Commit and push changes
         run: |

From 62d1454c5117571931fc0e292aa3d26c50662e95 Mon Sep 17 00:00:00 2001
From: MCAHITK-MINOR-PROJECT <mcahitkminorproject@gmail.com>
Date: Fri, 25 Oct 2024 13:51:05 +0530
Subject: [PATCH 74/76] Update update_leaderboard.py

---
 .github/scripts/update_leaderboard.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/.github/scripts/update_leaderboard.py b/.github/scripts/update_leaderboard.py
index e2688a6..3d49f6d 100644
--- a/.github/scripts/update_leaderboard.py
+++ b/.github/scripts/update_leaderboard.py
@@ -1,10 +1,11 @@
+import os
 import requests
 from collections import defaultdict
 
 # GitHub API URL for closed pull requests
 API_URL = "https://api.github.com/repos/rohitinu6/Stock-Price-Prediction/pulls?state=closed"
 
-GITHUB_TOKEN = None
+GITHUB_TOKEN = os.getenv("GH_TOKEN")
 
 # Points mapping based on the label names
 points_map = {
@@ -13,7 +14,7 @@
     "level3": 45
 }
 
-# Function to fetch closed pull requests
+# Function to fetch closed pull requests with pagination
 def get_closed_prs():
     headers = {}
     
@@ -30,7 +31,6 @@ def get_closed_prs():
         
         page_prs = response.json()
         
-        # Break the loop if there are no more PRs
         if not page_prs:
             break
             
@@ -55,7 +55,7 @@ def get_closed_prs():
             leaderboard[user]["points"] += points_map[label_name]
             leaderboard[user]["avatar_url"] = avatar_url  
 
-# Generate the leaderboard in markdown format
+# Function to generate the leaderboard in markdown format
 def generate_leaderboard_md(leaderboard):
     sorted_leaderboard = sorted(leaderboard.items(), key=lambda x: x[1]["points"], reverse=True)
     
@@ -72,5 +72,9 @@ def generate_leaderboard_md(leaderboard):
 
 # Generate the leaderboard markdown and save it to a file
 leaderboard_md = generate_leaderboard_md(leaderboard)
+
+# Save the leaderboard to leaderboard.md
 with open('leaderboard.md', 'w') as f:
     f.write(leaderboard_md)
+
+print("Leaderboard updated successfully!")

From 8deafac8dd4ed193690eba31a1cb65cf4879a53d Mon Sep 17 00:00:00 2001
From: MCAHITK-MINOR-PROJECT <mcahitkminorproject@gmail.com>
Date: Fri, 25 Oct 2024 13:55:48 +0530
Subject: [PATCH 75/76] Update update_leaderboard.yml

---
 .github/workflows/update_leaderboard.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/update_leaderboard.yml b/.github/workflows/update_leaderboard.yml
index 81398b6..33a178b 100644
--- a/.github/workflows/update_leaderboard.yml
+++ b/.github/workflows/update_leaderboard.yml
@@ -24,7 +24,7 @@ jobs:
 
       - name: Run leaderboard update script
         run: |
-          python .github/scripts/update_structure.py
+          python .github/scripts/update_leaderboard.py
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}  # GitHub token
           GITHUB_REPOSITORY: ${{ github.repository }}  # Repository info

From 27435f4a078bb159e0ebdee2fe22f2fa561b30bf Mon Sep 17 00:00:00 2001
From: MCAHITK-MINOR-PROJECT <mcahitkminorproject@gmail.com>
Date: Fri, 25 Oct 2024 19:59:18 +0530
Subject: [PATCH 76/76] Update update_leaderboard.yml

---
 .github/workflows/update_leaderboard.yml | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/update_leaderboard.yml b/.github/workflows/update_leaderboard.yml
index 33a178b..f7b6053 100644
--- a/.github/workflows/update_leaderboard.yml
+++ b/.github/workflows/update_leaderboard.yml
@@ -9,25 +9,29 @@ jobs:
     runs-on: ubuntu-latest
     
     steps:
+      
       - name: Checkout repository
         uses: actions/checkout@v3
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}  
         
+      
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
           python-version: '3.x'
-
+        
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           pip install requests PyGithub
-
+      
       - name: Run leaderboard update script
         run: |
           python .github/scripts/update_leaderboard.py
         env:
-          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}  # GitHub token
-          GITHUB_REPOSITORY: ${{ github.repository }}  # Repository info
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}  # Pass GitHub token to script
+          GITHUB_REPOSITORY: ${{ github.repository }}  # Pass repository information
 
       - name: Commit and push changes
         run: |
@@ -35,4 +39,4 @@ jobs:
           git config --global user.email "github-actions[bot]@users.noreply.github.com"
           git add leaderboard.md || echo "No changes to commit"  # Prevent errors if there's nothing to commit
           git commit -m "Update leaderboard" || echo "No changes to commit"
-          git push || echo "Nothing to push"
+          git push https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }} || echo "Nothing to push"